LISS 2024
14th International Conference on Logistics, Informatics and Service Sciences
- 2025
- Book
- 1. edition
- Editors
- Daqing Gong
- Yixuan Ma
- Jonathan Foster-Pedley
- Juliang Zhang
- Book Series
- Lecture Notes in Operations Research
- Publisher
- Springer Nature Singapore
About this book
This proceedings volume focuses on the “AI and data driven technical and management innovation in logistics, informatics and services”. In detail the included scientific papers analyze the latest fundamental advances in the state of the art and practice of logistics, informatics, service operations and service science. The proceedings volume is documentation of LISS 2024 at Cape Town and Beijing in July 26-29, 2024. It is co-organized by Beijing Jiaotong University, Henley Business School Africa, Beijing Information Science and Technology University and Beijing Wuzi University.
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Table of Contents
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Frontmatter
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A Study on Multi-objective Dynamic Pricing of Traditional Apparel: Application and Exploration of DDPG Method
Guanghui Mao, Qingcong ZhaoThis chapter delves into the dynamic pricing strategies for traditional apparel, focusing on the application of the Deep Deterministic Policy Gradient (DDPG) method. The study addresses the challenge of balancing profit and the promotion of cultural heritage by employing a refined multi-objective particle swarm algorithm. Key topics include the description of the dynamic pricing problem, the construction of a Markov Decision Process (MDP) model, and the integration of the DDPG algorithm to solve multi-objective optimization problems. The chapter also presents numerical experiments that compare the performance of different algorithms, including the multi-objective particle swarm algorithm, the multi-objective hybrid particle swarm algorithm, and the multi-objective particle swarm algorithm based on DDPG. The results highlight the superior performance of the DDPG-based algorithm in exploring the pricing objective space and achieving a more complete Pareto frontier. The conclusion emphasizes the practical implications of the findings for dynamic pricing in the traditional apparel industry, providing a reliable method and theoretical basis for decision-making.AI Generated
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AbstractResearch on dynamic pricing strategies for traditional apparel helps business managers better balance the dual objectives of profit maximization and cultural heritage preservation. This paper addresses the challenge of unknown demand distributions for traditional garments by employing deep reinforcement learning techniques. Specifically, it constructs a multi-objective dynamic pricing model for traditional apparel based on the Markov Decision Process (MDP). Furthermore, a multi-objective particle swarm algorithm, based on Deep Deterministic Policy Gradient (DDPG), is proposed to solve the dynamic pricing problem for traditional apparel. By comparing the Pareto optimal solutions obtained iteratively through the multi-objective particle swarm algorithm (MOPSO), the multi-objective hybrid particle swarm algorithm, and the multi-objective particle swarm algorithm based on DDPG, the algorithm based on DDPG demonstrates superior generality and convergence performance. -
Competitiveness Measurement and Evolution Pattern Analysis of CR Express Assembly Centers
Huiying Du, Shiyun Liu, Xingfen Wang, Hongjun WangThis chapter delves into the competitiveness measurement and evolution pattern analysis of China Railway Express (CR Express) assembly centers, focusing on four key areas: competitiveness level evaluation, analysis methods and model building, empirical analysis, and competitiveness improvement strategies. The study evaluates six assembly centers—Chongqing, Chengdu, Xi’an, Zhengzhou, Urumqi, and Shenyang—using a comprehensive index system that includes logistics scale, logistics resources, information degree, and economic development. The research employs the critic-entropy weight combination weighting method to measure competitiveness levels and uses the Gini coefficient and shift-share analysis to study competitive evolution characteristics from 2011 to 2023. The findings reveal that Chongqing and Chengdu assembly centers exhibit strong competitiveness, while Xi’an has shown rapid development. The study also highlights the fierce competition among assembly centers and suggests strategies for improving competitiveness, such as focusing on individual advantages, achieving differentiated development, and promoting high-quality development of CR Express. This analysis provides valuable insights for optimizing resource allocation, improving service quality, and enhancing the overall competitiveness of CR Express assembly centers.AI Generated
