Evaluating Advancements in Bluetooth Technology for Travel Time and Segment Speed Studies

2020 ◽  
Vol 2674 (4) ◽  
pp. 193-204
Author(s):  
Drew Cotten ◽  
Julius Codjoe ◽  
Matthew Loker
Keyword(s):  
Travel Time ◽  
Performance Metrics ◽  
Radar Data ◽  
Data Set ◽  
Video Footage ◽  
Benchmark Data ◽  
Match Rate ◽  
Significant Difference ◽  
Transportation Research ◽  
Urban Arterial

Application of Bluetooth technology has become ubiquitous in the current age of transportation research. The purpose of this article is to explore advancements of Bluetooth technology, applied to transportation studies, using performance metrics such as match rate, travel time, and segment speed through analyzing collected data from several Bluetooth devices. Specifically, the study explores the performance of two advanced Bluetooth devices coupled with classic Bluetooth technology: the demodulator (BT DM), and the low-energy Bluetooth signal additional component (BLE). Data were collected in two locations/phases: (1) along a 0.59-mi segment of interstate and (2) along a 0.52-mi segment of an urban arterial road in Baton Rouge, LA. The data collected were compared with benchmark data sets, gathered during the same period, using manual counts from video footage, radar data, and floating car data (FCD). Comparative analysis showed that BLE produced significantly higher matched rates than BT DM. Furthermore, BLE was able to maintain higher accuracy with increased levels of detection. Results of a Kruskal–Wallis test showed BT DM to have a statistically significant difference with FCD during only one out of three peak periods along the interstate segment. However, BLE matched closest with FCD along the interstate, but shared a significant difference with the benchmark data set during two peak periods along the urban arterial roadway. Considering the level of detection with the accuracy of travel times and segment speeds when compared with the benchmark data, it was evident that the BLE performed better than the BT DM.

Statistical Analysis of Mean Route Travel Times on Seven Primary Arterials in Chicago Metropolitan Region

1998 ◽  
Vol 1625 (1) ◽  
pp. 109-117
Author(s):  
Stephen C. Laffey ◽  
Louie Nan Liu ◽  
Ed J. Christopher
Keyword(s):  
Regression Analysis ◽  
Travel Time ◽  
Sampling Strategy ◽  
Metropolitan Region ◽  
Travel Times ◽  
Time Data ◽  
Data Set ◽  
Travel Time Data ◽  
Significant Difference ◽  
Single Data

Presented is a statistical approach for examining multiple years of travel time data to determine if any trends can be quantified reliably by using only 4 years of observed data. Empirical data for 80 routes covering more than 4025 km were collected in the summers of 1994, 1995, 1996, and 1997 and compiled into a database for public release in the fall of 1997. From this database, seven routes were selected then sampled over all 4 years. These seven routes are primary arterials of regional significance in northeastern Illinois. A multiple regression analysis was performed on the seven arterials to determine if there was a statistically significant difference in the observed mean route travel times over the 4-year period. The regression analysis indicated that only one of seven routes experienced a statistically significant change in directional route travel time between 1994 and 1997. Because only one of seven routes was significantly different, the analyst may merge multiple years of data for an individual route into a single data set to build a more robust database. Also, quantifiable change in travel time may be difficult to perceive year by year. This has significant implications for the design of a sampling strategy that needs to measure performance on approximately 28 980 directional route km of roadways. It would be better to sample fewer routes more intensely on a regular interval than to sample many routes lightly every year.

Finite mixture survival model for examining the variability of urban arterial travel time for buses, passenger cars and taxis

2020 ◽  
Vol 14 (12) ◽  
pp. 1524-1533 ◽  
Author(s):  
Xinzhi Zhong ◽  
Yajie Zou ◽  
Zhi Dong ◽  
Shaoxin Yuan ◽  
Muhammad Ijaz
Keyword(s):  
Travel Time ◽  
Survival Model ◽  
Finite Mixture ◽  
Passenger Cars ◽  
Urban Arterial

scholarly journals An efficient technique for CT scan images classification of COVID-19

2020 ◽  
pp. 1-14
Author(s):  
Esraa Hassan ◽  
Noha A. Hikal ◽  
Samir Elmuogy
Keyword(s):  
Neural Network ◽  
Diagnostic Tool ◽  
Deep Neural Network ◽  
Data Augmentation ◽  
Performance Metrics ◽  
Classification Model ◽  
Data Set ◽  
Training Models ◽  
The Earth ◽  
Diagnostic Time

