Search Results (10,275)

Objectives: There is a growing interest among practitioners in employing artificial intelligence (AI) to enhance the precision and efficiency of diagnostic methods. The objective of this study is to assess the precision of an AI-based method for facial asymmetry assessment using 3D facial images. Methods: The study included 130 patients (84 female, 46 male), analyzing 3D facial images from the Vectra^® M3 imaging system using both manual and AI-based methods. Seven bilateral facial landmarks were identified for manual analysis, calculating the asymmetry index for facial symmetry assessment. An AI-based program was developed to automate the identification of the same landmarks and calculate the asymmetry index. The reliability of the manual measurements was assessed using intraclass correlation coefficients (ICC) with 95% confidence intervals (CI). Precision of automated landmark identification was compared to the manual method. Results: The ICCs for the manual measurements demonstrated moderate to excellent reliability, both within raters (ICC = 0.62–0.99) and between raters (ICC = 0.72–0.96) each calculated with 95% CI. Agreement was observed between the manual and automated methods in calculating the asymmetry index for five landmarks. There was a statistically significant difference between the two methods in determining the asymmetry index for alare (median: 2.05 mm manual vs. 1.54 mm automated, p = 0.0056) and cheilion (median: 2.77 mm manual vs. 2.30 mm automated, p = 0.0081). Conclusions: The AI-based method provides efficient and comparable precision of facial asymmetry analysis using 3D images. The disagreement observed between the two methods can be addressed through further improvement and training of the automated software. This innovative approach opens doors to significant advancements in both research and clinical orthodontics. Full article

The purpose of this paper is to determine the local dimension of the critical locus of a generic singularity. We use combinatorial methods to calculate this dimension in terms of a convex object associated with the singularity, called the Newton polyhedron. In this article, we prove that the local dimension of the critical locus of a generic singularity $f:({\mathbb{C}}^n,0)⟶(\mathbb{C},0),$ $n\le 4,$ is equal to the combinatorial dimension of the Newton polyhedron of the gradient mapping $\nabla f.$ Therefore, there is some symmetry between combinatorial properties of the Newton polyhedron of a generic singularity and geometric properties of its critical locus. Full article

We theoretically investigate the supercurrent and superconducting diode effect (SDE) in an Aharonov–Bohm (AB) interferometer sandwiched between two aluminium-based superconducting leads. The interferometer features a quantum dot (QD), which is created in an indium arsenide (InAs) semiconductor nanowire by local electrostatic gating, inserted in one of its arms and a magnetic flux threading through the ring structure. The magnetic flux breaks the system time-reversal symmetry by modulating the quantum phase difference between electronic transport through the QD path and the direct arm, which enhances constructive interference in one direction and destructive interference in the other. This leads to a discrepancy between the magnitudes of the forward and reverse critical supercurrents and is the core mechanism that induces the SDE. We demonstrate that the critical supercurrents exhibit Fano line shapes arising from the interference between discrete Andreev bound states in the QD and continuous states in the direct arm. It is found that when electron transport is dominated by the QD-containing path as compared to the direct arm path of the interferometer, the diode efficiency reaches a maximum, with values as high as 80%. In contrast, when the direct arm path dominates transport, the diode efficiency becomes weak. This attenuation is attributed to the participation of higher-order quantum interference processes, which disrupt the nonreciprocal supercurrent balance. Importantly, the proposed AB interferometer system has a relatively simple structure, and the realization of the SDE within it is feasible using current nano-fabrication technologies. Full article

The purpose of this paper is to investigate a class of novel symmetric coupled fuzzy fractional partial differential equation system involving the Caputo–Katugampola (C-K) generalized Hukuhara (gH) derivative. Within the framework of C-K gH differentiability, two types of gH weak solutions are defined, and their existence is rigorously established through explicit constructions via employing Schauder fixed point theorem, overcoming the limitations of traditional Lipschitz conditions and thereby extending applicability to non-smooth and nonlinear systems commonly encountered in practice. A typical numerical example with potential applications is proposed to verify the existence results of the solutions for the symmetric coupled system. Furthermore, we introduce Ulam–Hyers stability (U-HS) theory into the analysis of such symmetric coupled systems and establish explicit stability criteria. U-HS ensures the existence of approximate solutions close to the exact solution under small perturbations, and thereby guarantees the reliability and robustness of the systems, while it prevents significant deviations in system dynamics caused by minor disturbances. We not only enrich the theoretical framework of fuzzy fractional calculus by extending the class of solvable systems and supplementing stability analysis, but also provide a practical mathematical tool for investigating complex interconnected systems characterized by uncertainty, memory effects, and spatial dynamics. Full article

