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Strategic partnership for Health in All Policies and sustainable transport in Scotland: a case study evaluation
Get PDFBackgroundHealth in All Policies aims to ensure policy decisions across sectors improve health and health equity. Principles of a Health in All Policies approach have been defined as Governance, Comprehensive approach to health, Collaboration, Equity, Participation, Evidence-based and Sustainability. Intersectoral partnerships are a recognised mechanism for Health in All Policies but few evaluations study partnerships that aim to influence policy. This case study evaluation studied a national Partnership focused on transport policy in Scotland. The evaluation aimed to assess the extent to which the Partnership meets the principles of Health in All Policies and informs policy and practice. It also identified actions to improve its impact.Study designAnonymous self-completion survey of members of the Partnership and its wider Learning Network.MethodsThe survey used Likert scales to assess respondents’ views on whether the Partnership was meeting its aims and supporting principles of Health in All Policies. Respondents also recorded whether the Partnership had increased their knowledge, supported wider collaboration or informed decision making. The Partnership used structured discussion in groups and an online poll to generate and prioritise improvement actions.ResultsA vast majority of respondents scored the Partnership highly for Comprehensive approach to health (82%), and being Evidence-based (78%). Most rated it highly for Governance (63%), Collaboration (62.5%) Equity (63%) and Sustainability (57%). However, less than half (43%) scored it highly for Participation. Respondents indicated a range of ways the Partnership impacted on their knowledge and practice. The top actions identified by the Partnership to improve its impact were to investigate car culture and identify specific national transport policies to influence.ConclusionsA national sector-specific Partnership can provide a constructive platform for a Health in All Policies approach to improve health and health equity, but further mechanisms are needed to support participation of affected populations
A Perceptual Approach to HRTF Personalisation: From Localisation to Spatial Literacy
Get PDFEfforts to personalise Head-Related Transfer Functions (HRTFs) have traditionally focused on anatomical accuracy, modelling the head, ears and torso to predict how sound should reach an individual’s ears. While valuable, such approaches often require specialist equipment and yield variable results. They also struggle to accommodate changes in hearing or playback context and rarely provide users with opportunities to build or refine spatial perception.This paper outlines an alternative, interaction-driven method: a perceptual approach to spatial calibration that learns from how listeners respond, rather than how they are built. Unlike anatomical or database-driven HRTF methods, this system adapts dynamically to how users perceive and respond to sound in space. It creates a calibration method that is accessible, reflexive and capable of evolving over time
CEO power and firm decarbonisation efforts
Get PDFUsing a global sample of 899 firms from 26 countries for the period 2000 to 2021, this study investigates the effect of CEO power on firms' decarbonisation efforts. We find that firms with higher levels of CEO power are associated with lower carbon emissions. Further analysis indicates that nationally diverse boards and older board members amplify the negative relationship between CEO power and carbon emissions. Similarly, powerful CEOs with high academic qualifications aggressively pursue corporate decarbonisation. The impact of CEO power on decarbonisation is more noticeable in carbon-intensive industries. Lastly, we document that climate legislation can be catalytic for decarbonisation
Application of Quantum Key Distribution to Enhance Data Security in Agrotechnical Monitoring Systems Using UAVs
Get PDFEnsuring secure data transmission in agrotechnical monitoring systems using unmanned aerial vehicles (UAVs) is critical due to increasing cyber threats, particularly with the advent of quantum computing. This study proposes the integration of Quantum Key Distribution (QKD), based on the BB84 protocol, as a secure key management mechanism to enhance data security in UAV-based geographic information systems (GIS) for monitoring agricultural fields and forest fires. QKD is not an encryption algorithm but a secure key distribution protocol that provides information-theoretic security by leveraging the principles of quantum mechanics. Rather than replacing traditional encryption methods, QKD complements them by ensuring the secure generation and distribution of encryption keys, while AES-128 is employed for efficient data encryption. The QKD framework is optimized for real-time operations through adaptive key generation and energy-efficient hardware, alongside Lempel–Ziv–Welch (LZW) compression to improve the bandwidth efficiency. The simulation results demonstrate that the proposed system achieves secure key generation rates up to 50 Mbps with minimal computational overhead, maintaining reliability even under adverse environmental conditions. This hybrid approach significantly improves data resilience against both quantum and classical cyber-attacks, offering a comprehensive and robust solution for secure agrotechnical data transmission
