Published papers

A health information system is a system of systems that serves as a foundation of health information used for the evidence-based decision making of public health actions. Since practical solutions for addressing system transformation and its effect on system management have not been extensively discussed, this study examines an approach to model and analyze the transforming health information system of systems. The process and architecture in the case of the Cambodian malaria surveillance system were modeled using the engineering system multiple-domain matrix modeling framework. By using the attributes of the process, architecture, and the risk associated with the environment within the model, relative weights of the constituent systems were scored at each interval of time. The simulated confidence intervals of the absolute difference in the scores indicated that this approach yields the transformation of the system under investigation. By comparing the calculated scores with the results of the simulation test employing an agent-based modeling method, further insights into the process, the limitations, and advantages of the scoring approach were discussed. This approach provides the first step in analyzing the transitional conditions of constituent systems as well as the entire health information system, whereby informed decision making for optimizing continuous system management is facilitated.

Area Business Continuity Management integrates both private and public sectors under the concept of Public-Private Partnership against common risks including natural disasters while previous research mainly focused within private sectors or critical infrastructures. Its issue is that the broader range of heterogeneous stakeholders gets involved the more complex and ambiguous their connectivity becomes. Although, a bottom up approach has been proposed that identifies interdependency of stakeholders by aggregating their dependent activities on business continuities, a practical method has not been proposed yet. This research therefore designed a tangible method which enables to elicit dependencies of individual stakeholder by extending ideas of Business Impact Analysis and Risk Assessment. The proposed method was verified through a workshop conducted in an educational program of disaster risk management. It was observed that the method enables to identify not only external dependent activities with necessary management resources for business continuity but also timings of those occurrence in chronological order that would help finding interdependency among stakeholders for formulating Area-BCM.

Financial exclusion has a major socio-economic impact on the poor and unbanked. Financially excluded smallholder farmers face challenges accessing credit facilities to fund their farming activities because they lack the financial history data required to create credit scores for credit risk evaluations. Non-financial data sources such as mobile applications have been proposed for the development of credit scoring models. However, context-specific alternative scoring factors which are independent of financial history information, must be developed for credit decision systems that use nonfinancial data. This research proposes an approach to developing alternative scoring factors based on stakeholder's requirements. An example of implementation of the proposed method is given using data collected from farmers in rural Cambodia through surveys and a mobile application. Alternative scoring factors are developed based on stakeholder's requirements and collected data. Multiple logistic regression and support vector machine models are trained and tested on this data to evaluate the selected factors. Models are compared by area under the receiver operating characteristics curve values and accuracy. Additional considerations are made to determine the most suitable model in this context. This stakeholder requirements-based approach can be used to design credit decision systems using nonfinancial data for financially excluded persons and facilitate greater financial inclusion.

This article deals with the possibilities and challenges of Japan's dual-use space situational awareness (SSA) systems. Japan is unique in a way that military use of outer space was completely prohibited for almost 40 years until the Basic Space Act (2008) became effective. The change in its space policy was decided because of the rapidly worsening security environment in East Asia, accompanied by preeminent threats to the safety, stability and sustainable use of outer space that constitutes a prerequisite for the safety and welfare of any country. This has led Japan to begin developing a full-scale SSA operation. However, Japan's long-standing non-military practice forces it to design and develop its SSA systems in a different manner than other advanced spacefaring nations. Given such conditions/restrictions, this article first identifies a Japanese means of constructing full-scale dual-use SSA systems, particularly taking into account the functions and capabilities of the Japan Aerospace Exploration Agency (JAXA), the significance of the Japan-U.S. Alliance and a series of recent governmental decisions. The merits and challenges of Japan's whole-of-government construction of the dual-use SSA will then be explored; this involves difficult processes to coordinate different defense and civil mission requirements. The conclusion envisions a balance between the Japan Air Self Defense Force's superior air defense capability and JAXA's accumulated technical capabilities, which together would enable full-fledged defense space utilization starting with the SSA.

