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The direct and indirect effects of road verges and urban greening on butterflies in a tropical city-state
No full textRoad verges have considerable potential to benefit wildlife, but in highly urbanised areas management often limits their value for biodiversity. Evaluating how the management of road verges affects wildlife, both directly and indirectly, provides opportunities to integrate biodiversity into urban planning, design, and management. We studied butterfly pollinators next to main roads across Singapore, a highly urbanised tropical city-state that envisions itself as ‘A City in Nature’. Using structural equation models we quantified how road verge habitat quality (nectar-floral diversity, structural complexity, size, and plant richness) and surrounding landscapes (traffic density and greenness as a ratio of green to concreted areas) directly and indirectly affected butterflies. We found direct positive effects of nectar-floral diversity and structural complexity within road verges on butterfly diversity (abundance and richness). While road verge size and plant richness had no direct effects on butterfly diversity, both had indirect positive effects by increasing nectar-floral diversity and structural complexity. Greenness at a landscape (≥ 500 m radius) rather than local (≤ 250 m radius) scale positively affected butterfly diversity. Traffic density had a direct negative effect on butterfly diversity likely though increased mortality due to collisions. Our findings offer valuable insights for city planners and policymakers, and suggest that simple management decisions, such as improving resource quality within verges, can have positive benefits for biodiversity in highly urbanised areas. As cities around the world develop policy mechanisms to create greener environments, our results highlight opportunities to improve road verges to benefit butterflies, a commonly used flagship taxon for biodiversity
An international consensus statement on the methodological standards for physical activity and sedentary behaviour guidelines development
Get PDFBackground: The World Health Organization and many national health bodies have released physical activity and sedentary behaviour guidelines; however, there are inconsistencies across jurisdictions, which may partly be due to variation in guideline development processes. This study aimed to develop international consensus on the methodological standards for the development of future physical activity and sedentary behaviour guidelines. Methods: We conducted a modified Delphi study. Experts in physical activity and/or guideline development rated a series of statements on stakeholder involvement, the types of evidence and study designs considered, and the utilisation of formal approaches in guideline development. Consensus was defined as group agreement of ≥80%. Results: Twenty-three participants from eight countries reached consensus that 1) different stages of the guidelines development process require the involvement of different stakeholders; 2) previous study-level synthesised evidence must be included in evidence reviews and individual studies can be included if published after the most recent review or where review evidence is unavailable; 3) parallel randomised controlled studies must be included in review processes (83.3% agreement), with observational cohort studies marginally missing the agreement criterion (79.2% agreement), while predictive modelling, crossover trials, non-randomised trials and case control studies can be included; and 4) formal approaches must be utilised to assess the quality of individual primary studies, the reporting and quality of systematic reviews, and the overall process for grading evidence. Conclusions: The findings provide a set of methodological standards to improve consistency and rigour in the development of future physical activity and sedentary behaviour guidelines
Aligning Institutional Resource Commitment with Strategic Pedagogical Development to Create Online Distance Learning Provision in UK HEIs
Get PDFPurpose:This study explores the various models influencing online distance learning growth in UK Higher Education Institutions (HEIs) due to the evolving politico-socio-economic environment and traditional educational challenges. It aims to understand their effectson teaching methods, resource allocation, and time investment. The research involves semi-structured interviews with 25 senior academic and management personnel involved in online education. The study uses theme analysis to understand current tactics and content generation processes, their benefits and drawbacks. The findings offer valuable insights for HEIs to align their resources with strategic pedagogicalgrowth in online distance learning.Design and methodology:Network and referral sampling techniques were employed to access individuals with direct experience in developing online provision within UK higher education institutions (HEIs). To understand institutional responses to political and socio-economic shifts, as well as declining international student enrolment, semi-structured interviews wereconducted with 25 senior academic and management staff from UK HEIs. The data were then manually analysed using thematic analysis, enabling an in-depth, nuanced interpretation through iterative reading and theme refinement. This study offers a comprehensive overview of the strategies adopted by UK HEIs, aiming to support strategic decision-making in online distance learning.Findings:The results of this study demonstrate that UK Higher Education Institutions (HEIs) use a wide variety of methods to create online distance learning programmes. An examination of semi-structured interviews reveals the diverse procedures involved in content production for each model. The selected models have varying effects on the advancement of teaching methods, allocation of