Modeling climate policy uncertainty into cryptocurrency volatilities
Get PDFClimate change is a highly controversial topic within the socioeconomic context. Climate Policy Uncertainty (CPU) arises from the process of climate policies formulation and implementation. This uncertainty impacts financial market volatilities, including cryptocurrency markets. In this paper, we demonstrate the substantial role of CPU in forecasting volatilities in cryptocurrency markets using Genetic Programming (GP). Our study shows that different cryptocurrency markets respond differently to CPU across time scales. Our paper contributes to the literature by illustrating the impact of CPU on cryptocurrency market volatilities and analyzes it across different time horizons. Second, we build three volatility forecasting models for different cryptocurrency markets by incorporating CPU, which outperform traditional models. Our models can thereby illuminate portfolio construction and hedging strategies, providing valuable insights for investors and policymakers
Count of Northern Gannets on the Bass Rock in July 2024
No full textA drone survey of Northern Gannets on the Bass Rock was carried out on 29 July 2024, following the protocol of the earlier surveys in 2023 and 2022. The resulting orthomosaic vertical image covered c.97% of the colony. Three independent counts of this area gave a mean count of 44,447 Apparently Occupied Sites (AOS) ± 192 standard deviation (SD). After scaling up this value to correct for the missing areas, the total Bass Rock population in 2024 was estimated to be 46,045 AOS. Comparison of the areas counted in both 2023 and 2024 indicated a 6.7% decline in AOS, while comparison of the estimated total population indicated a decrease of around 11%. Thus, there was no evidence of a population recovery after the high pathogenicity avian influenza (HPAI) outbreak in 2022. However, very few dead birds or unattended chicks were recorded indicating no recurrence of HPAI in 2024 and that foraging conditions for parents were favourable. A detailed analysis of inter-observer variation in counts of AOS was also carried out. Due to the high resolution of the drone image, inter-observer variation is substantially reduced compared to previous surveys using lower quality images from fixed-wing aircraft. Crucially, there was no statistical support that counters differed in how they counted areas varying in size and aspect. Thus, appreciable savings in time and/or expense needed to obtain population counts of gannetries may be possible by reducing the need for replicate counts
Priority Load Management for Improving Supply Reliability of Critical Loads in Healthcare Facilities Under Highly Unreliable Grids
Get PDFMany developing countries suffer from unreliable grids and rolling blackouts on a daily basis. Losing electricity in healthcare facilities can be detrimental to human life and the required health services. Thus, it is often necessary to keep critical loads operational even if the grid experiences a blackout. Such support is usually provided using battery storage or diesel generators. In the system design phase, it is often unknown how the priority-based load management will impact the battery life, sizing of the optimal battery, or operational cost in the long run. This paper presents a comprehensive analysis of a priority load management strategy for healthcare facilities in areas of highly unreliable grids. A grid-connected battery backup system is used for the evaluation. To operate the system, a priority-based dispatch algorithm is developed, which classifies medical loads into three tiers based on their criticality. Synthetic medical facility load profiles and blackout patterns are constructed to test the algorithm. The battery model was enhanced with the introduction of aging calculations spanning multiple years. It was found that the priority-based algorithm improved the reliability served to the most critical loads at the expense of the least critical. The load priority strategy slowed the battery pack degradation over time and reduced the number of replacement cycles, which is financially favorable in the long run. Finally, some insights for designing such a backup system are provided
Mixed methods evaluation of a digital resource to build students’ skills in AS sessing cardiovascular risk, MO tivating change, and SUS taining a healthier lifestyle in themselves and others- ASMOSUS: a study protocol