This work introduces a hybrid memory-based random walk model that combines the Elephant Random Walk with a modified Moran Random Walk. The model introduces a sequence of independent and identically distributed random variables with mean 1, representing step sizes. A particle starts at the origin and moves upward with probability r or remains stationary with probability $1-r$. From the second step onward, the particle decides its next action based on its previous movement, repeating it with probability p or taking the opposite action with probability $1-p$. The novelty of our approach lies in integrating a short-memory mechanism with variable step sizes, which allows us to derive exact distributions, recurrence relations, and central limit theorems. Our main contributions include (i) establishing explicit expressions for the moment-generating function and the exact distribution of the process, (ii) analyzing the number of stops through a symmetry phenomenon between repetition and inversion, and (iii) providing asymptotic results supported by simulations. Full article

In this work, it is shown that, based on the linear analysis, as long as a theory of gravity is diffeomorphism invariant and possesses the tensor degrees of freedom propagating at a constant, isotropic speed without dispersion, its asymptotic symmetry group of an isolated system contains the (extended/generalized) Bondi–Metzner–Sachs group. These asymptotic symmetries preserve the causal structure of the tensor degrees of freedom. They possess the displacement, spin and center-of-mass memory effects. These effects depend on the asymptotic shear tensor. The displacement memory effect is the vacuum transition and parameterized by a supertranslation transformation. All of these hold even when the Lorentz symmetry is broken by a special timelike direction. Full article

As rail transit systems advance toward greater automation and intelligence, cloud computing technology, with its remarkable scalability and robust data processing capabilities, has been steadily expanding its footprint in this domain. However, the adoption of cloud computing also introduces new safety challenges for train control systems. Traditional safety computers in train control systems rely on heterogeneous redundancy with symmetry to enhance safety. Nevertheless, the software in cloud computing environments, even if heterogeneous, may share the same source code, thereby triggering the risk of common-cause failures in the software. To address these issues, this study proposes a multi-level monitoring architecture system tailored to the characteristics of cloud-based safety computing platforms. This architecture innovatively integrates the three-level monitoring architecture pattern from the automotive field, the secure channel pattern, and the distributed safety mechanism architecture. It monitors the levels of common-cause software failures that cannot be eliminated through heterogeneity. The introduction of multi-level active monitoring for risk control has reduced the impact of common-cause software failures on system security. By constructing a formal security model, quantitative evaluations are conducted separately on the single-channel L2 and L3, the dual-channel L4 without degradation or monitoring, and the dual-channel L4 monitoring architecture with complete functions. This verifies the effectiveness of the proposed monitoring architecture in reducing the risk of common-cause software failures in the virtualization layer. This study provides a robust theoretical foundation and technical support for the security-oriented design and development of the next-generation intelligent rail transit systems. Full article

Insider threats remain a persistent challenge in cybersecurity, as malicious or negligent insiders exploit legitimate access to compromise systems and data. This study presents a bibliometric review of 325 peer-reviewed publications from 2015 to 2025 to examine how machine learning (ML) and deep learning (DL) techniques for insider threat detection have evolved. The analysis investigates temporal publication trends, influential authors, international collaboration networks, thematic shifts, and algorithmic preferences. Results show a steady rise in research output and a transition from traditional ML models, such as decision trees and random forests, toward advanced DL methods, including long short-term memory (LSTM) networks, autoencoders, and hybrid ML–DL frameworks. Co-authorship mapping highlights China, India, and the United States as leading contributors, while keyword analysis underscores the increasing focus on behavior-based and eXplainable AI models. Symmetry emerges as a central theme, reflected in balancing detection accuracy with computational efficiency, and minimizing false positives while avoiding false negatives. The study recommends adaptive hybrid architectures, particularly Bidirectional LSTM–Variational Auto-Encoder (BiLSTM-VAE) models with eXplainable AI, as promising solutions that restore symmetry between detection accuracy and transparency, strengthening both technical performance and organizational trust. Full article

