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Using our nursing superpower: Improving cancer care for older people and their care partners by addressing what matters most
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Sex differences in absolute and relative changes in muscle size following resistance training in healthy adults: a systematic review with Bayesian meta-analysis
Get PDFBackgroundMuscle hypertrophy may be influenced by biological differences between males and females. This meta-analysis investigated absolute and relative changes in muscle size following resistance training (RT) between males and females and whether measures of muscle size, body region assessed, muscle fibre type, and RT experience moderate the results.MethodsStudies were included if male and female participants were healthy (18–45 years old) adults that completed the same RT intervention, and a measure of pre- to post-intervention changes in muscle size was included. Out of 2,720 screened studies, 29 studies were included in the statistical analysis. Bayesian methods were used to estimate a standardised mean difference (SMD), log response ratio (lnRR) with exponentiated percentage change (Exp. % Change of lnRR), and probability of direction (pd) for each outcome.ResultsAbsolute increases in muscle size slightly favoured males compared to females (SMD = 0.19 (95% HDI: 0.11 to 0.28); pd = 100%), however, relative increases in muscle size were similar between sexes (Exp. % Change of lnRR = 0.69% (95% HDI: −1.50% to 2.88%)). Outcomes were minimally influenced by the measure of muscle size and not influenced by RT experience of participants. Absolute hypertrophy of upper-body but not lower-body regions was favoured in males. Type I muscle fibre hypertrophy slightly favoured males, but Type II muscle fibre hypertrophy was similar between sexes.ConclusionOur findings strengthen the understanding that females have a similar potential to induce muscle hypertrophy as males (particularly when considering relative increases in muscle size from baseline) and findings of our secondary analyses should inform future research that investigates sex differences in highly trained participants and muscle fibre type-specific hypertroph
The Accuracy of Artificial Intelligence in the Diagnosis of Soft Tissue Sarcoma: A Systematic Review and Meta-analysis
Get PDFIntroductionSoft-tissue sarcomas (STSs) constitute a rare group of malignancies. Diagnostic biopsies require experts and are invasive with long diagnostic intervals. Artificial intelligence (AI)-based STS models may serve as accurate detection and categorization tools; however, their diagnostic performance remains questionable.MethodThe PubMed, Scopus, and Web of Science databases were searched for related studies published until January 10, 2024. Studies that developed or used AI-based models to diagnose STS were included.ResultsNine studies were included in this meta-analysis. The common effects model yielded an accuracy of 0.8923 [0.8831; 0.9016], and the random effects model yielded an accuracy of 0.8524 [0.8132; 0.8916]. The Tau ^2 was 0.0094 [0.0055; 0.0202] and the I^2 statistic was 93.2% [91.1%, 94.7%], suggesting a high level of heterogeneity among the studies. The most accurate model was the decision tree (DT) model used in this study, with an accuracy of 0.9900 [0.9675–1.0125]. The least accurate model was the MLP model used in this study, which had an accuracy of 0.5720 [0.5107; 0.6333]. The pooled sensitivity of the AI model was 100%. Radiomics and simple vector-machine-based models are the most sensitive.ConclusionAI-based models show promising results for the diagnosis of STS. However, future studies should address the value of AI in the real-world setting. To provide precise comparisons across various models, it is essential to create a uniform training dataset to mitigate any sources of bias or heterogeneity. This information will assist scientists in identifying the optimal model for diagnosing STS and implementing this model in clinical practice
Event-triggered Bipartite Consensus for Multi-agent Systems via Model-free Sliding-mode Scheme
No full textThis paper addresses the challenge of achieving fully distributed event-triggered bipartite consensus in discrete-time nonlinear multi-agent systems (MASs) characterized by unknown dynamic models and antagonistic interactions. It begins by transforming the bipartite consensus issue into a standard consensus problem through a combined measurement error function. A dynamic linearization model is subsequently established for the input and the combined measurement error function, easing the strongly connected requirement of MASs' communication topology. To enhance performance, an event-triggered model-free sliding-mode bipartite consensus algorithm is proposed, designed to boost convergence speed, reduce steady-state error, and relieve communication burden. The convergence of the proposed method is rigorously proven, allowing for control of fine-tuned specific practical needs. Simulation studies are conducted to verify the effectiveness of the proposed scheme
Carbon-Optimised Refurbishment Measures for a Simulated House in Edinburgh
Get PDFThis research is conducted in the context of current efforts for net zero delivery in Scotland. Such a target is widely acknowledged as challenging by the industry necessitating some aspects of building design to change for higher performance. This study looks at ‘what is optimal’ in terms of carbon, both in operation and embodied in materials for dwellings by using the Pareto Optimal solution in a simulated case study in Edinburgh. At first, the potential of available glazing systems to find an optimum in operational and embodied carbon is investigated. The study then calculated the embodied carbon and operational carbon for added insulation and compared them against each other to find the most energy and environmentally-efficient option. An analysis to investigate the LCA of the 52 HVAC systems for the second phase of the building's life span is then conducted and finally a parametric analysis of using shading devices to observe if they can offer operational savings for the case study concludes the paper. This study demonstrates some recommended design solutions by globally used standards e.g. Passivhaus and common practices may not lead to carbon emissions minimisation in the refurbishment process
High Impact Biomass Valorization for Second Generation Biorefineries in India: Recent Developments and Future Strategies for Sustainable Circular Economy
