Search Results (3,518)

Building-integrated photovoltaic (BIPV) windows present a viable path towards carbon neutrality in the building sector. However, conventional BIPV windows, such as semi-transparent photovoltaic (STPV) glazings, still suffer from inadequate thermal insulation, which limits their effectiveness across different climate conditions. To address this issue, the cadmium telluride-based vacuum PV glazing has been developed to enhance the thermal performance of BIPV applications. To fully understand the complex thermal behaviour under real-world operational scenarios, this study introduces a one-dimensional transient heat transfer model that can efficiently capture the time-dependent thermal dynamics of this novel glazing system. Based on the numerical solutions using the explicit finite difference method (FDM), the temperature profile of the vacuum PV glazing can be obtained dynamically. Consequently, the heat gain of the semi-transparent vacuum PV glazing can be calculated under time-varying outdoor and indoor conditions. The validated heat transfer model was applied under four different scenarios, viz. summer daytime, summer nighttime, winter daytime, and winter nighttime, to provide a detailed analysis of the dynamic thermal behaviour, including the temperature variation and the energy flow. The dynamic thermal characteristics of the vacuum PV glazing calculated by the transient heat transfer model demonstrate its excellent thermal insulation and solar control capabilities. Moreover, the thermal performance of vacuum PV glazing was compared with a standard double-pane window under various weather conditions of a typical summer day and a typical winter day. The results indicate that the vacuum PV glazing can effectively minimise both heat gain and heat loss. The fluctuation of the inner surface temperature can be controlled within a limited range away from the set point of the indoor room temperature. Therefore, the vacuum PV glazing contributes to stabilising the temperature of the indoor environment despite the fluctuating solar radiation and periodic outdoor temperature. It is suggested that the vacuum PV glazing has the potential to enhance the climate adaptability of BIPV windows under different climate backgrounds. Full article

Grassland ecosystems are fundamentally shaped by the complex behaviours of livestock. While most previous studies have monitored grassland health using vegetation indices, such as NDVI and LAI, fewer have investigated livestock behaviours as direct drivers of grassland degradation. In particular, the spatial clustering and temporal concentration patterns of livestock behaviours are critical yet underexplored factors that significantly influence grassland ecosystems. This study investigated the spatiotemporal patterns of livestock behaviours under different grazing management systems and grazing-intensity gradients (GIGs) in Wenchang, China, using high-resolution GPS tracking data and machine learning classification. the K-Nearest Neighbours (KNN) model combined with SMOTE-ENN resampling achieved the highest accuracy, with F1-scores of 0.960 and 0.956 for continuous and rotational grazing datasets. The results showed that the continuous grazing system failed to mitigate grazing pressure when grazing intensity was reduced, as the spatial clustering of livestock behaviours did not decrease accordingly, and the frequency of temporal peaks in grazing behaviour even showed an increasing trend. Conversely, the rotational grazing system responded more effectively, as reduced GIGs led to more evenly distributed temporal activity patterns and lower spatial clustering. These findings highlight the importance of incorporating livestock behavioural patterns into grassland monitoring and offer data-driven insights for sustainable grazing management. Full article

In recent years, carbon dioxide (CO₂) emissions have increased at the fastest rates ever recorded. This is a trend that contradicts global efforts to stabilise greenhouse gas (GHG) concentrations and prevent long-term climate change. Over 90% of global transport relies on oil-based fuels. The continued use of diesel and petrol raises concerns related to oil costs, supply security, GHG emissions, and the release of air pollutants and volatile organic compounds. This study explored electric vehicle (EV) charging networks by assessing environmental impacts through GHG and petroleum savings, developing dynamic pricing strategies, and forecasting infrastructure needs. A substantial dataset of over 259,000 EV charging records from Palo Alto, California, was statistically analysed. Machine learning models were applied to generate insights that support sustainable and economically viable electric transport planning for policymakers, urban planners, and other stakeholders. Findings indicate that GHG and gasoline savings are directly proportional to energy consumed, with conversion rates of 0.42 kg CO₂ and 0.125 gallons per kilowatt-hour (kWh), respectively. Additionally, dynamic pricing strategies such as a 20% discount on underutilised days and a 15% surcharge during peak hours are proposed to optimise charging behaviour and improve station efficiency. Full article

