Borassus husk fibre/epoxy composites experimental analysis of physical, thermal, flexural, and dynamic mechanical properties for high-performance applications

This study explores the impact of alkali treatment on the physical, thermal, flexural, and thermo-mechanical properties of Borassus flabellifer husk fiber-reinforced epoxy composites in accordance with standards. Using the hand layup method, composites were fabricated with 10% (wt.) untreated and alkali-treated fibers (0.25–2 hours). SEM analysis confirmed improved fiber-matrix adhesion, leading to enhanced properties. Treated fiber composites exhibited reduced moisture regain (0.57−1.28%) and water absorption (0.59−1.55%), indicating superior moisture resistance. Thermal stability increased with alkali treatment, with integral process decomposition temperature (IPDT) reaching 547°C for 2-hr treated fibers. The glass transition temperature (Tg) peaked at 94.5°C for the 0.5-hr treated Borassus fiber-reinforced epoxy (0.5TBHFE). Flexural modulus (up to 3.2 GPa) and strength (up to 108.7 MPa) exceeded many conventional bio-fibers-reinforced composites, making them rational for structural applications. Dynamic mechanical analysis showed enhanced damping properties (tan δ up to 1.21), improving energy dissipation and impact resistance. Overall, 0.5TBHFE offered an optimum balance between stiffness and damping, making it suitable for aerospace and automotive applications. This study highlights the potential of Borassus husk fibers as a sustainable reinforcement alternative, though further optimization and industrial processing are needed for broader application

Playful Citizen Discussion Space to Help Steer Society Towards Water Security

In this paper, we explore the use of a playful dilemma activity, a survey and public data analysis to spark citizen awareness and reflection on water use within the UK. Water security ensures access to fresh water for survival and society’s needs. There is significant citizen agency in water security that impacts the freshwater reserves. However, in many countries, water security is under-discussed. Encouraging spaces for individuals to assess their water use, identify waste, and adopt sustainable practices is key to sustaining freshwater resources for the future. We present a case study conducted as part of the GREAT project to stimulate citizen discussion on water use and its subsequent impact on the broader water system. Through a series of activities, citizens can reflect on their water use, consider the future forecast of water security within the UK, and experience a playful dilemma to save a city from the effects of drought. In this dilemma, players are part of an organisation tackling a city’s water crisis. Faced with limited water resources, players analyse data on the population's usage and suggest behaviour changes to reduce consumption. The challenge lies in balancing the need for water conservation with the risk of proposing changes that are resisted by the population. The players must convince the facilitator of both the practicality and acceptability of their plans. Success restores water security, but failure reveals the consequences of inaction. Within this playful discussion space, we create an exchange for citizens to elicit attitudes and preferences on current water use and explore how individuals might be “nudged” into less wasteful behaviour. In this paper, we describe the co-design of this qualitative approach, share findings on attitudes and preferences elicited (n = 19), and reflect on this method as a tool for fostering awareness and meaningful citizen discussions

Becoming a mental health professional two autoethnographic accounts of the clinical psychology journey

PurposeThis paper aims to highlight the arduous journey of becoming a clinical psychologist.Design/methodology/approachThe two authors provide autoethnographic accounts of their respective journeys to become clinical psychologists.FindingsA year after graduation, neither author had succeeded in their goal. They talk about the danger of getting stuck in “the doctoral rut”, where the ambition of becoming a clinical psychologist can take over the applicant’s life to the exclusion of other career options.Research limitations/implicationsThis is of course only the story of two clinical psychology applicants, yet it will resonate with the thousands of people who apply for clinical psychology training each year. Of the five mental health professions, there are more people wanting to become clinical psychologists. This represents a reservoir of talent wanting to enter the field of mental health.Practical implicationsThere needs to be other pathways for psychology graduates who want to work in the mental health field than just clinical psychology but which offer attractive career pathways.Social implicationsGiven the stigma attached to people with mental health problems, it is interesting that so many psychologists want to work in this field.Originality/valueThis paper highlights the commitment that many young psychologists have towards working in mental health services. While it reports on the stories of just two individuals, these accounts are typical of many clinical psychology applicants

Exploring Nature's Impact on the Development of Shared Leadership in Early-Stage Startup Co-founder Teams

