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Internet of Vehicles-based application using deep learning approach
No full textEdge Computing is an optimistic technology that can extend the necessary support for vehicular applications. In this paper, an effective edge-computing framework is developed to improvise task scheduling. A task partition and scheduling algorithm are developed to decide the workload allocation and schedule the execution order of tasks offloaded. Then, according to the characteristics of task scheduling, design the corresponding state-action space and reward function; and finally, taking into consideration the complexity of task scheduling and computing resource allocation, the pointer network is trained by multi-agent fuzzy deep reinforcement learning; this allows the pointer network to account for the dynamic nature of During the process of network fusion, it is used to find a solution for the issue of weight distribution for each agent.The simulation showcases that the proposed method is superior. Furthermore, it has significant advantages in terms of convergence speed and optimal performance. It has a high degree of flexibility in the ever-changing and intricate electromagnetic environment. The capabilities of the Internet of Vehicles' job offloading system have been significantly increased because of this improvement.It is widely believed that the Internet of Vehicles (IoV), which incorporates cutting-edge technologies such as connectivity, big data, and artificial intelligence, will play a significant role in the development of the next-generation intelligent transportation system. In recent years, the Internet of Vehicles has given rise to a significant number of novel computer jobs, such as augmented reality and autonomous driving, to name a few. The completion of these computer jobs must adhere to stringent real-time constraints, and it takes a significant amount of computing resources to bring these tasks to a successful conclusion. Since the volume, weight, and other limitations that restrict vehicles prevent them from being outfitted with powerful computing devices, the computing resources of the onboard devices that are now in use are often not enough to fulfill the processing requirements of these jobs. Install edge servers in the immediate area of the vehicle. Edge computing, in contrast to cloud computing, can provide consumers with computer services that are located relatively near them. Instead of being sent to the cloud, the computing duties that are created by the vehicle are immediately offloaded to the edge server. This reduces the amount of time it takes for computing activities to be transmitted.As a result, the implementation of edge computing in IOVs is a potential solution to the problem of inadequate processing power shown by vehicles and a means of satisfying the criteria of low latency imposed by tasks.The offloading of computational duties, in general, may effectively lower the amount of energy that the vehicle requires to operate. Offloading chores is something that consumers are often more likely to do in the interest of keeping the vehicle's battery alive for as long as possible. The number of responsibilities that must be offloaded and carried out inside the Internet of Vehicles will continue to grow because of this. When a significant number of tasks are offloaded and performed, the server is unable to provide computer resources for all the tasks at the same time.This means that tasks that are not allocated to computing resources must wait to be executed.At present, it is not possible to disregard the waiting time if the computing jobs that are now queued up to be done have delay requirements. Therefore, to effectively offer computing services for a greater number of offloading jobs, it is important to establish an acceptable scheduling strategy according to the execution time and delay needs of computing tasks.This paper integrates software-defined networking (SDN) into the Internet of Vehicles, constructs an SDN-assisted computing task offloading system for the Internet of Vehicles in an edge computing environment, and presents a task of computing offloading for vehicles. This is done since SDN can manage network resources more conveniently and effectively. Scheduling model. After that, an improved pointer network is trained using deep reinforcement learning to solve the offload scheduling problem of delay-constrained computing tasks in multi-edge servers on the Internet of Vehicles. This is done in consideration of the complexity of task scheduling and the allocation of computing resources
Personality and attitudinal predictors of Sportspersonship in recreational sport
No full textRespect and fair treatment among competitors is necessary for maintaining quality and enjoyment in sporting competitions. Yet despite the existence of rules and expectations, many athletes choose to violate such sporting norms. The present study examined whether individual differences in Sportspersonship within recreational sports could be explained by personality and sport-related attitudes. Ultimate Frisbee players (N =828) completed an online survey consisting of personality (Dark triad, HEXACO-60 and Sportspersonship), demographic and attitudinal questionnaires. Psychopathy was associated with low Sportspersonship, whereas Honesty-Humility, Agreeableness and Openness to Experience predicted greater Sportspersonship. Additionally, participants holding positive attitudes about the conduct of other players and the efficacy of self-governed regulation displayed higher Sportspersonship. Positive associations between personality traits and Sportspersonship may be attributed to positive views towards cooperation and enjoyment of the experience, whereas negative associations between Psychopathy and Sportspersonship are likely to be linked to the callous and risk-taking nature of such individuals
Reference values for wrist-worn accelerometer physical activity metrics in England children and adolescents
