Hasil untuk "Physical geography"

J. Dewey

Yu Shi

We explain the achievements that were awarded 2022 Nobel Prize in Physics, as well as the preceding and the later developments. The main notions and historic cornerstones of Bell inequalities, the related researches on quantum entanglement are reviewed, and the key physical ideas are emphasized. Among the early work, C. S. Wu's contributions using polarization-entangled photons from electron-positron annihilation are introduced.

Eoin Reddin, Jennifer Hanafin, Mingming Tong et al.

Water table depth is the primary consideration during peatland rewetting, as a post-industrial peatland transitions from a degraded system with bare peat surfaces to a natural one. For rewetting to be successful, water table depth should be maintained in the upper 0.2 m of the soil to promote carbon sequestration while minimising net greenhouse gas emissions. There is evidence that satellite remote sensing techniques may be effective tools at monitoring water table depth. However, these techniques have been seldom used on degraded bare peat bogs, despite their excellent potential as monitoring tools during the restoration process. The aims of this paper are to (1) systematically test the relationship between radar backscatter and water table depth (2) compare decision tree regression algorithms to evaluate the potential of multi-sensor remote sensing in peatland management, and (3) make novel estimations of site-wide water table depth using a multi-sensor approach. This paper applies multi-sensor machine learning techniques to two post-industrial harvesting degraded peatlands, which are currently undergoing rewetting. Combined, these peatlands have nearly three years (2021–2023) of water table measurements, from over 50 piezometers. These data were used to train machine learning models, resulting in R2 values ranging from 0.72 to 0.78, and RMSE values of 0.14 m and 0.12 m. Significant variation in water level throughout the year was observed, suggesting that the ability for a peatland to successfully sequester carbon may be temporally variable. With this study, we provide a timely assessment of restoration efforts at anthropologically degraded bare peat peatlands. This work proves the utility of remote sensing techniques in tracking restoration progress, and may inform future strategies in peatland restoration, rewetting, and monitoring.

Zhihua Hu, Wanjie Lu, Kao Zhang et al.

Estimating room layout from panoramas is a new trend in the holistic reconstruction of the 3D environment. However, a single panorama is easily occluded by walls and furniture, making it hard to reconstruct the whole indoor room accurately and completely. Besides, deep learning room layout estimating methods often perform poorly in unseen scenes. To address this need, this paper proposes an accurate room layout estimation method from multi-view panoramas with multi-label graph cut. The proposed method takes full advantage of each panorama by utilizing multi-label graph cut. First, room layouts of each panorama are estimated with pre-trained deep-learning models and projected to the ground as the labels; then, a geometry-aware ray-casting method is utilized to obtain the initial floorplan; next, the initial floorplan is regularized by multi-label graph cut with the estimated labels from each panorama; in the end, the final layouts of each panorama is obtained by transforming the regularized floorplans and estimated ceiling heights into layouts with panorama geometry. Experiments in the recently released multi-view panoramas dataset show that the proposed method can regularize the initial floorplan to a floorplan with accurate geometry. Furthermore, the accuracy of the layouts surpassed the layout estimation accuracy of the single panorama deep learning models (HorizonNet and LGTNet) and the state-of-the-art self-training layout estimation models with multi-view panoramas by a large margin.

Hongchao Zheng, Qi Liang, Xinli Hu et al.

Abstract Debris flows can erode mountainsides, cover alluvial fans, and bury people and property by rapid deposition. The deposition characteristics of debris flows are strongly affected by their dynamics and composition, which depend on upstream sediment erosion, but how is still under scientific debate. Here, we conduct a series of experiments to analyze the effects of debris flow grain‐size gradation and eroded bed sediment on deposition characteristics. Debris flows deposit on a gentle runout zone and form coarse‐grained lateral levees and front lobes and a finer‐grained channelized interior due to grain segregation. We show that affected by a high basal pore‐fluid pressure, released mud‐sand‐gravel flows present much flatter deposits than sand‐gravel flows. Runout distance, width and inundated area increase with higher bed water content due to the growths of flow volume and momentum. Inundated area correlates to deposition volume with a power relation for all experiments. Savage number shows the greatest positive correlation with runout and inundated area among all factors, suggesting that potential energy of debris flow is more strongly consumed by grain collision stress than by basal friction stress. Debris flows can deposit as a single nose or multiple fingers depending on the relative magnitude between the friction force at the flow front balanced by downslope gravity and the thrust force of the following channelized flow with a higher speed. Our results facilitate the mapping of debris‐flow impact zones and provide a mechanistic model for predicting deposit shape in debris flows and other geophysical flows like pyroclastic flows.

