Search Results (17,778)

Sewer systems are essential for sustainable infrastructure management, influencing environmental, social, and economic aspects. However, sewer network capacity is under significant pressure, with many systems overwhelmed by challenges such as climate change, ageing infrastructure, and increasing inflow and infiltration, particularly through groundwater infiltration (GWI). Current research in this area has primarily focused on general sewer performance, with limited attention to high-resolution, spatially explicit assessments of sewer exposure to GWI, highlighting a critical knowledge gap. This study responds to this gap by developing a high-resolution GWI assessment. This is achieved by integrating fuzzy-analytical hierarchy process (AHP) with geographic information systems (GISs) and machine learning (ML) to generate GWI probability maps across the Dawlish region, southwest United Kingdom, complemented by sensitivity analysis to identify the key drivers of sewer network vulnerability. To this end, 16 hydrological–hydrogeological thematic layers were incorporated: elevation, slope, topographic wetness index, rock, alluvium, soil, land cover, made ground, fault proximity, fault length, mass movement, river proximity, flood potential, drainage order, groundwater depth (GWD), and precipitation. A GWI probability index, ranging from 0 to 1, was developed for each 1 m × 1 m area per season. The model domain was then classified into high-, intermediate-, and low-GWI-risk zones using K-means clustering. A consistency ratio of 0.02 validated the AHP approach for pairwise comparisons, while locations of storm overflow (SO) discharges and model comparisons verified the final outputs. SOs predominantly coincided with areas of high GWI probability and high-risk zones. Comparison of AHP-weighted GIS output clustered via K-means with direct K-means clustering of AHP-weighted layers yielded a Kappa value of 0.70, with an 81.44% classification match. Sensitivity analysis identified five key factors influencing GWI scores: GWD, river proximity, flood potential, rock, and alluvium. The findings underscore that proxy-based geospatial and machine learning approaches offer an effective and scalable method for mapping sewer network exposure to GWI. By enabling high-resolution risk assessment, the proposed framework contributes a novel proxy and machine-learning-based screening tool for the management of smart cities. This supports predictive maintenance, optimised infrastructure investment, and proactive management of GWI in sewer networks, thereby reducing costs, mitigating environmental impacts, and protecting public health. In this way, the method contributes not only to improved sewer system performance but also to advancing the sustainability and resilience goals of smart cities. Full article

Background: Individuals who are victimized and exploited by the heinous crimes of human trafficking (HT) access healthcare during their exploitation, yet gaps in education on HT content exist in prelicensure nursing programs. This study explored the impact of an HT simulation on nursing students’ preparedness in the identification of victims as well as their perceptions of the impact of this educational intervention on future practices. Methods: A quasi-experimental design with a qualitative component was used. A convenience sample of 120 nursing students were recruited. The participants completed a pretest survey, viewed a preparatory education video, and participated in the simulation followed by a debriefing, a 20-min video, and posttest survey. Results: More than 3/4 of the participants reported no previous exposure to this content. A paired sample t-test showed efficacy (p < 0.001) with a Cohen’s d > 0.8, illustrating an increase in knowledge gained. The qualitative data yielded four themes: eye-opening, educational and informative, increased awareness, and preparedness. Conclusions: Nurses are well-positioned to identify, treat, and respond to victims of HT. The findings underscore the critical need to incorporate comprehensive HT content into prelicensure nursing curricula. Through integration of an HT simulation, future nurses can be better prepared to address this pervasive issue, ultimately improving victim outcomes and ensuring progress towards UN Sustainable Development Goal 5 of Gender Equality and Goal 16 of Peace, Justice, and Strong Institutions. In addition, addressing this topic in prelicensure nursing education ensures that future nurses are not only clinically competent but also morally and emotionally prepared to handle the complexities of HT in their professional roles. Full article

Background/Objectives: Home mechanical ventilation (HMV) is a therapeutic approach that enables individuals with chronic respiratory failure to be cared for in home settings, thereby improving their quality of life. However, it also imposes a substantial burden on informal caregivers. This scoping review aimed to explore and synthesize current research on the psychosocial well-being of informal caregivers of adults receiving HMV and to identify existing knowledge gaps. Methods: Following PRISMA-ScR guidelines, six electronic databases were systematically searched without language or date restrictions. Eligible studies were original, peer-reviewed publications focusing on informal caregivers of adults receiving HMV. Relevant data were extracted and analyzed. Results: A total of 38 studies met the inclusion criteria. The majority of caregivers were women, most commonly spouses or partners. Caregivers frequently experienced high levels of burden, anxiety, depression, fatigue, and reduced quality of life. Common challenges included social isolation, sleep disturbances, and financial difficulties. Caregivers employed a range of coping strategies, both adaptive and maladaptive. Many reported unmet needs, particularly in the areas of psychological, informational, and professional support. Conclusions: Providing care for individuals receiving HMV is complex and demanding. While some caregivers find meaning and fulfillment in their role, many experience significant physical, emotional, and psychological challenges. These findings highlight the urgent need for comprehensive, individualized interventions aimed at reducing caregiver burden, enhancing quality of life, and ensuring better integration of caregivers into the broader care continuum. Full article

