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THE work of Johnson, Mathur and Henderson on the ‘Creep Deflexion of Magnesium Alloy Struts’ raised the question in the present writer's mind of the stress distribution in the beam cross‐section, and the variation of this distribution with time. The precise computation of the stress distribution in an eccentrically loaded strut appears to be a very difficult problem, and the present note is concerned with the much simpler case of a beam subjected to a constant bending moment.

The purpose of this paper is to focus on the design of a counterfeit network analyzer (CNA) for aggregating all the problematic product flows in order to discover any counterfeit distribution source, and to control the spread of counterfeit goods. The analyzer leverages radio frequency identification (RFID) information stored within the electronic product code (EPC) network.

The system architecture of the proposed CNA is first discussed. A case study of the system application in a Hong Kong pharmaceutical manufacturing company then presents the adoption process and the challenges encountered in such technologies.

Compared with traditional approaches, the results show that the proposed RFID solution is reliable and is capable of discovering counterfeit distributions, as well as reducing the detection costs by means of higher product movement visibility within the supply chain.

A prototype system has been constructed and trial implemented in a pharmaceutical manufacturing company. It proved to be of benefit to the manufacturer who is able to deter product counterfeiting effectively and visualize real‐time supply chain data automatically.

The proposed CNA is designed to overcome issues of information invisibility, which is considered a huge cost lost in the identification of counterfeit products, loss of sales and reputation associated with the counterfeiting, and ineffective product authentication in the manufacturing industry. This paper contributes to the RFID research in the counterfeiting area by studying the feasibility and practicality of shifting the focus of product identification from the traditional package redesign solutions to the visualization of the movement of the product (i.e. product supply chain) via the use of RFID and EPC. Furthermore, a pharmaceutical manufacturing site provided a case study for discussing the advantages, critical issues for implementation of the RFID system, and lessons learned.

This paper aims to introduce the weighted squared Euclidean distance between points in signal space, to improve the performance of the Wi-Fi indoor positioning. Nowadays, the received signal strength-based Wi-Fi indoor positioning, a low-cost indoor positioning approach, has attracted a significant attention from both academia and industry.

The local principal gradient direction is introduced and used to define the weighting function and an average algorithm based on k-means algorithm is used to estimate the local principal gradient direction of each access point. Then, correlation distance is used in the new method to find the k nearest calibration points. The weighted squared Euclidean distance between the nearest calibration point and target point is calculated and used to estimate the position of target point.

Experiments are conducted and the results indicate that the proposed Wi-Fi indoor positioning approach considerably outperforms the weighted k nearest neighbor method. The new method also outperforms support vector regression and extreme learning machine algorithms in the absence of sufficient fingerprints.

Weighted k nearest neighbor approach, support vector regression algorithm and extreme learning machine algorithm are the three classic strategies for location determination using Wi-Fi fingerprinting. However, weighted k nearest neighbor suffers from dramatic performance degradation in the presence of multipath signal attenuation and environmental changes. More fingerprints are required for support vector regression algorithm to ensure the desirable performance; and labeling Wi-Fi fingerprints is labor-intensive. The performance of extreme learning machine algorithm may not be stable.

The new weighted squared Euclidean distance-based Wi-Fi indoor positioning strategy can improve the performance of Wi-Fi indoor positioning system.

The received signal strength-based effective Wi-Fi indoor positioning system can substitute for global positioning system that does not work indoors. This effective and low-cost positioning approach would be promising for many indoor-based location services.

A novel Wi-Fi indoor positioning strategy based on the weighted squared Euclidean distance is proposed in this paper to improve the performance of the Wi-Fi indoor positioning, and the local principal gradient direction is introduced and used to define the weighting function.

Since the handling of environmentally sensitive products requires close monitoring under prescribed conditions throughout the supply chain, it is essential to manage specific supply chain risks, i.e. maintaining good environmental conditions, and ensuring occupational safety in the cold environment. The purpose of this paper is to propose an Internet of Things (IoT)-based risk monitoring system (IoTRMS) for controlling product quality and occupational safety risks in cold chains. Real-time product monitoring and risk assessment in personal occupational safety can be then effectively established throughout the entire cold chain.

In the design of IoTRMS, there are three major components for risk monitoring in cold chains, namely: wireless sensor network; cloud database services; and fuzzy logic approach. The wireless sensor network is deployed to collect ambient environmental conditions automatically, and the collected information is then managed and applied to a product quality degradation model in the cloud database. The fuzzy logic approach is applied in evaluating the cold-associated occupational safety risk of the different cold chain parties considering specific personal health status. To examine the performance of the proposed system, a cold chain service provider is selected for conducting a comparative analysis before and after applying the IoTRMS.

The real-time environmental monitoring ensures that the products handled within the desired conditions, namely temperature, humidity and lighting intensity so that any violation of the handling requirements is visible among all cold chain parties. In addition, for cold warehouses and rooms in different cold chain facilities, the personal occupational safety risk assessment is established by considering the surrounding environment and the operators’ personal health status. The frequency of occupational safety risks occurring, including cold-related accidents and injuries, can be greatly reduced. In addition, worker satisfaction and operational efficiency are improved. Therefore, it provides a solid foundation for assessing and identifying product quality and occupational safety risks in cold chain activities.

The cold chain is developed for managing environmentally sensitive products in the right conditions. Most studies found that the risks in cold chain are related to the fluctuation of environmental conditions, resulting in poor product quality and negative influences on consumer health. In addition, there is a lack of occupational safety risk consideration for those who work in cold environments. Therefore, this paper proposes IoTRMS to contribute the area of risk monitoring by means of the IoT application and artificial intelligence techniques. The risk assessment and identification can be effectively established, resulting in secure product quality and appropriate occupational safety management.

