Comparing locomotor intensity indicators in soccer training and competition across contextual factors: a study of replaced coaches in a Portuguese professional 1st league team.

This study aims to examine, for each head coach (HC) replaced, the association between training intensity and physical performances obtained in games. Furthermore, the study investigated how contextual factors influence locomotor and mechanical performance association. External load variables were collected using Global Positioning System (GPS) devices across the 4 weeks and 4 games before and after the replacement in a professional adult male soccer team. Six different HC records were analysed (48.8 ± 7.4 years of age; 11.2 ± 3.9 years as an HC) during a three-season span (2020/21-2022/2023). There were marked differences within player variability across the two coaching regimes. Game loads didn't reflect training-related performance, with differences ranging from -71.4% to -9.9%. Players under the outgoing coaches have greater coverage of meters per minute. Meters per minute, distance covered over 18 km/h and high-speed running (all in training) are found to be significant variables influenced by contextual factors. Within-subject and time, training loads did not reflect game-related loads/performances, with starters showing higher deficits (ranging from -79.0 to -14.5). The study suggests that changes in soccer HC can affect players' training intensity and game performance, influenced by various contextual factors and not directly correlated. This type of information might be very suitable to improve training load periodization and programming. For further research avenues, could be the study of the variation of the psychological states of the players at the time of the dismissal and hiring of the HCs, associating them with the physiological performance at the same moments.

Psychometric properties and gender invariance of the 8-item emotion regulation questionnaire (ERQ-8) among Chinese university students.

BACKGROUND: To assess emotion regulation strategies in a clear and direct manner, Emotion Regulation Questionnaire (ERQ) was developed based on the process model of emotion regulation. ERQ primarily assesses an individual's propensity for reappraisal (a cognitive change in the individual's psychological state in specific situations) and expressive suppression (a regulatory response where an individual alters their emotional response after the onset of an emotional reaction). Recent studies have suggested that the abbreviated 8-item version of the ERQ exhibits comparable model fit to the original version. The present study aimed to explore the psychometric properties and assess cross-gender invariance of the ERQ-8 in Chinese university students. METHODS: University students from Jiangsu Province participated in this study. Participants completed self-report surveys assessing emotion regulation strategies. It was conducted from May 2022 to July 2022. The study employed confirmatory factor analysis (CFA) to assess the two-factor model of ERQ-8 and measurement invariance across male and female samples. RESULTS: The mean age of 1534 participants was 19.83 years (SD = 1.54), and the majority were female (70.4%). The initial ERQ-10 model with ten items demonstrated good fit for all indicators, CFI (Comparative Fit index) = 0.967, TLI (Tucker-Lewis Index) = 0.957, RMSEA (Root Mean Square Error of Approximation) = 0.043, SRMR (Standardised Root Mean Square Residual) = 0.029. However, to assess the fit of the previously proposed ERQ-8 model, two items (Q1 and Q3) were excluded. The fit of the ERQ-8 model was further improved (CFI = 0.989, TLI = 0.984, RMSEA = 0.029, SRMR = 0.021). All item loadings exceeded or were equal to 0.573. Internal consistency analysis based on the ERQ-8 model revealed Cronbach's alpha values of 0.840 for reappraisal and 0.745 for suppression, and corresponding composite reliability (CR) values of 0.846 and 0.747, respectively. Test-retest reliability, assessed using the intraclass correlation coefficient (ICC) (95% CI) within a one-week interval, ranged from 0.537 to 0.679. The correlation coefficient between the two factors was 0.084, significantly below 0.85, which suggested a low correlation between the two factors. The results of the invariance analysis across gender demonstrated that the values of ΔCFI and ΔTLI were both below 0.01. It was supported the gender invariance of the ERQ-8 among university students. CONCLUSION: The eight-item ERQ demonstrated validity and reliability in evaluating emotion regulation strategies, and measurement invariance was observed across gender among university students. The ERQ-8 may prove to be a practical and cost-effective tool, particularly in time-constrained situations.

Assessing physical fitness adaptations in collegiate male soccer players through training load parameters: a two-arm randomized study on combined small-sided games and running-based high-intensity interval training.

