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Active attack and defense on attribute reduction with fuzzy rough sets
No full textAttribute reduction based on dynamically updated datasets in fuzzy rough sets plays a significant role in dealing with the uncertainty of time-evolving updated data. However, current research on attribute reduction lacks theoretical mechanisms to actively distinguish and defend against malicious interference in datasets. Aiming at this problem, an attribute reduction update framework with defense is proposed for dynamic datasets with adversarial attack. In this framework, an adversarial attack model is presented to select the optimal attacked attributes and construct the adversarial samples to generate the attack datasets. Based on this, a defense model is designed by constructing defense samples to avoid attacks. Firstly, the key identification sample pairs that determine the discernibility of the minimal element subset are defined, which are then used to define the attack target candidate set and construct adversarial samples. To alter the discernibility attributes of the key discernibility sample pairs, the attribute significance degree with attack preference is defined to select the unimportant attributes to attack. Then, the attack model is designed to select the optimal attacked candidate subset and generate the attack dataset. Targeting the attack strategy, defense samples for both the optimal attacked attribute subset and the useless attribute set are constructed to generate the defense matrix and defense datasets. Finally, a unified update strategy for attribute reduction after attack and defense is proposed to induce the updated reduct. Numerical experiments verify the rationality and effectiveness of the framework proposed in this paper based on the success rate of attack and defense, as well as the classification results
Quantum-enhanced federated learning for metaverse-empowered vehicular networks
No full textIn the rapidly evolving domain of vehicular metaverse, this study introduces a cutting-edge quantum-based decentralized and heterogeneity-aware federated learning framework for vehicular metaverse named QV-FEDCOM, which stands as a testament to the innovative fusion of quantum computing principles with federated learning (FL). This framework is ingeniously tailored to address the challenges in a vehicular metaverse, offering a cost-efficient and adaptive solution for the dynamic vehicular landscape. QV-FEDCOM is strengthened by key components like quantum sequential-training-program, with reinforcement learning-based dynamic mode switching to reduce communication costs and manage vehicle states adaptively, and the quantum vehicle-context-grouping utilizing hierarchical clustering and simulated annealing for effective vehicle grouping based on contextual data similarity, addressing the complexities of data heterogeneity. Additionally, the integration of quantum-inspired principal component analysis (Q-PCA) enhances memory efficiency, further optimizing the framework. These elements converge in the QV-FEDCOM algorithm, establishing a decentralized, efficient, and context-aware quantum federated learning (QFL) process that redefines learning dynamics in the vehicular metaverse. Our study also introduces an innovative quantum trajectory loss (QTL) function, specifically designed for trajectory prediction tasks, which combines the Huber loss with an angular deviation penalty to robustly handle errors and penalize large deviations in the predicted trajectory angle. The effectiveness of the QV-FEDCOM framework is rigorously validated through comprehensive simulations, with its performance meticulously compared against various adaptations, showcasing its transformative capabilities within the vehicular metaverse ecosystem.<br/
Incidence and risk factors for glaucoma and its clinical, mental health and economic impact in an elderly population: a longitudinal study
No full textObjectives To investigate the incidence and determinants of glaucoma in an elderly Chinese population, and clinical, mental health and economic impacts.Design This nationally representative, longitudinal study assessed self-reported 6-year (from 2011 to 2018) incident glaucoma diagnosis by a physician and measured biological, clinical and socioeconomical participant characteristics at baseline and endline.Setting In the first stage, 150 county-level units from across China were randomly selected with a probability-proportional-to-size sampling technique from a frame containing all county-level units nationwide. The sample was stratified by region and within region by urban district or rural county and per capita gross domestic product. The final sample of 150 counties included 30 out of 31 provinces and autonomous regions in China.Participants Consenting, community-dwelling Chinese persons aged 50 years and older.Primary and secondary outcome measures Incident glaucoma incidence (primary), factors associated with incident glaucoma (secondary), impact of glaucoma (secondary).Results Among 9973 individuals, 3.4% reported a glaucoma diagnosis