Nowadays, Coronavirus (COVID-19) considered one of the most critical pandemics in the earth. This is due its ability to spread rapidly between humans as well as animals. COVID_19 expected to outbreak around the world, around 70 % of the earth population might infected with COVID-19 in the incoming years. Therefore, an accurate and efficient diagnostic tool is highly required, which the main objective of our study. Manual classification was mainly used to detect different diseases, but it took too much time in addition to the probability of human errors. Automatic image classification reduces doctors diagnostic time, which could save human’s life. We propose an automatic classification architecture based on deep neural network called Worried Deep Neural Network (WDNN) model with transfer learning. Comparative analysis reveals that the proposed WDNN model outperforms by using three pre-training models: InceptionV3, ResNet50, and VGG19 in terms of various performance metrics. Due to the shortage of COVID-19 data set, data augmentation was used to increase the number of images in the positive class, then normalization used to make all images have the same size. Experimentation is done on COVID-19 dataset collected from different cases with total 2623 where (1573 training,524 validation,524 test). Our proposed model achieved 99,046, 98,684, 99,119, 98,90 In terms of Accuracy, precision, Recall, F-score, respectively. The results are compared with both the traditional machine learning methods and those using Convolutional Neural Networks (CNNs). The results demonstrate the ability of our classification model to use as an alternative of the current diagnostic tool.

scholarly journals Microarchitectural Characterization on a Mobile Workload

2021 ◽  
Vol 11 (3) ◽  
pp. 1225
Author(s):  
Woohyong Lee ◽  
Jiyoung Lee ◽  
Bo Kyung Park ◽  
R. Young Chul Kim
Keyword(s):  
Performance Monitoring ◽  
Performance Metrics ◽  
Performance Comparison ◽  
Instruction Level Parallelism ◽  
Data Set ◽  
Performance Events ◽  
Hardware Performance Counters ◽  
On Chip ◽  
The Comparative Study ◽  
Level Parallelism

Geekbench is one of the most referenced cross-platform benchmarks in the mobile world. Most of its workloads are synthetic but some of them aim to simulate real-world behavior. In the mobile world, its microarchitectural behavior has been reported rarely since the hardware profiling features are limited to the public. As a popular mobile performance workload, it is hard to find Geekbench’s microarchitecture characteristics in mobile devices. In this paper, a thorough experimental study of Geekbench performance characterization is reported with detailed performance metrics. This study also identifies mobile system on chip (SoC) microarchitecture impacts, such as the cache subsystem, instruction-level parallelism, and branch performance. After the study, we could understand the bottleneck of workloads, especially in the cache sub-system. This means that the change of data set size directly impacts performance score significantly in some systems and will ruin the fairness of the CPU benchmark. In the experiment, Samsung’s Exynos9820-based platform was used as the tested device with Android Native Development Kit (NDK) built binaries. The Exynos9820 is a superscalar processor capable of dual issuing some instructions. To help performance analysis, we enable the capability to collect performance events with performance monitoring unit (PMU) registers. The PMU is a set of hardware performance counters which are built into microprocessors to store the counts of hardware-related activities. Throughout the experiment, functional and microarchitectural performance profiles were fully studied. This paper describes the details of the mobile performance studies above. In our experiment, the ARM DS5 tool was used for collecting runtime PMU profiles including OS-level performance data. After the comparative study is completed, users will understand more about the mobile architecture behavior, and this will help to evaluate which benchmark is preferable for fair performance comparison.

scholarly journals Ownership Variation in Violated Regulations and National Care Standards: Evidence From Social Care Providers

2021 ◽  
pp. 089976402110014
Author(s):  
Anders M. Bach-Mortensen ◽  
Ani Movsisyan
Keyword(s):  
Social Care ◽  
Negative Binomial ◽  
Third Sector ◽  
Care Providers ◽  
Data Set ◽  
Logistic Regression Models ◽  
For Profit ◽  
Care Services ◽  
Significant Difference ◽  
Research Gap

