Search Results (275)

Oaxaca is one of the most biocultural diverse regions in Mexico. Although the white-nosed coati (Nasua narica L.; Procyonidae, Mammalia) is widely used by Indigenous and rural communities throughout the Neotropics, its role within subsistence hunting, traditional food systems, and local management practices in Oaxaca has remained undocumented. This study analyzes the biocultural significance of N. narica hunting and consumption among four Indigenous groups in northern Oaxaca. Data were collected through ethnographic techniques, i.e., semi-structured interviews with locals, ethnozoological field trips, and participatory workshops conducted with Zoque, Zapotec, Chinantec, and Mazatec communities. In addition, an extensive literature review was carried out to contextualize and compare the field data. N. narica hunting is predominantly diurnal and collective, frequently involving dogs (Canis lupus familiaris) and horses (Equus ferus caballus), and fulfills at least three functions, including food provisioning, crop protection, and limited wild meat trade. N. narica meat is incorporated into traditional diet and prepared in at least five culturally distinct culinary forms. The use of N. narica reflects complex ethnoecological knowledge and long-term human–fauna interactions embedded within Indigenous livelihoods. These findings underscore the relevance of a biocultural ethnoecological approach for understanding subsistence hunting, wild meat use, and rural livelihoods in Mesoamerica. Full article

This paper examines worked-bone production at the Miaodigou (MDG) site, a key Middle Neolithic settlement in northern China, to assess the role of bone objects in craft production, subsistence, and social life. During the expansion of millet agriculture and pig domestication, a small group of community members continued to acquire wild animal resources and engage in bone working. This study analyzes 26 finished bone objects and 69 modified osseous fragments through technological analysis, microscopic use-wear analysis, and replication experiments. Results reveal a simple yet pragmatic production system and provide the first reconstruction of the Middle Yangshao worked-bone production sequence. Small-scale bone production reflects a limited but persistent tradition of wild resource exploitation, integrating procurement, manufacture, and use. It also highlights the diversity of subsistence and craft activities within an agriculture-based society, where hunting and gathering practices are often underrepresented. This study further clarifies the functions of two common but understudied tool types in Chinese Neolithic assemblages. Replication experiments show that some awls were likely used for loosening threads or cords, while so-called “knives or daggers” might function as scrapers for bast fibers or animal hides. Full article

The modernization of legacy industrial machines is a major challenge in intelligent automation, particularly when critical assets must be upgraded without replacing high-value mechanical and hydraulic infrastructure. This paper presents an industrial case study on the intelligent PLC-based retrofit of an obsolete Neyrpic Digipid speed governor installed in a Kaplan turbine. The proposed solution replaces a closed, vendor-dependent controller with an open Siemens ET 200SP architecture programmed in TIA Portal, integrating existing sensors, hydraulic actuators, redundant speed acquisition, sequential state-machine control, and a digital distributor–runner blade Cam Curve. A key technical contribution is the diagnosis and mitigation of hydraulic hunting in the distributor position loop. The instability was traced to the interaction between integral control action and the intrinsic integrating behavior of the hydraulic actuator, leading to the adoption of a proportional-only position tracking strategy. The system was validated through Factory Acceptance Tests (FATs) and Site Acceptance Tests (SATs), including signal verification, startup, synchronization, load acceptance and emergency load rejection. Quantitative results demonstrate that during initial commissioning of the new PLC-based PI position loop, the LVDT position error reached 41.59% peak-to-peak, with 351.2 servo-valve reversals per minute. Disabling the integral action reduced the peak-to-peak position error to below 1.5% and eliminated steady-state valve reversals under the tested operating conditions. Separately, the historical 0.966 V oscillation detected in the legacy analog-input chain was resolved during the retrofit. During no-load startup, the unit reached 97% of nominal speed in 49.4 s, with a maximum overshoot of 2.3% and a speed tracking standard deviation of 1.1%. The complete operational cycle was successfully validated under real industrial conditions, including a near-nominal load-rejection test (approximately 2.25 MW), during which the measured speed peaked at 126.4% of nominal speed and the shutdown sequence was completed without protection-system malfunction. The results show that open PLC-based retrofits can improve maintainability, diagnostics and operational reliability in safety-critical industrial machines, while establishing a foundation for future SCADA integration and condition-based maintenance. Full article

