Journal of Resource Management and Decision Engineering
The Journal of Resource Management and Decision Engineering is a pioneering open access, peer-reviewed journal committed to disseminating cutting-edge research and practical insights into the complex world of resource management and decision-making processes. Our mission is to foster a dialogue among professionals, researchers, and practitioners to enhance the understanding and practice of efficient resource allocation and decision-making strategies across various disciplines. Our open access mandate supports a broader global exchange of knowledge, making research freely available to the international community without any barriers. <p style="padding: 0px; margin: 8px 0px 0px; word-break: break-word; font-size: 14px; line-height: var(--cib-type-body1-line-height); font-weight: 400; font-variation-settings: var(--cib-type-body1-font-variation-settings); color: #111111; font-family: -apple-system, Roboto, SegoeUI, 'Segoe UI', 'Helvetica Neue', Helvetica, 'Microsoft YaHei', 'Meiryo UI', Meiryo, 'Arial Unicode MS', sans-serif; font-style: normal; font-variant-ligatures: normal; font-variant-caps: normal; letter-spacing: normal; orphans: 2; text-align: left; text-indent: 0px; text-transform: none; widows: 2; word-spacing: 0px; -webkit-text-stroke-width: 0px; white-space: normal; background-color: #f3f3f3; text-decoration-thickness: initial; text-decoration-style: initial; text-decoration-color: initial;">This journal adheres to the ethical standards in publications and complies with the regulations of the Committee on Publication Ethics (COPE). It also follows the executive bylaw for the prevention and handling of academic misconduct. Aims and Scopes JRMDE aims to: Advance the theoretical foundations and practical applications of decision engineering and resource management across various engineering disciplines, including computer, electrical, civil, mechanical, and industrial engineering. Promote interdisciplinary research that addresses significant challenges in the allocation and management of resources across diverse sectors. Provide a platform for innovative ideas and novel methodologies that can influence policy-making and operational practices. Encourage the integration of technology and analytical tools in decision-making processes to enhance effectiveness and efficiency within the fields of computer, electrical, civil, mechanical, and industrial engineering. Scope The scope of JRMDE encompasses a wide array of topics within resource management and decision engineering, including but not limited to: Theoretical models and practical applications of decision analysis across computer, electrical, civil, mechanical, and industrial engineering. Resource allocation policies and strategies pertinent to various engineering disciplines. Sustainability and environmental impact assessments in resource management, with applications in civil and environmental engineering. Technological advancements and their impact on resource planning in computer, electrical, mechanical, and industrial engineering. Ethical considerations and societal impacts of decision-making processes in engineering fields. Fields and Areas Covered JRMDE covers a broad spectrum of fields and areas, reflecting the interdisciplinary nature of resource management and decision engineering: Business and Economics: Corporate decision-making processes, economic planning, and financial impacts of resource management strategies. Environmental Sciences: Environmental policy, natural resource management, and strategies for sustainable development. Engineering and Technology: Computer Engineering: Development of algorithms and software for decision support systems, and the role of artificial intelligence in resource management. Electrical Engineering: Optimization of electrical grids, smart grid technologies, and energy resource management. Civil Engineering: Infrastructure planning, construction resource allocation, and sustainable urban development. Mechanical Engineering: Efficiency improvements in mechanical systems, manufacturing resource management, and automation technologies. Industrial Engineering: Process optimization, supply chain management, and integration of industrial systems for resource efficiency. Public Policy and Administration: Governance of resources, public health decision-making, and infrastructure planning. Healthcare and Life Sciences: Resource allocation in healthcare, biomedical decision support systems, and public health logistics. Data Science and Analytics: Big data applications in decision-making, predictive analytics for resource management, and machine learning techniques.
Data-Driven Energy Efficiency Assessment and Environmental Performance Optimization in Industrial Systems
Industrial energy use accelerates environmental damage and increases greenhouse gas emissions. This data-oriented approach requires the collaboration of energy efficiency and environmental performance. The objective of this study was to design a data-driven methodology to evaluate energy efficiency and enhance environmental performance in industrial systems. Suppose we analyzed a fictitious dataset of 1,250 monthly observations from 125 industrial facilities. In our study, we would have focused on the following indicators: energy consumption, production output, electricity costs, fuel consumption, CO₂ emissions, operating hours, ambient temperature, and production intensity. Energy efficiency was defined as the ratio of production output to total energy consumption, while CO₂ emissions intensity represented the measure of environmental performance. Actually, predicting energy consumption and environmental performance of a facility with Random Forest, XGBoost and Support Vector Regression models was undertaken alongside feature-importance and multi-objective optimization. It was observed that average energy consumption was 4,820 MWh per facility per year and average CO₂ emission intensity was 0.74 tCO₂/MWh. XGBoost was better than all others, with an R2 of 0.91 and RMSE of 0.18, when it came to predicting energy consumption. Feature-importance analysis showed that production intensity (31.4%), operating hours (22.7%), equipment efficiency (18.5%), and ambient temperature (11.2%) were the most influential factors affecting energy consumption. An optimization analysis showed that the energy needs for production could be lowered by 13.6% with the better scheduling of production coupled with improved equipment and the better management of the production equipment’s energy dissipating needs. Improving scheduling and management of production equipment would further lower CO₂ emissions by 15.2% without significantly decreasing the production output. Integrating predictive analytics with the assessment of environmental performance is useful to industrial managers to understand opportunities to decrease energy consumption and develop operational strategies that sustain energy consumption.
Copyright (c) 2026 Mohammad Ali Niroomand, Rojeen Mamershafai, Fereshteh Khalili Peykani, Omid Shabkhizan (Author); Meysam Mahmoudi https://creativecommons.org/licenses/by-nc/4.0
Developing an Integrated Fuzzy Model for Ranking the Factors Causing Delay-Related Claims in Industrial Projects
This study aimed to develop an integrated fuzzy decision-making model for identifying and ranking the factors causing delay-related claims in industrial projects based on their probability of occurrence and effects on project time, cost, and quality. This applied, quantitative, cross-sectional study was conducted among 80 managers and specialists involved in industrial projects in Assaluyeh, Iran. Following a review of the literature and expert evaluation, 66 preliminary delay-related claim factors were identified and subsequently reduced to 48 factors. Data were collected using structured expert questionnaires assessing the probability of occurrence and the time, cost, and quality impacts of the identified factors. The analytical procedure combined statistical screening, probability-impact analysis, fuzzy evaluation, and multi-criteria decision-making. The relative importance of project-performance dimensions was determined through pairwise comparison, and the final prioritization of factors was performed using an integrated AHP-TOPSIS framework. Fuzzy logic was employed to accommodate uncertainty and imprecision in expert judgments. The final AHP-TOPSIS analysis assigned relative importance coefficients of 0.677, 0.674, and 0.655 to time, cost, and quality impacts, respectively. The highest-ranked factor was submission of an unrealistically low tender price solely to win the contract, followed by reduced foreign-contractor investment due to economic instability, prolonged activation of letters of credit, insufficient managerial focus caused by simultaneous involvement in multiple projects, and inadequate financial resources or diversion of project funds. Delayed contractor payments, governmental procurement regulations, delayed engineering responses, inaccurate estimation of work quantities, and delayed managerial problem resolution were also among the ten highest-ranked factors. The findings further showed that several high-priority factors simultaneously influenced more than one project-performance dimension, confirming the multidimensional nature of delay-related claims. Delay-related claims in industrial projects are primarily associated with interconnected contractual, financial, managerial, engineering, and procurement deficiencies, and the integrated fuzzy AHP-TOPSIS model provides a systematic mechanism for identifying and prioritizing these factors to support proactive risk and claim management.
