Abstract: Modern software application development imposes standards regarding high performance, scalability, and minimal system latency. Multi-threading asynchronous programming is one of the standard solutions proposed by the industry for achieving such objectives. However, the recent introduction of the reactive programming interface in Java presents a potential alterna-tive approach for addressing such challenges, promising performance improvements while minimizing resource utilization. The research examines the migration process from the asynchronous paradigm to the reactive paradigm, highlighting the implications, benefits, and challenges resulting from this transition. To this end, the architecture, technologies, and design of a support application will be pre-sented, designed to outline the practical aspects of this experimental process while closely monitoring the phased migration. The results are examined in terms of functional equivalence, testing, and comparative analysis of response times and resource utilization, as well as the cases where the reactive paradigm proves to be a solution worth considering. Additionally, possible directions for further research and development are presented. This paper not only investigates the design and implementation process but also sets a foundation for future research and innovation in dependable systems, collaborative technologies, sustainable solutions, and distributed system architecture.

Abstract: Artificial intelligence (AI) is transforming communication networks by enabling more efficient data management, enhanced security, and optimized performance across diverse environments, from dense urban 5G/6G networks to expansive IoT and cloud-based systems. Motivated by the increasing need for reliable, high-speed, and secure connectivity, this study explores key AI applications, including traffic prediction, load balancing, intrusion detection, and self-organizing network capabilities. Through detailed case studies, we illustrate AI’s effectiveness in managing bandwidth in high-density urban networks, securing IoT devices and edge networks, and enhancing security in cloud-based communications through real-time intrusion and anomaly detection. Our findings demonstrate AI’s substantial impact on creating adaptive, secure, and efficient communication networks, addressing both current challenges and future demands. Key directions for future work include advancing AI-driven network resilience, refining predictive models, and exploring ethical considerations for AI deployment in network management.

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Topological genomics offers a novel framework for understanding how genomic structures influence neural circuit differentiation, integrating principles of differential topology and statistical mechanics homology. This approach examines how genomic topological invariants, such as persistent loops and Betti numbers, correspond to functional transformations in neural circuits. By employing persistent homology to analyze genomic interaction maps and differential topology to model neural circuit formation, we identify key transitions that govern differentiation. Statistical mechanics complements this framework by modeling energy landscapes and phase transitions that reflect the emergent properties of neural architectures. Together, these interdisciplinary methods elucidate the role of topological features in genetic regulation and neural circuit specialization, with implications for neurodevelopment, pathology, and artificial intelligence.

Abstract: This paper presents a method for preserving the quality of video compressed with video codecs. The video codecs used in this article are H.264/AVC (Advanced Video Coding) and its successor H.265/HEVC (High Efficiency Video Coding). The aim of the article is to present a method for enhancing the video quality for high compression values by preprocessing video frames using Deep Neural Network (DNN). The proposed method reduces the degree of image quality degradation caused by lossy video compression by improving the detail quality of the output video frames after decompression. The proposed method improves video quality for high QP (Quantization Parameter) values beginning from 25 and above. Compared to the popular trend of improving the video quality of already degraded video, the presented method attempts to improve the quality by modifying the image before the compression process by the video codec. The image is modified by DNN to obtain the lowest possible loss of video quality, represented by quality coefficients, but above all by the visual reception of the video by the viewer, enabling increased perception of more details in conditions of high video compression.

Abstract: Artificial intelligence is moving at rapid speeds, which clearly affects user interaction with digital marketing applications. A huge stake for ML is to personalize content, optimize usability and target with precision. Plus it has reshaped the way businesses communicate with their audience. This paper delves into the dual applications of ML in digital marketing, while focusing mainly on how these technologies are influencing this key human-computer interface (HCI). Through a detailed analysis of ML technologies, and in particular by exploring their effectiveness and the ethical dimensions of their development, we are given the opportunity to gain a clear understanding of the potential of ML that is transforming digital commerce. Furthermore, this paper delves into the challenges as well as future trends in the HCI interface in the context of digital marketing using AI, while offering valuable insights regarding this evolving landscape and the challenges presented in it. Of particular interest is the way in which these advanced technologies have radically transformed the digital marketing landscape. The case studies presented highlight their impact on both user experience and customer retention, while also highlighting ethical issues such as data privacy and algorithmic transparency.

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This study explores the relationship between thermodynamic principles and the temporal dynamics of brain wave configurations, focusing on the processes underlying thought, sensation, and motor acts. By conceptualizing brain activity as a dynamic system that minimizes free energy, we analyze how energy landscapes govern transitions between cognitive states. Brain wave oscillations, including alpha, beta, theta, and gamma rhythms, are modeled as time-dependent attractors that reflect neural efficiency in processing sensory input and generating motor output. Additionally, we explore the thermodynamic time scales of thought (milliseconds to seconds), sensation (milliseconds), and motor acts (tens to hundreds of milliseconds), revealing how neural systems optimize energy dissipation during these processes. This interdisciplinary approach integrates neural dynamics, thermodynamic laws, and cognitive neuroscience to offer insights into the energetic constraints that shape mental and physical actions.

