Hasil untuk "Physical geography"

I. Fjørtoft

Abstract:This study investigated the impacts of playing in a natural environment on motor development in children. Methods from landscape ecology were applied for landscape analysis and entered into a Geographic Information System (GIS). Localization of play habitats was done by use of Global Positioning Systems (GPS). A quasi-experimental study was conducted on five-, six-, and seven-year old children with an experimental group playing in a natural environment and a control group playing in a more traditional playground. When provided with a natural landscape in which to play, children showed a statistically significant increase in motor fitness. There were also significant differences between the two groups in balance and co-ordination in favor of the experimental group. The findings indicate that landscape features influence physical activity play and motor development in children.

J. Lansford, K. Dodge, G. S. Pettit et al.

S. Parks, R. Housemann, R. Brownson

S. Brody, Sammy Zahran, A. Vedlitz et al.

Ekaterina Sorokovikova, Irina Tikhonova, Galina Fedorova et al.

<i>Microcystis</i>, a potentially toxigenic cyanobacterium known to form extensive blooms in eutrophic lakes globally, was investigated in the cold oligotrophic Lake Baikal. We report the isolation of two <i>Microcystis</i> strains, <i>Microcystis aeruginosa</i> and <i>M. novacekii</i>, and document the presence of the latter species in Lake Baikal for the first time. In <i>M. aeruginosa</i> strain BN23, we detected the microcystin synthetase gene <i>mcy</i>E. Liquid chromatography-mass spectrometry revealed the presence of two microcystin variants in BN23, with microcystin-LR, a highly potent toxin, being the dominant form. The concentration of MC-LR reached 540 µg/g dry weight. In contrast, <i>M. novacekii</i> strain BT23 lacked both microcystin synthesis genes and detectable toxins. The habitat waters were characterized as oligotrophic with minor elements of mesotrophy, exhibiting low phytoplankton biomass dominated by the chrysophyte <i>Dinobryon cylindricum</i> (76–77% of biomass), with cyanobacteria contributing 8–10%. The contribution of <i>Microcystis</i> spp. to the total phytoplankton biomass could not be quantified as they were exclusively found in net samples. The water temperature at both sampling stations was ~19 °C, which is considerably lower than optimal for <i>Microcystis</i> spp. and potentially conducive to enhanced microcystin production in toxigenic genotypes.

Ningsang Jiang, Peng Li, Zhiming Feng

Swidden agriculture, widely practiced by impoverished ethnic groups, continues to undergo rapid transition and transformation in tropical highlands. Exploring universal approaches for accurate mapping of newly-opened swiddens and fallows of different ages has not yet been stopped. The development of data-, information-, and knowledge-based algorithms for monitoring swidden agriculture requires integration of multi-dimensional features. The first part of the Continuous Change Detection and Classification (CCDC) algorithm holds promising potential in capturing abrupt changes. However, the CCD-derived temporal attributes and other multi-dimension features are seldom utilized to monitor swidden agriculture. Here, a combined algorithm integrating CCD and Support Vector Machine (SVM) is firstly developed to comprehensively highlight fundamental characteristics of swidden agriculture for maximumly and effectively mapping freshly opened swiddens. Local experimental results demonstrate that the CCD-SVM algorithm significantly enhances the performance of SVM in newly-opened swidden identification, with an average accuracy of over 85% (around a 10–20% improvement) under different land cover conditions. Next, CCD-SVM is applied to generate the 2019 map of newly-opened swidden in Laos using Landsat-8 dry-season (February to April) imagery. Comparisons with the same year results obtained from the CCDC-Spectral Mixture Analysis (SMA) show that CCD-SVM (94.69%) outperforms CCDC-SMA (87.52%) primarily due to less commission errors. Features inclusion of terrain and fire greatly improves classification accuracy. Additionally, over 60% of Laotian swiddens cross-validated by the 375-meter Visible Infrared Imaging Radiometer Suite active fires demonstrate CCD-SVM’s reliability and fidelity. The integration CCDC with SVM represents a novelty in combining time series analysis and machine learning techniques and helps monitor annual swidden agriculture in the tropics.

Martin Hruška, Martin Plesch

One of key goals of contemporary physics (and, realistically, STEM) education is to develop students' science literacy and critical thinking skills. In this paper, we present the construction and use of several versions of a simple school-based digital weather station that students can use to measure fundamental physical quantities (temperature, pressure, air humidity, light intensity) as part of school activities. The weather stations were constructed at our workplace using an Arduino microcontroller, BBC micro: bit, and the school measurement system Coach. This paper proposes not only the design and related programming of the weather stations but also how students can collect, analyse, and interpret measured data, thereby learning scientific methods and developing science literacy and critical thinking. This hands-on approach also develops students' experimental skills, emphasizes the cross-curricular relationships between physics, computer science and geography, and teaches them to work with accurate data in the context of real environmental problems.

