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Exercise based cardiac rehabilitation for atrial fibrillation: Cochrane systematic review, meta-analysis, meta-regression and trial sequential analysis
Get PDFObjective: To undertake a contemporary review of the impact of exercise based cardiac rehabilitation (ExCR) for patients with atrial fibrillation (AF). Data sources: CENTRAL, MEDLINE, Embase, PsycINFO, CINAHL, WoS Core Collection, LILACS and trial registers were searched from inception up to 24 March 2024. Eligibility criteria: Randomised clinical trials (RCTs) comparing ExCR with any non-exercise control. Design: Random effect meta-analyses presented as effect estimates and 95% CIs. Meta-regression examined study level effect modification. Cochrane risk of bias, GRADE (Grading of Recommendations Assessment, Development and Evaluation) and trial sequential analysis (RTSA) were applied. Results: 20 RCTs (n=2039) with a mean follow-up of 11 months showed that ExCR did not impact all cause mortality (8.3% vs 6.0%, relative risk (RR) 1.06, 95% CI 0.76 to 1.48) or serious adverse events (2.9% vs 4.1%, RR 1.30, 95% CI 0.66 to 2.56) but did reduce AF symptom severity (mean difference (MD) −1.61, 95% CI −3.06 to −0.16), AF burden (MD −1.61, 95% CI −2.76 to −0.45), episode frequency (MD −0.57, 95% CI −1.07 to −0.07), episode duration (MD −0.58, 95% CI −1.14 to −0.03), AF recurrence (RR 0.68, 95% CI 0.53 to 0.89), and improved exercise capacity (maximal oxygen consumption (VO2 peak) MD 3.18, 95% CI 1.05 to 5.31 mL/kg/min). There was benefit for the mental component but not the physical component of a health related quality of life questionnaire. No differential effects across AF subtype, ExCR dose, or mode of delivery were seen. Conclusion: Meta-analyses of RCT evidence for ExCR in patients with AF demonstrated several clinical benefits without an increase in serious adverse events. GRADE and RTSA assessments indicated further high quality and adequately powered RCTs are needed
Mangroves support an estimated annual abundance of over 700 billion juvenile fish and invertebrates
Get PDFMangroves are a critical habitat that provide a suite of ecosystem services and support livelihoods. Here we undertook a global analysis to model the density and abundance of 37 commercially important juvenile fish and juvenile and resident invertebrates that are known to extensively use mangroves, by fitting expert-identified drivers of density to fish and invertebrate density data from published field studies. The numerical model predicted high densities throughout parts of Southeast and South Asia, the northern coast of South America, the Red Sea, and the Caribbean and Central America. Application of our model globally estimates that mangroves support an annual abundance of over 700 billion juvenile fish and invertebrates. While abundance at the early life-history stage does not directly equate to potential economic or biomass gains, this estimate indicates the critical role of mangroves globally in supporting fish and fisheries, and further builds the case for their conservation and restoration
Improving Novelty Search with a Surrogate Model and Accuracy Objectives to Build High-Performing Ensembles of Classifiers
Get PDFNeuroevolution combined with Novelty Search to promote behavioural diversity is capable of constructing high-performing ensembles for classification. However, using gradient descent to train evolved architectures during the search can be computationally prohibitive. We have proposed a method to overcome this limitation by using a surrogate model which estimates the behavioural distance between two neural network architectures, required to calculate novelty scores. This has demonstrated a speedup of 10 times over previous work, significantly improving on previous reported results on three benchmark datasets from Computer Vision—CIFAR-10, CIFAR-100, and SVHN. This method makes an explicit search for diversity considerably more tractable for the same bounded resources. Here we investigate a range of search methods that span the full spectrum of favouring accuracy, diversity, or different combinations of both. Surprisingly, we show that multiple unique combinations between a diversity metric and accuracy give rise to similar results. This enables us to posit the existence of a diversity-accuracy duality in ensembles of classifiers, which suggests that there might not be a need to find a trade-off between the two
Shame- and guilt-proneness as mediators of PTSD/DSO symptoms in young adults
No full textThe aim of this study was to investigate the relationships between trauma exposure, shame and guilt proneness and the development of PTSD and Disturbances in Self‐Organisation (DSO) symptoms in young adults. Specifically, we hypothesised that trauma exposure would be positively correlated with PTSD and DSO symptoms and that shame and guilt would mediate this relationship. A total of 160 young adults participated in this study. Three models were tested: (1) a model with direct effects from trauma exposure to PTSD and DSO, (2) an indirect effects model where the direct paths were constrained and (3) a full model with both direct and indirect effects. Shame and guilt proneness showed a strong correlation with PTSD and DSO. Direct effects revealed that trauma exposure predicted PTSD, DSO, guilt and shame proneness. Guilt had a strong effect on PTSD, while shame had the strongest effect on DSO. Indirect effects showed that trauma exposure significantly predicted both PTSD and DSO through heightened guilt and shame. The strongest indirect relationships were trauma exposure to PTSD via guilt and trauma exposure to DSO via shame. This study demonstrates that trauma exposure is associated with heightened levels of shame and guilt proneness, which, in turn, predict greater severity of PTSD and DSO symptoms. These findings suggest that emotional regulation, particularly in relation to shame and guilt proneness, should be targeted in interventions for trauma‐related disorders. Future research should further explore the role of these emotions in the development of complex PTSD
