Search Results (14,128)

Background/Objectives: Greece has been a major host country for refugee populations, operating under conditions of limited resources and strained healthcare services. Refugee women residing in accommodation facilities face barriers to accessing primary prevention and health promotion services, including limited health literacy and cultural and linguistic challenges. This study aimed to assess the level of primary prevention and health promotion among refugee women living in accommodation facilities in Greece and to identify their health needs and barriers to accessing healthcare services. Methods: A quantitative cross-sectional study was conducted among 150 adult refugee women residing in the Malakasa accommodation facility in Greece. Participants voluntarily agreed to take part in the study. Data were collected between December 2024 and March 2025 using a structured questionnaire assessing sociodemographic characteristics, primary prevention, health promotion, and barriers to healthcare access. Descriptive statistical analysis was performed. The study was approved by the relevant ethics committee, and informed consent was obtained from all participants. Results: The study included 150 refugee women, primarily young adults with low educational attainment. Familiarity with primary prevention was reported as moderate or lower by the majority of participants, with only 24% indicating high or excellent familiarity, while familiarity with health promotion was even lower (8%). Participation in preventive practices varied, with 42.7% reporting frequent health check-ups; however, uptake of key preventive behaviors remained limited, including vaccination (30%) and adoption of a healthy diet (32.7%). During their stay in Greece, 97.3% participated in regular health check-ups, 32.7% adopted a healthy diet and 30% were vaccinated. Cardiovascular and gynecological conditions were the most frequently reported health problems (76.7% and 73.3%, respectively). The most prominent barrier to healthcare access was long distance from health facilities (97.3%), followed by lack of information or health education (24.7%). Conclusions: The study identified low levels of preventive health knowledge and limited uptake of key preventive practices among refugee women, alongside persistent barriers to healthcare access, underscoring the need for targeted and culturally sensitive health promotion interventions. Full article

Exercise stress testing remains one of the most widely used and cost-effective diagnostic tools in clinical cardiology. Beyond the traditional evaluation of induced ischemia, it provides valuable information on functional capacity, blood pressure response and arrhythmic behavior during exercise. In particular, the test plays a crucial role in assessing and interpreting exercise-induced arrhythmias, including tachyarrhythmias, such as premature ventricular beats (PVBs) and bradyarrhythmias, as well as corroborating the suspicion of some ion channel diseases. The usefulness of exercise testing is also highlighted in patients with devices, where it can help evaluate their function and exercise adaptation, as well as in specific conduction disorders, such as Wolff–Parkinson–White syndrome. This practical guide summarizes the key aspects of performing and interpreting the exercise stress test, focusing on hemodynamic and arrhythmic findings and their clinical implications, and includes several illustrative clinical cases. Full article

Background/Objectives: Dementia represents a complex syndrome in which biological, psychological, social, and cultural dimensions intersect. While its clinical features are well documented, less is known about how lived experiences, caregiving contexts, and cultural beliefs shape the trajectory of illness. This study explored clinical, behavioral, and socio-cultural dimensions related to the quality of life of people living with dementia from an anthropological perspective, focusing on the interaction between comorbidities, cognition, lifestyle, and caregiving environments as reported by their informal caregivers. Methods: We conducted a single-center, observational cross-sectional study including 73 family caregivers of patients with clinically diagnosed dementia who accessed care at the Neurology–Psychiatry Department of the C.F.2 Clinical Hospital (Bucharest, Romania) between November 2023 and April 2024. Caregivers provided socio-demographic, behavioral, lifestyle, and cultural information using a newly developed anthropological questionnaire. Descriptive and exploratory inferential analyses were performed to examine relationships between cognitive performance, comorbidities, lifestyle factors, and socio-cultural variables. Results: People with dementia had a mean age of 79.2 ± 7.5 years (range 66–95) and were predominantly female (71.2%). Multimorbidity was common, averaging 2.22 ± 1.03 chronic conditions, mainly neurological (84.9%) and cardiovascular (68.5%). The mean BMI was 26.1 ± 3.8 kg/m². Cognitive impairment was substantial (MMSE mean 11.47 ± 7), with descriptively lower scores among older individuals and those with lower education or income, although inferential tests were underpowered. Appetite and sleep disturbances were frequent and tended to co-occur with lower activity levels. Disclosure of diagnosis occurred in 74% of cases; reactions varied widely, ranging from acceptance to denial, confusion, anxiety, and sadness. Family responses likewise reflected a heterogeneous and often ambivalent adjustment process. Cultural beliefs and spirituality played a salient role in shaping explanatory models and coping strategies, with many caregivers attributing importance to religious practices and, to a lesser extent, alternative treatments. Conclusions: In this Romanian cohort, dementia was shaped not only by age-related multimorbidity and cognitive decline but also by caregiving practices, socioeconomic constraints and culturally grounded interpretations of illness. These findings highlight the relevance of integrative approaches to dementia care that consider medical, behavioral, and socio-cultural dimensions and that incorporate caregiver perspectives to improve the quality of life of both patients and families. Full article