Dynamic mobilization exercises (DME) are an effective strategy to prevent musculoskeletal injuries and promote back health in sport horses. Previous studies focused mainly on multifidus muscle cross-sectional area, with limited data on locomotion and adaptation timing. This study evaluated locomotor changes using accelerometry, over 8 weeks of DME application in 14 sedentary horses: a DME group (n = 8) performing 10 different DME (3 neck flexions, 1 neck extension and 3 lateral bending exercises to each side), 5 repetitions of each DME per session, 3 sessions/week, and a control group (n = 6), that continued with their daily routine activities without any other training. During the study period, all horses were housed in medium-sized paddocks. Accelerometric measurements were performed at walk and trot before intervention, 2 h and 24 h after a DME session, and at 2, 4, 6, and 8 weeks. The DME group showed significant increases in dorsoventral displacement and dorsoventral and mediolateral activities from week 4, at both walk and trot, which then stabilized. Longitudinal activity increased from week 2 on trot and from week 4 at walk. Locomotor symmetry and stride length improved at week 6, while stride frequency decreased at week 8; velocity remained unchanged. These findings indicate that DME enhances dorsoventral, mediolateral and longitudinal activities, producing longer, more symmetrical strides. Overall, DME appears to promote more symmetrical movement patterns. Full article

Practical problems often drive the development of new statistical methods by presenting real-world challenges. Testing the homogeneity of n independent Poisson means when only one observation per population is available is considered in this paper. This scenario is common in fields where limited data from multiple sources must be analyzed to determine whether different groups share the same underlying event rate or mean. These settings often exhibit underlying structural or spatial symmetries that influence statistical behavior. Traditional methods that rely on large sample sizes are not applicable. Hence, it is crucial to develop techniques tailored to the constraints of single observations. Under the null hypothesis, with large n and a fixed common mean $\lambda$, the likelihood ratio test statistic (LRTS) is shown to be asymptotically normally distributed, with the mean and variance being approximated by a truncation method and a parametric bootstrap method. Moreover, with fixed n and large $\lambda$, under the null hypothesis, the LRTS is shown to be asymptotically distributed as a chi-square with $n-1$ degrees of freedom. The Bartlett correction method is applied to improve the accuracy of the asymptotic distribution of the LRTS. We highlight the practical relevance of the proposed method through applications to wildfire and radioactive event data, where correlated observations and sparse sampling are common. Simulation studies further demonstrate the accuracy and robustness of the test under various scenarios, making it well-suited for modern applications in environmental science and risk assessment. Full article

This paper presents a new coupled fixed-point theorem for a pair of set-valued mappings acting on the Cartesian product of $(m_1, m_2)$- and $(n_1, n_2)$-quasi-metric spaces. Within the general, non-symmetric quasi-metric setting, we establish the existence of an approximate coupled fixed point. Moreover, under the additional assumption of $q_0$-symmetry, we guarantee the existence of a coupled fixed point. Together, these results extend and unify several known theorems in fixed-point theory for quasi-metric and asymmetric spaces. We illustrate the obtained results regarding fixed points when the underlying space is equipped with a graph structure and, thus, sufficient conditions are found to guarantee the existence of a subgraph with a loop with a length greater than or equal to 2. Full article

