A hybrid predictive methodology for head checks in railway infrastructure

2021 ◽  
pp. 095440972199361
Author(s):  
Annemieke Meghoe ◽  
Ali Jamshidi ◽  
Richard Loendersloot ◽  
Tiedo Tinga
Keyword(s):  
Hybrid Method ◽  
Data Driven ◽  
Evolution Analysis ◽  
Railway Infrastructure ◽  
Model Defect ◽  
The Us ◽  
Defect Evolution ◽  
Different Characteristics ◽  
Future Condition ◽  
Rail Surface Defect

This paper presents a hybrid method to assess the rail health with the focus on a specific type of rail surface defect called head check. The proposed method uses physics-based and data-driven models in order to model defect initiation and defect evolution on a rail for a given rail traffic tonnage. Ultrasonic (US) and Eddy Current (EC) defect detection measurements are used to provide Infrastructure Managers (IMs) with insight in the current rail condition. The defect initiation results obtained from the first part of the hybrid method which consists of the physics-based model is successfully validated with the EC measurements. Furthermore, the US and EC measurements are utilized to derive a data-driven model for defect evolution. Finally, a set of robust and predictive Key Performance Indicators (KPIs) are proposed to quantify the future condition of the rail based on different characteristics of rail health resulting from the defect initiation and defect evolution analysis.

scholarly journals Data-Driven Study on the Sustainable Log Movements: Impact of Rail Car Fleet Size on Freight Storage and Car Idling

2020 ◽  
Vol 12 (11) ◽  
pp. 4563
Author(s):  
Sangpil Ko ◽  
Pasi Lautala ◽  
Kuilin Zhang
Keyword(s):  
Service Life ◽  
Seasonal Dynamics ◽  
Lake Superior ◽  
Data Driven ◽  
Short Term ◽  
Fleet Size ◽  
Interesting Observation ◽  
The Us ◽  
Term Storage ◽  
Scale Data

Rail car availability and the challenges associated with the seasonal dynamics of log movements have received growing attentions in the Lake Superior region of the US, as a portion of rail car fleet is close to reaching the end of its service life. This paper proposes a data-driven study on the rail car peaking issue to explore the fleet of rail cars dedicated to being used for log movements in the region, and to evaluate how the number of cars affects both the storage need at the sidings and the time the cars are idled. This study is based on the actual log scale data collected from a group of forest companies in cooperation with the Lake State Shippers Association (LSSA). The results of our analysis revealed that moving the current log volumes in the region would require approximately 400–600 dedicated and shared log cars in ideal conditions, depending on the specific month. While the higher fleet size could move the logs as they arrive to the siding, the lower end would nearly eliminate the idling of rail cars and enable stable volumes throughout the year. However, this would require short-term storage and additional handling of logs at the siding, both elements that increase the costs for shippers. Another interesting observation was the fact that the reduction of a single day in the loading/unloading process (2.5 to 1.5 days) would eliminate almost 100 cars (20%) of the fleet without reduction in throughput.

Generalized Cumulative Residual Entropy of Time Series Based on Permutation Patterns

2021 ◽  
pp. 2150055
Author(s):  
Qin Zhou ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Stock Markets ◽  
Multiple Scales ◽  
Permutation Entropy ◽  
Residual Entropy ◽  
Permutation Patterns ◽  
Cumulative Residual Entropy ◽  
The Us ◽  
Cumulative Residual ◽  
Different Characteristics

Cumulative residual entropy (CRE) has been suggested as a new measure to quantify uncertainty of nonlinear time series signals. Combined with permutation entropy and Rényi entropy, we introduce a generalized measure of CRE at multiple scales, namely generalized cumulative residual entropy (GCRE), and further propose a modification of GCRE procedure by the weighting scheme — weighted generalized cumulative residual entropy (WGCRE). The GCRE and WGCRE methods are performed on the synthetic series to study properties of parameters and verify the validity of measuring complexity of the series. After that, the GCRE and WGCRE methods are applied to the US, European and Chinese stock markets. Through data analysis and statistics comparison, the proposed methods can effectively distinguish stock markets with different characteristics.