The persistent financial exclusion of micro-enterprises is fundamentally driven by information asymmetry, as traditional credit scoring models rely heavily on audited financial statements that small entities rarely possess. To address this “thin-file” challenge, this study proposes a shift from asset-based valuation to behavioral algorithmic profiling, hypothesizing that high-frequency operational risk patterns can serve as informative proxies for solvency compared to static liquidity ratios. Using an Extreme Gradient Boosting (XGBoost) architecture on a synthetic dataset of 5000 micro-enterprise transaction logs, we develop a predictive framework that extracts latent features such as supply chain latency, inventory turnover consistency, and digital footprint intensity. The proposed model achieves an Area Under the Curve (AUC) of 0.94, outperforming traditional linear baselines and achieving performance levels above those commonly reported in micro-enterprise solvency prediction studies. The results indicate that operational stability emerges as a strong indicator of repayment capacity within the evaluated context, outperforming static liquidity-based measures. These findings suggest that computational intelligence approaches grounded in high-frequency operational data may contribute to mitigating information asymmetries in micro-enterprise credit assessment, particularly in environments characterized by limited financial disclosure, although further empirical validation is required prior to large-scale deployment. Full article

Global urbanization redirects attention toward the sensory quality of the built environment as a decisive factor in public health and psychological resilience. In automated retail, façades function as sensory interfaces to mitigate the psychological alienation and sensory deprivation inherent in automated nodes. This preliminary study proposes and empirically validates a multimodal evaluation framework for assessing WELL-informed, AI-generated biophilic façade designs in automated retail contexts. Grounded in Environment-Based Design (EBD) theory, the framework systematically integrates health-oriented design logic with generative AI–based façade synthesis and multimodal human-response evaluation. To evaluate the effectiveness of the proposed methodology, this study pursued three specific objectives: (1) to utilize a curated series of architectural façade variations with calibrated biophilic complexity derived from an environment-based AI generative framework, as experimental stimuli, (2) to quantify subconscious responses represented by gaze patterns and behavioral indicators elicited by these configurations, and (3) to analyze the correlation and potential divergence between implicit physiological responses and explicit conscious aesthetic appraisals. The multimodal experiment involving 30 participants integrated eye-tracking, facial expression analysis, and Semantic Differential (SD) scales. Area of Interest (AOI)-based visual attention analysis indicated that biophilic complexity, particularly the integration of organic patterns and natural materials, significantly enhanced subconscious visual interest and sustained engagement within specific design zones. The findings unveiled a complexity–aesthetic paradox where subconscious physiological and behavioral indicators exhibited peak engagement with high-complexity patterns while conscious aesthetic preference favored material-driven structural clarity. Statistical verification via repeated measures correlation analysis revealed a lack of significant linear association between instinctive physiological engagement and explicit aesthetic appraisal, highlighting a notable divergence between implicit and explicit responses. In conclusion, grounded in an EBD–driven evaluation framework, this research establishes a systematic evaluation methodology for health-conscious design, recommending a material-first strategy with pattern as an enhancement to align subconscious fascination with psychological comfort. Full article

