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Search Results (314)

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Keywords = subjective time awareness

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30 pages, 1456 KiB  
Article
Adaptive Stochastic GERT Modeling of UAV Video Transmission for Urban Monitoring Systems
by Serhii Semenov, Magdalena Krupska-Klimczak, Michał Frontczak, Jian Yu, Jiang He and Olena Chernykh
Appl. Sci. 2025, 15(17), 9277; https://doi.org/10.3390/app15179277 - 23 Aug 2025
Abstract
The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and [...] Read more.
The growing use of unmanned aerial vehicles (UAVs) for real-time video surveillance in smart city and smart region infrastructures requires reliable and delay-aware data transmission models. In urban environments, UAV communication links are subject to stochastic variability, leading to jitter, packet loss, and unstable video delivery. This paper presents a novel approach based on the Graphical Evaluation and Review Technique (GERT) for modeling the transmission of video frames from UAVs over uncertain network paths with probabilistic feedback loops and lognormally distributed delays. The proposed model enables both analytical and numerical evaluation of key Quality-of-Service (QoS) metrics, including mean transmission time and jitter, under varying levels of channel variability. Additionally, the structure of the GERT-based framework allows integration with artificial intelligence mechanisms, particularly for adaptive routing and delay prediction in urban conditions. Spectral analysis of the system’s characteristic function is also performed to identify instability zones and guide buffer design. The results demonstrate that the approach supports flexible, parameterized modeling of UAV video transmission and can be extended to intelligent, learning-based control strategies in complex smart city environments. This makes it suitable for a wide range of applications, including traffic monitoring, infrastructure inspection, and emergency response. Beyond QoS optimization, the framework explicitly accommodates security and privacy preserving operations (e.g., encryption, authentication, on-board redaction), enabling secure UAV video transmission in urban networks. Full article
(This article belongs to the Section Electrical, Electronics and Communications Engineering)
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18 pages, 2398 KiB  
Article
Real-Time Detection of Distracted Walking Using Smartphone IMU Sensors with Personalized and Emotion-Aware Modeling
by Ha-Eun Kim, Da-Hyeon Park, Chan-Ho An, Myeong-Yoon Choi, Dongil Kim and Youn-Sik Hong
Sensors 2025, 25(16), 5047; https://doi.org/10.3390/s25165047 - 14 Aug 2025
Viewed by 303
Abstract
This study introduces GaitX, a real-time pedestrian behavior recognition system that leverages only the built-in sensors of a smartphone eliminating the need for external hardware. The system is capable of detecting abnormal walking behavior, such as using a smartphone while walking, regardless of [...] Read more.
This study introduces GaitX, a real-time pedestrian behavior recognition system that leverages only the built-in sensors of a smartphone eliminating the need for external hardware. The system is capable of detecting abnormal walking behavior, such as using a smartphone while walking, regardless of whether the device is handheld or pocketed. GaitX applies multivariate time-series features derived from accelerometer data, using ensemble machine learning models like XGBoost and Random Forest for classification. Experimental validation across 21 subjects demonstrated an average classification accuracy of 92.3%, with notably high precision (97.1%) in identifying distracted walking. In addition to real-time detection, the system explores the link between gait variability and psychological traits by integrating MBTI personality profiling, revealing the potential for emotion-aware mobility analytics. Our findings offer a scalable, cost-effective solution for mobile safety applications and personalized health monitoring. Full article
(This article belongs to the Special Issue AI in Sensor-Based E-Health, Wearables and Assisted Technologies)
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23 pages, 19710 KiB  
Article
Hybrid EEG Feature Learning Method for Cross-Session Human Mental Attention State Classification
by Xu Chen, Xingtong Bao, Kailun Jitian, Ruihan Li, Li Zhu and Wanzeng Kong
Brain Sci. 2025, 15(8), 805; https://doi.org/10.3390/brainsci15080805 - 28 Jul 2025
Viewed by 475
Abstract
Background: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human–computer interaction, and brain–computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking [...] Read more.
