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Effect of plasma-activated water against E. coli and S. aureus: Influence of organic matter and impact on skin cell viability
Get PDFThe study evaluated the antimicrobial efficacy of plasma-activated water (PAW), generated using a plasma bubble reactor, against Escherichia coli O157:H7 and Staphylococcus aureus in both planktonic and biofilm states. The physicochemical properties of PAW, including pH, electrical conductivity, and reactive oxygen and nitrogen species concentrations, were analysed immediately after production and after 24 and 48 h of storage at 4 °C. Additionally, the impact of organic load on PAW's antibacterial activity and its cytotoxic effects on human keratinocytes were investigated. To assess its stability, PAW’s antimicrobial activity after storage was also evaluated in the presence and absence of organic matter. PAW treatment resulted in a significant reduction in bacterial counts, achieving inactivation below the detection limit (1 log CFU/mL) within 20 min for both planktonic and biofilm states. However, the presence of organic matter significantly impaired PAW’s antibacterial efficacy, with higher organic loads leading to significantly diminished bacterial inactivation. PAW stored for 24 h maintained strong antimicrobial activity, which declined after 48 h; the presence of organic matter further reduced its efficacy across all time points. Importantly, PAW’s exposure did not induce cytotoxic effects on human keratinocytes at treatment durations of up to 30 min. These findings highlight the potential of PAW as a sustainable disinfection strategy, demonstrating robust antimicrobial activity against Gram-negative and Gram-positive foodborne pathogens while maintaining biocompatibility. Further research is required to enhance PAW’s efficacy in complex environments with organic contamination to enhance its practical applications in agri-food settings. Key points: • PAW effectively inactivated E. coli and S. aureus in planktonic and biofilm states. • PAW antimicrobial activity is reduced in the presence of organic matter. • PAW showed minimal cytotoxic effects on human keratinocytes. Graphical Abstract
Priority research questions in microbiome-integrated urban design
Get PDFUrbanization is accelerating at an unprecedented pace, with 70% of the global population projected to live in cities by 2050. This shift presents significant challenges and opportunities for fostering sustainable urban ecosystems aligned with the United Nations Sustainable Development Goals. Microbiomes—the diverse communities of microorganisms that underpin ecosystem function—are increasingly recognized for their vital role in nutrient cycling, climate regulation, biodiversity support, and human well-being. However, their consideration and integration in urban design remain underexplored, often limited to disease mitigation. The emerging field of microbiome-integrated urban design seeks to leverage microbial activity to enhance urban health and resilience through a multispecies framework. To address critical gaps, the Probiotic Cities Working Group convened a global interdisciplinary workshop, engaging experts from ecology, architecture, urban planning, immunology, and social sciences. Using reverse brainstorming and thematic analysis, participants identified eight core themes and 40 priority research questions (via a modified Delphi technique). These themes span communication and policy, pollution prevention, interdisciplinary collaboration, experimental design, ethics, and public perception of microbiomes. A binomial concordance analysis revealed strong consensus on the top-ranked questions, which address urgent needs such as improving science communication, defining success metrics, and promoting evidence-based microbiome interventions. This paper discusses the top-ranked priority research questions and their broader implications for microbiome science, urban health, and sustainable development. By focusing on these priorities, researchers, policymakers, and practitioners can foster a transformative agenda to integrate microbiomes into urban design, advancing resilient and equitable cities for the future
Transonic aerodynamic performance analysis of a CRM joined-wing configuration
Get PDFThis study examines the aerodynamic performance of a joined-wing (JW) aircraft design based on the NASA Common Research Model (CRM), aiming to assess its potential for efficient commercial transport or cargo aircraft at transonic speed (Mach 0.85). The CRM wing, optimised for transonic flight, was transformed into a JW design featuring a high-aspect-ratio main wing. An initial parametric study using the vortex lattice minimum drag panel method identified viable designs. The selected JW configuration, comprising front and rear wings joined by a vertical fin, was analysed using ANSYS Fluent to understand flow interactions and aerodynamic performance. At an angle of attack (AoA) of −1°, the JW design achieved a peak lift-to-drag ratio (L/D) of 17.45, close to the CRM’s peak L/D of 19.64 at 2°, demonstrating competitive efficiency. The JW’s L/D exceeded the CRM’s between AoA −3° and 0.8°, but the CRM performed better above 0.8°, with differences decreasing at a higher AoA. Based on induced drag alone, the JW outperformed the CRM across AoA −3° to 8°, but flow complications restricted its L/D advantage to a small, low AoA range. A strong shock on the vertical fin’s inboard side due to high incoming flow speed delayed shock formation on the main wing near the joint. Optimising the vertical fin shape slightly improved L/D, suggesting potential for further enhancements or that other design factors significantly affect JW performance. This study provides insights into JW aerodynamics at transonic speeds, revealing its potential benefits and challenges compared to the CRM design