scholarly journals Interchangeability of biosimilars: A study of expert views and visions regarding the science and substitution

PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262537
Author(s):  
Louise C. Druedahl ◽  
Sofia Kälvemark Sporrong ◽  
Timo Minssen ◽  
Hans Hoogland ◽  
Marie Louise De Bruin ◽  
...  
Keyword(s):  
Pharmaceutical Industry ◽  
Scientific Data ◽  
Data Driven ◽  
Policy Debate ◽  
Structured Interviews ◽  
Study Objective ◽  
Scientific Certainty ◽  
The Us ◽  
Almost All ◽  
Near Future

Healthcare systems have reached a critical point regarding the question of whether biosimilar substitution should become common practice. To move the discussion forward, the study objective was to investigate the views of experts from medicines agencies and the pharmaceutical industry on the science underpinning interchangeability of biosimilars. We conducted an empirical qualitative study using semi-structured interviews informed by a cross-disciplinary approach encompassing regulatory science, law, and pharmaceutical policy. In total 25 individuals with experience within biologics participated during September 2018–August 2019. Eight participants were EU national medicines authority regulators, and 17 had pharmaceutical industry background: five from two originator-only companies, four from two companies with both biosimilar and originator products, and eight from seven biosimilar-only companies. Two analysts independently conducted inductive content analysis, resulting in data-driven themes capturing the meaning of the data. The participants reported that interchangeability was more than a scientific question of likeness between biosimilar and reference products: it also pertained to regulatory practices and trust. Participants were overall confident in the science behind exchanging biosimilar products for the reference products via switching, i.e., with physician involvement. However, their opinions differed regarding the scientific risk associated with biosimilar substitution, i.e., without physician involvement. Almost all participants saw no need for additional scientific data to support substitution. Moreover, the participants did not believe that switching studies, as required in the US, were appropriate for obtaining scientific certainty due to their small size. It is unclear why biosimilar switching is viewed as scientifically safer than substitution; therefore, we expect greater policy debate on biosimilar substitution in the near future. We urge European and UK policymakers and regulators to clarify their visions for biosimilar substitution; the positions of these two frontrunners are likely to influence other jurisdictions on the future of biosimilar use.

scholarly journals Suicide disparities across metropolitan areas in the US: A comparative assessment of socio-environmental factors using a data-driven predictive approach

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258824
Author(s):  
Sayanti Mukherjee ◽  
Zhiyuan Wei
Keyword(s):  
Environmental Factors ◽  
Goodness Of Fit ◽  
Metropolitan Areas ◽  
Predictive Accuracy ◽  
Climatic Factors ◽  
Data Driven ◽  
Us States ◽  
Suicide Rates ◽  
Significant Difference ◽  
The Us

Disparity in suicide rates across various metropolitan areas in the US is growing. Besides personal genomics and pre-existing mental health conditions affecting individual-level suicidal behaviors, contextual factors are also instrumental in determining region-/community-level suicide risk. However, there is a lack of quantitative approach to model the complex associations and interplays of the socio-environmental factors with the regional suicide rates. In this paper, we propose a holistic data-driven framework to model the associations of socio-environmental factors (demographic, socio-economic, and climate) with the suicide rates, and compare the key socio-environmental determinants of suicides across the large and medium/small metros of the vulnerable US states, leveraging a suite of advanced statistical learning algorithms. We found that random forest outperforms all the other models in terms of both in-sample goodness-of-fit and out-of-sample predictive accuracy, which is then used for statistical inferencing. Overall, our findings show that there is a significant difference in the relationships of socio-environmental factors with the suicide rates across the large and medium/small metropolitan areas of the vulnerable US states. Particularly, suicides in medium/small metros are more sensitive to socio-economic and demographic factors, while that in large metros are more sensitive to climatic factors. Our results also indicate that non-Hispanics, native Hawaiian or Pacific islanders, and adolescents aged 15-29 years, residing in the large metropolitan areas, are more vulnerable to suicides compared to those living in the medium/small metropolitan areas. We also observe that higher temperatures are positively associated with higher suicide rates, with large metros being more sensitive to such association compared to that of the medium/small metros. Our proposed data-driven framework underscores the future opportunities of using big data analytics in analyzing the complex associations of socio-environmental factors and inform policy actions accordingly.