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AbstractChina Railway Express assembly center occupies an important position in China Railway Express operation network. In order to promote the development of China Railway Express, this study takes the assembly centers as the research object. Firstly, it constructs a competitiveness index system based on relevant policy documents, and uses the critic-entropy weight combination weighting method to measure and analyze logistics competitiveness. Secondly, it combines the Gini coefficient and offset-share method were used to clarify its competitive evolution characteristics from 2011 to 2023. Finally, targeted strategies to enhance competitiveness were proposed to provide reference for the construction and development of China Railway Express. -
Identifying Malicious Comments by Dual-Channel Combined Multi-dimensional Feature Interaction
Yunjie Wang, Yiqing Lu, Linyu ZhangIn this chapter, the authors address the challenge of identifying malicious comments in online text, which has become increasingly difficult due to the vast volume of information on the internet. The proposed DCFI model incorporates a Multi-Dimensional Feature Interaction (FI) layer to effectively extract features of malicious comments. The model combines Graph Convolutional Networks (GCN) and Bidirectional LSTM (BiLSTM) to capture both spatial and temporal features, respectively. The unique Multi-Dimensional FI layer refines and integrates features from these dual channels, enhancing the model's performance. Experimental results demonstrate that the DCFI model outperforms six popular deep learning models in accuracy, precision, recall, and F1-score. The chapter also discusses the impact of different modules within the DCFI model and suggests future directions for research. By reading this chapter, professionals will gain insights into the latest advancements in automated malicious comment identification and the potential of combining GCN, BiLSTM, and attention mechanisms for text classification tasks.AI Generated
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AbstractThis paper proposes a Dual-channel model named DCFI (identifying malicious comments by dual-channel combined Multi-Dimensional FI) to address the problem of identifying malicious comments. The model combines the Graph Convolutional Network (GCN) and Bidirectional Long Short-Term Memory (BiLSTM) to extract different textual features, enabling a more comprehensive representation of the text’s meaning. The proposed Multi-Dimensional FI refines and fuses the extracted features, followed by a fully connected layer and softmax classification to achieve the identification of malicious comments. Experimental results demonstrate that the DCFI model outperforms mainstream classification models in terms of malicious comment identification. It improves classification accuracy by 1.06% to 2.89%. This approach effectively extracts textual features specific to malicious comments, enabling accurate identification. -
Research on Incentive Mechanism of Commodity Trading Body in Blockchain Environment
Genxiang Gao, Huiying Du, Qing YuThis chapter delves into the transformative potential of blockchain technology in the realm of commodity trading, focusing on the critical role of government incentive mechanisms. Through an evolutionary game model, the study examines the interplay between government policies and traders' adoption of blockchain transactions, highlighting the factors that influence this dynamic equilibrium. Key topics include the current state of commodity trading, the challenges of information asymmetry, and the potential of blockchain to enhance transparency and security. The chapter also explores the impact of government incentives on traders' behavior, using simulation models to validate the findings. The study concludes that differentiated government incentive policies, tailored to the stage of traders' smart transformation, are essential for promoting the successful integration of blockchain technology in commodity trading. By providing a detailed analysis of the trade-offs associated with blockchain implementation costs and benefits, this chapter offers valuable insights for professionals seeking to leverage this technology in the commodity trading industry.AI Generated
