Measuring Congestion and Reliability Impacts of Safety Projects

2021 ◽  
pp. 036119812110067
Author(s):  
Simona Babiceanu ◽  
Sanhita Lahiri ◽  
Mena Lockwood
Keyword(s):  
Performance Measures ◽  
Positive Impact ◽  
Operating Conditions ◽  
Vehicle Miles Traveled ◽  
Data Set ◽  
Data Points ◽  
Practical Recommendations

This study uses a suite of performance measures that was developed by taking into consideration various aspects of congestion and reliability, to assess impacts of safety projects on congestion. Safety projects are necessary to help move Virginia’s roadways toward safer operation, but can contribute to congestion and unreliability during execution, and can affect operations after execution. However, safety projects are assessed primarily for safety improvements, not for congestion. This study identifies an appropriate suite of measures, and quantifies and compares the congestion and reliability impacts of safety projects on roadways for the periods before, during, and after project execution. The paper presents the performance measures, examines their sensitivity based on operating conditions, defines thresholds for congestion and reliability, and demonstrates the measures using a set of Virginia safety projects. The data set consists of 10 projects totalling 92 mi and more than 1M data points. The study found that, overall, safety projects tended to have a positive impact on congestion and reliability after completion, and the congestion variability measures were sensitive to the threshold of reliability. The study concludes with practical recommendations for primary measures that may be used to measure overall impacts of safety projects: percent vehicle miles traveled (VMT) reliable with a customized threshold for Virginia; percent VMT delayed; and time to travel 10 mi. However, caution should be used when applying the results directly to other situations, because of the limited number of projects used in the study.

Road Closures and Freight Diversion

2012 ◽  
Vol 2269 (1) ◽  
pp. 51-57 ◽  
Author(s):  
David Pierce ◽  
Jeffrey Short
Keyword(s):  
Performance Measures ◽  
The United States ◽  
Valuable Insight ◽  
Data Set ◽  
Sensing Technology ◽  
Global Positioning ◽  
Data Points ◽  
System Data ◽  
The Impact

The FHWA-sponsored Freight Performance Measures (FPM) program generates and monitors a series of performance measures related to the freight transportation system of the United States. The primary information analyzed by the FPM program is a data set consisting of billions of Global Positioning System data points from trucks. These data can be used to demonstrate empirically changes in truck travel patterns and freight performance independent of the availability of roadside sensing technology. A case study that was based on the flooding closure of Arkansas Interstate 40 in May 2011 was presented to show how FPM data can be used to analyze diversion behavior around road closures. This type of empirical analysis is in contrast to the majority of current diversion analyses, which rely on modeling to generate results. Not only do FPM data provide a viable alternative to modeling for studying past events, but the data may provide valuable insight into the underlying assumptions of future models designed to predict the impact of disaster scenarios. By understanding more fully how previous events unfolded, planners can prepare better for the next disaster.

scholarly journals Increasing Superstructure Optimization Capacity Through Self-Learning Surrogate Models

2021 ◽  
Vol 3 ◽  
Author(s):  
Julia Granacher ◽  
Ivan Daniel Kantor ◽  
François Maréchal
Keyword(s):  
Active Learning ◽  
Optimization Problems ◽  
Simulation Models ◽  
Surrogate Models ◽  
Operating Conditions ◽  
Model Complexity ◽  
Computational Time ◽  
Data Set ◽  
Prediction Quality ◽  
Data Points

Simulation-based optimization models are widely applied to find optimal operating conditions of processes. Often, computational challenges arise from model complexity, making the generation of reliable design solutions difficult. We propose an algorithm for replacing non-linear process simulation models integrated in multi-level optimization of a process and energy system superstructure with surrogate models, applying an active learning strategy to continuously enrich the database on which the surrogate models are trained and evaluated. Surrogate models are generated and trained on an initial data set, each featuring the ability to quantify the uncertainty with which a prediction is made. Until a defined prediction quality is met, new data points are continuously labeled and added to the training set. They are selected from a pool of unlabeled data points based on the predicted uncertainty, ensuring a rapid improvement of surrogate quality. When applied in the optimization superstructure, the surrogates can only be used when the prediction quality for the given data point reaches a specified threshold, otherwise the original simulation model is called for evaluating the process performance and the newly obtained data points are used to improve the surrogates. The method is tested on three simulation models, ranging in size and complexity. The proposed approach yields mean squared errors of the test prediction below 2% for all cases. Applying the active learning approach leads to better predictions compared to random sampling for the same size of database. When integrated in the optimization framework, simpler surrogates are favored in over 60% of cases, while the more complex ones are enabled by using simulation results generated during optimization for improving the surrogates after the initial generation. Significant time savings are recorded when using complex process simulations, though the advantage gained for simpler processes is marginal. Overall, we show that the proposed method saves time and adds flexibility to complex superstructure optimization problems that involve optimizing process operating conditions. Computational time can be greatly reduced without penalizing result quality, while the continuous improvement of surrogates when simulation is used in the optimization leads to a natural refinement of the model.

