Search Results (3,974)

With the continuous maturation of prefabricated buildings, the errors and efficiency issues in the design of prefabricated buildings have gradually drawn the attention of architectural designers. The characteristics of standardized design for prefabricated buildings also provide a foundation for the application of computer-learning methods in the field of architectural design, thereby improving design quality and efficiency. This study combined BIM technology to construct the information data on prefabricated buildings, applied the transfer-learning method to build the training model, and utilized the traditional architectural design collision concept to construct a prediction model applicable to the collision detection of prefabricated building design. The training set and test set were constructed in a 9:1 ratio, and the loss function and accuracy function were calculated. The error rate of the model was verified to be within 10% through trial calculations based on engineering cases. The results show that, in the selected engineering cases, the collision detection accuracy of the model reached 90.3%, with an average absolute error (MAE) of 0.199 and a root mean square error (RMSE) of 0.245. The prediction error rate was controlled within 10%, representing an approximately 65% improvement in efficiency compared to traditional manual inspections. This method significantly improves the efficiency and accuracy of collision detection, providing reliable technical support for the optimization of prefabricated building design. Full article

With the continuous expansion of power grids and the advancement of ultra-high voltage (UHV) projects, transmission lines are increasingly traversing areas characterized by micro-terrain. These localized topographic features can intensify meteorological effects, thereby increasing the risks of hazards such as conductor icing and galloping, directly threatening operational stability. Enhancing the disaster resilience of transmission lines in such environments requires accurate and efficient terrain identification. However, conventional recognition methods often neglect the spatial alignment of the transmission lines, limiting their effectiveness. This paper proposes a deep learning-based recognition framework that incorporates a dual-branch network architecture and a cross-branch spatial attention mechanism to address this limitation. The model explicitly captures the spatial correlation between transmission lines and surrounding terrain by utilizing line alignment information to guide attention along the line corridor. A semi-synthetic dataset, comprising 6495 simulated samples and 130 real-world samples, was constructed to facilitate model training and evaluation. Experimental results show that the proposed model achieves classification accuracies of 94.6% on the validation set and 92.8% on real-world test cases, significantly outperforming conventional baseline methods. These findings demonstrate that explicitly modeling the spatial relationship between transmission lines and terrain features substantially improves recognition accuracy, offering important support for hazard prevention and resilience enhancement in UHV transmission systems. Full article

Background/Objectives: Human epidermal growth factor 2 (HER2) scoring is critical for modern breast cancer therapies, especially with emerging indications of antibody–drug conjugates for HER2-low tumors. However, inter-observer agreement remains limited in borderline cases. Automatic artificial intelligence-based scoring has the potential to improve diagnostic consistency and scalability. This study aimed to develop two transformer-based models for HER2 scoring of breast cancer whole-slide images (WSIs) and compare their performance. Methods: We adapted a large-scale foundation model (Virchow) and a lightweight model (TinyViT). Both were trained using patch-level annotations and integrated into a WSI scoring pipeline. Performance was evaluated on a clinical test set (n = 66), including clinical decision tasks and inference efficiency. Results: Both models achieved substantial agreement with pathologist reports (linear weighted kappa: 0.860 for Virchow, 0.825 for TinyViT). Virchow showed slightly higher WSI-level accuracy than TinyViT, whereas TinyViT reduced inference times by 60%. In three binary clinical tasks, both models demonstrated a diagnostic performance comparable to pathologists, particularly in identifying HER2-low tumors for antibody–drug conjugate (ADC) therapy. A continuous scoring framework demonstrated a strong correlation between the two models (Pearson’s r = 0.995) and aligned with human assessments. Conclusions: Both transformer-based artificial intelligence models achieved human-level accuracy for automated HER2 scoring with interpretable outputs. While the foundation model offers marginally higher accuracy, the lightweight model provides practical advantages for clinical deployment. In addition, continuous scoring may provide a more granular HER2 quantification, especially in borderline cases. This could support a new interpretive paradigm for HER2 assessment aligned with the evolving indications of ADC. Full article

