Hasil untuk "Dynamic and structural geology"

Marios Impraimakis, Feiyu Zhou, Andrew Plummer

The system identification capabilities of a novel information-theoretic method are examined here. Specifically, this work uses information-theoretic metrics and vibration-based measurements to enhance damping estimation accuracy in mechanical systems. The method refers to a key limitation in system identification, signal processing, monitoring, and alert systems. These systems integrate various components, including sensors, data acquisition devices, and alert mechanisms. They are designed to operate in an environment to calculate key parameters such as peak accelerations and duration of high acceleration values. The current operational modal identification methods, though, suffer from limitations related to obtaining poor damping estimates due to their empirical nature. This has a significant impact on alert warning systems. This occurs when their duration is misestimated; specifically, when using the vibration amplitudes as an indicator of danger alerts for monitoring systems in damage or anomaly detection scenarios. To this end, approaches based on the Shannon entropy and the Kullback-Leibler divergence concept are proposed. The primary objective is to monitor the vibration levels in near real-time and provide immediate alerts when predefined thresholds are exceeded. In considering the proposed approach, both new real-world data from the multi-axis simulation table at the University of Bath, as well as the benchmark International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring problem are considered. Importantly, the approach is shown to select the optimal model, which accurately captures the correct alert duration, providing a powerful tool for system identification and monitoring.

Soumyajit Mukherjee, Swagato Dasgupta, G. Ian Alsop

Mohammad Reza Fathi, Soraya Birami, Alireza Payvar et al.

The Viable System Model (VSM) is a foundational framework in organizational cybernetics, designed to manage complexity and ensure systemic viability in dynamic environments. Given the increasing importance of this model in addressing complex organizational challenges, the primary objective of this research is to conduct a comprehensive and systematic review of existing studies in the field of the Viable System Model. This review aims to identify and analyze the practical areas of this model, evaluate its challenges and opportunities in confronting contemporary systemic issues, and extract key insights from 21 peer-reviewed studies. By synthesizing and analyzing the findings of these studies, this paper intends to provide a clear and coherent picture of the Viable System Model's current state and future potential. The process of identifying relevant studies was conducted using the PRISMA method, which involved searching the Scopus database and performing manual searches. This study employs a bibliometric research design, utilizing a quantitative approach and combining bibliometric and network analysis to examine the landscape of VSM research. Key findings highlight VSM’s role in enhancing organizational resilience, improving decentralized decision-making, and enabling systemic adaptability. The integration of VSM with emerging technologies—such as artificial intelligence, digital twins, and big data analytics—demonstrates its potential to address contemporary organizational challenges. However, critical gaps remain, including limited empirical validation, insufficient applications in underrepresented sectors such as agriculture and education, and scalability issues for small and medium-sized enterprises (SMEs). The study emphasizes the need for longitudinal research, hybrid frameworks, and sector-specific models to enhance the theoretical and practical utility of VSM. By synthesizing recent applications and identifying research opportunities, this paper reinforces the significance of VSM as a robust approach to managing complexity and outlines pathways for future research.

Siddharth Tourani, Jayaram Reddy, Akash Kumbar et al.

Dynamic scene rendering and reconstruction play a crucial role in computer vision and augmented reality. Recent methods based on 3D Gaussian Splatting (3DGS), have enabled accurate modeling of dynamic urban scenes, but for urban scenes they require both camera and LiDAR data, ground-truth 3D segmentations and motion data in the form of tracklets or pre-defined object templates such as SMPL. In this work, we explore whether a combination of 2D object agnostic priors in the form of depth and point tracking coupled with a signed distance function (SDF) representation for dynamic objects can be used to relax some of these requirements. We present a novel approach that integrates Signed Distance Functions (SDFs) with 3D Gaussian Splatting (3DGS) to create a more robust object representation by harnessing the strengths of both methods. Our unified optimization framework enhances the geometric accuracy of 3D Gaussian splatting and improves deformation modeling within the SDF, resulting in a more adaptable and precise representation. We demonstrate that our method achieves state-of-the-art performance in rendering metrics even without LiDAR data on urban scenes. When incorporating LiDAR, our approach improved further in reconstructing and generating novel views across diverse object categories, without ground-truth 3D motion annotation. Additionally, our method enables various scene editing tasks, including scene decomposition, and scene composition.