Black Hole Prediction in Backbone Networks: A Comprehensive and Type-Independent Forecasting Model
Get PDFNetwork backbone black holes(BH) pose significant challenges in the Internet by causing disruptions and data loss as routers silently drop packets without notification. These silent BH failures, stemming from issues like hardware malfunctions or misconfigurations, uniquely affect point-to-point packet flows without disrupting the entire network. Unlike cyber attacks and network intrusions, BHs are often untraceable, making early detection vital and challenging. This study addresses the need for an effective forecasting solution for BH occurrences, especially in environments with unlabeled traffic data where traditional anomaly detection methods fall short. The Type-Independent Black Hole Forecasting Model is introduced to predict BH occurrences with high precision across various anomalies, including contextual and collective anomaly types. The three-stage methodology processes unlabeled time-series network data, where the data is not pre-labeled as anomaly or normal, using machine learning and deep learning techniques to identify and forecast potential BH occurrences. The ’Point BH Identification and Segregation’ stage segregates point BH traffic using Density-Based Spatial Clustering of Applications with Noise(DBSCAN), followed by Reintegration and Time Series Smoothing. The final stage, Advanced Contextual and Collective BH Detection leverages Convolutional AutoEncoder(Conv-AE) with window sliding for advanced anomaly detection. Evaluation using a dual-dataset approach, including real backbone network traffic and a time-series adapted public dataset, demonstrates the adaptability of the model to real backbone BH detection systems. Experimental results show superior performance compared to state-of-the-art unsupervised anomaly forecasting models, with a 98% detection rate and 90% F-1 score, outperforming models like MultiHeadSelfAttention, which is the main building block of Transformers
Federated Learning: Concepts, Challenges and Implementation
No full textFederated Learning (FL) has emerged as an innovative approach for distributed neural networks, allowing multiple clients to collaboratively train a model without centralising their data, thus preserving decentralisation and data privacy. This review provides a comprehensive discussion of FL's core concepts, including its components, key challenges, and distinctions from traditional machine learning. The paper outlines the various types of FL, highlighting applications in privacy-sensitive fields like healthcare and finance. It also addresses recent advancements in self-supervised learning, personalisation, and multi-modal applications within FL, as well as the integration of blockchain technology for enhanced privacy. Key advantages of FL are discussed, such as reduced communication overhead through the transmission of model parameters instead of raw data, which minimises network load and enhances privacy protection. Furthermore, the paper explores emerging questions for FL development, including scalability, fairness, and system standardisation. Real-world examples, such as Google Gboard and brain tumour segmentation, are presented to illustrate FL's practical impact. Finally, the paper discusses future directions, including potential integration with other AI techniques like reinforcement learning and transfer learning. This review provides valuable insights for researchers and professionals who are new to FL or seek a broader understanding of its ecosystem. While there are few studies that explore limited aspect of FL, this review adopts a holistic approach and covers all aspects of FL including foundational concepts, implementation, challenges faced by FL, and real-world implementation. The broader scope, which spans FL from concepts to practical implementation, makes it particularly distinctive and a valuable contribution
The Effects of Virtual Reality During Labour on Perceived Pain, Use of Pain Relief and Duration of Labour: A Pilot Matched Case–Control Study in Belgium
