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With the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.

With an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.

Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.

The concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.

Nowadays, in many organizations, products are not delivered instantly. So, the customers should wait to receive their needed products, which will form a queueing-inventory model. Waiting a long time in the queue to receive products may cause dissatisfaction and churn of loyal customers, which can be a significant loss for organizations. Although many studies have been done on queueing-inventory models, more practical models in this area are needed, such as considering customer prioritization. Moreover, in many models, minimizing the total cost for the organization has been overlooked.

This paper will compare several machine learning (ML) algorithms to prioritize customers. Moreover, benefiting from the best ML algorithm, customers will be categorized into different classes based on their value and importance. Finally, a mathematical model will be developed to determine the allocation policy of on-hand products to each group of customers through multi-channel service retailing to minimize the organization’s total costs and increase the loyal customers' satisfaction level.

To investigate the application of the proposed method, a real-life case study on vaccine distribution at Imam Khomeini Hospital in Tehran has been addressed to ensure model validation. The proposed model’s accuracy was assessed as excellent based on the results generated by the ML algorithms, problem modeling and case study.

Prioritizing customers based on their value with the help of ML algorithms and optimizing the waiting queues to reduce customers' waiting time based on a mathematical model could lead to an increase in satisfaction levels among loyal customers and prevent their churn. This study’s uniqueness lies in its focus on determining the policy in which customers receive products based on their value in the queue, which is a relatively rare topic of research in queueing management systems. Additionally, the results obtained from the study provide strong validation for the model’s functionality.

A new triage method, MBCE (Medical Bio Social Ethics), is presented with social justice, bio, and medical ethics for critical resource distribution during a pandemic. Ethical triage is a complex and challenging process that requires careful consideration of medical, social, cultural, and ethical factors to guide the decision-making process and ensure fair and transparent allocation of resources. When assigning priorities to patients, a clinician would evaluate each patient’s medical condition, age, comorbidities, and prognosis, as well as their cultural and social background and ethical factors.

A statistical analysis shows no interactions among the ethical triage factors. It implies the ethical components have no moderation effect; hence, each is independent. The result also points out that medical and bioethics may have an affinity for interactions. In such cases, there seem to be some ethical factors related to bio and medical ethics that are correlated. Therefore, the triage team should be careful in evaluating patient cases. The algorithm is explained with case histories of the selected patient. A group of triage nurses and general medical practitioners assists with the triage.

The MBCE triage algorithm aims to allocate scarce resources fairly and equitably. Another ethical principle in this triage algorithm is the principle of utility. In a pandemic, the principle of utility may require prioritizing patients with a higher likelihood of survival or requiring less medical care. The research presents a sensitivity analysis of a patient’s triage score to show the algorithm’s robustness. A weighted score of ethical factors combined with an assessment of triage factors combines multiple objectives to assign a fair triage score. These distinctive features of the algorithm are reasonably easy to implement and a new direction for the unbiased triage principle.

The idea is to make decisions about distributing and using scarce medical resources. Triage algorithms raise ethical issues, such as discrimination and justice, guiding medical ethics in treating patients with terminal diseases or comorbidity. One of the main ethical principles in triage algorithms is the principle of distributive justice.

Circumstances such as pandemics can cause individuals to fall into a state of need, so they turn to donation services for assistance. However, donation services can be designed based on supply-side considerations, e.g. efficiency or inventory control, which restrict consumer choice without necessarily considering how consumer vulnerabilities like low financial or interpersonal power might cause them to react to such restrictions. Thus, the purpose of this paper is to examine service designs that limit the choices consumers are given in terms of either the allowable quantity or assortment variety and examine effects on consumer perceptions of justice and satisfaction.

Three experiments are reported, including one manipulating the service design of an actual food pantry.

When consumers have low financial or interpersonal power, meaning their initial state of control is low, and they encounter a donation service that provides limited (vs. expanded) choice that drops control even lower, they perceive the situation as unjust and report lower satisfaction.

Donation service providers should strive to design services that allow for expanded consumer choice and use interpersonal processes that empower beneficiaries so they perceive the service experience as just and satisfying. Collecting feedback from beneficiaries is also recommended.

