Management Strategies and Engineering Sciences

The natural gas imbalance crisis in Iran has subjected the country's petrochemical industry, as the most profitable economic pillar and the primary source of non-oil foreign currency earnings, to profound technical and economic challenges. This study takes an applied approach with the aim of developing an economic evaluation model. It models and evaluates the option of importing liquefied natural gas (LNG) through Floating Storage Units (FSUs) at southern ports, distributing it in the ultra-cold liquid phase to destination petrochemical complexes via a virtual pipeline (cryogenic rail and road logistics), and performing decentralized regasification at the destination using Ambient Air Vaporizers (AAVs).

The landed cost of LNG ready for overland loading at the Iranian coastal terminal is estimated at approximately USD 13.5 per million British Thermal Units (mmBtu), based on the JKM index and FSU operating tariffs. At the destination, constructing a small-scale regasification facility, comprising AAV vaporizers that require no active energy input along with double-walled strategic storage tanks, adds a cost share of USD 2.0 per mmBtu, bringing the base cost excluding logistics to USD 15.5 per mmBtu.

The financial assessment results show that rail logistics, at a variable cost of USD 0.10 per mmBtu per 100 kilometers, is the most efficient transportation method. In the urea producer scenario (intrinsic value of USD 16.52 per mmBtu), the scheme has a definite and profitable economic justification for the Pardis, Shiraz, and Kermanshah petrochemical complexes. In the methanol producer scenario (intrinsic value of USD 12.65 per mmBtu), although the initial direct profit margin is negatively affected by current import prices, the supply of gas during the critical months of the year is evaluated as a fully justified strategic tool for peak shaving when accounting for the opportunity cost of complex survival, the prevention of severe losses from equipment deterioration and catalyst destruction during winter shutdowns, and the potential for optimizing feedstock consumption processes. Furthermore, given that the current imported LNG price of USD 13.5 per mmBtu is high by historical standards, a reduction in geopolitical instability across the Middle East would bring average spot LNG cargo prices in global markets down to the USD 8 to 10 per mmBtu range, rendering methanol producers profitable as well.

Automatic binary gender-label classification from facial images is a well-established face-analysis task. Although current systems perform well under controlled conditions, their accuracy can deteriorate in the presence of illumination changes, head-pose variation, age differences, facial expression, and partial occlusion. This study addresses the design of a framework that can extract stable facial representations from a limited training set and learn the classifier decision boundary without relying exclusively on gradient-based optimization. The proposed method comprises three components: the frozen convolutional part of an ImageNet-pretrained AlexNet used as a feature extractor, a multilayer perceptron with one hidden layer of 128 neurons used as the classifier, and the Grasshopper Optimization Algorithm (GOA) used to search for the classifier weights and biases. After resizing, illumination normalization, and limited data augmentation, input images are passed through the convolutional backbone, and the Pool5 output is flattened into a 9,216-dimensional feature vector. The features are normalized using parameters estimated exclusively from the training set and are then supplied to the MLP. The main evaluation uses the official identity-disjoint split of GENDER-FERET, comprising 474 training images and 472 test images. The reported results indicate a test accuracy of 98.94%. For the male class, precision, recall, and F1-score are 99.15%, 98.73%, and 98.94%, respectively; for the female class, the corresponding values are 98.73%, 99.15%, and 98.94%. The small difference between class-specific results indicates balanced errors on this split. Nevertheless, the limited dataset size, controlled image acquisition, absence of cross-dataset testing, and the substantial computational cost of directly optimizing more than one million parameters constrain the external validity of the results. From the perspective of reusing deep representations while separating feature extraction from classifier optimization, the proposed framework is a viable approach for further investigation in low-data settings.

