Search Results (90)

Crumb rubber concrete (CRC), as a heterogeneous multiphase composite material composed of coarse aggregate, rubber particles, cement mortar, pores, and other constituents, is frequently regarded as a homogeneous material in engineering applications. This study employs numerical homogenization to compute equivalent mechanical parameters for CRC. By establishing a two-dimensional parametric random aggregate model combined with Monte Carlo simulations and finite element computations, it systematically analyzes the influence of rubber content (0%, 5%, 10%, 15%) and specimen size (50–150 mm) on CRC’s macroscopic equivalent elastic modulus. The research reveals that stable homogenization results, usable as macroscopic equivalent material parameters, are attained when the Representative Volume Element (RVE) size of the CRC model is ≥5 times the maximum aggregate particle size (dₘₐₓ). The equivalent modulus E decreases rapidly initially with increasing size, followed by a decelerated decline toward stabilization. A predictive model based on the fitted formula ln Eᵣ = kᵣ ln L + bᵣ (where Eᵣ denotes reduced modulus) enables elastic modulus prediction for large-scale components up to 600 mm. This study elucidates the macro-mesoscopic linkage mechanism governing CRC’s equivalent elastic parameters, providing a theoretical foundation for engineering structural design. Full article

This study proposes a theoretical–empirical framework for analyzing political regimes based on a structural analogy between electoral behavior and spin-glass systems in statistical physics. Society is modeled as a system of interacting agents (voters) influenced by both interpersonal interactions and external factors such as media and institutions, formalized through a social Hamiltonian. By introducing a partition function and free energy, political regimes are interpreted as distinct macroscopic phases governed by four effective macro-parameters: external field, conformism, interaction heterogeneity, and inverse social temperature. Democratic societies correspond to a multistable regime characterized by sensitivity to initial conditions and replica symmetry breaking (RSB), reflecting the coexistence of competing social configurations. Authoritarian regimes, in contrast, arise when a strong unidirectional external field, high conformism, and low effective social temperature stabilize a single dominant macroscopic state, producing a regime analogous to replica symmetry (RS). A central result of the model is the distinction between the predictability of macroscopic outcomes and structural social multistability, as well as between natural and externally imposed homogenization of collective behavior. To illustrate the empirical relevance of the framework, the model is applied to the transition from the Weimar Republic to the National Socialist regime (1919–1933), using aggregated electoral data to construct proxy indicators for the effective parameters governing social interactions. The proposed approach enables structural identification of early signals of authoritarian transition through changes in the parameters of social dynamics. Full article

Current research lacks systematic understanding of cross-scale correlations between micro-texture geometry and macro-lubrication behavior. This study presents a multi-scale collaborative optimization methodology for gear Micro-Textured Meshing Interface (MTMI). An objective function targeting macroscopic interfacial performance is formulated, and a topology optimization strategy is employed to achieve optimal MET configuration. The homogenization analysis captures the modulating effects of MET on local flow and stress fields, while topology optimization transcends conventional parametric geometric constraints, enabling the generation of non-regular MET topological patterns tailored to complex operating conditions, thereby ensuring optimal macroscopic ASLBC. The proposed scheme is validated through numerical simulations of two representative problems capturing distinct lubrication regimes: (1) IEL, characterizing transient load-bearing dynamics governed by temporally evolving MET configurations; and (2) ASLBC, elucidating steady-state load-bearing capacity modulation via spatially heterogeneous MET distributions. A Taylor expansion-based surrogate model is developed to efficiently explore the MET configuration design space, significantly enhancing computational efficiency and solution accuracy for multi-scale optimization. While the gradient-based algorithm cannot guarantee global optimality, extensive numerical simulations and cross-validation studies demonstrate consistent convergence toward high-performance MET configurations, with sensitivity analyses of design parameters further confirming the engineering applicability of the optimized solutions. Full article

