Kerosene pollution, stemming from its widespread use as a fuel and solvent, poses significant health and environmental risks. This study aimed to isolate biosurfactant-producing Klebsiella pneumoniae from petroleum-contaminated soil and apply the biosurfactant to enhance kerosene biodegradation. Among twelve isolates screened, seven produced biosurfactants, with K. pneumoniae S9 exhibiting the highest emulsification index (E24 = 45%). The biosurfactant was extracted, purified, and characterized as a lipopeptide via Thin-Layer Chromatography (TLC) and Fourier Transform Infrared (FT-IR) spectroscopy. Supplementation with the biosurfactant significantly accelerated kerosene degradation, achieving 64% efficiency within an 11-day incubation period. These results demonstrate the potential of this biosurfactant as an effective agent for the bioremediation of kerosene-contaminated environments.
Lakes in mining areas face serious ecological degradation due to complex interactions between human activities, land use change, and industrial pressures. Globally, approximately 46.7% of lakes have lost their ecosystem resilience, with impacts such as declining water quality, sedimentation, heavy metal pollution, and biodiversity loss. While previous studies have mostly focused on post-mining pit lakes, limited attention has been given to conservation in active mining areas, leaving a critical research gap. This study aims to identify the factors influencing lake water resource conservation in mining regions, analyze the interrelationships among these factors, develop a conceptual model, and propose contextual strategies for sustainable conservation. A systematic literature review was conducted following the PRISMA 2020 protocol, using searches on Scopus and Web of Science for English-language publications from 2015 to 2025. Inclusion criteria emphasized empirical studies addressing lake conservation in mining areas. Study quality was assessed using the Mixed Methods Appraisal Tool (MMAT) version 2018, and data synthesis employed thematic analysis with NVivo 14 to identify key themes, factor relationships, and model design. From an initial 642 articles, 114 studies met the criteria. The analysis identified 13 key factors, with three dominant determinants: human–environment interaction, eco-friendly technology and innovation, and socio-economic pressures. Factor relationships included direct pathways such as institutional capacity and social capital, mediating roles such as environmental education and leadership, and negative moderation through economic pressures. The resulting conceptual model emphasizes integrating technological interventions, social capacity building, and environmental value internalization. Priority strategies include environmental education, institutional strengthening, community participation, and adoption of mitigation technologies. Overall, lake conservation in mining contexts requires an integrative social–ecological systems approach that balances technical innovation, social interventions, and mitigation of economic drivers.
Sustainable logistics hub planning in emerging economies is often challenged by high levels of uncertainty, limited data availability, and the need to balance economic, environmental, and social objectives. Supporting consistent and transparent decision-making under such conditions remains a key issue in infrastructure planning. To address this, the present study develops an intelligent decision-support framework for prioritizing logistics hubs in complex and uncertain environments. The proposed framework combines q-rung orthopair fuzzy sets with the ordinal priority approach, enabling the representation of imprecise expert judgments alongside ordinal preference information within a unified multi-criteria structure. The approach is applied to the case of Kenya, where logistics development involves multiple and often conflicting criteria. A comprehensive evaluation system is established, and expert assessments are incorporated to derive priority rankings. The results show that operational efficiency and economic considerations play a dominant role in the decision process, while environmental and social factors receive comparatively lower weights. Sensitivity and comparative analyses confirm the stability and reliability of the findings. The study provides a structured and uncertainty-aware decision-support tool that can assist infrastructure planning and offers practical insights for policy and managerial decision-making in logistics systems.
Remediating hydrocarbon-contaminated soils in rainforest ecosystems poses complex challenges, requiring strategies that balance ecological restoration with long-term sustainability. This study aimed to analyze stakeholder dynamics and identify collaborative approaches to support sustainable remediation in the Taman Hutan Raya Sultan Syarif Hasyim (TAHURA SSH) area in Sumatra. The Matrix of Alliances and Conflicts: Tactics, Objectives, and Recommendations (MACTOR) method was applied to examine interactions among eleven stakeholder groups. Data were collected through purposive interviews and focus group discussions to evaluate influence, dependence, and consensus across these groups. The findings revealed that Pertamina Hulu Rokan (PHR) and contractors function as central actors with the highest influence in advancing remediation practices. Conversely, local communities exhibited limited influence, suggesting their potential marginalization in decision-making processes. Although strong consensus was observed on ecological priorities—such as ecosystem restoration, long-term sustainability, and minimizing environmental impact—significant divergence regarding cost-effectiveness exposed underlying tensions between economic efficiency and environmental objectives. Sustainable remediation in rainforest ecosystems requires collaborative and inclusive strategies that foster partnerships among the private sector, government institutions, and local communities. These results provide practical implications for policymakers to develop environmentally responsible and socially equitable remediation frameworks in fragile ecosystems.