Antimicrobial resistance (AMR) presents a global health challenge, necessitating comprehensive surveillance and intervention strategies. Wastewater-based epidemiology (WBE) is a promising tool that can be utilized for AMR monitoring by offering population-level insights into microbial dynamics and resistance gene dissemination in communities. This review (n = 29 papers) examines the current landscape of utilizing WBE for AMR surveillance with a focus on methodologies, findings, and gaps in understanding. Reported methods from the reviewed literature included culture-based, PCR-based, whole genome sequencing, mass spectrometry, bioinformatics/metagenomics, and antimicrobial susceptibility testing to identify and measure antibiotic-resistant bacteria and antimicrobial resistance genes (ARGs) in wastewater, as well as liquid chromatography-tandem mass spectrometry to measure antibiotic residues. Results indicate Escherichia coli, Enterococcus spp., and Pseudomonas spp. are the most prevalent antibiotic-resistant bacterial species with hospital effluent demonstrating higher abundances of clinically relevant resistance genes including bla, bcr, qnrS, mcr, sul1, erm, and tet genes compared to measurements from local treatment plants. The most reported antibiotics in influent wastewater across studies analyzed include azithromycin, ciprofloxacin, clindamycin, and clarithromycin. The influence of seasonal variation on the ARG profiles of communities differed amongst studies indicating additional factors hold significance when examining the conference of AMR within communities. Despite these findings, knowledge gaps remain, including longitudinal studies in multiple and diverse geographical regions and understanding co-resistance mechanisms in relation to the complexities of population contributors to AMR. This review underscores the urgent need for collaborative and interdisciplinary efforts to safeguard public health and preserve antimicrobial efficacy. Further investigation on the use of WBE to understand these unique population-level drivers of AMR is advised in a proposed framework to inform best practice approaches moving forward. Full article

The traditional use of medicinal plants around the world has a long history, predominantly in low- and middle-income countries. Previous ethnobotanical research pertaining to urban environments demonstrated that the legacy of the use of medicinal plant species persists worldwide; however, information about the main city in the occidental part of Mexico is scarce regarding this traditional knowledge and its variation during the last few decades. A database was created from interviews with local people who had inhabited the oldest neighborhoods of Guadalajara for at least 30 years and by using different electronic databases. In addition, the correct taxonomic identification of species was supported via corroboration through local and other digital herbariums. Furthermore, a Principal Coordinate Analysis (PCoA) was performed on the database information to search for relationships among the medicinal plant species used. An inventory of 137 medicinal plants was created, where the plant species most commonly used in the five old neighborhoods of Guadalajara City were muicle (Justicia spicigera Schltdl.), pirul (Schinus molle L.), manzanilla (Matricaria chamomilla L.), valeriana (Valeriana sp.), calabaza (Cucurbita pepo L.), cola de caballo (Equisetum arvense L.), tepezcohuite (Mimosa tenuiflora Poir.), salvia (Salvia officinalis L.), canela (Cinnamomum verum J. Presl.), tila estrella (Tilia americana var. mexicana (Schltdl.) Hardin), cedrón (Aloysia citrodora Paláu), uva (Vitis vinifera L.), jengibre (Zingiber officinale Roscoe) and gobernadora (Larrea tridentata (DC.) Coville). Illnesses of the cardiovascular, digestive, urinary, respiratory, nervous, muscular and reproductive systems, as well as culture-bound syndromes, were mostly treated with these plant species. Moreover, J. spicigera, M. chamomilla and L. tridentata were used for eight medical purposes, followed by Z. officinale with five medicinal practices. In contrast, only two medicinal uses were recorded for C. pepo, M. tenuiflora and S. officinale. The PCoA explained 65.88% of the variation accumulated at the first three ordination axes and formed four groups of species, which were related to their geographical origin. Eight of the fourteen species that are commonly used as medicinal plants are from America, and the rest come from Europe and Asia. This study confirms the persistence of traditional knowledge related to medicinal plants, and the diseases empirically addressed among the inhabitants of Guadalajara City are common in other parts of the world and in different regions of Mexico. These findings are supported by electronic databases that comprise multiple studies related to the phytochemical compounds and medical validation regarding their biological activity, supporting the empirical use and efficacy of these medicinal plants. Full article

