Abstract: The Migdal effect has traditionally been viewed as a quantum phenomenon, with its explanation relying on assumptions such as non-adiabatic transitions and quantum coupling. This paper, based on the Great Tao Model, constructs a complete explanatory framework for this effect within classical physics: following an impact, the nucleus undergoes classical accelerated motion, inducing a dynamic distortion in its charge existence field, and transfers energy continuously to the extranuclear electrons via classical electrostatic interaction, leading to their excitation or ionization. Through quantitative derivation of the existence field distortion intensity, electron energy gain, and the geometric relationship of the double-track feature, all predictions of this theory are in complete agreement with the quantitative observational results of the direct measurement experiment, including the "co-vertex double-track" characteristic and the electron energy range.. Further analysis indicates that neutrinos, due to their extremely small mass, impart recoil energies far below the effect's threshold, while Subtrons, lacking charge interaction, cannot trigger the effect at all. This paper systematically analyzes the fundamental differences between the classical and quantum mechanical explanations regarding physical reality, energy transfer mechanisms, and theoretical self-consistency, and clarifies the underlying reason why the Migdal effect cannot be used to detect Subtrons (dark matter). This study not only confirms the universality of classical physical laws at the microscopic scale, providing a novel, physically real, and logically self-consistent paradigm for understanding the Migdal effect, but also offers clear guidance for the strategic direction of frontier experiments such as dark matter detection.

Abstract: The increasing adoption of artificial intelligence (AI) in cybersecurity has introduced new opportunities to enhance detection, response, and automation capabilities; however, applying AI within cybersecurity auditing remains constrained by traditional compliance-oriented approaches that rely profoundly on binary, checklist-based evaluations. Such approaches often reinforce a policing or “sheriff-style” perception of auditing, emphasizing enforcement rather than enablement, risk insight, and organizational improvement. This study proposes an Anti-Sherif AI-driven cybersecurity audit model that integrates AI-based analytics with human expert judgment to support a more adaptive, risk-informed auditing process. Grounded in design science research, the model combines conventional binary compliance checks with AI-derived intelligence and governance-based maturity assessments to evaluate cybersecurity controls across technical, operational, and organizational dimensions. The approach aligns with established standards and frameworks, including ISO/IEC 27001, the National Institute of Standards and Technology (NIST), and the Center for Internet Security (CIS) benchmarks, while extending their application beyond static compliance. A fictional case study is used to demonstrate the model’s applicability and to illustrate how hybrid scoring can reveal residual risk not captured by conventional audits. The results indicate that combining AI-driven insights with structured human judgment enhances audit depth, interpretability, and business relevance. The proposed model provides a foundation for evolving cybersecurity auditing from periodic compliance assessments toward continuous, intelligence-supported assurance.

Abstract: We show that the GBFA method of Elgindy (2025), originally developed for 0 < α < 1, extends to all α > 0 without altering its algorithmic structure. Elgindy's transformation τ = t(1 − y1/α) remains valid for all α > 0 and preserves the numerical framework, ensuring that interpolation, quadrature, and error analysis carry over unchanged. For α > 1, the mapping induces only Hölder regularity at y = 0, which a ects quadrature accuracy. We quantify this e ect and show that the interpolation error retains its original convergence properties. To restore higher-order endpoint smoothness, we introduce a ϕ(α)-generalized transformation that enforces Cr regularity for any prescribed r ≥ 0, accelerating quadrature convergence while preserving the GBFA structure. Numerical experiments con rm high accuracy and robustness across all α > 0, demonstrating that the uni ed GBFA formulation provides an e cient, non-adaptive, xed-node approach for arbitrary-order RLFIs.

