Local Mechanisms Research Articles

An adaptive SBFEM based on a nonlocal macro/meso damage model for fracture simulation of quasibrittle materials

An adaptive scaled-boundary finite element method (SBFEM) is proposed to simulate the crack damage and propagation of quasibrittle materials on the basis of a nonlocal macro/meso damage model. The mesoscopic damage is defined by the deformation of the material bond between the two pairs in the model. First, the structure is discretized via an arbitrary polygon–quadtree mesh. During the damage propagation process, the damage value in each damaged element is determined by the model. Once the damage of an element reaches the damage threshold, the damaged element automatically undergoes mesh refinement until the refined mesh size meets the minimum mesh size requirement. This refinement strategy does not require manual intervention and is automatically implemented by computational software, greatly enhancing the computational efficiency of crack simulation. The transition elements between the refined area and the original elements are discretized using elements with a 2:1 size ratio, ensuring the high quality and efficiency of the automatically subdivided mesh. The nonlinear damage problem is solved iteratively via the arc-length method. The accuracy and efficiency of the proposed algorithm are verified through three numerical examples. The results also indicate that our method is not sensitive to mesh configurations, as commonly encountered in classic local damage mechanics. Moreover, increasing the mesh density results in smoother crack paths and higher computational accuracy.

Disentangling regional climate change: assessing the contribution of global- and regional-scale anthropogenic factors to observed regional warming

Abstract While the influence of uniformly-distributed well-mixed greenhouse gas emissions on global warming is well-documented and robustly attributed through multiple lines of evidence, regional attribution remains more challenging and dependent on the performance and resolution of climate models. This study investigates the extent to which observed regional temperature trends are attributable to global-scale anthropogenic factors, mainly the direct effects of CO2, aiming to differentiate the portion of the change attributable to regional-scale drivers and local feedback mechanisms. To quantify the contribution of global- and regional-scale climate drivers to observed temperature change, I conducted regression analyses using the observed temperature change in HadCRUT4 and the global warming index derived from global radiative forcing series. Results indicate that in certain regions—specifically West Asia, East N. America, West Africa, and the Amazon— 62 ± 7%, 61 ± 13%, 58 ± 7%, and 61 ± 10% of the warming observed over 1991–2020 can be attributed to global anthropogenic warming, primarily the direct effects of CO2. The remaining portion, which represents 22 ± 8%, 21 ± 16%, 27 ± 9%, 23 ± 14% of the observed warming, is attributable to regional drivers. The Mediterranean showcases high sensitivity, with regional drivers contributing 31 ± 13% of the observed 1.2 ∘ C warming, amplifying the warming attributed to global anthropogenic drivers, estimated at 55 ± 10%. The Arctic Ocean along with the Russian-Arctic region exhibits a substantial contribution from regional drivers and local feedback mechanisms (the indirect effects of CO2) to the observed warming amplification, quantified at 43 ± 15% of the 3 ∘C warming over 1991–2020. Regional cooling drivers, however, are significant in East Asia and the Tibetan Plateau, with the latter experiencing a cooling contribution of − 42 ± 17%. By breaking down the contributions of climate drivers into global and regional-scale components, we gain valuable insights into the local climate mechanisms responsible for the observed regional warming, which is a crucial factor that affects agriculture, ecosystems and societies.

Enhancing wildfire detection: a novel algorithm for controllable generation of wildfire smoke images

Background The lack of wildfire smoke image data is one of the most important factors hindering the development of image-based wildfire detection. Smoke image generation based on image inpainting techniques is a solution worthy of study. However, it is difficult to generate smoke texture with context consistency in complex backgrounds with current image inpainting methods. Aims This work aims to provide a wildfire smoke image database for specific scenarios. Methods We designed an algorithm based on generative adversarial networks (GANs) to generate smoke images. The algorithm includes a multi-scale fusion module to ensure consistency between the generated smoke and backgrounds. Additionally, a local feature-matching mechanism in the discriminator guides the generator to capture real smoke’s feature distribution. Key results We generated 13,400 wildfire smoke images based on forest background images and early fire simulation from the Fire Dynamics Simulator (FDS). Conclusions A variety of advanced object detection algorithms were trained based on the generated data. The experimental results confirmed that the addition of the generated data to the real datasets can effectively improve model performance. Implications This study paves a way for generating object datasets to enhance the reliability of watchtower or satellite wildfire monitoring.

