Transformer applications have been widely used in the computer vision field. Many related literatures show that the advantages of the model such as increased receptive field and globality are gradually emerging in image processing. However, with the popularity of the transformer, whether it can compete with the convolutional neural network (CNN) in terms of performance is still questionable and remains to be further studied. This paper will use the most basic structural model in the visual transformer (ViT) to classify and identify Chinese characters that are frequently used in the field of transportation and logistics and compare them with two classical CNN models. The results demonstrate that the performance of the transformer is obviously better than that of the traditional CNN structure, and the final accuracy of character recognition is higher than that of CNN, up to 98.66 %. It fully shows the infinite potential and excellent performance of the transformer in the area of computer vision and has high reliability and generalization ability.

This article mainly introduces M/M/1 queue and M/M/S queue applied in nucleic acid tests which are applications of Markov chains in queuing theory. Firstly, it is pointed that in the two kinds of queuing models, the arrival time and the service time have no aftereffect which means the two kinds of time both belong to the Markov chain, and it is also illustrated that the arrival time and the service time obey the Poisson distribution, which reflects the uniqueness and stability of the two types of queuing models. The distribution functions of waiting time, service time queue length and so on could be obtained by solving the models. Therefore, by comparing the advantages and disadvantages of different models, the managers could make better decisions which are helpful to allocate resources reasonably, avoid overcrowding and decrease the risk of virus transmission. Furthermore, some other queuing models which are in the more special cases and the innovations of many queuing models are also presented briefly. In the end, the applications of such queuing models in other fields are shown.

The issue of bus scheduling has always been the focus of researchers, and the optimization algorithms proposed constantly aim at balancing the interests of both passengers and bus companies. This paper summarizes the application of the existing algorithm to optimize the bus scheduling problem and analyzes it. Small differences between different algorithms are compared. The convergence speed of different algorithms is accelerated with continuous improvement. But these algorithms are optimized on the existing real data and strive to achieve the best. However, improving the algorithm is not enough; reality does not always match the model and is often more complex. Therefore, future optimization research needs to combine the actual situation to adjust the real-time data in time, in order to achieve real-time optimization problems. The scheduling problem of buses is an important problem related to citizens' travel conditions and social benefits. Optimizing the bus scheduling scheme can effectively improve the traffic environment and passenger satisfaction. At the same time, the bus company can also gradually maximize the benefits.

The point of video games is that players can reap success and excitement in games that they cannot easily experience in the real world. The quality of translating in-game architectural images to photos determines whether the game has many players and good prospects for development. This research in this paper is to implement the function of image-to-image translation using Cycle-GAN. In this work, the dataset is pre-processed to make it more suitable for training the network. Then images are generated by the generator, and the discriminator determines whether the generated ones seem real or not. The confrontation loss and the cycle loss are performed to constrain the learning of the entire system. However, distractions still exist in this system, such as people in the background of the game could be wrongly identified as part of the building, or as a pillar and hence resulting in odd results. To mitigate it, a self-attention mechanism was added to the network to address this phenomenon, allowing the network to focus on the architecture and not disperse attention to some of the game characters. After optimization and testing, the results show that the network can be well-optimized for game-style images to resemble the realistic architecture more closely.

Face detection is a popular and challenging issue which is widely studied in the past few decades. Its application includes the identity authentication, human machine interaction, security surveillance and social network. To have a better insight of the application of one of the typical deep learning algorithms called Convolutional Neural Network (CNN) in this field, this paper aims to analyze the current literature and progress about the face detection of low image quality and face detection optimization. The literature of Convolutional Neural Network from 2015 was included in this paper. Past research topics of face detection includes the occlusion, scale, small face cluster, speed, precision and multi-task region proposal network. The comparison between various deep learning-based methods in terms of the performance indicated that there is still no high robustness solution to all problems. The future research agendas of face detection based on the Convolutional Neural Network was also summarized.

This article is going to discuss several important characteristics of Glauber model from lead isotopes Pb208-under computational analysis. At the start, this paper will provide data under Wood-Saxon distribution in. Then, there are collisions coded from Python, assuming all collisions have reaction cross section of 72 mb, both participant particles and particles under secondary collisions are collected and plot in scatter graphs under impact parameter from 0 fm to 20 fm. Lastly, within the collision area, this paper is going to find the distribution of path length under various parameters.

When building the Industrial Internet of Things (IIoT), it is estimated that over 60% of the cost is spent on wiring and installation. The application of wireless data transmission can eliminate some of the cable connections, and the use of wireless technology for power transmission can undoubtedly avoid even more wiring. However, in practice, traditional wireless power transmission technology has disadvantages such as low efficiency and poor reliability. The introduction of beamforming technology can improve the performance of wireless charging in terms of transmission efficiency and transmission distance. The introduction of beamforming technology can improve the performance of wireless charging in terms of transmission efficiency and distance. Considering the influence of the possible relative displacement between the transmitting and receiving devices on the beamforming during the charging process, a charging process model based on beamforming is developed in this paper using ANSYS for a one-to-one dynamic wireless charging scenario. ANSYS simulation software is used to simulate the array antennas, to investigate the electromagnetic field relationship between the antennas and to analyse the effect of beamforming on the efficiency and stability of the system. Combined with the characteristics of the array antennas shown in the simulation results, the use of beamforming in transmitting antennas in wireless power transmission systems can greatly improve the directivity, effectiveness and interference immunity of the system.

