Most Recent

Functionally graded materials (FGMs) are widely applied in spacecraft structural design, thermal protection systems, and planetary landing mechanisms, benefiting from their ability to resist large thermal, pressure, and force gradients. To assess str...

This article investigates the widespread occurrence of wave-like behavior in biological systems and reflects on the importance of reaction–diffusion frameworks to explain fast information transfer. Focusing on the classical Fisher–Kolmogo...

To address the critical demand for real-time dynamic tracking of personnel in complex buildings during emergency rescue, a novel system was proposed integrating Back Propagation (BP) neural networks with Double-Sided Two-Way Ranging (DS-TWR) technolo...

The purpose of this study is to construct diverse forms of exact soliton solutions and conduct a comprehensive qualitative analysis. For this aim, we use the Gross–Pitaevskii system, which belongs to the family of nonlinear Schrödinger equ...

The stability of deep neural networks during training is related to the structural symmetry between gradient dynamics and parameter updates. Existing Adam-type optimizers are prone to breaking this dynamic symmetry when the gradient scale changes dra...

Quantum biology is a multidiscipline which analyses possible critical aspects of life that could be based on the macroscopic expression of quantum phenomena. The high efficiency of light energy harvesting in green sulfur bacteria during photosynthesi...

Task-oriented dialogue systems face a tension between comprehensive constraint elicitation (task adequacy) and conversational efficiency (minimizing turns). Current preference learning frameworks treat preferences as static, unable to capture the dyn...

Due to phenomena, such as refraction, reflection, and light scattering, the three-dimensional (3D) reconstruction of transparent objects with complex geometric symmetry or contours is confronted with the challenges of insufficient extraction of featu...

The expansion of urban rail transit has exacerbated environmental issues related to low-frequency noise (LFN), yet the impact of geometric symmetry breaking on structure-borne noise remains underexplored. This study aims to quantify the mechanism by...

Truck platooning enabled by V2X and cooperative driving can reduce aerodynamic drag and consequently decrease fuel consumption and CO₂ emissions. Meanwhile, hub-and-spoke courier networks require strategic decisions on hub locations, allocation, and...

The eccentric permanent-magnet (PM) pole technique is widely recognized as an effective technique for reducing cogging torque in surface-mounted PM motors (SMPMMs). This paper proposes a novel analytical approach based on bilinear mapping to determin...

In this work, the Renormalization Method (RM) is used to analyze the dynamics of a nonlinear two-degree-of-freedom (2DOF) system under parametric excitation, with a focus on fractal vibration behavior. This procedure comprises transforming the system...

Traffic flow prediction is a pivotal task in intelligent transportation systems. The primary challenge lies in accurately modeling the dynamically evolving and directional spatio-temporal dependencies inherent in road networks. Existing graph neural...

Droplet impact dynamics is a critical subject in interfacial fluid mechanics, with applications in aerospace, energy transport, microfluidics, and chemical engineering. This study investigates the impact behavior of double droplets on cylindrical sur...

Differential network analysis provides a powerful framework for characterizing how biological networks change across different conditions or external stimuli. With recent advances in high-throughput technologies, matrix-valued data have become increa...

The identification of topological phases in disordered systems is a significant field in condensed matter physics, where disorder breaks translational symmetry and invalidates conventional topological invariants defined in momentum space. Currently,...

Continual learning of multivariate time series (MTS) forecasting is critical for process industries where working condition drift is frequent due to the variation in the feed properties and other factors. However, existing continual learning methods...

This study addresses the common challenges of complex soil behavior and the difficulties in achieving precise control during the construction of large open caissons. A centrifugal model test was conducted to investigate open caisson–fine sandy...

We considerthe single-machine group scheduling problem with controllable processing times (i.e., resource allocation) under a common due-window ($condw$) assignment. The objective is to minimize a total cost composed of earliness, tardiness, due-window...

This paper advances a Lie-group approach to measurement by identifying symmetry conditions that determine when effect sizes from different instruments can be meaningfully compared. Measurement transformations are modeled as elements of a two-paramete...

This study employs the Atangana–Baleanu–Caputo fractional derivative within the Moore–Gibson–Thompson heat conduction model to analytically investigate the thermoelastic vibrations in solid medium-containing voids. The ABC&nda...

Cross-domain sentiment analysis remains challenging due to distributional shifts and heterogeneous sentiment expressions across platforms. Existing domain adaptation approaches primarily focus on enforcing domain-invariant representations. However, s...

The Multi-Objective Flexible Job Shop Scheduling Problem (MOFJSSP) represents a core challenge in modern manufacturing: achieving synergistic optimization of multiple conflicting objectives while pursuing production efficiency and energy sustainabili...

3D vision-based groove detection is playing a critical role in enabling autonomous recognition. However, most existing interference evaluation strategies rely on height-based statistics or handcrafted heuristics, which (i) confuse genuine groove geom...

The reported evidence for an isotropic gravitational-wave background (GWB) from pulsar timing array (PTA) collaborations has motivated searches for extrinsic and intrinsic anisotropies. Kinematic anisotropies may arise as a consequence of a boosted o...