Copyright (c) 2026 Maziyar Morshedi (Author); Mohammad Ali Dashti Rahmatabadi https://creativecommons.org/licenses/by-nc/4.0
Designing an Organizational Decision-Making Mechanism in Iran’s Oil and Gas Industry: A Behavioral Game Theory Approach
The present study investigates organizational decision-making mechanisms and strategic contracts in Iran’s oil and gas industry through the lens of behavioral game theory. Given the complexity and significance of decision-making in this industry, the study was conducted using a qualitative, exploratory approach. Data were collected through observation, in-depth interviews with experts in Iran’s oil and gas industry, and simulations of managerial decision-making through experimental tests administered to students at universities in Tehran. The findings indicated that improving the decision-making process requires appropriate infrastructures for selecting qualified individuals, establishing suitable mechanisms at both micro and macro levels, and presenting information within an appropriate framework as structural controls. In addition, three behavioral dimensions were identified as behavioral controls: continuous vigilance during decision-making, accurate understanding and interpretation of relevant narratives and texts, and the resolution of conflicts of interest at personal and organizational levels. The results demonstrate that, given the complex and sensitive nature of decision-making in strategic contracts, the application of this model can significantly enhance the efficiency and effectiveness of managerial decisions and strengthen overall organizational performance.
Copyright (c) 2026 Mojtaba Asgharnia (Author); Vahid Mahmodi; Ezzatollah Abbasian (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing Multi-Criteria Decision-Making Models for the Selection of Organizational Managers: A Case Study of the Pars Special Economic Energy Zone Organization
The primary objective of this study was to design and validate a fuzzy multi-criteria decision-making model for selecting managers in the Pars Special Economic Energy Zone Organization. The significance of this issue arises from the sensitivity and complexity of the oil, gas, and petrochemical industries, in which managerial decisions have far-reaching consequences for safety, productivity, and the environment. Traditional methods of managerial selection are unable to adequately address the needs of this industry because of their subjectivity and insufficient transparency. Following a systematic review of the research literature and the identification of 42 preliminary criteria, the fuzzy Delphi technique was applied, and the opinions of 18 organizational experts were obtained. Consequently, 5 main criteria and 22 final subcriteria were confirmed. The criteria were weighted using the fuzzy analytic hierarchy process based on Buckley’s (1985) approach. The results indicated that “knowledge and expertise” (0.283), “managerial skills” (0.245), “personality traits” (0.215), “work experience and professional background” (0.142), and “vision and attitude” (0.115) were ranked in descending order of importance. In the next stage, to validate the model in a controlled simulated scenario, five hypothetical candidates were evaluated by three senior decision-makers and ranked using fuzzy TOPSIS and fuzzy ELECTRE. The results obtained from the two methods were fully consistent, and Candidate A2 was identified as the most suitable option. The sensitivity analysis demonstrated the high robustness of the model, as ±20% variations in the weight of each main criterion, accompanied by the proportional redistribution of the remaining weights, did not affect the final ranking. The findings indicate that an integrated fuzzy approach can serve as an effective tool for increasing the transparency and systematicity of the managerial selection process in Iran’s energy industry.
Copyright (c) 2026 Naser Saboori Nezhad; Saber Ghasemi, Shirmohamad Bahador, Seyed Hadi Sajjadi (Author) https://creativecommons.org/licenses/by-nc/4.0
Central Asia Oil and Gas Transfer to China, Cooperative Game Theory Approach
International institutions project a substantial increase in China's demand for oil and gas in the coming years. To secure its energy supply, China has pursued a strategy of diversifying import sources. Landlocked Central Asian nations and energy-rich Iran aim to capitalize on this demand by expanding their export capabilities. This study employs cooperative game theory to analyze the bargaining power of countries in potential oil and gas transmission scenarios from Central Asia and Iran to China through pipeline. By developing future scenarios and evaluating their economic feasibility, we assess the bargaining power of each country using the Shapley value. Our findings indicate that a country's geographic location and production capacity are key determinants of its bargaining power in energy geopolitics. Additionally, if Iran significantly increases its production and export capacity, and its pipeline network connects with Turkmenistan's, it could economically justify joining the coalition for natural gas exports through the Central Asia-China pipeline by supplying approximately 12 billion cubic meters of natural gas annually.
Copyright (c) 2026 Touraj Dehghani https://creativecommons.org/licenses/by-nc/4.0
Designing a Political Marketing Management Model
This study aimed to develop a comprehensive, context-sensitive model of political marketing management grounded in the institutional, cultural, communicative, and ethical characteristics of Iran’s political environment. This qualitative study was conducted within an interpretive-constructivist paradigm using an inductive approach. Data were collected through in-depth, semi-structured interviews with 10 experts, including university professors, political strategists, and former members of parliament. Participants were selected purposively based on their theoretical knowledge and practical experience in political and electoral affairs. The interview data were analyzed using Braun and Clarke’s six-phase thematic analysis method. Data management and coding were performed in MAXQDA software through open, axial, and selective coding. The analytical process included repeated familiarization with the transcripts, generation of initial codes, categorization of conceptually related codes, review and refinement of themes, definition of organizing and global themes, and development of the final conceptual model. The analysis generated 33 open codes that were integrated into six organizing themes: structural and institutional ecology, voter identity dynamics, social capital and the authority of traditional networks, localized political marketing-mix and brand architecture, organizational agility and media defense, and ethical and normative governance. These themes were subsequently organized into three overarching dimensions. The analytical-cognitive dimension comprised environmental intelligence and indigenous voter analysis; the design-strategic dimension incorporated value architecture, political branding, and strategic configuration; and the executive-normative dimension encompassed agile campaign operations, rapid media response, performance monitoring, and ethical governance. The resulting model indicated that political marketing management is shaped by reciprocal relationships among institutional constraints, identity-based voter behavior, traditional reference networks, localized communication channels, organizational responsiveness, and binding ethical principles. Effective political marketing management requires more than short-term promotional techniques. It depends on indigenous voter segmentation, credible relationships with traditional networks, verifiable political promises, agile campaign structures, rapid responses to misinformation, financial transparency, and strict adherence to ethical standards. Ethics functions as a strategic mechanism for protecting social capital and ensuring the long-term sustainability of the political brand.
