Search Results (364)

Background: As means of addressing ongoing challenges in accessing publicly funded specialist care, new models of care have been trialled. One such approach is using physiotherapists in advance practice roles, who in collaboration with other health professionals, act as an initial orthopedic point of contact and coordinate care. This research investigated the impact of a model of care, the Spinal Virtual Clinic Model, implemented for the first time in South Australia, using advanced practice physiotherapists in a large metropolitan hospital in South Australia. Although formally named the “Spinal Virtual Clinic” by the health service, this model does not involve direct patient contact and differs from traditional virtual or telehealth clinics. Instead, it is best understood as a physiotherapy-led referral triage and management service. Methods: This research was conducted in two stages. Stage 1 was a retrospective clinical audit of sequential patients triaged to the Spinal Virtual Clinic, as well as a follow up audit to capture any subsequent engagement with the Orthopaedic Spinal Service following the initial Spinal Virtual Clinic correspondence. Data were descriptively analysed. In Stage 2, semi-structured interviews were conducted with patients from the Spinal Virtual Clinic to explore their perspectives on this model of care. The interviews were transcribed verbatim and independently analysed using thematic analysis. The sequential use of quantitative and qualitative approaches enabled us to both describe engagement with this model of care and better understand the underlying perspectives. Results: Three hundred and nine referrals were triaged to the physiotherapy-led spinal virtual clinic over a six-month period from 1 January 2021 to 30 June 2021. Majority of referrals were triaged as low acuity did not need formal spinal specialist review and could be managed safely in primary care. Therapist-led active management strategies (80.8%), trial of neuropathic medication (35.6%) closely followed by advice regarding targeted spinal injections (foraminal and epidural), were the most common conservative management strategies recommended. Only a small proportion needed surgical review. Interviews with eleven patients revealed that while many valued the convenience, timely advice, and reassurance offered by the service, others expressed confusion about the referral process and disappointment at not seeing a specialist. A key recommendation identified was improved communication, including providing patients with direct feedback alongside general practitioner correspondence. Conclusions: This research, underpinned by quantitative and qualitative research, has showcased the potential of this model of care, the spinal virtual clinic, to have a positive impact on improving access and reducing the burden on the health system for low acuity patients. As historical models of care become unsustainable and obsolete, alternative models of care can be implemented in health care settings where outpatient demand significantly exceeds capacity. Full article

Traditional book recommendation methods predominantly rely on collaborative filtering and context-based approaches. However, existing methods fail to account for the order of users’ book borrowings and the duration they hold them, both of which are crucial indicators reflecting users’ book preferences. To address this challenge, we propose a book recommendation framework called DPBD, which disentangles preferences based on borrowing duration, thereby explicitly modeling temporal patterns in library borrowing behaviors. The DPBD model adopts a dual-path neural architecture comprising the following: (1) The item-level path utilizes self-attention networks to encode historical borrowing sequences while incorporating borrowing duration as an adaptive weighting mechanism for attention score refinement. (2) The feature-level path employs gated fusion modules to effectively aggregate multi-source item attributes (e.g., category and title), followed by self-attention networks to model feature transition patterns. The framework subsequently combines both path representations through fully connected layers to generate user preference embeddings for next-book recommendation. Extensive experiments conducted on two real-world university library datasets demonstrate the superior performance of the proposed DPBD model compared with baseline methods. Specifically, the model achieved 13.67% and 15.75% on HR@1 and 15.75% and 12.90% on NDCG@1 across the two datasets. Full article

