scholarly journals Reputation System for Increased Engagement in Public Transport Oriented-Applications

Electronics ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 1070
Author(s):  
David García-Retuerta ◽  
Alberto Rivas ◽  
Joan Guisado-Gámez ◽  
Eleni Antoniou ◽  
Pablo Chamoso
Keyword(s):  
Real Number ◽  
Real World ◽  
Public Transport ◽  
Synthetic Data ◽  
User Engagement ◽  
Maximum Score ◽  
Reputation Score ◽  
User Reputation ◽  
Logarithmic Functions ◽  
Positive Results

Increasing user engagement is one of the biggest challenges when a new application is developed. An engaged user is one who finds a product valuable; highly engaged users generate profit. This study focuses on increasing user engagement in a transport application, via a user reputation score feature. The score is to reward application users and activity organisers, as well as to motivate beginners by offering a high reputation score in the first days of use. The algorithms are based on exponential and logarithmic functions, and were first tested on synthetic data. Real-world tests have shown that the algorithms behave as expected, but the COVID-19 pandemic created a disturbance which prevented any user from achieving the maximum score and many users from registering altogether. Data show positive results, although the real number of users is not sufficient to certify a correct behaviour. Further tests will be carried out when transport activities return to normal.

scholarly journals Lightweight and Efficient Image Dehazing Network Guided by Transmission Estimation from Real-World Hazy Scenes

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 960
Author(s):  
Zhan Li ◽  
Jianhang Zhang ◽  
Ruibin Zhong ◽  
Bir Bhanu ◽  
Yuling Chen ◽  
...  
Keyword(s):  
Real World ◽  
Evaluation Criteria ◽  
Synthetic Data ◽  
Network Size ◽  
Image Dehazing ◽  
Depth Data ◽  
Network Training ◽  
Dark Channel ◽  
Processing Techniques ◽  
Transmission Estimation

In this paper, a transmission-guided lightweight neural network called TGL-Net is proposed for efficient image dehazing. Unlike most current dehazing methods that produce simulated transmission maps from depth data and haze-free images, in the proposed work, guided transmission maps are computed automatically using a filter-refined dark-channel-prior (F-DCP) method from real-world hazy images as a regularizer, which facilitates network training not only on synthetic data, but also on natural images. A double-error loss function that combines the errors of a transmission map with the errors of a dehazed image is used to guide network training. The method provides a feasible solution for introducing priors obtained from traditional non-learning-based image processing techniques as a guide for training deep neural networks. Extensive experimental results demonstrate that, in terms of several reference and non-reference evaluation criteria for real-world images, the proposed method can achieve state-of-the-art performance with a much smaller network size and with significant improvements in efficiency resulting from the training guidance.

Unresectable hepatocellular carcinoma: prospects for drug therapy with lenvatinib

2022 ◽  
Vol 11 (3) ◽  
pp. 45-52
Author(s):  
V.  V. Breder ◽  
D.  T. Abdurakhmanov ◽  
V.  V. Petkau ◽  
P.  V. Balakhnin ◽  
M.  V. Volkonsky ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Drug Therapy ◽  
Liver Function ◽  
Real World ◽  
Systemic Therapy ◽  
Efficacy And Safety ◽  
Study Results ◽  
Class B ◽  
Pugh Class ◽  
Positive Results

There is a number of unresolved issues regarding the systemic therapy administration for hepatocellular carcinoma (HCC). Their solution is facilitated by accumulating real‑world study results. Lenvatinib therapy is a recognized drug with a good efficacy and safety profile for the treatment of HCC. Subanalyses of the REFLECT study showed that the absence of stratification by baseline AFP and baseline liver function, as well as the lack of options for subsequent drug therapy after lenvatinib, also affects the outcomes. Once these factors are taken into account, the hypothesis of superiority of lenvatinib to sorafenib and other drugs can be tested. Real‑world clinical studies have demonstrated positive results of lenvatinib therapy in patients with Child‑Pugh class B liver function, provided recommendations on the sequence of systemic therapy after lenvatinib and on the use of lenvatinib in patients with BCLC stage B, along with considering the possibility of lenvatinib monotherapy and the prospects for its use in patients with nHCC. Further real‑world studies of lenvatinib for HCC in the Russian population are required.

scholarly journals Using Synthetic Data to Improve and Evaluate the Tracking Performance of Construction Workers on Site

