A KD framework in football data analytics: a value co-creation framework for the use of knowledge discovery technologies in the football industry

Investment in sport technologies are expected to grow by 40.1% during 2016-2022 reaching approximately $3.97 billion by 2022. As well the recent changes in technology regulations by The Federation Internationale de Football Association (FIFA) since the 2018 World Cup created promising football technologies. This research questions addressing the issue of what is the value of such technologies for professional football teams? and what are the benefits of these technologies? This is achieved by developing a framework for understanding the value co-creation process from the knowledge discovery systems in the football industry. The framework aids in mapping the resources, pinpointing the outputs, identifying the competencies leading into capabilities, and finally in realisation of the value of the final outcomes in that journey. On another words, different teams have different resources that allow them to achieve certain outputs. These outputs enable the coaching team to achieve and maintain certain abilities. By changes in practice the will improve the team ability and enhance their analytical capabilities. Therefore, that will allow and aid the coaching team to gain new outcomes such as improving training strategies, transferring players, and informative match strategies. Additionally, improved understanding of the value co-creation process from the knowledge discovery systems in the football industry answering, why are some teams better able to gain value from investment in knowledge discovery technologies than other teams in the football industry. The framework has been developed in three phases in which semi-structured interviews where used in the first and second phases for developing and validating the framework respectively. The third and final phases is verifying the framework by developing a knowledge discovery maturity model as an online assessment s tool in operationalising the research findings. The main contributions of this research are the adaptation and customisation of Melville et al. (2004) to develop a value co-creation process form knowledge discovery resources. Moreover, applying Agile (APM, 2015) artefacts and techniques and tools in improving the value co-creation process between coaches and data analysts. That s aided in developing the value co-creation knowledge discovery framework in football analytics. Additionally, the development of a key performance indicators balanced scorecard and its adaptation as a in understanding the relationships between the key performance indicators (i.e. physical, psychological, technical and tactical performance indicators). Finally, the development of the knowledge discovery maturity model in football analytics which was used in understanding and pinpointing areas of strength and weakness in the utilisation of the various football resources used in football analytics (human resources, technological resources, value co-creation resources and analytical models used)

Extrusion based 3D printing as a novel technique for fabrication of oral solid dosage forms

Extrusion based three dimensional (3D) printing is defined as a process used to make a 3D object layer by layer directly from a computer aided device (CAD). The application of extrusion based 3D printing process to manufacture functional oral solid tablets with relatively complex geometries is demonstrated in this thesis. In Chapter 3 the viability of using a basic desktop 3D printer (Fab@Home) to print functional guaifenesin bilayer tablets (GBTs) is demonstrated. Guaifenesin is an over the counter (OTC) water soluble medicine used as expectorant for reduction of chest congestion caused by common cold and infections in respiratory system. The bilayer tablets were printed using the standard pharmaceutical excipients; hydroxypropyl methyl cellulose (HPMC) 2208, 2910, sodium starch glycolate (SSG), microcrystalline cellulose (MCC) and polyacrylic acid (PAA) in order mimic the commercial model formulation (Mucinex®) guaifenesin extended-release bilayer tablets. The 3D printed guaifenesin bilayer tablets (GBTs) were evaluated for mechanical properties as a comparison to the commercial GBTs and were found to be within acceptable range as defined by the international standards stated in the USP. Drug releases from the 3D printed GBTs were decreased as the amount of HPMC 2208 increased due to the increased wettability, swelling properties and gel barrier formation of the HPMC. The 3D printed GBTs also showed, as required, two release profiles: immediate release (IR) from the top layer containing disintegrants; SSG and MCC and sustained release (SR) profile from the lower layer containing HPMC 2208. The kinetic drug release data from the 3D printed and commercial GBTs were best modelled using the Korsmeyer–Peppas model with n values between 0.27 and 0.44. This suggests Fickian diffusion drug release through a hydrated HPMC gel layer. Other physical characterisations: X-Ray Powder Diffraction (XRPD), Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR), and Differential Scanning Calorimetry (DSC) showed that there was no detectable interaction between guaifenesin and the used excipients in both 3D printed and commercial GBTs. A more complex printer (RegenHu 3D bioprinter) was subsequently used to print complex multi-active tablets containing captopril, nifedipine, and glipizide as a model therapeutic combination. These drugs are frequently used to treat hypertension and diabetes mellitus. The 3D printed tablets were evaluated for drug release and showed that captopril was released by osmosis through permeable cellulose acetate (CA) film and both glipizide and nifedipine were released by diffusion through the hydrophilic HPMC 2208 matrix. According to XRPD and ATR-FTIR results, there was no detectable interaction between the actives and the used excipients. In the final experimental chapter, a combined treatment regimen: atenolol, ramipril, hydrochlorothiazide (anti-hypertensive medications), pravastatin (cholesterol lowering agent), and aspirin (anti-platelets) were printed into more complex geometry (polypill) using the RegenHu 3D bioprinter. This combined drug regimen is manufactured by Cadila Pharmaceuticals Limited as a capsule formulation under the trade name of Polycap™ and is currently the only polypill formulation commercially available and is used to treat and prevent cardiovascular diseases. The printed polypills were characterized for drug release using USP dissolution testing and showed the intended immediate and sustained release profiles based upon the active/excipient ratio used. Aspirin and hydrochlorothiazide were immediately released after the polypill contacted the dissolution medium, and atenolol, ramipril, and pravastatin were released over a period of 12 hrs. XRPD and ATR-FTIR showed that there was no detectable interaction between the actives and the used excipients. In this work, extrusion based 3D printing technique was used to print oral solid dosage forms with complex and well-defined geometries and function. The technology of 3D printing could offer the opportunity to print oral tablets with high and precise drug dosing and controlled drug release profiles tailored for sub-populations or individuals. If the manufacturing and regulatory issues associated with 3DP can be resolved such personalised medicine delivered by 3D printing could improve patient compliance and provide more effective treatment regimes

