Distribution Scheme Research Articles

Distributed model predictive control for asynchronous multiagent systems with self‐triggered coordinator

AbstractThis paper investigates a distributed model predictive control strategy for asynchronous nonlinear multiagent systems (MASs) with external disturbances via self‐triggered generators and prediction horizon regulators. First, a shrinking constraint related to the error between the actual state and the predicted state is introduced into the optimal control problem to enhance the robustness of the system. Then, the triggering interval and the corresponding prediction horizon are determined by altering the expression of the Lyapunov function, thus achieving a trade‐off between control performance and energy loss. By implementing the proposed algorithm, the coordination objective of the MASs is achieved under asynchronous communication. Finally, the recursive feasibility and closed‐loop stability are proven successively. An illustrative example is conducted to demonstrate the merits of the presented approach.

Parasitic taxa are key to the vertical stratification and community variation of pelagic ciliates from the surface to the abyssopelagic zone.

An increase in upper-ocean thermal stratification is being observed worldwide due to global warming. However, how ocean stratification affects the vertical profile of plankton communities remains unclear. Understanding this is crucial for assessing the broader implications of ocean stratification. Pelagic ciliates cover multiple functional groups, and thus can serve as a model for studying the vertical distribution and functional strategies of plankton in stratified oceans. We hypothesize that pelagic ciliate communities exhibit vertical stratification caused by shifts in functional strategies, from free-living groups in the photic zone to parasitic groups in deeper waters. 306 samples from the surface to the abyssopelagic zone were collected from 31 stations in the western Pacific and analyzed with environmental DNA (the V4 region of 18S rDNA) metabarcoding of pelagic ciliates. We found a distinct vertical stratification of the entire ciliate communities, with a boundary at a depth of 200m. Significant distance-decay patterns were found in the photic layers of 5m to the deep chlorophyll maximum and in the 2,000m, 3000m and bottom layers, while no significant pattern occurred in the mesopelagic layers of 200m - 1,000m. Below 200m, parasitic Oligohymenophorea and Colpodea became more prevalent. A linear model showed that parasitic taxa were the main groups causing community variation along the water column. With increasing depth below 200m, the ASV and sequence proportions of parasitic taxa increased. Statistical analyses indicated that water temperature shaped the photic communities, while parasitic taxa had a significant influence on the aphotic communities below 200m. This study provides new insights into oceanic vertical distribution, connectivity and stratification from a biological perspective. The observed shift of functional strategies from free-living to parasitic groups at a 200m transition layer improves our understanding of ocean ecosystems in the context of global warming.

A study on the spatial distribution and optimization strategies of day care centers for the elderly

Background: At present, the degree of ageing in China is becoming severe, especially in Shanghai, which is the largest city with the highest degree of aging in China. How to adjust the community to effectively meet the needs of the elderly groups has become the focus of social attention. Objectives: In this study, spatial analysis was used to explore the matching degree of the elderly population with the distribution of day care centers in Xuhui District, Shanghai, China, to explore the problems, and to propose optimization strategies with a view to providing feasible suggestions for the construction of old-age daycare. Methods: This research adopts the methods of literature study, map analysis, questionnaire survey, especially tries to use the spatial analysis software of Arc GIS in order to set up a spatial database of Xuhui District's elderly population based on street administrative territorial entity. Results: The results show that the quantity and quality of the day care centers in Xuhui District can not meet the actual needs of the elderly. Secondly, the services provided in daycare centers deviate from the actual needs of older population. Thirdly, the day care service content lists are quite homogeneous, which neglects the diversified and personalized needs . Main Contribution to Evidence-Based Practice: From the perspective of spatial distribution study, this article enriches the theory of the construction and management of the elderly care facilities in urban areas, and provides reference for the formulation of aging policy. Meanwhile, it gives some suggestions on optimizing the spatial distribution of community facilities to fulfil the actual needs for optimal matching of the elderly care resources.