Feature selection is crucial to the credit-scoring process, allowing for the removal of irrel-evant variables with low predictive power. Conventional credit-scoring techniques treat this as a separate process wherein features are selected based on improving a single statistical measure, such as accuracy; however, recent research has focused on meaningful business parameters such as profit. More than one factor may be important to the selection process, making multi-objective optimization methods a necessity. However, the comparative performance of multi-objective methods has been known to vary depending on the test problem and specific implementation. This research employed a recent hybrid non-dominated sorting binary Grasshopper Optimization Algorithm and compared its performance on multi-objective feature selection for credit scoring to that of two pop-ular benchmark algorithms in this space. Further comparison is made to determine the impact of changing the profit-maximizing base classifiers on algorithm performance. Experiments demon-strate that, of the base classifiers used, the neural network classifier improved the profit-based measure and minimized the mean number of features in the population the most. Additionally, the NSBGOA algorithm gave relatively smaller hypervolumes and increased computational time across all base classifiers, while giving the highest mean objective values for the solutions. It is clear that the base classifier has a significant impact on the results of multi-objective optimization. Therefore, careful consideration should be made of the base classifier to use in the scenarios.

Cross-sectional communication for data sharing among multiple stakeholders involved in disaster responses is one of the fundamental non-structural measures that directly influence the performance of disaster risk reduction. Taking the event of the 2008 Cyclone Nargis as the watershed experience, Myanmar has been developing a nationwide disaster risk reduction scheme. Transition from the past structure of a vertically divided administration to cross-sectional interaction is underway, making use of lessons learned from past disaster events, yet many challenges remain in communications among stakeholders. To address the issue, this research proposes a communication scheme for data sharing among multiple stakeholders to complement the current scheme for better decision making of the stakeholders during both normal times and emergencies. The proposed scheme is evaluated by the stakeholders, and it is shown that the benefits would include not only the strengthening of the current scheme for decision making but also a contribution to the design of long-term plans in areas such as agriculture, irrigation, and disaster preparedness. This research anticipates further development of the scheme by employing more concrete needs of the stakeholders by reiterating contextual inquiries as well as by physically modeling a database taking case scenarios into account for its design.

With the widespread use of indoor positioning technology, various services based on this technology are beginning to be offered to consumers and industrial applications. In the case of logistics facilities, in addition to indoor and outdoor spaces, there are top-bounded spaces (TBSs): elongated areas that are covered with roofs or eaves on the upper parts of buildings. The sides of such spaces are open, and workers and forklifts work in these areas. Only a few studies have been conducted on positioning methods for this unusual environment, and the way by which Signal-to-Noise Ratio (SNR) of Global Positioning System (GPS) changes with the stay in TBSs is unclear. Therefore, we conducted preliminary experiments and confirmed that TBS dwellings are difficult to stably detect with existing methods due to the combination of satellites with variable and unchanged SNRs. In this study, we designed a simple processing flow for selecting satellites with high probabilities of changing SNRs by using the spatial characteristics of TBSs as parameters (height, depth, and side opening orientation). We propose a method to detect the stay in TBSs using the SNR change rates of the selected satellites. As a result of evaluation experiments with three TBSs, we successfully detected the stay in TBSs with about 30% higher probability than those of an existing method.

Numerous studies have been conducted on indoor and outdoor seamless positioning and indoor–outdoor detection methods. However, the classification of real space into two types, outdoor space and indoor space, is difficult. One type of space that is difficult to classify is top-bounded space, which can be observed in commercial facilities, logistics facilities, and street-facing sidewalks. In this study, we designed a method for detecting stays in three spatial contexts: Outdoor, top-bounded space, and indoor. This method considers elongated top-bounded spaces covered with a roof and open on one of the sides. Specifically, we selected Global Positioning System (GPS) satellites for stay detection based on the simple extraction of the spatial characteristics of a top-bounded space and designed a decision flow using fuzzy inference based on the signal-to-noise ratio (SNR) of the selected GPS satellites. Moreover, we conducted an evaluation experiment to verify the effectiveness of the proposed method and confirmed that it could correctly detect the stay in three spatial contexts, outdoor, top-bounded space, and indoor, with a high probability of 93.1%.