No full textBackground: Over the last decade use of raw acceleration metrics to assess physical activity has increased. Metrics such as Euclidean Norm Minus One (ENMO), and Mean Amplitude Deviation (MAD) can be used to generate metrics which describe physical activity volume (average acceleration), intensity distribution (intensity gradient), and intensity of the most active periods (MX metrics) of the day. Presently, relatively little comparative data for these metrics exists in youth. To address this need, this study presents age- and sex-specific reference percentile values in England youth and compares physical activity volume and intensity profiles by age and sex. Methods: Wrist-worn accelerometer data from 10 studies involving youth aged 5 to 15 y were pooled. Weekday and weekend waking hours were first calculated for youth in school Years (Y) 1&2, Y4&5, Y6&7, and Y8&9 to determine waking hours durations by age-groups and day types. A valid waking hours day was defined as accelerometer wear for ≥ 600 min·d−1 and participants with ≥ 3 valid weekdays and ≥ 1 valid weekend day were included. Mean ENMO- and MAD-generated average acceleration, intensity gradient, and MX metrics were calculated and summarised as weighted week averages. Sex-specific smoothed percentile curves were generated for each metric using Generalized Additive Models for Location Scale and Shape. Linear mixed models examined age and sex differences. Results: The analytical sample included 1250 participants. Physical activity peaked between ages 6.5–10.5 y, depending on metric. For all metrics the highest activity levels occurred in less active participants (3rd-50th percentile) and girls, 0.5 to 1.5 y earlier than more active peers, and boys, respectively. Irrespective of metric, boys were more active than girls (p < .001) and physical activity was lowest in the Y8&9 group, particularly when compared to the Y1&2 group (p < .001). Conclusions: Percentile reference values for average acceleration, intensity gradient, and MX metrics have utility in describing age- and sex-specific values for physical activity volume and intensity in youth. There is a need to generate nationally-representative wrist-acceleration population-referenced norms for these metrics to further facilitate health-related physical activity research and promotion
"No country for old men": the Lawnmower Maintenance Society
No full textPurposeThis paper aims to describe a link between and benefits of two initiatives targeted at mental health and social inclusion. One being informal, the sport of walking football; the other a formal self-help well-being group. Design/methodology/approachThis reflective commentary describes two group activities which are aimed at addressing physical and mental health challenges of older adults. These are described by the author to highlight the benefits that they provide for some of the challenges faced by men in particular in later life. Using a narrative approach to describe a synergy between the two initiatives created by the link between the activities, and the participants. FindingsThe Lawnmower Maintenance Society has proved to be a successful model for promoting and supporting the participant's mental health and well-being. As we emerge from the trauma of COVID-19 and the isolation of lockdown, such initiatives may help redress the imbalance in health which resulted. Although there are several similar groups, there seems to be a positive link between the physical and mental health benefits of using Walking Football as a shared interest of the attendees. Feedback from participants and interest from the wider community suggest the positive impact of such groups, particularly as National Health Service resources are stretched beyond capacity. Research limitations/implicationsAlthough this study reports on one new group for men, to the best of the author's knowledge, this is the first to bring together the areas of Walking Football and a men's support group. Practical implicationsThis type of support group could easily be extended if the footballing authorities wish to replicate it in other parts of the country. Social implicationsIt is well known that men are very reluctant to talk about personal problems. Other workers have also tried innovative solutions to this problem, such as Men in Sheds. This case study offers a further contribution to this area. Originality/valueThere are several academic papers, as well as information on well-being groups in both the print and social media. This commentary outlines a small, but contemporary, description of one such initiative
A perceptual study of relationship between emotional intelligence and job performance among higher education sector employees in Saudi Arabia
No full textPurpose The purpose of this research is to investigate faculties' perceptions of emotional intelligence about job performance (task and contextual performance (TP and CP)) in the higher education sector in the Kingdom of Saudi Arabia. Design/methodology/approach This research employed an explanatory research design method. A quantitative design approach is adopted by implementing a survey-based study. Quantitative data have been collected anonymously from faculty members (n = 277) working in different higher educational institutes in Saudi Arabia. Findings The research findings revealed a positive relationship between others' emotions appraisal and use of emotions with CP, whilst all the dimensions of emotional intelligence, namely self-emotions appraisal (SEA), others' emotions appraisal (OEA), use of emotions (UOE) and regulation of emotions (ROE) revealed a significant positive relationship with the TP. Nevertheless, the research supports the argument that emotional intelligence is considered an essential contributor to faculty members' job performance. Practical implications This research study provides empirical support for the argument that emotional intelligence is a direct driver for enhancing job performance through the appraisal, use and regulation of emotions. In terms of practical implications, the research findings will encourage higher education institutions to take specific actions that will help to enhance awareness of and build emotional intelligence skills amongst faculty staff. Originality/value This study is the first that has sought to investigate the impact of emotional intelligence on employees' performance in the Saudi Arabian higher education sector. Additionally, this study has developed a new psychometric scale that is useful in the higher education sector
Polydimethylsiloxane (PDMS) Coated Broadband Tunable Vanadium Dioxide (VO2) Based Linear Optical Cavity Temperature Sensor
