Search Results (478)

Protection of coastal cultural heritage is among the most urgent global priorities, as these sites face increasing threats from climate change, sea level rise, and human activity. This study emphasises the value of innovative geospatial tools and data ecosystems for timely risk assessment. The role of land administration systems, geospatial documentation of coastal cultural heritage sites, and the adoption of innovative techniques that combine various methodologies is crucial for timely action. The coastal management infrastructure in Greece is presented, outlining the key public authorities and national legislation, as well as the land administration and geospatial ecosystems and the various available geospatial ecosystems. We profile the Hellenic Cadastre and the Hellenic Archaeological Cadastre along with open geospatial resources, and introduce TRIQUETRA Decision Support System (DSS), produced through the EU’s Horizon project, and a Digital Twin methodology for hazard identification, quantification, and mitigation. Particular emphasis is given to the role of Digital Twin technology, which acts as a continuously updated virtual replica of coastal cultural heritage sites, integrating heterogeneous geospatial datasets such as cadastral information, photogrammetric 3D models, climate projections, and hazard simulations, allowing for stakeholders to test future scenarios of sea level rise, flooding, and erosion, offering an advanced tool for resilience planning. The approach is validated at the coastal archaeological site of Aegina Kolona, where a UAV-based SfM-MVS survey produced using high-resolution photogrammetric outputs, including a dense point cloud exceeding 60 million points, a 5 cm resolution Digital Surface Model, high-resolution orthomosaics with a ground sampling distance of 1 cm and 2.5 cm, and a textured 3D model using more than 6000 nadir and oblique images. These products provided a geospatial infrastructure for flood risk assessment under extreme rainfall events, following a multi-scale hydrologic–hydraulic modelling framework. Island-scale simulations using a 5 m Digital Elevation Model (DEM) were coupled with site-scale modelling based on the high-resolution UAV-derived DEM, allowing for the nested evaluation of water flow, inundation extents, and velocity patterns. This approach revealed spatially variable flood impacts on individual structures, highlighted the sensitivity of the results to watershed delineation and model resolution, and identified critical intervention windows for temporary protection measures. We conclude that integrating land administration systems, open geospatial data, and Digital Twin technology provides a practical pathway to proactive and efficient management, increasing resilience for coastal heritage against climate change threats. Full article

Emergency medical services confront significant challenges in delivering timely patient assessments within geographically isolated or disaster-impacted regions. While drones (unmanned aircraft systems, UAS) show transformative potential in healthcare, standardized protocols for drone-assisted patient evaluations remain underdeveloped. This study introduces the ABCD-IN-BARS protocol, a 9-step telemedicine checklist integrating patient-assisted maneuvers and drone technology to systematize remote emergency assessments. A wait-list randomized controlled trial with 68 first-aid-trained volunteers evaluated the protocol’s feasibility. Participants underwent web-based modules and in-person simulations and were randomized into immediate training or waitlist control groups. The ABCD-IN-BARS protocol was developed via a content validity approach, incorporating expert-rated items from the telemedicine literature. Outcomes included time-to-assessment, provider confidence (Modified Cooper–Harper Scale), measured at baseline, post-training, and 3-month follow-up. Ethical approval and informed consent were obtained. Most of the participants can complete the assessment with a cue card within 4 min. A mixed-design repeated measures ANOVA assessed the effects of Time (baseline, post-test, 3-month follow-up within subject) on assessment durations. Assessment times improved significantly over three time points (p = 0.008), improving with standardized protocols, while patterns were similar across groups (p = 0.101), reflecting skill retention at 3 months and not affected by injury or not. Protocol adherence in simulated injury identification increased from 63.3% pre-training to 100% post-training. Provider confidence remained high (MCH scores: 2.4–2.7/10), and Technology Acceptance Model (TAM) ratings emphasized strong Perceived Usefulness (PU2: M = 4.48) despite moderate ease-of-use challenges (EU2: M = 4.03). Qualitative feedback highlighted workflow benefits but noted challenges in drone maneuvering. The ABCD-IN-BARS protocol effectively standardizes drone-assisted emergency assessments, demonstrating retained proficiency and high usability. While sensory limitations persist, its modular design and alignment with ABCDE principles offer a scalable solution for prehospital care in underserved regions. Further multicenter validation is needed to generalize findings. Full article

