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Aroma of the essential oil of peppermint reduces aggressive driving behaviour in healthy adults
Get PDFAggressive driving is of increasing concern in modern society. This study investigated the potential for the presence of an ambient aroma to reduce aggressive responses in a simulated driving situation. Previous literature has demonstrated the beneficial effect of peppermint (Mentha piperita) aroma on driver alertness and we aimed to identify any impact on aggressive driver behaviour. Fifty volunteers were randomly assigned to one of two conditions (peppermint essential oil aroma and no aroma). Aggressive driving behaviours were measured in a virtual reality driving simulator. The analysis indicated that the peppermint aroma significantly reduced aggressive driving behaviours. The presence of the aroma also produced medium sized effects on some aspects of mood from pre-test levels. These results provide support for the use of ambient aromas for the modification of driving behaviours. It is proposed that applying peppermint into daily driving may be a beneficial for reducing driver aggression
Insights into the accuracy of social scientists’ forecasts of societal change
Get PDFHow well can social scientists predict societal change, and what processes underlie their predictions? To answer these questions, we ran two forecasting tournaments testing accuracy of predictions of societal change in domains commonly studied in the social sciences: ideological preferences, political polarization, life satisfaction, sentiment on social media, and gender-career and racial bias. Following provision of historical trend data on the domain, social scientists submitted pre-registered monthly forecasts for a year (Tournament 1; N=86 teams/359 forecasts), with an opportunity to update forecasts based on new data six months later (Tournament 2; N=120 teams/546 forecasts). Benchmarking forecasting accuracy revealed that social scientists? forecasts were on average no more accurate than simple statistical models (historical means, random walk, or linear regressions) or the aggregate forecasts of a sample from the general public (N=802). However, scientists were more accurate if they had scientific expertise in a prediction domain, were interdisciplinary, used simpler models, and based predictions on prior data
Safety and efficacy of an artificial intelligence-enabled decision tool for treatment decisions in neovascular age-related macular degeneration and an exploration of clinical pathway integration and implementation: protocol for a multi-methods validation study
Get PDFIntroduction Neovascular age-related macular degeneration (nAMD) management is one of the largest single-disease contributors to hospital outpatient appointments. Partial automation of nAMD treatment decisions could reduce demands on clinician time. Established artificial intelligence (AI)-enabled retinal imaging analysis tools, could be applied to this use-case, but are not yet validated for it. A primary qualitative investigation of stakeholder perceptions of such an AI-enabled decision tool is also absent. This multi-methods study aims to establish the safety and efficacy of an AI-enabled decision tool for nAMD treatment decisions and understand where on the clinical pathway it could sit and what factors are likely to influence its implementation.
Methods and analysis Single-centre retrospective imaging and clinical data will be collected from nAMD clinic visits at a National Health Service (NHS) teaching hospital ophthalmology service, including judgements of nAMD disease stability or activity made in real-world consultant-led-care. Dataset size will be set by a power calculation using the first 127 randomly sampled eligible clinic visits. An AI-enabled retinal segmentation tool and a rule-based decision tree will independently analyse imaging data to report nAMD stability or activity for each of these clinic visits. Independently, an external reading centre will receive both clinical and imaging data to generate an enhanced reference standard for each clinic visit. The non-inferiority of the relative negative predictive value of AI-enabled reports on disease activity relative to consultant-led-care judgements will then be tested. In parallel, approximately 40 semi-structured interviews will be conducted with key nAMD service stakeholders, including patients. Transcripts will be coded using a theoretical framework and thematic analysis will follow.
