Search Results (2,868)

Cities are significant contributors to climate change, environmental degradation, and resource depletion. To address these challenges, sustainable strategies in building design, construction, and management are essential, and digitalisation through the integration of Building Information Modelling (BIM) and Life Cycle Assessment (LCA) can enable it. However, the environmental benefits of BIM–LCA integration remain underexplored, limiting broader practical adoption. This study systematically reviews 80 case studies (2015–2025) to assess how recent applications address known barriers and to identify enablers of successful BIM–LCA workflows. The analysis highlights a growing alignment between technological, regulatory, and methodological advancements and practical implementation needs, especially as technical barriers are increasingly overcome. Nevertheless, systemic challenges related to institutional, behavioural, and socio-economic factors persist. From a stakeholder perspective, four thematic drivers were identified: material circularity and resource efficiency; integration with complementary assessment tools; energy-performance strategies for comfort and efficiency; and alignment with international certification systems. The study offers a stakeholder-oriented framework that demonstrates the multi-level value of BIM–LCA integration not only for environmental impact assessment but to support informed decision-making and reduce resource consumption. These insights aim to bridge the gap between academic research and practical implementation, contributing to the advancement of sustainable practices in the built environment. Full article

Attending puppy training classes may help to improve the human–dog relationship and the overall behaviour and trainability of the dog, yet class attendance and the structure and content of classes are not well known. This study aimed to describe the size and structure of classes attended and reasons for non-attendance, and to identify factors associated with training class attendance, by owners of puppies under 19-weeks-old. In a sample of 2187 owners participating in the ‘Generation Pup’ study, 67% reported attending at least one training class. Factors associated with increased odds of class attendance were higher household income, previous intention to attend, first-time ownership, and/or those who received a puppy information pack at acquisition. The likelihood of attending decreased as the acquisition age of the puppy increased. Classes were reported to vary in relation to the number, age, and size of puppies. Class content also varied, for example, in relation to opportunities for puppies to play with each other and training advice given. Common reasons for non-attendance included owners wanting to work with the puppy themselves and/or no suitable classes being available. These data may help to better understand training class attendance for young dogs and could inform strategies to encourage attendance amongst the dog-owning population. Full article

Understanding the social relationships and behaviours of pet rats is important, particularly because they are comparatively understudied compared to their laboratory and wild counterparts, and little is known about their welfare. Here, open-ended interviews, with a particular focus on rat social relationships and behaviours, were conducted with 23 pet rat owners in the United Kingdom and a reflective thematic analysis was conducted on the resulting, transcribed dataset. Seven main themes were generated: Social Behaviours, Social Life and Group Dynamics, Introducing New Rats and Repairing Social Bonds, Owner Practices, Participant and Rat Contextual Background, Owner Narratives and Shared Understandings, and Owner Research Interests. Owners described rat social relationships and behaviours with a high level of consistency and reported the techniques they employ to manage the social dynamics of their rat groups, including the first scientific report of rat introductions. We propose that these qualitative findings can inform future research, including observational studies of captive (pet and non-pet) rat management and welfare. Full article

In developing countries, indoor air pollution in rural areas is often attributed to the use of solid biomass fuels for cooking. Such fuels generate particulate matter (PM), carbon monoxide (CO), carbon dioxide (CO₂), polyaromatic hydrocarbons (PAHs), and volatile organic compounds (VOCs). PM created from biomass combustion is a pollutant particularly damaging to health. This rigorous study employed a personal sampling device and multi-stage cascade impactor to collect airborne PM (including PM_2.5) and deposited ash from 20 real-world kitchen microenvironments. A robust analysis of the PM was undertaken using a range of morphological, physical, and chemical techniques, the results of which were then compared to a controlled burn experiment. Results revealed that airborne PM was predominantly carbon (~85%), with the OC/EC ratio varying between 1.17 and 11.5. Particles were primarily spherical nanoparticles (50–100 nm) capable of deep penetration into the human respiratory tract (HRT). This is the first systematic characterisation of biomass cooking emissions in authentic rural kitchen settings, linking particle morphology, chemistry and toxicology at health-relevant scales. Toxic heavy metals like Cr, Pb, Cd, Zn, and Hg were detected in PM, while ash was dominated by crustal elements such as Ca, Mg and P. VOCs comprised benzene derivatives, esters, ethers, ketones, tetramethysilanes (TMS), and nitrogen-, phosphorus- and sulphur-containing compounds. This research showcases a unique collection technique that gathered particles indicative of their potential for penetration and deposition in the HRT. Impact stems from the close link between the physico-chemical properties of particle emissions and their environmental and epidemiological effects. By providing a critical evidence base for exposure modelling, risk assessment and clean cooking interventions, this study delivers internationally significant insights. Our methodological innovation, capturing respirable nanoparticles under real-world conditions, offers a transferable framework for indoor air quality research across low- and middle-income countries. The findings therefore advance both fundamental understanding of combustion-derived nanoparticle behaviour and practical knowledge to inform public health, environmental policy, and the UN Sustainable Development Goals. Full article