While researchers have started to look at the service experiences of vulnerable populations, they have focused primarily on financial service designs. The authors look at donation service designs and identify problems with supply-side limits to choice quantity and assortment.

Throughout human history, the occurrence of disasters has been inevitable, leading to significant human, financial and emotional consequences. Therefore, it is crucial to establish a well-designed plan to efficiently manage such situations when disaster strikes. The purpose of this study is to develop a comprehensive program that encompasses multiple aspects of postdisaster relief.

A multiobjective model has been developed for postdisaster relief, with the aim of minimizing social dissatisfaction, economic costs and environmental damage. The model has been solved using exact methods for different scenarios. The objective is to achieve the most optimal outcomes in the context of postdisaster relief operations.

A real case study of an earthquake in Haiti has been conducted. The acquired results and subsequent management analysis have effectively assessed the logic of the model. As a result, the model’s performance has been validated and deemed reliable based on the findings and insights obtained.

Ultimately, the model provides the optimal quantities of each product to be shipped and determines the appropriate mode of transportation. Additionally, the application of the epsilon constraint method results in a set of Pareto optimal solutions. Through a comprehensive examination of the presented solutions, valuable insights and analyses can be obtained, contributing to a better understanding of the model’s effectiveness.

At the beginning of the Corona Virus Disease 2019 (COVID-19) pandemic, a digitalized construction environments surfaced in the heating, ventilation and air conditioning (HVAC) systems in the form of a modern delivery system called demand controlled ventilation (DCV). Demand controlled ventilation has the potential to solve the building ventilation's biggest problem of managing indoor air quality (IAQ) for controlling COVID-19 transmission in indoor environments. However, the improper evaluation and information management of infection prevention on dense crowd activities such as measurement errors and volatile organic compound (VOC) generation failure rates, is fragmented so the aim of this research is to integrate this and explore potentials with machine learning algorithms (MLAs).

The method used is a thorough systematic literature review (SLR) approach. The results of this research consist of a detailed description of the DCV system and digitalized construction process of its IAQ elements.

The discussion revealed that DCV has a potential for being further integrated by perceiving it as a MLAs and hereby enabling the management of IAQ level from the perspective of health risk function mechanism (i.e. VOC and CO₂) for maintaining a comfortable thermal environment and save energy of public and private buildings (PPBs). The appropriate MLA can also be selected in different occupancy patterns for seasonal variations, ventilation behavior, building type and locations, as well as current indoor air pollution control strategies. Furthermore, the conceptual framework showed that MLA application such as algorithm design/Model Predictive Control (MPC) integration can alleviate the high spread limitation of COVID-19 in the indoor environment.

Finally, the research concludes that a large unexploited potential within integration and innovation is recognized in the DCV system and MLAs which can be improved to optimize level of IAQ from the perspective of health throughout the building sector DCV process systems. The requirements of CO₂ based DCV along with VOC concentrations monitoring practice should be taken into consideration through further research and experience with adaption and implementation from the ventilation control initial stage of the DCV process.

This paper aims to present a comprehensive overview of the emergence and significance of digital payments, focusing on their impact on competitiveness and the need for policy interventions. In addition, it explores the design of policies that promote the adoption of digital payments, highlighting the benefits they offer to providers and users.

The paper examines the technological advances that have driven the growth of digital payment systems. It identifies key requirements for successful adoption and discusses the associated risks, along with potential strategies to mitigate these risks.

The findings emphasize the importance of responsible implementation and safeguarding the well-being of end users to fully realize the benefits of digital payment adoption. Understanding the inherent risks and establishing effective risk mitigation mechanisms are crucial. This necessitates the development of appropriate infrastructure to support the provision of digital payment services.

More research is needed to gain deeper insights into how emerging global trends in financial technology should be analyzed and understood by policymakers, service providers and users.

The findings of this study can guide policymakers, private sector managers and consumers in comprehending the effects of emerging digitalization trends and determining their adoption responses accordingly.

