17628 research outputs found
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Forecasting the Potential, Power and Energy (PPE) of Both Building Integrated PV and Traditional PV (BIPV-PV) Systems Using State-of-the-Art AI Methods
No full textForecasting the potential and output of building-integrated photovoltaic (BIPV) and traditional photovoltaic (PV) systems, including rooftop, ground-mounted and industrial-shed installations, has become increasingly important, as these technologies hold substantial potential for meeting a significant share of energy demand. Artificial intelligence (AI) approaches, including machine learning (ML) and deep learning (DL) models, are widely recognised as powerful tools for forecasting solar resource potential and system performance. These models play an essential role in accelerating the integration of renewable energy within urban energy planning frameworks. In this context, forecasting for BIPV-PV systems can be broadly classified into three domains: potential, power and energy (PPE). Given the rapid advances in the field of DL over the past few years, numerous studies have made targeted efforts to improve the forecasting accuracy for both BIPV-PV systems by enhancing input data quality and applying advanced, complex and hybrid models. Most of these efforts have mainly narrowed their focus to one of the three forecasting domains rather than adopting a more integrated approach. This systematic literature review (SLR) aims to provide a comprehensive review of PPE forecasting approaches to enable more robust assessment and deeper insights into the feasibility and viability of BIPV-PV systems. The review further highlights key methodological challenges, outlines limitations and offers practical guidance for researchers, policymakers and developers, while identifying emerging trends and future opportunities in AI-based forecasting for BIPV-PV applications
A Novel Network-Level Fused Self-Attention Deep Neural Network for Cervical Cancer Classification from Cervicography Images
Full text linkIntroduction: cervical cancer ranks as the fourth most common cancer among females worldwide. Approximately 528,000 new cases of cervical cancer are reported annually, and about 85% of them occur in less-developed countries. The lack of skilled medical staff and pre-screening procedures is the main cause of the high fatality rate in these countries. Cervicography images are the gold standard procedure for the evaluation of cervical cancer; however, the high intra-class inconsistency makes the diagnosis process more challenging for skilled medical specialists. Method: In this work, we propose a fully automated computer-aided diagnosis (CAD) system for classifying cervical cancer using Cervicography images. Data augmentation is performed in the initial phase to address dataset imbalance. Subsequently, we proposed two novel deep learning modules: the 11-Parallel Inverted Residual Bottleneck Blocks (11-PIRBnet) architecture and the 9-Parallel Inverted Residual blocks with Self-Attention Mechanism (9-PIRSANet). Both modules are fused at the network level via a depth concatenation layer to form a new network, 375NFNet. The proposed network is trained on the selected dataset, whereas the hyperparameters are initialized through Bayesian Optimization (BO). For feature extraction, a depth concatenation layer is used during testing to combine information from both deep learning modules. Finally, the extracted features are classified using a shallow neural network (SNN) to produce the final classification. Result: To evaluate the model, experiments were conducted on a publicly available cervical screening dataset of Cervicography images, and results demonstrate an accuracy of 95.5%, a precision of 95.4%, and an area under the curve of 0.97. When compared with several pre-trained techniques, the proposed architecture achieved significant improvement in accuracy, precision, and number of trainable parameters. Conclusion: The proposed 375NFNet architecture demonstrates remarkable accuracy and efficiency in classifying cervical cancer through cervicography images, which shows its potential as a valuable tool in resource-constrained environments
How do boundary objects influence people-centered smart cities? A systematic literature review
Full text linkThis study explores the critical role of boundary objects in the context of people-centred smart cities, a new paradigm in urban development that emphasises citizen participation in planning and decision-making. Boundary objects—artefacts, documents, or concepts that facilitate communication and collaboration across different knowledge domains—are increasingly recognized as essential tools in the complex, multi-stakeholder environment of urban governance. Despite extensive research on boundary objects in organizational contexts, their specific application in smart cities remains underexplored. This study addresses this gap by investigating how BOs, particularly collaborative tools and spaces, contribute to innovation, engagement, and knowledge-sharing in people-centred smart cities. Employing a Systematic Literature Review following the PRISMA protocol, this research synthesizes key insights from scholarly articles to comprehensively understand boundary objects' role in urban governance. This study offers a theoretical framework for leveraging boundary objects to enhance the inclusivity and sustainability of smart cities. It suggests avenues for future research, including empirical validation and exploration of boundary objects in diverse geographic and cultural contexts
Who cuts emissions, who turns up the heat? causal machine learning estimates of energy efficiency interventions
Full text linkReducing domestic energy demand is central to climate mitigation and fuel poverty strategies, yet the impact of energy efficiency interventions is highly heterogeneous. Using a causal machine learning model trained on nationally representative data of the English housing stock, we estimate average and conditional treatment effects of wall insulation on gas consumption, focusing on distributional effects across energy burden subgroups. While interventions reduce gas demand on average ( ≈ –19%), low energy burden groups achieve substantial savings, whereas those experiencing high energy burdens see little to no reduction. This pattern reflects a behaviourally-driven mechanism: households constrained by high costs-to-income ratios ( > 10%) reallocate savings toward improved thermal comfort rather than lowering consumption. Far from wasteful, such responses represent rational adjustments in contexts of prior deprivation, with potential co-benefits for health and well-being. These findings call for a broader evaluation framework that accounts for both climate impacts and the equity implications of domestic energy policy