Compliance management in business operations is often addressed through fragmented procedures that are difficult to coordinate and evaluate in a consistent manner. This study develops a structured compliance management framework grounded in a system engineering perspective, with the aim of linking regulatory requirements to operational processes in a coherent way. The framework is constructed by organizing compliance activities into a set of interrelated components, including regulatory interpretation, process integration, monitoring mechanisms, and feedback loops. On this basis, an evaluation scheme is established to examine the consistency and effectiveness of compliance implementation across operational stages. Particular attention is given to the identification of critical control points and the interaction between compliance measures and routine business processes. The proposed framework is examined through its application to typical organizational settings, where it allows a more transparent mapping between compliance requirements and operational execution. The analysis shows that a system-based structure supports clearer identification of process dependencies and facilitates more consistent evaluation outcomes. The study provides a structured basis for understanding compliance as an integrated operational system rather than a set of isolated practices, and offers a foundation for more informed decision-making in compliance management.
Automated grading has become an important component of digital transformation in K-12 education, yet the structured recognition of handwritten responses on answer sheets remains a practical challenge. General-purpose vision-language models often show limited robustness when applied directly to school assessment materials, particularly in the presence of fixed answer regions, mixed Chinese-English content, and diverse handwriting styles. To address this issue, this study develops a task-oriented fine-tuning framework for automated recognition of handwritten answer sheets in K-12 educational settings. A multimodal dataset was constructed from Chinese and English answer sheets, with region-level annotations designed to support structured text extraction. Based on this dataset, the Qwen2.5-VL-7B-Instruct model was adapted through LoRA-based fine-tuning under a dual-A16 GPU environment to reduce computational cost while preserving practical deployment feasibility. An end-to-end workflow covering data preparation, model training, weight merging, and inference was then established for structured JSON output. Experimental results show that the fine-tuned model achieved stable convergence in both small-sample and medium-sample settings and improved the extraction quality of handwritten responses within predefined answer regions. The proposed framework provides a practical and reproducible solution for deploying vision-language models in school grading scenarios with limited computing resources. The study also offers an application-oriented reference for the integration of multimodal large models into educational assessment systems.
This study aimed to demonstrate the application of environmental activity-based costing (EABC) and its impact on supporting environmental sustainability, in accordance with ISO 14001 and 14051 standards for material flow cost accounting (MFCA) and GRI 300 standards for materials, energy, water, compliance, waste, and environmental performance improvement. EABC is an environmental accounting tool that identifies activities and allocates environmental costs to those activities, then to products, thereby assigning each product its actual costs and providing more accurate data. The research was conducted at the General Company for Fertilizer Industries in the Southern Region of Basra, Iraq. The researcher employed a practical approach by comparing the system implemented in the company under study with EABC. The main reason for using this technique is the inefficient use of resources and the resulting environmental pollution and fines imposed for exceeding permissible pollution limits. These costs have come to constitute a large percentage of the company’s total costs, thus impacting its profitability. The research contributed to identifying areas of waste resulting from the inefficient use of available resources and assisted management in making sound and accurate decisions related to environmental and economic aspects. It also helped improve environmental performance and enable the allocation of environmental costs to products based on their resource consumption. This, in turn, leads to the sustainability of resources through optimal use, thus achieving environmental sustainability. The study concluded that adopting cash flow statements helps improve various administrative decision-making processes, including pricing decisions, by allocating environmental costs to products and the activities that generate them. Furthermore, some reasons for waste in raw materials are attributed to the poor quality of those materials and the manual addition of materials. Therefore, the model directs management’s attention and efforts towards purchasing less environmentally damaging materials and using a pump for material application.
This paper presents a genetic algorithm (GA) tuned Mamdani type fuzzy logic control (FLC) framework for trajectory tracking of a quadrotor unmanned aerial vehicle (UAV) using a nonlinear rigid body model. The proposed architecture adopts a cascaded structure in which an outer loop position controller generates attitude and thrust references $(\phi_{\mathrm{ref}},\theta_{\mathrm{ref}},T_{\mathrm{ref}})$, while an inner loop attitude controller generates body torques $(\tau_\phi,\tau_\theta,\tau_\psi)$. Both loops employ a shared Mamdani fuzzy inference system with normalized inputs (tracking error and error-rate) and a normalized control output. The GA automatically tunes scaling gains $(K_e,K_d,K_u)$ across all axes to minimize a robust objective that averages tracking error, control effort, and constraint violations over multiple scenarios with mass uncertainty and wind disturbances. Simulation results on a three dimensional figure eight trajectory indicate that GA tuning can reduce position and attitude errors while respecting actuator saturation and tilt safety limits, demonstrating a practical route to performance enhancement without requiring a high fidelity aerodynamic model. The methodology leverages the interpretability of fuzzy rules and the global search capabilities of evolutionary optimization within a UAV modeling framework consistent with established quadrotor dynamics literature.
