<p>Climate extremes exact a heavy toll on society, with adverse impacts unequally distributed across populations. In this perspective, we outline key challenges and opportunities for advancing research on understanding societal impacts of climate extremes. We identify three key challenges: limited availability and quality of impact data, difficulties in understanding the processes leading to impacts and lack of reliable impact projections. We argue that there is a window of opportunity to address several dimensions of these challenges, and we highlight recent examples and ongoing developments that hold transformative potential for the research field. We conclude with a call to build momentum by fostering interdisciplinary research and collaboration across sectors.</p>
As a primary slope stabilization technique, anchor support encompasses traditional engineering anchors, green anchors, and ecological restoration methods. This review synthesizes two decades of literature to evaluate these approaches. Current research disproportionately focuses on engineering anchors, while green anchor systems remain less studied despite their dual advantages: reduced labor/economic costs and environmental benefits. Notably, most green anchor studies originate from low-altitude plains, with minimal attention to high-altitude cold-arid regions such as plateaus. We therefore identify slope reinforcement using green anchors in plateau environments as a critical emerging research frontier.
Geophysics. Cosmic physics, Dynamic and structural geology
The spectral function of density fluctuations, also known as the dynamic structure factor, of a monatomic cubic crystal with vacancies is derived from the macroscopic equations describing transport in crystalline solids. The resonances of the spectral function are identified as a Brillouin doublet of sound propagation, a central Rayleigh peak of heat diffusion, as for perfect crystals, and another central sharp peak associated with vacancy diffusion. Analytical expressions for the heat and vacancy diffusivities, speeds of sound, and sound damping coefficients are obtained. The theoretical results are compared to molecular dynamics simulations of a face-centered cubic crystal of hard spheres.
<p>Machine learning is a powerful yet underutilised tool in geomorphology, commonly used for image-based pattern recognition. Analysing new high-resolution (1–10 m) elevation datasets, we investigate its usefulness for detecting discrete geomorphological features. This study develops a machine-learning-based method for identifying ribbed moraines in digital elevation data and progresses to test its performance versus time-consuming, manual methods. Ribbed moraines share geomorphometric characteristics with other glacial landforms, hence representing a valuable test of our new methodology in terms of differentiating between similar features, and for detecting landforms with similar characteristics. Furthermore, mapping ribbed moraines may provide valuable indications of their origin, a topic of debate within glacial geomorphology. To automatically detect ribbed moraines, we extract simple morphometrics from high-resolution digital elevation model data and mask regions where ribbed moraines are unlikely to form. We then test several machine learning algorithms before examining the best performer (<span class="inline-formula"><i>K</i></span>-means clustering) for three study areas of 15 km<span class="inline-formula"><sup>2</sup></span> in Norway. Our results demonstrate a balanced accuracy of 65 %–75 % when validating versus ground-truthing. The performance depends on the availability of high-resolution elevation data in Norway that are needed to resolve the spatial scale of the target (10–100 m). We find the method effective at detecting both fields of ribbed moraines, as well as individual ribbed moraines. We propose pathways for the future implementation of this method on a large scale and for increasing the detail of information gained about detected landforms. In conclusion, we demonstrate <span class="inline-formula"><i>K</i></span>-means clustering as a promising method for detecting ribbed moraines, with great potential to reduce the time needed to produce landform maps.</p>
In the past decades, as the world has placed emphasis on green energy, solar energy has become a favorable option. Different piled foundations have been designed to strengthen the structure supporting the solar panels. These piled foundations include rectangular and circular hollow section piles, as well as H-shaped piles. With various environmental loadings, lateral soil displacement will be encountered when large solar panels are installed on the supporting structure at an inclined angle. Presently, helical pipe piles are widely used in solar farms as part of the supporting structure. In this paper, the pile–soil interaction of steel pipe piles and helical pipe piles with wind loads is analyzed using ABAQUS. The Finite Element Method (FEM) models are assessed with varying strength moduli and cohesions of clay. Further, this paper examines the pile soil system, considering different clay stiffnesses, including very soft, soft, firm, stiff, very stiff, and hard. It is found that the helical piles’ horizontal capacity increases with soil strength and Young’s modulus, but the capacity increment rate reacts differently. This study has a guiding effect on the construction of solar farms using the “tracker” solar system.
Over the past few years, equation discovery has gained popularity in different fields of science and engineering. However, existing equation discovery algorithms rely on the availability of noisy measurements of the state variables (i.e., displacement {and velocity}). This is a major bottleneck in structural dynamics, where we often only have access to acceleration measurements. To that end, this paper introduces a novel equation discovery algorithm for discovering governing equations of dynamical systems from acceleration-only measurements. The proposed algorithm employs a library-based approach for equation discovery. To enable equation discovery from acceleration-only measurements, we propose a novel Approximate Bayesian Computation (ABC) model that prioritizes parsimonious models. The efficacy of the proposed algorithm is illustrated using {four} structural dynamics examples that include both linear and nonlinear dynamical systems. The case studies presented illustrate the possible application of the proposed approach for equation discovery of dynamical systems from acceleration-only measurements.