Copyright (c) 2026 Maryam Sayedan Ghaleh Jourtan (Author); Mohammad Reza Dalvi; Zahra Dashtlaali (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing a Risk Management Model for Power Plant Maintenance Using a Grounded Theory Approach
Maintenance and repair processes in thermal power plants continually face serious risk management challenges because of equipment complexity and the high-risk nature of operations. The present study aimed to develop a comprehensive risk management model in this field to enhance reliability and improve managerial decision-making. In terms of purpose, this study was applied, and methodologically, it employed a qualitative grounded theory approach based on the Strauss and Corbin framework. Data were collected through semi-structured interviews with 14 academic and industry experts in the thermal power plant sector and analyzed in MAXQDA using the three stages of open, axial, and selective coding. The findings resulted in the identification of 122 initial codes and 41 key components, which were ultimately explained within a paradigmatic model comprising six main categories: causal conditions, contextual conditions, the central phenomenon, intervening conditions, strategies, and consequences. By explaining the actions and interactions among technical, behavioral, structural, and environmental factors, the resulting model provides a comprehensive perspective on the logic of risk management within the indigenous context of Iran’s power generation industry. The results not only enrich the risk management literature but also provide an operational framework that can assist power plant managers in designing effective risk management systems and optimizing resource allocation.
Copyright (c) 2026 Mehdi Baghandeh (Author); Arshad Farahmandian; Firouzeh Haj Ali Akbari, Mohammad Hassan Abdollahi (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing an Intelligent Decision-Support Model for Risk and Delay Management in Industrial Projects Using an Integration of Monte Carlo Simulation and Machine Learning in Dynamic Uncertainty Environments
In recent years, industrial projects have faced increasing complexity, uncertainty, and interdependence among project activities. According to international project management reports, approximately 60% to 80% of large-scale industrial projects experience schedule delays, and nearly 45% encounter cost overruns of more than 20% compared with the initial budget. The objective of this study is to design an intelligent decision-support model for predicting and managing risk and delay in industrial projects by integrating Monte Carlo simulation and machine learning algorithms. In this study, performance data from previous projects, including activity schedules, resource consumption levels, cost variations, and risk indicators, are first collected. Then, using Monte Carlo simulation, more than 10,000 probabilistic project execution scenarios are generated to model uncertainty in time and cost from a probabilistic perspective. Subsequently, machine learning algorithms, such as Random Forest and Gradient Boosting, are applied to identify delay patterns and predict schedule deviation with a targeted accuracy of approximately 85% to 92%. The expected results indicate that the proposed hybrid model can improve the accuracy of project delay prediction by up to 30% compared with traditional project planning methods, including CPM/PERT, and reduce the mean error in estimating project completion time from approximately 18% to less than 10%. Furthermore, the model is capable of identifying critical risks at least 2 to 4 weeks earlier than conventional methods, which can play an important role in reducing additional costs and improving resource productivity.
Copyright (c) 2026 Ehsan Najafi Chimeh https://creativecommons.org/licenses/by-nc/4.0
A Comparative Study of Residential Satisfaction Indicators in Oil Company Towns: A Case Study of Newside and Naft Town, Ahvaz
Oil company towns are among the settlements in which housing, in addition to its residential function, is linked to organizational structure, spatial order, and the social relations of residents. Therefore, evaluating residential satisfaction in these settlements requires simultaneous attention to the physical, social, perceptual, and managerial dimensions of residential quality. The present study aims to analyze the components affecting residential satisfaction in the oil company towns of Ahvaz by comparing two residential complexes, namely Newside and Naft Town. The conceptual framework of the study was developed based on human–environment relationship theory, and residential quality was examined across five dimensions: spatial identity and attachment, social and ethical values, functionalism and quality of life, dynamism and physical quality, and urban management and governance. In terms of purpose, this research is applied, and in terms of method, it is quantitative–qualitative. Data were collected using a researcher-made questionnaire based on cluster sampling among 384 residents of the two neighborhoods. The results showed that Naft Town is at a high level in the components of spatial identity and social values, and at a moderate level in the physical, functional, and managerial components. In contrast, Newside is below the moderate level in all components, with its weaknesses being particularly evident in dynamism and physical quality, functionalism, and urban management. The comparison of the two neighborhoods indicates that residential satisfaction in oil company towns is not merely a function of the physical characteristics of housing units, but also depends on spatial legibility, the quality of neighborhood relations, the possibility of developing a sense of belonging, access to services, and the efficiency of urban management. Therefore, achieving desirable housing in these settlements requires a multidimensional and context-sensitive approach to housing planning and design.
Copyright (c) 2026 Amin Kamali (Author); Akbar Mousaeijoo; Ahmad Reza Kaboli, Seyedeh Sedigheh Mirgozar Langaroudi (Author) https://creativecommons.org/licenses/by-nc/4.0
Development of an intelligent framework for semantic road segmentation using depth and camera motion constraints: An approach for resource management and engineering decision making in transportation
One of the major challenges in semantic segmentation of road videos is to effectively exploit the 3D structure of the scene and camera motion, while only 2D pixel labeling is available. In this paper, we introduce a semi-supervised learning framework for semantic segmentation that exploits geometric constraints imposed by depth and camera motion in the video. In this approach, for each 15-frame sequence, only the first frame is manually labeled pixel-wise, and for subsequent frames, pseudo-labels are generated by “geometrically mapping” the labeled frame mask to other frames. To estimate monocular depth and relative camera motion between frames, the Monodepth2 model is used, which performs self-supervised learning of depth and camera motion using new view reconstruction and photometric reprojection error. Based on the estimated depth and motion, each pixel from the reference frame mask is mapped to 3D space and then to the destination frame, and by comparing the mapped depth and the estimated depth in the destination frame, a validation mask is constructed to refine the pseudo-labels. These pseudo-labels are used for semi-supervised training of the same LR-ASPP segmentation network. Evaluation on the KITTI-STEP dataset shows that the semi-supervised model based on geometric mapping (SSL-Warp) achieves mIoU of 25.73% and pixel accuracy of 71.42%, providing a slight improvement in performance over the baseline frame-only model. Analysis of the results shows that the depth and camera motion constraints are very useful for static classes such as road and vegetation, but for objects with independent motion, they require combination with explicit motion information.
