Search Results (20,515)

This study explores digital inequality as a socio-spatial phenomenon within the context of smart inclusion, focusing on the Regional Unit of Xanthi, Greece—a region marked by ethno-cultural diversity and pronounced urban–rural contrasts. Using a mixed-methods design, this research integrates secondary quantitative data with qualitative insights from semi-structured interviews, aiming to uncover how spatial, demographic, and cultural variables shape digital engagement. Geographic Information System (GIS) tools are employed to map disparities in internet access and ICT infrastructure, revealing significant gaps linked to geography, education, and economic status. The findings demonstrate that digital inequality is particularly acute in rural, minority, and economically marginalized communities, where limited infrastructure intersects with low digital literacy and socio-economic disadvantage. Interview data further illuminate how residents navigate exclusion, emphasizing generational divides, perceptions of technology, and place-based constraints. By bridging spatial analysis with lived experience, this study advances the conceptualization of digitally inclusive smart regions. It offers policy-relevant insights into how territorial inequality undermines the goals of smart development and proposes context-sensitive interventions to promote equitable digital participation. The case of Xanthi underscores the importance of integrating spatial justice into smart city and regional planning agendas. Full article

As digital technologies and food environments evolve, ensuring children’s food safety has become a pressing public health priority. This study examines how the policy discourse on children’s dietary safety in Korea has shifted over time by applying Latent Dirichlet Allocation (LDA) topic modeling to news articles from 2010 to 2024. Using a large-scale news database (BigKinds), the analysis identifies seven key themes that have emerged across five phases of the national Comprehensive Plans for Safety Management of Children’s Dietary Life. These include experiential education, data-driven policy approaches, safety-focused meal management, healthy dietary environments, nutritional support for children’s growth, customized safety education, and private-sector initiatives. A significant increase in digital keywords—such as “big data” and “artificial intelligence”—highlights a growing emphasis on data-oriented policy tools. By capturing the evolving language and priorities in food safety policy, this study provides new insights into the digital transformation of public health governance and offers practical implications for adaptive and technology-informed policy design. Full article

Background/Objectives: Elevated serum uric acid levels are associated with the occurrence, development, and adverse events of coronary heart disease (CHD) and CHD risk factors. However, the extent of any pathogenic effect of the serum uric acid on CHD and whether CHD risk factors play a confounding or mediating role are still unclear. Methods: The potential causal associations of serum uric acid with CHD were evaluated via cross-trait linkage disequilibrium score regression analysis and Mendelian randomization. The pleiotropy of genetic tools was analyzed via a Bayesian colocalization approach. Moreover, we utilized two-step MR to identify risk factors mediating the relationship between uric acid and CHD. Results: Mendelian randomization results derived from two genetic instrument selection strategies support that serum uric acid levels have a significant causal relationship with coronary artery disease, stable angina pectoris, and myocardial infarction. This causal relationship was partially mediated by diastolic blood pressure, mean arterial pressure, and serum triglycerides. Transcriptomic analysis revealed that serum uric acid may directly contribute to the development of atherosclerosis by inducing transcriptomic changes in macrophages. Conclusions: Our findings highlight that the control of serum urate concentration in the long-term management of CHD patients may be necessary. Well-designed clinical trials and foundational research are presently required to furnish conclusive proof regarding the specific clinical scenarios in which adequate reduction in urate concentrations can confer cardiovascular advantages. Full article

Due to their nutritional value and sustainability, edible insect-based foods are gaining popularity in Europe. Their use is regulated by EU legislation, which defines authorised species and sets labelling requirements. Molecular tools are being developed to authenticate such products. In this study, yellow mealworm (Tenebrio molitor) larvae authorised for human consumption were fed wheat flour-based diets containing varying proportions of house cricket (Acheta domesticus) flour for 21 days. This was followed by a 48 h starvation period to assess the persistence of insect DNA in the digestive tract. Two novel, species-specific, single-copy markers were designed: ampd gene for the Acheta domesticus and MyD88 gene for the Tenebrio molitor. These were applied using qPCR and ddPCR. Both methods successfully detected cricket DNA in the guts of starved larvae. Linear regression analysis revealed a strong, statistically significant correlation between the proportion of Acheta domesticus flour in the diet and the normalised relative quantity of DNA. ddPCR proved to be more sensitive than qPCR, particularly in the detection of low DNA levels. These results suggest that the presence of DNA from undeclared insect species in edible insects may be indicative of their diet rather than contamination or adulteration. This highlights the importance of contextual interpretation in food authenticity testing. Full article

