Search Results (242)

Convective instabilities at various boundary layers in the earth’s mantle—including the core–mantle boundary, mantle transition zone and lithosphere-asthenosphere boundary— result in upwellings (mantle plumes) and downwellings (subducting slabs). While hotspot volcanism is traditionally linked to mantle plumes, their structure, origins, evolution, and death remain subjects of ongoing debate. Recent progress in seismic tomography has revealed a complex plumbing system connecting the core–mantle boundary and the surface. In particular, recent seismic imaging results suggest the presence of large-scale ponding zones between 660 km and ∼1000 km, associated with several mantle plumes around the globe. The broad upwellings originating from the CMB spread laterally beneath the 660 km seismic discontinuity, forming extensive ponding zones several thousand kilometers wide and extending up from an approximately 1000 km depth. Similar ponding zones are also observed for downwellings, with stagnant subducting slabs, within the 660–1000 km depth range. Here, we review evidence for wide ponding zones characterized by low seismic velocities and anomalous radial and azimuthal anisotropies in light of recent high-resolution regional studies below La Réunion Island in the Indian Ocean and below St Helena/Ascension in the southern Atlantic Ocean. We review and discuss possible interpretations of these structures, as well as possible mineralogical, geodynamic implications and outlook for further investigations aiming to improve our understanding of the mantle plumbing system. Full article

Discontinuity trace provides critical geological data for engineering design and construction optimization. However, current extraction methods relying on discontinuity intersection fitting are highly sensitive to the segmentation accuracy of individual discontinuity, while trace segment connectivity remains suboptimal. To address these challenges, we propose an ARCG (Adaptive Region Contour Growing) method using 3D point clouds. By dynamically adjusting parameter thresholds, our approach simultaneously extracts both discontinuities and their boundaries. We then evaluate the fitting performance of different discontinuity models using area ratios, identifying the parallelogram as the most suitable representation. The method then detects intersection lines between paired discontinuities through spatial intersection analysis, with dynamic partitioning preserving original geometric properties. Finally, a bidirectional weighted graph-based growth algorithm connects intersection lines belonging to the same discontinuity, generating the final trace results. The proposed method was validated using slope data from two case studies. Results demonstrate that, compared to existing methods and point cloud processing software, our approach achieves robust extraction of complex traces while maintaining high connectivity. Moreover, it improves computational efficiency by 48.8% without compromising trace accuracy. Thus, this method offers a novel solution for the digital characterization of rock mass discontinuity parameters. Full article

The optimal computing ability of spiking neural networks (SNNs) mainly depends on the connection weights of their synapses and the thresholds that control the spiking. In order to realize the optimization calculation of different objective functions, it is necessary to modify the connection weights adaptively and make the thresholds dynamically self-learning. However, it is very difficult to construct an adaptive learning algorithm for spiking neural networks due to the discontinuity of neuron spike sending process, which is also a fatal problem in this field. In this paper, an efficient adaptive learning algorithm for spiking neural networks is proposed, which adjusts the weights of synaptic connections by a learning factor adaptively and adjusts the probability of spike sending by the self-organizing learning method of the dynamic threshold, so as to achieve the goal of automatic global search optimization. The algorithm is applied to the learning task of global optimization, and the experimental results show that this algorithm has good stability and learning ability, and is effective in dealing with complex multi-objective optimization problems of spatiotemporal spike mode. Moreover, the proposed framework explicitly leverages problem and model symmetries. In Traveling Salesman Problems, distance symmetry (d(i, j) = d(j, i)) and tour permutation symmetry are preserved by our spike-train-based similarity and energy updates, which do not depend on node labels. Together with the homogeneous neuron dynamics and balanced excitatory–inhibitory populations, these symmetry-aware properties reduce the effective search space and enhance the convergence stability. Full article

Sustainable urban mobility is gaining importance as cities seek to address congestion and environmental concerns, with cycling infrastructure being an essential component of urban transportation systems. This study proposes a novel integrated, data-driven modeling framework that uniquely combines sustainability and ergonomic design to evaluate and optimize urban cycling networks. A computational model incorporating graph-based analysis, isochrone mapping, and network discontinuity identification was used to assess cycling safety and accessibility within MEL. The findings highlight significant accessibility shortcomings caused by network discontinuities, unsafe segments, and missing links—issues frequently overlooked in conventional cycling network planning. Key employment centers in MEL were found to have limited cycling access, highlighting the need for cross-regional connectivity. The study suggests that targeted micro-interventions and improved connectivity can improve the sustainability and ergonomics of urban cycling networks. The methodological framework developed is scalable and adaptable, making it applicable to other metropolitan regions. This study offers actionable insights for urban planners, advocating for data-driven decision-making and micro-scale network improvements to create a more connected, efficient, and inclusive cycling network. Full article