This study investigates the spatiotemporal optimization of small-scale emergency infrastructure in high-density urban environments, using nucleic acid testing sites in Macao as a case study. The objective is to enhance emergency responsiveness during future public health crises by aligning infrastructure deployment with dynamic patterns of population influx. A behaviorally informed spatial decision-making framework is developed through the integration of kernel density estimation, point-of-interest (POI) distribution, and origin–destination (OD) path simulation based on an Ant Colony Optimization (ACO) algorithm. The results reveal pronounced temporal fluctuations in testing demand—most notably with crowd peaks occurring around 12:00 and 18:00—and highlight spatial mismatches between existing facility locations and key residential or functional clusters. The proposed approach illustrates the feasibility of coupling infrastructure layout with real-time mobility behavior and offers transferable insights for emergency planning in compact urban settings. Full article

Most of the failures during the actual operation of equipment are caused by improper human operation, tools, spare parts, and environmental factors. These faults are routine. Conventional faults have been validated during equipment development, testing, identification, and maintenance processes, with clear definitions and clear fault tree analysis (FTA) conclusions. Digital twins can offer rapid and interactive diagnostic capabilities for routine equipment failures. To enhance the efficiency of routine fault diagnosis and the interactive experience of the diagnosis process, this paper proposes a digital twin-based equipment routine fault diagnosis model. On this basis, considering the excellent interactivity of the Interactive Electronic Technical Manual (IETM), a conventional equipment fault diagnosis scheme based on twin data and IETM is designed. This scheme converts the equipment fault tree into an IETM fault data model (DM), which is structured and stored in a database to form a fault database. Using real-time twin data of equipment as input, the FTA method is adopted to perform step-by-step fault diagnosis and isolation guidance operation through the IETM process DM combined with fault, while providing maintenance operation guidance. When the real-time twin data of the equipment is not completely consistent with the fault information in the fault library, the case analysis method is used to calculate the similarity between the real-time twin data of the equipment and the clearly defined fault symptom information in the fault library. Based on the set similarity threshold, IETM pushes fault DMs above the threshold for corresponding fault diagnosis isolation guidance. Full article

Background/Objectives: This study investigated whether serum salusin-α and salusin-β levels could support the diagnosis and prognosis of confirmed acute pulmonary embolism (APE) cases. Methods: A prospective observational study was conducted including 57 patients diagnosed with APE using computed tomography pulmonary angiography (CTPA) and 30 control participants without any acute or chronic disease. APE patients were categorized based on the Pulmonary Artery Obstruction Index (PAOI) into low (≤20) and high (>20) thrombus burden groups. Serum salusin-α and salusin-β levels were measured at diagnosis using an enzyme-linked immunosorbent assay. Associations with PAOI and Pulmonary Embolism Severity Index (PESI) scores were analyzed. Results: Salusin-α and salusin-β levels were markedly reduced in APE patients versus controls (p < 0.001). In multivariate analysis, salusin-α remained independently associated with APE (p = 0.042), whereas salusin-β was not significant. A receiver operating characteristic analysis showed good diagnostic performance for salusin-α (AUC = 0.799; sensitivity = 89.5%; specificity = 46.7%). Neither peptide correlated with PAOI or PESI. At a 305.85 pg/mL cut-off, salusin-α yielded a positive predictive value of 76.1% and a negative predictive value of 70% in this cohort. Conclusions: The findings suggest that salusin-α has high sensitivity in detecting acute pulmonary embolism and may serve as a supportive diagnostic marker in emergency settings. Although its specificity is limited, it could contribute to guiding additional testing. While salusin-β showed no significant diagnostic value, the potential role of salusin peptides in prognostic evaluation requires further exploration. Full article

This paper investigates the utilization of artificial neural networks (ANNs) for the proactive identification of gearbox failures in wind turbines, boosting the use of operational SCADA data for predictive analysis. Avoiding gearbox failures, which can strongly impact the functioning of wind turbines, is crucial for ensuring high reliability and efficiency within wind farms. Early detection can be achieved though the development of a normal behavior model based on ANNs, which are trained with data from healthy conditions derived from selected SCADA variables that are closely associated with gearbox operations. The objective of this model is to forecast deviations in the gear bearing temperature, which serve as an early warning alert for potential failures. The research employs extensive SCADA data collected from January 2018 to February 2022 from a wind farm with multiple turbines. The study guarantees the robustness of the model through a thorough data cleaning process, normalization, and splitting into training, validation, and testing sets. The findings reveal that the model is able to effectively identify anomalies in gear bearing temperatures several months prior to failure, outperforming simple data processing methods, thereby offering a significant lead time for maintenance actions. This early detection capability is highlighted by a case study involving a gearbox failure in one of the turbines, where the proposed ANN model detected the issue months ahead of the actual failure. The present paper is an extended version of the work presented at the 5th International Conference of IFToMM ITALY 2024. Full article

