Hasil untuk "Physical geography"

Roland Baumhauer

Rafael de Souza Mendes da Silva, Letícia Oliveira Nicácio, Carem Aparecida Mesquita et al.

O solo é vital para a vida humana e os ecossistemas, mas está sendo degradado devido à erosão, seja por causas naturais ou humanas, com esta última sendo mais prejudicial. A Equação Universal da Perda de Solos é um modelo matemático que ajuda a estimar essa redução. Este estudo utilizou geoprocessamento para mapear a perda de solos, através da equação supracitada, na bacia do Córrego Vargem de Caldas, mostrando que mais de ⅓ da área está sofrendo perda moderada a alta, principalmente em seu norte e oeste, o que pode levar a inundações e prejuízos agrícolas. Propõe-se uma solução integrada que inclui proteção da vegetação nativa, manejo sustentável das áreas agrícolas, educação ambiental, fiscalização e incorporação da conservação do solo no planejamento territorial no intuito de promover a sustentabilidade ambiental.

A. Paetznick, M. P. da Silva, C. Ryan-Anderson et al.

The promise of quantum computers hinges on the ability to scale to large system sizes, e.g., to run quantum computations consisting of more than 100 million operations fault-tolerantly. This in turn requires suppressing errors to levels inversely proportional to the size of the computation. As a step towards this ambitious goal, we present experiments on a trapped-ion QCCD processor where, through the use of fault-tolerant encoding and error correction, we are able to suppress logical error rates to levels below the physical error rates. In particular, we entangled logical qubits encoded in the [[7,1,3]] code with error rates 9.8 times to 500 times lower than at the physical level, and entangled logical qubits encoded in a [[12,2,4]] code based on Knill's C4/C6 scheme with error rates 4.7 times to 800 times lower than at the physical level, depending on the judicious use of post-selection. Moreover, we demonstrate repeated error correction with the [[12,2,4]] code, with logical error rates below physical circuit baselines corresponding to repeated CNOTs, and show evidence that the error rate per error correction cycle, which consists of over 100 physical CNOTs, approaches the error rate of two physical CNOTs. These results signify a transition from noisy intermediate scale quantum computing to reliable quantum computing, and demonstrate advanced capabilities toward large-scale fault-tolerant quantum computing.

Augusto T. Chantada, Pavlos Protopapas, Luca Gomez Bachar et al.

The use of neural networks to solve differential equations, as an alternative to traditional numerical solvers, has increased recently. However, error bounds for the obtained solutions have only been developed for certain equations. In this work, we report important progress in calculating error bounds of physics-informed neural networks (PINNs) solutions of nonlinear first-order ODEs. We give a general expression that describes the error of the solution that the PINN-based method provides for a nonlinear first-order ODE. In addition, we propose a technique to calculate an approximate bound for the general case and an exact bound for a particular case. The error bounds are computed using only the residual information and the equation structure. We apply the proposed methods to particular cases and show that they can successfully provide error bounds without relying on the numerical solution.

Shaofei Huang, Christopher M. Poskitt, Lwin Khin Shar

Cyber-physical systems are at the intersection of digital technology and engineering domains, rendering them high-value targets of sophisticated and well-funded cybersecurity threat actors. Prominent cybersecurity attacks on CPS have brought attention to the vulnerability of these systems and the inherent weaknesses of critical infrastructure reliant on them. Security modelling for CPS is an important mechanism to systematically identify and assess vulnerabilities, threats, and risks throughout system life cycles, and to ultimately ensure system resilience, safety, and reliability. This survey delves into state-of-the-art research on CPS security modelling, encompassing both threat and attack modelling. While these terms are sometimes used interchangeably, they are different concepts. This paper elaborates on the differences between threat and attack modelling, examining their implications for CPS security. We conducted a systematic search that yielded 449 papers, from which 32 were selected and categorised into three clusters: those focused on threat modelling methods, attack modelling methods, and literature reviews. Specifically, we sought to examine what security modelling methods exist today, and how they address real-world cybersecurity threats and CPS-specific attacker capabilities throughout the life cycle of CPS, which typically span longer durations compared to traditional IT systems. This paper also highlights several limitations in existing research, wherein security models adopt simplistic approaches that do not adequately consider the dynamic, multi-layer, multi-path, and multi-agent characteristics of real-world cyber-physical attacks.

