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Search Results (1,318)

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19 pages, 913 KiB  
Article
Understanding Diversity: The Cultural Knowledge Profile of Nurses Prior to Transcultural Education in Light of a Triangulated Study Based on the Giger and Davidhizar Model
by Małgorzata Lesińska-Sawicka and Alina Roszak
Healthcare 2025, 13(15), 1907; https://doi.org/10.3390/healthcare13151907 - 5 Aug 2025
Abstract
Introduction: The increasing cultural diversity of patients poses new challenges for nurses. Cultural competence, especially knowledge of the cultural determinants of health and illness, is an important element of professionalism in nursing care. The aim of this study was to analyse nurses’ self-assessment [...] Read more.
Introduction: The increasing cultural diversity of patients poses new challenges for nurses. Cultural competence, especially knowledge of the cultural determinants of health and illness, is an important element of professionalism in nursing care. The aim of this study was to analyse nurses’ self-assessment of cultural knowledge, with a focus on the six dimensions of the Giger and Davidhizar model, prior to formal training in this area. Methods: A triangulation method combining qualitative and quantitative analysis was used. The analysis included 353 statements from 36 master’s student nurses. Data were coded according to six cultural phenomena: biological factors, communication, space, time, social structure, and environmental control. Content analysis, ANOVA, Spearman’s rank correlation, and cluster analysis (k-means) were conducted. Results: The most frequently identified that categories were environmental control (34%), communication (20%), and social structure (16%). Significant knowledge gaps were identified in the areas of non-verbal communication, biological differences, and understanding space in a cultural context. Three cultural knowledge profiles of the female participants were distinguished: pragmatic, socio-reflective, and critical–experiential. Conclusions: The cultural knowledge of the participants was fragmented and simplified. The results indicate the need to personalise cultural learning and to take into account nurses’ level of readiness and experience profile. The study highlights the importance of the systematic development of reflective and contextual cultural knowledge as a foundation for competent care. Full article
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24 pages, 4314 KiB  
Article
Hyperparameter Optimization of Neural Networks Using Grid Search for Predicting HVAC Heating Coil Performance
by Yosef Jaber, Pasidu Dharmasena, Adam Nassif and Nabil Nassif
Buildings 2025, 15(15), 2753; https://doi.org/10.3390/buildings15152753 - 5 Aug 2025
Abstract
Heating, Ventilation, and Air Conditioning (HVAC) systems represent a significant portion of global energy use, yet they are often operated without optimized control strategies. This study explores the application of deep learning to accurately model heating system behavior as a foundation for predictive [...] Read more.
Heating, Ventilation, and Air Conditioning (HVAC) systems represent a significant portion of global energy use, yet they are often operated without optimized control strategies. This study explores the application of deep learning to accurately model heating system behavior as a foundation for predictive control and energy-efficient HVAC operation. Experimental data were collected under controlled laboratory conditions, and 288 unique hyperparameter configurations were developed. Each configuration was tested three times, resulting in a total of 864 artificial neural network models. Five key hyperparameters were varied systematically: number of epochs, network size, network shape, learning rate, and optimizer. The best-performing model achieved a mean squared error of 0.469 and featured 17 hidden layers, a left-triangle architecture trained for 500 epochs with a learning rate of 5 × 10−5, and Adam as the optimizer. The results highlighted the importance of hyperparameter tuning in improving model accuracy. Future research should extend the analysis to incorporate cooling operation and real-world building operation data for broader applicability. Full article
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14 pages, 1926 KiB  
Article
Research on Data-Driven Drilling Safety Grade Evaluation System
by Shuan Meng, Changhao Wang, Yingcao Zhou and Lidong Hou
Processes 2025, 13(8), 2469; https://doi.org/10.3390/pr13082469 - 4 Aug 2025
Abstract
With the in-depth application of digital transformation in the oil industry, data-driven methods provide a new technical path for drilling engineering safety evaluation. In this paper, a data-driven drilling safety level evaluation system is proposed. By integrating the three-dimensional visualization technology of wellbore [...] Read more.
