High-risk Zone Research Articles

Unveiling scorpionism in Northern Algerian Sahara (El Oued Province): Epidemiological trends and faunistic diversity.

Narrative literature review. To review the current evidence and technological advancements related to enabling technologies, including navigation, robotics, and extended reality (XR) for the surgical management of cervical spine deformity, with a focus on their safety, efficacy, and limitations. Cervical spine deformity is associated with significant morbidity due to neurological compromise and structural imbalance. Surgical correction often involves complex decompression and instrumentation in high-risk anatomical zones. Recent advances in enabling technologies have been increasingly applied to spinal surgery, demonstrating improvements in accuracy, safety, and clinical workflow. However, literature specifically addressing their use in the cervical spine, especially in deformity cases, remains limited. A comprehensive review of published studies was performed to assess the application of enabling technologies in cervical spine deformity surgery. Databases were searched for studies involving computer-assisted navigation, robotics, and augmented or virtual reality in cervical procedures, with particular attention to deformity correction, instrumentation accuracy, and perioperative outcomes. A total of 119 studies were reviewed, and the 35 most relevant to cervical spine deformity were included for this review. Enabling technologies have demonstrated improvements in screw placement accuracy, reductions in radiation exposure, shorter hospital stays, and improved visualization during cervical spine surgery. Navigation systems have been particularly useful in high-stakes regions such as C1-C2 and the subaxial spine, while robotics has enabled precise, reproducible instrumentation in complex or revision cases. Extended reality applications, including AR and VR, offer enhanced 3D visualization and are emerging as tools for both intraoperative support and surgical training. Despite promising early results, limitations include steep learning curves, cost barriers, and limited data on long-term patient-reported outcomes. Enabling technologies are transforming cervical spine surgery by improving accuracy, safety, and surgical efficiency. Given the anatomical complexity and risks involved, their application in cervical deformity correction holds particular promise. Future studies should aim to standardize protocols and assess long-term outcomes to optimize their integration into surgical practice.

Predicting future tropical theileriosis risk in China using tick distribution and climate models.

Cost assessment of a preventive vaccination program against highly pathogenic avian influenza in Austrian poultry farms.

Urbanization markedly influences the microclimatic conditions in semi-arid regions by elevating land surface temperatures (LST) and contributing to ecological degradation. This study examined the spatial and temporal evolution of LST and urban heat island (UHI) effects in Benguerir, Morocco, over a 30-year period (1994–2024), employing high-resolution satellite imagery and in situ sensor data. Urban expansion was quantified using thermal bands from Landsat imagery, the Normalized Difference Built-up Index (NDBI), and the Built-up Index (BU), whereas thermal comfort was evaluated through the Universal Thermal Climate Index (UTCI) and Predicted Mean Vote (PMV) using air temperature and humidity data collected via spatial sensor and the Sniffer Bike mobile sensor network. These urban transformations have intensified the UHI effect, resulting in a 29.34 °C increase in mean LST to 41.82 °C in 2024 across built-up areas. Statistical modeling revealed strong linear relationships between LST and urban indices, with R2 values ranging from 0.93 to 0.96, and correlation coefficients around 0.98 (all p-values < 0.001), indicating a reliable model fit. Furthermore, the analysis of thermal comfort trends underscores urbanization’s impact on human well-being. In 1994, 34.2% of the population experienced slight warmth and 65.8% experienced hot conditions. By 2024, conditions had shifted dramatically, with 76.7% experiencing hot conditions and 16.2% exposed to very hot conditions, leaving only 7.1% in the slight warmth category. These findings highlight the urgent need for adaptive urban planning strategies. The implementation of urban greening initiatives, the use of reflective materials, and the integration of data-driven planning approaches are essential to mitigate thermal stress and enhance urban resilience. Leveraging climate modeling and spatial analytics can support the identification of high-risk zones and inform targeted interventions to effectively address the escalating UHI phenomenon.