Djenabou Bayo, Burak Çivitcioğlu, Joseph J Webb et al.

The detection of phase transitions is a fundamental challenge in condensed matter physics, traditionally addressed through analytical methods and direct numerical simulations. In recent years, machine learning techniques have emerged as powerful tools to complement these standard approaches, offering valuable insights into phase and structure determination. Additionally, they have been shown to enhance the application of traditional methods. In this work, we review recent advancements in this area, with a focus on our contributions to phase and structure determination using supervised and unsupervised learning methods in several systems: (a) 2D site percolation, (b) the 3D Anderson model of localization, (c) the 2D $J_1$-$J_2$ Ising model, and (d) the prediction of large-angle convergent beam electron diffraction patterns.

Franck Rothen, Samuel Klein, Matthew Leigh et al.

Machine learning is becoming increasingly popular in the context of particle physics. Supervised learning, which uses labeled Monte Carlo (MC) simulations, remains one of the most widely used methods for discriminating signals beyond the Standard Model. However, this paper suggests that supervised models may depend excessively on artifacts and approximations from Monte Carlo simulations, potentially limiting their ability to generalize well to real data. This study aims to enhance the generalization properties of supervised models by reducing the sharpness of local minima. It reviews the application of four distinct white-box adversarial attacks in the context of classifying Higgs boson decay signals. The attacks are divided into weight-space attacks and feature-space attacks. To study and quantify the sharpness of different local minima, this paper presents two analysis methods: gradient ascent and reduced Hessian eigenvalue analysis. The results show that white-box adversarial attacks significantly improve generalization performance, albeit with increased computational complexity.

Maximilian Schwarz, Windpanga Aristide Ouédraogo, Issouf Traoré et al.

Increasing frequencies of climate change-induced extreme weather events like prolonged droughts pose significant challenges for small-scale subsistence farmers in sub-Saharan Africa, who rely on the yearly harvest by more than 80 % of their nutritional needs. However, we do not have a good understanding of yield estimates at the field and household level (with a mean field size of < 2 ha) to understand nutritional priorities in vulnerable communities due to their scarcity in the literature, particularly yield estimates that do not require re-collection of in-situ data. Statistical models for estimating regional food crop yields based on high-resolution satellite data at the field level may provide better insights into how to address health risks such as child malnutrition. Especially in low-resource contexts, where the burden is greatest and expected to worsen in future climate projections. Our study developed crop-specific, satellite-based yield models using a novel three-year data set of in-situ yield measurements as exemplified for a rural region in Burkina Faso. The aim of the model is to reduce the need for in-situ field data collection while still assuring accurate yield estimates at the field level. The model employed LASSO regression and was based on monthly vegetation index composites from Sentinel-2 and weekly accumulated Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) rainfall data. Our yield modeling results showed that there was less overfitting when there was more training data over three years that demonstrated a wider range of yields, which also led to better model fits. R² values ranged from 0.62 (Maize) to 0.3 (Sorghum) for the three-year yield models, with normalized root mean square error (nRMSE) values ranging from 12 % − 16 %. An additional plausibility check confirmed the validity of our models, as we compared the magnitude of our yield estimation with national yield statistics for Burkina Faso. We demonstrated that our models based on three years of in-situ data may capture some of the inter-annual variability in yields, which could be a step toward minimizing the necessity for in-situ measurements in the future. Our advances in predicting yield estimates at the field level enable a linkage between household-level yields, socioeconomic indicators, nutritional status of children, and the health status of the household members. A further application is linking high-resolution yield data to farmers’ productivity losses from increasing heat under climate change.

Ruth Mace

Yuting Gong, Huifang Li, Huanfeng Shen et al.

