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An improved pelican optimization-kernel extreme learning machine for highly accurate state of charge estimation of lithium-ion batteries in energy storage systems.
No full textThe accurate estimation of the state of charge (SOC) of lithium-ion batteries is crucial for real-time monitoring and safety control. This paper proposes a novel method for estimating SOC by optimizing the kernel extreme learning machine (KELM) with a radial basis function (RBF) kernel using an enhanced pelican optimization algorithm (POA), termed TWCS-PO-KELM. This approach addresses the challenges of real-time estimation and low accuracy in conventional methods. This paper improves the basic POA by incorporating Tent chaotic mapping to diversify the initial population, a nonlinear inertia weight factor to improve local optimization, and a Cauchy variation alongside a sparrow alert mechanism to enhance the algorithm's robustness and optimization performance. The KELM model, based on the RBF kernel, enables efficient non-linear mapping of the input features, improving the accuracy of SOC estimation. Experimental results demonstrate that the TWCS-PO-KELM model offers superior SOC estimation with a mean absolute error (MAE) of 0.143%, root mean square error (RMSE) of 0.172%, and mean absolute percentage error (MAPE) of 1.344% under BBDST conditions, showcasing its strong tracking ability and robustness in comparison to other methods
An exploration of student experiences and perceptions of a physiotherapy student-led clinic.
Get PDFStudent-Led Clinics (SLCs) offer a structured, real-life learning environment for healthcare students, where they provide services under supervision. SLCs can facilitate increased confidence with clinical reasoning, improve skill development and enhance leadership skills. Existing research has generally captured students' experiences at the end of their SLC participation, lacking a longitudinal perspective and therefore, little is known about how students' perceptions develop over time. This study aimed to explore physiotherapy students' experiences in a neurological rehabilitation SLC, with a focus on participants' perceptions captured after attending specific SLC sessions and over the course of multiple SLC sessions. Using interpretative phenomenology, video diaries were used to explore student experiences over six weeks at a physiotherapy neurological rehabilitation SLC. Thematic analysis and methodological rigour ensured credibility and trustworthiness of the findings. To appraise changes over time, data were analysed week by week on an individual subject basis and within and between participants. Themes from participant experiences included: 1) student professional development through engagement in SLC; 2) influence of collaborative environment in SLC on student learning and development; 3) the role and impact of patients on the student experience; and 4) efficacy of SLC management. Participants highlighted positive aspects such as skill development, supervision quality, teamwork, and patient interactions. Challenges included feeling unprepared for the clinic environment and managing complex patient cases. This study explored the evolving perceptions and experiences of physiotherapy students participating in a neurological rehabilitation SLC over time using a diary method and has demonstrated that SLCs allow for student development over time and can be a useful adjunct for development of practice-based learning experiences. SLCs provide an environment for students to learn and offer time, space and constructive challenge to problem solve in a supported real-life setting. SLCs can offer a distinct and beneficial learning experience for physiotherapy students, fostering skill development and a deeper understanding of patient-centred care
I did it before, so I can do it again(?): recalling sucess, expectations of future success and the impact of ease-of retrieval and attributions.
Get PDFIt is widely assumed that recalling past success raises expectations of future success ("expectancy"). However, experimental research investigating that assumption has generated mixed results. The present study examined two (meta)cognitive factors that may play a role during "recall success" interventions: ease-of-retrieval (i.e. the ease/difficulty with which success is recalled) and causal attributions (i.e. the factors to which the success is attributed). Three experiments were conducted with English-speaking adults across the world. After being asked to recall either attraction "success(es)" or attraction "failure(s)," participants reported the extent to which they expected to attract someone in the future ("expectancy"). Results suggest that difficulty in retrieving examples of success and failure to attribute recalled success to stable/general factors have a negative impact on expectancy. Moreover, individuals with low self-perceived mate value are apparently more likely to experience difficulty-in-retrieval and less likely to attribute (attraction) success to stable/general factors. Unless ease-of-retrieval and attributions are addressed, those most in need of an expectancy boost may not benefit from "recall success" interventions
The conundrum of antidepressant-induced anhedonia: a blended patient-psychologist perspective.