Background: In a global panorama marked by a progressive rise in obesity, metabolic syndrome, and chronic non-communicable disease prevalence, nutrition education (NE) might play a pivotal role in restoring adoption and strengthening adherence to dietary patterns that protect human health. Therefore, the primary purpose of this work is to review the existing scientific literature studying NE programs aimed at schoolchildren in the decade 2014–2024 and evaluate the effectiveness of intervention tools. Methods: During the first phase of this research, a qualitative analysis was conducted to track similarity in intervention tools and strategies used in nutrition education programs. In the second phase, a quantitative analysis was carried out, extracting common parameters among studies and assessing their potential influence in improving adherence to the Mediterranean diet (MD). Results: A high degree of heterogeneity was observed in educational program designs and intervention tools, which were usually not properly described and justified. All studies that measured adherence to the MD registered an improvement after the intervention, in some cases even higher than 10%. However, this study found no relationship between common parameters (i.e., number of formal tools, number of non-formal tools, lesson duration, and program length) used in NE and the improvement in students’ adherence to MD. Conclusions: This research has contributed to outlining a general framework of NE and to promoting a systematic approach in this research field. Full article

Designing optimal heritage tourism routes in secondary cities involves complex trade-offs between cultural richness, travel time, carbon emissions, spatial coherence, and group satisfaction. This study addresses the Personalized Group Trip Design Problem (PGTDP) under real-world constraints by proposing DRL–IMVO–GAN—a hybrid multi-objective optimization framework that integrates Deep Reinforcement Learning (DRL) for policy-guided initialization, an Improved Multiverse Optimizer (IMVO) for global search, and a Generative Adversarial Network (GAN) for local refinement and solution diversity. The model operates within a digital twin of Warin Chamrap’s old town, leveraging 92 POIs, congestion heatmaps, and behaviorally clustered tourist profiles. The proposed method was benchmarked against seven state-of-the-art techniques, including PSO + DRL, Genetic Algorithm with Multi-Neighborhood Search (Genetic + MNS), Dual-ACO, ALNS-ASP, and others. Results demonstrate that DRL–IMVO–GAN consistently dominates across key metrics. Under equal-objective weighting, it attained the highest heritage score (74.2), shortest travel time (21.3 min), and top satisfaction score (17.5 out of 18), along with the highest hypervolume (0.85) and Pareto Coverage Ratio (0.95). Beyond performance, the framework exhibits strong generalization in zero- and few-shot scenarios, adapting to unseen POIs, modified constraints, and new user profiles without retraining. These findings underscore the method’s robustness, behavioral coherence, and interpretability—positioning it as a scalable, intelligent decision-support tool for sustainable and user-centered cultural tourism planning in secondary cities. Full article

Penicillium astrolabium is a primary pathogenic fungus that causes grape blue mold during postharvest, leading to substantial losses in the grape industry. Nevertheless, hypovirulence-associated mycoviruses can attenuate the virulence of postharvest grape-rot pathogens, thereby offering a promising biocontrol tool. Characterizing the mycovirus repertoire of P. astrolabium is imperative for grape protection, yet remains largely unexplored. Here, we screened six strains harboring viruses in 13 P. astrolabium isolates from rotted grapes. Using high-throughput sequencing, four novel dsRNA viruses and two +ssRNA viruses were identified from the six P. astrolabium strains. The dsRNA viruses belonged to two families—Chrysoviridae and Partitiviridae—and were designated to Penicillium astrolabium chrysovirus 1 (PaCV1), Penicillum astrolabium partitivirus 1′ (PaPV1′), Penicillum astrolabium partitivirus 2 (PaPV2), and Penicillum astrolabium partitivirus 3 (PaPV3). For the +ssRNA viruses, one was clustered into the Alphaflexiviridae family, while the other one was clustered into the Narnaviridae family. The two +ssRNA viruses were named Penicillium astrolabium alphaflexivirus 1 (PaAFV1) and Penicillium astrolabium narnavirus 1 (PaNV1), respectively. Moreover, several viral genomic contigs with non-overlapping and discontinuous sequences were identified in this study, which were probably representatives of five viruses from four families, including Discoviridae, Peribunyaviridae, Botourmiaviridae, and Picobirnaviridae. Taken together, our findings could expand the diversity of mycoviruses, advance the understanding of mycovirus evolution in P. astrolabium, and provide both potential biocontrol resources and a research system for dissecting virus–fungus–plant interactions. Full article