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As libraries undergo digital transformation, these technologies redefine current services and create new opportunities for innovation. The research examines the primary applications of Artificial Intelligence (AI) and machine learning in libraries, including information retrieval, automation, and data analysis. This analysis examines how these technologies enhance user experiences, optimize processes, and facilitate personalized solutions. We offer insights into the digital future of libraries through the analysis of current implementations and trends. The study examines the potential advantages and obstacles of integrating AI into library systems, encompassing privacy, ethics, and the evolving responsibilities of librarians. We emphasize innovative strategies for smart library development by integrating new literature and case experiences. This study contributes to the ongoing discourse over the implementation of contemporary technologies in libraries. It offers a prospective perspective on the transformation of these institutions by AI and machine learning, along with a framework for library professionals and policymakers to create more efficient, user-focused, and innovative library services in the digital era, emphasizing the importance of ethical considerations and user-centered design in the advancement of smart libraries. This research explores the emerging concept of "smart libraries" at the intersection of machine learning and artificial intelligence.

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In recent years, deep network-based hashing has emerged as a prominent technique, especially within image retrieval by generating compact and efficient binary representations. However, many existing methods tend to solely focus on extracting semantic information from the final layer, neglecting valuable structural details that encode crucial semantic information. As structural information plays a pivotal role in capturing spatial relationships within images, we propose the enhanced image retrieval using Multiscale Deep Feature Fusion in Supervised Hashing (MDFF-SH), a novel approach that leverages multiscale feature fusion for supervised hashing. The balance between structural information and image retrieval accuracy is pivotal in image hashing and retrieval. Striking this balance ensures both precise retrieval outcomes and meaningful depiction of image structure. Our method leverages multiscale features from multiple convolutional layers, synthesizing them to create robust representations conducive to efficient image retrieval. By combining features from multiple convolutional layers, MDFF-SH captures both local structural information and global semantic context, leading to more robust and accurate image representations. Our model significantly improves retrieval accuracy, achieving higher Mean Average Precision (MAP) than current leading methods on benchmark datasets such as CIFAR-10, NUS-WIDE and MS-COCO with observed gains of 9.5%, 5% and 11.5%, respectively. This study highlights the effectiveness of multiscale feature fusion for high-precision image retrieval.

Abstract: All characterizations of Shannon’s entropy include the so-called chain rule, a formula on a hierarchically structured probability distribution, which is based on at least two elementary distributions. We show that the chain rule can be split into two natural components, the well-known additivity of the entropy in case of cross-products and a variant of the chain rule that involves only a single elementary distribution. The latter is given as a proportionality relation and hence allows a vague interpretation as self-similarity, hence intrinsic property of the Shannon entropy. A similar characterization is given also for the Rényi entropy and the min-entropy.

Abstract: With the emergence of Artificial Intelligence (AI) chatbot technologies such as ChatGPT, which utilize AI and Machine Learning (ML) techniques to generate advanced responses to users, the field of education has been transformed drastically. The latest online learning models empowered by AI have proven to have several benefits for students, but these benefits also come with some inherent challenges, which impede students’ learning process and create hurdles for educators. The study aims to identify and analyze the benefits and drawbacks of AI chatbots in educational settings to help overcome existing educational barriers. The paper begins by highlighting the historical evolution of chatbots along with key elements that encompass the architecture of an AI chatbot. The paper also delves into the challenges and limitations associated with the integration of AI chatbots into learning platforms. A systematic literature review methodology has been adopted by dividing the research into several phases, using techniques of inclusion and exclusion criteria to identify appropriate scholarly articles for review. The research findings from this review reveal several benefits of leveraging AI chatbots in learning platforms. AI chatbots like ChatGPT can serve as a virtual tutoring assistant to foster an adaptive learning environment by aiding students with various learning activities, such as learning programming languages, foreign languages, understanding complex concepts, assisting with research activities and providing real-time feedback. Educators can leverage such chatbots to create course content, generate assessments, evaluate students’ performance and use it for data analysis and research. However, this technology possesses significant challenges concerning data security and privacy. In addition, ethical concerns impacting academic integrity and reliance on technology are some of the key concerns. Ultimately, AI chatbots can provide endless opportunities by creating a dynamic interactive learning environment, however, to allow students and teachers to maximize the potential of such a robust technology, it is imperative to first analyze and address the potential risks that AI chatbots pose.