Jungho Kim, Sang-ri Yi, Ziqi Wang

Accurate prediction of nonlinear structural responses is essential for earthquake risk assessment and management. While high-fidelity nonlinear time history analysis provides the most comprehensive and accurate representation of the responses, it becomes computationally prohibitive for complex structural system models and repeated simulations under varying ground motions. To address this challenge, we propose a composite learning framework that integrates simplified physics-based models with a Fourier neural operator to enable efficient and accurate trajectory-level seismic response prediction. In the proposed architecture, a simplified physics-based model, obtained from techniques such as linearization, modal reduction, or solver relaxation, serves as a preprocessing operator to generate structural response trajectories that capture coarse dynamic characteristics. A neural operator is then trained to correct the discrepancy between these initial approximations and the true nonlinear responses, allowing the composite model to capture hysteretic and path-dependent behaviors. Additionally, a linear regression-based postprocessing scheme is introduced to further refine predictions and quantify associated uncertainty with negligible additional computational effort. The proposed approach is validated on three representative structural systems subjected to synthetic or recorded ground motions. Results show that the proposed approach consistently improves prediction accuracy over baseline models, particularly in data-scarce regimes. These findings demonstrate the potential of physics-guided operator learning for reliable and data-efficient modeling of nonlinear structural seismic responses.

Mirac Suzgun, Mert Yuksekgonul, Federico Bianchi et al.

Despite their impressive performance on complex tasks, current language models (LMs) typically operate in a vacuum: Each input query is processed separately, without retaining insights from previous attempts. Here, we present Dynamic Cheatsheet (DC), a lightweight framework that endows a black-box LM with a persistent, evolving memory. Rather than repeatedly re-discovering or re-committing the same solutions and mistakes, DC enables models to store and reuse accumulated strategies, code snippets, and general problem-solving insights at inference time. This test-time learning enhances performance substantially across a range of tasks without needing explicit ground-truth labels or human feedback. Leveraging DC, Claude 3.5 Sonnet's accuracy more than doubled on AIME math exams once it began retaining algebraic insights across questions. Similarly, GPT-4o's success rate on Game of 24 increased from 10% to 99% after the model discovered and reused a Python-based solution. In tasks prone to arithmetic mistakes, such as balancing equations, DC enabled GPT-4o and Claude to reach near-perfect accuracy by recalling previously validated code, whereas their baselines stagnated around 50%. Beyond arithmetic challenges, DC yields notable accuracy gains on knowledge-demanding tasks. Claude achieved a 9% improvement in GPQA-Diamond and an 8% boost on MMLU-Pro problems. Crucially, DC's memory is self-curated, focusing on concise, transferable snippets rather than entire transcript. Unlike finetuning or static retrieval methods, DC adapts LMs' problem-solving skills on the fly, without modifying their underlying parameters. Overall, our findings present DC as a promising approach for augmenting LMs with persistent memory, bridging the divide between isolated inference events and the cumulative, experience-driven learning characteristic of human cognition.

Ian Dunn, Liv Toft, Tyler Katz et al.

Structure-based drug design (SBDD) focuses on designing small-molecule ligands that bind to specific protein pockets. Computational methods are integral in modern SBDD workflows and often make use of virtual screening methods via docking or pharmacophore search. Modern generative modeling approaches have focused on improving novel ligand discovery by enabling de novo design. In this work, we recognize that these tasks share a common structure and can therefore be represented as different instantiations of a consistent generative modeling framework. We propose a unified approach in OMTRA, a multi-modal flow matching model that flexibly performs many tasks relevant to SBDD, including some with no analogue in conventional workflows. Additionally, we curate a dataset of 500M 3D molecular conformers, complementing protein-ligand data and expanding the chemical diversity available for training. OMTRA obtains state of the art performance on pocket-conditioned de novo design and docking; however, the effects of large-scale pretraining and multi-task training are modest. All code, trained models, and dataset for reproducing this work are available at https://github.com/gnina/OMTRA

D. Muheki, A. A. J. Deijns, A. A. J. Deijns et al.

<p>Co-occurring extreme climate events exacerbate adverse impacts on humans, the economy, and the environment relative to extremes occurring in isolation. While changes in the frequency of individual extreme events have been researched extensively, changes in their interactions, dependence, and joint occurrence have received far less attention, particularly in the East African region. Here, we analyse the joint occurrence of pairs of the following extremes within the same location and calendar year over East Africa: river floods, droughts, heatwaves, crop failures, wildfires and tropical cyclones. We analyse their co-occurrence on a yearly timescale because some of the climate extremes we consider play out over timescales up to several months. We use bias-adjusted impact simulations under past and future climate conditions from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP). We find an increase in the area affected by pairs of these extreme events, with the strongest increases for joint heatwaves and wildfires (<span class="inline-formula">+940 <i>%</i></span> by the end of the century under RCP6.0 relative to present day), followed by river floods and heatwaves (<span class="inline-formula">+900 <i>%</i></span>) and river floods and wildfires (<span class="inline-formula">+250 <i>%</i></span>). The projected increase in joint occurrences typically outweighs historical increases even under an aggressive mitigation scenario (RCP2.6). We illustrate that the changes in the joint occurrences are often driven by increases in the probability of one of the events within the pairs, for instance heatwaves. The most affected locations in the East Africa region by these co-occurring events are areas close to the River Nile and parts of the Congo basin. Our results overall highlight that co-occurring extremes will become the norm rather than the exception in East Africa, even under low-end warming scenarios.</p>

Anthony Lomax, Matteo Bagagli, Sonja Gaviano et al.

