Search Results (186)

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

For a non-conservative nonlinear oscillator (NCNO) having a periodic solution, the existence of the first integral is a certain symmetry of the nonlinear dynamical system, which signifies the balance of kinetic energy and potential energy. A first-order nonlinear ordinary differential equation (ODE) is used to derive the first integral, which, equipped with a right-end boundary condition, can determine an implicit potential function for computing the period by an exact integral formula. However, the integrand is singular, which renders a less accurate value of the period. A generalized integral conservation law endowed with a weight function is constructed, which is proved to be equivalent to the exact integral formula. Minimizing the error to satisfy the periodicity conditions, the optimal initial value of the weight function is determined. Two non-iterative methods are developed by integrating three first-order ODEs or two first-order ODEs to compute the period. Very accurate value of the period can be observed upon testing five examples. For the NCNO without having the first integral, the integral-type period formula is derived. Four examples belong to the Liénard equation, involving the van der Pol equation, are evaluated by the proposed iterative method to determine the oscillatory amplitude and period. For the case with one or more limit cycles, the amplitude and period can be estimated very accurately. For the NCNO of a broad type with or without having the first integral, the present paper features a solid theoretical foundation and contributes integral-type formulations for the determination of the oscillatory period. The development of new numerical algorithms and extensive validation across a diverse set of examples is given. Full article

Collective intelligence systems have demonstrated considerable potential in dynamic adversarial environments due to their distributed, self-organizing, and highly robust characteristics. The crux of an efficacious defense lies in establishing a dynamically adjustable, non-uniform defense structure through the differentiation of internal member roles. The proposed model is a heterogeneous-swarm adaptive-defense model based on symmetry breaking and skin effect. The model draws from symmetry theory, incorporating the skin effect of conductor currents and the hierarchical structural characteristics of biological groups, such as starlings. The construction of a radially symmetric dynamic hierarchical swarm structure is achieved by assigning different types of individuals with distinct safety radius preferences. Secondly, the principle of symmetry breaking is employed to establish a phase transition mechanism from radial symmetry to directed defense, thereby achieving an adaptive barrier formation algorithm. This algorithm enables the defensive group to assess threat characteristics and dynamically adjust defense resource deployment. The simulation results obtained from this study validate the phase transition process from continuous rotational symmetry to directed defense. This process demonstrates the barrier formation mechanism and ensures the safety and integrity of the core units within the group. Full article

Lead-free perovskite variants have emerged as promising candidates due to their self-trapped exciton emission. However, in ASnX₃ systems, facile oxidation of Sn(II) to Sn(IV) yields A₂SnCl₆ vacancy-ordered derivatives. Paradoxically, despite possessing a direct bandgap, these variants exhibit diminished photoluminescence (PL). Doping engineering thus becomes essential for precise optical tailoring of A₂SnX₆ materials. Herein, through integrated first-principles calculations and spectroscopic analysis, we elucidate the luminescence mechanism in Te⁴⁺-doped (NH₄)₂SnCl₆ lead-free perovskites. Density functional theory, X-ray diffraction (XRD) patterns and X-ray photoelectron spectroscopy (XPS) confirm Te⁴⁺ substitution at Sn sites via favorable chemical potentials. Spectral interrogations, including absorption and emission profiles, reveal that the intense emission originates from the triplet STE recombination (³P₁ → ¹S₀) of Te centers. Temperature-dependent PL spectra further demonstrate strong electron–phonon coupling that induces symmetry-breaking distortions to stabilize STEs. Complementary electronic band structure and molecular orbital calculations unveil the underlying photophysical pathway. Leveraging these distinct thermal responses of PL intensity and peak position, 0.5%Te:(NH₄)₂SnCl₆ emerges as a highly promising candidate for non-contact, dual-parameter optical thermometry over an ultra-broad range (100–400 K). This work provides fundamental insights into the exciton dynamics and thermal engineering of optical properties in this material system, establishing its significant potential for advanced temperature-sensing applications. Full article