2020 ◽  
Vol 10 (14) ◽  
pp. 4948
Author(s):  
Marcel Neuhausen ◽  
Patrick Herbers ◽  
Markus König
Keyword(s):  
Real World ◽  
Safety Management ◽  
Tracking System ◽  
Synthetic Data ◽  
Tracking Systems ◽  
Construction Sites ◽  
Virtual Construction ◽  
Percentage Points ◽  
Video Frames ◽  
Graphics Software

Vision-based tracking systems enable the optimization of the productivity and safety management on construction sites by monitoring the workers’ movements. However, training and evaluation of such a system requires a vast amount of data. Sufficient datasets rarely exist for this purpose. We investigate the use of synthetic data to overcome this issue. Using 3D computer graphics software, we model virtual construction site scenarios. These are rendered for the use as a synthetic dataset which augments a self-recorded real world dataset. Our approach is verified by means of a tracking system. For this, we train a YOLOv3 detector identifying pedestrian workers. Kalman filtering is applied to the detections to track them over consecutive video frames. First, the detector’s performance is examined when using synthetic data of various environmental conditions for training. Second, we compare the evaluation results of our tracking system on real world and synthetic scenarios. With an increase of about 7.5 percentage points in mean average precision, our findings show that a synthetic extension is beneficial for otherwise small datasets. The similarity of synthetic and real world results allow for the conclusion that 3D scenes are an alternative to evaluate vision-based tracking systems on hazardous scenes without exposing workers to risks.

Characteristics of Education and the Current State of Teacher Effectiveness in Japan

2021 ◽  
pp. 148-171
Author(s):  
Takeshi Miyazaki ◽  
Hideki Watanabe ◽  
Emiko Furukawa ◽  
Masako Nezu ◽  
Shigeki Ohono ◽  
...  
Keyword(s):  
Elementary Schools ◽  
Teacher Effectiveness ◽  
Real World ◽  
Educational Policies ◽  
Postwar Period ◽  
Positive Role ◽  
Current State ◽  
The Difference ◽  
Positive Results ◽  
The Way

This chapter analyzed how Japanese teachers' qualifications and abilities, as well as educational policies, have been promoted since the postwar period to the present day and summarized these results. There are discrepancies between the needs of students' families and the real world and the ideas and contents required by the Course of Study. Teachers have tried to play a positive role in bridging the gap and in merging the reality and the ideals. In order to bridge the “difference” faced by school sites, it is necessary to start by examining the contents of the reforms required from the bottom and reforms required from the top. As an initiative from below, in most elementary schools in Japan, groups of teachers have voluntarily gathered, and “Jugyo-Kenkyu” have been conducted for many years to analyze the challenges of their own school's students as a team. Although Jugyo-Kenkyu has achieved some positive results, the way to measure the effectiveness of the research is still an issue.

scholarly journals Effects of Human Factors on Public Use of Standing-Type Personal Mobility Vehicle

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Naohisa Hashimoto ◽  
Kohji Tomita ◽  
Osamu Matsumoto ◽  
Ali Boyali
Keyword(s):  
Future Development ◽  
Real World ◽  
Public Transport ◽  
Urban Areas ◽  
Traffic Accidents ◽  
Transport Systems ◽  
Personal Mobility ◽  
Mobility Devices ◽  
Personal Mobility Vehicle ◽  
Mobility Device

To significantly reduce the occurrence of severe traffic accidents, reducing the number of vehicles in urban areas should be considered. Personal mobility is essential for realizing this reduction, which requires consideration of the last-/first-mile problem. The overall objective of our research is to solve this problem using standing-type personal mobility vehicles as transportation devices; however, to evaluate the feasibility of such vehicles as future mobility devices, it is necessary to evaluate their operation under real-world conditions. Therefore, in this study, experimental and survey data relating to the velocity, stability, safety, and comfort of a standing-type personal mobility device are obtained to evaluate its performance in three different scenarios. The results show that the personal mobility vehicle is socially well received and can be safely operated on sidewalks, irrespective of the gender or age of the driver; moreover, the results suggest that subjects who routinely use a bicycle are adept at avoiding and absorbing the impacts of small holes and bumps, thereby yielding reduced acceleration values (in all directions) and pitch, roll, and yaw rates. This is anticipated to benefit the future development of personal mobility devices and help realize effective and accessible public transport systems, as well as reduce the number of vehicles in urban areas.

scholarly journals An empirical analysis of dealing with patients who are lost to follow-up when developing prognostic models using a cohort design