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AbstractWith the rapid development of blockchain technology, its application in the commodity electronic trading market is becoming increasingly widespread. This study aims to construct a blockchain environment, explore the use of the evolutionary game method to analyze the impact of government behavior on traders’ adoption of blockchain transactions, put forward the “incentive-positive” game model, use the model example theory and incentive theory to analyze the two sides in the blockchain transaction of the evolution of the game of payoffs and benefits, and based on this simulation using Matlab software to study the transformation cost, government subsidy coefficients, and other factors. On this basis, Matlab software is used to conduct simulations to study the impact of the transformation cost of traders, government subsidy coefficient, and other factors on the game strategy. The study shows that when the initial willingness coefficient is 0.5, the final evolution of the game between the two sides tends to be similar to the government incentive, and the traders are active; thus, it can be seen that the initial willingness of the two sides affects the final strategy of the evolution of the system game, and the government raises the subsidy incentive coefficient based on which it has a significant effect on promoting the traders to use the blockchain transaction, so the government pays attention to the incentive strength in the designation of the policy, to formulate flexible incentive policy and maximize the social benefit—policies to achieve the goal of maximizing social benefits. -
Information Revelation Decision Considering Brand Spillover
Sheng Jin, Hui Yang, Kui Song, Ying LiThis chapter delves into the complex world of co-opetition in supply chains, focusing on the strategies employed by weak brand firms and e-commerce platforms. The study explores how weak brand firms can leverage brand spillover to enhance their perceived product quality and compete more effectively with strong brand firms. It also examines the e-commerce platform's information revelation strategy and how it influences consumer purchasing decisions. The research is based on a game-theoretic framework that analyzes the equilibrium wholesale and retail prices under different brand spillover and information revelation strategies. The findings reveal that brand spillover can benefit both weak and strong brand firms, and that the platform's information revelation strategy significantly impacts consumer preferences and pricing. The study provides valuable insights into the dynamics of co-opetition and the strategies that can be employed to enhance competitiveness in the market.AI Generated
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AbstractWhen a weak brand firm outsources its product from a strong brand firm, the weak brand firm can show this relationship to promote its product, which is referred to as brand spillover. At the same time, the platform can provide information that can alleviate consumers’ uncertainty about their preference for the product. We build a game-theoretic model to study the information revelation and brand spillover strategies in the context where an e-commerce platform sells products of two brands: weak brand firm and strong brand firm’s products. The weak brand firm outsources its product’s manufacturing to the strong brand firm. The equilibrium analysis shows that the weak brand firm always prefers to use brand spillover and the platform prefers to reveal preference information when the ratio of the two products’ cost-quality efficiencies is low. -
How Digital Transformation Impacts ESG Performance of Listed Companies
Gaojing Zhao, Xiaolan GuanThis chapter delves into the relationship between digital transformation and the ESG performance of listed companies, focusing on the mediating role of green technology innovation. Through empirical analysis of data from Shanghai and Shenzhen A-share listed companies between 2015 and 2022, the study finds that digital transformation significantly enhances ESG performance. The research also reveals that green technology innovation acts as a mediator in this process, promoting corporate sustainability and governance. The study provides practical recommendations for governments, enterprises, and society to leverage digital technologies for improving ESG performance. Key topics include the impact of digital transformation on ESG performance, the role of green technology innovation, and strategies for enhancing corporate sustainability through digital means.AI Generated
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AbstractIn the context of high-quality economic development and green transformation of enterprises, it is of great practical significance to explore how digital transformation affects the ESG performance of listed companies. The article firstly combed the related literature on digital transformation and ESG performance and secondly investigated the relationship between digital transformation and corporate ESG performance by empirically analyzing A-share listed companies from 2015 to 2022, and reached the conclusion that digital transformation can significantly enhance ESG performance, and the effect still holds after the robustness test. Finally, the mechanism by which digital transformation affects ESG performance is examined, and the conclusion is that digital transformation significantly enhances corporate ESG performance by promoting green technology innovation. -
Can Digital Construction Service Improve the Value Transformation of Green Innovation?