Social care services are increasingly provisioned in quasi-markets in which for-profit, public, and third sector providers compete for contracts. Existing research has investigated the implications of this development by analyzing ownership variation in latent outcomes such as quality, but little is known about whether ownership predicts variation in more concrete outcomes, such as violation types. To address this research gap, we coded publicly available inspection reports of social care providers regulated by the Care Inspectorate in Scotland and created a novel data set enabling analysis of ownership variation in violations of (a) regulations, and (b) national care standards over an entire inspection year ( n = 4,178). Using negative binomial and logistic regression models, we find that for-profit providers are more likely to violate non-enforceable outcomes (national care standards) relative to other ownership types. We did not identify a statistically significant difference between for-profit and third sector providers with regard to enforceable outcomes (regulations).

scholarly journals Adverse reactions of targeted therapy in cancer patients: a retrospective study of hospital medical data in China

BMC Cancer ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ruofei Du ◽  
Xin Wang ◽  
Lixia Ma ◽  
Leon M. Larcher ◽  
Han Tang ◽  
...  
Keyword(s):  
Targeted Therapy ◽  
Cancer Patients ◽  
Adverse Reactions ◽  
Cox Regression ◽  
Target Therapy ◽  
Skin Damage ◽  
Data Set ◽  
Patients With Cancer ◽  
Number Of Patients ◽  
Significant Difference

Abstract Background The adverse reactions (ADRs) of targeted therapy were closely associated with treatment response, clinical outcome, quality of life (QoL) of patients with cancer. However, few studies presented the correlation between ADRs of targeted therapy and treatment effects among cancer patients. This study was to explore the characteristics of ADRs with targeted therapy and the prognosis of cancer patients based on the clinical data. Methods A retrospective secondary data analysis was conducted within an ADR data set including 2703 patients with targeted therapy from three Henan medical centers of China between January 2018 and December 2019. The significance was evaluated with chi-square test between groups with or without ADRs. Univariate and multivariate logistic regression with backward stepwise method were applied to assess the difference of pathological characteristics in patients with cancer. Using the univariate Cox regression method, the actuarial probability of overall survival was performed to compare the clinical outcomes between these two groups. Results A total of 485 patients were enrolled in this study. Of all patients, 61.0% (n = 296) occurred ADRs including skin damage, fatigue, mucosal damage, hypertension and gastrointestinal discomfort as the top 5 complications during the target therapy. And 62.1% of ADRs were mild to moderate, more than half of the ADRs occurred within one month, 68.6% ADRs lasted more than one month. Older patients (P = 0.022) and patients with lower education level (P = 0.036), more than 2 comorbidities (P = 0.021), longer medication time (P = 0.022), drug combination (P = 0.033) and intravenous administration (P = 0.019) were more likely to have ADRs. Those with ADRs were more likely to stop taking (P = 0.000), change (P = 0.000), adjust (P = 0.000), or not take the medicine on time (P = 0.000). The number of patients with recurrence (P = 0.000) and metastasis (P = 0.006) were statistically significant difference between ADRs and non-ADRs group. And the patients were significantly poor prognosis in ADRs groups compared with non-ADRs group. Conclusion The high incidence of ADRs would affect the treatment and prognosis of patients with cancer. We should pay more attention to these ADRs and develop effective management strategies.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

scholarly journals Constraint-Based Hierarchical Cluster Selection in Automotive Radar Data

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3410
Author(s):  
Claudia Malzer ◽  
Marcus Baum
Keyword(s):  
Moving Objects ◽  
Hierarchical Cluster ◽  
Autonomous Driving ◽  
Radar Data ◽  
Background Information ◽  
Automotive Radar ◽  
Data Set ◽  
Radar Sensors ◽  
Small Clusters ◽  
Cluster Level

High-resolution automotive radar sensors play an increasing role in detection, classification and tracking of moving objects in traffic scenes. Clustering is frequently used to group detection points in this context. However, this is a particularly challenging task due to variations in number and density of available data points across different scans. Modified versions of the density-based clustering method DBSCAN have mostly been used so far, while hierarchical approaches are rarely considered. In this article, we explore the applicability of HDBSCAN, a hierarchical DBSCAN variant, for clustering radar measurements. To improve results achieved by its unsupervised version, we propose the use of cluster-level constraints based on aggregated background information from cluster candidates. Further, we propose the application of a distance threshold to avoid selection of small clusters at low hierarchy levels. Based on exemplary traffic scenes from nuScenes, a publicly available autonomous driving data set, we test our constraint-based approach along with other methods, including label-based semi-supervised HDBSCAN. Our experiments demonstrate that cluster-level constraints help to adjust HDBSCAN to the given application context and can therefore achieve considerably better results than the unsupervised method. However, the approach requires carefully selected constraint criteria that can be difficult to choose in constantly changing environments.

