Search Results (470)

The Liuwan burial complex is the largest known prehistoric clan-based cemetery in the upper Yellow River region, making its preservation vital for Chinese cultural heritage and sustainable local development. To address threats from unregulated agricultural activities and illegal looting, this study proposes a non-invasive preventive protection approach. Surface-visible tombs were identified using low-altitude UAV imagery and deep learning models (YOLOv8n, YOLOv5n, RT-DETR-l, and Hyper-YOLO). By incorporating environmental factors such as elevation, slope, aspect, distance to water, Topographic Wetness Index, and Topographic Position Index, potential tomb distributions were modeled on the Biomod2 platform and key environmental drivers were analyzed. Hyper-YOLO achieved the highest identification accuracy (94.4%). The optimal model, EMwmean (TSS = 0.492, AUC = 0.798), showed that high-potential tomb areas are mainly concentrated in the central region, with tombs preferring elevations of 1964–1978 m, south-facing slopes, and slopes of 13.14–19.19°. This study demonstrates the feasibility of using deep learning to identify surface-visible tombs and predict their potential distributions based on environmental characteristics, thereby providing priority references for heritage protection in Liuwan rather than a definitive inventory of all subsurface remains or cultural phases. Full article

Wild birds are increasingly exposed to anthropogenic threats that compromise population viability. Wildlife rehabilitation centers provide valuable data to monitor these pressures and their conservation implications. This retrospective study analyzed wild bird admissions to the Wildlife Recovery Center of the University of Trás-os-Montes and Alto Douro (CRAS-UTAD) in northern Portugal between January 2007 and October 2025. A total of 5090 birds from 135 species and 44 families were admitted. Causes of admission were grouped into 11 categories, and outcomes into 7. Admissions increased over time, rising from approximately 160 birds in 2007 to more than 430 in 2025, although the overall temporal trend was not statistically significant. Birds of prey were the most frequently admitted group, particularly Strix aluco (9.16%) and Buteo buteo (8.00%). The most common causes of admission were orphaned birds (26.2%), followed by seizures from illegal captivity (12.2%) and collisions (5.0%). Overall, 43.2% of birds were released, while 29.4% died and 18.3% were euthanized. Admission cause was strongly associated with outcome, with electrocution showing the poorest prognosis and seizure from illegal captivity the highest probability of release. These findings highlight the major impact of human activities on wild bird morbidity and mortality and reinforce the importance of rehabilitation centers as sentinels for conservation and mitigation strategies. Full article

Ecological risk assessment of ecosystems facing anthropogenic pressures informs coastal management. This study evaluated the ecological risk of ecosystems in two coastal municipalities in the Gulf of California, Mexico. The coastal area under study spans 175 km of coastline and includes various ecosystems, as well as the cities of Guaymas and Empalme (~160,000 inhabitants). Ecological risk was assessed by surveying the opinions of experts on local and global activities and influences (climate change), the ecological consequences of hazards, and the resilience (fragmentation) and natural recovery of ecosystems. In addition, potential synergies between human activities and the effects of climate change were identified. The results showed that the main threats are discharges of raw or poorly treated wastewater into the sea, the generation and dumping of garbage, and illegal fishing. Wastewater discharges represent the local threat that interacts most intensively with the effects of climate change. Mangroves, coastal water bodies, and rocky shores face the greatest ecological risk due to continuous exposure to anthropogenic threats, poorly planned urban growth, and industrial development. Approximately 20% of the coastal zone is estimated to correspond to the metropolitan areas of Guaymas and Empalme, where the greatest ecological risk occurs, and these represent opportunities to promote coastal management processes aimed at ecosystem restoration and planned urban development to prevent the loss of coastal ecosystem functions and the services they provide to society. Full article

Illegal, unreported, and unregulated (IUU) fishing activities have become one of the most critical challenges facing the global fish industry, particularly in developing countries, with the economic impact of fish fraud reaching billions of dollars annually. A major contributor to this problem is the limitation of conventional fish supply chain systems, which lack secure data sharing among stakeholders, fail to provide trusted product information to consumers, and offer insufficient transparency for regulatory authorities. These shortcomings facilitate fraud and weaken trust and oversight across the supply chain. Blockchain technology has demonstrated strong capability to address key cybersecurity challenges by enhancing traceability, transparency, and tamper-resistant data integrity across distributed supply chain stakeholders. In this paper, we present an enterprise-oriented prototype of a secure, permissioned blockchain-based fish supply chain system designed to enable trusted data sharing and end-to-end traceability across multi-stakeholder environments. Building upon our prior work in Ethereum-based seafood quality monitoring, this study contributes: (1) a modular, consortium-grade architecture implemented using Hyperledger Fabric and containerized via Docker, supporting scalable organizational participation; (2) formal UML-based system modeling of supply chain actors, assets, and lifecycle transitions; and (3) custom chaincode logic that enforces ownership transfer workflows and regulatory compliance policies. In addition, the architecture is designed as agent-ready, exposing standardized APIs that enable future integration of autonomous AI-driven client applications for proactive supply chain orchestration. By leveraging a private, permissioned network model, the functional prototype demonstrates the feasibility of improving data veracity and providing a practical foundation for mitigating fraud and enhancing regulatory oversight in the global fish industry. Full article