This research examines the ongoing challenge of depopulation in rural areas, focusing on the municipality of Buenavista del Norte in the Canary Islands. The objective is to analyse how governance and local community participation can contribute to reversing rural decline and to identify concrete strategies for sustainable development. Depopulation significantly impacts the social and economic viability of small rural municipalities, exacerbating marginalisation and isolation. The study applies a qualitative methodology, including interviews with public representatives, key strategic sector informants, and participatory group dynamics, to identify projects and resources that could foster local development. A core focus is placed on the integration of tourism, particularly wine tourism, as a tool for economic diversification and combating rural decline. Despite the Canary Islands’ status as a mature tourist destination, rural areas have not equally benefited, with some experiencing stagnation. Results point to the relevance of public–private collaboration, community-based innovation, and participatory approaches that engage key actors from various sectors. These processes facilitate the identification of viable projects and reveal the potential of tourism and sustainable community initiatives to reduce regional disparities. The implications of this research highlight the need for integrated local development strategies, improved infrastructure, and quality public services as essential measures to confront demographic challenges in remote areas. The inclusive governance, combined with strategic planning and tourism-based innovation, offers a viable roadmap for revitalising rural municipalities and ensuring their long-term resilience. Full article

Heavy-load collaborative robots are increasingly used in fields such as industrial handling and precision assembly. With the increase in the end load of the robotic arm and the acceleration of its movement speed, after the robotic arm completes a preset trajectory, due to factors such as inertia, the flexibility of the robotic arm’s rods and the harmonic reducer materials at the joints, there will still be residual vibration for a period of time after the robotic arm reaches the end point. On the one hand, residual vibration will have an adverse impact on the high-precision and high-performance operations of the robotic arm, affecting the operation accuracy and thus the production quality. On the other hand, many operations need to wait until the robotic arm completely stops before proceeding. In practical applications, the time spent waiting for the robotic arm to stop significantly affects efficiency. Therefore, effectively suppressing residual vibration is crucial to improving the performance of the robotic arm. To solve the problem of end residual vibration in heavy-load six-axis collaborative robots, this paper conducts research on input shaping and the estimation of robot end vibration parameters in arbitrary poses. The innovation is that vibration parameters in arbitrary poses are estimated based on the established vibration parameter model. An input shaper is designed according to the derived design method of the input shaper, achieving a certain suppression effect on the residual vibration of the robot end. When the parameter identification error is small, the optimized vibration suppression effect reaches more than 70%, realizing rapid and robust vibration suppression. This research is of great significance for enhancing the application value of collaborative robots in precision manufacturing and heavy-duty handling. Full article

The increasing complexity of maritime traffic, driven by the expansion of international trade and growing shipping demand, has resulted in frequent ship collisions with significant consequences. This paper evaluates the credibility of the risk, calculated using the automatic identification system (AIS), in busy waterways and integrates AIS data with video surveillance data to comprehensively analyze the risk of ship collision. Specifically, this study utilizes the IALA Waterways Risk Assessment Program (IWRAP) tool to simulate maritime traffic flow and assess collision risk probabilities across various study areas and time periods. In addition, we analyze data from 2019 to 2022 to explore the impact of the COVID-19 pandemic on maritime traffic and find that the number of ship arrivals during the epidemic has decreased, resulting in a decrease in accident risk. We identify four traffic conflict areas in the real-world study area and point out that there are multi-directional ship interactions in these areas, but compliance with traffic rules can effectively reduce the risk of accidents. Additionally, simulations suggest that even a 13.5% increase in ocean-going vessel (OGV) traffic would raise collision risk by only 0.0247 incidents/year. To more accurately analyze the risk of waterways, we investigate the capture of dynamic information for ships in waterways by using the learning-driven detection model for real-time ship detection. These findings highlight the effectiveness of combining AIS and visual data for waterway risk assessment, offering critical insights for improving safety measures and informing policy development. Full article

