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Integrating probabilistic trees and causal networks for clinical and epidemiological data
No full textHealthcare decision-making requires not only accurate predictions but also insights into how factors influence patient outcomes. While traditional machine learning (ML) models excel at predicting outcomes, such as identifying high-risk patients, they are limited in addressing “what if” questions about interventions. This study introduces the Probabilistic Causal Fusion (PCF) framework, which integrates Causal Bayesian Networks (CBNs) and Probability Trees (PTrees) to extend beyond predictions. PCF leverages causal relationships from CBNs to structure PTrees, enabling both the quantification of factor impacts and the simulation of hypothetical interventions. The framework is evaluated on three clinically diverse, real-world datasets, MIMIC-IV, Framingham Heart Study, and BRFSS (Diabetes), demonstrating consistent predictive performance comparable to conventional ML models, while offering enhanced interpretability and causal reasoning capabilities. In contrast to conventional approaches focused solely on prediction, PCF offers a unified framework for prediction, intervention modelling, and counterfactual analysis, forming a holistic toolkit for clinical decision support. To enhance interpretability, PCF incorporates sensitivity analysis and SHapley Additive exPlanations (SHAP). Sensitivity analysis quantifies the influence of causal parameters on outcomes such as Length of Stay (LOS), Coronary Heart Disease (CHD), and Diabetes, while SHAP highlights the importance of individual features in predictive modelling. This dual-layered interpretability offers both macro-level insights into causal pathways and micro-level explanations for individual predictions. By combining causal reasoning with predictive modelling, PCF bridges the gap between clinical intuition and data-driven insights. Its ability to uncover relationships between modifiable factors and simulate hypothetical scenarios provides clinicians with a clearer understanding of causal pathways. This approach supports more informed, evidence-based decision-making, offering a robust framework for addressing complex questions in diverse healthcare settings
Eruption-related ultraviolet irradiance enhancements associated with flares
No full textLarge solar flares (GOES M-class or higher) are usually associated with eruptions of material. However, when considering flare irradiance enhancements and dynamics such as chromospheric evaporation, potential contributions from erupted material have historically been neglected. We analyse nine eruptive M- and X-class flares from 2024 to early 2025, quantifying the relative contributions of erupted material to irradiance enhancements during the events. Atmospheric Imaging Assembly (AIA) images from four different channels had ribbon and eruption irradiance contributions separated using a semi-automated masking method. The sample-averaged percentages of excess radiated energy by erupted material over the impulsive phase were 10−4+4%, 24−14+14%, 21−10+14% and 13−9+6% for the 131 Å, 171 Å, 304 Å and 1600 Å channels, respectively. For three events that were studied in further detail, hard X-ray (HXR) imaging showed little to no signatures of nonthermal heating within the eruptions. Our results suggest that erupted material can be a significant contributor to UV irradiance enhancements during flares, with possible heating mechanisms including nonthermal particle heating, Ohmic heating, or dissipation of MHD waves. Future work may clarify the heating mechanism and evaluate the impact of eruptions on spectral variability, particularly in Sun-as-a-star and stellar flare observations.<br/
What are the needs of people who have a chronic respiratory disease in relation to their sexual health? A scoping review protocol
No full textClinical remission in severe asthma treated with biologics and macrolides: Definition, prevalence, associated factors, and future perspectives
No full textSevere asthma is associated with persistent symptoms, frequent exacerbations, oral corticosteroid dependence, and reduced lung function. The emergence of biologic therapies targeting type 2 (T2) cytokines, including IL-5, IL-4, IL-13, thymic stromal lymphopoietin, and circulating IgE, has changed disease management and led to substantial improvement. This approach has also introduced the concept of clinical remission (defined as controlled symptoms, no maintenance corticosteroid use, no exacerbations, and optimized/stabilized lung function) as a potential treatment target. Although some guidelines propose remission criteria, no universally accepted definition exists, and reported prevalence varies depending on definitions, and therapies, and patient groups. Clinical remission has been achieved in approximately one-third of patients receiving T2-targeted biologics. Factors associated with achieving clinical remission include less severe disease (less symptoms, fewer exacerbations, and better lung function), fewer comorbidities (e.g. obesity, anxiety/depression), greater T2-disease activity (the presence of nasal polyps and higher T2 biomarkers), and early treatment response. Azithromycin therapy can also contribute to achieving remission in both T2-high and T2-low moderate to severe asthma phenotypes. Future perspectives on asthma remission include integrating a treatable traits approach, establishing and validating the definition of complete remission, and assessing the long-term benefits of achieving remission. Complete remission may encompass clinical remission, inflammatory remission (normalization of T2 biomarkers), and structural/functional remission (resolution of bronchial hyperresponsiveness, mucus plugging, and airway remodeling). Standardizing remission criteria will enable the identification of predictive factors and facilitate personalized, treat-to-target strategies. Early induction of clinical remission may be a promising strategy for optimizing outcomes in severe asthma
Squash under strain: a systematic review and meta-analysis of injuries and illnesses in squash players
