Contract Mechanism of Water Environment Regulation for Small and Medium Sized Enterprises Based on Optimal Control Theory

The small and scattered enterprise pattern in the county economy has formed numerous sporadic pollution sources, hindering the centralized treatment of the water environment, increasing the cost and difficulty of treatment. How enterprises can make reasonable decisions on their water environment behavior based on the external environment and their own factors is of great significance for scientifically and effectively designing water environment regulation mechanisms. Based on optimal control theory, this study investigates the design of contractual mechanisms for water environmental regulation for small and medium-sized enterprises. The enterprise is regarded as an independent economic entity that can adopt optimal control strategies to maximize its own interests. Based on the participation of multiple subjects including the government, enterprises, and the public, an optimal control strategy model for enterprises under contractual water environmental regulation is constructed using optimal control theory, and a method for calculating the amount of unit pollutant penalties is derived. The water pollutant treatment cost data of a paper company is selected to conduct empirical numerical analysis on the model. The results show that the increase in the probability of government regulation and public participation, as well as the decrease in local government protection for enterprises, can achieve the same regulatory effect while reducing the number of administrative penalties per unit. Finally, the implementation process of contractual water environmental regulation for small and medium-sized enterprises is designed.

Research on the Optimization of Enterprise Financial Management under the Financial Sharing Model

With the advancement of globalization and information technology,the financial sharing mode has gradually emerged as a crucial means for enterprises to optimize their financial management.Particularly within the context of economic globalization,informatization,and digital transformation,enterprises find themselves navigating a rapidly evolving market environment by intensifying competition.To enhance efficiency and competitiveness,many enterprises have embraced the financial sharing model to streamline financial management processes,curtail costs,and bolster the execution of corporate strategies.This article aims to dissect the definition and essence of the financial sharing model and its significance in the realm of enterprise financial management.Drawing upon this analysis and aligning with the needs of enterprise financial management,the article proposes ideas for optimizing management practices,aspiring to foster reform and innovation in enterprise financial management while enhancing its level of financial management and ability to respond to financial risks.This contribution seeks to provide valuable insights for practitioners in the field.

Computational uncertainty and optimal grid size and time step of the Lax-Friedrichs scheme for the 1D advection equation

本文对于应用Lax-Friedrichs格式数值求解一维平流方程,研究数值求解过程中产生的截断误差与舍入误差,以及两种误差逐层向高时间层传播的累积,得到新的数值解总误差上界的理论近似公式,以及最优格距和最优时间步长的理论公式.通过数值算例验证了所得公式的可靠性。然后,发现了两种不同机器精度下最优时间步长之比满足的一个仅与机器精度有关的普适关系.最后,理论验证了在网格比固定的情况下,此问题满足数值计算的不确定性原理,以及在机器有限精度下最优时间步长的必然存在。

Application of Stork Optimization Algorithm for Solving Sustainable Lot Size Optimization

The efficiency of businesses is often hindered by the challenges encountered in traditional Supply Chain Manage-ment(SCM),which is characterized by elevated risks due to inadequate accountability and transparency.To address these challenges and improve operations in green manufacturing,optimization algorithms play a crucial role in supporting decision-making processes.In this study,we propose a solution to the green lot size optimization issue by leveraging bio-inspired algorithms,notably the Stork Optimization Algorithm(SOA).The SOA draws inspiration from the hunting and winter migration strategies employed by storks in nature.The theoretical framework of SOA is elaborated and mathematically modeled through two distinct phases:exploration,based on migration simulation,and exploitation,based on hunting strategy simulation.To tackle the green lot size optimization issue,our methodology involved gathering real-world data,which was then transformed into a simplified function with multiple constraints aimed at optimizing total costs and minimizing CO_(2) emissions.This function served as input for the SOA model.Subsequently,the SOA model was applied to identify the optimal lot size that strikes a balance between cost-effectiveness and sustainability.Through extensive experimentation,we compared the performance of SOA with twelve established metaheuristic algorithms,consistently demonstrating that SOA outperformed the others.This study’s contribution lies in providing an effective solution to the sustainable lot-size optimization dilemma,thereby reducing environmental impact and enhancing supply chain efficiency.The simulation findings underscore that SOA consistently achieves superior outcomes compared to existing optimization methodologies,making it a promising approach for green manufacturing and sustainable supply chain management.

