Search Results (2,511)

Reliable visual perception is a prerequisite for autonomous cable duct inspection, particularly for recognizing pipe-joint dislocations and obstruction-related hazards. Images acquired inside cable ducts are often affected by restricted viewpoints, uneven illumination, wall-texture interference, partial occlusion, and substantial variations in target geometry and scale. These factors increase the likelihood of missed targets, false alarms, and inaccurate bounding boxes. This study develops DPR-YOLOv9 from the YOLOv9c detector, where DPR represents deformable-strip feature extraction, position-aware attention, and regression optimization. In the backbone, a Deformable Strip Convolution Network (DSCN) adjusts its sampling pattern to better describe elongated boundaries, displaced joints, and irregular obstacle contours. CoordAttention is introduced into the multi-scale fusion path to retain directional coordinate cues and emphasize spatially relevant features. In addition, Inner-IoU modifies the regression constraint through auxiliary boxes, providing more effective optimization for small or partially occluded targets. Across three independent runs, DPR-YOLOv9 achieved mean Precision, Recall, mAP@0.5, and mAP@0.5:0.95 values of 0.944, 0.933, 0.940, and 0.751, respectively, while maintaining an inference speed of 67.85 FPS. The results indicate that the proposed detector improves both recognition reliability and localization quality for robotic cable duct inspection. Full article

Facade debonding may lead to falling components and pose safety risks in dense urban environments, but infrared thermal responses may also arise from non-defect facade components and environmental conditions. Conventional facade inspection methods are often labor-intensive, hazardous, and difficult to integrate into digital maintenance workflows. To support safer and more efficient facade inspection and maintenance information management, this study develops an engineering-oriented inspection and management framework that integrates unmanned aerial vehicle infrared thermography, intelligent defect recognition, visual result verification, and defect information management. A UAV-based infrared data acquisition scheme was established, and a self-constructed dataset containing 1035 thermal images was developed for the detection of suspected facade debonding regions and common thermal interference sources, including windows, air-conditioning units, and signage. A lightweight detection model was embedded as the recognition engine of the framework to balance detection reliability and deployment efficiency under practical inspection conditions. Experimental results show that the proposed method achieved an mAP@0.5 of 87.8%, with 1.64 million parameters and 4.2 GFLOPs, indicating its potential for rapid preliminary facade screening under the tested computing configuration. Beyond model evaluation, an application platform was developed to support infrared image and video input, automatic detection, result visualization, statistical analysis, and defect record storage. The proposed framework demonstrates the potential of combining UAV infrared inspection and digital management tools for preliminary facade screening and inspection documentation, providing supporting information for subsequent engineering review and maintenance planning. Full article

Climate-related and environmental hazards affect cultural heritage sites in markedly different inland, coastal, lacustrine and underwater settings, creating documentation requirements that cannot be addressed by a single sensing approach. This study presents the multi-sensor geometric documentation of eight cultural heritage sites. Unmanned aerial vehicle (UAV) photogrammetry was applied to six inland and coastal sites, while underwater photogrammetry, unmanned surface vehicles (USVs), acoustic sounding and a prototype green-wavelength flash LiDAR were used at three shallow-water sites. The campaigns produced orthomosaics, elevation models, dense point clouds, textured meshes, bathymetric maps and underwater LiDAR point clouds at scales appropriate to the conservation problem of each site. The resulting products document exposed architectural remains, excavation areas, cliffs and unstable slopes, lake-margin changes, submerged masonry, wooden structures and lakebed morphology. Their main contribution is the establishment of spatially explicit, site-specific baselines that provide measurable geometric and visual evidence for condition assessment, future repeat-survey comparisons and the spatial integration of environmental, archaeological and conservation information. The study demonstrates the operational and information complementarity of optical, acoustic and active ranging approaches, which address different documentation scales, environmental constraints and heritage targets, and provide distinct spatial evidence that can serve as potential inputs to subsequent digital twin and decision support applications. Full article