The iconic Dali’s painting The Temptation of St. Anthony dated 1946, housed in the Royal Museums of Fine Arts of Belgium since 1965, displays worrying surface conditions in specific areas, notably the figure of St. Anthony. The problematic paint layers similarly exhibit uneven transparency and a rugged surface irrespective of their color, raising questions about whether these features reflect deliberate artistic intent or material degradation. To evidence potential degradation mechanisms and to identify the associated painting materials, Dali’s picture has been investigated through a large panel of imaging and analytical techniques, including digital microscopy, MA-XRF, Raman and FT-IR spectroscopies, XRD and Py-GC–MS. The obtained results were subsequently assessed against the material and technical information collected from Dali’s 50 Secrets of Magic Craftsmanship, as well as against archival photographs. By combining historical and multi-analytical approaches, it was possible to diagnose the altered condition of the artwork, but above all to determine when and how the deterioration patterns took place. Visible changes of appearance occurred prior to 1965 and were most probably already initiated during the curing and drying processes of the paint films. The present study tends to demonstrate the key roles of mobile resin acids from amber, reactive zinc oxide pigment suspected of containing crystal defects, uncured lead-white-rich underlayers, and chlorine environmental contamination, regarding the early and peculiar degradation behavior observed on Dali’s masterpiece. Full article

This study examines the impact of social media use on public behavioral intentions regarding waste sorting in China, integrating the Cognition–Affect–Conation model with the Social Amplification of Risk Framework. The proposed framework explores how social media exposure and gratification influence waste sorting intentions through anticipated emotions and environmental risk perception. Regression analysis confirms that information gratification primarily activates positive emotions, while information exposure has a stronger effect on negative emotions. Both affective pathways significantly predict waste sorting intentions, with comparable predictive strengths. Mediation analysis further reveals that information gratification and information exposure indirectly influence behavioral intention through dual emotional pathways and environmental risk perception. Qualitative interviews highlight two structural deficiencies: fragmented knowledge dissemination, which weakens environmental norm internalization, and uneven community integration, which limits behavioral translation. These findings underscore the need for diversified communication strategies and community-based policy interventions to enhance public participation in waste sorting. Full article

Retailers must reevaluate store positioning when implementing omnichannel strategies. This study examines retailers’ strategic preferences toward in-store services, including Buy-Online-Pickup-in-Store (BOPS), Buy-Online-Return-in-Store (BORS), and their combined offering. A stylized model incorporating capacity constraints and strategic consumers’ purchase behavior is developed to analyze omnichannel impacts on brick-and-mortar operations from an inventory perspective. Firstly, profit-maximizing retailers benefit from reducing online channel inventory when handling product returns. Under high online return rates or stringent capacity constraints, retailers prefer maintaining physical-only channels to mitigate returns and capture cross-selling opportunities. Secondly, offering BOPS services remains a strategic advantage during periods of moderate capacity utilization. When the online consumer market expands, spillover effects increase, return rates decrease, and capacity constraints ease, it becomes feasible to consider jointly providing BORS services. Finally, BORS may migrate offline shoppers online, making it unsuitable for high-return-rate products. For omnichannel retailers, this study offers valuable insights into implementing omnichannel strategies under capacity constraints, empowering practitioners to make more informed decisions and optimize operational tactics. Full article

Real-time acoustic signal enhancement in non-stationary noise remains challenging, especially for sensing systems that must be causal, low latency, and interpretable. This paper proposes a unified Bayesian–Kalman estimator (UBKE) that analytically fuses a spectral Bayesian MMSE estimator with a temporal Kalman state-space tracker via a variance optimal fusion weight α(k). The UBKE is derived in closed form from a shared probabilistic model, yielding an estimator that adaptively balances spectral and temporal information as noise statistics evolve. We establish theoretical properties including bias–variance behavior, stability conditions, and analytical expressions for output SNR, SNR improvement, and log-spectral distortion. Under typical short-time processing (32 ms frame, 50% overlap), the proposed method operates causally with an algorithmic delay of 16 ms and real-time factors below 0.5 on a modern CPU. Analytical and empirical results show that UBKE achieves up to +9.8 dB ΔSNR and approximately +17% PESQ improvement over a baseline MMSE estimator in highly non-stationary noise, while also reducing log-spectral distortion. Experiments on standard speech corpora with real-world noise confirm that the empirical trends closely follow the analytical predictions, with small mismatch between theoretical and measured gains. The UBKE thus offers an interpretable, low-latency, and quantitatively validated framework for real-time acoustic sensing and speech enhancement, and can serve as a foundation for future hybrid model-driven and learning-augmented systems. Full article