With the in-depth application of digital transformation in the oil industry, data-driven methods provide a new technical path for drilling engineering safety evaluation. In this paper, a data-driven drilling safety level evaluation system is proposed. By integrating the three-dimensional visualization technology of wellbore trajectory and the prediction model of friction torque, a dynamic and intelligent drilling risk evaluation framework is constructed. The Python platform is used to integrate geomechanical parameters, real-time drilling data, and historical working condition records, and the machine learning algorithm is used to train the friction torque prediction model to improve prediction accuracy. Based on the K-means clustering evaluation method, a three-tier drilling safety classification standard is established: Grade I (low risk) for friction (0–100 kN) and torque (0–10 kN·m), Grade II (medium risk) for friction (100–200 kN) and torque (10–20 kN·m), and Grade III (high risk) for friction (>200 kN) and torque (>20 kN·m). This enables intelligent quantitative evaluation of drilling difficulty. The system not only dynamically optimizes bottom-hole assembly (BHA) and drilling parameters but also continuously refines the evaluation model’s accuracy through a data backtracking mechanism. This provides a reliable theoretical foundation and technical support for risk early warning, parameter optimization, and intelligent decision-making in drilling engineering. Full article
(This article belongs to the Section AI-Enabled Process Engineering)
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19 pages, 7432 KiB  
Article
Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
by Jun-Hyung Kim and Goo-Rak Kwon
Appl. Sci. 2025, 15(15), 8613; https://doi.org/10.3390/app15158613 (registering DOI) - 4 Aug 2025
Abstract
This study proposes a simple deep learning-based framework for image-level anti-personnel landmine detection in long-wave infrared imagery. To address challenges posed by the limited size of the available dataset and the small spatial size of anti-personnel landmines within images, we integrate two key [...] Read more.
This study proposes a simple deep learning-based framework for image-level anti-personnel landmine detection in long-wave infrared imagery. To address challenges posed by the limited size of the available dataset and the small spatial size of anti-personnel landmines within images, we integrate two key techniques: transfer learning using pre-trained vision foundation models, and attention-based multiple instance learning to derive discriminative image features. We evaluate five pre-trained models, including ResNet, ConvNeXt, ViT, OpenCLIP, and InfMAE, in combination with attention-based multiple instance learning. Furthermore, to mitigate the reliance of trained models on irrelevant features such as artificial or natural structures in the background, we introduce an inpainting-based image augmentation method. Experimental results, conducted on a publicly available “legbreaker” anti-personnel landmine infrared dataset, demonstrate that the proposed framework achieves high precision and recall, validating its effectiveness for landmine detection in infrared imagery. Additional experiments are also performed on an aerial image dataset designed for detecting small-sized ship targets to further validate the effectiveness of the proposed approach. Full article
(This article belongs to the Special Issue Application of Artificial Intelligence in Image Processing)
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33 pages, 8886 KiB  
Article
Unsupervised Binary Classifier-Based Object Detection Algorithm with Integrated Background Subtraction Suitable for Use with Aerial Imagery
by Gabija Veličkaitė, Ignas Daugėla and Ivan Suzdalev
Appl. Sci. 2025, 15(15), 8608; https://doi.org/10.3390/app15158608 (registering DOI) - 3 Aug 2025
Viewed by 56
Abstract
This research presents the development of a novel object detection algorithm designed to identify humans in natural outdoor environments using minimal computational resources. The proposed system, SARGAS, combines a custom convolutional neural network (CNN) classifier with MOG2 background subtraction and partial affine transformations [...] Read more.