The Internet of Things (IoT) comprises many devices and produces huge amounts of data that need efficient methods of resource finding. Resource discovery is required to locate and use devices, sensors, services, and data in IoT networks. Traditional approaches, however, have limitations in terms of scalability, efficiency, and adapting to changing environments. In this study, we present a novel model of resource discovery in IoT based on learning automata for efficiency, scalability, and energy efficiency. The approach integrates the Pastry DHT with Bloom filters for fast and large-scale resource discovery. It uses hashing mechanisms for better identification and learning automata for reinforcement learning to make resource access adaptive to real-time feedback. Large-scale simulation shows great improvement in data access speed, energy consumption, and cost over traditional methods. This paper presents an efficient, scalable, and versatile solution for IoT resource discovery, surmounting key challenges and enabling effective applications in smart homes, smart cities, and industrial automation. This research has important implications for IoT applications that require low latency, scalability, and energy efficiency. For instance, the 17.14% decrease in latency at 60 requests allows for real-time data requests in smart city traffic applications. Likewise, the 40% energy savings at 80 requests allows battery-operated healthcare devices to last longer. In comparison to other methods, such as Liu and Deng (2024), CoAP, and mDNS, the proposed method of mapping data in a hierarchical way achieved the best time efficiency and energy efficiency results, representing a groundbreaking solution for smart cities, healthcare, and any industrial IoT deployments.

This study aimed to examine the effects of lean–green–digital integration on productivity, waste reduction, and sustainable competitive advantage in mechanical manufacturing firms in Tehran. This applied quantitative study used a descriptive-correlational and cross-sectional survey design. The statistical population consisted of managers, production supervisors, industrial engineers, quality-control experts, environmental and safety personnel, supply-chain officers, and digital transformation specialists working in mechanical manufacturing firms in Tehran. Using purposive sampling, 286 complete questionnaires were collected and analyzed. Data were gathered using a structured questionnaire measuring lean manufacturing practices, green manufacturing practices, digital manufacturing capability, lean–green–digital integration, productivity, waste reduction, and sustainable competitive advantage. All constructs were assessed using five-point Likert-scale items. The validity of the measurement model was examined through confirmatory factor analysis, and reliability was evaluated using Cronbach’s alpha and composite reliability. Data were analyzed using descriptive statistics, correlation analysis, and structural equation modeling. The structural model showed that lean–green–digital integration had a significant positive effect on productivity (β = 0.62, p < 0.001), waste reduction (β = 0.66, p < 0.001), and sustainable competitive advantage (β = 0.35, p < 0.001). Productivity had a significant positive effect on sustainable competitive advantage (β = 0.31, p < 0.001), and waste reduction also significantly predicted sustainable competitive advantage (β = 0.27, p < 0.001). The indirect effect of lean–green–digital integration on sustainable competitive advantage through productivity was significant (β = 0.19, p < 0.001), as was the indirect effect through waste reduction (β = 0.18, p < 0.001). The model explained 38% of the variance in productivity, 44% of the variance in waste reduction, and 61% of the variance in sustainable competitive advantage. The findings indicate that lean–green–digital integration is a strategic operational capability that improves productivity, reduces waste, and strengthens sustainable competitive advantage in mechanical manufacturing firms. Integrated implementation of lean practices, environmental practices, and digital manufacturing capabilities can help firms achieve both operational efficiency and long-term competitiveness.

The development of green buildings is considered one of the most effective strategies for reducing energy consumption and greenhouse gas emissions in the building sector. However, the implementation of this approach in developing countries faces multiple barriers across economic, social, and politico-legal dimensions. The aim of this study was to identify, structurally analyze, and prioritize the barriers to the development of green construction in existing buildings, with a focus on energy conservation. To this end, eighteen key barriers were extracted based on a review of previous studies and classified into three main criteria: economic, social, and politico-legal. Subsequently, using the hybrid DEMATEL-based Analytic Network Process (DANP) approach, the causal relationships among the barriers and their final weights were determined. The required data were collected through questionnaires and the opinions of experts in the fields of building engineering and energy, and the analyses were performed using RStudio software. The results of the DEMATEL technique showed that the politico-legal criterion, with a positive causality index (+0.759), was identified as the root and principal influential factor in the system, whereas the economic and social criteria were classified as affected factors. Based on the DANP results, the most important barriers to the development of green buildings were the absence of comprehensive and locally adapted green building standards (19.31%), the lack of binding laws and regulations (14.41%), ineffective supervision of the implementation of building regulations (10.02%), the lack of public acceptance of energy consumption standards (8.51%), and non-specialized implementation and the use of traditional construction methods (8.41%). The findings indicate that economic barriers, including high initial costs, are not the primary causes but are rather consequences of weaknesses in legal frameworks, standards, and supportive policies. Accordingly, reforming regulations and legal requirements, strengthening the construction supervision system, establishing financial incentives, and enhancing public awareness are proposed as the most important policy strategies.