Despite the extensive adoption of radio-frequency identification (RFID) technology across many industry supply chains, the extent of adoption in healthcare is far behind the earlier expectation. The purpose of this study is to better understand the current RFID adoption in healthcare by looking beyond the existing body of work using both the task-technology fit (TTF) framework and network externalities theories.

A survey is employed in this study, and the structural equation modeling (SEM) technique is used to test the hypotheses of the proposed model.

The findings are twofold. First, both TTF and network externalities exert a positive impact on the RFID adoption in the healthcare sector; and second, no synergistic effect can be found between these two for further increasing the adoption. This is different from what the extant research found on other technology adoptions across various supply chains.

This paper provides contributions to both researchers and practitioners. For researchers, this study enriches the body of knowledge of RFID adoption by being the first to apply the network externalities and TTF theories to predict the adoption of RFID in healthcare. For healthcare practitioners, to make the RFID adoption easier and more effective, any initial applications of RFID tools should be centered on those for which there is a more natural application. Further, for those who propose an RFID adoption should start with a product that has a sizable adoption community; this may help persuade senior management to make the adoption decision.

The Internet of Things (IoT) is expected to have a huge impact on businesses and, especially, the way we think about supply chain management (SCM). However, there is still a paucity of studies on the impact of IoT adoption on supply chains and on different aspects of the business in general. The purpose of this paper is to examine the perception of the academic community of the impact of the IoT adoption in organizational supply chains with a view to verify potential key benefits and challenges existent in the literature. The research presents the impact on an organization along with the impact across its entire supply chain.

Data were collected through the use of an online survey and 87 participants completed the survey. Participants were mainly from the academic community and were university scholars based in different countries located in six continents. Participants were authors, or co-authors, of academic papers published in the Decision Science Institute 2015 and 2016 annual conference proceedings, the 21st International Symposium of Sustainable Transport and Supply Chain Innovations, the Supply Chain Management: An International Journal 2016 issues, and the Operations and Supply Chain Management: An International Journal 2016 issues.

The authors were able to confirm the significance of some of the examined potential benefits to individual organizations and their entire supply chains. However, the study identified other potential benefits that were not seen as a direct impact of IoT adoption. Most of the examined potential benefits were found to contribute to a number of critical success factors for implementing successful SCM. The authors were also able to confirm that some of the examined potential challenges were still perceived as key hinders to IoT adoption but examined potential challenges were not seen as hurdles to IoT adoption.

To the best of the authors’ knowledge, this is the first study of its kind. Although some literature attempted to provide an overview about the IoT management, no study has specifically explored potential benefits and challenges related to the adoption of IoT in supply chains and ranked them based on their significance. The results can be beneficial to academic scholars interested in the researched topic, business professionals, organizations within different sectors, and any other party interested in understanding more about the impact of adopting IoT on SCM.

Technology makes knowledge management easier. Knowledge sharing is essential for organizational development. Job satisfaction fosters knowledge sharing. Hence, this study aims to develop an understanding of the mutual relationship between knowledge sharing and job satisfaction when both are predicted by information technology (IT)-competency dimensions such as IT-operations, IT-knowledge and IT-infrastructure in the context of company performance.

The results were achieved based on the examination of 910 Polish knowledge workers with different roles and experiences across various sectors. Data were analyzed using structural equation modeling method.

The findings suggest that the company’s IT-competency drives job satisfaction and knowledge sharing more strongly for IT industry knowledge workers than for other industries. Also, a stronger mediation function of knowledge sharing and job satisfaction is observed for IT-operations when the IT industry is controlled.

The main value of the study is the empirical evidence that the influence of a particular IT-competency dimension varies for industries when it comes to job satisfaction and knowledge sharing processes.

The purpose of this paper is to analyze and describe the profiles of mental health professionals and their relationship to professional reactions and changes in working practice following a patient suicide.

Data from 713 mental health professionals working in various institutional settings and in private practice in French-speaking Switzerland were collected by written questionnaires.

Four distinct profiles with low to moderate professional reactions and changes in working practice were identified by cluster analysis. The type and intensity of relationship between professional and patient, and psychological and/or social support following the patient suicide were the most discriminant factors of the four profiles.

The findings contribute to the understanding of professional consequences of patient suicide on mental health professionals.

The purpose of this paper is to identify the two kinds of supply chain quality management (SCQM) capabilities: intra-SCQM and inter-SCQM, and explore the effect of intra- and inter-food SCQM on food safety and quality, and the effect of food SCQM on domestic and export performance through food certification and corporate reputation.

Secondary data on food selling or processing firms in Western China were collected to test all the hypotheses. The data were then analyzed using the statistical technique of stepwise regression and inference was drawn based on the result.

Through utilizing secondary data sources, it is found that intra-SCQM promotes sales in domestics market with mediating role of corporate reputation, while the realization of overseas performance depend both on intra- and inter-SCQM with mediating role of food certification.

The paper study on food supply chain quality problem, the suggested approaches can be easily realized by agro-food companies to achieve international competitiveness by implementing both intra- and inter-SCQM. Meanwhile to Chinese domestic companies, it is important to strengthen inter-SCQM and food certification in order to achieve competitive advantage.

Review of literature indicated that there is a dearth of open literature that discusses food safety from the perspective of inter- and intra-organizational management. Furthermore, it was also observed that there has been no study that has taken the safety signaling perspective. The current research tires to shed some light on this important, but sparely discussed issue.

This study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.

This study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.

Six clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.

This study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.