Donald Cameron, Md Rajibul Karim, Tim Johnson
et al.
Expansive soils change volume due to changes in moisture content, which results in ground movement. Lightweight shallow-depth structures such as pavements, residential footings, and pipelines can suffer distress as they face additional stresses from the moving ground. The soil reactivity (the ability of soil to expand or contract due to change in moisture content) and the interactions at the soil-atmosphere-vegetation boundary are two of the major contributing factors to the ground movement. The current methodology used in design to account for ground movement is based on limited field and laboratory observations. Aiming at a better understanding of the interaction at the soil-atmosphere-vegetation boundary and its correlation with ground movement, this paper presents results from a field study. The research site was located in a semi-arid climate area and consisted of moderately to highly reactive clay soils. Part of the site was vegetated with mature trees, and part was occupied by grass. The order of 10 s of mm difference was observed in the magnitude of movement across the site owing to site soil variability as well as differences in vegetation. The areas with large trees showed relatively lower ground movement over the study period compared to the area occupied by grass, which was attributed to the microclimate created by the tree canopy and the already established deeper drying of the soil near the trees.
The indirect tension test is an important laboratory test for rock characterization. The presence of rock fabric, such as schistosity, complicates the assessment of test results. One hundred and forty-five indirect tension tests were conducted on mica schist specimens to investigate the effect of schistosity orientation on failure mode and tensile strength. Tensile strength results did not provide a clear relationship between schistosity orientation and tensile strength, so the failure patterns were investigated. A new naming scheme for failure modes was developed, incorporating fracture patterns observed in the specimen faces and edges. The Single Mode failure group specimens had only one failure pattern that appeared on both specimen faces, either axial failure (seventy-three specimens), schistosity failure (six specimens), or out-of-plane failure (seven specimens). The Mixed Mode failure group had thirty-two specimens that exhibited one failure pattern on one face and another on the other. The Hybrid Mode failure group had twenty-seven specimens with multiple failure patterns on both specimen faces. It was noted that Mixed Mode and Hybrid Mode specimens with components of axial failure had higher indirect tensile strengths than specimens without elements of axial failures. Statistical analyses of the tensile strength data using Levene’s Test for equal variances and two-sample <i>t</i>-tests showed no statistical difference between the Mixed Mode and Hybrid Mode failure groups. However, there was a statistical difference between the tensile strengths of the Single Mode axial failure specimens and the combined Mixed Mode and Hybrid Mode failure groups. These results clearly emphasize that indirect tensile strength should be assessed using schistosity orientation and failure mode.
Tatiana A. Mingaleva, Sergey V. Shakuro, Natalia P. Senchina
et al.
The information on geological structure as well as on the degree of contamination and geometrical parameters of a pollutant in oil-contaminated areas is necessary for risk assessment, planning of oil products recovery and territory remediation. Geophysical methods are actively used for solving such problems. The work considers the site on the Volga River bank, where soils are contaminated with petroleum products. The aim of the work is to delineate the distribution area of petroleum products. In order to achieve the goal, the set of near-surface geophysical methods (vertical electric sounding, seismic survey) and gas geochemistry were implemented. The results of a new approach to characterization of contaminated sites by RGB-data synthesis have been demonstrated as one of the ways of data interpretation. The method is based on the generalization of the available materials by optically mixing of the data of three spatially distributed characteristics presented in the form of three channels – red, green, and blue – for the purpose of localizing the lenses of gravity-mobile and immobilized oil products. According to the results of the qualitative interpretation of geophysical information, the authors have built a scheme with the proposed contour of oil products distribution in the studied territory. The proposed method can be used for the delineation of oil spills along with the sufficient information obtained by geophysical or other methods (at least three) at the stage of determining the spread of contamination for the sites. This approach can speed up the interpretation process, as such maps overlaying sets the color distribution of different petrophysical characteristics of the soils for the selected depth level, and also eases the task of determination of coordinates when correlating various anomalies, identified by different methods.