Copyright (c) 2026 Mohammad Mohammadi https://creativecommons.org/licenses/by-nc/4.0
An Infrastructure Model for Lean Banking Human Resource Management in State-Owned Banks
In an era of intensifying competition and rapid technological transformation, human resource management within the national banking system faces significant challenges related to productivity, service quality, and the elimination of process waste. Despite the importance of lean thinking in enhancing organizational performance, a substantial research gap exists regarding the application of this approach in the human resource management domain of state-owned banking institutions. The present study was conducted with the aim of developing an infrastructure model for lean banking human resource management in the country's state-owned banks. This research employed a qualitative approach and qualitative content analysis method within the framework of the naturalistic paradigm. The statistical population consisted of 20 banking and academic experts who were selected through purposive sampling, and the sample size was determined based on the principle of theoretical saturation. Data were collected through semi-structured and in-depth interviews, with an average duration of 45 to 75 minutes. Data analysis was conducted using thematic analysis and MAXQDA Version 12 software through three stages of open, axial, and selective coding. The data analysis process resulted in the extraction of 189 basic themes, 41 organizing themes, and 10 overarching themes. The identified overarching themes included participatory culture (34 basic themes), technology in banks (31 basic themes), strategy and objectives in banks, banking laws and regulations, employee selection and recruitment in banks, employee empowerment, performance management in banks, motivation and communication in banks, banking compensation management, and organizational design of banks. The findings indicate that the infrastructure model of lean banking human resource management possesses a multidimensional and complex nature that extends beyond the traditional functions of recruitment and compensation. The successful implementation of this model requires the simultaneous alignment of organizational culture, technological infrastructure, legal systems, and human resource strategies. The results of this study can be utilized in designing administrative transformation programs, redesigning human resource processes, and enhancing the effectiveness of state-owned banks.
Copyright (c) 2026 Reza Zarei Einalghasi, Reza Taghvaei, Reza keyhanihekmat , Alireza Eslambolchi , Javad Nik Nafas (Author) https://creativecommons.org/licenses/by-nc/4.0
The Role of Moral Responsibility in Decision-Making Models Inspired by Islamic Religious Traditions
Despite the proliferation of ethical decision-making frameworks in contemporary management literature, the integration of religiously grounded moral responsibility remains underexplored, particularly within Islamic traditions. This paper addresses this lacuna by developing a comprehensive conceptual model of decision-making that foregrounds the Islamic conception of moral responsibility (al-mas’uliyyah al-akhlaqiyyah). Drawing on an integrative review of Islamic theology, jurisprudence, and business ethics literature, the study synthesises foundational Islamic principles—amanah (trust), taklif (moral obligation), khilafah (vicegerency), shura (consultation), and maqasid al-shari’ah (higher objectives of Islamic law)—into a phased, processual framework termed the Islamic Moral Responsibility-Driven Decision-Making (IMR-DM) model. The model comprises four interconnected stages: ethical intentionality and framing, deliberative consultation, maqasid-based assessment, and post-decision moral reflection. A qualitative conceptual analysis was conducted using primary Islamic sources and secondary scholarly works, guided by an interpretivist epistemology. The findings reveal that Islamic moral responsibility operates as a dynamic, spiritually informed meta-norm that permeates every phase of decision-making, embedding a transcendent accountability absent in secular models. The paper contributes to the fields of Islamic business ethics, religious-based management, and ethical decision-making by providing a theoretically robust and practically adaptable framework. It also highlights how the vertical dimension of divine accountability, combined with the horizontal obligations of communitarian welfare, offers a unique alternative to purely rational-choice or stakeholder-centric models. Implications for organisational practice, leadership development, and corporate governance in Islamic contexts are discussed, along with the challenges of operationalising such a model in pluralistic environments. The paper concludes by outlining a research agenda for empirical testing and cross-cultural comparative studies.
Copyright (c) 2026 Zeinab Sedaghatian https://creativecommons.org/licenses/by-nc/4.0
Presenting a LARG Supply Chain Model with a Hybrid Approach in the Poultry Industry in Northern Iran
The aim of the present study is to present a LARG supply chain model with a hybrid approach in the poultry industry in northern Iran. In terms of purpose, the present study is applied, and in terms of data nature, it was conducted using a mixed-methods approach (qualitative–quantitative). The qualitative section was carried out using a multiple fuzzy Delphi analysis approach, both deductively through a systematic review of the theoretical literature and inductively through semi-structured interviews with experts. The participants in the qualitative section included 20 university faculty members and senior managers of poultry industries, who were selected through non-probability theoretical sampling. Interpretive Structural Modeling (ISM) was used to analyze the data. The qualitative findings showed that the final indicators were classified into four main paradigms of the LARG supply chain, namely lean, agile, resilient, and green. To calculate the final weight of each indicator, the fuzzy Analytic Network Process (fuzzy ANP) method was used, and the results showed that just-in-time production, information technology, and continuous improvement obtained the highest final weights among the LARG supply chain indicators, respectively. The DEMATEL results indicate that, in the poultry industry, the lean paradigm has the highest importance with a weight of 35%, reflecting the industry’s focus on cost reduction and efficiency improvement. The agile and resilient paradigms ranked next, whereas the green paradigm has received comparatively less attention in this industry.
Copyright (c) 2026 Kaveh Kafili Kasmaei (Author); Arshad Farahmandian; Homa Doroudi, Nabiollah Mohammadi (Author) https://creativecommons.org/licenses/by-nc/4.0
Sustainable Resource Management in Nanofluid Cooling Systems for Electronic Boards: A Systematic Review
The present study aimed to systematically review and synthesize the available evidence regarding sustainable resource management in nanofluid cooling systems for electronic boards, with a particular focus on thermal performance enhancement, energy efficiency, resource optimization, environmental sustainability, and future technological development. This study was conducted as a systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework. A comprehensive search was performed in major scientific databases, including Scopus, Web of Science, IEEE Xplore, ScienceDirect, SpringerLink, Wiley Online Library, and Google Scholar, covering publications from 2010 to 2025. The research team consisted of six researchers from Tehran, Iran, who independently participated in literature screening, quality assessment, and data extraction. A total of 1,284 records were initially identified. After duplicate removal and eligibility assessment, 126 studies met the predefined inclusion criteria and were included in the final synthesis. Data were extracted using a structured review form and evaluated through descriptive and thematic synthesis methods. Methodological quality was assessed using the Mixed Methods Appraisal Tool (MMAT). The findings revealed that nanofluid cooling systems consistently outperformed conventional cooling fluids across diverse electronic thermal management applications. Hybrid nanofluids demonstrated the highest thermal conductivity enhancement (up to 41.3%) and heat transfer improvement (up to 36.8%), particularly in direct liquid cooling and battery thermal management systems. Graphene-based and carbon nanotube-based nanofluids showed superior performance compared with many traditional metal oxide formulations. More than 90% of reviewed studies reported significant improvements in cooling efficiency, while 70.6% identified reductions in energy consumption. Furthermore, 77.8% of studies documented reductions in thermal hotspot formation, and 72.2% reported improved system reliability. The evidence also suggested that nanofluid technologies contribute to sustainable resource management through enhanced equipment lifespan, optimized resource utilization, and improved lifecycle performance. However, challenges related to nanoparticle stability, environmental impacts, and large-scale implementation remain important considerations.
