Deep Learning for Osteoporosis Classification Using Hip Radiographs and Patient Clinical Covariates

This study considers the use of deep learning to diagnose osteoporosis from hip radiographs, and whether adding clinical data improves diagnostic performance over the image mode alone. For objective labeling, we collected a dataset containing 1131 images from patients who underwent both skeletal bone mineral density measurement and hip radiography at a single general hospital between 2014 and 2019. Osteoporosis was assessed from the hip radiographs using five convolutional neural network (CNN) models. We also investigated ensemble models with clinical covariates added to each CNN. The accuracy, precision, recall, specificity, negative predictive value (npv), F1 score, and area under the curve (AUC) score were calculated for each network. In the evaluation of the five CNN models using only hip radiographs, GoogleNet and EfficientNet b3 exhibited the best accuracy, precision, and specificity. Among the five ensemble models, EfficientNet b3 exhibited the best accuracy, recall, npv, F1 score, and AUC score when patient variables were included. The CNN models diagnosed osteoporosis from hip radiographs with high accuracy, and their performance improved further with the addition of clinical covariates from patient records

Field Survey of Flank Collapse and Run-up Heights due to 2018 Anak Krakatau Tsunami

Dataset: https://data.4tu.nl/articles/dataset/Bathymetry_data_underlying_the_publication_Field_survey_of_flank_collapse_and_run-up_heights_due_to_the_2018_Anak_Krakatau_Tsunami/1421561

Relative Preference for Living in a Safer Place from Natural Disasters: A Case Study at Tokyo, Japan

While it would be desirable to encourage people to live in places that are safer from natural disasters to minimize casualties and property damage, few studies have focused on people’s relative preference for living in such places. The present study has sought to clarify the extent to which Tokyo residents consider safety from natural disaster to be more important than other factors relevant to the choice of residential location, as well as what personal attributes may be correlated with this perception. An online survey was conducted to collect 1554 valid responses from residents in the 23 city wards of Tokyo, Japan, and statistical analysis (a chi-square test and multivariable logistic regression analysis) was then applied to the collected responses. The results demonstrated that, on average, 45.1% of the respondents considered that “safety from natural disasters” was relatively important among twelve such factors related to the selection of a suitable residential location. It was also found that showing a hazard map to Tokyo residents or educating them to take more interest in their health and the surrounding natural environment could be effective to increase the number of people preferring to live in safer places

Relative Preference for Living in a Safer Place from Natural Disasters: A Case Study at Tokyo, Japan

While it would be desirable to encourage people to live in places that are safer from natural disasters to minimize casualties and property damage, few studies have focused on people’s relative preference for living in such places. The present study has sought to clarify the extent to which Tokyo residents consider safety from natural disaster to be more important than other factors relevant to the choice of residential location, as well as what personal attributes may be correlated with this perception. An online survey was conducted to collect 1554 valid responses from residents in the 23 city wards of Tokyo, Japan, and statistical analysis (a chi-square test and multivariable logistic regression analysis) was then applied to the collected responses. The results demonstrated that, on average, 45.1% of the respondents considered that “safety from natural disasters” was relatively important among twelve such factors related to the selection of a suitable residential location. It was also found that showing a hazard map to Tokyo residents or educating them to take more interest in their health and the surrounding natural environment could be effective to increase the number of people preferring to live in safer places

複数の海底地すべりによる津波の増幅機構の解明とその予測手法の開発

本研究では複数の地すべり津波を対象とした水理実験および数値解析を行うことで，(1)地すべり津波は多くの場合，非線形波に分類されるため，重なり合った場合には単純な重ね合わせでは最大水位を正確に表現できないこと，（2）複数の地すべり津波を予測するためには，本研究で導出した単独の地すべり津波に対する最大水位の予測式に数％程度の安全率を加える，もしくは非線形性・分散性を考慮可能な数値解析モデルを用いる必要があること，（3）地すべり津波は地震発生後，即座に来襲する恐れがあるため，地すべり津波に対する適切な避難計画の策定が必要であること，を明らかにした．In the present study, I performed hydrodynamic experiments and numerical simulations targeting multiple landslide-generated tsunamis. As a result, the present study revealed that: (1) landslide-generated tsunamis, being predominantly classified as nonlinear waves, cannot be accurately represented by a simple superposition when overlapping, for the maximum water level; (2) to predict the height of the multiple landslide-generated tsunamis, it is necessary to add a safety margin of several percent to the prediction equations for the maximum water level of a single landslide-generated tsunamis, derived in the present study, or to use a numerical simulation model that can consider both nonlinearity and dispersion effects; (3) as landslide-generated tsunamis could potentially strike immediately after an earthquake occurs, it is necessary to formulate appropriate evacuation plans against landslide-generated tsunamis.研究分野：海岸工

A Comparison between Agent-Based and GIS-Based Tsunami Evacuation Simulations: A Case Study for Tofino, BC

Soft measures such as evacuation planning are recommended to mitigate the loss of life during tsunamis. Two types of evacuation models are widely used: (1) Agent-based modelling (ABM) defines sets of rules that individual agents in a simulation follow during a simulated evacuation. (2) Geographical information systems (GIS) are more accessible to city planners, but cannot incorporate the dynamic behaviours found in ABMs. The two evacuation modelling methodologies were compared through a case study by assessing the state of evacuation preparedness and investigating potential mitigation options. The two models showed different magnitudes for mortality rates and facility demand but had similar trends. Both models agreed on the best solution to reduce the loss of life for the community. GIS may serve as a useful tool for initial investigation or as a validation tool for ABMs. ABMs are recommended for use when modelling evacuation until GIS methodologies are further developed.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author