Self-supervised monocular depth estimation from oblique UAV videos is crucial for enabling autonomous navigation and large-scale mapping. However, existing self-supervised monocular depth estimation methods face key challenges in UAV oblique video scenarios: depth discontinuity from geometric distortion under complex viewing angles, and spatial ambiguity in weakly textured regions. These challenges highlight the need for models that combine global reasoning with geometric awareness. Accordingly, we propose RMTDepth, a self-supervised monocular depth estimation framework for UAV imagery. RMTDepth integrates an enhanced Retentive Vision Transformer (RMT) backbone, introducing explicit spatial priors via a Manhattan distance-driven spatial decay matrix for efficient long-range geometric modeling, and embeds a neural window fully-connected CRF (NeW CRFs) module in the decoder to refine depth edges by optimizing pairwise relationships within local windows. To mitigate noise in COLMAP-generated depth for real-world UAV datasets, we constructed a high-fidelity UE4/AirSim simulation environment, which generated a large-scale precise depth dataset (UAV SIM Dataset) to validate robustness. Comprehensive experiments against seven state-of-the-art methods across UAVID Germany, UAVID China, and UAV SIM datasets demonstrate that our model achieves SOTA performance in most scenarios. Full article

We introduce a novel technique for computing the periods of $(d,k)$-IETs based on Rauzy induction $\mathcal{R}$. Specifically, we establish a connection between the set of periods of an interval exchange transformation (IET) T and those of the IET $T^{\prime }$ obtained either by applying the Rauzy operator $\mathcal{R}$ to T or by considering the Poincaré first return map. Rauzy matrices play a central role in this correspondence whenever T lies in the domain of $\mathcal{R}$ (Theorem 4). Furthermore, Theorem 6 addresses the case when T is not in the domain of $\mathcal{R}$, while Theorem 5 deals with IETs having associated reducible permutations. As an application, we characterize the set of periods of oriented 3-IETs (Theorem 8), and we also propose a general framework for studying the periods of $(d,k)$-IETs. Our approach provides a systematic method for determining the periods of non-transitive IETs. In general, given an IET with d discontinuities, if Rauzy induction allows us to descend to another IET whose periodic components are already known, then the main theorems of this paper can be applied to recover the set of periods of the original IET. This method has been also applied to obtain the set of periods of all $(2,k)$-IETs and some $(3,k)$-IETs, $k\ge 1$. Several open problems are presented at the end of the paper. Full article

Reinforced concrete (RC) non-seismic frames in Middle Eastern multistory buildings often have beam–column connections with discontinuous bottom reinforcement, heightening the risk of progressive collapse if an outer column fails. This study aimed to reduce the potential for progressive collapse when a column is lost by investigating the use of bolted steel plates to enhance the beam–column joints of such frames. In this regard, high-fidelity finite element (FE) analysis was carried out on ten half-scale, two-span, two-story RC frames to simulate the removal of a center column. The numerical analysis accounted for the nonlinear rate-dependent response of steel and concrete, as well as the bond-slip model at steel bars/concrete interaction. The analysis matrix had three unstrengthened specimens that served as references for comparison, in addition to seven assemblies, which were strengthened using bolted steel plates. In the upgraded assemblies, the studied variables were the grade of steel plate (three grades were examined) and the upgrading scheme (three different schemes were investigated). The performance of the specimens was evaluated by comparing their failure patterns and the characteristics of load versus displacement of the middle column during both flexural and catenary action phases. Based on this comparison, the most efficient strengthening method was suggested. Full article

This study systematically investigates the piezoelectric performance of cement-based composite materials integrated with triply periodic minimal surface (TPMS) piezoelectric ceramic architectures, including Schwarz P and Neovius structures, in comparison with conventional 0–3 and 1–3 connectivity models. Under mechanical loading conditions, finite element analysis was employed to evaluate the average piezoelectric coefficients, voltage coefficients, and potential outputs of composites with varying piezoelectric ceramic volume fractions. Key findings reveal that the Neovius structure exhibits superior performance: at a 20% ceramic volume fraction, its average piezoelectric coefficient reaches 116 pC/N under 15 kN loading, surpassing the 0–3 type by approximately 12-fold. Both Schwarz P and Neovius structures demonstrate approximately 12× higher average piezoelectric coefficients than the 0–3 model, attributed to their continuous charge transfer pathways and efficient stress distribution enabled by TPMS geometry. Additionally, the piezoelectric voltage coefficients of TPMS-based composites significantly exceed those of traditional 1–3 and 0–3 structures. The potential generation capacity of Neovius composites peaks at 6.7 V under high loading, highlighting their superiority in charge accumulation. The results underscore the critical role of piezoelectric ceramic architecture: the bicontinuous TPMS configurations mitigate phase discontinuity issues, enhancing both mechanical–electrical coupling and energy conversion efficiency. This study provides a novel framework for optimizing cement-based piezoelectric composites toward applications in structural health monitoring, energy harvesting, and smart infrastructure. Full article

