Search Results (313)

Via a One Health approach, this study concomitantly assessed the susceptibility of humans and dogs to Trypanosoma cruzi infections on three islands and in two mainland seashore areas of southern Brazil. Human serum samples were tested using an enzyme-linked immunosorbent assay (ELISA) to detect anti-T. cruzi antibodies, while dog serum samples were tested using indirect fluorescent antibodies in an immunofluorescence assay (IFA). Seropositive human and dog individuals were also tested using quantitative polymerase chain reaction (qPCR) in corresponding blood samples. Overall, 2/304 (0.6%) human and 1/292 dog samples tested seropositive for T. cruzi by ELISA and IFA, respectively, and these cases were also molecularly positive for T. cruzi by qPCR. Although a relatively low positivity rate was observed herein, these cases were likely autochthonous, and the individuals may have been infected as a consequence of isolated events of disturbance in the natural peridomicile areas nearby. Such a disturbance could come in the form of a fire or deforestation event, which can cause stress and parasitemia in wild reservoirs and, consequently, lead to positive triatomines. In conclusion, T. cruzi monitoring should always be conducted in suspicious areas to ensure a Chagas disease-free status over time. Further studies should also consider entomological and wildlife surveillance to fully capture the transmission and spread of T. cruzi on islands and in seashore mainland areas of Brazil and other endemic countries. Full article

The manuscript examines the cause-and-effect relationships of fires in the Republic of Serbia over a fifteen-year period, primarily from the aspect of human safety. For this purpose, numerical variables describing the number of injuries and deaths in fires were introduced, on which various analysis and modeling techniques were implemented, which can be viewed in the context of data mining (DM). First, for both observed variables, stochastic modeling of their temporal dynamics was analyzed, and subsequently, cluster analysis of the values of these variables was performed using two different methods. Finally, by interpreting these variables as outputs (objectives) for the classification problem, several decision trees were formed that describe the influence and relationship of different fire causes on situations in which injuries or human casualties occur or not. In that way, several different types of fires have been identified, including rare but deadly incidents that require urgent preventive measures. Key risk factors such as fire cause, location, season, etc., have been found to significantly influence human casualties. These findings provide practical insights for improving fire protection policies and emergency response. Through such a comprehensive analysis, it is believed that some important results have been obtained that precisely describe the specific relationships between the causes and consequences of fires occurring in the Republic of Serbia. Full article

The Mediterranean region, identified by scientists as a ‘climate hot spot’, is experiencing warmer and drier conditions, along with an increase in the intensity and frequency of extreme weather events. One such extreme phenomena is droughts. The recent wildfires in this region are a concerning consequence of this phenomenon, causing severe environmental damage and transforming natural landscapes. However, droughts involve a two-way interaction: On the one hand, climate change and various human activities, such as urbanization and deforestation, influence the development and severity of droughts. On the other hand, droughts have a significant impact on various sectors, including ecology, agriculture, and the local economy. This study investigates drought dynamics in four Mediterranean countries, Greece, France, Italy, and Spain, each of which has experienced severe wildfire events in recent years. Using satellite-based Earth observation data, we monitored drought conditions across these regions over a five-year period that includes the dates of major wildfires. To support this analysis, we derived and assessed key indices: the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), and Normalized Difference Drought Index (NDDI). High-resolution satellite imagery processed within the Google Earth Engine (GEE) platform enabled the spatial and temporal analysis of these indicators. Our findings reveal that, in all four study areas, peak drought conditions, as reflected in elevated NDDI values, were observed in the months leading up to wildfire outbreaks. This pattern underscores the potential of satellite-derived indices for identifying regional drought patterns and providing early signals of heightened fire risk. The application of GEE offered significant advantages, as it allows efficient handling of long-term and large-scale datasets and facilitates comprehensive spatial analysis. Our methodological framework contributes to a deeper understanding of regional drought variability and its links to extreme events; thus, it could be a valuable tool for supporting the development of adaptive management strategies. Ultimately, such approaches are vital for enhancing resilience, guiding water resource planning, and implementing early warning systems in fire-prone Mediterranean landscapes. Full article