In a dynamic and uncertain environment, maintaining a high level of business process (BP) reliability represents a key long-term objective for organizations. The manufacturing process, as the most critical business process in manufacturing enterprises, is emphasized due to its potential to cause significant disruptions across other BPs if it fails. This paper proposes a two-stage model. In the first stage, failures leading to lean waste are evaluated and ranked using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) combined with interval-valued intuitionistic fuzzy numbers (IVIFNs), referred to as IF-TOPSIS. The model is grounded in the Failure Mode and Effect Analysis (FMEA) framework. In the second stage, a modified fuzzy logic system with IVIFN-based rules is applied to determine the risk level of the manufacturing process. This approach is based on the property of symmetry in the decision-making process, ensuring that criteria are treated in a balanced manner and inference rules are applied consistently. A case study based on real-life data demonstrates that the obtained results identify measures that can enhance business strategy and reduce failure rates. Thus, the model is validated and shown to contribute to lean waste reduction. It can be concluded that the proposed methodology provides clear and practical guidance to enterprise management, as well as to all sectors and individuals involved in ensuring a reliable manufacturing process, for defining failure priorities and implementing preventive measures. Full article

The increasing demand for portable health monitoring has highlighted the need for automated sleep staging systems that are both accurate and computationally efficient. However, most existing deep learning models for electroencephalogram (EEG)-based sleep staging suffer from parameter redundancy, fixed dilation rates, and limited generalization, restricting their applicability in real-time and resource-constrained scenarios. In this paper, we propose ADG-SleepNet, a novel lightweight symmetry-aware multi-scale dilation-gated temporal convolutional network enhanced with adaptive attention mechanisms for EEG-based sleep staging. ADG-SleepNet features a structurally symmetric, parallel multi-branch architecture utilizing various dilation rates to comprehensively capture multi-scale temporal patterns in EEG signals. The integration of adaptive gating and channel attention mechanisms enables the network to dynamically adjust the contribution of each branch based on input characteristics, effectively breaking architectural symmetry when necessary to prioritize the most discriminative features. Experimental results on the Sleep-EDF-20 and Sleep-EDF-78 datasets demonstrate that ADG-SleepNet achieves accuracy rates of 87.1% and 85.1%, and macro F1 scores of 84.0% and 81.1%, respectively, outperforming several state-of-the-art lightweight models. These findings highlight the strong generalization ability and practical potential of ADG-SleepNet for EEG-based health monitoring applications. Full article

We consider domain-type patterns in biological membranes that possess an in-plane membrane order. Domains are inseparably linked to topological defects, and many features related to them can be guessed based on universal topological arguments. However, much more complex membrane patterns are typically observed. As possible generators of such configurations, we analyze two relatively simple and universal phenomena. Both are based on continuous symmetry breaking (CSB), which manifests ubiquitously in all branches of physics. We present the Imry–Ma argument which, in addition to CSB, requests the presence of uncorrelated random-field-type disorder. Next, we discuss the Kibble–Zurek mechanism. In addition to CSB it considers dynamical slowing when a relevant phase transition is approached. These approaches were originally introduced in magnetism and cosmology, respectively. We adapt them to effectively two-dimensional membranes and discuss their potential role in membrane structure formation. Full article

In recent years, the generation of circularly polarized attosecond pulses has garnered significant attention due to their potential applications in ultrafast spectroscopy and, notably, in chiral-sensitive molecular detection. The traditional methods for generating such pulses often involve complex laser configurations or specially engineered targets, limiting their experimental feasibility. In this study, we present a streamlined and effective approach to producing circularly polarized attosecond pulses by employing a linearly polarized laser field in conjunction with a stereosymmetric linear molecule, 1-butyne (C₄H₆). The generation of high-order harmonics by this molecular system reveals a distinct plateau in the perpendicular polarization component, which facilitates the generation of isolated attosecond pulses with circular polarization. Through a detailed analysis of the time-dependent charge density dynamics across atomic sites, we identify the atoms primarily responsible for the emission of circularly polarized harmonics in the plane orthogonal to the driving field. Moreover, we explore the role of multi-orbital contributions in shaping the polarization properties of the harmonic spectra. Our findings underscore the importance of molecular symmetry and the electronic structure in tailoring the harmonic polarization, and they demonstrate a viable pathway for using circularly polarized attosecond pulses to probe molecular chirality. This method offers a balance between simplicity and performance, opening new avenues for practical applications in chiral recognition and ultrafast stereochemical analysis. Full article