Qing Zhong, Mamattursun Eziz, Mireguli Ainiwaer et al.

Mercury (Hg) is one of the most toxic heavy metals to the human body. Conventional methods for measuring Hg content in soil are time-consuming and expensive. In order to select a high-effective method for estimating soil Hg content based on hyperspectral remote sensing techniques, a total of 85 soil samples were collected from the Urumqi city, northwest China, to obtain the Hg contents and related hyperspectral data. A total of 12 spectral transformation methods were used to the original spectral data for selecting significant wavebands. The partial least squares regression (PLSR), random forest regression (RFR) and support vector machine regression (SVMR) were used to establish hyperspectral inversion models for soil Hg content using selected significant wavebands. The results showed that the Hg content of soil was significantly higher than its corresponding background value, which obviously enriched in soil in the study area. The spectral transformation of the original wavebands can effectively reduce the interference of the background noise and can improve the correlations between the spectral data and the soil Hg content. The RFR model based on logarithmic first-order differential (LTFD–RFR) or on reciprocal logarithmic first-order differential (ATFD–RFR) had the best inversion effects, with the highest prediction ability (R2 = 0.856, RMSE = 0.002 and MAE = 0.072). The LTFD–RFR or ATFD–RFR methods can be used as a means of inversion of Hg content of soil in oasis cities. The novel contribution of this work is to construct hyperspectral inversion model which can accurately estimate the Hg content of urban soils in arid zones. Results of this study can provide a technical support for hyperspectral estimation of soil Hg content.

Kathryn J. Allen, Danielle C. Verdon-Kidd, Mandy B. Freund et al.

Study region Western Tasmania, southeastern Australia.Study focus We present two new tree-ring based inflow reconstructions from western Tasmania in southeastern Australia.The warm season reconstruction (Dec–Feb) extends from 1030–2007 CE and explains up to 42% of the variance in instrumental flow, while the cool season (JA) extends from 1550–2007 CE and explains 27% of instrumental flow variance. Key features include an extended pluvial period in the 11th Century and a protracted dry period in ∼1500CE, neither of which are represented in the DJF instrumental record. Decreasing JA flow since the 19th Century is consistent with a local sediment-based hydroclimate record.New hydrological insights for the region The reconstructions confirm that the instrumental data do not capture how protracted past low or high flow periods have been. It is therefore important to consider pre-instrumental flow data when planning for the future. The reconstructions provide new insights into regional variability through their association with the Subtropical Ridge (STR) and the Southern Annular Mode (SAM). Differing spatial signatures of the seasonal reconstructions, and their associations with season-specific impacts of STR and SAM, highlight the need for caution when considering the use of remote hydroclimate proxy records with strong seasonal signatures. The reconstructions suggest that extrapolation of seasonally defined reconstructions to represent annual flow for regions beyond the extent of their spatial footprint may be problematic.

Ясін Агоно Аввал, Рукая Мухаммад Фатіху

Introduction. Spatial variability of soil properties as influenced by both intrinsic and extrinsic factors, plays a pivotal role in agricultural productivity. Understanding this variability is critical for implementing site-specific management, which optimizes resource allocation while sustaining soil health. This study investigates the spatial variability of selected soil properties in agricultural fields around Zaria, Kaduna State, Nigeria, utilizing geostatistical techniques to provide insights for sustainable land management. Materials and Methods. The study was conducted in an 85-hectare area located in Zaria, Kaduna State. Seventy soil samples were collected using a grid sampling approach across 85 hectares. Following standard laboratory procedures, the samples were analysed for properties, including particle size distribution, bulk density (BD), pH, organic carbon (OC), and cation exchange capacity (CEC). Geostatistical analysis using Kriging interpolation and semivariogram modelling was employed to determine spatial dependence. Normal Distribution Test and Data Transformation. Laboratory data from the studied soil properties were tested for normality using the Ryan-Jover test, which revealed that most soil properties did not follow a normal distribution (P<0.05). Johnson trans-formation was hence applied to improve normality for reliable geostatistical modelling, as confirmed by the residuals from QQ Plots. Descriptive Statistics of Soil Properties. Clay content exhibited the highest variability (CV = 43.09%), ranging from 60 to 420 g kg-1. CEC showed moderate to high fertility potential, ranging from 6.33 to 25.50 cmol kg-1, while OC were generally rated low. BD and pH showed weak spatial variability (CV < 15%) due to the influence of intrinsic soil factors. Geostatistical Analysis of Soil Properties. Semivariogram modelling revealed strong spatial dependence for most soil properties (nugget ratio < 0.25), including BD, OC, and pH, suggesting intrinsic factors as key drivers. Spatial ranges varied across properties, with clay and CEC extending to 339.9 m and 347.6 m, respectively, while pH and BD showed shorter ranges of 85.4 m and 93.3 m. Spatial patterns in sand and clay demonstrated inverse relationships, as areas with higher clay contents exhibited higher CEC and pH levels. Spatial Distribution Maps. Kriging interpolation highlighted distinct spatial patterns, such as higher clay and CEC concentrations in specific zones, and lower pH in sandy areas, indicative of leaching effects. Maps showed that the spatial distribution of OC and BD is influenced by short-range processes, requiring localized management strategies. Conclusion. This study demonstrates the necessity of addressing spatial variability in soil management plans. Strong correlations between clay and CEC emphasize the critical role of texture in influencing soil fertility. Properties like OC and BD, with weak spatial dependence, demand immediate attention through targeted interventions such as organic amendments and improved tillage practices.