Increasing wildfires are causing global concerns about ecosystem functioning and services. Although some wildfires are caused by natural ignitions, it is also important to understand how human ignitions and human-related factors can contribute to wildfires. While dynamic global vegetation models (DGVMs) have incorporated fire-related modules to simulate wildfires and their impacts, few models have fully considered various human-related factors causing human ignitions. Using global examples, this study aims to identify key factors associated with human impacts on wildfires and provides suggestions for enhancing model simulations. The main categories explored in this paper are human behavior and activities, socioeconomic background, policy, laws, regulations, and cultural and traditional activities, all of which can influence wildfires. Employing an integrated and interdisciplinary assessment approach, this study evaluates existing DGVMs and provides suggestions for their improvement. Full article

Forest fire detection is an essential application in environmental surveillance since wildfires cause devastating damage to ecosystems, human life, and property every year. The effective and accurate detection of fire is necessary to allow for timely response and efficient management of disasters. Traditional techniques for fire detection often experience false alarms and delayed responses in various environmental situations. Therefore, developing robust, intelligent, and real-time detection systems has emerged as a central challenge in remote sensing and computer vision research communities. Despite recent achievements in deep learning, current forest fire detection models still face issues with generalizability, lightweight deployment, and accuracy trade-offs. In order to overcome these limitations, we introduce a novel technique (FireNet-KD) that makes use of knowledge distillation, a method that maps the learning of hard models (teachers) to a light and efficient model (student). We specifically utilize two opposing teacher networks: a Vision Transformer (ViT), which is popular for its global attention and contextual learning ability, and a Convolutional Neural Network (CNN), which is esteemed for its spatial locality and inductive biases. These teacher models instruct the learning of a Swin Transformer-based student model that provides hierarchical feature extraction and computational efficiency through shifted window self-attention, and is thus particularly well suited for scalable forest fire detection. By combining the strengths of ViT and CNN with distillation into the Swin Transformer, the FireNet-KD model outperforms state-of-the-art methods with significant improvements. Experimental results show that the FireNet-KD model obtains a precision of 95.16%, recall of 99.61%, F1-score of 97.34%, and mAP@50 of 97.31%, outperforming the existing models. These results prove the effectiveness of FireNet-KD in improving both detection accuracy and model efficiency for forest fire detection. Full article

Since 2020, the Gs/Gd H5N1 influenza virus (clade 2.3.4.4b) has established itself within wild bird populations across Asia, Europe, and the Americas, causing outbreaks in wild mammals, commercial poultry, and dairy farms. The impacts on the bird populations and the agricultural industry has been significant, requiring a One Health approach to enhanced surveillance in both humans and animals. To support pandemic preparedness efforts, we evaluated the Cepheid Xpert Xpress CoV-2/Flu/RSV plus kit and the BioFire Respiratory 2.1 Panel for their ability to detect the presence of non-seasonal, zoonotic influenza A viruses, including circulating H5N1 viruses from clade 2.3.4.4b. Both assays effectively detected the presence of influenza virus in clinically-contrived nasal swab and saliva specimens at low concentrations. The results generated using the Cepheid Xpert Xpress CoV-2/Flu/RSV plus kit and the BioFire Respiratory 2.1 Panel, in conjunction with clinical and epidemiological findings provide valuable diagnostic findings that can strengthen pandemic preparedness and surveillance initiatives. Full article

