Intelligent scheduling method for life science automation systems

2016 ◽  
Author(s):  
X. Gu ◽  
S. Neubert ◽  
N. Stoll ◽  
K. Thurow
Keyword(s):  
Life Science ◽  
Automation Systems ◽  
Scheduling Method ◽  
Intelligent Scheduling

scholarly journals Flexible IoT Gas Sensor Node for Automated Life Science Environments Using Stationary and Mobile Robots

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7347
Author(s):  
Sebastian Neubert ◽  
Thomas Roddelkopf ◽  
Mohammed Faeik Ruzaij Al-Okby ◽  
Steffen Junginger ◽  
Kerstin Thurow
Keyword(s):  
Mobile Robots ◽  
Gas Sensors ◽  
Sensor Node ◽  
Occupational Safety ◽  
Life Science ◽  
Practical Investigations ◽  
Vapor Source ◽  
Fast Detection ◽  
Automation Systems ◽  
Science Laboratories

In recent years the degree of automation in life science laboratories increased considerably by introducing stationary and mobile robots. This trend requires intensified considerations of the occupational safety for cooperating humans, since the robots operate with low volatile compounds that partially emit hazardous vapors, which especially do arise if accidents or leakages occur. For the fast detection of such or similar situations a modular IoT-sensor node was developed. The sensor node consists of four hardware layers, which can be configured individually regarding basic functionality and measured parameters for varying application focuses. In this paper the sensor node is equipped with two gas sensors (BME688, SGP30) for a continuous TVOC measurement. In investigations under controlled laboratory conditions the general sensors’ behavior regarding different VOCs and varying installation conditions are performed. In practical investigations the sensor node’s integration into simple laboratory applications using stationary and mobile robots is shown and examined. The investigation results show that the selected sensors are suitable for the early detection of solvent vapors in life science laboratories. The sensor response and thus the system’s applicability depends on the used compounds, the distance between sensor node and vapor source as well as the speed of the automation systems.

scholarly journals Intelligent Scheduling Method Supporting Stadium Sharing

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lei Fang
Keyword(s):  
Task Scheduling ◽  
User Satisfaction ◽  
Mobile Terminal ◽  
Load Level ◽  
Edge Computing ◽  
Average System ◽  
Calculation Results ◽  
Lower Load ◽  
Scheduling Method ◽  
Intelligent Scheduling

At present, the fast-paced work and life make people under great pressure, and people’s enthusiasm for fitness is getting higher and higher, which is in contradiction with the shortage of existing stadiums. So it is considerably significant to open shared stadiums near where citizens live for booking. Therefore, how to allow citizens to book a suitable stadium according to their own needs through mobile phones or computers is an urgent problem to be solved. The booking of the shared stadium can be regarded as a mobile edge computing (MEC) scenario, and the problem can be transformed into task scheduling research under MEC through intelligent scheduling method. When using edge computing (EC) technology for service calculation, the mobile terminal needs to offload the service to the edge computing server. After the server completes the calculation, the calculation results will be sent back to the mobile terminal. Therefore, the calculation time and system energy consumption in the calculation process can be further reduced through task scheduling to improve user satisfaction. In this study, joint scheduling of service caching and task algorithm is proposed to reduce the latency of booking shared stadium request and improve user experience. The simulation results show that the proposed algorithm with edge cooperation idea can achieve lower average system latency at lower load level and can significantly reduce the cloud offloading ratio under low and middle pressure. In addition, the proposed algorithm uses the secondary transfer of more tasks to reduce the pressure of local task running. Finally, the quality of experience (QoE) satisfaction rate under low pressure is guaranteed.

scholarly journals Analysis of an Optimal Model for Liquidity Management of Financial Assets Using an Intelligent Scheduling Approach

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yi Zhou ◽  
Weili Xia ◽  
Shengping Peng
Keyword(s):  
Risk Management ◽  
Liquidity Risk ◽  
Research Priorities ◽  
Management Research ◽  
Liquidity Management ◽  
Management Capability ◽  
Operation Process ◽  
Depth Study ◽  
Scheduling Method ◽  
Intelligent Scheduling

This paper adopts the intelligent scheduling method to conduct an in-depth study and analysis on the optimization of financial asset liquidity management model, elaborates and analyzes the liquidity risk management theory of commercial banks, and reviews the progress of liquidity risk management research in domestic and foreign academia as the theoretical basis of this paper. After that, we analyze the liquidity risk management of Anhui Tianchang Rural Commercial Bank from both qualitative and quantitative levels and further review and analyze the problems and causes. Finally, the full research is summarized and reviewed, theoretical and practical insights are discussed and analyzed, and future liquidity risk management research priorities and directions are elaborated. Based on the analysis results, the problems of the bank in liquidity risk management are described one by one, and further deep-seated cause discovery is carried out to summarize the problems of liquidity risk management which exist in the bank’s operation process due to the lack of liquidity risk management, unbalanced asset, and liability allocation, as well as weak emergency management capability, insufficient day-to-day liquidity monitoring, and lack of professional talents. For the problems and causes of the study, effective suggestions on how to strengthen the bank’s liquidity risk management in multiple aspects are proposed. It is hoped that, by improving the bank’s liquidity risk management and reducing the chance of liquidity risk occurrence, the bank’s sustainable development can be enhanced, and it is also hoped that it can provide some reference for the liquidity risk management of similar rural small- and medium-sized financial institutions.

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