Search Results (12,774)

Background and Objectives: Clinical ethical judgments are often elicited through scenario-based (vignette-based) dilemmas that guide interpretation, reasoning, and moral judgment. Despite its importance, little is known about how healthcare professionals and students respond to such scenario-based dilemmas in Eastern European settings. This study explored differences in ethical decision-making between senior medical/dental students and practicing clinicians in Romania, focusing on how scenarios-based dilemmas influence conditional versus categorical responses. Materials and Methods: A cross-sectional survey was conducted with 244 participants (51 senior students; 193 practitioners). Respondents completed a validated 35-item questionnaire presenting hypothetical ethical scenarios across seven domains: informed consent, confidentiality, medical errors, public health duties, end-of-life decisions, professional boundaries, and crisis ethics. Each scenario used a Yes/No/It depends response structure. Group comparisons were analyzed using chi-square and non-parametric tests (α = 0.05). Results: Scenario-based dilemmas elicited frequent conditional reasoning, with “It depends” emerging as the most common response (47.8%). Strong consensus appeared in rejecting concealment of harmful errors and in treating unvaccinated families, reflecting robust professional norms. Divergences arose in areas where scenario-based dilemmas emphasized system-level duties: students more often supported annual influenza vaccination (52.9% vs. 32.6%, p = 0.028) and organ purchase authorization (76.47% vs. 62. 18%, p = 0.043), while practitioners more frequently endorsed higher insurance contributions for unhealthy lifestyles (48.7% vs. 23.5%, p = 0.003). Conclusions: Scenario-based dilemmas strongly shape moral decision-making in healthcare. While students tended toward principle-driven transparency, practitioners showed pragmatic orientations linked to experience and system stewardship. To promote high-quality clinical work and align decision-making with best practice and health policy, our findings support institutional protocols for transparent error disclosure, continuing professional development in ethical communication, the possible adoption of annual influenza vaccination policies for healthcare personnel as policy options rather than categorical imperatives, and structured triage frameworks during crisis situations. These proposals highlight how scenario-based ethics training can strengthen both individual reasoning and systemic resilience. Full article

International medical graduates (IMGs) make up a significant proportion of general practitioners (GPs) in high-income countries such as the United Kingdom (UK), the United States of America (USA), Australia, and Canada. This paper compares views about IMGs with their own views in relation to the timing of GP placements in GP specialty training programs in the UK. It presents an inductive thematic analysis of focus groups with GP specialty trainers and trainees (149 participants across 32 focus groups), examining opinions about the ideal timing of GP placements. Trainers and home graduates argued that for home graduates, the ideal sequence depends on the trainee’s previous experience. They also suggested that IMGs should start in a hospital placement to develop familiarity with the healthcare system. In contrast, most IMGs expressed a preference for starting in a GP placement, so that they can gain an understanding of the requirements of their specialty as early as possible. There is a contrast between what IMGs said about themselves and the views shared by trainers and home graduates. This highlights the need to involve IMGs in the design of support programs targeted towards them. Recommendations include tailoring training to account for individual career paths and providing training about the healthcare system before the start of the first placement. This could improve the efficiency of GP training programs at a time of extreme pressure on healthcare systems and training providers. Full article

The rapid growth of Internet of Medical Things (IoMT) devices offers promising avenues for real-time, personalized healthcare while also introducing critical challenges related to data privacy, device heterogeneity, and deployment scalability. This paper presents E-DMFL (Enhanced Distributed Multimodal Federated Learning), an Enhanced Distributed Multimodal Federated Learning framework designed to address these issues. Our approach combines systems analysis principles with intelligent model design, integrating PyTorch-based modular orchestration and TensorFlow-style data pipelines to enable multimodal edge-based training. E-DMFL incorporates gated attention fusion, differential privacy, Shapley-value-based modality selection, and peer-to-peer communication to facilitate secure and adaptive learning in non-IID environments. We evaluate the framework using the EarSAVAS dataset, which includes synchronized audio and motion signals from ear-worn sensors. E-DMFL achieves a test accuracy of 92.0% in just six communication rounds. The framework also supports energy-efficient and real-time deployment through quantization-aware training and battery-aware scheduling. These results demonstrate the potential of combining systems-level design with federated learning (FL) innovations to support practical, privacy-aware IoMT applications. Full article

