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Advanced machine learning algorithms for blood pressure classification: early detection or prevention could save lives
Full text linkObjectives: The primary objective of the study is to classify the blood pressure (BP) levels using advanced machine learning (ML) techniques for predictive purposes. The study assesses the efficacy of the Naïve Bayes, AdaBoost, feedforward neural networks (FNNs), and long short-term memory (LSTM) algorithms over the conventional multinomial logistic model using standard performance evaluation metrics.
Methods: The dataset comprised 15,000 entries obtained from the National Health Service, England, each containing eight variables. The variables include BP, age, weight, height, gender, smoking habit, alcohol consumption, and fitness level. The Naïve Bayes, AdaBoost, FNN, LSTM, and multinomial logistic models were employed in the study. Each model underwent training, testing, validation, and evaluation using suitable metrics such as accuracy, F1-Score, kappa statistics, sensitivity, specificity, and area under the curve score.
Results: The FNN model gives the highest test accuracy of 89.47% and balanced performance, making it the most appropriate model for predicting BP levels. The LSTM model demonstrated strong proficiency in capturing temporal patterns. AdaBoost was highly effective for dealing with class imbalance, but Naïve Bayes was a dependable benchmark. The multinomial logistic model established a reliable and stable reference point. The results represented a notable improvement over previous research, which typically reported median accuracy rates in the 80–85% range.
Conclusion: The study reveals that knowing an individual’s age, weight, height, gender, smoking habit, alcohol consumption, and fitness level is useful in predicting his/her BP level. Thus, the advanced ML algorithms demonstrate potential in accurately classifying BP levels and can aid in the prevention, detection, and management of hypertension
Biomedical and computational exploration of rationally designed ciprofloxacin tethered β-amino alcohols as anti-cancer agents
No full textTo explore the biological potential of ciprofloxacin-endowed derivatives, herein, we have outlined the synthesis, in vitro and in silico anti-proliferative assessment of a catalog of ciprofloxacin-based β-amino alcohols (3a–h) by using computer-aided drug design (CADD) and MTT assay. The targeted ciprofloxacin derivatives were accessed in good to efficient yields (70–85 %) by the ring opening of substituted epoxides with ciprofloxacin ester (employed as N-nucleophile). The characterization of the synthesized derivatives was accomplished through spectroscopic techniques followed by their in vitro cytotoxic evaluation against the liver cancer cell line (Hep-G2) and lung cancer cell line (A549). The majority of the synthesized ciprofloxacin-based beta-amino alcohols demonstrated noteworthy anti-cancer activities. Particularly, compound 3b emerged as the most promising anticancer agent with 11.35 ± 0.59 % cell viability against the Hep-G2 cell line, and 19.08 ± 0.34 % cell viability against the A549 cell line in comparison with standard reference drugs. DFT studies and the in silico molecular docking studies provided insights into the reactivity of the most bioactive molecule 3b. Moreover, MD simulations results were also found to be in agreement with in vitro findings which validated the anticancer therapeutic efficacy of the compound 3
Enhancing gender-based violence research: holistic approaches to data collection and analysis
Full text linkGender-based violence (GBV) is a profound and pervasive societal issue, disproportionately affecting women across diverse settings, including homes, workplaces, and public spaces. Despite its prevalence, significant challenges impede research on GBV, particularly regarding data collection, analysis, and ethical handling. This study investigates the complexities inherent in GBV research, focusing on the obstacles posed by under-reporting, ethical considerations, data quality, and the need for cross-comparative standards. Using a combination of police records, web scraping, news reports, and survey data from USAID’s Demographic and Health Surveys (DHS), our study examines strategies to work with sensitive GBV datasets, while maintaining data integrity. Our study advocates for improved demographic surveying and data integration methodologies that can enhance data accuracy and comparability. The findings suggest that while technological advancements, particularly generative AI and machine learning approaches, offer promising avenues for automating survey processes, reducing costs, and enhancing data collection efficiency, they present the limitations of secondary datasets, a lack of data disaggregation, and discrepancies in data coding systems, which highlight the necessity of refining global data standards
Epistemic decay: generative artificial intelligence and the recombination of culture
Full text linkGenerative Artificial Intelligence is a transformational technology that augurs profound socio-cultural change on a scale that may ultimately surpass the impact of the Internet and the World Wide Web. But although offering clear benefits and opportunities, its rise has also been met with anxiety about its near and long term effects. We have previously addressed in Business Information Review for example the impact of generative technologies on professional roles (Tredinnick, 2017) and the ethical implications of artificial intelligence (Laybats and Tredinnick, 2024). There has also been widespread alarm at the growing use of AI in the creative industries (Amankwah-Amoah et al., 2024; Bender, 2025) particularly advertising, publishing and the media. In addition, apocalyptic fears attend to the anxiety of a coming technological singularity, the point at which machines will surpass humans intelligence, initiating a snowball effect of every increasing machine capabilities and ultimately dominance (Shanahan, 2015).