The transition toward Industry 5.0 requires safety management systems that are not only intelligent and data-driven but also human-centric, resilient, and aligned with sustainable operations. However, conventional safety risk assessment in steel manufacturing remains heavily dependent on expert judgment and often lacks the adaptability required to address complex and dynamic production environments. This study proposes a text-driven intelligent framework for sustainable safety risk governance by integrating natural language processing, topic modeling, objective indicator weighting, and Bayesian decision fusion. The framework establishes a closed-loop process encompassing risk identification, quantitative assessment, risk classification, and hierarchical control. It automatically extracts risk-related information from unstructured safety records, maps the identified hazards onto a human–machine–environment–management structure, quantifies multidimensional risk indicators, and translates assessment outcomes into differentiated control measures. The framework was evaluated using field safety records collected from Tianjin Iron and Steel Group. The topic modeling results identified four major dimensions of operational risk, while the CRITIC–Bayesian weighting mechanism combined data-driven indicator differentiation with context-sensitive probabilistic reasoning. Following its integration into the company’s intelligent safety management platform and one year of operational use, the framework reduced the time required to formulate safety inspection plans by 70%, supported dynamic four-level risk classification, and achieved a 97% task completion rate. The number of recorded safety accidents also decreased by 35% compared with the pre-deployment baseline. These findings demonstrate that unstructured safety text can be transformed into actionable risk intelligence, enhancing the proactive, systematic, and adaptive governance of safety risks. The proposed framework provides a practical pathway for advancing human-centric safety management, operational resilience, and sustainable production in the steel industry. Full article

Large-area remote-sensing data provide essential pre-mission information for unmanned ground vehicles, but their spatial support and temporal latency may obscure local terrain changes. A remaining challenge is to translate heterogeneous regional evidence and recent local observations into a consistent, updateable, and planner-ready map. This study presents a satellite–unmanned aerial vehicle (UAV) workflow for constructing and incrementally maintaining an off-road traversability map for mission-level global planning. A common H3 index organizes satellite imagery, terrain, soil, road evidence, and local UAV semantic observations while retaining their native spatial support and provenance. The map separates environmental-prior, semantic, and traversability-cost layers to support interpretable fusion and independent updating. A confidence-hierarchical conflict resolution mechanism resolves inconsistencies in the regional prior, while an observer-agnostic interface projects UAV semantic observations onto local map cells. RGB imagery is used by the primary UAV observer, and digital surface model (DSM) is evaluated as an optional semantic-observation modality. Evaluation included a manually reviewed regional benchmark, a unified buffered spatial holdout, cell-level update assessment, and 40 fixed replanning tasks. Conflict resolution reduced high-risk omissions. RGB-only SegFormer-B2 achieved the highest semantic accuracy with moderate computational complexity. UAV override achieved a cell-level F1 score of 96.96% and limited the false-positive accumulation associated with conservative union. Replanning further revealed a trade-off between hazardous-cell avoidance and search-graph connectivity. The proposed workflow provides a maintainable interface between multi-source remote sensing and global UGV planning rather than a replacement for onboard perception, local obstacle avoidance, or vehicle control. Full article

Background and Objectives: Bile duct injury remains a major safety concern during laparoscopic cholecystectomy, and reliable interpretation of hepatocystic anatomy is fundamental to safe dissection. Artificial intelligence (AI)-based computer vision may support anatomical recognition, critical view of safety (CVS) assessment, and intraoperative decision support. This narrative review synthesized the current evidence, clinical applications, readiness for implementation, and future requirements of anatomy-aware AI during laparoscopic cholecystectomy. Materials and Methods: Five bibliographic databases were searched through 15 August 2026, supplemented by citation tracking and targeted searches. Studies were classified according to clinical task, dataset, reference standard, validation design, performance metrics, real-time capability, human factor assessment, and clinical readiness stage. The evidence base comprised 105 verified references: 50 primary AI reports and 55 contextual or methodological sources; 39 direct model development or evaluation reports were characterized in detail. Results: Investigated applications included landmark detection, semantic segmentation, CVS assessment, safe and hazard zone mapping, multimodal analysis incorporating indocyanine green fluorescence, automated documentation, education, and real-time perceptual prompting. Among the 39 direct reports, 24 remained at the offline proof of concept or internal validation stage, eight achieved temporal, external, or multicenter validation, six demonstrated prospective operating-room feasibility, and one reached post-deployment surveillance. The latter evaluated a surgical-process outcome rather than patient morbidity. Generalizability was constrained by dataset overlap, heterogeneous reference standards and metrics, domain shift, and underrepresentation of difficult cholecystectomy. No included study demonstrated reduced bile duct injury or other patient-level benefit. Conclusions: Despite progression to prospective feasibility and one post-deployment process surveillance report, no included study demonstrated a reduction in bile duct injury or another patient-level outcome. Current evidence supports adjunctive applications in documentation, video triage, education, coaching, and quality assurance, while surgeon-facing deployment requires further multicenter, human factor, and comparative-effectiveness evaluation. Full article