The classification of urban residential settlements through Machine Learning (ML) and Deep Learning (DL) remains a complex task due to the intrinsic heterogeneity of urban environments and the scarcity of large, accurately labeled training datasets. To overcome these limitations, this study introduces a novel GIS-based unsupervised classification framework that exploits Fuzzy C-Means (FCM) clustering for the detection and interpretation of urban morphologies. Compared to unsupervised classification approaches that rely on crisp-based clustering algorithms, the proposed FCM-based method more effectively captures heterogeneous urban fabrics where no clear predominance of specific building types exists. Specifically, the method applies fuzzy clustering to census units—considered the fundamental scale of urban analysis—based on construction techniques and building periods. By grouping census areas with similar structural features, the framework provides a flexible, data-driven approach to the characterization of urban settlements. The identification of cluster centroids’ dominant attributes enables a systematic interpretation of the spatial distribution of the built environment, while the subsequent mapping process assigns each cluster a descriptive label reflecting the prevailing building fabric. The generated thematic maps yield critical insights into urban morphology and facilitate evidence-based planning. The framework was validated across ten Italian cities selected for their diverse physical, morphological, and historical characteristics; comparisons with the results of urban zone classifications in these cities conducted by experts show that the proposed method provides accurate results, as the similarity to the classifications made by experts, measured by the use of the Adjusted Rand Index, is always higher than or equal to 0.93; furthermore, it is robust when applied in heterogeneous urban settlements. These results confirm the effectiveness of the method in delineating homogeneous urban areas, thereby offering decision makers a robust instrument to guide targeted interventions on existing building stocks. The proposed framework advances the capacity to analyze urban form, to strategically support renovation and urban regeneration policies, and demonstrates a strong potential for portability, as it can be applied to other cities for urban scale analyses. Full article

Few-Shot Semantic Segmentation (FSS) faces significant challenges in modeling complex backgrounds and maintaining prediction consistency due to limited training samples. Existing methods oversimplify backgrounds as single negative classes and rely solely on pixel-level alignments. To address these issues, we propose a symmetry-aware superpixel-enhanced FSS framework with a symmetric dual-branch architecture that explicitly models the superpixel region-graph in both the support and query branches. First, top–down cross-layer fusion injects low-level edge and texture cues into high-level semantics to build a more complete representation of complex backgrounds, improving foreground–background separability and boundary quality. Second, images are partitioned into superpixels and aggregated into “superpixel tokens” to construct a Region Adjacency Graph (RAG). Support-set prototypes are used to initialize query-pixel predictions, which are then projected into the superpixel space for cross-image prototype alignment with support superpixels. We further perform message passing/energy minimization on the RAG to enhance intra-region consistency and boundary adherence, and finally back-project the predictions to the pixel space. Lastly, by aggregating homogeneous semantic information, we construct robust foreground and background prototype representations, enhancing the model’s ability to perceive both seen and novel targets. Extensive experiments on the PASCAL-$5^i$ and COCO-${20}^i$ benchmarks demonstrate that our proposed model achieves superior segmentation performance over the baseline and remains competitive with existing FSS methods. Full article

