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Antimicrobial impact of locally generated antimicrobial impact of locally generated piezoelectric fields from solution-blown PVDF/PVDF-TPU nanofiber mats
No full textThe need for biomedical composite materials with specific toughness, non-toxicity, and biocompatibility with bending and twisting characteristics has increased recently. In our study, a piezoelectric polyvinylidene fluoride (PVDF) blended with thermoplastic polyurethane (TPU) nanofibers were manufactured using solution blow spinning method. The fabricated nanofibers were characterized morphologically by SEM, mechanically by texture analyzer CTX, and piezo-electrically by high impedance oscilloscope. The antibacterial influence of localized piezoelectric fields generated from the nanofibers was studied against K. pneumoniae in this research. Remarkable mechanical improvement of fabricated PVDF: TPU 10% membranes to get maximum elongation of 9.2% at tensile strengths of 46.5 MPa. The PVDF pure material achieved a peak output voltage of 0.76 V, while the PVDF: TPU 10% nanofibrous composite reaches 0.7 V. Under cyclic mechanical loading (2.5 N), PVDF: TPU 10% exhibited nonlinear voltage behavior with a peak output of 3.3 V at 1 Hz, whereas pure PVDF maintained a constant output of 0.5 V across frequencies. On the other hand, the maximum antibacterial effect was obtained by applying weak localized electrical fields at 1.5 Hz generated from pure PVDF nanofibrous. It showed maximum growth inhibition by 50% reduction, highest LDH and protein leakage levels by 250% and 143% respectively, and remarkable dielectric dispersions at the frequency range < 2 MHz and ≈ 5 MHz. In conclusion, the fabricated solution blown nanofibrous PVDF-TPU showed mechanical improvement, exhibiting a maximum elongation of 9.2% at 46.5 MPa and 6.9% at 14.9 MPa for nanofibrous PVDF alone. The novelty of using nanofibrous membranes as an antibacterial agent with a higher antibacterial effect that resulted from PVDF pure nanofibrous membranes
A Digital Twin model for predicting wind turbine performance using federated learning
No full textGenerating electricity from wind power is a crucial aspect of any renewable energy strategy, and onshore and offshore wind farms are among the most effective renewable energy sources. However, existing wind turbines and farm management have significant drawbacks, as wind turbines are distributed, and data collection, monitoring, configuration, and optimization need to be improved. Additionally, onsite data processing is necessary for network efficiency, privacy, and security. This study proposes a Digital Twin (DT) modeling architecture to emulate wind turbine operations and data workflows through virtual containers at the edge. The proposed system employs the Federated Learning (FL) algorithm with Age of Twin (AoT) data sampling based on Root Mean Square Error (RMSE) differences. This approach ensures reactive data sampling to maintain the freshness of the DT model data. This approach constructs global wind turbine models by processing and training at the edge, eliminating the need for centralized data aggregation. The wind energy model forecasts power generation with Deep Sequential Neural Networks (Deep Seq. NN) and XGBoost Machine Learning (ML) algorithms to evaluate model performance with two different datasets for homogeneous and heterogeneous wind turbine environments. The results show that the proposed models have a 0.9952 prediction accuracy with a 0.0354 Root Mean Square Error (RMSE) in the homogeneous environment, and 0.9949 value and 0.0396 in the heterogeneous environment. In addition, it has been demonstrated that the FL method can utilize AoT to achieve highly identical DTs with a reactive algorithm
Past, present and future of conscientious brands
No full textThis editorial first looks to the past to review the origins of the construct of conscientious brands, which was launched in a 2011 Special Issue of the Journal of Brand Management. Then, it presents the evolution of the construct, which leads us to the eight papers in this Special Issue which both deepen and extend the understanding of conscientious brands. Building on the inspiration of the papers, we look to the future and posit the issues that research should focus on in exploring how conscientious brands can promote systemic transformative change through stakeholder co-creation networks
APOLLO: A Proximity-Oriented, Low-Layer Orchestration Algorithm for Resources Optimization in Mist Computing