The transition toward carbon-neutral energy systems has revived interest in nuclear technologies, particularly small and micro modular reactors (SMRs and MMRs) as flexible, safe and efficient alternatives to conventional large-scale power plans. In the Czech Republic, Centrum výzkumu Řez (CVŘ) is developing Energy Well (EW), a molten salt-cooled micro modular reactor concept employing FLiBe (Fluoride Lithium Beryllium) as primary and secondary coolant and a supercritical CO₂ (sCO₂) tertiary loop. A dedicated experimental facility was built to reproduce EW operating conditions and provide critical data on thermohydraulic behavior, fuel properties and heat-transfer mechanisms. This paper presents the development and assessment of a TRACE (TRAC/RELAP Advanced Computational Engine) model of the experimental facility, including specific methodologies for the main heater and the heat exchanger. Model accuracy was assessed through comparison with experimental commissioning data. The simulations demonstrated overall model consistency, especially regarding the heat exchanger and the main heater general performances, while some discrepancies were observed inside the main heater graphitic core. Other discrepancies were observed along the loop, mainly resulting from modeling simplifications and lack of information regarding certain experimental loop phenomena. In particular, the pressure calculation showed large inconsistencies mainly connected to the complexity of pressure measurements in molten salt circuits and the lack of specific head loss correlations. This study also helped identify broader issues in both the code (persistent error in generating CO₂ property tables and instabilities resulting from FLiBe interactions with non-condensable gases) and the experimental loop (defect in the heat exchanger filling and uncertainties on sensors location), also contributing to resolving sensor-related inconsistencies in the facility. Results confirm TRACE as a reliable tool for modeling molten salt systems, regarding the temperature distribution and the heat transfer. However, depending on the specific experimental case, this paper introduces specific limitations, such as some inconsistencies in the pressure drops distribution, in order to support the future development of TRACE code. Beyond technical advances, this work provides unique experimental data and fosters international collaboration in advancing SMR and molten salt reactor technologies. Full article

Engineering education is increasingly shaped by two converging developments: accelerating sustainability transitions and rapid advances in artificial intelligence (AI). However, in many application-oriented undergraduate programs, sustainability learning remains fragmented, methodologically limited, and weakly connected to authentic engineering decision-making. To address this gap, this study proposes AI-SEE (Artificial Intelligence-Integrated Sustainable Engineering Education), a pedagogical framework that integrates AI across the curriculum as both a cognitive scaffold and a resource for system-level analysis. Emphasizing human–AI collaboration, AI-SEE is designed to be feasible and scalable within application-oriented higher education contexts. The framework comprises four interrelated pillars: intelligence-driven, green-empowered, responsibility-leading, and practice-integrated. Drawing on an empirical case from transportation-related programs at Nantong University, the study employs a qualitative comparative design and conducts semi-structured interviews with 144 undergraduates at the end of their eighth semester (control group n = 70; pilot group n = 74). Interview data were analyzed using thematic analysis informed by constructivist grounded theory and the Gioia coding approach. The findings suggest that participation in AI-SEE is associated with differentiated patterns of sustainability consciousness. At the knowledge level, students reported more systematic and interdisciplinary understandings that extended beyond environmentally reductionist perspectives to include life-cycle thinking, social equity, and long-term considerations. At the attitudinal level, students described enhanced ethical reflexivity and evolving professional self-concepts, shifting from a focus on technical execution toward broader value-oriented roles. At the behavioral level, students reported more extensive knowledge-to-action translation across personal, academic, and career-related domains. Overall, AI-SEE provides a transferable pedagogical pathway for integrating AI into engineering education to support the development of sustainability consciousness in higher education. Full article

Caudal type homeobox 2 (CDX2) is an intestine-specific transcription factor that serves as a diagnostic marker of enteric differentiation and may also reflect tumor behavior and therapeutic susceptibility. Emerging evidence suggests that CDX2 expression may predict sensitivity to fluoropyrimidine-based therapy independent of tissue of origin. We report a retrospective case series of three patients with metastatic adenocarcinoma (aggressive variant prostate, minor salivary gland, and intestinal-type sinonasal tract) exhibiting strong CDX2 nuclear expression. In all cases, tumors were refractory to or lacked established standard systemic therapy. Treatment decisions were informed by the CDX2-positive enteric phenotype, leading to the initiation of fluoropyrimidine-based regimens. Response was assessed using PET-CT and MRI. All three patients achieved marked metabolic and clinical responses, including a sustained complete metabolic response in the prostate cancer case and durable disease control in the salivary gland and sinonasal tumors. These findings highlight CDX2 as a potential biomarker requiring validation, which may identify tumors intrinsically susceptible to fluoropyrimidines regardless of anatomical origin. CDX2 immunohistochemistry is widely available and inexpensive, and may complement genomic profiling in rare malignancies or in settings where standard treatment algorithms are limited. This report is hypothesis-generating and not intended to estimate response rates or treatment efficacy. Full article