Integrated multi-manned disassembly line balancing problem with reverse supply chain design strategies by considering lot sizing

ABSTRACT Due to strict governmental regulations, manufacturers have increasingly focused on recovering end-of-life (EoL) products through disassembly lines, aiming to achieve economic benefits while addressing the environmental aspects of supply chain sustainability. Disassembly line balancing (DLB) has become a key challenge for enterprises seeking to perform environmentally conscious manufacturing as part of a closed-loop supply chain policy. Therefore, designing a sustainable closed-loop supply chain requires analyzing interrelated problems by focusing on strategic-, tactical-, and operational-level decisions simultaneously. This study introduces an integrated problem that addresses multi-manned DLB, lot sizing, and reverse supply chain (RSC) design problems. To mathematically represent the problem, a generic optimization model is developed to minimize overall costs, including station opening, inventory holding, shortage, transportation, and collection center/facility opening costs. This study addresses several research questions to explore the impact of distribution strategies (centralized and decentralized) on multi-manned DLB and lot size decisions within the context of reverse supply chain design.

Cutting-Edge Strategies for Overcoming Therapeutic Barriers in Alzheimer's Disease.

Alzheimer's disease (AD) remains one of the hardest neurodegenerative diseases to treat due to its enduring cognitive deterioration and memory loss. Despite extensive research, few viable treatment approaches have been found; these are mostly due to several barriers, such as the disease's complex biology, limited pharmaceutical efficacy, and the BBB. This presentation discusses current strategies for addressing these therapeutic barriers to enhance AD treatment. Innovative drug delivery methods including liposomes, exosomes, and nanoparticles may be able to pass the blood-brain barrier and allow medicine to enter specific brain regions. These innovative strategies of medicine distribution reduce systemic side effects by improving absorption. Moreover, the development of disease-modifying treatments that target tau protein tangles, amyloid-beta plaques, and neuroinflammation offers the chance to influence the course of the illness rather than only treat its symptoms. Furthermore, gene therapy and CRISPR-Cas9 technologies have surfaced as potentially groundbreaking methods for addressing the underlying genetic defects associated with AD. Furthermore, novel approaches to patient care may involve the utilization of existing medications having neuroprotective properties, such as those for diabetes and cardiovascular conditions. Furthermore, biomarker research and personalized medicine have made individualized therapy approaches possible, ensuring that patients receive the best care possible based on their unique genetic and molecular profiles.

Load forecasting based on multi-core learning Support Vector Machine (SVM)

Abstract The development of smart grids requires enhanced data integration, robust risk assessment, and dynamic response optimization. In this paper, a multi-core learning Support Vector Machine (SVM) model is presented to improve the accuracy and efficiency of load and photovoltaic output forecasting. The model leverages kernel function optimization and parallel computing frameworks to handle large-scale data efficiently. Additionally, a comprehensive risk assessment system is developed to quantify risks such as overvoltage, undervoltage, line overload, and load loss in distribution networks. An adaptive genetic algorithm-based risk control model is also proposed, optimized in two stages—day-ahead and intra-day—to achieve minimal comprehensive risk through real-time adjustments in distributed power output and electric vehicle charging strategies. Furthermore, an integrated virtual synchronous control online verification method for source-network-load-storage is introduced, enhancing system response speed and control accuracy. These innovations collectively provide a solid theoretical foundation and technical support for the efficient and safe operation of smart grids, addressing the increasing demands of modern energy systems.

Research on energy interactive operation optimization strategy of multi-energy network with integrated energy based on cooperative game

Abstract Integrated energy includes multi-energy networks such as electricity, gas, cold, and heat. Horizontal multi-energy coordination and vertical source network storage load interaction can effectively support large-scale new energy consumption. In this paper, a multi-energy flow model of the integrated energy multi-energy network is constructed, an optimization model of energy sharing and mutual benefit of the multi-energy network is established based on Nash negotiation, and then the model is solved to obtain the optimal distribution scheme of cooperation benefits. Finally, a numerical example is given to verify the proposed energy sharing and mutual benefit method. The results show that the total power generation cost of each park is reduced by 27%, and the profit distribution of each park is more reasonable.

