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Risks in implementing green building (GB) projects have emerged as a significant obstacle for GB development, especially in developing countries. In recent years, both academics and construction practitioners have paid considerable attention to the risks associated with GB. In this study, the authors aimed to create a comprehensive risk assessment model that considers three crucial risk features: impact level, probability of occurrence and risk manageability.

In the research, authors adopted the mean scoring and fuzzy synthetic evaluation method to assess GB risks. Based on expert assessments, this model can determine the significance of risk factors, risk groups and overall risk. Notably, this research applied the proposed model to assess GB risks in Vietnam by surveying 58 GB experienced professionals.

The findings revealed that GB risks are relatively high in Vietnam, implying that risk management is essential for GB projects to succeed. The results also showed that “lack of experience of GB designers” is the most critical factor, and “human resources risk in the design phase” is the top crucial risk group.

This study contributes a novel and practical model to help practitioners assess risks in GB projects. In addition, this research offers detailed GB risk evaluations in Vietnam and thus could be a valuable reference for construction practitioners and future studies.

Halal integrity assurance is the primary objective of Halal supply chain management. Several halal-related risks are present that have the potential to breach halal integrity. Therefore, this study aims to develop the framework for the assessment of halal-related risk from a supply chain perspective.

Risk related to halal is identified through the combined approach of the systematic literature review and experts’ input. Further, these risks are assessed using the integrated approach of intuitionistic fuzzy number (IFN) and D-number based on their severity score. This integrated approach can handle fuzziness, inconsistency and incomplete information that are present in the expert’s input.

Eighteen significant risks related to halal are identified and grouped into four categories. These risks are further prioritised based on their severity score and classified as “high priority risk” or “low priority risks”. The findings of the study suggests that raw material status, processing methods, the wholesomeness of raw materials and common facilities for halal and non-halal products are more severe risks.

This study only focusses on halal-related risks and does not capture the other types of risks occurring in the supply chain. Risks related to halal supply chain management are not considered in this study. Prioritisation of the risks is based on the expert’s input which can be biased to the experts' background.

The proposed risk assessment framework is beneficial for risk managers to assess the halal related risks and develop their mitigation strategies accordingly. Furthermore, the prioritisation of the risks also assists managers in the optimal utilisation of resources to mitigate high-priority risks.

This study provides significant risks related to halal integrity, therefore helping in a better understanding of the halal supply chain. To the best of the authors' knowledge, this is the first comprehensive study for developing a risk assessment model for the halal supply chain.

Unforeseen events can disrupt the operational process and negatively impact emergency resources optimization and its supply chain. A limited number of studies have addressed risk management issues in the context of emergency supply chains, and this existing research lacks inbuilt and practical techniques that can significantly affect the reliability of risk management outcomes. Therefore, this paper aims to identify and practically analyze the specific risk factors that can most likely disrupt the normal functioning of the emergency supply chain in disaster relief operations.

This paper has used a three-step process to investigate and evaluate risk factors associated with the emergency supply chain. First, the study conducts a comprehensive literature review to identify the risk factors. Second, the research develops a questionnaire survey to validate and classify the identified risk factors. At the end of this step, the study develops a hierarchical structure. Finally, the research investigates the weighted priority of the validated risk factors using the fuzzy-analytical hierarchy process (FAHP) methodology. Experts were required to provide subjective judgments.

This paper identified and validated 28 specific risk factors prevalent in emergency supply chains. Based on their contextual meanings, the research classified these risk factors into two main categories: internal and external risk factors; four subcategories: demand, supply, infrastructural and environmental risk factors; and 11 risk types: forecast, inventory, procurement, supplier, quality, transportation, warehousing, systems, disruption, social and political risk factors. The most significant risk factors include war and terrorism, the absence of legislative rules that can influence and support disaster relief operations, the impact of cascading disasters, limited quality of relief supplies and sanctions and constraints that can hinder stakeholder collaboration. Therefore, emergency supply chain managers should adopt appropriate strategies to mitigate these risk factors.

