Search Results (8,171)

Failure analysis has always been among the key research focuses in underground tunneling, particularly in forecasting the collapse risk of tunnel crowns, which bears great engineering and practical significance for tunnel safety assessment. In practical engineering, the soil surrounding shallow tunnels and other underground chambers is typically unsaturated. With the advancement of tunneling technology, shallow tunnels affected by ground temperatures are increasingly common, making it essential to incorporate temperature effects into the stability analysis of unsaturated shallow tunnels. This paper proposes a novel framework for analyzing the stability of shallow rectangular tunnel crowns under temperature influence. By adopting a temperature-dependent effective stress model for unsaturated soils combined with the soil–water characteristic curve, temperature influence is integrated into the calculation of apparent cohesion in unsaturated soils. The upper bound theorem and a multi-rigid-block failure mechanism are adopted to assess crown stability, with the geometry of the failure mechanism determined through a compatible velocity field. New analytical expressions are derived. Through calculating the internal energy dissipation rate, considering temperature effects and external work rate, the critical support pressure at the tunnel crown is obtained using the Sequential Quadratic Programming (SQP). Discussions of temperature and other unsaturated soil parameters are carried out to explore their effects on the stability of shallow tunnels. Results demonstrate that temperature significantly influences the tunnel’s critical support pressure, with the extent of this impact primarily dependent on the unsaturated soil type and seepage conditions. Furthermore, the theoretical framework developed in this study provides a more accurate description for unsaturated fine-grained soils. This study introduces a novel integration of thermal influences into the upper bound theorem, applying this enhanced methodology to the stability assessment of shallow rectangular tunnel crowns. The resulting failure model and analytical framework establish a rigorous upper bound solution for crown stability, thereby furnishing a more accurate theoretical foundation for subsequent tunnel face support strategies. Full article

This manuscript presents a comprehensive Lie symmetry analysis of the KdV-Burgers equation, a prototypical model for nonlinear wave dynamics incorporating dissipation and dispersion. We systematically derive its six-dimensional Lie algebra and construct an optimal system of one-dimensional subalgebras. This framework is used to perform a symmetry reduction, transforming the governing partial differential equation into a set of ordinary differential equations. A key contribution of this work is the identification and analysis of several non-trivial invariant solutions, including a new Galilean-boost-invariant solution related to an accelerating reference frame, which extends beyond standard traveling waves. Through a detailed physical interpretation supported by phase plane analysis and asymptotic methods, we elucidate how the mathematical symmetries directly manifest as fundamental physical behaviors. This reveals a clear classification of distinct wave regimes—from monotonic and oscillatory shocks to solitary wave trains governed by the interplay between nonlinearity, dissipation and dispersion. The numerical validation verify the accuracy and physical relevance of the derived invariant solutions, with errors less than $0.5\%$ in the Burgers limit and $3.2\%$ in the weak dissipation regime. Our work establishes a direct link between the model’s symmetry structure and its observable dynamics, providing a unified framework validated both analytically and through the examination of universal scaling laws. The results offer profound insights applicable to fields ranging from plasma physics and hydrodynamics to nonlinear acoustics. Full article

The AASHTO Manual for Assessing Safety Hardware (MASH) replaced the NCHRP Report 350 in 2009, becoming the new standard for evaluating safety hardware devices, including concrete bridge parapets; all new permanent installations of bridge rails on the National Highway System must be compliant with the 2016 MASH requirements after 31 December 2019, as agreed by the FHWA and AASHTO. However, due to the complexity of vehicular impact events, there are several different methods for estimating vehicular impact force on the parapets. They can be grouped into three main categories: theoretical, numerical and measurement methods. This paper presents a practical method based on analytical concepts for providing impact force estimates that can help bridge owners to evaluate the structural capacity of bridge parapets at a fraction of the cost of full-scale crash tests and finite element numerical simulations. This approach was developed based on fundamental dynamic principles and refined dynamic analysis of vehicle rigid-body motions during multi-phased impact events. Principles of impulse and momentum were first applied to determine both linear and angular velocities of a vehicle immediately after the initial impact; then coupled differential equations of motion were derived and solved to describe the vehicle’s plane-motion during the subsequent stage, which includes both translational and rotational movements. The proposed method was shown to be capable of providing reasonably accurate force estimates with significantly less demand for time and effort compared to other complex methods. These estimates can help infrastructure owners to make informed and sustainable decisions for bridge projects, which include selecting the most efficient bridge design alternatives, in a cost-effective and timely manner. Recommendations for future studies were also discussed. Full article