This research presents the development of a novel object detection algorithm designed to identify humans in natural outdoor environments using minimal computational resources. The proposed system, SARGAS, combines a custom convolutional neural network (CNN) classifier with MOG2 background subtraction and partial affine transformations for camera stabilization. A secondary CNN refines detections and reduces false positives. Unlike conventional supervised models, SARGAS is trained in a partially unsupervised manner, learning to recognize feature patterns without requiring labeled data. The algorithm achieved a recall of 93%, demonstrating strong detection capability even under challenging conditions. However, the overall accuracy reached 65%, due to a higher rate of false positives—an expected trade-off when maximizing recall. This bias is intentional, as missing a human target in search and rescue applications carries a higher cost than producing additional false detections. While supervised models, such as YOLOv5, perform well on data resembling their training sets, they exhibit significant performance degradation on previously unseen footage. In contrast, SARGAS generalizes more effectively, making it a promising candidate for real-world deployment in environments where labeled training data is limited or unavailable. The results establish a solid foundation for further improvements and suggest that unsupervised CNN-based approaches hold strong potential in object detection tasks. Full article
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22 pages, 13770 KiB  
Article
Prediction Model of Powdery Mildew Disease Index in Rubber Trees Based on Machine Learning
by Jiazheng Zhu, Xize Huang, Xiaoyu Liang, Meng Wang and Yu Zhang
Plants 2025, 14(15), 2402; https://doi.org/10.3390/plants14152402 - 3 Aug 2025
Viewed by 126
Abstract
Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into [...] Read more.
Powdery mildew, caused by Erysiphe quercicola, is one of the primary diseases responsible for the reduction in natural rubber production in China. This disease is a typical airborne pathogen, characterized by its ability to spread via air currents and rapidly escalate into an epidemic under favorable environmental conditions. Accurate prediction and determination of the prevention and control period represent both a critical challenge and key focus area in managing rubber-tree powdery mildew. This study investigates the effects of spore concentration, environmental factors, and infection time on the progression of powdery mildew in rubber trees. By employing six distinct machine learning model construction methods, with the disease index of powdery mildew in rubber trees as the response variable and spore concentration, temperature, humidity, and infection time as predictive variables, a preliminary predictive model for the disease index of rubber-tree powdery mildew was developed. Results from indoor inoculation experiments indicate that spore concentration directly influences disease progression and severity. Higher spore concentrations lead to faster disease development and increased severity. The optimal relative humidity for powdery mildew development in rubber trees is 80% RH. At varying temperatures, the influence of humidity on the disease index differs across spore concentration, exhibiting distinct trends. Each model effectively simulates the progression of powdery mildew in rubber trees, with predicted values closely aligning with observed data. Among the models, the Kernel Ridge Regression (KRR) model demonstrates the highest accuracy, the R2 values for the training set and test set were 0.978 and 0.964, respectively, while the RMSE values were 4.037 and 4.926, respectively. This research provides a robust technical foundation for reducing the labor intensity of traditional prediction methods and offers valuable insights for forecasting airborne forest diseases. Full article
(This article belongs to the Section Plant Modeling)
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26 pages, 9940 KiB  
Article
Assessing Model Trade-Offs in Agricultural Remote Sensing: A Review of Machine Learning and Deep Learning Approaches Using Almond Crop Mapping
by Mashoukur Rahaman, Jane Southworth, Yixin Wen and David Keellings
Remote Sens. 2025, 17(15), 2670; https://doi.org/10.3390/rs17152670 - 1 Aug 2025
Viewed by 118
Abstract
This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse [...] Read more.
This study presents a comprehensive review and comparative analysis of traditional machine learning (ML) and deep learning (DL) models for land cover classification in agricultural remote sensing. We evaluate the reported successes, trade-offs, and performance metrics of ML and DL models across diverse agricultural contexts. Building on this foundation, we apply both model types to the specific case of almond crop field identification in California’s Central Valley using Landsat data. DL models, including U-Net, MANet, and DeepLabv3+, achieve high accuracy rates of 97.3% to 97.5%, yet our findings demonstrate that conventional ML models—such as Decision Tree, K-Nearest Neighbor, and Random Forest—can reach comparable accuracies of 96.6% to 96.8%. Importantly, the ML models were developed using data from a single year, while DL models required extensive training data spanning 2008 to 2022. Our results highlight that traditional ML models offer robust classification performance with substantially lower computational demands, making them especially valuable in resource-constrained settings. This paper underscores the need for a balanced approach in model selection—one that weighs accuracy alongside efficiency. The findings contribute actionable insights for agricultural land cover mapping and inform ongoing model development in the geospatial sciences. Full article
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21 pages, 33884 KiB  
Article
Rapid Detection and Segmentation of Landslide Hazards in Loess Tableland Areas Using Deep Learning: A Case Study of the 2023 Jishishan Ms 6.2 Earthquake in Gansu, China
by Zhuoli Bai, Lingyun Ji, Hongtao Tang, Jiangtao Qiu, Shuai Kang, Chuanjin Liu and Zongpan Bian
Remote Sens. 2025, 17(15), 2667; https://doi.org/10.3390/rs17152667 - 1 Aug 2025
Viewed by 201
Abstract
Addressing the technical demands for the rapid, precise detection of earthquake-triggered landslides in loess tablelands, this study proposes and validates an innovative methodology integrating enhanced deep learning architectures with large-tile processing strategies, featuring two core advances: (1) a critical enhancement of YOLOv8’s shallow [...] Read more.
