scholarly journals Determinants of dengue virus dispersal in the Americas

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Orchid M Allicock ◽  
Nikita Sahadeo ◽  
Philippe Lemey ◽  
Albert J Auguste ◽  
Marc A Suchard ◽  
...  
Keyword(s):  
Generalized Linear Model ◽  
Urban Environments ◽  
Urban Centers ◽  
Data Set ◽  
The Past ◽  
Mobility Data ◽  
Statistical Framework ◽  
Phylogeographic History ◽  
Transport Mobility ◽  
Model Approach

Abstract Dengue viruses (DENVs) are classified into four serotypes, each of which contains multiple genotypes. DENV genotypes introduced into the Americas over the past five decades have exhibited different rates and patterns of spatial dispersal. In order to understand factors underlying these patterns, we utilized a statistical framework that allows for the integration of ecological, socioeconomic, and air transport mobility data as predictors of viral diffusion while inferring the phylogeographic history. Predictors describing spatial diffusion based on several covariates were compared using a generalized linear model approach, where the support for each scenario and its contribution is estimated simultaneously from the data set. Although different predictors were identified for different serotypes, our analysis suggests that overall diffusion of DENV-1, -2, and -3 in the Americas was associated with airline traffic. The other significant predictors included human population size, the geographical distance between countries and between urban centers and the density of people living in urban environments.

scholarly journals Change in mean and extreme temperature at Yingkou station in Northeast China from 1904 to 2017

2021 ◽  
Vol 164 (3-4) ◽  
Author(s):  
Xiaoying Xue ◽  
Guoyu Ren ◽  
Xiubao Sun ◽  
Panfeng Zhang ◽  
Yuyu Ren ◽  
...  
Keyword(s):  
Northeast China ◽  
Extreme Temperature ◽  
Maximum Temperature ◽  
Data Set ◽  
The Past ◽  
Significant Downward Trend ◽  
Cold Events ◽  
Temperature Indices ◽  
Increasing Trends ◽  
Change In Mean

AbstractThe understanding of centennial trends of extreme temperature has been impeded due to the lack of early-year observations. In this paper, we collect and digitize the daily temperature data set of Northeast China Yingkou meteorological station since 1904. After quality control and homogenization, we analyze the changes of mean and extreme temperature in the past 114 years. The results show that mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin) all have increasing trends during 1904–2017. The increase of Tmin is the most obvious with the rate of 0.34 °C/decade. The most significant warming occurs in spring and winter with the rate of Tmean reaching 0.32 °C/decade and 0.31 °C/decade, respectively. Most of the extreme temperature indices as defined using absolute and relative thresholds of Tmax and Tmin also show significant changes, with cold events witnessing a more significant downward trend. The change is similar to that reported for global land and China for the past six decades. It is also found that the extreme highest temperature (1958) and lowest temperature (1920) records all occurred in the first half of the whole period, and the change of extreme temperature indices before 1950 is different from that of the recent decades, in particular for diurnal temperature range (DTR), which shows an opposite trend in the two time periods.

scholarly journals Mobility patterns are associated with experienced income segregation in large US cities

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Esteban Moro ◽  
Dan Calacci ◽  
Xiaowen Dong ◽  
Alex Pentland
Keyword(s):  
Large Scale ◽  
Mobility Model ◽  
Coarse Grained ◽  
Spatial Proximity ◽  
Mobility Patterns ◽  
Data Set ◽  
Residential Patterns ◽  
Income Segregation ◽  
Mobility Data ◽  
Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

Spin clusters and low-energy excitations in rare earth kagome systems

2017 ◽  
Vol 31 (01) ◽  
pp. 1630010
Author(s):  
M. J. R. Hoch
Keyword(s):  
Rare Earth ◽  
Muon Spin ◽  
Correlation Lengths ◽  
Dynamic Correlation ◽  
The Past ◽  
Spin Resonance ◽  
Muon Spin Resonance ◽  
Spin Clusters ◽  
Dependent Correlation ◽  
Model Approach