Qin Liu, Yuzuo LiuThis study delves into the critical role of digital construction services in enhancing the value transformation of green technology innovation within the new energy vehicle (NEV) industry. Through an analysis of NEV venture enterprises listed on the China Growth Enterprise Market and Science and Technology Innovation Board from 2011 to 2020, the research constructs a measurement system for green technology innovation and examines its impact on green innovation value. The study highlights the moderating role of digital construction services in this relationship, demonstrating how these services can facilitate the networking, intelligence, and collaborative upgrading of traditional infrastructure, ultimately promoting the productization and marketization of green technology innovations. Additionally, the research explores the influence of profitability on the moderating effect of digital construction services, revealing that higher profitability can strengthen the positive impact of these services on green innovation value. The findings provide valuable insights into how enterprises can leverage digital construction services to overcome the challenges of green technology innovation and achieve sustainable development goals.AI Generated
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AbstractThe vigorous development of digital construction service provides an important opportunity for the value transformation of green innovation of new energy vehicle (NEV) enterprises. Based on this, this study explores the influence of green technology innovation on green innovation value, and studies the moderating role of digital construction service on this relationship. Furthermore, this study adopts heterogeneity analysis from the perspective of profitability to explore how to optimize the effect of digital construction service on the value transformation of green innovation. After analyzing panel data of Chinese NEV venture enterprises from 2015 to 2021, we find that green technology innovation can improve green innovation value, and this process is positively moderated by digital construction service. Moreover, the results of heterogeneity analysis indicate that under higher profitability, the moderating effect of digital construction service is strenghthen. This study has made theoretical contributions and practical implications for government and managers to develop digital construction service rationally, and optimize the value transformation process of green innovation to improve the value. -
Case Studies and Identification of Financial Risks in Pharmaceutical Enterprises——Based on Random Forest Model
Lijun Liang, Litong Cui, Guoyu ChenThis chapter delves into the critical issue of financial risk identification in pharmaceutical enterprises, leveraging the power of the Random Forest model, a robust machine learning technique. The study meticulously analyzes financial anomalies and their implications, drawing from real-world case studies of listed pharmaceutical companies in China. It constructs a comprehensive financial risk identification model, comparing its effectiveness with other models like GBDT and BAGGING. The research identifies key financial indicators such as Operating Profit Ratio, Total Assets Turnover, and Equity Multiplier as crucial for risk assessment. The findings reveal that the Random Forest model achieves a high accuracy rate of 78%, outperforming other models in identifying financial risks. The chapter concludes with strategic recommendations for pharmaceutical enterprises to mitigate financial risks, emphasizing the importance of real-time monitoring and strengthening internal controls. This detailed analysis provides valuable insights for professionals looking to enhance their financial risk management strategies in the pharmaceutical sector.AI Generated
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AbstractIn recent years, domestic listed pharmaceutical enterprises frequent financial fraud incidents, to the society and investors have a great negative impact. How to effectively identify financial anomalies of pharmaceutical enterprises and avoid financial risks is the focus of academic and audit circles. First of all, based on the relevant literature of this study, the causes and characteristics of financial risks of pharmaceutical enterprises are sorted out, and the index system for financial risk identification of pharmaceutical enterprises is summarized, including six first-level indicators and 19 s-level financial indicators. Secondly, the financial risk identification model of pharmaceutical enterprises is constructed, and the financial data of 25 normal and 53 abnormal pharmaceutical enterprises from 2013 to 2022 are obtained according to CSMAR database. Finally, the characteristics of the selected indicators are described by clustering method, and then incorporated into the random forest model for statistical testing and evaluation of the index results. The research results show that among the many models that can identify the financial risks of pharmaceutical enterprises, the random forest model constructed in the paper has a good recognition effect, and the stable accuracy rate reaches 77% in capturing high-latitude financial data and output data, which is beneficial to lay a good foundation for the establishment of a suitable financial risk early warning mechanism for pharmaceutical enterprises. Finally, based on the results of random forest evaluation, the paper puts forward the prospect of preventing financial anomaly identification of pharmaceutical enterprises. -