Abstract Purpose This study aims to evaluate complication rates following cryoablation and radiofrequency ablation of painful musculoskeletal tumors and to assess the impact of lesion proximity to critical structures and the role of passive and active thermoprotective strategies on procedural safety. Methods This retrospective, IRB-approved study included 364 percutaneous ablation procedures (100 cryoablation, 264 RFA) performed between January 2013 and August 2023. Patient demographics, lesion characteristics, procedural details, and thermoprotection techniques were analyzed. Lesion distances from critical structures (e.g., spinal cord, peripheral nerves, skin, viscera) were recorded pre- and postthermoprotection. Complications were classified using the Common Terminology Criteria for Adverse Events (CTCAE v5.0). Results The overall complication rates were 4% for cryoablation and 3.4% for RFA, with no procedure-related mortality. The most common complications for cryoablation were transient weakness and hypertensive urgency (2%, 1%), and for RFA were arrhythmia and nausea (1.1%, 0.8%). No complications occurred in patients who underwent RFA with active thermoprotection. Thermoprotective strategies increased the distance between lesions and adjacent vital structures, particularly in high-risk anatomical zones. Conclusions Both cryoablation and RFA are safe and effective for treating painful musculoskeletal tumors. Active thermoprotection significantly reduces complication risk, enabling safe ablation near critical structures. Lesion proximity alone should not be considered a contraindication when protective techniques are appropriately employed. These findings support routine prophylactic use of thermoprotection during thermal ablation procedures.

Indonesia’s location at the convergence of three active tectonic plates makes it highly susceptible to various natural disasters, with landslides being among the most frequent and destructive, particularly in mountainous and densely populated urban areas. Central Bogor District in West Java represents a vulnerable area where steep topography, high rainfall intensity, and dense population heighten landslide risk. Despite recurrent landslide events, comprehensive vulnerability assessments integrating both physical and socio-environmental factors remain limited. This study aims to produce a spatially explicit landslide vulnerability map for Central Bogor District by utilizing open geospatial data and applying a GIS-based multi-criteria decision-making approach. The Analytical Hierarchy Process (AHP) was employed to assign weights to four primary physical parameters—rainfall, slope, lithology, and land cover—based on their relative contribution to landslide susceptibility. Supporting data were derived from Sentinel-1A imagery (InSAR), Landsat-8 classification, CHIRPS precipitation records, and official geological maps. These physical layers were then integrated with exposure indicators, including population density, infrastructure distribution, and accessibility data from OpenStreetMap. The results delineated three landslide vulnerability zones: high (49.87 ha), moderate (481.82 ha), and low (236.45 ha). High-risk zones, such as Gudang and Paledang Sub-districts, feature steep slopes, weak geological formations, and dense settlements. Overlay analysis also revealed a significant concentration of critical infrastructure within moderate-to-high vulnerability zones, highlighting exposure and potential service disruption during hazard events. The study underscores the critical value of combining open geospatial data with AHP-based weighting to inform targeted disaster mitigation, infrastructure planning, and resilient urban development. The resulting maps can guide policy and preparedness strategies to reduce landslide impacts in high-risk urban areas.

This study presents a fire risk assessment of traditional wooden dwellings in Southern Hunan, focusing on Zhoujia Compound—a nationally protected cultural heritage site. By applying Pyrosim fire simulation software, we modeled fire spread, smoke dispersion, and temperature variation under localized architectural and environmental conditions. The simulations, informed by real-time wind speed monitoring, revealed that key fire risks stem from open flame activities during festivals, charcoal heating, and inadequate electrical wiring. Structural features such as interconnected wooden beams and open courtyards exacerbate fire spread. The results identified high-risk zones and demonstrated that wind speed and building orientation significantly affect fire dynamics. Based on these findings, we propose targeted fire prevention strategies, including fire-retardant treatments, improved compartmentalization, and community-level fire education. This research offers a novel, simulation-based approach to improving fire safety in traditional villages, contributing to both cultural heritage protection and rural fire risk mitigation.

High-altitude ecosystems face growing threats from natural hazards and human activities, intensifying socio-economic and environmental risks. The Nilgiris District, Tamil Nadu, is a hotspot where steep terrain, fragile ecosystems, climate variability, and anthropogenic pressures converge. This study integrates geospatial technologies and machine learning (XGBoost) to map multi-hazard risk zones and assess their implications for ecosystem stability and contaminant susceptibility. The GIS-based multi-moderated evaluation was applied using slope, elevation, land use/land cover (LULC), drainage density and proximity to roads and settlements. Improved the accuracy of the XGBoost classification by capturing complex spatial relationships. The multi-hazard risk zone map identified five classes with very low-risk zones with high-risk zones cantered near Coonoor and Kotagiri, which are associated for landslides and contaminated mobility, while in the lower-way areas cluster around AU. Combining crisis weakness with environmental fragility, this structure supports durable land-use management, ecosystem conservation and reducing pollution. The integration of geospatial analytics and machine learning provides a strong tool for disaster preparedness, risk reducing and elasticity in ecological sensitive hill districts.

