Applications of data science to football are blooming such as to calculate prediction of matches by applying machine learning. In this paper, we propose an implementation method of geometric analysis system for football that converts an actual coordinate data set into the system to geometrically calculate indicators describing game dynamism. The purpose of this project is to design and implement a system that dynamically extract knowledge of football in visible form. As an example of the indicators, Shooting Degree Value, SDV, is defined and implemented, and an example study is illustrated with the prototype system and an actual data set of J.LEAGUE.

The Paris Agreement of 2015 has prompted countries to accelerate their efforts to become carbon neutrality efforts, which meant reducing CO2 emissions to virtually zero. Limiting global warming to less than 1.5°C by 2050 rely on technologies that remove CO2 from the atmosphere faster than humans release it. This implies that CO2 will be removed at a rate of 1-30 gigaton per year by 2050. Carbon Capture Storage / Sequestration (CCS) and Utilization (CCU) are concepts and technologies that collect emitted CO2 store it permanently underground, or recycle it as energy or chemicals for use in manufacturing and other economic activities. CCS and CCU have been discussed globally, but have not reached local and practical levels. Currently planned large-scale CCS requires significant government investment and new technological developments for capture, transport, and storage / sequestration, therefore implementation is expected to start in the second half of 2030 towards the 2050 goal. The need to start acting now where possible rather than waiting for the distant future, makes it important to implement CCS and CCU on a small scale and build towards future scale-up as an immediate solution. This study proposes a support method and system to help companies that emit large amounts of CO2 such as power plants, cement, petrochemicals, and steel industries, to decide how to treat their CO2 emissions in the context of decarbonization. In this study, a Simple, Measurable, Attainable, Relative, and Time-Bound (SMART) decision support method and Direct Air Capture Location and Cost Simulator (DLCS) system were developed to provide a solution to the Negative Emission 5W1H “What, Who, Which, When, Why, and How” from the perspective of a company that emits CO2. A prototype model with parameter settings was proposed based on knowledge gained from practical experience. The functionality of the SMART method and DLCS system was confirmed by applying sample data from the actual data of the ‘Tokyo Region’ as a Proof of Concept (PoC). In this PoC, characteristics of direct air capture which is a critical technology for negative emissions, were verified. The core of the SMART and DLCS model entails combinatorial optimization, distance calculation, cost estimation, and market projection including constraint solution.

We propose a multidimensional corporate analysis system architecture, Dynamic Corporate Inspector (DCI), for stakeholders. This system architecture provides multifaceted corporate analysis of companies by changing text datasets and keyword sets. In this paper, We implemented the system using 4225 Japanese annual securities reports as text data and 173 keywords of SDGs as keyword sets. The analysis visualizes each company’s interest and activities for the SDGs.

This paper proposes a new system architecture that includes human action. The system is composed of three parts; sensing, processing, and actuation. Based on the concept, a virtual museum with a spatial immersive display and a locomotion interface was developed. It provides a physical walking experience in the virtual exhibition space. We developed a digital twin of the Artizon Museum as the system’s content. The locomotion interface records the walking trajectory of the participant, which represents their action in the virtual exhibition space. The system was implemented and operated in public for four weeks.

In this paper, we proposed an implementation method of a system according to ‘tri-knowledge base with personal context vectors model’ that proposed in previous study. The system aims not only a single set of contexts and knowledge base, but also create snapshot and stored it as the memories of the changes in timeseries. Organisational contexts vectors were added to the system. As well as the personal context vectors to absorb personal preferences, the organisational context vectors can absorb organisational preference without changing the knowledgebase. Experiments were conducted to verify the functionality of memorising the changes in timeseries and the effects of the organisational context vector.

This study investigates the complex dynamics of team sports, with a focus on the role of team members’ impressions of each other. We propose a novel team building method that incorporates dynamic elements to promote team formation and development, based on these impressions. A web-based system was developed to manage these impressions and collect data. This data was then analyzed to evaluate the impact of these impressions on team performance. Two team sports with distinct characteristics, American football and basketball, were examined. The results revealed that mutual impressions among team members significantly influenced team performance. This study underscores the importance of the interplay of team members’ impressions in team performance.

