Farm-level evaluation is considered an essential element that assists farmers in managing their farms in the wake of rising emphasis on agriculture. However, it is difficult for farmers to choose appropriate and effective farm-level agricultural decision support tools (ADSTs) due to multiple assessment criteria, data fluctuation, and a myriad of ADSTs. Previous studies have implemented the Multi-Attribute Decision-Making (MCDM) technique to evaluate the ADSTs. However, due to some inherent MCDM issues, it is not recommended to be an appropriate technique that aligns with ADSTs evaluation needed for agriculture 4.0. Accordingly, this study addresses this gap and proposes a novel decision-matrix ADSTs evaluation technique by integrating fuzzy-weighted zero-inconsistency (FWZIC) and fuzzy decision-making opinion score method (FDOSM) approaches. To this end, the methodology of the study contains two steps: first, an initial decision matrix is established based on “9 main criteria” and 16 ADSTs; second, the steps of Z-Cloud (ZC) FWZIC to get the weight of every criterion were done, while ZC-FDOSM steps are defined to rank ADSTs. The results of the analysis show that Farm Sustainability Assessment (FSA), DairySAT, and FieldPrint Calculator were found to be the best tools for the 1st, 2nd, and 3rd groups in a sequence, while COMER-FARM, Dairy GEM, and Ofoot were found to be the worst in the first, second, and third groups, respectively. This study provides essential implications for farmers and policymakers to increase productivity and reduce the use of resources.

Abstract The Industrial Internet of Things (IIoT), as a matter of fact, allows for operational efficiency through integrating real-time data from various resources; however, on the other hand, it also opens new frontiers for cybersecurity risks since the attack surface increases and the environment becomes resource constrained. Conventional intrusion detection systems in most scenarios do not adapt to the changing security requirements of the IIoT. In this research article, a basic lightweight intrusion detection framework that synthesizes Principal Component Analysis (PCA) for reducing dimensions with machine-learning-based ensembles, specifically Naïve Bayes and Random Forest classifiers. Having applied PCA for reducing the original features to 25 and 8 features, correspondingly, the information is well maintained (over 95% of the data variance), with substantially less computational complexity. The proposed system is evaluated on three benchmark datasets, CSE-CIC-IDS2018, CIC-IDS2017, and NSL-KDD, demonstrating robust performance with high detection accuracy, low mean squared error, and sub-millisecond inference latency. The results prove that the new framework maintains high detection performance, increases model generalizability and greatly decreases training and inference times compared with those of full-feature models. These results have justified the framework’s ability to balance accuracy with computational efficiency, offering a scalable and practical solution for real-time intrusion detection in industrial IIoT environments.

Research on environmental performance has been emerging as an important field of inquiry. Businesses are increasingly becoming aware of their corporate social responsibility practices. Adherence to the United Nations’ agenda for sustainable development goals has become central to companies’ strategic policies. Climate action is one of the 13th Sustainable Development Goals and thus requires immediate consideration by practitioners and researchers. This paper examines the determinants of environmental performance levels among Iraqi oil firms. The methodology is divided into two phases. Firstly, data were gathered through a structured questionnaire completed by 483 managers across eleven oil firms and analysed using partial least squares structural equation modelling (PLS-SEM). Secondly, interval-valued neutrosophic with fuzzy-weighted zero-inconsistency (IVN-FWZIC) and IVN with fuzzy-decision-by-opinion-score-method (IVN-FDOSM). The results indicated numerous inhibitors of environmental adaptation, including the costs associated with technology, emphasis on other external priorities, inadequate industry regulation, lack of access to equipment and information, and insufficient organizational, strategic, and financial abilities, all of which greatly affect environmental performance. The results also reflect the mediating relationship of the hybrid strategy between the result and predictors, and the mediating relationship of readiness for change between the hybrid strategy and the environmental performance. Additionally, the results of IVN-FWZIC indicate that an Inadequate Industry. Regulation is the most significant criterion (0.131). The IVN-FDOSM ranked the best and worst oil companies in terms of environmental performance. The results could assist businesses and other policymakers in coming up with corrective strategies aimed at improving environmental performance.

