Search Results (383)

As urbanization accelerates, façade defects in existing residential buildings have become increasingly prominent, posing serious threats to structural safety and residents’ quality of life. In the high-density built environment of Shenzhen, traditional manual inspection methods exhibit low efficiency and high susceptibility to omission errors. This study proposes an integrated framework for façade defect detection that combines unmanned aerial vehicle (UAV)-based visible-light and thermal infrared imaging with deep learning algorithms and parametric three-dimensional (3D) visualization. Three representative residential communities constructed between 1988 and 2010 in Shenzhen were selected as case studies. The main findings are as follows: (1) the fusion of visible and thermal infrared images enables the synergistic identification of cracks and moisture intrusion defects; (2) shooting distance significantly affects mapping efficiency and accuracy—for low-rise buildings, 5–10 m close-range imaging ensures high mapping precision, whereas for high-rise structures, medium-range imaging at approximately 20–25 m achieves the optimal balance between detection efficiency, accuracy, and dual-defect recognition capability; (3) the developed Grasshopper-integrated mapping tool enables real-time 3D visualization and parametric analysis of defect information. The Knet-based model achieves an mIoU of 87.86% for crack detection and 79.05% for leakage detection. This UAV-based automated inspection framework is particularly suitable for densely populated urban districts and large-scale residential areas, providing an efficient technical solution for city-wide building safety management. This framework provides a solid foundation for the development of automated building maintenance systems and facilitates their integration into future smart city infrastructures. Full article

Evaluating electric vehicle (EV) motor performance over dynamic drive cycles is essential for accurate energy efficiency prediction and system-level optimization. While conventional steady-state models enable rapid generation of efficiency maps, they can introduce significant errors due to grid interpolation and the omission of transient dynamics. Limited understanding exists regarding how grid coarseness and modeling approach affect the discrepancy between steady-state and time-stepping solutions. This study quantifies these differences for a laboratory-scale induction motor (IM) operating under down-scaled drive cycles, using experimental time-stepping measurements as a reference. Efficiency maps are developed using three methods—analytic modeling, finite element analysis (FEA), and experimental testing—while time-stepping simulations are conducted using an analytic model. The study evaluates both total drive cycle energy efficiency errors and pointwise deviations across the torque–speed envelope for various grid resolutions. Results are compared against laboratory-based time-stepping measurements to identify trade-offs between computational efficiency and accuracy. Additionally, the analysis evaluates the impact of operating point (OP) placement within the grid and temperature variation on the accuracy of efficiency maps. Full article

Audits of medication charts conducted by Royal Hobart Hospital Pharmacy revealed that dose omission was the most common medication error experienced by patients. Investigation of these errors also found that nurses spend significant time organising medication for inpatients. To address the issues contributing to these problems, an alternative model of medication management was implemented and tested. This model of bedside medication management involves medication supply managed by ward pharmacy technicians who review charts daily for changes to medicines and obtain the medicines needed for each patient. Outcomes on two intervention wards showed that the model, combined with technician involvement in controlled medicines stock management, resulted in 29.78 h of nursing time released to patient care per 20-bed ward per week, for an investment of 22.28 h of ward pharmacy technician time; a 75% reduction in delayed doses; a 44% reduction in missed doses; and an average decrease of two hours in the turnaround time for supply of inpatient medication. Introducing bedside medication management and controlled medicines stock management activities can release 1.34 h of nursing time to patient care for every hour of ward pharmacy technician time (at a lower hourly salary cost), decrease dose delays and omissions, and improve patient safety. Full article

