Heavy metals tracing and magnetic susceptibility are generally used as a proxy indicator of pollution in various depositional environments. This research focused on tracing the significant record of pollution of the Mutingia Calabura tree to understand the sensitivity in recording pollution and mining production. The area of this research is a hydrocarbon mining site of Wonocolo Geopark in the Bojonegoro, East Java Indonesia. The samples were taken both polluted and non-polluted leaf and bark, from 20 sampling points. Polluted leaves then were characterized by the existence of elevated levels of Pb, Fe, Cu and Zn. The average magnetic susceptibility of leaves increases from 0.86 × 10$^{-8}$ m$^3$/kg in non-polluted samples to 13.55 × 10$^{-8}$ m$^3$/kg in polluted samples and in the same way, increasing of magnetic susceptibility was also seen in the barks, from an average of 0.21 × 10$^{-8}$ m$^3$/kg in non-polluted sample, to 2.55 × 10$^{-8}$ m$^3$/kg in polluted sample. The pattern of magnetic susceptibility on leaves and barks at each sampling point is also the same as the pattern of hydrocarbon production which is related to the level of pollution in the area. The increase of magnetic susceptibility in polluted leaves and barks is thought caused by input of magnetic minerals and heavy metals from the fly ash of diesel engines used for hydrocarbon mining process. The heavy metal concentration has the average of Pb (0.0705 ppm), Fe (13.3219 ppm), Cu (8.4339 ppm), and Zn (11.6679 ppm). This value has exceeded the threshold of heavy metals content and have a worst impact on health and the environment. Based on Pollution Load Index (PLI) calculations, the most of areas affected by heavy metal pollution in very high and extremly high levels with the highest pollutants input are Fe, Cu, Zn and Pb respectively.
The Fallujah Cement Plant constitutes a cornerstone of reconstruction efforts in Al-Anbar Province, yet it simultaneously represents one of the largest stationary sources of air pollution in the region. This study presents the first integrated assessment of ambient air quality impacts from a major Iraqi cement facility by combining field measurements with the American Meteorological Society/Environmental Protection Agency Regulatory Model (AERMOD). Concentrations of seven pollutants—sulfur dioxide (SO$_2$), nitrogen dioxide (NO$_2$), carbon monoxide (CO), carbon dioxide (CO$_2$), total suspended particulates (TSP), particulate matter $\leq$ 10 $\mu$m (PM$_{10}$), and fine particulate matter $\leq$ 2.5 $\mu$m (PM$_{2.5}$)—were monitored at three receptor sites surrounding the plant. Results revealed that measured concentrations consistently exceeded model predictions, particularly for CO$_2$ (+221%) and CO (+441%). Field data indicated exceedances of Iraqi national standards and World Health Organization (WHO) guidelines by up to 23-fold for SO$_2$ and 12-fold for PM$_{2.5}$. Spatial analysis confirmed that pollutant plumes predominantly extend southeastward under prevailing northwesterly winds, with the highest risks observed in nearby residential complexes located 2 km downwind. Overall, the findings demonstrate that the Fallujah Cement Plant poses significant public health risks, underscoring the urgent need for advanced emission-control technologies and the establishment of vegetative buffer zones to mitigate environmental and health impacts.
This study examines the relationship between seawater quality in the vicinity of the Labuan 2 Steam Power Plant (SPP) and the distribution of plankton and benthos communities. Seawater quality was assessed in accordance with the Minister of Environment Decree No. 115/2003, while biodiversity was evaluated using the Shannon–Wiener diversity index, the uniformity index, and the Simpson dominance index. Sampling was conducted at seven monitoring points during the dry season in July 2021. The results indicated that seawater quality at all sampling locations met the established quality standards. A total of 27 phytoplankton species were identified, with Skeletonema consistently observed as the dominant genus across all sites. The phytoplankton community exhibited high uniformity, moderate diversity, and moderate dominance. Zooplankton analysis identified 17 species, dominated by Temora (Copepoda), reflecting its role as a key primary consumer linking phytoplankton to higher trophic levels. Zooplankton communities showed high uniformity, low dominance, and moderate diversity. In addition, ten benthic species were recorded, with Arenicola sp. as the dominant taxon. The benthos community was characterized by moderate uniformity, low dominance, and relatively high diversity. Overall, the findings indicate that the waters surrounding the Labuan 2 SPP remain ecologically balanced, with plankton and benthos communities supporting stable marine food web structures.