between 2011 and 2018; Central China had the highest incidence (3.95%) and Eastern China the lowest (2.64%) between 2011 and 2018. Those diagnosed with glaucoma during 2011 and 2018 were of older age (beta coefficient: 0.050, 95% CI: 0.001, 0.001, p<0.001), had higher prevalence of diabetes (beta coefficient: 0.049, 95% CI: 0.028, 0.032, p<0.001), hypertension (beta coefficient: 0.019, 95% CI: 0.006, 0.008, p<0.001), smoking (beta coefficient: 0.029, 95% CI: 0.004, 0.020, p=0.004), alcohol consumption (beta coefficient: 0.026, 95% CI: 0.002, 0.017, p<0.009) and illiteracy (beta coefficient: −0.057, 95% CI: −0.030, –0.015, p<0.001). Logistic regression models showed significant association between incidence of the following characteristics and baseline glaucoma: poor self-reported distance vision (beta coefficient: 1.106, 95% CI: 0.701, 1.511, p<0.001), having hypertension (beta coefficient: 0.545, 95% CI: 0.496, 0.593, p<0.001), having diabetes (beta coefficient: 0.388, 95% CI: 0.326, 0.449, p<0.001), not having obesity (beta coefficient: −0.184, 95% CI: −0.239, –0.129, p<0.001) and lower mean value of health utility score of residents’ quality of life (beta coefficient: −0.040, 95% CI: −0.006, 0.776, p<0.001).Conclusions Glaucoma incidence rate varies among geographical regions in China. Several risk factors for incident glaucoma were identified. In addition, glaucoma was found to be associated with multiple physical and psychosocial outcomes. Targeted public health strategies are needed, emphasising early detection and better vision care, to alleviate the burden of glaucoma and improve well-being.<br/
Ensemble learning for short circuit fault location estimation in distribution networks
No full textOne of the most challenging tasks in power system operation is finding the exact location of a short circuit fault especially in distribution networks with branched structure. In this article a novel neural network ensemble model-based methodology is presented for fault location determination, which combines fault type classification, fault section identification and fault location estimation, and uses only 3 phase V-I measurements from a single sending end monitoring point as inputs. Two ensemble modelling paradigms are considered, namely, Neural Network (Multilayer Perceptron) Ensembles (NNE) and Random Forests (RF). Several different ensemble learning based structures are created using the proposed models and evaluated for fault location estimation on the IEEE-34 feeder benchmark. The average and maximum prediction errors under different fault conditions are used as performance metrics. The results for both the RF and stacked ensemble methods demonstrate that combining predictions enhances overall performance. State-of-the-art performance is achieved with a confidence-weighted stacked NNE-RF ensemble model.<br/
Scoping review of clinical decision aids in the assessment and management of febrile infants under 90 days of age
No full textBackgroundClinical decision aids (CDA) play an important role in the management of young febrile infants (under 90 days of age) who are at risk of serious or invasive bacterial infections (SBI/IBI). Since 2010, a number of tailored CDAs have been developed that allow for lower-risk infants to be managed safely while undergoing fewer investigations and not receiving parenteral antibiotics. We aimed to map the CDAs developed since 2010, their derivation methodology, and their variable components.MethodsA scoping review based on the Joana Briggs Institute framework was conducted for studies published between 2010 and 2025. A database search was conducted using Medline, Embase, Scopus, Web of Science, Google Scholar, and the Cochrane library. Studies evaluating the derivation, validation, and application of CDAs for the assessment of febrile infants were eligible for inclusion. Two reviewers independently screened, analysed, and extracted data from the literature.ResultsA total of 32 studies met the inclusion criteria. The majority of studies were conducted in North America and Canada (56%), followed by Europe (28%), and Asia (16%). Of the 32 studies, 14 were retrospective, 9 prospective and 9 secondary analysis of an available dataset. There were 32 CDAs that were either derived or validated across 32 studies. The derivation methodology was classified into four themes: (i) expert consensus and evidence synthesis; (ii) regression analysis; (iii) recursive partitioning; and (iv) machine learning. CDAs typically either identified a low-risk cohort through sequential assessment (n = 12) or predicted the risk of IBI/SBI using prediction models (n = 20). CDA sensitivity and specificity ranged from 46 – 100% and 9 – 95% respectively for SBI/IBI. The majority (n = 18) of the more complex CDA prediction models have been published in the last five years. The most common variables included within the CDAs were age, urinalysis, height of fever, C-reactive protein, and absolute neutrophil count.ConclusionThis scoping review highlights a wide range of CDAs with a trend towards prediction modelling rather than sequential assessment in the last five years. There is still variability in CDA properties, applicability, and diagnostic performance, necessitating further validation of common CDA and prediction models.<br/