Identifying the impact of climate change and changes in underlying surface conditions on river runoff changes is critical for sustainable water resource use and watershed management in arid regions. The Kriya River is not only a key support for water resources in the arid environment of the Tarim Basin, but also a solid foundation for the survival and development of agricultural oases. In this study, the Kriya River Basin in Xinjiang, China, was taken as the research object, and the Mann–Kendall, Sen’s Slope, Cumulative Sum, and other methods were used to systematically analyze the temporal evolution law and multi-modal characteristics of runoff in the basin. Based on the Budyko hydrothermal coupling equilibrium equation, the contribution of temperature, evaporation, and the underlying surface to runoff variation was quantitatively interpreted. The study found that the annual runoff depth of the Kriya River Basin has shown a significant positive evolution trend in the past 60 years, with an increase rate of 0.5189 mm/a (p ≤ 0.01). Through the identification of mutation points, the runoff time series of the Kriya River was divided into the base period 1957–1999 and the change period 2000–2015. Without considering the supply of snowmelt runoff, the contribution rate of precipitation to runoff change was 75.23%, followed by the change in underlying surface (23.08%), and the potential evapotranspiration was only 1.69%. The results of this study provide a good scientific reference for water resources management and environmental governance in the Kriya River Basin. Full article

Control point detection is a critical initial step in camera calibration. For checkerboard corner points, detection is based on inferences about local gradients in the image. Infrared (IR) imaging, however, poses challenges due to its low resolution and low signal-to-noise ratio, hindering the identification of clear local features. This study proposes a physics-informed neural network (PINN) based on the YOLO target detection model to detect checkerboard corner points in infrared images, aiming to enhance the calibration accuracy of infrared thermal cameras. This method first optimizes the YOLO model used for corner detection based on the idea of enhancing image gradient information extraction and then incorporates camera physical information into the training process so that the model can learn the intrinsic constraints between corner coordinates. Camera physical information is applied to the loss calculation process during training, avoiding the impact of label errors on the model and further improving detection accuracy. Compared with the baselines, the proposed method reduces the root mean square error (RMSE) by at least 30% on average across five test sets, indicating that the PINN-based corner detection method can effectively handle low-quality infrared images and achieve more accurate camera calibration. Full article

This study delivers a pioneering, high-resolution hydrogeochemical assessment of surface waters in the Upper Velhas and Upper Paraopeba river basins within Brazil’s Iron Quadrangle—an area of critical socioeconomic importance marked by intensive mining and urbanization. Through a dense sampling network of 315 surface water points (one every 23 km²), the research generates an unprecedented spatial dataset, enabling the identification of contamination hotspots and the differentiation between lithogenic and anthropogenic sources of potentially toxic elements (PTEs). Statistical methods, including exploratory data analysis and cluster analysis, were applied to determine background and anomalous concentrations of potentially toxic elements (PTEs). Geospatial distribution maps were generated using GIS. The results revealed widespread contamination by As, Cd, Cr, Ni, Pb, and Zn, with many samples exceeding Brazilian, European, and global drinking water standards. Arsenic and cadmium anomalies in rural and peri-urban communities raise concerns due to the direct consumption of contaminated water. The innovative application of dense spatial sampling and integrated geostatistical methods offers new insights into the pathways and sources of PTE pollution, identifying specific lithological units (e.g., gold schists, mafic intrusions) and land uses (e.g., urban effluents, mining sites) associated with elevated contaminant levels. By establishing robust regional geochemical baselines and source attributions, this study sets a new standard for environmental monitoring in mining-impacted watersheds and provides a replicable framework for water governance, environmental licensing, and risk management in similar regions worldwide. Full article

Lake Taihu, a large, shallow freshwater lake in China, has experienced severe eutrophication for decades under intense human activities occurring around cities. Through long-term water quality management since 1995, the eutrophication of Lake Taihu has been controlled. This review examines the eutrophication characteristics, source identification methods, and control measures in Lake Taihu. Phosphorus is a primary driver of eutrophication, correlating strongly with chlorophyll a. The lake exhibits significant temporal and spatial variability in nutrient dynamics, influenced by human activities and the climate. Historical data show fluctuating nutrient levels and persistent algal blooms despite government efforts. A critical assessment of various source apportionment methods, including statistical analysis, physical modeling, and empirical models, is presented to elucidate the relative contributions of different nutrient sources. These methods identify agricultural non-point and urban point sources as major external contributors, with sediment nutrient release as a significant internal source. Implemented controls, including wastewater treatment plants and non-point-source management, have had limited success. Increased sewage and sediment nutrients necessitate integrated watershed management. Future research should prioritize advanced source tracking, sediment dynamics, climate impacts, and integrated ecological models. Sustainable eutrophication management in Lake Taihu requires integrated science, policy, and public engagement to ensure ecosystem health. Full article