The transition to a decarbonised energy system in Ireland presents significant socio-technical challenges. This paper, focused on the work of the SMARTLAB project at the Citizen Innovation Lab in Limerick city, investigated the potential of a localised living lab approach to address these challenges. Engaging across 70 buildings and their inhabitants, the project captured the evolution of attitudes and intentions towards the clean energy transition in ways directly relevant to future policy implementation across grid redevelopment, smart service design, and national retrofit. Project methodology was framed by a living lab approach, with wireless energy and indoor environment sensors installed in participant buildings and participant journeys developed by harnessing the Citizen Innovation Lab ecosystem. The results indicate behaviour changes among participants, particularly focusing on indoor environmental conditions. The study concludes that embedded, localised living labs offer a methodological framework which can capture diverse datasets and encompass complex contemporary contexts towards transition goals. Full article

This study investigates the cybersecurity landscape of Aotearoa-New Zealand through a culturally grounded lens, focusing on the integration of Indigenous Māori values into cybersecurity frameworks. In response to escalating cyber threats, the research adopts a mixed-methods and interdisciplinary approach—combining surveys, focus groups, and case studies—to explore how cultural principles such as whanaungatanga (collective responsibility) and manaakitanga (care and respect) influence digital safety practices. The findings demonstrate that culturally informed strategies enhance trust, resilience, and community engagement, particularly in rural and underserved Māori communities. Quantitative analysis revealed that 63% of urban participants correctly identified phishing attempts compared to 38% of rural participants, highlighting a significant urban–rural awareness gap. Additionally, over 72% of Māori respondents indicated that cybersecurity messaging was more effective when delivered through familiar cultural channels, such as marae networks or iwi-led training programmes. Focus groups reinforced this, with participants noting stronger retention and behavioural change when cyber risks were communicated using Māori metaphors, language, or values-based analogies. The study also confirms that culturally grounded interventions—such as incorporating Māori motifs (e.g., koru, poutama) into secure interface design and using iwi structures to disseminate best practices—can align with international standards like NIST CSF and ISO 27001. This compatibility enhances stakeholder buy-in and demonstrates universal applicability in multicultural contexts. Key challenges identified include a cybersecurity talent shortage in remote areas, difficulties integrating Indigenous perspectives into mainstream policy, and persistent barriers from the digital divide. The research advocates for cross-sector collaboration among government, private industry, and Indigenous communities to co-develop inclusive, resilient cybersecurity ecosystems. Based on the UTAUT and New Zealand’s cybersecurity vision “Secure Together—Tō Tātou Korowai Manaaki 2023–2028,” this study provides a model for small nations and multicultural societies to create robust, inclusive cybersecurity frameworks. Full article

Cardiovascular disease (CVD) remains the foremost global cause of mortality, driven significantly by modifiable lifestyle factors. This study employs a data-driven approach to identify and evaluate these risk factors using advanced machine learning techniques. Analysing a large publicly available dataset of over 300,000 adult health records containing lifestyle behaviours, clinical risk factors, and self-reported health indicators, this research implemented traditional classifiers, ensemble methods, and deep learning architectures to examine the impact of behaviours such as smoking, diet, physical activity, and alcohol consumption on CVD risk. The Random Forest model demonstrated superior performance, achieving high accuracy, recall, and ROC-AUC scores. To demonstrate real-world utility, the model was deployed as an interactive Streamlit web application. This tool allows individuals to input lifestyle and health data to receive real-time CVD risk predictions, offering a novel, user-friendly prototype that bridges machine learning insights with personalised digital health engagement. This tool can facilitate personalised health monitoring and supports early detection by providing actionable insights. The findings underscore the efficacy of predictive modelling in informing targeted interventions and public health strategies. By bridging advanced analytics with practical applications, this research offers a scalable framework for reducing CVD burden, paving the way for precision medicine and improved population health outcomes through data-driven decision-making. Full article