Social networks generate vast amounts of data that can reveal patterns of human behaviour, social attachment, and mental states. This paper explores advanced machine learning techniques to detect and model such patterns, focusing on community structures, influential users, and information diffusion pathways. To address the scale, noise, and heterogeneity of social data, we leverage recent advances in graph theory, natural language processing, and anomaly detection. Our framework combines clustering for community detection, sentiment analysis for emotional state inference, and centrality metrics for influence estimation, while integrating multimodal data—including textual and visual content—for richer behavioural insights. Experimental results demonstrate that the proposed approach effectively extracts actionable knowledge, supporting mental well-being and strengthening digital social ties. Furthermore, we emphasise the role of privacy-preserving methods, such as federated learning, to ensure ethical analysis. These findings lay the groundwork for responsible and effective applications of machine learning in social network analysis. Full article

The rapid emergence of generative artificial intelligence (GenAI) has sparked growing interest across educational disciplines, reshaping how knowledge is produced, represented, and assessed. While recent research has increasingly explored the implications of text-based tools such as ChatGPT in education, far less attention has been paid to image-based GenAI tools—despite their particular relevance to fields grounded in visual communication and creative exploration, such as architecture and design. These disciplines raise distinct pedagogical and ethical questions, given their reliance on iteration, authorship, and visual representation as core elements of learning and practice. This exploratory study investigates how architecture and interior architecture students perceive the use of AI-generated images, focusing on ethical responsibility, educational relevance, and career implications. To ensure participants had sufficient exposure to visual GenAI tools, we conducted a series of workshops before surveying 42 students familiar with image generation processes. Findings indicate strong enthusiasm for GenAI image tools, which students viewed as supportive during early-stage design processes and beneficial to their creativity and potential future professional competitiveness. Participants regarded AI use as ethically acceptable when accompanied by transparent acknowledgment. However, acceptance declined in later design stages, where originality and critical judgment were perceived as more central. While limited in scope, this exploratory study foregrounds student voices to offer preliminary insights into evolving conversations about AI in creative education and to inform future reflection on developing ethically and pedagogically responsive curricula across the design disciplines. Full article

In Chinese language semantic analysis, the processed languages often reveal similar representations in models for different application scenarios, resulting in similar language models. With that characteristic, evolutionary multitask optimization (EMTO) algorithms, which realize the synergy optimization for multiple tasks, have the potential to optimize such models for different scenarios. EMTO is an emerging topic in evolutionary computation (EC) for solving multitask optimization problems (MTOPs) with the help of knowledge transfer (KT). However, the current EMTO algorithms often involve two limitations. First, many KT methods usually ignore the distribution information of populations to evaluate task similarity. Second, many EMTO algorithms often directly transfer individuals from the source task to target task, which cannot guarantee the quality of the transferred knowledge. To overcome these challenges, an auxiliary–population–based multitask optimization (APMTO) is proposed in this paper, which will be further applied to Chinese semantic understanding in our future works. We first propose an adaptive similarity estimation (ASE) strategy to exploit the distribution information among tasks and evaluate the similarity of tasks, so as to adaptively adjust the KT frequency. Then, an auxiliary-population-based KT (APKT) strategy is designed, which uses auxiliary population to map the global best solution of the source task to target task, offering more useful transferred information for the target task. APMTO is tested on multitask test suite CEC2022 and compared with several state–of–the–art EMTO algorithms. The results show that APMTO outperforms the compared state–of–the–art algorithms, which fully reveals its effectiveness and superiority. Full article

