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PBatch: Pseudonym Certificate Batch Authentication With Generative AI-Based Cache for Cooperative Intelligent Transportation Systems
Get PDFAuthentication and revocation are the key mechanisms to ensure the security of the Cooperative Intelligent Transportation System (C-ITS). C-ITS relies on the Vehicular Public Key Infrastructure (VPKI) for anonymous authentication and device revocation. Several works complemented the VPKI-based authentication and revocation process. However, several security and performance issues exist in both mechanisms. This article presents PBatch: Pseudonym Certificate Batch Authentication based on Distributed Ledger Technology. PBatch addresses challenges specific to the authentication and revocation process to achieve 1000 authentications per second. PBatch relies on the concept of batching pseudonym certificates by offloading heavy validation operations such as certificate chain and revocation status validation to local edge servers. This enables vehicles to validate a batch of pseudonym certificates with a fixed number of verification operations, thus simplifying the authentication of the pseudonym certificate at the end devices. Furthermore, a caching-based message authentication mechanism is introduced to validate a relatively larger number of safety messages. We also introduced a Generative Artificial Intelligence (GAI) based cache management mechanism for safety messages caching and fetching. Finally, experiments and security analysis are conducted to investigate PBatch performance and security. The results show that PBatch is more secure, feasible, and scalable than the leading VPKI-based authentication proposals
Trade Risk Society - Understanding Trade Policymaking in the 2020s
Get PDFProfound changes in trade policymaking are taking place in the 2020s in response to a complex set of increasingly salient risks shaping the international trade system. Drawing upon the influential theory of risk society, this study develops a new trade risk society framework providing original insights and new conceptual thinking on the subject. This analytical approach extends beyond merely a topical evaluation of current risks to one embedding trade in deeper underlyingdevelopments in our contemporary world and challenges facing it. Key elements of risk society theory are deployed to this end across four risk domains: 1. Economic security. 2. Geopolitical volatility. 3. Climate–environmental. 4. Technology control. Close interconnections exist between these domains, as shown in the framework’s applied analysis of the 30 or so most significant trade policymaking initiatives introduced thus far this decade up to and including US President Trump’s aggressive tariff protectionism. It is argued this pattern of initiatives are indicative of a paradigm shift in trade policy norms emerging in an increasingly volatile and contested world that can be best understood in a trade risk society context
Erratum:Correction: Towards an affect intensity regulation hypothesis: Systematic review and meta-analyses of the relationship between affective states and alcohol consumption (PloS one (2022) 17 1 DOI: 10.1371/journal.pone.0262670)
No full text[This corrects the article DOI: 10.1371/journal.pone.0262670.].</p
International Society for Pediatric and Adolescent Diabetes Clinical Practice Consensus Guidelines 2024:Insulin and Adjunctive Treatments in Children and Adolescents with Diabetes
Get PDFThe International Society for Pediatric and Adolescent Diabetes (ISPAD) guidelines represent a rich repository that serves as the only comprehensive set of clinical recommendations for children, adolescents, and young adults living with diabetes worldwide. This chapter builds on the 2022 ISPAD guidelines, and updates recommendations on the principles of intensive insulin regimens, including more intensive forms of multiple daily injections with new-generation faster-acting and ultra-long-acting insulins; a summary of adjunctive medications used alongside insulin treatment that includes details on pramlintide, metformin, glucagon-like peptide-1 (GLP-1) receptor agonists (GLP-1RA) and sodium-glucose cotransporter inhibitors; and key considerations with regard to access to insulin and affordability to ensure that all persons with diabetes who need insulin can obtain it without financial hardship.</p
Physical Activity Volume and Intensity for Healthy Body Mass Index and Cardiorespiratory Fitness: Enhancing the Translation of Children's and Adolescents' Accelerometer Physical Activity Reference Values
Get PDFThis secondary data analysis aimed to demonstrate the utility of physical activity (PA) wrist accelerometer outcome reference values by identifying the PA volume (average acceleration) and intensity distribution (intensity gradient) centiles and values associated with body mass index (BMI) status (normal weight, overweight, and obese) and cardiorespiratory fitness (CRF, multistage shuttle runs test) status (low, moderate, and high) in children and adolescents. We assessed the dose–response associations between average acceleration and intensity gradient with BMI and CRF outcomes using restricted cubic spline linear mixed models. To aid translation of the findings, we calculated the increases in average acceleration needed to shift exemplar participants to “healthy” weight and CRF status. For boys and girls, there was a nonlinear inverse association between average acceleration and BMI. In both sexes, a positive dose–response was observed between average acceleration and intensity gradient with CRF. The values and centiles of average acceleration and intensity gradient that aligned with BMI and CRF statuses were identified. To move from an average acceleration associated with overweight to healthy weight, 10‐year‐old boys and girls would need to increase daily average acceleration by 23 mg (~30‐min running) and 16 mg (~18‐min running), respectively. These findings further demonstrate the importance of PA in relation to BMI and CRF and the utility of PA reference values for the translation of accelerometer outcomes into meaningful information. Additional studies demonstrating how PA reference values can be used to track behaviors and provide insights into health associations could inform practice further
Home advantage in English rugby union: A study of divisional differences since 2000/01
Get PDFThis descriptive study aimed to explore longitudinal variation in home advantage (HA) among the top four tiers of English rugby union, based on the matches played from 2000/01 to 2024/25 seasons. Home advantage was quantified using Pollard’s traditional and rescaled methods. Initial one-sample t-tests revealed that the mean HA values were significantly greater than 50% across all tiers (p < 0.001), indicating a clear presence of HA. Thereafter, outcomes of a one-way repeated measures ANOVA concluded that HA significantly differed between competition levels (p < 0.001). Post-hoc tests revealed it was significantly greater in the Gallagher Premiership (64.37%) than the RFU Championship (59.69%)across the considered period (p = 0.003). Although XmR control charts identified potential non-random variation in HA during the 2005–2008 RFU Championship seasons, it appears to have stabilised over the past decade across all the examined tiers. This judgement was further supported by sensitivity analyses conducted using linear mixed models, which highlighted no significant seasonal variations in HA in the top two tiers since 2014/15 season. Overall, the findings signify the influence of HA on English rugby union and offer opportunities for future research to explore root causes of the observed divisional and longitudinal variations
Can a novel computer vision-based framework detect head-on-head impacts during a rugby league tackle?
