Prediction and test of stable transmission time of spiral bevel gear during a loss-of-lubrication event in helicopter transmission system

Purpose This paper aims to establish a prediction model of stable transmission time of spiral bevel gear during a loss-of-lubrication event in helicopter transmission system. Design/methodology/approach To observe the temperature change of spiral bevel gear during working condition, a test rig of spiral bevel gear was developed according to the requirements of experiments and carried out verification experiments. Findings The prediction is verified by the test of detecting the temperature of oil pool. The main damage form of helicopter spiral bevel gears under starved lubrication is tooth surface burn. The stable running time under oil-free lubrication is mainly determined by the degree of tooth surface burn control. Originality/value The experimental data of the spiral bevel gear oil-free lubrication process are basically consistent with the simulation prediction results. The results lay a foundation for the working life design of spiral bevel gear in helicopter transmission system under starved lubrication.

Study of brainstem auditory evoked response and biochemical markers in patients of alcohol use disorder

Objectives: Data available on brainstem auditory evoked response (BAER) and its correlation with biochemical parameters in patients of alcohol use disorder (AUD) in Indian population is scanty. Therefore, this study was undertaken to focus on the effects of AUD on BAER and liver enzymes. Materials and Methods: This case-control study included 40 males in the study group who had AUD and 40 healthy males in the control group in the age group of 20–60 years. The BAER was performed using octopus NCS/ EMG/EP (Clarity) machine. The levels of aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase and serum bilirubin were estimated in all the subjects. Results: We observed a highly significant increase in the absolute latencies of waves III and V and interpeak latencies (IPL) I-III and I-V of BAER in the patients of AUD in this study. Significant increase in the liver enzymes and especially AST/ALT ratio of patients of AUD was seen which indicated towards subclinical alcoholic hepatitis. The latencies of waves of EPs (waves III, V, IPL I-III and IPL I-V) were positively correlated with the biochemical parameters and duration of AUD. Conclusion: Our findings indicated that AUD lead to the increase in brainstem transmission time and also lead to subclinical alcoholic hepatitis which is reflected by the increase in the liver enzymes. We concluded that chronic alcohol consumption affected the auditory pathways and delayed the auditory transmission time which was suggestive of possible demyelination of auditory tracts.

Performance Evaluation of Routing Metrics for Community Wireless Mesh Networks

<p>With the growth of different types of Internet traffic there is a compelling need to provide better quality of service, especially, over the increasing number of wireless networks. Expected Transmission Count (ETX) is a high throughput route selection metric that measures link loss ratios. ETX of a path reflects the total number of packet transmissions (including retransmission) required to successfully deliver a data packet along that path. Expected Transmission Time (ETT) is an improvement of ETX. ETT of a path is a measure of the transmission time needed to successfully deliver a packet along the path. ETT measures the loss ratio and the bandwidth of the link. Both, ETX and ETT, in comparison to hop count, provide better route selection for routing protocols widely used in Wireless Mesh Networks (WMNs). Using minimum hop count to find the shortest path has been shown to be inadequate for WMNs, as the selected routes often include the weakest links. This thesis presents a performance evaluation comparing hop count, ETX and ETT when used with the Optimized Link State Routing version 2 (OLSRv2) protocol. This study is based on the wireless mesh topology of a suburban residential area in New Zealand, and analyses the performance of three common Internet traffic types in terms of throughput, end-to-end delay, jitter and packet loss ratio, and presents findings that are closer to the perspective of what an enduser experiences. Also, a grid network of 121 nodes was used to analyze how the metrics choose paths, the performance changes (for different path lengths) and other conditions that affect the performance of the three metrics.</p>

Performance Evaluation of Routing Metrics for Community Wireless Mesh Networks

<p>With the growth of different types of Internet traffic there is a compelling need to provide better quality of service, especially, over the increasing number of wireless networks. Expected Transmission Count (ETX) is a high throughput route selection metric that measures link loss ratios. ETX of a path reflects the total number of packet transmissions (including retransmission) required to successfully deliver a data packet along that path. Expected Transmission Time (ETT) is an improvement of ETX. ETT of a path is a measure of the transmission time needed to successfully deliver a packet along the path. ETT measures the loss ratio and the bandwidth of the link. Both, ETX and ETT, in comparison to hop count, provide better route selection for routing protocols widely used in Wireless Mesh Networks (WMNs). Using minimum hop count to find the shortest path has been shown to be inadequate for WMNs, as the selected routes often include the weakest links. This thesis presents a performance evaluation comparing hop count, ETX and ETT when used with the Optimized Link State Routing version 2 (OLSRv2) protocol. This study is based on the wireless mesh topology of a suburban residential area in New Zealand, and analyses the performance of three common Internet traffic types in terms of throughput, end-to-end delay, jitter and packet loss ratio, and presents findings that are closer to the perspective of what an enduser experiences. Also, a grid network of 121 nodes was used to analyze how the metrics choose paths, the performance changes (for different path lengths) and other conditions that affect the performance of the three metrics.</p>

An NB-IoT-Based Edge-of-Things Framework for Energy-Efficient Image Transfer

Machine Learning (ML) techniques can play a pivotal role in energy efficient IoT networks by reducing the unnecessary data from transmission. With such an aim, this work combines a low-power, yet computationally capable processing unit, with an NB-IoT radio into a smart gateway that can run ML algorithms to smart transmit visual data over the NB-IoT network. The proposed smart gateway utilizes supervised and unsupervised ML algorithms to optimize the visual data in terms of their size and quality before being transmitted over the air. This relaxes the channel occupancy from an individual NB-IoT radio, reduces its energy consumption and also minimizes the transmission time of data. Our on-field results indicate up to 93% reductions in the number of NB-IoT radio transmissions, up to 90.5% reductions in the NB-IoT radio energy consumption and up to 90% reductions in the data transmission time.

Research on Diagnostic Information of Smart Medical Care Based on Big Data

The medical field has gradually become intelligent, and information and the research of intelligent medical diagnosis information have received extensive attention in the medical field. In response to this situation, this paper proposes a Hadoop-based medical big data processing system. The system first realized the ETL module based on Sqoop and the transmission function of unstructured data and then realized the distributed storage management function based on HDFS. Finally, a MapReduce algorithm with variable key values is proposed in the data statistical analysis module. Through simulation experiments on the system modules and each algorithm, the results show that because the traditional nondistributed big data acquisition module transmits the same scale of data, it consumes more than 3200 s and the transmission time exceeds 3000 s. The time consumption of smart medical care under the 6G protocol is 150 s, the transmission time is 146 s, and the time is reduced to 1/10 of the original. The research of intelligent medical diagnosis information based on big data has good rationality and feasibility.