Search Results (6,091)

The present article focuses on the assessment of the potential advantages and disadvantages of the utilisation of modern building inventory technologies in crisis situations, using a case study of Ukraine, currently engulfed in armed conflict. The following methods are described in detail: laser scanning, 360-degree camera images, and photo series. The authors conducted an in-depth SWOT/TOWS analysis, adapted to the specifics of the post-conflict environment, with a view to the future reconstruction of damaged buildings. The originality of the study lies in the use of a modified, quantitative version of the conventional SWOT analysis, supplemented with a weighting and rating system, which allowed for a more accurate assessment of the effectiveness of various technologies, including laser scanning. While the study focuses on the Ukrainian context, the authors emphasise that the developed methodology is universal and can be successfully applied to other critical areas, such as regions affected by earthquakes, floods, fires, or technological disasters. A modified SWOT/TOWS analysis can serve as a valuable tool in crisis management and infrastructure reconstruction during emergencies, providing the data necessary for making rational and effective decisions regarding the use of modern technologies in construction. The analysis revealed that, of the analysed inventory strategies, only laser scanning technology fits the so-called “maxi-maxi” strategy, a scenario in which both internal resources and external capabilities are maximised. The remaining two strategies were designated as “maxi-mini,” signifying that their implementation is associated with elevated levels of risk despite their inherent advantages. It is imperative to acknowledge the existence of substantial external threats that persist. Nevertheless, this does not constitute a complete rejection of the concept. This study examines armed conflict as a research context for a selection of buildings in Ukraine. The analysis was constrained to the three most prevalent methods: The use of TLS, SfM, and 360-degree cameras is also a key component of the methodology. Full article

With the rapid advancement of aviation technology, modern aircraft cockpits are evolving toward high automation and intelligence, making pilot-cockpit interaction a critical factor influencing flight safety and efficiency. Pilot pose estimation and behavior recognition are critical for monitoring pilot state, preventing operational errors, and enabling adaptive human–machine interaction, thus playing an essential role in aviation safety assurance and intelligent cockpit development. However, existing methods face challenges in real-time performance, reliability, and computational complexity in practical applications. Traditional approaches, such as wearable sensors and image-processing-based algorithms, demonstrate certain effectiveness but still exhibit limitations in aviation environments. To address these issues, this paper proposes a lightweight pilot pose estimation and behavior recognition framework, integrating Vision Transformer with depth-wise separable convolution to optimize the accuracy and efficiency of keypoint detection. Additionally, a novel multimodal data fusion technique is introduced, along with a scientifically designed evaluation system, to enhance the robustness and security of the system in complex environments. Experimental results on a pilot keypoint detection dataset captured in a simulated cockpit environment show that the proposed method achieves 81.9 AP, while substantially reducing model parameters and notably improving inference efficiency compared with HRNet. This study provides new insights and methodologies for the design and evaluation of aviation human-machine interaction systems. Full article

Ground Penetrating Radar (GPR) is a powerful tool for imaging shallow stratigraphic and structural features. This study shows that it is particularly effective also in detecting near-surface evidence of active faulting. In the Southern Apennines (Italy), one of the most seismically active regions of the Mediterranean area, the shallow expression of active faults is often poorly constrained due to limited or ambiguous surface evidence. Low-frequency GPR profiles were acquired in the Calore River Valley (Campania Region), an area historically affected by large earthquakes and characterized by debated seismogenic sources. The surveys employed multiple antenna frequencies (30, 60, and 80 MHz) and both horizontal and vertical acquisition geometries, enabling penetration depths ranging from ~5 m to ~50 m. The acquired GPR profiles, integrated with high-precision georeferencing, were able to reveal the presence of shallow steeply dipping active normal faults striking E–W to ENE–WSW, here named the Postiglione Fault System. Therefore, this study highlights the methodological potential of low-frequency GPR for investigating active faults in carbonate substratum and fine-to-coarse-grained sedimentary units and thus contributing to refining the seismotectonic framework and improving seismic hazard assessment of seismically active areas such as the Southern Apennines. Full article

Digital image correlation (DIC) technology is widely employed in speckle-based measurement techniques, including X-ray speckle tracking. By enhancing DIC’s measurement capability to the subpixel scale through subpixel registration technology, the accuracy of such tracking methods is significantly improved. Consequently, selecting an appropriate subpixel registration algorithm becomes crucial for advancing the precision of both DIC and its application in tracking methods. Nevertheless, current evaluation approaches for these algorithms overlook spatial resolution—an essential metric not only for X-ray speckle tracking but also for other comparable methodologies. Inspired by the modulation transfer function, this study proposes a novel evaluation method that uses the root mean square error of displacement measurement at different spatial frequencies to assess spatial resolution. Two widely used subpixel registration algorithms—the peak-finding algorithm and the iterative spatial domain cross-correlation algorithm—are evaluated and compared. The result strongly contrasts with traditional evaluations based on ideal translational conditions, and underscores the necessity of incorporating spatial resolution and speckle size into algorithm selection criteria for practical applications. Full article