Ethics and dissemination NHS Research Ethics Committee and UK Health Research Authority approvals are in place (21/NW/0138). Informed consent is planned for interview participants only. Written and oral dissemination is planned to public, clinical, academic and commercial stakeholders
Co-designed or evidenced based? Developing digital self-management interventions for long-term conditions
Get PDFSelf-management interventions for long-term conditions may improve the quality of life. Psychosocial techniques can be delivered via digital technologies such as smartphone applications. Interventions should be useful and meaningful to users, but also theory- and evidence-based. This chapter explores some of the processes and challenges involved in developing self-management interventions using co-design, theory, and evidence. Drawing on app development research focused on supporting those with Sjögren’s syndrome (an autoimmune and rheumatic disease with complex symptom patterns), the chapter discusses the practical difficulties we had in generating and respecting individuals’ ideas, preferences, requirements, and creativity, while acknowledging the need for an intervention to include established cognitive components, behaviour change techniques, and “active ingredients”. The chapter is organised around three tensions from our project where our ambition to co-design the intervention with participants was challenged and contradicted. In such situations, we often found ourselves having to judge the relevancy or compatibility of participants’ viewpoints and lived experiences against the evidence base. We close with critical reflections on these tensions, discussing the potential limits of co-design and user-led approaches to creating health and care interventions in a disciplinary culture of evidence-based practice. We offer a series of implications for conducting design research in the area of self-managing long-term conditions, identifying ways for the competing paradigms of co-design and evidence-based design to be bridged
Si-Cu nanocomposite as an effective sensing layer for H2S based on quartz surface acoustic wave sensors
Get PDFIt has been a critical challenge to develop highly sensitive H2S gas sensors, due to its wide-range application in industry and frequent leakage, which endangers people's lives. In this work, a highly sensitive room-temperature SAW H2S gas sensor based on Si-Cu nanocomposite was developed. The Cu content in this composite layer plays a key role for H2S response of the sensor because CuO, an excellent adsorption site for H2S, presents on the surface of Cu. The amorphous Si content results in the highly porous structure enhancing the interaction between CuO and H2S. The interaction leads to the formation of CuS and a negative frequency response of the sensor. H2O molecules effectively participate the reactions between CuO and H2S, and the responses of H2S were significantly enhanced in a moister environment. With the relative humidity of 60%, the sensor can detect 50 ppb H2S with a response of -225 Hz
Secure energy management of multi-energy microgrid: A physical-informed safe reinforcement learning approach
Get PDFThe large-scale integration of distributed energy resources into the energy industry enables the fast transition to a decarbonized future but raises some potential challenges of insecure and unreliable operations. Multi-energy Microgrids (MEMGs), as localized small multi-energy systems, can effectively integrate a variety of energy components with multiple energy sectors, which have been recently recognized as a valid solution to improve the operational security and reliability. As a result, a massive amount of research has been conducted to investigate MEMG energy management problems, including both model-based optimization and model-free learning approaches. Compared to optimization approaches, reinforcement learning is being widely deployed in MEMG energy management problems owing to its ability to handle highly dynamic and stochastic processes without knowing any system knowledge. However, it is still difficult for conventional model-free reinforcement learning methods to capture the physical constraints of the MEMG model, which may therefore destroy its secure operation. To address this research challenge, this paper proposes a novel safe reinforcement learning method by learning a dynamic security assessment rule to abstract a physical-informed safety layer on top of the conventional model-free reinforcement learning energy management policy, which can respect all the physical constraints through mathematically solving an action correction formulation. In this setting, the secure energy management of the MEMG can be guaranteed for both training and test procedures. Extensive case studies based on two integrated systems (i.e., a small 6-bus power and 7-node gas network, and a large 33-bus power and 20-node gas network) are carried out to verify the superior performance of the proposed physical-informed reinforcement learning method in achieving a cost-effective MEMG energy management performance while respecting all the physical constraints, compared to conventional reinforcement learning and optimization approaches
Understanding COVID-19 vaccine hesitancy: A cross-sectional study in Malang District, Indonesia
Get PDFIntroduction: Vaccine hesitancy could undermine efforts to reduce incidence of coronavirus disease 2019 (COVID-19). Understanding COVID-19 vaccine hesitancy is crucial to tailoring strategies to increase vaccination acceptance. This study aims to investigate the prevalence of and the reasons for COVID-19 vaccine hesitancy in Malang District, Indonesia.
Methods: Data come from a cross-sectional study among individuals aged 17-85 years old (N = 3,014). Multivariate ordered logistic regression was used to identify factors associated with postponing or refusing COVID-19 vaccines. The Oxford COVID-19 vaccine hesitancy scale was used to measure vaccine hesitancy. A wide range of reasons for hesitancy, including coronavirus vaccine confidence and complacency, vaccination knowledge, trust and attitude in health workers and health providers, coronavirus conspiracy, anger reaction and need for chaos, populist views, lifestyle, and religious influence, was examined.