Background: Decoding mental attention states from electroencephalogram (EEG) signals is crucial for numerous applications such as cognitive monitoring, adaptive human–computer interaction, and brain–computer interfaces (BCIs). However, conventional EEG-based approaches often focus on channel-wise processing and are limited to intra-session or subject-specific scenarios, lacking robustness in cross-session or inter-subject conditions. Methods: In this study, we propose a hybrid feature learning framework for robust classification of mental attention states, including focused, unfocused, and drowsy conditions, across both sessions and individuals. Our method integrates preprocessing, feature extraction, feature selection, and classification in a unified pipeline. We extract channel-wise spectral features using short-time Fourier transform (STFT) and further incorporate both functional and structural connectivity features to capture inter-regional interactions in the brain. A two-stage feature selection strategy, combining correlation-based filtering and random forest ranking, is adopted to enhance feature relevance and reduce dimensionality. Support vector machine (SVM) is employed for final classification due to its efficiency and generalization capability. Results: Experimental results on two cross-session and inter-subject EEG datasets demonstrate that our approach achieves classification accuracy of 86.27% and 94.01%, respectively, significantly outperforming traditional methods. Conclusions: These findings suggest that integrating connectivity-aware features with spectral analysis can enhance the generalizability of attention decoding models. The proposed framework provides a promising foundation for the development of practical EEG-based systems for continuous mental state monitoring and adaptive BCIs in real-world environments. Full article
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20 pages, 1258 KiB  
Article
The Crime of Vehicular Homicide in Italy: Trends in Alcohol and Drug Use in Fatal Road Accidents in Lazio Region from 2018 to 2024
by Francesca Vernich, Leonardo Romani, Federico Mineo, Giulio Mannocchi, Lucrezia Stefani, Margherita Pallocci, Luigi Tonino Marsella, Michele Treglia and Roberta Tittarelli
Toxics 2025, 13(7), 607; https://doi.org/10.3390/toxics13070607 - 19 Jul 2025
Viewed by 489
Abstract
In Italy, the law on road homicide (Law no. 41/2016) introduced specific provisions for drivers who cause severe injuries or death to a person due to the violation of the Highway Code. The use of alcohol or drugs while driving constitutes an aggravating [...] Read more.
In Italy, the law on road homicide (Law no. 41/2016) introduced specific provisions for drivers who cause severe injuries or death to a person due to the violation of the Highway Code. The use of alcohol or drugs while driving constitutes an aggravating circumstance of the offence and provides for a tightening of penalties. Our study aims to report on the analysis performed on blood samples collected between January 2018 and December 2024 from drivers convicted of road homicide and who tested positive for alcohol and/or drugs. The majority of the involved subjects were males belonging to the 18–30 and 41–50 age groups. Alcohol, cocaine and cannabinoids were the most detected substances and the most frequent polydrug combination was alcohol and cocaine. We also investigated other influencing factors in road traffic accidents as the day of the week and the time of the day in which fatal road traffic accident occurred, and the time elapsed between the road accident and the collection of biological samples. Our data, in line with the international scenario, strongly support that, in addition to the tightening of penalties, raising awareness plays a key role in preventing alcohol- and drug-related traffic accidents by increasing risk perception and encouraging safer driving behaviors. Full article
(This article belongs to the Special Issue Current Issues and Research Perspectives in Forensic Toxicology)
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34 pages, 3704 KiB  
Article
Uncertainty-Aware Deep Learning for Robust and Interpretable MI EEG Using Channel Dropout and LayerCAM Integration
by Óscar Wladimir Gómez-Morales, Sofia Escalante-Escobar, Diego Fabian Collazos-Huertas, Andrés Marino Álvarez-Meza and German Castellanos-Dominguez
Appl. Sci. 2025, 15(14), 8036; https://doi.org/10.3390/app15148036 - 18 Jul 2025
Viewed by 447
Abstract
Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability [...] Read more.