TMIC-49. TUMOR-DERIVED SPERMIDINE DRIVES IMMUNE SUPPRESSION IN THE GLIOBLASTOMA MICROENVIRONMENT VIA CD8 T CELL DEPLETION AND FUNCTIONAL ATTENUATION

Abstract A hallmark of glioblastoma (GBM) is an immunosuppressive microenvironment that is partially driven by tumor cell-derived secreted factors, highlighting the need for a more complete understanding of local immune suppressive mechanism. Altered secreted metabolite levels are observed in the GBM microenvironment; specifically, spermidine (a polyamine), which functions to drive cell proliferation, apoptosis, T cell lineage commitment, and myeloid cell-mediated suppression. However, the direct link between GBM cell-derived spermidine and the tumor microenvironment has yet to be determined. In syngeneic mouse GBM models, spermidine levels were increased in the tumor microenvironment and exogenous administration of spermidine accelerated tumor aggressiveness in an immune dependent-manner. Mechanistically, spermidine administration increased apoptosis of CD8+ T-cells and generated an exhausted phenotype. When ornithine decarboxylase (ODC1, the rate limiting enzyme in spermidine synthesis), was knocked down in tumor cells, no direct effect was observed on tumor cell growth. However, there was an increase in time to tumor succumbence and a concomitant increase in CD8+ T-cells and decrease in CD8+ T cell exhaustion. In GBM patients, there was a negative correlation between ODC1 expression and CD8+ T-cells present near the tumor. Additionally, patients with a favorable prognosis had significantly lower intratumoral levels of spermidine compared to patients with a poor prognosis. Taken together, these results demonstrate that spermidine functions as a tumor cell-derived metabolite that drives tumor progression through reducing CD8+T cell number and altering their phenotype. These findings have clinical implications as clinical trials using a polyamine biosynthesis inhibitor alone have not been very effective in GBM patients, suggesting the need for additional combination immunotherapies. Finally, spermidine is also produced at prominent levels by commensal gut microbes as well as absorbed from consumption of foods such as mushrooms; investigation into how these distal spermidine sources impact GBM growth via the gut-brain axis is actively ongoing.

An in-situ investigation of microstructure neighborhood effects on hydrogen-induced dislocation activity

The interactions between hydrogen and crystallographic defects are the key to understanding hydrogen induced localized plasticity or damage mechanisms. However, unraveling such interactions based on the resulting mechanical effects alone can be challenging, due to the spectrum of various effects hydrogen can create in a given microstructure. Pre-deformation or post-mortem characterization of hydrogen segregation has similar limitations, as they can only provide snapshots of these complex processes with challenging spatial and temporal scales. To tackle this, we design in situ experiments that combine electron channeling contrast imaging (ECCI), electron backscattered diffraction (EBSD), and desorption spectroscopy, to investigate hydrogen defect interactions, under no external loading. We show that the inhomogeneous hydrogen distribution within microstructures can cause long-range (>200 nm) dislocation rearrangements. Interpretation of these results as manifestations of boundary-segregation induced stress suggests that microstructural characteristics on either side of the boundary should also play an important role. We provide quantitative assessments of these microstructure neighborhood effects and discuss additional contributions from hydrogen-induced changes in stacking fault energy and elastic modulus.

