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The purpose of this paper is to research and design a feasible automatic identification and data capture (AIDC) system for a manufacturing small to medium enterprise (SME) that is able to facilitate the flow of accurate and real‐time data throughout the manufacturing process.

The processes and operations conducted at a manufacturing SME were critically analysed in order to identify areas, where the use of an AIDC system could be used to improve the efficiency and visibility of the processes throughout manufacture. The areas for improvement could then be identified and solved through specific applications and/or systems of which a cost benefit analysis could be conducted.

Significant cost savings are found through the implementation of a radio frequency identification (RFID) system based on the reduction of safety stock, the elimination of manual job tracking and the reduction of the manual input and written data throughout the process.

The read range of the technology outlined in this project was found to be limited due to the metal interference of the products, which should be aimed to be improved through the detection of other RFID transponders or a better adhesive medium used.

The most common limitations were found to be the lack of IT infrastructure, limited knowledge on the benefits of the system and also cultural resistance to change. However, appropriate training is to be provided to overcome any problems.

AIDC systems utilising data carrier technologies have been successfully implemented within many large multinational organisations but research into the implementation of AIDC systems within SMEs is far more limited.

Currently, the health system is a treatment-oriented system focused on service providers. In this system, the main focus is on the health market, with little attention on insured. One way to get out of existing conditions is to empower the insured in order to involve them actively in maintaining and improving health. The paper aims to discuss these issues.

This qualitative study was done using the content analysis method. Based on the purposive sampling method and theoretical saturation criterion, 24 individuals including 12 health insurance experts and 12 insured participated in the study in 2018. The semi-structured interview method was used to collect data. Data were analyzed using MAXQDA10 software.

Having analyzed the interviews, 750 codes were obtained. These codes were categorized into two categories of “insurance experts” and “insured” and ten subcategories of “informing and educating, cost reduction, intersectional activities, expectations from the insured, services package, access to services, inability to pay costs, participation, and expectations from the insurance organization.”

This qualitative study was conducted to assess and determine the effective strategies for empowering the insured under health insurance. The results of this study are helpful to the health insurance organizations and health decision makers to detect the effective ways to develop the quality of insurance services, improve the status of insured, and increase access to health care goods and services.

The purpose of this paper was to identify Serratia marcescens to extract and purify prodigiosin pigment to evaluate the antibacterial potential of the pigment.

Samples were collected from shrimp aquaculture ponds. Species identification was conducted using morphological, biochemical and molecular tests. Pigment extraction and purification were carried out using column chromatography. The antibacterial effect of crude and purified prodigiosin pigment was evaluated on Escherichia coli, Bacillus subtilis, Pseudomonas aeruginosa and Staphylococcus aureus as biofouling bacteria. In addition, the interaction between prodigiosin and proteins involved in biofilm formation was evaluated using molecular docking.

The results of prodigiosin extraction with solvents showed the highest percentage of pigment presence with methanol solvent in the second day of culture. The chemical structure of pure prodigiosin obtained from the column chromatography was confirmed by Fourier-transform infrared spectroscopy. Both crude and purified pigments exhibited antibacterial effects against selected bacterial strains. The antibacterial effect of the purified pigment was higher, and the highest antibacterial effect was observed on B. subtilis. Prodigiosin docking was carried out with all target proteins, and the docked energy in all of them was at an acceptable level.

Prodigiosin extracted from S. marcescens can be used as a bioactive compound to design and manufacture of anti-biofouling and anti-biofilm formation products to use extensively for industrial applications as a natural color in marine industries, food industry, cosmetics and textile productions.

Hospitals recently search for more accurate forecasting systems, given the unpredictable demand and the increasing occurrence of disruptive incidents (mass casualty incidents, pandemics and natural disasters). Besides, the incorporation of automatic inventory and replenishment systems – that hospitals are undertaking – requires developed and accurate forecasting systems. Researchers propose different artificial intelligence (AI)-based forecasting models to predict hospital assets consumption (AC) for everyday activity case and prove that AI-based models generally outperform many forecasting models in this framework. The purpose of this paper is to identify the appropriate AI-based forecasting model(s) for predicting hospital AC under disruptive incidents to improve hospitals' response to disasters/pandemics situations.

The authors select the appropriate AI-based forecasting models according to the deduced criteria from hospitals' framework analysis under disruptive incidents. Artificial neural network (ANN), recurrent neural network (RNN), adaptive neuro-fuzzy inference system (ANFIS) and learning-FIS (FIS with learning algorithms) are generally compliant with the criteria among many AI-based forecasting methods. Therefore, the authors evaluate their accuracy to predict a university hospital AC under a burn mass casualty incident.

The ANFIS model is the most compliant with the extracted criteria (autonomous learning capability, fast response, real-time control and interpretability) and provides the best accuracy (the average accuracy is 98.46%) comparing to the other models.