Nonunions and fracture-related infections represent a significant complication in orthopedic and trauma care, with their incidence rising due to an aging, more comorbid global population and the escalating threat of multi-resistant pathogens. This narrative review highlights pivotal advancements in diagnostics and therapeutic approaches, while also providing an outlook on future directions. Diagnostic methodologies have significantly evolved from traditional cultures to sophisticated molecular techniques like metagenomic next-generation sequencing and advanced imaging. Simultaneously, therapeutic strategies have undergone substantial refinement, encompassing orthoplastic management for infected open fractures and the innovative application of antibiotic-loaded bone substitutes for local drug delivery. The effective integration of these possibilities into daily patient care critically depends on specialized centers. These institutions play an indispensable role in managing complex cases and fostering innovation. Despite considerable progress over the past 25 years, ongoing research, interdisciplinary collaboration, and a steadfast commitment to evidence-based practice remain crucial to transforming management for the future. Full article

Background: The morphology of the acromion has long been implicated in shoulder pathology, particularly in relation to subacromial impingement and rotator cuff disease. More recently, interest has shifted toward the posterior acromion, with studies examining its potential role in posterior instability, eccentric glenohumeral osteoarthritis, and massive rotator cuff tears. Methods: A critical literature review of nine studies assessing sagittal acromial tilt, posterior coverage, and acromial height was conducted, emphasizing reproducibility and clinical significance across different shoulder disorders. Results: In posterior instability and eccentric osteoarthritis, the acromion is generally described as more horizontally oriented, less covering posteriorly, and positioned higher. Conversely, in massive cuff tears, it tends to appear more posteriorly covering without consistent change in tilt. Although these trends suggest a possible biomechanical role for the acromion, reported values vary widely between studies, and significant overlap exists between pathological and control groups. Such variability is compounded by differences in imaging modality, definitions of anatomical landmarks, and the frequent reduction of three-dimensional structures into two-dimensional projections. These methodological inconsistencies undermine reproducibility and limit the clinical applicability of posterior acromial parameters. Conclusions: Posterior acromial morphology appears to influence shoulder biomechanics, but existing measurements should be considered population-level markers rather than diagnostic thresholds. Future research should adopt standardized, three-dimensional, pathology-independent reference models anchored to stable scapular landmarks and validated across imaging modalities to improve reproducibility and clinical utility. Full article

This study discusses the challenges encountered in implementing a detailed protocol for upper abdominal imaging using magnetic resonance imaging (MRI), ranging from patient preparation and sequence selection to clinical applications. MRI is a valuable non-invasive imaging modality employed both in the early detection of diseases and as a complementary tool for the detailed characterization of various pathologies. Nevertheless, performing an abdominal MRI examination can be challenging; therefore, the understanding of sequences is particularly important, as changing the parameters can not only influence the quality of the images but also optimize scanning time improve patient experience during the examination. The methodology illustrates the purpose of each sequence and the critical role of appropriate patient preparation. Results highlighted the significance of these factors in the evaluation of hepatic lesions, showing that the proper choice of sequences and parameters is essential for distinguishing benign from malignant findings and for achieving an accurate diagnosis. It was also shown that MRI plays an important role as a complementary technique in investigation of upper abdominal pathologies in order to avoid overexposure to radiation. Full article

Image semantics, by revealing rich structural information, provides crucial guidance for image inpainting. However, current semantic-guided inpainting frameworks generally operate unidirectionally, relying on pre-trained segmentation networks without a feedback mechanism to adapt segmentation dynamically during inpainting. To address this limitation, we propose an innovative inpainting methodology that incorporates semantic segmentation feedback recorrection via semi-supervised learning. Specifically, the fundamental concept involves enabling the initial inpainting network to deliver feedback to the semantic segmentation model, which subsequently refines its predictions by leveraging cross-image semantic consistency. The iteratively corrected semantic segmentation maps serve to direct the inpainting neural network toward improved reconstruction quality, fostering a synergistic interaction that enhances both segmentation accuracy and inpainting performance. Furthermore, a semi-supervised learning strategy is implemented to reduce reliance on ground truth labels and improves generalization by utilizing both labeled and unlabeled datasets. We conduct our methodology on the CelebA-HQnd Cityscapes datasets, employing multiple quantitative metrics including LPIPS, PSNR, and SSIM. Results demonstrate that the proposed algorithm surpasses current methodologies: on CelebA-HQ dataset, it achieves a 5.89% reduction in LPIPS and a 0.52% increase in PSNR, with notable improvements in SSIM; on the Cityscapes dataset, LPIPS decreases by 6.15% and SSIM increases by 1.58%. Ablation studies confirm the effectiveness of the feedback recorrection mechanism. This research provides novel insights into synergistic interactions between segmentation and inpainting, demonstrating that fostering such interactions can substantially improve image processing performance. Full article