Background/Objective: Accurate determination and documentation of causes of death in children are essential for generating reliable mortality statistics and guiding public health strategies. Previous studies have reported frequent inaccuracies in pediatric death certificates (DCs), including the use of vague terms, omissions of relevant conditions, and variability across physician specialties. This study evaluated the accuracy of pediatric DCs in Shiga Prefecture, Japan; identified common errors in these DCs; and examined changes in the underlying causes of pediatric death before and after the COVID-19 pandemic. Methods: We performed a population-based retrospective review of 391 DCs for individuals under 18 years issued between 2015 and 2023. Two pediatricians and two forensic pathologists independently reviewed each DC, assessed accuracy, and classified errors using predefined criteria. Error rates were compared by physician specialty. Underlying causes of death were reassessed into ten categories, and their distributions were compared between 2015–2019 and 2020–2023. Results: Overall, 30.9% of DCs contained errors. The error rates differed by physician specialty: obstetricians had the highest error rate (92.9%), whereas forensic physicians had the lowest (8.4%). The most common error type was the use of non-specific mechanisms such as “cardiac arrest” or “respiratory failure”, rather than the actual causes of death. Congenital anomalies were often listed under other significant conditions contributing to death and not as an underlying cause of death. After the onset of the COVID-19 pandemic, deaths from acute diseases declined from 16.8% to 4.0%, while deaths from congenital disorders increased from 12.6% to 24.3%. Conclusions: Pediatric DCs often contain errors, particularly those completed by obstetricians. Misclassifying mechanisms as causes of death and underreporting congenital anomalies remain the main challenges. Strengthening physician education and introducing systematic review processes are essential to improve accuracy, clarify regional mortality trends, and guide effective public health interventions. Full article

The field of underwater robotics is experiencing rapid growth, wherein accurate object detection constitutes a fundamental component. Given the prevalence of false alarms and omission errors caused by intricate subaquatic conditions and substantial image noise, this study introduces an enhanced detection framework that combines the YOLOv8 architecture with multimodal visual fusion methodology. To solve the problem of degraded detection performance of the model in complex environments like those with low illumination, features from Visible Light Image are fused with the Thermal Distribution Features exhibited by Infrared Image, thereby yielding more comprehensive image information. Furthermore, to precisely focus on crucial target regions and information, a Multi-Scale Cross-Axis Attention Mechanism (MSCA) is introduced, which significantly enhances Detection Accuracy. Finally, to meet the lightweight requirement of the model, an Efficient Shared Convolution Head (ESC_Head) is designed. The experimental findings reveal that the YOLOv8-FUSED framework attains a mean average precision (mAP) of 82.1%, marking an 8.7% enhancement compared to the baseline YOLOv8 architecture. The proposed approach also exhibits superior detection capabilities relative to existing techniques while simultaneously satisfying the critical requirement for real-time underwater object detection. Moreover, the proposed system successfully meets the essential criteria for real-time detection of underwater objects. Full article

Background/Objectives: To evaluate the viability of using ChatGPT in a real clinical environment for patient education during informed consent for flexible cystoscopy, assessing its practicality, patient perceptions, and clinician evaluations within a urological cohort. Methods: A prospective feasibility study was conducted at a single institution involving patients with haematuria who attended an in-person clinic review with access to ChatGPT-4o mini. Using predetermined prompts regarding haematuria, we evaluated the accuracy, consistency, and suitability of the ChatGPT information. Responses were appraised for errors, omission of key information, and suitability for patient education. The functionality, usability, and quality of ChatGPT for patient education were assessed by three urologists using the Patient Education Materials Assessment Tool (PEMAT) and DISCERN tools. Readability was assessed using the Flesch–Kincaid tests. Further clinician questionnaires evaluated ChatGPT’s accuracy, reproducibility, and integration potential. Results: Ten patients were recruited, but one patient was excluded because he refused to use ChatGPT due to language barriers. All patients found ChatGPT to be useful, but most believed it could not entirely replace the doctor, especially for obtaining informed consent. There were no significant errors. The mean PEMAT score for understandability was 77.8%, and actionability was 63.8%. The mean DISCERN score was 57.7, corresponding to a ‘good’ quality score. The Flesch Reading Ease score was 30.2, with the writing level comparable to US grade level 13. Conclusions: ChatGPT offers valuable support for patient education, delivering accurate and comprehensive information. However, challenges with readability, contextual understanding, and actionability highlight the need for development and careful integration. Generative artificial intelligence (AI) should augment, not replace, clinician–patient interactions, emphasising ethical considerations and patient trust. This study provides a basis for further exploration of AI’s role in healthcare. Full article