(1) Background: Changes in the spatial, temporal, and magnitude-related patterns of fires caused by humans are expected to exacerbate with climate change, significantly impacting ecosystems and societies worldwide. However, our understanding of fire regimes in many regions remains limited, largely due to the inherent complexity of fire as an ecological process. Pyrogeography, combined with unsupervised learning methods and the availability of long-term satellite data, offers a robust framework for approaching this problem. The purpose of the study is to identify the pyroregions of the Argentine Gran Chaco, the world’s largest continuous tropical dry forest region. (2) Methods: Using globally available fire occurrence datasets, we computed five fire metrics, related to the extent, frequency, intensity, size, and seasonality of fires at three spatial scales (5, 10, and 25 km). In addition, we tested two widely used cluster algorithms, the K-means algorithm and the Gaussian Mixture Model (GMM). (3) Results and Discussion: The identification of pyroregions was dependent on the clustering algorithm and scale of analysis. The GMM algorithm at a 25 km scale ultimately demonstrated more coherent ecological and spatial distributions. GMM identified six pyroregions, which were labeled based on three metrics in the following order: annual burned area (categorized in low, regular or high), interannual variability of fire (rare, occasional, frequent), and fire intensity (low, moderate, intense). The values were as follows: LRM (22% of study area), ROI (19%), ROM (14%), LOM (10%), ROL (9%), and HFL (4%). (4) Conclusions: Our study provides the most comprehensive delineation of the Argentine Gran Chaco’s Dry Forest pyroregions to date, and highlights both the importance of determining the optimal scale of analysis and the critical role of clustering algorithms in efforts to accurately characterize the diverse attributes of fire regimes. Furthermore, it emphasizes the importance of integrating fire ecology principles and fire management perspectives into pyrogeographic studies to ensure a more comprehensive and meaningful characterization of fire regimes. Full article

Wildfires, whether natural or human-caused, significantly alter soil properties and increase soil erosion susceptibility, particularly through changes in rill detachment capacity (Dc). This study aimed to evaluate the influence of fire intensity on key soil properties and to recognize their relationships with Dc under controlled laboratory conditions. The research was conducted in the Darestan Forest, Guilan Province, northern Iran, a region characterized by a Mediterranean semi-arid climate. Soil samples were collected from three fire-affected conditions: unburned (NF), low-intensity fire (LF), and high-intensity fire (HF) zones. A total of 225 soil samples were analyzed using flume experiments at five slope gradients and five flow discharges, simulating rill erosion. Soil physical and chemical characteristics were measured, including hydraulic conductivity, organic carbon, sodium content, bulk density, and water repellency. The results showed that HF soils significantly exhibited higher rill detachment capacity (1.43 and 2.26 times the values compared to the LF and NF soils, respectively) and sodium content and lower organic carbon, hydraulic conductivity, and aggregate stability (p < 0.01). Strong correlations were found between Dc and various soil properties, particularly a negative relationship with organic carbon. The multiple linear equation had good accuracy (R² > 0.78) in predicting rill detachment capacity. The findings of the current study show the significant impact of fire on soil degradation and rill erosion potential. The study advocates an urgent need for effective post-fire land management, erosion control, and the development of sustainable soil restoration strategies. Full article

Forest fires are a global environmental threat to human life and ecosystems. This study compiles smoke alarm images from five high-definition surveillance cameras in Foshan City, Guangdong, China, collected over one year, to create a smoke-based early warning dataset. The dataset presents two key challenges: (1) high false positive rates caused by pseudo-smoke interference, including non-fire conditions like cooking smoke and industrial emissions, and (2) significant regional data imbalances, influenced by varying human activity intensities and terrain features, which impair the generalizability of traditional pre-train–fine-tune strategies. To address these challenges, we explore the use of visual language models to differentiate between true alarms and false alarms. Additionally, our method incorporates a prompt tuning strategy which helps to improve performance by at least 12.45% in zero-shot learning tasks and also enhances performance in few-shot learning tasks, demonstrating enhanced regional generalization compared to baselines. Full article