Falling risk analysis at workplaces through an accident data-driven approach based upon hybrid Artificial Intelligence (AI) techniques
No full textThis study proposed an accident data-driven approach using hybrid AI techniques for the quantification of falling risks at workplaces. Six machine learning models and one ensemble learning model were deployed for automatic extraction of causal factors. These causal factors were taken as main nodes in the falling risk Bayesian network (FRBN). Data-driven and knowledge-driven methods were combined for structure learning of the FRBN, based upon algorithms of hill climbing and tree augmented naive Bayes firstly and modification of FRBN through incorporation of knowledge. Sensitive causal factors were determined using parameter- based and evidence-based sensitivity analysis approaches. The FRBN was further adopted for forward and backward causal inferences. The accident data-driven approach through hybrid AI techniques contributes to substantial learning from fall-related accidents. Measures would be tailored according to causal inferences within the FRBN, so that the probability of falling risk will be reduced and negative impacts of fall-from-height (FFH) accidents will be decreased
On fault-tolerant Boolean functions in proteinoids–ZnO colloids
Get PDFThis study investigates the computational properties of ZnO colloids in combination with proteinoid microspheres within an unconventional computing framework. We propose a method for creating flexible and fault-tolerant logic gates utilising this colloidal system. The colloidal matrix receives binary strings with an electrical impulse representing a logical “True” and its absence representing a “False”. Electrical responses are recorded, and Boolean functions are extracted. This nano-bio hybrid of ZnO colloids and proteinoids has the potential to power next-generation unconventional computing systems that can adapt to changing environments, paving the way for novel nano-bio hybrid computing architectures
Effect of iodine nutrition during pregnancy and lactation on child cognitive outcomes: A review
Get PDFIodine deficiency remains one of the most serious global public health challenges, recognised as the leading cause of preventable brain damage worldwide. It is widely accepted as the primary aetiological factor underlying iodine deficiency disorders (IDD). Inadequate maternal iodine intake reduces thyroxine synthesis, impairing foetal brain development and leading to long-term deficits in cognitive function across childhood and adulthood. However, emerging evidence also suggests that excessive iodine intake may disrupt thyroid function, particularly in individuals with underlying thyroid autoimmunity, potentially leading to adverse neurodevelopmental outcomes. In this state-of-the-art review, we examine the effects of iodine nutrition during pregnancy and lactation on child cognitive outcomes. We provide an overview of the recent global iodine status, critically appraise the current evidence linking both iodine deficiency and excess to neurodevelopmental outcomes, and offer expert interpretation of the key findings. We further highlight areas of uncertainty, introduce emerging evidence from contemporary studies, and propose directions for future research to inform and optimise public health policies and clinical practice. Our findings highlight a U-shaped association, whereby both insufficient and excessive iodine exposure during pregnancy and lactation may impair optimal brain development in the offspring
Investigation of brittle adhesive reinforcement for single lap shear joints with composite adherends
No full textAdhesive bonding of composite structures is a promising alternative to mechanical fastening due to improved load distribution and reduced stress concentrations. However, the brittleness of high-stiffness epoxy adhesives poses challenges, especially under high loads. This study investigates reinforcement strategies for brittle adhesives in single lap shear joints using micro-scale glass beads (GB) and milled carbon fibres (MCF), both individually and in combination. Four adhesive configurations were tested: pure epoxy (XA120), GB-reinforced (10 %, 15 %, 20 % wt), MCF-reinforced (2.5 %, 5 %, 7.5 % wt), and a hybrid formulation (10 % GB + 5 % MCF). Moderate filler contents (10 % GB, 5 % MCF) enhanced joint stiffness and peak force, while excessive filler loading led to agglomeration and reduced performance. The hybrid formulation demonstrated balanced stiffness and strength due to the combined effects of spherical and fibrous fillers, reducing stress concentrations. Failure analysis revealed that pure XA120 and MCF-7.5 % joints primarily exhibited cohesive failure, while high GB content (20 %) increased interfacial debonding. The hybrid configuration showed mixed failure modes, suggesting variable stress distribution from non-uniform filler dispersion. Bootstrap resampling (n = 1000) confirmed that moderate GB content (15 %) provided consistent performance with narrow confidence intervals despite limited sample sizes. These findings underscore the importance of optimising filler content to balance stiffness, strength, and durability. While the hybrid formulation offers promising strength–toughness balance, further work is needed to improve filler dispersion and consistency for critical applications
Advancing explainable autonomous vehicle systems: A comprehensive review and research roadmap