Copyright (c) 2026 Sadegh Rasoul Ahari, Mohammad Ali Niroomand, Meysam Mahmoudi (Author); Mahdi Rezaei https://creativecommons.org/licenses/by-nc/4.0
Identifying Components and Indicators of Social Media Usage Among Bank Users Based on the Consequences Element of Means-End Chain Theory
The present study aimed to validate the components and indicators of social media usage among bank users based on the consequences element of the Means–End Chain (MEC) theory (attributes, consequences, and values). The expansion of social media and the transformation of user behavior have compelled banks to utilize these platforms to establish effective interactions, enhance customer satisfaction, and create value for customers. Despite extensive research on technology adoption and social media usage, the components and indicators of the Means–End Chain theory have not yet been comprehensively developed within the context of banking users. In terms of purpose, this study is applied research, and in terms of methodology, it is a quantitative descriptive-survey study. The statistical population consisted of social media users of Bank Sepah across the four regions of Tehran Province. Using Cochran’s formula, a sample size of 384 participants was determined, and 362 valid questionnaires were ultimately collected and analyzed. Data were gathered through a researcher-developed questionnaire based on the dimensions of the Means–End Chain theory, consisting of 86 items measured on a five-point Likert scale. The validity of the instrument was confirmed through content validity, confirmatory factor analysis, and convergent and discriminant validity assessments. Reliability was also verified using Cronbach’s alpha and composite reliability coefficients. Data were analyzed using SPSS and SmartPLS software through Structural Equation Modeling (SEM). The findings indicated that the chain of “Attributes → Consequences → Values” was significant in explaining bank users’ social media usage behavior. The effect of perceived attributes on usage consequences was confirmed with a path coefficient of 0.532, while the effect of consequences on perceived values was confirmed with a path coefficient of 0.647. The most influential attributes included ease of access to services, up-to-date information, and information transparency. Furthermore, time savings, reduced need for in-person visits, and increased satisfaction with the bank were validated as the most important consequences of social media usage. At the value level, feelings of security, trust in the bank, and overall satisfaction with the experience were found to be the most important values for users. Therefore, banks can utilize the Means–End Chain framework to design more effective social media management strategies and foster sustainable customer relationships.
Copyright (c) 2026 Amirhossein Tavaf (Author); Reza Ebrahimzadeh Dastjerdi; Mehrdad Sadeghi, Mehraban Hadi Peykani (Author) https://creativecommons.org/licenses/by-nc/4.0
Integrated Queue Management and Service Composition for Enhancing Manufacturing-as-a-Service Performance
This study aimed to develop and empirically evaluate an integrated framework examining the effects of integrated queue management and service composition capability on Manufacturing-as-a-Service (MaaS) performance, while assessing the enabling role of technological support factors within service-oriented manufacturing environments. This applied quantitative study employed a descriptive-correlational design using structural equation modeling. The statistical population consisted of manufacturing managers, industrial engineers, production planners, and digital manufacturing specialists working in manufacturing organizations in Tehran, Iran. A total of 384 participants were selected through stratified random sampling. Data were collected using a researcher-developed questionnaire measuring integrated queue management, service composition capability, technological support factors, and MaaS performance. Content validity was confirmed by an expert panel, while construct validity was assessed through confirmatory factor analysis. Reliability was established using Cronbach’s alpha, composite reliability, and average variance extracted indicators. Data analysis was performed using SPSS 27 and SmartPLS 4. The measurement model demonstrated satisfactory reliability and validity, with Cronbach’s alpha values ranging from 0.887 to 0.925, composite reliability values between 0.909 and 0.938, and average variance extracted values exceeding 0.50 for all constructs. Structural equation modeling revealed that integrated queue management had a significant positive effect on MaaS performance (β = 0.421, t = 8.974, p < 0.001). Service composition capability also exerted a significant positive effect on MaaS performance (β = 0.366, t = 7.852, p < 0.001). Technological support factors significantly influenced MaaS performance directly (β = 0.214, t = 4.986, p < 0.001) and indirectly through their effects on integrated queue management (β = 0.492, t = 9.843, p < 0.001) and service composition capability (β = 0.547, t = 11.308, p < 0.001). The proposed model explained 68.7% of the variance in MaaS performance (R² = 0.687), indicating substantial explanatory power and predictive relevance. The findings demonstrate that integrated queue management and service composition capability are critical drivers of Manufacturing-as-a-Service performance, while technological support factors provide the essential infrastructure enabling their effectiveness.
Copyright (c) 2026 Mina Ravanesh; Emad Roghanian (Author) https://creativecommons.org/licenses/by-nc/4.0
Designing an Effective Model of Virtual Education Using Artificial Intelligence in the Educational System of Islamic Azad Universities of West Azerbaijan Province
Higher education in the twenty first century has undergone extensive technological transformations. One of the most significant of these changes is the expansion of virtual education and its integration with modern technologies such as artificial intelligence. Virtual education is no longer merely an alternative to face to face instruction; rather, it has become a strategic approach within educational systems that can enhance learning quality, educational equity, and equal access to opportunities. Accordingly, the present study was conducted with the aim of designing an effective model of virtual education using artificial intelligence within the educational system of Islamic Azad Universities in West Azerbaijan Province. This research employed a sequential exploratory design, meaning that qualitative data were first collected and analyzed to identify the key dimensions and components. In the qualitative phase, the statistical population consisted of 15 experts in virtual education, educational technology, and key administrators of the Azad Universities in the province. Participants were selected based on criteria such as specialized knowledge, practical experience, credible research background, familiarity with the challenges and opportunities of virtual education, and the ability to provide analytical insights. Purposeful and snowball sampling methods were used until theoretical saturation was achieved. Data were collected through semi structured interviews and analyzed using thematic analysis with MAXQDA 11 software. The coding process consisted of three stages: open coding, axial coding, and selective coding. Ultimately, seven main dimensions of AI based virtual education effectiveness were identified: educational, technological, psychological, social, managerial, human, and organizational. Overall, the findings indicate that the effectiveness of AI based virtual education requires strategic synergy between data driven management, sustainable technological infrastructure, human capital empowerment, and organizational support. The proposed model can serve as a valid framework for the design, evaluation, and improvement of intelligent virtual education in Iranian universities.
