De Montfort University

Public project performance is shaped by both organisational capability and governance controls, yet the interaction between these factors remains underexamined in decentralised public administration contexts. This study examines the direct effects of team capability and ethical compliance on project performance, and tests whether ethical compliance conditions the capability–performance relationship. A quantitative explanatory survey design was adopted. Structured questionnaires were administered to 320 senior officers involved in project evaluation, procurement, budgeting and technical oversight, and the data were analysed using PLS-SEM to estimate the hypothesised direct and moderating relationships. Team capability and ethical compliance each have a significant positive effect on project performance, and team capability is positively associated with ethical compliance. The moderating effect of ethical compliance is significant but small in magnitude, indicating that higher levels of compliance modestly attenuate the marginal performance gains associated with greater team capability. These findings suggest that while compliance mechanisms strengthen accountability and directly support performance, they may simultaneously constrain the discretionary flexibility through which capable teams generate incremental improvements. The study contributes to public management research by empirically demonstrating a conditional capability–governance relationship in a local government project context. Given the moderate explanatory power of the model, future research should incorporate additional institutional and political variables to further clarify performance drivers in public-sector project systems.

The high energy consumption in the building sector and the increasing impacts of climate change have necessitated the transition to net-zero-energy buildings (NZEBs), in which energy retrofit strategies play a key role. However, neglecting moisture transport and climatic design while improving energy efficiency often leads to reduced building performance, material deterioration, worse occupant health, and indoor environmental problems. This review examines in detail the basic mechanisms of moisture transport, including diffusion, capillary action, and airborne moisture transport, and illustrates how poor moisture control affects thermal performance and resident health. Additionally, a comparative analysis of the impact of retrofit strategies implemented in different climatic regions on energy efficiency, carbon emission reduction, moisture-related failures and net-zero goals is conducted. At the same time, the need exists to incorporate awareness regarding the adverse effects on the health of occupants. This systematic review analyzed 120 peer-reviewed studies published from 1994 to 2026, covering different climatic regions (e.g., cold, temperate, warm–humid, etc.). The analysis found that the energy savings rates were reported to range from 18% to 45%, while the moisture-related failures in inappropriately retrofitted buildings were observed to increase by up to 32% in some cold regions. This research review provides a comprehensive advisory framework for domestic residents to take remedial steps until retrofit experts gain access in order to prevent health risks from mold and moisture ingress, which can contribute to a healthy lifestyle and a net-zero-energy building.

The use of textile fibre-reinforced composite materials for many alternative applications has significantly increased in the twenty-first century due to their lightweight nature and high strength-to-weight ratio. In this study, false banana fibres were used as reinforcement and unsaturated polyester resin as the matrix. The optimal ratio of fibre to matrix was established through an analysis of physico-mechanical parameters, including tensile, compressive, and flexural strengths, water absorption, and void fraction, utilising Design Expert software. Additionally, deformation, Von Mises stress, Von Mises strain, and velocity were analyzed using ANSYS simulation software. The composite exhibited water absorption of 1.5% over 24 to 48h, a void fraction of 1.02%, a tensile strength of 33.15MPa, a compressive strength of 29.69MPa, and a bending or flexural strength of 28.85MPa. Furthermore, the ANSYS results showed a maximum deformation of 0.60887mm, a maximum equivalent elastic strain of 0.0018815, a minimum value of 1.0375 × 10-10, a maximum equivalent stress of 22.27MPa, a minimum of 1.3877 × 10-5MPa, and a velocity streamline of 14.97m/s at 21rad/s. The simulated stresses were well below the material's measured strength limits, indicating a safe design under the analysed conditions.The weight of the developed composite blade was 31% lower than that of a conventional aluminum blade.