Get PDFThis article constitutes a Patient Perspective, grounded in lived experience. Its primary aim is to enhance awareness of antidepressant-induced anhedonia by providing experience-based insights, relevant to clinicians, researchers, and caregivers. My own experiences with treatment-resistant depression-anxiety have been significant and long-lasting. In my 22-year-plus journey of illness experience - and having taken over twenty-three antidepressant medications - emotional-blunting, anhedonia, and mania have all, at times, been side-effect-related factors. This work explores the conundrum of antidepressant-induced anhedonia, developing an in-depth patient perspective useful for mental health practitioners, psychiatrists, psychologists and for wider formal professional and informal non-professional caring actors. I write this via a reflexive lens as a long-term mental health patient, while also recognising my dual-positionality as a Chartered Psychologist and an academic with a PhD working in the field of mental health. Thus, my dual-perspective provides a unique lens useful for translating the patient experience to a wider caregiving audience: fostering understanding and deepened awareness of the anhedonia experience. Implications for patient-care are discussed
Transforming manufacturing quality management with cognitive twins: a data-driven, predictive approach to real-time optimization of quality.
Get PDFIn the ever-changing world of modern manufacturing, maintaining product quality is of great importance, yet extremely difficult due to complexities and the dynamic production paradigm. Currently, quality is rather reactively measured through periodic inspections and manual assessments. Traditional quality management systems (QMS), through these reactive measures, are often inefficient because of their higher operational cost and delayed defect detection and mitigation. The paper introduces a novel cognitive twin (CT) framework, which is the next evolved version of digital twin (DT). It is designed to advance the current quality management in flexible manufacturing systems (FMSs) through real-time, data-driven, and predictive optimization. This proposed framework uses four data types, namely feedstock quality (Qf), machine degradation (Qm), product processing quality (Qp), and quality inspection (Qi). By utilizing the power of machine learning algorithms, the cognitive twin constantly monitors and then analyzes real-time data. The cognitive twin optimizes the above quality components. This enables a very proactive decision making through an augmented reality (AR) interface by providing real-time visual insights and alerts to the operators. Thorough experimentation was conducted on the aforementioned FMS. Through the experiments, it was revealed that the proposed cognitive twin outperforms conventional QMSs by a great margin. The cognitive twin achieved a 2% improvement in the total quality scores. A 60% decrease in defects per unit (DPU) is observed as well as a sharp 40% decrease in scrap rate. Furthermore, the overall equipment efficiency (OEE) increased to 93–96%. The overall equipment efficiency increased by 11.8%, on average, from 82% to 93%, and the scrap rate decreased by 33.3% from 60% to 40%. The excellent results showcase the effectiveness of cognitive twin quality management via minimum wastage, continuous quality improvement, and enhancement in operational efficiency in the paradigm of smart manufacturing. This research study contributes to the field of industry 4.0 by providing a comprehensive, scalable, and adaptive quality management solution, thus leading the way for further advancements in intelligent manufacturing systems
Standardization and compliance challenges for arc flash protection.
Get PDFArc flash events pose significant hazards to personnel and equipment in electrical systems, particularly in industrial and utility operations. Accurate estimation of incident energy and effective risk mitigation remain critical for compliance with established electrical safety standards. This paper presents a comprehensive evaluation of three principal arc flash protection frameworks: NFPA 70E, IEEE 1584-2018, and OSHA 1910.269. The study integrates empirical modelling, historical incident data, and machine learning techniques to assess the predictive accuracy and compliance challenges associated with each standard. Incident data from OSHA and NFPA sources (2010-2024) were analysed to identify patterns in fault current, voltage class, arc duration, and PPE usage. Incident energy was computed using IEEE 1584-2018 equations and compared with reported injury severities. The findings indicate that while IEEE 1584 predictions align with observed outcomes in most configurations, notable underestimations occur in horizontal conductor and open-air systems. NFPA 70E, although widely adopted, provides qualitative guidelines and relies on external methods such as IEEE 1584 for energy calculation. A logistic regression model trained on the incident dataset achieved 87% accuracy in predicting severe injury outcomes based on system parameters. This model was extended with a neural network architecture to support real-time classification of arc flash risk. The integration of sensor data through IoT enabled monitoring and predictive analytics enables dynamic hazard assessment and supports pre-emptive mitigation. A comparative analysis highlights the strengths and limitations of each standard. IEEE 1584-2018 offers robust empirical modelling but depends on configuration-specific inputs. NFPA 70E provides structured procedural guidance but lacks inherent computational capabilities. OSHA 1910.269 enforces general safety compliance but does not prescribe detailed modelling techniques. This study proposes a data-driven framework that enhances arc flash hazard prediction through validated equations, statistical analysis, and AI-based risk models. Recommendations for standard refinement and predictive system integration are presented to support proactive electrical safety management
A critical review of AI-based battery remaining useful life prediction for energy storage systems.