The Influence of Company Performance on Company Value(K-Pop Entertainment Company Registered on KOSPI.KRX South Korea 2018-2022)

A well-managed company is a company that maximizes the value of its company,which is aimed at achieving maximum profits for shareholders.This research aims to determine and analyze the influence of company size,profitability,and financial leverage on the value of companies listed on the South Korean Stock Exchange KOSPI 2018-2022.The population consists of 8 South Korean K-Pop entertainment companies registered on KOSPI 2018-2022.The sampling technique used was purposive sampling with a total sample of 8 companies and a 5-year observation period.So that 40 data were processed.The analysis technique is multiple linear regression.The results obtained show that partially company size has no significant effect on company value,profitability has a significant positive effect on company value,and financial leverage has a significant negative effect on company value.Meanwhile,simultaneously company size,profitability,and financial leverage influence company value.

Optimal integration of solar home systems and appliance scheduling for residential homes under severe national load shedding

In developing countries like South Africa,users experienced more than 1030 hours of load shedding outages in just the first half of 2023 due to inadequate power supply from the national grid.Residential homes that cannot afford to take actions to mitigate the challenges of load shedding are severely inconvenienced as they have to reschedule their demand involuntarily.This study presents optimal strategies to guide households in determining suitable scheduling and sizing solutions for solar home systems to mitigate the inconvenience experienced by residents due to load shedding.To start with,we predict the load shedding stages that are used as input for the optimal strategies by using the K-Nearest Neighbour(KNN)algorithm.Based on an accurate forecast of the future load shedding patterns,we formulate the residents’inconvenience and the loss of power supply probability during load shedding as the objective function.When solving the multi-objective optimisation problem,four different strategies to fight against load shedding are identified,namely(1)optimal home appliance scheduling(HAS)under load shedding;(2)optimal HAS supported by solar panels;(3)optimal HAS supported by batteries,and(4)optimal HAS supported by the solar home system with both solar panels and batteries.Among these strategies,appliance scheduling with an optimally sized 9.6 kWh battery and a 2.74 kWp panel array of five 550 Wp panels,eliminates the loss of power supply probability and reduces the inconvenience by 92%when tested under the South African load shedding cases in 2023.

Research on the Optimization and Upgrading Path of Enterprise Financial Management from the Perspective of the Digital Economy

At present,China has entered the era of the digital economy,the business environment faced by enterprses has changed significantly,and the traditional financial management model is no longer adaptable due to market demand.Hence,enterprises need to study the characteristics of the digital economy and adopt ffective financial management oplimization and upgrading paths.This article summarizes the characteristics of the digital economy,concludes and analyzes the opportunities and challenges faced by corporate financial management from the perspective of the digital economy,investigates the necessity ol optimizing and upgrading corporate financial management,and examines the efective optimization and upgrading paths,hoping to provide reference information for corporate financial managers.

On the optimal harvesting of size-structured population dynamics

This work is concerned with a kind of optimal control problem for a size-structured biological population model.Well-posedness of the state system and an adjoint system are proved by means of Banach＇s fixed point theorem.Existence and uniqueness of optimal control are shown by functional analytical approach.Optimality conditions describing the optimal strategy are established via tangent and normal cones technique.The results are of the first ones for this novel structure.

Impacts of sample size for stomach content analysis on the estimation of ecosystem indices

This study used Ecopath model of the Jiaozhou Bay as an example to evaluate the effect of stomach sample size of three fish species on the projection of this model. The derived ecosystem indices were classified into three categories:(1) direct indices, like the trophic level of species, influenced by stomach sample size directly;(2)indirect indices, like ecology efficiency(EE) of invertebrates, influenced by the multiple prey-predator relationships;and(3) systemic indices, like total system throughout(TST), describing the status of the whole ecosystem. The influences of different stomach sample sizes on these indices were evaluated. The results suggest that systemic indices of the ecosystem model were robust to stomach sample sizes, whereas specific indices related to species were indicated to be with low accuracy and precision when stomach samples were insufficient.The indices became more uncertain when the stomach sample sizes varied for more species. This study enhances the understanding of how the quality of diet composition data influences ecosystem modeling outputs. The results can also guide the design of stomach content analysis for developing ecosystem models.

A Simple Method of Calculating the Optimal Step Size in 4DVAR Technique

In four—dimensional variational data assimilation (4DVAR) technology, how to calculate the optimal step size is always a very important and indeed difficult task. It is directly related to the computational efficiency. In this research, a new method is proposed to calculate the optimal step size more effectively. Both nonlinear one—dimensional advection equation and two—dimensional inertial wave equation are used to test and compare the influence of different methods of the optimal step size calculations on the iteration steps, as well as the simulation results of 4DVAR processes. It is in evidence that the different methods have different influences. The calculating method is very important to determining whether the iteration is convergent or not and whether the convergence rate is large or small. If the calculating method of optimal step size is properly determined as demonstrated in this paper, then it can greatly enlarge the convergence rate and further greatly decrease the iteration steps. This research is meaningful since it not only makes an important improvement on 4DVAR theory, but also has useful practical application in improving the computational efficiency and saving the computational time. Key words 4DVAR - Optimal step size - Iterative convergence rate This work was supported by the National Natural Science Foundation under grants: 49735180 and 49675259, the “973 Project? CHERES(G 1998040907), the Project of Natural Science Foundation of Jiangsu Province(BK99020), and the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars.