Experimental and Numerical Analysis of PIP Slip Joint Subjected to Bending

Detachable circular hollow sections (CHSs) offer an innovative solution to tackle the complexities of installation, maintenance, upgrades, and repairs in offshore monopile systems, particularly in challenging environments with limited access. As an alternative to traditional tubular joints, the PIP slip joint presents advantages in terms of ease of installation, time efficiency, and reduced susceptibility to failure. This study conducts an experimental investigation on PIP (Pile-in-Pile) slip joints under pure bending conditions, accompanied by comprehensive numerical analyses to examine the relationship between section slenderness, contact properties, and structural performance. The results highlight a strong correlation between force-displacement curves and include a comparison of compressive and tensile strain values for both experimental and numerical models. The experimental and numerical models showed strong agreement across all results, demonstrating the robustness of the findings. Additionally, numerical models were utilized to investigate various D/t ratios, revealing insights into the normalized moment, rotational capacity, and the impact of local buckling and contact mechanics. Furthermore, a comparison of these findings with established code guidelines, such as Eurocode and AISC-LRFD, has been conducted and reviewed in the context of this study. From analysis, it was found that the rise in the D/t ratio prompted a transformation in the buckling mode, which substantially altered the rotational ratio. This shift indicates the importance of understanding how these variables interact in engineering applications. These findings significantly enhance the understanding of PIP slip joints and emphasize their potential as a compelling alternative for offshore wind turbine support structures.

Unveiling the Local Strain-Induced Structural Degradation Mechanisms in Li Excess Manganese Cathodes

Unveiling the Local Strain-Induced Structural Degradation Mechanisms in Li Excess Manganese Cathodes

Structural and Phase Engineering of a Hierarchical 2D-2D Nickel MOF/Hydroxide-Derived Ni0.85Se/NiTe2 Heterointerface for Robust HER, OER, and Overall Water Splitting.

The development of a nonnoble metal-based cost-effective, efficient, and durable bifunctional electrocatalyst is crucial to achieving the goal of carbon neutrality. In this study, a structural and interfacial engineering approach is employed to design a 2D-2D hierarchical nickel MOF/nickel hydroxide-derived nickel selenide/nickel telluride dual-phase material through a single-step selenotellurization process. The rational design of highly ordered nanoarchitectures provides well-defined voids and ample pathways for ion diffusion. Furthermore, hierarchical nickel selenide/nickel telluride works synergistically at heterojunctions, providing a local ion enrichment mechanism for the catalytic process. As a result, Ni0.85Se/NiTe2@Ni-NH@CC needs an overpotential of 69 and 240 mV to deliver a current density of 10 mA cm-2 for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively, with an outstanding stability observed for over 100 h. Moreover, the Ni0.85Se/NiTe2@Ni-NH@CC (+, -) device exhibits excellent overall water-splitting performance with a cell voltage of 1.50 V at 10 mA cm-2 and can be operated steadily for >100 h at 100 mA cm-2. Density functional theory (DFT) calculations indicate favorable kinetics for H-adsorption at the selenotelluride heterojunction, thereby promoting the HER. This work highlights a new approach for designing a unique nanoarchitecture of MOF/hydroxide-derived selenotelluride heterojunctions for high-efficiency energy conversion applications.

Inter-city firm connections and the scaling of urban economic indicators

Abstract Cities exhibit consistent returns to scale in economic outputs, and urban scaling analysis is widely adopted to uncover common mechanisms in cities' socioeconomic productivity. Leading theories view cities as closed systems, with returns to scale arising from intra-city social interactions. Here, we argue that the interactions between cities, particularly via shared organizations such as firms, significantly influence a city's economic output. By examining global data on city connectivity through multinational firms alongside urban scaling GDP statistics from the US, EU, and China, we establish that global connectivity notably enhances GDP, while controlling for population. After accounting for global connectivity, the effect of population on GDP is no longer distinguishable from linear. To differentiate between local and global mechanisms, we analyzed homicide case data, anticipating dominant local effects. As expected, inter-city connectivity showed no significant impact. Our research highlights that inter-city effects affect some urban outputs more than others. This empirical analysis lays the groundwork for incorporating inter-city organizational connections into urban scaling theories and could inform future model development.