Abstract: Traveling wave ultrasonic motors (TWUMs) are critical components in precision systems, their performance is susceptible to degradation under dynamic disturbances in harsh operating environments. This paper presents a monolithic U-shaped rotor designed to intrinsically achieve quasi-zero stiffness (QZS). Unlike conventional QZS systems that rely on assembling discrete positive and negative stiffness elements, the proposed design generates the target mechanical characteristic through the tailored nonlinear response of a unified U-shaped structure, thereby improving preload stability. Through exploring the critical parameters of the rotor cross-section, the finite element method (FEM) is employed to optimize the geometry configuration and characterize the mechanical performances. Simulation results show that the QZS behavior, demonstrating a stable force plateau of 320 ± 10 N across a 0.7 mm displacement range. A maximum von Mises stress of 788 MPa is obtained, well within the material's safety margin, thereby ensuring the structural integrity. Experimental tests validate the effectiveness of the proposed design. This compact, monolithic U-shaped rotor provides a robust and reliable QZS solution, demonstrating significant potential for enhancing the stability of TWUMs in applications prone to harsh environments such as extreme high and low temperatures, thermal cycling conditions, shock environments.

Abstract: Based on the latest cosmological observational and theoretical advancements, this paper proposes a unified systems theory framework, conceptualizing the universe as an adaptive ecosystem with a fundamental architecture of "spacetime-dark matter-dark energy". Through the metaphor of a "computer operating system", it elaborates on the roles of spacetime as a structural entity, dark matter as a gravitational framework, and dark energy as a functional core. It also explores the implications of this framework for understanding physical laws, cosmic evolution, and the nature of information, attempting to establish a unified theory for comprehending the universe. It is important to emphasize that all physical theories are essentially sets of "models" or "metaphorical systems" used to explain and predict observational phenomena. The "operating system" paradigm presented herein is also an approximate description of the complex cosmic reality, not the ultimate answer.

Abstract: The advent of autonomous systems has propelled the integration of artificial intelligence (AI) and machine learning (ML) techniques, particularly deep reinforcement learning (DRL), to enhance decision-making capabilities. This research paper conducts an exhaustive survey of state-of-the-art DRL algorithms, focusing on their applicability and performance within the realm of autonomous systems. To find out how flexible and useful DRL algorithms are in real life, our research covers a lot of different areas, such as robots, self-driving cars, and unmanned flying vehicles. The study takes a close look at the most important parts of these algorithms, like neural network designs, exploration-exploitation strategies, and payment processes, to see how they affect how well independent systems work. Additionally, the study goes into detail about the problems and restrictions that come with using DRL in self-driving systems, covering everything from sample waste to safety concerns. Wealso look at newdevelopments andimprovements in DRLthatmightbeable to get around current problems and make way for future innovations in driverless technology. As a valuable resource for researchers, engineers, and practitioners working on the development and deployment of autonomous systems, this brief survey shows the pros, cons, and opportunities that come with different DRL algorithms in this ever-changing field.

Abstract: This review highlights the pathophysiology and pathogenesis of diabetic retinopathy, the main complication of diabetes. The Neurovascular Unit (NVU) is brought to the surface for its importance to retinal physiological function. Diabetes impairs the NVU leading to the production of causative factors, such as ischemia, oxidative stress and excitotoxicity. The interplay between members of the above triad leads to the main pathological factors of diabetic retinopathy, namely neurodegeneration, neuroinflammation and vasculopathy. Emphasis is given to the pathology of the early stage of diabetic retinopathy (ESDR) and the putative new therapeutic treatments that will prevent/delay the development of the advanced stage of DR in which vision is compromised. NADPH Oxidases (NOX1-NOX5), whose main function is to produce reactive oxygen species (ROS) and induce oxidative/nitrative stress will be presented as novel therapeutic targets for the impaired neurovascular unit. The knowledge of the molecular mechanisms involved in the neuroprotection induced by novel specific inhibitors of NOX2 and NOX4 against the diabetic insults will confer the hope that therapeutic treatments for ESDR will evolve in the near future and be beneficial to the millions of patients who are in the early stage of diabetic retinopathy, as well as patients with other complications of diabetes.

Abstract: Plants exhibit complex internal dynamics in response to environmental conditions, yet whether these dynamics reflect structured affective regimes remains unclear. This study investigates whether internal plant signals encode information about affective states defined relationally by sustained environmental conditions. Valence and arousal were operationalised using temperature, humidity, and residualised light. Using only internal plant measurements—including bioelectrical activity and volatile gas emissions—we evaluated whether machine learning models could decode affective structure without access to environmental variables. Binary classification revealed that valence was reliably decoded over longer temporal windows, whereas arousal required shorter windows, suggesting distinct underlying timescales. Direct multiclass quadrant classification proved unstable, but an Echo State Network capturing temporal dependencies achieved improved performance. These results indicate that plant internal dynamics carry a learnable, temporally extended signature of environmentally defined affective regimes, supporting an interpretation of plant affect as embodied environmental engagement.