Motor Imagery (MI) classification plays a crucial role in enhancing the performance of brain–computer interface (BCI) systems, thereby enabling advanced neurorehabilitation and the development of intuitive brain-controlled technologies. However, MI classification using electroencephalography (EEG) is hindered by spatiotemporal variability and the limited interpretability of deep learning (DL) models. To mitigate these challenges, dropout techniques are employed as regularization strategies. Nevertheless, the removal of critical EEG channels, particularly those from the sensorimotor cortex, can result in substantial spatial information loss, especially under limited training data conditions. This issue, compounded by high EEG variability in subjects with poor performance, hinders generalization and reduces the interpretability and clinical trust in MI-based BCI systems. This study proposes a novel framework integrating channel dropout—a variant of Monte Carlo dropout (MCD)—with class activation maps (CAMs) to enhance robustness and interpretability in MI classification. This integration represents a significant step forward by offering, for the first time, a dedicated solution to concurrently mitigate spatiotemporal uncertainty and provide fine-grained neurophysiologically relevant interpretability in motor imagery classification, particularly demonstrating refined spatial attention in challenging low-performing subjects. We evaluate three DL architectures (ShallowConvNet, EEGNet, TCNet Fusion) on a 52-subject MI-EEG dataset, applying channel dropout to simulate structural variability and LayerCAM to visualize spatiotemporal patterns. Results demonstrate that among the three evaluated deep learning models for MI-EEG classification, TCNet Fusion achieved the highest peak accuracy of 74.4% using 32 EEG channels. At the same time, ShallowConvNet recorded the lowest peak at 72.7%, indicating TCNet Fusion’s robustness in moderate-density montages. Incorporating MCD notably improved model consistency and classification accuracy, especially in low-performing subjects where baseline accuracies were below 70%; EEGNet and TCNet Fusion showed accuracy improvements of up to 10% compared to their non-MCD versions. Furthermore, LayerCAM visualizations enhanced with MCD transformed diffuse spatial activation patterns into more focused and interpretable topographies, aligning more closely with known motor-related brain regions and thereby boosting both interpretability and classification reliability across varying subject performance levels. Our approach offers a unified solution for uncertainty-aware, and interpretable MI classification. Full article
(This article belongs to the Special Issue EEG Horizons: Exploring Neural Dynamics and Neurocognitive Processes)
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32 pages, 8958 KiB  
Article
A Monte Carlo Simulation Framework for Evaluating the Robustness and Applicability of Settlement Prediction Models in High-Speed Railway Soft Foundations
by Zhenyu Liu, Liyang Wang, Taifeng Li, Huiqin Guo, Feng Chen, Youming Zhao, Qianli Zhang and Tengfei Wang
Symmetry 2025, 17(7), 1113; https://doi.org/10.3390/sym17071113 - 10 Jul 2025
Viewed by 289
Abstract
Accurate settlement prediction for high-speed railway (HSR) soft foundations remains challenging due to the irregular and dynamic nature of real-world monitoring data, often represented as non-equidistant and non-stationary time series (NENSTS). Existing empirical models lack clear applicability criteria under such conditions, resulting in [...] Read more.
Accurate settlement prediction for high-speed railway (HSR) soft foundations remains challenging due to the irregular and dynamic nature of real-world monitoring data, often represented as non-equidistant and non-stationary time series (NENSTS). Existing empirical models lack clear applicability criteria under such conditions, resulting in subjective model selection. This study introduces a Monte Carlo-based evaluation framework that integrates data-driven simulation with geotechnical principles, embedding the concept of symmetry across both modeling and assessment stages. Equivalent permeability coefficients (EPCs) are used to normalize soil consolidation behavior, enabling the generation of a large, statistically robust dataset. Four empirical settlement prediction models—Hyperbolic, Exponential, Asaoka, and Hoshino—are systematically analyzed for sensitivity to temporal features and resistance to stochastic noise. A symmetry-aware comprehensive evaluation index (CEI), constructed via a robust entropy weight method (REWM), balances multiple performance metrics to ensure objective comparison. Results reveal that while settlement behavior evolves asymmetrically with respect to EPCs over time, a symmetrical structure emerges in model suitability across distinct EPC intervals: the Asaoka method performs best under low-permeability conditions (EPC ≤ 0.03 m/d), Hoshino excels in intermediate ranges (0.03 < EPC ≤ 0.7 m/d), and the Exponential model dominates in highly permeable soils (EPC > 0.7 m/d). This framework not only quantifies model robustness under complex data conditions but also formalizes the notion of symmetrical applicability, offering a structured path toward intelligent, adaptive settlement prediction in HSR subgrade engineering. Full article
(This article belongs to the Section Engineering and Materials)
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17 pages, 267 KiB  
Article
Albinism in Tanzania: A Ritual Politics of Silence, Fear, and Subservience
by Francis Semwaza
Religions 2025, 16(7), 846; https://doi.org/10.3390/rel16070846 - 26 Jun 2025
Viewed by 492
Abstract
Violence against people with albinism (PWAs) in Tanzania continues nearly two decades after mass media reported the first incidents in the mid-2000s. The violence is linked to organ trafficking for use in “magical rituals” that allegedly help politicians and businesspeople to succeed in [...] Read more.