Optimal Size for Maximal Energy Efficiency in Information Processing of Biological Systems Due to Bistability

Energy efficiency is closely related to the evolution of biological systems and is important to their information processing. In this work, we calculate the excitation probability of a simple model of a bistable biological unit in response to pulsatile inputs, and its spontaneous excitation rate due to noise perturbation. Then we analytically calculate the mutual information, energy cost, and energy efficiency of an array of these bistable units. We find that the optimal number of units could maximize this array＇s energy efficiency in encoding pulse inputs, which depends on the fixed energy cost. We conclude that demand for energy efficiency in biological systems may strongly influence the size of these systems under the pressure of natural selection.

Analysis on the financing strategies of small and medium sized clothing enterprises under the Internet financial model

Small and medium sized clothing enterprises have become an important force to promote China＇s economic transformation. But the shortage of funds in the development of small and medium sized clothing enterprises is still a major problem. The development of Intemet financial model is of great significance to the small and Medium Sized Clothing Enterprises .It is helpful to solve the problems of information asymmetry, credit rationing and high loan cost in the financing of small and medium sized clothing enterprises..In many financial models on the interne＇., to our vast number of small and medium-sized enterprises provide convenient services, but in the face of this model using diffcrent opinions, and there is no pertinence, for most of the enterprises are usually similar situation was a summary and recommendations, there are a handful of targeted research ,This research is for research, in order to solve the financing problems of China＇s small and medium-sized garment enterprises countermeasures were studied to find a suitable China＇s small and medium-sized garment enterprises financing path with, for small and medium-sized garment enterprises in the capital needs of the guiding role, for the enterprise to avoid financial risk. the lnternet financial model to solve the financing problem of small and medium-sized garment enterprises play the important role, we should expand Internet banking, so that more small and medium-sized garment enterprises get better development.

Gold/Mg-Al mixed oxides catalysts for oxidative esterification of methacrolein:Effects of support size and composition on gold loading

Gold catalysts supported on Mg-Al mixed oxides for oxidative esterification of methacrolein are prepared by impregnation.Effects of the support particle size,concentration of HAuCl4 solution and Mg/Al ratio on gold loading and catalytic properties are investigated.The catalysts are characterized by CO_(2)-TPD,EDS,XPS,STEM and XRD techniques.Catalysts with smaller support particle size show more uniform gold distribution and higher gold dispersion,resulting in a higher catalytic performance,and the uniformity of gold and the activity of the catalysts with larger support particle size can be improved by decreasing the concentration of HAuCl4 solution.The Mg/Al molar ratio has significant effect on the uniformity of gold and the activity of the catalyst,and the optimum Mg/Al molar ratio is 0.1–0.2.This study underlines the importance of engineering support particle size,concentration of HAuCl4 solution and density of adsorption sites for efficient gold loading on support by impregnation.

Reduced differential transform and Sumudu transform methods for solving fractional financial models of awareness

In that paper,we new study has been carried out on previous studies of one of the most important mathematical models that describe the global economic movement,and that is described as a non-linear fractional financial model of awareness,where the studies are represented at the steps following:One:The schematic of the model is suggested.Two:The disease-free equilibrium point(DFE)and the stability of the equilibrium point are discussed.Three:The stability of the model is fulfilled by drawing the Lyapunov exponents and Poincare map.Fourth:The existence of uniformly stable solutions have discussed.Five:The Caputo is described as the fractional derivative.Six:Fractional optimal control for NFFMA is discussed by clarifying the fractional optimal control through drawing before and after control.Seven:Reduced differential transform method(RDTM)and Sumudu Decomposition Method(SDM)are used to take the resolution of an NFFMA.Finally,we display that SDM and RDTM are highly identical.

Effective Return Rate Prediction of Blockchain Financial Products Using Machine Learning

In recent times,financial globalization has drastically increased in different ways to improve the quality of services with advanced resources.The successful applications of bitcoin Blockchain(BC)techniques enable the stockholders to worry about the return and risk of financial products.The stockholders focused on the prediction of return rate and risk rate of financial products.Therefore,an automatic return rate bitcoin prediction model becomes essential for BC financial products.The newly designed machine learning(ML)and deep learning(DL)approaches pave the way for return rate predictive method.This study introduces a novel Jellyfish search optimization based extreme learning machine with autoencoder(JSO-ELMAE)for return rate prediction of BC financial products.The presented JSO-ELMAE model designs a new ELMAE model for predicting the return rate of financial products.Besides,the JSO algorithm is exploited to tune the parameters related to the ELMAE model which in turn boosts the classification results.The application of JSO technique assists in optimal parameter adjustment of the ELMAE model to predict the bitcoin return rates.The experimental validation of the JSO-ELMAE model was executed and the outcomes are inspected in many aspects.The experimental values demonstrated the enhanced performance of the JSO-ELMAE model over recent state of art approaches with minimal RMSE of 0.1562.