Addressing the technical demands for the rapid, precise detection of earthquake-triggered landslides in loess tablelands, this study proposes and validates an innovative methodology integrating enhanced deep learning architectures with large-tile processing strategies, featuring two core advances: (1) a critical enhancement of YOLOv8’s shallow layers via a higher-resolution P2 detection head to boost small-target capture capabilities, and (2) the development of a large-tile segmentation–tile mosaicking workflow to overcome the technical bottlenecks in large-scale high-resolution image processing, ensuring both timeliness and accuracy in loess landslide detection. This study utilized 20 km2 of high-precision UAV imagery acquired after the 2023 Gansu Jishishan Ms 6.2 earthquake as foundational data, applying our methodology to achieve the rapid detection and precise segmentation of landslides in the study area. Validation was conducted through a comparative analysis of high-accuracy 3D models and field investigations. (1) The model achieved simultaneous convergence of all four loss functions within a 500-epoch progressive training strategy, with mAP50(M) = 0.747 and mAP50-95(M) = 0.46, thus validating the superior detection and segmentation capabilities for the Jishishan earthquake-triggered loess landslides. (2) The enhanced algorithm detected 417 landslides with 94.1% recognition accuracy. Landslide areas ranged from 7 × 10−4 km2 to 0.217 km2 (aggregate area: 1.3 km2), indicating small-scale landslide dominance. (3) Morphological characterization and the spatial distribution analysis revealed near-vertical scarps, diverse morphological configurations, and high spatial density clustering in loess tableland landslides. Full article
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14 pages, 2796 KiB  
Article
Obtaining Rotational Stiffness of Wind Turbine Foundation from Acceleration and Wind Speed SCADA Data
by Jiazhi Dai, Mario Rotea and Nasser Kehtarnavaz
Sensors 2025, 25(15), 4756; https://doi.org/10.3390/s25154756 - 1 Aug 2025
Viewed by 174
Abstract
Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus [...] Read more.
Monitoring the health of wind turbine foundations is essential for ensuring their operational safety. This paper presents a cost-effective approach to obtain rotational stiffness of wind turbine foundations by using only acceleration and wind speed data that are part of SCADA data, thus lowering the use of moment and tilt sensors that are currently being used for obtaining foundation stiffness. First, a convolutional neural network model is applied to map acceleration and wind speed data within a moving window to corresponding moment and tilt values. Rotational stiffness of the foundation is then estimated by fitting a line in the moment-tilt plane. The results obtained indicate that such a mapping model can provide stiffness values that are within 7% of ground truth stiffness values on average. Second, the developed mapping model is re-trained by using synthetic acceleration and wind speed data that are generated by an autoencoder generative AI network. The results obtained indicate that although the exact amount of stiffness drop cannot be determined, the drops themselves can be detected. This mapping model can be used not only to lower the cost associated with obtaining foundation rotational stiffness but also to sound an alarm when a foundation starts deteriorating. Full article
(This article belongs to the Special Issue Sensors Technology Applied in Power Systems and Energy Management)
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48 pages, 5229 KiB  
Article
Enhancing Ship Propulsion Efficiency Predictions with Integrated Physics and Machine Learning
by Hamid Reza Soltani Motlagh, Seyed Behbood Issa-Zadeh, Md Redzuan Zoolfakar and Claudia Lizette Garay-Rondero
J. Mar. Sci. Eng. 2025, 13(8), 1487; https://doi.org/10.3390/jmse13081487 - 31 Jul 2025
Viewed by 228
Abstract
This research develops a dual physics-based machine learning system to forecast fuel consumption and CO2 emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte [...] Read more.