This study conducts a high-resolution forensic evaluation of stern structural damage resulting from a tugboat collision during berthing, integrating real-world measurement data with calibrated nonlinear finite element analysis. Based on field-acquired deformation geometry and residual dent profiles at Frame 76, five distinct collision scenarios varying in impact orientation, contact area, and load path were simulated using shell-based nonlinear plastic analysis. Particular attention is given to comparing the plastic equivalent strain (PEEQ), von-Mises stress fields, and residual deformation contours at Point A—the critical zone identified from damage surveys. Among the five cases, Case-2, defined by a vertically eccentric external impact, demonstrated the highest plastic strain intensity (PEEQ > 2.0%), the sharpest post-yield drops in stiffness, and the closest match to the residual dent profile observed in the actual structure. The integrated correlation between field damage and some of the results (strain, stress, and deformed shape) enabled clear identification of the most probable accident mechanism with engineering accuracy. This study proposes a validated, measurement-calibrated nonlinear finite element analysis framework to diagnose stern damage from tugboat collisions, enhancing repair decision-making and structural safety assessment. Such a calibrated forensic strategy enhances the reliability of structural safety predictions in marine collision incidents and supports eco-friendly rescue engineering by minimizing unnecessary structural renewal through precise damage localization. The proposed approach establishes a new benchmark for scenario-driven collision assessment, particularly relevant to sustainable, automation-compatible, and damage-tolerant ship design practices. Full article

Backgrounds: The incidence of gastrointestinal cancers (GICs), though decreased in recent years, still accounts for 35% of all cancer-related mortality. The proper identification of risk factors, early diagnosis, and therapy optimization represent the three cornerstones of GIC treatment. In four-stage diseases, chemotherapy embodies target therapy that may prolong patients’ expectancy when suitably applied. Patients and Methods: There were 133 (82 (62%) male and 51 (38%) female) consecutive patients with a median age of 70 (64–74) years who underwent palliative treatment due to four-stage colorectal cancer (CRC) between 2022 and 2024. The demographic, clinical, and laboratory data and applied chemotherapeutic protocols were evaluated regarding the response to applied therapy, resulting in complete or partial tumor regression. The advancement of the tumor was based on computed tomography (CT) performed before and 6 months after the chemotherapy. Results: The multivariable model revealed red cell distribution width (RDW) from peripheral blood analysis (OR: 0.81, 95% CI: 0.65–1.00, p = 0.049) as a possible predictor for systemic treatment response in colorectal cancer. The receiver operating characteristic curve revealed a predictive value of male sex and RDW prior to systemic therapy, with an area under the curve of 0.672, yielding a sensitivity of 70.0% and specificity of 58.1%. Conclusions: The results of our analysis point out the possible modulatory impact of RDW on six-month systemic therapy in colorectal terminal cancer management. Further studies are required to confirm the presented results. Full article

Background: Fear of falling (FOF) is a significant concern among older adults, especially after total knee arthroplasty (TKA). FOF can limit daily activities, reduce quality of life, and hinder recovery. This study aimed to investigate the prevalence, severity, and impacts of FOF in patients undergoing TKA and identify factors contributing to increased FOF. Methods: A prospective observational study was conducted at King Abdulaziz Medical City in Riyadh, Saudi Arabia, from April 2024 to December 2024. This study included 52 participants aged 20 to 75 years who had undergone primary TKA. Data were collected at two time points: after TKA and at three months post-surgery. The Short Falls Efficacy Scale-International (SFES-I) was used to assess the severity of FOF, and the Short Form 36 (SF-36) was used to measure the quality of life. Descriptive statistics, t-tests, and logistic regression were used for analysis. Results: This study included 52 participants (mean age: 63.77 ± 6.65 years; 82.7% female). Post-TKA, all participants exhibited high FOF (mean SFES-I score: 56.75 ± 8.30). After three months, the mean SFES-I score decreased significantly to 49.04 ± 12.45 (t = 4.408, p < 0.05). Post-TKA, SF-36 showed significant improvements in the physical function, role of physical limitations, bodily pain, vitality, social function, role of emotional limitations, and mental health subdomains. Bilateral total knee arthroplasty, body mass index, and some SF-36 subcomponents—such as general health, vitality, and role of emotional limitations—were identified as factors leading to increased FOF. Conclusions: FOF remains prevalent and severe in TKA patients, even at three months post-surgery, affecting rehabilitation outcomes. Early identification and tailored interventions for FOF should be considered essential components of comprehensive TKA recovery programs. Full article

