Search Results (163)

Effective web service selection and recommendation are critical for ensuring high-quality performance in distributed and service-oriented systems. Recent research has increasingly explored the use of BERT (Bidirectional Encoder Representations from Transformers) to enhance semantic understanding of service descriptions, user requirements, and Quality of Service (QoS) prediction. This systematic review examines the application of BERT-based models in QoS-aware web service selection and recommendation. A structured database search was conducted across IEEE, ACM, ScienceDirect, and Google Scholar covering studies published between 2020 and 2024, resulting in twenty-five eligible articles based on predefined inclusion criteria and PRISMA screening. The review shows that BERT improves semantic representation and mitigates cold-start and sparsity issues, contributing to better service ranking and QoS prediction accuracy. However, challenges persist, including limited availability of benchmark datasets, high computational overhead, and limited interpretability of model decisions. The review identifies five key research gaps and outlines future directions, including domain-specific pre-training, hybrid semantic–numerical models, multi-modal QoS reasoning, and lightweight transformer architectures for deployment in dynamic and resource-constrained environments. These findings highlight the potential of BERT to support more intelligent, adaptive, and scalable web service management. Full article

This study is to explore information synthesis on research topics and emerging trends in privacy within the context of technology adoption. A search for the terms privacy and technology adoption in the Web of Science database yielded information on 2910 publications from 2005 to 2025. The analysis was conducted using CiteSpace, incorporating cluster analysis, timeline analysis, and burst detection to identify key patterns and developments. Fifteen sub-areas of privacy related to technology adoption were identified, including health information exchange, blockchain adoption, artificial intelligence, Internet banking, smart home devices, location-based services, mobile commerce, ubiquitous commerce adoption, tracing apps, metaverse adoption, and facial recognition payment. Timeline analysis provided insights into the growth or decline of these research clusters over time. Based on the findings, a framework was developed to illustrate key insights and their interconnections, offering guidance for future research. The study concludes by discussing its implications, limitations, and recommendations for further research. Full article

As the global demand for seaweed products increases, resource managers, conservation groups, and other stakeholders strive to protect wild seaweed populations and the ecosystem services they provide from the damaging effects of over-harvesting. Ascophyllum nodosum (rockweed) is a slow-growing, intertidal brown alga of the North Atlantic that is commercially harvested for crop biostimulants, soil conditioners, and other products. Rockweed is considered a foundation species due to its high abundance, tall canopy, habitat characteristics, and role in detrital food webs. Rockweed shoots survive after harvesting if the holdfast remains intact, but rates of canopy and biomass recovery depend on the intensity of harvesting. In Maine, USA, and eastern Canada, little is known about how harvesting rockweed at various intensities affects recovery rates of algal height or biomass. Herein, we evaluate published studies and suggest improved experimental designs. Most experimental studies focus on a single harvest event, often with incomplete data on control plots, amount of biomass removed, or previous harvesting history at study sites. Much has been learned from previous work, but more rigorous studies are needed to develop harvest recommendations that address both commercial and conservation-related goals. Importantly, experimental studies of the effects of repeated harvesting on rockweed beds are lacking. Full article

Background: Age-related hearing loss (ARHL) is associated with decreased communication, reduced social engagement, cognitive decline and an increased risk of dementia globally. Although increasing studies report the benefits of combing auditory and cognitive training for older adults with ARHL, more evidence is needed to examine its effects. Moreover, existing training programs have been developed with minimal end-user involvement leading to low adherence rates. This study aimed to investigate the role of co-design in the development of an auditory–cognitive training system for older adults with ARHL. Methods: A co-design methodology was employed. Digital recordings of the co-design workshops were transcribed verbatim. An established reflexive thematic analysis methodology was used. Results: Fifteen older adults with ARHL, referred to as “co-researchers”, participated in three co-design workshops until data saturation was achieved. Consultations were held with two key service providers. Three key themes emerged: (1) older adults with ARHL prefer a user-friendly auditory–cognitive training system; (2) clear, localized and colloquial instructions for the training tasks are necessary; and (3) diversified, tailor-made and dual-task training tasks, performed in an interactive and game-like mode, can motivate and sustain usage of the training system. As a result, a prototype of a web-based, gamified, and adaptive auditory–cognitive dual-task training system was co-designed. Conclusions: Our findings affirmed the importance of genuinely listening to the voices of end-users and creating a system that is responsive to their needs and preferences. Future study is recommended to examine the effects of this system on older adults with ARHL. Full article