A Bilateral Self-Recursive ‘STA’ Contextualized Teaching Framework Based on Generative Artificial Intelligence
Xiang Li, Han Zhang, Shaozhong CaoThis chapter delves into the application of generative AI, specifically the ChatGPT model, to revolutionize education by creating contextualized exercises tailored to students' interests. The research highlights the prevalence of boredom among students and its negative impact on academic performance, proposing a solution through contextualized teaching. The study explores various prompt engineering techniques, including zero-shot and few-shot prompting, to generate engaging and diverse exercises. The effectiveness of these methods is validated through experiments, demonstrating the model's ability to create contextualized exercises and assist in marking. A bilateral self-recursive 'STA' framework is introduced, integrating students, teachers, and AI to streamline the educational process. This framework aims to enhance students' motivation, alleviate teachers' workload, and foster a more interactive and personalized learning environment. The findings underscore the potential of generative AI to transform educational practices, making learning more engaging and effective.AI Generated
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AbstractEducation is vital for the development of the country. However, there are students who experience boredom during the school year, which undoubtedly leads to a decline in their learning status. Studies have shown that using contextualized exercises can boost students’ interest, but it would be very exhausting for teachers to consider each student’s interest. With the development of generative AI, we can utilize large language models to help us do this. In this paper, we validate the capability of the ChatGPT model and propose a bilateral self-recursive STA contextualized teaching framework based on generative AI, and explore the application of AI in education. -
Transfer Pricing and Offshore Strategies for Competitive Multinational Companies
Ying Yuan, Hongfu Huang, Fei XuThis chapter delves into the strategic decisions of multinational companies (MNCs) regarding offshore production and transfer pricing, focusing on the influence of tax rate differences and the Arm's Length Regulation (ALR). It explores how MNCs can optimize their profits by relocating production departments to countries with lower tax rates, while retaining sales and finance departments in their home countries. The analysis compares scenarios with and without ALR, revealing how regulatory constraints impact transfer pricing and corporate profits. The study introduces a game model involving two competitive MNCs, using a Cournot model to determine equilibrium results. Key findings include the tendency of MNCs to prefer offshore production in the absence of ALR, the potential for ALR to induce a 'back to shore fever' when tax rate differences are small, and the possibility of ALR increasing firms' profits under high competitive intensity. The chapter concludes with practical implications for MNCs' offshore strategies, emphasizing the importance of considering tax rates, competition intensity, and regulatory environments.AI Generated
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AbstractWith the development of economic and the social levels, offshore production is in full swing in many countries. Offshoring by multinational companies (MNCs) needs to take into account the effects of costs, tax differences, transfer pricing and other factors, and competition between companies may change their equilibrium strategies as well. We construct a model of two MNCs with competitive relationships, explore the impact of the tax difference and arm’s length regulation (ALR) on transfer pricing for two MNCs’ offshore production strategies. We find that: the ALR can restrict offshore production by MNCs to prevent unreasonable tax evasion. When the market is highly competitive and the tax is low, the profits of MNCs will increase, this may be due to the positive effects of healthy competition between MNCs. Moreover, our research can reflect the influence of tax rate difference and ALR on the MNCs, which provides a certain reference for MNCs in the competitive environment. -
Decision Models for Cross-Sell Product with Two Ordering Opportunities
Ding Ran, Chen JieThis chapter examines decision models for cross-selling products with two ordering opportunities, focusing on the challenges of managing seasonal goods. The study explores the impact of cross-selling relationships on order quantities and expected returns, highlighting the importance of balancing inventory and sales to maximize profitability. Key topics include the development of a quadratic ordering newsboy model, sensitivity analysis of cross-selling coefficients, and the influence of maximum replenishment quantities on ordering strategies. The research concludes that leveraging cross-selling relationships and adopting secondary ordering strategies can significantly enhance retailer returns and mitigate risks associated with demand uncertainty. Through numerical examples and sensitivity analyses, the study provides valuable insights into optimizing order quantities and improving overall inventory management.AI Generated