scholarly journals The Study of Multiple Classes Boosting Classification Method Based on Local Similarity

Algorithms ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 37
Author(s):  
Shixun Wang ◽  
Qiang Chen
Keyword(s):  
Image Retrieval ◽  
Loss Function ◽  
Single Mode ◽  
Local Similarity ◽  
Text And Image ◽  
Data Set ◽  
Standard Data ◽  
Weak Learner ◽  
Great Progress ◽  
Data Points

Boosting of the ensemble learning model has made great progress, but most of the methods are Boosting the single mode. For this reason, based on the simple multiclass enhancement framework that uses local similarity as a weak learner, it is extended to multimodal multiclass enhancement Boosting. First, based on the local similarity as a weak learner, the loss function is used to find the basic loss, and the logarithmic data points are binarized. Then, we find the optimal local similarity and find the corresponding loss. Compared with the basic loss, the smaller one is the best so far. Second, the local similarity of the two points is calculated, and then the loss is calculated by the local similarity of the two points. Finally, the text and image are retrieved from each other, and the correct rate of text and image retrieval is obtained, respectively. The experimental results show that the multimodal multi-class enhancement framework with local similarity as the weak learner is evaluated on the standard data set and compared with other most advanced methods, showing the experience proficiency of this method.

352 Feasibility of Hypnosis as Adjunctive Treatment for Subjective Sleep Disturbance: A Pilot Study and Proof of Concept

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A140-A141
Author(s):  
Emma Zhao ◽  
Afik Faerman ◽  
David Spiegel
Keyword(s):  
Sleep Quality ◽  
Sleep Disturbances ◽  
Positive Impact ◽  
Self Report ◽  
Adjunctive Treatment ◽  
Current Evidence ◽  
Data Set ◽  
Sleep Complaints ◽  
Subjective Sleep ◽  
Before And After

Abstract Introduction Hypnosis-based interventions have been shown to have a positive impact on several dimensions of sleep health. However, current evidence is limited as only a paucity of studies included populations with sleep complaints. Here we present a pilot data set to demonstrate the feasibility of developing a hypnosis-based adjunctive treatment for subjective sleep complaints. Methods Eleven adults (42% female; mean age 45±16.87 years) who sought treatment at the Stanford Sleep Medicine Center or Center for Integrative Medicine for subjective sleep complaints received hypnosis as adjunctive treatment. Self-report questionnaires were used to assess the weekly frequency of subjective sleep disturbances experienced before and after treatment, as well as 5-point Likert scale ratings of perceived qualitative improvement in symptom severity and overall sleep quality. Results Five participants (45%) reported a reduction in symptom frequency and severity after hypnosis treatment. All five participants attributed at least some of the improvement to hypnosis treatment. Most participants (63%) observed post-treatment improvements in their overall sleep quality. No participants reported adverse effects of hypnosis. Conclusion Results suggest hypnosis-based adjunctive treatment may be effective for alleviating subjective sleep disturbances. The findings serve as preliminary support for further randomly controlled trials in larger samples. Support (if any):

Railroad Integration and Uneven Development on the European Periphery, 1870–1910

2021 ◽  
Vol 45 (2) ◽  
pp. 261-289
Author(s):  
Eduard J. Alvarez-Palau ◽  
Alfonso Díez-Minguela ◽  
Jordi Martí-Henneberg
Keyword(s):  
Economic Development ◽  
Positive Impact ◽  
Data Set ◽  
Per Capita Gdp ◽  
Catching Up ◽  
Nation States ◽  
Railroad Network ◽  
Per Capita ◽  
European Periphery ◽  
The Relationship

AbstractThis study explores the relationship between railroad integration and regional development on the European periphery between 1870 and 1910, based on a regional data set including 291 spatial units. Railroad integration is proxied by railroad density, while per capita GDP is used as an indicator of economic development. The period under study is of particular relevance as it has been associated with the second wave of railroad construction in Europe and also coincides with the industrialization of most of the continent. Overall, we found that railroads had a significant and positive impact on the growth of per capita GDP across Europe. The magnitude of this relationship appears to be relatively modest, but the results obtained are robust with respect to a number of different specifications. From a geographical perspective, we found that railroads had a significantly greater influence on regions located in countries on the northern periphery of Europe than in other outlying areas. They also helped the economies of these areas to begin the process of catching up with the continent’s industrialized core. In contrast, the regions on the southern periphery showed lower levels of economic growth, with this exacerbating the preexisting divergence in economic development. The expansion of the railroad network in them was unable to homogenize the diffusion of economic development and tended to further benefit the regions that were already industrialized. In most of the cases, the capital effect was magnified, and this contributed to the consolidation of newly created nation-states.

