Michal Schmirler

Modern energy systems are increasingly exposed to critical transitions due to high renewable penetration, power electronic dominance, distributed generation, and electrification of transport and industry. These changes have increased the risk of voltage collapse, frequency instability, cascading outages, converter-driven oscillations, and storage failures, while traditional monitoring frameworks remain largely reactive. This review examines scaling-based early warning signals derived from fractal and multifractal analysis as a proactive alternative for detecting resilience loss before overt instability emerges. It synthesizes the theoretical foundations of critical transitions and nonlinear dynamics, reviews key indicators such as the Hurst exponent and singularity spectrum evolution, and assesses existing energy-related applications. Building on the identified gaps, the paper proposes a structured framework for integrating fractal metrics into real-time monitoring, digital twins, and AI-assisted resilience management. The review highlights scaling analysis as a promising pathway toward proactive and physically grounded early warning architectures for next-generation energy systems.

The rapid integration of renewable generation, power electronic converters, and distributed energy resources has fundamentally altered the dynamical behavior of modern energy systems, reducing inertia, increasing stochastic variability, and introducing new instability mechanisms. Traditional stability assessment methods rely primarily on physics-based models or data-driven prediction, each with inherent limitations. Physics-based models provide interpretability but may not capture evolving stochastic structure, while purely data-driven approaches often lack physical grounding and robustness. To address this gap, this paper proposes a methodologically novel physics-informed fractal–AI framework that unifies deterministic stability analysis, multiscale fractal diagnostics, and AI-based probabilistic prediction within a single predictive resilience monitoring architecture. Fractal analysis quantifies scaling behavior, long-range dependence, and multiscale correlation structure that may reveal early signs of resilience erosion. The proposed framework integrates physical system models, real-time fractal scaling diagnostics, and artificial intelligence to enable early detection and probabilistic prediction of instability. Physics-based indicators characterize deterministic stability margins, fractal metrics capture multiscale dynamical evolution, and AI models learn nonlinear relationships between scaling indicators and failure risk. The framework also supports integration with digital twin platforms for real-time resilience monitoring and anticipatory control. By explicitly linking physical principles, scaling theory, and machine learning, the proposed approach provides a robust and predictive foundation for proactive stability management in next-generation renewable-integrated energy systems.

This study aims to analyze physiological reactions — skin conductance and facial muscle activity — in response to auditory stimuli of varying tempo levels using complexity-based nonlinear analysis. Thirty healthy adults (15 males, 15 females; aged 18–24) participated in an experiment involving a 2-min baseline followed by sequential exposure to four musical pieces with tempos of 40, 80, 120, and 160[ ]BPM. Galvanic skin responses (GSR) and electromyographic (EMG) signals were recorded using Shimmer devices at 128[ ]Hz and 256[ ]Hz, respectively, in an acoustically controlled environment. We analyzed the signals using fractal dimension (FD), sample entropy (SampEn), and approximate entropy (ApEn). The results indicated an increase in complexity in EMG and GSR signals as the tempo of the music increased, with strong correlations across modalities, suggesting a coordinated modulation of physiological systems with auditory input. These findings may advance affective neuroscience, emotion-aware systems, and human–computer interaction research by highlighting complexity as a robust measure of psychophysiological adaptation.

Fractal theory, renowned for its ability to model self-similar and complex patterns, introduces transformative capabilities to 3D printing. This paper explores the integration of fractal principles across various facets of additive manufacturing, including design optimization, material characterization, process enhancement, and quality assurance. Specifically, we demonstrate how fractal-based methods enable the creation of intricate, lightweight structures with superior strength-to-weight ratios, significantly improve material properties through precise microstructural control, and optimize process parameters to enhance printing accuracy and efficiency. By reviewing recent advancements, this study elucidates the role of fractal geometry in addressing challenges such as material efficiency, structural integrity, and manufacturing scalability. Future opportunities for fractal-based solutions in diverse industrial applications are also discussed, positioning fractal theory as a cornerstone for advancing additive manufacturing.