Peer assessment has emerged as a crucial approach for scaling evaluation in educational scenarios, fostering learner engagement, critical thinking, and collaborative learning. Nevertheless, traditional aggregation-based and probabilistic methods often fail to capture the intricate relational dependencies among students and submissions, thereby limiting their capacity to ensure reliable and equitable outcomes. Recent advances in graph neural networks (GNNs) offer promising avenues for representing peer-assessment data as graphs. However, most existing approaches treat all relations uniformly, overlooking variations in the reliability of evaluative interactions. To bridge this gap, we accordingly propose ReSAN (Relation-Sensitive Assessment Network), a novel framework that integrates relation-sensitive attention into the message-passing process. ReSAN dynamically evaluates and weights relationships, enabling the model to distinguish informative signals from noisy or biased assessments. Comprehensive experiments on both synthetic and real-world datasets demonstrate that ReSAN consistently surpasses strong baselines in prediction accuracy and robustness. These findings underscore the importance of explicitly modeling evaluator reliability for effectively capturing the dynamics of peer-assessment networks. Overall, this work advances reliable graph-based evaluation methods and provides new insights into leveraging representation learning techniques for educational analytics. Full article

Existing pneumatic artificial muscle (PAM) static geometrical models based on the principle of virtual work provide only approximate force predictions since they neglect the effects of volume change and radial bladder deformation work loss. In this study, we propose an improved geometrical static model called the Accurate Volume and Bladder Deformation Loss (AVBDL) model. This model introduces a physically consistent calculation of muscle volume at different contractions and pressures and incorporates a new way of describing work losses due to radial deformation of the bladder. The hyperelastic properties of the bladder were experimentally characterized and modeled using the Mooney–Rivlin formulation. The AVBDL model was validated against experimental data from four types of pneumatic muscles and compared with three established analytical models. Results show that the AVBDL model significantly improves force prediction accuracy, achieving a normalized root mean square (NRMS) error of 6.7–16.4%, compared to 20–68% for existing models. Due to its analytical transparency, reduced error, and broad applicability, the AVBDL model provides a robust basis for accurate simulation and control of pneumatic artificial muscles. Full article

Symmetry in vehicle body morphology is a crucial factor for achieving visual sensory balance in users, and it also serves as an important method for enhancing the efficiency of vehicle body research and development. This study proposes an AHP-SD-TOPSIS-AIGC integrated morphological design method to address multi-factorial design complexities in new energy commercial vehicle body styling under emotion-driven frameworks. Through literature retrieval and survey analysis, a Kansei evaluation system was constructed, with hierarchical design indicators established via Analytic Hierarchy Process (AHP) and weights determined through consistency matrices. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) identified optimal style forms exhibiting high emotional intention coupling, while edge detection algorithms extracted symmetrical spline features for body contour modeling. Artificial Intelligence Generated Content (AIGC) tools subsequently generated innovative solutions, validated through truck design applications to confirm method rationality and effectiveness. The results of the study show that the styling elements are accurately matched to user preferences and can identify target improvement points, and that the method can effectively achieve the output of the proposal for the design of commercial vehicle body morphology and is also applicable to passenger car-type vehicles to achieve the adaptation of multi-intentional emotional design. Full article

Genealogy is a powerful analytical framework for understanding the cultural evolution of tribes, communities, and societies. This article demonstrates that the recurrent reliance on genealogical structures is a common feature of human societies, serving as a fundamental mechanism for cultural evolution through time, space, and culture. Based on comparative analysis of indigenous tribal societies (e.g., Aboriginal Australian kinship, Polynesian chiefly genealogies), agrarian civilizations (e.g., European feudal lineages, Chinese patriliny), and modern nation-states (e.g., nationalist mythmaking, DNA-based ancestry movements), this study reveals consistent patterns in genealogical functions. Drawing on an interdisciplinary perspective from anthropology, sociology, history, and evolutionary biology, it is argued that genealogical systems are not passive records of descent but dynamic forces of cultural continuity and adaptation. The evidence shows that, despite vast sociocultural differences, genealogy widely operates as a dual-purpose instrument. It preserves cultural memory and legitimizes political authority while simultaneously facilitating social adaptation and innovation in response to new challenges. The paper also critiques contemporary trends like commercial genetic genealogy, highlighting its potential for reconnecting diasporic communities alongside its risks of biological essentialism. Ultimately, the work establishes that the persistent and patterned reliance on genealogy from oral traditions to genetic data offers a critical lens for understanding the deep structures of cultural continuity and transformation in human societies. It further underscores the importance of genealogy in cultural evolution, historical persistence, societal transformation, and the construction of belonging in an increasingly globalized world. Full article