Reconstruction of cloud-covered thermal infrared land surface temperature (LST) is vital for the measurement of physical properties in land surface at regional and global scales. In this paper, a novel reconstruction method for Moderate Resolution Imaging Spectroradiometer (MODIS) LST data with a 1-km spatial resolution is proposed by combining assimilation data and remote sensing data through a nonlocality-reinforced network (NRN) model. Firstly, a data grading criterion is introduced to evaluate the importance of the various datasets, forming four combinations of multi-modal datasets for the training and testing of the NRN model. Secondly, the NRN model with a multiscale encoding–decoding structure considering the nonlocality-reinforced module is proposed for LST reconstruction. The results suggest that the proposed method can precisely reconstruct cloud-covered LST, with a mean absolute error (MAE) less than 0.8 K, even when no auxiliary remote sensing LST are used (Combination 1). The best result is the full combination (Combination 4), in which the coefficient of determination is 0.8956, the MAE is 0.5219 K, and the root-mean-square error is 0.7622 K. Compared with the traditional harmonic analysis of time series method, the improved enhanced spatial and temporal adaptive reflectance fusion method and the multiscale feature connected convolutional neural network method for LST reconstruction, the proposed method can achieve superior results. The proposed method with Combination 1 has been implemented to reconstruct the daily LST in the study area for 2019. Referring to the meteorological station observations, the reconstructed bias absolute value is less than 1 K, indicating that the proposed model is very effective and valid for regional cloud-covered LST reconstruction.

Nazgul Zh. ZHENSIKBAYEVA, Nazym K. KABDRAKHMANOVA, Aigul Y. YEGINBAYEVA et al.

In this article, a study was conducted to analyze the factors leading to the occurrence of one of the natural disasters - fires on the territory of Eastern Kazakhstan. This work examined the consequences of forest fires that occurred before 2022 and analyzed changes in the state of forest cover in recent years using satellite images. The article also describes the methodology and application of geographic information technologies for assessing the potential damage caused by fires based on data from space. This technology provides a quick assessment of possible damage from forest and steppe fires, which can be supplemented with data from the area. Based on space monitoring data, areas affected by fires are identified, and a rapid assessment of such areas is carried out using information from the MODIS system, after which it is recommended to supplement it with more detailed medium-resolution data, such as Landsat images. In addition, the article determined the structure of forest cover, and also identified factors influencing the occurrence of fire conditions in the territory of Eastern Kazakhstan. As a result of the study, a set of proposals was developed to assess the level of damage caused by forest fires and measures to prevent such fires.

J. Jie, S. Zhong, Q. Zhang et al.

$^{23}$Na spin-1 Bose-Einstein condensates are used to experimentally demonstrate that mean-field physics beyond the single-mode approximation can be relevant during the non-equilibrium dynamics. The experimentally observed spin oscillation dynamics and associated dynamical spatial structure formation confirm theoretical predictions that are derived by solving a set of coupled mean-field Gross-Pitaevskii equations [J. Jie et al., Phys. Rev. A 102, 023324 (2020)]. The experiments rely on microwave dressing of the $f=1$ hyperfine states, where $f$ denotes the total angular momentum of the $^{23}$Na atom. The fact that beyond single-mode approximation physics at the mean-field level, i.e., spatial mean-field dynamics that distinguishes the spatial density profiles associated with different Zeeman levels, can -- in certain parameter regimes -- have a pronounced effect on the dynamics when the spin healing length is comparable to or larger than the size of the Bose-Einstein condensate has implications for using Bose-Einstein condensates as models for quantum phase transitions and spin squeezing studies as well as for non-linear SU(1,1) interferometers.

Debojyoti Kundu, Santosh Kumar, Subhra Sen Gupta

We study the spectral properties of and spectral-crossovers between different random matrix ensembles (Poissonian, GOE, GUE) in correlated spin-chain systems, in the presence of random magnetic fields, and the scalar spin-chirality term, competing with the usual isotropic and time-reversal invariant Heisenberg term. We have investigated these crossovers in the context of the level-spacing distribution and the level-spacing ratio distribution. We use random matrix theory (RMT) analytical results to fit the observed Poissonian-to-GOE and GOE-to-GUE crossovers, and examine the relationship between the RMT crossover parameter λ and scaled physical parameters of the spin-chain systems in terms of a scaling exponent. We find that the crossover behavior exhibits universality, in the sense that it becomes independent of lattice size in the large Hamiltonian matrix dimension limit.

Sophia Perdikaris, Roberto Abadie, Edith Gonzalez et al.

The islands of Barbuda and Puerto Rico share a history of dispossession and exploitation, occupying a peripheric position in a core–periphery world system. Yet, each island's response to COVID-19, and the subsequent effects of the pandemic, could not be more different. This paper examines how colonialism and neocolonialism affected the islands’ ability to respond to COVID-19. Barbuda relied on community traditions of support and self-reliance and was able to restrict all travel to and from the island, including travelers from the diaspora and those participating in its informal economic sector. In doing so, Barbuda effectively isolated itself from infection. On the other hand, Puerto Rico, in a protracted economic crisis, was particularly vulnerable to touristic flows, diasporic movements, and a large informal sector. The Puerto Rican response was shaped by deep politicization in the mainland U.S., which complicated an evidencebased strategy to combat the emergency. These cases show that islands, particularly those located in peripheric or subaltern spaces, cannot isolate themselves from the worst effects of COVID-19 through mere geography. Pandemics are not only driven by biological events but also by the narratives of colonialism, encompassing political, economic, and cultural factors, which determine their trajectories — sometimes with devastating outcomes.