The permutation flow shop scheduling problem (PFSP) is one of the hot issues in current research, and its production methods are widely used in steel, medicine, semiconductor, and other industries. Due to the characteristics of permutation flow (optimize the production process through the principle of symmetry to achieve efficient allocation and balance of resources), its task processes only need to be sorted on the first machine, and the subsequent machines are completely symmetrical with the first machine. This paper proposes an enhanced particle swarm optimization algorithm (EPSO) for the PFSP. Firstly, in order to enhance the diversity of the algorithm, a new dynamic inertia weight method was introduced to dynamically adjust the search range of particles. Secondly, a new speed update strategy was proposed, which makes full use of the information of high-quality solutions and further improves the convergence speed of the algorithm. Subsequently, an interference strategy based on individual mutations was designed, which improved the universality of the model’s global search. Finally, to verify the effectiveness of the EPSO algorithm, six benchmark functions were tested, and the results proved the superiority of the EPSO algorithm. In addition, the average relative error of the improved algorithm is at least 21.6% higher than that of the unimproved algorithm when solving large-scale PFSPs. Full article

This study presents a novel and efficient iterative scheme in the setting of CAT(0) spaces and investigates the convergence properties for a generalized class of mappings satisfying the Garcia–Falset property using the proposed iterative scheme. Strong and weak convergence results are established in CAT(0) spaces, generalizing many existing results in the literature. Furthermore, we discuss the stability and data dependence of the new iterative process. Numerical experiments include an analysis of error values, the number of iterations, and computational time, providing a comprehensive assessment of the method’s performance. Moreover, graphical comparisons demonstrate the efficiency and reliability of the approach. The obtained results are utilized in solving integral equations. Additionally, the paper concludes with a polynomiographic study of the newly introduced iterative process, in comparison with standard algorithms, such as Newton, Halley, or Kalantari’s $B_4$ iteration, emphasizing symmetry properties. Full article

Furosemide (FUR) is a loop diuretic widely used in pediatric care. However, no standardized oral liquid formulation exists due to degradation concerns, particularly the formation of furosemide-related compound B (FUR-B). This study aimed to develop and validate the HPLC method for the simultaneous quantification of FUR, FUR-B, methylparaben (MP), and propylparaben (PP) in pediatric extemporaneous oral solutions. Chromatographic separation was achieved using a Symmetry^® C18 column (4.6 × 250 mm, 5 µm) with a mobile phase of 0.1% acetic acid in water and acetonitrile (60:40, v/v) at 1.0 mL/min of flow with injection volume at 10 µL. Detection at 272 nm provided optimal sensitivity, especially for low concentrations of FUR-B. Forced degradation confirmed baseline separation of FUR from its degradation products. The condition showed high linearity (R² > 0.995), accuracy (recoveries 98.2–101.0%), and precision (RSD ≤ 2%). Robustness and ruggedness tests under varied conditions, analysts, and intra-day yielded consistent performance. Application to extemporaneous formulations showed that refrigeration (2–8 °C) retained initial composition, while elevated temperatures (30 °C and 40 °C) promoted FUR degradation, with FUR-B increasing to 6.84% after 90 days and greater MP and PP degradation. This validated method offers a reliable analytical tool for monitoring chemical changes and supporting quality control of pediatric FUR extemporaneous formulations. Full article

The integration of AI-driven optimization into Electronic Design Automation (EDA) enables smarter and more adaptive circuit design, where symmetry and asymmetry play key roles in balancing performance, robustness, and manufacturability. This work presents a model-driven optimization methodology for sizing low-phase-noise LC voltage-controlled oscillators (VCOs) at 5 GHz, targeting Wi-Fi, 5G, and automotive radar applications. The approach uses the EKV transistor model for analytical CMOS device characterization and applies a diverse set of metaheuristic algorithms for both single-objective (phase noise minimization) and multi-objective (joint phase noise and power) optimization. A central focus is on how symmetry—embedded in the complementary cross-coupled LC-VCO topology—and asymmetry—introduced by parasitics, mismatch, and layout constraints—affect optimization outcomes. The methodology implicitly captures these effects during simulation-based optimization, enabling design-space exploration that is both symmetry-aware and robust to unavoidable asymmetries. Implemented in CMOS 180 nm technology, the approach delivers designs with improved phase noise and power efficiency while ensuring manufacturability. Yield analysis confirms that integrating symmetry considerations into metaheuristic-based optimization enhances performance predictability and resilience to process variations, offering a scalable, AI-aligned solution for high-performance analog circuit design within EDA workflows. Full article