Since 1975, it has been known that the Hurwitz zeta-function has a unique property to approximate by its shifts all analytic functions defined in the strip $\mathfrak{D}=\{s=\sigma +it:1/2<\sigma <1\}$. However, such an approximation causes efficiency problems, and applying short intervals is one of the measures to make that approximation more effective. In this paper, we consider the simultaneous approximation of a tuple of analytic functions in the strip $\mathfrak{D}$ by discrete shifts $(\zeta (s+ik{h}_1,{\alpha }_1),\dots ,\zeta (s+ik{h}_r,{\alpha }_r))$ with positive $h_1,\dots ,h_r$ of Hurwitz zeta-functions in the interval $[N,N+M]$ with $M={max}_{1⩽j⩽r}\left(h_j^{-1}{\left(N{h}_j\right)}^{23/70}\right)$. Two cases are considered: $1^{°}$ the set $\{(h_{j}log(m+{\alpha }_j),m\in {\mathbb{N}}_0,j=1,\dots ,r),2\pi \}$ is linearly independent over $\mathbb{Q}$; and $2^{°}$ a general case, where ${\alpha }_j$ and $h_j$ are arbitrary. In case $1^{°}$, we obtain that the set of approximating shifts has a positive lower density (and density) for every tuple of analytic functions. In case $2^{°}$, the set of approximated functions forms a certain closed set. For the proof, an approach based on new limit theorems on weakly convergent probability measures in the space of analytic functions in short intervals is applied. The power $\eta =23/70$ comes from a new mean square estimate for the Hurwitz zeta-function. Full article

Conventional pixel-wise satellite-derived bathymetry (SDB) models face dual challenges: physical ambiguity from variable water quality and spatial incoherence from ignoring geographic context. This study addresses these limitations by proposing and validating OptiFusionStack, a novel two-stage physio-spatial synergistic framework that operates without in situ optical data for model calibration. The framework first generates diverse, physics-informed predictions by integrating Quasi-Analytical Algorithm (QAA)-derived inherent optical properties (IOPs) with multiple base learners. Critically, it then constructs a multi-scale spatial context by computing neighborhood statistics over an experimentally optimized 9 × 9-pixel window. These physical priors and spatial features are then effectively fused by a StackingMLP meta-learner. Validation in optically diverse environments demonstrates that OptiFusionStack significantly surpasses the performance plateau of pixel-wise methods, elevating inversion accuracy (e.g., R² elevated from 0.66 to >0.92 in optically complex inland waters). More importantly, the framework substantially reduces spatial artifacts, producing bathymetric maps with superior spatial coherence. A rigorous benchmark against several state-of-the-art, end-to-end deep learning models further confirms the superior performance of our proposed hierarchical fusion architecture in terms of accuracy. This research offers a robust and generalizable new approach for high-fidelity geospatial modeling, particularly under the common real-world constraint of having no in situ data for optical model calibration. Full article

Nonlinear evolution equations play a key role in modeling various physical processes, such as wave propagation in nonlinear optical and hydrodynamic media, as well as in the dynamics of plasma and quantum systems. In this paper, we study an integrable generalization of the nonlinear Schrödinger equation: the Fokas–Lenells (FL) equation. We derive a new (1+1)-dimensional FL equation with self-consistent sources, which enables modeling the interaction of solitons with external disturbances within the framework of integrable systems. For the frist time, we obtain, two distinct types of solutions for the spin system of the FL equation, namely, a traveling wave and a one-soliton solution, derived using the Darboux transformation (DT). We also construct exact one-soliton and two-soliton solutions for the (2+1)-dimensional FL equation using the DT. These results advance analytical methods in the theory of integrable nonlinear systems, including spin models widely used to describe magnetic, quantum, and soliton phenomena. We illustrate the dynamics of the solutions graphically. Full article

This study is devoted to the detailed examination of the concept of $(a,b)$-deformation, defined for parameters $a\in \mathbb{R}$ and $b>0$. The analysis is conducted within the framework of Cauchy–Stieltjes kernel (CSK) families of probability measures, with particular attention given to the role of their variance functions (VFs). Using the VF as the main analytical tool, it is shown that the $(a,b)$-deformation of any measure belonging to the free Meixner family (FMF) remains within the same family. Moreover, the VF framework provides a powerful and flexible means for establishing new limit theorems associated with $(a,b)$-deformed free convolution. In particular, several novel limiting behaviors are derived, which naturally encompass both free and Boolean additive convolutions as special cases. Full article