The rapid diffusion of artificial intelligence (AI) into decision-making processes has raised critical questions about how AI reshapes human behavior, judgment, and responsibility. While existing studies often emphasize technical performance, less attention has been given to the behavioral dynamics that emerge when humans interact with AI-supported systems. This study addresses this gap by proposing an integrated Strengths, Weaknesses, Opportunities, and Threats–Analytic Hierarchy Process–Technique for Order Preference by Similarity to an Ideal Solution (SWOT–AHP–TOPSIS) framework to systematically evaluate the behavioral impact of AI-assisted decision making. First, key behavioral factors are identified using SWOT analysis, where strengths and weaknesses represent internal human behavioral traits, and opportunities and threats capture external and contextual influences related to human–AI interaction. These factors are then weighted using AHP based on expert judgments, with consistency checks ensuring methodological reliability. Finally, TOPSIS is applied to rank three AI-assisted decision scenarios—human-dominant, shared-control, and AI-dominant decision making—according to their overall behavioral performance. The results indicate that behavioral weaknesses, such as over-reliance on AI and reduced critical thinking, exert the strongest influence on decision quality. Among the evaluated scenarios, human-dominant decision making achieves the highest closeness coefficient, followed by shared-control and AI-dominant scenarios. Sensitivity analysis confirms the robustness of these rankings under reasonable variations in criterion weights. Methodologically, this study demonstrates that the SWOT–AHP–TOPSIS approach, traditionally used in strategic and operational research, can be effectively adapted to behavioral and socio-technical contexts. Substantively, the findings highlight the importance of preserving human cognitive agency in AI-assisted environments. The proposed framework offers a practical and theoretically grounded tool for researchers, designers, and policymakers to assess and guide the behavioral implications of AI-supported decision systems.
The learning process plays a crucial role in developing character values, including environmental care value. Given the critical importance of the environment to human existence, addressing the increasing environmental challenges today requires the integration of environmental education into core learning processes. Developing learning that promote environmental values is essential for effective implementation. This study aims to: (1) develop a Green Education model through Eco-Clubs for students at Universitas Negeri Malang (UM), (2) evaluate the effectiveness of the Green Education model through Eco-Clubs for students, and (3) assess the wastepreneurship skills of students’ participation in the Green Education model through Eco-Clubs. The study employed a Research and Development (R&D) methodology utilizing the Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model. The research subjects were students of UM enrolled in the Entrepreneurship course. Data analysis employed a mixed-method approach. Results indicate that the Green Education model implemented through Eco-Clubs significantly enhances students’ wastepreneurship competencies. Achieving successful environmental projects requires high levels of cooperation, creativity, and a strong sense of environmental responsibility. These factors significantly influence students’ ability to address environmental issues, particularly waste management, both on campus and within the community. Furthermore, the implementation of the Green Education model using Eco-Clubs has enhanced student engagement in environmental initiatives, yielding beneficial outcomes for both the campus and broader community stakeholders.
Logic-based machine learning models such as the Tsetlin Machine (TM) have recently gained attention for their energy efficiency and inherent interpretability. However, existing TM-based architectures remain limited in their ability to perform hierarchical feature learning, adapt dynamically to task complexity, and process temporal data efficiently. This paper proposes the Adaptive Logic Learning Architecture (ALLA), a novel hierarchical and energy-aware logic learning framework that addresses these limitations through adaptive clause networks (ACNs), multi-layer logical composition, and TLUs. ALLA enables dynamic clause growth and pruning, supports hierarchical abstraction, and integrates temporal reasoning within a unified propositional logic framework. Experimental results across image classification and sequential recognition tasks show that ALLA improves accuracy over conventional TM models while maintaining substantially lower energy consumption than deep neural network baselines. Hardware synthesis results further confirm the suitability of ALLA for low-power and edge-intelligent systems.
Cooking-related fires and combustible-gas leaks remain recurring domestic hazards, while lights and ventilation fans are often left running in empty rooms. This paper presents the design and experimental validation of a low-cost retrofit IoT node that integrates occupancy-aware actuation with early smoke and gas monitoring under a safety-first policy. An Arduino UNO executes time-critical sensing and relay control, and an ESP8266 provides Wi-Fi connectivity and a lightweight smartphone interface. Occupancy is inferred using a passive infrared (PIR) sensor to gate a lamp and fan, while an MQ-2 module monitors smoke and combustible gases. The control logic is implemented as an event-driven state machine that prioritises safety events, enforces minimum on and off timing to suppress relay chattering, and stabilises the gas channel using clean-air baseline normalisation (R/R0) with hysteresis. Bench verification confirmed I/O mapping and electrical isolation via an opto-isolated relay stage, and repeated switching did not reveal relay instability under the prototype loads. Scenario trials in a two-zone mock-up demonstrated reliable manual overrides, motion-triggered actuation without oscillation, and consistent alert generation during staged smoke exposures. The results support feasibility for incremental residential retrofits and identify deployment priorities, including sensor drift management, power integrity, and installation practice.
