Hasil untuk "Physical geography"

G. McCormack, A. Shiell

BackgroundEmpirical evidence suggests that an association between the built environment and physical activity exists. This evidence is mostly derived from cross-sectional studies that do not account for other causal explanations such as neighborhood self-selection. Experimental and quasi-experimental designs can be used to isolate the effect of the built environment on physical activity, but in their absence, statistical techniques that adjust for neighborhood self-selection can be used with cross-sectional data. Previous reviews examining the built environment-physical activity relationship have not differentiated among findings based on study design. To deal with self-selection, we synthesized evidence regarding the relationship between objective measures of the built environment and physical activity by including in our review: 1) cross-sectional studies that adjust for neighborhood self-selection and 2) quasi-experiments.MethodIn September 2010, we searched for English-language studies on built environments and physical activity from all available years in health, leisure, transportation, social sciences, and geographical databases. Twenty cross-sectional and 13 quasi-experimental studies published between 1996 and 2010 were included in the review.ResultsMost associations between the built environment and physical activity were in the expected direction or null. Land use mix, connectivity and population density and overall neighborhood design were however, important determinants of physical activity. The built environment was more likely to be associated with transportation walking compared with other types of physical activity including recreational walking. Three studies found an attenuation in associations between built environment characteristics and physical activity after accounting for neighborhood self-selection.ConclusionMore quasi-experiments that examine a broader range of environmental attributes in relation to context-specific physical activity and that measure changes in the built environment, neighborhood preferences and their effect on physical activity are needed.

Ariane L. Bedimo-Rung, A. Mowen, D. Cohen

J. Briere, Diana M. Elliott

B. Chopard

B. Giles-Corti, R. Donovan

M. Stoltenborgh, M. Bakermans‐Kranenburg, Marinus H. van IJzendoorn et al.

Gordon Burtch, A. Ghose, S. Wattal

Adiba Amira Siddiqa, Sayed Shafaat Mahmud, Rafael Martinez-Galarza

We introduce a Variational Autoencoder (VAE)--Normalizing Flow (NF) framework for rapid probabilistic inference of galaxy properties and emission line fluxes at $z \leq 0.3$ from SDSS \textit{gri} imaging and photometry. Our model probabilistically infers stellar mass, star formation rate (SFR), redshift, gas-phase metallicity, and central black hole mass for a given galaxy. The model accruacy matches current non-spectroscopic methods for stellar mass and redshift, surpasses them for SFR and metallicity, and introduces the first probabilistic central black hole mass estimates from imaging + photometry. It also delivers probabilistic estimates of H$α$, H$β$, [N~\textsc{ii}], and [O~\textsc{iii}] emission line fluxes directly from imaging, enabling SFR, metallicity, dust, and AGN/shock diagnostics without spectroscopy. This approach opens new pathways for scalable, physics-informed inference in upcoming surveys such as Roman and Rubin LSST.

Zhao Liu, Wei Liu, Huajie Zhu et al.

The next Point-of-Interest (POI) recommendation task aims to predict users' next destinations based on their historical movement data and plays a key role in location-based services and personalized applications. Accurate next POI recommendation depends on effectively modeling geographic information and POI transition relations, which are crucial for capturing spatial dependencies and user movement patterns. While Large Language Models (LLMs) exhibit strong capabilities in semantic understanding and contextual reasoning, applying them to spatial tasks like next POI recommendation remains challenging. First, the infrequent nature of specific GPS coordinates makes it difficult for LLMs to model precise spatial contexts. Second, the lack of knowledge about POI transitions limits their ability to capture potential POI-POI relationships. To address these issues, we propose GA-LLM (Geography-Aware Large Language Model), a novel framework that enhances LLMs with two specialized components. The Geographic Coordinate Injection Module (GCIM) transforms GPS coordinates into spatial representations using hierarchical and Fourier-based positional encoding, enabling the model to understand geographic features from multiple perspectives. The POI Alignment Module (PAM) incorporates POI transition relations into the LLM's semantic space, allowing it to infer global POI relationships and generalize to unseen POIs. Experiments on three real-world datasets demonstrate the state-of-the-art performance of GA-LLM.