In last decades, dynamic resource programming in partial resource domains has been extensively investigated for single time slot optimizations. However, with the emerging real-time media applications in fifth-generation communications, their new quality of service requirements are often measured in temporal dimension. This requires multistage optimization for full resource domain dynamic programming. Taking experience rate as a typical temporal multistage metric, we jointly optimize time, frequency, space and power domains resource for multistage optimization. To strike a good tradeoff between system performance and computational complexity, we first transform the formulated mixed integer non-linear constraints into equivalent convex second order cone constraints, by exploiting the coupling effect among the resources. Leveraging the concept of structural sparsity, the objective of max-min experience rate is given as a weighted 1-norm term associated with the precoding matrix. Finally, a low-complexity iterative algorithm is proposed for full resource domain programming, aided by another simple conic optimization for obtaining its feasible initial result. Simulation verifies that our design significantly outperform the benchmarks while maintaining a fast convergence rate, shedding light on full domain dynamic resource programming of multistage optimizations.
The paper presents the results of studying soil lichens in areas of thermal habitats on the Kuril Islands, including the features of lichen distribution on the isles. Totally four species of epigeic lichens were found when studying the thermal fields on Kunashir and Iturup isles: Cladonia graciliformis, C. granulans, C. vulcani, and C. furcata. First three species are closest to active fumaroles and have the highest rates of occurrence frequency for these habitats.
Jianning Wang, Guangyu Zhang, Haiyang Zhuang
et al.
A design procedure for improving the seismic performance of unequal-span underground structures by installing isolation devices at the top end of columns is proposed based on the seismic failure mode of frame-type underground structures and the design concept of critical support columns. A two-dimensional finite element model (FEM) for a soil-underground structure with an unequal-span interaction system was established to shed light on the effects of a complex subway station with elastic sliding bearings (ESB) and lead rubber bearings (LRB) on seismic mitigation. It was found that the stiffness and internal force distribution of the underground structure changed remarkably with the installation of isolation devices at the top end of the columns. The constraints of the beam-column joints were significantly weakened, resulting in a decrease in the overall lateral stiffness and an increase in the structural lateral displacement. The introduction of the isolation device effectively reduces the internal force and seismic damage of the frame column; however, the tensile damage to the isolation structure, such as the roof, bottom plate, and sidewall, significantly increased compared to those of the non-isolation structure. Although the relative slip of the ESB remains within a controllable range under strong earthquake excitation as well as frame columns with stable vertical support and self-restoration functions, the LRB shows a better performance during seismic failure and better lateral displacement response of the unequal-span underground structure. The analysis results provide new ideas and references for promoting the application of seismic isolation technology in underground structures.
Geophysics. Cosmic physics, Dynamic and structural geology
Structural monitoring for complex built environments often suffers from mismatch between design, laboratory testing, and actual built parameters. Additionally, real-world structural identification problems encounter many challenges. For example, the lack of accurate baseline models, high dimensionality, and complex multivariate partial differential equations (PDEs) pose significant difficulties in training and learning conventional data-driven algorithms. This paper explores a new framework, dubbed NeuralSI, for structural identification by augmenting PDEs that govern structural dynamics with neural networks. Our approach seeks to estimate nonlinear parameters from governing equations. We consider the vibration of nonlinear beams with two unknown parameters, one that represents geometric and material variations, and another that captures energy losses in the system mainly through damping. The data for parameter estimation is obtained from a limited set of measurements, which is conducive to applications in structural health monitoring where the exact state of an existing structure is typically unknown and only a limited amount of data samples can be collected in the field. The trained model can also be extrapolated under both standard and extreme conditions using the identified structural parameters. We compare with pure data-driven Neural Networks and other classical Physics-Informed Neural Networks (PINNs). Our approach reduces both interpolation and extrapolation errors in displacement distribution by two to five orders of magnitude over the baselines. Code is available at https://github.com/human-analysis/neural-structural-identification
Kernel density estimation (KDE) stands out as a challenging task in machine learning. The problem is defined in the following way: given a kernel function $f(x,y)$ and a set of points $\{x_1, x_2, \cdots, x_n \} \subset \mathbb{R}^d$, we would like to compute $\frac{1}{n}\sum_{i=1}^{n} f(x_i,y)$ for any query point $y \in \mathbb{R}^d$. Recently, there has been a growing trend of using data structures for efficient KDE. However, the proposed KDE data structures focus on static settings. The robustness of KDE data structures over dynamic changing data distributions is not addressed. In this work, we focus on the dynamic maintenance of KDE data structures with robustness to adversarial queries. Especially, we provide a theoretical framework of KDE data structures. In our framework, the KDE data structures only require subquadratic spaces. Moreover, our data structure supports the dynamic update of the dataset in sublinear time. Furthermore, we can perform adaptive queries with the potential adversary in sublinear time.