The urban forest canopy provides critical ecosystem services, including carbon storage and sequestration. Healthy, well-managed trees in an urban setting can provide these services in a way comparable to forests managed for production or as nature preserves. Disturbance events threaten these benefits by reducing canopy cover and biomass. A tornado struck Ruston, Louisiana, on 25 April 2019, resulting in severe canopy damage through a swatch of the city. We used iTree Canopy to obtain estimates of ecosystem services (carbon sequestration, etc.) and converted this to a per-pixel value before interpolating for the study area. Fractional vegetation estimates obtained from spectral unmixing were obtained from pre- and post-tornado images using Sentinel-2 data and applied to weight damage. Pre- and post-tornado assessments revealed that Ruston’s urban forest canopy sequestered 85% of its pre-storm capability, with an estimated decline in social value of approximately $36,000. Assessing disturbance-based landscape changes, and subsequently calculating fractional changes in biomass and corresponding monetary impacts, will increasingly be looked to as ecosystem services and severe weather events are expected to become more commonplace in the future. The methodology employed demonstrates a cost-effective way to assess disturbance impacts in small urban areas, offering a framework to small municipalities to monitor canopy dynamics. Full article

We developed a machine vision-based robotic system to address automation challenges in pharmaceutical pill sorting and packaging. The hardware platform integrates a high-resolution industrial camera with an HSR-CR605 robotic arm. Image processing leverages the VisionMaster 4.3.0 platform for color classification and positioning. Coordinate mapping between camera and robot is established through a three-point calibration method, with real-time communication realized via the Modbus/TCP protocol. Experimental validation demonstrates that the system achieves 95% recognition accuracy under conditions of pill overlap ≤ 30% and dynamic illumination of 50–1000 lux, ±0.5 mm picking precision, and a sorting efficiency of108 pills per minute. These results confirm the feasibility of integrating domestic hardware and algorithms, providing an efficient automated solution for the pharmaceutical industry. This work makes three key contributions: (1) demonstrating a cost-effective domestic hardware-software integration achieving 42% cost reduction while maintaining comparable performance to imported alternatives, (2) establishing a systematic validation methodology under industrially-relevant conditions that provides quantitative robustness metrics for pharmaceutical automation, and (3) offering a practical implementation framework validated through multi-scenario experiments that bridges the gap between laboratory research and production-line deployment. Full article

The implementation strategy of a nowcasting methodology can be crucial to pursue skillful results in an operational context to obtain reliable short forecasts with as much as possible reduced errors. In this work, a spectral nowcasting algorithm was considered to carry out rainfall prediction at the Italian national scale, instead of the traditional “single-piece area” approach; strategies were tested to dynamically split the precipitation zone into smaller sub-regions by identifying connected components within the precipitation area. These strategies rely on image-processing techniques, and they were tested over a long period of time which includes several events with a variety of rainfall typologies (stratiform, thunderstorms, persistent rainfall). Traditional standard skill scores widely used in hydro-meteorology are exploited to quantify the improvements. The strategy that leads to the best performance is the one that results in smaller spatial calculation domains; this demonstrates the importance of correctly modeling and interpreting the different types of rain structures that may be present simultaneously in the rain field across a large domain. Full article

Satellite-derived bathymetry (SDB) using Ice, Cloud, and Land Elevation satellite-2 (ICESat-2) LiDAR data and remote sensing images faces challenges in the difficulty of uniform coverage of the inversion area by the bathymetric control points due to the linear sampling pattern of ICESat-2. This study proposes a novel virtual control point optimization framework integrating inverse distance weighting (IDW) and spectral confidence analysis (SCA). The methodology first generates baseline bathymetry through semi-empirical band ratio modeling (control group), then extracts virtual control points via SCA. An optimization scheme based on spectral confidence levels is applied to the control group, where high-confidence pixels utilized a residual correction-based strategy, while low-confidence pixels employed IDW interpolation based on a virtual control point. Finally, the preceding optimization scheme uses weighting-based fusion with the control group to generate the final bathymetry map, which is also called the optimized group. Accuracy assessments over the three research areas revealed a significant increase in accuracy from the control group to the optimized group. When compared with in situ data, the determination coefficient (R²), RMSE, MRE, and MAE in the optimized group are better than 0.83, 1.48 m, 12.36%, and 1.22 m, respectively, and all these indicators are better than those in the control group. The key innovation lies in overcoming ICESat-2’s spatial sampling limitation through spectral confidence stratification, which uses SCA to generate virtual control points and IDW to adjust low-confidence pixel values. It is also suggested that when applying ICESat-2 satellite data in active–passive-fused SDB, the distribution of training data in the research zone should be adequately considered. Full article