This study proposes an intelligent speed-loop controller for a brushless DC motor (BLDCM) drive implemented under a field-oriented control (FOC) scheme. The controller is constructed by embedding the coati optimization algorithm (COA) into a composite sliding mode theory (CSMT) control structure. In sliding mode control (SMC), the use of only one reaching law normally produces a design trade off: increasing the reaching speed tends to aggravate overshoot or chattering, whereas reducing switching activity often slows convergence. To mitigate this compromise, the proposed controller adopts a composite reaching law (CRL) formed by an exponential component and a power component. When the system trajectory is distant from the sliding surface, the exponential component strengthens the reaching action and shortens the transient interval. When the trajectory moves close to the sliding surface, the power component decreases the effective switching intensity, thereby attenuating high-frequency chattering and reducing the overshoot associated with an aggressive exponential action. For adaptive gain selection, the COA search variables are chosen as four controller parameters: the sliding mode gain, the exponential reaching gain, the power reaching gain, and the power exponent. The fitness index is established from the rotor-speed tracking error and the time variation in that error. By imitating the hunting and predator-avoidance behaviors of coatis, the optimization process updates candidate solutions and selects the parameter combination that best matches the current operating condition. The resulting gains are supplied to the composite sliding mode controller (CSMC) so that the BLDCM can follow speed commands rapidly while preserving stable regulation. Because the proposed method performs online optimization of controller gains rather than data-driven training, it can be realized without a large training dataset. MATLAB/Simulink simulations are carried out to examine the effectiveness of the proposed strategy. The controller is compared with four benchmark methods, namely power reaching law (PRL)-based SMC, exponential reaching law (ERL)-based SMC, non-optimized composite reaching law SMC, and zebra optimization algorithm (ZOA)-assisted ERL-based SMC. The simulation results demonstrate that the proposed COA-based composite sliding mode controller improves both speed command-tracking and load-disturbance rejection relative to the comparative controllers. Full article

This study introduces the ’Black-Backed Jackal Optimization’ (BBJO), a nature-inspired meta-heuristic algorithm designed for complex, non-linear, and high-dimensional search spaces. The fundamental mathematical model of BBJO relies on the opportunistic hunting behavior and survivability strategies of the black-backed jackal (Lupulella mesomelas). We use non-linear energy decrease and adaptive Lévy flight to maintain the equilibrium of the search. This allows the algorithm to scan large areas first, then zoom in with a high degree of precision once it has identified a suitable location. This configuration prevents the algorithm from getting stuck on a suboptimal local solution, which is a frequent danger during searches in complex spaces. BBJO has been validated against 23 standard benchmark functions, demonstrating significantly greater accuracy than Particle Swarm Optimization (PSO) on complex and large-scale search spaces. On fixed-size domains ($F_{21}$–$F_{23}$), the BBJO algorithm achieved a 100% success rate with zero standard deviation, surpassing the Grey Wolf Optimizer (GWO) and Differential Evolution (DE), which frequently suffered from structural stagnation. Visual convergence study shows that BBJO efficiently identifies optimal search regions early in the iteration budget, saving time compared to traditional linear decay models. BBJO optimizes fuzzy inference systems (FISs) for two practical applications: autonomous car speed control and industrial furnace regulation. Experimental results indicate that BBJO significantly decreased cumulative penalties and improved steady-state error reduction compared to baseline configurations and established meta-heuristic methods. The results show that BBJO is a reliable and useful technique for engineering optimization. Full article

Wild boar populations have expanded globally due to factors such as habitat restoration, climate change, and adjustments in hunting policies. This expansion has led to agricultural losses, threats to public safety, disease transmission, and ecological damage, thereby exacerbating human–animal conflicts. Current strategies for mitigating conflicts between wild boars and humans face challenges, including insufficient assessment of long-term control effects and limitations in population dynamics monitoring technologies. This paper reviews mitigation strategies for global wild boar conflicts from ecological, economic, and social perspectives and provides a comprehensive evaluation for their effectiveness. Existing mitigation strategies include population control (hunting management and reproductive regulation), habitat management (landscape barriers and food resource regulation), physical protection (electric fences and multimodal deterrence devices), and community engagement mechanisms (ecological compensation and participatory management). The effectiveness of these strategies varies significantly across regions. Global case studies on human–wild boar conflicts demonstrate that multidimensional collaborative governance has achieved notable success in mitigating these conflicts. For example, dynamic management systems based on intelligent monitoring and community participation can effectively reduce the incidence of wild boar conflicts. Given the limitations of the current single-indicator evaluations, this paper proposes a comprehensive evaluation framework encompassing “strategy effectiveness, cost, and sustainability”. The application of synergistic multi-strategy approaches produces significant synergistic effects, exhibiting nonlinear superposition characteristics. Therefore, future efforts should integrate intelligent early warning systems and policy reforms to develop an adaptive social–ecological coupling framework. Full article

