The Whale Optimization Algorithm (WOA) is an effective meta-heuristic. However, whale optimization algorithms tend to converge slowly and are prone to fall into local optimal solutions. Therefore, this paper proposes an algorithm based on the exploration idea of the Aquila Optimizer (AO) to solve global optimization problems. The improved algorithm is inspired by the exploration idea of the Aquila Optimizer. Firstly, Aquilas conduct exploration to expand the search range, thereby enhancing the global search capability and reducing the likelihood of getting trapped in local optima. Secondly, it incorporates the hunting behavior of whales to balance the exploration and exploitation phases of the algorithm. To validate the effectiveness of the algorithm, this study uses benchmark test functions as experimental objects and compares it with other popular metaheuristic algorithms. The experimental results demonstrate that the proposed algorithm in this study has shown good convergence speed, optimization accuracy, and stability.

In order to teach students more intuitively the phenomenon of race-hazard in combinational logic circuits, this study builds a simulation module circuit based on Proteus simulation software, and carries out simulation experiments to test the generation and elimination of circuit race-hazard. The simulation interface clearly displays the peak pulse of race-hazard, as well as the elimination results. Through this simulation module circuit, students not only observe the process of race-hazard more intuitively, but also can take different measures to eliminate circuit race-hazard. Teaching practice shows that the simulation module circuit provides a new scene for students to learn and understand the race-hazard phenomenon, thus improving the teaching effect.

The new technology and new concept in the era of digital intelligence have profoundly changed the way of operation, organization, production and decision-making in all walks of life. Under the impact of Big Data and Artificial Intelligence, the employment position has higher requirements for the computer skills and literacy of applied talents. The course of Computer Public Basic aims at cultivating students' computational thinking, computer technology and literacy, which plays an important role in improving the vocational skills of applied talents. Take Daqing Normal University, an application-oriented undergraduate university with characteristics in Heilongjiang Province, for example, this paper introduces the reform and practice of the education model of Computer Public Basic. On the basis of analyzing the computer ability required for application-oriented talents in the new era, this paper introduces the reform and practice path from four aspects: hierarchical curriculum system, mixed teaching mode, experimental teaching reform and innovation and entrepreneurship ability training.

In order to alleviate the problem of seat tension in university canteens, it is urgent to develop a kind of software to solve this problem and improve the dining experience for students. This article discusses the use of the current mainstream microservice framework Spring Boot, combined with technologies such as MyBatis Plus and Vue.js, to design and implement a campus ordering system backend, which facilitates platform administrators and merchant administrators to manage dishes, packages, orders, etc., providing a solution for the problem of tight dining for college students.

To address the issues of slow convergence and low success rate in robot path planning associated with the DQN algorithm, this study introduces an enhanced version of the DQN algorithm named MDQN. This enhancement involves modifying the configuration of the state space to more accurately distinguish different states, resulting in a more realistic observation space. Additionally, by altering the reward function, the algorithm is capable of obtaining more reward values, thereby improving learning efficiency. Experimental evaluations conducted on grid maps demonstrate that the improved algorithm, when compared to the traditional DQN algorithm and other related approaches, offers advantages such as higher success rates, faster convergence, and shorter paths.