Some of these perceived risks are no doubt overstated; while significant challenges and some structural transformation will accompany the wider use of generative technologies there will also be new opportunities and emerging markets. However, one potential risk has garnered less attention despite being perhaps the most immediate of them all. Generative artificial intelligence may be contributing to a gradual erosion of the epistemic foundations of our technologically and scientifically dependent culture. This possibility arises not from their apparent ability to create new knowledge, nor from the quality and reliability of the outputs that they produce, but from the ways in which generative applications have become implicated in a progressive recirculation of material culture. Successive generations of generative technologies may bring improved accuracy and fewer hallucinations, but these iterative improvements may have little or no impact of the problem of epistemic decay. This editorial explores the profound threat posed by generative artificial intelligence to our long-term understanding of what we believe we know, and what steps we can take to mitigate those risks
Brain tumour segmentation using Choquet integrals and coalition game
Full text linkArtificial Intelligence (AI) and computer-aided diagnosis (CAD) have revolutionised various aspects of modern life, particularly in the medical domain. These technologies enable efficient solutions for complex challenges, such as accurately segmenting brain tumour regions, which significantly aid medical professionals in monitoring and treating patients. This research focuses on segmenting glioma brain tumour lesions in MRI images by analysing them at the pixel level. The aim is to develop a deep learning-based approach that enables ensemble learning to achieve precise and consistent segmentation of brain tumours. While many studies have explored ensemble learning techniques in this area, most rely on aggregation functions like the Weighted Arithmetic Mean (WAM) without accounting for the interdependencies between classifier subsets. To address this limitation, the Choquet integral is employed for ensemble learning, along with a novel evaluation framework for fuzzy measures. This framework integrates coalition game theory, information theory, and Lambda fuzzy approximation. Three distinct fuzzy measure sets are computed using different weighting strategies informed by these theories. Based on these measures, three Choquet integrals are calculated for segmenting different components of brain lesions, and their outputs are subsequently combined. The BraTS-2020 online validation dataset is used to validate the proposed approach. Results demonstrate superior performance compared with several recent methods, achieving Dice Similarity Coefficients of 0.896, 0.851, and 0.792 and 95% Hausdorff distances of 5.96 mm, 6.65 mm, and 20.74 mm for the whole tumour, tumour core, and enhancing tumour core, respectively
Considerations in recycling research: laboratory practices for fibrous and plastic materials at Russian and Uzbekistan universities
Full text linkAs the demand for sustainable recycling of fibrous and polymeric waste increases, university laboratories play a crucial role in developing new technologies while training future professionals. This study presents a practical model for conducting safe, student-led laboratory research on the chemical recycling of textile waste with a focus on silk and cotton materials. It outlines safety measures for managing chemical and biological hazards including waste classification, disinfection protocols, and risk assessment procedures adapted for educational settings. Key innovations include the use of express tests for verifying bacterial decontamination, tailored workspace organization, and the application of solvent-based cleaning for material purity, a general approach to laboratory management that emphasizes student and staff responsibilities to health and safety. The study also reviews regulatory compliance and engineering controls specific to Russian and Uzbekistan academic settings. The proposed approach, supported by case studies, demonstrates the safe engagement of students in meaningful recycling research while mitigating risks associated with fibrous waste handling and chemical processing under the guidance of staff members who are not specialist health and safety professionals
A meta-analytic perspective on the impact of MBI’s (mindfulness based interventions) in depression treatment
Full text linkDepression, affecting over 280 million people globally, is a leading cause of disability and suicide, with more than 700,000 deaths annually. Certain groups, including older adults and postpartum women are at elevated risk, with depression prevalence estimated at 13–20% among new mothers and up to 25% among older adults. These disparities highlight the urgent and effective need for accessible, sustainable, and culturally sensitive interventions.
Mindfulness-based interventions (MBIs), including Mindfulness-Based Cognitive Therapy (MBCT) and Mindfulness-Based Stress Reduction (MBSR), have emerged as promising, non-pharmacological approaches, in alleviating depressive symptoms and preventing relapse by promoting emotional regulation and cognitive flexibility.
First developed by Teasdale et al. (2000), MBCT integrates cognitive therapy with mindfulness training to prevent depressive relapse by enhancing metacognitive awareness and emotional regulation.