Didactic digital tools can commence, enhance, and strengthen reading fluency in children undergoing long-term hospitalization due to oncology conditions. However, resources specifically designed to support rapid naming and decoding in Spanish remain scarce. This study presents the design, development, and evaluation of a game prototype aimed at addressing this gap among Spanish-speaking preschoolers in hospital settings. Developed using Unity through a design-based research methodology, the game comprises three narratively linked levels targeting rapid naming, decoding, and fluency. A sequential exploratory mixed-methods design (QUAL-quan) guided the evaluation. Qualitative data were obtained from a focus group of hospital teachers (N = 6) and interviews with experts (N = 30) in relevant fields. Quantitative validation involved 274 experts assessing the game’s contextual, pedagogical, and technical quality. The prototype was also piloted with four end-users using standardised tests for rapid naming, decoding, and fluency in Spanish. Results indicated strong expert consensus regarding the game’s educational value, contextual fit, and usability. Preliminary findings suggest potential for fostering and supplementing early literacy skills in hospitalised children. Further research with larger clinical samples is recommended to validate these outcomes. Full article

Multiple studies have assessed the potential for improvement for genetic and antigenic match of influenza vaccines to circulating viruses by altering the timing of vaccine strain decisions. The advent of new technologies for vaccination has generated global discussion around moving the seasonal influenza strain recommendations closer to the start of the vaccination period. The window between influenza vaccine strain recommendations and the availability of vaccine supply for immunization comprises sequential processes required to produce vaccine components, reagents for manufacture and release, and regulatory approvals. This commentary examines one company’s perspective on requirements for enabling manufacture and release of seasonal influenza vaccine in more detail, describes preparations to reduce risk, and highlights the potential impact on vaccine supply for all platforms (egg, cell, mRNA) when strain decisions are issued closer to the desired vaccination timing. Full article

The aim of this study was to examine teachers’ belief levels regarding education for sustainable development (ESD), to identify the factors behind these beliefs, and to reveal their suggestions for improving the quality of ESD. The study employed an explanatory sequential mixed-methods design. Data were collected from 409 teachers working at primary and secondary schools in Türkiye through the “Beliefs About Education for Sustainable Development Scale” and semi-structured interviews. The quantitative data were analyzed using an independent samples t-test, one-way Analysis of Variance (ANOVA), and Pearson product-moment correlation, and the qualitative data were analyzed through content analysis. The results indicated that the participants’ beliefs in ESD practices were high, but these beliefs were negatively affected by challenges due to SD goals, policymakers, students, and parents. In addition, the participants made recommendations for improving the quality of ESD to policymakers, the Turkish Council of Higher Education, the Ministry of National Education, and their colleagues. The findings of the study present significant implications for policymakers and educators for more effective implementation of ESD in the education system. Full article

This mixed-methods study investigated the dispositions and motivations of 118 K-12 teachers in Abu Dhabi regarding lifelong learning. Employing a sequential explanatory design, quantitative data were collected using a validated 40-item Likert scale survey across five domains: Goal setting, Application of knowledge and skills, Self-direction and evaluation, Locating information, and Adaptable learning strategies. Results indicated a moderate overall disposition toward lifelong learning, with the highest motivation observed in Self-direction and evaluation. Significant gender differences favored male teachers across all domains. The recommendations stress the need for developing goal-setting abilities, improving information accessibility, and encouraging adaptive learning strategies through focused professional development programs. Full article

Sugar-sweetened beverages (SSBs) are among the most traded and a significant component of global food and beverages. The consumption of these beverages is widely believed to be a major contributing factor to overweight, diabetes, tooth decay, and other noncommunicable diseases. To reduce the intake of these beverages, the World Health Organisation (WHO) encouraged countries and jurisdictions to introduce a sugar tax policy as an approach to reduce the sales and intake of the beverages. The purpose of this study is to evaluate the sustainability of the relationship that exists among the factors that influence the intake of SSBs in enhancing sugar tax in South Africa. A mixed research methods were used to explore the relationships among the variables. The Exploratory Sequential Design (ESD) was deemed appropriate to deal with the introduction of a sugar tax to reduce the intake of the SSB, most especially in Africa where the tax is a new concept. The Exploratory Sequential Design began with the collection of the structured interview qualitative data and analysis using the thematic analysis procedure, then followed by quantitative data collection and analysis using the confirmatory factor analysis method. This study used mainly primary data collected from the Gauteng Province of South Africa for both the qualitative and quantitative phases of the study. The study found that a sustainable effective sugar tax can be achieved when the public is aware of the existence, purpose, and acceptance of the sugar tax. Furthermore, the tax can become relevant and sustainable when it leads to a significant reduction in intake, contributing to negative consumer behaviour and attitude towards the intake of SSBs in South Africa. A synthesis of the integrated results confirmed that the recognition of the relationship among the factors influencing the intake of SSB and penalising the beverage manufacturers who do not reduce the sugar content in all their beverages as recommended by the WHO are vital in leading to a sustainable enhancement of an effective sugar tax in South Africa. Full article