Researchers are turning to nanofluids in PV/T hybrid systems for enhanced efficiency due to nanoparticle dispersion, improving thermal and optical properties over conventional fluids. Three different concentrations of formulated soybean oil based MXene nanofluids are considered 0.025, 0.075 and 0.125 wt.%. Maximum specific heat capacity nanofluids ( $c_{pNF}$ ) augmentation is 24.49% at 0.125 wt.% loading of Ti3C2 in the base oil. The calculation of the $c_{pNF}$ based on the temperature and nanoflakes concentration is very expensive and time-consuming as it should be calculated via the practical test investigation. This study employs a long short-term memory (LSTM) as an efficient machine learning method to extract the surrogate model for calculating the $c_{pNF}$ based on the temperature and nanoflakes concentration. In addition, a couple of other machines learning methods, including support vector regression (SVR), group method of data handling (GMDH), and multi-layer perceptron (MLP), are developed to prove the higher efficiency of the recently proposed LSTM model in the calculation of the $c_{pNF}$ . In addition, the Bayesian optimization technique is employed to calculate the optimal hyperparameters of the developed SVR, GMDH, MLP and LSTM to reach the highest efficiency of the system in predicting the $c_{pNF}$ based on temperature and nanoflakes concentration. Notably, 95% of the recorded data via differential scanning calorimetry (DSC) is used for training machine learning techniques. In comparison, 5% is used for testing and validation purposes of the developed algorithm. The newly proposed optimized SVR, GMDH, MLP, and LSTM are modelled in MATLAB software. The results show that the newly proposed optimized LSTM model can reduce the mean square error in calculating the $c_{pNF}$ by 99%, 99% and 91% compared with SVM, GMDH and MLP, respectively. The proposed methodology can be used to calculate other thermophysical properties of nanofluids.

Colloidal suspensions of nanoparticles in a base fluid, known as nanofluids, have gained attention as a promising green technology with a lot of promise to address issues with sustainability and energy efficiency in a variety of industries. The main features and uses of nanofluids as a sustainable solution are summarized in this paper. Due to their high thermal conductivity and high surface area to volume ratio, nanoparticles give off exceptional thermal and heat transmission capabilities. They are a desirable option for boosting the effectiveness of heat exchange systems, such as refrigeration, air conditioning, and cooling in electronic equipment, due to their improved qualities. Higher heat transfer rates and lower energy consumption can be achieved by using nanofluids as coolants or heat transfer fluids, which lowers greenhouse gas emissions and energy expenditures. Nanofluids have also found use in the realm of renewable energy, where they can improve the performance of geothermal and solar thermal collectors. The capture and conversion of renewable energy sources can be greatly enhanced by using nanofluids as working fluids in these systems, helping to create a greener and more sustainable energy landscape. Additionally, environmental cleanup and pollution management could benefit from the use of nanofluids. They are appropriate for uses including wastewater treatment, oil spill cleanup, and air purification because of their special features that allow for effective heat transfer and pollutant absorption. Furthermore, nanofluids can significantly contribute to lowering the amount of water and energy used in industrial operations, thus advancing sustainability objectives. The numerous uses of nanofluids as a green technology are highlighted in this paper, with an emphasis on their potential to improve energy efficiency, lessen environmental impact, and contribute to a more sustainable future. As this area of study and development develops, nanofluids will be in a position to play a crucial part in resolving the urgent problems of our day.

Due to its potential to increase lubrication effectiveness and reduce wear, nanofluids have drawn substantial interest in the field of mechanical systems. Colloidal suspensions of nanoparticles dispersed across a base fluid to create nanofluids. This comprehensive study's goal is to examine recent developments, scientific discoveries, and possible applications of nanofluids in tribology. The scientific and technical characteristics of materials which move in relation to one another are the subject of the academic topic of tribology. The aim of this review paper includes a thorough investigation of phenomena like lubrication mechanism, wear and friction. Because of their unique features at the nanoscale, nanoparticles offer a special opportunity to mitigate enduring problems in tribological systems. This review critically evaluates the process utilized to create nanofluids, examines their tribological properties, and considers how they affect the effectiveness of how mechanical systems function. The higher lubrication effectiveness and wear resistance are the main points of attention. This study also investigates several methods for characterizing nanofluids to examine their behavior. The assessment also emphasizes important elements that affect the effectiveness of nanofluids, including the composition, concentration, size, and choice of nanoparticles, in addition to the choice of the base fluid. This study examines many problems and probable future endeavors within the industry, encompassing inquiries pertaining to long-term durability, and scalability. The primary objective of this review paper is to conduct a comprehensive analysis of the current state of nanofluid research within the domain of tribology. The objective is to foster further progress and encourage the extensive adoption of nanofluids as an innovative lubricating technology.

The journal retracts the article entitled “Fractional Model of Electron–Phonon Interaction” by Kulish et al