Search results

In 2020, the logistics market in Malaysia was valued at USD 37.60 billion, and it is projected to grow to more than USD 55.0 billion by 2026 at a compound annual growth rate (CAGR) of more than 4%. However, more information is needed about the impact of green logistic practice determinants by the local SMEs on the market share. This study serves as a focal point by examining the factors involved by offering a conceptual framework of determinants and their potential outcomes. This study contributes by demonstrating a conceptual, theoretical framework derived from the synthesis of two theory such as the Resource-Based View theory and the Diffusion of Innovation Theory. At the same time, it offers a holistic approach with an in-depth understanding of the Technological and Organizational factors of SMEs. The relationship between the implementation of green practices and organizational performance is also explored.

This study aims to empirically analyse the influence mechanism of perceived interactivity in real estate APP which affects consumers' psychological well-being. With the growing application of human–machine interaction in real estate APP, it is crucial to utilize human–machine interaction to stimulate perceived interactivity between humans and machines to positively impact consumers' psychological well-being and sustainable development of real estate APP. However, it is unclear whether perceived interactivity improves consumers' psychological well-being.

This study proposes and examines a theoretical model grounded in the perceived interactivity theory, considers the relationship between perceived interactivity and consumers' psychological well-being and explores the mediating effect of perceived value and the moderating role of privacy concerns. It takes real estate APP as the research object, analyses the data of 568 consumer samples collected through questionnaires and then employs structural equation modelling to explore and examine the proposed theoretical model of this study.

The findings are that perceived interactivity (i.e. human–human interaction and human–information interaction) positively influences perceived value, which in turn affects psychological well-being, and that perceived value partially mediates the effect of perceived interaction on psychological well-being. More important findings are that privacy concerns not only negatively moderate human–information interaction on perceived value, but also negatively moderate the indirect effects of human–information interaction on users' psychological well-being through perceived value.

This study expands the context on perceived interaction and psychological well-being in the field of real estate APP, validating the mediating role and boundary conditions of perceived interactivity created by human–machine interaction on consumers' psychological well-being, and suggesting positive implications for practitioners exploring human–machine interaction technologies to improve the perceived interaction between humans and machines and thus enhance consumer psychological well-being and span sustainable development of real estate APP.

Since the performance of vehicular users and cellular users (CUE) in Vehicular networks is highly affected by the allocated resources to them. The purpose of this paper is to investigate the resource allocation for vehicular communications when multiple V2V links and a V2I link share spectrum with CUE in uplink communication under different Quality of Service (QoS).

An optimization model to maximize the V2I capacity is established based on slowly varying large-scale fading channel information. Multiple V2V links are clustered based on sparrow search algorithm (SSA) to reduce interference. Then, a weighted tripartite graph is constructed by jointly optimizing the power of CUE, V2I and V2V clusters. Finally, spectrum resources are allocated based on a weighted 3D matching algorithm.

The performance of the proposed algorithm is tested. Simulation results show that the proposed algorithm can maximize the channel capacity of V2I while ensuring the reliability of V2V and the quality of service of CUE.

There is a lack of research on resource allocation algorithms of CUE, V2I and multiple V2V in different QoS. To solve the problem, one new resource allocation algorithm is proposed in this paper. Firstly, multiple V2V links are clustered using SSA to reduce interference. Secondly, the power allocation of CUE, V2I and V2V is jointly optimized. Finally, the weighted 3D matching algorithm is used to allocate spectrum resources.

A cross-domain intelligent software-defined network (SDN) routing method based on a proposed multiagent deep reinforcement learning (MDRL) method is developed.

First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between the root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to acquire global network state information in real time. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a network traffic state prediction mechanism is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time.

Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and open shortest path first (OSPF) routing methods.

Message transmission and message synchronization for multicontroller interdomain routing in SDN have long adaptation times and slow convergence speeds, coupled with the shortcomings of traditional interdomain routing methods, such as cumbersome configuration and inflexible acquisition of network state information. These drawbacks make it difficult to obtain global state information about the network, and the optimal routing decision cannot be made in real time, affecting network performance. This paper proposes a cross-domain intelligent SDN routing method based on a proposed MDRL method. First, the network is divided into multiple subdomains managed by multiple local controllers, and the state information of each subdomain is flexibly obtained by the designed SDN multithreaded network measurement mechanism. Then, a cooperative communication module is designed to realize message transmission and message synchronization between root and local controllers, and socket technology is used to ensure the reliability and stability of message transmission between multiple controllers to realize the real-time acquisition of global network state information. Finally, after the optimal intradomain and interdomain routing paths are adaptively generated by the agents in the root and local controllers, a prediction mechanism for the network traffic state is designed to improve awareness of the cross-domain intelligent routing method and enable the generation of the optimal routing paths in the global network in real time. Experimental results show that the proposed cross-domain intelligent routing method can significantly improve the network throughput and reduce the network delay and packet loss rate compared to those of the Dijkstra and OSPF routing methods.