On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, we propose a context-driven recommendation framework that integrates a hybrid sequence modeling architecture with a Large Language Model for post hoc reasoning and reranking. Specifically, the solution tackles several key issues: (1) integration of multimodal features to achieve explicit context fusion through a hybrid fusion strategy; (2) introduction of a context capture layer and a context propagation layer to enable effective encoding of implicit contextual states hidden in the heterogeneous long and short term; (3) cross attention mechanisms to facilitate context retrospection, which allows implicit contexts to be uncovered; and (4) leveraging the reasoning capabilities of DeepSeek-R1 as a post-processing step to perform open knowledge-enhanced reranking. Extensive experiments on a real-world dataset show that our approach significantly outperforms strong baselines in both prediction accuracy and Top-K recommendation quality. Case studies further demonstrate the model’s ability to uncover nuanced, implicit contextual cues—such as family roles and holiday-specific behaviors—making it particularly effective for personalized, dynamic recommendations in high-frequency scenes. Full article

Introduction: The combination of ceftazidime−avibactam (CAZ-AVI) with aztreonam (ATM) may be an option for the treatment of infections due to metallo-β-lactamases (MBLs) producing bacteria, as recommended by current guidelines. MBLs protect the pathogen from any available β-lactam/β-lactamase inhibitor (BL/BLI). Moreover, in vitro and clinical data suggest that double carbapenem therapy (DCT) may be an option for such infections. Materials and Methods: This retrospective study was conducted in two mixed intensive care units (ICUs) at the University Hospital of Larissa, Thessaly, Greece, and the General Hospital of Larissa, Thessaly, Greece, during a three-year period (2022−2024). Mechanically ventilated patients with bloodstream infection (BSI) caused by K. pneumoniae resistant to all BL/BLI combinations were studied. Patients were divided into three groups: in the first, patients were treated with CAZ-AVI + ATM; in the second, with DCT; and in the third, with antibiotics other than BL/BLIs that presented in vitro susceptibility. The primary outcome of the study was the change in Sequential Organ Failure Assessment (SOFA) score between the onset of infection and the fourth day of antibiotic treatment. Secondary outcomes were SOFA score evolution during the treatment period, total duration of mechanical ventilation (MV), ICU length of stay (LOS), and ICU mortality. Results: A total of 95 patients were recruited. Among them, 23 patients received CAZ-AVI + AZT, 22 received DCT, and 50 patients received another antibiotic regimen which was in vitro active against the pathogen. The baseline characteristics were similar. The mean (SE) overall age was 63.2 (1.3) years. Mean (SE) Acute Physiology and Chronic Health Evaluation II (APACHE II) and SOFA scores were 16.3 (0.6) and 7.6 (0.3), respectively. The Charlson Index was similar between groups. The control group presented a statistically lower SOFA score on day 4 compared to the other two groups [mean (SE) 8.9 (1) vs. 7.4 (0.9) vs. 6.4 (0.5) for CAZ-AVI + ATM, DCT and control group, respectively (p = 0.045)]. The duration of mechanical ventilation, ICU LOS, and mortality were similar between the groups (p > 0.05). Comparison between survivors and non-survivors revealed that survivors had a lower SOFA score on the day of BSI, higher PaO₂/FiO₂ ratio, higher platelet counts, and lower lactate levels (p < 0.05). Septic shock was more frequent among non-survivors (60.3%) in comparison to survivors (27%) (p = 0.0015). Independent factors for mortality were PaO₂/FiO₂ ratio and lactate levels (p < 0.05). None of the antibiotic regimens received by the patients was independently associated with survival. Conclusions: Treatment with CAZ-AVI + ATM or DCT may offer similar clinical outcomes for patients suffering from BSI caused by K. pneumoniae strains resistant to all available BL/BLIs. However, larger studies are required to confirm the findings. Full article