Baseline correction techniques are highly applicable in analytical chemistry. Consequently, there is a constant demand for universal and automated baseline correction methods. Our new procedure, based on the Convolutional Autoencoder (ConvAuto) model and combined with an automated implementation algorithm (ApplyModel procedure), meets these expectations. The key advantage of this approach is its ability to handle 1D signals of various lengths and resolutions, which is a common limitation encountered in deep neural network models. The proposed procedure is fully automatic and does not require any parameter optimization. As our experiments show, the ApplyModel procedure can also be easily combined with other baseline correction methods that utilize deep neural networks, such as the ResUNet model, which also extends its practical applicability. The usability of our new approach was tested by implementing it for both simulated and experimental signals, ranging from 200 to 4000 points in length. For complex signals characterized by multiple peaks and a nonlinear background, the ConvAuto model achieved an RMSE of 0.0263, compared to 1.7957 for the ResUNet model. In the determination of Pb(II) in a certified reference material, a recovery of 89.6% was obtained, which was 1% higher than that achieved with the ResUNet model. Full article

Companies face multi-criteria problems every day, such as prioritizing projects, investments, and suppliers. In this respect, the literature offers countless methods, some of which provide partial and flawed solutions. Therefore, identifying and replacing a flawed method with a more efficient one is fundamental. However, this replacement is not straightforward because each method has a different evaluation structure. More specifically, in this study, the Mapping method uses scoring evaluations, whereas the Analytic Hierarchy Process (AHP) uses pairwise comparisons. In other words, scoring evaluations are incompatible with pairwise comparison evaluations. This incompatibility prevents one method from being replaced by another without loss of information. This is a significant gap because the re-evaluation process is expensive, time-consuming, and may even be impossible if the experts are no longer available. This study presents a novel approach to automatically substitute prioritization methods without loss of information. The approach was applied to a real-world case involving forty-four Brazilian companies. The specific case shows the prioritization of four projects evaluated by scores, combined with three new projects evaluated by pairwise comparisons. The application of the approach offers specific and general contributions. For example, substituting prioritization methods without loss of information, such as the Mapping method with AHP. Flexibility in choosing the evaluation method that offers greater psychological comfort to the experts. Obtaining transitive pairwise comparison matrices independently of the number of new projects evaluated. Full article

DC distribution networks exhibit inherent symmetry in their balanced power distribution and modular structure, offering high operational flexibility and making them particularly suitable for the integration of distributed generation and modern loads. This symmetric framework positions DC networks as a vital component of new power systems and a key development direction for future power supply systems in industrial and mining enterprises. However, pole-to-pole short-circuit faults disrupt this symmetry, characterized by low system damping, high fault currents, and extremely rapid current rise rates, which pose serious threats to system security and necessitate ultra-fast fault clearance. To address this issue, this paper proposes a novel pilot protection scheme inspired by symmetry principles, based on the slope of the line-mode current for pole-to-pole short-circuit faults in DC distribution networks. First, an equivalent circuit of the system before converter blocking under a pole-to-pole fault is established, and an analytical expression of the fault current is derived, incorporating symmetric analysis of modal components. Subsequently, the variation trends, amplitudes, and phase characteristics of the fault current under faults occurring in different zones of the DC line are analyzed from the perspective of modal symmetry, highlighting the symmetric and asymmetric behaviors of line-mode and zero-mode currents. Furthermore, considering the distinct symmetric properties of these currents during lightning disturbances and pole-to-pole faults, the least squares method is employed to perform linear fitting on the line-mode current, thereby capturing its symmetric variation trend. A pilot protection scheme utilizing the slope of the line-mode current is then proposed, leveraging symmetry in fault discrimination. Finally, simulation models built in MATLAB/Simulink (R2022a) are used for validation. The results demonstrate that the proposed protection method can quickly identify faults within 1.5 ms while exhibiting strong tolerance to a 20 Ω transitional resistance and 50 dB signal noise, indicating good feasibility and broad applicability, with symmetry-based analysis enhancing robustness. Full article

With the aging of China’s expressway network, the number of maintenance projects continues to increase, and issues such as construction safety, driving risk, and traffic efficiency have become increasingly prominent. This paper systematically reviews relevant research progress from four aspects: safety characteristics, traffic capacity, work-zone layout, and speed limit management. The review indicates that Western scholars have made extensive use of rich data resources—such as traffic parameters and accident records from expressway maintenance road sections—and have developed relatively systematic and well-established research frameworks in theoretical analysis, practical application, and evaluation methods. In contrast, Chinese studies have mainly relied on specific maintenance projects, commonly employing on-site investigations and traffic simulations to address particular problems, with limited systematization and generalization. Looking forward, it is essential to further strengthen the standardized collection and statistical analysis of traffic data (including accident data) for expressway maintenance road sections. Meanwhile, for complex scenarios such as multi-lane segments, special road sections, reconstruction and expansion sections, as well as extreme climatic conditions and nighttime operations, comprehensive research should be conducted by leveraging new-generation driving simulation, big data analytics, and artificial intelligence technologies, thereby providing scientific support and methodological foundations for building a systematic theoretical framework for traffic safety in expressway maintenance road sections. Full article