Development of Rail Temperature Predictions to Minimize Risk of Track Buckle Derailments

2013 ◽  
Author(s):  
Radim Bruzek ◽  
Larry Biess ◽  
Leith Al-Nazer
Keyword(s):  
Weather Forecast ◽  
Model Verification ◽  
Order Process ◽  
Railroad Track ◽  
Significant Cost ◽  
Web Based ◽  
Increased Risk ◽  
Forecast Data ◽  
Verification Process ◽  
Web Based System

Track buckling due to excessive rail temperature is a major cause of derailments with serious consequences. To minimize the risk of derailments, slow orders are typically issued on sections of track in areas where an elevated rail temperature is expected and risk of track buckling is increased. While the slow orders are an important preventive safety measure, they are costly as they disrupt timetables and can affect time-sensitive shipments. Optimizing the slow order process would result in significant cost saving for the railroads. The Federal Railroad Administration’s (FRA’s) Office of Research and Development has sponsored the development of a model for predicting rail temperatures using real time weather forecast data and predefined track parameters and a web-based system for providing resulting information to operators. In cooperation with CSX Transportation (CSX), ENSCO Inc. conducted a model verification study by comparing actual rail temperatures measured by wayside sensors installed on railroad track near Folkston, GA, with the rail temperatures predicted by the model based on weather forecast data over the course of summer 2011. The paper outlines the procedure of the verification process together with correlation results, which are favorable. The paper also presents results of several case studies conducted on derailments attributed to track buckling. These investigations improve our understanding of conditions and temperature patterns leading to increased risk of rail buckles and validate further use of the Rail Temperature Prediction Model as track buckling prediction tool and as an aid to the railroads in making more informed decisions on slow order issuing process.

scholarly journals Development of a Web-Based System for Exploring Cancer Risk With Long-term Use of Drugs: Logistic Regression Approach

10.2196/21401 ◽  
2021 ◽  
Vol 7 (2) ◽  
pp. e21401
Author(s):  
Hsuan-Chia Yang ◽  
Md Mohaimenul Islam ◽  
Phung Anh Alex Nguyen ◽  
Ching-Huan Wang ◽  
Tahmina Nasrin Poly ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cancer Risk ◽  
Drug Exposure ◽  
Conditional Logistic Regression ◽  
Population Based ◽  
Research Database ◽  
Web Based ◽  
Increased Risk ◽  
Web Based System

Background Existing epidemiological evidence regarding the association between the long-term use of drugs and cancer risk remains controversial. Objective We aimed to have a comprehensive view of the cancer risk of the long-term use of drugs. Methods A nationwide population-based, nested, case-control study was conducted within the National Health Insurance Research Database sample cohort of 1999 to 2013 in Taiwan. We identified cases in adults aged 20 years and older who were receiving treatment for at least two months before the index date. We randomly selected control patients from the patients without a cancer diagnosis during the 15 years (1999-2013) of the study period. Case and control patients were matched 1:4 based on age, sex, and visit date. Conditional logistic regression was used to estimate the association between drug exposure and cancer risk by adjusting potential confounders such as drugs and comorbidities. Results There were 79,245 cancer cases and 316,980 matched controls included in this study. Of the 45,368 associations, there were 2419, 1302, 662, and 366 associations found statistically significant at a level of P<.05, P<.01, P<.001, and P<.0001, respectively. Benzodiazepine derivatives were associated with an increased risk of brain cancer (adjusted odds ratio [AOR] 1.379, 95% CI 1.138-1.670; P=.001). Statins were associated with a reduced risk of liver cancer (AOR 0.470, 95% CI 0.426-0.517; P<.0001) and gastric cancer (AOR 0.781, 95% CI 0.678-0.900; P<.001). Our web-based system, which collected comprehensive data of associations, contained 2 domains: (1) the drug and cancer association page and (2) the overview page. Conclusions Our web-based system provides an overview of comprehensive quantified data of drug-cancer associations. With all the quantified data visualized, the system is expected to facilitate further research on cancer risk and prevention, potentially serving as a stepping-stone to consulting and exploring associations between the long-term use of drugs and cancer risk.