Contextual multi-armed bandits (CMABs) are vital for sequential decision-making in areas such as recommendation systems, clinical trials, and finance. We propose a simulation framework integrating Gaussian Process (GP)-based CMABs with vine copulas to model dependent contexts and GARCH processes to capture reward volatility. Rewards are generated via copula-transformed Beta distributions to reflect complex joint dependencies and skewness. We evaluate four policies—ensemble, Epsilon-greedy, Thompson, and Upper Confidence Bound (UCB)—over 10,000 replications, assessing cumulative regret, observed reward, and cumulative reward. While Thompson sampling and LLM-guided policies consistently minimize regret and maximize rewards under varied reward distributions, Epsilon-greedy shows instability, and UCB exhibits moderate performance. Enhancing the ensemble with copula features, GP models, and dynamic policy selection driven by a large language model (LLM) yields superior adaptability and performance. Our results highlight the effectiveness of combining structured probabilistic models with LLM-based guidance for robust, adaptive decision-making in skewed, high-variance environments. Full article

Multi-behavior sequential recommendation (MBSRec) is a form of sequential recommendation. It leverages users’ historical interaction behavior types to better predict their next actions. This approach fits real-world scenarios better than traditional models do. With the rise of the transformer model, attention mechanisms are widely used in recommendation algorithms. However, they suffer from low-pass filtering, and the simple learnable positional encodings in existing models offer limited performance gains. To address these problems, we introduce the context-aware multi-behavior sequential recommendation model (CAMBSRec). It separately encodes items and behavior types, replaces traditional positional encoding with context-similarity positional encoding, and applies the discrete Fourier transform to separate the high and low frequency components and enhance the high frequency components, countering the low-pass filtering effect. Experiments on three public datasets show that CAMBSRec performs better than five baseline models, demonstrating its advantages in terms of recommendation performance. Full article

Next Point-of-Interest (POI) recommendation is aimed at predicting users’ future visits based on their current status and historical check-in records, providing convenience to users and potential profits to businesses. The Graph Neural Network (GNN) has become a common approach for this task due to the capabilities of modeling relations between nodes in a global perspective. However, most existing studies overlook the more prevalent heterogeneous relations in real-world scenarios, and manually constructed graphs may suffer from inaccuracies. To address these limitations, we propose a model called Heterogeneous Graph Structure Learning for Next POI Recommendation (HGSL-POI), which integrates three key components: heterogeneous graph contrastive learning, graph structure learning, and sequence modeling. The model first employs meta-path-based subgraphs and the user–POI interaction graph to obtain initial representations of users and POIs. Based on these representations, it reconstructs the subgraphs through graph structure learning. Finally, based on the embeddings from the reconstructed graphs, sequence modeling incorporating graph neural networks captures users’ sequential preferences to make recommendations. Experimental results on real-world datasets demonstrate the effectiveness of the proposed model. Additional studies confirm its robustness and superior performance across diverse recommendation tasks. Full article