institutional resources, and time dedication. The benefits of this approach include increased adaptability and ease of use, while the drawbacks are difficulties in obtaining resources and possible limits inteaching methods. This study provides a fundamental paradigm for integrating institutional resources with strategic pedagogical growth, delivering useful insights for HEIs who are considering or actively involved in online distant learning.Originality/value:This study is unique because it explores and examines the many approaches used by HEIs to construct online distance learning programmes. The research addresses the contemporary political, social, and economic context and the problems faced by traditional educational paradigms. It makes a unique contribution by conducting interviews with senior academic and management personnel who are actively involvedin online education. An examination of the lived experiences provides detailed insights into the processes of content production, as well as the benefits and drawbacks of each model. This study is the first complete analysis that establishes an original basis for aligning institutional resources with strategic pedagogical growth in the ever-changing landscape of UK HEIs
Better financing: Signal match between growth in the digital economy and renewable energy business
No full textThe advancement of the digital economy propels decarbonization processes forward, with industrial digitalization anticipated to mitigate intermittent renewable energy generation, and digital industrialization amplifies the projected energy demand. Prior research has indicated that enterprises engaged in renewable businesses can alleviate their financing constraints through market signals. Given a deepening interconnection between the digital economy and renewable energy, a pertinent question arises - will the expansion of the digital economy effectively serve as an environmental signal to enhance the financing effect from renewable energy businesses? To address this, we analyze business data from all listed companies in China involved in renewable energy from 2003 to 2023. Our findings reveal: (1) Consistent with signaling theory, growth signals related to industrial digitalization and digital industrialization facilitate corporate financing through signals from renewable energy businesses. (2) We uncover dynamic relationships between these factors by progressively narrowing the time window for industrial digitalization and digital industrialization signal expression. (3) We validate our conclusions' robustness by testing how business cycles within the renewable energy sector affect the signal match. The findings offer insights for governmental efforts in formulating industry-specific plans, facilitating the enhancement of industrial collaboration
An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia
Get PDFThe early and accurate diagnosis of Alzheimer's Disease and Frontotemporal Dementia remains a critical challenge, particularly with traditional machine learning models which often fail to provide transparency in their predictions, reducing user confidence and treatment effectiveness. To address these limitations, this paper introduces an explainable and lightweight deep learning framework comprising temporal convolutional networks and long short-term memory networks that efficiently classifies Frontotemporal dementia (FTD), Alzheimer's Disease (AD), and healthy controls using electroencephalogram (EEG) data. Feature engineering has been conducted using modified Relative Band Power (RBP) analysis, leveraging six EEG frequency bands extracted through power spectrum density (PSD) calculations. The model achieves high classification accuracies of 99.7% for binary tasks and 80.34% for multi-class classification. Furthermore, to enhance the transparency and interpretability of the framework, SHAP (SHapley Additive exPlanations) has been utilized as an explainable artificial intelligence technique that provides insights into feature contributions
Towards transformatory critique: reframing curriculum for student focus
Get PDFIn this conceptual paper I present an argument from a critical theoretical perspective that it is the role of all universities to enable students’ criticality development. Considering criticality development as ‘critical being’, I argue that higher education needs to focus less on what students know and more on how they can enact the knowledge, skills and competencies they develop, and their critical engagement with the world they inhabit. To do this, I suggest reframing curriculum with an ontological focus to consider students’ development in domains beyond knowledge promoting their critical engagement with themselves, others and the world through active learning. I outline a framework that promotes students’ holistic development moving from ‘knowing’, through ‘becoming’, to ‘being’ emphasising dialogue, peer diversity and the exchange of differing perspectives in learning and teaching – what I term ‘contexts of difference’ – to stimulate criticality towards transformatory critique
Federated Learning-based Sensor Fault Classification in Label-Scarce Sensor Networks