The rise in online communication platforms has significantly increased exposure to harmful discourse, presenting ongoing challenges for digital moderation and user well-being. This paper introduces the EsCorpiusBias corpus, designed to enhance the automated detection of sexism and racism within Spanish-language online dialogue, specifically sourced from the Mediavida forum. By means of a systematic, context-sensitive annotation protocol, approximately 1000 three-turn dialogue units per bias category are annotated, ensuring the nuanced recognition of pragmatic and conversational subtleties. Here, annotation guidelines are meticulously developed, covering explicit and implicit manifestations of sexism and racism. Annotations are performed using the Prodigy tool (v1. 16.0) resulting in moderate to substantial inter-annotator agreement (Cohen’s Kappa: 0.55 for sexism and 0.79 for racism). Models including logistic regression, SpaCy’s baseline n-gram bag-of-words model, and transformer-based BETO are trained and evaluated, demonstrating that contextualized transformer-based approaches significantly outperform baseline and general-purpose models. Notably, the single-turn BETO model achieves an ROC-AUC of 0.94 for racism detection, while the contextual BETO model reaches an ROC-AUC of 0.87 for sexism detection, highlighting BETO’s superior effectiveness in capturing nuanced bias in online dialogues. Additionally, lexical overlap analyses indicate a strong reliance on explicit lexical indicators, highlighting limitations in handling implicit biases. This research underscores the importance of contextually grounded, domain-specific fine-tuning for effective automated detection of toxicity, providing robust resources and methodologies to foster socially responsible NLP systems within Spanish-speaking online communities. Full article

Parkinson’s disease (PD), a progressive neurodegenerative disorder, imposes growing clinical and socioeconomic burdens worldwide. Despite landmark discoveries in dopamine biology and α-synuclein pathology, translating mechanistic insights into effective, personalized interventions remains elusive. Recent advances in molecular profiling, neuroimaging, and computational modeling have broadened the understanding of PD as a multifactorial systems disorder rather than a purely dopaminergic condition. However, critical gaps persist in diagnostic precision, biomarker standardization, and the translation of bench side findings into clinically meaningful therapies. This review critically examines the current landscape of PD research, identifying conceptual blind spots and methodological shortfalls across pathophysiology, clinical evaluation, trial design, and translational readiness. By synthesizing evidence from molecular neuroscience, data science, and global health, the review proposes strategic directions to recalibrate the research agenda toward precision neurology. Here I highlight the urgent need for interdisciplinary, globally inclusive, and biomarker-driven frameworks to overcome the fragmented progression of PD research. Grounded in the Accelerating Medicines Partnership-Parkinson’s Disease (AMP-PD) and the Parkinson’s Progression Markers Initiative (PPMI), this review maps shared biomarkers, open data, and patient-driven tools to faster personalized treatment. In doing so, it offers actionable insights for researchers, clinicians, and policymakers working at the intersection of biology, technology, and healthcare delivery. As the field pivots from symptomatic relief to disease modification, the road forward must be cohesive, collaborative, and rigorously translational, ensuring that laboratory discoveries systematically progress to clinical application. Full article

Introduction. Singapore had previously embraced at least two types of pre-participation questionnaires for those intending to take up or enhance their level of physical activity (PA). Concern over the usefulness of and difficulty in understanding these questions led to the design of a Singapore Physical Activity Readiness Questionnaire (SPARQ). The primary objective of this study was to review the level of difficulty in understanding the seven SPARQ questions. Secondary objectives included the rate of identifying individuals as unfit for PA and to seek public feedback on this tool. Method. A public, cross-sectional survey on the SPARQ was carried out, obtaining participants’ bio-characteristics, having them completing the SPARQ and then providing feedback on the individual questions. Results. Of the 1136 who completed the survey, 35.7% would have required referral to a medical practitioner for further evaluation before the intended PA. Significant difficulty was experienced with one question, moderate difficulty with four and only slight difficulty with the remaining two. The length of the questions and use of technical terms were matters of concern. Suggestions were provided by the participants on possible amendments to the questions. Conclusions. The very high acceptance rate of the SPARQ will need to be tempered with modifications to the questions to enhance ease of understanding and use by members of the public. Full article

To tackle the inefficiencies in 3D mine tunnel modeling and the tedious task of drawing centerlines, this study introduces a faster method for generating centerlines using CAD secondary development. Starting with the tunnel centerline, the research then dives into techniques for creating detailed 3D tunnel models. The team first broke down the steps and logic behind tunnel modeling, designing a 3D tunnel framework and its data structure—complete with key geometric components like traverse points, junctions, nodes, and centerlines. By refining older centerline drawing techniques, they built a CAD-powered tool that slashes time and effort. The study also harnessed advanced algorithms, such as surface fitting and curve lofting, to swiftly model tricky tunnel sections like curves and crossings. This method fixes common problems like warped or incomplete surfaces in linked tunnel models, delivering precise and lifelike 3D scenes for VR-based mining safety drills and simulations. Full article

