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Estimating hydrogen demand function: A structural time series model
No full textThis paper utilizes a Structural Time Series Model (STM) with an underlying component to estimate the global hydrogen demand function. This approach allows for the discernment of the ongoing impact of technology and the dynamic changes in consumer behavior that affect hydrogen demand over time. To estimate the hydrogen demand function, the analysis incorporates key variables, including hydrogen price, natural gas price, oil price, and GDP (Gross Domestic Product) per capita. The study utilizes quarterly global data from the first quarter of 2009 to the fourth quarter of 2021. In comparing the underlying components influencing hydrogen demand, the study suggests that advancements in production technology, organizational technology, and changes in consumer behavior collectively contribute to a gradual leftward shift in the global hydrogen demand curve over time. The study uncovered that, in the short term, global hydrogen demand demonstrates high inelasticity. Furthermore, the results reveal. a complementary relationship between natural gas and hydrogen, although this complementarity diminishes significantly over time. Additionally, the findings suggest that oil. can function as a substitute for hydrogen, with the substitution effect intensifying in the long term. Interestingly, hydrogen is initially perceived as a luxury commodity, yet over the long term, it transitions to behaving as a normal commodity
Association between Interleukin-6 Gene Polymorphism (rs1800795 and rs1800796) and Type 2 Diabetes Mellitus in a Ghanaian Population: A Case-Control Study in the Ho Municipality
No full textBackground. There is no conclusive evidence on the association between interleukin- (IL-) 6 gene polymorphism and type 2 diabetes mellitus (type 2 DM). Thus, this study is aimed at evaluating the role of rs1800795 and rs1800796 polymorphisms in the pathogenesis of type 2 DM among Ghanaians in the Ho Municipality. Materials and Methods. We recruited into this hospital-based case-control study 174 patients with type 2 DM (75 DM alone and 99 with DM+HTN) and 149 healthy individuals between 2018 and 2020. Demographic, lifestyle, clinical, anthropometric, and haemodynamic variables were obtained. Fasting blood samples were collected for haematological, biochemical, and molecular analyses. Genomic DNA was extracted, amplified using Tetra-primer amplification refractory mutation system-polymerase chain reaction (T-ARMS-PCR) technique, and genotyped for IL-6 gene polymorphism. Logistic regression analyses were performed to assess the association between IL-6 gene polymorphism and type 2 DM. Results. The minor allele frequency (MAF) of the rs1800795 and rs1800796 polymorphisms was higher in DM alone (57.5%, 62.0%) and DMwith HTN groups (58.3%, 65.3%) than controls (33.1%, 20.0%). Carriers of the rs1800795GC genotype (aOR = 2 35, 95% CI: 1.13-4.90, p =0022) and mutant C allele (aOR =241, 95% CI: 1.16-5.00, p=0019) as well as those who carried the rs1800796GC (aOR =867, 95% CI: 4.00-18.90, p<0001) and mutant C allele (aOR =884, 95% CI: 4.06-19.26, p=0001) had increased odds of type 2 DM. For both polymorphisms, carriers of the GC genotype had comparable levels of insulin, HOMA-IR, and fasting blood glucose (FBG) with those who carried the GG genotype. IL-6 levels were higher among carriers of the rs1800796GC variant compared to carriers of the rs1800796GG variant (p =0023). The rs1800796 polymorphism, dietary sugar intake, and exercise status, respectively, explained approximately 3% (p =0046), 3.2% (p =0038, coefficient = 1456), and 6.2% (p =0004, coefficient = −2754) of the variability in IL-6 levels, suggesting weak effect sizes. Conclusion. The GC genotype and mutant C allele are risk genetic variants associated with type 2 DM in the Ghanaian population. The rs1800796 GC variant, dietary sugar intake, and exercise status appear to contribute significantly to the variations in circulating IL-6 levels but with weak effect sizes
Fingertip Video Dataset for Non-invasive Diagnosis of Anemia using ResNet-18 Classifier
