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How will smart technology support SDG 12? an empirical study on sustainability in Indian agricultural operations
Full text linkIndia is one of the fastest growing economies with significant potential for the use of smart farming operations. Although agriculture is a major sector, implementation of smart technologies in the agriculture sector has not progressed in India. We use a mixed-methods approach to develop knowledge on the factors determining this slow adoption of smart technology and develop strategies for large-scale adoption in the Indian agriculture sector. First, qualitative interviews are used to understand the factors behind the slow diffusion of smart technology in the agriculture sector. Based on the responses, we link the results of the qualitative study from the agri-sector to the well-known Diffusion of Innovations (DoI) theory. We then develop a framework for applying Fuzzy-set Qualitative Comparative Analysis (fsQCA) to analyze the impact of multiple causal factors. We apply our research findings to help achieve SDG 12 in the agriculture sector. Our findings indicate individual factors on their own may influence adoption, but some reasonable combinations of factors (e.g., a combination of technology, knowhow, experience, benefits-operation, and finance and reliability) could also result in the large-scale adoption of smart technologies in improving Indian agricultural operations. By doing so, we provide a contextual empirical configurational test of the DoI theory in the Indian smart agricultural context.</p
Corrigendum to "Genetically modified metallothionein/cellulose composite material as an efficient and environmentally friendly biosorbent for Cd<sup>2+</sup> removal" [Int. J. Biol. Macromol. 218 (2022) 543-555
No full textThe authors regret that several incorrect sub-images were mistakenly included in the original publication during the image preparation. Specifically, these errors occurred in Figs. 1, 2, 7 and 9. The figures have now been verified, and are provided in this corrigendum on the following pages. The authors confirm that these corrections do not affect the results, interpretations, or conclusions presented in the article.The authors would like to apologise for any inconvenience caused
How can we optimise co-living for the digital nomad era?
Full text linkPurposeThis research examines digital nomads’ customer priorities and opinions of the services offered by co-living accommodation providers, aiming to understand their needs through an analysis of their experiences, and identifying implications for hospitality.Design/methodology/approachAn inductive qualitative approach was used to identify what digital nomads look for and value in co-living spaces. Using netnography and thematic analysis in NVivo, 1,052 online user reviews of 10 co-living providers on Trustpilot from 2018 to 2024 were collected and analysed.FindingsFindings identified a variety of benefits and challenges of co-living for digital nomads. Data showed that the ability to integrate work, life and community in co-living environments were priorities for digital nomads, an integration which allows them to balance professional productivity with social and emotional well-being. Accommodation quality, workspace functionality, logistical support and privacy also emerged as priorities: inadequacies in these areas were among the most frequently reported challenges.Practical implicationsThis research provides practical and managerial insights for co-living operators and hospitality stakeholders to consider when designing accommodation and organising services, to best cater to the unique needs of digital nomads.Originality/valueThis study advances theoretical understanding of co-living by applying spillover theory as a sensitising concept to explore digital nomads’ work−life integration. It fills a gap in hospitality research by linking digital nomadism with co-living experiences, offering actionable insights into customer priorities
Building a corpus of digitally mediated EMI classroom interactions:challenges and opportunities for EMI research and pedagogy
Full text linkWhile research on English-medium instruction (EMI) has grown considerably in recent years, we still know relatively little about what happens inside EMI classrooms, particularly in terms of interactional practices. There remains a shortage of empirical resources, such as corpora, that can offer systematic insights into the features of EMI classroom discourse. This paper addresses some of the gaps by outlining the methodological and ethical procedures involved in building and annotating a corpus of digitally mediated EMI interactions, drawn from 24 university seminars in the social sciences across two EMI contexts. Using the Multimodal EMI Corpus (MEMIC) as a case study, we demonstrate how such a resource can be used to examine lexical and discursive features of EMI interaction across modes and registers. We also show how these insights can inform the development of EMI-relevant teaching materials and assessment tasks. The overarching aim of this contribution is to encourage other scholars and practitioners to collect and analyse data from their own local EMI contexts, in order to advance corpus-based research into EMI classroom practices and support more effective, evidence-informed pedagogy.</p
