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State-of-the-art flocking strategies for the collective motion of multi-robots
No full textThe technological revolution has transformed the area of labor with reference to automation and robotization in various domains. The employment of robots automates these disciplines, rendering beneficial impacts as robots are cost-effective, reliable, accurate, productive, flexible, and safe. Usually, single robots are deployed to accomplish specific tasks. The purpose of this study is to focus on the next step in robot research, collaborative multi-robot systems, through flocking control in particular, improving their self-adaptive and self-learning abilities. This review is conducted to gain extensive knowledge related to swarming, or cluster flocking. The evolution of flocking laws from inception is delineated, swarming/cluster flocking is conceptualized, and the flocking phenomenon in multi-robots is evaluated. The taxonomy of flocking control based on different schemes, structures, and strategies is presented. Flocking control based on traditional and trending approaches, as well as hybrid control paradigms, is observed to elevate the robustness and performance of multi-robot systems for collective motion. Opportunities for deploying robots with flocking control in various domains are also discussed. Some challenges are also explored, requiring future considerations. Finally, the flocking problem is defined and an abstraction of flocking control-based multiple UAVs is presented by leveraging the potentials of various methods. The significance of this review is to inspire academics and practitioners to adopt multi-robot systems with flocking control for swiftly performing tasks and saving energy
The association between physical outputs and match outcome across different playing styles for a professional second-tier football team across two complete seasons
No full textBACKGROUND: Elite-level football requires an array of physical, technical, psychological, and tactical skills. The aim of this study was to measure the association between physical outputs (distance, decelerations, accelerations) and the match outcome (win, draw, lose) in professional football. This research also examined whether the same association is influenced if a team adopts a possession or transition-based playing style.METHODS: Thirty-six elite-outfield football players from an English Championship team participated in the study during the 2020/2021 and 2021/2022 seasons using physical and event data collected from an English Championship club over the 2020-2021 and 2021-2022 seasons, this study conducted a univariate analysis of variance (ANOVA) and Hedge g effect size (ES) to measure the research aims.RESULTS: The results showed no significant differences were found between match outcomes for each physical output metric calculated. There was a trivial ES shown for all conditions except decelerations, with win/lose having a moderate ES (g=0.53). When playing a possession-based playing style there was no significant difference or non-trivial ES found for any physical output and match outcomes. When playing a transition-based playing style there was a moderate ES found for win/draw (P=0.38, g=0.90) and win/loss (P=0.98, g=0.64).CONCLUSIONS: This research provides important evidence for utilizing intense deceleration actions as a physical KPI during match play for teams adopting a transitional playing style. Subsequently, training interventions should be adopted to physically prepare players to complete and sustain intense deceleration actions during match play.</p
Machine learning approaches for obesity classification and prediction: an analysis of demographic, lifestyle, and health factors
No full textThe high level of obesity poses a serious public health problem across the globe, being a leading cause of various chronic diseases and substantial threats to the quality of people's lives. Given the ever-growing rates of obesity and its impact on individual well-being, the current study aims to explore numerous factors that drive obesity, using machine learning algorithms to solve classification tasks. By working on a rich dataset that contains a range of demographic, lifestyle, and health parameters, several classifiers were developed and tested, namely Decision Tree, Random Forest, Gradient Boosting, Support Vector Machine, and K-Nearest Neighbors. The resulting outcomes indicated that both Random Forest and Gradient Boosting algorithms were highly accurate in the classification of obesity, with 95.0% and 95.3% accuracy rates, respectively. The obtained results confirm the critical role of various machine learning approaches in understanding obesity and developing predictions for more focused intervention. This study offers considerable input into obesity epidemiology literature and demonstrates the utility of advanced analytical appraisal in public health
Exploring the correlation between academic pressure and mental health: a study of anxiety, stress, and depression levels among university students in Bangladesh