Steel stresses and shear forces in reinforcing bars due to dowel action

AbstractReinforcing bars in structural concrete are typically designed to carry axial forces. Nevertheless, due to their bending stiffness, the bars can also carry transverse forces, that are associated with the localized bending mechanism (dowel action) resulting from the relative displacements (or slip) wherever a crack interface or a discontinuity interface (between two concrete parts cast at different times) intercept the bar. Such localized bending induces stress concentrations in both the bars and the concrete. The relative displacement can occur at interfaces either perpendicular to the bar or inclined with respect to its axis. Thanks to steel ductility, the bending stresses in the bars due to dowel action do not impair the sectional capacity at the ultimate limit state. Fatigue verifications, however, require an accurate evaluation of these stresses under imposed transverse displacements or shear forces. As well known, dowel action can be described by means of the traditional unidimensional Winkler's model (beam on an elastic foundation), where the bearing stiffness of the concrete embedment is typically introduced through a couple of parameters, namely the bar diameter and the concrete strength in compression. The actual behavior of a dowel, however, is definitely more complex and for such a reason, improvements are needed for the Winkler's model to introduce other parameters typical of actual structures. Hence, a new formulation is introduced in this study for the bearing stiffness, that is calibrated based on mechanical considerations and measurements with optical fibers. The proposed formulation also accounts for the following parameters: angle between the crack and the bar, concrete‐cover thickness, number of load cycles and the softening effect caused by the local secondary cracks radiating from bar ribs during the pull‐out process. The predictions of the model—implemented with the proposed bearing stiffness—fit fairly well the test results under both monotonic and cyclic loads, in terms of shear force–transverse displacement response and peak stress in the reinforcing bars.

Iron-Induced Localized Oxide Path Mechanism Enables Efficient and Stable Water Oxidation.

The sluggish reaction kinetics of the anodic oxygen evolution reaction (OER) and the inadequate catalytic performance of non-noble metal-based electrocatalysts represent substantial barriers to the development of anion exchange membrane water electrolyzer (AEMWE). This study performed the synthesis of a three-dimensional (3D) nanoflower-like electrocatalyst (CFMO) via a simple one-step method. The substitution of Co with Fe in the structure induces a localized oxide path mechanism (LOPM), facilitating direct O-O radical coupling for enhanced O2 evolution. The optimized CFMO-2 electrocatalyst demonstrates superior OER performance, achieving an overpotential of 217 mV at 10 mA cm-2, alongside exceptional long-term stability with minimal degradation after 1000 h of operation in 1.0 M KOH. These properties surpass most of conventional noble metal-based electrocatalysts. Furthermore, the assembled AEMWE system, utilizing CFMO-2, operates with a cell voltage of 1.65 V to deliver 1.0 A cm-2. In situ characterizations reveal that, in addition to the traditional adsorbate evolution mechanism (AEM) at isolated Co sites, a new LOPM occurred around the Fe and Co bimetallic sites. First-principles calculations confirm the LOPM greatly reduced the energy barriers. This work highlights the potential of LOPM for improving the design of non-noble metal-based electrocatalysts and the development of AEMWE.

Iron‐Induced Localized Oxide Path Mechanism Enables Efficient and Stable Water Oxidation

The sluggish reaction kinetics of the anodic oxygen evolution reaction (OER) and the inadequate catalytic performance of non‐noble metal‐based electrocatalysts represent substantial barriers to the development of anion exchange membrane water electrolyzer (AEMWE). This study performed the synthesis of a three‐dimensional (3D) nanoflower‐like electrocatalyst (CFMO) via a simple one‐step method. The substitution of Co with Fe in the structure induces a localized oxide path mechanism (LOPM), facilitating direct O–O radical coupling for enhanced O2 evolution. The optimized CFMO‐2 electrocatalyst demonstrates superior OER performance, achieving an overpotential of 217 mV at 10 mA cm–2, alongside exceptional long‐term stability with minimal degradation after 1000 h of operation in 1.0 M KOH. These properties surpass most of conventional noble metal‐based electrocatalysts. Furthermore, the assembled AEMWE system, utilizing CFMO‐2, operates with a cell voltage of 1.65 V to deliver 1.0 A cm–2. In situ characterizations reveal that, in addition to the traditional adsorbate evolution mechanism (AEM) at isolated Co sites, a new LOPM occurred around the Fe and Co bimetallic sites. First‐principles calculations confirm the LOPM greatly reduced the energy barriers. This work highlights the potential of LOPM for improving the design of non‐noble metal‐based electrocatalysts and the development of AEMWE.