Unmanned aerial vehicles, also termed as unarmed aerial vehicles, are used for various purposes in and around the environment, such as delivering things, spying on opponents, identification of aerial images, extinguishing fire, spraying the agricultural fields, etc. As there are multi-functions in a single UAV model, it can be used for various purposes as per the user’s requirement. The UAVs are used for faster communication of identified information, entry through the critical atmospheres, and causing no harm to humans before entering a collapsed path. In relation to the above discussion, a UAV system is designed to classify and transmit information about the atmospheric conditions of the environment to a central controller. The UAV is equipped with advanced sensors that are capable of detecting air pollutants such as carbon monoxide (CO), carbon dioxide (CO₂), methane (CH₄), ammonia (NH₃), hydrogen sulfide (H₂S), etc. These sensors present in the UAV model monitor the quality of air, time-to-time, as the UAV navigates through different areas and transmits real-time data regarding the air quality to a central unit; this data includes detailed information on the concentrations of different pollutants. The central unit analyzes the data that are captured by the sensor and checks whether the quality of air meets the atmospheric standards. If the sensed levels of pollutants exceed the thresholds, then the system present in the UAV triggers a warning alert; this alert is communicated to local authorities and the public to take necessary precautions. The developed UAV is furnished with cameras which are used to capture real-time images of the environment and it is processed using the YOLO V3 algorithm. Here, the YOLO V3 algorithm is defined to identify the context and source of pollution, such as identifying industrial activities, traffic congestion, or natural sources like wildfires. Full article

Fire is a natural disturbance in the Brazilian Cerrado that modulates the vegetation structure. Protium heptaphyllum, a woody species of the family Burseraceae, is common in this biome. The resin produced in secretory canals immersed in the phloem of the stem and leaves of this species plays important ecological and industrial roles. The aim of this study was to investigate the influence of fire on the development of resin canals in the leaves and stem of P. heptaphyllum and on the chemical profile of substances produced in the leaves. Young plants were subjected to controlled fire experiments. Leaf and stem portions were analyzed using light microscopy; the chemical compounds in the leaves were identified through gas chromatography–mass spectrometry. The percentage area occupied by secretory canals in the leaf midrib was higher in fire-treated plants than in control plants. Similarly, the density of secretory canals and their lumen area were higher in young stems (primary growth) of fire-treated plants. By contrast, although the canal density in the secondary phloem was lower in older stem portions (secondary growth) in fire-treated plants, their lumens were larger, resulting in similar data regarding the total lumen area of the secretory canals in fire-treated and control plants. The main chemical compounds identified in the leaves were vitamin E, sitosterol, α-amyrin, squalene, and β-amyrin. Three compounds showed significant increases in fire-treated plants, with vitamin E being the only one reduced by fire. Our findings reveal the plasticity of the secretory system and of the biochemical properties of the leaves of P. heptaphyllum in response to fire. These results are important when considering the current increase in fires caused by climate change and human activity in different ecosystems around the world. Full article

Conveyor belts are critical in various industries, particularly in the barrier eddy current separator systems used in recycling processes. However, hidden issues, such as belt misalignment, excessive heat that can lead to fire hazards, and the presence of sharp or irregularly shaped materials, reduce operational efficiency and pose serious threats to the health and safety of personnel on the production floor. This study presents an intelligent monitoring and protection system for barrier eddy current separator conveyor belts designed to safeguard machinery and human workers simultaneously. In this system, a thermal camera continuously monitors the surface temperature of the conveyor belt, especially in the area above the magnetic drum—where unwanted ferromagnetic materials can lead to abnormal heating and potential fire risks. The system detects temperature anomalies in this critical zone. The early detection of these risks triggers audio–visual alerts and IoT-based warning messages that are sent to technicians, which is vital in preventing fire-related injuries and minimizing emergency response time. Simultaneously, a machine vision module autonomously detects and corrects belt misalignment, eliminating the need for manual intervention and reducing the risk of worker exposure to moving mechanical parts. Additionally, a line-scan camera integrated with the YOLOv11 AI model analyses the shape of materials on the conveyor belt, distinguishing between rounded and sharp-edged objects. This system enhances the accuracy of material separation and reduces the likelihood of injuries caused by the impact or ejection of sharp fragments during maintenance or handling. The YOLOv11n-seg model implemented in this system achieved a segmentation mask precision of 84.8 percent and a recall of 84.5 percent in industry evaluations. Based on this high segmentation accuracy and consistent detection of sharp particles, the system is expected to substantially reduce the frequency of sharp object collisions with the BECS conveyor belt, thereby minimizing mechanical wear and potential safety hazards. By integrating these intelligent capabilities into a compact, cost-effective solution suitable for real-world recycling environments, the proposed system contributes significantly to improving workplace safety and equipment longevity. This project demonstrates how digital transformation and artificial intelligence can play a pivotal role in advancing occupational health and safety in modern industrial production. Full article