Get PDFThe fusion of satellite technologies with the Internet of Things (IoT) has propelled the evolution of mobile computing, ushering in novel communication paradigms and data management strategies. Within this landscape, the efficient management of computationally intensive tasks in satellite-enabled mist computing environments emerges as a critical challenge. These tasks, spanning from optimizing satellite communication to facilitating blockchain-based IoT processes, necessitate substantial computational resources and timely execution. To address this challenge, we introduce APOLLO, a novel low-layer orchestration algorithm explicitly tailored for satellite mist computing environments. APOLLO leverages proximity-driven decision-making and load balancing to optimize task deployment and performance. We assess APOLLO’s efficacy across various configurations of mist layer devices while employing a round-robin principle for equitable task distribution among the close low-layer satellites. Our findings underscore APOLLO’s promising outcomes in terms of reduced energy consumption, minimized end-to-end delay, and optimized network resource utilization, particularly in targeted scenarios. However, the evaluation also reveals avenues for refinement, notably in CPU utilization and slightly low tasks success rates. Our work contributes substantial insights into advancing task orchestration in satellite-enabled mist computing with more focus on energy and end-to-end sensitive applications, paving the way for more efficient, reliable, and sustainable satellite communication systems
‘To whom am I speaking?’; Public responses to crime reporting via live chat with human versus AI police operators
Get PDFDriven by social and technological change and the imperative to enhance efficiency, police have in recent years adopted various technologies to transform their interactions with the public. In the UK, these initiatives often fall under ‘transformation’ agendas, promoting ‘channel choice’ strategies to facilitate public interactions through various technologically mediated platforms, such as reporting crimes online using form-based or chat functions. Artificial Intelligence already plays a role in some of these interactions, which is likely only to increase in the future. In this study we examine preferences and perceptions in online crime reporting. Participants read a fictitious ‘chat’ between a victim of crime and a police operator identified as either a human or a chatbot. Although the chats were identical, we find a consistent preference for human operators over chatbots across all scenarios. Human operators were thought to provide clearer explanations, although there were no significant differences in judgements of interpersonal treatment or decision neutrality between human and chatbot operators. Participants also responded more positively to the process when (a) the crime involved was less serious and (b) when the outcome was active (police attendance) rather than passive (simple recording). Our findings underscore the importance of procedural justice and communication clarity in online crime reporting systems – and perhaps of human interaction when reporting crimes
Generative AI and LLMs for Critical Infrastructure Protection: Evaluation Benchmarks, Agentic AI, Challenges, and Opportunities
Get PDFCritical National Infrastructures (CNIs)—including energy grids, water systems, transportation networks, and communication frameworks—are essential to modern society yet face escalating cybersecurity threats. This review paper comprehensively analyzes AI-driven approaches for Critical Infrastructure Protection (CIP). We begin by examining the reliability of CNIs and introduce established benchmarks for evaluating Large Language Models (LLMs) within cybersecurity contexts. Next, we explore core cybersecurity issues, focusing on trust, privacy, resilience, and securability in these vital systems. Building on this foundation, we assess the role of Generative AI and LLMs in enhancing CIP and present insights on applying Agentic AI for proactive defense mechanisms. Finally, we outline future directions to guide the integration of advanced AI methodologies into protecting critical infrastructures. Our paper provides a strategic roadmap for researchers and practitioners committed to fortifying national infrastructures against emerging cyber threats through this synthesis of current challenges, benchmarking strategies, and innovative AI applications
Discovery of novel naphthalene-based diarylamides as pan-Raf kinase inhibitors with promising anti-melanoma activity: rational design, synthesis, in vitro and in silico screening
Get PDFRaf kinase enzymes are often dysregulated in melanoma. While sorafenib demonstrates strong activity against wild-type B-Raf, it fails to effectively inhibit the mutated form of B-Raf. In this study, sorafenib served as a lead compound for the development of new derivatives designed to enhance inhibitory activity across multiple Raf isoforms (pan-Raf inhibitors). Novel naphthalene-based diarylamide derivatives were subsequently designed, synthesized, and evaluated for their biological activity against various Raf kinase isoforms and the melanoma A375 cell line. Among these, compound 9a, containing a difluoromethoxy group, demonstrated strong inhibitory activity across B-RafWT, B-RafV600E, and c-Raf. Additionally, it induced G2/M phase arrest and triggered dose-dependent apoptosis, effectively suppressing both cell proliferation and survival. Compound 9a also exhibited high selectivity for Raf isoforms with minimal off-target effects, underscoring its specificity and therapeutic potential for Raf-driven malignancies
Synergistic integration of digital twins and zero energy buildings for climate change mitigation in sustainable smart cities: A systematic review and novel framework
