A Novel Real-Time Autonomous Crack Inspection System Based on Unmanned Aerial Vehicles

Traditional methods on crack inspection for large infrastructures require a number of structural health inspection devices and instruments. They usually use the signal changes caused by physical deformations from cracks to detect the cracks, which is time-consuming and cost-ineffective. In this work, we propose a novel real-time crack inspection system based on unmanned aerial vehicles for real-world applications. The proposed system successfully detects and classifies various types of cracks. It can accurately find the crack positions in the world coordinate system. Our detector is based on an improved YOLOv4 with an attention module, which produces 90.02% mean average precision (mAP) and outperforms the YOLOv4-original by 5.23% in terms of mAP. The proposed system is low-cost and lightweight. Moreover, it is not restricted by navigation trajectories. The experimental results demonstrate the robustness and effectiveness of our system in real-world crack inspection tasks.

Determinants and Prediction of Injury Severities in Multi-Vehicle-Involved Crashes

Multi-vehicle (MV) crashes, which can lead to great damages to society, have always been a serious issue for traffic safety. A further understanding of crash severity can help transportation engineers identify the critical reasons and find effective countermeasures to improve transportation safety. However, studies involving methods of machine learning to predict the possibility of injury-severity of MV crashes are rarely seen. Besides that, previous studies have rarely taken temporal stability into consideration in MV crashes. To bridge these knowledge gaps, two kinds of models: random parameters logit model (RPL), with heterogeneities in the means and variances, and Random Forest (RF) were employed in this research to identify the critical contributing factors and to predict the possibility of MV injury-severity. Three-year (2016–2018) MV data from Washington, United States, extracted from the Highway Safety Information System (HSIS), were applied for crash injury-severity analysis. In addition, a series of likelihood ratio tests were conducted for temporal stability between different years. Four indicators were employed to measure the prediction performance of the selected models, and four categories of crash-related characteristics were specifically investigated based on the RPL model. The results showed that the machine learning-based models performed better than the statistical models did when taking the overall accuracy as an evaluation indicator. However, the statistical models had a better prediction performance than the machine learning models had considering crash costs. Temporal instabilities were present between 2016 and 2017 MV data. The effect of significant factors was elaborated based on the RPL model with heterogeneities in the means and variances.

Vehicle Detection under Adverse Weather from Roadside LiDAR Data

Roadside light detection and ranging (LiDAR) is an emerging traffic data collection device and has recently been deployed in different transportation areas. The current data processing algorithms for roadside LiDAR are usually developed assuming normal weather conditions. Adverse weather conditions, such as windy and snowy conditions, could be challenges for data processing. This paper examines the performance of the state-of-the-art data processing algorithms developed for roadside LiDAR under adverse weather and then composed an improved background filtering and object clustering method in order to process the roadside LiDAR data, which was proven to perform better under windy and snowy weather. The testing results showed that the accuracy of the background filtering and point clustering was greatly improved compared to the state-of-the-art methods. With this new approach, vehicles can be identified with relatively high accuracy under windy and snowy weather.

Neuroprotective potential of ceftriaxone in in vitro models of stroke

Astrocytic glutamate transporters are considered an important target for neuroprotective therapies as the function of these transporters is abnormal in stroke and other neurological disorders associated with excitotoxicity. Recently, Rothstein et al., [Rothstein JD, Patel S, Regan MR, Haenggeli C, Huang YH, Bergles DE, Jin L, Dykes Hoberg M, Vidensky S, Chung DS, Toan SV, Bruijn LI, Su ZZ, Gupta P, Fisher PB (2005) Beta-lactam antibiotics offer neuroprotection by increasing glutamate transporter expression. Nature 433:73-77] reported that beta-lactam antibiotics (including ceftriaxone, which easily crosses the blood-brain barrier) increase glutamate transporter 1 (GLT-1) expression and reduce cell death resulting from oxygen-glucose deprivation (OGD) in dissociated embryonic cortical cultures. To determine whether a similar neuroprotective mechanism operates in more mature neurons, which show a different pattern of response to ischemia than primary cultures, we exposed acute hippocampal slices obtained from rats treated with ceftriaxone for 5 days (200 mg/kg; i.p.) to OGD. Whole-cell patch clamp recording of glutamate-induced N-methyl-d-aspartate (NMDA) currents from CA1 pyramidal neurons showed a larger potentiation of these currents after application of 15 microM dl-threo-beta-benzyloxyaspartic acid (TBOA; a potent blocker of glutamate transporters) in ceftriaxone-injected animals than in untreated animals, indicating increased glutamate transporter activity. Western blot analysis did not reveal GLT-1 upregulation in the hippocampus. Delay to OGD-induced hypoxic spreading depression (HSD) recorded in slices obtained from ceftriaxone-treated rats was longer (6.3+/-0.2 vs. 5.2+/-0.2 min; P<0.001) than that in the control group, demonstrating a neuroprotective action of the antibiotic in this model. The effect of ceftriaxone was also tested in organotypic hippocampal slices obtained from P7-9 rats (>14 days in vitro). OGD or glutamate (3.5-5.0 mM) damaged CA1 pyramidal neurons as assessed by propidium iodide (PI) fluorescence. Similar damage was observed after pre-treatment with ceftriaxone (10-200 microM; 5 days) and ceftriaxone exposure did not result in GLT-1 upregulation as assayed by Western blot. Treatment of slice cultures with dibutyryl cAMP (100-250 microM; 5 days) increased GLT-1 expression but did not reduce cell damage induced by OGD or glutamate. Thus we confirm the neuroprotective effect of antibiotic exposure on OGD-induced injury, but suggest that this action is related to independent modulation of transporter activity rather than to the level of GLT-1 protein expression. In addition, our results indicate that the protective effects of beta-lactam antibiotics are highly dependent on the experimental model.

Experimental design: a useful tool for PCR Optimization

Because of complex interactions among the components of PCR and the wide and increasing variety of applications in which this technique is used, optimization is necessary for every reaction. Here we describe the use of experimental design techniques (2k fractional factorial design and central composite design) to attain easier, quicker and cheaper PCR optimization of DNA from blood spots. By determining the factors affecting the product yield first (factors screening), the quantity of template DNA needed for PCR and the Mg2+ concentration are easily optimized (factors optimization).