Get PDFThis paper provides a comprehensive review of recent advances in remaining useful life prediction for lithium-ion battery energy storage systems. Existing approaches are generally categorized into model-based methods, data-driven methods, and hybrid methods. A systematic comparison of these three methodological paradigms is presented, with hybrid methods further divided into filter-based hybrids and data-driven hybrids, followed by a comparative analysis of remaining useful life prediction accuracy. The literature analysis indicates that data-driven hybrid methods, by integrating the strengths of physical mechanism modeling and machine learning algorithms, exhibit superior robustness under complex operating conditions. Among them, the hybrid framework combining long short-term memory networks with an eXtreme Gradient Boosting model optimized by the Binary Firefly Algorithm demonstrates the highest stability and accuracy in the reviewed studies, achieving a root mean squared error below 2% and a mean absolute percentage error below 1%. Future research may further enhance the generalization capability of this framework, reduce computational cost, and improve model interpretability
Chicken nuggets deportation myth shows dangers of fake news about European Convention on Human Rights
Get PDFThe current system of UK human rights is under threat. Both the Conservative and Reform parties have announced that they will withdraw from the European Convention on Human Rights if they form a government. Labour, while not going that far, has also advocated changes with the Prime Minister saying that his government will look again at the application of the convention
A novel in vitro model of trauma-induced endotheliopathy provides a platform for studying mechanisms of coagulopathy.
Get PDFTrauma-induced coagulopathy (TIC) significantly contributes to trauma-related mortality, driven by dysregulated coagulation and fibrinolysis. Endotheliopathy of trauma (EoT) is central to TIC, yet its underlying mechanisms remain unclear. Current in vitro models fail to replicate the complex trauma environment, including haemorrhagic shock, tissue injury, and inflammation. This study aimed to develop a novel in vitro model of EoT that mimics key TIC features, enabling the investigation of endothelial contributions to TIC. Endothelial colony-forming cells (ECFCs) were exposed to trauma-relevant factors, including epinephrine, TNF-α, IL-6, HMGB1, hydrogen peroxide, and hypoxia. Endothelial injury markers (syndecan-1, thrombomodulin), haemostatic protein expression, coagulation, and fibrinolysis were analysed using ELISA, immunofluorescence, global haemostasis assays, and RNA sequencing. Plasma from healthy donors and trauma patients was used to assess clinical relevance. Traumatised ECFCs exhibited progressive dysfunction, with early surface damage and sustained fibrinolytic dysregulation. Transcriptomic analysis showed activation of inflammatory pathways, metabolic shifts, and epigenetic changes. Surface expression of anticoagulant proteins decreased, while procoagulant tissue factor increased, heightening thrombogenic potential. Initially, traumatised ECFCs promoted fibrinolysis via thrombomodulin shedding but later secreted antifibrinolytic PAI-1, mimicking the biphasic TIC phenotype. Plasma assays revealed thrombin generation and clot lysis changes similar to trauma patients. This in vitro model successfully replicates EoT and TIC-associated haemostatic imbalances, capturing the time-dependent evolution of endothelial dysfunction. It provides mechanistic insights into TIC and serves as a platform for testing targeted interventions to mitigate endothelial-driven coagulopathy in trauma
Citrus fruit detection based on an improved YOLOv5 under natural orchard conditions.
Get PDFAccurate detection of citrus can be easily affected by adjacent branches and overlapped fruits in natural orchard conditions, where some specific information of citrus might be lost due to the resultant complex occlusion. Traditional deep learning models might result in lower detection accuracy and detection speed when facing occluded targets. To solve this problem, an improved deep learning algorithm based on YOLOv5, named IYOLOv5, was proposed for accurate detection of citrus fruits. An innovative Res-CSPDarknet network was firstly employed to both enhance feature extraction performance and minimize feature loss within the backbone network, which aims to reduce the miss detection rate. Subsequently, the BiFPN module was adopted as the new neck net to enhance the function for extracting deep semantic features. A coordinate attention mechanism module was then introduced into the network's detection layer. The performance of the proposed model was evaluated on a home-made citrus dataset containing 2000 optical images. The results show that the proposed IYOLOv5 achieved the highest mean average precision (93.5%) and F1-score (95.6%), compared to the traditional deep learning models including Faster R-CNN, CenterNet, YOLOv3, YOLOv5, and YOLOv7. In particular, the proposed IYOLOv5 obtained a decrease of missed detection rate (at least 13.1%) on the specific task of detecting heavily occluded citrus, compared to other models. Therefore, the proposed method could be potentially used as part of the vision system of a picking robot to identify the citrus fruits accurately
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