Hasil untuk "Physical geography"

Newbold

C. Mcilwain

Yian Wang, Han Yang, Minghao Guo et al.

Automatically generating interactive 3D environments is crucial for scaling up robotic data collection in simulation. While prior work has primarily focused on 3D asset placement, it often overlooks the physical relationships between objects (e.g., contact, support, balance, and containment), which are essential for creating complex and realistic manipulation scenarios such as tabletop arrangements, shelf organization, or box packing. Compared to classical 3D layout generation, producing complex physical scenes introduces additional challenges: (a) higher object density and complexity (e.g., a small shelf may hold dozens of books), (b) richer supporting relationships and compact spatial layouts, and (c) the need to accurately model both spatial placement and physical properties. To address these challenges, we propose PhyScensis, an LLM agent-based framework powered by a physics engine, to produce physically plausible scene configurations with high complexity. Specifically, our framework consists of three main components: an LLM agent iteratively proposes assets with spatial and physical predicates; a solver, equipped with a physics engine, realizes these predicates into a 3D scene; and feedback from the solver informs the agent to refine and enrich the configuration. Moreover, our framework preserves strong controllability over fine-grained textual descriptions and numerical parameters (e.g., relative positions, scene stability), enabled through probabilistic programming for stability and a complementary heuristic that jointly regulates stability and spatial relations. Experimental results show that our method outperforms prior approaches in scene complexity, visual quality, and physical accuracy, offering a unified pipeline for generating complex physical scene layouts for robotic manipulation.

Ehsan Zeraatkar, Rodion Podorozhny, Jelena Tešić

Reconstructing continuous physical fields from sparse, irregular observations is a central challenge in scientific machine learning, particularly for systems governed by partial differential equations (PDEs). Existing physics-informed methods typically enforce governing equations as soft penalty terms during optimization, often leading to gradient imbalance, instability, and degraded physical consistency under limited data. We introduce the Physics-Guided Transformer (PGT), a neural architecture that embeds physical structure directly into the self-attention mechanism. Specifically, PGT incorporates a heat-kernel-derived additive bias into attention logits, encoding diffusion dynamics and temporal causality within the representation. Query coordinates attend to these physics-conditioned context tokens, and the resulting features are decoded using a FiLM-modulated sinusoidal implicit network that adaptively controls spectral response. We evaluate PGT on the one-dimensional heat equation and two-dimensional incompressible Navier-Stokes systems. In sparse 1D reconstruction with 100 observations, PGT achieves a relative L2 error of 5.9e-3, significantly outperforming both PINNs and sinusoidal representations. In the 2D cylinder wake problem, PGT uniquely achieves both low PDE residual (8.3e-4) and competitive relative error (0.034), outperforming methods that optimize only one objective. These results demonstrate that embedding physics within attention improves stability, generalization, and physical fidelity under data-scarce conditions.

Wenhai Kou, Jiahao Zhai

As an integral component of China’s intangible cultural heritage (ICH), sports intangible cultural heritage (SICH) holds immense significance and importance in cultural inheritance, social cohesion, health promotion, values education, cultural innovation. However, the spatial distribution characteristics and influencing factors of SICH have not been extensively explored. Therefore, we conducted an in-depth analysis of the spatial patterns and influencing factors of SICH utilizing Geographic Information System (GIS) spatial analysis methods such as geographic concentration index and kernel density estimation. The results reveal that SICH exhibits a spatially clustered distribution, with the highest concentrations in Hebei, Guangdong, and Zhejiang provinces. Notably, the Beijing-Tianjin-Hebei region and the Yangtze River Delta region are identified as areas with particularly high densities of SICH. The analysis of natural and human factors indicates that altitude, climate, rivers, GDP, and population density significantly influence the distribution of SICH, while the presence of core cities does not have a notable impact. This research provides valuable insights into the spatial distribution patterns of SICH and offers a foundation for future preservation and promotion strategies.

Yan An, Qiang Shen, C.K. Shum et al.

