Benzene is a toxic airborne contaminant and a recognized cancer-causing agent that presents substantial health hazards even at minimal concentrations. The precise prediction of benzene concentrations is crucial for reducing exposure, guiding public health strategies, and ensuring adherence to environmental regulations. Because of benzene's high volatility and prevalence in metropolitan and industrial areas, its atmospheric levels can vary swiftly influenced by factors like vehicular exhaust, weather patterns, and manufacturing processes. Predictive models, especially those driven by machine learning algorithms and real-time data streams, serve as effective instruments for estimating benzene concentrations with notable precision. This research emphasizes the use of recurrent neural networks (RNNs) for this objective, acknowledging that careful selection and calibration of model hyperparameters are critical for optimal performance. Accordingly, this paper introduces a customized version of the elk herd optimization algorithm, employed to fine-tune RNNs and improve their overall efficiency. The proposed system was tested using real-world air quality datasets and demonstrated promising results for predicting benzene levels in the atmosphere.
Image segmentation plays a crucial role in medical imaging, remote sensing, and object detection. However, challenges persist due to uncertainty in region classification, sensitivity to noise, and discontinuities in object boundaries. To address these issues, a novel segmentation framework is proposed, integrating Complex Pythagorean Fuzzy Aggregation Operators (CPFAs) with a level-set-based optimization strategy to enhance both precision and adaptability. The proposed model leverages complex Pythagorean fuzzy membership functions, incorporating both magnitude and phase components, to effectively manage overlapping intensity distributions and classification uncertainty. Additionally, geometric constraints, including gradient and curvature-based regularization, are employed to refine boundary evolution, ensuring accurate edge delineation in noisy and complex imaging conditions. A key contribution of this work is the formulation of a complex fuzzy energy functional, which synergistically integrates fuzzy region classification, phase-aware boundary refinement, and geometric constraints to guide segmentation. The level-set method is utilized to iteratively minimize this functional, facilitating smooth transitions between segmented regions while preserving structural integrity. Experimental evaluations conducted across diverse imaging domains demonstrate the robustness and versatility of the proposed approach, highlighting its efficacy in medical image segmentation, remote sensing analysis, and object detection. The integration of complex fuzzy logic with geometric optimization not only enhances segmentation accuracy but also improves resilience to noise and irregular boundary structures, making this framework particularly suitable for applications requiring high-precision image analysis.
Transformer-based language models have demonstrated remarkable success in few-shot text classification; however, their effectiveness is often constrained by challenges such as high intraclass diversity and interclass similarity, which hinder the extraction of discriminative features. To address these limitations, a novel framework, Adaptive Masking Bidirectional Encoder Representations from Transformers with Dynamic Weighted Prototype Module (AMBERT-DWPM), is introduced, incorporating adaptive masking and dynamic weighted prototypical learning to enhance feature representation and classification performance. The standard BERT architecture is refined by integrating an adaptive masking mechanism based on Layered Integrated Gradients (LIG), enabling the model to dynamically emphasize salient text segments and improve feature discrimination. Additionally, a DWPM is designed to assign adaptive weights to support samples, mitigating inaccuracies in prototype construction caused by intraclass variability. Extensive evaluations conducted on six publicly available benchmark datasets demonstrate the superiority of AMBERT-DWPM over existing few-shot classification approaches. Notably, under the 5-shot setting on the DBpedia14 dataset, an accuracy of 0.978±0.004 is achieved, highlighting significant advancements in feature discrimination and generalization capabilities. These findings suggest that AMBERT-DWPM provides an efficient and robust solution for few-shot text classification, particularly in scenarios characterized by limited and complex textual data.
Sentiment analysis in legal documents presents significant challenges due to the intricate structure, domain-specific terminology, and strong contextual dependencies inherent in legal texts. In this study, a novel hybrid framework is proposed, integrating Graph Attention Networks (GATs) with domain-specific embeddings, i.e., Legal Bidirectional Encoder Representations from Transformers (LegalBERT) and an aspect-oriented sentiment classification approach to improve both predictive accuracy and interpretability. Unlike conventional deep learning models, the proposed method explicitly captures hierarchical relationships within legal texts through GATs while leveraging LegalBERT to enhance domain-specific semantic representation. Additionally, auxiliary features, including positional information and topic relevance, were incorporated to refine sentiment predictions. A comprehensive evaluation conducted on diverse legal datasets demonstrates that the proposed model achieves state-of-the-art performance, attaining an accuracy of 93.1% and surpassing existing benchmarks by a significant margin. Model interpretability was further enhanced through SHapley Additive exPlanations (SHAP) and Legal Context Attribution Score (LCAS) techniques, which provide transparency into decision-making processes. An ablation study confirms the critical contribution of each model component, while scalability experiments validate the model’s efficiency across datasets ranging from 10,000 to 200,000 sentences. Despite increased computational demands, strong robustness and scalability are exhibited, making this framework suitable for large-scale legal applications. Future research will focus on multilingual adaptation, computational optimization, and broader applications within the field of legal analytics.