Rail Temperature Prediction Model and Heat Slow Order Management

2014 ◽  
Author(s):  
Radim Bruzek ◽  
Larry Biess ◽  
Leopold Kreisel ◽  
Leith Al-Nazer
Keyword(s):  
Prediction Model ◽  
Weather Forecast ◽  
Ambient Air ◽  
Detection Theory ◽  
Model Verification ◽  
Temperature Prediction ◽  
Order Management ◽  
Wide Range ◽  
Forecast Data ◽  
The Impact

Track buckling due to excessive rail temperature may cause derailments with serious consequences. To minimize the risk of derailments, slow orders are typically issued on sections of track in areas where an elevated rail temperature is expected and risk of track buckling is increased. While slow orders are an important preventive safety measure, they are costly as they disrupt timetables and can affect time-sensitive shipments. Optimizing the slow order management process would result in significant cost saving for the railroads. The Federal Railroad Administration’s (FRA’s) Office of Research and Development has sponsored the development of a model for predicting rail temperatures using real time weather forecast data and predefined track parameters and a web-based system for providing resulting information to operators. In cooperation with CSX Transportation (CSX) and FRA, ENSCO Inc. conducted a comprehensive model verification study by comparing actual rail temperatures measured by wayside sensors installed at 23 measurement sites located across the CSX network with the rail temperatures predicted by the model based on weather forecast data over the course of spring and summer 2012. In addition to the correlation analysis, detection theory was used to evaluate the model’s ability to correctly identify instances when rail temperatures are elevated above a wide range of thresholds. Detection theory provides a good way of comparing the performance of the model to the performance of the current industry practice of estimating rail temperature based on constant offsets above predicted daily peak ambient air temperatures. As a next step in order to quantify the impact of implementation of the model on CSX operations, heat slow orders issued by CSX in 2012 on 10 selected subdivisions were compared to theoretical heat slow orders generated by the model. The paper outlines the analysis approach together with correlation, detection theory and slow order comparison results. The analysis results along with investigation of past heat related track buckle derailments indicate that the railroad would benefit from adopting the rail temperature prediction model along with flexible rail temperature thresholds. The implementation of the model will have a positive impact on safety by allowing for issuing of advance heat slow orders in more accurate, effective and targeted way.

scholarly journals Development of a Web-Based System for Exploring Cancer Risk With Long-term Use of Drugs: Logistic Regression Approach (Preprint)

2020 ◽  
Author(s):  
Hsuan-Chia Yang ◽  
Md Mohaimenul Islam ◽  
Phung Anh Alex Nguyen ◽  
Ching-Huan Wang ◽  
Tahmina Nasrin Poly ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cancer Risk ◽  
Drug Exposure ◽  
Conditional Logistic Regression ◽  
Population Based ◽  
Research Database ◽  
Web Based ◽  
Increased Risk ◽  
Web Based System

BACKGROUND Existing epidemiological evidence regarding the association between the long-term use of drugs and cancer risk remains controversial. OBJECTIVE We aimed to have a comprehensive view of the cancer risk of the long-term use of drugs. METHODS A nationwide population-based, nested, case-control study was conducted within the National Health Insurance Research Database sample cohort of 1999 to 2013 in Taiwan. We identified cases in adults aged 20 years and older who were receiving treatment for at least two months before the index date. We randomly selected control patients from the patients without a cancer diagnosis during the 15 years (1999-2013) of the study period. Case and control patients were matched 1:4 based on age, sex, and visit date. Conditional logistic regression was used to estimate the association between drug exposure and cancer risk by adjusting potential confounders such as drugs and comorbidities. RESULTS There were 79,245 cancer cases and 316,980 matched controls included in this study. Of the 45,368 associations, there were 2419, 1302, 662, and 366 associations found statistically significant at a level of <i>P</i>&lt;.05, <i>P</i>&lt;.01, <i>P</i>&lt;.001, and <i>P</i>&lt;.0001, respectively. Benzodiazepine derivatives were associated with an increased risk of brain cancer (adjusted odds ratio [AOR] 1.379, 95% CI 1.138-1.670; <i>P</i>=.001). Statins were associated with a reduced risk of liver cancer (AOR 0.470, 95% CI 0.426-0.517; <i>P</i>&lt;.0001) and gastric cancer (AOR 0.781, 95% CI 0.678-0.900; <i>P</i>&lt;.001). Our web-based system, which collected comprehensive data of associations, contained 2 domains: (1) the drug and cancer association page and (2) the overview page. CONCLUSIONS Our web-based system provides an overview of comprehensive quantified data of drug-cancer associations. With all the quantified data visualized, the system is expected to facilitate further research on cancer risk and prevention, potentially serving as a stepping-stone to consulting and exploring associations between the long-term use of drugs and cancer risk.