This paper redefines point cloud density by utilizing statistical skewness derived from the geometric relationships between points and their local centroids. By comparing with a symmetric uniform reference model, this method can efficiently describe distribution patterns and detect non-uniform regions. Furthermore, a deep learning model trained on these skewness features achieves 85.96% accuracy in automated boundary extraction, significantly reducing omission errors compared to conventional density-based methods. The proposed framework offers an effective solution for automated point cloud segmentation and modeling. Full article

Objectives: This study investigated the effects of healthy aging on sentence production in narrative discourse and examined the role of cognitive abilities and Lifelong Learning (LLL) in mitigating age-related decline. Methods: Three hundred and seven Italian-speaking adults (26–89 years) completed a narrative task elicited from five picture stimuli, alongside assessments of verbal working memory, sustained attention, and inhibitory control. Morphological and morphosyntactic measures (morphological errors and omissions of content and function words) and syntactic variables (complete sentences, subordinate clauses, and passive sentences) were analyzed. Results: Aging was associated with increased morphological and morphosyntactic errors and reduced syntactic complexity. These effects were non-linear for the % of morphological errors, the % of omission of content words, and the % of complete sentences and were more pronounced after age 70. LLL was negatively associated with morphological and morphosyntactic errors and positively associated with sentence production. Verbal working memory and sustained attention explained additional variance only for omissions of function words, whereas the passive component of verbal working memory only explained additional variance for complete sentence production. Conclusions: These findings suggest that aging affects both simple and complex sentence production, with declines related to morphological errors and omissions. LLL appears to buffer against some grammatical declines, suggesting a role for educational engagement in maintaining syntactic abilities. Clinically, assessing complex sentence production and considering LLL may improve diagnosis and intervention for language disorders in older adults. Full article

Complex terrain conditions and dense vegetation cover in a vegetation area present significant challenges for point cloud data processing and the accurate extraction of ground points. This work integrates the physical characteristics between ground and non-ground points from the traditional Cloth Simulation Filter (CSF) algorithm and the strong learning capability of the machine learning Random Forest (RF) framework, developing the CSF-RF fusion algorithm for filtering ground points in vegetated areas, which can improve the accuracy of point cloud filtering in complex terrain environments. Both type I and type II errors do not exceed 0.05%, and the total error is maintained within 0.03%. Particularly in areas with dense vegetation and severe terrain undulations, the advantages are evident: the CSF-RF algorithm achieves a total error of only 0.19%, representing a 79.6% relative reduction compared with the 0.93% error of the CSF algorithm, while also reducing cases of ground point omission. Thus, it can be seen that the CSF-RF algorithm can effectively reduce vegetation interference and exhibits good stability, providing effective technical support for the accurate extraction of Digital Elevation Models (DEMs) in vegetated areas. Full article

Mapping forest tree species is vital for the habitat assessment, ecosystem services estimation, and implementation of European environmental policies such as the Habitats Directive. This study explores how repeated satellite observations over time, known as multitemporal data, can improve the mapping of tree species in riparian forests. Although many studies have shown that the use of multitemporal data improves tree species classification accuracies, there is a lack of research on how different multitemporal models perform compared to each other. We compared three multitemporal remote sensing approaches using Sentinel-2 imagery to map tree species within the Austrian riparian Natura 2000 site, Salzachauen. Seven tree species (five native and two non-native riparian species) were mapped using random forest models trained on a dataset of 444 validated tree samples. The three multitemporal approaches tested were: (i) multi-date image stacking, (ii) seasonal mean composites, and (iii) spectral–temporal metrics (STMs). The three approaches were compared to twenty single-date image classifications. The multitemporal models achieved 62 to 65% overall accuracy, while the median accuracy of single-date classification was 50% (SD = 6%). The seasonal model obtained the highest overall accuracy (65%), with F1 scores exceeding 73% for four individual species. However, differences among the three multitemporal approaches were not statistically significant. The mapping of native versus non-native riparian species achieved 92% accuracy. We evaluated misclassification patterns of individual species according to the two riparian forest habitats, 91E0* and 91F0, as defined in Annex I of the Habitats Directive. Most omission and commission errors occurred between species within the same habitat type. These findings underline the potential of translating tree species mapping to habitat-type classifications and the need to further explore the capabilities of satellite remote sensing to fill data gaps in Natura 2000 areas. Full article

