This PDF file contains the front matter associated with SPIE Proceedings Volume 12756, including the Title Page, Copyright information, Table of Contents, and Conference Committee information

The study of coastal hydrodynamics demands a thorough understanding of the properties of water waves. Following a brief introduction to the fundamental concepts of wavelength, dispersion, and ocean water wave mechanism, this paper covers the essentials of water wave theory, emphasizing wavelength in greater detail. Additionally, the mathematical models of the Navier-Stokes Equation were derived. Furthermore, the water wave speed equation is derived using gravitational force and dispersion. Most often, models are simplified so that they may be applied to specific waves, such as deep-sea waves, which are more difficult to predict. The amplitude and wavelength of a water wave are frequently classified based on the depth of the water in which they happen. Ocean waves of great amplitude and long wavelength, for example, are created in the deep ocean as waves of small amplitude but long-wavelength; when they approach the shoreline, however, they increase in amplitude while reducing in wavelength.

Nowadays many different physical structures, such as crystal structure and hydrogen atom structure, can be modelled using group theory methods. This paper mainly focuses on four aspects: infinite groups in general, the irrecognizable infinite groups, cosets of infinite groups, Sylow’s theorems and the Sylow tower group. In this research, different fundamental aspects of group theory in linear algebra and their applications in both finite and infinite groups are explored. The definition and examples of groups are first given, and the irrecognizable infinite group is then introduced. In addition, the concept of cosets of infinite groups is explained with the definition of subgroups, especially normal subgroups, as well as the Lagrange’s theorem. Lastly, Sylow’s first, second and third theorems, and the properties of Sylow tower group are also demonstrated. Therefore, readers are able to use the knowledge of groups to analyse symmetric mathematical phenomena since group theory can be described as a study of symmetry.

Mathematics is a subject that has been developed for thousands of years. Alongside everything else, prime numbers and perfect numbers are studied since the earliest of time, and still today. Despite all the improvements in mathematics and computer power, mysteries of prime numbers and perfect numbers still remain. From trial division, to Mersenne primes and Fermat numbers, all the way to Reimann Hypothesis, mathematicians kept attempting to capture the pattern of primes. Although a lot of progress are made, it is still not known precisely. Searching of big primes and constructing special composite numbers contributed to other areas a lot, from encryption to proofs of other number theoretic concepts. Perfect numbers are even rarer than primes. The study of perfect numbers can be traced back to the Pythagorean brotherhood. In this article, we will discuss about brief history of the research, theorems, proofs, as well as some useful algorithms.

This paper completes the macro and micro joint classification system by establishing a Support Vector Machine (SVM) regression prediction model. On this basis, we use Principal Component Analysis (PCA) and Entropy Weight Method (EWM) method to select and calculate the weight of five dimension reduction classification indicators. We classify the types by K-means clustering algorithm. The K-center is used to calculate the classification center and euclidean distance of unknown glass, so as to obtain the calculation method of glass in cultural relics identification. Finally, we use multiple regression fitting analysis of the relationship between the components. This study is helpful to the correct identification of the glass relics and plays an important role in the study of history.

The study of optical line difference is mostly derived from the fluctuation perspective based on the phase invariance^[1], which is a relatively abstract process and not easy to understand. Considering the wave-particle duality of light, this paper derives the determination method of optical line difference based on Lorentz transformation and light speed invariance principle from the particle nature of light, which is simple and clear, and the physical image is clear.

In order to better understand and explore the soil moisture prediction method, this paper establishes a soil moisture prediction model based on soil moisture data and related meteorological data from 2012-2021 in Xilin Gol grassland of Inner Mongolia, while keeping the current grazing strategy unchanged, based on Occam's Razor principle, using soil moisture and existing data such as soil evaporation and seasonal changes. Firstly, the ARIMA (12,1,0) model was developed to predict the precipitation and soil evaporation in 2022 and 2023. Second, according to the transformation relationship between evaporated water and latent heat flux, a linear model of soil moisture and independent variables such as precipitation and evaporation was established, and the expressions of different soil moisture and each influencing factor were obtained by fitting, and the land moisture data at different depths were calculated as the prediction results, in order to provide technical reference for further exploration of soil moisture forecasting and better meteorological services for agriculture. The results are intended to provide a technical reference for further exploration of soil moisture forecasting and to lay the foundation for better meteorological services for agriculture.

This paper predicts the success probability of successful or unsuccessful multivariate tests, uses Lasso regression method to fit the input data, mining information from different dimensions of input data to analyze the success probability, and makes experiments to compare and verify the effectiveness of the methods.

The extremal problem we consider is to study the maximum cardinality of a minimal suitable set of permutations. We give a lower bound of this maximum cardinality. Proof for the lower bound is constructive and produces corresponding sets of permutations. Some exact values are also determined.

Motzkin path is a type of lattice paths, which attract many combinatorists’ attention. In a Motzkin path, a node 𝑢 is called a sharp peak if it is between two steps 𝑈𝐷. A Motzkin path with no sharp peak is denoted by sp-free. In this paper, we focus on the distribution of the number of sharp peaks on Motzkin paths. In precise, by using Lagrange inversion formula, we obtain a formula to enumerate the number of Motzkin paths with length 𝑛 and containing 𝑘 sharp peaks. As the special case of the formula, we provide an elegant formula for the number of sp-free Motzkin paths.

The article improves the Newton iterative method and combines the matrix splitting technique to propose a series of matrix splitting iterative methods based on Newton's method, and the convergence theorems and error estimates about the iterative methods are also given in the paper. It also provides a new and efficient method for solving large-scale nonlinear systems of equations.

The Schrödinger equation is widely used in theoretical research in the fields of atomic and molecular physics, particle physics and nuclear physics, solid state physics, and photonics. As a first principle, it plays an important role in these applications. However, solving the Schrödinger equation numerically requires a relatively large amount of storage space and calculation. The emergence of modern graphics processing units (GPUs) provides an opportunity for the efficient solution of this equation. In this paper, on the basis of using the finite difference method to solve the three-dimensional Schrödinger equation algorithm, using the graphics processor Tesla V100 as the computing platform, through theoretical analysis and numerical simulation, using data structure layout and organization optimization, data merge reduction, synchronization elimination and merge kernel function optimization and other methods give full play to the hardware characteristics of the GPU and optimize the three-dimensional time-dependent algorithm for solving Schrödinger equation on the GPU. Experimental results show that, compared with the original GPU-based algorithm, the optimization method used in this paper can speed up the program by 1.476 times under the same number of loop iterations.

The development of energy-saving building projects has been progressing for many years and with the emergence of carbon peaking and carbon neutrality, it faces a greater risk. To ensure its continued success, effective risk management is required. Therefore, a tripartite evolutionary game model for the collaborative risk management of the government, property owners and contractors has been created. Through the use of Matlab software for numerical simulation, the factors that affect the three-party strategy selection have been analyzed, and the results demonstrate the varying influence the initial willingness to participate has on the three-party strategic choice. It was found that the contractor is more sensitive to the government’s preferential policies, while the owner is more responsive to the government’s financial support. Besides, the increase in penalty is seen to be beneficial in promoting collaboration between the owner and contractor.

Based on the theory of molecular dynamics and thermodynamics, the dynamics model of the large span dust particles in the air of the earth surface with the size of 10-7~10-5m was established, and the air drag coefficient of the particles under the model was obtained. The motion characteristics of spherical, cube and bicone particles in the model are studied by numerical analysis. In the process of falling, the drag coefficient gradually increases, and the air resistance is not proportional to the speed of movement. For a particle of the same shape, the time it takes to fall to the ground is affected by its size radius and the temperature of the air at the ground. The smaller the particle size radius (the decrease of gravity and air resistance at the same time), the higher the surface air temperature, the longer the time to fall to the ground.

As a topological index closely related to the physical, chemical and biological properties of compounds, graph indexes have been concerned and studied by scholars. This paper defined the concept of " Level-Contribution " from the view of degree and distance. On the basis of this, we studied the Vertex-Gutman-Index and Graph-Gutman-Index of the concurrent and double circle graph, and obtained the extremal values of the Vertex-Gutman of the vertices and extremal graphs of the Graph-Gutman-Index of the concurrent and double circle graph.

The environment in which ancient glass is buried can easily affect it and cause it to weather. During the weathering process, an extensive exchange of internal elements with environmental elements can change the composition ratio, thus affecting the correct determination of its category. Therefore, studying surface weathering characteristics and classifying glass artifacts is very important in archaeology. For the existing batch of relevant data (surface weathering, color, decoration, type, and chemical composition) of ancient glass artifacts in China, we first did the pre-processing work of relevant data, removing the outliers and adding the missing values for subsequent use. Then we used R-type and Q-type systematic cluster analysis to construct a subclass classification model based on systematic cluster analysis to obtain the appropriate chemical composition and use it as an indicator to classify glass artifacts into subclasses. Then we used the fisher discriminant analysis method to construct a fisher discriminant analysis-based glass-type identification model. With the help of the above model, we identified the type of glass artifacts belonging to unknown categories.

In order to analyze quantitatively the seismic damage on disconnectors, a fragility curve is built by case investigation according to the statistical theory and simulations are carried out by Monte-Carlo method. The fragility curve describes the relationship between the earthquake impact level and the damage probability. A regional electric power grid with three substations is selected as an example to calculate the disconnectors damage probability under seismic magnitude M=6, 6.5, 7, 7.5 and 8. Earthquake occurrence is simulated by Monte-Carlo method. The results indicate that the damage probability increases with the increase of seismic magnitude. The damage probability for disconnectors in substation A is 0.14 approximately when M=6 while rises up to 0.30 approximately when M=8. In addition, the epicenter distance is another main parameter which determines the damage probability. The damage probability for disconnectors in substation A is higher than substation B and C under same seismic magnitude because of the smaller epicenter distance. This paper proposed a way to build fragility curve on the basic of statistical theory, and gives references to the risk analysis, damage control and emergency management.

The study of species population change is one of the most classic topics in biology. In this paper, we consider not only intra-race competition but also competition between two species. It is assumed that the resources in the environment are finite in this paper. Improved on the Logistic Population Growth Model from Malthusian model, we built the Competitive Hunter Model for trout and bass referring to the Lotka-Volterra Model. In this paper, we presented the assumptions of the model and analyzed the equilibrium points of the model in numerical analysis and the typical trajectories in phase planes in graphical analysis.

The PMF model is effective for addressing high-dimensional, large-scale, sparse, and imbalanced rating data, yet it may suffer from limitations in generalization and prediction accuracy in certain scenarios. To address these limitations, we propose a hybrid AdaBoost ensemble method within the PMF model. In this paper, we use two-stage algorithms in the model. Our approach uses a two-stage algorithm, whereby the first stage involves fuzzy clustering to calculate the scoring matrix of user-items, followed by neural network training to further enhance scoring prediction accuracy. The second stage involves using the rating matrix as the basis learner for training by different neural networks, and the final score prediction result is obtained through ensemble learning. Our proposed model was evaluated on the MovieLens and FilmTrust datasets, and its effectiveness was demonstrated. Due to its well-crafted architecture and robust representation learning capability, our model can be readily applied to various PMF model settings, such as PMF, FCM-PMF, Bagging-BP-PMF, and AdaBoost-SVM-PMF models. The experiments show that the mean absolute error(MAE) of the proposed method increases by 1.24% and 0.79% compared with the Bagging-BP-PMF model on two different datasets, and the root mean square error(RMSE) increases by 2.55% and 1.87%, respectively. Finally, our experiments show that our proposed approach performs well in various settings. By utilizing ensemble learning to train the weight of the base learner from different neural networks, our method improves the stability of score prediction. Additionally, our results verify the universality of our approach.

In order to realize the research on the extraction of non-stationary process information of real ocean waves, a time frequency analysis method that based on variational modal decomposition (VMD) and Hilbert transform of measured ocean waves was proposed. The comparison of the measured waves and the waves obtained by the existing random wave model was analyzed by wavelet transform, which indicates that the wave spectrum model based on the Fourier transform cannot effectively reflect the non-stationary characteristics of the real waves. Furthermore, the non-stationary time frequency information obtained by the proposed method was used to reconstruct the wave surface, which was also verified by wavelet transform. Results show that the proposed method can be more accurate in reflecting the non-stationary information of the measured waves and can be more precise in restoring the real ocean waves.