This work addresses for the first time the effect of anode serpentine channel depth on Methanol Electrolysis Cells (MECs) and Direct Methanol Fuel Cells (DMFCs) for improving performance of both devices. Anode plates with serpentine flow fields of 0.5 mm, 1.0 mm and 1.5 mm depths are designed and tested in single-cells to compare their behaviour. Performance was evaluated through methanol crossover, polarization and power density curves. Results suggest shallower channels enhance mass transfer efficiency reducing MEC energy consumption for hydrogen production at 40 mA∙cm⁻² by 4.2%, but increasing methanol crossover by 30.3%. The findings of this study indicate 1.0 mm is the best depth among those studied for a MEC with 16 cm² of active area, while 0.5 mm is the best for a DMFC with the same area with an increase in peak power density of 14.2%. The difference in results for both devices is attributed to higher CO₂ production in the MEC due to its higher current density operation. This increased CO₂ production alters anode two-phase flow, partially hindering the methanol oxidation reaction with shallower channels. These findings underscore the critical role of channel depth in the efficiency of both MEC and DMFC single-cells. Full article

Riverbed dynamics are natural processes that are strongly driven by human and climatic factors. In the last two centuries, the anthropogenic influence and impact of climate change on European rivers has resulted in significant degradation of riverbeds. This research paper aims to determine the historical evolution (1856–2021) and future trends of the Ialomița riverbed (Romania) under the influence of anthropogenic impact and climate change. The case study is a reach of 66 km between the confluences with the Ialomicioara and Pâscov rivers. The localisation in a contact zone between the Curvature Subcarpathians and the Târgoviște Plain, the active recent tectonic uplift of the area, and the intense anthropogenic intervention gives to this river reach favourable conditions for pronounced riverbed dynamics over time. To achieve the aim of the study, we developed a complex methodology which involves the use of Geographical Information System (GIS) techniques, hierarchical cluster analysis (HCA), the Mann–Kendall test (MK), and R programming. The results indicate that the evolution of the Ialomița River aligns with the general trends observed across Europe and within Romania, characterised by a reduction in riverbed geomorphological complexity and a general transition from a braided, multi-thread into a sinuous, single-thread fluvial style. The main processes consist of channel narrowing and incision alternating with intense meandering. However, specific temporal and spatial evolution patterns were identified, mainly influenced by the increasingly anthropogenic local influences and confirmed climate changes in the study area since the second half of the 20th century. Future evolutionary trends suggest that, in the absence of river restoration interventions, the Ialomița riverbed is expected to continue degrading on a short-term horizon, following both climatic and anthropogenic signals. The findings of this study may contribute to a better understanding of recent river behaviours and serve as a valuable tool for the management of the Ialomița River. Full article

