清木 康

This paper proposes a “Zero-Plastic Model” that focuses on the complete elimination and systematic replacement of plastic products. The model represents the deviation from the “reduce, reuse, recycle” framework, emphasizing prevention over treatment and introducing innovative technological solutions for plastic alternatives. The paper illustrates the limitations of existing recycling systems. The paper presents three aspects, including (1) the systematic reduction of plastic dependency through frameworks and behavioral interventions, (2) the development and implementation of sustainable replacement materials, and (3) the integration of technologies to facilitate this transition. The proposed “Zero Plastic Model” establishes sustainability frameworks while introducing a novel approach focused on complete plastic elimination rather than recycling. The framework integrates circular economy principles and draws inspiration from pre-plastic era solutions to achieve zero residual plastic in the system. The research contributes to the growing body of knowledge on sustainable practices and offers a practical framework for organizations and communities seeking to eliminate their plastic dependency.

This paper aims to analyze the phenomenon of inapplicability of experience, which means that sometimes we make mistakes when we use our past experience to solve current problems. We propose a knowledge model based on the concept of “dark-matter”, which is a term used to describe the time-related data that is hidden from our observation. We use a two-dimensional matrix to represent both time-related and non-time-related data, and we call it space. We also introduce the concept of parallel spaces, which are composed of several spaces that can explain different situations and outcomes. We use case studies to illustrate how knowledge is generated and expressed using “dark-matter” and parallel spaces. We also reveal the reason for the inapplicability of experience and suggest some solutions. The contribution of this paper is that we provide a new perspective and a new model to understand and process knowledge based on “dark-matter” and parallel spaces.

“Semantic space creation” and “distance-computing” are basic functions to realize semantic computing for environmental phenomena memorization, retrieval, analysis, integration and visualization. We have introduced “SPA-based (Sensing, Processing and Actuation) Multi-dimensional Semantic Computing Method” for realizing a global environmental system, “5-Dimensional World Map System”. This method is important to design new environmental systems with Cyber-Physical Space-integration to detect environmental phenomena occurring in a physical-space (real space). This method maps those phenomena to a multi-dimensional semantic-space, performs semantic computing, and actuates the semantic-computing results to the physical space with visualizations for expressing environmental phenomena, causalities and influences. As an actual system of this method, currently, the 5D World Map System is globally utilized as a Global Environmental Semantic Computing System, in SDG14, United-Nations-ESCAP: (https://sdghelpdesk.unescap.org/toolboxes). This paper presents a semantic computing method, focusing on “Time-series-Analytical Semantic-Space Creation and Semantic Distance Computing on 5D World Map System” for realizing global environmental analysis in time-series. This paper also presents the time-series analysis of actual environmental changes on 5D World Map System. The first analysis is on the depth of earthquakes Earthquake with time-series semantic computing on 5D World Map System, which occurred around the world during the period from Aug. 23rd to Aug. 28th, 2014, and Jan 7th to Jan. 13th, 2023. The second is the experimental analysis of the time-series difference extraction on glacier melting phenomena in Mont Blanc, Alps, during the period from 2013 to 2022, and Puncak Jaya (Jayawijaya Mountains), Papua, during the period from 1991 to 2020 as important environmental changes.

This paper proposes a spatio-temporal and categorical correlation computing method for induction and deduction analysis. This method is a data analytics method to reveal spatial, temporal, and categorical relationships between two heterogeneous sets in past events by correlation calculation, thereby finding insights to build new connections between the sets in the future. The most significant feature of this method is that it allows inductive and deductive data analysis by applying context vectors to compute the relationship between the sets whose elements are time, space, and category. Inductive analysis corresponds to data mining, which composes a context vector as a hypothesis to extract meaningful relationships from trends and patterns of past events. Deductive analysis searches past events similar to a context vector’s temporal, spatial, and categorical conditions. Spatio-temporal information about the events and information such as frequency, scale, and category are used as parameters for correlation computing. In this method, a multi-dimensional vector space that consists of time, space, and category dimensions is dynamically created, and the data of each set expressed as vectors is mapped onto the space. The similarity degree of the computing shows the strength of relationships between the two sets. This context vector is also mapped onto the space and is calculated distances between the context vector and other vectors of the sets. This paper shows the details of this method and implementation method and assumed applications in commerce activities.

Semantic space creation and computing are essentially significant to realize semantic interpretations of situations and symptoms in human-health. We have presented a semantic space creation and computing method for domain-specific research areas. This method realizes semantic space creation with domain-oriented knowledge and databases. This paper presents a semantic space creation and computing method for “Human-Health Database” with the implementation process for “Human-Health-Analytical Semantic Computing”. This paper also presents a new knowledge base creation method for personal health data for preventive care and potential risk inspection with global and geographical mapping and visualization in 5-Dimensional World Map System. This method focuses on the analysis of personal health and potential-risk inspection and realizes a set of semantic computing functions for semantic interpretations of situations and symptoms in human-health. This method is applied to “Human-Health-Analytical Semantic Computing” to realize world-wide evaluation for (1) multi-parameterized personal health data, such as various biomarkers, clinical physical parameters, lifestyle parameters, other clinical/physiological or human health factors, etc., for health monitoring, and (2) time-series multi-parameterized health data in the national/regional level for global analysis of potential cause of disease. This Human-Health-Analytical Semantic Computing method realizes a new multidimensional data analysis and knowledge sharing for a global-level health monitoring and disease analysis. The computational results are able to be visualized in the time-series difference of the values in each place, the difference between the values of multiple places in a focused area, and the time-series differences between the values of multiple places to detect and predict a potential-risk of diseases.