Mirjam P Bak, Ilaria Micella, Edward R Jones et al.

Future climate-driven hydrological changes may strongly affect river exports of multiple pollutants to coastal waters. In large-scale water quality (WQ) models the effects are, however, associated with uncertainties that may differ in space and time but are hardly studied worldwide and for multiple pollutants simultaneously. Moreover, explicit ways to assess climate-driven uncertainties in large-scale multi-pollutant assessments are currently limited. Here, we aim to build trust in future river exports of nutrients (i.e. nitrogen and phosphorus), plastics (i.e. micro and macroplastics), and chemicals (i.e. diclofenac and triclosan) under climate-driven hydrological changes on the sub-basin scale worldwide. We used a soft-coupled global hydrological (VIC) and WQ (MARINA-Multi) model system, driven by five Global Climate Models (GCMs), to quantify river exports of selected pollutants to seas for 2010 and 2050 under an economy-driven and high global warming scenario. Subsequently, we developed and applied a new approach to build trust in projected future trends in coastal water pollution for the selected pollutants. Results reveal that in arid regions, such as the Middle East, East Asia, and Northern Africa, climate-driven uncertainties play a key role in future river exports of pollutants. For African sub-basins, high increases in river exports of pollutants are projected by 2050 under climate-driven hydrological uncertainty. Nevertheless, over 80% of the global sub-basin areas agree on the direction of change in future river exports of individual pollutants for at least three GCMs. Multi-pollutant agreements differ among seas: 53% of the area agrees on increasing river exports of six pollutants into the Indian Ocean by 2050, whereas 17% agrees on decreasing trends for the Mediterranean Sea. Our study indicated that even under climate-driven hydrological uncertainties, large-scale WQ models remain useful tools for future WQ assessments. Yet, awareness and transparency of modelling uncertainties are essential when utilising model outputs for well-informed actions.

Masahito Mochizuki

A new type of multiferroicity was experimentally discovered in 2003 in a perovskite manganite TbMnO$_3$ where its ferroelectricity is induced by cycloidally ordered Mn spins. Susequently, such spin-cycloid multiferroic phase was also discovered in $R$MnO$_3$ with other rare-earth ions $R$=Dy, Eu$_{1-x}$Y$_x$, Tb$_{1-x}$Gd$_x$, etc. In this class of materials, the magnetism and ferroelectricity are inseparably coupled, and resulting strong magnetoelectric coupling enables us to control/manipulate the electricity (magnetism) by magnetic (electric) fields. Moreover, many interesting magnetoelectric phenomena due to their cross correlation have been discovered. In this article, we discuss a microscopic theoretical model for $R$MnO$_3$ constructed by taking into account their precise electronic and lattice structures and overview the theoretical works based on this model which elucidated rich magnetoelectric phenomena of $R$MnO$_3$. The perovskite manganites are not only the first-discovered spin-spiral multiferroic materials but also a typical class of materials that exhibits most of the magnetoelectric phenomena manifested in many other multiferroics. Therefore, the comprehensive understanding of $R$MnO$_3$ directly leads to the clarification of universal physics of magnetoelectric phenomena in multiferroic materials.

Almo Senja Kulinan, Younghyun Cho, Minsoo Park et al.

