Abstract:

Synchronization of complex networks has been widely studied. Current research on the synchronization of complex networks is based on concepts from graph theory and statistical physics. However, the study of real network synchronization remains present substantial obstacles. To overcome the difficulties caused by the complexity of the network, I report a simple synchronization stability boundary equation and identify a spontaneous synchronization structure in power grids for the first time. The findings indicate that both the synchronization stability boundary and the location of spontaneous synchronization occurred are independent of the network. The boundary equation harmonizes two contradictory conclusions well and reveals the mechanism of the synchronization of different individuals through coupling. These results offer a new direction for synchronization research, providing a means to overcome the challenges posed by network complexity, nonlinearity, and uncertainty, and enabling a unified approach to analyzing the synchronization stability of grids.

Abstract: The question of whether the same laws of science can describe living and non-living matter has been debated since thermodynamics was invented. We show that E.~Schr\ödinger's and R.P.Feynman's predictions were correct that instead of a new interaction (and the force it generates), the interaction of two scientific disciplines is responsible for the thermodynamics's inverse relations, which serve as a fundament for processes of life. The thermodynamical and the electrical interaction speeds differ by several orders of magnitude, and classical physics is not prepared for handling such a case. We develop a mathematical method based on a physical transformation, for deriving the interrelations of the time derivatives of the interactions, essentially as Einstein did. We provide exact descriptions for charge-related transport processes needed in many fields. Based on assumptions, touching fundamental science principles, we explain why the former attempts describing life failed, and why non-ordinary laws are needed. We derive the presumed/suspected non-ordinary (non-disciplinary) laws of science describing life.

Abstract: Underwater target detection and segmentation in Side-Scan Sonar (SSS) imagery is challenged by low signal-to-noise ratios, geometric distortions, and Unmanned Underwater Vehicles (UUV) computational constraints. This paper proposes CKAN-YOLOv8, a lightweight multi-task network integrating Kolmogorov-Arnold Networks Convolution (KANConv) into YOLOv8. The core innovation replaces conventional convolutions with KANConv blocks using learnable B-spline activations, dynamically adapting to noise and multi-scale targets while ensuring parameter efficiency. KANConv-based feature pyramid (KANConv-PANet) mitigates geometric distortions through spline-optimized multi-scale fusion. A dual-task head combines CIoU loss-driven detection and a boundary-sensitive segmentation module with Dice loss. Evaluated on a dataset (50 raw images augmented to 2000), CKAN-YOLOv8 achieves state-of-the-art performance: 0.869 AP@0.5 and 0.72 IoU, alongside real-time inference at 66 FPS. Ablation studies confirm the contributions of KANConv modules to noise robustness and multi-scale adaptability. The framework demonstrates exceptional robustness to noise, scalability across target sizes.

Abstract: Solar energy has emerged as one of the most abundant and sustainable sources of power, with solar cells (photovoltaic cells) at the core of this transformation. These cells convert sunlight directly into electricity through the photovoltaic effect, and advancements in their technologies and materials have significantly enhanced their efficiency. This review explores the key solar cell technologies, including silicon-based, thin-film, organic, and perovskite solar cells, highlighting the scientific principles, material properties, efficiency improvements, and challenges in the industry. Furthermore, it delves into the future prospects of solar cells, focusing on emerging technologies, scalability, and sustainability, which could drive the next generation of solar power applications.

Abstract: Spiking Neural Networks (SNNs) have emerged as a promising paradigm for biologically inspired computing, offering advantages in energy efficiency, temporal processing, and event-driven computation. As research advances, scaling SNNs to large networks remains a critical challenge, requiring innovations in efficient training algorithms, neuromorphic hardware, and real-world deployment. This survey provides a comprehensive overview of large-scale SNNs, discussing state-of-the-art neuron models, training methodologies, and hardware implementations. We explore key applications in neuroscience, robotics, computer vision, and edge AI, highlighting the advantages and limitations of SNN-based systems. Additionally, we identify open challenges in scalability, energy efficiency, and learning mechanisms, outlining future research directions to bridge the gap between theory and practice. By addressing these challenges, large-scale SNNs have the potential to revolutionize artificial intelligence by providing more efficient, brain-inspired computation frameworks.

Abstract: Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data, yet retain low local entropy, are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMM), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies.