Objective: To evaluate the effects of a 4-week intervention combining small-sided games (SSGs) and high-intensity interval training (HIIT) on physical fitness in collegiate male soccer players. Methods: Twenty-one soccer players were randomly assigned to either the HIIT + SSGs group (n = 11) or a control group (n = 10). Physical fitness was assessed at baseline and 1-week post-intervention, including countermovement jump (CMJ), change of direction (COD) test, sprint test, repeated sprint ability (RSA) test, and 30-15 Intermittent Fitness Test (30-15IFT). The intervention comprised eight sessions over 4 weeks: four SSGs and four HIIT. Results: The intervention group showed small to moderate improvements: mean RSA improved by 4.5% (p = 0.07), CMJ increased by 3.2% (p = 0.12), and 30-15IFT scores enhanced by 6.8% (p = 0.09). Key predictors of group membership included heart rate load per minute (OR 1.602) and various GPS variables. Conclusion: The 4-week intervention combining SSGs with HIIT did not produce statistically significant improvements in most physical fitness variables compared to the control group. Although there were positive trends in variables such as RSA and 30-15IFT, these changes were modest and not statistically significant. The results suggest that while the combined SSGs and HIIT approach shows potential, its impact on physical fitness over a 4-week period is limited, with some variables, like CMJ, even showing decreases.

Patterns in assignment submission times: Analysis of factors contributing to undergraduate students' commitment to core-curriculum related course.

Co-curricular activities equip students with essential skills and knowledge for personal and professional growth. Despite their importance, many students exert minimal effort to complete the assigned tasks. Instructors perceive that the lack of emphasis on final exams in co-curricular subjects reduces student effort and commitment. Moreover, poor time management and lack of effort in completing tasks have increased across various subjects in recent years. Therefore, it is important to investigate the factors that contribute to student commitment towards co-curricular subjects. In this study, the submission status of 339 tasks was retrieved from the student learning system to measure student commitment based on whether tasks were submitted on time, delayed, or not submitted. A chi-square test f was used to investigate the relationship between students' demographic characteristics and their commitment. The findings revealed a significant association between student commitment and the type of task given (p < 0.001). Students were more likely to submit presentations on time compared to written assignments. Projects were more likely to be delayed, while written assignments had a high frequency of no submission. Age was a significant predictor of commitment (p < 0.05), with students over 20 more likely to submit on time and students under 20 more likely to ignore submission. Gender was also a significant predictor of commitment (p < 0.001), with female students having a higher percentage and frequency of on-time submissions while male students having a higher number of no submissions. However, no significant association was found between the study year and commitment (p > 0.05), indicating that the year of the study could not determine the level of commitment to the course. Overall, these findings could be used to guide the preparation of tasks and assignments in co-curricular subjects to enhance student commitment and holistic development.

Disciplinary measures defining referee activity in top-European football leagues: A cross-sectional investigation.

The success and enjoyment of a football match depend heavily on referees and their ability to ensure fair play and uphold the rules of the game. However, there is limited research investigating the disciplinary measures and booking activities of referees in top European football leagues. In the current investigation, we explored the disciplinary measures and booking activities of top-European football league referees. The dataset of the referee activities concerning 15 indicators containing 602 matches from five consecutive seasons across the five top European leagues, namely, the English Premier League, Spanish Laliga, Italian Serie A, French Ligue1, and German Bundesliga were utilized for this study. K-means cluster analysis was used to define the activity levels of the referees. The Mann-Whitney U test was employed to determine the differences in the levels of the referees' activity with respect to the disciplinary measures, while binary regression analysis was applied to examine the association between the disciplinary measures and the activity levels of the referees. Two groups of activities were defined by k-means, that is, high and low activity. The Mann-Whitney U test revealed statistically significant differences in all 15 indicators examined between high and low activity. However, the regression model demonstrated that only fouls, yellow cards, and air challenges could significantly describe referees' activity levels. These indicators appear to be predictors of high referee activity in elite European Football. Specific training on dealing with increased aggression and foul behaviour coupled with improved game organisational management could be further incorporated into referees' training programmes amongst other measures.

Physical fitness and motor ability parameters as predictors for skateboarding performance: A logistic regression modelling analysis.