This study investigates the dynamic impacts of renewable energy consumption, tourism, and foreign direct investment (FDI) on Tunisia's ecological footprint from 1994 to 2022. We apply the Autoregressive Distributed Lag (ARDL) approach to examine these relationships. The results confirm cointegration among the variables and reveal distinct short-run and long-run dynamics. The long-run results indicate that tourism significantly increases ecological footprint, whereas FDI decreases it. Most notably, renewable energy consumption exhibits no statistically significant long-run impact. However, renewable energy significantly moderates environmental degradation in the short term. Additionally, FDI and tourism demonstrate complex, lagged short-run effects. The findings underscore the critical importance of distinguishing between short-run and long-run environmental impacts. The study concludes by offering specific policy recommendations to enable Tunisia to balance economic development with environmental sustainability.
The rapid integration of technology, with increasing speeds, has transformed vehicles into cyber-physical systems by connecting them to each other Vehicle-to-Everything (V2X), significantly expanding the attack surface and leaving them vulnerable to network-based threats. Current cyber intrusion detection systems (CIDS) exhibit performance degradation due to significant class imbalance, limited resilience against adversarial attacks, and insufficient interpretability for security-critical environments. To overcome the identified issues in this study, we propose Hierarchical Classifier-Agnostic Boosted Stacking for Network Intrusion Detection (HCABS-NID), a hierarchical classifier-agnostic boosted stacking architecture for network intrusion detection in connected device ecosystems. The proposed framework adds the Synthetic Minority Over-sampling Technique for Nominal and Continuous features (SMOTENC)-based adaptive class balancing to increase minority attack detection and TreeSHAP to make it multi-level interpretable. As a hierarchical stacking strategy, a two-layer structure includes heterogeneous learners together with meta-learning, calibrated with LightGBM, XGBoost, CatBoost, and TabNet to take advantage of the complementary decision boundaries. Extensive experiments performed on the benchmark dataset from University of New South Wales Network-Based 15 (UNSW-NB15) should enhance generalization performance. HCABS-NID achieved 98.20% accuracy, 97.10% macro F1 score, and 0.989 macro Receiver Operating Characteristic Area Under the Curve (ROC-AUC), in contrast to the latest community-based methods found in the literature. The proposed model achieves 3.40 ms average inference latency, satisfying the real-time processing requirement of the V2X safety systems. Indeed, other analysis architectures show the same 96.8% accuracy at 5% corruption, which underscores their practicality. The results validate that hierarchical ensemble learning, with adaptive imbalance management artificial intelligence (AI) mechanisms, provides a sound, interpretable, and ready-to-use intelligent transportation security package.
This study presented a theory-informed bibliometric review that explored the intersection of adaptation finance, vulnerability, and development cooperation within the climate finance literature. Anchored in the vulnerability-resilience framework, the study aims to map the conceptually-aligned financial models on adaptation, particularly how policy-driven instruments such as Official Development Assistance (ODA) have evolved within the world economy and debates about global macroeconomic policy. Utilizing a conceptually integrated search strategy, the analysis combined bibliographic coupling, thematic clustering, and theory-informed mapping techniques. The findings revealed that although adaptation-related concepts held a central place in global policy frameworks (e.g., Sustainable Development Goals (SDGs) 13 and 17), their representations in the academic literature remained uneven and fragmented. Structural clusters reflected the dominance of Global North institutions and mitigation-centered research whereas emerging thematic patterns indicated growing emphasis on context-specific and vulnerability-sensitive adaptation finance. Comparative insights from sectoral ODA data confirmed the thematic gaps identified in the bibliometric analysis and underscored the persistent disconnect between financial flows and local adaptation needs. By linking bibliometric insights with patterns of institutional finance, this study offered an integrative perspective on climate-oriented development and contributed to the agenda of global economic transformation. In doing so, it addressed a significant research gap via combining integrated theory-driven bibliometric mapping with analysis of policy-centered development finance.
For Authors
For Reviewers
For Editors
Open Access