Psychological support systems for retiring elite athletes can assist them in making career transitions out of sport. The purpose of this study is to further advance research in this area, with a specific focus the developmental experiences related to the sport career transition process in Japan. One retired Olympic medalist was analyzed with a life story analysis method. The findings highlight the psychological difficulties experienced by retiring athletes and the system requirements to help them make an optimal transition.

The control of economic activities by governments for the purpose of minimizing COVID-19 spread influenced atmospheric conditions. Satellite-based technology is promising to monitor these changes and even non-specialists in geospatial analysis can use it through cloud-based open platforms. This study aims to give scientific interpretation on the causes of the atmospheric changes based on these governmental controls on human activity. The nitrogen dioxide (NO2) levels were monitored in Ayutthaya Province in Thailand in an urban area including industrial zones during January 1 to April 30, 2020. An analysis was conducted with Google Earth Engine by using four datasets including NO2 data from Sentinel-5P. The mean value of NO2 density in 2020 decreased 12.5% compared with that in 2019. The closure of industrial facilities by the government seemed to be a key cause of the phenomena. The decline was statistically tested and resulted in a significant difference between the two periods. As the new daily cases decreased to single digits, the difference became non-significant from May 1, 2020. Single timeseries analysis on NO2 density change enabled visualization of the brief impact of responses and policies made by the government and subsequent economic activities; however, further atmospheric and industrial activity data are required for further assessment.

Various interventions were made by the Thai government to prevent the COVID-19 spread by controlling socioeconomic activities, but the effectiveness of these interventions and other factors have not yet been fully clarified. Thus, this study aims to provide further scientific evidence on those potential factors which affect the socioeconomic activities changes during the pandemic, by using spatial analysis on atmospheric composition. By taking Phra Nakhon Si Ayutthaya Province, Thailand as the case area. Results of the government's COVID-19 measures and statuses of industries were compared with changes in aerosols, including PM 2.5 which was analyzed by Google Earth Engine with nine open datasets including meteorological and hydrological factors. The analysis revealed that the aerosol index in ueban area of the province decreased at 28.03% in 2020 compared in 2019. Besides, PM 2.5 drastically decreased from March 2020, even without the influence of wind speed which as the highest causal relationship, and kept low level compared with previous years. The reason of the tendency would be explained that other than government interventions including national-level state of the emergency decree, reduction of factories’ activities at Rojana Industrial Park and reduction of the number of tourists had significant influence to reduce the mean value of PM 2.5.

Despite the substantial efforts of various stakeholders, numerous people do not have sufficient access to healthcare services. Though the reliability and timeliness of the health information collected in the healthcare system play critical roles in realizing appropriate healthcare resource allocation, the quality of the health information continues to be of concern. Because the health information system is a system-of-systems that transforms over time in response to the changing environment and needs of the stakeholders, it is important to understand the conditions of transitioning it continuously to improve the data quality. This study examined the continuous management of the health information system-of-systems by the engineering systems multiple-domain modeling approach. The engineering systems multiple-domain matrix framework was applied to model the Cambodian malaria surveillance system. By including the spatiotemporal dynamics of the environmental attributes in the model along with the architectural and process transformation, the transitions of the relative weights of the constituent systems were scored at each interval of time. These scores could be used to optimize the healthcare resource allocation continuously, whereas the considered architectural, process, and environmental changes could contribute to the sustained healthcare access by people.

Road pavements need to be efficiently maintained under budget constraints. A pavement management system supports a road administrator's decision making based on the prediction of pavement deterioration. However, the prediction of pavement deterioration is complicated and uncertain because there are many unobservable variables, and the highly accurate prediction of deterioration is difficult. For pavement administrators to use such predictions in decision making, it is necessary to quantify the reliability of prediction. This paper proposes a prediction interval estimation method by applying the bootstrap method with a reduced computational cost to the deterioration prediction model using a neural network. The proposed method is applied to the rutting depth prediction in the inspection history of road pavement surface, and the estimation accuracy of the prediction interval is verified. In the prediction model, because the inspection history is time-series data, a recurrent neural network model that extends neural networks to time series prediction is used. Verification shows that not only is the computational cost reduced but also the accuracy of the prediction interval is higher than that of the conventional method.