The identification and prediction of athletic talent are pivotal in the development of successful sporting careers. Traditional subjective assessment methods have proven unreliable due to their inherent subjectivity, prompting the rise of data-driven techniques favoured for their objectivity. This evolution in statistical analysis facilitates the extraction of pertinent athlete information, enabling the recognition of their potential for excellence in their respective sporting careers. In the current study, we applied a logistic regression-based machine learning pipeline (LR) to identify potential skateboarding athletes from a combination of fitness and motor skills performance variables. Forty-five skateboarders recruited from a variety of skateboarding parks were evaluated on various skateboarding tricks while their fitness and motor skills abilities that consist of stork stance test, dynamic balance, sit ups, plank test, standing broad jump, as well as vertical jump, were evaluated. The performances of the skateboarders were clustered and the LR model was developed to classify the classes of the skateboarders. The cluster analysis identified two groups of skateboarders: high and low potential skateboarders. The LR model achieved 90% of mean accuracy specifying excellent prediction of the skateboarder classes. Further sensitivity analysis revealed that static and dynamic balance, lower body strength, and endurance were the most important factors that contributed to the model's performance. These factors are therefore essential for successful performance in skateboarding. The application of machine learning in talent prediction can greatly assist coaches and other relevant stakeholders in making informed decisions regarding athlete performance.

A generalized distributed delay model of COVID-19: An endemic model with immunity waning.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been spreading worldwide for over two years, with millions of reported cases and deaths. The deployment of mathematical modeling in the fight against COVID-19 has recorded tremendous success. However, most of these models target the epidemic phase of the disease. The development of safe and effective vaccines against SARS-CoV-2 brought hope of safe reopening of schools and businesses and return to pre-COVID normalcy, until mutant strains like the Delta and Omicron variants, which are more infectious, emerged. A few months into the pandemic, reports of the possibility of both vaccine- and infection-induced immunity waning emerged, thereby indicating that COVID-19 may be with us for longer than earlier thought. As a result, to better understand the dynamics of COVID-19, it is essential to study the disease with an endemic model. In this regard, we developed and analyzed an endemic model of COVID-19 that incorporates the waning of both vaccine- and infection-induced immunities using distributed delay equations. Our modeling framework assumes that the waning of both immunities occurs gradually over time at the population level. We derived a nonlinear ODE system from the distributed delay model and showed that the model could exhibit either a forward or backward bifurcation depending on the immunity waning rates. Having a backward bifurcation implies that $ R_c < 1 $ is not sufficient to guarantee disease eradication, and that the immunity waning rates are critical factors in eradicating COVID-19. Our numerical simulations show that vaccinating a high percentage of the population with a safe and moderately effective vaccine could help in eradicating COVID-19.

Physical predictors for retention and dismissal of professional soccer head coaches: an analysis of locomotor variables using logistic regression pipeline.

Introduction: Soccer has enormous global popularity, increasing pressure on clubs to optimize performance. In failure, the tendency is to replace the Head coach (HC). This study aimed to check the physical effects of mid-season replacements of HCs, investigating which external load variables can predict retention or dismissal. Methods: The data was collected in training and matches of a professional adult male soccer team during three complete seasons (2020/21-2022/2023). The sample included 6 different HCs (48.8 ± 7.4 years of age; 11.2 ± 3.9 years as a HC). The 4 weeks and 4 games before and after the replacement of HCs were analysed. External load variables were collected with Global Positioning System (GPS) devices. A logistic regression (LR) model was developed to classify the HCs' retention or dismissal. A sensitivity analysis was also conducted to determine the specific locomotive variables that could predict the likelihood of HC retention or dismissal. Results: In competition, locomotor performance was better under the dismissed HCs, whereas the new HC had better values during training. The LR model demonstrated a good prediction accuracy of 80% with a recall and precision of 85% and 78%, respectively, amongst other model performance indicators. Meters per minute in games was the only significant variable that could serve as a potential physical marker to signal performance decline and predict the potential dismissal of an HC with an odd ratio of 32.4%. Discussion: An in-depth analysis and further studies are needed to understand other factors' effects on HC replacement or retention.

Factorial validity and measurement invariance of the psychological need satisfaction in exercise scale across gender.