The Influences of Reaction Conditions on Phenanthrene Hydrogenation over NiW/Al2O3 Catalyst

2012 ◽  
Vol 512-515 ◽  
pp. 2200-2206
Author(s):  
Kun Wang ◽  
Jun Guan ◽  
De Min He ◽  
Qiu Min Zhang
Keyword(s):  
Positive Impact ◽  
Volume Ratio ◽  
Operating Conditions ◽  
Reaction Conditions ◽  
Remarkable Effect ◽  
Ms Analysis ◽  
Temperature And Pressure ◽  
Cracking Products

Hydrogenation of phenanthrene (PHE HYD) was investigated over a commercial NiW/Al2O3catalyst under practical reaction conditions. GC-MS analysis was utilized to identify the numerous products formed during PHE HYD. The products included dihydrophenanthrene (DHP), 1,2,3,4-tetrahydrophenanthrene (THP), sym-octahydrophenanthrene (1,8-OHP), asym-octahydrophenanthrene (1,10-OHP) and perhydrophenanthrene (PHP), but the cracking products were not found under the reaction conditions. The effects of operating conditions such as temperature, pressure and H2/liquor on PHE HYD were tested in detail. It is found that temperature and pressure had remarkable effect on PHE HYD, but volume ratio of H2/liquor had little effect on PHE HYD at the observation range. The addition of decalin had a positive impact on PHE HYD; it could increase the conversion of PHE and the selectivity to PHP.

scholarly journals Enhancing the performance of gastrointestinal tumour board by improving documentation

2018 ◽  
Vol 7 (1) ◽  
pp. e000168 ◽  
Author(s):  
Roaa Saleh Alsuhaibani ◽  
Hajer Alzahrani ◽  
Ghada Algwaiz ◽  
Haneen Alfarhan ◽  
Ashwaq Alolayan ◽  
...  
Keyword(s):  
Performance Measures ◽  
Multidisciplinary Team ◽  
Positive Impact ◽  
Team Approach ◽  
Rapid Improvement ◽  
Past Performance ◽  
Pdsa Cycle ◽  
Tumour Board ◽  
Medical Records System ◽  
Documentation Tool

Tumour board contributes to providing better patient care by using a multidisciplinary team approach. In the efforts of evaluating the performance of the gastrointestinal tumour board at our institution, it was difficult to assess past performance due to lack of proper use of standardised documentation tool. This project aimed at improving adherence to the documentation tool and its recommendations in order to obtain performance measures for the tumour board. A multidisciplinary team and a plan were developed to improve documentation. Four rapid improvement cycles, Plan–Do–Study–Act (PDSA) cycles, were conducted. The first cycle focused on updating the case discussion summary form (CDSF) based on experts’ input and previous identified deficiencies to enhance documentation and improve performance. The second PDSA cycle aimed at incorporating the CDSF into the electronic medical records system and assessing its functionality. The third cycle was to orient and train staff on using the form and launching it. The fourth PDSA cycle aimed at assessing the ability to obtain tumour board performance measures. Adherence to completion of the CDSF improved from 82% (baseline) to 94% after the fourth PDSA cycle. Over 104 consecutive cases discussed in the tumour board between January and July 2016 and 76 cases discussed in 2015, results were as follows: adherence to National Comprehensive Cancer Network guidelines in 2016 was observed in 141 (95%) recommendations, while it was observed in 90 (92%) recommendations in 2015. Changes in the management plans were observed in 37 (36%) cases in 2016 and in 6 (8%) cases in 2015. Regarding tumour board recommendations, 87% were done within 3 months of tumour board discussion in 2016, while 69% were done in 2015. Implementing electronic standardised documentation tool improved communication among the team and enabled getting accurate data about performance measures of the tumour board with positive impact on healthcare process and outcomes.

Generation of a Complete Profile for Porosity Log While Drilling Complex Lithology by Employing the Artificial Intelligence

2021 ◽  
Author(s):  
Ahmed Al-Sabaa ◽  
Hany Gamal ◽  
Salaheldin Elkatatny
Keyword(s):  
Artificial Intelligence ◽  
Prediction Model ◽  
Real Time ◽  
Storage Capacity ◽  
Data Set ◽  
Drilling Parameters ◽  
Unseen Data ◽  
Rock Porosity ◽  
Data Points ◽  
Logging Tool