This study develops a novel Genetic Algorithm (GA)-based framework for multi-objective optimization of workshop layouts in dam construction projects, explicitly addressing curved dam geometry, crane operational constraints, and differentiated safety requirements. The proposed approach employs a hierarchical penalty mechanism to enforce constraint feasibility and a three-phase optimization strategy combining global exploration and adaptive genetic operators to improve convergence and solution quality.The framework is validated through a real-world case study of the Divarsh Rudbar Dam in Iran, optimizing the placement of worker accommodation, cement silos, and material storage facilities. The optimized layout achieves a 21% reduction in transportation distance while satisfying all safety and operational constraints. Sensitivity analysis demonstrates the robustness of the solution under variations in safety parameters.The results highlight the effectiveness of the proposed method as a practical decision-support tool and its capability to address highly constrained, real-world engineering optimization problems.

This study aimed to systematically identify and analyze circular economy strategies in the construction industry and develop an integrated strategic framework for enhancing environmental sustainability, improving resource efficiency, and reducing waste generation through the transition from linear to circular construction systems. This research employed a qualitative systematic review design. Relevant scientific articles, policy documents, review studies, and international reports addressing circular economy implementation in the construction sector were identified and selected through a structured literature review process. The collected documents were analyzed using qualitative content analysis. Through iterative coding and thematic synthesis, major strategic categories, implementation barriers, institutional drivers, sustainability outcomes, and managerial implications associated with circular construction were identified and integrated into a comprehensive conceptual framework. The analysis revealed four major thematic dimensions governing the transition toward circular construction. First, macro-level circular strategies were classified into waste prevention, design for durability, design for disassembly, material reuse, advanced recycling, circular supply chains, digital material management, and service-based business models. Second, implementation barriers included information asymmetry, fragmented governance structures, regulatory complexity, insufficient market transparency, and limited institutional coordination, while government support, financial incentives, technological innovation, stakeholder collaboration, and public awareness emerged as key institutional drivers. Third, circular economy implementation demonstrated substantial positive effects on environmental sustainability indicators, including carbon footprint reduction, resource conservation, waste minimization, landfill diversion, improved energy efficiency, and enhanced urban environmental quality. Fourth, the synthesis of findings resulted in an integrated strategic conceptual framework in which digital infrastructure, institutional capacity, economic incentives, and technical strategies operate synergistically to facilitate circular economy adoption and long-term sustainability within the construction industry. The findings indicate that successful implementation of circular economy principles in the construction industry requires an integrated and systemic approach that combines strategic planning, supportive governance, technological innovation, economic incentives, and stakeholder collaboration. Circular construction represents not only an environmental necessity but also a strategic pathway toward sustainable resource management, enhanced competitiveness, and long-term resilience. The proposed conceptual framework provides a practical roadmap for policymakers, managers, and industry stakeholders seeking to accelerate the transition toward a sustainable and circular built environment.

Road traffic accidents remain a critical challenge in road safety, where rapid detection and accurate severity assessment play a vital role in reducing fatalities. This study presents an intelligent system for the automatic evaluation of vehicle accidents based on the EfficientNet-B0 model enhanced with the Convolutional Block Attention Module (CBAM). The primary objective is to overcome the severe class imbalance issue, particularly in the “low-risk” class, and to improve detection accuracy under real-world conditions. The proposed architecture consists of two main modules: a multi-task convolutional neural network for accident detection and severity classification, and a YOLOv8-Nano module for real-time fire and smoke identification. To address data imbalance, a combined strategy involving external data augmentation, oversampling, and an eight-stage training pipeline was employed. Experimental evaluation on the Accident Images Analysis dataset demonstrated that adding the CBAM mechanism led to a 38% improvement in the F1-score of the minority class. The final system achieved an accuracy of 94% in accident detection and 74% in severity classification (with a Macro F1 score of 0.61). Comparison with Pashaei et al. (2020) on the public version of the dataset showed a 4.49% improvement in the Macro F1 metric. The results confirm that integrating attention mechanisms with efficient architectures and smart data management significantly enhances the performance of emergency response support systems.