BACKGROUND: Based on the self-determination theory, the psychological requirements for competence, autonomy, and relatedness boost beneficial exercise behaviour for healthy living. However, there is no valid, reliable Malay version scale to investigate the extent to which these psychological needs are met. The main purpose of this study was to examine the psychometric properties of a Malay version of the Psychological Need Satisfaction in Exercise (PNSE-M) scale. In addition, the purpose of this study was to confirm the measurement and structural invariance of the PNSE-M across gender. METHODS: The study participants included 919 students (male: 49.6%, female: 50.4%), with a mean age of 20.4 years (standard deviation = 1.5). The participants were selected through convenience sampling. The 18-item PNSE-M was used to measure psychological need satisfaction in exercise. The English version of the PNSE was translated into Malay using standard forward-backward translation. Confirmatory factor analysis (CFA) and invariance tests were performed on the three domains of the PNSE-M model. Composite reliability (CR), average variance extracted (AVE), internal consistency based on Cronbach's alpha, and test-retest reliabilities using intraclass correlation coefficient (ICC) were also computed. RESULTS: After some model re-specification, the CFA findings based on the hypothesised measurement model of three factors and 18 items indicated acceptable factor structure (CFI = .936, TLI = .923, SRMR = .054, RMSEA = .059). The CR and AVE values were .864-.902 and .573-.617, respectively. Cronbach's alpha was .891-.908, and the ICC was .980-.985. The findings supported the full measurement and structural invariance of the PNSE-M for both male and female participants. The CFA model matched the data well for both male (CFI = .926, SRMR = .057, RMSEA = .066) and female (CFI = .926, SRMR = .060, RMSEA = .065) participants. CONCLUSION: The PNSE-M with three factors and 18 items is considered to be a valid, reliable instrument for university students in Malaysia. It is valid for use to make meaningful comparisons across gender.

Transmission dynamics of Monkeypox virus: a mathematical modelling approach.

Monkeypox (MPX), similar to both smallpox and cowpox, is caused by the monkeypox virus (MPXV). It occurs mostly in remote Central and West African communities, close to tropical rain forests. It is caused by the monkeypox virus in the Poxviridae family, which belongs to the genus Orthopoxvirus. We develop and analyse a deterministic mathematical model for the monkeypox virus. Both local and global asymptotic stability conditions for disease-free and endemic equilibria are determined. It is shown that the model undergo backward bifurcation, where the locally stable disease-free equilibrium co-exists with an endemic equilibrium. Furthermore, we determine conditions under which the disease-free equilibrium of the model is globally asymptotically stable. Finally, numerical simulations to demonstrate our findings and brief discussions are provided. The findings indicate that isolation of infected individuals in the human population helps to reduce disease transmission.

Design and Validation of a Virtual Physical Education and Sport Science-Related Course: A Learner's Engagement Approach.

Learners' engagement is shown to be a major predictor of learning, performance, and course completion as well as course satisfaction. It is easier to engage learners in a face-to-face teaching and learning format since the teacher can observe and interpret the learner's facial expression and body language. However, in a virtual setting with the students sitting behind cameras, it is difficult to ascertain engagement as the students might be absent-mindedly attending the class. Henceforth, with the rapid transition to online learning, designing course content that could actively engage the students towards achieving the said elements is, therefore, necessary. We applied a data-driven approach in designing a virtual physical education and sport science−related course via a learner engagement model. A fully online course catering to 132 students that runs for a total of 14 weeks was used as a case study to develop the course. The study was conducted during the 2020/2021 academic year, which was the period of the peak COVID-19 pandemic in Malaysia. The delivery of the course content was implemented in stages to achieve three essential educational outcomes namely, skill and knowledge acquisition, and personal development as well as course satisfaction. We hypothesised that the developed learners' engagement approach will promote the students' acquisition of skills and knowledge and foster the personal development of the students through fitness improvement. It is also hypothesised that the students will be satisfied with the course developed upon successful completion. A chi-square analysis projected a statistically significant difference in the skill and knowledge acquisition before and after the programme (p < 0.001). A Wilcoxon rank-sum test demonstrated personal improvement in the overall fitness of the student upon completing the prescribed activity of the course content. Moreover, a total of 96.2%, 95.5% and 93.2% of students expressed their satisfaction with the clarity of the learning objectives, good organisational and course content plan, and appropriate workload of the course designed, respectively. There is sufficient evidence to accept all hypotheses formulated, and hence, we postulated that, since students spend more time outside the classroom, out-of-class learners' engagement activity should be considered when designing a virtual course to promote lifelong learning, experience, and higher-order thinking. The techniques presented herein could be useful to academics, professionals, and other relevant stakeholders in developing virtual course content within a specific domain of interest.