Geopolymer-based modification of blasting sealing materials and optimization of blasting block size in coal seams of open pit mines

This research proposes the utilization of a geopolymer-based blasting sealing material to improve the profitability of coal sales and reduce the rate of coal fragmentation during blasting in open pit mines.The study first focused on optimizing the strength of the sealant material and reducing curing time.This was achieved by regulating the slag doping and sodium silicate solution modulus.The findings demonstrated that increasing slag content and improving the material resulted in an early rise in strength while increasing the modulus of the sodium silicate solution extended the curing time.The slag doping level was fixed at 80 g,and the sodium silicate solution modulus was set at 1.5.To achieve a strength of 3.12 MPa,the water/gel ratio was set at 0.5.The initial setting time was determined to be 33 min,meeting the required field test duration.Secondly,the strength requirements for field implementation were assessed by simulating the action time and force destruction process of the sealing material during blasting using ANSYS/LS-DYNA software.The results indicated that the modified material meets these requirements.Finally,the Shengli Open Pit Coal Mine served as the site for the field test.It was observed that the hole-sealing material’s hydration reaction created a laminated and flocculated gel inside it.This enhanced the density of the modified material.Additionally,the pregelatinized starch,functioning as an organic binder,filled the gaps between the gels,enhancing the cohesion and bonding coefficient of the material.Upon analyzing the post-blasting shooting effect diagram using the Split-Desktop software,it was determined that the utilization of the modified blast hole plugging material resulted in a decrease in the rate of coal fragmentation from 33.2%to 21.1%.This reduction exhibited a minimal error of 1.63%when compared to the field measurement,thereby providing further confirmation of the exceptional plugging capabilities of the modified material.This study significantly contributes to establishing a solid theoretical basis for enhancing the blasting efficiency of open pit mines and,in turn,enhancing their economic advantages.

Deep Learning Enabled Financial Crisis Prediction Model for Small-Medium Sized Industries

Recently,data science techniques utilize artificial intelligence(AI)techniques who start and run small and medium-sized enterprises(SMEs)to take an influence and grow their businesses.For SMEs,owing to the inexistence of consistent data and other features,evaluating credit risks is difficult and costly.On the other hand,it becomes necessary to design efficient models for predicting business failures orfinancial crises of SMEs.Various data classification approaches forfinancial crisis prediction(FCP)have been presented for predicting thefinancial status of the organization by the use of past data.A major process involved in the design of FCP is the choice of required features for enhanced classifier out-comes.With this motivation,this paper focuses on the design of an optimal deep learning-basedfinancial crisis prediction(ODL-FCP)model for SMEs.The proposed ODL-FCP technique incorporates two phases:Archimedes optimization algorithm based feature selection(AOA-FS)algorithm and optimal deep convo-lution neural network with long short term memory(CNN-LSTM)based data classification.The ODL-FCP technique involves a sailfish optimization(SFO)algorithm for the hyperparameter optimization of the CNN-LSTM method.The performance validation of the ODL-FCP technique takes place using a benchmarkfinancial dataset and the outcomes are inspected in terms of various metrics.The experimental results highlighted that the proposed ODL-FCP technique has out-performed the other techniques.

Back Frame Section Size Optimization of Large Aperture Telescope

The back frame structure of a large radio telescope is an important component supporting the reflecting surface,which is directly related to the surface precision.Its optimal design is of key significance for ensuring the surface precision and reducing structural weight.Two methods are constructed to optimize the cross-section size of the telescope back frame in this paper,the criterion method and the first-order optimization method.The criterion method is based on the Lagrangian multiplier method and Kuhn-Tucker condition.This method first establishes the mathematical model by taking the inner and outer radiuses of the back frame beams as the design variables,the structural weight as the constraint condition,and the structural compliance as the objective function,then derives the optimization criterion.The first-order optimization method takes the inner and outer radiuses of the beams as the design variables,the back frame RMS as the objective function,and the structural weight as the constraint condition.Comparison of RMS,structural stress uniformity and optimization efficiency shows that both algorithms can effectively reduce structural deformation and improve RMS,but the criterion method has relatively better result than the first-order method.