Copyright (c) 2026 Mehdi Tabarok Soreh (Author); Taghi Mohammadi https://creativecommons.org/licenses/by-nc/4.0
Designing an Exploitation-Oriented Digital Innovation Model Based on the Maximum Functional Performance of Mining Industries
The purpose of this study was to design an exploitation-oriented digital innovation model for Iran’s mining industries, with an emphasis on improving performance, sustainability, and competitiveness. The research adopted a mixed-methods qualitative–quantitative grounded theory approach. The literature review indicated that international models, due to infrastructural and cultural differences, do not fully meet the needs of Iran’s mining industry, highlighting the need for an indigenous model. The statistical population of the qualitative phase consisted of 15 experts and managers in the mining industry, who were selected through purposive sampling based on the criteria of expertise, experience, and diversity. Data were collected through semi-structured interviews and analyzed using grounded theory analysis. In the quantitative phase, data from 220 questionnaires distributed among managers and experts of mines across the country were collected through stratified sampling and analyzed using structural equation modeling (SEM). The qualitative results identified weak technological infrastructures, the absence of an innovation culture, and the lack of data-driven governance frameworks as key barriers. The key factors identified included managerial support, transformational leadership, technology localization, and the integration of managerial software systems. The results of this study show that strengthening human capital, data-driven decision-making, and macro-level policymaking can provide the foundation for achieving the highest level of exploitation, profitability, and sustainability across the entire mining value chain. The quantitative findings confirmed the validity and fit of the proposed model and extracted operational strategies for developing digital innovation. This study provides a novel basis for policymaking and the development of an indigenous digital transformation model in Iran’s mining industry.
Copyright (c) 2026 Samira Rahmani Shiviari, Younos Vakil Alroaia (Author); Seyed Hossein Hosseini; Mohammad Reza Rostami (Author) https://creativecommons.org/licenses/by-nc/4.0
A Hybrid Statistical–MCDM Framework for Integrated Risk Prioritization and Sustainability Assessment in Construction Supply Chains: Bridging Local Data with Global Theory
This study aimed to develop and empirically validate a hybrid statistical–multi-criteria decision-making (MCDM) framework for identifying, prioritizing, and managing sustainability-related risks and strategic interventions in construction supply chains within the context of the Qeshm Free Trade Zone. A practical descriptive–inferential research design was employed using a structured Delphi survey administered to 33 experts, including project managers, engineers, contractors, consultants, and supply chain professionals involved in major construction projects in Qeshm. Following expert consensus, a questionnaire measuring economic, social, environmental, and structural risks, as well as key supply chain success drivers, was developed and validated. Reliability was confirmed through Cronbach’s alpha coefficients. One-sample t-tests were conducted to determine statistically significant risk factors. The Analytic Network Process (ANP) was applied to prioritize critical supply chain drivers and capture interdependencies among criteria, while the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used to rank sustainability-oriented management strategies based on multiple performance criteria. The results revealed that economic risks, including supplier financial challenges, material price volatility, exchange-rate fluctuations, transportation costs, and delay-related expenses, were statistically significant. Environmental risks, such as pollution, resource depletion, non-renewable resource usage, and environmental compliance issues, also demonstrated significant effects. ANP results indicated that Material Supply Management was the most influential driver (weight = 0.553), followed by Design and Engineering Management (0.294), Manufacturing and Production Management (0.094), and Workforce Supply and Safety Management (0.059). At the sub-criterion level, provision of high-quality domestic building materials (0.304) and optimal foundation design (0.215) received the highest priorities. TOPSIS analysis identified sustainable supply chain management software (0.8962) and sustainable material selection (0.8664) as the most effective strategic interventions. The integration of statistical analysis, ANP, and TOPSIS provides a robust decision-support framework for sustainable construction supply chain management. The findings demonstrate that economic volatility, environmental pressures, material quality, and engineering effectiveness are dominant determinants of sustainable performance.
Copyright (c) 2026 Mahyar Amin Forghani (Author); Sohail Dadkhah; Mohsen Dadras (Author) https://creativecommons.org/licenses/by-nc/4.0
Explaining the Relationship between Organizational Humanitarianism and Employees’ Social Accountability: The Moderating Role of Employee Self-Leadership
This study seeks to demonstrate how employee self-leadership, as an individual facilitating factor, can moderate and strengthen the effect of organizational humanitarianism on social accountability within Iran Khodro Company. Therefore, the present study aimed to explain the relationship between organizational humanitarianism and employees’ social accountability, emphasizing the moderating role of employee self-leadership in Iran Khodro Company. The present research is descriptive–analytical in nature, applied in terms of purpose, and categorized as a survey study in terms of methodology. The statistical population included all employees of Iran Khodro Company, comprising 10,000 official employees. Among them, 269 individuals were selected through simple random sampling. A total of 300 questionnaires were distributed, of which 190 valid questionnaires were returned. To conduct the study, a 51-item closed-ended questionnaire based on a five-point Likert scale was distributed among employees, and the questionnaire measured the study variables. Content validity was confirmed through expert opinions, construct validity was assessed using confirmatory factor loading analysis, and reliability was verified through Cronbach’s alpha and composite reliability coefficients. After data collection and classification, the data were analyzed using SPSS version 24 and Smart PLS software. The findings indicated that among the variables examined in the present study, employee self-leadership obtained the highest mean score (3.94), whereas organizational humanitarianism obtained the lowest mean score (3.30). The results of structural equation modeling using PLS software demonstrated that organizational humanitarianism had a 55% effect on employees’ social accountability. Furthermore, employee self-leadership moderated the relationship between organizational humanitarianism and employees’ social accountability by 3%.
Copyright (c) 2026 Fatemeh Movahed Nia; Monireh Tahaei, Mahdi Fardi Kalahroudi, Reza Kazemi (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification of the Components of Environmental Innovation, Green Supply Chain Management, Circular Economy Capabilities, and Financial Performance Through Qualitative Content Analysis
The present study aimed to identify and explain the components of environmental innovation, green supply chain management, circular economy capabilities, and financial performance through qualitative content analysis using the multi-grounded theory approach. This study was conducted using a qualitative exploratory design based on multi-grounded theory. The statistical population included university experts, senior managers, sustainability specialists, and experienced professionals in environmental management, green innovation, and supply chain systems associated with airport organizations in Tehran. Participants were selected through purposive and snowball sampling, and sampling continued until theoretical saturation was achieved. Finally, 17 experts participated in semi-structured in-depth interviews. In addition to field interviews, 40 scientific studies published between 2015 and 2025 were systematically reviewed through meta-synthesis to strengthen the theoretical grounding of the extracted concepts. Data analysis was conducted through open, axial, and selective coding using continuous comparative analysis. To ensure trustworthiness, member checking, peer review, rich description, and inter-coder reliability assessment based on Cohen’s Kappa were employed. The findings led to the extraction of a comprehensive qualitative model consisting of causal conditions, contextual conditions, enabling factors, strategies, the central category, and multidimensional consequences. Environmental innovation in the context of green supply chain management emerged as the central category of the model. The main causal conditions included institutional sustainability requirements, organizational environmental responsibility, and competitive green market pressures. Contextual conditions involved green governance, environmental culture, technological infrastructure, and socio-environmental justice. Knowledge-based, technological, digital, and financial capabilities were identified as major enabling factors. Strategic responses included green product design, reverse logistics, green procurement, smart environmental monitoring, and sustainability-oriented governance systems. The consequences of the model included improved financial performance, operational efficiency, green competitiveness, social sustainability, environmental performance, and institutional learning.