Multilevel attention mechanism for motion fatigue recognition based on sEMG and ACC signal fusion.

This study aims to develop a cost-effective and reliable motion monitoring device capable of comprehensive fatigue analysis. It achieves this objective by integrating surface electromyography (sEMG) and accelerometer (ACC) signals through a feature fusion strategy. The study introduces a multi-level attention mechanism for classification, leveraging convolutional neural networks (CNNs). The preprocessing phase involves a local feature attention mechanism that enhances local waveform features using the amplitude envelope. A dual-scale attention mechanism, operating at both channel and neuron levels, is employed to enhance the model's learning from high-dimensional fused data, improving feature extraction and generalization. The local feature attention mechanism significantly improves the model's classification accuracy and convergence, as demonstrated in ablation experiments. The model, optimized with multi-level attention mechanisms, excels in accuracy and generalization, particularly in handling data with pseudo-artifacts. Computational analysis indicates that the proposed optimization algorithm has minimal impact on CNN's training and testing times. The study achieves recognition accuracies of 92.52%, 92.38%, and 92.30%, as well as F1-scores of 91.92%, 92.13%, and 92.29% for the three fatigue states, affirming its reliability. This research provides technical support for the development of affordable and dependable wearable motion monitoring devices.

Artificial intelligence-based optimization techniques for optimal reactive power dispatch problem: a contemporary survey, experiments, and analysis

The optimization challenge known as the optimal reactive power dispatch (ORPD) problem is of utmost importance in the electric power system owing to its substantial impact on stability, cost-effectiveness, and security. Several metaheuristic algorithms have been developed to address this challenge, but they all suffer from either being stuck in local minima, having an insufficiently fast convergence rate, or having a prohibitively high computational cost. Therefore, in this study, the performance of four recently published metaheuristic algorithms, namely the mantis search algorithm (MSA), spider wasp optimizer (SWO), nutcracker optimization algorithm (NOA), and artificial gorilla optimizer (GTO), is assessed to solve this problem with the purpose of minimizing power losses and voltage deviation. These algorithms were chosen due to the robustness of their local optimality avoidance and convergence speed acceleration mechanisms. In addition, a modified variant of NOA, known as MNOA, is herein proposed to further improve its performance. This modified variant does not combine the information of the newly generated solution with the current solution to avoid falling into local minima and accelerate the convergence speed. However, MNOA still needs further improvement to strengthen its performance for large-scale problems, so it is integrated with a newly proposed improvement mechanism to promote its exploration and exploitation operators; this hybrid variant was called HNOA. These proposed algorithms are used to estimate potential solutions to the ORPD problem in small-scale, medium-scale, and large-scale systems and are being tested and validated on the IEEE 14-bus, IEEE 39-bus, IEEE 57-bus, IEEE 118-bus, and IEEE 300-bus electrical power systems. In comparison to eight rival optimizers, HNOA is superior for large-scale systems (IEEE 118-bus and 300-bus systems) at optimizing power losses and voltage deviation; MNOA performs better for medium-scale systems (IEEE 57-bus); and MSA excels for small-scale systems (IEEE 14-bus and 39-bus systems).