Ester Gruppelli Kurz, William Daldegan

A pesquisa investiga como o BRICS acomoda os interesses chineses na economia-mundo capitalista. O objetivo é compreender de que forma o grupo pode contribuir para um possível próximo Ciclo Sistêmico de Acumulação liderado pela China. São examinadas as Declarações de Cúpula do BRICS, os Planos Quinquenais e os Relatórios do Congresso do Partido Comunista da China desde 2011. A hipótese é que, por meio do BRICS, a China constrói consenso internacional, elemento necessário à hegemonia. O grupo reforça a política externa chinesa ao projetar a imagem de um país em desenvolvimento preocupado com a periferia e disposto a assumir papel ativo na governança global. Nesse processo, a China oferta bens públicos internacionais, como estabilidade econômica, segurança, proteção ambiental e infraestrutura.

Favour Nerrise, Andrew Sosa Sosanya, Patrick Neary

Magnetic navigation (MagNav) is a rising alternative to the Global Positioning System (GPS) and has proven useful for aircraft navigation. Traditional aircraft navigation systems, while effective, face limitations in precision and reliability in certain environments and against attacks. Airborne MagNav leverages the Earth's magnetic field to provide accurate positional information. However, external magnetic fields induced by aircraft electronics and Earth's large-scale magnetic fields disrupt the weaker signal of interest. We introduce a physics-informed approach using Tolles-Lawson coefficients for compensation and Liquid Time-Constant Networks (LTCs) to remove complex, noisy signals derived from the aircraft's magnetic sources. Using real flight data with magnetometer measurements and aircraft measurements, we observe up to a 64% reduction in aeromagnetic compensation error (RMSE nT), outperforming conventional models. This significant improvement underscores the potential of a physics-informed, machine learning approach for extracting clean, reliable, and accurate magnetic signals for MagNav positional estimation.

Yulei Mu, Jue Huang, Mingxin Song et al.

Study region: Laptev Sea Study focus: The absorption slopes of colored dissolved organic matter (CDOM) in the spectral range of 275–295 nm (S275–295) is a reliable parameter of the source and transformation of CDOM and dissolved organic carbon (DOC) in the estuarine environment. Studying S275–295 in coastal areas can provide important information about CDOM and DOC. This study proposed a new multilayer backpropagation neural network (MBPNN) based on the relationship between S275–295 and aCDOM(443) (absorption of CDOM at 443 nm) as a customization function to invert S275–295. The model accurately estimated S275–295 with a MAPE and RMSE of 6.12 % and 0.0012 nm−1, respectively, and revealed the spatiotemporal distribution of S275–295 in the Laptev Sea between 2002 and 2022 (July to September). The effects of influencing factors on temporal and spatial variations of S275–295 were analyzed. New hydrological insights for the region: The MBPNN model with a customization function has good performance to invert S275–295 in Laptev Sea. The S275–295 in the Laptev Sea ranged from 0.014 to 0.022 nm−1. S275–295 increased gradually from coastal waters to the open sea. Inter-annual S275–295 fluctuated, but no significant trend was observed during the study period (2002–2022). S275–295 was negatively correlated with river discharge (r=-0.41), permafrost thaw depth (r=-0.42), ice extent (r=-0.55) and Normalized Difference Vegetation Index (r=-0.46) but positively correlated with salinity (r=0.86) and wind speed (r=0.46).

Greg Brown, Morgan F. Schebella, Delene Weber

Yangqiuting Li, Chandralekha Singh

Students' self-efficacy, interest, and perceived recognition from others have been shown to be very important for the development of their identity in a given field, which is a critical predictor of students' career decisions. Prior research suggests that students' self-efficacy and interest play an important role in their performance and persistence in STEM fields. However, very little has been investigated about the role of perceived recognition and validation by instructors on students' self-efficacy and interest. Moreover, prior quantitative studies show that women often report a lower level of physics perceived recognition, self-efficacy and interest. In this study, we analyzed data from individual interviews with 38 female students to investigate their learning experiences in physics courses in order to obtain a qualitative understanding of the factors that shape their self-efficacy and interest. We find that female students' negative and positive perceived recognition from instructors and teaching assistants (TAs) greatly influenced their self-efficacy and interest and even impacted their desire to persist in STEM majors. We categorize different types of perceived recognition that women reported in our interviews and how they influenced them. For example, many women reported that they felt belittled for their questions or efforts in physics courses, which often negatively influenced their self-efficacy. These findings can help physics educators develop better ways to interact with students in order to provide positive recognition and validation, such as acknowledging students' efforts and questions, expressing faith in students' ability to excel, and being careful not to give unintended messages to students. Our research also suggests that it is important for instructors/TAs to note that it is not their intentions that matter but the impact they are having on their students.