This study investigates atmospheric dust pollution generated by quarrying activities, particularly the impact of traffic on access roads. The task addressed relates to the lack of a comprehensive assessment of dust levels and associated health risks, considering the actual operation of quarry infrastructure and seasonal variability. Emissions from quarrying during 2020–2024 have been analyzed, which made it possible to evaluate anthropogenic pressure. PM2.5 and PM10 measurements were conducted along the access road to the Rybalskyi quarry (Ukraine); the results were used for statistical processing and dust load modeling. Correlation-regression models were built to assess the impact of environmental and transport factors, identifying key pollution drivers. A mathematical model of the spatial distribution of concentrations was constructed, including an evaluation of health risks for people. Maximum recorded PM10 concentrations reached 312 μg/m3, thereby exceeding the permissible limit by 6.2 times. Considering meteorological conditions, vehicle types, as well as traffic intensity enabled quantitative assessment of each factor's contribution to dust load and identification of high-risk zones. The results are attributed to the high sensitivity of dust concentrations to local changes, confirmed by determination coefficients and spatial modeling outcomes. The proposed approach is suitable for environmental protection measures aimed at reducing dust emission impact on the environment and public health. It could be applied to plan sanitary-protection zones, regulate traffic, and optimize logistics according to local conditions. This approach requires the availability of meteorological data and traffic information to provide reliable forecasts

Previous studies on land subsidence identified Bangladesh and the Ganges-Brahmaputra Delta (GBD) region as a high-risk zone that is vulnerable to sea-level rise and climate change. This regional subsidence will affect the stability of linear rail infrastructure. The possible effect of this subsidence on rail infrastructure health was not scientifically examined before. Dhaka-Kasiani-Gopalganj Railway, which connects the capital city Dhaka to Gopalganj city in the southern part of Bangladesh, is chosen as a case study. This railway is about 150 km long, and most of the stations, except the ones within Dhaka city, have been newly constructed during the last few years (construction started in late 2016). In this study, SBAS-InSAR time-series analysis was used to extract LOS displacement along the 60 m buffer width of the railway embankment. Sentinel-1 A images from both ascending and descending passes were used covering the temporal resolution from January 2020 to October 2023. A mean displacement rate of around − 10 mm/year was observed for ascending satellite line-of-sight LOS direction and − 14 mm/year for descending LOS direction. The ascending LOS displacement rate was compared with GPS data to check the instability trend, and the result showed an agreement in LOS displacement trend. The 2.5D analysis was also conducted to compute the quasi-vertical component along railway line for better understanding of local instability. A mean quasi-vertical displacement rate of approximately − 16 mm/year was observed along the railway line, and it indicates that the railway embankment is subsiding in general. To enhance the accuracy and reliability of displacement interpretation, InSAR measurements were supplemented with contextual analysis incorporating land use/land cover (LULC) and soil data. In regions covered by “Crops” land cover type, a high rate of quasi-vertical displacement was observed indicating that railway line passing through paddy fields are highly prone to instability. Similarly, significant displacement rates were noted in “Deltaic silt” soil type, followed by “Alluvial silt and clay” and “Marsh clay and peat” type. Railway embankment built on these soil types should be prioritized for inspection. The threshold displacement velocity value was also defined based on the results so that it can serve as an early indicator of damage, aiding in the prioritization of inspection and maintenance work. This study aims to broaden the use of InSAR data in engineering practices in linear rail infrastructure health monitoring. Future research will focus on a detailed analysis of current damage conditions compared with satellite-based instability results.