No full textBackground: Squash, a high-intensity sport with growing global popularity and an upcoming 2028 Olympic debut, is known to pose a wide range of potential health risks. However, epidemiological research of squash-related injuries and illnesses lacks con-sistency regarding reporting metrics and methodological standardisation. Therefore, this study aimed to systematically review the global literature to identify the incidence, prevalence, and anatomical distribution of reported squash-related health issues, calculate a pooled injury rate, and highlight research gaps. Methods: Following PRISMA guide-lines (PROSPERO ID: CRD420251081709), a search conducted across MEDLINE, Embase, and Web of Science (from inception to 12 June 2025) yielded 12 studies, and a ran-dom-effects model estimated the pooled injury rate. Results: The pooled injury rate ap-proximated 0.74 injuries per 365 athlete-days (95% CI: 0.26–2.07) and 2.01 injuries per 1000 athlete-days (95% CI: 0.72–5.67); however, extremely high heterogeneity (I2 = 99.65%) revealed significant methodological inconsistencies. Lower limb soft tissue injuries were most common, though regional patterns varied substantially. Additionally, risks from cardiovascular strain and hyperthermia were noted within the literature, alongside a generally poor uptake of protective equipment and a significant research gap on squash-related illnesses. Conclusions: Lack of standardisation hinders risk assessment and prevention within squash; therefore, future research requires an international con-sensus on injury surveillance, particularly as squash enters its Olympic era
Electrochemical synthesis of Ni-Co-W-Zr(P) quinary medium entropy alloy for enhanced hydrogen evolution reaction
No full textOver the course of history, the principles of alloying have evolved, with the past fifteen years witnessing the rise of high-entropy alloying theory, which has fundamentally transformed our approach to alloy design. Developing cost-effective and efficient electrocatalysts is critical for large-scale hydrogen production via water splitting. The Ni-Co-W-Zr-P alloy coating offers a promising alternative to noble metal-based electrocatalysts. In this study, we developed a Co-W-Zr-incorporated Ni(P) coating using the electroless plating method. The integration of Co-W-Zr into the Ni(P) matrix notably enhances the number of active sites during the hydrogen evolution reaction. Electrochemical studies revealed a low overpotential of 413.5 mV of the coating when the current density is at −10 mA cm −2 . Kinetic parameters were analyzed using EIS measurements, and a potential mechanism for the hydrogen evolution reaction (HER) was proposed. The coating demonstrated exceptional stability, with no surface degradation even after prolonged electrochemical testing, making it suitable for large or irregularly shaped electrodes required in industrial applications
Hybrid orchestration of AI services and microservices in cloud-edge collaboration
No full textThe rapid development of AI accelerates the implementation and delivery of AI applications in diverse fields. In cloud-edge collaboration, delivering a complete AI application relies on the robust coordination between AI-supporting microservices and AI services. However, most existing studies only coarsely considered monolithic AI service orchestration while neglecting microservice orchestration. Such coarse-grained orchestration severely impacts application performance. To enable diverse high-performance AI applications, fine-grained hybrid orchestration of AI services and microservices (HOAIM) is highly desirable, yet presents formidable challenges. Due to heterogeneous services, call dependencies, and service multiplexing, fine-grained hybrid orchestration modeling is highly non-trivial. Moreover, the tight coupling between deployment and routing results in a complex joint optimization problem. To address this, we first propose a heterogeneous service orchestration network that supports orchestration optimization and automated management. Then, based on queuing networks and multi-instance models, we conduct an accurate analysis of delay and load. Furthermore, to achieve efficient hybrid orchestration, we propose preference-driven resource allocation and instance computation algorithms, along with reinforcement learning with action masking and reward shaping. Finally, extensive trace-driven simulations demonstrate that our algorithms optimize average response delay by up to 41.83%, and achieve significant advantages in load balancing, response success rate, and resource efficiency
Green orchestra: joint spatiotemporal task scheduling and hybrid energy coordination in computing power networks
No full textRecent advancements in information and communication technologies necessitate powerful computing power and network capabilities. Fortunately, Computing Power Networks (CPNs) have emerged to seamlessly integrate distributed computing resources via network orchestration, enabling high-throughput and on-demand computing services. However, CPNs consume substantial energy and generate significant carbon emissions when processing massive data. What’s worse, the interplay between CPN nodes and network paths, and spatiotemporal variations in renewable energy complicate energy-efficient task scheduling. To address these issues, we design a joint spatiotemporal task scheduling and hybrid energy coordination mechanism to efficiently manage and allocate computing, network, and energy resources, achieving green orchestra in CPNs. Firstly, we design a novel green CPN framework that synergizes computing resources, network resources, and enhanced energy coordination systems. Then, we propose a spatiotemporal task scheduling scheme with a triple selection of CPN nodes, routing paths, and forwarding time. The scheme can optimize energy consumption and carbon emissions while ensuring delay constraints and load balancing. Lastly, we formulate the problem as a Markov Decision Process (MDP) and design a customized Deep Reinforcement Learning (DRL) approach to solve it. Simulation results validate our scheme outperforms the benchmark schemes in learning efficiency, energy savings, carbon reduction, and renewable energy utilization efficiency
A near-field extension of the 3GPP geometry-based stochastic channel model for sub-Thz communications
No full textMany conventional radio channel models, such as 3GPP TR 38.901, assume far-field conditions and rely on the plane wave approximation. However, as antenna arrays grow in size and link distances decrease, near-field propagation effects become significant, particularly at sub-THz frequencies. This letter proposes an initial extension to the 3GPP channel model to incorporate near-field characteristics at these frequencies. The approach models both line-of-sight and non-line-of-sight paths using exact geometric distances. An image-theory-based method is introduced to model specular reflections. The extended model maintains consistency with the existing 3GPP framework while enhancing modeling accuracy in the near-field