Yangqiuting Li, Chandralekha Singh

Structural equation modeling (SEM) is a statistical method widely used in educational research to investigate relationships between variables. SEM models are typically constructed based on theoretical foundations and assessed through fit indices. However, a well-fitting SEM model alone is not sufficient to verify the causal inferences underlying the proposed model, as there are statistically equivalent models with distinct causal structures that equally well fit the data. Therefore, it is crucial for researchers using SEM to consider statistically equivalent models and to clarify why the proposed model is more accurate than the equivalent ones. However, many SEM studies did not explicitly address this important step, and no prior study in physics education research has delved into potential methods for distinguishing statistically equivalent models with differing causal structures. In this study, we use physics identity model as an example to discuss the importance of considering statistically equivalent models and how other data can help to distinguish them. Previous research has identified three dimensions of physics identity: perceived recognition, self-efficacy, and interest. However, the relationships between these dimensions have not been thoroughly understood. In this paper, we specify a model with perceived recognition predicting self-efficacy and interest, which is inspired by individual interviews with students in physics courses to make physics learning environments equitable and inclusive. We test our model with fit indices and discuss its statistically equivalent models with different causal inferences among perceived recognition, self-efficacy, and interest. We then discuss potential experiments that could further empirically test the causal inferences underlying the models, aiding the refinement to a more accurate causal model for guiding educational improvements.

Luca Gamberi, Alessia Annibale, Pierpaolo Vivo

Information in the form of data, which can be stored and transferred between users, can be viewed as an intangible commodity, which can be traded in exchange for money. Determining the fair price at which a string of data should be traded is an important and open problem in many settings. In this work we develop a statistical physics framework that allows to determine analytically the fair price of information exchanged between players in a game of chance. For definiteness, we consider a game where $N$ players bet on the binary outcome of a stochastic process and share the entry fees pot if successful. We assume that one player holds information about past outcomes of the game, which they may either use exclusively to improve their betting strategy or offer to sell to another player. We find a sharp transition as the number of players $N$ is tuned across a critical value, between a phase where the transaction is always profitable for the seller and one where it may not be. In both phases, different regimes are possible, depending on the "quality" of information being put up for sale: we observe symbiotic regimes, where both parties collude effectively to rig the game in their favor, competitive regimes, where the transaction is unappealing to the data holder as it overly favors a competitor for scarce resources, and even prey-predator regimes, where an exploitative data holder could be giving away bad-quality data to undercut a competitor. Our analytical framework can be generalized to more complex settings and constitutes a flexible tool to address the rich and timely problem of pricing information in games of chance.

Cícero Francisco de Lima, Erivelton de Souza Nunes, Filipe Augusto Xavier Lima

Este artigo tem como objetivo verificar os condicionantes do diferencial de rendimentos entre as famílias pluriativas, agrícolas e não agrícolas no meio rural brasileiro. Foram utilizados, como base, os microdados da Pesquisa Nacional por Amostra de Domicílios (PNAD) de 2014, por meio do método não paramétrico de Ñopo (2008). Os resultados evidenciaram que as famílias pluriativas ganham aproximadamente 64% a mais do que as famílias agrícolas e cerca de 6% a menos do que as famílias não agrícolas. Os resultados econométricos reforçaram a existência de disparidades de rendimentos em benefício das famílias pluriativas (0,1169 Lnw) em relação às famílias agrícolas, e diferenciais de renda negativo (-0,0647 Lnw) para a pluriatividade, comparados à atividade não agrícola.

Chien-Hsiang Lin, Siao-Man Wu, Chia-Yen Lin et al.