The risk of forest fires is affected by various factors such as vegetation density, topography, human activities, and climate patterns. These factors remain relatively constant over time, at least during the fire season. To manage forests and ensure protection against fires, fire-cycle analysis is performed which includes creating a map of potential fire ignition and preparing a vulnerability map that can assist in controlling the spread of fire. Accurate data is crucial for forest management, and geospatial technology provides reliable information. By providing accurate information, geospatial technology can help prevent and mitigate damage caused by forest fires, while also promoting sustainable land use practices. The study focused on assessing forest fire risk in the Malkangiri district of Odisha, India, using geospatial technology and the AHP method. The final risk map was categorized into five zones, namely very high, high, moderate, low, and very low, which can help guide forest management and firefighting efforts in the area. To validate these forest fire risk zones, the study used fire points data from the office of PCCF, Odisha from FIRMS. The results showed that the forest fire risk was high in the low to moderate elevation ranges, with most fire points overlapping in the very high-risk zones of the map. Anthropogenic activities have been a major cause of forest fires in tropical regions. Overall, the study demonstrated the effectiveness of using geospatial technologies and the AHP method for assessing forest fire risk. The results can help in developing strategies to prevent and mitigate the impact of forest fires, particularly in areas with high-risk zones, such as the Malkangiri district of Odisha, India.

The increasing demand for tall buildings in seismically active regions has necessitated innovative structural systems that combine architectural aesthetics with superior seismic performance. Diagrid structural systems have emerged as a promising solution, offering enhanced lateral load resistance through their triangulated geometric configuration. This research investigates the optimization of diagrid angles across various building geometries—square, rectangular, circular, and hexagonal—subjected to seismic loading conditions in high-risk zones. Through comprehensive dynamic analysis using finite element modeling, this study evaluates the structural behavior of diagrid systems with varying angles (ranging from 35° to 75°) under earthquake loads corresponding to Zone V seismic conditions. The research employs ETABS software for modeling and analysis, examining critical parameters including story drift, displacement, base shear, and acceleration responses. Results indicate that optimal diagrid angles vary significantly with building geometry, with square and rectangular structures performing best at 65-70°, while circular configurations show superior performance at 55-60°. The findings reveal that properly optimized diagrid angles can reduce lateral displacement by up to 35% and improve overall structural efficiency by 28% compared to conventional configurations. This research contributes valuable insights for structural engineers and architects designing tall buildings in earthquake-prone areas, providing evidence-based guidelines for selecting appropriate diagrid angles based on building shape and seismic requirements.

There has been a rise in the incidence of esophageal adenocarcinoma (EAC) over the past five decades in the United States, and it remains a highly lethal malignancy due to frequent late-stage diagnosis. Barrett’s esophagus (BE), a well-established precursor to EAC, presents a critical window for early intervention through screening, surveillance, and endoscopic eradication therapy. Despite gastrointestinal society guideline recommendations for screening, the majority of patients with BE or early EAC remain undiagnosed until symptoms of late-stage cancer emerge. This review outlines current challenges and evolving strategies in the United States in BE detection and management, including risk stratification models, non-endoscopic screening tools, high-quality endoscopic techniques, tissue-based biomarkers, and artificial intelligence-enhanced imaging. We highlight best practices for surveillance, emphasizing the importance of thorough inspection of high-risk anatomic zones and the integration of advanced imaging. Endoscopic eradication therapy, including endoscopic mucosal resection and ablation, achieves high rates of complete eradication when performed with meticulous technique, especially with comprehensive treatment of the gastroesophageal junction and gastric cardia. Long-term surveillance remains essential due to the risk of recurrence. As new technologies continue to emerge, integrating precision tools into routine practice will be key to improving outcomes and reducing EAC mortality.