Automated seismic arrival picking on large and real-time seismological waveform datasets is fundamental for monitoring and research. Recent, high-performance arrival pickers apply deep-neural-networks to nearly raw seismogram inputs. However, there is a long history of rule-based, automated arrival detection and picking methods that efficiently exploit variations in amplitude, frequency and polarization of seismograms. Here we use this seismological domain-knowledge to transform raw seismograms as input to a deep-learning picker. We preprocess 3-component seismograms into 3-component characteristic functions of a multi-band picker, plus modulus and inclination. We use these five time-series as input instead of raw seismograms to extend the deep-neural-network picker PhaseNet. We compare the original, data-driven PhaseNet and our domain-knowledge PhaseNet (DKPN) after identical training on datasets of different sizes and application to in- and cross-domain test datasets. We find DKPN and PhaseNet show near identical picking performance for in-domain picking, while DKPN outperforms PhaseNet for some cases of cross-domain picking, particularly with smaller training datasets; additionally, DKPN trains faster than PhaseNet. These results show that while the neural-network architecture underlying PhaseNet is remarkably robust with respect to transformations of the input data (e.g. DKPN preprocessing), use of domain-knowledge input can improve picker performance.

L. Zacherl, T. Baumann

<p>In Bavaria, the exploration of geothermal energy from the carbonates in the Upper Jurassic reservoir is a promising and growing field, but the efficiency is seriously affected by scaling, i.e., the precipitation of <span class="inline-formula">CaCO₃</span>. Predictive maintenance tools aim to reduce service intervals and unexpected downtimes based on measurements and a prediction of the state of the installations. With regard to scaling, this requires forward modeling of carbonate precipitation. However, standard models overpredict the amount of precipitates, and data required for localized process parametrization under dynamic flow conditions is scarce. For hybrid multiphase models, this data has to include local hydrogeochemistry, shear forces, and interaction forces of the precipitates with the matrix. Our new experimental approach combines the quantitative measurement of the amount of precipitates using a highly sensitive quartz crystal microbalance (QCM) measurement with the qualitative analysis of the individual crystals with Raman microspectroscopy. The setup consists of a microfluidic flow channel in which NaOH solution and Munich tap water (carbonate-rich) were injected and allowed to mix under varying flow conditions. The increase in the pH value caused nucleation and precipitation, which was monitored in real-time. The experiments showed many newly formed carbonate particles, but only some of the particles were actually deposited on the QCM crystal. The remaining particles were not able to settle at the given flow velocities and flushed out of the microfluidic channel. The stability of the signal degraded in long-term experiments. Therefore, quantitative measurements are limited to shorter times (up to 1 day in our case) with semi-quantitative data beyond that time. For those short time frames, the combination of Raman microscopy and QCM allows to quantify the process of scaling formation under very controlled dynamic conditions.</p>

Jun Zheng, Jing Wang, Fuwei Zhao et al.

Video try-on stands as a promising area for its tremendous real-world potential. Previous research on video try-on has primarily focused on transferring product clothing images to videos with simple human poses, while performing poorly with complex movements. To better preserve clothing details, those approaches are armed with an additional garment encoder, resulting in higher computational resource consumption. The primary challenges in this domain are twofold: (1) leveraging the garment encoder's capabilities in video try-on while lowering computational requirements; (2) ensuring temporal consistency in the synthesis of human body parts, especially during rapid movements. To tackle these issues, we propose a novel video try-on framework based on Diffusion Transformer(DiT), named Dynamic Try-On. To reduce computational overhead, we adopt a straightforward approach by utilizing the DiT backbone itself as the garment encoder and employing a dynamic feature fusion module to store and integrate garment features. To ensure temporal consistency of human body parts, we introduce a limb-aware dynamic attention module that enforces the DiT backbone to focus on the regions of human limbs during the denoising process. Extensive experiments demonstrate the superiority of Dynamic Try-On in generating stable and smooth try-on results, even for videos featuring complicated human postures.