With the Internet industry's development and the economy's rapid growth, the logistics industry has been overgrowing in recent years. It plays an irreplaceable role in the institutions of economic systems worldwide. However, the A* algorithm and the D* algorithm, which currently dominate the logistics industry's path planning algorithms, still have problems, such as long planning times and long planning paths, and there is much room for optimization. Starting with the conventional A* and D* algorithms, this study improves the planning times of the former by enhancing the heuristic function and the latter by increasing the judgment condition. After verification, the average optimization rate of both improved methods reaches more than 5%, improving the transport efficiency of the logistics industry.

In the modern society of digitalization, integration, intelligence, and networking, while people enjoy the convenience of information technology to their production and lives, information security in the network, as the cornerstone of information communication, becomes more and more important. The research is to establish a new image encryption (IE) method based on LFSR state sequence (SS)s in VHDL language, and the stream cipher of LFSR is studied in detail to induce the idea of LFSR SSs based on VHDL language. The correlation coefficient (CC) of the original image (OI) and encrypted image (EI) pixel points (PP) are analyzed from horizontal direction, vertical direction and diagonal direction, and the results show that the CC of adjacent PP of the OI is large, which approaches 1. However, using the encryption algorithm proposed in this paper, the correlation coefficient of the PP of the EI is -0.0282 in the diagonal direction, and the highest correlation coefficient in the horizontal direction is only 0.0122, which indicates that the adjacent PP of the EI are almost uncorrelated with each other. Therefore, the encryption method can well resist statistical attacks, which illustrates the effectiveness, security, and reliability of this new IE method.

Convolutional Neural Network (CNN) tend to have better results on large data sets and poor performance on small data sets, so the data augmentation is crucial for a CNN to get better performance based on the dataset with limited size. In this paper, Deep Convolution Generative Adversarial Network (DCGAN) was used to augment data to make the AlexNet perform better on an image classification task with small data sets. AlexNet was trained on a small anime face training set with only 160 samples to determine whether the anime face was male or female, and then tested its accuracy on a test set with 240 sample. Then, a pre-trained DCGAN was transferred to train on the male and female training sets respectively. And 2 DCGANs were obtained, one could generate male cartoon faces and another could generate female cartoon faces. The images generated by DCGANs were put in train set, which was used to train AlexNet again and the result was recorded. Other data augmentation methods such as cutout, cutmix and Noise Injection were compared as well. Finally, it is found that AlexNet has the best performance when using the DCGAN augmentation method, which can significantly improve the verification accuracy of the model.

The mathematical method known to be Quadratic Programming is a branch of Convex Programming or Convex Optimization, which is then a peculiar case of Mathematical Optimization given a series of restrictions including a convex function to minimize. The Smallest Ball Problem is a problem where we seek to find the smallest enclosing ball of given points on a plane. Convex Optimization provides a solution to the Smallest Ball Problem. There are several ways to characterize a convex programming problem and its solutions, the most important of which is called the KKT conditions. By relating the Smallest Ball Problem to solving a Convex Programming Problem and using the Python packages, the radius, as well as the central point of the Smallest Ball, will be found. In addition, the underlying algorithm for solving Convex Programming Problems is studied. It can be concluded that Convex Programming, or more specifically, Quadratic Programming, gives a feasible solution to the Smallest Ball Problem.

This paper first takes an overview of the current development of high-energy physics, including an introduction to the fundamental particles and forces described by the standard model, physics beyond the standard model like super-symmetry and matter-antimatter asymmetry, and current high-energy physics experiments such as ATLAS and CMS at the large hadron collider. Then we focus on the prospective dark matter detections at the high-luminosity large hadron collider (HL-LHC) and circular electron-positron collider (CEPC), especially the upgrades of the current LHC and different working modes of the CEPC. Finally, the paper discusses the prospects of dark matter detection and possibly dark matter candidates.

This review study will give a brief introduction to dark matter, Large Hadron colliders (LHC), and circular electron-positron colliders(CEPC). The first part of the paper will discuss the fundamental properties of dark matter and the evidence for its existence. There will be a brief discussion of the theories used to explain dark matter. There will be three dark matter profiles and dark halo introductions. The basic configuration, ideas, and dark matter detection of LHC will then be covered in the study. The detection process includes missing momentum signals, bump hunting, and limiting the WIMP zone. The final section will describe CEPC's basic setup and its benefits for locating dark matter.