ABSTRACTPurposeTobacco smoking is a major driver of chronic disease, and e‐cigarette use is increasingly common, yet their associations with prescribed medication burden remain unclear.MethodsWe analysed data from 1657 adults aged ≥ 16 years who completed a nurse visit in the Health Survey for England 2021. Smoking and e‐cigarette status were categorized into four mutually exclusive groups: never users, current smokers, current exclusive e‐cigarette users, and ex‐smokers. Prescribed medication use in the past 7 days was verified by nurses and grouped into seven BNF‐based therapeutic categories: cardiovascular, metabolic, respiratory, pain and anti‐inflammatory, gastrointestinal, mental health, and anti‐infectives. Overall, medication burden was categorized as none, one, two, or three or more prescribed medicines. Generalized ordered logistic regression estimated associations with overall burden, and Firth penalized logistic regression examined therapeutic categories, adjusting for age, sex, education, and area‐level deprivation. Sensitivity analyses restricted the sample to adults aged ≥ 45 years.ResultsEx‐smokers reported the highest medication burden, with 20.3% prescribed three or more medicines compared with 8.6% of never users, 10.9% of current smokers, and 12.2% of current e‐cigarette users (p < 0.001). In adjusted analyses using never users as the reference group, ex‐smokers had significantly greater odds of higher overall burden (OR = 1.50, 95% CI: 1.21–1.87, p < 0.001). Current smokers (OR = 0.86, 95% CI: 0.60–1.24, p = 0.42) and e‐cigarette users (OR = 1.63, 95% CI: 0.93–2.83, p = 0.09) did not differ significantly from never users. By therapeutic category, current e‐cigarette users, compared with never users, had higher odds of pain and anti‐inflammatory medication use (OR = 3.61, 95% CI: 1.70–7.67, p = 0.001). Ex‐smokers, relative to never users, also had elevated odds of respiratory (OR = 1.72, 95% CI: 1.17–2.55, p = 0.006), gastrointestinal (OR = 1.56, 95% CI: 1.17–2.07, p = 0.002), and anti‐infective prescriptions (OR = 3.32, 95% CI: 1.63–6.79, p = 0.001). Sensitivity analyses restricting to those aged ≥ 45 years produced consistent results.ConclusionsEx‐smokers had the highest medication burden compared with never users, which may partly reflect reverse causation, whereby illness and associated treatment prompt smoking cessation, though residual effects of prior smoking cannot be excluded. Current smokers showed no significant excess burden, possibly due to a “healthy smoker” effect, and e‐cigarette use was not associated with overall burden compared with never users, although it was linked to greater use of pain and anti‐inflammatory medicines.

Conventional textile dyeing remains one of the most resource-intensive stages of garment production, characterised by high water and energy use and the generation of chemically contaminated effluent. This study explores an alternative approach to conventional dyeing through the development and evaluation of a laser dyeing process termed peri-dyeing , a digitally driven, non-contact colouration technique in which dye fixation was initiated by targeted laser irradiation directly at the fibre surface. Optimisation of laser parameters and dye application methods enabled controlled surface colouration of wool fabrics. Colour measurements, SEM imaging, and tensile strength analysis confirmed that high dye fixation efficiencies (82–96%) were achieved without compromising fibre integrity. Standardised testing demonstrated strong wash and rub colour fastness, indicating technical performance compatible with commercial textile applications. Design sampling validated the technique's ability to produce fine linear detail, smooth tonal gradients, and multicolour imagery on both flat and textured substrates. The peri-dyeing process demonstrates the technical feasibility of a digitally controlled approach to textile colouration that avoids immersion dye baths and enables targeted dye application. The results indicate potential for reduced resource use and increased production flexibility. The paper highlights opportunities for integration into direct-to-garment and on-demand manufacturing contexts, supporting the development of more efficient and adaptable textile colouration workflows. Graphical Abstract - Peri-dyeing: Laser dye fixation for efficient textile colouration and design. • A digitally driven laser dyeing process for textile colouration and design. • Enables precise dye–fibre interaction and fixation through targeted laser irradiation. • Offers resource-aware, localised colouration, strong fastness and fibre integrity. • Achieves high design precision, flexibility, repeatability, and process efficiency. • Supports non-contact, digitally controlled surface design for on-demand production.