Violence against people with albinism (PWAs) in Tanzania continues nearly two decades after mass media reported the first incidents in the mid-2000s. The violence is linked to organ trafficking for use in “magical rituals” that allegedly help politicians and businesspeople to succeed in their endeavors. Over time, as societal awareness grows, the attacks become increasingly clandestine and complex. PWAs themselves, the public, and gray literature frequently relate the violence to the increased political and economic activity and participation following Tanzania’s adoption of political and economic liberalization. However, scholarly research is either silent or mentions the occult practices only in passing. This paper, therefore, explores Tanzania’s institutional arrangements both driving the violence and crippling the efforts at promoting the rights and welfare of PWAs in the wake of increasing political and economic participation in the country. It discusses the ways in which violence against PWAs has evolved alongside political and economic dynamics from the time such incidents came to public attention until the present. I argue that the current approach, whereby advocacy about the rights of PWAs relies on appeasing the state, appears to perpetuate the very beliefs and practices driving the violence. The exploration makes use of first-hand experience through my participation in numerous formal and informal interactions with PWAs, internal and external meetings within the Tanzania Albinism Society (TAS), interviews, and gray literature on the subject. Full article
20 pages, 1120 KiB  
Article
Safe and Sound: Governance for Planning Public Space in a Security-by-Design Paradigm
by Martina Massari, Danila Longo and Sara Branchini
Urban Sci. 2025, 9(7), 241; https://doi.org/10.3390/urbansci9070241 - 26 Jun 2025
Viewed by 600
Abstract
Security in public spaces has long been the subject of debate and extensive experimentation. With the exponential growth in risks (both expected and unexpected) that public spaces are exposed to, further exacerbated by the pandemic crisis, urban security management increasingly conflicts with the [...] Read more.
Security in public spaces has long been the subject of debate and extensive experimentation. With the exponential growth in risks (both expected and unexpected) that public spaces are exposed to, further exacerbated by the pandemic crisis, urban security management increasingly conflicts with the right to social interaction in space. To avoid creating overly controlled spaces that are unsuitable for generating sociality and spontaneous interactions, and which often reproduce discriminatory social dynamics, while at the same time ensuring users’ awareness of being in a safe environment, it is necessary for all three dimensions of public space security—policy, design, and governance—to converge. This study focuses on governance, exploring how security management shapes public life and how it can align with planning that supports vibrant, spontaneous interaction. Using a multi-method qualitative approach, including a critical literature review, EU policy analysis, and empirical research from the Horizon Europe SAFE CITIES project, the study introduces two tools: the Security and Vulnerability Assessment (SVA) framework and the Atlas for Safe Public Spaces Design. These were tested in pilot sites, including the Gorizia-Nova Gorica cross-border square. Results support a governance model integrating “security by design,” which aligns with Foucault’s view of governance as adaptable to uncertainty and flow. This mixed-method approach allowed for a comprehensive examination of the governance dynamics shaping urban security, ensuring that the study’s conclusions are grounded in theoretical insights and practical implementation, though necessarily limited in generalizability. By framing security as a process of negotiated governance rather than a set of technical constraints, the study offers a conceptual contribution to urban security discourse and practical guidance for planning secure, inclusive public spaces. Full article
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16 pages, 477 KiB  
Article
Pubertal Timing and Health-Related Quality of Life—A Cross-Sectional Study of Polish Adolescents
by Zbigniew Izdebski, Alicja Kozakiewicz, Katarzyna Porwit, Michalina Aleksandra Gryglewska and Joanna Mazur
Pediatr. Rep. 2025, 17(3), 69; https://doi.org/10.3390/pediatric17030069 - 18 Jun 2025
Viewed by 566
Abstract
Background/Objectives: In research on the relationship between pubertal timing and adolescent health, more attention is typically given to early rather than late maturation, as well as the associated risk of engaging in health-compromising behaviors. The aim of this study was to assess changes [...] Read more.
