Network Analysis of Research Project Members in Grant-in-Aid for Scientific Research in Japan

The researcher network that appeared in research projects funded by the Japanese government was analyzed. Several static and dynamic network analysis methods were applied to the data for 20 years to explore the fine structure of the researcher’s network for grants. Our analysis shows that the long-term trend of researchers’ group sizes has become smaller, particularly rapidly decreasing in recent years. Some findings on researcher behavior in joining a project have also been reported.

Investigating Economic Growth, Trade Issues and Future Energy Strategies for Central Asian Countries

We examine the economic growth, energy development policies and strategies for the CAC from various perspectives. We apply statistical data analysis techniques and mathematical modelling methodologies focusing upon regression model analysis in order to deal with the economic and energy related data during the period 1990-2014, and to investigate the relationship among economic growth, energy production, and the trade of energy resources in order to find future desirable policies and strategies for the CAC. Findings show that energy production growth would bring statistically significant positive impact on GDP growth in fossil-fuel rich Kazakhstan, Uzbekistan, and Turkmenistan while we also find negative impact of GDP growth on the trade balance of the CAC except for Turkmenistan. Another finding follows that the foreign direct investment has a significant influence on the trade balance in the cases of Uzbekistan and Turkmenistan, which institute import substitution policies right from their initial years of independence. Based on these quantitative investigations on economy, energy, and trades we propose future energy strategies for the CAC, stressing the importance of diversification of economies.

Relative Performance Evaluation of Competing Crude Oil Prices’ Volatility Forecasting Models: A Slacks-Based Super-Efficiency DEA Model

With the increasing number of quantitative models available to forecast the volatility of crude oil prices, the assessment of the relative performance of competing models becomes a critical task. Our survey of the literature revealed that most studies tend to use several performance criteria to evaluate the performance of competing forecasting models;however, models are compared to each other using a single criterion at a time, which often leads to different rankings for different criteria—A situation where one cannot make an informed decision as to which model performs best when taking all criteria into account. In order to overcome this methodological problem, Xu and Ouenniche [1] proposed a multidimensional framework based on an input-oriented radial super-efficiency Data Envelopment Analysis (DEA) model to rank order competing forecasting models of crude oil prices’ volatility. However, their approach suffers from a number of issues. In this paper, we overcome such issues by proposing an alternative framework.

Multivariate Analyses for Finding Significant Track Irregularities to Generate an Optimal Track Maintenance Schedule

We first discuss the relationship between the optimal track maintenance scheduling model and an efficient detection method for abnormal track irregularities given by the longitudinal level irregularity displacement (LLID). The results of applying the cluster analysis technique to the sampling data showed that maintenance operation is required for approximately 10% of the total lots, and these lots were further classified into three groups according to the degree of maintenance need. To analyze the background factors for detecting abnormal LLID lots, a principal component analysis was performed;the results showed that the first principal component represents LLIDs from the viewpoints of the rail structure, equipment, and operating conditions. Binomial and ordinal logit regression models (LRMs) were used to quantitatively investigate the determinants of abnormal LLIDs. Binomial LRM was used to characterize the abnormal LLIDs, whereas ordinal LRM was used to distinguish the degree of influence of factors that are considered to have a significant impact on LLIDs.

医院管理实践的过程评价

通过对JCI国际评审标准、德国医疗透明制度标准委员会(KTQ)标准、日本医院机能评价标准以及LSE医院管理过程评价方法的介绍和对比分析,讨论了各自的特点和优缺点,结合目前医改对医院管理提出的客观要求,建议我国应重视和加强对医院管理实践过程的评价,并有必要开发适合我国医疗卫生体系特点、简单、易行的医院管理实践评价工具和方法。

孟加拉国曼尼普尔部落对部族药用植物的利用(英文)

通过问卷调查的方式,调查了孟加拉国曼尼普尔部落对民族药用植物的利用。发现有26个科29个属32种药用植物用于治疗37种疾病。结果表明:所调查的32种植物中,多利用其地上部分(86%),而地下部分的利用相对较少(14%)。在植物药用制备中,叶片的使用最高(17个种),接下来依次是树皮、果实、根/地下茎、整个植株、种子和花。在32个植物科中,用于治疗痢疾的物种有10个,治疗发烧和风湿病的物种各5个,治疗哮喘、便秘、创伤和皮肤病的各4个物种,低烧病、咳嗽、腹泻各3种。32个物种中,72%的药用植物都能治疗至少一种疾病:75%口服使用,9%外服用,16%口服和外服结合使用。本研究突出了民族植物研究的潜力和迫切需求获取药用植物在人类健康利用的健康医疗实践知识。图4表3参45。

Case study of debris flow disaster scenario caused by torrential rain on Kiyomizu-dera, Kyoto, Japan - using Hyper KANAKO system

This paper presents debris-flow numerical simulations using the Hyper KANAKO system,developed by the authors.The system uses the debris flow simulator KANAKO 2D equipped with a graphical user interface(GUI);hence,a user can easily produce appropriate landform data for simulations using standard laser profiler data,and visualize the results using a GIS.Hyper KANAKO was applied to the streams around Kiyomizu-dera in Kyoto,Japan.Kiyomizu-dera is a famous temple in Japan which is visited by numerous tourists throughout the year.We simulated a disaster scenario of debris flow caused by torrential rain.We set the hydrograph using rainfall intensity data,and set the landform data using information from the Geospatial Information Authority of Japan(GSI) and a digital elevation model(DEM).We evaluated different mesh sizes and also used a digital surface model(DSM) to consider the building heights.The simulation results showed that with small mesh size,the debris flowmoved through the roads,which seems realistic for a disaster situation.When buildings were considered,the flow direction changed,and a 1-m flow depth,which was deeper than in other cases,appeared in the flow path.This may pose a dangerous situation for evacuations.