Neuromarketing studies the brain function as a response to marketing stimuli. A large amount of neuromarketing research uses data from electroencephalography (EEG) recordings as a response of individuals’ brains to marketing stimuli, aiming to identify the factors that influence consumer behaviour that they cannot articulate or are reluctant to reveal. Evidence suggests that individuals’ processing styles affect their reaction to marketing stimuli. In this study, we propose and evaluate a predictive model that classifies consumers as verbalizers or visualizers based on EEG signals recorded during exposure to verbal, visual, and mixed advertisements. Participants (N = 22) were categorized into verbalizers and visualizers using the Style of Processing (SOP) scale and underwent EEG recording while viewing ads. The EEG signals were preprocessed and the five EEG frequency bands were extracted. We employed three classification models for every set of ads: SVM, Decision Tree, and kNN. While all three classifiers performed around the same, with accuracy between 86 and 93%, during cross-validation SVM proved to be the more effective model, with kNN and Decision Tree showing sensitivity to data imbalances. Additionally, we conducted independent t-tests to look for statistically significant differences between the two classes. The t-tests implicated the Theta frequency band. Therefore, these findings highlight the potential of leveraging EEG-based technology to effectively predict a consumer’s processing style for advertisements and offers practical applications in fields such as interactive content designs and user-experience personalization. Full article

This study explores the factors influencing the willingness of Cashew Feni producers to adopt GI certifications, delving deeper into the behavioural factors. This study is guided by the extended Theory of Planned Behaviour. This study was conducted in Goa, India, from June 2024 to January 2025 using a quantitative approach. Face-to-face interviews using structured questionnaires were conducted with Cashew Feni producers actively producing, processing, and distributing Feni in the key production regions. A total of 200 producers were approached, and after validation, 148 responses were considered valid for analysis. The respondents were chosen using a stratified random sampling method. This study employed Partial Least Squares-based Structural Equation Modelling (PLS-SEM) in the SmartPLS 4 software to analyse the data. This study found that attitude is a strong predictor significantly driving adoption. Perceived economic benefits also impact attitudes and directly affect the willingness to adopt GIs, emphasising the role of economic factors. Additionally, awareness influences attitudes and subjective norms, indicating that informed producers are likelier to have a positive attitude towards GI adoption. This study also found a significant impact of subjective norms on attitudes and perceived behavioural control. These insights can assist policy formulation and boost sustainable growth and cultural preservation. Full article

E-commerce platforms offering regional fresh produce often face a trade-off between logistics costs and product quality. Due to limited use of cold chain logistics, consumers frequently receive damaged goods, resulting in negative post-purchase experiences. This study examines how logistics service encounters, as reflected in consumer reviews, influence subsequent purchase behaviour, and how the alignment between review images and text moderates this relationship. We analyse sales and review data from 694 fruit products on Tmall between February and April 2024. Latent Dirichlet Allocation (LDA) is applied to extract logistics-related review content. At the same time, image–text similarity is assessed using the Chinese-CLIP model. Regression analysis reveals that positive logistics service encounters significantly enhance purchase intention. However, high image–text similarity weakens this positive effect, suggesting that overly repetitive content may reduce informational value for prospective buyers. These findings advance understanding of consumer behaviour in online fresh produce markets by highlighting the interactive effects of logistics experiences and user-generated content. The results offer practical implications for improving logistics services, enhancing content diversity in review systems, and increasing consumer trust in e-commerce environments. Full article