Study region: Hengshui City, situated in the North China Plain (NCP), China, is a semi-arid area characterized by intensive agricultural activities and chronic groundwater overdraft due to scarce surface water availability. Study focus: This study aims to quantify long-term groundwater storage changes and reveal the aquifer system's response mechanisms in a typical multi-aquifer setting. We employ Sentinel-1A data for multi-year time-series interferometric synthetic aperture radar (InSAR) analysis to assess surface deformation patterns in Hengshui City from 2017 to 2024. Seasonal deformation was separated, phase lag was corrected, and confined aquifer head changes incorporated to estimate the elastic skeletal storage coefficient (ESSC) and groundwater storage change (GWSC) in deep aquifers. New hydrological insights for the region: Results show subsidence dominates in Hengshui City, with rates up to 141 mm/year (2017–2024), mainly due to falling confined aquifer heads and delayed aquitard drainage. ESSC ranges from 0.98×10−3 to 3.63×10−3, with annual deep groundwater loss around −0.57 km³ . Overall, aquifer heterogeneity contributes to spatial variability in parameters, causing uneven subsidence and water storage dynamics. This work offers new insights into groundwater monitoring in Hengshui, constraining groundwater-subsidence modeling. It also demonstrates InSAR’s strong capability in detecting subsurface deformation and multi-scale hydrological variations.

Mahdieh Hosseinjanizadeh, Hassanzadeh Reza, Mehdi Honarmand

This study aims to enhance lithological mapping by employing Support Vector Machine (SVM) classification on integrated visible-near infrared (VNIR)-shortwave infrared (SWIR), and thermal infrared (TIR) datasets from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The study focuses on a part of Kerman Province, the Dehsard area, which was selected due to its diverse lithological units, including igneous, sedimentary, and metamorphic rocks, as represented in the 1: 100,000 geological map of Dehsard. SVM classification was applied on the three datasets using training areas derived from the geological map, along with principal component analysis (PCA) and minimum noise fraction (MNF). The results indicated that the SVM classification on the 14-band ASTER data yielded more accurate results compared to the 9 and 5 band datasets. This study underscores the effectiveness of integrating multiple spectral bands in improving the precision of lithological mapping, which is essential for mineral exploration and geological studies.

Sergey Parnovsky, A. A. Varlamov

Olive oil is an integral part of Mediterranean culture, shaped by more than three thousand years of history, rich traditions, evolving technologies, and fundamental physical principles. This article explores the role of physics in the production of olive oil, highlighting how physical laws govern each stage of the process.

E. Nevo

Victor Lundström, David Simpson, Peter Yaworsky

At the end of the Pleistocene as temperatures warmed, new habitats opened up to human occupation as the Fennoscandian Ice Sheet receded. Along the west coast of modern-day Norway, human populations of coastal foragers slowly transitioned from short-term settlement patterns in the Early Mesolithic (ca. 11,500–10,000 cal BP), to more lasting ones during the Late Mesolithic (8500-6000 BP) and Early Neolithic (ca. 6000–5200 BP) as climatic conditions improved and stabilized. Here, using spatially and temporally resolved archaeological observations, paleoclimate data, and a spatiotemporal species distribution model, we test whether a) improvements in climate resulted in expansion of the available human niche space allowing for human population growth, and b) whether increasing population densities and ensuing deprecation of habitat suitability pushed people into occupying successively lower ranked habitats as predicted by the Ideal Free Distribution model. We find that a) climate gradually improved and stabilized during the Holocene, with the effect of improving general habitat suitability, which in turn led to an increase in human population size, b) that immediate proximity to sheltered coastal areas was central to settlement decisions but that c) increasing populations did not drive dispersal patterns into lower ranked habitats. The latter is likely attributable to the general improvements in habitat suitability due to the warming climate and the relative abundance of coastal habitats found in Norway.

Daniel Amaro Medina, Sean K. Carey

Study Region: Athabasca Oil Sands Region, Alberta, Canada Study Focus: Evapotranspiration (ET) is the greatest loss of water for ecosystems in the subhumid Athabasca Oil Sands Region (AOSR), and its long-term change helps determine the ability of reconstructed boreal forests to return to a functional state following mining disturbances. This article analyzes 16 years (2008–2023) of growing season eddy covariance (EC) data from a constructed aspen-spruce-dominated boreal forest and assesses ET dynamics and energy partitioning over time. New Hydrological Insights for the Region: The findings demonstrate that (1) Evapotranspiration increased by 15 % from the early (2008–2012) to middle (2013–2017) stages of forest development and declined in the late stage (2018–2023), nearing early period rates. (2) Air temperature was the major factor controlling ET in the study forest. (3) Interannual climate variability resulted in an ET increase during wetter and warmer growing seasons with abundant precipitation and elevated air temperature, and a decline in ET during dry years with reduced precipitation and soil moisture. (4) A multi-year increase in site greenness during winter, a decline in growing season albedo, and decreased ET in later years are attributed to an increase in white spruce species on the site. This study enhances the understanding of hydrometeorological processes in the boreal forest ecosystem and emphasizes the importance of long-term observations for the successful application of reclamation strategies in the region.

Zeyu Li, Hongkun Dou, Shen Fang et al.

