Applications and Challenges of Smart Technologies in Supply Chain Management

The advent of intelligent technologies in supply chain management(SCM)through the use of artificial intelligence(AI),the Internet of Things(IoT),blockchain,big data analytics,and others is a game changer.These improvements increase clarity,effectiveness,and flexibility,allowing companies to quickly deal with market changes and interruptions.Nevertheless,adopting such technologies comes with difficulties,such as information security,compatibility,exorbitant prices,and employee opposition.The applications of intelligent technologies for SCM are examined in this article,as well as the importance of their implementation and solutions that provide a competitive advantage for businesses.

Logistics,Supply Chain Management,and Taiwan residents Immigration Assessed in Light of Globalization

Organizations regardless of size can take advantage of the market dynamics brought about by globalization.Supply chain management is crucial for large organizations,especially with regard to the costs,complexity,and risks of its large,often global,supply chains.From technology to risk management,learning what it takes to navigate transportation and logistics business management in today’s volatile world is critical.Human migration has accelerated in light of globalization and rapid advances in transportation(particularly aviation)technology.This study investigated the connection between logistics and supply chain management in the age of globalization;reviewed the migration,settlement,and naturalization of Taiwan residents immigrants along the West Coast of the United States;and theoretically analyzed the forces that have led to the construction of the Taiwan residents immigrant community.Taiwan residents immigrants and their settlement are affected by global forces,and factors such as their cross-cultural trade,the characteristics of diasporic communities,and their national identity can provide scholarly and pedagogical insights.

Enhancing Enterprise Efficiency through Cross-Domain Supply Chain Linkage Management

In recent years,with the complexity and variability of international finance,debt,and geopolitical risks,the growth rate of domestic economic operations has slowed down.More enterprise groups are facing the transformation and upgrading from domestic leadership to internationalization,achieving revenue growth of nearly 10 billion yuan per year against the trend.The severe and complex internal and external environment has brought many challenges to the supply chain for enterprises.This article focuses on the goal of an“enterprise supply chain system focusing on value creation,strengthening system linkage,and improving overall chain efficiency,”aiming to find solutions to problems such as strong demand variability,frequent emergency needs,coexistence of project shortages and inventory surplus,long material turnover cycles,and high organizational complexity.By searching for key activities in the entire business chain of demand,design,production,service,and finance,implementing source planning,process control,system linkage,analysis and summary,and team collaboration,we have achieved more scientific demand forecasting,more accurate planning,more effective procurement cost reduction,more stable production performance supply,more convenient inventory sharing services,and more efficient integration of business and financial policies.

Research on Innovation Strategy of Supply Chain Management of Agricultural Enterprises Under the Background of Big Data

With the rapid development and widespread application of Big Data technology, the supply chain management of agricultural products enterprises is facing unprecedented reform and challenges. This study takes the perspective of Big Data technology and collects relevant information on the application of supply chain management in 100 agricultural product enterprises through a survey questionnaire. The study found that the use of Big Data can effectively improve the accuracy of demand forecasting, inventory management efficiency, optimize logistics costs, improve supplier management efficiency, enhance the overall level of supply chain management of enterprises, and propose innovative strategies for supply chain management of agricultural products enterprises based on this. Big Data technology brings a new solution for agricultural products enterprises to enhance their supply chain management level, making the supply chain smarter and more efficient.

Research on Procurement Cost Control Based on Supply Chain Management

In modern enterprise management,strengthening the control and management of procurement processes can effectively reduce operational costs.However,with the continuous reform and improvement of China’s market economy system,competition among supply chain enterprises has intensified.The traditional procurement management model is no longer able to meet the needs of enterprise development in the current era.As a result,procurement management based on supply chain management has emerged,offering significant improvements in the scientific management of procurement processes.It has become an important means of promoting the long-term development of enterprises.This paper primarily analyzes and studies procurement cost control based on supply chain management,providing insights for reference.

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

Crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation management.Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced data.However,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update module.This paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing system.Here,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and tamper-resistance.Computation of user reputation scores relies on their behavioral history,actively discouraging undesirable conduct.Additionally,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and reputation.Our scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior.This research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in crowdsourcing across diverse domains.