Abstract Understanding the diversity of deep-sea fish fauna based on otoliths in the tropical and subtropical West Pacific has been limited, creating a significant knowledge gap regarding regional and temporal variations in deep-sea fish fauna. To address this gap, we collected a total of 122 bulk sediment samples from the Lower Pliocene Gutingkeng Formation in southwestern Taiwan to reconstruct the otolith-based fish fauna. Using planktonic foraminiferal biostratigraphy, we determined the age of the samples to be 5.6 to 3.1 Ma. A total of 8314 otoliths were assigned to 64 different taxa from 33 families, including the discovery of one new genus, Gutingichthys gen. nov., and three new species: Benthosema duanformis sp. nov., Benthosema parafibulatum sp. nov., and Gutingichthys changi sp. nov. Comparisons with other regional otolith-based assemblages highlighted the exceptional diversity of our collection, making it the most diverse fossil fish fauna reported from Taiwan to date. Otolith diversity analysis revealed very few taxa were dominant in the assemblage, particularly the mesopelagic Myctophidae, with a wide variety of minor taxa. The co-occurrence of shallow-water elements suggests episodic storm events as a potential source. The predominance of deep-sea and oceanic fishes indicated an outer-shelf to upper slope environment, resembling the modern outer-shelf and upper slope fish fauna in the region. Our findings suggest an early establishment and persistent presence of the mesopelagic fish community since the Early Pliocene. Further investigations of the Upper Miocene and Pleistocene sections of the Gutingkeng Formation would provide valuable insights into the evolution of deep-sea fish fauna in the area. ZooBank LSID: urn:lsid:zoobank.org:pub:7A336FC7-0D9A-4D17-B212-0B51427945DD.

P. -C. Chiang, S. C. Lin, C. -Y. Chiang et al.

Recent quasi-2D systems with judicious exploitation of the atomic monolayer or few-layer architecture exhibit unprecedented physical properties that challenge the conventional wisdom on the condensed matter physics. Here we show that the infinite layer SrCuO2 (SCO), a topical cuprate Mott insulator in the bulk form, can manifest an unexpected metallic state in the quasi-2D limit when SCO is grown on TiO2-terminated SrTiO3 (STO) substrates. Hard x-ray core-level photoemission spectra demonstrate a definitive Fermi level that resembles the hole doped metal. Soft x-ray absorption spectroscopy also reveals features analogous to those of a hole doped Mott insulator. Based on these results, we conclude that the hole doping does not occur at the interfaces between SCO and STO; instead, it comes from the transient layers between the chain type and the planar type structures within the SCO slab. The present work reveals a novel metallic state in the infinite layer SCO and invites further examination to elucidate the spatial extent of this state.

Milad Niroumand-Jadidi, Carl J. Legleiter, Francesca Bovolo

The rapid expansion of CubeSat constellations could revolutionize the way inland and nearshore coastal waters are monitored from space. This potential stems from the ability of CubeSats to provide daily imagery with global coverage at meter-scale spatial resolution. In this study, we explore the unique opportunity to improve the retrieval of bathymetry offered by CubeSats, specifically those of the PlanetScope constellation. The orbital design of the PlanetScope constellation enables the acquisition of image sequences with short time lags (from seconds to hours). This characteristic allows multiple images to be captured during a short period of steady bathymetric conditions, especially in dynamic environments like rivers. We hypothesize that taking the ensemble mean of a CubeSat image sequence can enhance bathymetry retrieval compared to standard single-image analysis. Along with the existing optimal band ratio analysis (OBRA) algorithm, we also use a new neural network-based depth retrieval (NNDR) technique to infer bathymetry from both individual and time-averaged images. The two methodologies are evaluated using field data from five different river reaches with depths up to 15 m and both top-of-atmosphere (TOA) radiance and bottom-of-atmosphere (BOA) surface reflectance PlanetScope data products. Despite low spectral resolution and concerns about the radiometric quality of CubeSat imagery, accuracy assessment based on in-situ comparisons indicates the potential (0.52 < R2 < 0.7 for the NNDR method) of PlanetScope imagery to retrieve depths up to ∼ 10 m in clear water conditions. The proposed image averaging consistently improves bathymetry retrieval over single image analysis. The NNDR technique was found to outperform OBRA, illustrating the importance of leveraging all spectral bands through machine learning approaches. TOA data provided more robust bathymetry results than BOA data for the OBRA technique, but the NNDR technique was minimally impacted by the type of data product.