This paper investigates the stabilization problem of a class of nonlinear systems characterized by non-minimum phase behavior within each subsystem, with a focus on an application to a gantry crane system that employs friction to control its swing angle. In practical crane operations, the demand for accelerated system response is critical to improving productivity; however, this often induces significant variations in the swing angle, potentially destabilizing the system. To overcome this challenge, we propose a hybrid control approach that combines the concept of multi-diffeomorphism with symmetry considerations to enhance the smoothness of transient responses. Unlike classical input–output feedback linearization, which typically relies on a single diffeomorphism and may compromise the zero dynamics stability, the proposed method distributes the transformation across multiple diffeomorphisms, ensuring balanced and coordinated transient behavior. The design involves the simultaneous development of subsystem-dependent feedback controllers, which collaboratively guarantee the global stability of the overall closed-loop nonlinear gantry crane system. The Lyapunov stability framework is employed to rigorously demonstrate that the tracking errors converge asymptotically to meet the desired performance specifications. In addition, the simulation results demonstrate that the developed hybrid control approach notably enhances the system’s responsiveness while preserving both symmetry and the stability of the zero dynamics. Specifically, the swing angle decreases by over 90% in less than 2 s, highlighting the method’s efficiency in minimizing oscillations during fast operations. This study highlights the practical benefits of integrating symmetry-aware multi-diffeomorphism techniques into nonlinear control design. Such techniques are found to be particularly effective for underactuated mechanical systems like gantry cranes. Full article

This study investigates natural convection of liquid metal in an annular enclosure with a square cross-section using three-dimensional numerical simulations. Liquid metals, with low Prandtl numbers, exhibit oscillatory transitions at lower Rayleigh numbers than conventional fluids. While previous studies focused on full-circle domains where steady or irregular flows were observed, this work examines the effect of angular partitions on flow dynamics. The results reveal that periodic three-dimensional oscillatory flows arise in domains with specific angular sizes, such as quarter circles, whereas full-circle domains produce irregular or steady flows. Angular wave numbers vary spatially and temporally during transitional growth. The emergence of half-symmetric oscillatory modes highlights the role of symmetry constraints imposed by the geometry and boundary conditions. These transitions are closely tied to symmetry breaking and mode selection. A linear stability perspective helps clarify the critical factors that determine the transition type. These findings underscore that angular segmentation and periodic boundary conditions are essential for sustaining regular oscillatory convection. This study contributes to the understanding of symmetry-governed convection transitions in low-Prandtl-number fluids and has potential implications for industrial processes, such as semiconductor crystal growth, where flow uniformity and thermal stability are crucial. Full article

Core problems in agile software project scheduling, such as resource-constrained balancing and iteration cycle optimization, embody the pursuit of symmetry. Simultaneously, optimization algorithms find extensive applications in symmetry problems, for example, in graphs and pattern recognition. Considering the cooperation among multiple teams and environmental changes in complex agile software development, a dynamic periodic scheduling model for multi-team agile software project is constructed, which includes three tightly coupled sub-problems, namely user story selection, user story-development team allocation, and task-employee allocation. To solve the model, a group learning particle swarm optimization algorithm is proposed, which includes three novel strategies. First, the population is divided into four groups based on dual indicators of objective values and potential values. Second, different learning objects are selected according to the characteristic of each group so that the search diversity can be improved. Third, to react to the environmental changes and enhance the mining ability, heuristic population initialization and local search strategies are designed by utilizing the problem-specific information. Systematic experimental results on 13 instances indicate that compared with the state-of-the-art algorithms, the proposed algorithm is able to provide a schedule with better precision for the project manager in each sprint of the agile development. Full article

This paper presents a novel four-dimensional (4D) chaotic system exhibiting parametric symmetry breaking and multistability. Through equilibrium stability analysis, attractor reconstruction, Lyapunov Exponent spectra (LEs), and bifurcation diagrams, we reveal a continuous transition from symmetric period attractors to asymmetric chaotic states and rich dynamical behaviors. Additionally, considering the potential of this system in practical applications, a feedback control simulation circuit is designed and implemented to ensure its stability and effectiveness under real-world conditions. Finally, among various control strategies, this paper proposes an innovative Fixed-Time Sliding Mode Synchronization (FTSMS) strategy, determines its synchronization convergence time, and provides an important theoretical foundation for the practical application of the system. Full article