Results and discussion: The results show that 60.2% of the respondents were hesitant to receive the COVID-19 vaccine. Low confidence and complacency beliefs about the vaccine (OR = 1.229, 95% CI = 1.195–1.264) and more general sources of mistrust within the community, particularly regarding health providers (OR = 1.064, 95% CI = 1.026–1.102) and vaccine developers (OR = 1.054, 95% CI = 1.027–1.082), are associated with higher levels of COVID-19 vaccine hesitancy. Vaccine hesitancy is also associated with anger reactions (OR = 1.019, 95% CI = 0.998–1.040), need for chaos (OR = 1.044, 95% CI = 1.022–1.067), and populist views (OR = 1.028, 95% CI = 1.00–1.056). The findings were adjusted for socio-demographic factors, including age, sex, education, marital status, working status, type of family, household income, religious beliefs, and residency. The results suggest the need for an effective health promotion program to improve community knowledge of the COVID-19 vaccine, while effective strategies to tackle “infodemics” are needed to address hesitancy during a new vaccine introduction program
Holocene hydroclimate variability along the Southern Patagonian margin (Chile) reconstructed from Cueva Chica speleothems
Get PDFPatagonia is ideally situated to reconstruct past migrations of the southern westerly winds (SWWs) due to its southerly maritime location. The SWWs are an important driver of Southern Ocean upwelling and their strength and latitudinal position changed during the Holocene, leading thus to different responses of the vegetation to past climate changes along the Chilean continental margin. A new speleothem record from Cueva Chica (51°S) is investigated to reconstruct past climatic changes throughout the Holocene in conjunction with other marine and paleoenvironmental records of the region and better constrain the regional paleoclimatic evolutions of SWWs. Samples comprising both a flowstone core and a stalagmite were radiometrically dated (Usingle bondTh & 14C) to construct age-depth models for the highly-resolved proxy profiles (δ13C, δ18O, chemical composition). The Cueva Chica record provides a highly-resolved isotopic and elemental curves for the last 12 ka, albeit with a hiatus from 5.8 to 4 ka BP. The multi-proxy analysis suggests three climatic regimes throughout the Holocene in Southern Patagonia: i) an early Holocene wet period (with the exception of two dry excursions at 10.5 ka and 8.5 ka BP), ii) a mid-Holocene dry period and iii), a return to generally wet conditions over the late Holocene. The global drivers for these tri-phased climatic regimes are likely related to oceanic and South polar feedbacks. The early Holocene was the warmest period and might be attributable to changes in global ocean circulation which involved a rise in air T° and a strength in SWW from 50°S, and therefore higher precipitations over landmass. After 9 ka BP, an intensified deglaciation dynamic along the Antarctic Peninsula is concordant with increasing summer insolation in the Southern hemisphere, leading to a poleward shift of the SWWs in response to global warming and thus to a reduction in moisture supply from the Pacific onto the Patagonian shore. After 5 ka BP, a gradual SST decline is consistent with an equatorward shift of the SWWs in response to a cooling Southern hemisphere. The SWW storm tracks extended to lower latitudes, inducing a return to wetter conditions with highly variable moisture patterns along the Patagonian landmass. Clumped isotope (Δ 47) analyses at lower resolution reflect the degree of kinetic isotope fractionation at the time of carbonate deposition, especially during the dry interval around 8.5–5.5 ka BP. Reduced kinetic isotope fractionation is observed since at least 2.6 ka BP, a period marked by (slightly) wetter conditions
Deep reinforcement learning-based long-range autonomous valet parking for smart cities
Get PDFIn this paper, to reduce the congestion rate at the city center and increase the traveling quality of experience (QoE) of each user, the framework of long-range autonomous valet parking is presented. Here, an Autonomous Vehicle (AV) is deployed to pick up, and drop off users at their required spots, and then drive to the car park around well-organized places of city autonomously. In this framework, we aim to minimize the overall distance of AV, while guarantee all users are served with great QoE, i.e., picking up, and dropping off users at their required spots through optimizing the path planning of the AV and number of serving time slots. To this end, we first present a learning-based algorithm, which is named as Double-Layer Ant Colony Optimization (DLACO) algorithm to solve the above problem in an iterative way. Then, to make the fast decision, while considers the dynamic environment (i.e., the AV may pick up and drop off users from different locations), we further present a deep reinforcement learning-based algorithm, i.e., Deep Q-learning Network (DQN) to solve this problem. Experimental results show that the DL-ACO and DQN-based algorithms both achieve the considerable performance
Towards sustainable development in the hospitality sector: Does green human resource management stimulate green creativity? A moderated mediation model
Get PDFGreen human resource management (GHRM) is an important organisational approach to promote the sustainable development of organisations. Although the literature regarding the effect of GHRM is growing, little is known about the mechanisms and boundary conditions that may facilitate the link between GHRM and green outcomes. Through a combined underpinning of ability–motivation–opportunity, job demands–resources and social exchange theories, this study examines the relationship between GHRM and green creativity through green work engagement, with spiritual leadership moderating the GHRM–green work engagement relationship. Also, we explore the links between GHRM, spiritual leadership, green work engagement and green creativity using a moderated mediation model. Using survey data of 271 front-line hotel employees in UAE, we use a partial least squares structural equation modelling to conduct our statistical analysis. The results show that GHRM positively influences green work engagement and green creativity, while green work engagement positively influences green creativity and mediates the GHRM–green creativity nexus. In addition, spiritual leadership amplifies the nexus between GHRM and green work engagement and the mediating effect of green work engagement in the nexus between GHRM and green creativity in the context of the hospitality sector in the UAE. Our study offers industry-specific practical implications and suggests agendas for further research