Get PDFBackground: Cardiovascular disease (CVD) is a prevalent cause of morbidity and mortality globally. Nurses and nursing students are in an optimum role to assess, manage and promote lifestyle changes associated with CVD risk. Patients and service users are more likely to adopt these changes if the person delivering the information embodies this lifestyle themselves. Literature suggests that nurses and nursing students show detrimental behaviours in association with smoking, obesity, nutrition, physical inactivity and alcohol. It is therefore essential to address CVD risk factors, management and lifestyle promotion early on in a healthcare professionals’ career- ideally the university delivering their nursing program. This aligns with the Nursing and Midwifery Council curricula in the United Kingdom (UK) on the topic of public health and health promotion. Although already taught there is a gap between knowledge and adoption of healthy lifestyle behaviours. This is potentially resolved through consolidating self-efficacy in nursing students and their ability to apply theory to practice. Methods: This study will evaluate a digital educational resource: ASMOSUS. This resource was co-designed with nursing students, academic and clinical staff to provide the skills to assess CVD risk, motivate change and encourage adoption of a healthy lifestyle in themselves and others. All nursing students will receive the ASMOSUS digital resource as part of their routine teaching, followed by either a 90-minute face to face tutor-led class or via a live online platform such as Microsoft Teams to consolidate skills with their peers. A mixed-methods study will be carried out in two phases. Phase one will use two questionnaires to investigate student knowledge on CVD risk and self-efficacy, using a pre-post test design. Phase two will explore the experience of the students in using the resource and the impact on their skills and self-efficacy using focus groups. Discussion: This study has the potential to engage nursing students as the health professionals of the future in the early adoption of the knowledge and skills in CVD risk assessment, management and promotion of a healthy lifestyle. This will inform not only the health and wellbeing of nursing students themselves but translate into role modelling for patients and optimal patient care
HoloJig: Interactive Spoken Prompt Specified Generative AI Environments
Get PDFHoloJig offers an interactive, speech-to-VR, virtual reality experience that generates diverse environments in real-time based on live spoken descriptions. Unlike traditional VR systems that rely on pre-built assets, HoloJig dynamically creates personalized and immersive virtual spaces with depth-based parallax 3D rendering, allowing users to define the characteristics of their immersive environment through verbal prompts. This generative approach opens up new possibilities for interactive experiences, including simulations, training, collaborative workspaces, and entertainment. In addition to speech-to-VR environment generation, a key innovation of HoloJig is its progressive visual transition mechanism, which smoothly dissolves between previously generated and newly requested environments, mitigating the delay caused by neural computations. This feature ensures a seamless and continuous user experience, even as new scenes are being rendered on remote servers
A Novel Approach to Fire Detection With Enhanced Target Localisation and Recognition
No full textReal-time monitoring of fires is crucial for safeguarding lives and property. However, current fire detection methods still suffer from issues such as redundant feature information, poor network generalisation capabilities and low perception of target location information. To address these challenges, a novel fire detection method called YOLO-FDI has been proposed. This method utilises partial convolution and coordinate convolution with attention mechanisms and Alpha loss at different stages. Specifically, to enhance target localisation accuracy, an attention mechanism is integrated into the model to autonomously focus on fire-affected areas. In terms of feature extraction, partial convolution is employed to reduce computational redundancy and memory access, improving performance and effectively extracting spatial features. During the feature fusion stage, coordinate convolution embeds feature information into coordinate data, further enhancing the coordinate perception capabilities of pixels on the feature map, thereby improving adaptability and accuracy in detecting fire targets. Additionally, the model utilises Alpha loss to enhance flexibility and robustness in fire object detection and recognition. Experimental results demonstrate the effectiveness of the proposed model based on three self-constructed datasets. Compared to the baseline YOLOv7 model, its mAP has improved by 4.5 percentage points, 1.7 percentage points and 2.6 percentage points, respectively. This method demonstrates the capability to accurately represent fire targets and exhibits better stability and reliability in fire target detection, effectively reducing false positives and missed detections
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