This article explores how Text-to-Speech (TTS) and Speech-to-Text (STT) technologies are being harnessed in education to enhance accessibility, language development, and overall learner engagement. Drawing upon theoretical frameworks in linguistics and educational psychology, we highlight the benefits TTS and STT can offer to diverse student populations, including students with disabilities, language learners, and those seeking personalized or self-paced instruction. We discuss methods for integrating TTS and STT into the classroom (hardware, software, and practical considerations) and offer case studies of effective implementations in areas such as literacy support, foreign language acquisition, and assessment. We then address the pedagogical benefits these tools provide—such as differentiated instruction, immediate feedback, and a heightened sense of learner autonomy—along with limitations and challenges that educators may encounter. In conclusion, we suggest future directions for research and practice, underscoring the importance of teacher training, ethical considerations, and ever-evolving advancements in natural language processing. Full article

Background: Neural efficiency theory proposes that expert athletes optimize brain resource allocation and functioning. Beta band oscillations are associated with attention, motor preparation, and emotional control, reflecting adaptive patterns of reduced cortical energy expenditure (absolute power) and greater temporal precision (peak frequency). Slow cortical potential (SCP) neurofeedback has emerged as a method to train voluntary cortical regulation, yet its application in sports—particularly in precision-demanding disciplines such as golf—remains underexplored. The aim of this study was to evaluate the effects of SCP neurofeedback on beta band activity in competitive golfers. Methods: Forty-two golfers were randomly assigned to either an intervention group (n = 21), which completed 16 SCP neurofeedback sessions (2560 trials), or a control group (n = 21). SCP activity was measured during activation and deactivation trials, while EEG beta oscillations were analyzed in terms of peak frequency and absolute power at C3, O2, F8, and T5. These sites were chosen for their relevance to golf: C3 (motor execution), O2 (visual processing), F8 (inhibitory and emotional control), and T5 (visuospatial integration). Results: The intervention group showed significant increases in positive SCP trials, reflecting improved voluntary cortical inhibition. Peak frequency increased in Beta 1 (C3) and Beta 2 (O2), while absolute power decreased at F8 and T5, which seems to indicate a reduced cortical overload and enhanced visuospatial integration. Conclusions: SCP neurofeedback modulated beta activity in golfers, enhancing neural efficiency and supporting its potential as an innovative tool to optimize performance in precision sports. Full article

Understanding the relationship between environmental variables and gas concentrations from livestock production is essential for evaluating the impact of pollutants on animal housing and surrounding areas. This study investigates the use of ANNs to predict NH₃ concentrations in a Mediterranean dairy barn under seasonal conditions—namely, hot, cold, and transitional weather. A Multi-Layer Perceptron (MLP) structure was employed, trained using Levenberg–Marquardt and Bayesian Regularization algorithms. The input dataset included ten variables related to internal and external environmental conditions, NH₃ concentrations, and time of day. The models were evaluated using R², R, MAE, MSE, and RMSE as performance metrics. Results showed strong predictive capabilities, with R² values ranging from 0.75 to 0.96 and RMSE values between 0.47 and 0.80 due to the number of input data (different days) and environmental conditions. These findings highlight the potential of ANNs as effective tools for real-time pollutant prediction, supporting Precision Livestock Farming (PLF) strategies. Full article

Audio steganography exploits redundancies in the human auditory system to conceal secret information within cover audio, ensuring that the hidden data remains undetectable during normal listening. However, recent research shows that current audio steganography techniques are vulnerable to detection by deep learning-based steganalyzers, which analyze the high-dimensional features of stego audio for classification. While deep learning-based steganography has been extensively studied for image covers, its application to audio remains underexplored, particularly in achieving robust embedding and extraction with minimal perceptual distortion. We propose a diffusion-based audio steganography model comprising two primary modules: (i) a diffusion-based embedding module that autonomously integrates secret messages into cover audio while preserving high perceptual quality and (ii) a corresponding diffusion-based extraction module that accurately recovers the embedded data. The framework supports both pre-existing cover audio and the generation of high-quality steganographic cover audio with superior perceptual quality for message embedding. After training, the model achieves state-of-the-art performance in terms of embedding capacity and resistance to detection by deep learning steganalyzers. The experimental results demonstrate that our diffusion-based approach significantly outperforms existing methods across varying embedding rates, yielding stego audio with superior auditory quality and lower detectability. Full article