Background/Objectives: In research on the relationship between pubertal timing and adolescent health, more attention is typically given to early rather than late maturation, as well as the associated risk of engaging in health-compromising behaviors. The aim of this study was to assess changes in HRQL (health-related quality of life) depending on subjectively perceived pubertal timing, measured in five categories. Methods: A cross-sectional online survey was conducted in spring 2024 in a western region of Poland (N = 9411; mean age 15.15 ± 1.56 years). Mean KIDSCREEN-27 index scores were compared according to self-reported pubertal timing, and five relevant general linear models were estimated, adjusting analyses for respondents’ age, sex, and the remaining four HRQL scores. Results: In the study group, 49.0% of students assessed their pubertal timing as typical, 28.5% as earlier, and 22.5% as later compared to peers of the same sex. For all five KIDSCREEN-27 dimensions, adolescents who matured at a pace perceived as typical achieved the highest quality-of-life index scores. Significantly earlier or significantly later pubertal timing was associated with a notable decrease in these indices. Some significant interactions were identified between sex or age and pubertal timing as predictors of HRQL. The strongest association with pubertal timing was observed for the Psychological Well-being dimension, where differences unfavorable to older age groups were additionally linked to delayed pubertal timing. Conclusions: Greater awareness of the relationship between perceived pubertal timing and adolescents’ well-being is warranted among preventive care physicians, parents, and school psychologists and educators. Full article
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28 pages, 5423 KiB  
Article
Design Strategies for Mobile Click-and-Load Waiting Scenarios
by Yang Yin, Yingpin Chen, Chenan Wang, Yuching Chiang, Pinhao Wang, Haoran Wei, Haibo Lei, Chunlei Chai and Hao Fan
Appl. Sci. 2025, 15(12), 6717; https://doi.org/10.3390/app15126717 - 16 Jun 2025
Viewed by 583
Abstract
The optimization of design strategies in loading and waiting scenarios is of great significance for enhancing user experience. This study focuses on the click-to-load waiting scenario in mobile device interfaces and systematically analyzes the user experience performance of three design strategies—the interface type, [...] Read more.
The optimization of design strategies in loading and waiting scenarios is of great significance for enhancing user experience. This study focuses on the click-to-load waiting scenario in mobile device interfaces and systematically analyzes the user experience performance of three design strategies—the interface type, loading indicator, and layout—across different page transition types (including the tab page, content page, and half-screen overlay). Based on questionnaire responses and experimental data (N = 90) collected from participants aged 20–29, we assessed subjective user perceptions across five validated metrics: time perception, loading speed, satisfaction, emotional valence, and arousal level. The results revealed significant differences among strategies in terms of loading speed perception, time awareness, and emotional responses. Notably, progressive loading strategies proved particularly effective in enhancing user satisfaction and alleviating temporal cognitive load. This study summarizes the characteristics of strategy applicability and proposes general optimization recommendations, offering both theoretical insights and practical guidance for designing loading feedback in mobile device interfaces. Full article
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30 pages, 648 KiB  
Systematic Review
Positive Psychology Interventions in Early-Stage Cognitive Decline Related to Dementia: A Systematic Review of Cognitive and Brain Functioning Outcomes of Mindfulness Interventions
by Dimitra Vasileiou, Despina Moraitou, Konstantinos Diamantaras, Vasileios Papaliagkas, Christos Pezirkianidis and Magda Tsolaki
Brain Sci. 2025, 15(6), 580; https://doi.org/10.3390/brainsci15060580 - 28 May 2025
Viewed by 1223
Abstract
Background: Dementia is a global condition affecting over 55 million people. Since there is no treatment, non-pharmacological interventions aim to delay its progression in a safe and cost-effective way. The extant literature suggests that Positive Psychology Interventions (PPIs) can probably be effective [...] Read more.
