Research on Recognition of Road Hypnosis in the Typical Monotonous Scene.

Road traffic safety can be influenced by road hypnosis. Accurate detection of the driver's road hypnosis is a very important function urgently required in the driver assistance system. Road hypnosis recurs frequently in a certain period, and it tends to occur in a typical monotonous scene such as a tunnel or a highway. Taking the scene of a tunnel or a highway as a typical example, road hypnosis was studied through simulated driving experiments and vehicle driving experiments. A road hypnosis recognition model based on principal component analysis (PCA) and a long short-term memory network (LSTM) was proposed, where PCA was used to extract various parameters collected by the eye tracker, and the LSTM model was constructed to identify road hypnosis. The accuracy rates of 93.27% and 97.01% in simulated driving experiments and vehicle driving experiments were obtained. The proposed method was compared with k-nearest neighbor (KNN) and random forest (RF). The results showed that the proposed PCA-LSTM model had better performance. This paper provides a novel and convenient method to realize the driver's road hypnosis detection function of the intelligent driver assistance system in practical applications.

Fe3O4@uio66 core-shell composite for detection of electrolyte leakage from lithium-ion batteries.

Fe3O4is an environmentally friendly gas sensing material with high response, but the cross-response to various analytes and poor thermal stability limit its practical applications. In this work, we prepared Fe3O4@uio66 core-shell composite via a facile method. The selective response to volatile organic compounds, especially to electrolyte vapors of lithium-ion batteries, as well as long-term stability of Fe3O4@uio66 has been dramatically enhanced compared to pure Fe3O4, due to the preconcentrator feature and thermal stability of the uio66 thin shell. Real-time detection of electrolyte leakage for an actual punctured lithium-ion battery was further demonstrated. The Fe3O4@uio66 sensor, after aging for 3 months, was able to detect the electrolyte leakage in 30 s, while the voltage of the punctured battery was maintained at the same level as that of a pristine battery over 6 h. This practical test results verified ability of the Fe3O4@uio66 sensor with long-term aging stability for hours of early safety warning of lithium-ion batteries.

A real-time driver fatigue identification method based on GA-GRNN.

It is of great practical and theoretical significance to identify driver fatigue state in real time and accurately and provide active safety warning in time. In this paper, a non-invasive and low-cost method of fatigue driving state identification based on genetic algorithm optimization of generalized regression neural network model is proposed. The specific work is as follows: (1) design simulated driving experiment and real driving experiment, determine the fatigue state of drivers according to the binary Karolinska Sleepiness Scale (KSS), and establish the fatigue driving sample database. (2) Improved Multi-Task Cascaded Convolutional Networks (MTCNN) and applied to face detection. Dlib library was used to extract the coordinate values of face feature points, collect the characteristic parameters of driver's eyes and mouth, and calculate the Euler Angle parameters of head posture. A fatigue identification model was constructed by using multiple characteristic parameters. (3) Genetic Algorithm (GA) was used to find the optimal smooth factor of Generalized Regression Neural Network (GRNN) and construct GA-GRNN fatigue driving identification model. Compared with K-Nearest Neighbor (KNN), Random Forest (RF), and GRNN fatigue driving identification algorithms. GA-GRNN has the best generalization ability and high stability, with an accuracy of 93.3%. This study provides theoretical and technical support for the application of driver fatigue identification.

A Safety Warning Algorithm Based on Axis Aligned Bounding Box Method to Prevent Onsite Accidents of Mobile Construction Machineries.

Mobile construction machineries are accident-prone on a dynamic construction site, as the site environment is constantly changing and continuous safety monitoring by human beings is impossible. These accidents usually happen in the form of machinery overturning or collapsing into risk areas, including the foundation pit, slopes, or soft soil area. Therefore, preventing mobile construction machineries from entering risk areas is the key. However, currently, there is a lack of practical safety management techniques to achieve this. Utilizing a wireless sensor device to collect the location information of mobile construction machineries, this research develops a safety warning algorithm to prevent the machineries moving into risk area and reduces onsite overturning or collapsing accidents. A modified axis aligned bounding box method is proposed according to the movement patterns of mobile construction machineries, and the warning algorithm is developed based on the onsite safety management regulations. The algorithm is validated in a real case simulation when machinery enters the warning zone. The simulation results showed that the overall algorithm combining the location sensing technology and the modified bounding box method could detect risk and give warnings in a timely manner. This algorithm can be implemented for the safety monitoring of mobile construction machineries in daily onsite management.

Evaluate driver response to active warning system in level-2 automated vehicles.