Startups are often exhilarating, akin to an outdoor adventure, but challenges can quickly arise, exacerbating stressors. This study investigates the underexplored potential of how exposure to nature can impact early-stage startup co-founder teams. It explores how a nature-based retreat influences shared leadership development particularly in the areas of trust and cohesion. Using a hybrid methodology integrating phenomenology and case study approaches, the study is guided by Tuckman's Group Development Model

Special Floating Wind-H2 Design For Ajman - UAE: Navigating The Turbulent Waters Of The Energy Transition And Building Climate Resilience

A Floating Wind-H2 System can support efforts toward a net-zero future through its synergistic potential. This paper overviews the powerful synergy between floating wind and hydrogen technologies. Floating wind technology has made significant advances in terms of turbine size and efficiency, opening a vast offshore wind resource. H2's multifaceted advantages and the trajectory of these systems within the broader energy landscape are analysed in a multidimensional approach to assessing their transformative potential. Floating Wind-H2 Systems are examined, revealing current and prospects based on existing projects. As floating wind technology evolves, they shed light on H2's nuanced role in the energy transition and provide insights into the feasibility and impact of integrating these technologies at scale. Using Ajman, UAE, as a case study to illustrate regional adaptation, this paper discusses the significance of floating wind and hydrogen technologies for achieving global net-zero targets. The comparison of centralised onshore and decentralised offshore electrolysis demonstrated the importance of flexible solutions, particularly in regions like Ajman with limited land availability. An innovative approach to clean energy infrastructure is demonstrated by the proposed wind-H2 system in Ajman, which relocates offshore facilities to overcome land constraints, reduce costs, and facilitate free trade. The system can produce more than 5 Tons of H2 per day considering power generation of 3.4 MW per wind turbine for 3 turbines

Addiction recovery stories: Emma Roughley in conversation with Lisa Ogilvie

PurposeThe purpose of this paper is to examine recovery through lived experience. It is part of a series that explores candid accounts of addiction and recovery to identify important components in the recovery process.Design/methodology/approachThe growth, connectedness, hope, identity, meaning in life,empowerment (G-CHIME) model comprises six elements important to addiction recovery (growth, connectedness, hope, identity, meaning in life and empowerment). It provides a standard against which to consider addiction recovery. It has been used in this series, as well as in the design of interventions that improve well-being and strengthen recovery. In this paper, a firsthand account is presented, followed by a semistructured e-interview with the author of the account. Narrative analysis is used to explore the account and interview through the G-CHIME model.FindingsThis paper shows that addiction recovery is a remarkable process that can be effectively explained using the G-CHIME model. The significance of each component in the model is apparent from the account and e-interview presented.Originality/valueEach account of recovery in this series is unique and as yet untold

Air Quality Management Strategic Framework For Future Sustainable Cities

Pollution can originate from fixed, mobile, and local sources, due to human activity or naturally occurring processes. The well-developed cities contribute to over 70% of global carbon emissions. This paper analyses the parameters that contribute to achieving healthy, environmentally sustainable cities. A strategic planning framework is introduced to implement efficient and effective strategies for future sustainable cities. Therefore, the paper aims to identify and examine successful factors within a framework to reduce negative environmental impacts from air pollution designed for future sustainable cities. Data is collected from locations that reflect the nature of human settlement and well-being, and Advanced SWOT analysis was conducted to see the success factors. Data analyses reveal many factors that must be monitored to achieve SDG 11 targets. The results show how air quality affects people's health and social living conditions in urbanised areas. Comparisons between pre-and-post Covid 19 indicated the impact of the pandemic on air quality and showed evidence of possible reductions in air pollution when activities are reduced. The method used for this research is analysing the data recorded from a network of environmental stations constructed at different sites in the Emirate of Ajman. The achieved framework consists of strategies categorised into five main categories, formed by different functional layers, to demonstrate actions needed by the government. Recommendations have been drawn from the findings, and if considered, it could be possible to achieve sustainable air quality. Keywords: Aerodynamics, Forebody and afterbody, Next keyword, Projectile, Supersonic speed

Machine learning applications for wind resource mapping in Ajman, UAE towards sustainable energy solutions