The installation process of numerous Bluetooth Low Energy (BLE) beacons in the indoor positioning environment usually requires the manual measurement of the beacon's physical coordinates which tends to be a labor-intensive process. In this work, we aim to create a BLE beacon map by obtaining their coordinates from in situ Received Signal Strength (RSS) observations on a smartphone in a short period of time. This paper proposes a method of automatic localization for twin BLE beacons based on Range-Only Simultaneous Localization and Mapping (RO-SLAM) approach and multi-hypothesis Extended Kalman Filter (EKF), on the condition that only the relative distance of twin beacons is known. In this method, a person holding a smartphone collects RSS of BLE beacons by walking around with no requirement of predefined trajectory. Normally, the lack of angular information on the sensor involves the difficulty of finding a good initial value of the state vector in the EKF, that requires substantial time to evolve the estimation enough. In our proposed method, tracking the multiple hypotheses which represent the probability distribution of possible locations of the BLE beacon and smartphone makes the results obtainable in a brief RSS survey. The hypotheses generation process employs the interval information of twin BLE beacons to reduce the number of hypotheses and consequently the computational load. The experimental evaluation using commercial off-the-shelf devices show that the proposed method offers a significant improvement in the time required to evolve the location estimation, while the use of twin BLE beacons can suppress the total number of hypotheses.

Credit scoring of financially excluded persons is challenging for financial institutions because of a lack of financial data and long physical distances, which hamper data collection. The remote collection of alternative data has the potential to overcome these challenges, enabling credit access for such individuals. Whereas alternative data sources such as mobile phones have been investigated by previous researchers, this research proposes the integration of mobile-phone, satellite, and public geospatial data to improve credit evaluations where financial data are lacking. An approach to integrating these disparate data sources involving both spatial and temporal analysis methods such as spatial aggregation was employed, resulting in various data combinations. The resulting data sets were used to train classifiers of varying complexity, from logistic regression to ensemble learning. Comparisons were based on various performance metrics, including accuracy and the area under the receiver operating-characteristic curve. The combination of all three data sources performed significantly better than mobile-phone data, with the mean classifier accuracy and F1 score improving by 18% and 0.149, respectively. It is shown how these improvements can translate to cost savings for financial institutions through a reduction in misclassification errors. Alternative data combined in this manner could enhance credit provision to financially excluded persons while managing associated risks, leading to greater financial inclusion.

Understanding of system requirements that satisfy end users’ needs is fundamental of system development, yet challenging when end users are unable to address their needs explicitly. Although a number of scholars have been designing and applying requirement elicita-tion techniques, there is a research gap in Spatial Decision Support System (SDSS) with Web-based Geo-graphical Information System (Web-GIS) in water resource management for disaster risk reduction. The gap addresses especially design elicitation techniques and their performances 1) to understand data types used for decision making, 2) set timing for sharing the data to accomplish end users’ tasks, and 3) com-pile the data to be represented so as to facilitate end users’ decision making. This study therefore designed a requirement elicitation technique by advancing User Story Mapping (USM) and validated through a workshop using mock-up system interface with potential end users who are in charge of water resource management in Myanmar’s Bago River Basin. Through the research it could be validated that the user stories-based approach enabled end users to decompose their operation activities into tasks. It also allowed them to link to necessary data with visual image for facilitating their task accomplishments and decision making for water resource management. It was revealed that the benefits of using the designed approach are not only just to summarize necessary data and information for end users’ decision making but also to encourage them to proactively consider data utilization into their oper-ations. For further development of the requirement elicitation to understand end users needs, insights and recommendations for the proposed technique designing and conducting of the workshop were obtained.