No full textBy utilizing the scores of academic pressures and mental health indicators (anxiety, stress, depression levels) from different university students in Bangladesh. The present investigation aims to explore association between them. This study will determine if these mental health issues are more prevalent in certain universities and departments, by examining this dataset drawn from a diverse set of schools. These results provide useful information for the design of focused interventions that could help to improve well-being in students. Methods: Using data from this survey, we assessed mental health in a variety of ways. That all in entire system the interacting different universities, departments and academic year was implemented to figure out major factors that causes student towards anxiety stress or how much. The study's findings will help to identify the roots of poor mental health in students and inform interventions tailored for student well-being. It aims for meaningful meta-analytic conclusions and implications to policy makers as well university administrators regarding various student populations around the world; emphasizing specialized mental health support systems should be designed according to distinct issues met by students in each country
Machine learning model for predicting hepatocellular carcinoma in Hepatitis C patients
No full textAn estimated 58 million people suffer from chronic Hepatitis C virus around the world, while substantial evidence indicates that patients with Hepatitis C virus are at 17 times larger risk of developing Liver Cancer (Hepatocellular Carcinoma). Research has been carried out to predict hepatitis C and liver cancer at different stages in patients. In this research, we proposed the Classification model AdaBoost with Decision Tree as its base model to be trained and tested on patient dataset. The dataset contains records of clinical indicators and was acquired from the University of California Irvine Machine Learning Repository. The preparation of the dataset was done using balancing techniques i.e. SMOTE, it was encoded using Ordinal Encoding. The hyperparameters of AdaBoost model was tuned manually to find the most optimal combination. AdaBoost Classification Model achieved a 92.68% accuracy, and the AUROC of 0.97. The precision and recall differ for each class, “healthy” individuals were classified with a precision of 98% and a recall of 99% while patients with “Cirrhosis” (irreversible scarring of liver due to a tumor) were classified with 86% precision and 67% recall. The research concluded that machine learning has efficient applications in predicting diseases. Moreover, the clinical indicators mentioned in previous studies have proven to be vital in the prediction of HCC (liver cancer) however it is advised that, in future a larger dataset may be acquired to overcome any potential biases in the predictions. The current program successfully distinguishes between patients at different stages of HCC and HCV and can be further adapted to build decision systems to aid diagnosis
The Swinging Christies
No full textThe Swinging Christies podcast is an extensive in-depth analysis of one of the least-discussed areas of Agatha Christie’s works. The writing career of Christie spanned seven decades, almost up to her death in 1976, and she remains the best selling novelist of all time. However, historical and critical analysis of her works has tended to concentrate on the earlier periods of her work. The Swinging Christies offers original research and insight to boldly make the argument that Agatha Christie was always a contemporary writer. The podcast focuses on the 1960s, a time when Christie’s writings reacted to everything from the atom bomb to miniskirts.This is the first time that Christie’s later works have been scrutinised so closely, and the analysis encompasses more than just her novels. They include adaptations of her work in the decade, including those produced internationally (such as in India), as well as lesser-known theatrical works and short stories. Episodes are arranged by theme, covering such subjects as ‘Sex’, ‘Drugs’, ‘Armageddon’, ‘Empire’ and ‘Fashion’, and the discussions situate Christie’s works within their historical, social and political contexts,The Swinging Christies has been afforded privileged access to the Agatha Christie family archive, allowing some material to be discussed in-depth for the first time. This includes an unproduced play that Christie wrote in the 1960s, which offered original insights into how she was writing during this decade. The podcast also features an interview with Christie’s grandson, Mathew Prichard, who recalls much about his grandmother in the decade. This includes her reaction to television, and even the first time he played her a Beatles record. This new material allows the podcast to be unique both in its analysis as well as the new information uncovered for a global audience.The podcast is entirely written, produced and presented by Agatha Christie expert Dr Mark Aldridge and the writer Gray Robert Brown. It has been extremely well-received, with a total audience in excess of 100,000 listens, and a string of positive feedback. Some recenttestimonials are on the podcast’s website, with endorsements from our diverse listenership, from professors to performers: https://christietime.com/testimonials-1
Modelling environmental life cycle performance of alternative marine power configurations with an integrated experimental assessment approach: A case study of an inland passenger barge