This study applies InSAR time series analysis derived from Sentinel-1 satellite data (ascending and descending orbits) processed with ISCE2 and StaMPS (v.4.1) software to evaluate deformation dynamics in three landfill types near Gijón, Spain. Initially, the data were validated against the European Ground Motion Service (EGMS) dataset using a set of Persistent Scatterers (PS) in an urban control area through two analytical approaches (EGMS protocol and PSDefoPAT(2023)). The results showed high consistency, with 82–85% of points classified as highly reliable. Subsequently, this control group was compared with PS from each landfill type (active sanitary, operational inert, and closed inert). Significant deformation differences were found in each landfill type: the active sanitary landfill exhibited distinct anomalies depending on orbit, with strong residual variance instability detected (p < 0.003) in both. Operational inert landfills showed significant anomalies (p < 0.001) in both orbits with variable stability, while closed inert landfills displayed strong stability (p > 0.7) and variable anomalies. These results confirm the efficacy of InSAR approaches for detecting active landfill zones to aid in locating illegal or unauthorized dumping sites and to direct in situ inspection planning. Full article

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with Global Positioning System (GPS) tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system’s effectiveness in detecting elevated pollutant levels in rural areas with solid fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m³ in forested areas to as high as 160 μg/m³ in industrial zones, while CO₂ levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement. Full article

This study evaluates the costs and benefits of participatory forest management (PFM) versus non-participatory forest management based on the perceptions and involvement of local communities in the Kilombero Valley Ramsar site, Tanzania. The area hosts ecologically significant wetlands managed through different regimes: forests managed by local communities under PFM and protected areas controlled by national authorities. Using data collected through focus groups, key interviews, household surveys, and direct observations in two villages—Siginali (PFM) and Kilama (non-participatory)—this research explores perceptions of two different forest management approaches. The results revealed: (i) a generally low awareness and participation in forest management activities in both villages; (ii) restrictions on forest resource access, essential for local livelihoods, were common and often poorly accepted in the two villages; (iii) neither approach alleviates poverty, instead, strict regulations have worsened livelihoods by eliminating traditional income sources; (iv) forced participation in patrols and fire control was also noted as an unfair burden without direct compensation; and (v) the “fortress” model is perceived as more effective at improving forest health and stopping illegal activity due to stricter patrols. The study concludes that while PFM supports forest sustainability, it needs enhanced local engagement, benefit-sharing mechanisms, and complementary income-generating initiatives such as ecotourism to sustainably balance conservation and community welfare. Full article

Travel time prediction (TTP) is a fundamental pillar of intelligent transportation systems (ITS). However, deploying highly parameterized deep learning models in data-scarce environments—referred to as the “cold-start” problem—remains a critical bottleneck, frequently leading to overfitting and severe error accumulation on ultra-long trajectories. To surmount these limitations, this study proposes the Dual-Attentional Gated Residual Network (DAGRN), a data-efficient forecasting framework driven by a novel topology-temporal coordination mechanism. Specifically, the framework introduces three integrated innovations: (1) transforming the primal network into a physics-aware Line Graph to explicitly filter out illegal movements and dynamically modulating topological propagation via Feature-wise Linear Modulation (FiLM); (2) coupling a Bidirectional GRU backbone with a Multi-Head Attention module to simultaneously capture global trends and localized intersection delays; (3) employing a Gated Residual Fusion mechanism that preserves dimensional consistency and facilitates gradient flow in extensive sequences. To rigorously validate the model’s robustness, we conduct evaluations on a highly constrained, stratified dataset comprising merely 2000 trajectories. Experimental results demonstrate that DAGRN achieves state-of-the-art predictive precision with an RMSE of 415.485 s and an R² of 0.848, significantly outperforming 12 advanced baseline models and reducing error by up to 13.8% against the strongest graph baseline. Comprehensive ablation studies confirm the absolute necessity of the Multi-Head Attention module, whose removal causes the most severe performance degradation (RMSE surging to 521.495 s). Ultimately, DAGRN presents a readily deployable solution for sparse-data ITS regimes, actively paving the way for future hybrid integrations with microscopic traffic simulations and evolutionary road network optimization algorithms. Full article