The complex interaction between nature and human factors has led to frequent forest fires, but their combined effects in different areas remain unclear. Taking the Northeast China forest as the study area, this study integrates structural equation modeling (SEM) and Vine Copula analysis to quantify these drivers over 2001–2022. Results show that 70.42% of forest fires were caused by humans, clustering in populated low-elevation areas. SEM revealed partial correlations of 0.48 (weather conditions) and 0.59 (human activities) with forest fire frequency; canopy moisture was negatively correlated with fire (−0.38). Vine Copula indicated a joint probability of 0.32 between the human footprint index (HFI) and forest fires under high temperatures. This study can provide a framework for region-specific fire management in temperate forests by combining the effects of various influences. Full article

In recent years, large-scale wildfires have become a serious threat to terrestrial ecosystems and people in the Chiquitania region of Bolivia. Understanding public perceptions is fundamental to designing comprehensive and effective wildfire management strategies. The objectives of the study were to learn perception on the main causes of wildfires, to understand their perceptions of the impacts of these events, and to explore the most viable solutions to preventing future wildfires in the Chiquitania region of Bolivia. We developed a 15-questions online survey and disseminated it through social media platforms, mobile messaging service groups, and at two workshops held in two locations. A total of 597 people participated in the survey with a balanced sex distribution. The participants were mainly young people aged 18–24 (45.40%) and 25–34 (21.40%), representing university students (42.6%) and professionals (42.6%). The data came from seven departments, but Santa Cruz was more strongly represented (75.9%). In addition, although only 65% considered themselves part of the general population, the data shows that 76% had personal experience of wildfires. Respondents indicated that fires were caused by human activities (95.9%), mainly due to traditional agricultural practices. The most important perceived impacts included landscape and vegetation quality, fauna habitat and ecosystem regeneration. In addition, participants have prioritized the reinforcement of patrols and surveillance, the hiring of forest firefighters and the purchase of aerial firefighting units. For prevention, the most chosen was to change policies that promote fires, changing the vision for economic development and stricter penalties. The findings can be used to formulate public policies aimed at preventing wildfires, mitigating their impacts and promoting environmental conservation. Full article

Among the challenges the human population needs to address are threats of global pandemics, increasing socioeconomic inequality, especially in developing countries, and anthropogenic climate change. The latter’s effect has been amplified with the arrival of 2023/24 El Niño, causing an exceptional drought in the Amazon basin, significantly affecting fire conditions and hydroelectric power production in several South American countries, including Ecuador. This study analyzes five criteria pollutants—carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), and particulate matter ≤ 2.5 µm (PM_2.5)—during 2019–2024 in Quito, Ecuador, a high-elevation tropical metropolis. Despite long-term efforts to regulate emissions, air pollution levels continue to rise, driven by overlapping crises, including energy shortages, political unrest, and extreme weather events. The persistent failure to improve air quality underscores the vulnerability of developing nations to climate change-induced energy instability and the urgent need for adaptive, diversified, and resilient future energy planning. Without immediate shifts in climate adaptation policies, cities like Quito will continue to experience worsening air quality, with severe implications for public health and environmental sustainability. Full article