No full textHemoglobin is the iron containing protein in red blood cells which carries oxygen from lungs to rest of the body tissues.Accurate measurement of hemoglobin is essential for diagnosing anemia, a condition characterized by a deficiency of red blood cells. This measurement is particularly vital before initiating blood transfusions for thalassemia patients.Non-invasive estimation of hemoglobin levels can be achieved through photoplethysmography (PPG)-based methods.PPG is an optical method to measure blood volume changes in successive heart beats. PPG signals can be obtained from fingertip videos using a light source and a photodetector. Smartphone PPG utilizes a smartphone’s flashlight as a light source and its camera as a photodetector to acquire PPG signals, offering an affordable and portable point-of care tool. Despite the ubiquity of smartphones, signals from their cameras often contain noise, making feature selection from PPG characteristics challenging. While PPG-based methods are invaluable, the lack of real-world datasets poses a significant challenge in maximizing the benefits of PPG technology. In this paper, we introduce a dataset comprising 1-minute fingertip video recordings from 150 anemic patients, obtained using a smartphone’s camera. The dataset, publicly accessible for research purposes a, covers an age range of 6 months to 32 years, with diverse hemoglobin values (4.3 gm/dL - 12.4 gm/dL).Utilizing this dataset, we propose a deep learning-based technique employing the ResNet-18 architecture to estimate hemoglobin levels. This approach eliminates the need for manual feature extraction and selection from PPG signals, overcoming a limitation in existing smartphonePPG-based hemoglobin estimation systems. Our model achieves a hemoglobin level estimation with an RMSE of 0.81-1.39 when compared with the gold standard laboratory method, Complete Blood Count (CBC) test reports.In contrast, HemaApp, a state-ofthe-art research utilizing a machine learning-based classifier (SVM), yields an RMSE of 1.7 on our dataset. The accuracy and simplicity of our model position it as a promising alternative to existing non-invasive hemoglobin level estimation methods
An appetite to win: Disordered eating behaviours amongst competitive cyclists
No full textCompetitive cyclists may be vulnerable to disordered eating (DE) and eating disorders (ED) due to perceived body composition optimisation and external influences within cycling culture and from stakeholders. Therefore, this study aimed to assess DE and ED risk in competitive cyclists using the Eating Attitudes Test (EAT-26), explore differences in responses based on sex, discipline and level of competition, and to gain insights into contributing factors towards DE via open-ended survey questions. In total, 203 participants completed a mixed-method questionnaire. Eating disorders were reported by 5.7% (n = 11) of participants, with three being historic cases. The median (inter-quartile range) EAT-26 score was 8 (12) of a total possible score of 78. Disordered eating risk was observed in 16.7% of participants due to an EAT-26 score ≥20. Female participants had significantly higher scores than male participants (12.5 ± 17.5 vs. 6.5 ± 10.0; p = .004). There was no significant difference between road cyclists and off-road cyclists (7.0 ± 13.25 vs. 8.0 ± 10.5; p = .683). There was a significant difference in scores between novice/club/regional and national/elite/professional cyclists (6.0 ± 11.25 vs. 10.5 ± 12.0; p = .007). Thematic analysis of open-text responses found that the social environment of competitive cycling contributed towards DE behaviours and body image issues. These findings indicate competitive cyclists do appear to be an ‘at risk’ population for DE/ED. Therefore, there is need for stakeholders to enhance nutritional services, nutrition education and create supportive athlete environments
Relative Age Effects and the Premier League’s Elite Player Performance Plan (EPPP): A Comparison of Birthdate Distributions Within and Between Age Groups
No full textThe purpose of this study was to investigate the prevalence of relative age effects (RAEs) within and between U18, U21 and professional senior squads, that compete in the highest (respective) leagues within England. Birthdate, playing position and age (years) of U18 (n = 487), U21 (n = 350), and senior (n = 396) players from squads competing in the highest divisions of their respective age groups were obtained. Moreover, nationality (UK or Non-UK) was recorded for U21 and senior players, with estimated market value also obtained for senior players. Chi-square tests, Cramer’s V and odds ratios with 95% confidence intervals were used to compare observed and expected birthdate distributions. A selection bias toward relatively older players was evident within U18 and U21 squads, across all positions. Furthermore, analysis of age bands within each age group also revealed an increase in the prevalence of RAEs throughout each age group. In contrast, analysis of senior squads revealed no significant deviations in birthdate distributions when considered as a whole sample, as separate age bands or by position. However, although non-significant, Q4 players were found to have the highest estimated market value. Results demonstrate RAEs are prevalent across U18 and U21 age groups at the highest level of competitive football within England, yet this is not representative of the birthdate distributions within senior squads. Ongoing research is needed to highlight the prevalence of RAEs within academies, particularly when these RAEs are not representative of birthdate distributions within professional senior squads