A longitudinal analysis of injury characteristics among elite and amateur tennis players at different tournaments from electronic newspaper reports.

The non-complexity of tennis, coupled with its health benefits, renders it appealing and encourages varying competitions at different levels of age, gender, and expertise. However, the rapid increase in the participation rates witnesses a surge in injury occurrences, prompting the need for in-depth analysis to facilitate immediate intervention. We employed a media content analysis technique in which tennis-associated articles published in the last 5 years were examined. A total of 207 news reports were gathered and screened for analysis. Subsequently, 71 articles were excluded from the study due to content duplications or summary updates of existing news articles, while 23 news articles were also excluded from the study due to inappropriateness. Finally, 113 news reports directly related to injury in tennis were coded and analyzed. We examined various types of injuries reported from the screened articles with respect to their status (fresh, recurrent, and recovery) across expertise levels i.e., elite, or amateur. Similarly, the incidence of injury occurrences based on the types of tournaments the players engage in was also investigated. A chi-square analysis was employed to achieve the objectives of the study. Occurrences of tennis-associated injuries are disseminated across expertise levels [ χ ( 18 ) 2 = 16.542; p = 0.555], with knee, hip, elbow, and shoulder injuries being highly prevalent in both elite and amateur players. Nevertheless, it was noted that elite players suffered a staggering 72.60% of injury-related problems, while amateur players sustained 27.40% of injuries. Moreover, the status of injury spreads based on types of tournaments [ χ ( 4 ) 2 = 3.374; p = 0.497], with higher occurrences of fresh and recurrent injuries, while low recovery rates were observed. The findings further demonstrated that injuries are sustained regardless of tournament types [ χ ( 36 ) 2 = 39.393; p = 0.321]. However, most of the injuries occurred at international tournaments (85%). Whereas, only 5.30% of the injuries occurred at national/regional tournaments while 9.70% were unidentified. It could be deduced from the findings of this investigation that elite players are more prone to injuries compared with amateur players. Furthermore, the most common tennis-related injuries affect the lower, trunk, and upper regions of the body, respectively. A large number of the reported tennis injuries are fresh and recurrent, with a few recoveries. The international tennis tournaments are highly attributed to injury occurrences as opposed to the national/regional tournaments. The application of the media-based data mining technique is non-trivial in projecting injury-related problems that could be used to facilitate the development of an injury index peculiar to the tennis sport for prompt intervention.

Association of Physical Activity with Anthropometrics Variables and Health-Related Risks in Healthy Male Smokers.

Anthropometric variables (AV) are shown to be essential in assessing health status and to serve as markers for evaluating health-related risks in different populations. Studying the impact of physical activity (PA) on AV and its relationship with smoking is a non-trivial task from a public health perspective. In this study, a total of 107 healthy male smokers (37 ± 9.42 years) were recruited from different states in Malaysia. Standard procedures of measurement of several anthropometric indexes were carried out, and the International Physical Activity Questionnaire (IPPQ) was used to ascertain the PA levels of the participants. A principal component analysis was employed to examine the AV associated with physical activity, k-means clustering was used to group the participants with respect to the PA levels, and discriminant analysis models were utilized to determine the differential variables between the groups. A logistic regression (LR) model was further employed to ascertain the efficacy of the discriminant models in classifying the two smoking groups. Six AV out of twelve were associated with smoking behaviour. Two groups were obtained from the k-means analysis, based on the IPPQ and termed partially physically active smokers (PPAS) or physically nonactive smokers (PNAS). The PNAS were found to be at high risk of contracting cardiovascular problems, as compared with the PPAS. The PPAS cluster was characterized by a desirable AV, as well as a lower level of nicotine compared with the PNAS cluster. The LR model revealed that certain AV are vital for maintaining good health, and a partially active lifestyle could be effective in mitigating the effect of tobacco on health in healthy male smokers.

The classification of EEG-based winking signals: a transfer learning and random forest pipeline.