This study addresses a research gap in integrated environmental and spatial assessments of municipal solid waste (MSW) systems in rapidly growing secondary cities in Central Asia. Using a mixed-method approach that combines field audits, GIS-based spatial analysis, environmental monitoring, and greenhouse gas modeling, the study evaluates waste composition, infrastructure coverage, and ecological risks in Shymkent, Kazakhstan. The results reveal uneven distribution of legal waste containers, a 5–7% annual increase in illegal dumping sites, and dust (TSP) concentrations exceeding WHO thresholds near active disposal zones. Spatial hotspot mapping identifies critical pressure areas in peripheral districts, while morphological audits show a rising share of plastics and construction debris. The findings support district-specific policy interventions, infrastructure modernization, and behavior-driven recycling incentives. The proposed methodology provides a replicable framework for sustainable MSW governance in urban contexts. These results contribute to evidence-based municipal waste governance and regional sustainability planning. Full article

The natural resources and local communities of Madre de Dios, Peru, face severe environmental degradation due to illegal mining, deforestation, and the expansion of agricultural activities, threatening one of the most ecologically sensitive regions of the Amazon. This research proposes a low-carbon and bioclimatic architectural design for a Sustainable Interpretation and Research Center dedicated to the conservation of the ecosystems of Manu National Park. The study is based on an analysis of the surrounding environment in terms of flora, fauna, and climate, applying bioclimatic strategies focused on sustainability and supported by specialized digital tools (Revit 2024, Canva, Global Mapper 2024, SketchUp 2024, Photoshop 2022, and Illustrator 2022). The project presents a bioclimatic architectural design that integrates constructive techniques ensuring thermal comfort in a warm-humid climate, while promoting the use of clean technologies such as photovoltaic solar systems generating 15,571.8 kWh per year and a rainwater harvesting system collecting 70,675 L annually. The infrastructure is built with bamboo and locally sourced wood, renewable materials that ensure durability and low environmental impact. In addition, the design includes the reforestation of 17.92% of the total area and 3.46% of public spaces, incorporating native species such as Brazil nut, rosewood, and capirona to reinforce local biodiversity. Overall, this research demonstrates how low-carbon construction, renewable materials, and bioclimatic design can contribute to sustainable development, environmental awareness, and the preservation of natural ecosystems in tropical regions. Full article

Protected area management plays a crucial role in conserving biodiversity, especially in areas where increasing demand for natural resources is associated with fast-growing human populations, such as West Africa. Investigating the perceptions of people with first-hand knowledge of protected area management provides important insights about biodiversity conservation, wildlife law enforcement, and human activities to inform adaptive management. Using 442 semi-structured interviews, we assessed the perceptions of park rangers and local residents in and around Fazao-Malfakassa National Park in Togo, West Africa, which was managed by the non-profit Franz Weber Foundation from 1990 to 2015, and since 2015 has been managed by the government of Togo. Both rangers and residents reported significant economic concerns following the park’s transfer from private to state management, with salary declines negatively affecting rangers and declines in community development projects and income-generating activities negatively affecting residents. Law enforcement capacity and resources also declined under state management, severely undermining the ability of rangers to curb illegal activities in the park, especially poaching and the destruction of trees to harvest wild honey and produce commercial charcoal. All rangers and most residents who had experience with both private and state park management preferred private management. There is an urgent need to increase surveillance and law enforcement capacity in the park to combat poaching and other illegal activities, and to engage local communities in the park’s long-term protection. To this end, rangers and residents are calling for change, and specifically recommend returning the park to competent private management to safeguard Togo’s last large refuge for nature and wildlife. Full article

The illegal wildlife trade remains a significant threat to biodiversity in South Africa. The poaching of native species in the country has increased over the years, primarily driven by the demand for abalone, rhino horns, and pangolin scales. This study analysed TRAFFIC wildlife crime records between 1984 and 2025 to identify hotspots, trends in enforcement over time, and the most affected species. We found that provinces such as Gauteng and KwaZulu-Natal have the highest diversity of species affected, while the Western Cape recorded the highest number of incidents, predominantly seizures. Seizure was the most common wildlife activity, followed by poaching and illegal harvesting, with fewer cases of smuggling, breeding, and prosecution. A total of 50 species across nine animal classes were impacted, with white rhinoceros (Ceratotherium simum (Burchell, 1817)), abalone (Haliotis midae (Linnaeus, 1758)), lion (Panthera leo (Linnaeus, 1758)), and ground pangolin (Smutsia temminckii (Smuts, 1832)) among the most frequently targeted. Correlation analysis revealed a strong positive relationship between seizures and arrests (Pearson’s r = 0.90, p = 0.001) across provinces. This indicates a substantial strengthening of law-enforcement activity across provinces, likely driven by enhanced detection or reporting, as reflected in a rising proportion of cases resulting in arrests. Species such as elephants and pangolins were associated with enforcement outcomes, particularly those involving horns, tusks, scales, and dead specimens. There is a need for targeted interventions in high-risk areas, and provinces must collaborate in combating the wildlife trade. Limitations in data completeness and species representation suggest the need for improved surveillance and reporting mechanisms to fully understand and combat illegal wildlife trade in South Africa. Full article