Mushrooms, forest fruits and game meat represent three major categories of forest foods in Czechia. This study compares their acquisition mechanisms, market visibility and value-chain positions and provides reference-year, category-specific physical estimates and stage-specific indicative economic values. The analysis integrates pooled national survey data on mushrooms and forest fruits from 2021 to 2025 (N = 5025), a 2022 survey extension on game meat (N = 1000), qualitative interviews with 12 stakeholders in the Czech game-meat value chain conducted by the research team between 2023 and 2024, and official hunting statistics. In the 2024 reference year, mushrooms and forest fruits were estimated through household-collected quantities, whereas game meat was estimated as gross carcass-weight equivalent at the primary procurement stage. The three categories together represented an indicative stage-specific economic value of approximately EUR 324.3 million, but their physical quantities are interpreted as product-specific estimates rather than as directly equivalent units of provisioning value. Mushrooms showed the strongest household-collection profile: 70.4% of respondents reported collection and 20.1% reported purchase. Forest fruits displayed a more mixed acquisition pattern, with particularly high purchase shares for blueberries and raspberries. Collection and purchase were largely independent for mushrooms, whereas complementary relationships prevailed among forest fruits. Game meat had an indicative primary procurement value of EUR 33.57 million and reflected a regulated hunting-based value chain. The findings identify a differentiated forest-food system in which socio-economic significance is shaped by product-specific relationships among household acquisition, market access, value-chain organisation and stage-specific value creation. Full article

This study constructs a novel fractional-order eco-epidemiological predator–prey model, in which disease spreads among predators through environmental transmission, and both cooperative hunting behavior and disease latency delay are incorporated simultaneously. Different from classical integer-order predator–prey models, fractional derivative is adopted to describe the memory-dependent mechanism of ecological populations, and the infection can alter the hunting strategy of diseased predators. The existence, non-negativity, and boundedness of system solutions are proved theoretically. The local stability of all equilibrium points is analyzed, and the conditions for the occurrence of Hopf bifurcation induced by latency delay are derived. Numerical simulations further verify the theoretical results, and quantitatively reveal the separate and combined effects of the fractional order, cooperative hunting coefficient, and latency delay on the dynamical evolution of the population system. Full article

For radial distribution systems (RDSs) to operate efficiently, reliably, and sustainably, distributed generation (DG), capacitor banks (CBs), and network reconfiguration (NR) must be optimally allocated and sized. The main objectives considered in this research are minimizing real power losses, improving voltage profiles, enhancing energy utilization efficiency, and strengthening the operational reliability of distribution networks. To address this challenge, an Improved Frilled Lizard Optimizer (IFLO) is proposed to determine the optimal placement and sizing of DGs, CBs, and NR while satisfying system operational constraints. FLO is inspired by the adaptive survival and movement characteristics of frilled lizards in their natural ecosystem. The optimization mechanism of FLO is driven by hunting behavior for broad exploration and tree-climbing behavior for localized movement, enabling effective search and exploitation of promising regions. The IFLO introduces a defensive strategy phase, mimicking the lizard’s survival responses, and an adaptive local search phase, which models agile movement and stabilization behaviors. These enhancements improve the algorithm’s capability to reduce power losses, improve voltage regulation, increase network efficiency, and facilitate the effective integration of distributed energy resources into modern power distribution infrastructures. Comprehensive simulations on the IEEE 69-bus and the practical large-scale 141-bus RDS evaluate the impacts of DG and CB installation under practical operating constraints. This study investigates six scenarios involving different combinations of DG, CB, and NR to support efficient network planning and operation. Furthermore, recent optimization techniques, including Bezier Curve-Based Optimization (BCO), Horned Lizard Optimization Algorithm (HLOA), Whale Optimization Algorithm (WOA), Jaya Algorithm, and Particle Swarm Optimization (PSO), are implemented on the studied systems, and their results are compared with those of the proposed IFLO. The findings demonstrate that the suggested strategy outperforms existing optimization approaches in terms of convergence speed, solution quality, and network performance enhancement. The IFLO algorithm achieves an active power loss reduction of 92.69% for the large-scale system, while significantly improving voltage stability and operational efficiency. These outcomes contribute to the development of resilient, energy-efficient, and intelligent distribution infrastructures capable of supporting increased penetration of distributed energy resources under diverse operating conditions. Full article