Unsafe behavior of production commanding staff can cause tremendous accidents. Affected by random factors, the cognition state of production commanding staff has random fluctuation. The fluctuation of cognitive state can affect their behavior choice. For analyzing the behavior choice of production commanding staff under the influence of the random fluctuation of cognitive state, random evolution systems are constructed, and the stability of the system is analyzed in the mean sense. It is found that in the mean sense, when the supervision intensity and punishment intensity of supervisors to production commanding staff meet a certain condition, the unsafe behavior random evolution system will reach a stable state. This condition can provide basis for the design of the unsafe production behavior control strategy.

As an important water conservancy facility to resist flooding, embankment project has the risk of failure due to its complex structure and large variability of operating conditions. This paper establishes a single-objective, multi-level, semi-structural evaluation index system based on the inverse diffusion of consequences method, which comprehensively considers various failure forms of embankments; and constructs a comprehensive evaluation method for embankment safety, which determines the subjective and objective weights of evaluation indexes by introducing interval number theory, combining hierarchical analysis method and entropy value method, scoring intervals based on set value statistics, and finally carrying out comprehensive gray evaluation based on improved hybrid centroid-type triangular whitening function, and on this basis, a Monte Carlo mathematical model is established for the sensitivity analysis of indicators. The safety evaluation was carried out with Wuhan Yangtze River embankment as an example, and the result shows that the integrated evaluation method of embankment safety based on interval number decision matrix has good practicality and more adequate data mining, which can truly reflect the embankment safety condition and has strong engineering guidance significance.

The effective analysis of computer hardware problems and the construction of mathematical models can ensure the security and reliability of the internal hardware of computer equipment. Combined with the actual situation of the hardware operation in the computer, based on the reference index of the corresponding safe operation of each hardware, the function relationship between the hardware running time and temperature is analyzed, and the hardware safety evaluation model is built. The computer simulation technology is applied to the real computer hardware, through the virtual simulation technology combined with the model to show the three-dimensional form of the internal situation, combined with the experimental results to analyze the role of the computer simulation technology (VR) technology in this respect, and then analyze the important indicators to determine the current running state of the computer, monitoring and prevention of various possible situations.

Through the discretization of continuous design parameters to form various environment scenarios, stochastic and robust optimization approaches often led to the sub-optimal solution for the uncertain p-hub median problem. This paper presented a triangular fuzzy number-based model for Non-Strict Uncapacitated Multi-Allocation p-hub Median Problem (NSUMApHMP). To complete such kind of comparisons between different interval-valued solutions, a direct intervalvalued measurement approach was proposed by means of calculating the overlapping areas using the triangular fuzzy number membership function. Then a tailored Tabu search algorithm was devised to solve this model, in which an improved Floyd-Warshall algorithm was also developed to find the shortest paths for all Origin-Destination pairs. The classic Civil Aeronautics Board (CAB) data was used to verify this approach. Our investigation compared various algorithms and models, and the results showed a significant improvement in computed CPU time using our proposed algorithm compared to alternative algorithms. Furthermore, our proposed model outperformed other models in terms of total system cost. Notably, both the robust model and our proposed model are well-suited for uncertain environments, and we acknowledge the potential impact of uncertainty on the performance of these models.

In this paper, we study the existence of positive solutions for a Hénon elliptic system with boundary value problems involving the critical exponent. Based upon the variational methods, we prove the existence of a positive solution to this problem under some proper conditions.

Feature selection is a critical step in machine learning to reduce data dimensionality, whereas the conflicting objectives: minimizing the selected feature number, and minimizing the error rate of classification make it quite challenging. However, most existing feature selection algorithms only focus on one single objective. To address this issue, a hybrid multi-objective feature selection (MOFS) method based on Shapley value and binary state transition algorithm is proposed in this paper, named MOFS-BSTA. Additionally, to enhance the converge capability of MOFS-BSTA, a mutation operator is developed to produce more efficient candidate solutions, and a two-dimensional sorting strategy is designed to replace the fast non-dominated sorting strategy. Finally, MOFS-BSTA is tested on nine UCI datasets, and the results demonstrate that the MOFS-BSTA can obtain the better Pareto optimal solution set with less running time than conventional MOFS methods.

The Beta distribution can effectively describe the non-Gaussian statistical characteristics of the electromagnetic scattering. Conventional random electromagnetic fields always implicitly assume that the number of steps is infinite, but in practice, it is finite. In this paper, we investigate the statistical properties of the Beta distributed random light field, and analyze the new statistical theory by combining the resulting probability density function distribution of the Stokes parameters.

This paper proposes a method to solve (1) by approximating the unknown function with Bernoulli polynomials and then approximating the integral part with the quadrature formula. This reduces the original equation to an algebraic equation. And two numerical examples provide the absolute error to indicate the validity of the method.

Envelope numbers are a class of integers associated to irrational numbers, and envelope numbers have some specific properties. There is no envelope number between the two adjacent envelopes and their sum, the sum of the adjacent upper and lower envelope numbers is still the envelope numbers, and the sum of the functional values of the adjacent upper and lower envelope numbers is the functional value of their sum. The difference between the number of two adjacent envelopes is the lower envelope number, and the difference between two adjacent envelopes is the upper envelope number.

A deterministic model of COVID-19 transmission is established by using a differential equation system, which integrates the protective adaptive behavior of human population and the protective effect of vaccine. Based on the data of reported cases and vaccinations from February 1, 2020 to July 16, 2022, parameter fitting is performed by MCMC method to estimate the intervention effect and transmission trend. The results indicate that (1) this epidemic may level off around October 2021 if without any intervention, but in big cost of disease burden, leading to 5.46 billion cases (about 10 times of current infections); (2) 210 million people may be free of infection due to vaccination, which is 37% of the current number of infections; and (3) according to the current situation of intervention measures, the cumulative number of cases in the world would slowly increase and tend to be flat by 2024. The epidemic will be completely ended in February 2026, and the cumulative number of cases will reach 870 million. The findings show that protective behavior, vaccination and its protection rate are still the key factors to determine the global epidemic trend.

Recommender system is the most successful commercial technology in the past decade. Technical mammoth such as Temu, TikTok and Amazon utilize the technology to generate enormous revenues each year. Although there have been enough research literature on accuracy enhancement of the technology, explainable AI is still a new idea to the field. In 2022, the author of this paper provides a geometric interpretation of the matrix factorization-based methods and uses geometric approximation to solve the recommendation problem. We continue the research in this direction in this paper, and visualize the inner structure of the parameter space of matrix factorization technologies. We show that the parameters of matrix factorization methods are distributed within a hyper-ball. After further analysis, we prove that the distribution of the parameters is not multivariate normal.

In this paper, we present and analyze the Nonconforming finite element method (NFEM) for constrained optimal control problems governed by Maxwell’s equations. Firstly, based on Maxwell’s equations, we propose the elliptic optimal control problem in H(curl) space, establish an optimal control system and give the regularity property of the optimal control model. Then, the state and co-state variables are discretized by NFEM, the control variable is approximated by piecewise constants element. The L² -norm for control and the semi-norm for state and co-state are derived. At last, numering results illustrating the theoretical findings.

In this article, a new iteration method is raised for solving an optimal control problem (OCP) governed by elliptic equation with point-wise control constraint. The OCP is discretized by applying finite element method and transformed into KKT conditions by using Lagrange multiplier method. We use variable transformation to convert KKT conditions into quadratic equations and solve these nonlinear equations by Newton's iteration method. Finally, a numerical example is proposed to illustrate the effectiveness of this iteration method.

There are a lot of fuzzy phenomena and fuzzy concepts in the objective world, and there is no clear boundary between their opposite concepts. The evaluation model based on fuzzy mathematics can address this issue . This study selects a background in real life and the fuzzy mathematics method is used to process the data, uses the analytic hierarchy process to evaluate. The results show that the model is feasible in this aspect and provides reference for other aspects.

The frame is the "skeleton" of the college formula racing car. Its function is to carry the load of the internal system of the entire car and the connection arrangement of the parts. At the same time, it is a component connected to the external suspension system. Its structural strength affects the performance of the vehicle. Therefore, the structural strength and stiffness of the frame must meet the requirements. The frame of the second generation formula car of Beijing Institute of Technology Zhuhai was designed and optimized. Some software was used for structural design and finite element analysis, including CATIA modeling software and ANSYS finite element analysis.

The concept of derivative is identified from three perspectives, and specific ideas for calculation in different scenarios are summarized.

Nowadays, the impact of drought on plant communities is a significant concern in the face of global climate change. In this paper, we developed a dynamic vegetation model to investigate the impact of drought on a community of lichen, a symbiotic association of fungi and algae. Our model considers the impact of drought on population size and inter-specific relationships in the community, and predicts how the community will change over time during drought and rainy seasons. We found that the community's response to drought is highly dependent on the number of species present, and the types of species in the community can have a significant impact on the overall population. Our results suggest that a greater frequency and wider variation of the occurrence of droughts in future weather cycles could have significant implications for the stability of lichen communities.

Vaccinations can prevent the propagation of infectious diseases very effectively. In this paper, we consider the effects of vaccines on the spread of infectious diseases, compute the analytical expressions of the disease-free equilibrium point and the basic reproduction number, and analyze the local asymptotic stability conditions of the disease-free equilibrium point using the Hurwitz criterion. Numerical simulations are used to examine the stability of the endemic equilibrium point. We chose the vaccination rate as a control variable, and we estimated the optimal control problem with an inequality constraint. We used the symplectic pseudospectral method to solve optimal control problems. The results indicate that increasing vaccination rate can stop the spread of infectious diseases.

Due to that redundant feature will degrade the accuracy and efficiency of the displacement prediction model, a feature engineering strategy is proposed in this paper to prompt the displacement prediction. Firstly, the displacement-related factors are sorted out, and these factors are enriched by feature interaction. Then, the decision tree algorithm is combined with Spearman correlation coefficient in feature screening phase to eliminate the redundant features. Finally, based on the feature screening results, an integrated AdaBoost-BP neural network prediction model is constructed. Taking Xinpu landslide in Chongqing as an example, the prediction accuracy of MAE and MSE is 0.234mm and 0.099mm respectively, which performs better than that without feature engineering. It is demonstrated that the proposed feature engineering has superior applicability for landslides prediction.

This paper analyzes how the hotel make the optimal sales strategy decision and design a revenue sharing coordination mechanism to improve the operational efficiency of the supply chain. Some assumptions are there are two partners which are the hotel and the OTA, and the hotel cooperates with the OTA in travel package marketing. The hotel is risk aversion while the website is risk neutral. A mean-variance model is proposed to characterize the hotel front desk directly selling behavior. And then we can get Stacklberg game model between the hotel with the website. Finally ,the solution of the model yields some insights as follows:(1) the external market structure and the organizational feature of the hotel have effect on the optimal sales strategy;(2) Compared with the centralized setting, the utility of the whole supply chain will reduce in the decentralized;(3)The promotional revenue sharing coordination mechanism can lead to a Parteo improvement of the utility level of both the hotel and the OTA. These conclusions can provide decision-making reference for promoting the hotel to operate efficiently.

This paper studies the pricing strategy of closed-loop supply chain when consumers have reference effect and the thirdparty recycler has fairness concern preference. Firstly, this paper constructs three decision models：the decision model considering only fairness concerns, the decision model considering only the reference effect and the decision model considering both. Then, the optimal decision of each model is given under decentralized decision-making and centralized decision-making respectively. The results show that the fairness preference of the third party is beneficial to the third party itself, but not to the consumers and manufacturers and the reference effect of consumers is beneficial to itself and manufacturers, but not to the third party. Finally，numerical examples are used to verify the main theoretical results.

The experimental evaluation and design model of C4 olefin preparation based on high dimensional discrete space optimization was established in this paper. First, the relationship between ethanol conversion, C4 olefin selectivity and temperature was studied by fitting. Then, we took catalyst combination and temperature as independent variables, ethanol conversion and C4 olefin selectivity as dependent variables, conducted partial correlation analysis on the latter to observe their correlation degree, further used multivariate analysis of variance, and conducted inter-agent effect test. The effects of catalyst combination and temperature on ethanol conversion and C4 olefin selectivity were evaluated by the sum of type III squares. Subsequently, we used the control value of the aforementioned dependent variable to generate a high-dimensional discrete space, conducted stepwise regression of the aforementioned dependent variable and independent variable, and then tested its accuracy according to the objective function of maximizing the yield of C4 olefin. Combining with the constraints of discrete variables, the method of ergodic and genetic algorithm is used to search for the optimal value in high dimensional discrete space, and the corresponding results are obtained. Finally, as an innovative supplement, we used Pareto optimization to explore the method of producing more C4 olefin by ethanol coupling experiment under the same amount of ethanol substance as possible.