Barrier-based safety systems are fundamental to preventing accidents in high-hazard industrial operations. However, traditional Hazard Identification (HAZID) and risk screening frameworks aggregate safety safeguards at a macro-hazard level, creating a systemic blind spot that masks threat-specific vulnerabilities and single points of failure. To address this gap, this study develops ‘Maximum-Vulnerability Diagrams’ (MVD), a network-based modeling approach that maps and quantifies threat–barrier pathways using matrix algebra and conditional probability. Validated across empirical cases of working-at-height (H-06.01) and heavy rotary equipment (H-08.01) operations in the oil extraction industry, the MVD successfully isolates high-criticality, zero-redundancy pathways. We mathematically establish that multiplexed defenses require a target individual efficiency of η ≥ 95% to reliably suppress system failure probability below a strict 5% operational threshold. The findings demonstrate that aggregate safeguard volume is a deceptive safety metric, and that systemic resilience depends entirely on network architecture. This framework transitions risk governance from passive compliance checking to predictive, threat-driven barrier management, offering an actionable methodology to optimize safety resources before accidents occur. Full article

Airborne dust plumes are difficult to localize in underground mine-face video because the scene is dark, illumination moves with machinery, and dust is confused with lamp bloom, reflective steel, and water-spray aerosol. We present DustVeil, a label-free two-stage system for image-space plume localization. Its software teacher combines background-referenced veiling (C1), local texture decay (C2), and absolute dark-channel response (C3) with a probabilistic soft-OR, then applies glare and chroma gates. The teacher returns a dimensionless response map in [0, 1], a binary plume mask and the corresponding image-area ratio; it does not estimate dust concentration, particle-size distribution, respirable exposure, or hazard categories. Teacher outputs from 342 frames in 114 clips/24 sessions supervise a 0.47 M parameter TinyU-Net. Evaluation uses a 144-image synthetic calibration set and a 72-frame real test set drawn from 72 clips in 18 sessions, with all roles separated at clip and session levels. Thresholds are selected only on synthetic masks and frozen before real scoring. After replacing per-image score normalization with fixed baseline-normal calibration and using reference implementations of the anomaly methods, DustVeil obtains IoU/F1 of 0.366/0.500 and the lowest clean-frame false-positive area (3.7% versus 13.9–59.9%). A separate water-spray set quantifies visual specificity. TinyU-Net runs at 610 FPS for network-only inference and 233 FPS aggregate in the measured six-stream decode-to-mask pipeline; optical flow is excluded from these figures. The validated scope is six fixed visible-light RGB cameras with camera-specific unlabelled calibration at one site, rather than concentration monitoring or camera-disjoint deployment. Full article

Education on natural hazards is a non-structural component of Disaster Risk Reduction (DRR), but the evidence base spans curriculum studies, risk-perception research, educational interventions, geospatial approaches and analyses of education-system continuity. This systematic narrative review synthesizes 27 outcome-bearing studies published between 2013 and the partial year 2026. PRISMA 2020 was used as a reporting framework, while PRISMA-S informed a retrospective audit of the search documentation. A structured design-sensitive appraisal recorded evidence family, comparison or temporal structure, outcome directness, permitted inference and principal limitation. The studies were coded into six mutually exclusive primary axes: reviews and frameworks; curriculum and policy; knowledge and risk perception; educational interventions and active methodologies; GIS and geospatial technologies; and educational continuity and system resilience. The included literature suggests that locally situated problems, maps, simulations and inquiry can support knowledge, risk appraisal and preparedness intentions, although demonstrated effects on sustained performance or actual preparedness behavior remain limited. Cross-cutting gaps include weak longitudinal assessment, sparse attention to teacher professional development, limited treatment of indigenous or local knowledge, and no core study centered on learners with disabilities or special educational needs. The review defines critical territorial risk literacy as the capacity to interpret hazard, exposure, vulnerability, capacity and uncertainty through spatial evidence; evaluate their unequal territorial distribution; and translate that understanding into inclusive, proportionate preparedness and collective action. Full article

