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.
The Greek financial crisis erupted in 2009 led to unprecedented austerity policies in the public sector. This study examined how crisis-driven wage cuts were perceived to have affected the motivation and self-reported productivity of public employees. Building upon equity theory, fair wage–effort arguments, effort–reward imbalance, and public service motivation (PSM), the research developed a perception-based framework linking compensation fairness to motivation and performance in a post-crisis public administration context. These theoretical insights were combined with a cross-sectional survey of 112 employees working in the Greek public sector. Descriptive statistics summarized respondents’ demographic profiles and perceptions, while Likert-scale questions gauged the impact of the crisis on income, job satisfaction, and exposure to new management practices. Results demonstrated that 98.2% of the respondents experienced income reductions during the crisis and an overwhelming majority sought additional sources of income. Low compensation was widely perceived as a major impediment to productivity, with 74.1% identifying pay as a primary productivity driver and 80.2% affirming its key role in job satisfaction. Nearly all respondents (99.1%) agreed that job satisfaction enhanced productivity. Austerity-era reforms yielded mixed outcomes as performance evaluations were viewed ambivalently. While a novel employee mobility scheme was considered potentially productivity-enhancing, its effectiveness was viewed as contingent on fair and transparent implementation. The study contributes to debates on post-crisis European public administration by illustrating how compensation reforms are experienced from below and by outlining implications for other austerity-affected systems in Southern Europe and beyond.
This paper presents the design, development, and implementation of an offline chatbot system specialized in answering food safety-related questions, relying entirely on Vietnamese legal documents. The system employs Retrieval-Augmented Generation (RAG) to ensure accurate and contextually relevant responses without internet dependency, a critical feature for low-connectivity environments. Key highlights include robust Vietnamese language support, a flexible vector database using Chroma for seamless legal content updates, and the integration of Qwen2.5:7B-Instruct-Q4_0 as the local language model, selected after comparative testing against DeepSeek-R1, Gemma3:1B, and Mistral. Embeddings are generated using BAAI/bge-small-en-v1.5. By processing Vietnamese queries and retrieving from a localized knowledge base, the chatbot delivers reliable guidance to stakeholders such as food producers, traders, and consumers. Evaluations demonstrate high accuracy in Vietnamese Q&A, stable offline operation, and adaptability to evolving regulations, with discussions on limitations and future enhancements.
Determining strategic locations for the development of integrated agroindustry—encompassing aquaculture areas, industrial zones, and ecotourism sites within coastal regions—presents a complex challenge. Each sector carries distinct interests and characteristics, often leading to spatial conflicts. Moreover, ensuring coastal ecological sustainability must remain a top priority throughout the planning process. A comprehensive approach is required to identify locations that not only minimize environmental impacts but also maximize cross-sectoral economic value. This study aims to identify potential locations for the development of an integrated agroindustry in the aquaculture–ecotourism sector. The Analysis of Operational Area of Nature-Based Tourism Attractions (AOA-NBTA) was employed to assess ecotourism potential. The Analytical Hierarchy Process (AHP) was applied to assign weights to industrial development parameters, while Geographic Information Systems (GIS) were used for spatial analysis of potential locations for integrated agroindustrial development. The AOA-NBTA analysis identified Tanjung Pakis Beach as the most promising ecotourism location, with a cumulative score of 3.175. Spatial overlay analysis in the Bekasi–Karawang coastal region revealed that highly suitable (S1) areas account for 20.27% (1,950.961 ha), suitable (S2) areas 18.10% (1,742.823 ha), and unsuitable areas 61.63% (5,933.175 ha). These findings provide a foundation for spatial decision-making in formulating sustainable development policies, particularly in coastal zones.
This study investigates the factors influencing environmental sustainability by examining the roles of environmental knowledge, attitudes, behaviors, and awareness. Although these variables have been widely studied in global contexts, limited research addresses how they manifest among Indonesian students. This study fills that gap by focusing on 409 ninth-grade students from middle schools in Pekanbaru, Riau Province, and Solok City, West Sumatra, Indonesia. A quantitative approach using survey questionnaires was employed to measure students’ environmental knowledge, attitudes, behaviors, and sustainability awareness. Results showed that environmental knowledge, attitudes, and behaviors significantly influenced sustainability awareness, with standardized coefficients of 0.35, 0.42, and 0.28, respectively ($p < $ 0.001 for all). Among these, environmental attitude had the most substantial impact. These findings highlight the need for a multidimensional approach to environmental education that integrates cognitive, emotional, and behavioral components. By focusing on a regional context often underrepresented in sustainability research, this study contributes to a deeper, culturally grounded understanding of how young learners in Indonesia engage with environmental issues. It offers valuable insights for educators and policymakers in designing curricula and interventions that not only build knowledge but also nurture positive attitudes and sustainable behaviors among students.
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