Split averaging:bridging the heterogeneity gap in clients data for federated learning
Full text linkFederated Learning (FL) has gained significant prominence to overcome the issue of data silos in various domains. However, since its introduction FL has been confronted with the presence of Non-Independent and Identically Distributed (Non-IID) data, hindering its broad-scale adoption. In this paper, we present a novel method named Federated Split Averaging (FSA) to tackle the problem of Non-IID data. FSA solves the key challenge that classical FL fails to overcome, specifically accounting for real-world scenarios where data instances from certain classes are completely missing. Unlike conventional FL, where a cloud server blindly averages clients' model parameters, FSA classifies clients into strong and weak groups and aggregates their parameters separately. The spitted parameters are then used to compute dynamic penalty factors, which regularize clients' training and accelerate convergence. {Experimental results on real-world datasets demonstrated that the proposed method can significantly improve model accuracy in handling Non-IID data, achieving up to 7.23% improvement as compared to other state-of-the-art solutions
Self-efficacy, emotional loneliness, and psychological distress among women undergoing a domestic violence intervention
No full textThis study aimed to evaluate the effectiveness of a domestic violence intervention addressing abused mothers in England. It was hypothesized that (a) the women would improve significantly on various measures after the intervention, b) changes in the women’s psychological distress would be mediated by self-efficacy, emotional loneliness, social isolation, and parenting competence, and c) self-efficacy would mediate changes in parental competence, emotional loneliness, and social isolation. Ninety-five mothers of low socio-economic status, experiencing domestic violence completed questionnaires before and after the intervention: General Health Questionnaire, Loneliness Scale, Parenting Sense of Competence Scale, General Self-Efficacy Scale, and Distance from Child Scale. Post intervention participants improved in self-efficacy, psychological distress, emotional loneliness, and parenting competence. Self-efficacy at post-intervention mediated post-intervention change in psychological distress, emotional loneliness, and parenting competence, while emotional loneliness mediated change in psychological distress, and psychological distress mediated change in self-efficacy. The present study identified mothers’ pre-intervention vulnerability characteristics (high psychological distress and emotional loneliness; low parental competence and self-efficacy) and mechanisms by which such characteristics may affect outcomes. The findings suggest that interventions focusing on strengthening self-efficacy and reducing emotional loneliness may support the most disadvantaged of maltreated women toward attaining more positive outcomes.<br/
Investigating the determinants of intention to use IoT for preventing food waste by UK food supply chain companies
Full text linkPurpose - Food companies are still unsure about using IoT-based sensors and remain reluctant to adopt them for the purpose of food waste prevention. To address this problem, this study aims to examine the determinants of the intention to use IoT-based sensors for preventing food waste by the food supply chain companies in the UK. Design/methodology/approach - This research develops a comprehensive Motivation- OpportunityAbility-Trust (MOAT) model that extends and contextualises the original Motivation-OpportunityAbility (MOA) model in the context of using IoT sensors in food supply chain companies for preventing food waste. The MOAT model is tested using data collected from a questionnaire survey with 315 senior managers in the UK food sector. Findings - The findings show that opportunity and trust positively influence the managers’ behavioural intention to use IoT for food waste prevention, are therefore key determinants to IoT adoption for food waste prevention. Data analysis highlighted the role of trust as the underlying principle in food supply chains when adopting IoT sensors in all operational level activities. Originality – This study contextualises and extends the original MOA model into MOAT framework to reflect the characteristics and applications IoT in food waste prevention in food supply chains. The MOAT model provides a foundation for further research and practical strategies. This study makes valuable contributions to the theoretical development and practical understanding of the influence of ability, opportunity, motivation and trust on IoT sensor adoption for preventing food waste among food companies.<br/
Experiences and perceptions of care for medications with a risk of dependence:insights from patients and healthcare professionals