On Designs of Decentralized Reputation Management for Permissioned Blockchain Networks

In permissioned blockchain networks,the Proof of Authority(PoA)consensus,which uses the election of authorized nodes to validate transactions and blocks,has beenwidely advocated thanks to its high transaction throughput and fault tolerance.However,PoA suffers from the drawback of centralization dominated by a limited number of authorized nodes and the lack of anonymity due to the round-robin block proposal mechanism.As a result,traditional PoA is vulnerable to a single point of failure that compromises the security of the blockchain network.To address these issues,we propose a novel decentralized reputation management mechanism for permissioned blockchain networks to enhance security,promote liveness,and mitigate centralization while retaining the same throughput as traditional PoA.This paper aims to design an off-chain reputation evaluation and an on-chain reputation-aided consensus.First,we evaluate the nodes’reputation in the context of the blockchain networks and make the reputation globally verifiable through smart contracts.Second,building upon traditional PoA,we propose a reputation-aided PoA(rPoA)consensus to enhance securitywithout sacrificing throughput.In particular,rPoA can incentivize nodes to autonomously form committees based on reputation authority,which prevents block generation from being tracked through the randomness of reputation variation.Moreover,we develop a reputation-aided fork-choice rule for rPoA to promote the network’s liveness.Finally,experimental results show that the proposed rPoA achieves higher security performance while retaining transaction throughput compared to traditional PoA.

An Overview of the Application of Big Data in Supply Chain Management and Adaptation in Nigeria

That the world is a global village is no longer news through the tremendous advancement in the Information Communication Technology (ICT). The metamorphosis of the human data storage and analysis from analogue through the jaguars-loom mainframe computer to the present modern high power processing computers with sextillion bytes storage capacity has prompted discussion of Big Data concept as a tool in managing hitherto all human challenges of complex human system multiplier effects. The supply chain management (SCM) that deals with spatial service delivery that must be safe, efficient, reliable, cheap, transparent, and foreseeable to meet customers’ needs cannot but employ bid data tools in its operation. This study employs secondary data online to review the importance of big data in supply chain management and the levels of adoption in Nigeria. The study revealed that the application of big data tools in SCM and other industrial sectors is synonymous to human and national development. It is therefore recommended that both private and governmental bodies should key into e-transactions for easy data assemblage and analysis for profitable forecasting and policy formation.

Supply Chain Management Analysis and Design for a Variety of Economic Scenarios, Including Data and System Administration

Due to the rapid progress of information technology, organizations anticipate significant changes in the planning, scheduling, and optimization aspects of operation and supply chain management (SCM) shortly. Two primary types of risk have an impact on supply chain management and design. The first group deals with the difficulties in matching supply and demand, whereas the second group deals with disruptions to regular business operations. The essay offers a theoretical framework that combines the cooperative efforts of risk assessment and mitigation, which are critical for effectively handling potential supply chain interruptions. This content provides insightful viewpoints on the strategic resources and operational structure needed to improve organizational success. We utilized the partial least squares (PLS) method to address the problem of multicollinearity and measurement mistakes in examining cause-and-effect constructs. The statistical method, Least Squares (PLS), used in structural equation modeling, is based on partial variance. The Partial Least Squares (PLS) strategy uses a two-stage estimate procedure to calculate weights, loadings, and route estimations. Initially, several simple and complex regressions were performed with the provided model. The procedure was repeated until a solution was found, resulting in a set of weights used to determine the latent variable scores. In the second step, non-iterative PLS regression yields loadings, path coefficients, mean scores, and location parameters. According to the structural study, implementing Sustainable Supply Chain Management (SSCM) can significantly improve a business’s operational and financial performance. The findings offer a comprehensive understanding of several elements of supply chain management (SSCM), including information systems, organizational configurations, supply chain network architecture (SCND), and supply chain strategy (SCS). The supply chain is essential for effectively moving goods over great distances and encouraging cooperation between parties. Therefore, these connections are established precisely, quickly, and cheaply via a knowledgeable and efficient supply chain. Two key components are necessary for a supply chain (SC) to be successful: efficient collaboration and the smooth integration of information-sharing platforms.