Background/Objectives: This study evaluates students’ awareness and perceptions of interdisciplinary education. It focuses specifically on digital dentistry among students in the Department of Dental Technology. The findings aim to support the development of interdisciplinary courses and programs to enhance students’ skills in response to the growing digitalization of dental healthcare. Methods: A cross-sectional survey was conducted using a 23-item online questionnaire administered to a total of 203 students to collect data on general characteristics, perceptions of interdisciplinary education, the perceived necessity of such education, and the demand for interdisciplinary training, including topics related to CAD/CAM and 3D printing technologies. A t-test was performed to analyze grade-level differences in perceptions. Correlation analysis was conducted between perceptions and digital dental laboratory technology skills. Results: Despite the relatively low level of awareness regarding interdisciplinary education, students expressed a strong perceived need for it. A total of 76.6% of respondents preferred to collaborate with the Department of Dental Hygiene. No clear link exists between students’ perceptions of interdisciplinary education and their digital dental competencies. Practical training is more important than awareness. A significant difference in competencies was seen between lower- and higher-year students, indicating that advanced programs for higher-year students may be effective. Conclusions: Clear guidance on interdisciplinary education can enhance student understanding and acceptance. Interdisciplinary education with the dental hygiene department may increase engagement. Full article

Few-Shot Industrial Anomaly Detection (FSIAD) is an essential yet challenging problem in practical scenarios such as industrial quality inspection. Its objective is to identify previously unseen anomalous regions using only a limited number of normal support images from the same category. Recently, large pre-trained vision-language models (VLMs), such as CLIP, have exhibited remarkable few-shot image-text representation abilities across a range of visual tasks, including anomaly detection. Despite their promise, real-world industrial anomaly datasets often contain noisy labels, which can degrade prompt learning and detection performance. In this paper, we propose AnomalyNLP, a new Noisy-Label Prompt Learning approach designed to tackle the challenge of few-shot anomaly detection. This framework offers a simple and efficient approach that leverages the expressive representations and precise alignment capabilities of VLMs for industrial anomaly detection. First, we design a Noisy-Label Prompt Learning (NLPL) strategy. This strategy utilizes feature learning principles to suppress the influence of noisy samples via Mean Absolute Error (MAE) loss, thereby improving the signal-to-noise ratio and enhancing overall model robustness. Furthermore, we introduce a prompt-driven optimal transport feature purification method to accurately partition datasets into clean and noisy subsets. For both image-level and pixel-level anomaly detection, AnomalyNLP achieves state-of-the-art performance across various few-shot settings on the MVTecAD and VisA public datasets. Qualitative and quantitative results on two datasets demonstrate that our method achieves the largest average AUC improvement over baseline methods across 1-, 2-, and 4-shot settings, with gains of up to 10.60%, 10.11%, and 9.55% in practical anomaly detection scenarios. Full article

Agritourism is increasingly recognized as a driver of sustainable rural development, yet research has often focused on ecological and economic outcomes while neglecting the human capital that sustains service quality. This study introduces the concept of human sustainability capital and links it with the ESG (Environmental, Social, Governance) framework and emotional labor theory, using case studies from Maramureș and Bucovina, Romania. Data were collected in summer 2025 through two surveys: one of 120 tourists assessing satisfaction, challenges, and improvement needs, and one of 45 agritourism hosts and employees examining emotional labor, job satisfaction, and ESG-related practices. Tourists reported high satisfaction with hospitality, food, landscapes, and cultural authenticity but noted shortcomings in infrastructure, activity variety, and crowding during peak seasons. Hosts and employees showed strong motivation and cultural pride, with genuine engagement more frequent than surface acting, yet many reported fatigue, low pay, and limited access to training. Social and cultural benefits were evident, environmental practices were modest, and governance emerged as the weakest pillar. Strengthening governance through professional development, fair labor conditions, and infrastructural support is crucial to maintain authenticity, protect cultural heritage, and ensure the long-term resilience of agritourism. Full article