Abstract: Background/Objectives: Catalase (CAT) plays a pivotal role in cellular redox homeostasis by catalyzing the decomposition of hydrogen peroxide, thereby mitigating oxidative damage. Impaired CAT function has been linked to chronic inflammation, deregulated cell proliferation, and oncogenesis. While numerous CAT variants have been reported, their functional relevance in prostate cancer (PCa) remains unclear. This study investi-gated CAT genetic variants, enzymatic activity, and potential pathogenic significance in PCa. Methods: Peripheral blood samples from 17 patients with prostate cancer (PCa) and 17 patients with benign prostatic hyperplasia (BPH) were processed for genomic DNA extraction and quantification. Reported missense variants were screened using pub-lic genomic databases. Pathogenicity was predicted with SNPs&GO, PolyPhen-2, and I-Mutant 2.0. Protein modeling was performed with UCSF Chimera, and structural quality was assessed via Ramachandran plots. Two selected variants (L299F, A333T) were geno-typed using the rhAmp™ SNP assay. Serum CAT activity was quantified spectrophoto-metrically (240 nm) following Aebi’s protocol. Results: CAT activity was significantly re-duced in PCa compared to BPH (1.44 ± 0.24 vs. 1.91 ± 0.25 U/mg protein, p = 0.033), with no association to age, PSA levels, or comorbidities. In silico predictions identified variants K177T, G216V, and L351P as potentially deleterious, while N452S appeared benign. Gen-otyping revealed that all participants were wild-type homozygous for L299F and A333T. Conclusions: PCa is characterized by diminished CAT activity independent of the inves-tigated variants, suggesting an alternative regulatory mechanism, such as epigenetic reg-ulation or post-translational modifications, may drive CAT downregulation. These find-ings support further investigation into CAT as a potential biomarker and therapeutic tar-get in oxidative stress–mediated prostate carcinogenesis.

Abstract: Background/Objectives: Injuries caused by wild boar during hunting are repeatedly reported worldwide, yet the scientific literature is remarkably sparse and largely limited to isolated case reports, forensic analyses of fatal events or heterogeneous international compilations. Before this study, no structured nationwide data existed for Germany or comparable European hunting systems. This study aimed to systematically characterize wild boar–related hunting injuries and expand the empirical knowledge base. Methods: In this nationwide exploratory study, German hunters who had sustained at least one wild boar–related injury were recruited via hunting journals. Structured physician-led telephone interviews were conducted using a standardized questionnaire. Data were pseudonymized and analyzed descriptively, stratifying injuries into closed, outpatient open and inpatient open categories. Results: A total of 101 injured hunters were included, representing the largest systematically collected cohort of wild boar hunting injuries to date. Most were highly experienced male dog handlers injured during close-range tracking of wounded wild boar. Injuries predominantly involved the lower extremities. Open injuries—particularly those requiring inpatient treatment—were associated with extensive surgical management, higher complication rates, prolonged recovery and persistent functional impairment. Despite severe courses, all participants resumed hunting. Conclusions: This study substantially expands the previously limited evidence base on wild boar–related hunting injuries by providing structured nationwide data across the full spectrum of injury severity. The findings offer transferable insights relevant to hunting practices, prevention strategies and clinical management in international contexts where wild boar populations and hunting activity are increasing.

Abstract: Despite‍‍‍ Large Language Models (LLMs) exhibiting outstanding capabilities in various natural language processing tasks, they might still be unreliable. Actually, one of their main sources of unreliability is a phenomenon called hallucination, the creation of reasonable but false pieces of information. This work provides a comprehensive overview of advances in understanding, locating, and reducing hallucinations. We start by considering hallucination as the main obstacle in creating reliable AI, and define a taxonomy that follows the development of factual errors and the notion of unfaithfulness with respect to the model's accessible knowledge. Afterwards, we survey the detection methods that are classified depending on the degree of model access and also, and we also refer to the different cognitive processes used for their comparison, which comprise uncertainty estimation, consistency checking, and knowledge-grounding evaluation. In the end, we offer a well-organized representation of the interventions aimed at the abolition of the model hallucinations employed at various stages of the model lifecycle: (1) data-centric interventions exemplified by high-quality data curation, (2) model-centric alignment through preference optimization and knowledge editing, and (3) inference-time strategies such as retrieval-augmented generation (RAG) and self-correction. We affirm that the multilayer, defense-in-depth framework incorporating these non-overlapping strategies is crucial for robust hallucination abatement. Some of the ongoing difficulties are the scalable data curation, the trade-off between alignment and model capability, and the problem of editing the reasoning pathways instead of the surface ‍‍‍facts.