Attenuation of bulk waves using locally resonant soil-coupled metabarriers

Abstract Low frequency ground-borne vibrations generated by transport infrastructure are one of the most serious causes of disturbance to the general population. One possibility to reduce this problem is to use the wave filtering properties of elastic metamaterials. However, their integration in the soil complicates the prediction of their response, and the influence of soil-structure interaction needs to be correctly evaluated for an efficient design. The aim of this work is to experimentally evaluate the efficiency of metamaterial trench barriers set in soil in attenuating vibrations, using low-frequency local resonance mechanisms. A lab scale model is proposed comprising different resonating structures and a cylindrical encasement is adopted to couple the structure to the soil. The influence of various parameters is evaluated, such as metamaterial structure, geometrical characteristics of the resonator, and constituent materials. Finite Element simulations are used to develop a suitable design, analysing mode shapes and resonance frequencies of structures with and without the surrounding encasement. Experimental modal analysis is then performed on the corresponding fabricated samples, providing both model validation and out-of-soil mechanical characterization. Finally, vibration transmission loss measurements are performed in a setup in which different resonant metamaterial barriers are embedded into the soil sample, allowing the evaluation of barrier performance. Results indicate that the metamaterial structures provide good attenuation of vibrations in selected intervals in the low to high frequency range (1–5 kHz), demonstrating the feasibility of the approach in a scaled sample. Preliminary data regarding the structures providing preferable design characteristics is also obtained. These results can be useful for the design of trench barriers scaled to large dimensions in more realistic applicative settings.

Potassium ion channel modulation at cancer-neural interface enhances neuronal excitability in epileptogenic glioblastoma multiforme.

The central nervous system (CNS) is increasingly recognized as a critical modulator in the oncogenesis of glioblastoma multiforme (GBM), with interactions between cancer and local neuronal circuits frequently leading to epilepsy; however, the relative contributions of these factors remain unclear. Here, we report a coordinated intratumor shift among distinct cancer subtypes within progenitor-like families of epileptic GBM patients, revealing an accumulation of oligodendrocyte progenitor (OPC)-like subpopulations at the cancer-neuron interface along with heightened electrical signaling activity in the surrounding neuronal networks. The OPC-like cells associated with epilepsy express KCND2, which encodes the voltage-gated K+ channel KV4.2, enhancing neuronal excitability via accumulation of extracellular K+, as demonstrated in patient-derived exvivo slices, xenografting models, and engineering organoids. Together, we uncovered the essential local circuitry, cellular components, and molecular mechanisms facilitating cancer-neuron interaction at peritumor borders. KCND2 plays a crucial role in mediating nervous system-cancer electrical communication, suggesting potential targets for intervention.

Tunable underwater sound absorption via piezoelectric materials with local resonators

In recent years, piezoelectric composite materials have been widely used in the design of underwater anechoic coatings due to their adaptability to tuning parameters. However, there are also some shortcomings, such as a single dissipation mechanism, narrow bandwidth, and poor low-frequency sound absorption. This work proposes an acoustic composite structure combining piezoelectric composite materials with local resonance units, which effectively enhances the sound absorption performance of the structure through the coupling effect of the piezoelectric energy consumption mechanism and local resonance mechanism. Compared to conventional acoustic structures, the proposed acoustic composite structure not only has a strong low-frequency sound absorption effect but also enriches the mid-high frequency sound absorption modes by connecting shunt damping circuits. On this basis, the effect of piezoelectric parameters and resonator morphological properties on structural sound absorption performance is further investigated, and the results show that the designed structure has the characteristic of sound absorption performance that is tunable. In addition, key factors affecting the sound absorption performance of the structure have been optimized to achieve better broadband sound absorption performance. This work may provide valuable ideas for the development of low-frequency broadband adjustable underwater sound-absorbing coatings.

Structural basis of transcriptional regulation by UrtR in response to uric acid.