IntroductionParental mental illness is a major public health issue across the globe with well-known intergenerational impacts on children. There is a wide body of evidence supporting the effectiveness of a range of interventions supporting families, however, their implementation has rarely been sustained across public health systems. Systemic change is an important part of workforce development and known to be crucial to embed and sustain practice, policy and structural initiatives in services for families. While much is known about the barriers to implementing family focused approaches within organizations and systems, less is known about how systems change occurs and what supports systems change to improve outcomes for families.MethodsThis study uses a Delphi method, with 103 experts from 17 countries participating to identify systems change factors from their own experience and to build consensus about key strategies required across the globe to support systems change in health, education, social welfare and mental health services.ResultsThe findings identify that systems change can be defined as any workforce, policy, legislation or other mental health promotion strategy that collectively contributes to improving outcomes for parents with mental illness, their children and their families. A systems approach to improve outcomes for families where a parent has a mental illness requires partnerships and collaboration between services and sectors affecting families (mental health, welfare, primary health, education, social care, public health), social and health policy development, and families themselves. Success in system change requires a focus on change at all levels of the system for momentum building, leadership support, the use of relevant data and reporting mechanisms, establishing practice competency and collaborative care, and being able to reflect and adapt to changing conditions and structural barriers.DiscussionA focus on system change for supporting families where a parent has a mental illness appears to require the combination of many strategies and factors, with international approaches to knowledge sharing imperative to support implementing, resourcing and sustaining change.

High consumption of ultra-processed food (UPF) has been consistently linked to negative health outcomes, but existing tools for assessing UPF consumption are often lengthy, culturally specific, or psychometrically underdeveloped. The present research aimed to develop and validate the Ultra-Processed Food Consumption Scale (UPFCS); that is, a brief, psychometrically sound, and cross-culturally applicable measurement. In Study 1 (n = 325), exploratory factor analysis (EFA) was conducted to examine the factorial structure and internal consistency of the 30-item UPFCS. Study 2 (n = 129) evaluated test-retest reliability across a 2-week interval, and Study 3 (n = 144) assessed convergent validity by examining associations to Mediterranean diet adherence, body mass index (BMI), and food addiction, as well as testing the moderating role of Mediterranean diet adherence. EFA and internal consistency supported a unidimensional structure, while test-retest reliability was moderate to strong. UPFCS scores were positively associated with food addiction symptoms, but not with BMI or Mediterranean diet adherence. Exploratory moderation analysis indicated that adherence to a Mediterranean diet attenuated the relationship between UPF consumption and food addiction symptoms, with the effect strongest among those with low Mediterranean dietary adherence. The UPFCS demonstrates robust psychometric properties and practical utility for assessing ultra-processed food consumption in behavioural and nutritional research.

Oil spills represent a severe threat to aquatic ecosystems, requiring rapid and reliable detection methods to support environmental response. Hyperspectral imaging (HSI) offers high spectral resolution for distinguishing hydrocarbon types, but its effective use depends on the performance and robustness of deep learning (DL) models, especially under data-limited conditions. This study presents a systematic evaluation of convolutional neural network (CNN) configurations for oil spill classification in visible-near-infrared (VNIR) hyperspectral data, examining the influence of architectural depth and hyperparameters such as the number of convolutional kernels, neuron density, and dropout rate. Two architectures were tested across 54 configurations and two training set sizes (259 and 518 samples). Results show that a compact architecture with an additional max pooling layer achieved near-perfect accuracy (&gt;0.99) with reduced complexity and greater robustness, outperforming its deeper counterpart. Importantly, this study reveals that under small-sample scenarios, optimal performance can still be achieved by carefully balancing model capacity, favouring moderate convolutional depth and high neuron density, while avoiding over-regularisation. These findings provide practical guidance for designing efficient CNNs for UAV-based oil spill monitoring and lay the groundwork for future integration into local real-time processing pipelines and transfer learning applications.