Status of the non-native short-tailed field Vole (Microtus agrestis) (Rodentia, Cricetidae) in Ireland
No full textWe report the distribution of the Short-tailed Field Vole (Microtus agrestis) a recent arrival to Ireland, and discuss its probable deliberate introduction at multiple sites in the northern third of the island. It is a potential competitor of other small mammal species, both native and alien, and prey for a range of specialist and generalist avian and mammalian predators. It is not clear whether M. agrestis will simply replace existing prey items rather than become additional food for Irish raptors and carnivores. M. agrestis may also act as a reservoir for existing and novel pathogens of animals and humans. A first Draft Rapid Risk Assessment indicates that the introduction of M. agrestis represents a significant but not immediately severe threat to ecological sustainability and human health in Ireland. Deliberate introduction of non-native species and moving them within Ireland is illegal in northern and southern jurisdictions and further degrades Ireland’s biodiversity which is becoming less unique with each new arrival
Mechanisms and applications of manganese-based nanomaterials in tumor diagnosis and therapy
No full textTumors are the second most common cause of mortality globally, ranking just below heart disease. With continuous advances in diagnostic technology and treatment approaches, the survival rates of some cancers have increased. Nevertheless, due to the complexity of the mechanisms underlying tumors, cancer remains a serious public health issue that threatens the health of the population globally. Manganese (Mn) is an essential trace element for the human body. Its regulatory role in tumor biology has received much attention in recent years. Developments in nanotechnology have led to the emergence of Mn-based nanoparticles that have great potential for use in the diagnosis and treatment of cancers. Mn-based nanomaterials can be integrated with conventional techniques, including chemotherapy, radiation therapy, and gene therapy, to augment their therapeutic effectiveness. Further, Mn-based nanomaterials can play a synergistic role in emerging treatment strategies for tumors, such as immunotherapy, photothermal and photodynamic therapy, electromagnetic hyperthermia, sonodynamic therapy, chemodynamic therapy, and intervention therapy. Moreover, Mn-based nanomaterials can enhance both the precision of tumor diagnostics and the capability for combined diagnosis and treatment. This article examines the roles and associated mechanisms of Mn in the field of physiology and tumor biology, with a focus on the application prospects of Mn-based nanomaterials in tumor diagnosis and treatment
A dynamic surface roughness prediction system based on machine learning for the 3D-printed carbon-fiber-reinforced-polymer (CFRP) turning
No full textThis study proposes a novel surface roughness prediction system that uses machine learning and dynamic inputs for additively-manufactured, Carbon-Fiber-Reinforced-Polymer tubular workpieces. First, an investigation of the effects of standard machining conditions on the generated surface roughness was carried out, to assess the machinability of the 3D-printed, composite workpieces during turning. Two sets of specimens were fabricated, each with different wall layer thickness (WT) and a set of experiments was designed with respect to the selected range of cutting-speed (Vc), feed (f) and depth-of-cut (ap). As expected, it was found that all process parameters affected the generated roughness with cutting-speed and feed contributing the most to the results. The research hypothesis was that an Artificial Neural Network (ANN) that includes vibration signals together with the cutting conditions would provide better surface roughness predictions. Two shallow, three-layered ANN models were used. The first model utilized the machining parameters and the second model was based on the first one, with the addition that the acquired acceleration signals, to provide meaningful representations of vibrations with the aid of the Principal Component Analysis. The first model yielded a Mean Absolute Percentage Error (MAPE) equal to 2.59%. The second model provided more accurate surface roughness predictions, with MAPE being reduced to 1.51%. Finally, a Generic Algorithm (GA) was employed to identify the optimal process parameters for minimizing the response. The best combination was determined to be: WT = 0.50 mm, Vc = 173.2 m/min, f = 0.04 mm/rev and ap = 0.50 mm.<br/
Anterior right thoracotomy versus mini-sternotomy for surgical aortic valve replacement: a systematic review and meta-analysis. Society for Cardiothoracic Surgery in Great Britain and Ireland (SCTS) Annual Meeting 16th-18th March, 2025, Edinburgh International Conference Centre. In: Supplements | Journal of Cardiothoracic Surgery, 2026.
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