As vehicles with automated functions become more prevalent on U.S. roadways, maintaining driver attention while the vehicle is engaged in automation will be an important consideration for safe operation of these vehicles. The objective of this paper is to evaluate how drivers respond and adapt to active safety warning signals in a Level 2 automatic vehicle. Specifically, statistical analysis was conducted to evaluate whether the amount of inattention prompts that drivers received changed over time, possibly indicating a change in the amount of inattention that drivers exhibited. The driving performance data was collected from sixteen participants who drove a Level 2 vehicle in an experimental setting, as part of the study Human Factors Evaluation of Level 2 and Level 3 Driving Concepts. A proprietary driver inattention warning system was installed on the experiment vehicles. The system would send a warning signal if the driver's attention was not on the primary driving task for a pre-specified duration. This study focuses on driver's response when experiencing prompts after two seconds of inattention while operating a Level 2 vehicle in automated mode. The results show that on average, the frequency of prompts the participants received decreased over the course of the experiment from 29.9 in the first ten minutes to 18.1/10 min after 110 min. The decrease levelled off after about two hours. The fact that participants received fewer prompts over time suggests that they had fewer instances of inattention lasting at least two seconds as the experiment progressed. This suggests that drivers would adapt to the alert and adjust their behavior to avoid triggering the inattention alert. The results of this study provide evidence for a potential benefit of incorporating a prompting system in vehicles with automated functions.

The Drug Safety and Rational Use of Zolpidem Tartrate / 中国药学杂志

OBJECTIVE: To givesafety recommendations for the dose, time of administration, and precautions for zolpidem tartrate. METHODS: Search relevant websites and databases, the pharmaceutical characteristics, clinical application, adverse reactions, drug interactions and rational use of zolpidem tartrate were reviewed. RESULT:S In order to ensure the safety of medication for patients, it is recommended that women and the elderly aged 65 years and above should use the minimum effective dose of 5 mg•d-1. Short-term use, the course of medication should not exceed 4 weeks. Insomnia patients can choose to treat as needed (intermittent, not every night). Follow the prescribed course of treatment and dosage in strict accordance with the doctor's advice. CONCLUSIONS: Zolpidem tartrate can treat various types of insomnia with definite curative effect and mild adverse reactions.Clinicians and pharmacists should pay attention to the safety risks of zolpidem and inform patients of the contraindications and precautions.

Effects of safety warnings and risk management plan for Thiazolidinediones in Taiwan.

PURPOSE: To evaluate changes in thiazolidinedione use and quality of prescription following safety warnings for thiazolidinediones and cardiac risk in 2007, Risk Management Plan (RMP) policy for rosiglitazone in 2010, and warning for pioglitazone and bladder cancer risk in 2010 in Taiwan. METHODS: We obtained 2003-2011 claims data from Taiwan's National Health Insurance Research Database. Using an interrupted time series design and segmented regression, we estimated changes in monthly prescribing rates for thiazolidinediones among all and prevalent diabetes patients with and without cardiovascular disease history (CV history). We also compared time to prescription of thiazolidinediones among new diabetes patients with CV history before and after each regulatory action using survival analysis. RESULTS: Among prevalent patients with and without CV history, the prescribing rates of rosiglitazone decreased 36.88% and 28.92% after safety warnings in 2007 respectively. Pioglitazone prescriptions increased 13% among patients with CV history, but no changes were detected among patients without CV history. After rosiglitazone's RMP policy in 2010, large reductions in prescriptions were observed in patients with CV history (-101.67%) and those without CV history (-88.04%). Among new diabetes patients with CV history, cardiac safety warnings in 2007 significantly delayed the prescription of rosiglitazone, but no significant change was found for pioglitazone. CONCLUSIONS: The Taiwan FDA regulatory actions for thiazolidinediones communicated possible risks of cardiac events and bladder cancer. Different safety regulatory actions had differential impacts on the use of rosiglitazone and pioglitazone and the quality use of these drugs among the high-risk patients.

Assessing the safety features of electronic patient medication record systems used in community pharmacies in England.

AIMS: To evaluate the ability of electronic patient medication record (ePMR) systems used in community pharmacies in England to detect and alert users about clinical hazards, errors and other safety problems. METHODS: Between September 2012 and November 2012, direct on-site observational data about the performance of ePMR systems were collected from nine sites. Twenty-eight scenarios were developed by consensus agreement between a general practitioner and two community pharmacists. Each scenario was entered into the ePMR system, and the results obtained from the assessment of six unique systems in nine sites, in terms of the presence or absence of an alert, were recorded onto a prespecified form. RESULTS: None of the systems produced the correct responses for all of the 28 scenarios tested. Only two systems provided an alert to penicillin sensitivity. No dose or frequency check was observed when processing a prescription for methotrexate. One system did not warn about nonsuitability of aspirin prescribed to a child of 14 years of age. In another system, it was not possible to record a patient's pregnancy status. None of the six systems provided any warning for diclofenac overdose, high initiation dose of morphine sulfate or significant dose increase. Only one of the systems did not produce any spurious alerts. CONCLUSIONS: The performance of the ePMR systems tested was variable and suboptimal. The findings suggest the need for minimum specifications and standards for ePMR systems to ensure consistency of performance.