This study will contribute to the general knowledge of risk management in emergency supply chains. The identified risk factors and structural hierarchy taxonomic diagram will provide a comprehensive risk database for emergency supply chains.

The research findings will provide comprehensive and systemic support for respective practitioners and policymakers to obtain a firm understanding of the different risk categories and specific risk factors that can impede the effective functioning of the emergency supply chain during immediate disaster relief operations. Therefore, this will inform the need for the improvement of practices in critical aspects of the emergency supply chain through the selection of logistics and supply chain strategies that can ensure the robustness and resilience of the system.

This research uses empirical data to identify, categorize and validate risk factors in emergency supply chains. This study contributes to the theory of supply chain risk management. The study also adopts the fuzzy-AHP technique to evaluate and prioritize these risk factors to inform practitioners and policymakers of the most significant risk factors. Furthermore, this study serves as the first phase of managing risk in emergency supply chains since it motivates future studies to empirically identify, evaluate and select effective strategies that can eliminate or minimize the effects of these risk factors.

This paper aims to offer a tutorial/introduction to new statistics arising from the theory of optimal transport to empirical researchers in econometrics and machine learning.

Presenting in a tutorial/survey lecture style to help practitioners with the theoretical material.

The tutorial survey of some main statistical tools (arising from optimal transport theory) should help practitioners to understand the theoretical background in order to conduct empirical research meaningfully.

This study is an original presentation useful for new comers to the field.

This study aims to improve the rules and regulations system of high-speed rail emergency disposal.

Based on the analysis of the demands, rules and regulations of China concerning on-site high-speed rail emergency disposal, basic principles for revising the regulations on railway technical management (RRTM) are proposed and suggestions and evaluation methods according to the main clauses are put forward.

Basic principles for revising the RRTM are proposed, namely “to meet the actual needs of on-site high-speed railway emergency disposal, standardize the emergency disposal process, improve the efficiency of emergency disposal and keep the consistency between provisions of emergency disposal”. Existing provisions related to emergency disposal efficiency, scenarios, safety and service quality are made up for the deficiencies. To make up for the deficiencies of the existing provisions related to emergency disposal efficiency, improvement of emergency disposal scenarios and guarantee of emergency disposal safety and quality, this paper puts forward suggestions on revising 15 emergency disposal provisions of the RRTM with regard to earthquake monitoring and warning, in-station foreign body invasion warning, air conditioning failure of EMU trains and forced parking of trains in sections. A fuzzy comprehensive evaluation model based on the analytic hierarchy process (AHP) is constructed to evaluate the proposed revision scheme and suggestions, which has been highly recognized by experts.

This study implements the goal of high-quality railway development.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

With the help of basic physics, the application of computer algorithms in the form of recent advances such as machine learning and neural networking in textile Industry has been discussed in this review. Scientists have linked the underlying structural or chemical science of textile materials and discovered several strategies for completing some of the most time-consuming tasks with ease and precision. Since the 1980s, computer algorithms and machine learning have been used to aid the majority of the textile testing process. With the rise in demand for automation, deep learning, and neural networks, these two now handle the majority of testing and quality control operations in the form of image processing.

The state-of-the-art of artificial intelligence (AI) applications in the textile sector is reviewed in this paper. Based on several research problems and AI-based methods, the current literature is evaluated. The research issues are categorized into three categories based on the operation processes of the textile industry, including yarn manufacturing, fabric manufacture and coloration.

AI-assisted automation has improved not only machine efficiency but also overall industry operations. AI's fundamental concepts have been examined for real-world challenges. Several scientists conducted the majority of the case studies, and they confirmed that image analysis, backpropagation and neural networking may be specifically used as testing techniques in textile material testing. AI can be used to automate processes in various circumstances.

This research conducts a thorough analysis of artificial neural network applications in the textile sector.