Pengquan Wang, Runjie Li, Shengkui Cao

To achieve sustainable development goals in Huangshui River Basin (HRB), strengthening adaptive water resources management under the dual impact of climate change (CC) and human interventions (HI) is of great significance. Multiple mathematical and statistical methods were employed to determine the runoff trend and breakpoint in HRB. The elasticity of CC and HI on the runoff decline and their contributions were quantitatively discerned based on the Budyko hypothesis, complementary method, and SWAT hydrological model. The results show that (1) the runoff showed a decreasing trend, with a runoff breakpoint in 1990; (2) the elasticity coefficients indicated a 1% increase in P, ET0, and n, leading to a 2.19% increase, a 1.19% decrease, and a 1.52% decrease in the runoff, respectively; (3) the Budyko framework determined the contribution of CC and HI to runoff decline in HRB to be 37.98–41.86% and 58.14–62.02%, respectively, and that estimated by SWAT hydrological model to be 38.72 and 61.28%, respectively; (4) HI were the primary factor for runoff decline in HRB, where direct anthropogenic disturbances such as water withdrawals and water conservancy project construction were the main drivers. The findings have important scientific significance for water resources planning and management in HRB. HIGHLIGHTS We determined the change trend and breakpoint of annual runoff from 1959 to 2014.; The runoff elasticity was estimated theoretically based on the Budyko hypothesis for 20 mountainous catchments and 5 hydrographic cross-sections in HRB.; The complementary method calculated the contribution threshold of climate change and human interventions to runoff changes.; SWAT models were used to discern runoff change attributions.;

Bochuan Zhao, Yifei Wang, Huizhi Geng et al.

Urban agglomerations are increasingly becoming the primary regional units in global competition, characterized by the rapid expansion of impervious surface areas, which negatively impacts both society and the environment. This study quantifies the spatiotemporal expansion of these surfaces in the Yangtze River Delta urban agglomeration and explores its driving factors using a Geographically Weighted Random Forest model. The results demonstrate a transition from “point expansion” to “infill development”, while also revealing a gradual southward shift in the developmental focus of the Yangtze River Delta urban agglomeration. Although expansion intensity has decreased, spatial clustering has intensified. Based on the expansion patterns of impervious surface areas, we propose a novel regional classification method, dividing the Yangtze River Delta urban agglomeration into three zones: “A-Development Decline Zone”, “B-Development Core Zone”, and “C-Development Ascendance Zone”. Socio-economic factors are the primary drivers of this expansion, followed by science and education, and then the ecological environment, while physical geography factors have the least impact. The study reveals differentiated regional development characteristics and further refines the sub-regions within the urban agglomeration, providing a new perspective for future regional coordinated development policies.

