This paper describes the importance of robot path planning in artificial intelligence and control theory, and proposes three improvements to the A-algorithm: bi-directional A-search, improved heuristic functions and pruning strategies. The performance of the different algorithms in terms of computation time, path length and number of nodes is compared through experiments. Moreover, it is emphasised in the article that in practical applications suitable algorithms and their improvements are selected according to the characteristics of the specific problem and reasonable evaluation criteria are used to measure the performance of the algorithms.

As the digital age and globalization continue to evolve, the demand for accurate machine translation of tourism texts has increased substantially. This paper investigates how to improve the quality of machine translation (MT) and machine translation post-editing (MTPE) of Chinese tourism texts for non-native speakers. A review of the machine translation literature reveals a significant progression in translation methods from rule-based to corpus-based, statistical, and finally to the current neural machine translation (NMT) models. Despite its advanced capabilities, NMT requires large amounts of parallel data for training, which often presents challenges. This study proposes the use of Transformer-based models for MT and MTPE to improve translation quality. A dataset was curated from online sources, mainly Chinese tourism websites. The methodology involved pre-processing the data, performing machine translation using the Transformer model, and post-editing the results. The experiment demonstrated an increase in the BLEU score, suggesting an improvement in translation quality. However, challenges such as the handling of synonyms and geographical nouns were encountered, indicating the need for further research and model optimization.

C++, a compiled language, and Python, an interpreted language, are among those essential coding languages that function in diverse areas of the current computer industry. However, different languages have disparate benefits and fit in various circumstances. When large amounts of data are involved or fast execution speed is required, one should consider which language performs better. This research mainly aims to find out whether C++ or Python is more efficient through Topological Sorting, which is utilized to linearize the vertices of a Directed Acyclic Graph (DAG). In the approach of coding the Topological Sorting algorithm in C++ and Python and comparing their execution times on each matrix representing a DAG randomly generated by a Python program, it is concluded that C++ generally has a higher efficiency than Python.

Virtual screening by computer is of great scientific significance for drug research and development. In recent years, a large number of computer simulation methods have been developed and applied to drug development for a variety of diseases. This paper summarized and prospected the application progress of computer aided drug design (CADD) in the research and development of new drugs, focusing on the working principle of CADD, related algorithms, and the advantages and disadvantages of existing methods. Although CADD has been successfully applied to a number of drug development projects, the accuracy of auxiliary drug structure optimization is still not high. Therefore, it is urgent to develop more accurate and efficient CADD models and algorithms to promote the process of new drug discovery.

This paper focuses on how designers can find the right balance and new foundation between themselves and Artificial Intelligence Generated Content (AIGC) at a time when the current artificial intelligence trend is invading the design industry like a wave. This study uses two methods of text analysis and semi-structured interviews to explore the coexistence between AIGC and designers. The results show that, for now, AIGC can help solve some of the fundamental problems in the design process but not all of them. Almost all designers dare not underestimate the possibility of AIGC in the future, and the arrival of AIGC is already an irreversible fact. This study explores the future impact of AIGC on the creative design industry through the perspective of designers and critical theory. It provides practical inspiration and some valuable thinking for the design industry.

With the rapid development of artificial intelligence technology, content generated by artificial intelligence has been rapidly applied to people's lives. At the same time, it is also accompanied by many infringement lawsuits, whether AIGC has really caused different degrees of infringement to human artists. Through the analysis of the existing literature on copyright issues and the walkthrough of Stable Diffusion, an AI-generated image platform, this article digs into the main factors that the AI-generated platform causes infringements on human artists. Provide references for using AI by enterprises and related media, and let more scholars pay attention to this issue. The study found that in the workflow of the AI generation platform, taking Stable Diffusion as an example, the two processes of model training and image generation may cause copyright infringement to a certain extent. Based on this, the AI generation platform has unauthorized use of copyright works, excessive plagiarism and adaptation of copyright works, and the generated images are not marked with watermarks or sources, which damages the copyright owner's rights.

Modern instant messaging platforms often default to permanent data retention, raising concerns about data privacy and users' ability to manage their digital artifacts. In contrast, emerging applications like Snapchat introduce ephemeral communication, where messages automatically vanish after being viewed. This paper investigates the motivations and experiences of users utilizing Snapchat's disappearing message feature. The study employs semi-structured interviews with young adults aged 18-24, who are active Snapchat users. Findings reveal that Snapchat's primary appeal lies in entertainment, creative expression, and maintaining casual relationships. While the disappearing feature contributes to privacy perceptions, users' trust in the feature's privacy protection is not absolute. The paper highlights the challenge of conducting task-oriented or deep conversations due to the ephemerality, emphasizing Snapchat's utility for informal and lighthearted interactions. Ultimately, the study underscores the importance of aligning user experiences with application design, providing insights for practitioners seeking to enhance user-centered product development in the evolving landscape of digital communication.

Apple vision pro renewed interest in VR technology. Whether academia and communities all have many discussions. In this article, three main questions could be discussed after the selecting of some data. The first is how it is being applied in the future. The second is what experience does this current application provide to the user. The last question is what direction could be taken in the future. This research may answer some of the doubts and bring some thoughts to the table

Snow simulation is always a challenge in the computer graphics community due to its combined nature of solids and fluids. In the past, researchers usually applied different solvers to computationally simulate the behavior of snow at different phases, which made the simulation both slow and complicated. In 2013, the material point method, abbreviated as MPM, was first introduced for snow simulation, eliminating the need for multiple solvers. This paper investigates the history of the application of MPM to snow simulation in computer graphics specifically, and offers an overview of its evolution since the pioneering work by Stomakhin. It aims at showing the current state-of-art as well as any limitations. Nowadays, the development of MPM and snow simulation focuses on improvements of the stability and physical accuracy of the method itself, and the generalization of the application scope from snow to arbitrary granular materials. The trade-off between efficiency and accuracy remains a problem, thus it introduces more potential research directions, ranging from developing simpler mathematical models for better physical accuracy to incorporating machine learning techniques to accelerate the simulation process.

Nowadays, Virtual reality(VR) and Aug- mented reality(AR) have become one of the most popular format in many fields, for example video gaming, medical training and even aviation. VR and AR technique simulates images in an edge device, it gives an immersive experience to the users. AR/VR requires high resolution and high FPS for good experience. However, most of the AR/VR devices are made of embedded device due to the limitation of the size and weight of the headset. It is hard to render high quality frames in headset. Many popular VR/AR applications utilize the desktop and server to render the frames and transmit the frames to VR/AR for display. Data transmission from a more powerful device to the VR/AR device requires high transmission speed (1.6GB/s for Oculus quest 2), it is hard to provide the bandwidth with wireless protocol (WIFI/5G). HDMI or DP cable can be applied, but they limit the use case of the VR/AR devices. In this paper, we proposed a latency sensitive super sampling hardware accelerator for VR/AR devices based on machine learning which can significantly reduce the bandwidth requires to transmit frames to VR/AR. In our experiment, the super sampling can deliver high-resolution frames with 25% bandwidth which enable the wireless protocal for VR/AR devices. We implemented the accelerators in RTL and synthesis it with 130 nm skywater pkd. The power consumption of our accelerator at normal data rate for VR/AR devices is 20.97 w and the area is 299.602 mm2.