Night-time population consistently explains the transmission dynamics of coronavirus disease 2019 in three megacities in Japan.

Background: Mobility data are crucial for understanding the dynamics of coronavirus disease 2019 (COVID-19), but the consistency of the usefulness of these data over time has been questioned. The present study aimed to reveal the relationship between the transmissibility of COVID-19 in Tokyo, Osaka, and Aichi prefectures and the daily night-time population in metropolitan areas belonging to each prefecture.

Methods: In Japan, the population estimated from GPS-based location data from mobile phone users is regularly monitored by Ministry of Health, Labor, and Welfare and other health departments.

MobCovid: Confirmed Cases Dynamics Driven Time Series Prediction of Crowd in Urban Hotspot.

Monitoring the crowd in urban hot spot has been an important research topic in the field of urban management and has high social impact. It can allow more flexible allocation of public resources such as public transportation schedule adjustment and arrangement of police force. After 2020, because of the epidemic of COVID-19 virus, the public mobility pattern is deeply affected by the situation of epidemic as the physical close contact is the dominant way of infection.

EpiMob: Interactive Visual Analytics of Citywide Human Mobility Restrictions for Epidemic Control.

The outbreak of coronavirus disease (COVID-19) has swept across more than 180 countries and territories since late January 2020. As a worldwide emergency response, governments have implemented various measures and policies, such as self-quarantine, travel restrictions, work from home, and regional lockdown, to control the spread of the epidemic. These countermeasures seek to restrict human mobility because COVID-19 is a highly contagious disease that is spread by human-to-human transmission.

FedVoting: A Cross-Silo Boosting Tree Construction Method for Privacy-Preserving Long-Term Human Mobility Prediction.

The prediction of human mobility can facilitate resolving many kinds of urban problems, such as reducing traffic congestion, and promote commercial activities, such as targeted advertising. However, the requisite personal GPS data face privacy issues. Related organizations can only collect limited data and they experience difficulties in sharing them.

Applying State-of-the-Art Deep-Learning Methods to Classify Urban Cities of the Developing World.

This paper shows the efficacy of a novel urban categorization framework based on deep learning, and a novel categorization method customized for cities in the global south. The proposed categorization method assesses urban space broadly on two dimensions-the states of urbanization and the architectural form of the units observed. This paper shows how the sixteen sub-categories can be used by state-of-the-art deep learning modules (fully convolutional network FCN-8, U-Net, and DeepLabv3+) to categorize formal and informal urban areas in seven urban cities in the developing world-Dhaka, Nairobi, Jakarta, Guangzhou, Mumbai, Cairo, and Lima.

Epidemic versus economic performances of the COVID-19 lockdown: A big data driven analysis.

Lockdown measures have been a "panacea" for pandemic control but also a violent "poison" for economies. Lockdown policies strongly restrict human mobility but mobility reduce does harm to economics. Governments meet a thorny problem in balancing the pros and cons of lockdown policies, but lack comprehensive and quantified guides.

Context-aware heatstroke relief station placement and route optimization for large outdoor events.

Background: Heatstroke is becoming an increasingly serious threat to outdoor activities, especially, at the time of large events organized during summer, including the Olympic Games or various types of happenings in amusement parks like Disneyland or other popular venues. The risk of heatstroke is naturally affected by a high temperature, but it is also dependent on various other contextual factors such as the presence of shaded areas along traveling routes or the distribution of relief stations. The purpose of the study is to develop a method to reduce the heatstroke risk of pedestrians for large outdoor events by optimizing relief station placement, volume scheduling and route.

On-site Dining in Tokyo During the COVID-19 Pandemic: Time Series Analysis Using Mobile Phone Location Data.

Background: During the second wave of COVID-19 in August 2020, the Tokyo Metropolitan Government implemented public health and social measures to reduce on-site dining. Assessing the associations between human behavior, infection, and social measures is essential to understand achievable reductions in cases and identify the factors driving changes in social dynamics.

Objective: The aim of this study was to investigate the association between nighttime population volumes, the COVID-19 epidemic, and the implementation of public health and social measures in Tokyo.

1.6 Million transactions replicate distributed PV market slowdown by COVID-19 lockdown.

Solar PV has seen a spectacular market development in recent years and has become a cost competitive source of electricity in many parts of the world. Yet, prospective observations show that the coronavirus pandemic could impact renewable energy projects, especially in the distributed market. Tracking and attributing the economic footprint of COVID-19 lockdowns in the photovoltaic sector poses a significant research challenge.

Ex-ante online risk assessment for building emergency evacuation through multimedia data.

Ex-ante online risk assessment for building emergency evacuation is essential to protect human life and property. Current risk assessment methods are limited by the tradeoff between accuracy and efficiency. In this paper, we propose an online method that overcomes this tradeoff based on multimedia data (e.

Pedestrian Trajectory Prediction in Extremely Crowded Scenarios.

Pedestrian trajectory prediction under crowded circumstances is a challenging problem owing to human interaction and the complexity of the trajectory pattern. Various methods have been proposed for solving this problem, ranging from traditional Bayesian analysis to Social Force model and deep learning methods. However, most existing models heavily depend on specific scenarios because the trajectory model is constructed in absolute coordinates even though the motion trajectory as well as human interaction are in relative motion.

Measuring spatio-temporal accessibility to emergency medical services through big GPS data.

Medical accessibility is an important indicator for evaluating the effectiveness of public health services. However, the previous medical accessibility studies mainly focus on spatial accessibility without considering temporal variation in population distribution which is significant for evaluating access to emergency medical service (EMS). This paper proposes a model of spatio-temporal accessibility to EMS called ST-E2SFCA based on adapting the enhanced two-step floating catchment area (E2SFCA) method.