The focus of this paper is efficiently and accurately solving the stochastic problems with multimodal distribution. Based on the stochastic perturbation method, the divided-difference method is adopted to approximate the partial derivative term in the perturbation method, and regard the divided-difference nodes as the collocation points to construct a nonintrusive statistical method. In addition, we also propose an adaptive method to select perturbation expansion terms, which can improve the calculation efficiency of statistical moments by ignoring the insignificance items. Because the Gaussian mixture model is often used to deal with the random problem of multimodal distribution, combined with the proposed method in this paper, the calculation processes of the statistical moments and probability density function are given. Finally, the accuracy and efficiency of the method proposed in this paper and the quasi-Monte Carlo method are compared by two numerical examples. The results show that the proposed method can efficiently and accurately obtain the statistical moments and probability density function of random response.

The structure of an artificial excavation foundation pit is a typical semi-enclosed space, wherein the working space is small, and the natural ventilation is not smooth. This makes it easy to cause the accumulation of harmful gases and threaten the safety of operators. In this work, an artificial excavation foundation pit was used as the study area. Computational fluid dynamics software was employed to simulate the dynamic change law of the gas environment of artificial excavation foundation pits of different sizes (diameter and depth) under the action of natural and mechanical ventilation. The simulation results showed that under natural ventilation, the harmful gas barely dissipates, and thus, mechanical ventilation must be adopted. Under mechanical ventilation conditions, at the same digging depth, the lower the position of the diffusion source, the faster the mass fraction of harmful gas at the monitoring point when it reaches peak value; the deeper the hole, the longer the mass fraction of harmful gases to reach the peak value at the monitoring point, and the more difficult it is for the harmful gas to diffuse. Further, the larger the diameter, the faster the mass fraction of the monitoring point when it reaches the peak, while diameter size has little effect on the dissipation rate. By analyzing the law, a prediction model for changing carbon dioxide gas concentration at a distance of 1.2 m from the bottom of the foundation pit was established. The model can simulate and predict changes in harmful gas concentration in artificial excavation foundation pits.

Aiming at the common problem in the industry that response of the deviation correction mechanism of lithium-ion battery winding machine is not timely under the high-speed winding state of 3.5m/s, a theoretical dynamics model for displacement response of deviation correction mechanism of the lithium-ion battery high-speed winding machine based on lumped parameter method was established; the transient displacement response and error rate of the transient displacement response of the correction mechanism were analyzed, and the relationship between transient displacement response and the displacement input and the relationship between error rate of the transient displacement response and the displacement input were obtained. A finite element model of the transient displacement response of the deviation correction mechanism was established based on Ansys software, and the transient dynamic simulation of the deviation correction mechanism was carried out. An improved scheme was proposed to shorten the distance between motor and deviation correction roller, and change the deviation correction mechanism in series along the axis direction of the motor into a parallel arrangement. The effectiveness of the improved mechanism was verified through the high-speed winding test with winding rate of 3.5m/s. The results show that the main influencing factor of the transient displacement response of the deviation correction mechanism is overall stiffness. The time required to correct the deviation of the improved deviation correction mechanism is reduced by 38.6%. The proportion of pole pieces exceeding the allowable range of 0.2mm~0.8mm due to untimely deviation correction decreased from 12.9% to 0%. The results of test and simulation analysis conform well. The problem of untimely deviation correction was solved, which verified the effectiveness of the improved mechanism.

Data science, science and technology should be combined with practical applications. Scholars at home and abroad have developed many early warning models. Different models have different characteristics, and their scope of application, advantages and disadvantages are different. This research uses python to collect the financial data of small and mediumsized enterprises in recent years. learning models such as ridge regression, logistic regression, Lasso regression, support vector machine and decision tree model, and on this basis, use BP neural network to optimize the model. Among them, it introduces in detail the construction idea of enterprise financial early warning model based on neural network algorithm, clearly puts forward various indicators in the early warning mechanism, and implements further optimization through artificial neural network on the basis of the basic model. suggest an admission of the basic model.

Fungi plays an important role in the breakdown of wood, which is a part of carbon cycle. This kind of litter refers to plants that are no longer useful for humans to use. The aim of this paper is to model the decomposition process of wood fiber by fungi in the presence of multiple fungal interactions, different growth rates and niche width effects in a region. A set of differential equations are established to obtain the curve of the growth rate of fungal hyphae and the rate of wood decomposition increasing with time. Nextly, the Lotka Volterra model is adapted to established a model for the interaction of multiple kinds of fungi. Lastly, the effect of the diversity of fungal communities on the efficiency of decomposing wood is considered. It can be concluded that the greater the diversity of fungal communities, the greater the efficiency of the system in decomposition of wood.

The so-called aircraft stealth refers to coating or pasting absorbing materials on the relevant parts of the aircraft, reasonably designing the aircraft shape and layout, so that the enemy detection system (such as radio radar, infrared radar, lidar, etc.) can only receive the greatly weakened aircraft reflection signal. In this way, the possibility of aircraft being found or tracked is reduced. The basic research of stealth technology includes designing the absorbing material into a reasonable shape to maximize its efficiency. Its achievements can be applied not only to military equipment such as aircraft, ships and tanks, but also to other scientific and technological fields. Although the mathematical and physical problems composed of Maxwell equations and corresponding boundary conditions can be analyzed and calculated strictly in theory, the model is complex and the calculation is complicated. Focusing on the problem of radiation absorption, the microwave absorption performance of wedge shaped absorber is studied in this paper. Simulation results show the effectiveness of the research.

With the growth of age, the body function of the elderly gradually declines, and falling has become the main factor endangering the life safety of the elderly. In order to solve the problem of the lack of data support in the study of the elderly fall injury, the human body is simplified into a two-bar model articulated by two rigid bodies. Based on Kane method, the kinetic equation of the human fall process is derived and analyzed. By setting the inertia parameters and joint torque of the human body, the curve of the dynamic parameters of the human body with time is obtained. The simulation results show that the human joint Angle decreases gradually in the dynamic model, and the human body falls forward, which conforms to the law of human movement, and verifies the correctness of the dynamic model. It also plays an important role in the design of bionic machinery such as walking robots.

Using QR decomposition of a matrix, the inverse quadratic eigenvalue problem is transformed into an equivalent system of equations, the expressions of the general solution and the Necessity and sufficiency solution of the symmetric Orthogonal symmetric Matrix for the inverse Matrix problem are obtained. Furthermore, the optimal approximation problem for any given Matrix is considered and the optimal approximation symmetric orthogonal symmetric solution is obtained.

While the rapid development of highway construction, various highway emergencies have also brought a large negative impact to the society. Highway scenes have the characteristics of linear scene strip distribution, and other scene elements outside the linear scene area are often not needed or not preferentially needed in the construction of linear scenes. To address this problem, this paper proposes a method to construct highway emergency scenes based on unexpected events. Based on the theories related to event-driven and dynamic segmentation, the paper extends the dynamic segmentation technique by combining the data characteristics of highway emergencies and designing the event-based scene construction rules. An event-driven model is constructed to elaborate the mapping relationship between business events and geographic events, and a 3D linear scene dynamic feature description scheme is designed to realize accurate dynamic change expression of geographic elements in 3D linear scenes. Finally, a rescue simulation experiment of highway is conducted to realize the construction of emergency scenes of highway based on unexpected events, which proves the feasibility of the method.

Motivated by the importance of slow-speed underway replenishment of vessels in offshore shipping, this study aims to investigate the interaction and wave resistance phenomena between two ships sailing in close proximity, which may lead to lateral drift, yaw, and even collision, posing a great threat to the safety of both vessels. KVLCC2 and Aframax standard model ships were chosen as the research objects. To examine the ship-ship interaction and wave resistance phenomena in regular waves under the conditions of low-speed single ship and staggered navigation, RANS numerical simulation method and overset grid technique were employed. The simulation results indicate that the ship's motion and forces and moments between two ships are sensitive to the wave's wavelength, especially in the range of λ/Lpp=1~1.5, where response peaks were observed. Compared to single-ship navigation, the longitudinal pitching effect of two ships sailing staggered in waves is significant and increases with wave height. This study provides an important reference value for the safe navigation of ships during wave-induced staggered navigation and replenishment operations at sea.

Due to issues such as uneven spatial distribution of data sensors and data vulnerability, dynamic systems such as disease transmission and weather prediction that can be described by PDE often generate uneven spatiotemporal data. However, due to the limitations of module assumptions or convolutional calculations, existing data-driven deep learning models are often not suitable for spatiotemporal data that is not standardized in the spatiotemporal domain, and specific models are difficult to generalize to new dynamic systems. In this paper, graph neural network and method of line method are used to model the uneven spatio-temporal data modeling generated by dynamic systems, and a prediction model that allows efficient spatio-temporal input domain is proposed. It uses a differential graph network of approximate derivatives to learn the differential information in dynamic system data, combined with the dynamic system parameter information extracted from the parameter coding network, and finally realizes accurate prediction of dynamic systems through network approximate equations. The model achieves end-to-end system prediction, in which the parameter encoding network learns dynamic system parameters and other information, and is supplemented by a meta learning mechanism to improve the model's generalization ability on different PDE prediction tasks. The experimental results on the nonlinear reaction diffusion equation and heat equation show that the model can adapt to the data with spatiotemporal noise, and shows a very competitive prediction accuracy.

Ancient glasses identification and classification has been widely developed to investigate the ancient culture and historical issues. However, existing researches are concentrated on the classifications for ancient glass with unacceptable identification accuracy, which ignores the prediction method can also successful identify the types of ancient glass. Therefore, there is a demand for correctly predicting types of ancient glass with acceptable identification accuracy and reasonable system costs. Logistic regression models are statistical methods used to predict a binary (yes/no, 0/1) outcome based on one or more predictor variables. The aim of this paper is to make species predictions for ancient glass based on chemical composition distributions, and to determine whether an unknown glass sample is a high potassium or leadbarium glass. We collected data from 61 sets of samples, each corresponding to fourteen chemical compositions, and used a logistic regression algorithm to make species predictions for these sample glasses, solving for the parameters using Newton's method and gradient descent, and comparing them.

Ancient glasses sub-classification is an essential component for archaeological research and assist researchers to divide the ancient glasses. However, existing classification models are concentrated on life-related area and ignore the ancient glass. Subsequently, existing classification models are almost utilizing machine learning algorithm, which is a black-box model leaks the reasonable explanation and mathematical calculation procedures for the classification results and may lead low accuracy when learning a novel area. Therefore, we propose a novel classification model by utilizing clustering method to dispose the issue about ancient glass sub-classification. In this paper, we aim to sub-classify weathered ancient glass according to its chemical composition distribution. We collect 61 sets of sample data, each corresponding to fourteen chemical compositions, and use the k-means algorithm to sub-classify these sample glasses. Next, the cohesiveness and separability of the results are evaluated using the contour coefficient method and the model accuracy is checked using the elbow method .Finally, the elbow method is suitably improved.

At present, 3D printing technology has been widely used in machinery manufacturing, aerospace, automobile tires and many other aspects, especially in the machinery manufacturing industry application has spawned a variety of new technologies. The FDM 3D printer can make consumables accumulate gradually by melting deposition, and print 3D entity by supporting material. At present, this technology is relatively low cost, easy to operate, is the most commonly used technology, but it also has low precision, slow speed, uneven heat dissipation and other disadvantages. Based on the existing technology and equipment, the FDM 3D printer needs to be optimized in many aspects, including nozzle structure design, simulation analysis and parameter optimization.

As the core moving part of the laser cutting machine, the beam quality has a very important impact on the dynamic and static performance and processing accuracy of the laser cutting machine. This paper uses Solid Works to build the beam model of F3015 laser cutting machine, select 8 design variables and appropriate constraint conditions, and take the economic index quality of the beam as the objective function to carry out the lightweight design of the beam. The results show that the mass is reduced by 15.9%, and the total deformation is reduced by 10 .5%.