Climate change news risk and advertising spending
Full text linkWe examine the effect of climate change news risk on corporate advertising spending. Using a novel measure of media-driven climate risk matched to firm-level advertising data, we find a robust negative relationship between climate news risk and advertising spending. Mechanism tests show that financial constraints mediate this relationship. The effect is stronger for firms with low stock market liquidity and high cash-flow volatility. We also find that domestic firms reduce their advertising spending relative to their multinational counterparts, which aligns with the idea that international operations provide diversification and stronger cash flow benefits that enhance firms’ resilience to the effects of domestic climate risk shocks. The results remain consistent across different advertising measures, and after correcting for selection bias with a Heckman two-step method. We address endogeneity concerns through instrumental variable estimation. Our findings support the risk management hypothesis that firms proactively adjust their financial policies to mitigate the negative effects of rising climate risk exposur
A Survey on LoRaWAN MAC Schemes: From Conventional Solutions To AI-driven Protocols
Full text linkLong Range (LoRa) networks have emerged as a vital solution catering to applications requiring coverage over relatively long distances in the world of Internet of Things (IoT), where connectivity and efficient data transmission are paramount. LoRa’s utilization of the unlicensed Industrial, Scientific, and Medical (ISM) band not only underscores its cost-effectiveness but also positions it favorably against licensed technologies in terms of deployment cost. Consequently, LoRa has been used as the underlying communication technology for applications across various industrial IoT scenarios. Despite its immense promise in reshaping IoT connectivity, LoRa does have some shortcomings and challenges that the research community has yet to address to unleash its full potential. These limitations have triggered substantial attention from diverse entities, including research institutions, organizations, and industry stakeholders. This paper reviews the literature aimed at improving the capacity and scalability of LoRa networks specifically at the Medium Access Control (MAC) and data link layers. Unlike other surveys, this study focuses on these layers because they play a pivotal role in managing collision rates, which significantly impact network scalability. The paper suggests a comprehensive review of the literature, organizing it based on key limitations that could hinder the network’s ability to meet its performance objectives, including scalability, Packet Delivery Ratio (PDR), and energy efficiency. In addition, the paper provides a summary of these research efforts and offers insight into potential directions for future research in this area
State‐space models and inference approaches for aquatic animal tracking with passive acoustic telemetry and biologging sensors
Full text linkPassive acoustic telemetry systems are widely deployed to track animals in aquatic environments. However, investments in integrative methods of data analysis have remained comparatively limited, with current workflows typically considering individual movements separately from space use, home ranges and residency.This review presents a unifying perspective that bridges this divide. We argue that the core of animal-tracking analyses lies in the estimation of individual locations based on probabilistic principles. We formalise a generic state-space model for individual movements and a set of targets for statistical inference, unifying existing literature in a common framework. We critically assess inference algorithms and connect model-based inference to downstream ecological analyses of individual centres of activity, occurrence, residency, home ranges, habitat selection and behaviour.We provide guidance to practitioners on model formulation, algorithm choice and software suitability in different contexts and identify key avenues for future research.This review provides a roadmap for integrative data analysis in passive acoustic telemetry systems that should support research into the ecology and conservation of many aquatic species
Health service contacts for mental health and substance use on release from prison: a retrospective population-based data linkage study
No full textBackground: Mental health and substance use problems among people released from prison contribute substantially to premature mortality and emergency services demand. Understanding of mental health and substance use-related health service contacts prior to these severe and costly outcomes is limited. We assessed mental health and substance use-related contact with multiple services, comparing rates of contact among people released from prison to a matched general population sample who had not recently been in prison. Objectives: To compare rates of health service contacts for mental health and substance use between people released from prison and a matched general population sample. Design: We conducted a retrospective cohort study using linked administrative data with nationwide coverage. The cohort contained all people released from any Scottish prison in 2015 (exposed group), and a random general population sample matched (ratio 1:5) on sex, age, postcode and deprivation indices, who had no imprisonment in the 5 years prior (unexposed group). We linked individual-level administrative healthcare (prescriptions, outpatient, inpatient, emergency/unscheduled care: 2010–2020), prison (admissions, releases: 2010–2020) and deaths records (2015–2020). We estimated adjusted incidence rate ratios (aIRRs) with 95% CIs using fixed-effects Poisson regression with cluster-robust standard