Conventional non-partitioned Landslide Susceptibility Mapping (LSM), which neglects geospatial heterogeneity, often has limitations in accurately capturing local risk patterns. To address this challenge, this study investigated the effectiveness of localized modeling in the environmentally diverse state of Oregon, USA, by comparing ecoregion-based local models with the non-partitioned model. We partitioned Oregon into seven distinct units using the U.S. Environmental Protection Agency (EPA) Level III Ecoregions and developed one global and seven local models with the eXtreme Gradient Boosting (XGBoost) algorithm. A comprehensive evaluation framework, including the Area Under the Curve (AUC), Landslide Density (LD), and the Total Deviation Index (TDI), was used to compare the models. The results demonstrated the clear superiority of the partitioned strategy. Moreover, different ecoregions were found to have distinct dominant landslide conditioning factors, revealing strong spatial non-stationarity. Although all models generated high AUC values (>0.93), LD analysis showed that the local models were significantly more efficient at identifying high-risk zones. This advantage was particularly pronounced in critical, landslide-prone western areas; for instance, in the Willamette–Georgia–Puget Lowland, the local model’s LD value in the ‘very high’ susceptibility class was over 3.5 times that of the global model. High TDI values (some >35%) further confirmed fundamental spatial discrepancies between the risk maps obtained by the two strategies. This research substantiated that, in geographically complex terrains, partitioned modeling is an effective approach for more accurate and reliable LSM, providing a scientific basis for developing targeted regional disaster mitigation policies.

Abstract Rural depopulation and land abandonment in parts of Europe have reduced anthropogenic deterrents to large carnivores, leading to rewilding and escalating conflicts with the remaining inhabitants. Such conflicts deepen negative attitudes toward these species, challenging conservation and coexistence efforts. In Bulgaria, a significant year-to-year increase in bear-caused damages has raised concerns for human livelihoods and bear conservation. This study examines socio-demographic and environmental drivers of bear-damages in the Western Rhodopes – a depopulating mountainous region, and hotspot for human-bear conflict. Multiple linear regression was applied using demographic and spatial variables to identify factors influencing bear damage incidence within municipalities. MaxEnt was applied to identify high-risk zones, based on land use and elevation. Regression results identified human population decline as a key correlate of bear damages, suggesting that reduced human activity and fewer anthropogenic deterrents (e.g., noise, dogs, human presence) around settlements, combined with persistent attractants like orchards and trash, increase conflict risks despite a declining bear population. The MaxEnt model revealed that land use types with human activity of low-medium intensity (e.g. remote communities interspersed with agricultural land, natural vegetation, and houses) experience most conflicts. While most damages occur around the mean regional elevation, incidents both higher and lower are increasing between 2004 and 2022, likely due to unsupervised grazing at higher elevations and reduced human activity around lower-elevation settlements. Increasing conflicts erode local trust in institutional wildlife management and worsen attitudes toward bears. Our findings highlight the intensification of human-bear conflicts in depopulating regions, threatening livelihoods, sense of security and the support for conservation efforts. This pattern is especially concerning amid global trends of urbanization and rural decline, highlighting the need for targeted interventions.

This study examines sand encroachment in El Hajeb, Algeria, from 2000 to 2023—a region highly vulnerable to desertification. A multi-source remote sensing framework was used, combining optical and radar satellite data (Landsat, MODIS, Sentinel-1 SAR) with spectral indices (NDVI, MSAVI, BSI, NDESI) to monitor dune dynamics, vegetation health, and sand distribution. Pre-processing steps included atmospheric correction, cloud masking, and normalization. Advanced geospatial analysis and supervised classification were conducted using machine-learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—, which improved classification accuracy and land, cover discrimination. Results show an 11.90% reduction in stabilized dunes and a 6.05% increase in sparse sand areas. These shifts are linked to vegetation exploitation, soil moisture status, and intensified aeolian activity driven by prolonged droughts and prevailing winds. Sentinel-1 SAR data contributed to understanding surface roughness and moisture variability, reinforcing the analysis. Spatial maps revealed high-risk zones along the periphery of vegetated areas. To combat land degradation, the study recommends windbreaks, sand fences, and afforestation efforts. Overall, the research highlights the value of integrating remote sensing and machine learning for environmental monitoring and supports the development of adaptive land management strategies to enhance resilience in arid landscapes.