ПОДХОДЫ К ОСУЩЕСТВЛЕНИЮ РИСК-МЕНЕДЖМЕНТА В УСЛОВИЯХ НЕОПРЕДЕЛЕННОЙ И ТУРБУЛЕНТНОЙ ВНЕШНЕЙ СРЕДЫ (НА ПРИМЕРЕ ЗЕРНОВОГО ПРОИЗВОДСТВА)

In the modern Russian socio-economic system, there is an objective increase in uncertainty and turbulence, which requires adequate management responses. In connection with the sanctions pressure, in agriculture the strategy of reducing the risks of import dependence is becoming a stimulating factor for development. The study was conducted on the example of grain production development, which has some specifics from the standpoint of management and, in particular, risk management. The purpose of the study is to generalize approaches to risk management in an uncertain and turbulent external environment and to develop on this basis a risk distribution scheme between management entities in the risk management system adapted to the specifics of grain production. It is shown that the increase in the values of economic indicators of grain production is influenced by natural and climatic conditions, they determine the quality of grain products, costs and the complex of applied agrotechnological measures. In addition to the observed "standard" variability of natural and climatic conditions, the study also identifies extreme risk events that require forecasting and consideration when managing grain production. Also, risks in the considered sphere are caused by diversity and interdependence of such factors as natural and climatic, soil, technological, economic, social, legal, etc. In their mutual interweaving, these factors determine annual fluctuations of economic indicators (yield, gross harvest, sown area) in grain production in Russia. As a result of the study, it was established that risk management procedures in the sphere of grain production should be carried out according to the hierarchical principle with the allocation of at least two levels: the coordinating level - the executive level. Based on this idea, an industry structural and functional model of risk management at various levels of decision-making has been developed. This model has the property of dualism: some levels are simultaneously coordinating, offering strategic solutions to lower levels, and executive - for higher structures. Implementation of the developed model in the practice of risk management in grain production will increase its sustainability and predictability.

V2X Application Server and Vehicle Centric Distribution of Commitments for V2V Message Authentication

Safety applications, such as intersection collision warnings and emergency brake warnings, enhance road safety and traffic efficiency through periodic broadcast messages by vehicles and roadside infrastructure. While the Elliptic Curve Digital Signature Algorithm (ECDSA) is a widely used security approach, its performance limitations make it unsuitable for time-critical safety applications. As such, a symmetric cryptography-based technique called Timed Efficient Stream Loss-tolerant Authentication (TESLA) offers a viable alternative. However, applying standard TESLA in the context of vehicle-to-vehicle (V2V) communications has its own challenges. One challenge is the difficulty of distributing authentication information called commitments in the highly dynamic V2V environment. In this paper, we propose two novel solutions to this problem, namely, V2X Application Server (VAS)-centric and vehicle-centric. The former is an application-level solution that involves selective unicasting of commitments to vehicles by a central server, the VAS, and the latter is a reactive scheme that involves the periodic broadcast of commitments by the vehicles themselves. Extensive simulations are conducted using representatives of the real V2V environment to evaluate the performance of these approaches under different traffic situations; as well as performance comparison with a state-of-the-art distribution solution. The simulation results indicate that the VAS-centric solution is preferable for use in a TESLA-like V2V security scheme. It demonstrates desirable features, including timely delivery of commitments and high distribution efficiency, with over 95% of commitments sent by the VAS are associated with relevant safety messages when compared with the vehicle-centric and state-of-the-art solutions. Formal security analysis, conducted using the Random Oracle Model (ROM), proves the correctness of our proposed distribution schemes. Additionally, an informal security analysis shows the resilience of the proposed schemes against various attacks, including impersonation, replay, and bogus commitment messages.