Background: Artificial intelligence (AI) has been proposed as a transformative tool in suicide prevention, yet most evidence remains observational. To provide a rigorous benchmark, we systematically reviewed randomized controlled trials (RCTs) evaluating AI-based interventions targeting suicidal thoughts, behaviours, or help-seeking. Methods: Following PRISMA 2020 guidelines, MEDLINE, Web of Science, and Scopus were searched to 31 May 2025. Eligible studies were RCTs in humans that incorporated AI or machine learning for risk prediction, automated intervention, or treatment allocation. Methodological quality was assessed with the PEDro scale and certainty of evidence with GRADE. Results: From 1101 screened records, six RCTs (n = 793) met all criteria. Three studies tested machine learning risk prediction, two evaluated fully automated interventions (a transformer-based recommender and a digital nudge), and one examined AI-assisted treatment allocation. Risk-prediction models stratified short-term suicidal outcomes with accuracies of up to 0.67 and AUC values around 0.70. Digital interventions reduced counsellor response latency or increased crisis-service uptake by 23%. Algorithm-guided allocation reduced the occurrence of suicidal events when randomisation aligned with model recommendations. Methodological quality was moderate to high (median PEDro = 8/10), but GRADE certainty was low due to small samples and imprecision. Conclusions: AI can enhance discrete processes in suicide prevention, including risk stratification, help-seeking, and personalized treatment. However, the current evidence is limited, and larger multisite RCTs with longer follow-up, CONSORT-AI compliance, and equity-focused design are urgently required. Full article

Background: Nuclear medicine is a highly specialized field that combines advanced technology and multidisciplinary collaboration. Despite its complexity, the role of nurses in this context remains underexplored, especially regarding their clinical and administrative activities. Methods: This is a scoping review protocol developed according to the Joanna Briggs Institute (JBI) methodology and reported in accordance with the PRISMA-ScR guidelines, as recommended by the EQUATOR Network. The research question was structured using the PCC mnemonic (Population, Concept, and Context): What are the clinical and administrative activities performed by nurses in nuclear medicine services? A comprehensive search will be conducted in Web of Science, PubMed, Embase, Cochrane, SciELO, LILACS, Scopus, and CINAHL, complemented by grey literature sources such as Google Scholar and ProQuest Dissertations & Theses Global. No restrictions on language or publication date will be applied. Study selection and data extraction will be performed independently by two reviewers. This protocol is registered on the Open Science Framework (OSF) and is publicly accessible. The selection process will be detailed in a PRISMA-ScR flow diagram. A descriptive table will summarize the characteristics of the included studies, including the authors, year, country, study type, objectives, population, main nursing activities, and key findings. Expected Outcomes: The anticipated results are expected to clarify nurses’ contributions to patient safety and service quality in nuclear medicine. Conclusions: This review may also support the development of an assessment tool for nursing activities, guide professional training, and inform policies to strengthen nursing practice in this specialized field. Full article