Brain Computer-Interface (BCI) technology plays a considerable role in the control of rehabilitation or peripheral devices for stroke patients. This is particularly due to their inability to control such devices from their inherent physical limitations after such an attack. More often than not, the control of such devices exploits electroencephalogram (EEG) signals. Nonetheless, it is worth noting that the extraction of the features and the classification of the signals is non-trivial for a successful BCI system. The use of Transfer Learning (TL) has been demonstrated to be a powerful tool in the extraction of essential features. However, the employment of such a method towards BCI applications, particularly in regard to EEG signals, are somewhat limited. The present study aims to evaluate the effectiveness of different TL models in extracting features for the classification of wink-based EEG signals. The extracted features are classified by means of fine-tuned Random Forest (RF) classifier. The raw EEG signals are transformed into a scalogram image via Continuous Wavelet Transform (CWT) before it was fed into the TL models, namely InceptionV3, Inception ResNetV2, Xception and MobileNet. The dataset was divided into training, validation, and test datasets, respectively, via a stratified ratio of 60:20:20. The hyperparameters of the RF models were optimised through the grid search approach, in which the five-fold cross-validation technique was adopted. The optimised RF classifier performance was compared with the conventional TL-based CNN classifier performance. It was demonstrated from the study that the best TL model identified is the Inception ResNetV2 along with an optimised RF pipeline, as it was able to yield a classification accuracy of 100% on both the training and validation dataset. Therefore, it could be established from the study that a comparable classification efficacy is attainable via the Inception ResNetV2 with an optimised RF pipeline. It is envisaged that the implementation of the proposed architecture to a BCI system would potentially facilitate post-stroke patients to lead a better life quality.

Surveillance of Injury Types, Locations, and Intensities in Male and Female Tennis Players: A Content Analysis of Online Newspaper Reports.

The popularity of modern tennis has contributed to the increasing number of participants at both recreational and competitive levels. The influx of numerous tennis participants has resulted in a wave of injury occurrences of different types and magnitudes across both male and female players. Since tennis injury harms both players' economic and career development, a better understanding of its epidemiology could potentially curtail its prevalence and occurrences. We used online-based tennis-related injury reports to study the prevalence, location types, and injury intensities in both male and female tennis players for the past five years. It is demonstrated from the chi-square analysis that injury occurrences are significantly associated with a specific gender (χ2(18) = 50.773; p = 0.001), with male players having a higher risk of injury manifestation (68.10%) as compared with female players (31.90%). Nonetheless, knee, hip, ankle, and shoulder injuries are highly prevalent in both male and female players. Moreover, the injury intensities are distributed across gender (χ2(2) = 0.398; p = 0.820), with major injuries being dominant, followed by minor injuries, whilst a few cases of career-threatening injuries were also reported. It was similarly observed that male players recorded a higher degree of both major, minor, and career-threatening injuries than female players. In addition, male players sustained more elbow, hip, knee, shoulder, and thigh injuries than female players. Whereas, female players mostly suffered from Achilles and back injuries, ankle and hamstring injuries affected both genders. The usage of online newspaper reports is pivotal in characterizing the epidemiology of tennis-related injuries based on locations and gender to better understand the pattern and localization of injuries, which could be used to address the problem of modern tennis-related injuries.

The classification of motor imagery response: an accuracy enhancement through the ensemble of random subspace k-NN.

Brain-computer interface (BCI) is a viable alternative communication strategy for patients of neurological disorders as it facilitates the translation of human intent into device commands. The performance of BCIs primarily depends on the efficacy of the feature extraction and feature selection techniques, as well as the classification algorithms employed. More often than not, high dimensional feature set contains redundant features that may degrade a given classifier's performance. In the present investigation, an ensemble learning-based classification algorithm, namely random subspace k-nearest neighbour (k-NN) has been proposed to classify the motor imagery (MI) data. The common spatial pattern (CSP) has been applied to extract the features from the MI response, and the effectiveness of random forest (RF)-based feature selection algorithm has also been investigated. In order to evaluate the efficacy of the proposed method, an experimental study has been implemented using four publicly available MI dataset (BCI Competition III dataset 1 (data-1), dataset IIIA (data-2), dataset IVA (data-3) and BCI Competition IV dataset II (data-4)). It was shown that the ensemble-based random subspace k-NN approach achieved the superior classification accuracy (CA) of 99.21%, 93.19%, 93.57% and 90.32% for data-1, data-2, data-3 and data-4, respectively against other models evaluated, namely linear discriminant analysis, support vector machine, random forest, Naïve Bayes and the conventional k-NN. In comparison with other classification approaches reported in the recent studies, the proposed method enhanced the accuracy by 2.09% for data-1, 1.29% for data-2, 4.95% for data-3 and 5.71% for data-4, respectively. Moreover, it is worth highlighting that the RF feature selection technique employed in the present study was able to significantly reduce the feature dimension without compromising the overall CA. The outcome from the present study implies that the proposed method may significantly enhance the accuracy of MI data classification.