Machine learning-based multi-target regression to effectively predict turning movements at signalized intersections

Effective prediction of turning movement counts at intersections through efficient and accurate methods is essential and needed for various applications. Commonly predictive methods require extensive data collection, calibration, and modeling efforts to estimate turning movements. In this study, three models were proposed to estimate turning movements at signalized intersections using approach volumes. Two sets of data from the United States and Canada were obtained to develop and test the proposed models. Machine learning-based regression models, including random forest regressor (RFR) and multioutput regressor (MOR) in addition to an artificial neural network (ANN) model, were developed and trained to analyze the relationship between approach volumes and corresponding turning movements. Multiple evaluation measurements were utilized to compare the models. All models produced satisfactory results. The RFR regression model outperformed the MOR model. However, the ANN model had the best performance when compared to the other models. The proposed models provide traffic engineers and planners with reliable and fast methods to estimate turning movements. 2022 Tongji University, Tongji University PressThe authors would like to thank the reviewers for their dedicated work and insightful comments and recommendations.Scopu

Demand Response in HEMSs Using DRL and the Impact of Its Various Configurations and Environmental Changes

With smart grid advances, enormous amounts of data are made available, enabling the training of machine learning algorithms such as deep reinforcement learning (DRL). Recent research has utilized DRL to obtain optimal solutions for complex real-time optimization problems, including demand response (DR), where traditional methods fail to meet time and complex requirements. Although DRL has shown good performance for particular use cases, most studies do not report the impacts of various DRL settings. This paper studies the DRL performance when addressing DR in home energy management systems (HEMSs). The trade-offs of various DRL configurations and how they influence the performance of the HEMS are investigated. The main elements that affect the DRL model training are identified, including state-action pairs, reward function, and hyperparameters. Various representations of these elements are analyzed to characterize their impact. In addition, different environmental changes and scenarios are considered to analyze the model's scalability and adaptability. The findings elucidate the adequacy of DRL to address HEMS challenges since, when appropriately configured, it successfully schedules from 73% to 98% of the appliances in different simulation scenarios and minimizes the electricity cost by 19% to 47%. 2022 by the authors.This research was funded by the NPRP11S-1202-170052 grant from Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors.Scopu

StEduCov: An Explored and Benchmarked Dataset on Stance Detection in Tweets towards Online Education during COVID-19 Pandemic

In this paper, we present StEduCov, an annotated dataset for the analysis of stances toward online education during the COVID-19 pandemic. StEduCov consists of 16,572 tweets gathered over 15 months, from March 2020 to May 2021, using the Twitter API. The tweets were manually annotated into the classes agree, disagreeor neutral. We performed benchmarking on the dataset using state-of-the-art and traditional machine learning models. Specifically, we trained deep learning models-bidirectional encoder representations from transformers, long short-term memory, convolutional neural networks, attention-based biLSTM and Naive Bayes SVM-in addition to naive Bayes, logistic regression, support vector machines, decision trees, K-nearest neighbor and random forest. The average accuracy in the 10-fold cross-validation of these models ranged from 75% to (Formula presented.) % and from (Formula presented.) % to 68% for binary and multi-class stance classifications, respectively. Performances were affected by high vocabulary overlaps between classes and unreliable transfer learning using deep models pre-trained on general texts in relation to specific domains such as COVID-19 and distance education. 2022 by the authors.Scopu

Methodical evaluation of Arabic word embeddings

Many unsupervised learning techniques have been proposed to obtain meaningful representations of words from text. In this study, we evaluate these various techniques when used to generate Arabic word embeddings. We first build a benchmark for the Arabic language that can be utilized to perform intrinsic evaluation of different word embeddings. We then perform additional extrinsic evaluations of the embeddings based on two NLP tasks. 2017 Association for Computational Linguistics.This work was made possible by NPRP 6-716-1-138 grant from the Qatar National Research Fund (a member of Qatar Foundation). The statementsScopu

Association rule mining on five years of motor vehicle crashes

Every year, road accidents kill more than a million people and injure more than 20 million worldwide. This paper aims to offer guidance on road safety and create awareness by pinpointing the major causes of traffic accidents. The study investigates motor vehicle crashes in the Genesee Finger Lakes Region of New York State. Frequency Pattern Growth algorithm is utilized to cultivate knowledge and create association rules to highlight the time and environment settings that cause the most catastrophic crashes. This knowledge can be used to warn drivers about the dangers of accidents, and how the consequences are worse given a specific context. For instance, a discovered rule from the data states that 'most of the crashes occur between 12:00 pm and 6:00pm'; hence, it is suggested to modify existing navigation application to warn drivers about the increase in risk factor.Scopu

Survivable Cloud Network Mapping for Disaster Recovery Support

Network virtualization is a key provision for improving the scalability and reliability of cloud computing services. In recent years, various mapping schemes have been developed to reserve VN resources over substrate networks. However, many cloud providers are very concerned about improving service reliability under catastrophic disaster conditions yielding multiple system failures. To address this challenge, this work presents a novel failure region-disjoint VN mapping scheme to improve VN mapping survivability. The problem is first formulated as a mixed integer linear programming problem and then two heuristic solutions are proposed to compute a pair of failure region-disjoint VN mappings. The solution also takes into account mapping costs and load balancing concerns to help improve resource efficiencies. The schemes are then analyzed in detail for a variety of networks and their overall performances compared to some existing survivable VN mapping scheme