No full textSilicon on insulator (SOI) based sensors provide a reasonable solution to the issues common in traditional linear optical cavities such as wavelength dependant nature of mirrors, size, and maintaining the resonant condition. In this study we presented polydimethylsiloxane (PDMS) coated SOI based linear optical temperature sensing resonator model and analysed it in finite element method by using COMSOL Multiphysics. The phase changing material (PCM) VO2on each side of the Si waveguide helped to achieve the resonant condition and thermal tunability of the resonator. An almost linear variation in resonant frequency (wavelength)frdue to the temperature change in the range of 0–90 °C resulted in maximum sensitivity of 0.01 THz/°C or 79.4 pm/°C for the 10 µm cavity length. The recorded sensitivity is at least 5-times (or more) higher than the previous studies. The prominent reasons behind this improvement can be PDMS coating, adequate light matter interaction and proper confinement of resonating mode. The demonstrated sensor model has wide operational frequency range spanning from 10 to 210 THz. Moreover, the reported model also showed an increase in temperature sensitivity from 0.00967 to 0.01 THz/°C while the length of resonator was changed from 2 to 10 µm
What the Stories Tell Us
No full textTen stories of addiction and recovery have been presented. The authors of these are all very different. They include men and women from different cultural backgrounds, who were born or reside in one of eight different countries, and at the time of writing ranged in age from 19 to 70. The common factor that brought these 10 individuals together is they have a history of addiction, are now in recovery and have a story to tell about their experience. Each story is remarkable, recounting the deep unhappiness that is wrought by addiction, but also the remarkable capacity people have to change, to create a better life for themselves, where they live authentically to safeguard their future
Predicting fish habitat in the Persian Gulf using artificial intelligence
No full textThe Persian Gulf is one of the most important habitats in the Middle East. It can be extremely beneficial to aquatic species' survival and environmental preservation to continuous monitoring and collect data about aquatic animals, their habitats, and behaviours. Finding a novel and suitable method to carry out accurate and automatic monitoring with low timing and low cost for monitoring aquatic species’ behaviour in this high potential area is helpful. To predict fish habitat in Persian Gulf Convolutional Neural Network method and Naïve Bayes algorithm are used. Deep learning convolutional neural network technology is mostly used for data science classification and recognition because of its exceptional accuracy and to solve search and optimization issues, the Naïve Bayes algorithm is employed. Results indicate for predicting fish habitat in the Persian Gulf, the accuracy of the Convolutional Neural Network algorithm and the Naïve Bayes algorithms is 97.32% and 95.47%, respectively. With p=0.025 (p0.05), there is a substantial difference between the Naïve Bayes method and the Convolutional Neural Network algorithm. Therefore, The Convolutional Neural Network method seems to be more accurate than the Naïve Bayes method at predicting fish habitat in the Persian Gul
A novel approach of machine learning application in astrophysics: morphological feature wrapping based ensemble method for galaxy shape classification using GAMA dataset
No full textThe numerous strategies for the automated morphological categorization of galaxies, which uses a variety of supervised machine learning techniques, have not been well examined or compared. As the majority of star galaxy classifiers in use today use condensed summary data from catalogues, rigorous feature extraction and selection are required. With the aid of Deep Convolutional Neural Networks (CNN), a development in machine learning, it may automate the process of feature detection from data by a computer, therefore lowering the demand for qualified human input. Low-level artificial classification has made great progress. While this is the case, Deep Learning consistently outperforms traditional computers. analyzing large datasets while learning. We examine three machine learning techniques for categorizing morphological galaxies: Support Vector Machines (SVM), Random Forests (RF), and Naive Bayes (NB). We examine the efficacy of several machine learning algorithms on each feature representation of a galaxy using a collection of morphological features produced by image analysis as well as the raw image pixel data compressed using PCA (Principal Component Analysis) into PCA features. According to our experiments, RF outperformed SVM and NB. The remainder of the time, morphological features outperformed our PCA features in performance. Thus, the current mechanism is not extremely scalable. A probabilistic classifier that can scale, is based on source data, and requires the least amount of human interaction is essential to resolving these problems
Working with perfectionistic athletes in sport: an Acceptance and Commitment Therapy perspective.
No full textIn this chapter we draw on our applied experiences and research to highlight how perfectionistic athletes can be supported using Acceptance and Commitment Therapy (ACT). The first part of the chapter describes ACT and research that has examined its use for perfectionism. In keeping with other chapters in this section of the book, the second part of the chapter presents a case example of a perfectionistic athlete. Our case example is an aspiring young athlete who in making the transition to the senior performance squad has begun to experience emotional and behavioural problems. Our novel contribution to previous work of this kind is our focus on ACT. Few studies have adopted ACT interventions to reduce perfectionism even though we believe it to be a valuable way of doing so. In addition, there are even fewer exemplars of how to implement this type of intervention in sport. As such, our intention is that the chapter serves as a guide for practitioners unfamiliar with ACT and is a useful addition to other illustrative examples of how to work effectively with perfectionistic athletes