Applying a guarantee effectiveness simulation assessment method to combine harvester cross-zone operations improves the guarantee capability of the combined cross-zone operations. A combined harvester cross-zone operational simulation model was designed based on the Simlox simulation platform. The structure of the combine harvester and elementary mission characteristics of the cross-area operation were combined. Typical indicators for the simulation evaluation of guaranteed effectiveness were defined, and the simulation process, sampling, and statistical methods were determined. Operation availability (A_o) and mission completion probability (P_mcs) are the leading indicators for assessing the capability of the combine harvester in cross-zone operations. The analysis of the simulation results shows that the significant factor affecting the guarantee capability of the combine harvester is the shortage of spare parts for the unrepairable units (DU) after an equipment failure and that the guarantee capability can be optimized in the direction of the spare parts inventory. This study establishes a simulation model of the combine harvester cross-zone operation guarantee, which can be used to evaluate the guarantee capability of the combine harvester cross-zone operation and provide a reference for optimizing the guarantee model of the combine harvester cross-zone operation.

This paper mainly focus on the research of the active tip clearance control scheme for a high pressure turbine on aircraft engine. A brief interpretation of this model is illustrated. Then a rum-time based engine control model (including steady and transient control) is added to the high pressure turbine model, the high pressure tip clearance can be simulated upon the rebuilt model. The High pressure turbine tip clearance control method is realized by bleeding the cool air from the fan to the case shroud, the objective of this paper is to derive the reasonable control plan for the Active tip clearance.

In order to better target the protection of glass artifacts, this paper proposes a composition analysis and identification model of glass artifacts based on Bayesian and SVM, adopts a variety of analysis methods to extract and pre-process the data, establishes a composition analysis and identification model of artifacts and predicts the results for the next composition of glass artifacts, and finally validates the model, and the experimental results show that the SVM model obtains a high performance with high generalizability

The heteroscedasticity in training Machine Learning (ML) methods is vital for its prediction performance. To provide practical insights about this challenge, we modify the objective function to incorporate heterogeneous variance, which enhances ML training and improves the accuracy of prediction and evaluate the performance of the proposed heteroscedastic Support Vector Regression (HSVR) method using the Sieve simulation and Monte Carlo techniques. The results demonstrate that the performance of the HSVR method continually improves as the sample size increases, with a progressively decreased root mean squared error from 0.129 to 0.028. Our findings suggest that incorporating heterogeneous variance in the objective function significantly enhances predictive performance and provides a valuable contribution to the field of ML. The proposed method can be extended to other ML models that assume a fixed variance in the noise model.

In this paper, based on the principle of penalty function algorithm, combined with the gas-steam ejection classic launch internal ballistics model, the water depth and ambient temperature were selected as decision variables, the carrier velocity out of the cylinder as the objective function, to design an improved penalty function algorithm, so as to obtain the optimal parameter configuration of launch internal ballistic performance. Besides, the gas-steam launching interior ballistics calculation software is designed, and a certain type of carrier is selected to complete the launching interior ballistics analysis, proving the feasibility of the application of the improved penalty function algorithm in the launching interior ballistics technology. The calculation software of gas-steam launch interior trajectory was compiled, and a carrier was selected as an example to carry out the optimization calculation of launch interior trajectory. Through the analysis of the optimization results, it is proved that the improved penalty function algorithm can greatly improve the design efficiency and shorten the design cycle of the products under development and new development under the premise of satisfying the calculation accuracy of the launch interior trajectory.

To address the challenge of oversupply of emergency living materials during the initial stages of the public health emergency outbreak, we propose a novel combined distribution model that utilizes the Analytic Hierarchy Process and Genetic Algorithm and practices in the COVID-19 pandemic in Changchun, Jilin Province. Specifically, the proposed method aims to optimize the distribution scheme and ensure that the materials are allocated efficiently to those who need them the most. In this paper, we utilized the Analytic Hierarchy Process to establish an index system for the urgency of emergency living materials in each district. By doing so, we were able to determine the utility weights for each district. We then incorporated a Genetic Algorithm to optimize the distribution of emergency living materials, with the aim of maximizing the effectiveness of material distribution. Our proposed distribution model was compared and analyzed against the original distribution scheme, and the results demonstrated that our approach had better feasibility and effectiveness in distributing emergency living materials. This is accomplished despite the constraints posed by limited material availability and the urgency of demand in each district. Our approach enables a scientifically-based distribution of living materials

The world has witnessed a huge increase in the concentration of CO2. Some people believe that it may cause severe disasters. Thus, we develop mathematical models to predict the future trend of CO2 based on its atmospheric circulations. The effects of fossil burnings, plant respiration, oceanic absorption, policy and technology are analyzed and discussed. Results show that the concentration of CO2 will boost in the next 50 years, but the trend will be terminated and inversed if human begin to control the emissions of CO2.

In order to formulate high-quality and reliable supplier schemes in the enterprise's raw material ordering, according to the order and supply data of 402 raw material suppliers of an enterprise in recent five years, we have determined five indicators, including average supply quantity, supply times, supply qualification rate, average satisfaction rate and maximum capacity of suppliers. On this basis, a quantitative index evaluation model based on principal component analysis and "S" curve is established, Finally, 50 most important suppliers were selected. Then, taking 24 week ordering cost, minimum transport loss of forwarders and minimum suppliers as objective functions, a multi-objective programming model is established, we use Heuristics algorithm to solve the problem and determine the ordering and transshipment scheme. Finally, we use the method of comparative analysis to obtain that the effect of our scheme can stabilize the production capacity of the enterprise while reducing the cost of the enterprise.

The position of UAV formation must be adjusted by positioning method to keep the formation during flight. Maintaining formation is conducive to completing various tasks of UAVs. In order to reduce external interference, electromagnetic signal emission should be reduced as much as possible. However, the existing passive location and active location methods can not effectively solve this problem. Active positioning will increase the risk of exposing the position of the UAV, and the UAV should not be too far away from the ground signal source. Existing passive localization methods such as building nonlinear motion model and volume Kalman wave algorithm cannot adjust the position of UAV quickly without reducing electromagnetic signal emission.To maintain formation shape, an optimization model based on azimuth-only passive positioning and greedy strategy is established. We studied a particular case of drone formation. Firstly, we give the location model by geometric method. Next, we build an optimization model based on greedy strategy. At last, We randomly selected a set of data for simulation, the UAV position can be adjusted only 6 times to achieve a better effect. It means fewer electromagnetic signals are emitted and the adjustment speed is faster.

The high-pressure fuel system is an important part of the transmitter. In its working process, the oil enters or sprays out, which will cause pressure changes. The pressure in the high-pressure oil pipe is basically stable at about 100MPa, and the density pressure formula is specially corrected. The correction value is +0.018 through the weighted average. The classic PID control algorithm is used to easily realize the control process of voltage stabilization from 100MPa to 150MPa.This paper establishes three models, namely, the high-pressure oil pump model, the fuel injector model and the decompression model. The MATLAB simulation software is used to display the results, and the fuel pressure and quality in the high-pressure oil pipe are used to connect the three models. The parallel operation method reduces the overall operation workload of the system, with good robustness and good scalability.

The rapid development of renewable energy has effectively alleviated the problems of energy shortage and environmental pollution. At the same time, its randomness and volatility have increased the demand for flexible adjustment of power systems. The increasing proportion of renewable energy generation output exacerbates the risk of insufficient flexibility of the power system. In this context, this study quantifies the frequency support requirements of the power system, and establishes an energy storage configuration model considering flexible resources at multiple time scales, and uses gurobi as a solution tool to verify the model with the IEEE-39-bus system. Simulation result proves that the optimization model of energy storage allocation considering the flexibility of multi-time-scale resources can effectively improve the flexibility of the power system and meet the operational needs of the power system at various time scales.

This study employs the entropy-weighted-TOPSIS approach to evaluate the level of development of cold chain logistics across 30 provinces in mainland China. The findings indicate Guangdong, Shanghai, Shandong, Henan, Beijing, Tianjin and Jiangsu exhibit high level of development in cold chain logistics, whereas Qinghai, Gansu, Jilin, and Xinjiang exhibit low level of development in cold chain logistics. Jiangsu and its surrounding areas have formed a high-value agglomeration for the cold chain logistics development index, whereas Inner Mongolia, Ningxia, Qinghai, Xinjiang, Gansu and Sichuan have formed a low-value agglomeration. Therefore, it is essential for all regions to improve the technological level of cold chain enterprises, standardize cold chain infrastructure, and optimize the cold chain logistics network.

Script event prediction involves building a model using recent event sequence records to capture the correlation between events and predict future ones. However, existing methods require a large amount of training data to perform well. In many real-world scenarios, labeled data is scarce and data distribution is uneven, making it challenging to achieve satisfactory performance. Additionally, the learning process of many models demands significant computational resources, which can render them unsuitable for low-resource settings. In order to fill the task blank of event prediction in low resource scenarios, a general framework called Meta-memory low resource network learning model(MLN) is proposed. Firstly, MLN captures the memory of events that occurred in the history through trend learner, learns the context-implied representation between events, and can predict future events based on the implied representation. Secondly, a Meta-memory predictor is proposed, which can further aggregate similar contextual implicit representations to form meta-memory representations, To realize the long-term memory learning process under the condition of few-shot. Finally, a gated network combination method combines the trend learner with the Meta-memory predictor to improve the effect of event prediction. We conducted experiments on four real data sets, and the results showed the effectiveness of MLN.

With the depletion of mineral resources in the plains, Chinese mining industry has shifted to higher altitudes where low pressure and low oxygen has become a bottleneck limiting further development of mining technology. The traditional diffusion type of oxygen supply has low oxygen utilization. Controlled circulation ventilation offers a good alternative for oxygen supply to workers in high altitude mines. In this study, a CFD approach was used to conduct a single factor analysis of circulating duct height, distance between circulating duct outlet and heading face, oxygen supply duct height, forcing duct air velocity and circulating duct air velocity to investigate their effects on the oxygen mass fraction located at the heading face. In addition, an orthogonal experiment was designed to analyze the effect of the above five factors on the distribution of oxygen mass fraction in the heading face. The results showed that the effect of forcing duct air velocity on the oxygen mass fraction in the heading face was the greatest. The average oxygen mass fraction in the heading face is highest when the height of circulating duct was 0.9 m, the distance between circulating duct outlet and heading face was 2 m, the height of oxygen supply duct was 1.0 m, the air velocity of forcing duct was 3 m/s and the air velocity of circulating duct was 2.5 m/s.

The cold-formed thin-walled steel-timber composite component is a new type of composite component. The effective combination of timber and steel can utilize the mechanical properties of the two materials. In this paper, considering the effects of flange width thickness ratio, web height-thickness ratio, and axial compression ratio on the mechanical performance, the seismic performance of six cold-formed thin-walled steel columns and six cold-formed thin-walled steel-timber composite columns was analyzed using the software ABAQUS. The results show that the main factor affecting the seismic bearing capacity of components is axial compression ratio. When the axial compression ratio is large, the stiffness degradation of cold-formed thin-walled steel members is relatively obvious, especially in the late stage of loading, the performance of the composite members is better. It shows that timber boards play a key role in the buckling of steel sections, verifying the good working performance of the composite members.

To maximize the potential and value of enterprise data assets, this paper analyzes and evaluates the data asset management and analysis (D&A) system for logistics enterprises. A relatively complete system is put forward for enterprise data asset management, and a comprehensive evaluation model based on the Analytic Hierarchy Process (AHP) is constructed. The Key Performance Indicator (KPI) is determined by the key success factor method, and the right is further quantified. The comprehensive score of key indicators is obtained by expert score and linear weighted average method. It expounds on the connection and importance between internal personnel, technology and the data management process, the calculation is simple and the results are clear. By simulating the relevant situation of the enterprise, the effectiveness of the model is proved, which is strong, and the combination of qualitative and quantitative, can deal with many practical problems that cannot be started with the traditional optimization technology.

China is a large population country,whose food security issue is of great importance to people's livelihood. Therefore analyzing and predicting grain output is significant in China. In view of the second exponential smoothing model(SES) and Support Vector Regression algorithm(SVR), it proposes SES-SVR residual correction model to predict grain yield. It not only highlights the advantages of the trend of time series, but also increases the robustness of the sequence. For the grain yield data of Yunnan Province, the SES-SVR residual correction model has smaller error and higher prediction accuracy, which has certain guiding significance for grain yield data prediction.

