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Parameter estimation and validation of cascaded DC-DC boost converters for renewable energy systems using the IGWO optimization algorithm
Get PDFThe voltage amplitude generated by renewable energy sources is often unstable, necessitating the use of power electronic circuits for effective grid integration. Among these, DC-DC converters play a critical role in maintaining a constant DC link voltage, typically 400 V or 800 V, at the input of inverter circuits that supply power to the load or the grid. The study focuses on the voltage gain behavior of a high-gain dual cascaded DC-DC boost converter designed for PV (photovoltaic) power systems. Using ANSYS Electronics software with its parametric solver, a comprehensive dataset was generated based on key parameters such as input voltage, power switch duty ratio, and switching frequency.
The Improved Grey Wolf Optimizer (IGWO) algorithm was employed to estimate mathematical models for this dataset using linear and quadratic equations. The accuracy of the proposed models was validated across six test scenarios, demonstrating superior performance compared to traditional optimization algorithms, including Harmony Search (HS), Particle Swarm Optimization (PSO), Differential Evolution (DE), and the standard Grey Wolf Optimizer (GWO). Experimental validations yielded output voltages of 23.5 V and 36.1 V for input voltages of 4.8 V and 6.2 V, respectively, closely aligning with simulation results of 23.113 V and 36.447 V.
The findings, supported by detailed simulations and graphical analyses, highlight the IGWO algorithm's precision and reliability in predicting converter output voltages under variable input conditions. This work advances renewable energy systems integration by enhancing the modeling and performance of cascaded DC-DC boost converters
Accuracy of equations for calculating normal values of maximal inspiratory
Get PDFThe objective was to compare the accuracy of available prediction equations and consider the implications of using these equations in interpreting measured Maximal Inspiratory Pressure (MIP) in healthy Caucasian older adults (n = 129; 65–85 years). Eight studies out of 174 manuscripts were analysed, and predictive equations were extracted. Results show that predicted MIP is variable compared to measured MIP, demonstrating significant between-equation differences. Our analyses suggest that using age, sex, and height to predict ‘normal’ MIP values is insufficiently accurate. Hence, a multidimensional assessment of outcomes will enhance the periodisation and personalisation of rehabilitative and training programs associated with inspiratory muscles, such as inspiratory muscle trainin
Reversible dual stimuli-responsive polymer coatings with antimicrobial properties for oil–water separation
Get PDFEfficient separation of increasingly complex oil–water mixtures demands advanced materials capable of adaptive, on-demand performance under diverse environmental conditions. Conventional single-function separation materials often fail to meet these challenges, particularly in achieving controllable separation in dynamic or contaminated environments. This highlights the pressing need for intelligent materials to help mitigate global water pollution and oil scarcity. In this study, a series of reversible dual-stimuli-responsive polymers were synthesised via a straightforward one-step reversible addition-fragmentation chain transfer (RAFT) polymerisation method. Incorporating azobenzene-based photoswitches and pH-responsive units, these polymers exhibit significant and reversible responsiveness to both UV irradiation and pH variation. When coated onto stainless steel wire meshes, the resulting reusable membranes demonstrated excellent performance in separating oil–water mixtures. Under alkaline conditions or upon irradiation with 445 nm light, the membranes exhibited a maximum water contact angle of approximately 140°. In contrast, under acidic conditions or 365 nm UV exposure, the membranes switched to a superhydrophilic state with a contact angle of around 6°, resulting in a maximum contact angle variation of 134°. Additionally, antibacterial assays revealed a maximum inhibition rate of 75.1%, confirming the coatings’ potential effectiveness in biological environments. This work demonstrates a bright strategy for designing multifunctional and controllable oil–water separation coatings, with potential applications in complex, real-world settings requiring both separation efficiency and antibacterial properties
Improving diabetes and pre-diabetes detection in the uk: insights from hba1c screening in an acute hospital’s emergency department
Get PDFIntroduction
Many individuals in the community have undiagnosed glucose intolerance, type 2 diabetes (T2D), and pre-diabetes (Pre-DM). This study explored screening for unknown glucose intolerance in the emergency department (ED) in an acute hospital.
Methods
1382 persons attending the ED without T2D were screened using HbA1c. T2D and Pre-DM were classified using American Diabetes Association (ADA) and National Institute for Health and Care Excellence (NICE) criteria. The Finnish Diabetes Risk Score (FINDRISC) was calculated in all patients.