This work contributes to developing accurate forecasting systems for hospitals under disruptive incidents to improve their response to disasters/pandemics situations.

Supply chains (SCs) have been growingly virtualized in response to the market challenges and opportunities that are presented by new and cost-effective internet-based technologies today. This paper designed a virtual closed-loop supply chain (VCLSC) network based on multiperiod, multiproduct and by using the Internet of Things (IoT). The purpose of the paper is the optimization of the VCLSC network.

The proposed model considers the maximization of profit. For this purpose, costs related to virtualization such as security, energy consumption, recall and IoT facilities along with the usual costs of the SC are considered in the model. Due to real-world demand fluctuations, in this model, demand is considered fuzzy. Finally, the problem is solved using the Grey Wolf algorithm and Firefly algorithm. A numerical example and sensitivity analysis on the main parameters of the model are used to describe the importance and applicability of the developed model.

The findings showed that the Firefly algorithm performed better and identified more profit for the SC in each period. Also, the results of the sensitivity analysis using the IoT in a VCLSC showed that the profit of the virtual supply chain (VSC) is higher compared to not using IoT due to tracking defective parts and identifying reversible products. In proposed model, chain members can help improve chain operations by tracking raw materials and products, delivering products faster and with higher quality to customers, bringing a new level of SC efficiency to industries. As a result, VSCs can be controlled, programmed and optimized remotely over the Internet based on virtual objects rather than direct observation.

There are limited researches on designing and optimizing the VCLSC network. This study is one of the first studies that optimize the VSC networks considering minimization of virtual costs and maximization of profits. In most researches, the theory of VSC and its advantages have been described, while in this research, mathematical optimization and modeling of the VSC have been done, and it has been tried to apply SC virtualization using the IoT. Considering virtual costs in VSC optimization is another originality of this research. Also, considering the uncertainty in the SC brings the issue closer to the real world. In this study, virtualization costs including security, recall and energy consumption in SC optimization are considered.

1. Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

2. Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

3. Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

4. Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

Investigates the role of IoT for virtual supply chain profit optimization and mathematical optimization of virtual closed-loop supply chain (VCLSC) based on multiperiod, multiproduct with emphasis on using the IoT under uncertainty.

Considering the most important costs of virtualization of supply chain include: cost of IoT information security, cost of IoT energy consumption, cost of recall the production department, cost of IoT facilities.

Selection of the optimal suppliers in each period and determination of the price of each returned product in virtual supply chain.

Solving and validating the proposed model with two meta-heuristic algorithms (the Grey Wolf algorithm and Firefly algorithm).

This study aims to investigate the entrepreneurial actions of successful Iranian women entrepreneurs. It is an attempt to find the roots, motivations, challenges and strategies of successful actions of these women, despite the unequal and masculine structure of the society.

This research was conducted through the theoretical framework of poststructuralist feminism and the qualitative method. By purposive sampling, the researchers selected 10 women entrepreneurs living in Khorasan Razavi province for in-depth interviews. These women have created many jobs based on local potential and become recognized as top entrepreneurs. The narratives of these women were analyzed phenomenologically.

The actions of these women are fertilized under the influence of family background and early socialization and in the combination of internal motivations and external stimuli. These women resist social barriers such as widespread patriarchy, gender division of labor in society and complex bureaucratic processes through the “strategic cycle” resulting from the interaction of “reliance on intertwined social networks,” “social entrepreneurship,” “gradual formation of social capital” and “reliance on local potentials.”

The strategies to overcome gender inequalities at the micro (family) and macro (society) levels introduced in this research can inspire women entrepreneurs who seek success in their businesses.

To the best of the authors’ knowledge, this is the first qualitative research that, by studying the actions of successful Iranian women entrepreneurs, describes how they overcome structural inequalities and their success strategies.

This study aims to analyse and understand the current state of research in the field of digital marketing in “library and information science”.

This study used a “bibliometric research design.” A lexical title search was used to obtain the required data set for executing this study, and a comprehensive “indexing and abstracting” database, Web of Science, was used as a data harvesting source. Louvain’s clustering algorithm was used for network metrics.

The findings revealed that research productivity and impact have grown considerably over time, indicating significant attention towards digital marketing research in library and information science (LIS). Moreover, the results showed that the overall author collaboration patterns were weak, hence creating room for development in the author’s collaboration patterns.

The current study could be very beneficial in providing a comprehensive and up-to-date overview of the “digital marketing” research field scholarly output in LIS, which can be used by researchers, practitioners and policymakers to guide their work and make informed decisions.

The originality of this bibliometric study lies in its comprehensive and up-to-date analysis of the current state of research in the field of “digital marketing” in LIS. This study provides a unique and in-depth understanding of the key authors, venues and papers in the field, as well as the trends and patterns in the research.

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.