The efficient use of energy is a problem that is currently being given general attention. In this paper, the motion of the oscillator and float are analyzed from the perspectives of statics, dynamics, and hydrodynamics, respectively, and the force analysis of the wave energy device is performed to obtain the equations of motion of the oscillator and float. The maximum output power problem of wave energy is discussed using mathematical modelling, mainly using differential equation theory to study the motion of wave energy device floats and oscillators, damping coefficients and output power, and solved using the Runge-Kutta method, and finally obtained that when the scale factor of damping coefficient is 97975 and the power index n is 0.4437, the maximum average output power of 162.2131w exists

Based on the single-parameter Lie group method, the analytical properties of invariant manifolds of quasi-homogeneous autonomous systems are revealed, providing a more flexible and practical method for finding invariant manifolds of such systems. The analytical properties of invariant manifolds of quasi-homogeneous systems are applied to classical gyro systems, and several known methods for finding special solutions are unified; And restrict the distribution of rigid body center of gravity to the condition 𝒙𝑮 = 𝟎, a three-dimensional invariant manifold of the system is obtained, and the motion on the three-dimensional invariant manifold is discussed.

This paper will briefly describe the impact It is mainly predicted by wind power(WPP) error on power supply, point out the factors that affect wind farm ultra-short term WPP, and analyze the current research situation WPP. So far, many WPP methods have been studied internationally, which are generally divided into two categories: physical methods and statistical methods. The physical method is mainly to predict the wind speed and wind energy of the wind farm by using the numerical weather forecast and combining the physical information such as the terrain aroundGet wind farm; The statistical methods is to establish the mapping relationship between the historical data and the future wind speed using the linear or nonlinear method based on the statistical model method and the historical measured data of the wind farm.

In view of the problems of repeated modeling and low efficiency in the three-dimensional modeling of different types of drum brakes, this paper uses Visual Basic and ANSYS Parametric Design language command flow of ANSYS to develop the software interface program of the drum brake parametric modeling system. First of all, the three-dimensional modeling program of the drum brake is compiled by APDL command of ANSYS, and its geometric model is established to conduct mesh generation and component operation. It is the preparatory work for statics analysis, harmonic response analysis, and topology optimization in ANSYS. The research results show that the drum brake parametric modeling system developed in this paper can reduce the labor intensity of the drum brake designer and reduce the professional quality requirements of the designer on the finite element simulation software, so it is of great significance.

For the design problem of maximum output power of wave energy device, the motion law of the device is obtained by establishing the vertical and longitudinal motion model, and the motion law of the device is obtained by using the fourthorder Runge-Kutta method, and the simulation annealing algorithm is used to optimize and solve the problem of linear damper and rotational damper coefficient optimization. According to the experimental data, MATLAB is used to calculate the maximum output power of the wave energy device and the optimal damping coefficient of the linear damper and rotary damper.

The operation optimization of LNG supply chain is increasingly crucial. This paper investigates the operation optimization of LNG supply chain. The minimum total cost of the new LNG supply chain is set as the objective function. Under the constraints of station construction, route selection, equipment purchase, node flow balance, and service radius, a mixed integer linear programming (MILP) model is established. Finally, it can determine the liquefaction scheme of gas source, intermediate node construction scheme, reserve storage scheme, transportation scheme, purchase scheme of different equipment, etc. A practical case is used to illustrate the reliability of the model. The results show the proposed model can provide an admission of LNG production, transportation, and marketing business for natural gas-related enterprises, and also can help the government to coordinate resources.

The spread of SARS-CoV-2 poses threat to human health and negative impact on social economy and stability. This paper uses CoVid19 Modeling toolkit provided in the GAMA platform to establish SEIR model with contagious incubation period, and model parameters were determined from relevant research data. In terms of propagation trend of COVID-19, the use of different control measures was simulated to obtain relevant prediction data of different strains. Through simulation of the spread of COVID-19, it has reference significance for the development and prediction of other influenza

This paper proposes an anomaly detection method based on the combination of empirical mode decomposition (EMD) and sliding Interquartile range (SIQR) for detecting electromagnetic anomalies prior to earthquakes. The EMD method is used to effectively remove noise and trend components in the geomagnetic field data. Then, the SIQR algorithm is used to compare the raw data and the reconstructed data after EMD from multiple seismic stations. The results demonstrate that this method can more effectively detect electromagnetic disturbances prior to earthquakes.

Different species of plants have different adaptations to dry climates. The number of species may change with time under drought stress. Based on the improved logistic growth model (LGM), the ordinary differential equation was derived to predict the changes of plant community over time. The fourth order Runge Kutta equation was used to solve the solution, and the evolution process of plant community under drought stress was analyzed. First, as for the relationship between drought adaptability and the number of species, considering that the growth of plant community conforms to the law of biostatistics, this paper established the growth differential equation of species biomass by improving the logistic growth curve. In order to verify the feasibility of the model, two plants with competitive relationship, namely Rugondii and Artemisia orhaga, were selected for non-dimensional processing. Using fourth-order Runge-Kutta (ode45 function), the prediction curves of the two competing species with time can be obtained.

Vienna ab initio Simulation Package(VASP) is software for performing electronic structure calculations and quantum mechanical molecular dynamics simulations, and is widely used in materials simulation and computational material science research. Currently VASP is accelerated on NVIDIA GPUs via the OpenACC programming model, which cannot be directly compiled and used by domestic GPU like accelerator platform. We use the HIP programming model to port VASP to GPU like accelerator. Refer to the OpenACC port of VASP, we use the HIP API for device and data management, write kernel to implement cyclic accelerated computation, and use mathematical libraries such as hipBLAS and hipFFT to support the computation. After the porting was completed we compare the computed results of the HIP port version of VASP with the CPU version and the OpenACC version. We passed the validation of the correctness of the HIP ported version. We also analyze the principle of All-to-All parallel communication and propose optimization strategies such as aggregated communication and merged copy for the case of multiple bands communication, which are implemented on the HIP port version. We choose the B.hR105 testcase to test the All-to-All before and after optimization. The results demonstrate a 25.57% performance improvement after optimization.

Underground buried cables can improve the efficiency of power transmission and utilizing in the urban underground space. The current load capacity is an important parameter in cable design and operation. When the cables are concentrated in the concrete ducts, the mutual heating increases the temperature of the central cable. concrete pipe laying cable is a commonly used method for high voltage cable because of its high reliability, easy maintenance and convenient replacement. In given concrete duct cross sections and corresponding cable arrangements, the finite element method was used to analyze the temperature field distribution of concrete ducts section with different cable arrangements. The influence of buried depth of concrete duct and thermal conductivity of soil on cable current carrying capacity were discussed. Based on the temperature distribution of concrete pipe, the 'thermal-mechanical' coupling model of cable pipe was established by finite element method, and the distribution characteristics of temperature stress and strain were obtained. The research can provide scientific advice for the conduit’s arrangement and structural form optimization of concrete cable ducts.

This paper proposes an approach based on numerical simulation to enhance the accuracy and generality of fringe field modeling in Quadrupole Mass Filter (QMF) systems. The proposed method involves SIMION simulation calculations to achieve the desired outcomes. A numerical model of the potential distribution is constructed using the finite numerical difference method in the region where the QMF and end plate are connected. The exponential fringe field function is selected as the basis for approximation, and the fringe field function is obtained through the least squares method. The potential gradient curves for different end plate gaps are derived, revealing the impact of gaps on the fringe field intensity, length, and penetration into the QMF. The proposed method is validated by comparing the obtained fringe field analysis model with literature data The research establishes the reliability and validity of the proposed method for effectively analyzing the fringe fields between the end plate and the QMF electrode in QMF systems.This makes it possible to study the properties of such systems by phase-space dynamics methods.

Aiming at the fact that traditional signal reconstruction method based on traditional methods such as Fourier and wavelets transformations cannot preserve the sharp features in the original data well, a signal reconstruction method is proposed in this paper based on a class of orthogonal piecewise polynomial function system—named as V-system. Firstly, different types of feature points of the original data are obtained through the feature extraction algorithm; secondly, the feature points are reparametrized to the locations of different knots in the V-system; finally, the fitted signal is obtained by solving the least-squares linear equations with constrains. Because the V-system has basis functions with different smoothness, the reconstructed signal can capture different features in the original data. Numerical experimental results show that the proposed method in this paper not only obtains a reconstructed signal reflecting the global shape of the original data, but also can accurately reproduce sharp features.

At present, "COVID-19" has spread in 184 countries (regions) in the world, and more than 500 million patients have been diagnosed and caused 66.3 million deaths. The worldwide epidemic is still ongoing, and the epidemic in China is still increasing, therefore, the regular prevention and control of the epidemic is An important part of ensuring people live, live, live, especially in crowded places, there is an urgent need for artificial intelligence, biometrics and other technologies. This paper proposes the face recognition method of sparse enhancement and collaborative information fusion. The paper first explains and generalises the relevant terms, and finally, the practicality of the algorithm is demonstrated through experimental analysis. The recognition rate of the algorithm described in this paper is generally higher than 95% on different facial masking ratios, and the results show that the face recognition method of sparse enhancement and collaborative information fusion based on epidemic prevention and control is a very worthy research The results show that face recognition based on sparse enhancement and collaborative information fusion for epidemic prevention and control is a very worthwhile research technique that can help solve the critical problems of epidemic prevention and control today.

Considering the current rapid development of space equipment and the increasingly urgent demand for using space frequency and orbit resources (SFOR), and the future demand for using global orbital layout, this paper proposes a comprehensive method for evaluating the effectiveness of space frequency and orbit resources. To address this issue, we build the judgment matrix for evaluation in the absence of expert advice, and utilize the entropy weights method (EWM) to the analytical hierarchy process (AHP) method to measure the effectiveness of SFOR. The effectiveness of the proposed methods is finally validated by numerical tests.

During the normal operation of the spillway tunnel, the water velocity can reach 50m s-1. High-speed water flow leads to problems such as negative pressure and high-frequency noise above the tunnel, affecting the normal operation of various facilities in the tunnel and the health of the staff. In this study, a 3D numerical simulation of free surface spillway tunnel with a rectangular cross-section is carried out. The Euler multiphase model combined with the realizable k-epsilon dual turbulence equation was used to analyze velocity distribution of air above flow surface. The velocity distribution is maintained on the same curve with sufficient residual height. The calculation equation of air velocity distribution is fitted by the couette flow related theory, and the simulation results can be well predicted.

The operation data of the predictive control system is nonlinear, sequential and dynamic. The implicit nonlinear transformation of the traditional support vector data description (SVDD) cannot represent the timing and dynamic characteristics of the operation data of the control system. To solve this problem, this paper proposes a predictive control performance degradation detection method based on weighted Deep-SVDD. First of all, the target optimization function of SVDD is redefined based on the architecture of deep neural network, and the Deep-SVDD monitoring model is constructed using the extracted deep features. Secondly, the concept of sliding windows is introduced in view of the difference in sensitivity of deep features to predicted performance degradation of control systems. Using dynamic data from the window to weight the impact of sensitive features to highlight system performance degradation and construct a weighted Deep-SVDD monitoring model. Finally, the algorithm implementation process is theoretically deduced. The effectiveness of the proposed method is verified by the simulation of Wood-Berry distillation tower predictive control system.

The management of enterprises has long included manager management as a key responsibility. Recently, manager portrait has attracted attention as a research focus in the field of manager management. This paper aims to model the problem of manager portrait using neighborhood Rough Set theory. Personality characteristics of managers are extracted from document materials using Natural Language Processing algorithms and integrated into the rough set model. In addition, the distance formula of the KFHARA attribute reduction algorithm is improved. A label attributes construction method and a portrait method suitable for manager portrait are proposed based on the rough set model and the improved KFHARA algorithm. Finally, the practicability of the method is illustrated by an example of an enterprise manager selection and appointment business scenario.

