Optimal Detection of Faulty Traffic Sensors Used in Route Planning

In a smart city, real-time traffic sensors may be deployed for various applications, such as route planning. Unfortunately, sensors are prone to failures, which result in erroneous traffic data. Erroneous data can adversely affect applications such as route planning, and can cause increased travel time. To minimize the impact of sensor failures, we must detect them promptly and accurately. However, typical detection algorithms may lead to a large number of false positives (i.e., false alarms) and false negatives (i.e., missed detections), which can result in suboptimal route planning. In this paper, we devise an effective detector for identifying faulty traffic sensors using a prediction model based on Gaussian Processes. Further, we present an approach for computing the optimal parameters of the detector which minimize losses due to false-positive and false-negative errors. We also characterize critical sensors, whose failure can have high impact on the route planning application. Finally, we implement our method and evaluate it numerically using a real-world dataset and the route planning platform OpenTripPlanner.Comment: Proceedings of The 2nd Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE 2017), Pittsburgh, PA USA, April 2017, 6 page

Developing Sports Diplomacy Paradigm of Foreign Policy in Iran

Sports diplomacy is regarded as one of the most important communicative components among nations in their international relations. Regarding the mentioned fact, the main objective of this study was to provide a paradigm of sport diplomacy in the foreign policy of Iran. This is a qualitative study with exploratory nature by applying the Strauss and Corbin grounded theory approach. The data collection tool was semi-structured interviews with 14 elites including sports experts and policymakers. Findings demonstrated 133 initial concepts in open coding, reduced into 27 categories, and categorized into six main themes to support the initial development of the model. The results showed that adopting appropriate strategies like managerial evolution, developing indigenous models, national brand-making through sports, educating and empowering human resources, legal and structural development of sports diplomacy, and cultural changes in Iranian professional sports could bring greater consequences that encourage governing bodies to alter attitudes toward the role of sports diplomacy, and to empowers foreign policy for strengthening mutual relations worldwide

Role of Long Non-Coding RNAs in Conferring Resistance in Tumors of the Nervous System

Tumors of the nervous system can be originated from several locations. They mostly have high mortality and morbidity rate. The emergence of resistance to chemotherapeutic agents is a hurdle in the treatment of patients. Long non-coding RNAs (lncRNAs) have been shown to influence the response of glioblastoma/glioma and neuroblastoma to chemotherapeutic agents. MALAT1, NEAT1, and H19 are among lncRNAs that affect the response of glioma/glioblastoma to chemotherapy. As well as that, NORAD, SNHG7, and SNHG16 have been shown to be involved in conferring this phenotype in neuroblastoma. Prior identification of expression amounts of certain lncRNAs would help in the better design of therapeutic regimens. In the current manuscript, we summarize the impact of lncRNAs on chemoresistance in glioma/glioblastoma and neuroblastoma

Prevalence and predictors of low back pain among the Iranian population: Results from the Persian cohort study

Background and objectives: Low back pain (LBP) is a common health condition in populations. Limited large-scale population-based studies evaluated the prevalence and predictors of LBP in developing countries. This study aimed to evaluate the prevalence and factors associated with LBP among the Iranian population. Methods: We used baseline information from the Prospective Epidemiological Research Studies in Iran (PERSIAN), including individuals from 16 provinces of Iran. LBP was defined as the history of back pain interfering with daily activities for more than one week during an individual's lifetime. Various factors hypothesized to affect LBP, such as age, sex, marital status, educational status, ethnicity, living area, employment status, history of smoking, body mass index (BMI), physical activity, sleep duration, wealth score, history of joint pain, and history of morning stiffness in the joints were evaluated. Results: In total, 163770 Iranians with a mean age of 49.37 (SD = 9.15) were included in this study, 44.8% of whom were male. The prevalence of LBP was 25.2% among participants. After adjusting for confounders, the female gender [OR:1.244(1.02-1.50)], middle and older ages [OR:1.23(1.10-1.33) and OR:1.13(1.07-1.42), respectively], being overweight or obese [OR:1.13(1.07-1.19) and OR:1.21(1.16-1.27), respectively], former and current smokers (OR:1.25(1.16-1.36) and OR:1.28(1.17-1.39), respectively], low physical activity [OR:1.07 (1.01-1.14)], and short sleep duration [OR: 1.09(1.02-1.17)] were significantly associated with LBP. Conclusion: In this large-scale study, we found the lifetime prevalence of LBP to be lower among the Iranian population in comparison to the global prevalence of LBP; further studies are warranted to evaluate the causality of risk factors on LBP

Application-Aware Anomaly Detection of Sensor Measurements in Cyber-Physical Systems

Detection errors such as false alarms and undetected faults are inevitable in any practical anomaly detection system. These errors can create potentially significant problems in the underlying application. In particular, false alarms can result in performing unnecessary recovery actions while missed detections can result in failing to perform recovery which can lead to severe consequences. In this paper, we present an approach for application-aware anomaly detection (AAAD). Our approach takes an existing anomaly detector and configures it to minimize the impact of detection errors. The configuration of the detectors is chosen so that application performance in the presence of detection errors is as close as possible to the performance that could have been obtained if there were no detection errors. We evaluate our result using a case study of real-time control of traffic signals, and show that the approach outperforms significantly several baseline detectors