Accurate site-specific Sustainable Wind Resource Assessment (SWRA) remains a critical contemporarysustainable wind power development issue. Especially in regions like the Emirate of Ajman and theUnited Arab Emirates (UAE), site-specific wind data collection faces high challenges and constraints.Mainly due to the excessively high costs of measuring wind speeds at wind turbine hub heights level,leading to dependence on publicly available NASA satellite data or any other freely available wind data that requiresextensive error correction for reliable application in SWRA.This research develops a comprehensive methodology for site-specific SWRA in the Emirate of Ajman throughfive integrated objectives: developing machine learning (ML)-based error correction methodology for NASAsatellite wind data, determining site-specific surface parameters, predicting future wind speed trends using ARIMAmodelling, analysing wind potential variations, and creating GIS-based wind resource maps. A systematic mixed-methodsapproach was used, integrating multiple ML algorithms (Random Forest, Support Vector Machine, Gradient Boosting) for NASAwind speed data correction, determination of site-specific parameters (wind shear coefficients, roughness length, air density),statistical analysis of wind patterns, and GIS-based wind resource mapping. Ground-based measurements from strategicallylocated onshore monitoring stations validated the methodology and established site-specific correction factors across Ajman'sdiverse terrain. Results showed clear spatial and temporal variations in wind resources, with annual wind speeds rangingfrom 3.33- 3.74 m/s at 50m to 4.75-5.2 m/s at 100m height. Spring emerged as the optimal season, with wind speedsreaching 5.69-6.16 m/s at 100m height. The Random Forest model achieved the highest accuracy (R² = 0.5772) insatellite data correction. Surface roughness length varied from 0.0002 (offshore) to 0.50 (urban areas), while air densityranged between 1.146-1.166 kg/m³. Offshore locations showed higher wind power density, reaching 126.12 W/m².This study establishes Ajman's first validated, GIS integrated SWRA methodology, contributing to practical and theoreticaladvances in SWRA. While supporting the feasibility of hybrid wind-solar systems and offshore installations, the findingsalign with the UAE's Net Zero 2050 strategy and establish a systematic approach that other regions can follow to improvesatellite-derived wind speed data accuracy

Enhanced Intrusion Detection in IoT Networks Using Hybrid Machine Learning Technique

This study introduces a unique framework for an Intrusion Detection System (IDS) that employs an advanced machine learning approach to improve the Internet of Things (IoT) networks. IoT devices, now increasingly prevalent, rely on data which is a subject of interest to hackers/attackers who explore the present rise in network security vulnerabilities. There is therefore the need for a more robust and accurate intrusion detection system. The integration of Random Forest (RF) algorithms with Deep Neural Networks (DNNs) provides a significant increase in model evaluation metrics and robustness. A comprehensive CICIoT2023 dataset was adopted and used to meticulously train and evaluate the IDS model, resulting in an exceptional and effective system of identifying and preventing potential threats. Also, the study analysis highlights areas of improvement, particularly in detecting specific attack types such as SQL injection. Whilst these findings push the boundaries of IoT security using state-of-the-art machine learning techniques, they have also underlined the need for further studies to address the obvious gaps

Improving Telemedicine with Digital Twin-Driven Machine Learning: A Novel Framework

The convergence of digital twin technology and machine learning has ushered in a transformative era in patient monitoring and diagnosis within the healthcare sector. This review article explores the comprehensive integration of digital twin-driven machine learning frameworks, aiming to elucidate the core objectives, pivotal findings, and overarching implications. Our primary objectives encompass the exploration of digital twin technology's adaptation to healthcare, the augmentation of medical assessments through machine learning algorithms, the enabling of real-time monitoring with early anomaly detection capabilities, and the personalization of treatment plans rooted in patient profiles generated by digital twins. The key findings underscore the successful adaptation of digital twin technology for healthcare applications, emphasizing its potential to capture dynamic patient data and history. The synergy between machine learning and digital twins enhances the precision of diagnostics and predictive analytics, thus improving healthcare outcomes. Real-time monitoring, made possible through digital twins, ensures proactive patient care with timely interventions. Moreover, personalizing treatment plans, tailored to individual patient profiles, offers a promising avenue for more effective and less invasive interventions. The implications of this review extend to the transformative potential of digital twin-driven machine learning in healthcare, with the ability to revolutionize patient care, diagnostics, and monitoring. The review highlights data security and ethical challenges, stressing the need for standardized protocols to protect patient information. Ongoing research and innovation are crucial for maximizing these frameworks' potential, improving patient outcomes, and enhancing healthcare quality