ПОДХОДЫ К ОСУЩЕСТВЛЕНИЮ РИСК-МЕНЕДЖМЕНТА В УСЛОВИЯХ НЕОПРЕДЕЛЕННОЙ И ТУРБУЛЕНТНОЙ ВНЕШНЕЙ СРЕДЫ (НА ПРИМЕРЕ ЗЕРНОВОГО ПРОИЗВОДСТВА)

In the modern Russian socio-economic system, there is an objective increase in uncertainty and turbulence, which requires adequate management responses. In connection with the sanctions pressure, in agriculture the strategy of reducing the risks of import dependence is becoming a stimulating factor for development. The study was conducted on the example of grain production development, which has some specifics from the standpoint of management and, in particular, risk management. The purpose of the study is to generalize approaches to risk management in an uncertain and turbulent external environment and to develop on this basis a risk distribution scheme between management entities in the risk management system adapted to the specifics of grain production. It is shown that the increase in the values of economic indicators of grain production is influenced by natural and climatic conditions, they determine the quality of grain products, costs and the complex of applied agrotechnological measures. In addition to the observed "standard" variability of natural and climatic conditions, the study also identifies extreme risk events that require forecasting and consideration when managing grain production. Also, risks in the considered sphere are caused by diversity and interdependence of such factors as natural and climatic, soil, technological, economic, social, legal, etc. In their mutual interweaving, these factors determine annual fluctuations of economic indicators (yield, gross harvest, sown area) in grain production in Russia. As a result of the study, it was established that risk management procedures in the sphere of grain production should be carried out according to the hierarchical principle with the allocation of at least two levels: the coordinating level - the executive level. Based on this idea, an industry structural and functional model of risk management at various levels of decision-making has been developed. This model has the property of dualism: some levels are simultaneously coordinating, offering strategic solutions to lower levels, and executive - for higher structures. Implementation of the developed model in the practice of risk management in grain production will increase its sustainability and predictability.

IDL-LTSOJ: Research and implementation of an intelligent online judge system utilizing DNN for defect localization

The evolution of artificial intelligence has thrust the Online Judge (OJ) systems into the forefront of research, particularly within programming education, with a focus on enhancing performance and efficiency. Addressing the shortcomings of the current OJ systems in coarse defect localization granularity and heavy task scheduling architecture, this paper introduces an innovative Integrated Intelligent Defect Localization and Lightweight Task Scheduling Online Judge (IDL-LTSOJ) system. Firstly, to achieve token-level fine-grained defect localization, a Deep Fine-Grained Defect Localization (Deep-FGDL) deep neural network model is developed. By integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU), this model extracts fine-grained information from the abstract syntax tree (AST) of code, enabling more accurate defect localization. Subsequently, we propose a lightweight task scheduling architecture to tackle issues, such as limited concurrency in task evaluation and high equipment costs. This architecture integrates a Kafka messaging system with an optimized task distribution strategy to enable concurrent execution of evaluation tasks, substantially enhancing system evaluation efficiency. The experimental results demonstrate that the Deep-FGDL model improves the accuracy by 35.9% in the Top-20 rank compared to traditional machine learning benchmark methods for fine-grained defect localization tasks. Moreover, the lightweight task scheduling strategy notably reduces response time by nearly 6000ms when handling 120 task volumes, which represents a significant improvement in evaluation efficiency over centralized evaluation methods.

Asymmetric Charge Distribution in Atomically Precise Metal Nanoclusters for Boosted CO2Reduction Catalysis.

Recently, atomically precise metal nanoclusters (NCs) have been widely applied in CO2 reduction reaction (CO2RR), achieving exciting activity and selectivity and revealing structure-performance correlation. However, at present, the efficiency of CO2RR is still unsatisfactory and cannot meet the requirements of practical applications. One of the main reasons is the difficulty in CO2 activation due to the chemical inertness of CO2. Constructing symmetry-breaking active sites is regarded as an effective strategy to promote CO2 activation by modulating electronic and geometric structure of CO2 molecule. In addition, in the subsequent CO2RR process, asymmetric charge distributed sites can break the charge balance in adjacent adsorbed C1 intermediates and suppress electrostatic repulsion between dipoles, benefiting for C-C coupling to generate C2+ products. Although compared to single atoms, metal nanoparticles, and inorganic materials the research on the construction of asymmetric catalytic sites in metal NCs is in a newly-developing stage, the precision, adjustability and diversity of metal NCs structure provide many possibilities to build asymmetric sites. This review summarizes several strategies of construction asymmetric charge distribution in metal NCs for boosting CO2RR, concludes the mechanism investigation paradigm of NCs-based catalysts, and proposes the challenges and opportunities of NCs catalysis.