We propose a new strategy for constructing Runge–Kutta (RK) methods using evolutionary computation techniques, with the goal of directly minimising global error rather than relying on traditional local properties. This approach is general and applicable to a wide range of differential equations. To highlight its effectiveness, we apply it to two benchmark problems with oscillatory behaviour: the (2+1)-dimensional nonlinear Schrödinger equation and the N-Body problem (the latter over a long interval), which are central in quantum physics and astronomy, respectively. The method optimises four free coefficients of a sixth-order, eight-stage parametric RK scheme using a novel objective function that compares global error against a benchmark method over a range of step lengths. It overcomes challenges such as local minima in the free coefficient search space and the absence of derivative information of the objective function. Notably, the optimisation relaxes standard RK node bounds ($c_i\in [0,1]$), leading to improved local stability, lower truncation error, and superior global accuracy. The results also reveal structural patterns in coefficient values when targeting high eccentricity and non-sinusoidal problems, offering insight for future RK method design. Full article

People with moderate to severe intellectual disabilities (M/S ID) and behaviour that challenges are still almost exclusively encountered and understood within a highly specialized professional care system context. They are almost invisible in the societal mainstream, where a wider variety of perspectives on (everyday) manners, encounters, relationships and life applies. These (and other) exclusionary dynamics render everyday relations with residents with M/S ID whose behaviours challenge still largely dependent on the interpretative frameworks and actions of professionals. Professionals are trained and socialized within highly specialized professional care system contexts, despite a growing scientific and professional awareness that behaviour that challenges is a multifaceted and contextual phenomenon. In this paper, we report on a pioneering initiative (titled Project WAVE) which aimed to cultivate a fresh and comprehensive approach to behaviours that challenge within stagnant care practices. Our goal was to foster an innovative collaborative paradigm by facilitating an extensive and enduring exchange between “insiders”—professionals of specialized care system contexts—and “outsider-researchers”—individuals socialized through alternative avenues. We present our epistemological and methodological approach, the data collection process (a multiple case-informed community of practice), and the most important lessons learned. Full article

Thermoresponsive Pluronic hydrogels offer a promising platform for localised antibiotic delivery. However, how drug loading affects the structural integrity and gelation of these systems remains underexplored. This study evaluates the impact of vancomycin on the physicochemical and self-assembly behaviour of Pluronic F127, F108, and F68 hydrogels. Rheological analysis revealed that vancomycin altered the critical micellisation and gelation temperatures (CMT and CGT, respectively), accelerating gelation in weak gel systems but disrupting network formation in stronger gels. Small-angle X-ray scattering (SAXS) showed that vancomycin suppressed micellar ordering, particularly along FCC (111) planes in F127, without inducing a phase transition. Scanning electron microscopy (SEM) imaging confirmed reduced pore integrity in vancomycin-loaded F127 and F108 gels, while 35% F68 gels failed to form stable structures at the tested concentrations despite enhanced drug solubility. F127 (18%) and F108 (22–23%) maintained gelation at 37 °C with reasonable mechanical strength and partial cubic ordering, making them suitable candidates for drug-eluting gels. These findings inform the design of thermoresponsive hydrogels for localised, implant-associated antibiotic delivery. Full article

The prestressed concrete-filled double skin steel tube (CFDST) lattice tower has emerged as a promising structural solution for large-capacity wind turbine systems due to its superior load-bearing capacity and economic efficiency. The steel–concrete composite adapter (SCCA) is a key component that connects the upper tubular steel tower to the lower lattice segment, transferring axial loads. However, the compressive behaviour of the SCCA remains underexplored due to its complex multi-shell configuration and steel–concrete interaction. This study investigates the axial compression behaviour of SCCAs through refined finite element simulations, identifying diagonal extrusion as the typical failure mode. The analysis clarifies the distinct roles of the outer and inner shells in confinement, highlighting the dominant influence of outer shell thickness and concrete strength. A sensitivity-based parametric study highlights the significant roles of outer shell thickness and concrete strength. To address the high cost of FE simulations, a 400-sample database was built using Latin Hypercube Sampling and engineering-grade material inputs. Using this dataset, five neural networks were trained to predict SCCA capacity. The Dropout model exhibited the best accuracy and generalization, confirming the feasibility of physics-informed, data-driven prediction for SCCAs and outperforming traditional empirical approaches. A graphical prediction tool was also developed, enabling rapid capacity estimation and design optimization for wind turbine structures. This tool supports real-time prediction and multi-objective optimization, offering practical value for the early-stage design of composite adapters in lattice turbine towers. Full article