There is growing interest in the use of herbicide for the silvicultural practice of tree thinning (i.e., chemical thinning or e-thinning) in New Zealand. Potential benefits of this approach include improved stability of the standing crop in high winds, and safer and lower-cost operations, particularly in steep or remote terrain. As uptake grows, tools for monitoring treatment effectiveness, particularly during the early stages of stress, will become increasingly important. This study evaluated the use of UAV-based multispectral and hyperspectral imagery to detect early herbicide-induced stress in a nine-year-old radiata pine (Pinus radiata D. Don) plantation, based on temporal changes in crown spectral signatures following treatment with metsulfuron-methyl. A staggered-treatment design was used, in which herbicide was applied to a subset of trees in six blocks over several weeks. This staggered design allowed a single UAV acquisition to capture imagery of trees at varying stages of herbicide response, with treated trees ranging from 13 to 47 days after treatment (DAT). Visual canopy assessments were carried out to validate the onset of visible symptoms. Spectral changes either preceded or coincided with the development of significant visible canopy symptoms, which started at 25 DAT. Classification models developed using narrow band hyperspectral indices (NBHI) allowed robust discrimination of treated and non-treated trees as early as 13 DAT (F1 score = 0.73), with stronger results observed at 18 DAT (F1 score = 0.78). Models that used multispectral indices were able to classify treatments with a similar accuracy from 18 DAT (F1 score = 0.78). Across both sensors, pigment-sensitive indices, particularly variants of the Photochemical Reflectance Index, consistently featured among the top predictors at all time points. These findings address a key knowledge gap by demonstrating practical, remote sensing-based solutions for monitoring and characterising herbicide-induced stress in field-grown radiata pine. The 13-to-18 DAT early detection window provides an operational baseline and a target for future research seeking to refine UAV-based detection of chemical thinning. Full article

Q Fever is a zoonosis caused by Coxiella burnetii that affects domestic and wild ruminants, leading to reproductive disorders. In humans, the disease can manifest with acute and chronic clinical manifestations. Veterinarians, as healthcare professionals in close contact with animals, serve both as the first line of defence in preventing infection at the animal–human interface and as an important sentinel group for the rapid detection of outbreaks. The aim of this study was to assess the knowledge, perception, and attitude of veterinarians in Southern Spain regarding Q Fever. To this end, an online survey was designed, validated, and conducted among veterinarians in the province of Malaga, with a final participation of 97 individuals, predominantly from the private sector (clinic, livestock, agri-food, etc.). The data obtained reflected a general lack of knowledge about the disease, particularly concerning its epidemiology and infection prevention. Regarding perception and attitude, a significant percentage of respondents stated they did not use protective equipment when handling susceptible animals and only sought information about the disease in response to outbreak declarations. The study emphasised the significance of promoting training in zoonotic diseases during and after graduation, the relevance of official channels in occupational risk prevention, and the utility of epidemiological surveys as a tool to identify and address potential gaps in knowledge related to this disease. Full article

Maternal and child health during pregnancy is an important issue in global public health, and the classification accuracy of fetal cardiotocography (CTG), as a key tool for monitoring fetal health during pregnancy, is directly related to the effectiveness of early diagnosis and intervention. Due to the serious category imbalance problem of CTG data, traditional models find it challenging to take into account a small number of categories of samples, increasing the risk of leakage and misdiagnosis. To solve this problem, this paper proposes a two-step innovation: firstly, we design a method of adaptive adjustment of misclassification loss function weights (MAAL), which dynamically identifies and increases the focus on misclassified samples based on misclassification rates. Secondly, a primary and secondary correction network model (MAC-NET) is constructed to carry out secondary correction for the misclassified samples of the primary model. Experimental results show that the method proposed in this paper achieves 99.39% accuracy on the UCI publicly available fetal health dataset, and also obtains excellent performance on other domain imbalance datasets. This demonstrates that the model is not only effective in alleviating the problem of category imbalance, but also has very high clinical utility. Full article

Computational Fluid Dynamics (CFD) is regarded as an important tool for analyzing the flow field, thermal environment, and air quality around the built environment. However, for built environment applications, the high computational cost of CFD hinders large-scale scenario simulation and efficient design optimization. In the field of built environment research, surrogate modeling has become a key technology to connect the needs of high-fidelity CFD simulation and rapid prediction, whereas the low-dimensional nature of traditional surrogate models is unable to match the physical complexity and prediction needs of built flow fields. Therefore, combining machine learning (ML) with CFD to predict flow fields in built environments offers a promising way to increase simulation speed while maintaining reasonable accuracy. This review briefly reviews traditional surrogate models and focuses on ML-based surrogate models, especially the specific application of neural network architectures in rapidly predicting flow fields in the built environment. The review indicates that ML accelerates the three core aspects of CFD, namely mesh preprocessing, numerical solving, and post-processing visualization, in order to achieve efficient coupled CFD simulation. Although ML surrogate models still face challenges such as data availability, multi-physics field coupling, and generalization capability, the emergence of physical information-driven data enhancement techniques effectively alleviates the above problems. Meanwhile, the integration of traditional methods with ML can further enhance the comprehensive performance of surrogate models. Notably, the online ministry of trained ML models using transfer learning strategies deserves further research. These advances will provide an important basis for advancing efficient and accurate operational solutions in sustainable building design and operation. Full article