For Mega-Constellations: Edge Computing and Safety Management Based on Blockchain Technology

In mega-constellation Communication Systems, efficient routing algorithms and data transmission technologies are employed to ensure fast and reliable data transfer. However, the limited computational resources of satellites necessitate the use of edge computing to enhance secure communication.While edge computing reduces the burden on cloud computing, it introduces security and reliability challenges in open satellite communication channels. To address these challenges, we propose a blockchain architecture specifically designed for edge computing in mega-constellation communication systems. This architecture narrows down the consensus scope of the blockchain to meet the requirements of edge computing while ensuring comprehensive log storage across the network. Additionally, we introduce a reputation management mechanism for nodes within the blockchain, evaluating their trustworthiness, workload, and efficiency. Nodes with higher reputation scores are selected to participate in tasks and are appropriately incentivized. Simulation results demonstrate that our approach achieves a task result reliability of 95% while improving computational speed.

Blockchain-Based Key Management Scheme Using Rational Secret Sharing

Traditional blockchain key management schemes store private keys in the same location,which can easily lead to security issues such as a single point of failure.Therefore,decentralized threshold key management schemes have become a research focus for blockchain private key protection.The security of private keys for blockchain user wallet is highly related to user identity authentication and digital asset security.The threshold blockchain private key management schemes based on verifiable secret sharing have made some progress,but these schemes do not consider participants’self-interested behavior,and require trusted nodes to keep private key fragments,resulting in a narrow application scope and low deployment efficiency,which cannot meet the needs of personal wallet private key escrow and recovery in public blockchains.We design a private key management scheme based on rational secret sharing that considers the self-interest of participants in secret sharing protocols,and constrains the behavior of rational participants through reasonable mechanism design,making it more suitable in distributed scenarios such as the public blockchain.The proposed scheme achieves the escrow and recovery of personal wallet private keys without the participation of trusted nodes,and simulate its implementation on smart contracts.Compared to other existing threshold wallet solutions and keymanagement schemes based on password-protected secret sharing(PPSS),the proposed scheme has a wide range of applications,verifiable private key recovery,low communication overhead,higher computational efficiency when users perform one-time multi-key escrow,no need for trusted nodes,and personal rational constraints and anti-collusion attack capabilities.

Automobile Transportation Logistics Supply Chain Management Strategy

To enhance the management level and quality of the automobile transportation logistics supply chain and promote innovation and development in automobile transportation logistics enterprises,it is essential to strengthen the construction of the automobile transportation logistics supply chain management model.This can be achieved through the gradual improvement of the automobile transportation logistics management process,ensuring that the management of the automobile transportation logistics supply chain proceeds in an orderly manner.The aim is to improve automobile transportation and logistics service levels while meeting the changing market supply needs.This will enable automobile transportation and logistics enterprises to maintain steady economic benefits and enhance their core competitiveness in the market.Therefore,this paper has conducted a comprehensive exploration and research on managing the automobile transportation logistics supply chain.Corresponding management strategies are proposed as a starting point to achieve the aforementioned goals.

Blockchain and Digital Transformation of University Asset Management

As educational activities realize value transmission and value realization through a digital form, the Digital transformation of universities continues to advance. The quantity of asset data continues to grow, with internal value reflected in education, management, and services, while external value is reflected in policies, resources and reputation. Therefore, effective management of university assets is particularly important. Blockchain is an important infrastructure for Digital transformation, which provides a trusted environment for the occurrence of various collaborations and will reshape many collaboration mechanisms. This paper discusses the current situation and problems faced by universities in the process of Digital transformation. We analyze the development opportunities brought about by Digital transformation and the specific problems faced by physical asset management in detail. We discuss the data platform optimization, asset data application, digital asset security, planning strategy, and construction path of digital transformation involved in the physical asset management of universities.

Knowledge and associated factors of healthcare workers on measles vaccine and cold chain management at health institutions in Gondar,Ethiopia