The rare earth kagome systems R3Ga5SiO[Formula: see text] (R = Nd or Pr), which are weakly frustrated antiferromagnets, do not exhibit long-range order at temperatures down to 40 mK as revealed by neutron scattering. The neutron experiments provide evidence for the emergence at low temperatures of correlated spins in nanoscale cluster regions with magnetic field-dependent correlation lengths. A variety of techniques have been used to determine the magnetic and thermal properties of these systems. In particular, high-field electron spin resonance (ESR), nuclear magnetic resonance (NMR) and muon spin resonance ([Formula: see text]SR) experiments have established that dynamic correlation of spins remains significant at temperatures well above 1 K. ESR provides evidence for spin wave excitations in spin clusters and the spectra have been interpreted using a Heisenberg model approach. While Nd[Formula: see text] (J = 9/2) is a Kramers ion Pr[Formula: see text] (J = 4) is not. This difference leads to contrasts in the magnetic properties of the two systems. This review surveys the information that has been obtained on the properties of these kagome materials over the past decade.

scholarly journals Self-reflectivity: a moment of professionalization in psychotherapy training

2018 ◽  
Vol 21 (3) ◽  
Author(s):  
Margarete Finger-Ossinger ◽  
Henriette Löffler-Stastka
Keyword(s):  
Thematic Analysis ◽  
European Association ◽  
Psychotherapy Training ◽  
Self Awareness ◽  
Clear Indication ◽  
Learning Program ◽  
Data Set ◽  
Self Reflection ◽  
The Past ◽  
Essential Prerequisite

The required basic skills of European psychotherapists were published by the European Association of Psychotherapy in 2013. One of these abilities is self-reflection. To mentalize oneself, to reflect on what circumstances and experiences in the past and present have led to the present desires, thoughts and convictions is an essential prerequisite for professional work in the psychosocial field. With the help of the thematic analysis a data set of 41 self-reflection reports of students is analysed at the end of the training. Since the training should be evaluated and if necessary optimized, it should be examined which elements of the online preparation course make the selfreflection ability visible. The analysis of the students’ texts gives a clear indication of existing self-reflection skills. It was surprising that for some students, besides the great importance of self-awareness lessons, affective integration into the blended learning program was an essential impulse for self-reflection.

scholarly journals Augmented Data Selector to Initiate Text-Based CAPTCHA Attack

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Aolin Che ◽  
Yalin Liu ◽  
Hong Xiao ◽  
Hao Wang ◽  
Ke Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Training Data ◽  
Accuracy Rate ◽  
Data Set ◽  
Research Directions ◽  
Training Models ◽  
The Past ◽  
Future Work ◽  
Design Cost ◽  
Accuracy Rates

In the past decades, due to the low design cost and easy maintenance, text-based CAPTCHAs have been extensively used in constructing security mechanisms for user authentications. With the recent advances in machine/deep learning in recognizing CAPTCHA images, growing attack methods are presented to break text-based CAPTCHAs. These machine learning/deep learning-based attacks often rely on training models on massive volumes of training data. The poorly constructed CAPTCHA data also leads to low accuracy of attacks. To investigate this issue, we propose a simple, generic, and effective preprocessing approach to filter and enhance the original CAPTCHA data set so as to improve the accuracy of the previous attack methods. In particular, the proposed preprocessing approach consists of a data selector and a data augmentor. The data selector can automatically filter out a training data set with training significance. Meanwhile, the data augmentor uses four different image noises to generate different CAPTCHA images. The well-constructed CAPTCHA data set can better train deep learning models to further improve the accuracy rate. Extensive experiments demonstrate that the accuracy rates of five commonly used attack methods after combining our preprocessing approach are 2.62% to 8.31% higher than those without preprocessing approach. Moreover, we also discuss potential research directions for future work.

scholarly journals Conceptual Framework for Assessing Process Variables Salient for Service-Learning Experience

2020 ◽  
Author(s):  
Ka Hing Lau ◽  
Robin Snell
Keyword(s):  
Service Learning ◽  
Community Service ◽  
Course Design ◽  
Learning Experience ◽  
Community Partner ◽  
Positive Role ◽  
Data Set ◽  
The Past ◽  
Multi Stakeholder ◽  
Effective Integration