Abstract: In this study, the cumulative effect of the empirical probability distribution of a random variable is identified as a factor that amplifies the occurrence of extreme events in datasets. To quantify this observation, a corresponding information measure is introduced, drawing upon Shannon entropy for joint probabilities. The proposed approach is validated using selected market data as case studies, encompassing various instances of extreme events. In particular, the results indicate that the introduced cumulative measure exhibits distinctive signatures of such events, even when the data is relatively noisy. These findings highlight the potential of the discussed concept for developing a new class of related indicators or classifiers.

Abstract:

We have shown in an earlier publication9 that it is essential to include general relativity in order to stabilize the electron. Using our algorithm we calculated the radius and mass of the electron. It is re = α/4π G/c3 = α/4πlP ≈ 4 × 10−37m or α/4π of the Planck length lP . The radius is independent of ; it depends on e, G and c. The electron mass is µ∗= 1/2 α/4π c/G= (1/2) α/4πmP in terms of the Planck mass mP.The fields merge at µ∗= (1/2) 1/4π e2/G = 1017GeV. Since the unified field is independent of (it depends on e and G alone) we conclude that it is continuous. In this submission, apart from filling in some computations, we extend our previous result to calculate the pressure profile within the electron. We present both numerical, and analytical calculations based on approximations. The two results are consistent. We also calculate the speed of excitations within the electron which display two distinct regions; a hard shell surrounding a softer core. We also provide an explanation for the large discrepancy between the theoretical and measured mass of electrons.

Abstract: This paper presents the design and the solutions of the chopper for the injection line of the cyclotron of the SPES project at Laboratori Nazionali di Legnaro. The device aims to precisely modulate the average current injected into the cyclotron, thereby controlling the current it delivers. A precise control of the beam current is essential for many experiments foreseen for the cyclotron. Due to safety constraints and limited space, an innovative design has been developed. The chopper features a Wien filter configuration, where the electric field is pulsed and the magnetic field is generated by permanent magnets.

Abstract: The emergence of order from chaos is a fundamental question span- ning multiple scientific disciplines, from physics and biology to com- plex social and economic systems. Traditional deterministic perspec- tives often fail to account for the spontaneous organization observed in nature, where seemingly random interactions give rise to struc- tured patterns. This paper explores how stochastic processes, rather than opposing order, act as a crucial mechanism for self-organization. We propose that stochastic determinism, the interplay between ran- domness and structured evolution, underpins the formation of stable macroscopic laws. Entropy, commonly perceived as a driver of dis- order, is reinterpreted as an organizing force in open systems, where local randomness leads to global order through non-linear interactions and feedback mechanisms. By integrating mathematical models of stochastic differential equa- tions and the law of large numbers, we demonstrate how fluctuations in quantum mechanics, biological evolution, and socio-economic net- works contribute to emergent complexity. These processes, though driven by chance at a micro level, generate statistically predictable outcomes at a macro-level, bridging the gap between chaos and struc- ture. The implications of this study extend across disciplines, providing a unifying framework for understanding pattern formation, stability in dynamic systems, and the evolution of complexity. By recogniz- ing stochasticity as an essential principle of natural organization, this research paves the way for advancements in artificial intelligence, cli- mate modeling, and adaptive socio-economic policies. The findings challenge conventional views of randomness, presenting it not as an obstacle but as the fundamental catalyst for the emergence of order.

Abstract:

Titanium dioxide (TiO2) thin films were deposited onto a substrate via chemical bath deposition, a versatile and scalable coating process. This work comprehensively characterizes the resulting films, elucidating their key structural, optical, morphological, electrochemical, and photoelectrochemical properties. X-ray diffraction (XRD) analysis confirmed the formation of anatase TiO2 with a tetragonal crystal structure, providing insights into the film’s crystalline nature. Optical properties, including absorbance, energy band gap (3.07 eV by Tauc’s plot), and extinction coefficient, were determined using UV-visible spectroscopy, crucial for understanding the material’s interaction with light. Scanning electron microscopy (SEM) revealed the surface morphology and cross-sectional microstructure, enabling measurement of the average film thickness (9.875 μm). Electrochemical impedance spectroscopy, conducted with a polysulfide electrolyte, assessed the charge transfer resistance and electron lifetime in the absence of a dye sensitizer, providing fundamental electrochemical insights. Current-voltage (IV) characteristics demonstrated a nominal current density of 0.0884 mA/cm², a key parameter for potential applications. This detailed characterization provides a comprehensive understanding of the chemically deposited TiO2 thin films, relevant to various applications including functional coatings and surface modifications.