Providing long-term care for a person with Alzheimer’s disease is associated with chronic stress and emotional overload. One of the key predictors of emotional burden is the amount of time devoted to caregiving, which intensifies the experienced stress. Additional risk factors include the stage of the illness, difficulties in the care recipient’s activities of daily living, the caregiver’s neglect of their own needs, and challenging behaviours exhibited by the person receiving care. Therefore, it is essential to identify the psychological protective resources of caregivers that can buffer the impact of stress. Background/Objectives: The objective of the study was to explore the psychological mechanisms underlying the involvement of caregivers supporting individuals with Alzheimer’s disease. A moderated mediation model was employed, in which stress indirectly affects caregiver involvement through a sense of coherence, and the strength of this relationship is moderated by the amount of time devoted to caregiving. Methods: The bootstrapping method was applied using 5000 resamples within a 95% bias-corrected confidence interval. The analysis accounted for variables such as stress levels, sense of coherence, involvement in caregiving, duration of care, education, gender, age, and stage of the illness. Results: The sense of coherence mediated the relationship between stress and involvement in caring (B = 0.0063, SE = 0.0031, 95% CI [0.0012, 0.0135]), and this indirect effect was contingent upon the amount of time devoted to helping. The relationship between sense of coherence and involvement in caring was significant at the mean level (B = 0.005, SE = 0.002, 95% CI [0.0004, 0.0101]) and became stronger at high levels of time devoted to caring (+1 SD; B = 0.009, SE = 0.003, 95% CI [0.0030, 0.0148]). These results indicate that the positive association between sense of coherence and caregiver involvement increases with the amount of time spent caring. Conclusions: The results highlight the importance of strengthening caregivers’ resilience resources—such as a sense of coherence—in preventing overload. The model may serve as a foundation for developing interventions aimed at supporting caregivers’ mental health. Full article

Three-dimensionally printed cement-based composites emerge as a research hotspot in the fields of construction engineering in recent years. Current research primarily focuses on the reinforcement mechanisms of individually incorporated fibres, while a significant gap remains in the synergistic effects of hybrid fibre systems. This study investigates the effects of mono-doping (0.2 wt.% and 0.4 wt.% by the mass of the cement) and hybrid-doping (0.1 wt.% + 0.1 wt.% by the mass of the cement) with 3 mm polypropylene, basalt, and carbon fibres on the fresh-state properties and mechanical behaviours. Through quantitative characterisation of the flowability and mechanical performance of short-fibre-reinforced 3D-printed cementitious composites (SFR3DPC), coupled with comprehensive testing including digital image correlation, X-ray diffraction, and scanning electron microscopy, several key findings are obtained. The experimental results indicate that the addition of excess fibres reduces fluidity, which affects the mechanical performance and make the anisotropy of the composites more pronounced. While the single addition of 0.2 wt.% CF shows the most significant improvement in flexural and compressive strengths, the hybrid combination of 0.1 wt.% CF and 0.1 wt.% BF shows the greatest increase in interlayer bond strength by 26.7%. The complementary effect of the hybrid fibres contributes to the damage mode of the composites from brittle fracture to quasi-brittle behaviour at the physical level. These findings offer valuable insights into optimising the mechanical performance and improving defects of 3D-printed cementitious composites with short fibres. Full article

(1) Background: The sophisticated function of the aortic root relies on the coordinated movement of its constituent components. This study examines the extracellular components of the interleaflet triangles (ILTs) and characterises the cells that are present within this region of the aortic root. (2) Methods: A total of 10 human aortic valves and 6 porcine aortic valves were processed for immunohistochemical staining, scanning, and transmission electron microscopy. (3) Results: The three ILTs differed in size and macroscopic appearance. Each triangle comprised up to five distinct layers of tissue: an innermost endothelial layer, an inner elastin-rich layer, a thicker outer layer comprising densely packed layers of collagen and glycosaminoglycans, and an outer layer of intermingled myocardial and adipose tissue. A band of cells near the luminal surfaces of all ILTs expressed smooth muscle cell α-actin with variable expression of smooth muscle myosin heavy chain. In all the ILTs, there was evidence of neurofilament staining, indicating the presence of nerve fibres. (4) Conclusions: Each ILT is unique in its structure and organisation, with differing amounts of elastin and collagen, as well as myocardial, adipose, and fibrous content. The ILTs contain multiple cell types in varying abundance. Functional studies are required to determine the role of the different cells and their organisation in contributing to the sophisticated, dynamic behaviour of the aortic root. Full article