Makoto Ohsaki, Kentaro Hayakawa, Jingyao Zhang

We propose a two-level structural optimization method for obtaining an approximate optimal shape of piecewise developable surface without specifying internal boundaries between surface patches. The condition for developability of a polyhedral surface onto a plane is formulated using the area of discrete Gauss map formed by unit normal vectors at the faces adjacent to each vertex. The objective function of the lower-level optimization problem is the sum of square errors for developability at all interior vertices. The contribution of large error to the objective function is underestimated by filtering with hyperbolic tangent function so that the internal boundary between the surface patches can naturally emerge as a result of optimization. Vertices are located non-periodically to generate the internal boundaries in various unspecified directions. Simulated annealing is used for the upper-level optimization problem for maximizing stiffness evaluated by the compliance under the specified vertical loads. The design variables are the heights of the specified points. It is shown in the numerical examples that the compliance values of the surfaces with a square and a rectangular plan are successfully reduced by the proposed method while keeping the developability of each surface patch. Thus, a new class of structural shape optimization problem of shell surfaces is proposed by limiting the feasible surface to piecewise developable surfaces which have desirable geometrical characteristics in view of fabrication and construction.

P. P. Sergipe, V. Louro, Y. Marangoni et al.

The Rio Grande Rise (RGR) is an extensive structural high located in the South Atlantic Ocean, target of increasing exploratory interest. During the last decades, considerable attention has been given to its genesis, dynamic, regional tectonic, and composition. Some studies indicate the presence of volcanic rocks, mainly basaltic, related to their volcanic origin and Ferromanganese Crusts, boosting the research and economic interest. This study suggests the location of volcanic rocks and FeMn crusts at the north portion of Cruzeiro do Sul Rift within the RGR, characterizing the local geology and distribution pattern. We used multibeam bathymetry, sidescan sonar, dredges, and magnetic field data to integrate and better constrain the results. The magnetic field data highlighted the location of probable basaltic rocks, agreeing with the published literature, which was afterward confirmed by dredge samples. Their magnetic anomalies displayed the predominance of reverse polarization and less frequent normal polarization anomalies. FeMn crusts need a large volume of magnetite to cause anomalies in the local magnetic field, which does not happen in the RGR. There, they have reduced thickness and are frequently eroded, as displayed by the bathymetry, sidescan sonar, and geological data. Magnetic lineaments at the Rift margin defined a zone with a series of normal faults. During the Rift formation, transcurrent movements caused an intense fracturing, providing pathways for magma intrusion. Therefore, the fault zone could be related to the primary magnetic anomalies as a function of the magma intrusion and the occurrence of the rifting process and seafloor spreading. The new data presented in this paper brings valuable data for the comprehension and exploration effort of the RGR.

V. A. Ostra, V. Yavorska, S. Kudelina

Problem Statement and Purpose. In today's context, migration is becoming one of the most important issues for Ukraine. The socio-economic disparities in each region of the country are largely influenced by large-scale forced internal migration, which has both positive and negative aspects. Timely identification of problems and development of solutions to them makes it possible to bring the situation under control. Odesa region is a leader among the southern regions of the country in terms of the number of registered internally displaced persons (IDPs). The relevance of the study of this type of migration is due to its impact on the socio-economic status of communities and the level of integration of IDPs in the region. The article presents a structural, dynamic and regional analysis of the movement of internally displaced persons in Odesa region. In the course of the study, the trends in the distribution of internal migrants and the factors that influence it were identified. The positive and negative factors of this type of migration on the socio-economic status of the region's communities are analyzed. Ways to solve these problems were proposed. Data & Methods. The study of the processes of forced internal migration of the population was based on a documentary analysis of the legislative framework and policy on migration processes in Ukraine. For the statistical analysis, data from the official websites of the relevant executive authorities of the country, the administration of Odesa region and the International Organization for Migration in Ukraine were used. To achieve this goal, the following methods were used: dialectical, empirical, modeling, and comparison. Results. The study of the movement of forced internal migration in Odesa region found that the rapid growth of new population is affecting the economic, environmental, and socio-cultural spheres of the region. The analysis of migrants' settlement in the region showed that the settlement is uneven. The leader in terms of the number of registered migrants among the seven districts of the region is Odesa district with the regional center of Odesa, with 93524 people registered as of January 2023, which is 67% of the total number of registered IDPs. This indicates that IDPs choose settlements with developed infrastructure, high economic and social standards of living of the local population, and a larger number of support programs from local governments aimed at solving problems and supporting IDPs. The overwhelming majority of migrants come from Mykolaiv and Kherson regions, due to the territorial proximity of these southern regions. The study identified the factors of influence of forced internal migration on the socio-economic situation in Odesa region. Among the main positive factors are: increased production and attraction of new investments in the communities of the region. The following negative factors were identified: unregulated control of registered migrants, burden on the economic, educational and medical funds of the region, and increased anthropogenic impact on the environment. As a result of the analysis of the identified problems and needs, ways to solve them were proposed, namely: to increase investments to improve infrastructure in each district of the region, which will allow developing programs for the uniform resettlement of the population, improving the system of social and financial programs for different groups of migrants, and increasing the level of patriotic education among the local population. All this will help internally displaced persons to adapt better in new communities, and the local population will improve their living standards and reduce discrimination in the region.