Despite advances in climate modelling, observation systems, and risk assessment, a persistent gap remains between climate knowledge production and its translation into effective adaptation action. Existing approaches often identify physical hazards and vulnerabilities but provide limited mechanisms for integrating socio-cultural values and place-based conditions into adaptation decision-making. This paper proposes a Climate Knowledge Interpretation Framework that conceptualizes adaptation as a structured process connecting climate information, vulnerability contexts, decision systems, and socio-spatial outcomes. The framework draws on established climate datasets, assessment approaches, and socio-spatial analysis to establish a basis for interpreting climate knowledge within local contexts. It argues that adaptation effectiveness depends not only on the availability of climate information but also on the capacity to incorporate intangible values, relational networks, and place-based knowledge into planning processes. The study proposes Value Maps as a conceptual decision-support tool for revealing socio-spatial infrastructures and relational anchors that contribute to adaptive capacity. The proposed framework provides a pathway for developing more inclusive, context-sensitive, and transferable climate adaptation strategies across diverse climatic contexts. Full article

The Alternative Lengthening of Telomeres (ALT) pathway drives replicative immortality in aggressive malignancies, particularly sarcomas and gliomas. Clinical ALT stratification has relied on screening for structural ATRX and DAXX mutations. However, this genotypic approach fails to capture the dynamic macro-reprogramming required to sustain ALT. Here, we established and validated a 28-gene transcriptomic signature that captures the ALT-associated transcriptomic phenotype of the ALT phenotype. Using multivariate Cox proportional hazards models and time-dependent ROC analyses, we demonstrate that this signature is a robust, independent predictor of poor overall survival in Sarcoma (SARC) and Lower Grade Glioma (LGG) cohorts, outperforming the prognostic value of traditional ATRX/DAXX mutational status. Genomic mapping revealed this transcriptional synchrony is structurally facilitated by non-random focal clustering on Chromosome 8. To deconstruct the machinery driving this lethal phenotype, we employed an integromic approach, synthesizing protein–protein and metabolic flux networks. Topological algorithms prioritized five indispensable hubs: TP53, ATM, ATR, PCNA, and UBE2I. Gene–metabolite profiling identified PCNA as a bottleneck funneling extreme deoxyribonucleotide (dNTP) demand to sustain break-induced telomeric recombination. To translate these vulnerabilities into actionable treatments, we mapped these hubs to a precision pharmacological network. We propose a multi-targeted strategy combining FDA-approved PARP inhibitors to exploit ATR-mediated synthetic lethality, alongside antimetabolites to induce nucleotide starvation. This study redefines ALT risk stratification and provides a data-driven framework to target and treat resistant ALT-positive tumors. Full article

The active development of the foothill areas of the Almaty agglomeration (Southeastern Kazakhstan) requires reliable methods for landslide susceptibility assessment. This study aims to identify the spatial association between landslide occurrence and selected environmental and anthropogenic factors using the Frequency Ratio (FR) method and geographic information systems. The analysis is based on an inventory of 157 landslides, the SRTM digital elevation model, Landsat imagery, WorldClim climate data, geological maps, and OpenStreetMap data. Ten conditioning factors were analyzed: elevation, slope, aspect, precipitation, lithology, distance to faults, rivers and roads, the Normalized Difference Vegetation Index (NDVI), and land use. FR values were calculated for each factor and integrated to produce a landslide susceptibility map. Model performance was evaluated using Receiver Operating Characteristic (ROC) analysis and the Area Under the Curve (AUC). The resulting susceptibility map identifies areas with different levels of landslide susceptibility and provides a scientific basis for sustainable land-use planning, engineering-geological investigations, safer infrastructure development, natural hazard assessment, and disaster risk reduction in rapidly developing foothill regions. By supporting risk-informed land-use decisions and targeted mitigation measures, the study contributes to the long-term environmental safety and resilience of the Almaty agglomeration. Full article

