Green growth has emerged as a crucial central policy objective for reconciling economic performance with environmental sustainability. This study investigates the determinants of green growth in OECD countries over the period 1990−2022. Specifically, it evaluates the roles of environmental policies (EPS), research and development (R&D), and institutional quality, particularly the Rule of Law (IQRL), in shaping economic green growth. To address cross-country interdependence and structural heterogeneity, we employ the Common Correlated Effects Estimator with a Cross-Sectionally Augmented Autoregressive Distributed Lag (CS-ARDL) framework. The approach appropriates the dataset exhibiting unobserved common factors, cross-sectional dependence, and mixed order of integration. The model effectively manages cross-sectional dependence, accommodates heterogeneous dynamics, and addresses mixed stationarity. It therefore provides reliable estimates of both short-run and long-run effects and is well suited for modern applied panel data analysis. The empirical results show that an inclusive strategy promoting technological innovation, strong environmental governance, and following the Rule of Law is essential for advancing green growth in advanced economies. Trade openness, GDP per capita, and population growth are found to be significant positive drivers. In contrast, renewable energy consumption (REC) exerts a negative effect, suggesting the presence of short-term adjustment costs associated with the energy transition. Overall, these findings highlight the importance of coordinated environmental governance, sustained R&D support, and strong institutional frameworks in advancing green growth. Policymakers should prioritize targeted R&D investments, strengthen environmental policy design, and enhance the institutional frameworks that support the ecological transformation.
Deep neural network-based English handwriting recognition has revolutionised the security or verification system of today; nevertheless, there are certain risks that are inevitable. This paper examines the status of the present technologies in the handwriting recognition systems, and more particularly, by the various deep neural network architectures. It also evaluated common cybersecurity risks such as data poisoning, model inversion, and adversarial attacks, which can be devastating to such systems, as well as common privacy and ethical issues. The potential regulatory compliance and mitigation measures that can be taken to avert these risks and hurdles are also addressed in detail, with requisite emphasis being made on the future outlook of a more secure handwriting recognition system.
Urban public transport systems are required to respond to pronounced temporal variations in passenger demand driven by calendar effects, weather conditions, and evolving mobility patterns. Reliable short-term demand forecasts have therefore become an important role in supporting operational planning and service management in large-scale transit systems. This study examines the daily ridership dynamics of the Transjakarta bus rapid transit system and evaluates the forecasting performance of three modeling approaches: seasonal autoregressive integrated moving average with exogenous variables (SARIMAX), multilayer perceptron (MLP), and a dynamic moving-window model. The analysis is based on 851 daily observations from January 1, 2023 to April 30, 2025, with rainfall, working days, and national holidays included as exogenous variables. Each model is estimated using a training dataset and evaluated on a hold-out test set over a 30-day forecasting horizon. Forecast accuracy is assessed using the mean absolute percentage error (MAPE). The results indicate that the MLP model achieves the highest forecasting accuracy, with a MAPE of 8.547%, while SARIMAX and the dynamic model yield higher error levels of 33.345% and 37.754%, respectively. The findings suggest that non-linear modeling approaches are better suited to capturing the complex and irregular demand patterns observed in daily urban bus ridership data. The study provides empirical evidence that can support short-term planning and demand-aware operational decision-making in urban public transportation systems.
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 Muntingia 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 (0.070 ppm), Fe (13.322 ppm), Cu (8.434 ppm), and Zn (11.668 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.
In many Iraqi cities, urban traffic congestion is still a problem, and the use of sophisticated analytical methods is constrained by the lack of trustworthy data. The present research combines both supervised learning and clustering algorithms to create a data-driven model to classify traffic levels in Nasiriyah. An example of K-means clustering was used to derive a categorical congestion-level variable by using field data that were collected on sixteen different districts to explain the underlying traffic patterns. The ability of three classification algorithms—J48 decision tree, Naive Bayes, and random forest (RF) to differentiate between low, medium, and high congestion circumstances was then assessed. With an accuracy of 81.25% and a kappa value of 0.70, the J48 model outperformed the other classifiers on the short dataset and had the most consistent performance. The findings also suggest that the lightweight hybrid strategy can provide authoritative congestion information in data-limited settings and, therefore, present a useful tool to support planning and traffic management decisions in fast-growing cities.
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.
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