Unsponsoring football”: Sign value, symbolic exchange and simulacra in a gambling-related marketing campaign
No full textThis article utilises Baudrillard’s (1981/1994; 1981/2019) concepts of sign value and symbolic exchange to examine the “Unsponsoring Football” campaign which was designed by the marketing and creative agencies VCCP and Octagon, and carried out in conjunction with the bookmakers Paddy Power. Clubs were “unsponsored” by Paddy Power, which paid for the right to not display its logo on football shirts. The campaign concept involved “spoof” shirts which are simulations of the “real” items. A four step process through which the simulacra develop is outlined in the article. The campaign parodies UK government policy on gambling-related sponsorship, which has been criticised for its failure to regulate what is a globalised market. The 2023 White Paper “High Stakes: Gambling Reform for the Digital Age” is notably similar to the parodic position taken in “Unsponsoring Football”. Removing some elements of sponsorship while retaining a wider relationship with the gambling industry means that the gamblification of the sport remains in place
Green AI‐Driven Concept for the Development of Cost‐Effective and Energy‐Efficient Deep Learning Method: Application in the Detection of Eimeria Parasites as a Case Study
No full textAlthough large-scale pretrained convolutinal neural networks (CNN) models have shown impressive transfer learning capabilities, they come with drawbacks such as high energy consumption and computational cost due to their potential redundant parameters. This study presents an innovative weight-level pruning technique that mitigates the challenges of overparameterization, and subsequently minimizes the electricity usage of such large deep learning models. The method focuses on removing redundant parameters while upholding model accuracy. This methodology is applied to classify Eimeria species parasites from fowls and rabbits. By leveraging a set of 27 pretrained CNN models with a number of parameters between 3.0M and 118.5M, the framework has identified a 4.8M-parameter model with the highest accuracy for both animals. The model is then subjected to a systematic pruning process, resulting in an 8% reduction in parameters and a 421M reduction in floating point operations while maintaining the same classification accuracy for both fowls and rabbits. Furthermore, unlike the existing literature where two separate models are created for rabbits and fowls, this article presents a combined model with 17 classes. This approach has resulted in a CNN model with nearly 50% reduced parameter size while retaining the same accuracy of over 90%
Exploring the Impact of Passthrough on VR Exergaming in Public Environments: A Field Study
No full textSedentary behavior is becoming increasingly prevalent in daily work and study environments. VR exergaming has emerged as a promising solution in these places of work and study. However, private spaces in these environments are not easy, and engaging in VR exergaming in public settings presents its own set of challenges (e.g., safety, social acceptance, isolation, and privacy protection). The recent development of Passthrough functionality in VR headsets allows users to maintain awareness of their surroundings, enhancing safety and convenience. Despite its potential benefits, little is known about how Passthrough could affect user performance and experience and solve the challenges of playing VR exergames in real-world public environments. To our knowledge, this work is the first to conduct a field study in an underground passageway on a university campus to explore the use of Passthrough in a real-world public environment, with a disturbance-free closed room as a baseline. Results indicate that enabling Passthrough in a public environment improves performance without compromising presence. Moreover, Passthrough can increase social acceptance, especially among individuals with higher levels of self-consciousness. These findings highlight Passthrough's potential to encourage VR exergaming adoption in public environments, with promising implications for overall health and well-being