This research develops a dual physics-based machine learning system to forecast fuel consumption and CO2 emissions for a 100 m oil tanker across six operational scenarios: Original, Paint, Advanced Propeller, Fin, Bulbous Bow, and Combined. The combination of hydrodynamic calculations with Monte Carlo simulations provides a solid foundation for training machine learning models, particularly in cases where dataset restrictions are present. The XGBoost model demonstrated superior performance compared to Support Vector Regression, Gaussian Process Regression, Random Forest, and Shallow Neural Network models, achieving near-zero prediction errors that closely matched physics-based calculations. The physics-based analysis demonstrated that the Combined scenario, which combines hull coatings with bulbous bow modifications, produced the largest fuel consumption reduction (5.37% at 15 knots), followed by the Advanced Propeller scenario. The results demonstrate that user inputs (e.g., engine power: 870 kW, speed: 12.7 knots) match the Advanced Propeller scenario, followed by Paint, which indicates that advanced propellers or hull coatings would optimize efficiency. The obtained insights help ship operators modify their operational parameters and designers select essential modifications for sustainable operations. The model maintains its strength at low speeds, where fuel consumption is minimal, making it applicable to other oil tankers. The hybrid approach provides a new tool for maritime efficiency analysis, yielding interpretable results that support International Maritime Organization objectives, despite starting with a limited dataset. The model requires additional research to enhance its predictive accuracy using larger datasets and real-time data collection, which will aid in achieving global environmental stewardship. Full article
(This article belongs to the Special Issue Machine Learning for Prediction of Ship Motion)
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21 pages, 8731 KiB  
Article
Individual Segmentation of Intertwined Apple Trees in a Row via Prompt Engineering
by Herearii Metuarea, François Laurens, Walter Guerra, Lidia Lozano, Andrea Patocchi, Shauny Van Hoye, Helin Dutagaci, Jeremy Labrosse, Pejman Rasti and David Rousseau
Sensors 2025, 25(15), 4721; https://doi.org/10.3390/s25154721 - 31 Jul 2025
Viewed by 223
Abstract
Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree [...] Read more.
Computer vision is of wide interest to perform the phenotyping of horticultural crops such as apple trees at high throughput. In orchards specially constructed for variety testing or breeding programs, computer vision tools should be able to extract phenotypical information form each tree separately. We focus on segmenting individual apple trees as the main task in this context. Segmenting individual apple trees in dense orchard rows is challenging because of the complexity of outdoor illumination and intertwined branches. Traditional methods rely on supervised learning, which requires a large amount of annotated data. In this study, we explore an alternative approach using prompt engineering with the Segment Anything Model and its variants in a zero-shot setting. Specifically, we first detect the trunk and then position a prompt (five points in a diamond shape) located above the detected trunk to feed to the Segment Anything Model. We evaluate our method on the apple REFPOP, a new large-scale European apple tree dataset and on another publicly available dataset. On these datasets, our trunk detector, which utilizes a trained YOLOv11 model, achieves a good detection rate of 97% based on the prompt located above the detected trunk, achieving a Dice score of 70% without training on the REFPOP dataset and 84% without training on the publicly available dataset.We demonstrate that our method equals or even outperforms purely supervised segmentation approaches or non-prompted foundation models. These results underscore the potential of foundational models guided by well-designed prompts as scalable and annotation-efficient solutions for plant segmentation in complex agricultural environments. Full article
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27 pages, 1164 KiB  
Review
Physical Literacy as a Pedagogical Model in Physical Education
by Víctor Manuel Valle-Muñoz, María Mendoza-Muñoz and Emilio Villa-González
Children 2025, 12(8), 1008; https://doi.org/10.3390/children12081008 - 31 Jul 2025
Viewed by 386
Abstract
Background/Objectives: Legislative changes in educational systems have influenced how student learning is understood and promoted. In physical education (PE), there has been a shift from behaviorist models to more holistic approaches. In this context, physical literacy (PL) is presented as an emerging [...] Read more.
