No. 01 (2025) Volume 05 Issue 01

The rise of the Internet of Things has significantly impacted the field of computer science, revolutionizing the way we interact with and utilize technology. IoT has enabled the integration of physical devices with the digital world, allowing for the collection and exchange of vast amounts of data that can be used to enhance our understanding of the world around us.One of the primary applications of IoT in computer science is in the realm of big data and cloud computing. IoT devices generate massive amounts of data that can be leveraged to uncover valuable insights and inform decision-making processes.Analytics and machine learning techniques are being employed to extract meaningful information from this data, leading to the development of innovative applications and services across a wide range of domains, including healthcare, smart cities, and industrial automation ,However, the proliferation of IoT devices also brings about significant security challenges. IoT devices are inherently vulnerable to cyber attacks, as they are often connected to the internet and may lack robust security measures. Malicious actors can exploit these vulnerabilities to gain unauthorized access to sensitive data or disrupt critical systems.

Artificial intelligence and machine learning have revolutionized the pharmaceutical industry, offering new approaches to drug discovery and development. These techniques have the potential to improve the efficiency and accuracy of the drug discovery process, leading to the development of more effective medications.In particular, AI-based algorithms can be employed to predict the efficacy and toxicity of new drug compounds, as well as to identify new targets for drug development. This paper provides an overview of the current landscape of AI in large-molecule drug discovery, highlighting the increasing application of these techniques to areas such as antibodies, gene therapies, and RNA-based therapies. The paper also discusses the challenges and opportunities associated with the use of AI in pharmaceutical research and development, emphasizing the importance of balancing the promise of AI with a continued reliance on the scientific method. While the promise of AI in pharmaceutical research is significant, it is crucial to recognize the limitations of these technologies and to maintain a balanced approach that leverages the strengths of both AI-driven and traditional, scientific methods. By doing so, researchers and developers can harness the power of AI to accelerate the drug discovery process, while ensuring that the development of new drugs remains grounded in robust scientific principles.

Scientific laboratories in the field of computer science require stringent safety and security measures to ensure the protection of personnel, equipment, and sensitive information. This paper examines the key aspects of laboratory safety and security, including access control, chemical storage, and emergency preparedness. The paper highlights the importance of comprehensive safety protocols, regular training for laboratory staff, and robust security systems to mitigate risks and maintain a secure and productive research environment. Laboratory safety and security are of paramount importance in the field of computer science, where experiments and research often involve handling delicate equipment, sensitive data, and potentially hazardous materials. Proper safety measures are essential to safeguard personnel, protect valuable assets, and ensure the integrity of research activities.

This study examines the impact of rainfall on road traffic accidents in a metropolitan city in India, analyzing statistical data over a specified period. Using accident records from local traffic authorities, the research identifies patterns and correlations between rainfall intensity and the frequency of road accidents. The study focuses on various factors such as accident severity, types of collisions, and the time of occurrence, comparing data from both rainy and dry periods. Statistical analysis, including regression models and correlation tests, is employed to assess the relationship between rainfall and road accidents. The findings indicate a significant increase in accidents during rainfall, particularly in conditions of heavy rainfall, poor visibility, and wet road surfaces. The study highlights the need for improved road safety measures, such as better drainage systems, enhanced driver awareness during rainy seasons, and more effective traffic management strategies to reduce the risk of accidents in such weather conditions.

Foundry production inevitably generates significant volumes of waste, such as aspiration dust, sludge, and shavings. These materials present both environmental risks and economic potential due to their content of valuable metals. This paper examines traditional methods of recycling these wastes, their limitations, and innovative approaches utilizing rotary tilting furnaces (RTFs). The study includes research findings and practical case studies that confirm the effectiveness of RTFs in processing complex wastes and integrating them into production processes.

This article analyzes the influence of germanium on aluminum alloys. The article examines how germanium oxide is introduced into its composition during melting of aluminum-manganese, aluminum-copper, aluminum-magnesium alloys and its hardness changes. Based on the results of the conducted research, the conclusions and suggestions of the authors are presented at the end of the article.

The article considers one boundary value problem of parabolic type with a divergent main part, when the boundary condition contains the time derivative of the desired function. Such non-classical problems arise in a number of applied problems, for example, when a homogeneous isotropic body is placed in the inductor of an induction furnace and an electromagnetic wave falls on its surface. Such problems have been little studied, so the study of problems of parabolic type, when the boundary condition contains the time derivative of the desired function, is relevant. The work defines a generalized solution to the problem under consideration in the space  The purpose of the study is to prove the theorem of the existence and uniqueness of an approximate solution of the Bubnov-Galerkin method for the considered non-classical parabolic problem with a divergent main part, when the boundary condition contains the time derivative of the desired function.

This paper discusses the derivation of Bessel functions of the first kind using power series method and their properties. Additionally, the practical applications of these functions, their graphical analysis, and relationships with other special functions are examined. The research results serve to expand the theoretical and practical significance of Bessel functions.