The metaverse has revolutionized the computing landscape, transformed numerous aspects of life and added glamorous nuances to various industries. However, its darker aspects are generally overlooked. This study addresses a critical issue: the prevalence of deviant behaviors in the virtual world. Our research investigates the role of technical factors, such as immersion and 3D design, and social factors, including online disinhibition, loneliness, social presence, and authenticity, in the online abuse of the metaverse. The purported conceptual model also examines the privacy implications of deviant behaviors. To provide insight to academics and practitioners into the dark sides of the metaverse, we employed partial least squares structural equation modelling (PLS-SEM) to capture causal relationships and fuzzy set qualitative comparative analysis (fsQCA) to identify configurations of online abuse. The results of the linear relationships reveal that social and technical factors strongly influence online abuse in the metaverse, with users’ biometrics data risks driving the relationship between technical and social factors and user behaviour. The fsQCA results uncovered five solutions to address online abuse in the metaverse, primarily focusing on the role of technical aspects in designing safe and user-friendly platforms while providing social support and a sense of presence for users in the virtual world. The current study has serious implications for practitioners and developers to create immersive and user-friendly virtual world experiences. We recommend that practitioners and policymakers develop algorithms that prohibit profanity and block malicious users. This study is unique in that it investigates the relationship between the sociotechnical perspective and the dark sides of the metaverse using both symmetrical and asymmetrical analysis.

Blockchain technology is rapidly replacing traditional technologies. This technology offers numerous benefits, including transparency, safety and security, authenticity, and traceability, which drive business organizations to adopt it. Thus, the factors that generate resistance to the adoption of blockchain technology must be recognized, and an organization's strategy toward adopting blockchain technology must be influenced. In this study, the vital factors that produce resistance to blockchain technology from the perspective of oil companies operating in Iraq were determined. The three-pronged framework of technology, organization, and environment (TOE) was employed to identify the elements that affect resistance to blockchain technology, with subsequent effects on the blockchain strategy. Data were collected using a questionnaire distributed among the managers of the target companies. Among the 381 distributed questionnaires, 313 usable responses were gathered and analyzed via partial least square-structural equation modeling and artificial neural networks to test the proposed hypotheses. The findings show that all the elements of the TOE framework are crucially associated with resistance to blockchain technology and consequently shape the blockchain strategy. Furthermore, resistance to blockchain technology mediates the link between predictors of TOE factors and the blockchain strategy. Therefore, in this work, the present knowledge on resistance to blockchain technology and the blockchain strategy is enhanced by explicating the factors inhibiting blockchain technology adoption in a previously overlooked context. The findings provide practical insight for managers of oil companies to devise effective blockchain strategies for the optimal adoption of blockchain technology.

What turns process discipline into real performance in turbulent markets? This study examines how process-oriented supply chain management (SCM) practices relate to performance via digital innovation, and whether market turbulence conditions those relationships. We surveyed 387 managers and department heads in manufacturing firms across Cairo, Alexandria, and Aswan. The model includes five practice families (lean, agile, resilience, green, sustainable), four digital-innovation facets (user experience, value proposition, skills, digital human capital, improvisation), market turbulence, and multi-dimensional supply-chain performance. Using PLS-SEM (SmartPLS 4), the measurement model shows strong reliability and validity. Resilience, agile, and lean display consistent, positive, and significant associations with all digital facets; sustainable practices show more minor positive associations. Green practices are non-significant for skills and improvisation, marginal for value proposition, and null for user experience, indicating a primarily market-facing rather than capability-deepening role in this sample. Mediation through digital innovation is broadly supported; for green practices, only the user-experience and value-proposition channels are supported. Market turbulence shows no direct association with performance and negatively moderates only the improvisation–performance link. We specify a capability stack in which resilience, supported by lean and agile, provides the operational foundation for digital value creation, with value proposition (and user experience) emerging as the most potent levers of environmental, economic, operational, and social performance. A 10-fold cross-validated ANN complements the SEM by capturing nonlinearities and ranking predictors, highlighting value proposition as the strongest driver of supply-chain performance.