Get PDFGiven the uncertainty surrounding how existing explainability methods for autonomous vehicles (AVs) meet the diverse needs of stakeholders, a thorough investigation is imperative to determine the contexts requiring explanations and suitable interaction strategies. A comprehensive review becomes crucial to assess the alignment of current approaches with varied interests and expectations within the AV ecosystem. This study presents a review to discuss the complexities associated with explanation generation and presentation to facilitate the development of more effective and inclusive explainable AV systems. Our investigation led to categorising existing literature into three primary topics: explanatory tasks, explanatory information, and explanatory information communication. Drawing upon our insights, we have proposed a comprehensive roadmap for future research centred on (i) knowing the interlocutor, (ii) generating timely explanations, (ii) communicating human-friendly explanations, and (iv) continuous learning. Our roadmap is underpinned by principles of responsible research and innovation, emphasising the significance of diverse explanation requirements. To effectively tackle the challenges associated with implementing explainable AV systems, we have delineated various research directions, including the development of privacy-preserving data integration, ethical frameworks, real-time analytics, human-centric interaction design, and enhanced cross-disciplinary collaborations. By exploring these research directions, the study aims to guide the development and deployment of explainable AVs, informed by a holistic understanding of user needs, technological advancements, regulatory compliance, and ethical considerations, thereby ensuring safer and more trustworthy autonomous driving experiences
A transparency paradox? Investigating the impact of explanation specificity and autonomous vehicle imperfect detection capabilities on passengers
Get PDFTransparency in automated systems could be afforded through the provision of intelligible explanations. While transparency is desirable, might it lead to catastrophic outcomes (such as anxiety) that could outweigh its benefits? It's quite unclear how the specificity of explanations (level of transparency) influences recipients, especially in autonomous driving (AD). In this work, we examined the effects of transparency mediated through varying levels of explanation specificity in AD. We first extended a data-driven explainer model by adding a rule-based option for explanation generation in AD and then conducted a within-subject lab study with 39 participants in an immersive driving simulator to study the effect of the resulting explanations. Specifically, our investigation focused on: (1) how different types of explanations (specific vs. abstract) affect passengers' perceived safety, anxiety, and willingness to take control of the vehicle when the vehicle perception system makes erroneous predictions; and (2) the relationship between passengers' behavioural cues and their feelings during the autonomous drives. Our findings showed that abstract explanations did not make passengers safer despite being vague enough to conceal all perception system detection errors compared to specific explanations having a minimal amount of exposed perception system detection errors. Anxiety levels increased when specific explanations revealed perception system detection errors (high transparency). We found no significant link between passengers' visual patterns and their anxiety levels. We advocate for explanation systems in autonomous vehicles (AV) that can adapt to different stakeholders' transparency needs
Application of carbon-fiber-reinforced polymer rods and ultra-high-performance fiber-reinforced concrete jackets with mechanical anchorage systems to reinforced concrete slabs
Get PDFThe aim of this experimental study was to develop and evaluate the effectiveness of a new strengthening system for reinforced concrete slabs employing external jackets consisting of ultra-high-performance fiber-reinforced-concrete (UHPFRC) and mechanical anchor systems. The issue of debonding between old and fresh concrete layers, as well as the efficiency of utilizing CFRP rods, is the primary challenge of applying the UHPFRC jackets with embedded CFRP rods. In this study, we propose a novel retrofitting technique for implementing a mechanical anchor system to improve the binding of fresh UHPFRC jackets with old RC slabs. An experimental test was conducted by subjecting three slabs to cyclic loads by utilizing a dynamic actuator: a reference slab, a retrofitted slab with an external UHPFRC layer, and a retrofitted slab with an external UHPFRC layer incorporating CFRP bars. Furthermore, finite element models (FEMs) were utilized to investigate the responses of the retrofitted slabs and compare the novel method with traditional strengthening techniques, including near-surface-mounted (NSM) CFRP rods, externally bonded CFRP strips, and epoxy-bonded UHPFRC jackets, as well as two models that were the same as the experimental strengthened slab specimens except for the fact that they did not have a mechanical anchor system. Additionally, analytical mechanistic models were employed to determine the flexural moment capacity of the RC slabs. The experimental findings demonstrated that the proposed strengthening strategy considerably prevented premature debonding and enhanced the maximum load of retrofitted RC slabs by over 82%. Also, the FEM and analytical results are significantly consistent with the experimental outcomes. In conclusion, the newly suggested strengthening technique is a reliable system for enhancing the efficacy of slabs, effectively preventing early debonding between existing and new components