errors, controlling for time-in-community, pre-index mental health and substance use-related health service contacts, and comorbidities. We stratified models by mental health (MH), substance use (SU) and dual diagnosis (attributable to both MH and SU). Setting: Scotland. Results: We linked records for 8313 people released from prison, and 41 213 matched individuals. Mental health and substance use-related contact rates were significantly higher for people released from prison across all services, and particularly for emergency and unscheduled care. aIRRs for ambulance contacts were MH=7.75 (95% CI 5.76 to 10.42), SU=7.58 (95% CI 5.71 to 10.08), dual diagnosis=8.28 (95% CI 6.50 to 10.55); and accident and emergency department contacts were MH=4.88 (95% CI 3.78 to 6.29) and SU=7.98 (95% CI 5.71 to 11.17). aIRRs for community prescriptions were MH=1.80 (95% CI 1.67 to 1.94), SU=5.95 (95% CI 4.83 to 7.32), dual diagnosis=5.33 (95% CI 3.70 to 7.68); drug and alcohol services were 7.13 (95% CI 6.00 to 8.48); and outpatient attendances were 2.61 (95% CI 2.17 to 3.16). aIRRs for 24-hour unscheduled telephone support were MH=7.63 (95% CI 4.93 to 11.83) and SU=8.29 (95% CI 3.99 to 17.22); and out-of-hours general practice were MH=5.14 (95% CI 3.66 to 7.22), SU=5.89 (95% CI 3.11 to 11.14) and dual diagnosis=8.85 (95% CI 2.94 to 26.63). aIRRs for general/acute hospital admissions and day cases were MH=2.97 (95% CI 1.43 to 6.16), SU=7.85 (95% CI 4.42 to 13.91), dual diagnosis=13.11 (95% CI 7.95 to 21.61); and for psychiatric admissions were MH=3.62 (95% CI 2.39 to 5.49), SU=10.74 (95% CI 6.12 to 18.84) and dual diagnosis=7.74 (95% CI 4.30 to 13.94). Conclusions: Improved post-release mental health and substance use care is vital for individual and public health. Despite elevated rates of contact with community mental health and substance use services, people released from prison have disproportionately high rates of contact with emergency and unscheduled care services. This suggests that early support is either inadequate or not accessed by those in greatest need. Policymakers and service providers should consider investment in tailored transitional and post-release intervention at individual and population level, to improve health and thus prevent later high-cost service use and avoidable mortality. Our results also suggest high-quality care must be available and accessible beyond the immediate post-release period to permit sustained engagement or engagement at a later date
Native trees are related to advanced bird breeding phenology and increased reproductive success along an urban gradient
No full textUrban areas are altered from natural landscapes in several ways that can impact wildlife. Birds are widespread in urban areas, and it is well documented that there are phenotypic differences between urban and non-urban conspecifics. However, little is known about which characteristics of the urban environment are driving differences. We used 9 years of data from nest boxes spread across 20 sites along a 40-km urban–non-urban gradient in Scotland to test whether characteristics of the urban environment (native, non-native, native oak (Quercus spp.), birch (Betula spp.) foliage availability, temperature and human population density, and the interaction between foliage and temperature) influenced phenology and reproductive success in blue tits (Cyanistes caeruleus). We found that higher foliage availability of native foliage, and specifically of the most common native genus, oak, was associated at the territory level with earlier first egg laying date. Higher non-native foliage availability at both a site and territory level was negatively related to clutch size. The number of fledglings produced was reduced at sites with higher levels of non-native foliage and increased at sites with greater amounts of native oak foliage present. We also found territories with a higher human population density had reduced fledging success. Temperature was negatively related to first egg laying date, clutch size and the number of fledglings produced. Moreover, the number of Lepidopteran larvae, blue tits' preferred prey, that were collected over the breeding season was positively related to native oak foliage availability. Our results strongly indicate that the presence of native trees, such as oak, are beneficial to breeding insectivores by increasing the number of fledglings they can successfully raise, likely due to the increased availability of invertebrate prey. We suggest that urban planting regimes should be carefully considered, selecting tree species that are native or non-native congeneric species, and most importantly that will host Lepidoptera larvae. This will not only help to support complete food chains, but also to maximize biodiversity and ecosystem services of urban green spaces
Dark personality traits in project management: A bibliometric analysis and agenda for future research
No full textFor the past thirty-five years, project management research has been largely dominated by studies emphasizing the prosocial, and therefore positive, traits of project managers and their influence on project outcomes. Alongside this tradition, however, a growing body of work has begun to examine alternative traits, specifically project manager dark personality (PMDP) traits, which are frequently described as ‘dark’. These traits are generally characterized as aversive, deviant, or malevolent. Despite increasing scholarly interest, the intellectual structure of research on PMDP traits remains underexplored. To address this gap, the present study undertakes a comprehensive bibliometric analysis of PMDP literature published between 1989 and 2024, with the aim of uncovering the thematic focus and publication patterns within this field. Our analysis reveals seven recurring project-focused themes: ‘Corruption, unethical, and negative behaviours’; ‘Impact of dark traits’; ‘Causes and effects of biases’; ‘Dark leadership and well-being’; ‘Causes of selective reporting’; ‘Authoritarian leadership and toxicity’; and ‘PM traits and outcomes’. The study makes two key contributions: first, it provides a pioneering primer on PMDP traits and traces their developmental trajectory; second, it highlights emerging themes and trends that merit further investigation
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