The paper presents the results of research focused on configuring a system for stereoscopic view capturing and processing. The system is being developed for use in staff training scenarios based on Virtual Reality (VR), where high-quality, distortion-free imagery is essential. This research addresses key challenges in image distortion, including the fish-eye effect and other aberrations. In addition, it considers the computational and bandwidth efficiency required for effective and economical streaming and real-time display of recorded content. Measurements and calculations were performed using a selected set of cameras, adapters, and lenses, chosen based on predefined criteria. A comparative analysis was conducted between the nearest-neighbour linear interpolation method and a third-order polynomial interpolation (ABCD polynomial). These methods were tested and evaluated using three different computational approaches, each aimed at optimizing data processing efficiency critical for real-time image correction. Images captured during real-time video transmission—processed using the developed correction techniques—are presented. In the final sections, the paper describes the configuration of an innovative VR-based training system incorporating an edge computing device. A case study involving a factory producing wheel rims is also presented to demonstrate the practical application of the system. Full article

The harmonic balance method (HBM) is a widely used method for determining the forced response of non-linear systems such as bladed disks. This paper focuses on analyzing the sensitivity of this method to key computational parameters and its robustness. HBM and HBM coupled with pseudo arc length continuation are used in this paper to solve the equation of motion of a test case. The pseudo arc length continuation is necessary because when intermittent contact occurs, natural continuation cannot guarantee solver convergence. Intermittent contact, in addition to turning points, introduces further problems, which are caused by an infinite sequence of decaying, but not zero, Fourier coefficients. This results in the need to oversample the non-linear force time signal to avoid convergence problems. The computational parameters investigated in this paper are the samples per period, which determine the number of points in which the time signal is discretized, and the harmonic truncation order. In addition, the connection of contact parameters, such as friction and contact stiffness, with computational parameters is analyzed. This study shows that the number of time samples per period is the most limiting parameter when intermittent contact occurs; whereas, in the absence of intermittent contact convergence, problems can be avoided with a reasonable number of time points. Poor discretization of the signal leads to a bad computation of Fourier coefficients and thus a lack of convergence. Sensitivity analysis shows that the samples per period depend on the contact parameters, especially normal stiffness. To ensure the solver robustness, it is important to set the computation parameters appropriately to ensure the convergence of the solver while avoiding unnecessary computation effort. Full article

Mild–moderate equine asthma (MEA) is a very common but underdiagnosed pulmonary disease in horses, with mild cases not showing clinical respiratory signs. This study evaluates the influence of a standardized lunging exercise test (SLET) on bronchoalveolar lavage fluid (BALF) cytology in MEA horses. We hypothesized that SLET would increase the total nucleated cell count (TNCC) and/or percentages of inflammatory cells associated with EA. In a prospective, randomized, non-blinded, between-subjects study design of two independent groups, 39 horses (17 mild and 22 moderate) were included. They were chosen out of a cohort of horses undergoing respiratory investigations (history, clinical examination, and clinical pathology (white blood cells (WBC) and arterial blood gas analysis (aBGA)) consistent with MEA, using a scoring system in a clinical setting of an equine referral clinic. Bronchoalveolar lavage (BAL) was performed 30 min post-SLET in 16 randomly chosen horses. The other horses underwent BAL without SLET. The SLET resulted in a statistically significant increase (p < 0.001) in the proportions of neutrophils in BALF cytology, and in an increased chance of confirmation of the presumed diagnosis in horses with mild phenotypes (p < 0.001, OR = 8.00, CI = 1.28–50.04), while moderate phenotypes were less frequently diagnosed. Exercise had no association with cytology across all horses. Unexpectedly, the SLET group of horses with a moderate phenotype had a statistically significant lower TNCC (p = 0.035). In conclusion, an SLET prior to BAL might increase the chance of an MEA diagnosis. Full article