With the large-scale integration of high-penetration distributed photovoltaic (DPV) into distribution networks, its output volatility and reverse power flow characteristics are prone to causing voltage violations, necessitating the accurate identification of weak nodes to enhance operational reliability. This paper investigates the definition, quantification criteria, and multi-indicator comprehensive determination methods for weak nodes in distribution networks. A multi-criteria assessment method integrating voltage deviation rate, sensitivity analysis, and power margin has been proposed. This method quantifies the node disturbance resistance and comprehensively evaluates the vulnerability of voltage stability. Simulation validation based on the IEEE 33-node system demonstrates that the proposed method can effectively identify the distribution patterns of weak nodes under different penetration levels (20~80%) and varying numbers of DPV access points (single-point to multi-point distributed access scenarios). The study reveals the impact of increased penetration and dispersed access locations on the migration characteristics of weak nodes. The research findings provide a theoretical basis for the planning of distribution networks with high-penetration DPV, offering valuable insights for optimizing the siting of volatile loads such as electric vehicle (EV) charging stations while considering both grid safety and the demand for distributed energy accommodation. Full article

Environmental noise is inevitable during seismic data acquisition, with major sources including heavy machinery, rivers, wind, and other environmental factors. During field data acquisition, it is important to assess the impact of environmental noise and evaluate data quality. In subsequent seismic data processing, these noise components also need to be eliminated. Accurate identification of noise traces facilitates rapid quality control (QC) during fieldwork and provides a reliable basis for targeted noise attenuation. Conventional environmental noise identification primarily relies on amplitude differences. However, in seismic data, high-amplitude signals are not necessarily caused by environmental noise. For example, surface waves or traces near the shot point may also exhibit high amplitudes. Therefore, relying solely on amplitude-based criteria has certain limitations. To improve noise identification accuracy, we use the Mel-spectrogram to extract features from seismic data and construct the dataset. Compared to raw time-series signals, the Mel-spectrogram more clearly reveals energy variations and frequency differences, helping to identify noise traces more accurately. We then employ a Vision Transformer (ViT) network to train a model for identifying noise in seismic data. Tests on synthetic and field data show that the proposed method performs well in identifying noise. Moreover, a denoising case based on synthetic data further confirms its general applicability, making it a promising tool in seismic data QC and processing workflows. Full article

Amidst the intensification of global climate change and the increasing impacts of human activities, ecosystem patterns and processes have undergone substantial transformations. The distribution and evolutionary dynamics of mountain ecosystems have become a focal point in ecological research. The Surkhan River Basin is located in the transitional zone between the arid inland regions of Central Asia and the mountain systems, where its unique physical and geographical conditions have shaped distinct patterns of vertical zonation. Utilizing Landsat imagery, this study applies a hierarchical classification approach to derive land cover classifications within the Surkhan River Basin. By integrating the NDVI (normalized difference vegetation index) and DEM (digital elevation model (30 m SRTM)), an “NDVI-DEM-Land Cover” scatterplot is constructed to analyze zonation characteristics from 1980 to 2020. The 2020 results indicate that the elevation boundary between the temperate desert and mountain grassland zones is 1100 m, while the boundary between the alpine cushion vegetation zone and the ice/snow zone is 3770 m. Furthermore, leveraging DEM and LST (land surface temperature) data, a potential energy analysis model is employed to quantify potential energy differentials between adjacent zones, enabling the identification of ecological transition areas. The potential energy analysis further refines the transition zone characteristics, indicating that the transition zone between the temperate desert and mountain grassland zones spans 1078–1139 m with a boundary at 1110 m, while the transition between the alpine cushion vegetation and ice/snow zones spans 3729–3824 m with a boundary at 3768 m. Cross-validation with scatterplot results confirms that the scatterplot analysis effectively delineates stable zonation boundaries with strong spatiotemporal consistency. Moreover, the potential energy analysis offers deeper insights into ecological transition zones, providing refined boundary identification. The integration of these two approaches addresses the dimensional limitations of traditional vertical zonation studies, offering a transferable methodological framework for mountain ecosystem research. Full article