Objective:To assess the knowledge of healthcare workers on the measles vaccine and its cold chain management.Method:An institutional-based cross-sectional study was conducted from February 1 to March 30,2022 in Gondar City Administration public health institutions among 165 healthcare workers.Data were collected using a structured questionnaire.In addition,an on-spot observation checklist was used to assess the availability,status and management of the cold chain.A logistic regression model was used to assess the relationship between the outcome and predictor variables.Crude and adjusted odds ratios were calculated with 95%confidence intervals.Results:Overall,87(52.7%;95%CI 44.8%-60.5%)of the healthcare workers had unsatisfactory knowledge regarding the measles vaccine and its cold chain management.One hundred thirty-six(82.4%)healthcare workers correctly mentioned the recommended range of temperature(2-8℃)for measles vaccine storage.Healthcare workers aged 18-29 years(P=0.001)and 30-44 years(P=0.014)were observed as determinants of unsatisfactory knowledge on the measles vaccine and its cold chain management.One hundred and five(63.6%)of the healthcare workers did not correctly mention the type of measles vaccine used in routine immunization.More than one-third(36.4%)of the healthcare workers perceived that the measles vaccine is not safe and could cause measles.Conclusions:More than half of the healthcare workers in the study area had unsatisfactory knowledge on the measles vaccine and its cold chain management.It is necessary to provide technical support and in-service training for healthcare workers to ensure optimal immunization effectiveness.

A Blockchain-Based Life-Cycle Environmental Management Framework for Hospitals in the COVID-19 Context

During the coronavirus disease 2019 (COVID-19) emergency, many hospitals were built or renovated around the world to meet the challenges posed by the rising number of infected cases. Environmental management in the hospital life cycle is vital in preventing nosocomial infection and includes many infection control procedures. In certain urgent situations, a hospital must be completed quickly, and work process approval and supervision must therefore be accelerated. Thus, many works cannot be checked in detail. This results in a lack of work liability control and increases the difficulty of ensuring the fulfillment of key infection prevention measures. This study investigates how blockchain technology can transform the work quality inspection workflow to assist in nosocomial infection control under a fast delivery requirement. A blockchain-based life-cycle environmental management framework is proposed to track the fulfillment of crucial infection control measures in the design, construction, and operation stages of hospitals. The proposed framework allows for work quality checking after the work is completed, when some work cannot be checked on time. Illustrative use cases are selected to demonstrate the capabilities of the developed solution. This study provides new insights into applying blockchain technology to address the challenge of environmental management brought by rapid delivery requirements.

Leveraging Blockchain with Optimal Deep Learning-Based Drug Supply Chain Management for Pharmaceutical Industries

Due to its complexity and involvement of numerous stakeholders,the pharmaceutical supply chain presents many challenges that companies must overcome to deliver necessary medications to patients efficiently.The pharmaceutical supply chain poses different challenging issues,encompasses supply chain visibility,cold-chain shipping,drug counterfeiting,and rising prescription drug prices,which can considerably surge out-of-pocket patient costs.Blockchain(BC)offers the technical base for such a scheme,as it could track legitimate drugs and avoid fake circulation.The designers presented the procedure of BC with fabric for creating a secured drug supplychain management(DSCM)method.With this motivation,the study presents a new blockchain with optimal deep learning-enabled DSCM and recommendation scheme(BCODL-DSCMRS)for Pharmaceutical Industries.Firstly,Hyperledger fabric is used for DSC management,enabling effective tracking processes in the smart pharmaceutical industry.In addition,a hybrid deep belief network(HDBN)model is used to suggest the best or top-rated medicines to healthcare providers and consumers.The spotted hyena optimizer(SHO)algorithm is used to optimize the performance of the HDBN model.The design of the HSO algorithm for tuning the HDBN model demonstrates the novelty of the work.The presented model is tested on the UCI repository’s open-access drug reviews database.

Cross-Domain Authentication Scheme Based on Blockchain and Consistent Hash Algorithm for System-Wide Information Management

System-wide information management(SWIM)is a complex distributed information transfer and sharing system for the next generation of Air Transportation System(ATS).In response to the growing volume of civil aviation air operations,users accessing different authentication domains in the SWIM system have problems with the validity,security,and privacy of SWIM-shared data.In order to solve these problems,this paper proposes a SWIM crossdomain authentication scheme based on a consistent hashing algorithm on consortium blockchain and designs a blockchain certificate format for SWIM cross-domain authentication.The scheme uses a consistent hash algorithm with virtual nodes in combination with a cluster of authentication centers in the SWIM consortium blockchain architecture to synchronize the user’s authentication mapping relationships between authentication domains.The virtual authentication nodes are mapped separately using different services provided by SWIM to guarantee the partitioning of the consistent hash ring on the consortium blockchain.According to the dynamic change of user’s authentication requests,the nodes of virtual service authentication can be added and deleted to realize the dynamic load balancing of cross-domain authentication of different services.Security analysis shows that this protocol can resist network attacks such as man-in-the-middle attacks,replay attacks,and Sybil attacks.Experiments show that this scheme can reduce the redundant authentication operations of identity information and solve the problems of traditional cross-domain authentication with single-point collapse,difficulty in expansion,and uneven load.At the same time,it has better security of information storage and can realize the cross-domain authentication requirements of SWIM users with low communication costs and system overhead.KEYWORDS System-wide information management(SWIM);consortium blockchain;consistent hash;cross-domain authentication;load balancing.