The emergence of the Internet of Things (IoT) has introduced major security challenges. Deep learning models have shown strong potential for intrusion detection. However, they often require large datasets and high computational resources. In contrast, IoT environments are resource-constrained and lack sufficient labeled data. This paper proposes a lightweight intrusion detection approach based on Long Short-Term Memory (LSTM) networks and homogeneous transfer deep learning. The model is first trained on a subset of the BoT-IoT dataset as a source domain. It is then fine-tuned on a disjoint subset containing a rare attack type. This setup represents adaptation to unseen attack behaviors within the same environment. By freezing earlier layers and fine-tuning only the final layers, the method reduces training overhead while preserving performance. This is important to meet the IoT requirement for frequent, lightweight model updates on resource-constrained devices. The proposed model achieved 99.9% accuracy, a macro F1-score of 0.96, and a 47.8% reduction in training time compared to training from scratch. Extensive experiments confirm that it maintains balanced detection across both common and rare classes. Full article

This paper addresses the growing sustainability fatigue in advanced economies. By analyzing Amazon’s artificial intelligence (AI) agent strategy as a model for “Rational Sustainability”, the study identifies a self-propagating growth trajectory that reconciles economic rationality with value creation. It provides a theoretical and empirical framework to overcome technological saturation and strategic homogenization in the generative AI era. To ensure methodological transparency, the analysis was conducted through two distinct stages: (i) Techno-econometric analysis (macro-level): Using an empirical dataset of 160 countries (40 advanced, 70 emerging, and 50 developing) from 2014 to 2024, the study utilized regression models to quantify the correlations and elasticities between three key proxies: GDP per capita (Y); the Human Capital Index (HCI), representing Institutional Capacity Building (ICB); and the E-Government Development Index (EGI), representing Endogenous Institutional Evolution (EIE). (ii) Hybrid AI analysis (case study): Utilizing process-tracing research, the paper examines Amazon’s R&D structure and AI agent strategy. This qualitative and structural analysis identifies how Amazon co-evolves EIE and ICB to conceptualize tacit knowledge and operationalize it into a competitive advantage. The findings reveal a marked disruption of the co-evolutionary mechanism in advanced economies, where the elasticity of EGI to GDP has declined since 2019, leading to a withdrawal state. In contrast, Amazon’s model demonstrates that the co-evolution of EIE and ICB creates a self-propagating growth engine. This research concludes that “Rational Sustainability”—grounded in evidence, economic rationality, and clear trade-offs—offers a viable pathway for revitalizing sustainability strategies in mature digital economies. Full article

Long-term tillage management fundamentally reshapes soil’s physical and chemical environment, yet an integrated, predictive characterization of the distinct chemical signatures induced by no-tillage (NT) versus chisel tillage (CT) remains limited. We analyzed an eight-year dataset (2010–2017) from a long-term experiment in Iowa, USA, focusing on pH, available phosphorus (Bray1-P), and macro- and micronutrients (K, Ca, Mg, Cu, Fe, Zn) at two depths (0–5 and 5–15 cm). A convergent multi-method framework combined robust univariate statistics, multivariate ordination (PCA, PERMANOVA), linear mixed-effects models, and machine learning (Random Forest and Firth-penalized logistic regression). Results reveal a clear stratification–homogenization pattern. NT is associated with surface accumulation of Zn (+14%), Fe (+16%), and Cu (+5%), with mild acidification (−0.4 pH units) and high temporal stability. CT favored vertical nutrient redistribution, marked by subsurface K enrichment (up to 6% higher than NT), progressive alkalinization, and greater temporal variability. Predictive modeling highlighted subsurface K and surface Zn/Fe as key discriminators, with Firth regression confirming their complementary effects. These findings indicate that long-term NT and CT are associated with distinct, depth-specific chemical configurations—integrated systems defined by concentration gradients, temporal stability, and element covariation—rather than isolated element changes. This work provides a robust, quantitative framework for diagnosing soil management history and characterizing the pedochemical imprint of tillage. Full article