The challenge of improving long-lasting insecticidal nets coverage on Bioko Island: using data to adapt distribution strategies

BackgroundSince 2015, malaria vector control on Bioko Island has relied heavily upon long-lasting insecticidal nets (LLIN) to complement other interventions. Despite significant resources utilised, however, achieving and maintaining high coverage has been elusive. Here, core LLIN indicators were used to assess and redefine distribution strategies.MethodsLLIN indicators were estimated for Bioko Island between 2015 and 2022 using a 1x1 km grid of areas. The way these indicators interacted was used to critically assess coverage targets. Particular attention was paid to spatial heterogeneity and to differences between urban Malabo, the capital, and the rural periphery.ResultsLLIN coverage according to all indicators varied substantially across areas, decreased significantly soon after mass distribution campaigns (MDC) and, with few exceptions, remained consistently below the recommended target. Use was strongly correlated with population access, particularly in Malabo. After a change in strategy in Malabo from MDC to fixed distribution points, use-to-access showed significant improvement, indicating those who obtained their nets from these sources were more likely to keep them and use them. Moreover, their use rates were significantly higher than those of whom sourced their nets elsewhere.ConclusionsStriking a better balance between LLIN distribution efficiency and coverage represents a major challenge as LLIN retention and use rates remain low despite high access resulting from MDC. The cost-benefit of fixed distribution points in Malabo revealed significant advantages, offering a viable alternative for ensuring access to LLINs to those who use them.

Phase-Angle-Encoded Snake Optimization Algorithm for K-Means Clustering

The rapid development of metaheuristic algorithms proves their advantages in optimization. Data clustering, as an optimization problem, faces challenges for high accuracy. The K-means algorithm is traditaaional but has low clustering accuracy. In this paper, the phase-angle-encoded snake optimization algorithm (θ-SO), based on mapping strategy, is proposed for data clustering. The disadvantages of traditional snake optimization include slow convergence speed and poor optimization accuracy. The improved θ-SO uses phase angles for boundary setting and enables efficient adjustments in the phase angle vector to accelerate convergence, while employing a Gaussian distribution strategy to enhance optimization accuracy. The optimization performance of θ-SO is evaluated by CEC2013 datasets and compared with other metaheuristic algorithms. Additionally, its clustering optimization capabilities are tested on Iris, Wine, Seeds, and CMC datasets, using the classification error rate and sum of intra-cluster distances. Experimental results show θ-SO surpasses other algorithms on over 2/3 of CEC2013 test functions, hitting a 90% high-performance mark across all clustering optimization tasks. The method proposed in this paper effectively addresses the issues of data clustering difficulty and low clustering accuracy.

Improving racial/ethnic health equity and naloxone access among people at risk for opioid overdose: A simulation modeling analysis of community-based naloxone distribution strategies in Massachusetts, United States.