Determining constitutive model parameters from in situ tests offers several advantages, including reduced time, lower cost, and minimal soil disturbance. As part of a research project, an automated framework was developed to derive constitutive model parameters from in situ test results using a graph-based approach. Previous studies primarily focused on validating the framework’s output in terms of soil parameters by comparing them with values interpreted from laboratory tests. This study demonstrates the full capability of the framework, from importing raw in situ measurements and stratifying the soil profile to determining both soil and constitutive model parameters, and ultimately linking the results to numerical modelling. To assess the accuracy of the obtained material sets, two well-documented boundary value problems are modelled: one involving the long-term settlement behaviour of an embankment and the other addressing the failure load of shallow footings. The parameter determination framework proves particularly valuable in the early stages of geotechnical projects, offering enhanced insight and detailed soil characterisation when data is limited. Ongoing research aims to extend the framework by incorporating additional in situ tests and implementing statistical tools to better capture uncertainty and support informed decision-making. Full article

Background: Expected utility has been deployed in order to predict health behaviour in health economic evaluation. However, only limited variance in health behaviour is explained by this construct. This limited explained variance is often attributed to the dubious foundational postulates underlying the construct (e.g., absolute rationality, complete information, fixed preferences). Due to these limitations it has been hypothesized that substituting or complementing expected utility with experienced utility may enhance predictions of health behaviour. As this hypothesis has not yet been subjected to empirical scrutiny, this study examines if deployment of experienced utility or expected utility and experienced utility combined enhances predictions of health behaviour relative to expected utility separately. Methods: Online questionnaires were distributed across a panel of Dutch citizens (N = 2550). The questionnaire includes items and scales on sample characteristics, expected utility, experienced utility and health behaviour. Data analysis was conducted by employing descriptive, reliability, validity and model statistics. Results: Experienced utility has a significant direct effect on health behaviour that is stronger than expected utility. Experienced utility also explains more variance in health behaviour than expected utility. Expected utility and experienced utility combined have a significant direct and indirect effect on health behaviour that is stronger than each type of utility separately. Expected utility and experienced utility combined also explain more variance in health behaviour than each type of utility separately. Conclusions: Deploying experienced utility separately or in combination with expected utility in health economic evaluation seems pertinent as it has considerable impact on health behaviour and may provide health economists with an even sturdier foundation for conducting health economic evaluation. Full article

Background/Objectives: In Angola, malnutrition contributes each year to the deaths of an estimated 42,000 to 76,000 children under the age of 5. Addressing this issue must stand as a priority and requires providing local residents with access not only to nutritious food but also to adequate and accurate information in order to facilitate informed dietary choices. As part of the “Seigungo—Health, Education and Quality of Maternal and Child Life in Gungo project”, a nutrition-focused study was conducted in Gungo, Angola to evaluate the effectiveness of a training model designed to enhance food literacy among residents. Methods: Data were collected using a 14-item questionnaire developed to assess various key domains of food literacy: information seeking and access; comprehension and thematic knowledge; critical evaluation of information and behaviour; practical application and sound decision-making. Results: Thirty trainees took part in the study, of which 60% were men, with a mean age of 45.6 years. The majority were single (53.3%) and had completed six years of formal education (26.7%). Before attending the training program, 86.7% of the participants demonstrated inadequate or problematic food literacy. Following the intervention, the proportion of participants with adequate food literacy increased significantly from 13.3% to 73.3% (p < 0.001). Conclusions: The training program had a statistically significant impact on improving food literacy. Full article

This study explores how AI chatbot experiences on travel websites influence consumer engagement, with satisfaction from using AI chatbots as a mediating factor. Grounded in the Stimulus-Organism-Response (S-O-R) framework, the research shifts the focus from utilitarian models to examine how chatbot attributes—e.g., ease of use, information quality, security, anthropomorphism, and omnipresence—affect satisfaction of using AI chatbots and subsequent consumer engagement behaviours. Survey data from 519 Portuguese travellers were analysed using partial least squares structural equation modelling (PLS-SEM). The study contributes to theory by (1) demonstrating S-O-R’s advantages over utilitarian models in capturing relational and emotional dimensions of AI interactions, (2) identifying satisfaction with using AI chatbots as a pivotal mediator between AI chatbot experience and consumer engagement, and (3) revealing generational disparities in drivers of engagement. Notably, satisfaction strongly influences engagement for Generation X, while direct experience matters more for Generation Z. Millennials exhibit a distinct preference for hybrid human–AI service handoffs. The practical implications include prioritizing natural language processing for ease of use, implementing generational customization (e.g., gamification for Gen Z, reliability assurances for Gen X), and ensuring seamless human escalation for Millennials. These insights equip travel businesses to design AI chatbots that foster long-term loyalty and competitive differentiation. Full article