Search Results (1,572)

Background/Objectives: Although antimicrobial resistance (AMR) is recognized as a critical global health threat across human, animal, and environmental domains, evidence from AMR economic evaluations remains limited. This study systematically reviewed available studies, emphasizing existing evidence and reported limitations in AMR-related economic evaluations. Methods: A comprehensive review of peer-reviewed empirical studies was conducted, including publications up to July 2023 without temporal restrictions, but limited to English-language articles. Literature searches were undertaken in PubMed and Cochrane using a search strategy centered on the terms “economic evaluations” and “antimicrobial resistance.” Screening and data extraction were performed by two reviewers independently, with disagreements resolved through consensus or consultation with a third reviewer. Findings were synthesized narratively. Results: Of the 3682 records screened, 93 studies were included. Evidence gaps were identified across income and geographic regions, particularly in low- and middle-income countries (LMICs) and the African, Southeast Asian, and Eastern Mediterranean regions. Studies were comparatively more numerous in high-income countries (HICs) and the European and Americas regions. Substantial gaps also existed in one health approach and community-based evaluations. Nine major study limitations were identified, with many interlinked. The most frequent issues included limited generalizability primarily due to inadequate sampling approaches (n = 16), and single-center studies (n = 11), alongside errors in cost estimation (n = 4), and lack of consideration for essential features or information (n = 3). Conclusions: The review highlights persistent evidence gaps and recurring methodological shortcomings in AMR economic evaluations. Addressing these limitations, particularly in LMICs, will strengthen the evidence base and better inform policy implementation to combat AMR effectively. Full article

Hybrid education is a model that combines different settings within the learning process. In this paper, four dimensions related to different features of the learning process are considered, namely, space, time, language, and frameset. The first one relates to its location, the second one relates to when it takes place, the third one relates to how it is imparted, and the fourth one relates to the way in which it is conducted. The goal is to modify learning features in each session of a course to increase student engagement and improve academic performance. Additionally, this layout may also help students prepare for potential disruptive events in the future, which might have an impact on the way class sessions are run. The results obtained confirmed a statistically significant improvement in academic performance with respect to the previous course, which was taught in a traditional manner, as well as a high level of engagement. However, the actual sample size was not sufficient to detect the effect size achieved; hence, further research should be conducted with a larger sample size. Full article

Since the early 19th century, linguists have collected enough linguistic data to draw a remarkably stable Uralic language family tree. However, the traditional Uralic language family tree has two main problems. First, it lacks a reliable chronology because linguistic data can suggest that some languages are closer or farther from each other, but that gives only a relative instead of a precise chronology of the branching events. Second, the extinct Mezhovskaya culture in the Ural region and the Minoan civilization on the island of Crete were not incorporated into the Uralic language family, although recent archaeogenetic and linguistic data indicate that their languages also belonged to the Uralic language family. Some recent studies took an essentially purely archaeogenetic approach to the study of the evolution of the Uralic language family. These purely archaeogenetic studies propose linguistically perplexing solutions. This is the first study of the development of the Uralic language family that fully integrates the archaeogenetic, archaeological and linguistic data and proposes a new chronology of the Uralic language family that avoids the above inconsistencies. The new chronology relies on the best current estimates of the formation of the mitochondrial DNA haplogroups that are found among present Uralic language speakers and in samples from various archaeological sites that are associated with Uralic speakers. The new chronology places the various branching events of the Uralic language family tree much earlier than usual, including the split between Proto-Finno-Permic and Proto-Ugric, which is shown to have taken place in the Mesolithic period. The new proposal makes the Bronze Age Minoan language better fit chronologically as well as linguistically into the Uralic language family. Full article