Background: Dementia is a global condition affecting over 55 million people. Since there is no treatment, non-pharmacological interventions aim to delay its progression in a safe and cost-effective way. The extant literature suggests that Positive Psychology Interventions (PPIs) can probably be effective for this purpose. The systematic review aims to assess the effectiveness of PPIs as non-pharmacological interventions for mild cognitive decline related to dementia by evaluating their effectiveness in cognitive functions and brain functioning in people with Subjective Cognitive Decline (SCD), Mild Cognitive Impairment (MCI), and mild Alzheimer’s disease dementia (AD). Methods: A comprehensive search conducted in the databases Scopus, PubMed, ScienceDirect and PsychINFO (December 2024–March 2025) published between 2015 and 2025 to identify records that met inclusion criteria: studies included patients with SCD, MCI and mild AD dementia, implemented PPIs, Randomized controlled trials (RCTs) and pre–post intervention studies with measurable outcomes, assess at least one of the following: cognitive functions and brain functioning. Results: The systematic review included 12 studies (N = 669 participants) that can answer the research question. Only mindfulness interventions were identified. Findings suggest that different types of mindfulness interventions, such as the Mindfulness Awareness Program (MAP) and Mindfulness Training (MT), may be efficient for improving specific cognitive functions (e.g., working memory and attention) and influencing biological pathways related to cognitive decline. However, long-term efficacy has not been demonstrated, and results are mixed and unclear. Conclusions: Μindfulness interventions seem promising for enhancing cognition and brain functioning in older adults with cognitive decline, although the data is limited. However, limitations such as the heterogeneity of the studies and the diversity of the interventions make it necessary for more systematic and organized research to be conducted on the implementation of such interventions. At the same time, it is proposed to examine the effectiveness of other constructs of positive psychology, such as character strengths (CS). Full article
(This article belongs to the Section Neuropsychology)
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19 pages, 1840 KiB  
Article
Facial Analysis for Plastic Surgery in the Era of Artificial Intelligence: A Comparative Evaluation of Multimodal Large Language Models
by Syed Ali Haider, Srinivasagam Prabha, Cesar A. Gomez-Cabello, Sahar Borna, Ariana Genovese, Maissa Trabilsy, Adekunle Elegbede, Jenny Fei Yang, Andrea Galvao, Cui Tao and Antonio Jorge Forte
J. Clin. Med. 2025, 14(10), 3484; https://doi.org/10.3390/jcm14103484 - 16 May 2025
Cited by 1 | Viewed by 1064
Abstract
Background/Objectives: Facial analysis is critical for preoperative planning in facial plastic surgery, but traditional methods can be time consuming and subjective. This study investigated the potential of Artificial Intelligence (AI) for objective and efficient facial analysis in plastic surgery, with a specific focus [...] Read more.
Background/Objectives: Facial analysis is critical for preoperative planning in facial plastic surgery, but traditional methods can be time consuming and subjective. This study investigated the potential of Artificial Intelligence (AI) for objective and efficient facial analysis in plastic surgery, with a specific focus on Multimodal Large Language Models (MLLMs). We evaluated their ability to analyze facial skin quality, volume, symmetry, and adherence to aesthetic standards such as neoclassical facial canons and the golden ratio. Methods: We evaluated four MLLMs—ChatGPT-4o, ChatGPT-4, Gemini 1.5 Pro, and Claude 3.5 Sonnet—using two evaluation forms and 15 diverse facial images generated by a Generative Adversarial Network (GAN). The general analysis form evaluated qualitative skin features (texture, type, thickness, wrinkling, photoaging, and overall symmetry). The facial ratios form assessed quantitative structural proportions, including division into equal fifths, adherence to the rule of thirds, and compatibility with the golden ratio. MLLM assessments were compared with evaluations from a plastic surgeon and manual measurements of facial ratios. Results: The MLLMs showed promise in analyzing qualitative features, but they struggled with precise quantitative measurements of facial ratios. Mean accuracy for general analysis were ChatGPT-4o (0.61 ± 0.49), Gemini 1.5 Pro (0.60 ± 0.49), ChatGPT-4 (0.57 ± 0.50), and Claude 3.5 Sonnet (0.52 ± 0.50). In facial ratio assessments, scores were lower, with Gemini 1.5 Pro achieving the highest mean accuracy (0.39 ± 0.49). Inter-rater reliability, based on Cohen’s Kappa values, ranged from poor to high for qualitative assessments (κ > 0.7 for some questions) but was generally poor (near or below zero) for quantitative assessments. Conclusions: Current general purpose MLLMs are not yet ready to replace manual clinical assessments but may assist in general facial feature analysis. These findings are based on testing models not specifically trained for facial analysis and serve to raise awareness among clinicians regarding the current capabilities and inherent limitations of readily available MLLMs in this specialized domain. This limitation may stem from challenges with spatial reasoning and fine-grained detail extraction, which are inherent limitations of current MLLMs. Future research should focus on enhancing the numerical accuracy and reliability of MLLMs for broader application in plastic surgery, potentially through improved training methods and integration with other AI technologies such as specialized computer vision algorithms for precise landmark detection and measurement. Full article
(This article belongs to the Special Issue Innovation in Hand Surgery)
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16 pages, 749 KiB  
Article
The Use of 360-Degree Video to Reduce Anxiety and Increase Confidence in Mental Health Nursing Students: A Mixed Methods Preliminary Study
by Caroline Laker, Pamela Knight-Davidson and Andrew McVicar
Nurs. Rep. 2025, 15(5), 157; https://doi.org/10.3390/nursrep15050157 - 30 Apr 2025
Viewed by 483
Abstract
Background: Stress affects 45% of NHS staff. More research is needed to explore how to develop resilient mental health nurses who face multiple workplace stressors, including interacting with distressed clients. Higher Education Institutions are uniquely placed to introduce coping skills that help reduce [...] Read more.