Rafael Santos, Chellasamy Jebarathinam, Remigiusz Augusiak

We propose a family of noncontextuality inequalities and show that they can be used for certification of multiqubit quantum systems. Our scheme, unlike those based on non-locality, does not require spatial separation between the subsystems, yet it makes use of certain compatibility relations between measurements. Moreover, it is scalable in the sense that the number of expectation values that are to be measured to observe contextuality scales polynomially with the number of qubits that are being certified. In a particular case we also show our scheme to be robust errors and experimental imperfections. Our results seem promising as far as certification of physical set-ups is concerned in scenarios where spatial separation between the subsystems cannot be guaranteed.

Patrick Weigelt, W. Jetz, H. Kreft

Junxiong Zhou, Yuean Qiu, Jin Chen et al.

High spatial resolution images have been widely applied to the monitoring of land surfaces during the past decades. However, satellite and unmanned aerial vehicle (UAV) sensors (e.g., IKONOS, and QuickBird) usually only provide visible and near-infrared spectral bands with a high spatial resolution. Such limited spectral bands at a high spatial resolution are not adequate for crucial environmental and agricultural studies that require essential bands (e.g., the red-edge (RE) and the short-wave infrared (SWIR)), such as land cover classification and the estimation of plant physiological parameters. On the contrary, abundant spectral information is commonly available on sensors with a medium spatial resolution (e.g., Landsat, and Sentinel-2). Existing spatial-spectral fusion methods can leverage the unique advantages of these two types of sensors, and produce synthesized images with both spatial and spectral detail. However, most spatial-spectral methods ignore the inherent geometric registration errors when using multi-source data, inevitably introducing the errors into the fusion result. To address the limitation, this study presents a new spatial-spectral fusion method, the Misregistration-Resistant Data Fusion approach (MRDF), to add new spectral bands to the high spatial resolution images from medium resolution images. The proposed method is composed of a local-model-based similar pixel searching and a global partial least square (PLS) regression, and has the following advantages: (1) the local model utilizes neighborhood similar pixels to generate predictions robust against geometric misregistration; (2) the combined model integrates a global PLS regression to improve fusion accuracy for the SWIR bands; (3) the extended-box strategy and spatial filtering can relieve geometric errors when minimizing residuals for the SWIR bands. Satellite and airborne data from three sites were used to compare the performance of MRDF with seven other typical methods. Results showed that MRDF not only produced accurate fusion results with above-average efficiency but also improved the classification accuracy, which substantiated its great potential for various applications.

Andres Santamaria-Artigas, Eric F. Vermote, Belen Franch et al.

The long-term data record (LTDR) from the Advanced Very High-Resolution Radiometer (AVHRR) provides daily surface reflectance with global coverage from the 1980s to present day, making it a unique source of information for the study of land surface properties and their long-term dynamics. Surface reflectance is a critical input for the generation of products such as vegetation indices, albedo, and land cover. Therefore, it is of utmost importance to quantify its uncertainties to better understand how they might propagate into downstream products. Due to the prolonged length of the surface reflectance LTDR and previous unavailability of a well calibrated reference, no comprehensive evaluation of the complete record has been reported so far. Recently, the United States Geological Survey (USGS) began production of surface reflectance datasets from the Landsat 4–8 satellites, which provide a suitable reference against which the LTDR can be compared to. In this study, we evaluate the LTDRV5 between 1984 and 2011 using surface reflectance data from the Landsat-5 Thematic Mapper (TM5) Collection-1 as a reference. Data from TM5 was obtained from over 740,000 globally distributed scenes which gave a representative set of land surface types and atmospheric conditions. Differences due to observation geometry were accounted for using the Vermote-Justice-Breon (VJB) Bidirectional Reflectance Distribution Function (BRDF) normalization method to adjust the AVHRR surface reflectance to TM5 observation conditions; the spectral response differences were minimized using spectral band adjustment factors (SBAFs) derived from the Earth Observing One (EO-1) Hyperion atmospherically corrected hyperspectral spectra. The performance of the AVHRR record is reported in terms of the accuracy, precision, and uncertainty (APU). Results show that the LTDR performance is close or within the combined uncertainty specification of 0.071ρ + 0.0071, where ρ is the estimated reflectance.