Get PDFThere is pressure on the global shipping industry to move towards greener propulsion and fuel technologies to reduce greenhouse gas emissions. Hydrogen and electricity are both recognised as pathways to achieve a net-zero. However, in the evaluation of the environmental performance of these alternative marine power configurations, conventional life cycle assessment (LCA) methods have limitations reflecting the varied nature of ship design and operational modes. The integration of LCA with experimental assessment could remedy the shortcoming of conventional approaches to data generation. The system energy demand data in this study was generated based on specific ship design and directly fed into life cycle assessment. To demonstrate the effectiveness and potential the approach was applied to a case study of inland waterway vessel. Suitable hybrid PV/electricity/diesel and hydrogen powered fuel cell systems for the case vessel were modelled; and hydrodynamic testing and dynamic system simulation was undertaken to provide ship performance data under various operational/environmental profiles. Lifecycle assessment (LCA) indicated hydrogen and electrical propulsion technologies have the potential for 85.7 % and 56.2 % emissions reduction against an MGO base case, respectively. The results highlight that implementation of both technologies is highly dependent on energy production pathways. Hydrogen systems reliant on fossil feedstocks risk an increase in emissions of up to 6.3 % against the MGO base case. Sensitivity analysis indicated an electrical system with electricity production from 79.5 % renewables could achieve savings of 82.2 % in GHG emissions compared to the MGO base case. Crucially, the results demonstrate a further development of the LCA approach which can enable a more accurate environmental performance evaluation of alternative marine power configurations considering specific ship design and operational characteristics. Ultimately this addition makes the results more meaningful for commercial operations and decision making in the selection of alternative marine power systems to support the transition to net-zero
How do Legal Aid cuts in England and Wales impact LGBTQ+ people seeking asylum? Perspectives from providers and directly affected people
Get PDFIntroductionLGBTQ+ people seeking asylum in England and Wales may experience disproportionate risk due to recent cutbacks in legal aid services, including inconsistent standards for determining the credibility of asylum claims and the inability to obtain essential resources.MethodsInterviews were conducted with legal, social care, and mental health professionals (n = 17) and directly affected people (n = 9) from January to April 2023 to explore how legal aid cuts shape the experiences of LGBTQ+ people seeking asylum in England and Wales.ResultsGuided by the concept of structural violence and employing constructivist grounded theory analysis, this qualitative study identified four themes demonstrating the impact of legal aid cuts: making it difficult to find solicitors with expertise in working with LGBTQ+ people seeking asylum; forcing solicitors to make difficult choices about the clients they accept; compromising the ability of solicitors to build the trust needed to work with LGBTQ+ people seeking asylum; and compounding life instabilities for LGBTQ+ people seeking asylum.ConclusionsFindings reveal that legal aid cuts contribute to structural violence against LGBTQ+ people seeking asylum by constraining the ability of solicitors to properly represent their asylum claims and thus prolonging the deleterious conditions faced by this population.Policy ImplicationsEfforts are needed to ensure access to legal aid services for LGBTQ+ people seeking asylum in England and Wales. Adequately funding legal aid services would also better enable solicitors to apply trauma-informed legal practices, which is imperative for effectively engaging with and representing LGBTQ+ people seeking asylum
What is the criminological value of fiction? Examining Southern Postcolonial storytelling as a site for post-disciplinary criminological theory construction, analogy and pedagogy
No full textUnilateral high-load resistance training induced a similar cross-education of strength between the dominant and non-dominant arm
Get PDFIt was previously hypothesized that the cross-education of strength is asymmetrical, where a greater transfer of strength is observed from the dominant to the non-dominant limb. The purpose of this study was to examine if the magnitude of cross-education of strength differed between dominant and non-dominant limbs following unilateral high-load resistance training. One hundred and twenty-two participants were randomized to one of the three groups: 1) training on the dominant arm (D-Only), 2) training on the non-dominant arm (ND-Only) and 3) a time-matched non-exercise control (Control). The training groups completed 6?weeks (18 sessions) of unilateral elbow flexion exercise. Each training session started with one-repetition maximum (1RM) training (≤ five attempts), followed by four sets of high-load exercise (i.e. 8?12RM). Strength changes of the untrained arm were compared between groups. Changes in the strength of the untrained arm were greater in D-Only (1.5?kg) and ND-Only (1.3?kg) compared to Control (?0.2?kg), without differences between D-Only and ND-Only. Unilateral resistance training increased strength in the opposite untrained arm, and the magnitude of this effect was similar regardless of which arm was trained. However, there is still considerable uncertainty on this topic and additional research is warranted to confirm the current findings
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