Shipborne fighters play a critical role in far-sea operations. However, their landing process on aircraft carrier decks involves significant risks, where accidents can lead to substantial losses. Timely and accurate risk prediction is, therefore, essential for improving flight training efficiency and enhancing the combat capability of naval aviation forces. Machine-learning algorithms have been explored for predicting landing risks in land-based aircraft. However, owing to the challenges in acquiring relevant data, the application of such methods to shipborne aircraft remains limited. To address this gap, the present study proposes a deep learning-based method for predicting landing risks of shipborne aircraft. A dataset was constructed using simulated ship movements recorded during the sliding phase along with relevant flight parameters. Model training and prediction were conducted using up to ten different input combinations with artificial neural networks, long short-term memory, and transformer neural networks. Experimental results demonstrate that all three models can effectively predict landing parameters, with the lowest average test error reaching 3.5620. The study offers a comprehensive comparison of traditional machine learning and deep learning methods, providing practical insights into input variable selection and model performance evaluation. Although deep learning models, particularly the Transformer, achieved the highest accuracy, in practical applications, the support of hardware performance still needs to be fully considered. Full article

Healthcare workers’ (HCWs) wellness is a critical concern, particularly following the COVID-19 pandemic. Staff Wellness Rounding (SWR) has emerged as a leadership-driven strategy to support HCWs but research on its effectiveness remains limited. This study examines the impact of SWR within a large healthcare organisation in Australia and introduces the CALM (Conversation, Active Listening, Leadership Engagement, Mechanism for Feedback) Framework to enhance leadership-driven wellness initiatives. SWR was implemented across six acute hospitals and 14 community health centres in New South Wales, Australia (July to October 2021). A sequential mixed-methods design was used to evaluate SWR effectiveness, leadership engagement, and key components for a structured wellness approach. Phase One included a survey of 169 HCWs to capture their experiences, and Phase Two and Three comprised semi-structured interviews with SWR leaders, participants of SWR and analysis of 342 SWR records. Findings showed that informal conversations foster trust, active listening supports emotional well-being, and leadership engagement facilitates issue escalation. However, feedback mechanisms require improvement: 77.5% of HCWs felt able to escalate concerns but only 32.5% believed feedback was effectively addressed. These insights directly informed the development of the CALM Framework with implications for leadership training and digital wellness integration in healthcare settings. Full article

Human–Robot Collaboration (HRC) in construction projects promises enhanced productivity, safety, and quality, yet realizing these benefits requires understanding the multifaceted human and robotic factors that influence team performance. This study develops and validates a multidimensional framework that links key human abilities (operational skill, decision-making ability, and learning ability) and robot capacities (functionality and operability) to HRC team performance, with task complexity considered as contextual influence. A field survey of construction practitioners (n = 548) was analyzed using partial least squares structural equation modeling (PLS-SEM) to test direct effects and human–robot synergies. Results reveal that all five main effects are positive and significant, indicating that both human abilities and robot capacities have significant contribution. Moreover, every hypothesized two-way interaction is supported, evidencing strong interaction effects. Three-way moderation analyses further reveal that task complexity significantly strengthened the interactions of human abilities with robot functionality, whereas its interactions with robot operability were not significant. The study contributes an integrated and theory-driven model of HRC team performance that accounts for human abilities and robot capacities under varying task complexity, and validated constructs that can be used to diagnose and predict performance. The findings offer actionable guidance for project managers by recommending that they prioritize user-friendly robot operability to translate worker expertise into performance across a wide range of tasks, invest in training to strengthen operators’ skills and decision-making, and, for complex tasks, pair highly skilled workers with high-functionality robots to maximize performance gains. Full article