Background/Objectives: Legislative changes in educational systems have influenced how student learning is understood and promoted. In physical education (PE), there has been a shift from behaviorist models to more holistic approaches. In this context, physical literacy (PL) is presented as an emerging pedagogical model in school PE, aimed at fostering students’ motor competence in a safe, efficient, and meaningful way. The aim of this study is to analyze the origins, foundations, methodological elements, and educational value of PL, highlighting its potential to promote holistic and inclusive learning as the basis for an emerging PL model. Methods: A narrative review was conducted through a literature search in the Web of Science, PubMed, Scopus, and SportDiscus databases up to June 2025, focusing on scientific literature related to PL and PE. The analysis included its historical background, philosophical and theoretical foundations, and the key methodological elements and interventions that support its use as a pedagogical model. Results/Discussion: The findings indicate that the PL model can be grounded in key principles, such as student autonomy, teacher training, connection with the environment, inclusion, and collaboration. Additionally, motivation, enjoyment, creativity, and continuous assessment are identified as essential components for effective implementation. Moreover, this model not only guides and supports teachers in the field of PL but also promotes comprehensive benefits for students at the physical, cognitive, affective, and social levels, while encouraging increased levels of physical activity (PA). Conclusions: PL is understood as a dynamic and lifelong process that should be cultivated from early childhood to encourage sustained and active participation in PA. As a pedagogical model, PL represents an effective tool to enhance student learning and well-being in PE classes. Full article
(This article belongs to the Section Global Pediatric Health)
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22 pages, 1272 KiB  
Review
Pharmacy Technicians in Immunization Services: Mapping Roles and Responsibilities Through a Scoping Review
by Carolina Valeiro, Vítor Silva, Jorge Balteiro, Diane Patterson, Gilberto Bezerra, Karen Mealiff, Cristiano Matos, Ângelo Jesus and João Joaquim
Healthcare 2025, 13(15), 1862; https://doi.org/10.3390/healthcare13151862 - 30 Jul 2025
Viewed by 180
Abstract
Background: Pharmacy technicians are increasingly involved in immunization services, enhancing vaccine accessibility and reducing pharmacies’ workload. This scoping review aims to (1) provide a comprehensive overview of pharmacy technicians’ involvement in immunization services across various healthcare settings and countries, and (2) conduct a [...] Read more.
Background: Pharmacy technicians are increasingly involved in immunization services, enhancing vaccine accessibility and reducing pharmacies’ workload. This scoping review aims to (1) provide a comprehensive overview of pharmacy technicians’ involvement in immunization services across various healthcare settings and countries, and (2) conduct a comparative analysis of training curricula for pharmacy technicians on immunization. Methods: A scoping review was conducted following the Arksey and O’Malley framework. A comprehensive search of the PubMed and Scopus databases was performed using keywords and MeSH terms such as “pharmacy technician(s)”, “immunization”, “vaccination”, “role”, and “involvement”. Studies included assessed pharmacy technicians’ roles in vaccine administration, training, and public health outcomes. Descriptive and thematic analyses were used to synthesize the findings. In addition, a supplementary analysis of immunization training curricula was conducted, reviewing programs from different countries to identify similarities, differences, and gaps in course structure, content, and delivery formats. Lastly, a comprehensive toolkit was developed, offering guidelines intended to facilitate the implementation of immunization training programs. Results: A total of 35 articles met the inclusion criteria, primarily from the United States of America (n = 30), Canada (n = 2), Ethiopia (n = 1), Denmark (n = 1) and United Kingdom (n = 1). The findings indicate that pharmacy technicians contribute significantly to vaccine administration, patient education, and workflow optimization, particularly in community pharmacies. The COVID-19 pandemic accelerated their involvement in immunization programs. Key challenges include regulatory barriers, a lack of standardized training, and resistance from other healthcare professionals. Facilitators include legislative support (e.g., the PREP Act), structured training programs, and collaborative pharmacist–technician models. Conclusions: Pharmacy technicians can play a vital role in expanding immunization services, improving vaccine uptake, and reducing pharmacist workload. Addressing regulatory inconsistencies, enhancing training, and fostering interprofessional collaboration are crucial for their effective integration of immunization programs. Since immunization by pharmacy technicians is not yet allowed in many EU countries, this review will provide a foundational basis to address their potential to support the healthcare workforce and improve access to immunization services. Full article
(This article belongs to the Special Issue Policy Interventions to Promote Health and Prevent Disease)
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17 pages, 5455 KiB  
Article
A Hybrid Deep Learning Architecture for Enhanced Vertical Wind and FBAR Estimation in Airborne Radar Systems
by Fusheng Hou and Guanghui Sun
Aerospace 2025, 12(8), 679; https://doi.org/10.3390/aerospace12080679 - 30 Jul 2025
Viewed by 222
Abstract
Accurate prediction of the F-factor averaged over one kilometer (FBAR), a critical wind shear metric, is essential for aviation safety. A central F-factor is used to compute FBAR. i.e., compute the value of FBAR at a point using a spatial [...] Read more.