This paper stands out as one of the few research contributions that provide comprehensive and actionable policy recommendations to facilitate a smooth transition to a digital payments ecosystem that benefits all stakeholders.

Greener on the Outside for Prisons (GOOP) is a therapeutic horticulture programme targeting the high levels of complex health and social care needs in prisons in England. The COVID-19 pandemic and resulting lockdowns led to unprecedented disruption in prisons in England. This paper examines the experiences of prisoners both during and post-lockdowns in four prisons, to understand the effects of participation in GOOP on health and wellbeing after the disruption of restrictions, and identify implications for developing this programme further.

The paper is based on original qualitative data gathered from in-depth narrative-based interviews and focus groups with prisoners and staff in four English prisons. Audio data was transcribed and subject to a thematic analysis, drawing from a realist-informed lens.

Thematic analysis revealed five key themes: reimagining the GOOP context; increasing empathy between participants; building sense of coherence; reconnecting with nature and a joined-up connection with provider services. The main arguments centre on horticulture in prisons remaining under-utilised as a means of promoting good health and wellbeing, although there is enthusiasm from staff to provide green spaces for the most vulnerable prisoners and develop a range of mechanisms to connect people in prison with nature.

This paper focuses on new knowledge arising from an unprecedented situation in English prisons, from key stakeholders on the frontline of garden activities. Accounts demonstrate the extent of the health and wellbeing benefits of participation in such activities in this challenging environment, which has implications for practice for prisons more widely.

The purpose of this study was to establish the cause-effect relationship between determinants of trust in the buyer–supplier integration and the procurement performance of large manufacturing firms in Tanzania.

The study surveyed 52 firms from Temeke Municipality, Tanzania using questionnaire subjected to one procurement manager and one stores manager tallying a sample size of 104 respondents. Explanatory design was employed due to the presence of cause–effect relationship and the null hypotheses were tested using binary logistic regression technique at p values < 0.05 and ExpB > 1.

Mutual goals, geographical vicinity among partners, and supplier reliability are significant for the procurement performance of the manufacturing firms in Tanzania, whereas interpersonal and inter-organizational trusts and perceived buyers’ confidence are of no significant impact.

Buyer–supplier integration is a recently embraced and paramount practice for the manufacturing firms in Tanzania. Therefore, longitudinal study would further add value. The presence of the causality from the tested hypothesis appeals for the necessity of progress tracking.

Causality has been established, and a framework has been developed for the performance of large manufacturing firms using trust of buyer–supplier integration.

There shall be creation of more employment opportunities and timely availability of materials from large manufacturing firms in Tanzania.

Anchored on transaction cost economics and resource dependency theories, the study disclosed the root cause of procurement performance in the context of manufacturing firms in Tanzania whilst considering trust as a resource advantage of buyer–supplier integration.

The study aims to address a critical gap in existing healthcare payment schemes and care service pricing by recognizing the influential role of patients' decisions on self-management efforts. These decisions not only impact health outcomes but also shape the demand for care, subsequently influencing care costs. Despite the significance of this interplay, current payment schemes often overlook these dynamics. The research focuses on investigating the implications of a novel behavior-based payment scheme, designed to align incentives and establish a direct connection between patients' decisions and care costs. The primary objective is to comprehensively understand whether and how this innovative payment scheme structure influences key stakeholders, including patients, care providers, insurers and overall social welfare.

In this paper, we propose a game-theoretical model to incorporate the performance of self-management with the demand for healthcare service, compare the patient's effort decision for self-management and provider's price decision for healthcare service under a behavior-based scheme with that under two implemented widely payment schemes, that is, co-payment scheme and co-insurance scheme.

Our findings confirm that the behavior-based scheme incentives patient self-management more than current schemes while reducing their possibility of seeking healthcare service, which indirectly induces the provider to lower the price of the service. The stakeholders' utility under various payment schemes is sensitive to the cost of treatment and the perceived health utility of patients. Especially, patient health awareness is not always benefited provider profit, as it motivates patient self-management while diminishing the demand for care.

We provide a novel framework for characterizing behavior-based payment schemes. Our results confirm the need for modification of the current payment scheme to incentivize patient self-management.