Smart cities offer new opportunities for urban governance and sustainable development. However, at the current stage, the construction and development of smart cities generally exhibit a technology-driven tendency, neglecting real resident demand, which contradicts the “human-centric” principle. Traditional top-down methods of demand collection struggle to capture the dynamics and heterogeneity of public demand. At the same time, government service platforms, as one dimension of smart city construction, have accumulated massive amounts of user-generated data, providing new solutions for this challenge. This paper aims to construct a big data-driven analytical framework for dynamically identifying and accurately forecasting core resident demand. The study uses Xi’an City, Shaanxi Province, China, as a case study, utilising user messages from People.cn spanning 2011 to 2023. These messages cover various domains, including urban construction, healthcare, education, and transportation, as the data source. The People.cn message board is China’s most significant nationwide online political platform. Its institutionalised feedback mechanism ensures data content focuses on highly representative specific grievances, rather than the broad emotional expressions on social media. The study employs user messages from People.cn from 2011 to 2023 as its data source, encompassing urban construction, healthcare, education, and transportation. First, a large language model (LLM) was used to preprocess and clean the raw data. Subsequently, the BERTopic model was applied to identify ten core demand themes and construct their monthly time series, thereby overcoming the limitations of traditional methods in short-text semantic recognition. Finally, by integrating variational mode decomposition (VMD) with support vector machines (SVMs), a hybrid demand forecasting model was established to mitigate the risk of overfitting in deep learning when forecasting small-sample time series. The empirical results show that the proposed LLM-BERTopic-VMD-SVM framework exhibits excellent performance, with the goodness-of-fit (R2) on various demand themes ranging from 0.93 to 0.96. This study proposes an effective analytical framework for identifying and forecasting resident demand. It provides a decision-support tool for city managers to achieve proactive and fine-grained governance, thereby offering a viable empirical pathway to promote the transformation of smart cities from technology-centric to human-centric. Full article

This cross-sectional study examined whether physical activity (PA) in nature and overall PA mediate the relationship between nature exposure and quality of life (QoL) across four domains: physical, psychological, social, and environmental, while controlling for perceived financial security. A cross-sectional online survey was conducted, involving 924 adults aged 18 to 79 years (m = 40.0, SD = 12.4); 73.6% were women. Nature exposure, PA in nature, overall PA, and financial security were assessed using nationally language-validated self-report scales and questionnaires. QoL was measured using the WHOQOL-BREF, covering four domains. Mediation models were tested using the regression-based PROCESS macro with 5000 bootstrapped samples. Nature exposure was positively associated with both types of PA and all QoL domains, while financial security was positively linked to PA in nature. PA in nature significantly mediated the relationship between nature exposure and psychological QoL, but not the other domains. In contrast, overall PA was a significant mediator across all QoL domains. In all models, nature exposure and financial security remained significant direct predictors of QoL. Bootstrapped confidence intervals confirmed the significance of indirect effects through overall PA for physical, psychological, social, and environmental QoL. While nature exposure was independently associated with better QoL, this relationship was partly explained by PA. These findings highlight the broader role of PA in linking nature exposure to QoL and underscore the importance of supporting active lifestyles in nature to enhance QoL. To achieve a higher QoL, policies that increase access to and opportunities for nature-based physical activity should be implemented. Full article