According to a report released by the World Health Organization in 2011, the number of disabled people worldwide accounts for about 15% of humanity, many of whom are lower limb amputees who lost the ability to walk and climb stairs. However, for various reasons, the proportion of amputees fitted with prostheses or orthotic devices is deficient, and society has a significant demand for prostheses. Since the end of the 20th century, prosthetic intelligence has gradually become the primary goal of prosthesis research. Intelligent prostheses can better restore the function of the inherent limb. There are various solutions for both the mechanical structure and control systems of prostheses, such as stepper motor drives, magneto-rheological liquid intelligent control drives, hydraulic and pneumatic dual-cylinder type drives, and pneumatic device drives. This paper will analyze these solutions and discuss how better integration of different solutions can bring new ideas to the research of intelligent prostheses.

Music generation is a cutting edge and useful research filed, which is helpful for artists to compose novel melodies as well as revealing potential patterns of music. Recurrent neural network (RNN) is a member of the neural network family, which is commonly used for processing sequential data. It can deal with sequential changes in data compared to normal neural networks. Long short-term memory (LSTM) aims at improving the conventional RNN. It is designed to alleviate the deficiencies of gradient disappearance and gradient explosion that possibly happened in RNN during training. In simple terms, LSTM is superior at grasping long term information than normal RNN. It can record the information that requires to be recorded for a long time and abandon these unimportant features. Unlike RNN, which have merely one way of stacking long-term information. It's quite useful for tasks that require long range dependence. In this work the effectiveness of the LSTM is validated on the music generation task.

With the progress of the social economy, the pace of people's life and work is accelerating, and queuing has gradually become a key factor affecting the service level and quality of enterprises. Although the queuing problem has been gradually optimized, there are still some problems such as queue congestion and confusion, inadequate work of staff, and low efficiency. It can be seen that effective measures have been taken to properly solve the queuing problem in China's service industry and other industries. This paper is aimed at using the method that combined linear programming with queue theory to develop 3 steps to optimize the manpower scheduling problem for a real-world situation. A case study of the local government service counter is provided. After applying our model to the case problem, we have achieved three goals: to reduce the number of staff to a more accurate state. Our research finds that there exist some real problems with the service like their service rate is a bit low and needs to improve. In addition, this model has application value for a wide range of other fields, such as banks, post offices and other scenarios.

Emergent phenomena near the quantum critical point at sufficiently low temperature attracts accumulating attentions with respect to both theories and applications. On the contrary to materials with itinerant electrons, ferroelectric materials are relatively less studied but promising in studying novel quantum orders. In this report, we focus on one clean model system, strontium titanite oxide, to explore the quantum criticality. The stabilization of a quantum paraelectric phase has been verified by the previous experimental observation of the dielectric permittivity saturating at a rather high value to the order of 104 under 4 Kelvin. To understand the underlying mechanism, we apply the quantum generalization of Ginzburg-Landau theory as well as lattice dynamics, i.e., the stiffness of soft phonon mode to rationalize the deviation from the classical paraelectric to ferroelectric phase transitions. Besides, under the effective upper critical dimension, a logarithmic correction of the relationship between relative permittivity and temperature could explain the upturn found in diagram.

This study examined the causal effect of the Early Childhood Longitudinal Study, Kindergarten Class of 1998–1999 (ECLS-K) program on students’ academic achievements in math. Moreover, factors heavily impacting students’ fifth grade math score are also explored. The result indicates that the ECLSK program has insignificant negative impact on student’s math score. Kindergarten math score, fine motor skill and gender are top three positive factors and child’s age at k entry, ECLS training program, attended head start are top three negative factors. These results provide a framework for educators to help children improve their math score.

In order to maintain a fair competition environment and enjoyable experience for players, millions of dollars have been spent on against cheating in video games. There is limited research on more sophisticated forms of cheating like “play-for-hire” whereby players pay others to play for themselves. Our work develops a model to identify each player from player behavioural characteristics, which will contribute to solve the “play-for-hire” problem. Firstly, we recorded interactions between players and the game as multivariate time series. Next, we tried to use CNN and LSTM to classify data as corresponding players and we do some feature processing and parameter optimization to improve our result. We found that LSTM is acting better than CNN in higher dimensions, which achieved an accuracy of nearly 87%.

Due to the improvement of online shopping mode, an increasing number of customers rely on reviews displayed on online shopping websites to choose products, and there are also more and more sellers taking consumers' text reviews into consideration to modify their products. Therefore, understanding and analyzing these reviews are getting increasingly significant. This study utilized natural language processing on E-Commerce Reviews. First, I used the Naïve Bayes model and Support Vector Machine to classify whether a reviewer recommends the reviewed product; the accuracies are both 87%. Then I used the random forest to classify the reviewer's positive, neutral, and negative sentiment on each review, which gave 86% precision.