Exploration of the Relationships between Hazard Perception and Eye Movement for Young Drivers

Aiming at young drivers’ hazard perception (HP) and eye movement, a cross-sectional study was conducted in the city of Xi’an, China. 46 participants were recruited, and 35 traffic scenes were used to test drivers’ hazard perception and eye movement. The difference analysis and correlation analysis were carried out for the acquired data. The results suggest that some indices of hazard perception and eye movement are significantly correlated. A higher saccade speed is in the direction of higher hazardous scenes. Higher complex scenes result in smaller saccade angle. The number of hazards unidentified is negatively influenced by complexity degree and hazardous degree of traffic scenes, and similar associations are found between hazard identification time, complexity degree, and hazardous degree. The hazard identification time and the number of hazards slowly identified are positively affected by the number of fixations and the number of saccades. Meanwhile, differences in the hazardous degree evaluation, hazard identification time, number of hazards unidentified, number of fixations, and number of saccades are found in different types of traffic scenes. The results help us to improve the design of road and vehicle devices, as well as the assessment and enhancement of young drivers’ hazard perception skills.

Impact of Short-Incubation MALDI-TOF MS on Rapid Identification of Pathogens and Antibiotic Therapy

Background: It can be a critical point for reducing pathogen identification time and accurate antibiotic treatment for patients with blood circulation infection since it causes high mortality. Objective: The objectives of this study were to evaluate the time differences between conventional identification and MALDI-TOF conventional identification and short-incubation MALDI-TOF identification for positive blood cultures, and to explore the impact of short-incubation MALDI-TOF identification on empirical antibiotic therapy. Methods: Positive blood cultures were collected in our hospital from 2017 to 2019, clinical data were collected from the medical records, which were analyzed retrospectively to determine the empirical antibiotic therapy. Results: Compared with the conventional identification method, the short-incubation MALDI-TOF identification time to initial identification of Klebsiella pneumoniae, Escherichia coli, Pseudomonas aeruginosa, Acinetobacter baumannii, Staphylococcus aureus, Enterococcus faecium, and E. faecalis decreased by 22.28 h, 22 h, 23.59 h, 23.63 h, 22.63 h, 23.92 h, and 21.59 h, respectively (P < 0.05). The time to final reporting was decreased by 48.85 h, 47.99 h, 55.40 h, 51.07 h, 49.60 h, 51.78h, and 51.73h, respectively (P < 0.05). However, the antimicrobial susceptibility test time of E. coli, A. baumannii, and S. aureus increased to 2.02 h, 2.19 h, and 3.86 h, respectively (P < 0.05). The coincidence rate of antimicrobial susceptibility was 98.48% between short-incubation MALDI-TOF identification and conventional identification method of all Gram-negative bacilli, and there were no extremely major errors or major errors. The coincidence rate of antimicrobial susceptibility of Gram-positive cocci was 99.53%, one strain of E. faecium and S. aureus had major errors. Patients received earlier correct empirical antibiotic 19.89 h earlier by short-incubation MALDI-TOF identification than the conventional identification method (P < 0.001). Conclusions: The short-incubation MALDI-TOF identification significantly shortens the pathogen identification time and the final report time, it is a reliable method for rapid identification of positive blood cultures; the results of antimicrobial susceptibility are highly consistent, which significantly lead to earlier appropriate empirical therapy of bacteremia.

Evaluation of Microarray and Lysis Filtration Combined MALDI TOF MS Procedures for the Identification of Gram Negative Bacteremia