Application mapping strategies in Network-on-Chip (NoC)-based Multiprocessor System-on-Chip (MPSoC) are critical for achieving efficient communication and reduced energy consumption. Therefore, choosing the optimal mapping strategy is of significant importance. However, due to the numerous evaluation criteria, trade-offs, conflict, and criteria importance, the assessment and selection of mapping strategies remain a complex challenge. Despite the importance of this issue, current literature reveals a significant research gap in comprehensive comparative evaluations of these strategies using systematic and quantitative methods. Previous researchers recommended multi-criteria decision-making (MCDM) to address the issue of identity best mapping strategy. Remarkably, the literature has reported a paucity of evaluations of the optimal mapping strategies. The present study aims to determine the most effective application mapping strategies in certain situations by using fuzzy MCDM methods. The design and methods of this study involve two phases. The first phase involves the evaluation decision matrix, which is derived through the intersection of the evaluation criteria and the mapping strategies list. The second phase includes the proposed MCDM methods, namely the Weight Fuzzy Judgment Method with Triangular Fuzzy (Tr-WFJM) for determining the weights for the criteria of mapping strategies and Multi-Attributive Border Approximation Area Comparison (MABAC) to rank the mapping strategies based on the weight assigned. The findings of Tr-WFJM revealed that PIP Cost has the highest final weight (0.2326) and MPEG-4 Cost has the lowest weight (0.0887), respectively. In terms of the MABAC method, the Integer Linear Programming (ILP) is the most efficient mapping strategy. This study is exceptional because it provides academics and practitioners insight into reducing resources and energy consumption.

The implementation of Countermeasure Techniques (CTs) in the context of Network-On-Chip (NoC) based Multiprocessor System-On-Chip (MPSoC) routers against the Flooding Denial-of-Service Attack (F-DoSA) falls under Multi-Criteria Decision-Making (MCDM) due to the three main concerns, called: traffic variations, multiple evaluation criteria-based traffic features, and prioritization NoC routers as an alternative. In this study, we propose a comprehensive evaluation of various NoC traffic features to identify the most efficient routers under the F-DoSA scenarios. Consequently, an MCDM approach is essential to address these emerging challenges. While the recent MCDM approach has some issues, such as uncertainty, this study utilizes Fuzzy-Weighted Zero-Inconsistency (FWZIC) to estimate the criteria weight values and Fuzzy Decision by Opinion Score Method (FDOSM) for ranking the routers with fuzzy Single-valued Neutrosophic under names (SvN-FWZIC and SvN-FDOSM) to overcome the ambiguity. The results obtained by using the SvN-FWZIC method indicate that the Max packet count has the highest importance among the evaluated criteria, with a weighted score of 0.1946. In contrast, the Hop count is identified as the least significant criterion, with a weighted score of 0.1090. The remaining criteria fall within a range of intermediate importance, with enqueue time scoring 0.1845, packet count decremented and traversal index scoring 0.1262, packet count incremented scoring 0.1124, and packet count index scoring 0.1472. In terms of ranking, SvN-FDOSM has two approaches: individual and group. Both the individual and group ranking processes show that (Router 4) is the most effective router, while (Router 3) is the lowest router under F-DoSA. The sensitivity analysis provides a high stability in ranking among all 10 scenarios. This approach offers essential feedback in making proper decisions in the design of countermeasure techniques in the domain of NoC-based MPSoC.