Full text linkBackground: Medications with a risk of dependence are widely prescribed but have been associated with a poor experience of care for patients. This study aimed to understand patient and healthcare staff perspectives in the prescription, management, and deprescription of benzodiazepines, z-drugs, opioids for chronic noncancer pain, gabapentinoids, and antidepressants. Methods: Online semistructured interviews were conducted with 20 patients and 15 healthcare professionals from five different GP practices. Data were analyzed using codebook thematic analysis. Results: Patients and healthcare professionals shared concerns about medications with a risk of dependence and described deprescription as a challenging and complex process. While the value of providing patients with detailed medication-related information was recognized by healthcare professionals, patients felt that more information was needed. The use of regular, personalized medication reviews was seen as important for patient care and medication management, but patients felt this was lacking from current care. Conclusion: The findings of this study provide new insights into how medications with a risk of dependence are managed and how care is experienced by patients. The findings have clear implications for improving patient experience, which is a key aspect of quality care.</p
Co-production of a sarcopenia and frailty screening and intervention programme for older people from a culturally diverse population
Full text linkPurpose: Physical activity has been shown to enhance health, particularly for older people who typically have lower levels of physical activity. Many factors influence physical activity level, including culture and socioeconomic status, which in turn increases the risk of developing age-related conditions such as frailty and sarcopenia. The Bedford, Luton, and Milton Keynes Integrated Care Service (BLMK ICS) has developed a screening and intervention programme called the Healthy Ageing Programme (HAP) which identifies people at risk of frailty and sarcopenia. The programme has been developed as a pilot in Luton, which is a town in the BLMK area that has a culturally diverse population. Project Description: The project is co-produced with a diverse group of stakeholders, including older community members from many different cultures, leaders within these communities, members of local authorities, as well as professionals in health and social care. People at risk of developing frailty and sarcopenia are identified through both electronic health record screening, for people who routinely access primary care, while community-based screening is used to ensure people that do not routinely access health and social care services are included. The interventions were also co-developed, with activities chosen based on the preferences of participants and scientific literature. The range of activities cover a wide spectrum, including walking sports such as football and cricket, golf, chair-based activities, as well as fitness classes and gym membership. A mixed methods evaluation includes pre- and post- assessment of physical function and quality of life, while pre- and post-intervention interview enable attitudes and experiences to be explored. The project will be scaled up to other areas within the BLMK Region, contingent upon results of this pilot study. Final result of the study will be disseminated via academic publications, while discussions have started with neighbouring areas of the United Kingdom looking to adopt a similar approach.<br/
PyBayesDM: Hybrid Framework for Bayesian Adaptive Management under Deep Uncertainty
No full textDecision-making under deep uncertainty presents a persistent challenge for adaptive management in complex systems. Standard Bayesian Decision Theory performs well for prescriptive optimization under confident beliefs but is less robust when uncertainty is profound. Philosophical frameworks, which emphasize resilience and learning, typically lack a formal integration with Bayesian rigor. This paper develops a Hybrid Bayesian Adaptive Management Framework that unifies these prescriptive and philosophical paradigms within a single Bayesian formulation. The framework formally unifies both decision rules as parametric instances of a Bayesian adaptive management problem. It employs an entropy-driven dynamic weighting mechanism to continuously adjust the balance between efficiency and resilience based on posterior uncertainty, and introduces a suite of performance metrics designed to evaluate multi-dimensional outcomes beyond conventional measures. Experiments conducted on simulations inspired by climate-vulnerable agriculture in Vietnam show that the hybrid approach achieves statistically significant improvements (p < 0.05) in resilience and risk-adjusted performance under high uncertainty. The prescriptive framework excels in short-term reward but degrades under increased noise, while the philosophical framework maintains robustness, sometimes at a cost to efficiency. The hybrid strategy delivers balanced performance across uncertainty levels, confirming the utility of entropy-based arbitration. All experiments are reproducible using the developed Python implementations. This work offers a principled approach to adaptive decision-making under deep uncertainty, with implications for climate adaptation, resource management, and policy desig
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