Abstract:

The objective was to detect T. gondii and various protozoan oocysts in the feces of sheltered cats in Thailand using two molecular approaches, Sanger sequencing and un-targeted Next-generation sequencing (NGS). A total of 166 fecal samples from shelter cats, in 26 samples, Toxoplasma gondii oocyst–like structures were detected. The harvested oocysts were grouped into nine pooled samples. DNA was extracted from all pooled samples and tested using quantitative PCR (qPCR), employing coc1 and coc2 primers, which are commonly used to amplify Apicomplexan DNA. The sequenced of the qPCR products were analyzed by Sanger sequencing. Sequence from all pooled samples had similarity to Cystoisospora spp. To further characterize the oocyst species, NGS was performed. Bioinformatic analysis was conducted using a de novo assembler to generate scaffolds, which were then aligned against a custom database of coccidian whole-genome references. This analysis revealed the presence of T. gondii DNA in three pooled samples. In addition, DNA from other protozoan parasites—including Eimeria spp., Cystoisospora spp., Besnoitia besnoiti, Hammondia hammondi, and Cryptosporidium parvum—was also detected. These findings indicate that T. gondii is circulating among shelter cats, many of which were formerly stray. Moreover, NGS sequencing provided more comprehensive information on the diversity of coccidian species in cat feces compared with Sanger sequencing of PCR-amplified targets.

Abstract: Ni@TiO₂ core–shell nanoparticles were synthesized by magnetron sputtering and their structure verified by HRTEM and EDS analysis. The thermal stability of these particles was investigated using in situ TEM annealing and compared with that of pure Ni nanoparticles. While pure Ni particles sinter already at 450 °C and exhibit significant growth at 800 °C, Ni@TiO2 nanoparticles remain stable up to 700 °C, with the sintering onset between 700 and 800 °C. A simple thermal-mismatch model was applied to explain the stabilizing effect of the TiO2 shell, demonstrating that differences in thermal expansion between Ni and TiO2 generate interface stresses sufficient to crack the shell after the amorphous–rutile transformation. The TiO2 coating effectively delays Ni coalescence by 250 °C relative to bare Ni, highlighting its role as a protective shell against high-temperature sintering.

Abstract: Background: Eosinophilic esophagitis (EoE) pathophysiological mechanism is complex and it s still being investigated. We believe there is a group of patients with eosinophilic esophagitis which we could differentiate as having an allergic phenotype, who exhibit a sensitization profile (aeroallergens, panallergens, foods and specific IgG4 levels) with significant differences when compared to patients with conventional allergic disease without associated eosinophilic esophagitis and healthy controls. Method: We have measured the prevalence of sensitization to aeroallergens, foods and panallergens by means of molecular diagnostic techniques (ImmunoCAPTM ISAC) and determined the levels of specific IgG4 against foods and eosinophilic derived neurotoxin (EDN) (ImmunoCAP technology) in patients with EoE of allergic phenotype to study if there are statistically significant differences with respect to the control groups (patients with different allergic pathologies without EoE and healthy patients without documented allergies). The total number of patients under study was 118, distributed among the different study groups. The case group (Allergic phenotype EoE patients) has 48 subjects. The food and respiratory allergy control groups, 30 subjects each. Finally, we included 10 in the healthy control group. Results: We were able to identify statistically significant differences when comparing levels of food-specific IgG4. Milk, egg, wheat, nuts, soy, cod, and LTP stood out. We did not observe significant differences in relation to sensitization to aeroallergens, foods, or panallergens. We also did not observe differences in EDN levels. Conclusion: We present a study in which statistically significant differences in IgG4 levels were observed in response to different types of food, comparing patients with eosinophilic esophagitis of allergic phenotype (case group) against subjects with allergic pathology without EoE and healthy subjects (control groups). Determining whether the detected foods are clinically relevant or not in these patients would be fundamental to establishing their usefulness as a treatment alternative in our patients.

