Design of the School-Enterprise Cooperation Management Information Platform Based on the B/S Architecture

The SEC teaching management platform provides a good support for school-enterprise cooperation. This paper conducts research on the SEC management information platform based on the B/S architecture. On the basis of the analysis of the relevant functional needs of the system, the overall functional framework of the system includes the college management platform, enterprise management platform, resource-sharing platform, and user login platform, then the B/S architecture is used to develop the SEC teaching management platform, and the test experiments of the SEC teaching management platform are carried out. The maximum number of concurrent tests shows that the maximum number of concurrencies of the platform is 989. Through the system response time test, the system response time is between 0.2 and 0.45, which meets the system response time requirements. In the CPU use test, the CPU share of the system was between 25% and 31%, meeting the needs of the system. From the above experimental results, the system is relatively high.

Analysis of the use of the Redis in the distributed order processing system in the restaurant network

The object of research is a distributed order processing system for a restaurant chain. The subject of the research is the analysis of the use of Redis for managing event queues in distributed systems. When implementing a distributed order processing system in a restaurant chain with a possible load of up to 20,000 users per day, the Redis system was used. Management of 9 distributed subsystems was organized through Redis. This solution showed an increase in the performance of the system under heavy load (from 50 transactions per second), but the response time of the system in some cases of its operation was longer than without using Redis. When working systems using Redis, it is necessary to take into account the amount of data with which Redis will work, since it does not exceed the amount of RAM, the absence of differentiation into users and groups, and the absence of a query language, which is replaced by a key-value scheme. This research is aimed at analyzing the operation of the system during trial operation under real load. We compared the operation of a configured system with Redis enabled and disabled. The main indicators for the analysis were the system response time and the maximum request execution time. The research was carried out for 2 weeks, the first week using the system settings with disabled Redis, the second – with enabled Redis. We selected 2 days with a similar load on the system to each other. Especially indicative are the results of comparing the durations of the longest queries, which show an almost constant value of the duration for the system in the mode of enabled Redis. The hypothesis of an increase in the system response time at low loads was confirmed, but this value not only leveled off at a load of 500 unique users but also became less at loads of 1000 unique users.

Applicability analysis of prediction methods in the system for selection personalized offers by analytical modeling

The relevance of the work is justified by the frequent occurrence of the need to solve the problems of choosing personalized offers in information systems and the many possible methods of machine learning, among which it is necessary to choose the most suitable one. The purpose of this study is to simulate a system for selecting personalized offers as a queuing system for estimating equipment costs when using each of the methods necessary to service the required part of requests for a given time limit. This solves the problem of assessing the minimum number of servicing devices (backend servers) required to ensure the operation of the system at a given level. The paper shows that the system can be described by a multichannel queuing system without losses. The distribution function of the spent time of the request in the system (the service time plus the waiting time in the queue) is calculated, since in the literature for such systems only the distribution function of the waiting time in the queue is described. Transformations of the expression for the probability of waiting are given, which solve the overflow problem in the software implementation. In the final part, as an example, the system was modeled according to the given parameters, and the minimum number of servicing devices was estimated to ensure a given system response time. Based on the data obtained, it is possible to make a decision on the advisability of using one or another method for predicting the frequency of user clicks or ranking.

The Application of Acceptance Models to Human Resource Information Systems: A Literature Review

Technology acceptance by users has been extensively studied in recent years in various fields such as technologies for learning, e-commerce, and business technologies. This review focuses specifically on Human Resource Information Systems (HRIS) and its acceptance by users. Given their widespread use in organisations, HRIS acceptance has been researched but not synthesised in any way. This article aims to review the effectiveness of the classical TAM and UTAUT models commonly used for new technologies and to identify the variables added to these models to better predict HRIS acceptance by employees. It also highlights the importance of the human-machine-organisation relationship to contribute to the understanding of HRIS acceptance in professional environments. This review confirms the effectiveness of the TAM and UTAUT models and proposes to develop them by (a) variables reffering to technological characteristics (security, system response time, and the data quality implemented in the system), (b) user satisfaction with the system, and (c) organisational variables (expected role of the HR department). The discussion focuses on the retroaction possibilities between the different Human-Machine-Organisation relation levels.