Frost damage to concrete proceeds by two distinct mechanisms: deep sustained freezing, which governs buried elements, and repeated freeze–thaw cycling, which governs exposed ones. Romanian practice still assigns durability requirements from maps built for other purposes: a 1977 frost-depth zonation and a thermal zoning derived from building heat-loss temperatures. Daily records from 156 meteorological stations were processed by Gumbel extreme value analysis at 2–100-year mean recurrence intervals to map the freezing index (FI), freeze–thaw cycle (FTC) counts, day-counts below −5 °C and −10 °C, the 50-year minimum temperature, and frost penetration depth. The two hazards are spatially decoupled: across the 135 stations below 1000 m, the freezing index explains only 31% of the variance in cycle count, falling to 14% once altitude is partialled out. Low-altitude cold pockets in northern Oltenia reach top-class cycling at moderate minima (Apa Neagră, 250 m, FTC₅₀ = 134.6) yet are currently placed in the second-mildest zone. A two-axis zonation with Jenks-calibrated thresholds is therefore proposed, aggregated into five zones that preserve the existing numbering while correcting four misclassifications, with the axes being assigned to buried and above-ground concrete. Requirements matched to local severity extend service life and avoid premature repair and over-specification. Full article

The increasing frequency and intensity of extreme rainfall events due to climate change have significantly heightened the risk of natural disasters such as floods and landslides in South Korea. The 2011 Mt. Majeok landslide in Chuncheon resulted in 13 fatalities and 26 injuries, revealing critical limitations of conventional 1:5000-scale topographic map-derived DEMs in capturing fine-scale terrain features essential for accurate debris flow and slope failure analysis. This study quantifies systematic differences in terrain derivative outputs and hazard classification patterns between a high-resolution LiDAR-derived DEM (<0.5 m, resampled to 5 m) and a conventional 1:5000-scale contour-derived TIN DEM (5 m) for landslide hazard assessment in a documented historical disaster catchment. FLO-2D and SINMAP models were applied to debris flow and slope stability simulations within the Mt. Majeok watershed. Since slope stability indices are computed quantities dependent on input terrain data, the LiDAR result was adopted as the high-resolution reference. SINMAP analysis revealed a sequential pattern of resolution-induced discrepancies from flow direction through terrain derivatives to stability index classification (CSI = 0.565, Kappa = 0.589). FLO-2D simulation showed that the TIN-based DEM overestimated inundation extent by 10.5 percentage points, with the LiDAR-based simulation reducing false alarm inundation area by 10.5 percentage points relative to the TIN-based result—a margin with direct operational implications for evacuation zone delineation in high-risk mountain communities. These findings demonstrate that LiDAR-derived DEMs produce markedly different spatial patterns of hazard classification and flow simulation compared to contour-derived DEMs; the reported discrepancy magnitudes are specific to the geomorphological context of the Mt. Majeok watershed and should be interpreted as inter-product differences rather than absolute accuracy measures, with direct implications for sustainable disaster risk management in mountainous environments. Full article

The U.S. Geological Survey collects remote sensing data to support national scientific assessments of natural resources, hazards, and landscape change. Spectrometers and spectroradiometers are essential for gathering point-based spectral measurements used in applications such as uncrewed aerial systems (UAS) multispectral image calibration, validation, and analysis. Evaluating how different instruments perform in laboratory and field environments helps determine whether they provide consistent, interoperable measurements. Such verification can expand access to spectral ground data during UAS operations by allowing scientists to use alternative instruments when budgets, logistics, or field conditions limit options. We propose and test a methodological framework for evaluating spectrometers for measurement consistency during UAS multispectral vegetation mapping applications. There are three central evaluation components to the framework: laboratory, field, and relative to UAS multispectral imagery. By evaluating the instruments in both relatively controlled and uncontrolled environments, we thoroughly examine measurement consistency and when/why measurements may differ. We opportunistically selected two instruments for a case study in a coastal marsh setting: a compact laboratory spectrometer we modified for field use and a field-ready spectroradiometer. The instruments produced consistent measurements in both environments. We found differences between the field spectra and UAS spectra that likely reflect the perspectives of ground vs. aerial data and indicate that further radiometric calibration may be needed. Full article