Enhancing Tennis Serve Scoring Efficiency: An AI Deep Learning Approach.

The playing field of a tennis competition is a dynamic and complex formative environment given the following preliminary knowledge: (a) the basic technical, tactical, situational, and special types of shots used by the opponent; (b) the hitting area of the tennis player; (c) the place of service; (d) the ball drop position; and (d) batting efficiency and other related information that may improve the chances of victory. In this study, we propose an AI classification model for tennis serve scores. Using a deep learning algorithm, the model automatically tracks and classifies the serve scores of professional tennis players from video data. We first defined the players’ techniques, volleys, and placements of strokes and serves. Subsequently, we defined the referee's tennis terms and the voice in deciding on a serve score. Finally, we developed a deep learning model to automatically classify the serving position, landing position, and use of tennis techniques. The methodology was applied in the context of 10 matches played by Roger Federer and Rafael Nadal. The proposed deep learning algorithm achieved a 98.27% accuracy in the automatic classification of serve scores, revealing that Nadal outscored Federer by 2.1% in terms of serve-scoring efficiency. These results are expected to facilitate the automatic comparison and classification of shots in future studies, enabling coaches to adjust tactics in a timely manner and thereby improve the chances of winning

Building information modelling for sustainability appraisal of conceptual design of steel-framed buildings

In the construction sector, capturing the building product in a single information model with good interoperable capabilities has been the subject of much research efforts in at least the last three decades. Contemporary advancements in Information Technology and the efforts from various research initiatives in the AEC industry are showing evidence of progress with the advent of building information modelling (BIM). BIM presents the opportunity of electronically modelling and managing the vast amount of information embedded in a building project, from its conception to end-of-life. Researchers have been looking at extensions to expand its scope. Sustainability is one such modelling extension that is in need of development. This is becoming pertinent for the structural engineer as recent design criteria have put great emphasis on the sustainability credentials in addition to the traditional criteria of structural integrity, constructability and cost. Considering the complexity of nowadays designs, there is a need to provide decision support tools to aid the assessment of sustainability credentials. Such tools would be most beneficial at the conceptual design stage so that sustainability is built into the design solution starting from its inception. This research work therefore investigates how contemporary process and data modelling techniques can be used to map and model sustainability related information to inform the structural engineer’s building design decisions at an early stage. The research reviews current design decisions support systems on sustainability and highlights existing deficiencies. It examines the role of contemporary information modelling techniques in the building design process and employs this to tackle identified gaps. The sustainability of buildings is related to life cycle and is measured using indicator-terms such as life cycle costing, ecological footprint and carbon footprint. This work takes advantage of current modelling techniques to explore how these three indicators can be combined to provide sustainability assessment of alternative design solutions. It identifies the requirements for sustainability appraisal and information modelling to develop a requisite decision-support framework vis-à-vis issues on risk, sensitivity and what-if scenarios for implementation. The implementation employed object-oriented programming and feature modelling techniques to develop a sustainability decision-support prototype. The prototype system was tested in a typical design activity and evaluated to have achieved desired implementation requirements. The research concludes that the utilized current process and data modelling techniques can be employed to model sustainability related information to inform decisions at the early stages of structural design. As demonstrated in this work, design decision support systems can be optimized to include sustainability credentials through the use of object-based process and data modelling techniques. This thesis presents a sustainability appraisal framework, associated implementation algorithms and related object mappings and representations systems that could be used to achieve such decision support optimization

Building information modelling for sustainability appraisal of conceptual design of steel-framed buildings

In the construction sector, capturing the building product in a single information model with good interoperable capabilities has been the subject of much research efforts in at least the last three decades. Contemporary advancements in Information Technology and the efforts from various research initiatives in the AEC industry are showing evidence of progress with the advent of building information modelling (BIM). BIM presents the opportunity of electronically modelling and managing the vast amount of information embedded in a building project, from its conception to end-of-life. Researchers have been looking at extensions to expand its scope. Sustainability is one such modelling extension that is in need of development. This is becoming pertinent for the structural engineer as recent design criteria have put great emphasis on the sustainability credentials in addition to the traditional criteria of structural integrity, constructability and cost. Considering the complexity of nowadays designs, there is a need to provide decision support tools to aid the assessment of sustainability credentials. Such tools would be most beneficial at the conceptual design stage so that sustainability is built into the design solution starting from its inception. This research work therefore investigates how contemporary process and data modelling techniques can be used to map and model sustainability related information to inform the structural engineer’s building design decisions at an early stage. The research reviews current design decisions support systems on sustainability and highlights existing deficiencies. It examines the role of contemporary information modelling techniques in the building design process and employs this to tackle identified gaps. The sustainability of buildings is related to life cycle and is measured using indicator-terms such as life cycle costing, ecological footprint and carbon footprint. This work takes advantage of current modelling techniques to explore how these three indicators can be combined to provide sustainability assessment of alternative design solutions. It identifies the requirements for sustainability appraisal and information modelling to develop a requisite decision-support framework vis-à-vis issues on risk, sensitivity and what-if scenarios for implementation. The implementation employed object-oriented programming and feature modelling techniques to develop a sustainability decision-support prototype. The prototype system was tested in a typical design activity and evaluated to have achieved desired implementation requirements. The research concludes that the utilized current process and data modelling techniques can be employed to model sustainability related information to inform decisions at the early stages of structural design. As demonstrated in this work, design decision support systems can be optimized to include sustainability credentials through the use of object-based process and data modelling techniques. This thesis presents a sustainability appraisal framework, associated implementation algorithms and related object mappings and representations systems that could be used to achieve such decision support optimization

Open Source Studie Schweiz 2024

Die «Open Source Studie Schweiz 2024» bietet einen umfassenden Überblick über aktuelle Trends und Entwicklungen im Bereich Open Source Software in der Schweiz sowie Hintergrundinformationen zu Verbreitung, wirtschaftlichem Nutzen, Lizenzwahl und Geschäftsmodellen. Die Befragung von über 175 Unternehmen und Verwaltungen zeigt Einsatzgebiete, Vorteile und Hindernisse beim Einsatz von Open Source Software auf und untersucht die Gründe für die Freigabe von Open Source Software. Zudem enthält die Studie zahlreiche Fachartikel und Praxisbeispiele aus verschiedenen Sektoren sowie ein Verzeichnis von Open Source Anbietern und weiteren Akteuren. Die Open Source Studie Schweiz 2024 wurde von den Verbänden CH Open und swissICT herausgegeben und vom Institut Public Sector Transformation der Berner Fachhochschule erarbeitet