A Proposed Feature Selection Particle Swarm Optimization Adaptation for Intelligent Logistics--A Supply Chain Backlog Elimination Framework

The diversity of data sources resulted in seeking effective manipulation and dissemination.The challenge that arises from the increasing dimensionality has a negative effect on the computation performance,efficiency,and stability of computing.One of the most successful optimization algorithms is Particle Swarm Optimization(PSO)which has proved its effectiveness in exploring the highest influencing features in the search space based on its fast convergence and the ability to utilize a small set of parameters in the search task.This research proposes an effective enhancement of PSO that tackles the challenge of randomness search which directly enhances PSO performance.On the other hand,this research proposes a generic intelligent framework for early prediction of orders delay and eliminate orders backlogs which could be considered as an efficient potential solution for raising the supply chain performance.The proposed adapted algorithm has been applied to a supply chain dataset which minimized the features set from twenty-one features to ten significant features.To confirm the proposed algorithm results,the updated data has been examined by eight of the well-known classification algorithms which reached a minimum accuracy percentage equal to 94.3%for random forest and a maximum of 99.0 for Naïve Bayes.Moreover,the proposed algorithm adaptation has been compared with other proposed adaptations of PSO from the literature over different datasets.The proposed PSO adaptation reached a higher accuracy compared with the literature ranging from 97.8 to 99.36 which also proved the advancement of the current research.

A Systematic Literature Review on Blockchain Consensus Mechanisms’ Security: Applications and Open Challenges

This study conducts a systematic literature review(SLR)of blockchain consensus mechanisms,an essential protocols that maintain the integrity,reliability,and decentralization of distributed ledger networks.The aim is to comprehensively investigate prominent mechanisms’security features and vulnerabilities,emphasizing their security considerations,applications,challenges,and future directions.The existing literature offers valuable insights into various consensus mechanisms’strengths,limitations,and security vulnerabilities and their real-world applications.However,there remains a gap in synthesizing and analyzing this knowledge systematically.Addressing this gap would facilitate a structured approach to understanding consensus mechanisms’security and vulnerabilities comprehensively.The study adheres to Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)guidelines and computer science standards and reviewed 3749 research papers from 2016 to 2024,excluding grey literature,resulting in 290 articles for descriptive analysis.The research highlights an increased focus on blockchain consensus security,energy efficiency,and hybrid mechanisms within 60%of research papers post-2019,identifying gaps in scalability,privacy,and interoperability for future exploration.By synthesizing the existing research and identifying the key trends,this SLR contributes to advancing the understanding of blockchain consensus mechanisms’security and guiding future research and structured innovation in blockchain systems and applications.

Personal Thermal Management by Radiative Cooling and Heating

Maintaining thermal comfort within the human body is crucial for optimal health and overall well-being.By merely broadening the setpoint of indoor temperatures,we could significantly slash energy usage in building heating,ventilation,and air-conditioning systems.In recent years,there has been a surge in advancements in personal thermal management(PTM),aiming to regulate heat and moisture transfer within our immediate surroundings,clothing,and skin.The advent of PTM is driven by the rapid development in nano/micro-materials and energy science and engineering.An emerging research area in PTM is personal radiative thermal management(PRTM),which demonstrates immense potential with its high radiative heat transfer efficiency and ease of regulation.However,it is less taken into account in traditional textiles,and there currently lies a gap in our knowledge and understanding of PRTM.In this review,we aim to present a thorough analysis of advanced textile materials and technologies for PRTM.Specifically,we will introduce and discuss the underlying radiation heat transfer mechanisms,fabrication methods of textiles,and various indoor/outdoor applications in light of their different regulation functionalities,including radiative cooling,radiative heating,and dual-mode thermoregulation.Furthermore,we will shine a light on the current hurdles,propose potential strategies,and delve into future technology trends for PRTM with an emphasis on functionalities and applications.