Get PDFBACKGROUND: Head-on-head impacts are a risk factor for concussion, which is a concern for sports. Computer vision frameworks may provide an automated process to identify head-on-head impacts, although this has not been applied or evaluated in rugby.METHODS: This study developed and evaluated a novel computer vision framework to automatically classify head-on-head and non-head-on-head impacts. Tackle events from professional rugby league matches were coded as either head-on-head or non-head-on-head impacts. These included non-televised standard-definition and televised high-definition video clips to train (n=341) and test (n=670) the framework. A computer vision framework consisting of two deep learning networks, an object detection algorithm and three-dimensional Convolutional Neural Networks, was employed and compared with the analyst-coded criterion. Sensitivity, specificity and positive predictive value were reported.RESULTS: The overall performance evaluation of the framework to classify head-on-head impacts against manual coding had a sensitivity, specificity and positive predictive value (95% CIs) of 68% (58% to 78%), 84% (78% to 88%) and 0.61 (0.54 to 0.69) in standard-definition clips, and 65% (55% to 75%), 84% (79% to 89%) and 0.61 (0.53 to 0.68) in high-definition clips.CONCLUSION: The study introduces a novel computer vision framework for head-on-head impact detection. Governing bodies may also use the framework in real time, or for retrospective analysis of historical videos, to establish head-on-head rates and evaluate prevention strategies. Future work should explore the application of the framework to other head-contact mechanisms and also the utility in real time to identify potential events for clinical assessment.</p
Collaborative Learning Integration for Enhanced Photovoltaic System Power Forecasting: A Novel Ensemble Approach
No full textThe integration of photovoltaic (PV) systems into power distribution networks presents challenges due to the unpredictable nature of power output, which affects grid reliability and energy management. Accurate forecasting of a PV power output is therefore critical. This study proposes a novel collaborative learning intergration (CLI) stacking ensemble energy forecasting model approach, designed to forecast real-time hourly power output of a 39.02 kWp photovoltaic system using the historical real time power generation output of the system and the hourly time correlating meteorological data. Unlike conventional methods that simply average model predictions or develop a meta learner, the proposed topology allows individual machine learning models to interact and learn from each other through mutual reinforcement, thereby enhancing forecasting accuracy. The algorithm is evaluated against benchmark independent and stacked models to analyze its performance as an improved forecasting model. The experiment results show that the proposed collaborative learning integration stacked ensemble model outperforms the five benchmark models, achieving an R-square (R2), mean absolute error (MAE) and root mean square error (RMSE) accuracy of 0.89, 1.44 and 2.47 respectively. The residual analysis highlighted that the collaborative learning intergration model was more effective at minimising long-term forecast errors compared to other models. The proposed model can be highly useful for predicting the performance of small and large-scale solar PV power plants in a multi-step ahead forecasting
Critical evaluation of animal-component-free media in human keratinocytes and human fibroblasts
No full textThe MuM (Mums Using Music) online programme: A mixed methods feasibility study to promote perinatal wellbeing
Get PDFBackground There is mounting evidence to support use of music for perinatal wellbeing yet few supports exist to inform pregnant women about using music for this purpose. In response to this the ‘Mums Using Music’(MuM) online programme was co-designed by music therapists, midwives and a Public and Patient Involvement Panel to empower pregnant women with knowledge on how to use music for their wellbeing. This mixed-method quasi-experimental study aimed to examine the feasibility of a MuM pilot among pregnant women. Methods N= 9 pregnant women between 18-35 weeks gestation were recruited to an intervention group [n= 5] or a control group [n= 4]. The intervention group attended four 1-hour weekly online MuM sessions while the control group received care as usual. All participants completed the Warwick Edinburgh Mental Wellbeing Scale and the Prenatal Attachment Inventory at baseline and at follow up. Afterwards the control group received the MuM intervention, and all participants attended focus groups to gain qualitative feedback. Findings This study found that: (1) participants were drawn to MuM for a various reasons related to maternal wellbeing, (2) the online format was both accessible and supportive, (3) MuM seemed to enhance maternal wellbeing and maternal-foetal attachment and, (4) participants directly attributed their engagement with music to their maternal wellbeing. Conclusions The findings suggest that MuM, an online music programme for maternal wellbeing, is feasible among pregnant women. Progression to a larger trial is recommended