Results
According to NICE criteria, 80.1% (1107 individuals) exhibited normal glucose tolerance, 11.6% (160 individuals) exhibited pre-diabetes, and 8.3% (115 individuals) exhibited diabetes. Each unit increase in FINDRISC score, using multinomial regression, corresponded to an 8% (5–12%; p < 0.001) higher risk for pre-diabetes and a 16% (10–23%; p < 0.001) higher risk for diabetes (NICE). The risk remained elevated even after adjusting for age, sex, and ethnicity. South-Asians had higher glucose intolerance rates than white British (34.8% versus 18.5%) using the NICE criteria, and even greater at 50.0% versus 37.6% using ADA criteria. The adjusted relative risk of having pre-diabetes in people of color compared with white British individuals was 1.77 (1.04–3.00; p = 0.034, ADA) and 2.84 (1.41–5.65; p = 0.003, NICE). The multinomial relative-risk ratio (RRRs) for having diabetes by ethnicity was 2.97 (1.73–5.08; p < 0.0001, ADA) and 2.80 (1.59–4.94; p < 0.0001, NICE).
Conclusions
Routine HbA1c screening in the ED, with FINDRISC scoring, successfully identifies individuals with diabetes and pre-diabetes. This approach could enable early intervention, particularly in groups at higher risk of glucose intolerance.
Trial registration
ClinicalTrials.gov identifier, NCT04653545
The Development and Validation of the Adolescent Self-Criticism Scale (ASCS)
Get PDFBackground
In adolescence, self-criticism represents a central phenomenon in a variety of mental disorders, and can portend emotional, social, and behavioural problems.
Objective
We outline the development and testing of the first bespoke measure of self-criticism for adolescents aged 11–16 years, the Adolescent Self-Criticism Scale (ASCS), informed by adolescents and experts in child mental health/scale development, via two studies.
Methods
In study one, an item pool was generated via: (i) a literature review of existing self-criticism measures to produce an initial pool of 141 items; (ii) eight subject matter experts’ assessment of the content validity of the 141-items; and (iii) three focus groups with 18 adolescents in total, to produce a final pool of 70 items. In study 2, this pool of 70 items was administered to a sample of 305 secondary school pupils at time point one. Factor Analysis revealed two latent factors based on a reduced set of 24 items characterised as: Criticising Self and Sensitivity to Failure. Both subscales demonstrated high internal consistency (Factor 1 α.94; Factor 2 α.91). At time point two, the 24-item ASCS was administered to 206 adolescents, alongside standardised scales of wellbeing.
Results
The ASCS showed high correlations with depression, self-compassion, self-criticism, and perfectionism, with significant correlations between subscales and validation measures. Test-retest reliability at four weeks was excellent (0.87).
Conclusion
Overall, as a short psychometrically robust scale, the ASCS offers an excellent ecologically valid tool for professionals interested in measuring the emotional wellbeing of young people and/or the effectiveness of youth wellbeing interventions
Future of coral bleaching research
Get PDFCoral bleaching is the largest global threat to coral reef ecosystem persistence this century. Advancing our understanding of coral bleaching and developing solutions to protect corals and the reefs they support are critical. In the present article, we, the US National Science Foundation–funded Coral Bleaching Research Coordination Network, outline future directions for coral bleaching research. Specifically, we address the need for embedded inclusiveness, codevelopment, and capacity building as a foundation for excellence in coral bleaching research and the critical role of coral-bleaching science in shaping policy. We outline a path for research innovation and technology and propose the formation of an international coral bleaching consortium that, in coordination with existing multinational organizations, could be a hub for planning, coordinating, and integrating global-scale coral bleaching research, innovation, and mitigation strategies. This proposed strategy for future coral bleaching research could facilitate a step-function change in how we address the coral bleaching crisis
A novel real-time battery state estimation using data-driven prognostics and health management
Get PDFThis paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R2 values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation
Supportive Management Mindset Model for IT System Implementation: A Case Study of the UAE Oil and Gas Sector
Get PDFProblem Statement, Background, and Rationale: The successful implementation of IT systems in the UAE oil and gas sector is influenced by management mindset. However, the role of internal and external management mindset factors in determining IT system effectiveness remains an underexplored area. This research problem centres on investigating how a supportive management mindset facilitates IT implementation through dynamic capabilities and IT quality standards. Current literature inadequately addresses the inverse relationship between management mindset and IT system adoption in the UAE oil and gas industry, necessitating an in-depth investigation on addressing this research gap. This study fills this research gap by identifying key internal (IT competencies, leadership, systems awareness) and external (PESTLE, competition, customer demands) factors influencing IT system implementation. It examines how these factors affect organisational dynamic capabilities and IT quality standards, thereby ensuring technological effectiveness. The study is envisaged to be beneficial and particularly relevant as the UAE oil and gas sector faces increasing digital transformation pressures, requiring structured and adaptive management strategies.