Satellite data are essential during wildfires for understanding its adverse effects and improving the effectiveness of rapid disaster management. However, existing techniques used for damage assessments are inaccurate and lack automation. In this study, we propose an integrated machine learning approach with auto-generated training samples for a rapid wildfire disaster response framework using Sentinel-2 imagery at 10 m resolution from Google Earth Engine (GEE). First, training samples of burned areas were obtained by utilizing textural data based on features that had changed because of the wildfire, and samples of unburned areas were obtained using the normalized difference vegetation index (NDVI). The images were categorized as burned and unburned images using the object-based image analysis (OBIA) classification method. Finally, using the classified maps, burn severity maps and estimated pixel counts for each severity class were generated and compared. The proposed method was implemented to put out a wildfire that broke out in Uljin, Gyeongsangbuk-do, South Korea in March 2022 and the transferability of the model was evaluated in Gangneung, Gangwon-do, South Korea. The study findings indicate that the random forest (RF) classifier acquired the greatest overall accuracy (OA) of 97.6 % in Uljin; additionally, the model transferability performed well in Gangneung with an OA of 93.8 %. The RF also generated the fewest pixels of the unchanged class when the burn severity map was evaluated. Overall, our study proposes a quick and automated approach for estimating wildfire damage that could be used for immediate mitigation actions.

Minji Kim, Tianshu Wen, Kookjin Lee et al.

This study presents the conditional neural fields for reduced-order modeling (CNF-ROM) framework to approximate solutions of parametrized partial differential equations (PDEs). The approach combines a parametric neural ODE (PNODE) for modeling latent dynamics over time with a decoder that reconstructs PDE solutions from the corresponding latent states. We introduce a physics-informed learning objective for CNF-ROM, which includes two key components. First, the framework uses coordinate-based neural networks to calculate and minimize PDE residuals by computing spatial derivatives via automatic differentiation and applying the chain rule for time derivatives. Second, exact initial and boundary conditions (IC/BC) are imposed using approximate distance functions (ADFs) [Sukumar and Srivastava, CMAME, 2022]. However, ADFs introduce a trade-off as their second- or higher-order derivatives become unstable at the joining points of boundaries. To address this, we introduce an auxiliary network inspired by [Gladstone et al., NeurIPS ML4PS workshop, 2022]. Our method is validated through parameter extrapolation and interpolation, temporal extrapolation, and comparisons with analytical solutions.

Genilson Santana Cornélio, Marcio Douglas Amaral

O objetivo central do trabalho é analisar as principais transformações espaciais ocorridas na relação da cidade com o rio, em Vitória do Xingu, no estado do Pará, em face da instalação da Usina Hidrelétrica de Belo Monte e sua inserção na fronteira energética imposta à Amazônia. Em termos metodológicos, foi feita a revisão teórico-conceitual e o trabalho de campo (com registros fotográficos, produção de mapas e aplicação de formulários). Argumenta-se que através da construção da hidrelétrica houve mudanças significativas na dinâmica urbana, reveladas na requalificação do porto da cidade (sua orla fluvial), no adensamento e modernização da área principal de comércio e serviços e no surgimento de novos espaços de assentamentos no interior da cidade, principalmente, no eixo rodoviário.

Daigo Ishikita, Yuya Haraguchi, Hiroko Aruga Katori

We have successfully synthesized four Mo$_3$O$_8$-type cluster Mott insulators (CMI) by intercalating lithium into nonmagnetic precursors to regulate the Mo$_3$ cluster valence. The resulting materials are Li$_{1+x}$$R$Mo$_3$O$_8$ ($R$ = Sc, Y, Lu) and Li$_x$Zn$_2$Mo$_3$O$_8$. Our magnetic susceptibility measurements revealed that these materials display characteristics akin to a valence bond glass state and suggest the presence of short-range ordering when the Mo$_3$ cluster valence approximates its ideal value. These findings challenge the prevailing belief that the plaquette charge ordering state is an inherent feature of Mo$_3$O$_8$-type CMI. Instead, they underscore the importance of Mo$_3$ cluster valence in determining the physical properties of these systems. These insights furnish a fresh understanding of the Mo$_3$O$_8$-type CMI and open new research opportunities in highly frustrated magnetism.