2020 ◽  
Author(s):  
Jenna M Reps ◽  
Peter Rijnbeek ◽  
Alana Cuthbert ◽  
Patrick B Ryan ◽  
Nicole Pratt ◽  
...  
Keyword(s):  
At Risk ◽  
Real World ◽  
Synthetic Data ◽  
Real World Data ◽  
Test Set ◽  
Loss To Follow Up ◽  
World Data ◽  
Cohort Design ◽  
Lost To Follow Up

Abstract Background: Researchers developing prediction models are faced with numerous design choices that may impact model performance. One key decision is how to include patients who are lost to follow-up. In this paper we perform a large-scale empirical evaluation investigating the impact of this decision. In addition, we aim to provide guidelines for how to deal with loss to follow-up.Methods: We generate a partially synthetic dataset with complete follow-up and simulate loss to follow-up based either on random selection or on selection based on comorbidity. In addition to our synthetic data study we investigate 21 real-world data prediction problems. We compare four simple strategies for developing models when using a cohort design that encounters loss to follow-up. Three strategies employ a binary classifier with data that: i) include all patients (including those lost to follow-up), ii) exclude all patients lost to follow-up or iii) only exclude patients lost to follow-up who do not have the outcome before being lost to follow-up. The fourth strategy uses a survival model with data that include all patients. We empirically evaluate the discrimination and calibration performance.Results: The partially synthetic data study results show that excluding patients who are lost to follow-up can introduce bias when loss to follow-up is common and does not occur at random. However, when loss to follow-up was completely at random, the choice of addressing it had negligible impact on model discrimination performance. Our empirical real-world data results showed that the four design choices investigated to deal with loss to follow-up resulted in comparable performance when the time-at-risk was 1-year but demonstrated differential bias when we looked into 3-year time-at-risk. Removing patients who are lost to follow-up before experiencing the outcome but keeping patients who are lost to follow-up after the outcome can bias a model and should be avoided.Conclusion: Based on this study we therefore recommend i) developing models using data that includes patients that are lost to follow-up and ii) evaluate the discrimination and calibration of models twice: on a test set including patients lost to follow-up and a test set excluding patients lost to follow-up.

scholarly journals Assessing Digital Health Implementation for a Pediatric Chronic Pain Intervention: Comparing the RE-AIM and BIT Frameworks Against Real-World Trial Data and Recommendations for Future Studies (Preprint)

2020 ◽  
Author(s):  
Rocio de la Vega ◽  
Lee Ritterband ◽  
Tonya M Palermo
Keyword(s):  
Chronic Pain ◽  
Real World ◽  
Digital Health ◽  
Controlled Trial ◽  
User Engagement ◽  
Maintenance Period ◽  
Treatment Implementation ◽  
Positive Attitudes ◽  
Pediatric Chronic Pain ◽  
Implementation Costs

BACKGROUND Digital health interventions have demonstrated efficacy for several conditions including for pediatric chronic pain. However, the process of making interventions available to end users in an efficient and sustained way is challenging and remains a new area of research. To advance this field, comprehensive frameworks have been created. OBJECTIVE The aim of this study is to compare the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) and Behavior Interventions using Technology (BIT) frameworks with data collected from the web-based management of adolescent pain (WebMAP Mobile; WMM) randomized controlled trial (RCT). METHODS We conducted a hybrid effectiveness-implementation cluster RCT with a stepped wedge design in which the intervention was sequentially implemented in 8 clinics, following a usual care period. Participants were 143 youths (mean age 14.5 years, SD 1.9; 117/143, 81.8% female) with chronic pain, from which 73 were randomized to receive the active intervention. Implementation outcomes were assessed using the RE-AIM and BIT frameworks. RESULTS According to the RE-AIM framework, the WMM showed excellent reach, recruiting a sample 19% larger than the size originally planned and consenting 79.0% (143/181) of eligible referred adolescents. Effectiveness was limited, with only global impression of change showing significantly greater improvements in the treatment group; however, greater treatment engagement was associated with greater reductions in pain and disability. Adoption was excellent (all the invited clinics participated and referred patients). Implementation was acceptable, showing good user engagement and moderate adherence and positive attitudes of providers. Costs were similar to planned, with a 7% increase in funds needed to make the WMM publicly available. Maintenance was evidenced by 56 new patients downloading the app during the maintenance period and by all clinics agreeing to continue making referrals and all, but one, making new referrals. According to the BIT, 82% (60/73) of adolescents considered the treatment acceptable. In terms of adoption, 93% (68/73) downloaded the app, and all of them used it after their first log-in. In terms of appropriateness at the user level, 2 participants were unable to download the app. Perceptions of the appearance, navigation, and theme were positive. Providers perceived the WMM as a good fit for their clinic, beneficial, helpful, and resource efficient. In terms of feasibility, no technical issues were reported. In terms of fidelity, 40% (29/73) completed the treatment. Implementation costs were 7% above the budget. With regard to penetration, 56 new users accessed the app during the maintenance period. In terms of sustainability, 88% (7/8) of clinics continued recommending the WMM after the end of the study. CONCLUSIONS For the first time, a real-world digital health intervention was used as a proof of concept to test all the domains in the RE-AIM and BIT frameworks, allowing for comparisons. INTERNATIONAL REGISTERED REPORT RR2-10.1016/j.cct.2018.10.003

