Volume 110

The intensification of global climate change and rising temperatures have underscored the need for energy-efficient building designs, particularly in southern China. This study proposes a particle swarm optimization (PSO) approach to optimize the thickness of triple-glazed windows (L1, L2, L3) to minimize sunlight transmission energy within the wavelength range of 300-2000 nm. A multilayer glass model was constructed, accounting for light transmission, reflection, and absorption properties. By simulating various thickness combinations, the transmitted energy was calculated across the wavelength spectrum. The PSO algorithm was then employed to search for the optimal thickness configuration that minimizes the total transmitted sunlight energy. Experimental results indicate that the proposed method significantly reduces indoor sunlight transmission compared to traditional design methods, enhancing both energy efficiency and occupant comfort. The findings highlight the importance of optimizing glass thickness and material selection in window design, balancing light transmission and shading to suit specific architectural needs. Additionally, this study underscores the impact of PSO parameters on convergence and solution diversity, suggesting further refinements for practical applications. The approach provides a scientific basis for building energy-saving designs and can be adapted to various climatic conditions and material specifications. Future work should focus on incorporating weather variations and expanding model complexity to enhance the algorithm's applicability in real-world scenarios.

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With the rapid development of AI, machine learning has become a hot topic. Among them, reinforcement learning is an important branch of machine learning. With the continuous efforts of scholars, various algorithms emerge in an endless stream. Q-Learning algorithm is a very classic reinforcement learning algorithm, which is the basis of many algorithms. Basically, the Q-table is updated by iteration, so that the agent can choose the best action in the corresponding situation, so as to get closer to the optimal solution. In essence, Q-Learning is sequential difference of different strategies. In the process of learning different strategies, there are two different strategies, goal strategy and behavior strategy. In order to balance the relationship between exploration and exploitation, the ε-greedy strategy is selected to maintain a certain exploratory property of the agent, and relevant hyperparameters such as learning rate (alpha) and discount factor (gamma) are set. However, the research on Q-Learning hyperparameters is not clear enough. In this paper, the author will study the influence of Q-Learning algorithm hyperparameters on its convergence speed under a relatively simple model.

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The application of artificial intelligence (AI) in game development has significantly transformed the dynamics, interactivity, and personalized experiences of games. This paper explores the key applications of AI in game development, including procedural content generation (PCG), non-player character (NPC) behavior and control, as well as automated game testing and quality assurance. PCG enhances replayability and user engagement by automatically generating game content, such as levels and maps, through algorithms. AI-driven NPC behavior makes game characters more adaptive, intelligent, and lifelike, thereby enhancing player immersion. Automated testing utilizes machine learning algorithms to improve testing efficiency and game quality. Despite the numerous advantages that AI brings, there are also challenges related to ethics, technology, creativity, and economics. This paper discusses the potential benefits and challenges of AI in game development and emphasizes the importance of responsible and sustainable application of AI technologies.

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This study scrutinizes the important factors behind the success of video games,Targeting a comprehensive paradigm which merges game quality, brand influence, and market strategies. By investigating the top 100 best-selling titles on Steam, the research looks into the interconnections between creative gameplay, the depth of storytelling, audiovisual quality, and sales results. Analysis of quantitative regression shows that although game quality is important, it is not the main factor for success. A qualitative assessment of high-priced, low-rated games including Overwatch 2 and NBA 2K25, and high-rated, low-selling games like Stardew Valley and Euro Truck Simulator 2, shows how significant brand loyalty, multiplayer interaction, and market segmentation are. This study demonstrates the value of original gameplay, successful marketing approaches, and technological innovations, like VR and superior graphics, in increasing sales and supporting player retention. The results provide key understanding for game developers and marketers who want to improve game performance in a cutthroat and rapidly evolving market.

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With the acceleration of urbanization and rapid economic development, the problem of urban solid waste (SW) has become increasingly prominent. Traditional SW disposal treatment methods, mainly landfill and incineration, pose issues of environmental pollution and resource waste. This study aims to analyze the recycling pathways of urban SW and establish a multi-objective optimization model to achieve comprehensive optimization of economic, environmental, and social goals. Through data collection, quantification and standardization, establishment of a multi-objective optimization problem model, NSGA-II algorithm solution, and simulation and result analysis, this paper seeks to provide scientific decision support and technical guidance for urban SW management. Taking Wuhan as an example, this study analyzes the treatment methods of six different types of SW and seeks the optimal treatment pathways through the multi-objective optimization model. The results show that the multi-objective optimization model can effectively balance economic costs, environmental costs, and utilization efficiency in SW treatment and utilization, providing new ideas for achieving urban sustainable development and resource recycling.