A real-time approach of diagnosing rice leaf disease using deep learning-based faster R-CNN framework.

The rice leaves related diseases often pose threats to the sustainable production of rice affecting many farmers around the world. Early diagnosis and appropriate remedy of the rice leaf infection is crucial in facilitating healthy growth of the rice plants to ensure adequate supply and food security to the rapidly increasing population. Therefore, machine-driven disease diagnosis systems could mitigate the limitations of the conventional methods for leaf disease diagnosis techniques that is often time-consuming, inaccurate, and expensive. Nowadays, computer-assisted rice leaf disease diagnosis systems are becoming very popular. However, several limitations ranging from strong image backgrounds, vague symptoms' edge, dissimilarity in the image capturing weather, lack of real field rice leaf image data, variation in symptoms from the same infection, multiple infections producing similar symptoms, and lack of efficient real-time system mar the efficacy of the system and its usage. To mitigate the aforesaid problems, a faster region-based convolutional neural network (Faster R-CNN) was employed for the real-time detection of rice leaf diseases in the present research. The Faster R-CNN algorithm introduces advanced RPN architecture that addresses the object location very precisely to generate candidate regions. The robustness of the Faster R-CNN model is enhanced by training the model with publicly available online and own real-field rice leaf datasets. The proposed deep-learning-based approach was observed to be effective in the automatic diagnosis of three discriminative rice leaf diseases including rice blast, brown spot, and hispa with an accuracy of 98.09%, 98.85%, and 99.17% respectively. Moreover, the model was able to identify a healthy rice leaf with an accuracy of 99.25%. The results obtained herein demonstrated that the Faster R-CNN model offers a high-performing rice leaf infection identification system that could diagnose the most common rice diseases more precisely in real-time.

The classification of skateboarding tricks via transfer learning pipelines.

This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 ± 7 years of age with at least 5.0 years of experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data, a total of six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.

Kids motor performances datasets.

These datasets described the data of the Motor Performance Index for 7 years old kids in Malaysia based on Malaysia's physical fitness test SEGAK. This database has been designed and created with data analysis to create the index from the factor and variable of the test and the test was conducted in the majority of the national primary school in Malaysia. Gender, state of origin, and residential location of the school were the factors used to categorize the participant of the test. The factor of age, weight, height, body mass index (BMI), power, flexibility, coordination, and speed were used for the measurement to relate with the participant's physical fitness. Kids Motor Performances Index data can be reused for talent identification in sport talent scout and to create a baseline for kid's biology growth specifically in gross motor skills and cognitive growth measurement.

Current Status, Challenges, and Possible Solutions of EEG-Based Brain-Computer Interface: A Comprehensive Review.

Brain-Computer Interface (BCI), in essence, aims at controlling different assistive devices through the utilization of brain waves. It is worth noting that the application of BCI is not limited to medical applications, and hence, the research in this field has gained due attention. Moreover, the significant number of related publications over the past two decades further indicates the consistent improvements and breakthroughs that have been made in this particular field. Nonetheless, it is also worth mentioning that with these improvements, new challenges are constantly discovered. This article provides a comprehensive review of the state-of-the-art of a complete BCI system. First, a brief overview of electroencephalogram (EEG)-based BCI systems is given. Secondly, a considerable number of popular BCI applications are reviewed in terms of electrophysiological control signals, feature extraction, classification algorithms, and performance evaluation metrics. Finally, the challenges to the recent BCI systems are discussed, and possible solutions to mitigate the issues are recommended.