Objective: Each hour of delay in antibiotics administration increases mortality in sepsis. The aim of this study was to decrease the bacteria identification time to initiate appropriate antibiotic treatment as early as possible. Method: Tests were applied to 39 Gram negative bacteria isolated from blood cultures sent to our laboratory from intensive care units between November 2015- February 2016. The results of bacterial identification tested on both microarray and LFM methods were compared. Results: In the comparison of MALDI-TOF MS after sub-culture, MALDI-TOF after lysis centrifugation and microarray methods, sensitivity was determined as 82% (32/39) in LFM and as 87.1% (34/39) in the microarray method. All three methods had a concordance of 76.9% (30/39). Most common species identified in this study were Acinetobacter spp., Klebsiella spp. and Escherichia spp., and their Cohen’s Kappa coefficients for LFM and post-subculture MALDI were calculated as 0.715, 0.843, and 0.938, respectively. In addition, their BC-GN microarray and post-subculture MALDI concordance rates calculated with Cohen’s Kappa were 0.935, 0.753 and 0.938, respectively. Both methods showed good correlations with the post-culture MALDI method. Conclusion: Lysis centrifugation and microarray platforms decrease the identification time in blood culture processing successfully. Results of this study suggest that for the laboratories with MALDI-TOF mass spectrophotometer, the lysis filtration method is a fast and cost-effective method that may be suitable for routine procedures.

Acoustic Emission Signal Classification Using Feature Analysis and Deep Learning Neural Network

With the development of artificial intelligence in recent years, deep learning has been widely used in mechanical system signal classification but the impact of different feature extractions on the efficiency and effectiveness of deep learning neural networks is more important. In this study, a vehicle classification based on engine acoustic emission signal in the time domain, the frequency domain and the wavelet transform domain for deep learning network techniques is presented and compared. In signal classification, different feature extractions will show in different decomposition levels and can be used to recognize the various acoustic conditions. In the experimental work, as engines from 10 different ground vehicles operate, the measured sound signal is converted into a digital signal, and the established data set is classified and identified by the deep learning method. The number of samples, identification rate and identification time in the various signal domains are compared and discussed in this study. Finally, the experimental results and data analysis show that by using the wavelet signal and the deep learning method, excellent identification time and identification rate can be achieved, compared with traditional time and frequency domain signals.

MVDT-SI: A Multi-View Double-Triangle Algorithm for Star Identification

Recently, the triangle algorithm has become the most widely used star identification algorithm because of its simplicity and convenience, where the magnitude information plays a key role in the construction of star map features. However, in practice, the magnitude information of the observed star map is often difficult to use, because they might contain errors or be lost in some worst cases. To solve this problem, we proposed a multi-view double-triangle algorithm for star identification in this paper. This algorithm constructs double-triangle features of stars with the angle and distance information of star points. Moreover, to reduce the influence of noise interference on the identification accuracy of the model, we built multi-view double-triangle features for the observed star map to improve the robustness of the algorithm. Synthetic and real experiments show that our algorithm has a high identification accuracy of more than 98.4% in face of “false star” noises and “missing star” noises, and our algorithm is not affected by the focal length and the shooting angle of the star sensor. Moreover, the results also show that our algorithm has good robustness, short identification time and reduced storage costs, which could be beneficial in practice.

Modelling the set of earthworks machinery with the use of computer simulation

The paper presents the comparison of deterministic and stochastic approach for modeling the set of earthworks machinery. Simulation takes into account the normal distribution of cycle time and efficiency of machines and points out its influence for total construction works time. Results of the simulation indicate the need of identification time and efficiency deviation as a risk factor, which can cause delay whenever earthworks cycle includes the serial work of several machines.

Hybrid Dynamic-Binary ALOHA Anti-Collision Protocol in RFID Systems

The main techniques for identifying objects in an Internet of things environment are based on radio frequency identification, in which a specific object is identified by the reader through the tag mounted on the object. When there are multiple tags in the reader’s interrogation zone, they respond simultaneously to the reader’s request, thus causing a collision between the signals sent simultaneously to the reader from those tags. Such collisions reduce the data accuracy and prolong the identification time, thus making it difficult to provide a rapid service. This paper explores a hybrid anti-collision protocol, namely, the hybrid dynamic-binary ALOHA anti-collision protocol, which is designed to prevent tag collision and to enable more stable information transmission by improving the existing tag anti-collision protocols. The proposed protocol has achieved performance enhancement by shortening the tag identification process when tag collision occurs by combining the ALOHA and binary search protocols. In contrast to the existing protocols, whereby the reader’s request is repeated after detecting a collision, the proposed protocol shortens the tag identification time by requesting only the collision bits. This contributes to a substantial reduction in the object identification time in an IoT environment.