Wildfire escalation is increasingly threatening ecosystems and communities in Khyber Pakhtunkhwa (KP), Pakistan, particularly in forest and rangeland landscapes where ecological flammability interacts with human activity. While environmental and climatic drivers are well studied, governance factors remain underexplored despite their decisive role in shaping how ecological risk translates into disasters. Regional forests show considerable ecological diversity, including chir pine-dominated stands, mixed temperate conifer forests, broadleaved oak-associated systems, and shrub rangeland mosaics, each differing in fuel structure and fire behavior. Dependence on fuelwood collection, grazing, and forest access further influences ignition probability and fire spread. This study examines how governance failures influence wildfire risk and severity through a Governance-Fire Risk Framework. Governance is treated as a determining institutional condition affecting prevention capacity, regulation of hazardous land use, fuel management, and emergency response effectiveness. A cross-sectional survey of 540 stakeholders from rural (Dir Lower, Dir Upper) and peri-urban districts (Swat, Mansehra, Abbottabad) was analyzed using SPSS (version 26) and AMOS (version 24) (CFA and SEM). Governance failure significantly escalates wildfire risk through delayed emergency response, regulatory non-compliance, political interference, and weak institutional coordination. Institutional preparedness and response capacity reduce risks, whereas corruption intensifies them. Corruption functions through illegal land conversion, diversion of fire management resources, procurement irregularities, nepotistic staffing, and selective enforcement, increasing ignition sources, fuel accumulation, and response delays. Rural districts show stronger governance-fire linkages. Wildfire escalation in KP is governance-driven in interaction with ecological conditions and community dependence on forest resources. Effective mitigation requires anti-corruption measures, rapid response systems, stronger enforcement, and improved preparedness. The study offers a transferable governance-focused framework for wildfire management in fire-prone developing regions. Full article

Due to people’s increasing access to computers, IT security has become extremely important in today’s society. This increase in connectivity has also led cybercriminals to take advantage of the anonymity and privacy offered by the Internet to carry out illegal activities. One of the most innovative solutions for protecting systems and networks is the use of artificial intelligence. However, these same technologies can become attractive targets for attackers seeking to compromise an organisation’s security. This paper analyses attacks targeting machine learning algorithms used in the classification of messaging application traffic, using Generative Adversarial Networks. Three algorithms were specifically evaluated and the results obtained were compared. The analyses show that all algorithms have a certain degree of vulnerability to malicious manipulation, highlighting the need to strengthen their defence mechanisms. Full article

Al-Musk Lake, an artificial waterbody of 2.9 km² formed by illegal dumping of 9.5 million cubic meters of raw sewage near Jeddah, Saudi Arabia, remains a significant subsurface environmental hazard after drainage activities in 2010. The current research employs a multidisciplinary approach, integrating geological mapping, aeromagnetic and electromagnetic surveys, Landsat imagery, and chemical analyses, to investigate contamination migration and accumulation. The objective is to delineate subsurface contamination pathways and assess their impact on soil and groundwater quality. Frequency-domain electromagnetic (FDEM) surveys identified areas of high apparent conductivity (up to 200 mS/m at 2000 kHz), indicative of deep contamination saturation. Chemical analysis of water and soil samples revealed distressing levels of heavy metals, Na⁺ up to 2400 mg/L, Ca²⁺ up to 3648 mg/L, and Fe up to 4150 mg/L, far exceeding irrigation safe standards. Findings locate two at-risk areas several kilometers from the lake, where contaminants accumulate through basement depressions controlled by faults. These pose immediate risks to adjacent residential areas and expanding agricultural belts. In short, subsurface contamination continues to spread westward. Short-term remedies include halting agricultural activities, treating in-storage water, and paving infiltration zones. A larger-scale geophysical survey, along with denser geochemical sampling and analysis, is necessary to guide long-term remediation and to protect public health. Full article