In order to investigate the impact mechanism of explosion fragment size on its collision probability, five typical fragments that often appear in industrial accidents were selected. Taking into account the rotation of the fragments during ejection, a correction factor for collision probability was proposed, and a modified model for fragment collision probability was established. The results of the case analysis show that the maximum ejection distance of the five types of fragments can reach 550-950 m, and the maximum possible ejection distance is distributed between 76-544 m. The ejection distance of CE-type fragments is relatively small, while that of PT-type fragments is relatively large. In addition, under the condition of considering the size of the fragments, increasing the size of the fragments can improve the collision probability. The impact of the fragment size on the collision probability is more significant within a close range of 60 m. When the distance between the fragment and the target exceeds 60 m, the impact of the fragment size on the collision probability can be ignored.

The issue of light pollution has been arousing more and more attention on a global scale. Oceans and atmosphere on earth are seriously threatened by light pollution. In order to identify and assess the light pollution risk level of a location, this paper establishes a two-phase comprehensive environmental assessment model for regional light pollution. In phase I model, this paper selected 12 indicators from three aspects: source of light pollution, severity of pollution problem, and resource availability of pollution. Subsequently, the Logistic Regression model (LR) was utilized to obtain the models of the light pollution source and the resource availability of the treated pollution, and the regression model was developed through several significant tests. In phase II, this paper used Spearman correlation coefficient analysis to obtain 10 effective indicators that are highly correlated with the amount of light used. Then we used the comprehensive weighting method based on the Ana-lytic Hierarchy Process (AHP) and Entropy Weight Method (EWM) to give weight to 10 indicators, and finally we evaluated the risk of light pollution in four different types of locations based on the TOPSIS comprehensive evaluation method. Finally, the sensitivity analysis of the model is carried out, and the results show that our model has good stability and is very reliable.

Convolutional neural network-related applications have now been developed on a variety of platforms, including CPUs, GPUs, and others,but most of them sacrificed energy consumption to achieve good performance. Therefore, in recent years, more and more hot spots have shifted to how to achieve related applications such as low energy consumption research methods. In order to make the steel surface defect detection system meet the requirements of real -time detection, this article proposes that VGG16 is used as the main network to complete the design of the FPGA's rapid detection and identification system for the FPGA. This system conducts software and hard-hard design based on the ZYNQ-7000 platform (1) The design of parallelization of convolution on the PL side The hardware acceleration is achieved by accelerating the data flow design. Each IP core accelerated with the PL end. (2) In the method of quantifying the data, in the case of almost little loss of data accuracy, the use of resources on FPGA films has been greatly reduced to achieve acceleration of algorithms. The final experimental results show that compared with the CPU, this algorithm has increased by 6 times. The power consumption ratio of the CPU platform and the FPGA platform is 12.6, and the power consumption ratio of the GPU platform and the FPGA platform is 38.2, which is more suitable for applications on the embedded platform.

Buffer is an important safety protection device in the event of vehicle accident and its working stability directly affects the life of the occupants. Due to the influence of strain gauge size and other factors, the conventional experimental measurement data is not accurate enough. Moreover, the buffer experiment is disruptive and costly. In order to obtain more accurate data and reduce the experimental cost, finite element modeling method was used to carry out numerical calculation of aluminum alloy tubular buffer, and the energy absorption capacity and the fluctuation of crushing force were studied. A large amount of data and nephogram were obtained, which provided support for the in-depth study of the working principle of the buffer. The results show that with the continuous pressure of the indenter, the different parts of the aluminum alloy tube will undergo elastoplastic deformation and absorb energy continuously from the outside. Data analysis results show that the average crushing force is 2.625 kN. Although the stress distribution of pipe wall is more uniform, the fluctuation of crushing force is still very obvious. This study provides data support for the design and optimization of buffer parameters in automobile enterprises.

Electricity generation is a major contributor to carbon emissions in the power system. However, the current carbon emission reduction strategies focus mostly on the production and consumption aspects of the power industry, without taking into account the potential for reducing emissions through the use of power transmission and transformation equipment.This paper investigates the carbon emission efficiency of power transmission and transformation equipment by combining Life Cycle Assessment (LCA) and Data Envelopment Analysis (DEA). First, data related to the power transmission and transformation projects are collected, organized, and cleaned to set weights for different stages of the equipment life cycle. Then, a whole life cycle total factor carbon emission efficiency measurement model (e-LCA-SBM model) of power transmission and transformation equipment is constructed to measure the total factor carbon emission efficiency of the equipment. The results show that the whole life cycle carbon efficiency is modest in the equipment with lower whole life cycle fixed capital investment, and vice versa. This study can provide a reference for selecting equipment with higher carbon emission efficiency and reducing carbon emissions.

Ca²⁺ in cardiac myocyte are mainly stored in the sarcoplasmic reticulum (SR), the disturbance of Ca²⁺ dynamics in the SR can cause various cardiovascular diseases. However, there is a lack of research on regulatory mechanism of SR Ca²⁺ . In this paper, we developed a spatially-detailed numerical model for Ca²⁺ blink to analyze Ca²⁺ spatiotemporal dynamic characteristics in junctional SR (jSR) cisternae of rabbit cardiomyocytes. The geometric model is constructed by 2- dimensional unstructured triangular meshes with a combined method of Delaunay Triangulations (DT) and Advancing Front Technique (AFT). The numerical method for solving the governing equations adopts Basic Function Method based on unstructured triangular meshes. Finally, our model is well reproduced the SR Ca²⁺ kinetics of Ca²⁺ blink and simulated results well match the results of a recent biological experiment.

Given the importance of Ca²⁺ spark and Ca²⁺ nanospark and the lack of relevant simulation, the numerical model of Ca²⁺ nanospark is constructed in this paper. We constructed a kinetic model according to the physiological mechanism of Ca²⁺ nanospark and Ca²⁺ spark. Then, a geometric model is constructed for the occurrence space of Ca²⁺ nanospark and Ca²⁺ nanospark and a structured grid for it is generated. Finally, the numerical model of Ca²⁺ nanospark is constructed by using the finite difference method. We use the model to simulate the basic Ca²⁺ spark and Ca²⁺ nanospark, and the coincidence degrees of most temporal properties are about 90%. In addition, the numerical model can explore the corresponding kinetics changes of Ca²⁺ spark and Ca²⁺ nanospark by adjusting the model parameters.

The spread of rumors in social networks can do great harm to the society, so it is significant to limit the propagation of misinformation. One solution to block rumor is to broadcast the anti-rumor information. How to select users of social networks to spread information against rumor can be abstracted as the problem of influence blocking maximization (IBM). The problem can be described as choosing k nodes from a social graph to minimize the number of nodes that adopt rumor at the end of the spread process. The state-of-the-art IBM algorithm IBMM has a ( 1 - 1/e - ε)-approximation guarantee. However, we noticed that the algorithm could cause unnecessary cost of calculation. Therefore, we modify IBMM and propose the IBM-N algorithm. And the results of a series of experiments conducted on both synthetic and real world data sets demonstrate the performance efficiency of our algorithm.

To capture the inherent temporal variability of the degradation process over time, stochastic degradation models have become a popular topic of reliability evaluation for complex systems with high reliability and long lifetime. For a practical degrading product, several stochastic degradation models may be a candidate for its reliability modelling, and it is significant to measure their incompleteness and construct an effective model selection criterion. Traditional model selection criterion mainly emphasizes the balance between model complexity and degradation data fitting effect, but the extrapolation and prediction effect of the product reliability model under accelerated degradation test is limited. Therefore, based on the accelerated degradation test and long-term degradation data of a real product, this paper adapts the traditional Bayesian Information Criterion and proposes a new model selection criterion. By balancing model complexity, data fitting effect and prediction effect, the optimal selection of the Wiener degradation process, gamma degradation process and inverse Gaussian degradation process can be realized, and we can obtain more accurate reliability evaluation and lifetime prediction results.

With the increasing penetration of renewable energy, flexible hydropower is regarded as an effective regulating source for power dispatch. The optimal coordinated operation of hydropower and other energy is becoming more important for power systems. By compromising practical feasibility and calculation accuracy, an annual dispatch strategy based on a two-stage optimization model is proposed in this paper. The model is formulated overall and simulated in a provincial hydrodominated power system. The results show that the method can efficiently schedule the power generation of multiple power sources and the operation dispatch of reservoirs and units.

In order to study the mechanical properties of aluminum alloy, this paper adopts the birth-death element method (BDEM) to numerically analyze the behavior of tensile cracking of aluminum alloy specimens. A finite element model was established using Abaqus, and the BDEM were set in the deformation region. The results show that the BDEM accurately simulates the cracking behavior of the aluminum alloy. The specimen began to fracture when the experimental time was 18 s. The maximum Mises stress during the whole tensile process is 445.3 MPa. Compared with the traditional mechanical experiments, this method can obtain the stress/strain data during the large-scale plastic deformation of the aluminum alloy before rupture, which provides data support for the design and optimization of aluminum alloy parts.

Aiming at the development and utilization of underground space in soft soil area, in order to study the influence law of pipe jacking construction on the deformation disturbance of existing subway tunnel in soft soil area, the numerical simulation method combined with field monitoring data and machine learning algorithm was used to simulate and analyze the deformation disturbance law of existing subway tunnel during pipe jacking construction. The results show that the HSS model can better simulate the influence of excavation disturbance on the vertical displacement of the tunnel measuring point. Only when the pipe jacking is out of the hole, the vertical displacement of the measuring point fluctuates greatly, while the deformation of the jacking process is relatively stable. The maximum vertical settlement of the measuring point of the downward tunnel is about 11.3 mm, and the minimum is about 6.81 mm. The maximum vertical displacement of the measuring point of the ascending tunnel is about 17.4mm, and the minimum is about 12.7mm

In industrial production, metal level detection is very important. LFMCW millimeter wave radar is suitable for such harsh environments that require high-precision short distance measurement. This paper discusses the reasons that affect the accuracy of millimeter wave ranging, and gives the specific radar parameters and ranging module configuration based on the actual situation of metal level detection, and analyzes them; Based on FFT frequency estimation algorithm, rough target peak is provided, and then frequency spectrum is refined near the peak point through two CZTs to solve phase ambiguity. On this basis, phase correction method is used to further improve the accuracy of the algorithm. Finally, the AWR1843 millimeter wave radar and DCA1000 are used to conduct the cold state experiment on the surface position of the metal liquid level, obtain the IF signal and process the IF signal data. The results show that when the measuring system detects the position of the liquid metal surface 1 m away from the AWR1843 signal transmitting end, the experimental measurement error does not exceed 0.1 mm.

Intelligent defect detection is a key section of the PDCA comprehensive quality management in the solar cells production. deep learning-based models for its powerful capacity of extracting features has successfully applied to defect detection tasks. Nevertheless, the deep learning-based models is susceptible to these problems such as insufficient labelled data and class imbalance, which deteriorates the performance of the deep models. What's worse, the new unknown defects without labels in the target domain tend to be recognized as the unknown defects in the source domain. To solve these problems, a novel method named IDATLGAN is proposed. Experimental results show that the proposed model outperforms the DCNN model in feature extraction ability and fulfil the transfer tasks even when faced with new unknown defects without labels.

In order to evaluate the performance of the aircraft cargo compartment smoke detection system, this paper uses the method of artificial simulation of smoke and the Fire Dynamics Simulator (FDS) to study the alarm time of the smoke detection system and the propagation characteristics of smoke in the cargo compartment. The flow of smoke and heat transport from fires is simulated by large-eddy simulation (LES) method with an emphasis on smoke and heat transport from fires and the simulation result is consistent with the test result.

In this study, the influence of brash ice on propeller performance is numerically analyzed using the coupling method of Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM). Among them, fluid part is modeled with CFD method, while DEM method is applied to model the brash ice and evaluate the ice-ice and ice-propeller interactions. Firstly, we carry out the numerical prediction of propeller performance under ice-free condition, and obtained efficiency coefficient corresponding to different advance coefficients, which show good agreement with experimental data. Subsequently, hydrodynamic performance of propeller in different ice-level sea areas is analyzed by gradually increasing the number of ice injectors in the calculation domain. The results show that when the content of brash ice is relatively limited, the flow characteristics around propeller is less affected, so propeller performance does not change significantly. With the increase of brash ice, the non-uniformity of flow field around the propeller rises rapidly, which leads to the fast deterioration of propeller performance. In the case with 12 DEM ice injectors, the thrust and torque coefficients increase about 10% and 6%, respectively.