Objective.Electromagnetic (EM) modeling is an effective method for evaluating the gradient safety of magnetic resonance imaging (MRI) for patients with active implantable medical devices (AIMDs). However, the combined effects of multiple factors-including gradient coil design constraints, implanted lead path, gradient strength, and scan configuration-on gradient-induced voltage (GIV) risk has not been systematically investigated. In particular, the magnetic field distribution outside the region of linearity (ROL) of gradient coils cannot be uniquely determined from their nominal gradient profile, and its impact on AIMD gradient safety assessment remains poorly understood.Approach.This study presents a multifactorial analysis of MRI gradient safety by integrating gradient coil modeling with anatomical lead path tracing using a reference human body shell. We examine how variations in coil design constraints affect magnetic field distributions and how these, in turn, influence GIV for three representative AIMDs' pathways: deep brain stimulators (DBSs), cardiac pacemakers (PMs), and sacral nerve stimulators (SNMs). Multiple gradient strengths, coil excitation modes, and scanning positions are assessed.Results.Magnetic field distributions vary significantly between coil designs, particularly in the concomitantBxandBycomponents, with differences reaching up to 53%. These variations result in GIV difference that increases with gradient strength. The maximum GIV differences for DBS, SNM, and PM reach 1.08 V, 0.52 V, and 0.93 V, respectively, underY-axis excitation. The concomitant field plays a significant role in these differences. Simultaneous excitation of all axes does not always produce the highest GIV due to cancellation effects. Cross-AIMD analysis shows high-risk zones are concentrated in and around the ROL.Significance.This work fills a gap by systematically evaluating how coil design, implant characteristics, gradient strength, and scan configurations influence GIV risk, providing a foundation for more comprehensive, individualized MRI gradient safety assessments.

This study presents a comprehensive spatial, temporal, and predictive analysis of urban fire risk across Türkiye from 2012 to 2022, with a particular focus on the provinces of Adana and Antalya—regions identified as the most vulnerable to wildfire hazards. Leveraging high-confidence VIIRS satellite data, the research employs advanced spatial analytics, including Getis–Ord Gi* hot spot detection, inverse distance weighting, and a weighted overlay analysis structured through the analytic hierarchy process. These methods are integrated within a geographic information system framework to produce high-resolution fire risk maps that account for climatic, environmental, and anthropogenic drivers. Results reveal that high and very high fire risk zones comprise over 52 percent of Antalya and nearly 41 percent of Adana, with climate variables—particularly declining precipitation and rising temperatures—emerging as the dominant risk factors. Trend analyses further highlight Adana’s rapidly intensifying fire climate, and predictive modeling under moderate and severe 2050 climate scenarios projects a significant expansion of high-risk zones, particularly in interior areas and areas of dense vegetation. These findings provide actionable insights for urban planners and policymakers, offering a robust, spatially resolved foundation for developing adaptive strategies to mitigate escalating fire threats in Türkiye’s urban environments.

BackgroundRecent international developments have led to increased terrorist activity and intentional violence across Europe. Interagency collaboration has repeatedly been identified as a key challenge in previous scenarios. This study explores the perspectives of European pre-hospital medical providers and law enforcement officers on collaboration during violent incidents, aiming to identify barriers, best practices, and opportunities for improvement.MethodsTwelve modified focus group interviews (3–8 individuals per group) with embedded polls were conducted online. Deductive coding was performed for predefined topics, e.g., Command, Communication, Hot Zone Care, Provider Safety, and Interagency Understanding. Inductive coding was applied to additional content arising from the interview dynamics.Results64 participants from 20 European countries were interviewed. 41% (26/64) were non-physician pre-hospital providers, 38% (24/64) worked as pre-hospital emergency physicians, and 22% (14/64) served in law enforcement, including special forces. 49% (30/61) of participants denied feeling adequately trained to collaborate with other responding services in a tactical scenario. Training methods were criticised for neglecting situational awareness and interpersonal competencies. Overall, joint training opportunities are reportedly rare, leading to misconceptions of the capabilities and priorities of interagency counterparts on scene. 64% (39/61) of participants reported police as the primary care provider for casualties in the hot zone, with many regional protocols extending this reliance into the warm zone and specifically onto special forces. However, the interviews indicated that police are unlikely to deliver such care without significant delay. Thus, casualty evacuation emerged as a key bottleneck and priority for further debate.ConclusionsThis study revealed a significant paucity of joint education and training opportunities. Training concepts should place greater emphasis on behavioural competencies instead of rigid protocol compliance. Although promising response frameworks exist across Europe, various deployed strategies risk prolonging the therapeutic vacuum of casualties inside high-risk zones and impairing provider safety. Joint strategies and interprofessional exchange will be essential to decrease such systemic vulnerabilities.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13049-025-01482-4.