Abstract: Background and Objectives: Studies in hypertension (HTN), migraine (MIG) and HTN associated MIG (HMIG) in women present a link between vascular disorders and migraine. However, there are controversial findings yet for the important factors involved in overweight (OW) women with HTN and HMIG in postmenopause (PMP). Therefore, we planned to investigate the interactive role of body mass index (BMI) based serum levels of interleukin-6 (IL-6) and vitamin D (VitD) in PMP women with HTN, and low frequency episodic MIG and HMIG with aura. Materials and Methods: The subject groups of normal weight normotensives (NW-NTN), OW-NTN, NW-HTN, OW-HTN, NW-MIG, OW-MIG, NW-HMIG and OW-HMIG in PMP women (n:1008) were studied for investigating the BMI based variations and associations of IL-6 and VitD. Age range in each PMP women group (n:126) was 51-60 years. BMI range respectively in NW and OW participants was ≥ 18.5 - ≤ 24.9 and ≥ 25 - ≤29.9 kg/m2. Results: Among groups variations indicated highly significant change in serum IL-6 and VitD. The post hoc Tukey Kramer test for HTN groups indicated significant increased VitD in OW-HTN compared to OW-NTN and NW-HTN, and significantly increased IL-6 in OW-HTN compared to OW-NTN and NW-HTN as well as OW-NTN compared to NW-NTN. Significant increased IL-6 was obtained in OW-MIG compared to NW-MIG and OW-HTN, NW-HMIG compared to NW-NTN, and OW-HMIG compared to OW-MIG, NW-HMIG and OW-NTN. The multiple linear regression indicated collective significant effect among BMI, IL-6, and VitD in OW-HTN, NW-MIG, OW-MIG, NW-HMIG and OW-HMIG. BMI and IL-6 presented significant inverse association with VitD in these groups. The remaining groups presented non-significant effect. Conclusion: Current study shows significant role of serum IL-6 and VitD in HTN and low-frequency episodic MIG and HMIG especially with OW status in PMP women. BMI based significant variation and negative association of IL-6 with vitamin D in HTN, and low-frequency episodic MIG and HMIG with aura in the present report provides evidence of the pathophysiological impact of IL-6 and vitamin D in HTN, and episodic MIG and HMIG.

Abstract: The increasing volume and complexity of digital evidence pose significant challenges to its lawful collection and admissibility, particularly in on-site investigative contexts. Selective seizure has emerged as a critical approach for minimizing unnecessary data acquisition while ensuring procedural legality, privacy protection, and investigative efficiency. However, despite its growing importance, systematic evaluation criteria for selective seizure capabilities in digital forensic tools remain underdeveloped. This study proposes a structured evaluation framework for assessing selective seizure functions in Windows-based forensic tools, with a focus on live-response environments. Essential selective seizure functions were identified and organized into three investigative phases—search, selection, and seizure—reflecting practical field procedures. Based on this framework, a dedicated evaluation dataset was constructed, and six representative portable forensic tools were empirically evaluated under a controlled Windows 10 (NTFS) environment simulating active system conditions. The experimental results demonstrate notable differences in tool capabilities across investigative phases. In the search phase, variations were observed in NTFS parsing and Windows artifact analysis, while the selection phase revealed disparities in file filtering, keyword search, encrypted file handling, and preview functions. In the seizure phase, only a subset of tools sufficiently supported evidence collection, integrity verification, and reporting requirements necessary for selective seizure. These findings highlight that no single tool uniformly satisfies all functional requirements, underscoring the need for context-dependent tool selection.　The proposed framework and evaluation results provide practical guidance for digital forensic practitioners in selecting appropriate tools for selective seizure in field investigations. Moreover, this study contributes a reproducible methodological foundation for future research on selective seizure evaluation, supporting the development of more precise, proportionate, and legally robust digital evidence collection practices in Windows-based forensic investigations.