Village Building Identification Based on Ensemble Convolutional Neural Networks.

In this study, we present the Ensemble Convolutional Neural Network (ECNN), an elaborate CNN frame formulated based on ensembling state-of-the-art CNN models, to identify village buildings from open high-resolution remote sensing (HRRS) images. First, to optimize and mine the capability of CNN for village mapping and to ensure compatibility with our classification targets, a few state-of-the-art models were carefully optimized and enhanced based on a series of rigorous analyses and evaluations. Second, rather than directly implementing building identification by using these models, we exploited most of their advantages by ensembling their feature extractor parts into a stronger model called ECNN based on the multiscale feature learning method.

Object Discovery: Soft Attributed Graph Mining.

We categorize this research in terms of its contribution to both graph theory and computer vision. From the theoretical perspective, this study can be considered as the first attempt to formulate the idea of mining maximal frequent subgraphs in the challenging domain of messy visual data, and as a conceptual extension to the unsupervised learning of graph matching. We define a soft attributed pattern (SAP) to represent the common subgraph pattern among a set of attributed relational graphs (ARGs), considering both their structure and attributes.

Full-length transient receptor potential vanilloid 1 channels mediate calcium signals and possibly contribute to osmoreception in vasopressin neurones in the rat supraoptic nucleus.

Neurones in the supraoptic nucleus (SON) of the hypothalamus possess intrinsic osmosensing mechanisms, which are lost in transient receptor potential vanilloid 1 (Trpv1)-knock-out mice. The molecular nature of the osmosensory mechanism in SON neurones is believed to be associated with the N-terminal splice variant of Trpv1, although their entire molecular structures have not been hitherto identified. In this study, we sought for TRPV1-related molecules and their function in the rat SON.

Weather effects on the patterns of people's everyday activities: a study using GPS traces of mobile phone users.

This study explores the effects that the weather has on people's everyday activity patterns. Temperature, rainfall, and wind speed were used as weather parameters. People's daily activity patterns were inferred, such as place visited, the time this took place, the duration of the visit, based on the GPS location traces of their mobile phones overlaid upon Yellow Pages information.

GPS-supported visual SLAM with a rigorous sensor model for a panoramic camera in outdoor environments.

Accurate localization of moving sensors is essential for many fields, such as robot navigation and urban mapping. In this paper, we present a framework for GPS-supported visual Simultaneous Localization and Mapping with Bundle Adjustment (BA-SLAM) using a rigorous sensor model in a panoramic camera. The rigorous model does not cause system errors, thus representing an improvement over the widely used ideal sensor model.

A vehicle-borne urban 3-D acquisition system using single-row laser range scanners.

In this research, a novel vehicle-borne system of measuring three-dimensional (3-D) urban data using single-row laser range scanners is proposed. Two single-row laser range scanners are mounted on the roof of a vehicle, doing horizontal and vertical profiling respectively. As the vehicle moves ahead, a horizontal and a vertical range profile of the surroundings are captured at each odometer trigger.

Analysis of 1/f fluctuations of heart rate response while walking or listening to sounds.

This study analyzes the heart rate and voice response of young students while walking or listening to sounds based on 1/f (f=frequency) fluctuation using FFT spectral analysis. There was more positive 1/f fluctuation in heart rate response in normal walking or while listening to comfortable sounds than under stressful conditions, whereas the response was negatively influenced by walking with a heavy bag or while listening to noisy sounds. This study provides a useful method for preventive medicine, physical fitness, and a means for young subjects to maintain a state of well-being.

Walking analysis using an acceleration sensor.

It is important to evaluate walking stability to improve people's health. There are many cases of unstable walking even if people think they are in good health. A small, light acceleration sensor was attached to subjects' center of gravity to measure the acceleration displacement while the subjects were walking.

Spectral analysis of walking with shoes and without shoes.

This study analyzes the walking balance of young students based on 1/f fluctuations using auto-regressive (AR) modeling. There was more good walking balance than bad, influenced positively or negatively by the students' shoes. After the subjects understood their own walking condition, based on 1/f fluctuation, and had received suitable rehabilitation and shoes, their walking balance became better.

Spectral analysis in walking balance by elderly subjects.

Elderly people lose their rhythm of walking balance, because their body alignment and standing balance become less stable through aging. The fluctuations of both hips movement while walking were measured using a portable measurement system with two accelerometers. Before they have had physical exercise and when walking without shoe insoles, the fluctuations of the hip movement, having back and knee pain, was not rhythmic, and also not proportional to 1/f (f=frequency) fluctuation, their walking was unstable.

Three-dimensional image mosaicking using multiple projection planes for 3-D visualization of roadside standing buildings.

A novel image-mosaicking technique suitable for 3-D visualization of roadside buildings on websites or mobile systems is proposed. Our method was tested on a roadside building scene taken using a side-looking video camera employing a continuous set of vertical-textured planar faces. A vertical plane approximation of the scene geometry for each frame was calculated using sparsely distributed feature points that were assigned 3-D data through bundle adjustments.

Analysis of impulse response on walking stability using accelerometers.

The walking stability becomes less stable under the influence of a problematic posture or back. This problem was analyzed on the basis of two new approaches. The first approach is a newly developed measurement system, consisting of two accelerometers for walking analysis.

Ribulose-1,5-bisphosphate carboxylase/oxygenase from an ammonia-oxidizing bacterium, Nitrosomonas sp. K1: purification and properties.

The ammonia-oxidizing chemoautotrophic Nitrosomonas sp. strain K1 exhibited marked ribulose-1,5-bisphosphate carboxylase (RubisCO) activity. The RubisCO [EC 4.