No full textEnsuring reliable data collection from sensor nodes is vital for the success of IoT applications such as smart cities, industrial automation, and other data-driven systems. As sensor data underpins key decision-making processes, undetected faults can compromise system performance, leading to misinterpretations and costly operational disruptions. Therefore, timely and accurate fault classification is essential to preserve the integrity and resilience of IoT deployments. This paper proposes a novel method for classifying five common types of sensor faults across homogeneous sensor nodes using a combination of federated learning and transfer learning. The approach supports two categories of client nodes: source nodes with labeled data and target nodes without labels. Federated training at source nodes exploits dataset similarity with the unlabeled target node, while a central server adaptively weights each client’s contribution based on its relevance to the target sensor before aggregation. Across various source-target configurations, the proposed method consistently outperforms baseline approaches. It yields 2% to 11% higher accuracy compared to the widely used FedAvg method. Additionally, it improves over a centralized LSTM-based model by +3.5% to +5.4%, and surpasses traditional classifiers—such as SVM, Decision Tree, Random Forest, and KNN—by up to +33.6% in accuracy and +40.2% in F1-score, especially in target nodes lacking labeled data. These results highlight the effectiveness of the proposed approach in enabling robust, scalable, and privacy-preserving sensor fault classification in real-world IoT systems
Public Acceptance of a Proposed Sub-Regional, Hydrogen–Electric, Aviation Service: Empirical Evidence from HEART in the United Kingdom
Get PDFThis paper addresses public acceptance of a proposed sub-regional, hydrogen–electric, aviation service reporting initial empirical evidence from the UK HEART project. The objective was to assess public acceptance of a wide range of service features, including hydrogen power, electric motors, and pilot assistance automation, in the context of an ongoing realisable commercial plan. Both qualitative and quantitative data collection instruments were leveraged, including focus groups and stakeholder interviews, as well as the questionnaire-based Scottish National survey, coupled with the advanced discrete-choice modelling of the data. The results from each method are presented, compared, and contrasted, focusing on the strength, reliability, and validity of the data to generate insights into public acceptance. The findings suggest that public concerns were tempered by an incomplete understanding of the technology but were interpretable in terms of key service elements. Respondents’ concerns and opinions centred around hydrogen as a fuel, single-pilot automation, safety and security, disability and inclusion, environmental impact, and the perceived usefulness of novel service features such as terminal design, automation, and sustainability. The latter findings were interpreted under a joint framework of technology acceptance theory and the diffusion of innovation. From this, we drew key insights, which were presented alongside a discussion of the results
Tractor and Semitrailer Scheduling with Time Windows in Highway Ports with Unbalanced Demand Under Network Conditions
Get PDFTo address the challenges of unbalanced demand and high operational costs in highway port logistics, this study investigates the scheduling of tractors and semitrailers under time window constraints in a networked environment, where geographically distributed ports are interconnected by fixed routes, and tractors dynamically transport semitrailers between ports to balance asymmetric demands. A mathematical optimization model is developed, incorporating multiple car yards, diverse transport demands, and temporal constraints. To solve the model efficiently, an Adaptive Large Neighborhood Search (ALNS) algorithm is proposed and benchmarked against an improved Ant Colony System (IACS). Simulation results show that, compared to traditional scheduling methods, the proposed approach reduces the number of required tractors by up to 61% and operational costs by up to 21%, depending on tractor working hours. The tractor-to-semitrailer ratio improves from 1.00:1.10 to 1.00:2.59, demonstrating the enhanced resource utilization enabled by the ALNS algorithm. These findings offer practical guidance for optimizing tractor and semitrailer configurations in highway port operations under varying conditions
Measurement properties of the German version of the birth satisfaction scale-revised (BSS-R) in women with pre-existing medical conditions and high-risk pregnancy
Get PDFBackground: The Birth Satisfaction Scale-Revised (BSS-R) is a validated questionnaire for assessment of childbirth experience which has been translated into many languages. It is the instrument of choice in the International Consortium for Health Outcomes Measurement (ICHOM) standard set for ‘Pregnancy and Childbirth’. Translation of the key outcome measures from English into German language was previously performed, but its validation is pending. Aim and Objectives: To analyze the key psychometric properties of the German version of the BSS-R (Ger-BSS-R), and to evaluate its application in women with chronic conditions. Methods: 248 women with pre-existing medical conditions were provided with the Ger-BSS-R during hospital inpatient stay for childbirth. The 10-item measurement contains three sub-scales for assessing quality of care provision (QC), women’s personal attributes (WA), and stress experienced during labor (SE). Results: Complete data was available in N = 224 cases. After removal of four multivariate outliers, N = 220 were available for psychometric evaluation. The cesarean section rate was 50.5%, prematurity occurred in 14.5% of deliveries and induction of labor was performed in 49.7% of cases with planned vaginal delivery. Mean total BSS-R score was 25.7 (SD 5.94). In the confirmatory factor analysis, the tri-dimensional measurement model was found to offer a good fit to Ger-BSS-R data. For internal consistency, the total, SE and QC sub-scale Cronbach’s alphas were significantly lower than those of the founder version. Conclusions: The Ger-BSS-R is a robust instrument for assessing birth satisfaction. This is the first study to apply the BSS-R in women with pre-existing medical conditions. Compared to the founder UK version, differences in total BSS-R scores and sub-scales for experience of stress and quality of care are present, requiring further investigations
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