During the COVID-19 pandemic, there was a surge in opioid overdose deaths (OODs) in Massachusetts, USA, particularly among Black and Hispanic/Latinx populations. Despite the increasing racial and ethnic disparities in OODs, there was no compensatory increase in naloxone distributed to these groups. We aimed to evaluate two community-based naloxone expansion strategies, with the objective of identifying approaches that could mitigate mortality and racial and ethnic disparities in OODs. Individual-based simulation model. We measured naloxone availability using naloxone kits per OOD and evaluated scenarios of achieving higher benchmarks for naloxone availability (i.e. 40, 60 and 80 kits per OOD) from 2022 levels (overall: 26.0, White: 28.8, Black: 17.3, Hispanic/Latinx: 18.9). We compared two naloxone distribution strategies: (1) proportional distribution: achieving the benchmark ratio at the overall population level while distributing additional kits proportional to the 2022 level for each racial/ethnic group (at 40 kits per OOD benchmark: overall: 40, White: 44.3, Black: 26.6, Hispanic/Latinx: 29.1), and (2) equity-focused distribution: achieving the benchmark ratio among each racial/ethnic group (at 40 kits per OOD benchmark: 40 for all groups). Massachusetts, United States. People at risk of OOD. Annual number and rate of OODs, total healthcare costs of increasing naloxone availability. Both naloxone distribution strategies yielded comparable predicted reductions in total OODs in 2025 and incurred similar incremental costs. However, the relative reduction in the rate of OODs differed across groups. For achieving an 80 kits per OOD benchmark, proportional distribution resulted in a projected 6.7%, 6.5% and 7.1% reduction in annual OODs in 2025 among White, Black and Hispanic/Latinx populations, respectively. In contrast, equity-focused distribution achieved a reduction of 5.7%, 11.3% and 10.2% in the respective groups. In all scenarios, the cost per OOD averted was lower than the generally accepted thresholds for cost per life saved. An equity-focused naloxone distribution strategy designed to reduce racial and ethnic disparities in naloxone availability could improve health equity among racial and ethnic groups while potentially improving overall population health at lower healthcare costs per opioid overdose death averted than a proportional distribution strategy.

Order-restricted statistical inference and optimal censoring scheme for Gompertz distribution based on joint type-II progressive censored data

This paper considers the order-restricted statistical inference for two populations based on the joint type-II progressive censoring scheme. The lifetime distributions of the two populations are supposed to follow the Gompertz distribution with the same shape parameter but different scale parameters. The maximum likelihood estimates of the unknown parameters are derived by employing the Newton-Raphson algorithm and the expectation-maximization algorithm, respectively. The Fisher information matrix is then employed to construct asymptotic confidence intervals. For Bayes estimation, we assume an ordered Beta-Gamma prior for the scale parameters and a Gamma prior for the common shape parameter. Bayes estimations and the highest posterior density credible intervals for unknown parameters under two different loss functions are obtained with the importance sampling technique. To evaluate the performance of order-restricted inference, extensive Monte Carlo simulations are performed and two air-conditioning systems datasets are used to illustrate the proposed inference methods. In addition, the results are compared with the case when there is no order restriction on the parameters. Finally, the optimal censoring scheme is obtained by four optimality criteria.

Research on Dynamic Adaptation of Supply and Demand of Power Emergency Materials based on Cohesive Hierarchical Clustering

Taking the dynamic adaptation of supply and demand of emergency materials as the research object, the condensed analytic hierarchy process was used to analyze the supply and demand risk factors in the supply chain of emergency materials from the supply side and the demand side. A new two-objective optimization model based on cohesive hierarchical clustering and electricity is proposed. Three distribution schemes are developed based on customer demand, shortest transportation time and the combination of both. When the overall satisfaction of the supply chain is the highest, the total revenue is maximized, which is suitable for the optimization of the supply chain of emergency materials under emergencies. Firstly, the power data and behavior data of power users are cross-analyzed to obtain the typical data characteristics of power users. Secondly, the cohesive hierarchical clustering algorithm is used to analyze the power users, and the cohesive hierarchical clustering algorithm is used to improve the calculation operation of score similarity. The adjustment factor is introduced to improve the calculation operation, and the comprehensive weight of each emergency point is determined as the parameter of the objective function by considering the subjective and objective factors that affect the importance of emergency points. Finally, through the simulation analysis of an emergency materials scheduling based on electric power, and compared with the results of random scheduling, the science and effectiveness of the proposed model are verified.