Meagan Sundstrom, Logan Kageorge

Students' beliefs about the extent to which meaningful others, including their peers, recognize them as a strong science student are correlated with their persistence in science courses and careers. Yet, prior work has found a gender bias in peer recognition, in which student nominations of strong peers disproportionately favor men over women, in some instructional contexts. Researchers have hypothesized that such a gender bias diminishes over time, as determined by students' academic year: studies have found a gender bias in science courses aimed at first-year students, but not in science courses aimed at beyond first-year students. This hypothesis that patterns of peer recognition change over time, however, has yet to be tested with longitudinal data--previous studies only examine snapshots of different students in different science courses. In this study, we isolate the effect of time on peer recognition by analyzing student nominations of strong peers across a two-semester introductory physics course sequence, containing the same set of students and the same instructor in both semesters, at a mostly-women institution. Using a combination of social network analysis and qualitative methods, we find that while many students receive similar levels of peer recognition over time, the four most highly nominated students--the recognition celebrities--exhibit some change between semesters even in this highly controlled setting. Furthermore, we observe that these changes in the celebrities track closely with changes in student outspokenness and that being outspoken is likely more important for gaining recognition than earning a high grade in the class. These findings lend support to prior work's hypothesis that peer recognition changes over time, but also challenge the generalizability of previous results (i.e., that patterns of recognition are related to students' academic year).

L. Campbell, E. Svendsen, Michelle L. Johnson et al.

ABSTRACT Stewardship consists of acts of claims-making on space and caretaking of place that activate urban environments to function as social infrastructure. While stewardship practices are enacted by actors across the governance network, there is a need to better understand the role of civil society. Civic stewardship groups care and advocate for green, grey, and blue spaces, and can strengthen social trust and foster civic engagement. We conducted semi-structured interviews (n = 26) with a sample of New York City civic stewardship groups from a previous survey dataset (n = 754); the sample was stratified by network position and geographic scale. This paper analyzes how these groups operate in physical geographies and through relational networks. We describe the practices by which stewards activate and transform urban environments to create more sustainable cities, finding that activation of social infrastructure depends upon the degree of group connectivity and scale at which groups work.

I. Altman, M. M. Chemers

L. Richardson

ABSTRACT The platform is a flexible spatial arrangement that does not have a fixed territory but rather draws on other territorialized networks to actualize in urban form. The capacity for the platform to act occurs through its ability to articulate together more or less territorialized urban elements. It implies a reorganization of urban operations (such as transport, housing, and so on) not through new physical infrastructures, but instead through novel technologies of coordination of those already existing. At present, discussion of platforms in cities is dominated by the platform as company, which generates private value from the coordination of differently networked actors. However, appreciating the urban geography of the platform as a flexible spatial arrangement indicates that platforms can hold much promise for the organization of cities but requires a more equitable distribution of the value generated by coordination of urban actors.

Elvira TURYSPEKOVA, Nurgul RAMAZANOVA, Еmin АTASOY

National parks belong to the most significant category of specially protected natural areas in the Republic of Kazakhstan. One of the important tasks of national parks is the development of ecological tourism and education, considering the nature and cultural characteristics of the territory. Katon-Karagai State National Nature Park is the largest national park in Kazakhstan in terms of area with a rapid pace of development in the field of tourism, since the geosystems of the state nature park have a diverse landscape and many attractions. The purpose of this work is to identify the most attractive areas in terms of recreation, as well as provide recommendations and proposals for the development of ecotourism in the East Kazakhstan region using the example of the Katon-Karagay State National Nature Park. Research methods - data collection and analysis, descriptive, cartographic. The results of the study can be used to develop recommendations for rationally organizing and planning in the area of recreational and tourism industry at the regional level. Conclusions are drawn about the prospects for the development of ecotourism in the territory of the Katon-Karagai State Natural National Park.