Building on randomized controlled trials, neuroimaging studies, and meta-analyses, the presentation demonstrates that MBIs offer moderate but meaningful improvements in emotional regulation, reduced rumination, and relapse prevention, particularly in individuals with three or more depressive episodes. Neuroimaging findings (e.g., Hölzel et al., 2011) link mindfulness practice to increased grey matter in brain regions associated with emotion regulation and self-awareness. Comparative trials further suggest MBCT is as effective as maintenance antidepressants, offering a viable non-pharmacological treatment pathway.
MBIs also show promise for vulnerable populations. MBSR has been found to significantly reduce depressive symptoms in older adults, and MBCT-PD has reduced relapse risk in postpartum women while being well-accepted by participants. Despite these encouraging findings, the field faces notable limitations: short follow-up durations, sample homogeneity, underrepresentation of minority groups, and inconsistencies in intervention delivery.
Critics further question the theoretical and ethical dilution of mindfulness in Western clinical settings. This presentation argues for a reinvigorated research agenda prioritizing rigorous methodologies, diverse populations, and culturally grounded practices. While not a universal remedy, MBIs represent a hopeful, patient-centred complement to traditional therapies in the ongoing fight against depression: one that demands continued, targeted inquiry amid an escalating global mental health crisis
Impact of patriarchy and gender stereotypes on working women: exploring its past, present and future
Full text linkThis book explores the meaning, perceptions, historical and current cultural and psychological roots of gender stereotypes and patriarchy in the workplace. It provides a comprehensive analysis of the types of stereotypes, their origins, and theoretical underpinnings as well as a comparison of the different paradigms across cultures. As the narrative progresses, the book then provides a conceptual model of impact of gender stereotyping on female expatriates and provides evidence of women’s experiences at work and in the society from across different countries. It also shows mindsets across different generations and examines the possible impact of generative AI tools. This all reveals how this phenomenon still exists despite the increased number of women in workforce and how these stereotypes perpetuate harmful norms that limit individual potential, reinforce inequality, and enhance discrimination. Relevant for scholars, researchers, students, practitioners, and policy makers, this book encourages readers to s
What makes a good article for leadership? Thoughts and views from our associate editors, part 2
Full text linkLast year we published an editorial that included thoughts and views about what makes a good article for Leadership from three of our associate editors (see Edwards et al., 2024 - https://repository.londonmet.ac.uk/9083/). As this editorial was so well received, we wanted to do the same this year and publish a part 2. This time we hear from three more associate editors, Sarah Robinson, Richard Bolden and Suze Wilson, as they too respond to the following questions:
1. What do you look for in a strong article, suitable for submission to Leadership?
2. What do you see as a critical contribution to leadership studies?
3. Can you highlight and/or explore some past articles published in Leadership that exemplify your views?
As you will see below, the Associate Editors push us towards key thinking around building and traversing bridges in leadership studies to enable areas to be uncovered that have been inaccessible previously. We are also taken back 20 years to the birth of the journal Leadership and are reminded of the original aims; from this we are encouraged to never stop questioning. Lastly, we are also pushed to think differently about leadership with exemplars of critical leadership scholarship. We hope that you enjoy the read once more
Integrating generative artificial intelligence into green logistics: a systematic review and policy-oriented research agenda
No full textIn light of mounting environmental issues, the logistics industry plays a critical role in promoting sustainability. While generative artificial intelligence (GAI) has the potential to revolutionize green logistics, several barriers still prevent its widespread adoption. In existing literature, little is known about applications, drivers, enablers, critical barriers, and challenges associated with implementing GAI along with green logistics. To fill this gap, this study aims to systematically identify and assess the existing body of knowledge on the GAI and green logistics nexus, drawing on a systematic literature review carried out in compliance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) protocol. The study identifies 34 key GAI-driven green logistics applications, 23 drivers and enablers, and 38 major barriers and challenges. The findings illustrate that GAI-driven green logistics applications, such as risk assessment and mitigation, decision support and real-time environmental response, resilience testing and scenario planning, are essential for developing sustainable logistics ecosystems. Organizational readiness, stakeholder collaboration, and supportive regulatory frameworks emerge as crucial enablers, while lack of digital infrastructure, investment costs, and regulatory gaps constitute significant barriers. The study proposes a decision-making framework to prioritize policy initiatives that could promote GAI adoption in green logistics. This research fills current knowledge gaps and has significant implications for supply chain stakeholders, scholars, and policymakers aiming to support sustainable and cutting-edge logistics systems