Amidst rapid urbanization and modernization, numerous traditional villages in China face severe challenges, including landscape homogenization and the erosion of their distinctive characteristics. Addressing this issue requires a method capable of systematically identifying, analyzing, and reconstructing both the landscape and its underlying cultural features. This study proposes a digital analytical approach that integrates multimodal artificial intelligence with landscape language theory to address the homogenization of cultural landscapes in traditional Chinese villages. Taking Xinye Village in Zhejiang Province as a case study, the research systematically decodes its landscape spatial narratives and underlying cultural genes. This framework systematically deconstructs village landscapes across four levels: “vocabulary, context, grammar, and semantics”. The village image database is first automatically recognized and statistically analyzed by computer vision technology, which extracts 31 core landscape vocabulary items from three main categories and nine subcategories. Second, Retrieval-augmented Generation technology is employed to synthesize from the constructed domain-specific corpus, a natural context structured around Yuhua Mountain and Daofeng Mountain, as well as a cultural context based on ancestral hall order, connected through folk activities, and idealized by farming and reading passed down through generations. Building on this framework, a multimodal model was used to examine the spatial composition and combinatorial laws of landscape features. Six essential dimensions—spatial layout, visual order, element combination, functional relationships, circulation layout, and scale correlations—revealed the spatial grammar of shuikou landscape. Lastly, the semantic values conveyed by the landscape vocabulary were thoroughly analyzed across three dimensions—form, function, and culture—by integrating a knowledge base. This work creates a landscape language atlas of Xinye Village by combining these studies and using a linguistic model of “character-word-sentence-paragraph”. By methodically deciphering the clan’s cultural code of “farming and reading passed down through generations”, this clearly reconstructs the spatial narrative logic from micro-elements to macro-patterns. This research not only advances the study of landscape language in traditional villages from qualitative description toward a systematic, digital, and interpretable paradigm but also provides an operational theoretical and methodological foundation for the in-depth interpretation, conservation, and transmission of traditional village cultural landscapes. Full article

Existing macroscopic finite element models for electron beam welding (EBW) typically assume isotropic material behavior, often failing to accurately predict residual stresses induced by strong crystallographic textures. To address this limitation, this study established a sequential dual-scale coupled numerical model bridging micro-texture to macro-mechanics by combining the crystal plasticity finite element method (CPFEM) with thermal-elastic-plastic theory. Representative volume elements (RVEs) incorporating α and β dual-phase characteristics were constructed based on electron backscatter diffraction (EBSD) data from the TA15 weld cross-section. Through simulated tensile and shear calculations on the RVEs, homogenized orthotropic stiffness matrices and Hill yield constitutive parameters were derived and mapped onto the macroscopic model. Simulation results indicate that the proposed model maintains the prediction error for molten pool morphology within 16.3%, while effectively correcting the stress overestimation inherent in isotropic models. Specifically, it adjusts the peak longitudinal residual stress at the weld center from 800 MPa to approximately 350 MPa, significantly reducing the anomalous “M-shaped” stress distribution. By successfully capturing shear stress components, this work provides a high-fidelity computational approach for predicting complex stress states in welded joints, offering critical insights for structural integrity assessment. Full article

Carbon fiber-reinforced thermoplastic composites (CFRTP) are widely used in automotive, aerospace, and other industries due to their lightweight, high specific strength, recyclability, and superior thermal properties. However, their non-homogeneity and anisotropy present challenging machining characteristics, often leading to damage that deteriorates component performance. It is imperative to conduct numerical simulation and experimental studies on CFRTP to systematically analyze the relationship between cutting mechanisms and the surface integrity of CFRTP. This study aimed to establish an innovative three-dimensional micro-scale cutting numerical model that integrates the differentiated constitutive behaviors and damage criteria of carbon fibers, matrices, and fiber–matrix interfaces—enabling precise characterization of micro-scale damage evolution during cutting. By combining simulation with experimental verification, it unveils the material removal mechanisms and processing damage causes of CF/PEEK, and further pioneers the quantification of the gradient correlation between fiber orientations (0°, 45°, 90°, and 135°) and fracture modes, cutting forces, and surface integrity, thereby addressing the gap of micro-mechanism and quantitative analysis in CFRTP machining. The micro-scale damage mechanisms revealed by the model directly reflect the intrinsic response of individual fibers in the tow, and the collective effect of these micro-behaviors determines the macro-scale machining performance observed in the experiments. A right-angle cutting experiment was conducted to validate the accuracy of the micro-scale numerical model. The mechanisms of fiber fracture, damage patterns, and chip morphology were systematically compared. The experimental results demonstrate good agreement with the outcomes of the numerical simulations. This study aims to bridge the gap between theoretical understanding and practical application of the cutting mechanisms in CFRTP, providing valuable insights for advancements in manufacturing processes. Full article