Mariana Betzabeth Pelayo

En México, la construcción de megaproyectos hidroeléctricos representa profundas consecuencias socioambientales; la degradación ecológica, la reorganización territorial y productiva, así como diversos conflictos ecoterritoriales. Dichas condiciones estimulan la conformación de procesos micropolíticos por parte de actores locales como enunciaciones de resistencia y re-existencia. Como muestra de esas acciones de resistencia comunitaria, este trabajo tiene como objetivo explorar la respuesta local de los habitantes de Las Blancas para proteger y mitigar los impactos en sus espacios de vida y sus fuentes materiales y simbólicas de existencia ante el establecimiento del proyecto hidroeléctrico Aguamilpa en el estado de Nayarit (México). A partir de una investigación etnográfica, con el apoyo de la observación participante, entrevistas abiertas, grupos focales y el análisis de variables cualitativas, se concluye que las comunidades configuran nuevas formas de refuncionalidad productiva, territorialidades múltiples y nuevas formas de existencia y resignificación alrededor del agua para la producción y reproducción comunitaria.

Marcelo Saúl de la Fuente

Las dos instituciones más relevantes dedicadas a la promoción de las ciencias naturales en el sur de la provincia de Mendoza son el Museo de Historia Natural de San Rafael (MHNSR) (Departamento de San Rafael) y el Museo Regional de Malargüe “Jorge Luna” (MRM) (Departamento de Malargüe). La primera institución fue fundada en el año 1955 mientras que la segunda se erigió en el año 1973 y ambas se incorporaron a la órbita municipal en los primeros años de la década del ‘70 del siglo pasado. Estos museos han tenido un diferente desarrollo y proyección en lo que respecta a su actividad científica. Esto también se reflejó en el desarrollo de la paleoherpotología en el MHNSR con la incorporación de investigadores y becarios del CONICET acontecida a partir del año 2002. En el año 2006 se originó el Centro Regional de Investigación y Desarrollo Cultural, institución que desarrolló actividades paleontológicas en Malargüe principalmente entre los años 2006 y 2015. Con la reciente creación del Instituto de Evolución, Ecología Histórica y Ambiente, unidad ejecutora de doble dependencia CONICET y Universidad Tecnológica Nacional, los paleontólogos continuaron su desempeño en esta unidad sin dejar el MHNSR, donde se alojan las colecciones paleontológicas. En el MRM, las colecciones se encuentran tanto en el antiguo molino del casco de la estancia “La Orteguina”, edificación colindante, y en la sala de exposición de paleontología de dicho museo. Estas instituciones resguardan pequeñas colecciones de reptiles fósiles.

Thai Duong, Nikolay Atanasov

In real-world robotics applications, accurate models of robot dynamics are critical for safe and stable control in rapidly changing operational conditions. This motivates the use of machine learning techniques to approximate robot dynamics and their disturbances over a training set of state-control trajectories. This paper demonstrates that inductive biases arising from physics laws can be used to improve the data efficiency and accuracy of the approximated dynamics model. For example, the dynamics of many robots, including ground, aerial, and underwater vehicles, are described using their $SE(3)$ pose and satisfy conservation of energy principles. We design a physically plausible model of the robot dynamics by imposing the structure of Hamilton's equations of motion in the design of a neural ordinary differential equation (ODE) network. The Hamiltonian structure guarantees satisfaction of $SE(3)$ kinematic constraints and energy conservation by construction. It also allows us to derive an energy-based adaptive controller that achieves trajectory tracking while compensating for disturbances. Our learning-based adaptive controller is verified on an under-actuated quadrotor robot.

R. John

Sand is the world's most used construction material forming the physical backbone of the built environment, while its extraction is causing severe socioecological damages and political–economic frictions. This paper answers the need for more scientific attention, by tracing sand and the geographies of its multiple entanglements from the global economy to the local socioecological effects of its exploitation. First, the article reviews existing literature, providing an introduction into sand's political relevance, economic use, and socioenvironmental effects of its extraction. Second, it proposes a sociomaterial geography of sand centred around resource geographies, calling for a stronger engagement with the material foundations of urbanisation and its spatiotemporal effects. Overall, the article calls for more sociomaterial analyses of sand in order to challenge its normalisation as a universal, readily available, cheap and conflict ‐ free construction material. and fast growing economies the of understanding the past and logics of The for construction sand and socioecological peaked in