Get PDFBackground: Virtual reality has been shown to reduce pain during labour. We aimed to determine whether virtual reality reduces analgesia use and shortens labour duration. Methods: A non-randomised pilot study was conducted, using a matched case–control design (1:2 ratio). Cases were women who voluntarily used virtual reality alongside standard intrapartum pain management, including non-pharmacological methods and/or epidural analgesia. Controls received standard intrapartum pain management. Results: A total of 108 women were included for analysis (36 cases vs. 72 controls). Perceived pain scores before and after virtual reality use did not differ significantly (p = 0.43, p = 0.73), suggesting a limited immediate analgesic effect under current conditions. Epidural analgesia rates and cervical dilation at initiation of analgesia did not show significant differences between cases and controls (p = 0.13, p = 0.42). After adjusting for induction of labour and cervical dilation at admission, there were no significant differences for duration of epidural analgesia (p = 0.86, p = 0.56), duration of labour (p = 0.64, p = 0.55), or vaginal birth (p = 0.23). Adjusted models indicated a non-significant trend toward shorter durations of labour, birth, and epidural exposure for cases. Conclusions: Our pilot study did not reveal a decrease in perceived pain or epidural analgesia use or an effect on duration of labour and vaginal birth
Design Optimization of Surface Seawater Intake Piping for Hybrid Ocean Thermal Energy Conversion Pilot Plant
Get PDFHybrid Ocean Thermal Energy Conversion (H‐OTEC) systems are characterized by the adoption of both open‐loop and closed‐loop Rankine cycles. In the closed‐loop configuration, a working fluid such as ammonia is evaporated in a heat exchanger, utilizing the heat from water vapor generated in a vacuum chamber by warm surface seawater introduction. The vapor is then expanded through a turbogenerator to produce electricity before being condensed in a cold‐water heat exchanger using cold water. In Malaysia, significant advancements are being made in the technology for seawater suction systems, particularly for applications in fish breeding, farming, desalination plants, and power generation. The operation of an H‐OTEC Experimental system at UPM I‐AQUAS, Port Dickson, Malaysia depends on surface seawater for turbine operation, necessitating the installation of a piping system spanning 336 m from the H‐OTEC facility to the suction location. Challenges associated with seawater intake systems include pump cavitation due to high suction head, pipe contamination by organisms such as barnacles and algae, pump placement, strainer size, and pipe diameter intake. The primary objective of this study is to provide valuable insights, conduct field testing, and gather necessary data for the development of the first‐of‐its‐kind surface seawater piping system for H‐OTEC in the Asian region. This objective was accomplished through the installation of a centrifugal pump unit with a flow rate of 40 m3/h (600 L/min), the laying of 106 mm inner diameter parallel pipes, installation of strainers, and a booster pump connected to a 125 A HDPE pipe. The collected data provides the necessary input in establishing the layout design and location selection of the seawater intake pipe, introduce a novel helical crossflow self‐cleaning suction screen water intake system, facilitate weight structure design, and enable pump sizing and suction pump analysis
Careers work in Scottish state secondary schools: The guidance teacher as pastoral firefighter
Get PDFThis article examines the delivery of career education, information, advice and guidance (CEIAG) to young people in Scottish state secondary schools. It explores the role of guidance teachers, and the influence of national policy initiatives, curriculum guidelines and external agencies on their work. The perspective of teachers is the central focus for this research. Research interviews were conducted with 10 guidance teachers and contextualised with input from five topic experts. Data were analysed using thematic analysis. Key issues reported by participants included growing demands on their time, difficulty with role boundaries, lack of training, and regional variation in provision for education-employer links. Areas for policy and practice improvement are identified. These include recommendations for staff roles, quality standards, and training
The eyes eat first: Improving consumer acceptance of plant-based meat alternatives by adjusting front-of-pack labeling
Get PDFThe substitution of meat products with plant-based meat (PBM) alternatives is seen to foster sustainable consumption. It can play an important role in helping reach greenhouse gas emission targets. While consumers generally perceive PBM alternatives as more environmentally friendly and healthier than meat, they often find them less hedonically appealing and too expensive, which hinders their widespread adoption. One effective strategy to encourage consumers toward more sustainable choices is the use of front-of-pack information, such as claims and labels. This study identifies the most effective labeling strategy to increase consumers' preference for PBM burger patties through a three-fold research approach, namely, a supermarket audit in the UK, a best-worst scaling study (i.e., Maximum Difference Scaling), and a discrete choice experiment (i.e., choice-based conjoint analysis). In the UK market, front-of-pack labels and claims presented on PBM products can be categorized into those primarily related to nutrition, ecological welfare, and taste. These categories correspond to three distinct consumer segments extracted from a best-worst scaling study. A subsequent discrete choice experiment, which compared labeled PBM patties vis-à-vis meat patties, revealed that a third-party accredited taste label has the potential to gain the highest market share and willingness-to-pay among all types of labels/claims. Our findings underscore the importance of adopting an appropriate labeling strategy to foster sustainable food consumption
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