scholarly journals The Lyme and Tickborne Disease Dashboard: A map-based resource to promote public health awareness and research collaboration

PLoS ONE ◽  
2021 ◽  
Vol 16 (12) ◽  
pp. e0260122
Author(s):  
Frank C. Curriero ◽  
Cara Wychgram ◽  
Alison W. Rebman ◽  
Anne E. Corrigan ◽  
Anton Kvit ◽  
...  
Keyword(s):  
Public Health ◽  
Spatial Data ◽  
Scientific Collaboration ◽  
One Health ◽  
Research Collaboration ◽  
Data Driven ◽  
Data Sets ◽  
Future Directions ◽  
The Us ◽  
Spatial Data Sets

With the incidence of Lyme and other tickborne diseases on the rise in the US and globally, there is a critical need for data-driven tools that communicate the magnitude of this problem and help guide public health responses. We present the Johns Hopkins Lyme and Tickborne Disease Dashboard (https://www.hopkinslymetracker.org/), a new tool that harnesses the power of geography to raise awareness and fuel research and scientific collaboration. The dashboard is unique in applying a geographic lens to tickborne diseases, aiming not only to become a global tracker of tickborne diseases but also to contextualize their complicated geography with a comprehensive set of maps and spatial data sets representing a One Health approach. We share our experience designing and implementing the dashboard, describe the main features, and discuss current limitations and future directions.

scholarly journals Mutation density changes in SARS-CoV-2 are related to the pandemic stage but to a lesser extent in the dominant strain with mutations in spike and RdRp

2020 ◽  
Author(s):  
Doğa Eskier ◽  
Aslı Suner ◽  
Gökhan Karakülah ◽  
Yavuz Oktay
Keyword(s):  
Viral Genome ◽  
Time Dependent ◽  
Origin And Evolution ◽  
Gene Level ◽  
The Us ◽  
The World ◽  
Before And After ◽  
Different Characteristics ◽  
First Time ◽  
New Mutations

AbstractSince its emergence in Wuhan, China in late 2019, the origin and evolution of SARS-CoV-2 have been among the most debated issues related to COVID-19. Throughout its spread around the world, the viral genome continued acquiring new mutations and some of them became widespread. Among them, 14408 C>T and 23403 A>G mutations in RdRp and S, respectively, became dominant in Europe and the US, which led to debates regarding their effects on the mutability and transmissibility of the virus. In this study, we aimed to investigate possible differences between time-dependent variation of mutation densities (MDe) of viral strains that carry these two mutations and those that do not. Our analyses at the genome and gene level led to two important findings: First, time-dependent changes in the average MDe of circulating SARS-CoV-2 genomes showed different characteristics before and after the beginning of April, when daily new case numbers started levelling off. Second, this pattern was much delayed or even non-existent for the “mutant” (MT) strain that harbored both 14408 C>T and 23403 A>G mutations. Although these differences were not limited to a few hotspots, it is intriguing that the MDe increase is most evident in two critical genes, S and Orf1ab, which are also the genes that harbor the defining mutations of the MT genotype. The nature of these unexpected relationships warrant further research.

Data driven condition assessment of railway infrastructure

2021 ◽  
pp. 693-693
Author(s):  
C. Hoelzl ◽  
V. Dertimanis ◽  
E. Chatzi ◽  
D. Winklehner ◽  
S. Züger ◽  
...  
Keyword(s):  
Condition Assessment ◽  
Data Driven ◽  
Railway Infrastructure
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