Background and objectives: Evaluating Large Language Models (LLMs) presents two interrelated challenges: the general problem of assessing model performance across diverse tasks and the specific problem of using LLMs themselves as evaluators in pedagogical and educational contexts. Existing approaches often rely on single metrics or opaque preference-based methods, which fail to capture critical dimensions such as explanation quality, robustness, and argumentative diversity—attributes essential in instructional settings. This paper introduces PEARL, a novel framework conceived, operationalized, and evaluated in the present work using LLM-based scorers, designed to provide interpretable, reproducible, and pedagogically meaningful assessments across multiple performance dimensions. Methods: PEARL integrates three specialized rubrics—Technical, Argumentative, And Explanation-focused—covering aspects such as factual accuracy, clarity, completeness, originality, dialecticality, and explanatory usefulness. The framework defines seven complementary metrics: Rubric Win Count (RWC), Global Win Rate (GWR), Rubric Mean Advantage (RMA), Consistency Spread (CS), Win Confidence Score (WCS), Explanation Quality Index (EQI), and Dialectical Presence Rate (DPR). We evaluated PEARL by evaluating eight open-weight instruction-tuned LLMs across 51 prompts, with outputs scored independently by GPT-4 and LLaMA 3:instruct. This constitutes LLM-based evaluation, and observed alignment with the GPT-4 proxy is mixed across metrics. Results: Preference-based metrics (RMA, RWC, and GWR) show evidence of group separation, reported with bootstrap confidence intervals and interpreted as exploratory due to small samples, while robustness-oriented (CS and WCS) and reasoning-diversity (DPR) metrics capture complementary aspects of performance not reflected in global win rate. RMA and RWC exhibit statistically significant, FDR-controlled correlations with the GPT-4 proxy, and correlation mapping highlights the complementary and partially orthogonal nature of PEARL’s evaluation dimensions. Originality: PEARL is the first LLM evaluation framework to combine multi-rubric scoring, explanation-aware metrics, robustness analysis, and multi-LLM-evaluator analysis into a single, extensible system. Its multidimensional design supports both high-level benchmarking and targeted diagnostic assessment, offering a rigorous, transparent, and versatile methodology for researchers, developers, and educators working with LLMs in high-stakes and instructional contexts. Full article

Uncertainty quantification (UQ) for open-ended language generation remains a critical yet underexplored challenge, particularly in settings where token-level log-probabilities are available during decoding. We present Token-Entropy Conformal Prediction (TECP), which treats a log-probability-based token-entropy statistic as a nonconformity score and integrates it with split conformal prediction to construct prediction sets with finite-sample coverage guarantees. We work in a white-box regime in which per-token log-probabilities are accessible during decoding. TECP estimates episodic uncertainty from the token-entropy structure of sampled generations and calibrates thresholds via conformal quantiles to ensure provable error control. Empirical evaluations across six large language models and two QA benchmarks (CoQA and TriviaQA) show that TECP consistently achieves reliable coverage and compact prediction sets, outperforming prior self-UQ methods. These results provide a principled and efficient solution for trustworthy generation in white-box, log-probability-accessible LLM settings. Full article

Aim: The aim of this study was to develop and test a surface electromyography (sEMG) assessment protocol to characterise the activity of the extrinsic laryngeal muscles (suprahyoid and infrahyoid) during phonatory tasks and vocal techniques. Methodology: The protocol of assessment was based on electromyographic assessment guidelines and on clinical voice evaluation needs and was tested in six healthy adults with no vocal disorders. Surface electromyographic activity of suprahyoid and infrahyoid muscles was acquired during different reference tasks (rest, reading, maximum contractions) and six vocal tasks, including nasal sounds, fricatives, and semi-occluded vocal tract exercises. A laryngeal accelerometer was used for detecting the beginning and end of each exercise. The average activity during each task was normalised by the signal obtained in the incomplete swallowing task for the SHM and by the sniff technique for the IHM. Results: The range of activation values varied across tasks, with higher percentages observed in plosive production and in the “spaghetti” technique, while nasal and fricative sounds tended to show lower activation values within the group. A consistent pattern of simultaneous activation of suprahyoid and infrahyoid muscles was observed during phonation. Conclusions: The protocol proved potential for clinical application in speech–language pathology as it enabled the characterisation of muscle activity in determinant muscles for vocal function. Larger samples and further validation of the time-marking system are needed. This study provides a foundation for integrating sEMG measures into functional voice assessment. Full article