Background: Clinical experience with semaglutide in patients with type 2 diabetes mellitus shows that its benefits extend far beyond glucose regulation. This study examines whether this drug is indeed popular among the Croatian population and explores whether factors such as gender or proximity to the healthcare sector influence its potential use, attitudes toward weight loss, and knowledge regarding its application and possible adverse effects. Methods: This was a cross-sectional population study. In this study we focused on the brand name Ozempic^® for semaglutide as it is the most commonly searched term for semaglutide. Results: The study included 290 participants, most of who were women (N = 243, 83.8%). As many as 214 (73.8%) people stated they had heard of Ozempic^®; however, there was no significant difference in whether people had heard of Ozempic^® if they had type 2 diabetes mellitus (p = 0.415). In total, 23.4% of people stated they knew someone who took Ozempic^®. Women were significantly more likely to feel pressure about their appearance than men, with 51.1% of men reporting no pressure at all compared to only 39.9% of women (p = 0.015). A majority of participants agreed that social media strongly affects perception on the use of medications for weight loss (73.8%). Individuals with a family member in the healthcare field were significantly more informed about the possible adverse reactions of semaglutide compared to those without such a connection. Among participants without a healthcare professional in the family, 75.0% reported being unaware of potential adverse effects, compared to 47.9% of those with a family member in healthcare. Moreover, participants with a healthcare professional in the family were more likely to know the correct route of administration for Ozempic^® (68.1% vs. 54.6%, p = 0.025); Conclusions: The results of this study show that three-quarters of people had heard of Ozempic^®, regardless of whether they had an indication for its use or not. In addition, the results indicate that although both men and women share satisfaction with their bodies, women feel more pressured by societal expectations related to their appearance. Full article

Self-medication is increasingly prevalent among healthcare students, raising concerns about the adequacy of current medical education in promoting safe medication practices. This study aimed to assess the frequency, motivations, and perceptions of self-medication among medical and pharmacy students and to identify educational gaps. A cross-sectional survey was conducted using a structured, anonymous questionnaire distributed to medical and pharmacy students at a single academic institution. The questionnaire assessed self-medication frequency, substances used, motivations, perceived risks, confidence in knowledge, sources of information, and attitudes toward curriculum improvements. Over 50% of participants reported practicing self-medication at least once a month. The most commonly used substances were analgesics and dietary supplements. Main motivations included recognition of symptoms, confidence in personal knowledge, and avoidance of waiting times. Despite receiving university instruction on self-medication risks, students continued to self-medicate, with many relying on the internet as a primary source of information. Only 8% felt very confident in counseling patients on self-medication. A majority (over 70%) expressed a strong interest in integrating dedicated educational modules into the curriculum. There is a clear need for improved, practice-oriented education on self-medication. Future interventions should focus on interdisciplinary teaching, digital literacy, and simulation-based training to foster safer medication practices. Full article

Background: Trust in information sources is essential to enhance an individual’s understanding of the message and boost their willingness to change or act on specific health behavior, including vaccine uptake. This study explores the association between trust in information sources and parents’ knowledge, attitudes, and practices regarding their children’s 13-valent pneumococcal conjugate vaccine (PCV13) uptake across seven cities in the Yangtze River Delta (YRD) region in China. Methods: A cross-sectional web-based survey was conducted from May to June 2023. Adult parents (N = 1304) who had at least one child aged 24 months or less and lived in the YRD region were recruited. The Adjusted Ordinary Least Squares (OLSs) regression model was applied to estimate the association between participants’ level of trust in different information sources and their knowledge, attitudes, and practices of children’s PCV13 vaccination. Results: Information from the Disease Control and Prevention Center (CDC) source received the highest trust score. Age, gender, education, and annual household income were related to varied trust levels in specific sources. Trust in the health service provider source was significantly associated with a better command of PCV13 knowledge, acceptance of PCV13, and a higher likelihood of vaccination. Trust in online community sources was positively associated with vaccine uptake. Conclusions: The study participants highly trusted information from health service provider sources. These sources may be effective channels with potential to enhance parents’ vaccine knowledge and acceptance of PCV13. Public health workers could utilize trusted sources to disseminate the benefits of the PCV13 and encourage the uptake of the vaccine. Full article

Disaster risk reduction requires efficient flood control in lowland and flood-prone areas, especially in agricultural areas like the Bang Rakam model area in Phitsanulok province, Thailand. In order to improve flood prediction and response, this study proposes the creation of a low-cost, real-time water-level monitoring integrated with spatial data analysis using Geographic Information System (GIS) technology. Ten ultrasonic sensor-equipped monitoring stations were installed thoughtfully around sub-catchment areas to provide highly accurate water-level readings. To define inundation zones and create flood depth maps, the sensors gather flood level data from each station, which is then processed using a 1-m Digital Elevation Model (DEM) and Python-based geospatial analysis. In order to create dynamic flood maps that offer information on flood extent, depth, and water volume within each sub-catchment, an automated method was created to use real-time water-level data. These results demonstrate the promise of low-cost IoT-based flood monitoring devices as an affordable and scalable remedy for communities that are at risk. This method improves knowledge of flood dynamics in the Bang Rakam model area by combining sensor technology and spatial data analysis. It also acts as a standard for flood management tactics in other lowland areas. The study emphasizes how crucial real-time data-driven flood monitoring is to enhancing early-warning systems, disaster preparedness, and water resource management. Full article