The Trepponti bridge in Comacchio (Italy) is a significant masonry landmark characterised by a complex geometry. Its structure comprises five irregularly connected segments, creating pronounced geometric discontinuities. Accurately modelling this configuration is challenging due to the highly complex mechanical behaviour of masonry. This study presents a robust computational strategy for the nonlinear structural assessment of such heritage bridges. The methodology integrates a parametric meshing environment (PoliBrick plugin) with nonlinear finite-element analysis in Straus7. An initial discretisation is generated through PoliBrick, undergoes geometric optimisation to produce an analysis-ready model. The bridge is homogeneously modelled and meshed through macro-blocks obeying a Mohr–Coulomb failure criterion. Material parameters are defined according to the LC1 knowledge level stipulated by the Italian structural code. Differential settlement scenarios are simulated by imposing controlled vertical displacements on individual and paired piers. This approach enables evaluation of structural displacement, stress distribution, and crack propagation. The analyses reveal a markedly asymmetric structural response, identifying two specific piers as critical vulnerable elements. The proposed framework demonstrates that parametric meshing effectively reconciles accurate geometric representation with computational efficiency. It offers a practical tool for guiding the conservation and safety evaluation of irregular vaulted masonry bridges. Full article

Simulating damage phenomena in masonry structures remains a significant challenge because of the intricate and heterogeneous nature of this material. An accurate evaluation of fracture behavior is essential for assessing the bearing capacity of these structures, thereby mitigating dramatic failures. This paper proposes an innovative adaptive concurrent multiscale model for evaluating the bearing capacity of in-plane masonry structures under in-plane loadings. Developed within a Finite Element (FE) set, the proposed model employs a domain decomposition scheme to solve a combination of fine- and coarse-scale sub-models concurrently. In regions requiring less detail, the masonry is represented by homogeneous linear elastic macro-elements. The material properties for these macro-elements are derived through a first-order computational homogenization strategy. Conversely, in areas with higher resolution needs, the masonry is modeled by accurately depicting individual brick units and mortar joints. To capture strain localization effectively in these finer regions, a Phase Field Cohesive Zone Model (PF-CZM) formulation is employed as the fracture model. The adaptive nature derives from the fact that at the beginning of the analysis, the model is entirely composed of coarse regions. As nonlinear phenomena develop, these regions are progressively deactivated and replaced by finer regions. An activation criterion identifies damage-prone regions of the domain, thereby triggering the transition from macro to micro scales. The proposed model’s validity was assessed through multiscale numerical simulations applied to a targeted case study, with the results compared to those from a direct numerical simulation. The results confirm the effectiveness and accuracy of this innovative approach for analyzing masonry failure. Full article

Static mixers are widely used devices for efficient fluid mixing, homogenization, and enhancement of heat transfer, with applications ranging from chemical processing and pharmaceutical manufacturing to wastewater treatment. This review provides a structured overview of mixing processes and the key metrics used to assess mixing quality in static mixers. Conceptual models such as dispersive versus distributive mixing and the classification into macro-, meso-, and micromixing are introduced as a basis for understanding mixing phenomena. Subsequently, a comprehensive set of quantitative measures, including G-value, residence time distribution, intensity of segregation, coefficient of variation, striation-based descriptors, Lyapunov exponent, extensional efficiency, and shear rate, is discussed in detail. Correlations and relationships among these measures are highlighted to facilitate their application in characterizing mixing quality in static mixers. By systematically summarizing the theoretical background, definitions, and interconnections of mixing quality measures, this review aims to provide researchers and engineers with a clear framework for evaluating and comparing mixing quality in static mixers, thereby supporting both academic studies and practical design considerations. Full article