The monitoring of turbidity in estuarine environments is a challenging essential task for managing water quality and ecosystem health. This study focuses on the lower reaches of the River Mersey, Liverpool. Harmonized Sentinel-2 MSI Level-2A imagery was integrated with in situ measurements from seven Environment Agency monitoring stations for two consecutive years (January 2023–January 2025). The workflow included image preprocessing, spectral index calculation, and the application of four machine learning algorithms: Gradient Boosting Regressor, XGBoost, Support Vector Regressor, and K-Nearest Neighbors. Among these, Gradient Boosting Regressor achieved the highest predictive accuracy (R² = 0.84; RMSE = 15.0 FTU), demonstrating the suitability of ensemble tree-based methods for capturing non-linear interactions between spectral indices and water quality parameters. Residual analysis revealed systematic errors linked to tidal cycles, depth variation, and salinity-driven stratification, underscoring the limitations of purely data-driven approaches. The novelty of this study lies in demonstrating the feasibility and proof-of-concept of using machine learning to derive spatially explicit turbidity estimates under data-limited estuarine conditions. These results open opportunities for future integration with Computational Fluid Dynamics models to enhance temporal forecasting and physical realism in estuarine monitoring systems. The proposed methodology contributes to sustainable coastal management, pollution monitoring, and climate resilience, while offering a transferable framework for other estuaries worldwide. Full article

External insulation of coastal power grids transmitting offshore wind power faces significant threats from salt fog flashovers. Current arc monitoring and early warning technologies for flashover are severely inadequate. Research on salt fog discharge processes and determining the threshold at the flashover brink for transmission equipment external insulation is crucial for ensuring the safe operation of coastal grids delivering offshore wind power. Fiber Bragg Grating (FBG), with its advantages of compact size, excellent insulation, and fast response, enables effective discharge monitoring and identification of the critical flashover state on external insulation surfaces. In this study, FBGs were embedded at the interfaces of typical external insulation specimens, including silicone rubber plates and epoxy resin plates, to conduct contaminated AC salt fog discharge tests. Synchronized measurements of visible light images, infrared thermal images, and FBG interface temperature were conducted to investigate the discharge physical processes on silicone rubber insulating surfaces and the flashover threshold based on FBG temperature rise rate. The results indicate that discharge process can be divided into three phases: arc initiation, extension, and flashover based on the characteristics of arc visible light images. By comparing arc locations in infrared and visible light images with the corresponding FBG interface temperature rise, the arc phase criterion of FBG interface temperature rise rate and position were proposed. Furthermore, through multiple experiments, it has been found that flashover occurs when both interface temperatures reached above 4.6 × 10⁻² °C/s. This study provides a novel research methodology for physical process of external insulation discharge and flashover warning in coastal salt fog environments. Full article

Background: Prostate cancer is one of the most common malignancies in men and a leading cause of cancer-related mortality. Early detection is essential to ensure curative treatment and favorable outcomes, but traditional diagnostic approaches—such as serum prostate-specific antigen (PSA) testing, digital rectal examination (DRE), and histopathological confirmation following biopsy—are limited by suboptimal accuracy and variability. Multiparametric magnetic resonance imaging (mpMRI) has improved diagnostic performance but remains highly dependent on reader expertise. Artificial intelligence (AI) offers promising opportunities to enhance diagnostic accuracy, reproducibility, and efficiency in prostate cancer detection. Objective: To evaluate the diagnostic accuracy and reporting timeliness of AI-based technologies compared with conventional diagnostic methods in the early detection of prostate cancer. Methods: Following PRISMA 2020 guidelines, PubMed, Scopus, Web of Science, and Cochrane Library were searched for studies published between January 2015 and April 2025. Eligible designs included randomized controlled trials, cohort, case–control, and pilot studies applying AI-based technologies to early prostate cancer diagnosis. Data on AUC-ROC, sensitivity, specificity, predictive values, diagnostic odds ratio (DOR), and time-to-reporting were narratively synthesized due to heterogeneity. Risk of bias was assessed using the QUADAS-AI tool. Results: Twenty-three studies involving 23,270 patients were included. AI-based technologies achieved a median AUC-ROC of 0.88 (range 0.70–0.93), with median sensitivity and specificity of 0.86 and 0.83, respectively. Compared with radiologists, AI or AI-assisted readings improved or matched diagnostic accuracy, reduced inter-reader variability, and decreased reporting time by up to 56%. Conclusions: AI-based technologies show strong diagnostic performance in early prostate cancer detection. However, methodological heterogeneity and limited standardization restrict generalizability. Large-scale prospective trials are required to validate clinical integration. Full article