Incivility in the workplace refers to the violation of business standards, etiquette and ethics and is considered a global problem affecting the psychological condition and health of employees. Despite research indicating that 98

This study investigates the adoption of Generative Artificial Intelligence (GenAI) by street-level bureaucrats (SLBs) and examines its impact on their discretion in implementing sustainable policies in Iraq and Oman. By extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) to include sustainable policy alignment and policy ambiguity as moderating factors, the research explores how these policy elements influence the relationship between GenAI adoption and SLBs’ discretionary actions. Data was collected from 489 SLBs and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings demonstrate that performance expectancy, effort expectancy, hedonic motivation, and habit significantly drive the continuous intention to use GenAI. In contrast, social influence and facilitating conditions do not have a significant effect. Furthermore, the continuous intention to use GenAI positively influences SLBs’ discretion in policy implementation, with sustainable policy alignment strengthening this relationship and diminishing policy ambiguity. A multi-group analysis reveals notable differences between Iraq and Oman. In Oman, all UTAUT2 variables are significant, reflecting a supportive and stable governance environment. In contrast, in Iraq, individual perceptions dominate, likely due to higher policy ambiguity and weaker institutional support. These results underscore the importance of emphasizing GenAI’s practical benefits and ease of use and advocate for developing clear, supportive policies that empower SLBs. This study extends the theoretical foundations of UTAUT2 in the public sector, offering practical insights for policymakers and organizations seeking to leverage GenAI for enhanced sustainability outcomes.

The social commerce live-streaming feature has propelled the business model into the global spotlight by generating huge profits. Numerous studies in the past have evaluated different dimensions of live-stream selling from the consumer's perspective, however, there is little research on live-stream business from the viewpoint of online retailers. Accordingly, this study aimed to address the gap by evaluating the determinants of retailers' trust in live-stream selling. This study explores the determinants of retailers' trust in live-stream selling via social commerce platforms. An online questionnaire was developed, which was distributed among 572 Malaysian retailers who utilize social commerce platforms for live-stream selling purposes. The determinants, among others, include cost perception, scarcity persuasion, vicarious experience, anchor (parasocial interaction), audience (social contagion, the attractiveness of the online store), and situational (selling motivation, time pressure, product involvement). The results of causal relationships revealed the influence of content, anchor, audience, and situational factors on retailers' trustworthiness along with multi-way interactions of platform assistance and anchors' professionalism which strengthen this relationship. The results provided eight fundamental solutions to tackle the concern of retailers' trust in social commerce platforms' live-stream selling.

Background/Objectives: Viral diseases remain a major threat to global public health, particularly during outbreaks when limited therapeutic resources must be rapidly and fairly distributed to large populations. Although Convalescent Plasma (CP) transfusion has shown clinical promise, existing allocation frameworks treat patient prioritization, donor selection, and validation as separate processes. This study proposes a credible, converged smart framework integrating multicriteria decision-making (MCDM) and regression-based validation within a telemedicine environment to enable transparent, data-driven CP allocation. Methods: The proposed framework consists of three stages: (i) Analytic Hierarchy Process (AHP) for weighting five clinically relevant biomarkers, (ii) dual prioritization of patients and donors using Order Preference by Similarity to Ideal Solution (TOPSIS) and Višekriterijumsko Kompromisno Rangiranje (VIKOR) with Group Decision-Making (GDM), and (iii) regression-based model selection to identify the most robust prioritization model. An external dataset of 80 patients and 80 donors was used for independent validation. Results: The external GDM AHP-VIKOR prediction model demonstrated strong predictive performance and internal consistency, with R2 = 0.971, MSE = 0.0010, RMSE = 0.032, and MAE = 0.025. Correlation analysis confirmed biomarker behavior consistency and stability in ranking, thereby reinforcing the reliability of the prioritization outcomes. Conclusions: The proposed patient–donor matching framework is accurate, interpretable, and timely. This work presents an initial step toward realizing safe AI-enabled transfusion systems within telemedicine, supporting transparent and equitable CP allocation in future outbreak settings.