Multinational enterprises (MNEs) establish a wide range of alliances to access the critical resources that they may need at any one time. Although inter-organizational relationships (IORs) constitute the channels through which social capital flows, MNEs should consider which mechanisms or characteristics of the relations facilitate their actual mobilization.

A definition of alliance types yielded the parameters for an ordinary least squares regression of a sample from top global-reach MNEs from the airline industry.

The results showed that certain kind of alliances favored the actual mobilization of social capital.

Managers of MNEs must select the type of IOR taking into account the objective they pursue and the type of activity they will include.

Analyzing the factors that influence the degree of mobilization of social capital and how MNEs actually use the resources of the partners require the establishment of a theoretical framework and the development of empirical evidence.

las Empresas Multinacionales (MNEs) establecen una amplia gama de alianzas para acceder a los recursos críticos externos que puedan necesitar en cualquier memento. Las MNEs deben considerar qué mecanismos o características de las relaciones facilitan su movilización real.

una definición de los tipos de alianza produjo los parámetros para una regresión de mínimos cuadrados ordinarios de una muestra de las principales MNEs de alcance global de la industria de las aerolíneas.

Los resultados mostraron que ciertos tipos de alianzas favorecieron la movilización real del capital social.

Analizar los factores que influyen en el grado de movilización del capital social y cómo las MNEs utilizan en la práctica los recursos de sus socios, requiere del establecimiento de un marco teórico y el desarrollo de evidencia empírica.

This work aims at proposing a novel Internet of Things (IoT)-based and cloud-assisted monitoring architecture for smart manufacturing systems able to evaluate their overall status and detect eventual anomalies occurring into the production. A novel artificial intelligence (AI) based technique, able to identify the specific anomalous event and the related risk classification for possible intervention, is hence proposed.

The proposed solution is a five-layer scalable and modular platform in Industry 5.0 perspective, where the crucial layer is the Cloud Cyber one. This embeds a novel anomaly detection solution, designed by leveraging control charts, autoencoders (AE) long short-term memory (LSTM) and Fuzzy Inference System (FIS). The proper combination of these methods allows, not only detecting the products defects, but also recognizing their causalities.

The proposed architecture, experimentally validated on a manufacturing system involved into the production of a solar thermal high-vacuum flat panel, provides to human operators information about anomalous events, where they occur, and crucial information about their risk levels.

Thanks to the abnormal risk panel; human operators and business managers are able, not only of remotely visualizing the real-time status of each production parameter, but also to properly face with the eventual anomalous events, only when necessary. This is especially relevant in an emergency situation, such as the COVID-19 pandemic.

The monitoring platform is one of the first attempts in leading modern manufacturing systems toward the Industry 5.0 concept. Indeed, it combines human strengths, IoT technology on machines, cloud-based solutions with AI and zero detect manufacturing strategies in a unified framework so to detect causalities in complex dynamic systems by enabling the possibility of products’ waste avoidance.

This paper reviews the literature on zombie firms to provide a holistic view by delineating their formation, impact, widespread nature, prevention and policy implications.

This paper uses a systematic literature review methodology, in which 76 papers published in journals ranked on the Australian Business Deans Council (ABDC) 2022 list were reviewed. The study period was from 2000 to 2022.

Among the main findings, the widespread problems of zombie firms were evident. The authors found that consistent support, either in the form of government grants or a weak financial framework, was responsible for their formation. The suboptimal performance of factors of production, depressed job creation, low innovation and overall negative impact on economic activity are the consequences of zombification. This can be controlled by ensuring better bankruptcy codes, focused on government assistance, technology use and better due diligence by banks.

This review serves as a reference point for future researchers as a cohesive and holistic study presenting a full picture of the problem, so that the proposed solutions are robust and tenable.

This review is among the initial attempts to comprehensively study published work on zombie firms in terms of analyzing their region-specific nature, with an emphasis on definition, causes, impact and prevention.