Green infrastructure (GI) and nature-based solutions (NBSs) in land-use planning and landscape architecture represent a holistic, interdisciplinary response to environmental and societal challenges. Although integrated into Slovak legislation since 2019, their formal implementation has progressed rather slowly, creating a gap that has been increasingly addressed by civic initiatives (CIs) of diverse types and legal forms. This study examines approaches and methods of CIs in Slovakia implementing GI and NBSs, while focusing on their legal forms, activities, spatial delimitations, and their impact on green space development and governance. Analysis of seventeen case studies shows that many CIs operate at multiple levels—local, national, and international—often delivering professional, conceptually grounded solutions. The most frequent NBS activities involve creating or enhancing parks, green public spaces, and community gardens, as well as restoring natural and semi-natural areas through nature-based management. Knowledge creation and awareness-raising are central strategies, including environmental education centres, citizen science, public campaigns, and informal learning platforms. The transformation of derelict areas into multifunctional public spaces emerges as a notable practice, combining ecological regeneration with cultural and social uses. The findings highlight the growing role of civic actors in advancing inclusive, participatory, and knowledge-based environmental management and call for stronger institutional support to integrate their contributions into formal administrative frameworks. Full article

This study examines how educational attainment and creative-class identity influence public library use among Black homeowners in Ward 8, Washington, D.C., a historically disinvested, yet resilient, Black community. Using an adapted theoretical framework (Chatman’s Small World Theory, Florida’s creative class theory, and Crenshaw’s intersectionality), the research investigates how symbolic capital informs institutional engagement in a racially homogeneous but economically stratified setting. A survey of 56 Black homeowners examined the relationships among education, income, creative-class identity, and library use. Logistic regression analysis revealed that higher educational attainment was a significant predictor of identification with the Black Creative Class^TM. However, neither income nor creative-class identity significantly predicted public library use. These findings challenge the assumption that middle-class status or creative-class affiliation ensures participation in educational or cultural institutions. Instead, they suggest that deeper dynamics, such as cultural relevance, perceived alignment, and trust, may shape engagement with public libraries. The study advances knowledge in library and information science (LIS) and urban studies by demonstrating how spatial context and class distinctions within Black communities shape library engagement. The results underscore the need for culturally responsive library strategies that recognize class-based variation within racial groups, moving beyond monolithic models of community outreach. Full article

The estimation of accurate and precise Satellite-Derived Bathymetries (SDBs) is important in marine and coastal applications for a better understanding of the ecosystems and science-based decision-making. Despite the advancements in related Machine Learning (ML) studies, quantifying the anticipated bias per pixel in the SDBs remains a significant challenge. This study aims to address this knowledge gap by developing a spatially explicit uncertainty index of a ML-derived SDB, capable of providing a quantifiable anticipation for biases of 0.5, 1, and 2 m. In addition, we explore the usage of this index for model optimization via the exclusion of training points of high or moderate uncertainty via a six-fold iteration loop. The developed methodology is applied across the national coastal extent of Belize in Central America (~7017 km²) and utilizes remote sensing data from the European Space Agency’s twin satellite system Sentinel-2 and Planet’s NICFI PlanetScope. In total, 876 Sentinel-2 images, nine NICFI six-month basemaps and 28 monthly PlanetScope mosaics are processed in this study. The training dataset is based on NASA’s system Ice, Cloud and Elevation Satellite (ICESat-2), while the validation data are in situ measurements collected with scientific equipment (e.g., multibeam sonar) and were provided by the National Oceanography Centre, UK. According to our results, the presented approach is able to provide a pixel-based (i.e., spatially explicit) uncertainty index for a specific prediction bias and integrate it to refine the SDB. It should be noted that the efficiency of the optimization of the SDBs as well as the correlations of the proposed uncertainty index with the absolute prediction error and the true depth are low. Nevertheless, spatially explicit uncertainty information produced by a ML-related SDB provides substantial insight to advance coastal ecosystem monitoring thanks to its capability to showcase the difficulty of the model to provide a prediction. Such spatially explicit uncertainty products can also aid the communication of coastal aquatic products with decision makers and provide potential improvements in SDB modeling. Full article