scholarly journals EPID-22. CHARACTERIZATION OF RADIATION-INDUCED MENINGIOMAS IN A BRAIN TUMOR DATABASE

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii83-ii83
Author(s):  
Nilan Vaghjiani ◽  
Andrew Schwieder ◽  
Sravya Uppalapati ◽  
Zachary Kons ◽  
Elizabeth Kazarian ◽  
...  
Keyword(s):  
African American ◽  
Brain Tumor ◽  
Radiation Treatment ◽  
Latency Period ◽  
Data Set ◽  
Who Grade ◽  
Significant Difference ◽  
Radiation Induced ◽  
Grade Ii ◽  
The Mean

Abstract PURPOSE Radiation-induced meningiomas (RIMs) are associated with previous exposure to therapeutic irradiation. RIMs are rare and have not been well characterized relative to spontaneous meningiomas (SMs). METHODS 1003 patients with proven or presumed meningiomas were identified from the VCU brain tumor database. Chart review classified RIM patients and their characteristics. RESULTS Of the 1003 total patients, 76.47% were female with a mean ± SD age of 67.55 ± 15.50 years. 15 RIM patients were identified (66.67% female), with a mean ± SD age of 52.67 ± 15.46 years, 5 were African American and 10 were Caucasian. The incidence of RIMs was 1.49% in our data set. The mean age at diagnosis was 43.27 ± 15.06 years. The mean latency was 356.27 ± 116.96 months. The mean initiating dose was 44.28 ± 14.68 Gy. There was a significant difference between mean latency period and ethnicity, 258.3 months for African American population, and 405.2 months for Caucasian population (p = 0.003). There was a significant difference between the mean number of lesions in females (2.8) versus males (1.2; p = 0.046). Of the RIMs with characterized histology, 6 (55%) were WHO grade II and 5 (45%) were WHO grade I, demonstrating a prevalence of grade II tumors approximately double that found with SMs. RIMs were treated with combinations of observation, surgery, radiation, and medical therapy. Of the 8 patients treated with radiation, 4 demonstrated response. 8 of the 15 patients (53%) demonstrated recurrence/progression despite treatment. CONCLUSION RIMs are important because of the associated higher grade histology, gender, and ethnic incidences, and increased recurrence/progression compared to SMs. Despite the presumed contributory role of prior radiation, RIMs demonstrate a significant rate of responsiveness to radiation treatment.

Gender bias in reference letters for residency and academic medicine: a systematic review

2021 ◽  
pp. postgradmedj-2021-140045
Author(s):  
Shawn Khan ◽  
Abirami Kirubarajan ◽  
Tahmina Shamsheri ◽  
Adam Clayton ◽  
Geeta Mehta
Keyword(s):  
Systematic Review ◽  
Gender Bias ◽  
Academic Medicine ◽  
Medical Faculty ◽  
Gendered Language ◽  
Men And Women ◽  
Faculty Hiring ◽  
Match Rate ◽  
Significant Difference ◽  
Meta Analyses

Reference letters play an important role for both postgraduate residency applications and medical faculty hiring processes. This study seeks to characterise the ways in which gender bias may manifest in the language of reference letters in academic medicine. In particular, we conducted a systematic review in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched Embase, MEDLINE and PsycINFO from database inception to July 2020 for original studies that assessed gendered language in medical reference letters for residency applications and medical faculty hiring. A total of 16 studies, involving 12 738 letters of recommendation written for 7074 applicants, were included. A total of 32% of applicants were women. There were significant differences in how women were described in reference letters. A total of 64% (7/11) studies found a significant difference in gendered adjectives between men and women. Among the 7 studies, a total of 86% (6/7) noted that women applicants were more likely to be described using communal adjectives, such as “delightful” or “compassionate”, while men applicants were more likely to be described using agentic adjectives, such as “leader” or “exceptional”. Several studies noted that reference letters for women applicants had more frequent use of doubt raisers and mentions of applicant personal life and/or physical appearance. Only one study assessed the outcome of gendered language on application success, noting a higher residency match rate for men applicants. Reference letters within medicine and medical education exhibit language discrepancies between men and women applicants, which may contribute to gender bias against women in medicine.

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