Abstract: Cracks in planetary gearbox casings generate vibration responses, which, when properly isolated and analyzed, can be used for monitoring structural degradations. This paper provides a signal processing framework to effectively track casing crack related features in planetary gearboxes using carrier synchronous signal average (C-SSA). The proposed algorithm is based on processing the hunting-tooth synchronous signal average (H-SSA) to extract the C-SSA which contains the cyclic interaction between the gear loadings and the corresponding casing response. The root mean square (RMS) of the C-SSA signal can then serve as a health condition indicator (CI) to track crack propagation. Further enhancement can be achieved by applying the Hilbert transform (HT) on the C-SSA using the full bandwidth to derive squared envelope signal, which clearly shows the modulations from the crack response and further enhances the trending capability. To remove cyclic temperature influences observed in the trends, singular spectrum analysis technique (SSAT) has been used, ensuring the trend reflects the changes purely due to the damage progression. Experiments using three casing-mounted sensors show good capability to track crack progression. Tests under 100%, 125%, and 150% load levels show consistent performance across these operating conditions, with better results seen at higher loads. The results demonstrate that C-SSA and its squared envelope signal effectively enhances the sensitivity and reliability of vibration-based crack detection, providing a practical tool for long-term structural health monitoring of planetary gearbox.

Abstract: The pervasive volatility and structural complexity of decentralized assets present significant challenges for modern portfolio management. This paper introduces Coin Quest, a novel, high-fidelity cryptocurrency tracking and risk management platform designed to address critical shortcomings in existing market solutions, notably high data latency and the deficiency of robust quantitative risk tools. Our technical proposal mandates a resilient microservices architecture centered on Apache Kafka for high-throughput, low-latency data stream ingestion, ensuring real-time portfolio valuation across disparate exchanges and blockchains. The analytical core of Coin Quest implements the Monte Carlo Simulation (MCS) framework to compute Value at Risk (VaR) and the superior measure, Conditional Value at Risk (CVaR), recognizing the non-normal return distributions inherent to crypto assets. Furthermore, we detail specialized algorithms necessary for comprehensive tracking and valuation of complex Decentralized Finance (DeFi) positions, including the calculation of Impermanent Loss, and quantitative monitoring of NonFungible Tokens (NFTs) using floor price metrics. We conclude by outlining empirical validation requirements demonstrating the system’s capacity to maintain sub-100ms data latency and confirming the superior predictive accuracy of the MCS-based risk model against traditional historical simulations in highly volatile market environments.

Abstract: The increasing complexity of microwave and mm-wave devices and components that are required to meet the demands for 5G/6G communication, biomedical, security, and intelligent wireless systems necessitates the development of new methodologies that combine theoretical knowledge with computational power. Classic electromagnetic simulations, although accurate, are computationally intensive. Therefore, their application to high-scale optimization processes is limited. Exploiting machine learning techniques could provide an attractive alternative by providing fast and efficient design of microwave and mm-wave devices and components. Herein, we provide a thorough review of the research efforts made so far on this topic by presenting the current state-of-the-art techniques, methodologies, and systems, including surrogate modeling approaches, inverse design strategies, physics-aware learning schemes, and issues related to model generalization and reproducibility. Additionally, we address specific challenges and issues. Finally, we discuss emerging directions and future trends.

Abstract: Machine learning (ML) backtests in finance frequently overstate performance due to data leakage, non-point-in-time features, and evaluation procedures that inadvertently incorporate future information. This paper proposes a leakage-resistant, reproducible, and deployment-oriented framework, the Quant-Safe architecture, combining (i) point-in-time feature engineering with explicit reporting lags, (ii) walk-forward evaluation with out-of-sample (OOS) explainability, and (iii) a robust portfolio translation layer with transaction- cost modeling and execution-grade accounting logs. We validate the framework on the Dow Jones Industrial Average (DJI) constituent universe over 2015-2025, using gradient-boosted trees and Shapley Additive Explanations (SHAP) to demonstrate that macro regime variables (e.g., interest-rate proxies) become dominant drivers during stress periods. The primary contribution is an engineering methodology enabling other researchers to reproduce, extend, and audit financial ML results with explicit controls against common failure modes.