Landfill leachate poses a major challenge to urban waste management, particularly in tropical regions with high rainfall and heterogeneous waste composition. This study developed an artificial neural network (ANN) based on a multilayer perceptron (MLP) architecture to predict leachate volume at the Supit Urang landfill in Malang City, Indonesia. The dataset combined primary measurements of leachate discharge with secondary meteorological and environmental data, including rainfall, temperature, humidity, wind, and waste volume. Data preprocessing involved cleaning, imputation, transformation, and normalization to improve data quality and model readiness. The ANN model used two hidden layers with 64 neurons each and was optimized with the Adam algorithm, early stopping, and L2 regularization to balance predictive accuracy and generalization. The model achieved an R$^2$ of 0.61 and correlation coefficients above 0.82, indicating a good ability to capture nonlinear relationships and overall leachate trends. However, the relatively high RMSE values showed that individual predictions still deviated substantially from observed values. Overall, the findings indicate that ANN models are promising decision-support tools for sustainable landfill management, although further improvements in data quality and model optimization are still required. The study also offers practical insight for estimating leachate generation and planning treatment strategies in urban landfills.

The increasing complexity of modern urban traffic networks demands intelligent control strategies that can anticipate and adapt to dynamic traffic conditions. Model Predictive Control (MPC) is a framework that optimizes vehicle control by predicting future states and respecting real-time constraints, such as traffic signals at intersections. However, the computational complexity of MPC increases significantly with the number of decision variables and constraints, which is directly proportional to the length of the prediction horizon, creating a critical trade-off between control performance and computational efficiency. To address this challenge, this paper proposes an adaptive-horizon optimal driving (AHOD) bi-level optimization framework that incorporates a novel time-step discretization for real-time trajectory optimization and integrates it into a full traffic signal cycle. Unlike conventional MPC, which employs uniform time discretization leading to exponential growth in decision variables with horizon length, the proposed AHOD framework assigns finer time steps near signal phase transitions and coarser steps in the distant horizon, maintaining a fixed number of optimization nodes regardless of cycle length. The proposed framework comprises two controllers: the upper and lower controllers. The Upper controller employs finer resolution at critical times of signal change and coarser resolution in distant horizons, thereby reducing computational cost while maintaining prediction accuracy. The lower controller applies a practical MPC scheme to generate realtime control actions that are consistent with the long-term constraints of the upper controller. Simulation results demonstrate that the proposed framework achieves up to 17.6% fuel savings compared to traditional human driving and reduces computation time by approximately 61% compared to long-horizon MPC, while maintaining comparable control performance. The proposed framework enables real-time, cycle-aware predictive control for connected and automated vehicles (CAVs), and establishes a practical basis for embedding long-horizon prediction within an MPC-based trajectory-planning framework.

Padang City faces serious waste problems, including a 500-ton increase in daily waste generation to 500 tons and an annual accumulation of 236,296 tons (2023). Waste from the Final Processing Site is predicted to exceed its maximum limit by 2026; waste composition mainly comprises organic materials (62.53%) and plastics (13.6%), which have not been sufficiently managed through the Reduce, Reuse, and Recycle (3R) paradigm. This study analyzes the institutional, technical, regulatory, financial, and participatory barriers to waste management in Padang, as well as the policy implications from collaborative governance and circular economy perspectives. Using qualitative-descriptive methodology, with document analysis and policy evaluation, this study offers a unique contribution by combining polycentric governance defined as multi-level coordination and activity among government, private sector, and community actors with responsive regulation that situates punitive enforcement in the context of observed social behaviour and institutional capacity. The results indicate that institution fragmentation, under-enforcement of established laws, unsustainable funding mechanisms, and low community participation undermine the waste management practices in Padang. Integrated Waste Processing Place 3R and waste banks have, so far, not achieved optimal scale in terms of effectiveness. Contextualizing these outcomes through the lenses of polycentric governance, responsive regulation, circular economy, and community-based social marketing shows the role that cross-sectoral collaboration, participatory mechanisms, and adaptive regulatory tools played in building resilient urban waste systems. Theoretically, this study contributes to environmental governance scholarship by integrating governance design and regulatory innovation in the Global South context, while offering practical recommendations for performance contracts among stakeholders, as well as the adoption of Extended Producer Responsibility (EPR), decentralized technologies for organic waste, and digital-based incentives at the community level. Therefore, this study not only highlights the need for structural reforms but also contributes to establishing inclusive, adaptive, and sustainable waste management systems in Indonesia’s urban areas.

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.