Nature-based Solutions (NbS) are increasingly adopted in urban settings to restore ecological functions and enhance biodiversity. This study evaluates the effects of NbS interventions on bird, insect, and plant communities in the Cavalum Valley urban green area, Penafiel (northern Portugal). Over a three-year period, systematic field surveys assessed changes in species richness, abundance, and ecological indicators following actions such as riparian restoration, afforestation, habitat diversification, and invasive species removal. Results revealed a marked increase in bird overall abundance from 538 to 941 individuals and in average pollinator population size from 9.25 to 12.20. Plant diversity also improved, with a rise in native and RELAPE-listed species (5.23%). Functional group analyses underscored the importance of vegetative structure in supporting varied foraging and nesting behaviours. These findings highlight the effectiveness of integrated NbS in enhancing biodiversity and ecological resilience in urban landscapes while reinforcing the need for long-term monitoring to guide adaptive management and conservation planning. Future work could evaluate ecological resilience thresholds and community participation in citizen science monitoring. Full article

The increasing complexity of financial reporting has enabled the implementation of innovative accounting practices that often obscure a company’s actual performance. This project seeks to uncover manipulative behaviours by constructing an anomaly detection model that utilises unsupervised machine learning techniques. We examined a dataset of 149,566 Slovak firms from 2016 to 2023, which included 12 financial parameters. Utilising TwoSteps and K-means clustering in IBM SPSS, we discerned patterns of normative financial activity and computed an abnormality index for each firm. Entities with the most significant deviation from cluster centroids were identified as suspicious. The model attained a silhouette score of 1.0, signifying outstanding clustering quality. We discovered a total of 231 anomalous firms, predominantly concentrated in sectors C (32.47%), G (13.42%), and L (7.36%). Our research indicates that anomaly-based models can markedly enhance the precision of fraud detection, especially in scenarios with scarce labelled data. The model integrates intricate data processing and delivers an exhaustive study of the regional and sectoral distribution of anomalies, thereby increasing its relevance in practical applications. Full article

Timber and other biobased materials store carbon that has been captured from the atmosphere during photosynthesis and plant growth. The estimation of these biogenic carbon stocks in the harvested wood products (HWP) pool has received increasing attention since its inclusion in greenhouse gas reporting by the IPCC. It is of particular interest for long service life products such as timber in buildings; however, some aspects require further thought—in particular the handling of service lives as opposed to half-lives. The most commonly used model for calculating changes in the HWP pool uses first order decay based on half-lives. However other approaches are based on average service lives and estimates of residence times in the product pool, enabling different mathematical functions to be used. This paper considers the evolution of the two concepts and draws together data from a wide range of sources to consider service life estimation, which can be either related to design life or practical observations such as local environmental conditions, decay risk or consumer behaviour. As an increasing number of methods emerge for calculating HWP pool dynamics, it is timely to consider how these numerical inputs from disparate sources vary in their assumptions, calculation types, accuracy and results. Two groups are considered: half-lives for first order decay models, and service life and residence time population distributions within models based on other functions. A selection of examples are drawn from the literature to highlight emerging trends and discuss numerical constraints, data availability and areas for further study. The review indicated that issues exist with inconsistent use of nomenclature for half-life, average service life and peak flow from the pool. To ensure better sharing of data between studies, greater clarity in reporting function types used is required. Full article

Green apps have emerged as ways users can engage with climate action, covering ventures that plant trees as users search for information (e.g., Ecosia) through to apps that facilitate behaviour change (e.g., United Nation’s AWorld). But how much can these apps be trusted to facilitate long-term engagement with climate action? Setting our research within the literature on trust, we combine expert interviews (n = 20) with the academic literature to outline 14 trustworthiness indicators. Each indicator provides a clear statement about what makes a green app more or less trustworthy. The indicators are grouped into six core categories: going beyond the app, meaningful collective action, designing the app, accessibility and inequality, data, and organisation. In doing so, our indicators speak to a range of research from multiple disciplines. At the same time, they provide a toolkit for users, practitioners, and academics to critically and productively engage with green apps. Full article