Conventional signature-based defenses no longer protect the heterogeneous, large-scale infrastructures that the Internet of Things (IoT) now constitutes. Large language models (LLMs) and agentic artificial intelligence (AI)—systems that autonomously perceive, reason, plan, and act—open a path to self-defending IoT ecosystems, but the integrating literature remains fragmented. Within the IEEE Xplore, ACM Digital Library, and MDPI literature, this survey is, to the best of our knowledge, among the first systematic reviews of agentic AI and LLM-driven approaches for autonomous IoT cybersecurity. Following a PRISMA 2020 protocol, we analyze 153 peer-reviewed studies published between 2020 and 2026 in IEEE Xplore, the ACM Digital Library, and MDPI journals. We organize the corpus along a four-pillar taxonomy: agent architecture (single- vs. multi-agent), reasoning strategy (chain-of-thought, ReAct, plan-and-solve, tool use), action scope (detection, response, threat hunting, vulnerability discovery, deception), and deployment topology (edge, fog, cloud). We synthesize four flagship application domains, consolidate datasets and benchmarks, and analyze open challenges including hallucination, prompt-injection robustness, explainability, privacy, latency, and governance. A 2026 research roadmap identifies federated agentic learning, verifiable autonomous reasoning, trustworthy multi-agent collaboration, and resource-hardened edge agents as high-priority directions. A companion reproducibility kit—prompt templates, reference single- and multi-agent loops, and an Edge-IIoTset-style evaluation harness, released as illustrative scaffolding rather than a validated framework—is released publicly and archived on Zenodo (DOI 10.5281/zenodo.20726552). Full article

Location-based games (LBGs) have increasingly been adopted in education for experiential, situated, and collaborative learning. While used in subjects such as geography and history, their application in mathematics remains underexplored, partly because mathematical concepts are abstract and hard to embed in spatial game environments. This study examines the feasibility and educational potential of LBGs in lower-secondary mathematics. Using a mixed-methods approach, two location-based activities were tested with 28 students aged 12–14. In the first, students used Global Positioning System (GPS)-enabled devices to reach landmarks (e.g., a volleyball court, a church), where they took on-site measurements and applied geometric reasoning to calculate areas, perimeters, and volumes. In the second, they followed a treasure hunt, solving algebraic equations and word problems to form a secret word. Questionnaires, observations, and teacher interviews showed high engagement, participation, and collaboration, with students viewing the activities as meaningful revision. Teachers found them valuable and feasible within curricular limits, despite challenges such as preparation time, technical issues, and regulations. However, given the small sample and exploratory design, findings should be interpreted with caution: no general inferences can be drawn, and no direct learning-outcome measures were used. The study offers empirical insights into designing mathematics-oriented LBGs and future research directions. Full article

Machine learning (ML) methods for network anomaly detection are emerging as effective proactive strategies in threat hunting, substantially reducing the time required for threat detection and response. However, the challenges in training and maintaining ML models, coupled with frequent false positives, diminish their acceptance and trustworthiness. In response, Explainable AI (XAI) techniques have been introduced to enable cybersecurity operations teams to assess alerts generated by AI systems more confidently. Despite these advancements, XAI tools have encountered limited acceptance from incident responders and have struggled to meet the decision-making needs of both analysts and model maintainers. Large Language Models (LLMs) offer a unique approach to tackling these challenges. Through tuning, LLMs have the ability to discern patterns across vast amounts of information and meet varying functional requirements. In this research, we introduce the development of HuntGPT, a specialized intrusion detection dashboard created to implement a Random Forest classifier trained utilizing the KDD99 dataset. The tool incorporates XAI frameworks like SHAP and Lime, enhancing user-friendliness and intuitiveness of the model. When combined with a GPT-3.5 Turbo conversational agent, HuntGPT aims to deliver detected threats in an easily explainable format, emphasizing user understanding and offering a smooth interactive experience. We investigate the system’s comprehensive architecture and its diverse components, assess the prototype’s technical accuracy using the Certified Information Security Manager (CISM) Practice Exams, and analyze the quality of response readability across six unique metrics. Our results indicate that conversational agents, underpinned by LLM technology and integrated with XAI, can enable a robust mechanism for generating explainable and actionable AI solutions, especially within the realm of intrusion detection systems. Full article