Research Aims, Objectives, and Conceptual Framework: The overarching aim of this research is to develop a supportive management mindset model that enhances IT system implementation in the oil and gas sector. To achieve this aim, the study is designed to establish the following objectives: (1) identify the practices of a supportive management mindset and their influence on IT system implementation, (2) analyse the impact of these practices, and (3) develop an applicable model for IT system adoption in the UAE oil and gas industry. The research is guided by the following questions: How do internal and external management mindset factors influence IT system implementation? How does management mindset affect technology adoption through dynamic capabilities and IT quality standards? The study employs a conceptual framework integrating Dynamic Capabilities Theory and IT quality standards to explore these relationships systematically.
Data Collection, Analysis, and Key Findings: A quantitative research methodology was employed, involving the distribution of 382 questionnaires to employees across the UAE oil and gas sector, with 172 valid responses (45% response rate). Data analysis was conducted using SPSS, employing regression analysis to test the proposed hypotheses. Findings confirm that a supportive management mindset significantly influences IT system implementation. Internal factors such as IT competencies, leadership, and systems awareness, along with external factors like PESTLE conditions, competition, and customer demands, positively impact IT system adoption. Additionally, dynamic capabilities and IT quality standards were found to mediate the relationship between management mindset and IT system success. The study supports all proposed hypotheses, reinforcing the critical role of management mindset in fostering successful technology implementation. However, the hypotheses are considered in the context of this research design
Research Discussion and Contributions to Knowledge: This research makes significant academic and practical contributions. Academically, it develops a comprehensive framework integrating management mindset characteristics with IT system implementation, bridging gaps in existing literature. The study contributes by (1) identifying key internal and external factors influencing IT adoption, (2) integrating Dynamic Capabilities Theory to contextualize IT system adaptation, (3) incorporating IT quality standards for enhanced technological effectiveness, and (4) applying the framework to a real-world context, specifically ADNOC.
Practically, the research proposes a structured model that aids organisations in improving IT system implementation. The findings emphasize the need for management to align leadership practices with technological needs, foster a culture of continuous learning, and actively engage employees in IT transformation initiatives. The proposed model offers oil and gas sector stakeholders a strategic approach to enhancing IT project success rates, improving customer satisfaction, and aligning with international IT quality standards. As the UAE's oil and gas industry continues its digital transformation, this study provides contributes towards a foundational framework to guide effective IT system adoption and sustain competitive advantage
What If We Got It Wrong? The Gift of Failure: Unlearning success and reclaiming risk in civic practice, by embracing the uncomfortable in academic engagement.
No full textWhat If We Got It Wrong? The Gift of Failure: Unlearning success and reclaiming risk in civic practice, by embracing the uncomfortable in academic engagement.
Despite the growing emphasis on arts and creative practice in place management, there is often little space for honest discussion of what doesn’t go to plan — misaligned collaborations, disengaged communities, unanticipated resistance, or short-lived interventions. This webinar invites contributors to share insights from failure, friction, or adaptation in their use of creative engagement methods within place-based projects. We seek to explore the gap between theory and practice, and to create a space for constructive reflection
Allestree Park Rewilding: Qualitative evaluation report
No full textThe following evaluation was requested by Derbyshire Wildlife Trust (DWT) to enable findings to be drawn together from a variety of data sources aimed to elicit thoughts and feelings about the Allestree Park rewilding project from members of the public. Data was gathered in several ways. Firstly, members of the DWT team spoke with groups (e.g. the Friends of Allestree Park, groups at Darley Abbey, Swanick and at a Lake celebration at the Allestree Park). Secondly, community conversations events were held at the park and people passing volunteered to share their thoughts. At both of these, people’s verbal comments were noted down and open questions from questionnaires they completed were later transcribed. Thirdly, interviews were conducted with ten people who were recruited to take part via posters at Allestree Park, noticeboards at other Derby Parks, social media posts and DWT’s volunteering platform. Recruitment was conducted over 10 weeks and the inclusion criteria stipulated that participants needed to be over 18. Interviews were conducted via MS teams and were transcribed for data analysis.
The aim of the evaluation was to ascertain the perceptions of the rewilding project within its first year. The data was generated from November 2024-June 2025