The reconstruction of physical fields from sparse measurements is pivotal in both scientific research and engineering applications. Traditional methods are increasingly supplemented by deep learning models due to their efficacy in extracting features from data. However, except for the low accuracy on complex physical systems, these models often fail to comply with essential physical constraints, such as governing equations and boundary conditions. To overcome this limitation, we introduce a novel data-driven field reconstruction framework, termed the Physics-aligned Schrödinger Bridge (PalSB). This framework leverages a diffusion Schrödinger bridge mechanism that is specifically tailored to align with physical constraints. The PalSB approach incorporates a dual-stage training process designed to address both local reconstruction mapping and global physical principles. Additionally, a boundary-aware sampling technique is implemented to ensure adherence to physical boundary conditions. We demonstrate the effectiveness of PalSB through its application to three complex nonlinear systems: cylinder flow from Particle Image Velocimetry experiments, two-dimensional turbulence, and a reaction-diffusion system. The results reveal that PalSB not only achieves higher accuracy but also exhibits enhanced compliance with physical constraints compared to existing methods. This highlights PalSB's capability to generate high-quality representations of intricate physical interactions, showcasing its potential for advancing field reconstruction techniques.

Hakan Başağaoğlu, Chetan Sharma, Debaditya Chakraborty et al.

Study regionThe Edwards Aquifer Region is located in south-central Texas United States.Study focusThe paper focuses on the development and implementation of a data-inspired heuristic drought identification scheme to (i) quantify the intensity, duration, and frequency of precipitation deficit- and high temperature-driven meteorological droughts (PMet- and TMet-droughts), and (ii) link their propagation to groundwater droughts (GW-droughts) using baseline hydroclimatic measures and prevailing drought conditions derived from historical climate data and regional mitigation strategies.New hydrological insights for the regionBased on the intensity, duration, and timing of PMet- and TMet-droughts in the semi-arid karstic region, we identified three distinct GW-droughts, including persistence-driven, preconditions-driven, and intensity-driven droughts. The analysis revealed that successive heavy precipitation events are needed to end GW-droughts. The scheme also identified TMet-droughts with the longest dry spells, TMet- and PMet-droughts with the highest intensity, and GW-drought with the second-highest intensity on record all occurred over the past 15 years. These findings provide evidence for a warming climate, intensified meteorological droughts, and increasing stress on the aquifer. Among the artificial intelligence models used, Extremely Randomized Trees (ERT) regressor predicted time series of intensity & duration of GW-droughts from hydroclimatic features with high accuracy. The ERT classifier revealed that the duration of PMet droughts and the intensity of TMet droughts are the topmost decisive features in predicting GW-drought intensity in the region.

Shukai Du, Samuel N. Stechmann

Abstract With nested grids or related approaches, it is known that numerical artifacts can be generated at the interface of mesh refinement. Most of the existing methods of minimizing these artifacts are either problem‐dependent or numerical methods‐dependent. In this paper, we propose a universal predictor‐corrector approach to minimize these artifacts. By its construction, the approach can be applied to a wide class of models and numerical methods without modifying the existing methods but instead incorporating an additional step. The idea is to use an additional grid setup with a refinement interface at a different location, and then to correct the predicted state near the refinement interface by using information from the other grid setup. We give some analysis for our method in the setting of a one‐dimensional advection equation, showing that the key to the success of the method depends on an optimized way of choosing the weight functions, which determine the strength of the corrector at a certain location. Furthermore, the method is also tested in more general settings by numerical experiments, including shallow water equations, multi‐dimensional problems, and a variety of underlying numerical methods including finite difference/finite volume and spectral element. Numerical tests suggest the effectiveness of the method on reducing numerical artifacts due to mesh refinement.

Andrew Melnik, Robin Schiewer, Moritz Lange et al.

Physical reasoning is a crucial aspect in the development of general AI systems, given that human learning starts with interacting with the physical world before progressing to more complex concepts. Although researchers have studied and assessed the physical reasoning of AI approaches through various specific benchmarks, there is no comprehensive approach to evaluating and measuring progress. Therefore, we aim to offer an overview of existing benchmarks and their solution approaches and propose a unified perspective for measuring the physical reasoning capacity of AI systems. We select benchmarks that are designed to test algorithmic performance in physical reasoning tasks. While each of the selected benchmarks poses a unique challenge, their ensemble provides a comprehensive proving ground for an AI generalist agent with a measurable skill level for various physical reasoning concepts. This gives an advantage to such an ensemble of benchmarks over other holistic benchmarks that aim to simulate the real world by intertwining its complexity and many concepts. We group the presented set of physical reasoning benchmarks into subcategories so that more narrow generalist AI agents can be tested first on these groups.