Achieving the 2030 and 2050 Paris Agreement targets to improve the global environment and address climate change is critical despite the costs and time requirements to implement possible measures. However, current measures are primarily undertaken from a global macro perspective, and lack established means to examine them from micro, realistic, and practical perspectives. CO2 direct air capture (DAC) is an innovative negative CO2 emission technology in its early commercial stages that can help control and mitigate climate change in the long term. Despite technological advances in the past decade, there are still misconceptions about the current and long-term costs of DAC, and energy, water, and area demands. This paper presents a knowledge-based indication method with a prototype system for a DAC location and cost simulator to support early-stage decisions from a micro-local perspective and promote the use of the applicable and scalable DAC technology. Additionally, this study presents a method for determining optimal locations, estimating project costs, and prioritizing projects for early-stage feasibility assessments, policy, and business decisions on DAC investments to accelerate its deployment. The main feature of this approach is to provide a data model and unified cost index ($/CO2 Ton) to calculate the optimal location and cost projection of DAC implementation based on location characteristics and a quantified industry knowledge base considering various cost-carbon intensity constraints—such as reservoirs, infrastructure, low-carbon electricity, heat, transportation, and atmospheric conditions—that affect the suitability of specific locations.

In this paper, a dynamic curation method that uses pretrained models of art interpretation is proposed. The proposed method leverages exhibition catalogs (primary classifications and images) as a source of curatorial knowledge to construct a machine learning model, thereby using this model to evaluate, position, and visualize other artwork. This method enables the dynamic curation of artwork in a vast integrated art collection archive. A prototype system for the proposed method was implemented and applied to two exhibition catalogs at the Artizon Museum. In the experiment, artworks matching the purpose and art style of the modeled exhibition from the archives of the Metropolitan Museum of Art in New York City and Paris Musées were evaluated using the constructed model. The experiment demonstrated that the proposed method successfully classifies and enables the visualization of artwork and images available in the open data from the MET and Paris Musées in alignment with the intended theme of the exhibition for which the model was constructed. Feedback from curators and art professionals indicates that the proposed method can be used to compare museums with art collections of the same genre and to organize new exhibitions based on the curatorial model.

This paper introduces Art Sensorium Project that is founded in Asia AI Institute of Musashino University. A main target of the project is to design and implement a system architecture of unified art collections for virtual art experiences. To provide art experiences, a projection-based VR system, called Data Sensorium, is used to stage art materials in a form of real-sized virtual reality. Furthermore, a system architecture of a multidatabase system for heterogeneous art collection archives is presented, so a set of integrated art data is applied to Data Sensorium for newly generated art experiences.

In this paper, we proposed a new workplace data model and its calculation method. The method was designed to calculate appropriate workplace according to the intents (activities) and situations of a worker. The data model was designed as a semantic space with three knowledge bases: ‘Activity-affecting’, ‘Place-determining’, and ‘Activity and Place’. Experiments were conducted to show the different results depending on activities and the contexts of the workplace and presented the feasibility of the proposed data model and calculation method.

In this paper, an implementation method of GACA, Global Art Collection Archive, is proposed. Each museum maintains their own archives of art collections. GACA dynamically integrate those collection data of artworks in each museum archive and provide them with REST API. GACA works as a integrated data platform for various kinds of viewing environment of artworks such as virtual reality, physical exhibitions, smartphone applications and so on. It allows users not only to view artworks, but also to experience the creativity of artworks through seeing, feeling, and knowing them, inspiring a new era of creation.

In order to provide art exhibitions in a virtual space which integrates various data of an art museum and gives an emotional experience, the Virtual Art Exhibition System is proposed. This research uses a multi-database called Artizon Cloud to display museum data, combinate with technologies called Data Sensorium and Torus Treadmill to project images and enable visitors to walk around the virtual museum. Moreover, the virtual museum will exploit the users intentions and be personalized, automatically generating further art exhibitions.

This paper describes about project “Data Sensorium” launched at the Asia AI Institute of Musashino University. Data Sensoriumis a conceptual framework of systems providing physical experience of content stored in database. Spatial immersive display is a key technology of Data Sensorium. This paper introduces prototype implementation of the concept and its application to environmental and architectural dataset.

For dynamically integrating professional knowledge of curators, a multidatabase system architecture for an art museum, Artizon Cloud is proposed. A location based data provision is defined in the architecture for visitors. A system and applications are implemented and provided in an actual museum, and heterogeneous archives that were independently implemented as databases with Web UIs are dynamically extracted, integrated, and staged in visitors’ devices.