Cyber risks have been a major concern even if more advanced technologies have been used to deter or mitigate cyberattacks. Much research has been conducted in the areas of cyber risks and cybersecurity. Handling cyber risks needs the specific support of the theories, frameworks, and models of cyber risk management. This paper introduces theories for managing cyber risks, frameworks for handling cyber risks, models for managing cyber risks, and cyber risk management and practices. Cyber risk management and threat intelligence provide their technologies and standards. Healthcare organizations must provide robust cybersecurity procedures. Big data analytics, artificial intelligence (AI)/machine learning (ML)/deep learning (DL), etc., have thus far offered significant advances in cybersecurity for healthcare agencies. This paper will also present a case study of managing cyber risks, which will demonstrate how successful these theories, frameworks, models, and practices have been in healthcare. This paper is not a more in-depth qualitative or quantitative analysis but focuses on identifying, justifying, and describing certain key issues regarding cyber risks.

Integrating Large Language Models (LLMs) into traditional back-end systems can significantly reduce development overhead and enhance flexibility. This paper presents a novel approach using a fine-tuned LLama3 model as a modular back-end component capable of processing JSON-formatted inputs and outputs. We developed a specialized dataset through advanced prompt engineering with the Phi-3.5 model and fine-tuned LLama3 using Quantized Low-Rank Adaptation (QLoRA) on a single NVIDIA T4 GPU. An API layer was designed to facilitate seamless communication between clients and the LLM, effectively replacing conventional application logic. Our fine-tuned model achieved an average accuracy of 76.5¥% and a response time of 3.56 s across 100 test cases, demonstrating its effectiveness in handling back-end tasks. This work underscores the potential of LLMs to transform AI-driven back-end architectures, offering scalable and efficient solutions for modern web services.

This survey explores the integration of machine learning (ML), deep learning (DL), and reinforcement learning (RL) within wireless communications. It reviews various methods, algorithms, and applications while addressing the challenges and future research directions in this field. The paper highlights the necessity of intelligent techniques to enhance the performance and management of wireless networks, driven by the increasing complexity and demand for higher efficiency. Key areas of focus include network optimization, resource management, security, signal recognition, channel coding, traffic prediction, access control, and energy optimization. The survey also discusses emerging techniques such as federated learning, transfer learning, and multi-agent reinforcement learning, emphasizing their potential to revolutionize wireless communication systems.

For a long time, chaos was considered uncontrollable and unusable. However, over the past thirty years, researchers have formulated equations for certain chaotic phenomena, revealing a deterministic aspect to what initially seems random. The evolution of chaotic systems is characterized by strange attractors, which, despite their complex nature, do not allow precise long-term predictions of system behavior. The Lorenz attractor is the best-known example and was the first to be studied, though many other attractors with unusual shapes have since been discovered. The aim of this work is to perform a spectral analysis of the Lorenz attractor by examining the frequencies present in the time signals generated by the solutions of the Lorenz system of equations. To evaluate the frequency complexity of these signals, the discrete Fast Fourier Transform (FFT) is used to derive their frequency spectrum.