The development of a robust understanding of the response of the Antarctic Ice Sheet to present and projected future climatic change is a matter of key global societal importance. Numerical ice sheet models that simulate future ice sheet behaviour are typically evaluated with recourse to how well they reproduce past ice sheet behaviour, which is constrained by the geological record. However, subglacial topography, a key boundary condition in ice sheet models, has evolved significantly throughout Antarctica's glacial history. Since mantle processes play a fundamental role in the generation and modification of topography over geological timescales, an understanding of the interactions between the Antarctic mantle and palaeotopography is crucial for developing more accurate simulations of past ice sheet dynamics. This chapter provides a review of the influence of the Antarctic mantle on the long-term evolution of the subglacial landscape, through processes including structural inheritance, flexural isostatic adjustment, lithospheric cooling and thermal subsidence, volcanism and dynamic topography. The uncertainties associated with reconstructing these processes through time are discussed, as are important directions for future research and the implications of the evolving subglacial topography for the response of the Antarctic Ice Sheet to climatic and oceanographic change.
Among the versatile forms of dynamical patterns of activity exhibited by the brain, oscillations are one of the most salient and extensively studied, yet are still far from being well understood. In this paper, we provide various structural characterizations of the existence of oscillatory behavior in neural networks using a classical neural mass model of mesoscale brain activity called linear-threshold dynamics. Exploiting the switched-affine nature of this dynamics, we obtain various necessary and/or sufficient conditions on the network structure and its external input for the existence of oscillations in (i) two-dimensional excitatory-inhibitory networks (E-I pairs), (ii) networks with one inhibitory but arbitrary number of excitatory nodes, (iii) purely inhibitory networks with an arbitrary number of nodes, and (iv) networks of E-I pairs. Throughout our treatment, and given the arbitrary dimensionality of the considered dynamics, we rely on the lack of stable equilibria as a system-based proxy for the existence of oscillations, and provide extensive numerical results to support its tight relationship with the more standard, signal-based definition of oscillations in computational neuroscience.
Because gas injection into geological formations is a common technology deployed for enhanced oil recovery (EOR), it is important to understand at the molecular level the relations between competitive adsorption and fluid mobility at the single-pore level. To achieve such an understanding, we report here molecular dynamics simulation results to document structural and dynamical properties of n-octane confined in slit-shaped alumina and graphite pores in the presence of CO2 or H2S. The substrates are chosen as proxy models for natural hydrophilic and hydrophobic substrates, respectively. It was found that CO2 and H2S could displace n-octane from alumina but not from graphite surfaces. Analysis of the results demonstrates that more attractive n-octane – surface and weaker CO2/H2S – surface interactions in graphite compared to alumina are responsible for this observation. Regardless of pore type, the results suggest that adding CO2 or H2S suppresses the diffusion of n-octane due to pore crowding. However, the mechanisms responsible for this observation are different, wherein preferential adsorption sites are available on the alumina surface for both CO2 and H2S, but not on graphite. To contribute to designing advanced EOR technologies, possible molecular mechanisms are proposed to interpret the results. GRAPHICAL ABSTRACT
During the Paleozoic, sedimentary basins developed within Gondwana without evolving to diverging plate boundaries. Such intracontinental basins present long subsidence histories with multiple phases of accelerated subsidence that are not always easily explained by far‐field tectonic forces, and may be driven by processes other than rifting and thermal subsidence. Here we investigate the subsidence of Paleozoic Australian intracontinental basins by comparing one‐dimensional backstripped tectonic subsidence histories from the western Australian Canning and Southern Carnarvon Basins and the central Australian Cooper Basin to forward subsidence models for pure shear lithospheric thinning. We make the hypothesis that differences between observed and model subsidence may be explained by mantle‐flow driven topography, in addition to tectonic forces. To test this hypothesis, we compute dynamic topography from the first geodynamic models of mantle flow spanning the entire Phanerozoic Eon, and we analyse the relationship between dynamic topography and anomalous basin subsidence to dynamic topography and mantle flow. Although reconstructions of mantle flow in deep geological times are uncertain, our results suggest that long‐wavelength dynamic topography could explain aspects of the complex tectonic histories intracontinental basins. In the presented reconstruction of mantle flow, topographic rebound following the sinking of a Cambrian aged slab resulted in a minor phase of dynamic uplift in the Cooper Basin in middle Permian times. Throughout Carboniferous‐Triassic times Australia was positioned above a mantle upwelling driven by a hot structure at the base of the mantle. Structural uplift in the Canning and Southern Carnarvon basins during the Triassic‐Jurassic interval was augmented by dynamic uplift produced by that large‐scale upwelling, and possibly augmented by a focused active mantle plume during the Permo‐Triassic. In Late Jurassic‐Cretaceous times, Australia drifted east away from the mantle upwelling, resulting in a period of subsidence in the Canning and Southern Carnarvon basins. During the Cretaceous the Cooper Basin moved over a downwelling produced by long‐lived subduction along the east Australian margin, resulting in a period of accelerated subsidence.