Service-learning is an established pedagogy which integrates experiential learning with community service. It has been widely adopted in higher education around the world including in Hong Kong, yet the key ingredients that determine its successful impacts for its stakeholders have not been fully assessed. This study reviewed the past literature, which indicates the key ingredients that may be found in successful service-learning programmes. We identify six key ingredients: students provide meaningful service; the community partner representative plays a positive role; effective preparation and support for students; effective reflection by students; effective integration of service-learning within the course design; and stakeholder synergy in terms of collaboration, communication and co-ownership. In order to obtain an inter-subjectively fair and trustworthy data set, reflecting the extent to which those key ingredients are perceived to have been achieved, we propose a multi-stakeholder approach for data collection, involving students, instructors and community partner representatives.

scholarly journals Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space

2021 ◽  
Author(s):  
Yunhong Gong ◽  
Yanan Sun ◽  
Dezhong Peng ◽  
Peng Chen ◽  
Zhongtai Yan ◽  
...  
Keyword(s):  
Common Sense ◽  
Expert Knowledge ◽  
State Of The Art ◽  
Data Set ◽  
The Past ◽  
Computed Tomography Images ◽  
Intensive Use ◽  
Hospital Resources ◽  
The Common ◽  
Ct Data

AbstractThe COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations

scholarly journals CREATING MULTI-TEMPORAL MAPS OF URBAN ENVIRONMENTS FOR IMPROVED LOCALIZATION OF AUTONOMOUS VEHICLES

2020 ◽  
Vol XLIII-B2-2020 ◽  
pp. 317-323
Author(s):  
J. Schachtschneider ◽  
C. Brenner
Keyword(s):  
Urban Areas ◽  
Autonomous Vehicles ◽  
Weather Conditions ◽  
Point Clouds ◽  
Urban Environments ◽  
Seasonal Effects ◽  
Data Set ◽  
Multi Temporal ◽  
One Year

Abstract. The development of automated and autonomous vehicles requires highly accurate long-term maps of the environment. Urban areas contain a large number of dynamic objects which change over time. Since a permanent observation of the environment is impossible and there will always be a first time visit of an unknown or changed area, a map of an urban environment needs to model such dynamics.In this work, we use LiDAR point clouds from a large long term measurement campaign to investigate temporal changes. The data set was recorded along a 20 km route in Hannover, Germany with a Mobile Mapping System over a period of one year in bi-weekly measurements. The data set covers a variety of different urban objects and areas, weather conditions and seasons. Based on this data set, we show how scene and seasonal effects influence the measurement likelihood, and that multi-temporal maps lead to the best positioning results.

scholarly journals Seismic hazard evaluation

2000 ◽  
Vol 33 (3) ◽  
pp. 371-386 ◽  
Author(s):  
Paul Somerville
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Earthquake Source ◽  
Hazard Evaluation ◽  
Data Set ◽  
Motion Models ◽  
The Past ◽  
Ground Motion Models

This paper reviews concepts and trends in seismic hazard characterization that have emerged in the past decade, and identifies trends and concepts that are anticipated during the coming decade. New methods have been developed for characterizing potential earthquake sources that use geological and geodetic data in conjunction with historical seismicity data. Scaling relationships among earthquake source parameters have been developed to provide a more detailed representation of the earthquake source for ground motion prediction. Improved empirical ground motion models have been derived from a strong motion data set that has grown markedly over the past decade. However, these empirical models have a large degree of uncertainty because the magnitude - distance - soil category parameterization of these models often oversimplifies reality. This reflects the fact that other conditions that are known to have an important influence on strong ground motions, such as near- fault rupture directivity effects, crustal waveguide effects, and basin response effects, are not treated as parameters of these simple models. Numerical ground motion models based on seismological theory that include these additional effects have been developed and extensively validated against recorded ground motions, and used to estimate the ground motions of past earthquakes and predict the ground motions of future scenario earthquakes. The probabilistic approach to characterizing the ground motion that a given site will experience in the future is very compatible with current trends in earthquake engineering and the development of building codes. Performance based design requires a more comprehensive representation of ground motions than has conventionally been used. Ground motions estimates are needed at multiple annual probability levels, and may need to be specified not only by response spectra but also by suites of strong motion time histories for input into time-domain non-linear analyses of structures.

Sign in / Sign up

Export Citation Format

Share Document