Background and objectives: Desquamative gingivitis (DG) is a clinical term used to describe gingival conditions marked by erythema (unrelated to dental plaque), epithelial desquamation, and various intraoral lesions, with occasional extraoral involvement. It is typically linked to a range of underlying diseases. Materials and methods: A narrative literature review was conducted using PubMed, Scopus, Google Scholar, and the Cochrane Library, searching with keywords like “oral dysplasia”, “oral mucosa lesions”, or “desquamative gingivitis”. In addition to the literature review, a case report of a patient with DG is included to illustrate the diagnostic challenges and treatment considerations in a clinical setting, and to design and test simplified diagnosis and treatment-planning algorithms. Results: Diagnosis can be supported by a standard punch biopsy to obtain tissue samples for histopathological evaluation. The complex clinical case presented illustrates the clinical features of DG and highlights the challenges associated with its diagnosis and management. The mainstay of treatment, as resulted from 96 studies included in our review, involves topical and systemic corticosteroids, with topical calcineurin inhibitors serving as adjunctive therapy. Conclusions: A universally accepted treatment protocol is still lacking for DG, so this report outlines an effective, experience-based therapeutic approach. Additionally, it offers a simplified framework for diagnosis, treatment planning, and therapeutic management, contributing to the growing knowledge base needed for a decision-support algorithm development. Full article

Yellowfin tuna (Thunnus albacares) constitutes a critical global fishery resource, and its distribution pattern is correlated to varying degrees with the marine environment. This study utilized longline fishing data from the Western and Central Pacific Fisheries Commission (WCPFC) and the Inter-American Tropical Tuna Commission (IATTC) spanning 2004 to 2020, categorized by quarter and combined with surface and 0–200 m depth environmental variables. Geographical random forests (GRF) were employed to examine spatially non-stationary relationships between yellowfin tuna resources and environmental factors. Additionally, by integrating GRF with GeoShapley explainable methods, we quantitatively evaluated the mechanistic impacts of environmental drivers on tuna distribution across spatial scales. The findings indicated that (1) the GRF model demonstrated superior performance throughout all four quarters, with the goodness of fit on the 20% test set (R² = 0.72–0.85) consistently surpassing that of conventional random forest (RF) (R² = 0.68–0.79) and extreme gradient boosting random forest (XGBRF) (R² = 0.68–0.80). Moreover, in most cases, it had a lower RMSE and MAE, while effectively addressing spatial heterogeneity issues in yellowfin tuna fishery resources across most regions. (2) GeoShapley spatial explainable analysis revealed distinct environmental drivers, showing that the sea surface temperature and temperature at 105 m depth significantly influenced yellowfin tuna resources across all quarters, following a “high-value promotion, low-value inhibition” pattern, with salinity and dissolved oxygen at 105 m depth in Q2–Q3 and mixed-layer depth in Q3 also demonstrating notable effects. (3) Significant spatiotemporal heterogeneity was observed. The main spatial effects and temperature–depth–locality interactions remained significant throughout the year; mixed-layer depth–locality interactions were prominent in Q1, Q3, and Q4, dissolved oxygen–locality interactions in Q2 and Q4, and 105 m salinity–locality interactions exclusively in Q2. This study used geographical random forests (GRF) to integrate spatial statistics and machine learning to model the relationship between Pacific yellowfin tuna fishery resources and environmental factors. This approach demonstrates potential in improving spatial predictions of heterogeneous tuna resources and may help to identify key environmental drivers influencing their distribution. These findings provide essential insights for the formulation of science-based management strategies for Pacific yellowfin tuna fisheries. Full article

Mapping Soil Organic Carbon (SOC) at a regional scale is essential for assessing soil health and supporting sustainable land management. This study evaluates the potential of using Sentinel-2 imagery and regional calibration to predict SOC in salt-affected agricultural lands in Portugal while also assessing the influence of soil properties, such as texture and salinity, on SOC prediction. A per-pixel mosaicking approach was set to analyze the relationship of spectral reflectance indices linked to bare soil conditions with SOC. SOC prediction models were developed using linear regression (LR) and Partial Least Squares Regression (PLSR). Among the tested approaches, the combination of the maximum Bare Soil Index (maxBSI) with LR produced the most accurate SOC predictions, achieving moderate prediction performance (R² = 0.52; RMSE = 0.16%; LCCC = 70%). This approach slightly outperformed the application of the 90th percentile of bare soil pixels (R90 reflectance) and the median approaches with PLSR. Notably, our findings indicate that soil salinity did not significantly affect SOC predictions within the observed salinity range of EC_e between 1.2 and 10.4 dS m⁻¹ in topsoil. However, further case studies are needed to validate this observation across diverse agricultural conditions. In contrast, soil texture and moisture content emerged as the dominant factors influencing soil reflectance. The combination of per-pixel mosaicking and regional calibration provides a practical, scalable, and cost-effective method for generating SOC maps using open access satellite imagery. To support wider adoption and improve model generalizability, future studies should incorporate a larger number of fields with a wider range of soil properties, crop types, and management practices. Full article