Vineyard cover crops deliver well-documented ecosystem services, yet consistent establishment, especially of perennial grasses and legumes, remains a primary barrier to adoption. This review reframes “companion (nurse) cropping” not as a new crop class but as a facilitative establishment strategy within the broader cover-/service-crop literature. We (i) position our contribution relative to recent syntheses, (ii) synthesize evidence on companion crops practices that reduce cover cropping early failure risk, and (iii) propose a testable research agenda. A focused scoping review of peer-reviewed and extension literature indexed in Web of Science and Google Scholar was conducted using search terms encompassing cover/service crops and nurse/companion/facilitation in viticulture systems. Across climates, fast-establishing cereals (Avena sativa, Hordeum vulgare, Secale cereale, × Triticosecale Wittmack) and short-cycle legumes (Vicia sativa, Pisum sativum, Trifolium incarnatum) can reliably “nurse” slower perennials and legumes by providing early groundcover, weeds control, and microclimate buffering when sown at reduced rates (≈25–50% of monoculture) and terminated on time to limit vine competition. Evidence gaps persist for in-row applications, water-use penalties under drought, and long-term effects on yield and grape composition. Companion cropping is argued to be a design principle in vineyard cover-crop programs rather than a separate category. A decision framework and research agenda are presented to quantify establishment reliability, resource trade-offs, and wine-relevant outcomes, and it is recommended that future decision tools make the companion-phase logic explicit to de-risk adoption and align with regional guidelines. Full article

The global aging population, particularly those aged 60 and above, is increasingly vulnerable to communicable diseases. Building ventilation (BV) plays a key role in residential aged-care (RAC) facilities, where COVID-19 has had a significant impact. This study systematically reviews the published literature to examine the influence of BV systems (BVSs) on airborne disease (COVID-19) transmission in RACs and recommends strategies to protect vulnerable residents. Using the PRISMA framework, articles published in the last decade were sourced from Scopus, Web of Science, and PubMed. Bibliometric analyses revealed key research clusters on risk factors, transmission, facilities and services, and gender-based and retrospective studies. Australia, the USA, Africa, and the UK have made the most scholarly contributions to this field. Three main research areas emerged: BVS functionality, ventilation’s role in COVID-19 transmission, and spatial building design for effective airflow. Findings reveal that inadequate ventilation and poor indoor air quality are major contributors to disease spread, further influenced by ventilation rate, airflow, temperature, humidity, and air distribution. A hybrid ventilation design that integrates natural and mechanical systems with technologies such as HEPA filters, UVGI, and HVAC is recommended in the current study. In addition, building form and layout should incorporate spatial, engineering, administrative, and hierarchical controls in line with sustainable ventilation design guidelines. This study adds to the growing body of knowledge on the roles of ventilation and design in infection control. It offers practical recommendations for architects, RAC managers, government agencies, and policymakers involved in designing and managing RACs to reduce the risk of communicable disease transmission. Full article

Sustainable intensification requires metrics that are able to capture both economic performance and the often-hidden environmental inputs that support agriculture. Emergy analysis (EmA) meets this need by converting all inputs—free environmental flows and purchased goods/services—into a common unit (solar emjoules, sej). We conducted a PRISMA-documented bibliometric review of EmA in agroecosystems (Web of Science + Scopus, 2000–2022) using Bibliometrix and synthesized farm-scale indicators (ELR, EYR, ESI, %R). Our results show output has grown but is concentrated in a few countries (China, Italy and Brazil) and journals, with farm-level assessments dominating over regional and national assessments. Across cases, mixed crop–livestock systems tend to show lower environmental loading (ELR) and higher sustainability (ESI) than crop-only or livestock-only systems. %R is generally modest, indicating continued reliance on non-renewables, with fertilizers (crops) and purchased feed (livestock) identified as recurrent drivers. Thematic mapping reveals well-developed niche clusters but no single motor theme, consistent with the presence of incongruous baselines, transformities and boundaries that limit comparability. We recommend adoption of the 12.1 × 10²⁴ sej yr⁻¹ baseline, transparent transformity reporting and multi-scale designs that link farm diagnostics to basin and national trajectories. Co-reporting with complementary sustainability assessment methods (such as LCA and carbon footprint), along with appropriate UEV resources, would increase its reputation among policymakers while preserving EmA’s systems perspective, converting dispersed case evidence into cumulative knowledge for circular, resilient agroecosystems. Full article