With industrial transformation and the rise in the 15 min community life circle, optimizing walkability and preserving industrial heritage are key to revitalizing former industrial areas. This study, focusing on Shijingshan District in Beijing, proposes a walkability evaluation framework integrating multi-source big data and street-level perception. Using Points of Interest (POI) classification, which refers to the categorization of key urban amenities, pedestrian network modeling, and street view image data, a Walkability Friendliness Index is developed across four dimensions: accessibility, convenience, diversity, and safety. POI data provide insights into the spatial distribution of essential services, while pedestrian network data, derived from OpenStreetMap, model the walkable road network. Street view image data, processed through semantic segmentation, are used to assess the quality and safety of pedestrian pathways. Results indicate that core communities exhibit higher Walkability Friendliness Index scores due to better connectivity and land use diversity, while older and newly developed areas face challenges such as street discontinuity and service gaps. Accordingly, targeted optimization strategies are proposed: enhancing accessibility by repairing fragmented alleys and improving network connectivity; promoting functional diversity through infill commercial and service facilities; upgrading lighting, greenery, and barrier-free infrastructure to ensure safety; and delineating priority zones and balanced enhancement zones for differentiated improvement. This study presents a replicable technical framework encompassing data acquisition, model evaluation, and strategy development for enhancing walkability, providing valuable insights for the revitalization of industrial districts worldwide. Future research will incorporate virtual reality and subjective user feedback to further enhance the adaptability of the model to dynamic spatiotemporal changes. Full article

This article introduces two numerical methods based on the Theory of Functional Connections (TFC) for solving linear ordinary differential equations that involve step discontinuities in the forcing term. The novelty of the first proposed approach lies in the direct incorporation of discontinuities into the free function of the TFC framework, while the second proposed method resolves discontinuities through piecewise constrained expressions comprising particular weighted support functions systematically chosen to enforce continuity conditions. The accuracy of the proposed methods is validated for both a second-order initial value and boundary value problem. As a final demonstration, the methods are applied to a third-order differential equation with non-constant coefficients and multiple discontinuities, for which an analytical solution is known. The methods achieve error levels approaching machine precision, even in the case of equations involving functions whose Laplace transforms are not available. Full article

Structural safety of transmission towers is directly influenced by the behavior of bolted connections at discontinuity joints in the main steel angles. Thus, it is essential to investigate the axial compression behavior of double-shear splice connections of main steel angles. In this study, a total of 10 groups of discontinuous steel angle specimens with double-shear splice connections, comprising eight groups of specimens with the same upper and lower angles and two groups of specimens with different upper and lower angles, were designed and tested in compression. The axial deformation, out-of-plane deflection, and strain at the mid-height of steel angles were measured to analyze the influence of double-shear splice connections on the compression behavior of steel angles. Moreover, comparisons were made among discontinuous steel angles in terms of the ultimate load and the associated deformation to investigate the effects of splice steel ratio, slenderness, bolt spacing, and bolt torque, respectively. Based on the experimental results of steel angles in compression, comparisons with the values calculated using Chinese design codes suggest that present design methods show limited accuracy in calculating the axial compressive load capacity of steel angles with double-shear spliced connections, indicating the necessity for revising the design methods in relevant codes. Full article

Integrating carbon nanotubes (CNTs) into electrospun polyvinylidene fluoride (PVDF) fibers is a promising approach for developing conductive and multifunctional materials. This study systematically compared two CNT deposition techniques, electrophoretic deposition (EPD) and dip coating (DC), in terms of their effectiveness in modifying the surface of aligned electrospun PVDF mats. Morphological characterization revealed that EPD produced more homogeneous and compact CNT coatings. In contrast, DC resulted in discontinuous and irregular layers regardless of deposition time. A key distinction between the two methods was the tunability of the coating: EPD allowed for precise control over CNT layer thickness and mass accumulation by adjusting the deposition time. In contrast, DC showed no significant changes in thickness with longer immersion. These structural differences translated into distinct electrical behaviors. Resistance measurements showed that EPD samples exhibited a substantial decrease in resistance with increasing deposition time, from 5.9 ± 2.5 kΩ to 0.2 ± 0.1 kΩ, indicating the formation of well-connected conductive pathways. On the other hand, DC samples maintained relatively constant, higher resistance values across all conditions. Additionally, EPD-coated mats demonstrated enhanced touch sensitivity, generating higher and more stable current responses compared to DC-deposited samples. These results confirm that EPD is a more effective, tunable method for fabricating conductive CNT coatings on electrospun PVDF mats, particularly for applications in flexible electronics and wearable sensors. Full article

