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A reliable and accurate monitoring of traffic load is of significance for the operational management and safety assessment of bridges. Traditional weight-in-motion techniques are capable of identifying moving vehicles with satisfactory accuracy and stability, whereas the cost and construction induced issues are inevitable. A recently proposed traffic sensing methodology, combining computer vision techniques and traditional strain based instrumentation, achieves obvious overall improvement for simple traffic scenarios with less passing vehicles, but are enfaced with obstacles in complicated traffic scenarios. Therefore, a traffic monitoring methodology is proposed in this paper with extra focus on complicated traffic scenarios. Rather than a single sensor, a network of strain sensors of a pre-installed bridge structural health monitoring system is used to collect redundant information and hence improve accuracy of identification results. Field tests were performed on a concrete box-girder bridge to investigate the reliability and accuracy of the method in practice. Key parameters such as vehicle weight, velocity, quantity, type and trajectory are effectively identified according to the test results, in spite of the presence of one-by-one and side-by-side vehicles. The proposed methodology is infrastructure safety oriented and preferable for traffic load monitoring of short and medium span bridges with respect to accuracy and cost-effectiveness.

The aim of this study was to investigate the therapeutic efficacy and neuroprotective mechanisms of UCF-101, a novel Omi/HtrA2 inhibitor, following ischemia/reperfusion brain injury. Male Wistar rats were subjected to 2 hr of middle cerebral artery occlusion followed by reperfusion. Animals were divided into 3 groups: sham, vehicle-treated ischemia/reperfusion, and UCF-101 treatment. In the UCF-101 treatment group, rats were intraperitoneally administered UCF-101 (1.5 lmol/kg) 10 min prior to reperfusion. The rats were evaluated for neurological deficits, and brain infarct volume was assessed by 2,3,5-triphenyl tetrazolium chloride. TUNEL staining was utilized to evaluate the amount of apoptosis. In addition, expressions of protein caspase-8, caspase-3, FasL, and FLIP were examined by Western blot analysis. Results demonstrated that UCF-101 treatment significantly decreased cerebral infarct size by about 16.27% (P < 0.05) and also improved neurological behavior. TUNEL staining revealed that UCF-101 treatment significantly reduced TUNELpositive cells in the cerebral cortex. Furthermore, the upregulation in the expression of FasL and the cleavage products of active caspase-8 and caspase-3 induced by ischemia was attenuated in mice treated with UCF-101, whereas upregulation of FLIP levels was increased. The present results demonstrated that UCF-101 protects against cerebral ischemia/ reperfusion injury in mice. UCF-101 provided neuroprotection in vivo, and this was correlated with regulation of Fas-mediated apoptotic proteins. Taken together, the use of UCF-101 is a potent, neuroprotective factor for the treatment of focal cerebral ischemia. Anat Rec, 292:854-861, 2009. V V C 2009 Wiley-Liss, Inc.

Highway agencies are responsible for the optimal expenditure of taxpayer dollars allocated to highway infrastructure. Truck size and weight are regulated by federal legislation, and every state highway agency has its own legal load limits. In addition, state agencies issue permits for trucks with gross vehicle weights that are above legal load limits. However, the effect of overweight trucks on the service life of bridge structures, especially concrete decks, is not explicitly quantified. Detailed research on deterioration models for bridge decks was conducted. Condition ratings of bridge decks in New Jersey from the National Bridge Inventory were used to derive the deterioration of decks over time, and the expected service lives of decks on different highways were obtained. Weigh-in-motion data from stations in New Jersey were used to extract two data sets: “all trucks” and “legal trucks.” The “all trucks” data set was used to develop a deck deterioration model as a function of equivalent wheel load that could be used to estimate expected service life. Finally, bridge life-cycle cost analysis was conducted under two scenarios, one with and the other without overweight trucks, to quantify the economic impact of such trucks on bridge decks. The results indicate that overweight trucks caused more damage on New Jersey state highways than on Interstate highways because of a larger proportion of overweight trucks, heavy wheel loads from overweight trucks, and fewer axles per truck.

In recent years, an increasing amount of locker-ransomware has been posing a great threat to the Android platform as well as users' properties. Locker-ransomware blackmails victims for ransom by compulsorily locking the devices. What is worse, a mature locker-ransomware transaction chain has taken shape on Chinese social networks. The effective detection of locker-ransomware is an emergent yet crucial issue. To deal with this issue, in this paper, we are motivated to propose a light-weight and automated method for the detection of locker-ransomware. First, we conduct a thorough survey of the locker-ransomware's transaction market and perform a comprehensive analysis of locker-ransomware's behaviors. Second, to cope with the code obfuscation problem, we extract features of both displayed texts and background operations based on the observed behaviors. The fine-grained features are extracted from multiple sources, which can profile locker-ransomware in different aspects. Finally, we employ the ensemble of four machine learning algorithms for detection. The experimental results show that our method outperforms VirusTotal. It achieves the best performance with the detection accuracy of 99.98%. INDEX TERMSAndroid, locker-ransomware, malware detection. DAN SU received the B.S. degree from Beijing Jiaotong University, China, in 2014, where she is currently pursuing the Ph.D. degree with the School of Computer and Information Technology. Her main research interest includes mobile security.

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