Delivery strategies to improve piglets exposure to oral antimicrobials

BackgroundThe widespread practice of delivering antimicrobials through drinking water to livestock leads to considerable variability in exposure levels among animals, raising concerns regarding disease outbreaks and the emergence of antimicrobial resistance. This variability is primarily driven by three pivotal factors: fluctuations in drug concentration within water pipes, variances in drinking behavior among animals, and differences in individual pharmacokinetic parameters. The objective of this study is to develop and evaluate a strategy that tailors medication delivery based on the drinking patterns of pigs, aiming to improve medication distribution without increasing the overall dose of medication.ResultsOur results demonstrate that several distribution strategies based on the animals’ drinking behavior can effectively increase their overall exposure. These strategies include increasing the exposure of the least exposed animals, raising the average exposure, maximizing the exposure of the majority of the well-exposed animals, or increasing exposure to ensure that a Pharmacokinetics/Pharmacodynamics (PK/PD) criterion reaches a threshold value for a large number of the animals. In summary, constructing an effective distribution strategy for drinking water requires optimizing a specific criterion. The various criteria and methods for optimizing then are detailed.ConclusionsAs examples, this article demonstrate that incorporating the drinking behavior into the delivery of amoxicillin results in an increase in the percentage of piglets reaching an AUC/MIC ratio greater than 25h. Specifically, with Pasteurella multocida, the percentage rises from 30%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} to at least 60%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}, while with Actinobacillus pleuropneumoniae, it increases from 20%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document} to more than 70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\%$$\\end{document}.

An Exploratory Study of Entrepreneurs in the Textile Industry in India: Scope and Challenges

This paper presents an exploratory study of entrepreneurs in the textile industry in India, focusing on the scope, challenges, and strategic imperatives for establishing a textile plant. The research provides a comprehensive overview of the multifaceted aspects required for successful operations, including distribution channels, sales strategies, and marketing approaches. Through qualitative interviews with industry leaders and analysis of secondary data, the study highlights critical factors influencing the textile sector, such as supply chain management, regulatory compliance, and market entry strategies. It also examines the role of technology in enhancing operational efficiency and the importance of sustainability in production practices. The findings reveal that while there are significant growth opportunities in the Indian textile market, entrepreneurs must navigate various challenges, including competition, infrastructure constraints, and evolving consumer preferences. This research aims to equip prospective entrepreneurs with actionable insights to facilitate informed decision-making and foster sustainable growth in the textile industry. Key Words: Textile Industry, Entrepreneurs, India, Distribution Strategy, Sales and Marketing, Supply Chain Management, Regulatory Compliance, Technology Integration, Sustainability, Market Entry Strategies

Based on improved crayfish optimization algorithm cooperative optimal scheduling of multi-microgrid system

In order to solve the influence of the complex interaction relationships among subjects on the system solution accuracy and speed of the Multi-Microgrid system under the high penetration rate of new energy. Firstly, the paper establishes the bi-level optimal scheduling Stackelberg game model based on shared energy storage, considering the inter-subject interaction in MMG. Subsequently, based on the four improvement methods of Chaotic Map, Quantum Behavior, Gaussian Distribution, and Nonlinear Control Strategy, the Chaotic Gaussian Quantum Crayfish Optimization Algorithm is proposed to solve the optimization scheduling model. The improved algorithm exhibits superior initial solutions and enhanced search capability. In comparison to the original algorithm, the relative errors of the CGQCOA optimization outcomes are 98%, 20.96%, 98.74% and 16.55%, respectively, enhancing the model-solving accuracy and the speed of convergence to the optimal solution. Finally, the simulation demonstrates that the revenue of Microgrid 1, Microgrid 2, and Microgrid 3 have increased by 0.73%, 1.17%, and 1.04%, respectively. Concurrently, the penalty cost of pollutant emission has decreased by 5.9%, 11.5%, and 12.68%, respectively. Furthermore, the revenue of the shared storage have increased by 1.91%. These findings validate the efficacy of the methodology proposed in enhancing the revenue of the various subjects and reducing pollutant gas emission.