Burned area (BA) mapping and fire severity assessment are essential for understanding fire occurrence patterns, formulating post-fire restoration strategies and evaluating vegetation recovery processes. However, existing BA datasets are primarily derived from coarse-resolution satellite imagery and often lack sufficient consideration of fire severity. To address these limitations, this study utilized dense time-series Landsat imagery available on the Google Earth Engine, applying the qualityMosaic method to generate annual composites of minimum normalized burn ratio values. These composites imagery enabled the rapid identification of fire sample points, which were subsequently used to train a random forest classifier for estimating per-pixel burn probability. Pixels with a burned probability greater than 0.9 were selected as the core of the BA, and used as candidate seeds for region growing to further expand the core and extract complete BA. This two-stage extraction method effectively balances omission and commission errors. To avoid the repeated detection of unrecovered BA, this study developed distinct correction rules based on the differing post-fire recovery characteristics of forests and grasslands. The extracted BA were further categorized into four fire severity levels using the delta normalized burn ratio. In addition, we conducted a quantitative validation of the BA mapping accuracy based on Sentinel-2 data between 2015 and 2023. The results indicated that the BA mapping achieved an overall accuracy of 93.90%, with a Dice coefficient of 82.04%, and omission and commission error rates of 26.32% and 5.25%, respectively. The BA dataset generated in this study exhibited good spatiotemporal consistency with existing products, including MCD64A1, FireCCI51, and GABAM. The BA fluctuated significantly between 1985 and 2010, with the highest value recorded in 1987 (13,315 km²). The overall trend of BA showed a decline, with annual burned areas remaining below 2000 km² after 2010 and reaching a minimum of 92.8 km² in 2020. There was no significant temporal variation across different fire severity levels. The area of high-severity burns showed a positive correlation with the annual total BA. High-severity fire-prone zones were primarily concentrated in the northeastern, southeastern, and western parts of the study area, predominantly within grasslands and forest–grassland ecotone regions. Full article

Mangrove forests are among the world’s most vital coastal ecosystems. Mapping mangrove cover from local to global scales using spectral data and machine learning models is a well-established method. While non-spectral contextual datasets (spatial features) have also been incorporated into such models, the contribution of these additional features to improving mangrove mapping remains underexplored. Using the Mesoamerican Reef Region as a case study, we evaluate the effectiveness of incorporating spatial features in binary mangrove classification to enhance mapping accuracy. We compared an aspatial model that includes only spectral data with three spatial models: two included features such as geographic coordinates, elevation, and proximity to coastlines and streams, while the third integrated a geostatistical approach using Inverse Distance Weighted (IDW) interpolation. Spectral inputs included bands and indices derived from Sentinel-1 and Sentinel-2, and all models were implemented using the Random Forest algorithm in Google Earth Engine. Results show that spatial features reduced omission errors without increasing commission errors, enhancing the model’s ability to capture spatial variability. Models using geographic coordinates and elevation performed comparably to those with additional environmental variables, with storm frequency and distance to streams emerging as important predictors in the Mesoamerican Reef region. In contrast, the IDW-based model underperformed, likely due to overfitting and limited representation of local spectral variation. Spatial analyses show that models incorporating spatial features produced more continuous mangrove patches and removed some false positives in non-mangrove areas. These findings highlight the value of spatial features in improving classification accuracy, especially in regions with ecologically diverse mangroves across varied environments. By integrating spatial context, these models support more accurate, locally relevant mangrove maps that are essential for effective conservation and management. Full article