Get PDFSustainable smart cities are increasingly turning to innovative technologies, such as Urban Digital Twins (UDTs) and Zero Energy Buildings (ZEBs), which offer transformative opportunities to enhance energy management and reduce environmental impacts. Despite significant progress, research on the integration of UDTs and ZEBs remains limited, and the lack of a unified framework hampers their potential to fully contribute to environmental goals in smart cities. Therefore, this study conducts a comprehensive systematic review of how UDTs and ZEBs can be integrated to strengthen climate change mitigation efforts in sustainable smart cities. It primarily aims to develop a novel framework that leverages their synergies for maximizing their combined potential to advance environmentally sustainable urban development. The study reveals key trends in the convergence of UDTs and ZEBs, emphasizing the growing role of Artificial Intelligence (AI), the Internet of Things (IoT), and Cyber-Physical Systems (CPS), while also highlighting specific research patterns related to their synergistic interplay and its contribution to enabling the convergence. Moreover, it underscores the role of UDTs in enhancing the energy management and performance capabilities of ZEBs by improving energy efficiency, boosting renewable energy integration, and reducing carbon emissions through real-time monitoring, advanced data analytics, predictive maintenance, and operational optimization. Conversely, ZEBs provide real-time data and performance metrics that enhance UDTs’ analytical and predictive capabilities. Furthermore, the study introduces a comprehensive framework that integrates UDT technical and operational indicators with ZEB performance indicators. However, technical, ethical, environmental, financial, regulatory, and practical challenges must be addressed and overcome for large-scale implementation. The novelty and contribution of this study lies in the development of a unique integrated framework that bridges the gap between UDTs and ZEBs, highlighting their collective impact on sustainable urban development, an area that has not been explored in previous review research. Theoretical and practical insights are discussed to inform researchers, practitioners, and policymakers, showing how these findings can shape future research directions, guide technological implementation, and influence policy decisions in advancing sustainable smart cities
Evaluating the benefits of professional events and venues for academic scholars and institutions & their host cities
No full textThis study is response to a gap in knowledge regards higher education institutions – and their subject or discipline areas - and the motivations for hosting conferences in professional and purpose-built venues, as well any perceived barriers in doing so. Further, the research also seeks to gauge the importance given to the hosting of conferences within and for the host city . A Key Informant research method is applied, i.e., primary research was undertaken in 2024 with key research leaders in universities. Key informants are those whose social positions in a research setting give them specialist knowledge about other people, processes or happenings that is more extensive, detailed or privileged than ordinary people, and who are therefore particularly valuable sources of information to a researcher, not least in the early stages of a project (Payne & Payne, 2011)
Exploring parents’ experiences and holistic needs following late miscarriage: a narrative systematic review
Get PDFUp to 2% of all pregnancies result in pregnancy loss between 14 + 0 and 23 + 6 weeks’ gestation, which is defined as ‘late miscarriage’. Lack of consensus about definition of viability paired with existing multiple definitions of perinatal loss make it difficult to define the term ‘late miscarriage’. Parents who experience late miscarriage often have had reassuring scan-milestones, which established their confidence in healthy pregnancy progression and identity formation, which socially integrates their baby into their family. The clinical lexicon alongside the lack of support offered to parents experiencing late miscarriage may disclaim their needs, which has potential to cause adverse psychological responses.AimTo review what primary research reports about parents’ experiences and their perceived holistic needs following late miscarriage.MethodsA narrative systematic review was carried out. Papers were screened based on gestational age at time of loss (i.e. between 14 + 0 and 23 + 6 weeks’ gestation). The focus was set on experience and holistic needs arising from the loss rather than its clinical care and pathophysiology. Studies were selected using PRISMA-S checklist, and quality assessed using the Critical Appraisal Skills Program (CASP) tool. Thematic analysis was used to guide the narrative synthesis of findings.ResultsSix studies met the inclusion criteria. Three main themes emerged: communication and information-giving; feelings post-event; and impact of support provision.ConclusionLiterature about the experience of late miscarriage is scarce, with what was found reporting a lack of compassionate and individually tailored psychological follow-up care for parents following late miscarriage. Hence, more research in this arena is required to inform and develop this area of maternity care provision
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