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AbstractThis paper studies a newsvendor model for two types of products with one-way cross-selling and the retailer had two ordering opportunities. Introducing maximum replenishment. Building and solving the newsvendor model, proving that the model is a concave function by means of the Hesser matrix study; and derive the conditions for the existence of the optimal solution of the model. The numerical examples is verified validity of the model. The genetic algorithm is used to derive the optimal solution of numerical example. The sensitivity analysis of the different elements show that the model is effective. The results show that the total expected revenue of the condition of one-way cross-selling effect is superior to separate ordering with independent demand; The increase of the cross-selling coefficient, the model will order more major products and the less secondary products, and lower expected revenue; with the increase of the maximum replenishment quantity, the model will order less quantity of the main products,and the higher the expected revenue; As the profit from sales of the main products increases, the order quantity of the main products grows, leading to a higher expected return. -
Push, Pull, and Supply Chain Coordination with Overconfident Retailers
Jian Zhang, Shuang He, Ying ZhangThis chapter delves into the intricate world of supply chain management, focusing on the impact of overconfident retailers on push and pull supply chain models. It explores how overconfidence influences decision-making, operational efficiency, and profit distribution within these models. The study reveals that overconfidence can lead to suboptimal ordering decisions, affecting the overall performance of the supply chain. It compares the efficiency of push and pull supply chains under the influence of overconfident retailers, highlighting that pull supply chains may not always be superior. The chapter also introduces two innovative contracts—Advance-purchase Contract with Three-part Tariff (ACTT) for push supply chains and Revenue-Sharing Contract with Three-Part Tariff (RCTT) for pull supply chains—to achieve supply chain coordination. Through detailed analysis and numerical examples, the study provides valuable insights for managers looking to optimize their supply chain strategies in the face of overconfident retailers.AI Generated
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AbstractIn this paper, we investigate the impact of retailers’ overconfidence on the push and the pull supply chain performance. Traditionally, a pull supply chain generates a higher optimal order quantity and hence higher supply chain profit than a push supply chain when firms are entirely rational. Our analysis indicates that push can lead to a higher optimal order quantity than pull when the retailer is sufficiently overconfident. Meanwhile, with the increase of retailers’ overconfidence level, the optimal wholesale price of the push supply chain increases, the optimal order quantity approaches the mean of random demand, and the optimal wholesale price of the pull supply chain decreases, the optimal output decreases. We demonstrate that three-part tariff advance-purchase and revenue-sharing contracts can coordinate the push and the pull supply chains with overconfident retailers, respectively. -
Data Analysis and Prediction Study of Endangered Species Based on Ecological Environment
Chuan Zhao, Chunyu XingThis chapter focuses on the data analysis and prediction of endangered species based on ecological environment factors. It explores the impact of global natural disasters, surface temperature changes, greenhouse gas emissions, and forest cover on three categories of endangered species: vulnerable, endangered, and critically endangered. The study uses the ARIMA model to predict changes in the number of endangered species over the next five years. Key findings include a positive correlation between greenhouse gas emissions and the number of endangered species, as well as a significant negative correlation between forest cover and species populations. The chapter concludes that world forest cover, global greenhouse gas emissions, global surface temperature, and global natural disasters are ranked in order of their influence on endangered species. The predictions indicate a clear increasing trend in the number of endangered species, highlighting the urgent need for targeted conservation measures. The study provides specific recommendations for increasing public awareness, habitat conservation, and investment in scientific research to mitigate the unfavorable situation of endangered organisms.AI Generated