A Deep Neural Network Galerkin Method (DNNGM) is suggested in this paper to address the phase field system. Neural network approximation is utilized to obtain the solution. The neural network can reduce the amount of computation on account of meshless. The neural network is trained using randomly sampled time and space points. The stochastic gradient descent algorithm and minimization loss function are employed to fulfill the boundary and initial conditions, as well as the differential operators of the partial differential equations. Finally, numerical examples are utilized to verify the effectiveness of the algorithm.

Publisher's Note: This paper, originally published on 28 July 2023, was withdrawn on 4 December 2023 per author request.

In the process of converting the free-form surface of the blade of the centrifugal compressor impeller to the ruled surface, the cutter rotation method is a rotating search with the search center as the center of the circle and the radius is sequentially changed. The search in the meridian and circumferential directions of the blade is not completely independent, resulting in a certain range Search blind spots. We propose a new search method. The search of the cutter in the blade meridian direction and the tangential direction are independent of each other, which further expands the search range of the best position of the flank milling ruled surface cutter. The mathematical calculation method of this method is introduced, and two different free-form surface centrifugal compressor impellers are used as test examples. The results show that this method can improve the accuracy of transforming the free-form surface of the impeller blade into a ruled surface.

This paper presents a method for generating 3D and 4D star polytopes based on the Todd-Coxeter algorithm and Wythoff construction. This method can be used to calculate most of the 3-dimensional uniform star polytopes and all of the 4-dimensional uniform star polytopes. Its advantage is that it can obtain the group element representation corresponding to each vertex, edge, and face of the polytope without using floating-point arithmetic, where each group element is given by the product of the generators. With slight modifications, it can also be applied to calculate all convex Wythoff uniform polytopes.

The problem of path planning has attracted wide attention. Dispatching is also an extremely essential link in the field of train transportation, especially in the case of emergency, dispatching is more significant. When resolving the dispatching problem, this paper seeks the shortest path based on Dijkstra’s algorithm and sets the functional logic that the dispatching needs to follow. Finally, the experiments indicate that the dispatching method in this paper has achieved excellent outcomes.

The IT-based and state-of-the-art inventory management tools help in collecting the historical data of materials accurately and easily, laying a solid foundation for material demand forecasting. An accurate and science-based material demand forecasting however depends on the effective use of material demand forecasting models. To improve the accuracy of material demand forecasting in manufacturers and reduce the procurement cost and even the overall operation cost of the supply chain, this paper, with the Company D as a case study, adopts information technology to collect the historical data of the company's important material, and employs the cubic exponential smoothing method as well as the Winters linear and seasonal exponential smoothing forecasting method to forecast the material separately, Finally, SPSS statistical method is used to compare and analyze the prediction results of the two methods.. As a result of the study, it is found that the cubic exponential smoothing method is the optimal method for forecasting the demand of material color powder, which serves as a reference for the material demand forecasting of other materials of the company and even other enterprises of the same type.

To investigate the mechanism of the disease spreading, mathematical models are introduced, the first model I tried out is the logistic model, and it is too simple to give a full explanation of the development of the pandemic and especially, it cannot predict when will the pandemic fade out. The second model I used is the famous SIR model, and by fitting data, the parameters of the model are acquired precisely, and it gives a better illustration of the ongoing progress in preventing the disease from spreading. Even though a better-fitting result is given, it is still a naive model for explaining the spreading process of the pandemic. By comparing these two models with the data in the real world, I draw some conclusions on the mechanism pandemic spreading process and identified the key elements in preventing it from further spreading.

Real time state detection and fault diagnosis are very important to the normal use and safe operation of elevators. With the development of intelligent diagnosis technology, the powerful computing power and diagnosis ad-vantages of machine learning are increasingly prominent in elevator fault detection and diagnosis. This paper summarizes the application of four typical algorithms of artificial neural network (ANN), support vector machine (SVM), Bayesian network (BN) and deep learning (DL) in elevator fault diagnosis in machine learning technology in recent years, and analyzes the method expansion and application based on its basic theory and the problems encountered in practice, The advantages and disadvantages of each method in the diagnosis process are discussed, and the improvement measures proposed at this stage are analyzed. Finally, the future research and development direction are proposed.

With the development of genome-wide association analysis and sequencing techniques, lots of rare and common variants associated with complex traits or diseases have been detected. Besides, in recent years, the research based on family data has attracted wide attention, but most of the research only consider the data of unrelated individuals and siblings, and rarely consider the data of distant relatives like grandparent-grandchild pairs, uncle-nephew pairs, cousin pairs, and so on. In this paper, we propose an effective method for generating affected grandparent-grandchild pair data (called GAGP). Based on association analysis, we use a large number of simulation experiments to evaluate the effectiveness and application of the new sampling method. The simulation results show that in all cases, the new method is valid and obtains good test results compared with other methods, which indicates that the method has better performance. The new method is implemented by the software R.

With the rapid development of digital economy, online-shopping has become an indispensable part of people's lives. The current study explores product consumer satisfaction (PCS) which measures product success from the perspective of customer feedback. We collected 32,024 reviews of three markets on a leading e-commerce platform to analyze the sentiment of consumer text-based feedback with NLP algorithms. Considering informational efficiency theory, we constructed a product consumer satisfaction (PCS) evaluation system to calculate consumer satisfaction of each good with TOPSIS method. Our findings indicate that PCS is generally on the rise, and with the continuous improvement of the e-commerce, the growth rate has accelerated significantly. However, due to the impact of the COVID-19 epidemic, consumer satisfaction in some markets has declined. Heterogeneity is across different markets. Furthermore, we also found that the sentiment of the comments is mostly positive. And a high star rating for a product elicited more customer reviews, where consumers expressed affection and spontaneously recommended it. These findings will help merchants and e-commerce platforms to better understand consumer mindset and help them to develop more feasible marketing plans and customer review management strategies.

This paper mainly researched the basic principle of support vector machine based on Gaussian kernel function. Aiming at the defects of existing Gaussian kernel function of support vector machine, particle swarm optimization algorithm was used to optimize the penalty factor and Gaussian radial parameters to improve the generalization prediction ability of the support vector machine. Moreover, based on the support vector machine and particle swarm optimization, the rice canopy density prediction model and correlation analysis experiment were established using the rice canopy image data. The experiment shows that the absolute error and relative error of the rice canopy density prediction model based on support vector machine and particle swarm optimization can meet the requirements of real-time control of the feed amount of the combined harvest under normal and back light conditions.

The paper begins with an introduction to fuzzy subsets and their complementary definitions and introduces the concepts of anti-fuzzy subrings and anti-fuzzy ideals. This is followed by a discussion of the many properties of fuzzy ideals and the connection between anti-fuzzy subrings as well as anti-fuzzy ideals. Ultimately we rationalise the isomorphism theorem for the anti-fuzzy ring

The SEIR model is a classical model that describes the dynamic changes in the number of different groups of people under an epidemic. This paper modifies the classical SEIR model and adds corresponding rooms based on the consideration of people under centralized and home quarantine states, which realizes the separate prediction of the number of people in different infection states and different quarantine states under the epidemic situation. In addition, we have also established the mathematical relationship between the above-predicted number of people and the demand for emergency materials, therefore, providing support for the government to react more effectively and make better distribution of emergency supplies when facing epidemics. This paper uses data from the COVID-19 outbreak in Wuhan, China in 2020 to verify the effectiveness of the model.

With the development of the economy and society and the application of big data, UAVs have significantly been developed and widely used, but it also brings some security risks. In order to study the risk factors affecting the safety of UAVs and make a timely risk assessment and early warning, this paper establishes a UAV safety risk assessment and early warning model. First, the unsafe flight cases of UAVs are analyzed using grounded theory to determine the main risk factors. Then, we designed questionnaires to collect data, and structural equation models were used to determine the path coefficients of each risk factor affecting the safety of UAV flight. Finally, a multilevel fuzzy comprehensive evaluation method was used to establish a UAV safety risk evaluation and early warning model. The study shows an apparent correlation between personnel, environment, equipment, management factors, and the safety of UAV operations. The risk assessment and early warning model established by fully considering the interaction of these factors can enrich the research on UAV safety risk assessment and early warning and help provide constructive suggestions for developing this industry.

The problem of parameter estimation of inverse Topp-Leone distribution is studied under type-I censoring sample with missing data. It is proved that the maximum likelihood estimation of parameter satisfies the consistency and asymptotic normality. Moreover, the approximate confidence interval and hypothesis test on testing equality of the parameters from two inverse Topp-Leone distribution are given. Finally, the high accuracy of the estimation by Monte Carlo simulation shows that this estimation method is feasible.

This paper takes the analytic hierarchy process as the basic method to establish the air traffic control tower safety risk factor set. In combination with the gray theory and fuzzy mathematics principle to determine the gray fuzzy evaluation conclusion set and the index layer factor score value matrix, we calculate the gray evaluation coefficient and build the evaluation weight matrix to evaluate the control operation risk of air traffic control towers. Finally, the case verification is carried out, and the risk assessment value of the air traffic control tower used in the case is calculated as 5.6325, which belongs to the “general risk” level. The evaluation method adopted in this paper can provide reasonable suggestions for operational risk management of air traffic control towers.

Electroencephalogram (EEG) seizure examination is an important part of epilepsy diagnosis and evaluation. In this paper, a Deep domain adaptation-based seizure detection method is proposed for the automatic detection of epilepsy by transfer learning. First, a Butterworth bandpass filter is used for denoising and the EEG signals in the frequency of 0.5-50Hz are extracted. The filtered EEG signals were then sliced in 4-second segments. In the second step, the EEG signal is Pretreatment processed, and the two-dimensional spectrogram signal is obtained by short-time Fourier variation in a segment every 4 seconds at the same time. Scalp EEG at CHB-MIT was tested for a total of 915.34h, including 29.06 minutes of seizure data. The average sensitivity and specificity were 96.77% and 96.48%, respectively.

Predicting the average stature of trees is necessary to determine the biomass and carbon storage of a stand. The correlation between diameter at breast height and tree height has been extensively analysed and provides a reliable method to predict tree height. This study presents calculations of entropy information related to birch, the dominant tree species in a montane forest located in the northwestern region of Hebei Province. Six classical models (exponential, power, linear, logarithmic, Richard and logistic) are compared in this paper. Tests on the dataset showed that entropy data at breast diameter outperformed mean diameter in the ability to predict mean tree height. In addition, stratification of the data by breast diameter improved the accuracy of the model. This study proposes an optimisation method for predicting mean tree height by using entropy information at breast height and serves as a benchmark for comparable studies of multi-layered forests in the mountainous region of northwest Hebei Province, China. This paper examines and analyses the forest hierarchy based on a predictive model that analyses and predicts various factors of forest trees based on the underlying analytical model.

Alzheimer's disease (AD) is a kind of people memory decline, brain function slow and gradually mutated mental loss performance of the disease. Early diagnosis and prediction of AD can delay the onset of patients and may prolong the life span of patients, which is of great scientific significance to the whole society. In this paper, machine learning and other methods are used to analyze and model AD data, add brain characteristic indicators, and conduct data preprocessing. The intelligent diagnosis of AD is realized by using SVM, which is a paired classification method. The accuracy of five classifications obtained by SVM reaches 0.7. The accuracy of LMCI (68%), SMC (58%) and EMCI (76%) is good. By extracting and discovering key symptoms that affect the diagnosis and prediction of AD and classifying patients, early diagnosis and prediction of the incidence of AD can be achieved effectively and quickly, so as to achieve effective measures to prevent AD. SVM based on the pair classification method can realize the intelligent diagnosis of AD, and can be extended to more classification problems.

Aiming at the problem that the rate of ship identification is still low, a method of ship identification on river surface is proposed. In this paper, MATLAB software is first used to simulate the IF signal generated by millimeter-wave radar. Then, by setting the reflection coefficient of steel plate (ship), water (water surface), sand (river bank) and the relative distance of radar, the simulation experiment is carried out to obtain the generated time domain diagram and energy diagram. Then, the Fourier transform of the signal is carried out by MATLAB software to obtain its range spectrum diagram. Then the obtained time domain diagram, energy diagram and frequency domain diagram are processed through a series of images and input into the neural network for classification learning. Then, the experimental test was carried out through the millimeter wave radar signal acquisition plate with one round and four rounds of collection. By collecting the characteristic spectra of steel plate, sand and water at different distances and then classifying them, it was found that the recognition accuracy in the energy and time domain could reach 95%, effectively classifying and recognizing the three substances: steel plate (ship), water (water surface) and sand (river bank).