With the continuous growth of user reviews, identifying underlying sentiment across multi-source texts efficiently and accurately has become a significant challenge in NLP. Traditional single models in cross-domain sentiment analysis often exhibit insufficient stability, limited generalization capabilities, and sensitivity to class imbalance. Existing ensemble methods predominantly rely on static weighting or voting strategies among homogeneous models, failing to fully leverage the complementary advantages between models. To address these issues, this study proposes a heterogeneous ensemble sentiment classification model integrating multi-view features and dynamic weighting. At the feature learning layer, the model constructs three complementary base learners, a RoBERTa-FC for extracting global semantic features, a BERT-BiGRU for capturing temporal dependencies, and a TextCNN-Attention for focusing on local semantic features, thereby achieving multi-level text representation. At the decision layer, a meta-learner is used to fuse multi-view features, and dynamic uncertainty weighting and attention weighting strategies are employed to adaptively adjust outputs from different base learners. Experimental results across multiple domains demonstrate that the proposed model consistently outperforms single learners and comparison methods in terms of Accuracy, Precision, Recall, F1 Score, and Macro-AUC. On average, the ensemble model achieves a Macro-AUC of 0.9582 ± 0.023 across five datasets, with an Accuracy of 0.9423, an F1 Score of 0.9590, and a Macro-AUC of 0.9797 on the AlY_ds dataset. Moreover, in cross-dataset ranking evaluation based on equally weighted metrics, the model consistently ranks within the top two, confirming its superior cross-domain adaptability and robustness. These findings highlight the effectiveness of the proposed framework in enhancing sentiment classification performance and provide valuable insights for future research on lightweight dynamic ensembles, multilingual, and multimodal applications. Full article

Supercritical carbon dioxide (SC-CO₂) jetting has emerged as a promising technique for rock fracturing due to its superior physical properties such as low viscosity, high diffusivity, and zero surface tension. However, the complex interaction mechanisms between SC-CO₂ jets and heterogeneous rock media remain inadequately understood. In this study, a coupled Smooth Particle Hydrodynamics–Finite Element Method (SPH-FEM) framework is established to simulate the dynamic fracturing process of rocks under SC-CO₂ jet impact. The Riedel–Hiermaier–Thoma (RHT) constitutive model is incorporated to describe the nonlinear damage evolution of brittle rocks, and key material parameters are calibrated via sensitivity analysis and SHPB experimental validation. A series of numerical simulations are performed to investigate the effects of jet standoff distance, jet velocity, and rock lithology (marble, granite, red sandstone) on fracturing efficiency. Damage area, damage volume, and a novel metric—block size distribution—are employed to quantify the fracturing quality from both macro and meso scales. The results indicate that SC-CO₂ jets outperform conventional water jets in creating more extensive and homogeneous fracture networks. An optimal standoff distance of 1–2 cm and a velocity threshold of 0.2 cm/μs are identified for maximum fracturing efficiency in marble. Furthermore, smaller block sizes are achieved under higher velocities, indicating a more complete and efficient rock fragmentation process. This study provides a comprehensive numerical insight into SC-CO₂ jet-induced rock failure and offers theoretical guidance for optimizing green and water-free rock fracturing techniques in complex geological environments. Full article

In this work, we develop a method for homogenizing effective second-order gradient continuum models for 2D periodic composite materials. A constitutive law is formulated using a variational approach combined with the Hill macro-homogeneity condition for strain energy. Incorporating strain gradient effects enhances the constitutive law by linking the hyperstress tensor to the second-order gradient of displacement, capturing elastic size and microstructure effects beyond classical Cauchy elasticity. The effective strain gradient moduli are calculated for composites exhibiting strong internal length effects, validating the proposed approach by computing the strain energy at different scales. Additionally, we develop an inverse homogenization method to compute local mechanical properties (properties of the constituents) given known global properties (effective properties), showing good agreement with the literature data. This framework is extended to study wave propagation by analyzing longitudinal and shear waves in 2D composite materials. The effects of inclusion shape and volume percentage on wave propagation are examined, revealing that elliptic inclusions lead to a slight increase in both modes of propagation. Finally, we investigate the impact of property contrast between the inclusion and matrix, demonstrating its influence on wave dispersion. Full article