Abstract:

Acute and recurrent pustulosis (ARP), previously known as actinic folliculitis, superficial actinic folliculitis or even acne aestivalis, is a rare, underdiagnosed dermatological condition characterized by the sudden onset of monomorphic pustular eruptions on an erythematous background localised predominantely on the upper body. While typically associated with sun exposure, ARP can also be triggered by other factors, such as heat or sweating, underscoring its multifactorial etiology. We report the case of a 40-year-old woman with ARP, presenting diagnostic challenges due to overlapping clinical features and the coexistence of atopic dermatitis (AD), an association not previously documented in the literature. The patient exhibited recurrent pustular episodes localized on sun-exposed and non-exposed areas, unresponsive to conventional therapies. Comprehensive microbiological, histopathological, and clinical assessments excluded infectious, drug-induced, and other inflammatory pustular dermatoses, confirming the diagnosis of ARP. Importantly, treatment with baricitinib, a Janus kinase (JAK) inhibitor primarily prescribed for AD, resulted in marked improvement in both conditions, suggesting shared inflammatory pathways. This therapeutic response highlights the potential role of JAK inhibitors in ARP management, particularly in cases resistant to standard interventions. This report also proposes the inclusion of heat- and sweat-induced ARP as a distinct subtype, expanding the understanding of its diverse triggers beyond UV radiation. Furthermore, it underscores the need for standardized diagnostic criteria and a structured approach to differential diagnosis, given the condition’s underdiagnosed and often misinterpreted nature. By shedding light on the clinical and therapeutic aspects of ARP, this case contributes to a more nuanced understanding of this rare entity and its potential interplay with inflammatory skin disorders such as AD.

Abstract: Metrological traceability is essential for ensuring the accuracy of measurement results and enabling comparison of results to support critical decision-making in society. This paper explores a structured approach to modelling traceability chains, focusing on the role of residual measurement errors and their impact on measurement accuracy. This work emphasises a scientific description of these errors as physical quantities. By adopting a simple, static modelling framework grounded in physical principles, the paper offers a formal way to track the effects of errors through an entire traceability chain, from primary reference standards to end users. Real-world examples from microwave and optical metrology highlight the effectiveness of this rigorous modelling approach. Additionally, to further advance digital systems development in metrology, the paper advocates a formal semantic structure for modelling, based on principles of Model-Driven Architecture. This architectural approach will enhance the clarity of metrological practices and support ongoing efforts toward the digital transformation of international metrology infrastructure.

Abstract: The report discusses the impact of nanoparticles on the previtreous behavior of glass-forming systems. The presented studies were carried out via broadband dielectric spectroscopy (BDS) in nanocolloids composed of nematogenic liquid crystalline (LC) mixture E7 and paraelectric BaTiO3 nanoparticles. Tests started in the isotropic liquid phase, showing critical-like dielectric constant changes associated with prenematic fluctuations and weakly discontinuous isotropic–nematic phase transition. Subsequently, evolutions of the dielectric constant, two primary relaxation times (related to  τ and α τ' processes), and DC electric conductivity are considered in the nematic phase, down to the glass temperature Tg. The prevalence of the portrayal via the ‘double exponential’ MYEGA equation is shown for dynamic properties. Monitoring of the dielectric constant revealed the exogenic impact of nanoparticles, leading to the permanent arrangement of rod-like LC molecules. For the primary loss curve, critical-like changes of its maximum (peak) are evidenced: peak''∝1T-Tg* for Tg<T<Tg+25K, where Tg*<Tg is the extrapolated singular temperature. Such behavior is shown for both pure E7 and nanocolloids. The heuristic model commenting on this unique behavior is presented. It recalls a hypothetical link between the glass transition and a hidden near-critical phase transition, avoiding a symmetry change.

Abstract: Environmental issues have garnered significant attention from younger generations. Students recognize the importance of envisioning sustainable development for countries that have not yet reached full industrialization. Additionally, students are aware that productive processes in industrialized nations must undergo a transition to methods based on renewable energies. In this work, we elucidate the concept of sunlight trapping and suggest some educational applications. Furthermore, to encourage students' creativity and exploration, we briefly discuss feasible applications of the concept of sunlight trapping.

