Edge computing has emerged as a pivotal solution to meet the growing demand for real-time data processing and reduced latency in modern distributed systems. However, its decentralized nature increases the attack surface, making it vulnerable to Distributed Denial of Service (DDoS) attacks. Traditional security models, which rely on perimeter-based defenses, struggle to address these threats due to their inability to effectively manage insider threats, dynamic traffic, and sophisticated attack vectors. This paper explores the integration of Zero Trust Security Models (ZTSM) with behavioral analytics to enhance DDoS detection and mitigation in edge computing environments. Zero Trust principles, such as "never trust, always verify," enforce strict access control, continuous authentication, and micro-segmentation, reducing unauthorized access to critical resources. Behavioral DDoS detection complements this by analyzing patterns in network traffic, user behavior, and resource utilization to identify anomalies indicative of potential DDoS attacks. The proposed framework leverages machine learning algorithms to create adaptive and context-aware detection mechanisms that are scalable across edge nodes. By combining ZTSM with behavioral detection, the model offers proactive mitigation strategies, ensuring minimal disruption to edge services. This integration also addresses challenges like scalability, real-time decision-making, and false positive reduction. The study demonstrates the effectiveness of this approach through simulated DDoS scenarios in edge environments. Results indicate significant improvements in detection accuracy, response time, and overall resilience of the edge infrastructure. These findings underscore the importance of adopting Zero Trust principles and advanced behavioral analytics to safeguard edge computing systems against evolving DDoS threats. Future work will focus on enhancing model interoperability, extending the solution to heterogeneous edge environments, and incorporating predictive capabilities to anticipate and mitigate threats proactively.

The objective of this study was to develop and evaluate an acyclovir-loaded ophthalmic lyophilisate carrier system, aimed at enhancing ocular drug delivery for the treatment of viral eye infections. Acyclovir, an antiviral agent, is often prescribed for ocular herpes simplex virus infections, but its ocular bioavailability is limited due to rapid drainage and poor permeability. To address these limitations, a novel lyophilized formulation was designed using biocompatible and biodegradable polymers as carriers to prolong the retention time and facilitate sustained release of the drug at the site of infection. The acyclovir-loaded lyophilisates were prepared using a freeze-drying technique, with optimization of the formulation parameters including polymer concentration, cryoprotectants, and lyophilization conditions. The characterization of the lyophilized systems included evaluation of physical properties, drug content, reconstitution behavior, morphology, and in vitro release profiles. The formulations exhibited high drug entrapment efficiency, desirable rehydration properties, and sustained drug release over an extended period. In vivo ocular tolerability of the developed system was assessed in a rabbit model. The formulations showed good ocular compatibility with minimal irritation or inflammation, indicating their potential for safe and effective use in ocular therapy. The pharmacokinetic study revealed enhanced corneal retention and prolonged drug release compared to conventional aqueous solutions. This study demonstrates the potential of acyclovir-loaded ophthalmic lyophilisate carrier systems for improved ocular drug delivery. The developed formulation provides a promising alternative to conventional therapies, offering prolonged ocular residence time, improved drug bioavailability, and minimal side effects, thereby enhancing the therapeutic efficacy of acyclovir in treating viral ocular infections. Further clinical studies are warranted to confirm these findings and explore the potential for broader clinical application.

The rapid evolution of software systems, particularly multi-component architectures, has intensified the need for efficient and context-aware testing solutions. Traditional methods of test case generation often struggle to account for the dynamic interdependencies between system components, which can lead to incomplete or redundant test coverage. Leveraging Large Language Models (LLMs), such as GPT-based architectures, presents a novel approach to generating context-aware test cases that better reflect real-world usage scenarios and system interactions. By fine-tuning LLMs on domain-specific knowledge and historical test data, we can enhance their ability to generate meaningful, diverse, and context-sensitive test cases. This abstract discusses a methodology for utilizing LLMs to automatically produce test cases tailored to multi-component systems, accounting for both individual component behaviors and their interactions. We highlight the benefits of this approach, such as improved test coverage, reduced manual effort, and faster development cycles, while also addressing challenges like ensuring test case quality and managing model biases. Ultimately, this research aims to establish a robust framework for integrating LLMs into the software testing lifecycle, with the goal of enhancing software reliability and reducing time-to-market for complex systems.

The present study investigates the stability and in vitro bioavailability of an Acyclovir-loaded Ophthalmic Lyophilisate Carrier System (OLCS) designed for enhanced ocular drug delivery. Acyclovir, an antiviral drug commonly used for treating herpes simplex keratitis, faces challenges such as low bioavailability and limited corneal permeability in conventional formulations. The OLCS approach leverages lyophilized carriers to provide improved stability, controlled drug release, and targeted delivery to ocular tissues. The research evaluated the long-term stability of OLCS under different environmental conditions (accelerated and real-time) in terms of drug content, physical integrity, and reconstitution properties. Stability was assessed using analytical techniques such as high-performance liquid chromatography (HPLC) and differential scanning calorimetry (DSC). The in vitro bioavailability was studied through corneal permeation assays using excised corneal tissues, with comparison to standard acyclovir eye drops. Results showed that OLCS exhibited excellent stability over 12 months, with negligible drug degradation and preserved lyophilized structure. Reconstituted formulations displayed consistent physicochemical properties, including pH and osmolarity. In vitro bioavailability testing demonstrated significantly higher drug permeation and retention in corneal tissues compared to conventional formulations, attributed to the enhanced mucoadhesive and controlled release properties of the OLCS. The findings suggest that Acyclovir-loaded OLCS is a promising alternative to conventional ocular delivery systems, offering improved therapeutic performance and long-term efficacy. Further in vivo studies are warranted to validate its clinical applicability for managing ocular viral infections.