Caucasus University is a private university in Tbilisi, Georgia. The university was established in 2004 on the foundation of Caucasus School of Business, which was established in 1998 in partnership with Georgia State University, Atlanta, U.S., during the country's transitional period. This was when Georgia was making the first attempts to move from a planned to free market economy. Caucasus University has evolved and is now one of the most prominent higher education institutions in Georgia and the South Caucasus region.
Distributed Denial of Service (DDoS) attacks, including stealthy Low-Rate DDoS (LRDDoS) variants, pose critical challenges to network security by evading conventional detection systems that rely on traffic volume thresholds. Existing machine learning-based detectors often fail to generalize across diverse attack patterns, suffer from concept drift in evolving traffic, and cannot leverage distributed data due to privacy constraints. To address these limitations, we propose FLD-DDoS, an integrated detection framework based on asynchronous Federated Learning (FL) with Bidirectional LSTM (Bi-LSTM) and adaptive concept drift handling. Our approach enables collaborative model training across multiple network nodes without centralizing sensitive data, while maintaining detection accuracy under changing traffic conditions. The key contributions include: (1) a novel asynchronous FL architecture with intelligent main node selection; (2) a Bi-LSTM classifier enhanced with model drift detection using Kolmogorov-Smirnov testing; (3) a comprehensive evaluation on 800 million real-world corporate network packets showing 99.82% detection accuracy with sub-second latency; (4) experimental comparison demonstrating superiority over six baseline and state-of-the-art methods; (5) a comparative experimental evaluation considering two additional baseline models. The implemented system significantly reduces network load and demonstrates scalable performance with O(n) time complexity for the core algorithms, while providing robust protection against both volumetric and stealthy DDoS attacks.
Static analysis is a critical methodology for ensuring the quality, security, and safety of embedded, cyber-physical, and electronic software systems, particularly as such systems become increasingly complex and tightly coupled with hardware and real-time constraints. Through a systematic study of the literature, this paper summarizes the State-of-the-Art in static program analysis. We develop a comprehensive taxonomy of fundamental techniques, including model checking, abstract interpretation, data-flow analysis, and symbolic execution, and examine their application in modern analysis tools used in electronic and safety-critical systems. The survey thoroughly reviews applications across key domains, including vulnerability detection, automotive and embedded software verification, smart contract auditing, and AI-enabled electronic systems. We also critically analyze persistent challenges, including tool integration, scalability limitations, and the trade-off between analysis precision and soundness. Finally, by discussing emerging trends and future research directions-such as machine-learning-enhanced analysis and hybrid static-dynamic techniques-this work provides a structured framework to guide future research and industrial practice in the development of reliable electronic systems.
Objectives:Early childhood is a critical neurodevelopmental period during which environmental exposures may influence cognitive, language, and social outcomes. Screen media use has become increasingly prevalent in infancy and toddlerhood, yet evidence regarding its association with autism screening outcomes remains inconsistent. Methods:A cross-sectional observational study was conducted among children aged 16-30 months. Surveys were completed by primary caregivers between April 2025 and January 2026 through in-clinic recruitment and online distribution. Autism screening was performed using the Modified Checklist for Autism in Toddlers, Revised with Follow-Up (M-CHAT-R/F). All variables were parent-reported. Results:Higher average daily screen time at assessment and over the preceding six and twelve months was significantly associated with higher M-CHAT-R/F scores. Screen exposure before 12 months of age was associated with a greater likelihood of moderate- and high-risk screening classifications. In multivariate models, earlier exposure and longer screen time duration remained significant positive predictors of M-CHAT-R/F scores. Conclusion:Both the timing and duration of screen exposure were associated with increased autism screening risk scores. Although causal inferences cannot be made and effect sizes were small to moderate, the findings support current pediatric recommendations discouraging screen exposure during infancy.
Biodegradable municipal solid waste remains a major source of methane emissions in waste systems that rely on landfilling without gas recovery. This study tests the hypothesis that partial diversion of biodegradable municipal solid waste from landfill to biological treatment can provide substantial greenhouse gas mitigation in data-limited waste systems and that anaerobic digestion delivers higher mitigation than composting when biogas utilisation and digestate management are effective. A hybrid emission factor-based scenario framework is applied to Georgia as a transition-economy case where biodegradable waste represents a high share of municipal solid waste and separate biowaste treatment remains limited. The analysis compares baseline landfilling, source-separated composting, and anaerobic digestion with digestate stabilisation under equivalent diversion assumptions. Results indicate that 50
The study investigates the capillary electrophoretic enantioseparation of agrochemicals (dichloroprop and mecoprop) using modified cyclodextrins 2-hydroxypropyl-β-cyclodextrin (CD-1), heptakis (2,3-di-O-methyl)-β-cyclodextrin (CD-2), heptakis (2,3-di-O-methyl-6-sulfo)-β-cyclodextrin (CD-3), and heptakis (2,3,6-tri-O-methyl)-β-cyclodextrin (CD-4) in 2-(N-morpholino)ethanesulfonic acid (MES) buffer. Migration times varied depending on the selector type and inclusion complex type and stability, reflecting differences in enantiomer-selector interaction strengths. The reversal of enantiomer migration order observed among the cyclodextrins suggested that even small structural differences in the chiral selectors influenced changes in intermolecular interactions and complex stability. This reversal order is useful for understanding the mechanisms. The separations were due to inclusion complexes with neutral cyclodextrins, while electrostatic interactions were responsible for negatively charged cyclodextrins. Docking and ROESY-NMR studies were used to provide supportive structural insight into the nature of chiral recognition mechanisms. The docking provided general structural information about the potential binding modes of these systems. However, native β-cyclodextrin was used for the ROESY-NMR studies because it produced very clear spectra, allowing the comparison of the basic interaction patterns among the various β-cyclodextrin derivatives. Overall, the results demonstrated that capillary electrophoresis (CE) is a sensitive technique for investigating intermolecular interactions and gaining insight into enantioselective processes.