Copyright (c) 2026 Javad Shomali Nejad (Author); Kiamars Fathi Hafshejani; Nowrouz Nourollahzadeh (Author) https://creativecommons.org/licenses/by-nc/4.0
Decision-Making Models for Circular Economy Implementation in Waste Management
The transition from linear waste management systems to circular economy (CE) models creates the demand for advanced decision-making frameworks designed to tackle environmental, economic, social, and technical issues. This research presents the systematic literature review on decision-making models for the application of circular economy in waste management. The review processes the PRISMA framework and studies obtained from Scopus, Web of Science, and Google Scholar. Search terms included circular economy, waste management, sustainability, and MCDM, and were applied to articles from 2020 to 2026. Following the application of predefined criteria and title, abstract, and full text screenings, nine articles were chosen for qualitative synthesis. Studies were found to be moving away from classical MCDM methods, like AHP and TOPSIS, toward fuzzy-set and hybrid complex environmental system decision-making frameworks. The analyzed studies examined a variety of waste types, including medical, electronic, construction, food, organic, and plastic waste. Among these, construction and demolition waste was the most researched category. The most recent studies found that environmental factors, particularly regarding pollution, public health, and sustainability, were increasingly found among the most prominent factors of the decision-making process. Furthermore, the analysis demonstrated that hybrid and uncertainty-oriented models provide more robust and reliable support for circular economy implementation in waste management systems.
Copyright (c) 2026 Amirreza Soleimannezhad Aliabadi; Mohammad Ali Niroomand, Fereshteh Khalili Peykani (Author) https://creativecommons.org/licenses/by-nc/4.0
Sentiment Analysis of Customers in Iran’s Automotive Industry Using the LSTM Method
Given the large volume of customer opinions and complaints regarding automobiles in Iranian virtual platforms, this study aimed to develop an accurate sentiment analysis model based on Long Short-Term Memory (LSTM) neural networks for processing Persian texts. The primary objective was to improve the accuracy of sentiment classification (positive, negative, and neutral) by overcoming the challenges associated with imbalanced data and linguistic limitations. Data were collected from 12 valid online sources, including 4,348 customer comments regarding various automobile models. Three LSTM architectures (single-layer, two-layer, and three-layer) were implemented and compared using fixed parameters (Embedding = 128, Hidden Units = 64, Dropout = 0.3). To enhance performance, Data Augmentation, the SMOTE algorithm, and FastText word embeddings were employed. The three-layer LSTM model outperformed the shallower models, achieving a validation accuracy of 62.01%, which increased to 95.45% after applying optimization techniques. The two-layer model demonstrated superior performance in identifying positive comments, whereas the three-layer model showed a more balanced performance across all sentiment classes. The issue of overfitting in simpler models was mitigated through the addition of layers and the use of Dropout. The findings indicated that the two-layer model improved overall accuracy, particularly in recognizing positive sentiments, compared to the single-layer model. Furthermore, the three-layer LSTM model demonstrated greater balance and robustness across all classes. To further improve performance, the three-layer model was retrained using Data Augmentation techniques and the SMOTE algorithm for data balancing. The modified version substantially improved overall accuracy and validation performance.
Copyright (c) 2026 Lida Shojaei Barjouei (Author); Ghasem Bakhshandeh; Mahmood Daniali Deh houz (Author) https://creativecommons.org/licenses/by-nc/4.0
Presenting a Green Government Model for Renewable Energy Development in Iran’s Electricity Industry
The development of renewable energy in Iran’s electricity industry faces numerous structural, institutional, and policy-related barriers, among which the absence of a coherent and domestically adapted governance framework is one of the most significant. The present study aimed to design and validate a green government model for renewable energy development in Iran’s electricity industry. The study employed an exploratory sequential mixed-methods approach grounded in the pragmatist paradigm. In the qualitative phase, an interpretive approach and Braun and Clarke’s thematic analysis method were applied. Data were collected through semi-structured interviews with 20 experts, and sampling continued until theoretical saturation was achieved. In the quantitative phase, based on statistical power calculations conducted using G*Power, a sample of 188 experts and managers from the electricity industry was selected. The data were analyzed through variance-based structural equation modeling using SPSS version 24 and SmartPLS version 3. The findings of the qualitative phase resulted in the extraction of 102 basic themes, categorized into 33 organizing themes and 11 global constructs: green institutional capacity, green energy policymaking, green financing, green economic support, green diplomacy, green energy infrastructure, green technological innovation, green human capital, green social participation, environmental sustainability, and energy security. In the quantitative validation phase, the measurement and structural model fit indices were found to be satisfactory, and all 26 relationships in the model were confirmed with statistically significant path coefficients (p < .001). The strongest causal relationship was observed between green energy policymaking and green financing (β = .836).
Copyright (c) 2026 Mehran Golabkesh (Author); Seyed Ahmad Hosseini Gol Afshani; Hassan Givarian (Author) https://creativecommons.org/licenses/by-nc/4.0
Identification of Key Causes of Disputes in Construction Projects and Preventive Strategies: A Systematic Review
Construction disputes affect costs, timelines, and relationships among stakeholders. Therefore, the purpose of this study to collate the latest strategies for construction dispute resolution from various primary sources, using a systematic literature review method. The author conducted a structured query on Scopus, Web of Science, and Google Scholar for literature published for the years 2023-2026. Overall, the query returned 518 records, which after multiple iterations, were narrowed to 12 studies, which formed the basis of this review. The study identifies five predominant categories of dispute management strategies: preventive strategies, contractual and management strategies, alternative dispute resolution (ADR), technological strategies, and risk and context components. The findings suggest that the literature is moving from a focus on reactive dispute resolution to a focus on proactive dispute prevention, especially in the pre-contract and early phases of a project. The ability to reduce disputes was linked to communication, documentation, and a clearly defined scope. Concern around standardization and contractual risk allocation remains valid given that contractual issues continue to be a source of conflict. In addition, this study validates the increased use of negotiation and adjudication as alternative methods of dispute resolution that provide varying degrees of flexibility and legal certainty. Emerging technologies, including smart contracts and artificial intelligence, have the potential to improve transparency, automate parts of contracts, and address the issues of payment delays and unclear contractual obligations. Additionally, macro-level factors such as regional legal risks and technical uncertainties, particularly ground conditions, were found to significantly influence dispute occurrence.