Get PDFIndia’s rapidly growing automobile industry has intensified the need for sustainable fuel alternatives to reduce dependency on imported fossil fuels and mitigate greenhouse gas (GHG) emissions. This study examines the potential of second-generation biorefineries as a comprehensive solution for efficient biomass valorization in India. With a projected bioethanol demand of 10,160 million liters by 2025 for India’s 20% ethanol blending target, there is an urgent need to develop sustainable production pathways. The biorefinery approach enables simultaneous production of multiple valuable products, including bioethanol, biochemicals, and bioproducts, from the same feedstock, thereby enhancing economic viability through additional revenue streams while minimizing waste. This paper systematically analyzes available biomass resources across India, evaluates integrated conversion technologies (biochemical, thermochemical, and synergistic approaches), and examines current policy frameworks supporting biorefinery implementation. Our findings reveal that second-generation biorefineries can significantly contribute to reducing GHG emissions by up to 2.7% of gross domestic product (GDP) by 2030 while creating rural employment opportunities and strengthening energy security. However, challenges in supply chain logistics, technological optimization, and policy harmonization continue to hinder large-scale commercialization. The paper concludes by proposing strategic interventions to overcome these barriers and accelerate the transition toward a sustainable circular bioeconomy in India
Policing Together: a framework for innovating volunteer policing through community-driven partnerships
No full textPolice agencies face growing pressure from limited resources and increasingly complex crime patterns. Innovation in public safety is no longer optional, it is imperative. Volunteer policing has long played a vital role in supporting law enforcement. Today, however, it is hindered by declining participation, lack of diversity, and persistent retention challenges. Drawing on decades of collective experience, our international team comprising volunteer officers, police leaders, trainers, professors, researchers, and consultants proposes Policing Together, a community-driven framework. This framework integrates evidence from the 2024 International Symposium on Volunteering in Policing (Edinburgh) and the National Women in the Special Constabulary Conference 2024 (Birmingham), alongside international research and practice. We propose engaging various e groups such as capable and diverse citizens, specialists, employers, organizations and youths, and employing varied methods, including digital platforms, school engagement, and grassroots recruitment, to overcome systemic barriers. Rooted in pragmatist philosophy, Policing Together offers a flexible, inclusive framework that sustains public trust and community resilience
heARtbeat: Augmented reality for teaching electrocardiogram electrode placement
Get PDFThe 12-lead Electrocardiogram (ECG) is currently one of the most widely used, operator-dependent clinical skills in healthcare. To enable correct interpretation of ECG recordings, electrodes must be placed in specific anatomic locations on the chest, legs and arms. Traditional learning practices of ECG electrode placement are not standardized and burdening on faculty staff and resources. Thus, misplacement of ECG electrodes affects the accuracy of interpretation and referral to medical treatments. Augmented Reality (AR) offers a safe and scalable metric for students to learn and practice various procedures and skills by creating hyper-realistic simulations. Yet, within the healthcare domain, the main applications of AR are in the field of surgery, rehabilitation, and anatomy teaching. Therefore, this article documents the design of an AR application for teaching ECG electrode placement and the tool's Alpha evaluation by means of an expert validation and case study trial. Users of the application found it to be Inspiring (5.0) and Educational (4.9) but less effective in terms of User Friendliness (2.8) on a 7-point Likert scale. Regarding the educational potential, 57 % of the end users found the tool to increase their knowledge of ECG placement
Integrated Sensing and Communications for Reconfigurable Intelligent Surface-Aided Cell-Free Networks
No full textThis work explores a cell-free integrated sensing and communication (CF-ISAC) framework in which distributed access points work together to support communication users (UEs) with assistance from multiple reconfigurable intelligent surfaces (RISs) while simultaneously performing target sensing. An effective strategy is introduced for the joint optimization of communication parameters, sensing beamforming designs, and reflecting coefficients, with the goal of enhancing the minimum signal-to-interference-plus-noise ratio (SINR) among all UEs. To address the complexity of this non-convex optimization problem, a robust alternating optimization method is designed. The numerical results confirm that the proposed approach significantly boosts the minimum SINR in CF-ISAC systems, demonstrating the advantages of utilizing RISs
Balancing purity, pragmatism and partnership in occupational therapy clinical research: trials and tribulations of recruiting for a multisite stroke trial
Get PDFPurposeThe REfLECTS trial was a randomised controlled trial (RCT) testing effectiveness of mirror box therapy in upper limb rehabilitation among sub-acute stroke patients. REfLECTS was a large-scale, rigorously planned study; however, implementation was challenging due to low recruitment rates, 803 patients were screened and only 26 were recruited. The purpose of this study is to explore factors and challenges influencing the recruitment of participants to this multisite RCT.Design/methodology/approachA Communities of Practice (CoP) approach was used. Bi-monthly steering meetings were held to address recruitment issues and a focus group was conducted post-recruitment to identify influencing factors. Data from meeting minutes and the focus group were amalgamated and analysed using thematic analysis.FindingsThe trial team (n = 14) comprising academics (n = 5) and clinicians (n = 9) contributed to the steering meetings. The focus group (n = 9) included researchers (n = 5) and clinicians (n = 4). Two major themes were identified: impact of COVID-19, including shorter in-patient stays affecting trial recruitment and clinical trials (and tribulations) highlighting therapist-led dilemmas and patient-related factors leading to patients declining to participate.Research limitations/implicationsThe CoP identifies important contextual clinical service-based and therapist-led factors, which have pragmatic impacts on the design and implementation of high-quality occupational therapy clinical trials. High-quality occupational therapy evidence for stroke rehabilitation is essential, however, there is a need to critically reflect on how clinical research can best be implemented in clinical practice to ensure implementation and subsequent usability of findings. Provision of ongoing support for clinicians during trial implementation is essential to manage the therapist-led clinical dilemma.Originality/valueGood research helps us improve therapy, however, maintaining research purity in the pragmatic “real world” of stroke rehabilitation is challenging. Clinicians encounter ethical dilemmas with randomisation in high-quality clinical trial methodologies and this study identifies the need for ongoing trial implementation support to ensure clinician and patient engagement, enhance recruitment and maintain research integrity
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