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AbstractGiven the decreasing trend of global biodiversity year by year, preserving species diversity is significantly influenced by the ecological environment, making it a crucial factor of great importance. This study selected three categories of endangered animals: “vulnerable”, “endangered” and “critically endangered”, and analyzed the data in a clear and visually appealing manner from four different perspectives: Global Natural Disasters, Global Land Surface Temperature, Global Greenhouse Gas Emissions, and Worldwide Forest Cover Area, using relevant data from 2000 to 2022. By employing techniques such as correlation coefficient, linear regression equation, and heat map, the impact of various factors on endangered species was deeply discussed. The findings indicated that the quantity of three groups of endangered animals showed an overall upward trend. The order of the damage of the four ecological environments to endangered species is world forest coverage > global greenhouse gas emissions > global surface temperature > global natural disasters. By establishing the ARIMA prediction model, the number of endangered species of the three types was predicted and analyzed, and it was found that they all showed a significant increasing trend in five years. The results of this research can provide valuable guidance and solutions for reducing the threats caused to endangered animals in their ecological environment, to cope with the current challenges faced by biodiversity. -
Hash Chain Based Secure Communication for Internet of Things: Architecture and Schemes
Jinquan Li, Wenbao Jiang, Haibao ZhangThis chapter explores the implementation of hash chain-based secure communication for IoT devices, focusing on architecture and schemes. It delves into the challenges of traditional security methods like TLS and IPSec, highlighting the need for lightweight, scalable solutions. The chapter introduces hash chain-based authentication architectures, including traceable anonymous authentication and lightweight unicast/broadcast authentication. It presents the HMA and HBMA algorithms, which leverage the immutability of hash chains to ensure message integrity and provide security functions like privacy protection and traceability. The chapter also discusses the performance evaluation of these schemes, comparing them with existing methods and demonstrating their efficiency and security. Experimental results show that the proposed schemes can meet the unicast/broadcast authentication and microsecond-level secure communication requirements of IoT. The chapter concludes by outlining future research directions, emphasizing the need for privacy preservation, scalability, and efficient identity and access management in IoT authentication systems.AI Generated
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AbstractIn the era of the Internet of Things (IoT), the interconnection of physical information systems with the Internet is transforming various domains such as logistics, manufacturing, connected vehicles, and smart cities. Due to the proliferation of IoT devices, securing their communication becomes critical. IoT device communications are usually not authenticated or encrypted, and traditional authentication schemes based on asymmetric/symmetric cryptographic systems cannot cope with the unique challenges posed by IoT, such as resource constraints, large scale, scalability, and decentralized trust requirements. This research designs a novel IoT device authentication framework based on lightweight cryptography techniques. Firstly, we propose AMA, a lightweight anonymous authentication scheme for IoT. Then a reliable and scalable authentication scheme HBMA is provided by exploiting the immutability of hash chains. The lightweight cryptography technique ensures that the computational overhead is minimized for resource constrained IoT devices. The study also concludes with a detailed analysis of the security features of the proposed framework and summarizes future research directions. The experimental results demonstrate the feasibility and efficiency of the proposed scheme for the IoT ecosystem, paving the way for more secure and reliable IoT applications. -
Research on Night Cloud Amount Calculation Based on Transfer Learning
Hongrui Zhang, Lei Che, Leilei Li, Junling RenThis chapter explores advanced techniques for enhancing nighttime cloud detection and calculation. The research focuses on data enhancement methods, transfer learning, and model optimization to improve accuracy in nighttime cloud detection. Key topics include contrast-based data enhancement, transfer learning using pre-trained U-Net models, and the impact of these techniques on cloud amount calculation. The study demonstrates that the proposed method achieves high pixel accuracy and low average error in cloud amount calculation, outperforming traditional models like FCN and U-Net. The findings highlight the effectiveness of transfer learning and data enhancement in improving nighttime cloud detection, offering valuable insights for professionals in atmospheric science and remote sensing.AI Generated