Within the framework of evidence theory, maximum entropy is regarded as a measure of total uncertainty that satisfies a comprehensive set of mathematical properties and behavioral requirements. However, its practical applicability is severely questioned due to the high computational complexity of its calculation, which involves the manipulation of the power set of the frame of discernment. In the literature, attempts have been made to reduce this complexity by restricting the computation to singleton elements, leading to a formulation based on reachable probability intervals. Although this approach relies on a less specific representation of evidential information, it has been shown to provide an equivalent maximum entropy value under certain conditions. In this paper, we present an experimental comparative study of two algorithms for calculating maximum entropy in evidence theory: the classical algorithm, which operates directly on belief functions, and an alternative algorithm based on reachable probability intervals. Through numerical experiments, we demonstrate that the differences between these approaches are less pronounced than previously suggested in the literature. Depending on the type of information representations to which it is applied, the original algorithm based on belief functions can be more efficient than the one using the reachable probability interval approach. This is an interesting result, and a reason for choosing one algorithm over the other depending on the situation. Full article

Natural and human-made hazards are increasing due to global warming and human activities. Occupant evacuation in complex buildings remains challenging due to unfamiliar building layouts, communication failures, and unpredictable occupant behavior. Therefore, this study aims to explore how integrating digital technologies enhances emergency preparedness, supports occupant decision-making during evacuation, and improves occupants’ situational awareness. We conducted a PRISMA-guided systematic literature review across Scopus, IEEE Xplore, and ProQuest Discover, analyzing 31 high-quality journal articles relevant to the research. The focus was on integrating digital technologies to support occupant situational awareness and evacuation outcomes. This review explores the integration of Internet of Things (IoT), Building Information Modeling (BIM), Virtual Reality (VR) /Augmented Reality (AR), Artificial Intelligence (AI), and Digital Twins (DTs) for emergency preparedness, supporting real-world applications. This review highlights three research questions: (1) Evaluate how current digital technologies affect occupant emergency preparedness in buildings; (2) Identify the challenges that limit the effectiveness of digital technologies across key emergency preparedness stages; (3) Understand how digital technologies can support occupant emergency preparedness. The review compiles evidence and presents a conceptual framework to support the integration of digital technologies into occupant-focused emergency preparedness, providing practical guidance for the future direction of risk management research. Full article

Rural shrinkage is increasingly expressed through changing residential mobility, housing under occupancy, and intermittent dwelling use, rather than a simple linear process of permanent outmigration and abandonment. Yet empirical measurement of occupancy dynamics and the service-mediated mechanisms shaping residence stability remains limited. This study proposes an evidence-driven and explainable assessment framework that links energy-informed occupancy dynamics with settlement building area and mechanism identification, using Fuyuan City as a case study. Daily electricity consumption time series from 2021 to 2024 are used to infer occupancy dynamics and detect behavioral signatures of long term residence, seasonal residence, return visits, and vacancy. Shape-based temporal clustering identifies six occupancy trajectories, revealing pronounced heterogeneity in mobility rhythms within the rural settlement system. Settlement vacancy-related built-environment changes are characterized from 2 m remote sensing imagery, using a trained YOLO-based building detection workflow, producing settlement-level total building area as a physical indicator of the development intensity. Integrating these behavioral measures with multi-source spatial factors, the mechanism model shows that development, governance, and environmental conditions influence residence stability primarily through service provision. Among service domains, education services exhibit the strongest direct association with long-term residence stability, while transport and daily life services show modest positive effects and healthcare presents a smaller positive effect. Development conditions positively promote all service types, whereas governance and environmental context display differentiated and, in some pathways, opposing effects across services. Overall, the framework enables interpretable monitoring of rural shrinkage dynamics by jointly quantifying occupancy trajectories, settlement morphology, and service-mediated pathways shaping residential outcomes. Full article