Background: Temporomandibular disorders are a generic term referred to clinical conditions involving the jaw muscles and temporomandibular joint with multifactorial pattern and genetic background. The aim of this observational study was to investigate the correlation between craniomandibular disorders and the presence of occlusal alterations. A clinical evaluation of the occlusal and articular status of the patients was carried out, integrating the latter with the electromyographic recording the activity of the masseter and temporalis muscles. Methods: A clinical observational study on 20 adults assessed temporomandibular disorders using DC/TMD criteria, anamnesis, clinical exams, occlusal and electromyographic analyses. Occlusion was evaluated morphologically and functionally. Electromyography tested static/dynamic muscle activity. Data were statistically analyzed using t-tests and Pearson correlation (p < 0.05). Results: Electromyographic analysis revealed significant differences between subjects with and without visual correction, suggesting that visual input influences masticatory muscle activity. Correlations emerged between occlusal asymmetries and neuromuscular parameters. These findings highlight clinical implications for mandibular function, muscle symmetry, and the potential for therapeutic rebalancing through targeted interventions. Conclusions: The study demonstrates a significant correlation between visual–motor integration and masticatory muscle efficiency. It emphasizes lateralized neuromuscular activation’s influence on occlusal contact distribution. Moreover, it identifies mandibular torsion–endfeel inverse correlation as a potential diagnostic marker for craniomandibular dysfunctions via surface electromyography. Full article

Vanillin photoinduced deprotonation was evaluated and analyzed. Vibronic states and transitions were computationally investigated. Optimizations and vertical electron transitions in the gas phase and with the continuum solvation model were computed using the time-dependent density functional theory. Static absorption and emission (photostatic optical) spectra were statistically averaged over the excited instantaneous molecular conformers fluctuating on quantum–classical molecular dynamic trajectories. Photostatic optical spectra were generated using the hybrid quantum–classical molecular dynamics for explicit solvent models. Conical intersection searching and nonadiabatic molecular dynamics simulations defined potential energy surface propagations, intersections, dissipations, and dissociations. The procedure included mixed-reference spin–flip excitations for both procedures and trajectory surface hopping for photodynamics. Insignificant structural deformations vs. hydroxyl bond cleavage followed by deprotonation were demonstrated starting from different initial structural conditions, which included optimized, transition state, and several other important fluctuating configurations in various environments. Vanillin electronic structure changes were illustrated and analyzed at the key points on conical intersection and nonadiabatic molecular dynamics trajectories by investigating molecular orbital symmetry and electron density difference. The hydroxyl group decomposed on transition to a σ-molecular orbital localized on the elongated O–H bond. Full article

This study proposes an application framework based on Large Language Models (LLMs) to analyze multimodal heterogeneous data in the power sector and introduces the CLB-BER model for classifying user electricity consumption behavior. We first employ the Euclidean–Cosine Dynamic Windowing (ECDW) method to optimize the adjustment phase of the CLUBS clustering algorithm, improving the classification accuracy of electricity consumption patterns and establishing a mapping between unlabeled behavioral features and user types. To overcome the limitations of traditional clustering algorithms in recognizing emerging consumption patterns, we fine-tune a pre-trained DistilBERT model and integrate it with a Softmax layer to enhance classification performance. The experimental results on real-world power grid data demonstrate that the CLB-BER model significantly outperforms conventional algorithms in terms of classification efficiency and accuracy, achieving 94.21% accuracy and an F1 score of 94.34%, compared to 92.13% accuracy for Transformer and lower accuracy for baselines like KNN (81.45%) and SVM (86.73%); additionally, the Improved-C clustering achieves a silhouette index of 0.63, surpassing CLUBS (0.62) and K-means (0.55), underscoring its potential for power grid analysis and user behavior understanding. Our framework inherently preserves temporal symmetry in consumption patterns through dynamic sequence alignment, enhancing its robustness for real-world applications. Full article