Applying Network Flow Optimization Techniques to Improve Relief Goods Transport Strategies under Emergency Situation

Given the seriously damaged emergency situation occurring after a large-scale natural disaster, a critical and important problem that needs to be solved urgently is how to distribute the necessary relief goods, such as drinking water, food, and medicine, to the damaged area and how to transport them corresponding to the actual supply and demand situation as quickly as possible. The existing infrastructure, such as traffic roads, bridges, buildings, and other facilities, may suffer from severe damage. Assuming uncertainty related with each road segment’s availability, we formulate a transshipment network flow optimization problem under various types of uncertain situations. In order to express the uncertainty regarding the availability of each road segment, we apply the Monte Carlo simulation technique to generate random networks following certain probability distribution conditions. Then, we solve the model to obtain an optimal transport strategy for the relief goods. Thus, we try to implement a necessary and desirable response strategy for managing emergency cases caused by, for example, various natural disasters. Our modeling approach was then applied to the actual road network in Sumatra Island in Indonesia in 2009, when a disastrous earthquake occurred to develop effective and efficient public policies for emergency situations.

A Quantitative Factorial Component Analysis to Investigate the Recent Changes of Japan’s Weight-Based Food Self-Sufficiency Ratio

We investigate the weight-based food self-sufficiency ratio (WSSR) for Japan over a 50-year period (1961-2011) by applying factorial component analysis technique in order to measure the changes of the WSSR quantitatively. Quantitative data analysis is employed to determine the drivers of those changes. Numerical results show that Japan experienced a drastic decline in its food self-sufficiency ratio (FSSR) during the above period. The factorial component analysis shows that such a decline was caused by the changes in the FSSR of the food groups/items, not in the quantity of the food supply. A number of characteristics of those changes are presented and a list of major food groups that have major impacts on the changes is constructed. The findings in this paper reiterate the alarming food security problem in Japan and provide clear insight into the causes of this problem. The findings in this study pick up where previous studies have left off, aid the food-related policy-making process and identify new ideas for future food research.

Statistical Data Analyses on Aircraft Accidents in Japan: Occurrences, Causes and Countermeasures

We investigate the major characteristics of the occurrences, causes of and counter measures for aircraft accidents in Japan. We apply statistical data analysis and mathematical modeling techniques to determine the relations among economic growth, aviation demand, the frequency of aircraft/helicopter accidents, the major characteristics of the occurrence intervals of accidents, and the number of fatalities due to accidents. The statistical model analysis suggests that the occurrence intervals of accidents and the number of fatalities can be explained by probability distributions such as the exponential distribution and the negative binomial distribution, respectively. We show that countermeasures for preventing accidents have been developed in every aircraft model, and thus they have contributed to a significant decrease in the number of accidents in the last three decades. We find that the major cause of accidents involving large airplanes has been weather, while accidents involving small airplanes and helicopters are mainly due to the pilot error. We also discover that, with respect to accidents mainly due to pilot error, there is a significant decrease in the number of accidents due to the aging of airplanes, whereas the number of accidents due to weather has barely declined. We further determine that accidents involving small and large airplanes mostly occur during takeoff and landing, whereas those involving helicopters are most likely to happen during flight. In order to decrease the number of accidents, i) enhancing safety and security by further developing technologies for aircraft, airports and air control radars, ii) establishing and improving training methods for crew including pilots, mechanics and traffic controllers, iii) tightening public rules, and iv) strengthening efforts made by individual aviation-related companies are absolutely necessary.

Investigating Evacuation Trends and Restoration Processes from Serious Earthquake Damage in Japan

Japan has experienced many large-scale natural disasters, such as earthquakes, typhoons accompanied by heavy rain, and landslides. Based on data for the damage caused by four recent major earthquakes in Japan, we investigate the trends in the number of evacuees and evacuation centres after these disasters as well as the restoration processes for public utilities such as electricity, gas, water, and communication lines. We compare the restoration speeds and trends of the damaged infrastructure systems. We also propose various mathematical models to approximate the recovery trends using both evacuee-related data and damage recovery process data. These results can be used to design natural disaster mitigation policies not only in Japan, but also in other countries. The results of various statistical data analyses and mathematical modelling techniques are applied to provide policy suggestions.