Background: Stress affects 45% of NHS staff. More research is needed to explore how to develop resilient mental health nurses who face multiple workplace stressors, including interacting with distressed clients. Higher Education Institutions are uniquely placed to introduce coping skills that help reduce anxiety and increase confidence for pre-registration nurses entering placements for the first time. Methods: A convenience sample of first year mental health student nurses (whole cohort), recruited before their first clinical placement, were invited to participate. Following a mixed methods design, we developed a 360-degree virtual reality (VR) video, depicting a distressed service user across three scenes, filmed in a real-life decommissioned in-patient ward. Participants followed the service user through the scenes, as though in real life. We used the video alongside a cognitive reappraisal/solution-focused/VERA worksheet and supportive clinical supervision technique to explore students’ experiences of VR as an educative tool and to help build emotional coping skills. Results: N = 21 mental health student nurses were recruited to the study. Behavioural responses to the distressed patient scenario were varied. Students that had prior experience in health work were more likely to feel detached from the distress of the service user. Although for some students VR provided a meaningful learning experience in developing emotional awareness, other students felt more like a ‘fly on the wall’ than an active participant. Empathetic and compassionate responses were strongest in those who perceived a strong immersive effect. Overall, the supportive supervision appeared to decrease the anxiety of the small sample involved, but confidence was not affected. Conclusion: The use of 360-degree VR technology as an educative, classroom-based tool to moderate anxiety and build confidence in pre-placement mental health nursing students was partially supported by this study. The effectiveness of such technology appeared to be dependent on the degree to which ‘immersion’ and a sense of presence were experienced by students. Our cognitive reappraisal intervention proved useful in reducing anxiety caused by ‘the patient in distress scenario’ but only for students who achieved a deep immersive effect. Students with prior exposure to distressing events (in their personal lives and in clinical settings) might have developed other coping mechanisms (e.g., detachment). These findings support the idea that ‘presence’ is a subjective VR experience and can vary among users. Full article
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9 pages, 3797 KiB  
Brief Report
Role of Molecular Diagnosis in Imported Cutaneous Leishmaniasis and Its Public Health Significance in India
by Rohit Sharma, Deepti Singh, S. Muthukumaravel, S. L. Hoti, Laxmisha Chandrashekar and Manju Rahi
Pathogens 2025, 14(5), 436; https://doi.org/10.3390/pathogens14050436 - 30 Apr 2025
Viewed by 803
Abstract
Cutaneous leishmaniasis (CL) is a significant public health concern that affects many countries. This disease is caused by the protozoan parasite Leishmania spp. and is transmitted through the sandflies from the genus Phlebotomus and Lutzomyia. The clinical manifestations of CL can vary, [...] Read more.