Hospital-acquired infections (HAIs) remain a major clinical and economic burden, with pathogens such as Escherichia coli contributing to high rates of morbidity and mortality. Traditional manual disinfection methods are often insufficient, particularly in high-risk hospital environments. In this study, we investigated innovative strategies to enhance surface decontamination and reduce infection risk. First, we assessed the efficacy of the SMEG BPW1260 bedpan washer-disinfector, a thermal disinfection system for human waste containers. Our results demonstrated a reduction in Clostridium difficile and Escherichia coli contamination by >99.9% (>3 log reduction), as measured by colony-forming units (CFU) before and after treatment. Molecular techniques, including spectrophotometry, cell counting, and quantitative PCR (qPCR) for DNA quantification, confirmed reduction in bacterial contamination. Specifically, Clostridium difficile showed a reduction of approximately 89% in both optical density (OD) and cell count (cells/mL). In the case of Escherichia coli, a reduction of around 82% in OD was observed, with an even more pronounced decrease in cell count, reaching approximately 99.3%. For both bacteria, DNA quantification by qPCR was below detectable limits. Furthermore, we optimized the energy efficiency of the disinfection cycle, achieving a 45% reduction in power consumption compared to standard protocols without compromising antimicrobial efficacy. Secondly, we developed a sustainable cleaning solution based on methyl ester sulfonate surfactants derived from waste cooking oil. The detergent’s antibacterial activity was tested on contaminated surfaces and further enhanced through the incorporation of nanoassemblies composed of silver, electrostatically bound either to biomimetic magnetic nanoparticles or to conventional magnetic nanoparticles. Washing with the detergent alone effectively eliminated detectable contamination, while the addition of nanoparticles inhibited bacterial regrowth. Antimicrobial testing against E. coli revealed that the nanoparticle-enriched formulations reduced the average MIC values by approximately 50%, with MIC₅₀ values around 0.03–0.06 mg/mL and MIC₉₀ values between 0.06 and 0.12 mg/mL, indicating improved inhibitory efficacy. Finally, recognizing the infection risks associated with intra-hospital transport, we tested the SAFE-HUG Wheelchair Cover, a disposable non-woven barrier designed to reduce patient exposure to contaminated wheelchair surfaces. Use of the cover resulted in a 3.3 log reduction in surface contamination, based on viable cell counts. Optical density and bacterial DNA were undetectable in all covered samples at both 1 and 24 h, confirming the strong barrier effect. Together, these approaches—thermal no-touch disinfection, eco-friendly detergent boosted with nanoparticles, and protective transport barriers—respond to the urgent need for effective, sustainable infection control methods in healthcare settings. Our findings demonstrate the potential of these systems to counteract microbial contamination while minimizing environmental impact, offering promising solutions for the future of infection prevention in healthcare settings. Full article

Water quality deterioration poses a critical threat to ecological security and sustainable development, particularly in rapidly urbanizing regions. To enable proactive environmental management, this study develops a novel hybrid deep learning model, the NGO-CNN-GRU, for high-precision time-series water quality prediction in the Xijiang River Basin, China. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction and a Gated Recurrent Unit (GRU) for temporal dependency modeling, with hyperparameters optimized via the Northern Goshawk Optimization (NGO) algorithm. Using historical water quality (pH, DO, CODMn, NH3-N, TP, TN) and meteorological data (precipitation, temperature, humidity) from 11 monitoring stations, the model achieved exceptional performance: test set R² > 0.986, MAE < 0.015, and RMSE < 0.018 for total nitrogen prediction (Xiaodong Station case study). Across all stations and indicators, it consistently outperformed baseline models (GRU, CNN-GRU), with average R² improvements of 12.3% and RMSE reductions up to 90% for NH3-N predictions. Spatiotemporal analysis further revealed significant pollution gradients correlated with anthropogenic activities in the Pearl River Delta. This work provides a robust tool for real-time water quality early warning systems and supports evidence-based river basin management. Full article