The restoration of blurred images remains a critical challenge in computational image processing, necessitating advanced methodologies capable of reconstructing fine details while mitigating structural degradation. In this study, an innovative image restoration framework was introduced, employing Complex Interval Pythagorean Fuzzy Sets (CIPFSs) integrated with mathematically structured transformations to achieve enhanced deblurring performance. The proposed methodology initiates with the geometric correction of pixel-level distortions induced by blurring. A key innovation lies in the incorporation of CIPFS-based entropy, which is synergistically combined with local statistical energy to enable robust blur estimation and adaptive correction. Unlike traditional fuzzy logic-based approaches, CIPFS facilitates a more expressive modeling of uncertainty by leveraging complex interval-valued membership functions, thereby enabling nuanced differentiation of blur intensity across image regions. A fuzzy inference mechanism was utilized to guide the refinement process, ensuring that localized corrections are adaptively applied to degraded regions while leaving undistorted areas unaffected. To preserve edge integrity, a geometric step function was applied to reinforce structural boundaries and suppress over-smoothing artifacts. In the final restoration phase, structural consistency is enforced through normalization and regularization techniques to ensure coherence with the original image context. Experimental validations demonstrate that the proposed model delivers superior image clarity, improved edge sharpness, and reduced visual artifacts compared to state-of-the-art deblurring methods. Enhanced robustness against varying blur patterns and noise intensities was also confirmed, indicating strong generalization potential. By unifying the expressive power of CIPFS with analytically driven restoration strategies, this approach contributes a significant advancement to the domain of image deblurring and restoration under uncertainty.
The environmental performance and service quality of liquefied petroleum gas (LPG) vehicles were evaluated through a dual-phase analytical approach. In the first phase, exhaust emissions from LPG and petrol-powered vehicles were quantified using the CAPELEC 3010 gas analyzer, with concentrations of carbon monoxide (CO), carbon dioxide (CO$_2$), nitrogen oxides (NOx), and hydrocarbons being measured. The results demonstrated that LPG vehicles emitted significantly lower CO levels (0.09% on average) compared to petrol vehicles (0.18%), with corrected CO values also reduced (0.08% vs. 0.19%). These findings reinforce the environmental advantages of LPG as a cleaner fuel alternative. In the second phase, the SERVQUAL model was employed to assess user perceptions of service quality, focusing on five dimensions: reliability, responsiveness, assurance, empathy, and overall service quality. A negative overall SERVQUAL gap (-0.806) was identified, with the most pronounced discrepancies observed in reliability (-1.061) and responsiveness (-0.933), indicating unmet expectations in key service aspects. Despite these gaps, LPG vehicles were perceived as cost-effective and environmentally sustainable. The findings underscore the necessity for technical refinements in LPG vehicle systems and improvements in service infrastructure to enhance user satisfaction. The insights derived from this study offer valuable guidance for policymakers and industry stakeholders seeking to promote LPG as a viable component of sustainable transportation strategies.
In recent decades, the strategic placement of capacitors for compensating inductive reactive power has been extensively investigated by network operators and researchers globally, owing to its profound impact on minimizing power losses, improving voltage regulation, and enhancing overall voltage stability. The installation of shunt capacitors has been demonstrated to significantly improve the efficiency and performance of power systems by regulating voltage levels at load points, as well as at distribution and transmission system buses. This approach not only reduces inductive reactive power but also corrects the system’s power factor, thereby optimizing energy utilization. In this study, the optimal sizing and placement of capacitor banks within a specific section of the Duhok city distribution network were systematically analyzed. The Electrical Transient Analyzer Program (ETAP) software was employed to simulate and evaluate power losses and voltage drops both before and after capacitor installation. The findings reveal a marked improvement in the voltage profile across the network, accompanied by a substantial reduction in power losses. These results underscore the critical role of capacitor banks in enhancing the operational efficiency of distribution networks, providing a robust framework for future implementations in similar systems. The methodology and outcomes presented herein offer valuable insights for network operators seeking to optimize power system performance through reactive power compensation.
This study investigates the extent to which renewable energy adoption contributes to achieving a sustainable competitive advantage in Jordanian small and medium-sized enterprises (SMEs) through enhanced sustainability practices. A quantitative research design was employed, utilizing data collected from 467 administrative personnel across 43 SMEs operating in diverse industries to ensure representativeness. Structural equation modeling (SEM) was conducted using SmartPLS 4 to examine both the direct and indirect effects of renewable energy adoption on corporate sustainability practices and its subsequent impact on long-term competitiveness. The findings indicate that integrating renewable energy into business operations significantly strengthens sustainable competitive advantage by improving operational efficiency, reducing costs, and enhancing corporate reputation. Furthermore, the results highlight the role of renewable energy adoption in reinforcing sustainability initiatives, thereby aligning environmental stewardship with strategic business objectives. These insights provide valuable implications for SMEs seeking to enhance market positioning through sustainability-driven strategies. Additionally, the study contributes to the existing body of knowledge on corporate sustainability and strategic management by elucidating the mechanisms through which renewable energy facilitates long-term competitive positioning. Practical recommendations are offered to policymakers and business leaders to support the effective implementation of sustainability initiatives within the SME sector.