Cutaneous leishmaniasis (CL) is a significant public health concern that affects many countries. This disease is caused by the protozoan parasite Leishmania spp. and is transmitted through the sandflies from the genus Phlebotomus and Lutzomyia. The clinical manifestations of CL can vary, often leading to challenges in accurate diagnosis and treatment. In 2022, a 51-year-old male patient presented to a tertiary care hospital in Puducherry, India, with progressively worsening facial lesions and granulomatous plaques. The patient had recently returned from Saudi Arabia, where he likely contracted the infection. Before he visited the tertiary care hospital in Puducherry, the patient had been misdiagnosed and treated for conditions such as Erysipelas and Acute Cutaneous Lupus Erythematosus (ACLE), highlighting the diagnostic challenges associated with CL. Skin scrapings from the patient were subjected to real-time PCR, confirming Leishmania spp.’s presence. Cytological examinations revealed the amastigote-like structures within macrophages, thereby establishing the identity of the parasite. For precise species-level identification, PCR-Restriction Fragment Length Polymorphism (PCR-RFLP) and Sanger sequencing of the Internal Transcribed Spacer-1 (ITS-1) region were performed. Molecular techniques confirmed the infection as being caused by Leishmania tropica. Following the accurate diagnosis, the patient was successfully treated with Liposomal Amphotericin B, a treatment known for its efficacy against Leishmania infections. This case underscores the critical importance of considering cutaneous leishmaniasis in the differential diagnosis of travelers returning from endemic areas who present with dermatological manifestations. The initial misdiagnosis and inappropriate treatment highlight the need for heightened clinical awareness and the utilization of advanced diagnostic tools for accurate identification. Effective and timely treatment, as demonstrated in this case, is essential for the management and control of the disease. This report emphasizes the necessity of vigilance among healthcare providers to recognize and appropriately address imported cases of cutaneous leishmaniasis. Full article
(This article belongs to the Special Issue Zoonotic Vector-Borne Infectious Diseases: The One Health Perspective)
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15 pages, 1473 KiB  
Article
HECM-Plus: Hyper-Entropy Enhanced Cloud Models for Uncertainty-Aware Design Evaluation in Multi-Expert Decision Systems
by Jiaozi Pu and Zongxin Liu
Entropy 2025, 27(5), 475; https://doi.org/10.3390/e27050475 - 27 Apr 2025
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Abstract
Uncertainty quantification in cloud models requires simultaneous characterization of fuzziness (via Entropy, En) and randomness (via Hyper-entropy, He), yet existing similarity measures often neglect the stochastic dispersion governed by He. To address this gap, we propose HECM-Plus, an algorithm integrating [...] Read more.
Uncertainty quantification in cloud models requires simultaneous characterization of fuzziness (via Entropy, En) and randomness (via Hyper-entropy, He), yet existing similarity measures often neglect the stochastic dispersion governed by He. To address this gap, we propose HECM-Plus, an algorithm integrating Expectation (Ex), En, and He to holistically model geometric and probabilistic uncertainties in cloud models. By deriving He-adjusted standard deviations through reverse cloud transformations, HECM-Plus reformulates the Hellinger distance to resolve conflicts in multi-expert evaluations where subjective ambiguity and stochastic randomness coexist. Experimental validation demonstrates three key advances: (1) Fuzziness–Randomness discrimination: HECM-Plus achieves balanced conceptual differentiation (δC1/C4 = 1.76, δC2 = 1.66, δC3 = 1.58) with linear complexity outperforming PDCM and HCCM by 10.3% and 17.2% in differentiation scores while resolving He-induced biases in HECM/ECM (C1C4 similarity: 0.94 vs. 0.99) critical for stochastic dispersion modeling; (2) Robustness in time-series classification: It reduces the mean error by 6.8% (0.190 vs. 0.204, *p* < 0.05) with lower standard deviation (0.035 vs. 0.047) on UCI datasets, validating noise immunity; (3) Design evaluation application: By reclassifying controversial cases (e.g., reclassified from a “good” design (80.3/100 average) to “moderate” via cloud model using HECM-Plus), it resolves multi-expert disagreements in scoring systems. The main contribution of this work is the proposal of HECM-Plus, which resolves the limitation of HECM in neglecting He, thereby further enhancing the precision of normal cloud similarity measurements. The algorithm provides a practical tool for uncertainty-aware decision-making in multi-expert systems, particularly in multi-criteria design evaluation under conflicting standards. Future work will extend to dynamic expert weight adaptation and higher-order cloud interactions. Full article
(This article belongs to the Special Issue Entropy Method for Decision Making with Uncertainty)
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