Background and Aim: Long-term treatment with biologic therapy alongside immunomAfodulators in patients with inflammatory bowel disease (IBD) can be associated with severe side effects. The objective of this study was to determine whether discontinuing anti-TNF treatment after two years in patients who have achieved mucosal healing is associated with lower relapse rates. Materials and Methods: A total of 67 patients with IBD from a single tertiary IBD Center who had achieved mucosal healing were enrolled in this retrospective study. In this single-center retrospective study (January 2014–December 2022), we screened 67 IBD patients in deep remission (endoscopic mucosal healing after ≥2 years of anti-TNF therapy). After excluding three patients without histologic data, 64 patients (25 ulcerative colitis, 39 Crohn’s disease) were analyzed. Mayo endoscopic sub-score and SES-CD were used to evaluate endoscopic activity after two years of anti-TNF therapy. Histological activity was assessed using the GHAS (for CD) and Nancy index (for UC). Results: A total of 67 patients were screened, of whom 3 were excluded due to a lack of biopsies. Of the 64 included patients, 39.06% (25/64) had UC and 60.9% (39/64) had CD, with a mean disease duration of 11.6 ± 8.0 years. All patients were in endoscopic remission at the time of therapy de-escalation, and 60.9% (39/64) also achieved histological remission (“deep remission”). In the follow-up of 38.6 months (IQR 30–48) after biologic therapy was stopped, 57.8% (37/64) relapsed with a median time to relapse of 13.5 months (IQR 8–24) off anti-TNF—a total of 34 patients required a restarting of biologic therapy. Using Spearman’s correlation, a moderate connection was observed between histological activity at withdrawal and subsequent relapse (rho = 0.467, p < 0.001). The probability of relapsing within 4 years after anti-TNF cessation was significantly higher (OR 2.72) in patients with histologically active disease at the time of de-escalation. Conclusions: Achieving ‘deep remission’ (clinical, endoscopic, and histological healing) may be a suitable parameter for making decisions on when to de-escalate therapy; however, given that over half of patients in endoscopic remission relapse after discontinuation, any de-escalation should be approached with caution and individualized patient assessment. Full article

Ship structures are subjected to cyclic loading from waves and currents during operation, which can lead to fatigue failure, particularly at locations with structural discontinuities such as welds. Although various fatigue assessment methods have been developed, there is a lack of experimental data and comparative studies for actual ship structure details. This study addresses this limitation by evaluating the fatigue strength of longi-web connections in hull structures using local stress approaches, including hot spot stress, effective notch stress, notch stress intensity factor, and structural stress methods. Finite element analyses were conducted, and the predicted fatigue lives and failure locations were compared with experimental results. Although there are some differences between each method, all methods are valid and reasonable for predicting the primary failure locations and evaluating fatigue life. These findings provide a basis for considering suitable fatigue assessment methods for welded ship structures with respect to joint geometry and failure mechanisms. Full article

The zero-error capacity of discrete memoryless channels (DMCs), introduced by Shannon, is a fundamental concept in information theory with significant operational relevance, particularly in settings where even a single transmission error is unacceptable. Despite its importance, no general closed-form expression or algorithm is known for computing this capacity. In this work, we investigate the computability-theoretic boundaries of the zero-error capacity and establish several fundamental limitations. Our main result shows that the zero-error capacity of noisy channels is not Banach–Mazur-computable and therefore is also not Borel–Turing-computable. This provides a strong form of non-computability that goes beyond classical undecidability, capturing the inherent discontinuity of the capacity function. As a further contribution, we analyze the deep connections between (i) the zero-error capacity of DMCs, (ii) the Shannon capacity of graphs, and (iii) Ahlswede’s operational characterization via the maximum-error capacity of 0–1 arbitrarily varying channels (AVCs). We prove that key semi-decidability questions are equivalent for all three capacities, thus unifying these problems into a common algorithmic framework. While the computability status of the Shannon capacity of graphs remains unresolved, our equivalence result clarifies what makes this problem so challenging and identifies the logical barriers that must be overcome to resolve it. Together, these results chart the computational landscape of zero-error information theory and provide a foundation for further investigations into the algorithmic intractability of exact capacity computations. Full article