Accurate prediction of the F-factor averaged over one kilometer (FBAR), a critical wind shear metric, is essential for aviation safety. A central F-factor is used to compute FBAR. i.e., compute the value of FBAR at a point using a spatial interval beginning 500 m prior to the point and ending 500 m beyond the point. Traditional FBAR estimation using the Vicroy method suffers from limited vertical wind speed (W,h) accuracy, particularly in complex, non-idealized atmospheric conditions. This foundational study proposes a hybrid CNN-BiLSTM-Attention deep learning architecture that integrates spatial feature extraction, sequential dependency modeling, and attention mechanisms to address this limitation. The model was trained and evaluated on data generated by the industry-standard Airborne Doppler Weather Radar Simulation (ADWRS) system, using the DFW microburst case (C1-11) as a benchmark hazardous scenario. Following safety assurance principles aligned with SAE AS6983, the proposed model achieved a W,h estimation RMSE (root-mean-squared deviation) of 0.623 m s1 (vs. Vicroy’s 14.312 m s1) and a correlation of 0.974 on 14,524 test points. This subsequently improved FBAR prediction RMSE by 98.5% (0.0591 vs. 4.0535) and MAE (Mean Absolute Error) by 96.1% (0.0434 vs. 1.1101) compared to Vicroy-derived values. The model demonstrated a 65.3% probability of detection for hazardous downdrafts with a low 1.7% false alarm rate. These results, obtained in a controlled and certifiable simulation environment, highlight deep learning’s potential to enhance the reliability of airborne wind shear detection for civil aircraft, paving the way for next-generation intelligent weather avoidance systems. Full article
(This article belongs to the Section Aeronautics)
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16 pages, 5304 KiB  
Article
Regional Youth Population Prediction Using LSTM
by Jaejun Seo, Sunwoong Yoon, Jiwoo Kim and Kyusang Kwon
Sustainability 2025, 17(15), 6905; https://doi.org/10.3390/su17156905 - 29 Jul 2025
Viewed by 420
Abstract
Regional shrinkage, driven by declining birth rates, an aging population, and population concentration in the capital region, has become an increasingly serious issue in South Korea, threatening the long-term sustainability of local communities. Among various factors, youth out-migration is a key driver, undermining [...] Read more.
Regional shrinkage, driven by declining birth rates, an aging population, and population concentration in the capital region, has become an increasingly serious issue in South Korea, threatening the long-term sustainability of local communities. Among various factors, youth out-migration is a key driver, undermining the economic resilience and vitality of local areas. This study aims to predict youth population trends across 229 municipalities by incorporating diverse regional socioeconomic factors and providing a foundation for policy implementation to mitigate demographic disparities. To this end, a long short-term memory (LSTM) model, based on a direct approach that independently forecasts each future time point, was employed. The model was trained using the youth population data from 2003 to 2022 and socioeconomic variables, including employment, education, housing, and infrastructure. The results reveal a persistent nationwide decline in the youth population, with significantly sharper decreases in local areas than in the capital region. These findings underscore the deepening spatial imbalance and highlight the urgent need for region-specific demographic policies to address the accelerating risk of regional population decline. Full article
(This article belongs to the Section Sustainable Urban and Rural Development)
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