Smart living is strongly promoted to enhance the quality of life via the application of innovative solutions, and this is driven by domain specialists and policymakers, including designers, urban planners, computer engineers, and property developers. Nonetheless, the actual user, whose views ought to be considered during the design and development of smart living systems, has received little attention. Thus, this study aims to identify and examine the critical smart functions to achieve smart living in smart buildings based on occupants’ perceptions. The aim is achieved using a sequential quantitative research method involving a literature review and 221 valid survey data gathered from a case of a smart student residence in Hong Kong. The method is further integrated with descriptive statistics, the Kruskal–Walli’s test, and the criticality test. The results were validated via a post-survey with related experts. Twenty-six critical smart functions for smart living were revealed, with the top three including the ability to protect personal data and information privacy, provide real-time safety and security, and the ability to be responsive to users’ needs. A need was discovered to consider the context of buildings during the design of smart living systems, and the recommendation is for professionals to understand the kind of digital technology to be integrated into a building by strongly considering the context of the building and how smart living will be achieved within it based on users’ perceptions. The study provides valuable insights into the occupants’ perceptions of critical smart features/functions for policymakers and practitioners to consider in the construction of smart living systems, specifically students’ smart buildings. This study contributes to knowledge by identifying the critical smart functions to achieve smart living based on occupants’ perceptions of smart living by considering the specific context of a smart student building facility constructed in Hong Kong. Full article

Background/Objectives. This manuscript presents an overview of advances in oncological radiotherapy as an effective treatment method for cancerous tumors, focusing on mechanisms of action within metabolite–antimetabolite systems. The urgency of this topic is underscored by the fact that cancer remains one of the leading causes of death worldwide: as of 2022, approximately 20 million new cases were diagnosed globally, accounting for about 0.25% of the total population. Given prognostic models predicting a steady increase in cancer incidence to 35 million cases by 2050, there is an urgent need for the latest developments in physics, chemistry, molecular biology, pharmacy, and strict adherence to oncological vigilance. The purpose of this work is to demonstrate the relationship between the nature and mechanisms of past diagnostic and therapeutic oncology approaches, their current improvements, and future prospects. Particular emphasis is placed on isotope technologies in the production of therapeutic nuclides, focusing on the mechanisms of formation of simple and complex theranostic compounds and their classification according to target specificity. Methods. The methodology involved searching, selecting, and analyzing information from PubMed, Scopus, and Web of Science databases, as well as from available official online sources over the past 20 years. The search was structured around the structure–mechanism–effect relationship of active pharmaceutical ingredients (APIs). The manuscript, including graphic materials, was prepared using a narrative synthesis method. Results. The results present a sequential analysis of materials related to isotope technology, particularly nucleus stability and instability. An explanation of theranostic principles enabled a detailed description of the action mechanisms of radiopharmaceuticals on various receptors within the metabolite–antimetabolite system using specific drug models. Attention is also given to radioactive nanotheranostics, exemplified by the mechanisms of action of radioactive nanoparticles such as Tc-99m, AuNPs, wwAgNPs, FeNPs, and others. Conclusions. Radiotheranostics, which combines the diagnostic properties of unstable nuclei with therapeutic effects, serves as an effective adjunctive and/or independent method for treating cancer patients. Despite the emergence of resistance to both chemotherapy and radiotherapy, existing nuclide resources provide protection against subsequent tumor metastasis. However, given the unfavorable cancer incidence prognosis over the next 25 years, the development of “preventive” drugs is recommended. Progress in this area will be facilitated by modern medical knowledge and a deeper understanding of ligand–receptor interactions to trigger apoptosis in rapidly proliferating cells. Full article

News recommendation systems must simultaneously address multiple challenges, including dynamic user interest modeling, nonlinear popularity patterns, and diversity recommendation in cold-start scenarios. We present a Popularity-Aware Dynamic Multi-Perspective Fusion Network (PAD-MPFN) that innovatively integrates three key components: adaptive subspace projection for multi-source interest fusion, logarithmic time-decay factors for popularity bias mitigation, and dynamic gating mechanisms for personalized recommendation weighting. The framework uniquely combines sequential behavior analysis, social graph propagation, and temporal popularity modeling through a unified architecture. Experimental results on the MIND dataset, an open-source version of MSN News, demonstrate that PAD-MPFN outperforms existing methods in terms of recommendation performance and cold-start scenarios while effectively alleviating information overload. This study offers a new solution for dynamic interest modeling and diverse recommendation. Full article