Abstract: Many physical and biological phenomena are characterized by time asymmetry, and are referred to as irreversible. Time-reversal symmetry breaking is in fact the hallmark of systems operating away from equilibrium and reflects the power dissipated by driving the system away from it. Time asymmetry may manifest in a wide range of time scales; quantifying irreversibility in such systems thus requires methods capable of detecting time asymmetry in a multiscale fashion. In this contribution we review the main algorithmic solutions that have been proposed to detect time irreversibility, and evaluate their performance and limitations when used in a multiscale context using several well-known synthetic dynamical systems. While a few of them have a general applicability, most tests yield conflicting results on the same data, stressing that a “one size fits all” solution is still to be achieved. We conclude presenting some guidelines for the interested practitioner, as well as general considerations on the meaning of multiscale time irreversibility.

Abstract:

Microfluidic systems have become a hot topic in Micro-Electro-Mechanical System (MEMS) research, with micropumps serving as a key element due to their role in determining structural and flow dynamics within these systems. This study aims to analyze the influence of different structural obstacles within microfluidics on micropump efficiency and to offer guidance for improving microfluidic system designs. In this context, a MEMS-based micropump valve structure was developed, and simulations were conducted to examine the effects of the valve on microfluidic oscillations. The research explored various configurations, including valve positions and quantities, yielding valuable insights for optimizing microfluidic transport mechanisms at the microscale.

Abstract: We study the structural properties of networks formed by random sets of bit strings –namely, ordered arrays of binary variables representing, for instance, genetic information or cultural profiles. Two bit strings are connected by a network link when they are sufficiently similar to each other, i. e. when their Hamming distance is below a certain threshold. Using both analytical and numerical techniques, we determine the degree distribution and the conditions for the existence of a giant component in this kind of network. In addition, we analyze their clustering, assortativity, and mean geodesic distance. We show that these properties combine features specific to random networks with characteristics that derive from the Hamming metrics implicit in the definition of similarity between bit strings.

Abstract: This study introduces a new method for nowcasting UV Index maps within the framework of the Austrian Solar UV Measurement Network. The primary objective is to improve public health measures by providing timely and area-wide UV Index values. The UV Index maps are based on clear-sky calculations using data from the Copernicus Atmosphere Monitoring Service. Cloud effects are integrated using cloud modification factors determined from Meteosat Second Generation satellite imagery. To assess the representativeness of the calculated UV Index maps, the corresponding pixel values are compared to ground-based measurements of the year 2022 of 27 sites located in Germany, Austria and Switzerland (DACH region). A source of uncertainty in the comparison arises from the different measurement methods. Ground-based measurements reflect the UV Index in the immediate vicinity of the measurement device whereas the satellite-derived UV Index maps represent mean values over pixel-sized areas. For clear-sky conditions the most significant discrepancies occur at high-altitude sites with near-permanent snow cover, where surface albedo is not adequately represented by the satellite’s mean values. For all-sky conditions, cloudiness introduces additional uncertainties across all locations. Ground-based measurements can detect rapidly changing sun obstruction, whereas satellite-derived cloud information represents a single average value per pixel, lacking the resolution to identify sub-pixel variations. This effect is most prominent for sites located in complex terrain due to cloud climatology. This study emphasizes the potential of satellite data to inform public health initiatives, highlighting that the spatial representation of UV Index values from satellite data is a valuable complement to ground-based measurements.

Abstract: This study examined atmospheric mechanisms affecting the East Bay Hills Fire (1991) in Oakland, California, using the Advanced Weather Research and Forecasting numerical model (WRF) and North American Regional Reanalysis (NARR) dataset. High-resolution WRF simulations at 16km were downscaled to 4km and 1km to analyze primary and secondary circulations at synoptic and meso-α/meso-β scales before the fire. Findings indicate that a ridge over the Great Basin and a trough off the Pacific coast created a strong pressure gradient over northern California, resulting in favorable meso-α conditions for the hot, dry northeasterly winds, known as "Diablo winds," that initiated the wildfire. Additionally, mountain waves from the jet stream enhanced the sinking air on the Sierra Nevada's western side. The main conclusion is that jet circulations did not directly transport warm, dry air to the fire but established a vertical atmospheric structure conducive to wave amplification and breaking, and downward dry air fluxes, leading to the necessary warm and dry low-level air for the fire. The Hot-Dry-Windy (HDW) fire weather index also indicated how favorable the environment was for this tragic event.