Accurate estimation of tree height is fundamental to sustainable forest management, particularly in regions such as Kumrat Valley, Pakistan, where Deodar Cedar (Cedrus deodara) serves as a vital ecological and economic resource. Conventional height estimation models often exhibit limitations in capturing the inherent complexity of forest ecosystems, where multiple environmental factors interact non-linearly. To address this challenge, a hybrid predictive framework integrating fuzzy inference systems (FIS) and multiple linear regression (MLR) has been developed to enhance the accuracy of height estimation. The FIS model incorporates key environmental and physiological parameters, including trunk diameter, soil quality, temperature, and rainfall, which are classified into fuzzy sets—low, medium, and high—corresponding to distinct growth rates (slow, normal, fast) and developmental stages (early, average, late). This classification enables a nuanced representation of environmental variability and tree growth dynamics. Complementarily, the MLR model quantifies the statistical relationships between these variables and tree height, yielding an R² value of 0.85, an adjusted R² of 0.64, and a statistically significant p-value of 0.04. The integration of fuzzy logic with regression analysis offers a robust, data-driven approach to height prediction, effectively addressing the uncertainties associated with environmental fluctuations. By leveraging both rule-based inference and quantitative modeling, this method provides valuable insights for precision forestry, contributing to the sustainable management and conservation of Deodar Cedar in Kumrat Valley.
The increasing pace of urbanization has heightened the need for urban systems that are both sustainable and resilient. While extensive research has been conducted on these two concepts, the interplay between them remains insufficiently explored. In particular, sustainability is often associated with efficiency—maximizing resource utilization—whereas resilience emphasizes redundancy, ensuring the presence of backup systems to mitigate risks. To address this critical gap, a comprehensive framework is proposed that integrates these dual objectives within urban land-use planning. Geospatial technologies and multi-criteria decision analysis are employed to systematically assess the balance between efficiency and redundancy in urban environments. A machine learning (ML)-based classification of land use and built-up area changes, combined with demographic and infrastructural data, is utilized to quantify these factors. The proposed approach provides urban planners and policymakers with an adaptable decision-making tool, enabling context-specific prioritization of efficiency or redundancy based on local requirements. In high-density urban areas experiencing rapid expansion, efficiency is emphasized to optimize land and resource use, whereas in regions vulnerable to environmental hazards, redundancy is strategically incorporated to enhance resilience without undermining overall urban functionality. The flexibility of this method offers a significant advantage over rigid, predefined planning policies that may not be suited to specific urban contexts. By facilitating informed decision-making, the framework enhances risk management, optimizes resource allocation, and supports the development of customized urban strategies, ultimately improving long-term urban performance under diverse developmental scenarios.
In recent years, frequent natural disasters and public emergencies have emphasized the importance of emergency material distribution path planning. Aiming at the problems of neglecting the differences in the urgency of the demand at the disaster-stricken points and the lack of distribution fairness in traditional research, this study proposes an emergency material distribution path planning method that integrates the priority assessment of the disaster-stricken points and multi-objective optimization. First of all, a two-level evaluation system is constructed from the dimensions of disaster degree and material demand, including the number of rescue population and other indicators, and the combined weights are calculated by combining the subjective and objective methods of hierarchical analysis (AHP) and entropy weighting, so as to quantify the urgency coefficient of the demand at each disaster site and break through the limitations of the traditional “nearby distribution” mode. On this basis, a vehicle path planning model is established with the dual objectives of minimizing the total distribution cost and vehicle load balance, and the elite strategy non-dominated sorting genetic algorithm (NSGA-II) is introduced to solve the problem. Scenario analysis is carried out with the background of public health emergencies in Jingzhou City, and the effectiveness of the model is verified based on the actual data of 64 medical material demand points. The simulation results show that the total distribution distance and vehicle load balance are optimized after optimization. Finally, it is suggested in conjunction with the current situation of emergency material distribution in China. Through the quantification of demand urgency and multi-objective collaborative optimization, this study provides theoretical basis and practical reference for improving the fairness, timeliness and resource utilization efficiency of emergency logistics, and has important application value for improving disaster relief decision-making.
With the rapid advancement of modern robotics and artificial intelligence, intelligent picking robots have been widely adopted in agricultural production. Global path planning techniques have been applied to crop harvesting, such as oranges, apples, tea leaves, and tomatoes, yielding promising results. This study focuses on the path planning problem for a robotic arm used in premium tea leaf picking. Experimental simulations reveal that the Ant Colony Optimization (ACO) algorithm performs particularly well in solving small-scale Traveling Salesman Problems (TSP), as it can incrementally construct initial paths and, with properly tuned parameters, produce higher-quality solutions and achieve faster convergence compared to other algorithms. However, the traditional ACO algorithm tends to fall into local optima and suffers from slow convergence. To address these challenges, this paper proposes a dynamically optimized ACO algorithm that enhances the pheromone update rules and optimizes the $\alpha$ and $\beta$ parameters during the search process. These parameters are updated according to the optimization results, and a ranking factor is introduced to prevent the optimal picking path from being overlooked. The proposed method demonstrates superior performance over the traditional ACO algorithm in terms of path quality and convergence speed.
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