Background: Patient education (PE) is an essential component of quality healthcare and chronic disease management. However, effective implementation often faces patient-, nurse-, and organization-related barriers. This is particularly relevant in multicultural healthcare settings such as Saudi Arabia, where a highly diverse nursing workforce may influence PE practices. Aim: To examine the barriers and facilitators influencing patient education practices among nurses working in multiple hospitals in Saudi Arabia. Methods: A descriptive cross-sectional study was conducted among 289 registered nurses recruited through convenience sampling from various hospitals in Saudi Arabia. Data were collected using a validated self-administered questionnaire consisting of demographic items and structured scales assessing PE barriers and facilitators. Descriptive statistics were used to analyze the data. Results: Language differences (64.3%) and cultural barriers (59.2%) were the most commonly reported patient-related obstacles. Among nurse-related barriers, staff shortages (72.4%), heavy workload (72.0%), and time constraints (59.9%) were prominent. Organizational barriers included limited educational resources (39.4%) and unsupportive environments (35.6%). Key facilitators identified by nurses included availability of policies and procedures (63.6%), provision of PE training (63.7%), and integration of PE into clinical workflow and nurse appraisals. Conclusions: Despite strong professional support for PE, multiple barriers hinder its implementation in Saudi hospitals. Addressing these challenges requires institutional strategies such as workforce reinforcement, policy integration, and resource allocation. Future efforts should also include integrating patient perspectives, developing culturally tailored education resources, and evaluating the impact of targeted interventions to strengthen PE delivery in diverse hospital settings. Full article

Integrating literacy-focused curricula in preschool settings may help support children’s literacy learning. In this study, we explored the use of literacy-focused curricula and how it was associated with preschool children’s literacy gains (i.e., print and letter knowledge, phonological awareness, language and comprehension, and emergent writing) relative to non-literacy-focused curricula. We estimated multilevel structural equation models using data from an intervention study that included a sample of 571 children nested within 98 preschool classrooms. Because early disparities in emergent literacy are associated with later reading and writing difficulties, we examined how such associations might be moderated by child risk status, receipt of emergent literacy intervention, and program settings. We found that literacy-focused curricula were not often used by teachers in preschool classrooms, but teachers’ use of such curricula was positively associated with children’s phonological awareness gains. Risk status did not moderate the association between use of literacy-focused curricula and children’s emergent writing gains. Additionally, emergent literacy intervention and program settings did not moderate the associations. However, we found that teachers’ use of literacy-focused curricula was positively associated with print and letter knowledge, phonological awareness, and language and comprehension for children identified as at risk for later reading difficulties compared to those who were not at risk. As such, our findings suggest that integrating or supplementing existing classroom instruction with literacy-focused curricula could yield meaningful benefits for children identified as at risk for later reading difficulties. 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

This study analyzes factors influencing Ukrainian refugee labor market participation in Romania, based on a survey of 399 respondents. The analysis shows that only 21.7% of refugees were employed at the time of the survey. Significant predictors of employment were gender (men had higher employment rates), marital status (unmarried individuals were more active), and Romanian language proficiency, which tripled the chances of finding a job. Education level, English language skills, age, and number of children did not significantly predict employment. The findings also highlight a strong desire among refugees for language learning and professional training, indicating untapped integration potential. This research contributes to the literature on Ukrainian refugee integration by emphasizing the importance of language support policies and gender-sensitive measures. While acknowledging limitations such as convenience sampling and selection bias, our results offer valuable insights for public policy and future research on refugee integration in Europe. Full article