Medication discrepancies at hospital admission are common and may lead to adverse outcomes. Medication reconciliation is a critical process for minimizing medication discrepancies and medication errors at the time of hospital admission. This study aimed to evaluate the role of clinical pharmacists in identifying pharmacotherapy-related issues upon patient admission in a low-resource setting. A prospective observational study was conducted at a university hospital between 1 March and 31 May 2023. Within 24 h of admission, a clinical pharmacist documented each patient’s pre-admission medication regimen and compared it with the medication history obtained by the admitting physician. Discrepancies and pharmacotherapy problems were subsequently identified. Among 65 patients, pharmacists documented 334 medications versus 189 recorded by physicians (p < 0.01). The clinical pharmacist identified 155 discrepancies, 112 (72.26%) of which were unintentional. The most frequent type was drug omission (91.07%), followed by incorrect dosage (4.46%), incorrect dosing interval (2.68%), and medications with unknown indications (1.79%). Most discrepancies were classified as errors without harm (53.57%), while 41.07% were potentially harmful. These findings underscore the importance of integrating clinical pharmacists into the healthcare team. Their active participation during hospital admission can significantly enhance medication safety and reduce preventable adverse drug events. Full article

As artificial intelligence (AI) becomes increasingly utilized to perform tasks requiring human intelligence, patients who are pregnant may turn to AI for advice on over-the-counter (OTC) medications. However, medications used in pregnancy may pose profound safety concerns limited by data availability. This study focuses on a chatbot’s ability to accurately provide information regarding OTC medications as it relates to patients that are pregnant. A prospective, descriptive design was used to compare the responses generated by the Chat Generative Pre-Trained Transformer 3.5 (ChatGPT-3.5) to the information provided by UpToDate^®. Eighty-seven of the top pharmacist-recommended OTC drugs in the United States (U.S.) as identified by Pharmacy Times were assessed for safe use in pregnancy using ChatGPT-3.5. A piloted, standard prompt was input into ChatGPT-3.5, and the responses were recorded. Two groups independently rated the responses compared to UpToDate on their correctness, completeness, and safety using a 5-point Likert scale. After independent evaluations, the groups discussed the findings to reach a consensus, with a third independent investigator giving final ratings. For correctness, the median score was 5 (interquartile range [IQR]: 5–5). For completeness, the median score was 4 (IQR: 4–5). For safety, the median score was 5 (IQR: 5–5). Despite high overall scores, the safety errors in 9% of the evaluations (n = 8), including omissions that pose a risk of serious complications, currently renders the chatbot an unsafe standalone resource for this purpose. Full article

Accurate plant species identification in desert grasslands using hyperspectral data is a critical prerequisite for large-scale, high-precision grassland monitoring and management. However, due to prolonged overgrazing and the inherent ecological vulnerability of the environment, sericite–Artemisia desert grassland has experienced significant ecological degradation. Therefore, in this study, we obtained spectral images of the grassland in April 2022 using a Soc710 VP imaging spectrometer (Surface Optics Corporation, San Diego, CA, USA), which were classified into three levels (low, medium, and high) based on the level of participation of Seriphidium transiliense (Poljakov) Poljakov and Ceratocarpus arenarius L. in the community. The optimal index factor (OIF) was employed to synthesize feature band images, which were subsequently used as input for the DeepLabv3p, PSPNet, and UNet deep learning models in order to assess the influence of species participation on classification accuracy. The results indicated that species participation significantly impacted spectral information extraction and model classification performance. Higher participation enhanced the scattering of reflectivity in the canopy structure of S. transiliense, while the light saturation effect of C. arenarius was induced by its short stature. Band combinations—such as Blue, Red Edge, and NIR (BREN) and Red, Red Edge, and NIR (RREN)—exhibited strong capabilities in capturing structural vegetation information. The identification model performances were optimal, with a high level of S. transiliense participation and with DeepLabv3p, PSPNet, and UNet achieving an overall accuracy (OA) of 97.86%, 96.51%, and 98.20%. Among the tested models, UNet exhibited the highest classification accuracy and robustness with small sample datasets, effectively differentiating between S. transiliense, C. arenarius, and bare ground. However, when C. arenarius was the primary target species, the model’s performance declined as its participation levels increased, exhibiting significant omission errors for S. transiliense, whose producer’s accuracy (PA) decreased by 45.91%. The findings of this study provide effective technical means and theoretical support for the identification of plant species and ecological monitoring in sericite–Artemisia desert grasslands. Full article