Cost-schedule control in construction projects is inherently a continuous decision-making process conducted under conditions of uncertainty, rather than a purely technical or accounting activity. Conventional approaches, which rely on retrospective performance measurement and fragmented indicators, provide limited support for timely managerial intervention and often lead to delayed or suboptimal decisions. This study develops a decision-centric framework that integrates earned value analytics with organizational decision processes to enable proactive and structured cost–schedule control in small and medium-sized construction projects. The proposed framework conceptualizes cost control as a four-stage decision process—situational awareness, diagnostic analysis, predictive assessment, and intervention execution—and establishes explicit linkages between analytical signals and managerial actions. Within this structure, earned value metrics are reinterpreted as decision triggers rather than passive evaluation tools, while organizational roles are reconfigured to support timely interpretation and coordinated response. The framework is examined through an in-depth case study of a gas station construction project exposed to significant environmental and operational uncertainty. The findings indicate that cost overruns are primarily associated with delayed decision responses, fragmented information flows, and misaligned responsibility structures. By embedding real-time performance evaluation within a coherent decision architecture, the proposed approach enables earlier identification of deviations and more targeted managerial interventions. The study contributes to the literature on intelligent management decision-making by demonstrating how analytical tools can be operationalized within organizational contexts to enhance decision quality under uncertainty. It further provides a transferable framework for structuring data-informed decision processes in resource-constrained project environments.
Rolling bearings are critical components of marine shafting power transmission systems, and accurate prediction of their vibration signal trends is essential for predictive maintenance. To address the limited adaptability of conventional time-series forecasting models under varying operating conditions and their insufficient ability to capture strong noise and abrupt changes, this study proposes a vibration signal prediction method that integrates particle swarm optimization (PSO) with an improved Informer model. PSO is used to adaptively optimize key Informer hyperparameters for different operating conditions, while a rolling time-window mechanism is introduced to enhance the capture of abrupt signal variations. In addition, a mixture of sparse attention (MoSA) encoder with a collaborative dense-head/sparse-head structure is designed to balance global temporal dependency modeling and local fault feature extraction. Experimental results on the Case Western Reserve University (CWRU) bearing fault dataset show that the proposed model outperforms Long Short-Term Memory (LSTM), Transformer, Informer, iTransformer, and Flowformer in terms of Mean Squared Error (MSE), Mean Absolute Error (MAE), and Root Mean Squared Erro (RMSE). The model achieves an MSE of 0.2015, which is 25.5% lower than that of the second-best iTransformer model. It also demonstrates robust performance under four different bearing operating states, confirming its adaptability to complex operating conditions. The proposed method provides a promising technical route for the predictive maintenance of rolling bearings in marine shafting systems.
Marine debris is one of the major environmental concerns in the 21st century, owing to its impact on the ocean ecosystems, the biodiversity of marine inhabitants, and human well-being. Through the utilization of automated content analysis (ACA) and graph theory in the context of a systematic literature review (SLR), the purpose of this investigation is to comprehensively map and assess the global research landscape concerning marine trash. Leximancer was used in this study to extract semantic links among important ideas, which were then displayed as directed acyclic graphs (DAG). The research used 357 Scopus-indexed papers that were published between 2017 and 2024. Core conceptual clusters relating to microplastics, plastics, and soil were identified through the ACA method. These clusters each reflected a different aspect of marine pollution that was interrelated with the others. The utilization of graph theory enabled the identification of structural links and core nodes that were shared by several themes. These connection points might be quantified by adjacency matrices and normalized grouping was accomplished by k-means analysis. According to the findings, phrases such as “waste”, “plastics”, and “marine” were the most prominent notions, and they served as the foundation for study on marine debris on a worldwide scale. These findings not only contribute to the advancement of automated environmental informatics but also highlight how graph-based content analysis may be used to identify hidden patterns in scientific knowledge. Taking into account both theoretical and methodological considerations, this study have implications for academics who use computational bibliometric analysis in the field of environmental science.
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