Uric acid (UA)-responsive transcriptional regulators (UrtRs), which belong to the multiple antibiotic resistance regulator (MarR) superfamily, transcriptionally coordinate virulence and metabolism in bacteria by modulating interactions with operator DNA in response to UA. To elucidate the transcriptional regulatory mechanism of UrtR, we structurally analyzed UrtR proteins, including PecS, MftR, and HucR, alone and in complex with UA or DNA. UrtR contains a dimerization domain (DD) and a winged helix-turn-helix domain (wHTHD) and forms a homodimer primarily via the DD, as observed for other MarR superfamily proteins. However, UrtRs are characterized by a unique N-terminal α-helix, which contributes to dimerization and UA recognition. In the absence of UA, the UrtR dimer symmetrically binds to the operator double-stranded DNA (dsDNA) by inserting its α4 recognition helix and β-stranded wing within the wHTHD into the major and minor grooves of dsDNA, respectively. Upon exposure to UA, UrtR accommodates UA in the intersubunit pocket between the DD and wHTHD. UA binding induces a conformational change in the major groove-binding core element of the UrtR wHTHD, generating a DNA binding-incompatible structure. This local allosteric mechanism of UrtR completely differs from that generally observed in other MarR superfamily members, in which the entire wHTHD undergoes effector-responsive global shifts.

DAPNet: A Dual-Attention Parallel Network for the Prediction of Ship Fuel Consumption Based on Multi-Source Data

The precise prediction of ship fuel consumption (SFC) not only serves to enhance energy efficiency to benefit shipping enterprises but also to provide quantitative foundations to aid in carbon emission reduction and ecological environment protection. On the other hand, SFC-related data represent typical multi-source characteristics and heterogeneous features, which lead to several methodological issues (e.g., feature alignment and feature fusion) in SFC prediction. Therefore, this paper proposes a dual-attention parallel network named DAPNet to solve the above issues. Firstly, we design a parallel network structure containing two kinds of long short-term memory (LSTM) and improved temporal convolutional networks (TCNs) for time-series analysis tasks so that different source data can be applied to suitable networks. Secondly, a local attention mechanism is included in each single parallel network so as to improve the ability of feature alignment from different-scale training data. Finally, global attention is employed for the fusion of all parallel networks, which can enrich representation features and simultaneously enhance the performance of SFC prediction. In experiments, DAPNet is compared with 10 methods, including baseline and attention models. The comparison results show that DAPNet and several of its variants obtain the highest accuracy in SFC prediction.

Localized shear and distributed strain accumulation as competing shear accommodation mechanisms in crustal shear zones: constraining their dictating factors

Abstract. Understanding the underlying mechanisms of strain localization in the Earth’s lithosphere is crucial for explaining the mechanics of tectonic plate boundaries and various failure-assisted geophysical phenomena, such as earthquakes. Geological field observations suggest that shear zones are the most important lithospheric structures demonstrating intense shear localization at plate boundaries, accommodating a major portion of tectonic deformations. Despite extensive studies over the past several decades, the factors governing how shear zones accommodate bulk shear, whether via distributed strain (i.e. the development of macroscopic S (schistosity) foliations normal to the principal shortening strain axis) or via localized shearing (i.e. the formation of shear-parallel C bands, where C refers to the French “cisaillement” (shear)), remain largely unexplored. This study aims to address this gap in knowledge by providing observational evidence of varying S and C development in crustal shear zones from two geological terrains in eastern India. These field observations are complemented by 2D viscoplastic numerical simulations within a strain-softening rheological framework to constrain the factors controlling two competing shear accommodation mechanisms: distributed strain accumulation and shear band formation. The model-based analysis recognizes the bulk shear rate (γ˙b), initial viscosity (ηv), and initial cohesion (Ci) of a shear zone as the most critical factors determining the dominance of one mechanism over the other. For a given Ci value, low γ˙b and ηv values facilitate the formation of S foliation (uniformly distributed strain), which transitions to a C-dominated shear accommodation mechanism as ηv increases. However, increasing γ˙b facilitates shear accommodation through a combination of the two mechanisms, leading to S–C structures. The article finally discusses the conditions under which shear zones can significantly intensify rates of localized shear, producing rapid slip events, such as frictional melting and seismic activities.