A real-time production scheduling method for semiconductor back-end manufacturing process becomes increasingly important in industry 4.0. Semiconductor back-end manufacturing process is always accompanied by order splitting and merging; besides, in each stage of the process, there are always multiple machine groups that have different production capabilities and capacities. This paper studies a multi-agent based scheduling architecture for the radio frequency identification (RFID)-enabled semiconductor back-end shopfloor, which integrates not only manufacturing resources but also human factors.

The architecture includes a task management (TM) agent, a staff instruction (SI) agent, a task scheduling (TS) agent, an information management center (IMC), machine group (MG) agent and a production monitoring (PM) agent. Then, based on the architecture, the authors developed a scheduling method consisting of capability & capacity planning and machine configuration modules in the TS agent.

The authors used greedy policy to assign each order to the appropriate machine groups based on the real-time utilization ration of each MG in the capability & capacity (C&C) planning module, and used a partial swarm optimization (PSO) algorithm to schedule each splitting job to the identified machine based on the C&C planning results. At last, we conducted a case study to demonstrate the proposed multi-agent based real-time production scheduling models and methods.

This paper proposes a multi-agent based real-time scheduling framework for semiconductor back-end industry. A C&C planning and a machine configuration algorithm are developed, respectively. The paper provides a feasible solution for semiconductor back-end manufacturing process to realize real-time scheduling.

While many companies begin to promote ethically produced products, much remains to be known about consumers' buying intention toward these products. This paper attempts to integrate the theory of planned behavior and the Hunt–Vitell theory of marketing ethics to explore the buying intention toward ethically produced food products in a developing economy.

Data were collected through a questionnaire survey in Bangladesh. Structural equation modeling technique was used to test the research model.

Research findings showed that deontological evaluation and teleological evaluation have significantly positive effects on perceived behavioral control and subjective norm. Perceived behavioral control, subjective norm, attitude, hedonic and utilitarian value have significantly positive effects on buying intention toward ethically produced foods.

The results are practically and theoretically meaningful because the integrated model holds well explanatory power to predict consumers' intention toward buying ethical foods and thereby understand consumers' ethical decision-makings.

How to use a simple and classical topology to provide a high-efficiency excitation voltage for dielectric barrier discharge (DBD) loads is one of the primary problems to be solved for DBD application fields.

To address the issue, a set of modes that can generate a high-efficiency pulse excitation voltage in a full-bridge inverter are adopted. With the set of modes, the unique equivalent circuit of DBD loads and the parasitic parameter of the step-up transformer can be fully used. Based on the set of modes, a control strategy for the full-bridge inverter is designed. To test the performance of the power supply, a simulation model is established and an experimental prototype is made with a DBD excimer lamp.

The simulation and experimental results show that not only a high-efficiency excitation voltage can be generated for the DBD load, but also the soft switching of all power switch is realized. Besides this, with the set of modes and the proposed control strategy, the inverter can operate in a high frequency. Compared with other types of power supplies, the power supply used in the paper can fully take advantage of the potential of the excimer lamp at the same input power.

This work considers that how to use a simple and classical topology to provide a high-efficiency excitation voltage for DBD loads is one of the primary problems to be solved for DBD application fields.

How to identify the critical success factors (CSFs) of public health emergencies (PHEs) is of great practical significance to carry out a scientific and effective risk assessment. The purpose of this paper is to address this issue.

In this paper, the authors propose a new approach to identify the CSFs by hesitant fuzzy linguistic set and a Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. First, a larger group of experts are clustered into three groups according to similarity degree. Then, the weight of each cluster is determined by the maximum consensus method, and the overall direct influence matrix is obtained by clustering with hesitant fuzzy linguistic weighted geometric (HFLWG) operators. Finally, the overall direct influence matrix is transformed into the crisp direct impact matrix by the score function, and 11 CSFs of PHEs are identified by using the extended DEMATEL method.

In addition, an example of PHEs shows that the approach has good identification applicability. The approach can be used to solve the problems of fuzziness and subjectivity in linguistic assessments, and it can be applied to identify the customer service framework with the linguistic assessments process in emergency management.

This paper extends the above DEMATEL method to study in the hesitant fuzzy linguistic context. This proposed hybrid approach has a wider application in the high-risk area where disasters frequently occur.