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AbstractCloud amount calculation methods for ground-based cloud play an important role in accurate weather forecasting and climate model construction in the meteorological field. At present, the method for calculating cloud amount during the day has been relatively complete. However, due to the influence of light, ground-based cloud detection at night is difficult to distinguish the outline and size of clouds, which increases the difficulty of calculation, and there are few related studies. To address this problem, we propose a night cloud amount calculation method based on transfer learning. First, the nighttime cloud image is processed with contrast enhancement; secondly, transfer learning technology is used to transfer the weight parameters of the U-Net network model to the night cloud detection task; finally, on the basis of the original network model, two-layer skip connection is used to enhance the extraction of nighttime cloud layer by the model. So as to improve the accuracy of cloud calculation. Experimental results show that compared with the FCN model and the original U-Net model, the night cloud amount calculation based on transfer learning performs better in cloud image segmentation. Its detection speed is only half that of the U-Net model, and the average calculated error is reduced from 11.32% for the FCN model and 10.56% for the U-Net model to 8.62%, significantly improving the accuracy of the calculation results. -
Decisions and Coordination in Fresh Product Supply Chain with Dual Channels Under Government Subsidy
Changwang Zhang, Hongjie Lan, Zhengwei LyuThis study investigates the dynamics of fresh product supply chains operating through dual channels, with a particular focus on the influence of government subsidies. Four key areas are explored: the optimal decision-making processes for farmers and retailers, the impact of government subsidies on these decisions and overall profits, the design of a coordination contract to align the interests of all parties, and the effects of consumer preferences and revenue-sharing ratios on supply chain performance. The research reveals that government subsidies can significantly enhance product freshness and increase profits across the supply chain. It also highlights the importance of differentiated pricing strategies based on consumer preferences and the benefits of implementing a revenue-sharing and cost-sharing contract to achieve Pareto improvement. Through numerical analysis, the study provides actionable insights into how these factors interplay to optimize supply chain efficiency and profitability.AI Generated
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AbstractTo ensure the quality of fresh agricultural products, the Chinese government implemented financial subsidies to incentivize farmers to construct refrigerated facilities. Based on this, we consider a dual-channel fresh agricultural product (FAP) supply chain composed of a farmer and a retailer. The retailer sells the products online and offline, and the government provides subsidies to offset the farmer’s freshness-keeping cost. We develop decision models under decentralized and centralized scenarios, compare the optimal solutions with and without government subsidies for each scenario, and propose a contract mechanism to coordinate the decentralized supply chain. Our findings offer valuable insights for farmers and retailers in making optimal decisions with and without government subsidies. -
Research on Cement Inventory Control of Concrete Batching Plant Based on Batch Management
Xue Tan, Xiaochun Lu, Zheng NiThis chapter delves into the critical aspects of cement inventory control for concrete batching plants, emphasizing the importance of effective batch management. It explores two primary models: the 2-equal-bin system and the innovative multi-equal-bin model with continuous review. The study highlights the discrete nature of cement inventory changes and the challenges posed by batch restrictions. Through a detailed analysis, it demonstrates how the multi-equal-bin model can optimize inventory levels, reduce costs, and enhance the efficiency of construction projects. The research culminates in a case study that compares the two models, revealing a significant cost reduction of up to 9.07% with the multi-equal-bin approach. This chapter provides valuable insights into improving cement inventory control, offering practical strategies for professionals to implement in their projects.AI Generated
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AbstractEffective inventory control of cement silos in large-scale construction projects is crucial. Due to the unique chemical properties of cement, batches stored in the same silo must be identical, leading to the necessity of integer-based inventory control for cement storage. Consequently, the inventory control model for cement is quite specialized. This paper adopts a batch management equal-bin inventory model and extends it to a multi-equal-bin model with continuous checking based on the 2-equal-bin model. The paper also considers the integer relationship between the capacity of cement silos and the capacity of cement transport trucks. The inclusion of a coefficient representing the multiples of cement truck capacity results in unconventional variations in the inventory cost curve as the number of cement silos increases, exhibiting multiple peaks that are difficult to derive through theoretical deduction. The inventory control strategy for the equal-bin system studied in this paper can offer insights and recommendations for the storage of materials in other tank systems.
- Title
- LISS 2024
- Editors
-
Daqing Gong
Yixuan Ma
Jonathan Foster-Pedley
Juliang Zhang
- Copyright Year
- 2025
- Publisher
- Springer Nature Singapore
- Electronic ISBN
- 978-981-9696-97-0
- Print ISBN
- 978-981-9696-96-3
- DOI
- https://doi.org/10.1007/978-981-96-9697-0
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