DEA Scores’ Confidence Intervals with Past-Present and Past-Present-Future Based Resampling

In data envelopment analysis (DEA), input and output values are subject to change for several reasons. Such variations differ in their input/output items and their decision-making units (DMUs). Hence, DEA efficiency scores need to be examined by considering these factors. In this paper, we propose new resampling models based on these variations for gauging the confidence intervals of DEA scores. The first model utilizes past-present data for estimating data variations imposing chronological order weights which are supplied by Lucas series (a variant of Fibonacci series). The second model deals with future prospects. This model aims at forecasting the future efficiency score and its confidence interval for each DMU. We applied our models to a dataset composed of Japanese municipal hospitals.

Looking for and Aiming for an Asian OR Applicable to the Public Sector

Operations research(OR)is a scientific approach for solving various types of societal problems and making decisions to deal with these problems appropriately.First,we briefly describe the history of OR,focusing on applying OR to public sectors,and then provide its characterization as a scientific method for decision making.Then,OR activities in Japan are introduced,emphasizing three major roles:(i)quantitative data analysis,(ii)mathematical modeling analysis,and(iii)theory building analysis.We provide an example for each of these three types of major roles.Based upon the analyses,we seek an Asian OR applicable to policy making in the public sector.Finally,we provide a summary and discuss future perspectives for OR.

Forecasting Daily Electric Load by Applying Artificial Neural Network with Fourier Transformation and Principal Component Analysis Technique

In this paper,we propose a hybrid forecasting model(HFM)for the short-term electric load forecasting using artificial neural network(ANN),discrete Fourier transformation(DFT)and principal component analysis(PCA)techniques in order to attain higher prediction accuracy.Firstly,we estimate Fourier coefficients by the DFT for predicting the next-day load curve with an ANN and obtain approximate load curves by applying the inverse discrete Fourier transformation.Approximate curves,together with other input variables,are given to the ANN to predict the next-day hourly load curves.Furthermore,we predict PCA scores to obtain approximate load curves in the first step,which are then given to the ANN again in the second step.Both DFT and PCA models use input variables such as calendrical and meteorological data as well as past electric loads.Applying those models for forecasting hourly electric load in the metropolitan area of Japan for January and May in 2018,we train our models using historical data since January 2008.The forecast results show that the HFM consisting of“ANN with DFT”and“ANN with PCA”predicts next-day hourly loads more accurately than the conventional three-layered ANN approach.Their corresponding mean average absolute errors show 2.7%for ANN with DFT,2.6%for ANN with PCA and 3.0%for the conventional ANN approach.We also find that in May,when electric demand is smaller with smaller fluctuations,forecasting errors are much smaller than January for all the models.Thus,we can conclude that the HFM would contribute to attaining significantly higher forecasting accuracy.

Network-Based Multiple UAVs Search Planning for Disaster Relief

This paper studies the use of multiple unmanned aerial vehicles(UAVs)for searching a victim of disaster.We propose a network-based optimization model for planning the search path of multiple UAVs in a disaster-stricken area and estimating the number of UAVs necessary for search.A heuristic algorithm is devised to solve the optimization model and applied to the problem instances taken from potential hazard areas in Japan.Our computational result shows relatively small number of UAVs is enough to cover the area in most of cases.We also give an investigation on the relation between search area and the number of UAVs necessary for search via regression methods.

Renminbi in Ordinary Economies： A Demand-side Study of Currency Globalization

Most studies of renminbi internationalization focus on the supply side, by examining China＇s own economic and political conditions. In contrast, this study addresses the demand side of renminbi internationah＇zation, by providing an in-depth analysis of renminbi use in ordinary foreign economies from both economic and political perspectives, with a particular focus on South Korea, China＇s next-door neighbor. The study finds that sustainable indigenous market forces facilitating renminbi use in South Korea remain weak, despite the country＇s close economic ties with China. This research also shows, however, that the Korean Government has itself recently been able, through its policy measures, to generate new domestic support for greater use of the renminbi. These findings ultimately highlight the significant impact on the internationalization of a currency of the politics in the foreign countries using it.

How the iPhone Widens the U.S.Trade Deficit with China:The Case of the iPhone X

Through an examination of the case of the iPhone X,this paper demonstrates that Chinese companies involved in the production of the iPhone X have moved up along the global value chain.According to the bill of materials,those companies contributed 25.4%of the value added of the iPhone X.About 45%of the value added of the iPhone X originated in Japan,South Korea,and other economies.The iPhone trade remains a significant element of the statistical distortion of the China-US bilateral trade imbalance.In terms of gross value,the import of one iPhone X results in a USD332.75 trade deficit for the US;measured in terms oLthe value added,however the deficit is a mere USD 104.The depreciation of the Chinese yuan(CNY)has very limited power to counterbalance the tariffs imposed by the Trump administration because the foreign value added embedded in Chinese exports is 33.9%on average.Simulation results show that to counterbalance a 25%tariff,the CNY would have to depreciate by 43.3%against the US dollar on average,and to fully compensate for a 25%tariff burden on the iPhone X,a 400%depreciation of the CNY would be necessary.Hedging the risk of the punitive U.S.tariffs by depreciating the CNY is impossible.