In recent years, the effects of global warming has caused significant property damage and loss of life. Studying global warming is important to understand nature and protect ourselves. In order to explore the variation trend of the global warming, this paper collected authoritative data Based on the temperature data, by establishing grey prediction model (GM), we have made a reasonable prediction of the future temperature, calculated the expected time for the global temperature to reach 20 degrees Celsius, and reached the conclusion that the global temperature will continue to grow slowly.

Continuous casting and cutting machinery and equipment play an important role in industrial production. During continuous casting cutting, how to meet the actual operation requirements with the minimum loss and achieve the customer's target value as much as possible is the central element of cutting scheme design. This paper uses data mining model to guide the reuse of cutting equipment. Set the decision variable as the cutting length of each section of billet, Z as the objective function, combined with the basic requirements and normal requirements, and the specific model is as follows:Z1 = min Σ(l + |l2 − 8.5| + |l2 − 8.5|+. . . |lk − 8.5|) . st 8≤ li ≤9 ;l +l1 +l2 +...+lk =L ;4.8≤ l < 8.The above model is realized by matlab programming, and the cutting scheme is obtained by inputting different tail blank length values. On this basis, the cutting length and loss are adjusted manually according to the actual situation, and the optimal scheme is obtained. Here, the minimum loss section should be 4.8 meters, while the cutting frequency is 4 minutes/time, and the casting speed is 1 meter/minute, so the cutting frequency always meets the requirements of the scheme.

This paper focuses on predicting the results of wordle players' data. Firstly, the data is preprocessed according to the provided data, the ARIMA time series model is constructed, the ADF test is conducted, and then the correlation coefficient is obtained for model verification. The overall analysis of the model is made to determine that the model meets the requirements, and the future time results are predicted. Then, another fitting algorithm model is constructed, and the optimal function model is found through many attempts, its related parameters are calculated, its fitting effect is determined, and the subsequent results are predicted. Finally, we take the prediction results of the two models as the threshold of the prediction interval, and the result is [10860,18524].

In order to achieve efficiency and accurate identification of objects on high voltage transmission lines, this paper studies the improvement based on YOLO network. Traditional YOLO options a new basic model, called Darknet-19, including 19 voluntary layers and 5 maxpooling layers Darknet-19 is consistent with the VGG16 model design principle, mainly using 3 * 3 solution, After using 2 * 2 maxpooling layer, the feature map dimension is reduced by 2 times, while the channels of the feature map are doubled Similar to NIN, Darknet-19 ultimate uses global avgpooling for prediction, and uses 1 * 1 revolution between 3 * 3 revolution to compress the feature map to reduce the computational amount and parameters of the model In this paper, a two dimensional chaotic attractor of the sinusoidal basis function is generated by iterative operation to be adjusted to the image, and the attractor can be used as a feature input network of the image, replacing the one layer pooling operation of the Darkenet-19 network For objects on high voltage transmission lines, the improved network is used to identify them, improve accuracy, reduce calculation time, and reduce the structural complexity of the network The methods of other literature were applied to the database of this paper, and the indicators were recorded experimentally, and then compared and analyzed with the methods in this paper The mechanism of iterative attractor replacement revolution and pooling is studied, and the learning method of iterative function parameter change is realized

Based on MATLAB software and from the perspective of supply chain management, this paper models the sales volume of electric vehicles in Shanghai in the next few years with GM (1,1) model, and draws the conclusion that the sales volume of electric vehicles in Shanghai will increase exponentially in the next few years; At the same time, based on the prediction results, the possible charging load of electric vehicles in Shanghai in 2024 is simulated by Monte Carlo, which provides reference value for evaluating the impact of electric vehicles on the local grid load, and puts forward policy recommendations to the government.

Wordle is a free web crossword that has exploded into global popularity in 2022, and it has a lot to offer in terms of gameplay. In order to explore the reasons for the popularity of Wordle games, the bionic SIR Epidemic model is used to predict the number of Wordle players and analyze the reasons for the change of the number of players in combination with the mode of game transmission. Information entropy is used to process Wordle word data in the past, and the relationship between word attributes and players' completion rate is analyzed. A more accurate model is then built to predict the specific distribution of the number of times players in Wordle correctly guess the word. The input data set was constructed from the word attributes that mainly affect Wordle players, and the multi-output prediction model was built based on the bionic encircle tracking method of GWO. The mathematical model above is used to systematically predict the future trend of Wordle.

After the Industrial Revolution, global temperatures generally began to rise. With global warming as the background, this paper builds a forecast model of future temperature, studies to determine whether the temperature rise in March 2022 leads to a larger rise, and builds more than two models to predict the temperature and evaluate the model. SARIMA(1,1,1)(1,1) model and LSTM model were established for temperature prediction. The study found that the temperature will not reach 20°C in either 2050 or 2100, and the global average temperature is expected to reach 20°C around 2180. Through model evaluation, the r square of SARIMA model is 0.87, and the accuracy of LSTM model is 0.783. Therefore, we think the SARIMA model is superior.

The meltblown nonwoven material (MNM) is an important raw material for mask production. With good filtration performance, its production process is simple, low cost, and light quality. The research on the process parameters and structural variables of the MNM is widely concerned by domestic and foreign enterprises, and the relationship between structural variables and product performance is also. To the end, we study the process parameters and product performance of the MNM by using some mathematical methods for the high quality and high efficiency of the industry. We use a multiple nonlinear regression model to build a statistical model based on the data provided by the enterprise to reveal the variation pattern between process parameters and structural variables. The coefficient of decidability was calculated , and the correlation coefficient was above 0.9, which proved the accuracy of the model. Finally, in order to explore the relationship between structural variables and product performance, we used MATLAB to make a correlation four-dimensional diagram, used fitting polynomials to finally fit a quadratic correlation function, and used Monte Carlo multi-objective planning programming to obtain the maximum filtration efficiency of 96.8272% when the acceptance distance was 39.5cm and the hot air speed was 1188r/min.

In recent years, with the continuous development of big data and cloud computing services, the scale of data centers has become larger and larger, and the rapidly increasing network traffic has also put forward higher requirements for the load balancing technology of data centers. Most of the traditional traffic scheduling algorithms are derived from IP networks. These algorithms do not take into account performance parameters such as link bandwidth and delay of real networks, resulting in very unsatisfactory results. In view of the above problems, this paper proposes a traffic scheduling algorithm based on the Particle Swarm Optimization Fusion Ant Colony Optimization (P-ACO) algorithm, which is mainly for dynamic traffic scheduling of elephant flow. First, the Software Defined Networking (SDN) controller obtains the network performance parameters and topology, then uses the PSO algorithm to iteratively search for solutions, integrates the solutions into the initial pheromone distribution of the ACO algorithm, and finally combined with the global network state, the optimal solution is obtained through the improved ACO algorithm.

With the rapid development of science and knowledge economy, traditional supply chain management has been unable to adapt to the ever-changing competitive environment. When a large amount of data is exchanged, it is easy to cause various privacy and security issues, and data between enterprises cannot be accurately shared. At the same time, there are still a lot of manual operations in each link, with long process delays and low efficiency. This paper proposes a blockchain-based supply chain privacy protection and data collaboration scheme. By using blockchain, data privacy and collaboration can be guaranteed based on a more accurate assessment of supplier capabilities. At the same time, this solution improves the efficiency of cooperation between enterprises more safely and reasonably, and makes the supply chain system circulate more smoothly.

This paper explores the application of ARIMA(AutoRegressive Integrated Moving Average) and LSTM(Long Short- Term Memory) in predicting future global temperatures in the context of climate change. The authors employ both ARIMA and LSTM models to describe past temperature trends and forecast future temperatures. Moreover, they combine the two models and consider residual analysis to establish an ARIMA-LSTM model with enhanced predictive performance. Results indicate that this model effectively predicts future global temperature changes.

The use of electric buses is not only one of the important measures to respond to the national promotion of new energy vehicles, but it also has the benefit of low carbon and environmental protection. How to exploit the battery load characteristics of electric buses so that they can actively participate in the power grid's demand response planning is an urgent topic that needs to be solved right now.Based on this, this paper proposes a demand response aggregation service strategy for electric buses, which consists primarily of researching the battery load characteristics of electric buses, calculating the cost of electricity for electric buses to implement demand response, and proposing a demand response aggregation service strategy for electric buses.

This article mainly uses the data provided by Wordle player to predict the distribution of score results for a certain word in the future. However, the scoring results have high uncertainty. There are many evaluation indicators and impact factors. In order to predict the results more accurately, this paper proposes a BP neural network data prediction model based on genetic algorithm optimization(GA BP algorithm). We introduce three factors: time, prevalence of each word and number of repeated letters. We determine the BP neural network topology, each parameter of BP and each parameter of GA, then train it. The data correlation between the training set, validation set, test set and overall results after training is derived and the result is good. The predicted result for the term EERIE on March 1, 2023 is: 0.00%, 2.06%, 16.21%, 32.95%, 28.50%, 15.78%, 4.48% from 1 to X in that order.

Online rating platform represents the new trend of online cultural and commercial goods consumption. The user rating data on such platforms are foods for recommender system algorithms. Understanding the evolution pattern and its underlying mechanism is the key to understand the structures of input data for recommender systems. Prior research on input data analysis for recommender systems is quite limited, with a notable exception in 2018 [6]. In this paper, we take advantage of Poisson Process to analyze the evolution mechanism of the input data structures. We discover that homogeneous Poisson Process could not capture the mechanism of user rating behavior on online rating platforms, and inhomogeneous Poisson Process is compatible with the formation process.

Anagram is a popular daily puzzle offered by The New York Times. Players solve the puzzle by guessing a five-letter word in six or fewer attempts, with feedback for each guess. For this version, each guess must be an actual English word. The main purpose of this paper is to build a model to predict the future report results by taking the daily change in the number of report results, the change in date and the classification of difficulty as variables and taking "eerie" as an example. Classification of word difficulty is a problem of five inputs and seven outputs. In this paper, RSR model is established. Firstly, entropy weight method is used to evaluate 1try,2tries, 3tries,4tries, 5tries, 6tries. 7or more tries (X) to calculate the difficulty level of index weight division. Rank indexes from small to large, calculate the rank sum ratio, and finally make statistical regression ranking.

This article presents the implementation of the sparse twin least squares support vector regression (WTLSSVR) model, which is derived from the twin least squares support vector regression (TLSSVR) model. The WTLSSVR model achieves sparsity by eliminating sample points with small Lagrange multipliers.Our algorithm involves combining the LSSVR with the twin hyperplane algorithm, removing sample points with small Lagrange multipliers, and thinning the model to construct a sparse twin least squares support vector regression model. This approach enhances the predictive accuracy of current twin regressors, while maintaining excellent generalization performance.Therefore, the WTLSSVR is superior to the traditional LSSVR in generalization performance. In addition, compared with some other traditional algorithms, WTLSSVR has higher prediction accuracy, which is obvious when dealing with large-scale problems. To validate the proposed model’s soundness and practicality, we carried out numerous experiments, encompassing two UCI datasets and a genuine vegetable price dataset. The results of the experiments indicate that the suggested algorithm is efficacious, practical, and versatile.

In this paper, based on the Extended Mixed Finite Element Method (EMFEM), we discretize the pressure equation with full tensor permeability, which is often derived by applying upscaling technique to the huge geological grid model or in the heterogeneous porous medium case. We apply traditional Finite Element Method (FEM) to solve the concentration equation. We prove the optimal order convergence rate of the numerical solution of pressure p and concentration c in L² - norm.

Nine Point Cell-Centered Finite Difference Method (NPCCFDM) is generally used for deal with the pressure equation with full tensor permeability, which is often derived by upscaling technique to the permeability on the huge geological grid or in the model in heterogeneous porous medium. The method is derived from the Expanded Mixed Finite Element Method (EMFEM) which is more convenient to implement to the petroleum reservoir simulation. In this paper, we will prove the numerical solution of the pressure p and concentration c of NPCCFDM has the optimal order convergence in discrete norm. Numerical experiment is given to verify the convergence of NPCCFDM.