scholarly journals A Spatial Biosurveillance Synthetic Data Generator in R

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Drew Levin ◽  
Patrick Finley
Keyword(s):  
Power Law ◽  
Real World ◽  
Degree Distribution ◽  
Transportation Network ◽  
Synthetic Data ◽  
Data Sets ◽  
Real World Data ◽  
Data Set ◽  
Scale Free ◽  
Data Generator

ObjectiveTo develop a spatially accurate biosurveillance synthetic datagenerator for the testing, evaluation, and comparison of new outbreakdetection techniques.IntroductionDevelopment of new methods for the rapid detection of emergingdisease outbreaks is a research priority in the field of biosurveillance.Because real-world data are often proprietary in nature, scientists mustutilize synthetic data generation methods to evaluate new detectionmethodologies. Colizza et. al. have shown that epidemic spread isdependent on the airline transportation network [1], yet current datagenerators do not operate over network structures.Here we present a new spatial data generator that models thespread of contagion across a network of cities connected by airlineroutes. The generator is developed in the R programming languageand produces data compatible with the popular `surveillance’ softwarepackage.MethodsColizza et. al. demonstrate the power-law relationships betweencity population, air traffic, and degree distribution [1]. We generate atransportation network as a Chung-Lu random graph [2] that preservesthese scale-free relationships (Figure 1).First, given a power-law exponent and a desired number of cities,a probability mass function (PMF) is generated that mirrors theexpected degree distribution for the given power-law relationship.Values are then sampled from this PMF to generate an expecteddegree (number of connected cities) for each city in the network.Edges (airline connections) are added to the network probabilisticallyas described in [2]. Unconnected graph components are each joinedto the largest component using linear preferential attachment. Finally,city sizes are calculated based on an observed three-quarter power-law scaling relationship with the sampled degree distribution.Each city is represented as a customizable stochastic compartmentalSIR model. Transportation between cities is modeled similar to [2].An infection is initialized in a single random city and infection countsare recorded in each city for a fixed period of time. A consistentfraction of the modeled infection cases are recorded as daily clinicvisits. These counts are then added onto statically generated baselinedata for each city to produce a full synthetic data set. Alternatively,data sets can be generated using real-world networks, such as the onemaintained by the International Air Transport Association.ResultsDynamics such as the number of cities, degree distribution power-law exponent, traffic flow, and disease kinetics can be customized.In the presented example (Figure 2) the outbreak spreads over a 20city transportation network. Infection spreads rapidly once the morepopulated hub cities are infected. Cities that are multiple flights awayfrom the initially infected city are infected late in the process. Thegenerator is capable of creating data sets of arbitrary size, length, andconnectivity to better mirror a diverse set of observed network types.ConclusionsNew computational methods for outbreak detection andsurveillance must be compared to established approaches. Outbreakmitigation strategies require a realistic model of human transportationbehavior to best evaluate impact. These actions require test data thataccurately reflect the complexity of the real-world data they wouldbe applied to. The outbreak data generated here represents thecomplexity of modern transportation networks and are made to beeasily integrated with established software packages to allow for rapidtesting and deployment.Randomly generated scale-free transportation network with a power-lawdegree exponent ofλ=1.8. City and link sizes are scaled to reflect their weight.An example of observed daily outbreak-related clinic visits across a randomlygenerated network of 20 cities. Each city is colored by the number of flightsrequired to reach the city from the initial infection location. These generatedcounts are then added onto baseline data to create a synthetic data set forexperimentation.KeywordsSimulation; Network; Spatial; Synthetic; Data

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