Copyright (c) 2026 Mohammad Taghi Nazarpour; Negin Kheradmand (Author) https://creativecommons.org/licenses/by-nc/4.0
An Enhanced Hybrid Statistical–Learning Approach for Detecting Fake Feedback in Cloud Environments Using Hierarchical Clustering with Reptile Search Algorithm and Particle Swarm Optimized Extreme Learning Machine
The proliferation of fake feedback in cloud environments represents a major challenge in maintaining user trust and ensuring service quality. Accordingly, the development of intelligent approaches for distinguishing genuine feedback from fraudulent instances has become increasingly important. In this study, a hybrid framework based on statistical analysis and machine learning is proposed for the detection of fake feedback. The framework integrates improved hierarchical clustering using the Reptile Search Algorithm (AHC-RSA), the E-EDA statistical model for anomaly filtering, and an Extreme Learning Machine optimized with Particle Swarm Optimization (ELM-PSO). This integrated approach reduces intra-cluster dispersion, enhances data separability, improves prediction accuracy, and accelerates model convergence. Experimental evaluations conducted on the CloudArmor and Epinions datasets demonstrated that the proposed model achieved accuracies of 99.83% and 99.84%, respectively. The results indicate that the proposed model outperforms SVM, ANN, LSTM, and GRU algorithms in terms of accuracy, stability, and convergence speed. On average, the proposed model shows an improvement of approximately 5% to 8% in accuracy and F1-score, along with a significant reduction in MSE and RMSE compared to the best existing methods. Therefore, the proposed framework can be considered an effective and reliable approach for detecting fake feedback and enhancing trust in cloud services.
clustering with Reptile Search Algorithm
Particle Swarm Optimization-based Extreme Learning Machine
Copyright (c) 2026 Hussein Kadhim Almamoori (Author); Golnaz Aghaee Ghazvini; Ali Albu-Rghaif, Fariba Majidi (Author) https://creativecommons.org/licenses/by-nc/4.0
Investigating the Relationship between Job Characteristics and the Growth of Professional Ethics among Employees of the East Azerbaijan Gas Company
The aim of this study is to investigate the relationship between job characteristics and the growth of professional ethics among employees of the East Azerbaijan Gas Company. For this purpose, job characteristics were defined based on the theory of Richard Hackman and Greg Oldham across five dimensions: skill variety, task identity, task significance, autonomy and freedom, and job feedback. Professional ethics were defined based on the code of ethics of the East Azerbaijan Gas Company. Accordingly, one main hypothesis and five sub-hypotheses were formulated. The statistical population of this study consists of all employees of the East Azerbaijan Gas Company, totaling 380 individuals. The statistical sample size was estimated to be 192 individuals based on the Cochran formula, and participants were selected using stratified random sampling. Data collection tools included two questionnaires: the Hackman and Oldham Job Characteristics Questionnaire and a Professional Ethics Questionnaire based on the code of ethics of the East Azerbaijan Gas Company. After assessing and confirming their validity and reliability, the questionnaires were distributed to the statistical sample. Descriptive and inferential statistical methods were used to analyze the data. Specifically, descriptive statistics were employed for classification, summarization, and interpretation of the data, while the Kolmogorov-Smirnov test and Spearman's correlation test were used at the inferential level to test the research hypotheses. The results of hypothesis testing confirmed all hypotheses, indicating that there is a significant relationship between job characteristics and the growth of professional ethics among employees of the East Azerbaijan Gas Company.
Copyright (c) 2026 Ali Reza Abbasian (Author); Jafar Beikzad https://creativecommons.org/licenses/by-nc/4.0
Explanation and Prioritization of Factors Influencing the Location of Resilient Schools Against Natural Hazards with an Emphasis on Collective Intelligence (Case Study: Nowshahr City)
This study was conducted with the aim of identifying and prioritizing the factors influencing the location of resilient schools in the face of natural hazards. The research adopted a mixed-methods approach, and data were analyzed using the Analytic Network Process (ANP), Delphi method, Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Geographic Information System (GIS). The findings indicated that out of 22 initial items, 14 items were identified as key factors agreed upon by experts, encompassing physical, environmental, safety, accessibility, and socio-managerial dimensions. The results of spatial analysis, combined with collective intelligence algorithms, enabled the identification of safe and high-risk zones, as well as the optimization of evacuation routes and school site allocation. This framework can serve as a practical tool for urban planners, educational administrators, and designers of educational spaces to enhance the resilience and safety of schools.
Copyright (c) 2026 Mehdi Elkaei Behjati (Author); Seyed Ali Seydian; Mehdi MirzaGoltabar Roshan (Author) https://creativecommons.org/licenses/by-nc/4.0
Detection of Attacks in Internet of Things Devices Using an Optimized Ensemble Classification Based on Deep Transfer Learning and the Harris Hawks Algorithm
With the rapid expansion of the Internet of Things (IoT) and the increasing number of cyberattacks targeting these devices, the use of efficient methods for attack detection has become increasingly important. In this study, a novel approach based on ensemble deep transfer learning optimized by the Harris Hawks Optimization (HHO) algorithm is proposed for detecting attacks in IoT devices. In this method, multiple deep transfer learning models are employed, which are capable of transferring learned knowledge from data in other domains to IoT-related data. Subsequently, the Harris Hawks algorithm is utilized to optimize the model parameters and integrate them into an ensemble classifier. The Edge-IIoTset dataset is used to evaluate the performance of the proposed method. The obtained results indicate that the detection accuracy reaches 99.8%, while the false alarm rate is significantly reduced. These findings demonstrate the high effectiveness of the proposed method in enhancing the security level of IoT devices.
Copyright (c) 2026 Farqad Abdullah Mohammed (Author); Saba Joudaki; Mezher H Mezher, Mahdi Mosleh (Author) https://creativecommons.org/licenses/by-nc/4.0
Exploring the Meta-Synthesis Model of the Green Supply Chain in the Power Industry: An Industry 4.0 and Smart Sustainability Approach
The establishment of a green and sustainable supply chain in the power industry particularly in the manufacturing sector of electrical equipment and transmission/distribution facilities plays a vital role in supporting sustainable development and ensuring national energy security. This study aims to analyze and identify the primary and secondary variables of the green supply chain within the framework of Industry 4.0 and smart sustainability in companies producing electrical equipment and infrastructure in Mazandaran Province, Iran. The research employs a meta-synthesis technique combined with the Delphi method. Following the seven-step process of Sandelowski and Barroso (2007), theoretical foundations and prior research published between 2014–2025 (international sources) and 1393–1404 (Persian sources) were systematically reviewed. In total, 248 documents were initially identified, from which 34 core studies remained after a rigorous critical appraisal process. Through open, axial, and selective coding, five main categories and ten axial codes were extracted, including Smart Ecological Design, Digital Green Processes, Data-Driven Waste Management, AI-Based Green Marketing, and Smart Ecological Governance. The axial codes encompass Ecological Product Design, Eco-Friendly Raw Material Procurement, Ecological Production, Green Procurement, Primary and Secondary Waste Management, Ecological Information Exchange, and Green Marketing Contexts, originating from 46 open codes derived during the synthesis phase.
Copyright (c) 2026 Shafagh Shariati (Author); Yousef Gholipour Kanani; Fatemeh Harsej (Author) https://creativecommons.org/licenses/by-nc/4.0