Objective: This study explores the prevalence of violence and forced displacement as indicators of inequity among Bogotá’s elderly population, with a particular focus on how these factors affect their health and access to healthcare services. Methods: This is a subsidiary analysis of the SABE-Bogotá survey. The design was a probabilistic cluster sample of 2000 people aged 60 and over. The study was carried out by the Pontificia Universidad Javeriana’s Institute on Aging and cosponsored by Colciencias. The variables of interest were displacement and experiences of violence, assessed through self-reporting. A descriptive analysis of all variables was performed, calculating simple frequency distributions. Subsequently, dependency and association analyses were performed using Chi-square, T-tests, and multivariate logistic regressions, depending on each case. Results: 43.32% of the subjects were victims of some type of violence in the last year, among which offensive language was one of the most frequent. Individuals with severe depression (OR 2.10 [1.21–3.65]) and those who had been victims of displacement (OR 2.55, CI 95% [1.65–3.95]) had the highest risk of violence. The results reveal a direct correlation between these experiences and pre-existing health conditions. For instance, severe depression and a history of displacement were associated with a higher risk of experiencing violence, while the risk of displacement was higher among individuals with diabetes, severe depression, and, crucially, those who lacked access to health insurance. Conclusion: A high percentage of the elderly population in the city of Bogotá has been victims of different types of violence, including ones related to armed conflict and forced displacement, which is a particular and exclusive form of violence suffered by this group of people. These findings suggest that violence and displacement are social determinants of health that exacerbate inequities, underscoring the need for more inclusive health policies and improved access to medical care for this vulnerable population. Full article

Background: Hepatopancreatobiliary (HPB) surgery is among the most complex domains in oncologic care, where decisions entail significant risk–benefit considerations. Artificial intelligence (AI) has emerged as a promising tool for improving individualized decision-making through enhanced risk stratification, complication prediction, and survival modeling. However, its role in HPB oncologic surgery has not been comprehensively assessed. Methods: This systematic review was conducted in accordance with PRISMA guidelines and registered with PROSPERO ID: CRD420251114173. A comprehensive search across six databases was performed through 30 May 2025. Eligible studies evaluated AI applications in risk–benefit assessment in HPB cancer surgery. Inclusion criteria encompassed peer-reviewed, English-language studies involving human s ubjects. Two independent reviewers conducted study selection, data extraction, and quality appraisal. Results: Thirteen studies published between 2020 and 2024 met the inclusion criteria. Most studies employed retrospective designs with sample sizes ranging from small institutional cohorts to large national databases. AI models were developed for cancer risk prediction (n = 9), postoperative complication modeling (n = 4), and survival prediction (n = 3). Common algorithms included Random Forest, XGBoost, Decision Trees, Artificial Neural Networks, and Transformer-based models. While internal performance metrics were generally favorable, external validation was reported in only five studies, and calibration metrics were often lacking. Integration into clinical workflows was described in just two studies. No study addressed cost-effectiveness or patient perspectives. Overall risk of bias was moderate to high, primarily due to retrospective designs and incomplete reporting. Conclusions: AI demonstrates early promise in augmenting risk–benefit assessment for HPB oncologic surgery, particularly in predictive modeling. However, its clinical utility remains limited by methodological weaknesses and a lack of real-world integration. Future research should focus on prospective, multicenter validation, standardized reporting, clinical implementation, cost-effectiveness analysis, and the incorporation of patient-centered outcomes. Full article

This study investigates the determinants of Romanian workers’ migration intentions towards Italy, integrating economic, social, and psychological perspectives. Based on a sample of 358 respondents, four hypotheses were tested concerning perceived living standards, working conditions, quality of public services, and anticipated integration difficulties. Data were analysed using descriptive statistics, Spearman’s rho correlation, Mann–Whitney U, Chi-square, ANOVA, and ordinal logistic regression. The results confirm that higher perceived living standards and better working conditions in Italy significantly increase the likelihood of expressing migration intentions, while favourable evaluations of healthcare and education act as additional pull factors. Conversely, anticipated integration difficulties, particularly language barriers and cultural adaptation, reduce migration intentions, indicating that socio-psychological obstacles can counterbalance economic incentives. By combining non-parametric and multivariate analyses, the study demonstrates that migration is a multidimensional process shaped not only by structural opportunities but also by behavioural and psychological appraisals. These findings are consistent with recent research on European labour mobility and contribute to the literature by highlighting the role of subjective perceptions in shaping migration decisions. Implications for policy include the need to address both economic disparities and integration barriers to support more balanced mobility within the European space. Full article