George F. Fragulis

This paper studies the topics of chaos and cryptography, and how chaotic systems can be used in data encryption. Previous research show that chaos can generate pseudo-random sequences and help protect data, but there are problems because many studies do not analyze security deeply, and chaotic systems can be attacked. In this work, chaotic systems are studied, and a variation of a chaotic encryption algorithm is implemented. The security and performance of this algorithm are tested and analyzed. The results show some advantages but also limitations of chaotic encryption, and give important information for future use and research in cryptography.

The subject of this study is the control of a two-degree-of-freedom robotic arm. Initially, the theoretical foundation employed in this study is presented. Subsequently, homogeneous transformation matrices are computed utilizing the Denavit–Hartenberg (D-H) method. Subsequently, the problem of forward kinematics and inverse kinematics is resolved. Subsequently, state space matrices are calculated, and finally, the parameters of the PID (Proportional Integral Derivative) controller are determined to ensure that specific specifications (e.g., overshoot, settling time, steady-state error) are met, even in the presence of disturbances.

Federated learning (FL) and edge computing are transformative technologies that enhance privacy, efficiency, and real-time intelligence for distributed machine learning across diverse edge device networks. FL enables devices to collaboratively train models locally, so sensitive data remains on each device, ensuring privacy and regulatory compliance (like GDPR and HIPAA). Edge computing complements FL by bringing data processing closer to sources such as IoT sensors and smartphones, which reduces latency, bandwidth use, and dependence on cloud servers. This architecture is vital for smart cities, healthcare, industry, and autonomous systems, supporting real-time decision-making. The review details challenges such as resource heterogeneity, communication constraints, security risks, and management complexity, while highlighting opportunities for scalable orchestration, decentralized architectures, and blockchain integration. Together, FL and edge computing create a robust paradigm for scalable, privacy-aware distributed intelligence across multiple domains.

Autism Spectrum Disorder (ASD) is a heterogeneous neurodevelopmental disorder marked by persistent social-communication difficulties and restricted behavioral patterns. Impairments in Theory of Mind (ToM), defined as the capacity to infer and interpret others’ mental states, are widely recognized as central to the social challenges observed in ASD. Although ToM tasks have significantly advanced the theoretical understanding of socio-cognitive deficits, their application in early detection remains limited due to subjectivity, variability in behavioral responses, and time-intensive assessment procedures. This paper presents a theoretical examination of the contribution of Machine Learning (ML) to autism detection through data derived from ToM-based tasks. We argue that ML techniques can transform behavioral, eye-tracking, and neurophysiological responses into objective, multidimensional features, enabling the identification of discriminative patterns beyond conventional statistical analyses. By integrating socio-cognitive theory with computational modeling, we propose a conceptual framework in which ML-enhanced ToM paradigms support more accurate, scalable, and non-invasive approaches to ASD detection.

This study investigates Bitcoin price forecasting using integrated sentiment analysis and gradient boosting within digital financial ecosystems. Two XGBoost models were developed using sentiment scores derived from Bitcoin news (2021–2024) and technical indicators, including GARCH-estimated volatility, Bollinger Bands, MACD, and RSI. The analysis uses 1042 daily Bitcoin observations and 10,025 sentiment records. Two model configurations were evaluated: one using only technical indicators and another incorporating daily aggregated sentiment scores. Model performance was assessed using Diebold–Mariano tests with Newey–West HAC variance estimation and walk-forward validation across 40 rolling windows. Contrary to expectations, sentiment features provided no statistically significant improvement over the technical-only model (p = 0.4888). Both models achieved identical test performance (R² = −0.16%). Walk-forward validation revealed substantial temporal instability (Mean R² = −126.30%, Std = 233.05%), highlighting the challenges of forecasting daily Bitcoin returns. Nevertheless, both XGBoost models significantly outperformed the random walk benchmark (DM statistic = −8.58, p < 0.0001), indicating that technical indicators capture exploitable market structure despite limited predictive accuracy for practical trading. These findings support the efficient market hypothesis and have implications for digital financial ecosystems integrating multimodal information.

Path planning for autonomous robots is a key problem area, particularly when faced with complicated, dynamic, or uncertain environments. Even though traditional techniques (grid-based, graph-based, sampling, and optimization-based) have already been developed to solve this problem, there are notable limitations to scalability, adaptability, and responsiveness with these methods. In this paper, we explore an alternative approach based on chaotic dynamical systems, specifically chaotic attractors like those produced by the Lorenz and Rössler systems. Chaotic systems are defined by several properties that could be leveraged: non-linearity, sensitivity to initial conditions, and dense coverage of the state space are three notable properties that could be used to generate trajectories that are organized, yet ultimately unpredictable. By applying numerical integration (Runge–Kutta) directly to robot motion through MATLAB R2025b simulations, chaotic states support more effective exploration, better obstacle avoidance, and more robust navigation in dynamic or adversarial environments. The paper also examines whether chaotic path planning can be applied in multi-robot systems through state coupled robots that emerge coordinated behavior while maintaining autonomous movement. This paper is a framework for chaos theory supporting adaptable, robust navigating behaviors for purposes such as autonomous vehicles, swarm robotics, and search and rescue and surveillance applications.