Mohit Kumar, Hritu Raj, Nisha Chaurasia et al.

A cyber-physical system is considered to be a collection of strongly coupled communication systems and devices that poses numerous security trials in various industrial applications including healthcare. The security and privacy of patient data is still a big concern because healthcare data is sensitive and valuable, and it is most targeted over the internet. Moreover, from the industrial perspective, the cyber-physical system plays a crucial role in the exchange of data remotely using sensor nodes in distributed environments. In the healthcare industry, Blockchain technology offers a promising solution to resolve most securities-related issues due to its decentralized, immutability, and transparency properties. In this paper, a blockchain-inspired secure and reliable data exchange architecture is proposed in the cyber-physical healthcare industry 4.0. The proposed system uses the BigchainDB, Tendermint, Inter-Planetary-File-System (IPFS), MongoDB, and AES encryption algorithms to improve Healthcare 4.0. Furthermore, blockchain-enabled secure healthcare architecture for accessing and managing the records between Doctors and Patients is introduced. The development of a blockchain-based Electronic Healthcare Record (EHR) exchange system is purely patient-centric, which means the entire control of data is in the owner's hand which is backed by blockchain for security and privacy. Our experimental results reveal that the proposed architecture is robust to handle more security attacks and can recover the data if 2/3 of nodes are failed. The proposed model is patient-centric, and control of data is in the patient's hand to enhance security and privacy, even system administrators can't access data without user permission.

David Noever, Samuel Hyams

Physics-based simulations typically operate with a combination of complex differentiable equations and many scientific and geometric inputs. Our work involves gathering data from those simulations and seeing how well tree-based machine learning methods can emulate desired outputs without "knowing" the complex backing involved in the simulations. The selected physics-based simulations included Navier-Stokes, stress analysis, and electromagnetic field lines to benchmark performance as numerical and statistical algorithms. We specifically focus on predicting specific spatial-temporal data between two simulation outputs and increasing spatial resolution to generalize the physics predictions to finer test grids without the computational costs of repeating the numerical calculation.

B. Minten, C. Barrett

Q. Zhang, E. Berntell, J. Axelsson et al.

<p>As global warming is proceeding due to rising greenhouse gas concentrations, the Earth system moves towards climate states that challenge adaptation. Past Earth system states are offering possible modelling systems for the global warming of the coming decades. These include the climate of the mid-Pliocene (<span class="inline-formula">∼</span> 3 Ma), the last interglacial (<span class="inline-formula">∼</span> 129–116 ka) and the mid-Holocene (<span class="inline-formula">∼</span> 6 ka). The simulations for these past warm periods are the key experiments in the Paleoclimate Model Intercomparison Project (PMIP) phase 4, contributing to phase 6 of the Coupled Model Intercomparison Project (CMIP6). Paleoclimate modelling has long been regarded as a robust out-of-sample test bed of the climate models used to project future climate changes. Here, we document the model setup for PMIP4 experiments with EC-Earth3-LR and present the large-scale features from the simulations for the mid-Holocene, the last interglacial and the mid-Pliocene. Using the pre-industrial climate as a reference state, we show global temperature changes, large-scale Hadley circulation and Walker circulation, polar warming, global monsoons and the climate variability modes – El Niño–Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO) and the Atlantic Multidecadal Oscillation (AMO). EC-Earth3-LR simulates reasonable climate responses during past warm periods, as shown in the other PMIP4-CMIP6 model ensemble. The systematic comparison of these climate changes in past three warm periods in an individual model demonstrates the model's ability to capture the climate response under different climate forcings, providing potential implications for confidence in future projections with the EC-Earth model.</p>

Dragutin Mihailovic, Darko Kapor, Sinisa Crvenkovic et al.

This book represents a continuation, an elaboration, and possibly a clear explanation of the ideas which were expounded in the previous book Time and Methods in Environmental Interfaces Modeling (henceforth abbreviated as TM, Mihailovic et al 2016). In that book as well as in whole of our published scientific work we were either implicitly or explicitly driven by a need to understand how the space between the human mind and observed physical reality is bridged. Here we use synonymously the terms physical reality and reality since the reality is all of physical existence, and concepts related to it as opposed to those products of our mind which remain on the level of mind. Relying on that book we add our new experiences in research in which physics plays a dominant role. To these experiences we attached some epistemological features as well as a view of physics through the optics of Godel Incompleteness Theorems (Godel 1931). In the Prolegomena (Chapter 1) we consider some aspects of generality of physics (1.1 Generality of physics)