Rancang Bangun Sistem Penerimaan Dan Mutasi Barang Pada PDAM Tirta Kerta Raharja

Sensi Journal ◽  
2020 ◽  
Vol 6 (2) ◽  
pp. 236-246
Author(s):  
Ilamsyah Ilamsyah ◽  
Yulianto Yulianto ◽  
Tri Vita Febriani
Keyword(s):  
System Analysis ◽  
Object Oriented ◽  
Analysis Method ◽  
Web Based ◽  
Visual Modeling ◽  
Long Time ◽  
The Right ◽  
And Control ◽  
Web Based System ◽  
The Web

The right and appropriate system of receiving and transferring goods is needed by the company. In the process of receiving and transferring goods from the central warehouse to the branch warehouse at PDAM Tirta Kerta Raharja, Tangerang Regency, which is currently done manually is still ineffective and inaccurate because the Head of Subdivision uses receipt documents, namely PPBP and mutation of goods, namely MPPW in the form of paper as a submission media. The Head of Subdivision enters the data of receipt and mutation of goods manually and requires a relatively long time because at the time of demand for the transfer of goods the Head of Subdivision must check the inventory of goods in the central warehouse first. Therefore, it is necessary to hold a design of information systems for the receipt and transfer of goods from the central warehouse to a web-based branch warehouse that is already database so that it is more effective, efficient and accurate. With the web-based system of receiving and transferring goods that are already datatabed, it can facilitate the Head of Subdivision in inputing data on the receipt and transfer of goods and control of stock inventory so that the Sub Head of Subdivision can do it periodically to make it more effective, efficient and accurate. The method of data collection is done by observing, interviewing and studying literature from various previous studies, while the system analysis method uses the Waterfall method which aims to solve a problem and uses design methods with visual modeling that is object oriented with UML while programming using PHP and MySQL as a database.

Effectiveness of a web-based medication adherence tool with patient centered communication: Results of a clustered randomized controlled trial (Preprint)

2019 ◽  
Author(s):  
Jan van Lieshout ◽  
Joyca Lacroix ◽  
Aart van Halteren ◽  
Martina Teichert
Keyword(s):  
Randomized Controlled Trial ◽  
Medication Adherence ◽  
Controlled Trial ◽  
Intervention Group ◽  
Control Group ◽  
Community Pharmacies ◽  
Web Based ◽  
Randomized Controlled ◽  
Tailored Interventions ◽  
Increased Risk

BACKGROUND Growing numbers of people use medication for chronic conditions; non-adherence is common, leading to poor disease control. A newly developed web-based tool to identify an increased risk for non-adherence with related potential individual barriers might facilitate tailored interventions and improve adherence. OBJECTIVE To assess the effectiveness of the newly developed tool to improve medication adherence. METHODS A cluster randomized controlled trial assessed the effectiveness of this adherence tool in patients initiating cardiovascular or oral blood glucose lowering medication. Participants were included in community pharmacies. They completed an online questionnaire comprising an assessments of their risk for medication non-adherence and subsequently of barriers to adherence. In pharmacies belonging to the intervention group, individual barriers displayed in a graphical profile on a tablet were discussed by pharmacists and patients at high non-adherence risk in face to face meetings and shared with their general practitioners and practice nurses. Tailored interventions were initiated by the healthcare providers. Barriers of control patients were not presented or discussed and these patients received usual care. The primary outcome was the difference in medication adherence at 8 months follow-up between patients with an increased non-adherence risk from intervention and control group, calculated from dispensing data. RESULTS Data from 492 participants in 15 community pharmacies were available for analyses (intervention 253, 7 pharmacies; control 239, 8 pharmacies). The intervention had no effect on medication adherence (-0.01; 95%CI -0.59 – 0.57; P= .96), neither in the post hoc per protocol analysis (0.19; 95%CI -0.50 – 0.89; P=.58). CONCLUSIONS This study showed no effectiveness of a risk stratification and tailored intervention addressing personal barriers for medication adherence. Various potential explanations for lack of effect were identified. These explanations relate for instance to high medication adherence in the control group, study power and fidelity. Process evaluation should elicit possible improvements and inform the redesign of intervention and implementation. CLINICALTRIAL The Netherlands National Trial Register: NTR5186. Date: May 18, 2015 (http://www.trialregister.nl/trialreg/admin/rctview.asp?TC=5186)

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