National and international aged care frameworks recommend family-integrated care to enhance care quality and outcomes, supported by evidence demonstrating improvements in patient and clinician experiences. Yet uncertainty remains about how to integrate family carers effectively in diverse healthcare models and settings for neurodegenerative and mental health conditions. A systematic integrative review was conducted to answer two research questions: how do the studies describe the integration of family carers in health services design and delivery for older patients with neurodegenerative and mental health conditions? And what is the evidence for family-integrated care models impacting the health and wellbeing of these older patients? Structured and iterative searches of five databases (CINAHL, Medline (Ovid), Web of Science, PsycINFO, and ProQuest) and the Google Scholar search engine identified 2271 records. A Covidence screening process resulted in 14 studies for review, comprising randomised controlled trials, mixed methods studies, qualitative studies, and quasi-experimental designs. The following four themes emerged from the evidence synthesis: (1) family participation in service delivery, (2) health and wellbeing outcomes, (3) satisfaction with care, and (4) service dynamics in enabling family-integrated care successfully. This review highlights that while family-integrated care models contribute to positive health and wellbeing outcomes for older patients with neurodegenerative and mental health conditions, challenges remain for implementation due to the extent and variability in integration strategies, a lack of rigorous evaluation, and an absence of standardised frameworks. Full article

Background: Children with food allergies can present with paediatric feeding disorder (PFD). However, access to coordinated multidisciplinary services to support these children in Australia is inconsistent. To date, the availability of services or the perceived care needs of Australian health professionals working with this population have not been formally explored. Methods: A web-based survey was distributed to health professionals in Australia. Quantitative demographic data were summarised using descriptive statistics, and open-ended responses were analysed using content analysis. Results: The final sample comprised 98 responses, with speech pathologists representing the largest professional group (n = 39; 40%). A majority (59%) worked in hospital-based services. Open-ended responses were coded utilising content analysis. Three categories were developed including (1) service delivery, (2) intervention, and (3) resources. Services were commonly impacted by long wait times, limited staff training, and inconsistencies between hospital and community care. Additionally, mental health support was frequently reported as insufficient. Conclusions: The findings from this study underscore the need for integrated services for children with food allergies and paediatric feeding disorder. Recommended areas for future research include exploring caregiver perspectives and the impact of food allergies and paediatric feeding disorder, and consideration of co-designed studies to inform service improvement initiatives. Full article

Background: In elderly community services, information technology is reshaping the daily lives of older adults in unprecedented ways. It effectively addresses the issue of frailty in the community by strengthening support networks and dynamic risk management. Despite its vast potential, there remains a need to explore further enabling methods in the realm of elderly community services. Objectives: This study aims to provide a significant theoretical and practical foundation for information technology in this field by systematically analyzing the progress and trends of digital transformation facilitated by information technology. Materials and method: To map the advancements and emerging trends in this evolving field, this study conducts a bibliometric analysis of 461 relevant publications from the Web of Science Core Collection (2004–2024). The research employs bibliometric methods and utilizes tools such as CiteSpace and VOSViewer to analyze collaborations, keywords, and citations, as well as to perform data visualization. Results: The findings indicate that current research hotspots mainly focus on “community care”, “access to care”, “technology”, and “older adults”.Potential development trends include (1) further exploration of information technology in elderly care to provide more precise health management solutions; (2) systematically building community elderly service systems to offer more detailed elderly care services; (3) strengthening interdisciplinary information sharing and research collaboration to drive innovation in community elderly care models; and (4) introducing targeted policy and financial support to improve the specific implementation framework of information technology in elderly community services. Conclusions: This study provides empirical support for the development of relevant theories and practices. Furthermore, the research outcomes offer valuable insights into business opportunities for practitioners and provide important recommendations for formulating elderly service policies. Full article