Abstract The formation porosity of drilled rock is an important parameter that determines the formation storage capacity. The common industrial technique for rock porosity acquisition is through the downhole logging tool. Usually logging while drilling, or wireline porosity logging provides a complete porosity log for the section of interest, however, the operational constraints for the logging tool might preclude the logging job, in addition to the job cost. The objective of this study is to provide an intelligent prediction model to predict the porosity from the drilling parameters. Artificial neural network (ANN) is a tool of artificial intelligence (AI) and it was employed in this study to build the porosity prediction model based on the drilling parameters as the weight on bit (WOB), drill string rotating-speed (RS), drilling torque (T), stand-pipe pressure (SPP), mud pumping rate (Q). The novel contribution of this study is to provide a rock porosity model for complex lithology formations using drilling parameters in real-time. The model was built using 2,700 data points from well (A) with 74:26 training to testing ratio. Many sensitivity analyses were performed to optimize the ANN model. The model was validated using unseen data set (1,000 data points) of Well (B), which is located in the same field and drilled across the same complex lithology. The results showed the high performance for the model either for training and testing or validation processes. The overall accuracy for the model was determined in terms of correlation coefficient (R) and average absolute percentage error (AAPE). Overall, R was higher than 0.91 and AAPE was less than 6.1 % for the model building and validation. Predicting the rock porosity while drilling in real-time will save the logging cost, and besides, will provide a guide for the formation storage capacity and interpretation analysis.

Association of Medication-Assisted Treatment and Age With Treatment Completion Among Veterans Seeking Non-VA Treatment for Opioid use Disorder at Non-Veterans Health Administration Facilities

2021 ◽  
Author(s):  
Joseph G Pickard ◽  
Carissa van den Berk-Clark ◽  
Monica M Matthieu
Keyword(s):  
Positive Impact ◽  
Treatment Completion ◽  
Armed Forces ◽  
Opioid Use Disorder ◽  
Prescription Opioid ◽  
Health Administration ◽  
Opioid Use ◽  
Data Set ◽  
Medication Assisted Treatment

ABSTRACT Background Medication-assisted treatment has been shown to be effective in treating opioid use disorder among both older adults and veterans of U.S. Armed Forces. However, limited evidence exists on MAT’s differential effect on treatment completion across age groups. This study aims to ascertain the role of MAT and age in treatment completion among veterans seeking treatment in non–Department of Veterans Affairs healthcare facilities for opioid use disorder. Methods We used the Treatment Episode Data Set—Discharges (TEDS-D; 2006-2017) to examine trends in treatment and MAT usage over time and TEDS-2017 to determine the role of age and MAT in treatment completion. We examined a subset of those who self-identified as veterans and who sought treatment for an opioid use disorder. Results Veterans presented in treatment more often as heroin users than prescription opioid users, and older veterans were more likely to get MAT than younger veterans. We found that before propensity score matching, MAT initially appeared to be associated with a lower likelihood of treatment completion in inpatient ($\beta $ = −1.47, 95% CI −1.56 to −1.39) and outpatient ($\beta $ = −1.40, 95% CI −2.21 to −0.58) settings, and age (50+ years) appeared to mediate the effect of MAT on treatment completion ($\beta $ = −0.54, 95% CI −0.87 to −0.21). After matching, older veterans were more likely to complete substance use disorder treatment ($\beta $ = 0.21, 95% CI 0.01-0.42), while age no longer mediated the effect of MAT, and MAT had a significant positive impact on treatment completion in detox settings ($\beta $ = 1.36, 95% CI 1.15-1.50) and inpatient settings ($\beta $ = 1.54, 95% CI 1.37 -1.71). Conclusion The results show that age plays an important role in outpatient treatment completion, while MAT plays an important role in inpatient treatment completion. Implications for veterans are discussed.

A REVIEW OF PRACTICAL METHODS FOR REDUCING UNDERWATER NOISE POLLUTION FROM LARGE COMMERCIAL VESSELS

2021 ◽  
Vol 154 (A2) ◽  
Author(s):  
R C Leaper ◽  
M R Renilson
Keyword(s):  
Noise Pollution ◽  
Operating Conditions ◽  
Wake Flow ◽  
Underwater Noise ◽  
Data Set ◽  
Marine Life ◽  
Propeller Design ◽  
Widespread Agreement ◽  
Noise Measurements ◽  
Considerable Concern

Underwater noise pollution from shipping is of considerable concern for marine life, particularly due to the potential for raised ambient noise levels in the 10-300Hz frequency range to mask biological sounds. There is widespread agreement that reducing shipping noise is both necessary and feasible, and the International Maritime Organization is actively working on the issue. The main source of noise is associated with propeller cavitation, and measures to improve propeller design and wake flow may also reduce noise. It is likely that the noisiest 10% of ships generate the majority of the noise impact, and it may be possible to quieten these vessels through measures that also improve efficiency. However, an extensive data set of full scale noise measurements of ships under operating conditions is required to fully understand how different factors relate to noise output and how noise reduction can be achieved alongside energy saving measures.

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