Multi-objective optimization problems (MOPs) are common in practical scenarios where decision-makers need to accomplish several competing goals. Single-objective optimization techniques do not guarantee applicability in these scenarios. As such, there has been a need for the development of metaheuristics capable of generating multiple trade-off solutions. Harris Hawks Optimization (HHO) has been shown to possess strong exploration and exploitation capabilities for the solution of optimization problems, owing to the collaborative hunting tactics of Harris’s hawks. Therefore, the Multi-objective Harris Hawks Optimization (MHHO) algorithm was suggested to generalize HHO to handle MOPs. By combining the mechanisms of Pareto dominance, diversity preservation, elitism, adaptiveness, and others, MHHO approaches the Pareto-optimal front and provides decision-makers with several high-quality nondominated solutions. This study comprehensively examines MHHO, elaborating on its theoretical background, algorithmic variants, and fields of application. MHHO has been implemented in different disciplines. Using the Scopus database to conduct a bibliometric study, the publication growth, research development, and the application of MHHO in various fields of study were analyzed. By classifying the extant contributions into original, modified, and hybrid versions, the study provides a detailed outline of the algorithm’s progression. Applications spanning engineering, cloud computing, scheduling, networking, bioinformatics, and energy systems are analyzed, illustrating the broad adaptability of MHHO. A constructive critique has been conducted to evaluate some limitations including premature convergence, scalability issues, and difficulty in addressing disconnected Pareto regions. This review shows the versatility and potential of MHHO in tackling different optimization problems. In addition, further research is needed on the development of more sophisticated hybrid methods, tailored improvements, and more refined techniques for the preservation of diversity. Full article

Mediterranean coastal groundwater-dependent ecosystems are among the most vulnerable environments to the combined effects of climate change and local anthropogenic pressures, yet long-term quantitative assessments disentangling these drivers remain limited. The 41-year hydro-ecological dynamics (1984–2025) of “Le Soglitelle”, a transitional man-made coastal GDE located in the Campania Plain (southern Italy), were reconstructed across three management regimes: illegal hunting via electric pumps augmentation of flooded areas (1984–2004), post-seizure transition (2005–2015), and fenced natural reserve sustained by artesian wells flow (2016–2025). A monthly multi-sensor time series of seven spectral indices was derived from cross-calibrated Landsat program Surface Reflectance products via Google Earth Engine. Spectral indices were then combined with climatic variables (precipitation, reference evapotranspiration, air temperature) and then integrated in a statistical framework including Mann–Kendall test, Pettitt test, and Principal Component Analysis. Significant breakpoints were identified for the water fraction (2007; mean decrease from 0.18 to 0.09) and the Normalized Difference Vegetation Index (2009; mean increase from 0.30 to 0.42), consistent with a hydrological regime shift following the interruption of anthropogenic pressures. The relationship between the water fraction and the Vegetation Soil Salinity Index was 2.7 times steeper in the last period than the first one, indicating that, for an equivalent flooded extent, osmotic stress on vegetation is substantially higher under the artesian flow alone, likely due to reduced dilution of saline inputs combined with the effect of ongoing climate change. PCA showed that PC1 reflected the transition from anthropogenic to more natural system conditions, whereas PC2 was associated with increasing ET₀, became more prominent during the last period of management, suggesting a shift toward stronger climate-driven control. Long-term satellite monitoring provides a quantitative baseline for designing targeted management interventions aimed at sustaining ecosystem functioning under ongoing Mediterranean warming. Full article

Tropical forests of the Amazon support exceptional biodiversity while sustaining the livelihoods, cultures, and food systems of Indigenous communities. In Loreto, Peru, hunting remains central to both subsistence and market economies, yet its sustainability depends on ecological dynamics and sociocultural systems that shape harvest behavior. Here, we evaluate the potential for sustainable wildlife management in the Maijuna–Kichwa Regional Conservation Area (MKRCA) by integrating a spatially explicit biodemographic model of hunting with a targeted review of Maijuna hunting practices, governance, and economic context. Using participatory mapping data from 19 hunters in the community of Sucusari, we parameterized a model to estimate species-specific depletion under current and projected hunting scenarios. Model results suggest that current harvest rates are largely sustainable, with localized depletion near settlements but relatively intact populations across the broader landscape, supported by access to remote hunting areas and nearby source populations. The literature review reveals that Maijuna sociocultural systems, including territorial hunting norms, seasonal mobility, food-sharing practices, and species-specific taboos, may function as informal management institutions that distribute hunting pressure and limit overexploitation. Together, these findings suggest that both ecological conditions and sociocultural institutions in Sucusari are conducive to sustainable wildlife management if supported by adaptive co-management approaches. However, external pressures, particularly a proposed highway, may fragment existing source–sink dynamics and pose a significant risk to long-term sustainability. Full article