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Due to booming advance on generative models, people have great interest on designing model structure to produce wonderful pictures, or even 3d shapes. The motivation of this work is that the 3d modeling manufacturing in game development is still challenging and time-consuming, and 3d shape produced from generative models may be powerful tools for the problem. The work tried to build game scenes, such as a city and a cave, the typical scene that requires many random but similar objects. This paper aims to explore a complete workflow for applying the GANs to the game development. The structure of the paper is introducing the background of the game development and the progress of the generative model, giving interpretation about the principle of Generative Adversarial Networks, and propose the process on how to utilize it to improve the productivity in developing game scene. Finally, this work found that it could considerably reduce the repetitive work on making massive and similar objects.

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Supply chain is one of the use cases where blockchain itself has proven to be a powerful technology enabling increased transparency plus traceability and security. However, as acknowledged by many that consider it today one of the major technical challenges that issues related to scalability, high cost and data privacy paper investigates what benefits will lead to implementation of supply chain as well its architecture. Examples presented include e-commerce and logistics in which fraud can be reduced while operational efficiency improved through use of blockchain technology. Research also described solutions sharding and Layer 2 handling the scalability issue without specifying security or presenting another area of research needed information. It found the use of blockchain technology in supply chain management to be highly beneficial, though Technical and regulatory challenges to successful adoption impel industry stakeholders to address them, which, apart from being far from answered without common articulated goals through standardization efforts aimed at fostering innovation capable driving transformative changes across global supply chains empowered by realizing full potential offered through DLTs like blockchains.

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Artificial intelligence (AI) technology has witnessed unprecedented advancements and a gradual penetration into civilian applications. This paper aims to thoroughly investigate the application of AI in the entertainment industry, with a particular focus on the principles and cross-disciplinary implementations of 3D real-life scanning, AI for non-player characters (NPCs), and AI video generation. By synthesizing how these technologies streamline content creation processes, lower technical barriers, and inspire novel approaches to game design, we observe that AI is not only reshaping the ecosystem of the entertainment sector but also facilitating the entry of newcomers into game development. However, alongside the benefits, this study identifies several challenges and limitations associated with current AI technologies, such as accuracy, cost-effectiveness, and ethical concerns, which require attention and resolution in future research and practice. Through a detailed examination and synthesis of these phenomena, this research provides a reference for practitioners and suggests directions for subsequent studies.

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Hong Kong, as an international financial center, is widely admired for its iconic Central skyline. Central is not only the heart of the economy but also a hub for global financial activities, gathering numerous international banks and financial institutions. This study aims to explore the feasibility and efficiency of using OpenSCAD for 3D modeling of some buildings in the Central district of Hong Kong, thereby providing a new tool for architectural design and urban planning.In this study, this paper selected three representative buildings for 3D modeling: the Bank of China Tower, the International Finance Centre II, and the Hong Kong Ferris Wheel. These buildings are not only distinctive in their own right but also occupy significant positions in the Central skyline. Through their modeling, we demonstrated how to generate complex geometric structures using a programmatic approach, fully reflecting the efficiency and flexibility of OpenSCAD as a modeling tool. The results show that OpenSCAD has a clear advantage in handling regular geometric bodies and repetitive structures, especially in parametric and modular model design. In addition, by using OpenSCAD's scripted modeling capabilities, we can easily create buildings with different design intentions and styles, further enriching the three-dimensional visual effects of the city.

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This paper examines the evolution and application of OpenAI's advanced conversational AI, ChatGPT, particularly within the domain of cybersecurity. With an architecture built on the Transformer model, ChatGPT demonstrates significant capabilities in language understanding and generation. It leverages vast datasets, ranging from social media posts to technical documents, ensuring the model adapts to diverse fields and maintains compliance with privacy and security regulations. The paper explores ChatGPT's role in network security, highlighting its proficiency in threat detection, vulnerability assessment, and incident response, essential as regulations like GDPR and CCPA become more stringent. Furthermore, the study addresses potential security risks associated with AI, such as phishing and misinformation, and discusses mitigation strategies through advanced training techniques like adversarial training and multi-task learning. A novel variational autoencoder (VAE)-based method, T-VAE, is introduced, offering enhanced generalization capabilities across different tasks and scenarios. The findings suggest that while ChatGPT has made significant strides in cybersecurity applications, continuous improvements in model robustness and adaptability are necessary to mitigate emerging threats and adapt to evolving digital landscapes.

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