Multimodal named-entity recognition (MNER) aims to identify entity information by leveraging multimodal features. With recent research shifting to multi-image scenarios, existing methods overlook modality noise and lack effective cross-modal interact...

Generative AI (GenAI) is accelerating design space exploration and multimodal prototyping in industrial design (ID), bringing new efficiencies and possibilities to early-stage ideation and cross-media expression. Yet many studies do not clearly defin...

Traditional non-negative matrix factorization (NMF) faces challenges when dealing with complex data, primarily characterized by sensitivity to noise, neglect of data geometric structure, and inability to effectively utilize supervised information. To...

Distributed manufacturing introduces new challenges to traditional production shop scheduling, and the combination of machine learning and metaheuristic algorithms offers new approaches to solve these problems. To address the distributed hybrid flow...

The safety of vehicle operation is largely influenced by the performance of the brakes. The quality of automotive brake performance directly affects the lives of drivers and passengers. This paper conducts an in-depth study based on the structural ch...

The paper considers quantal many-boson systems that are described by a rotationally invariant and boson-number conserving Hamiltonian. The properties of a generic model are studied, which treats N bosons of p different kinds with non-zero angular mom...

This paper presents a cover lens concept for camera modules based on surface acoustic waves (SAW) to mitigate the degradation of physical AI optical sensor field-of-view performance caused by surface contamination. The proposed approach utilizes a si...

In the ever-evolving landscape of industrial design, the interplay between symmetry and asymmetry serves as both a philosophical cornerstone and a practical compass [...]

Sliding failure is a common form of instability for reverse rock slopes. The determination of the failure plane for such unstable slopes is currently a key and challenging issue in research. To pinpoint the position of the failure surface with higher...

Asymmetries in data distributions and performance across subgroups can induce systematic unfairness in real-world systems. A variety of previous studies have significantly ameliorated the fairness of deep learning models; however, most of them necess...

The rapid growth of renewable integration and active consumer participation has made modern power grids increasingly complex and dynamic, where maintaining balanced and efficient energy distribution remains a central challenge. This paper introduces...

The proliferation of wired Gigabit Ethernet has greatly increased communication bandwidth, while introducing new challenges for device identification and security. Conventional physical-layer fingerprinting techniques are constrained by the Nyquist s...

To tackle the safety performance concerns of Squat shear walls in nuclear island structures (which serve as shields for powerhouses) under seismic action, this research endeavors to explore the seismic performance of such shear walls with different r...

This study focuses on analyzing the dynamic process, strength, and orientation of risk spillovers in the Chinese banking system under the exogenous shock of the COVID-19 pandemic. Using daily closing prices of 25 representative banks, it stratifies t...

To overcome the limitations of the standard Student Psychology-Based Optimization (SPBO) algorithm, such as strategy homogeneity, insufficient elite-guided diversity, and inefficient evolution of low-quality individuals, this paper proposes a Hierarc...

Extracting structured threat intelligence from unstructured cybersecurity texts requires accurate identification of entities together with their underlying semantic relations. However, threat reports often exhibit intricate sentence structures, long-...

This paper introduces an LLM-driven design-time workflow for Internet of Things (IoT) and network management system development that combines the generative and summarization capabilities of Large Language Models with the formal rigor of Model-Driven...

We investigate the Kaluza–Klein modes of a bulk $U(1)$ gauge field coupled to a dilaton in six-dimensional brane-world scenarios. We demonstrate that four-dimensional gauge invariance is preserved via a Stueckelberg-like mechanism driven by scala...

For businesses to optimize management decisions in the digital transformation, a process inherently characterized by symmetry between feedback collection and strategic adjustment, it is essential to automatically extract fine-grained opinions from la...

This research proposes two new estimators which use auxiliary information to derive estimates of population means in cases where populations are considered rare and clustered in line with the General Inverse Adaptive Cluster Sampling (GIACS) framewor...

This paper develops a generalized Laplace transform framework on weighted function spaces $C_{{\delta }_{\psi },\gamma }^n[a,b]$, establishing a symmetry between integer-order ${\delta }_{\psi }$ operators and fractional $\psi$-Hilfer derivatives at the level of transf...

The (2+1)-dimensional Caudrey–Dodd–Gibbon (CDG) equation, which can frequently be used to be describe the propagations of shallow-water waves and plasma physics, is solved by various methods in this paper, thereby revealing its nonlinear...

Emergency vehicles (EmVs) play a crucial role in providing prompt services to public rescue activities. However, due to the impacts of ordinary vehicles (OVs), EmVs are often blocked and cannot reach the rescue site in time. Therefore, a cooperative...

Shadow removal aims to restore photometric, chromatic, and structural consistency between shadowed and non-shadowed image regions. Although weakly supervised shadow removal methods reduce the reliance on densely paired training data, they still strug...

An advanced filtration technique for reusing greywater is a critical and essential procedure for solving water scarcity and meeting the rising demand for clean water in densely populated and urban areas. Greywater or wastewater from washing machines,...