Optimization-driven distribution of relief materials in emergency disasters

The distribution of relief materials is an important part of post-disaster emergency rescue. To meet the needs of the relief materials in the affected areas after a sudden disaster and ensure its smooth progress, an optimized dispatch model for multiple periods and multiple modes of transportation supported by the Internet of Things is established according to the characteristics of relief materials. Through the urgent production of relief materials, market procurement, and the use of inventory collection, the needs of the disaster area are met and the goal of minimizing system response time and total cost is achieved. The model is solved using CPLX software, and numerical simulation and results are analyzed using the example of the COVID-19 in Wuhan City, and the dispatching strategies are given under different disruption scenarios. The results show that the scheduling optimization method can meet the material demand of the disaster area with shorter time and lower cost compared with other methods, and can better cope with the supply interruptions that occur in post-disaster rescue.

Development of a Test Stand to Quantify the Response of a Planter’s Automatic Downforce Control System

HighlightsThe developed downforce test stand simulated varying disc loads based on actual field data.The planter’s downforce control system was able to maintain the target gauge wheel load 94% of the time.The planter’s downforce control system managed disc load variations of up to 667 N within 1.3 s.Abstract. In recent years, precision planters have incorporated automatic control of the row unit downforce to reduce sidewall soil compaction, maintain proper seeding depth, and control row unit ride quality. By applying an appropriate row unit downforce, more uniform emergence and increased yield can be obtained. However, little research exists on evaluating the response and accuracy of downforce control systems during planting. Therefore, the objectives of this study were to (1) develop a laboratory-scale row unit downforce test stand and (2) use the test stand to evaluate the downforce control system response time and the load distribution between the gauge wheels, opening discs, and closing wheels using simulation scenarios based on real-world soil and terrain data. The downforce test stand was able to distribute the applied downforce to the row unit gauge wheels, opening discs, and closing wheels. It was also capable of varying the row unit ride height. The simulation scenarios using the test stand showed that the downforce control system maintained the target gauge wheel load (GWL) of 379 N within ±223 N for more than 94% of the time during all simulations. The downforce control system was also able to manage the GWL within 1.3 s for disc load variations up to 667 N. Keywords: Automatic downforce control, Downforce test stand, Gauge wheel load, Simulation.

Application of Machine Learning in Air Hockey Interactive Control System

In recent years, chip design technology and AI (artificial intelligence) have made significant progress. This forces all of fields to investigate how to increase the competitiveness of products with machine learning technology. In this work, we mainly use deep learning coupled with motor control to realize the real-time interactive system of air hockey, and to verify the feasibility of machine learning in the real-time interactive system. In particular, we use the convolutional neural network YOLO (“you only look once”) to capture the hockey current position. At the same time, the law of reflection and neural networking are applied to predict the end position of the puck Based on the predicted location, the system will control the stepping motor to move the linear slide to realize the real-time interactive air hockey system. Finally, we discuss and verify the accuracy of the prediction of the puck end position and improve the system response time to meet the system requirements.

Research on Optimal Control of Excavator Negative Control Swing System

In order to improve the energy efficiency and dynamic of negative control swing systems of excavators, this paper proposes a technical scheme of adding two PRVs (pressure reducing valves) to main valve pilot control circuit, which can adjust main value opening arbitrarily according to the working condition. A pump-value compound control strategy was formulated to regulate the system power flow. During swing motor acceleration, main pump and the two PRVs are controlled to match system supply flow with motor demand flow, thereby reducing motor overflow and shortening system response time. During swing motor braking, the channel from motor to tank is opened to release hydraulic brake pressure by controlling PRVs before swing speed reduces to zero, which prevents the motor from reversing and oscillating. A simulation model of 37-ton excavator was established, and the control strategy was simulated. The original and optimized performance of the swing system were compared and analyzed, and results show that the application of new scheme with the compound control strategy can reduce overflow and increase braking stability of the swing system. In addition, system response and speed control performance are also improved when excavator performs a single-swing action.

Ride-Hailing Order Dispatching at DiDi via Reinforcement Learning

Order dispatching is instrumental to the marketplace engine of a large-scale ride-hailing platform, such as the DiDi platform, which continuously matches passenger trip requests to drivers at a scale of tens of millions per day. Because of the dynamic and stochastic nature of supply and demand in this context, the ride-hailing order-dispatching problem is challenging to solve for an optimal solution. Added to the complexity are considerations of system response time, reliability, and multiple objectives. In this paper, we describe how our approach to this optimization problem has evolved from a combinatorial optimization approach to one that encompasses a semi-Markov decision-process model and deep reinforcement learning. We discuss the various practical considerations of our solution development and real-world impact to the business.