The concept of the 15-minutes city [15 MC] focuses on providing important services within proximity and accessibility through active travel like walking or biking. While this model is becoming popular in urban planning and academic discourse, its implementation faces challenges in both densely populated developing and developed countries. This study aims to conduct a systematic review of recent literature to (1) identify the core components of the 15 MC model, (2) examine planning tools and strategies used in its implementation, (3) compare successes and challenges across global contexts, particularly in densely populated and resource-constrained areas, and (4) offer practical recommendations for adapting the model to meet local needs. The study reviewed 33 research papers published in the last five years [2019–2024]. Following PRISMA guidelines, a structured screening and selection process was conducted using databases such as Scopus, Web of Science, and Google Scholar. Thematic analysis revealed major challenges for implementing the 15 MC in the Global South, including urban informality, gaps in infrastructure, and complex governance issues. Moreover, the review points out the potential risks of exclusion and gentrification if the specific needs of different contexts are not considered. While high-income countries tend to focus on sustainability and making neighborhoods walkable, developing countries struggle with deeper structural problems that make equitable implementation harder. This study emphasizes the need for locally adaptive frameworks in planning 15 MCs and aims to develop inclusive urban policies that support Sustainable Development Goal 11, which seeks to make cities inclusive, safe, resilient, and sustainable. Full article

This study presents a system for automatic cookie collection using bots that simulate user browsing behavior. Five bots were deployed, one for each of the most commonly used university browsers, enabling comprehensive data collection across multiple platforms. The infrastructure included an Ubuntu server with PiHole and Tshark services, facilitating cookie classification and association with third-party advertising and tracking networks. The BotSoul algorithm automated navigation, analyzing 440,000 URLs over 10.9 days with uninterrupted bot operation. The collected data established relationships between visited domains, generated cookies, and captured traffic, providing a solid foundation for security and privacy analysis. Machine learning models were developed to classify suspicious web domains and predict their vulnerability to XSS attacks. Additionally, clustering algorithms enabled user segmentation based on cookie data, identification of behavioral patterns, enhanced personalized web recommendations, and browsing experience optimization. The results highlight the system’s effectiveness in detecting security threats and improving navigation through adaptive recommendations. This research marks a significant advancement in web security and privacy, laying the groundwork for future improvements in protecting user information. Full article

Background: Implementing automatic classification of short texts in online healthcare platforms is crucial to increase the efficiency of their services and improve the user experience. A short text classification method combining the keyword expansion technique and a deep learning model is constructed to solve the problems of feature sparsity and semantic ambiguity in short text classification. Methods: First, we use web crawlers to obtain patient data from the online medical platform “Good Doctor”; then, we use TF-IWF to weight the keyword importance and Word2vec to calculate the keyword similarity to expand the short text features; and then we integrate the cue learning and deep learning models to construct a self-adaptive attention model to solve the problem of sparse features and unclear semantics in short text classification in the adaptive-attention-Prompt-BERT-RCNN model to realize effective classification of medical short texts. Results: Empirical studies show that the classification effect after keyword expansion is significantly higher than that before expansion, the accuracy of the model in classifying medical short texts after expansion is as high as 97.84%, and the model performs well in different categories of medical short texts. Conclusions: The short text expansion methods of TF-IWF and Word2vec make up for the shortcomings of not taking into account the keyword rarity and the contextual information of the subwords, and the model can achieve effective classification of medical short texts by combining them. The model further improves the classification accuracy of short text by integrating Prompt’s bootstrapping, self-adaptive attention’s keyword weight weighting, BERT’s deep semantic understanding, and RCNN’s region awareness and feature extraction; however, the model’s accuracy in individual topics still needs to be improved. The results show that the recommender system can effectively improve the efficiency of patient consultation and support the development of online healthcare. Full article