J. M. P. Carmelo, P. D. Sacramento, T. Stauber et al.

The coexistence in the low-temperature spin-conducting phases of the zigzag materials BaCo2V2O8 and SrCo2V2O8 of one-dimensional (1D) physics with important deviations from it is not well understood. The studies of this paper account for an important selection rule that follows from interchain spin states being coupled more strongly within the spin dynamical structure factor of such zigzag materials whenever they are connected by a specific symmetry operation of the underlying lattice. In the case of excited states, this symmetry operation is only a symmetry in spin-space ifno electronic spin flip is performed within the generation of such states. Our results on both the role of selective interchain couplings in protecting the 1D physics and being behind deviations from it and on the dynamical properties being controlled by scattering of singlet pairs of physical spins 1/2 open the door to a key advance in the understanding of the physics of the spin chains in BaCo2V2O8 and SrCo2V2O8.

Tran Van Tra, Ngo Thi Thuy, Van Thi Hang et al.

Study region: Ba River Basin, Viet Nam. Study focus: The impacts of deep uncertainties on the water resources system in the Ba River Basin are explored through the combined use of a climate stress-test and land use change. The performance of the water resources system is assessed using water supply reliability under various climate conditions and land use. A MIKE HYDRO Basin model is developed with 7 irrigation regions, 45 irrigation users, 8 regular users, 48 reservoirs, and 10 hydropower plants. 40 different combinations of climate conditions and land use change were simulated. New hydrological insights for the region: Climatic and land use change is expected to reduce water supply reliability in the river basin by between 2.1% and 5.2%. The most significant reliability decrease is in Nam Bac An Khe (−29.4%), while the most increase is in Upper Ayun (+10.9%). The spatial variation of climatic change impact is high and is reflected in the reliability range. The reliability range of Upper Ayun and Ayun Pa is narrow (1.2% in Upper Ayun and 0.7% in Ayun Pa). In contrast, the reliability range of Nam Bac An Khe, Krong Pa, Krong Hnang, and Upper Dong Cam is wide (14.3% in Nam Bac An Khe, 12.7% in Krong Pa, 12.4% in Krong Hnang, 11.7% in Upper Dong Cam, and 3.4% in Lower Dong Cam).

Daniel A. Knopf, Israel Silber, Nicole Riemer et al.

Abstract Mixed‐phase clouds (MPCs) have been identified as significant contributors to uncertainties in climate projections, attributable to model representation of processes controlling the formation and loss of supercooled water droplets and ice particles from the atmosphere. Arctic MPCs are commonly widespread and long‐lived, with sustained ice crystal formation processes that challenge current understanding. This study examines the ice‐nucleating particle (INP) reservoir dynamics governing immersion‐mode heterogeneous freezing in an observed case of Arctic MPCs using a simplified 1D aerosol‐cloud model. The model setup includes prescribed dynamical forcings and thermodynamic profiles, and represents INPs as multicomponent and polydisperse particle size distributions. Diagnostic and prognostic approaches to immersion freezing parameterization are compared, including time‐independent (singular) number‐ and surface area‐based descriptions and a time‐dependent description following classical nucleation theory (CNT). The choice of freezing parameterization defines the size of the INP reservoir. The CNT‐based description yields an orders of magnitude larger INP reservoir than the singular parameterizations, which is the dominant factor for sustained ice crystal formation. The efficiency of the freezing process and cloud cooling are of secondary importance. A diagnostic treatment neglecting INP loss is only accurate when the INP reservoir size is large and INP depletion weak. Since a larger INP reservoir sustains ice crystal formation substantially longer, and ice water path scales with ice crystal concentrations for the conditions considered, resolving the source of differences in INP reservoir dynamics due to model implementation is a high priority for advancing climate model physics.

Revaz Khazaradze, Elene Salukvadze

Xin Jiang, Alan D Ziegler, Shijing Liang et al.

Reforestation is an eco-friendly strategy for countering rising carbon dioxide concentrations in the atmosphere and the negative effects of forest loss and degradation. China, with one of the world’s most considerable afforestation rates, has increased its forest cover from 16.6% 20 years ago to 23.0% by 2020. However, the maximum potential forest coverage achieved via tree planting and restoration is uncertain. To map potential tree coverage across China, we developed a random forest regression model relating environmental factors and appropriate forest types. We estimate 67.2 million hectares of land currently available for tree restoration after excluding existing forested areas, urban areas, and agriculture land covers/uses, which is 50% higher than the current understanding. Converting these lands to the forest would generate 3.99 gigatons of new above- and belowground carbon stocks, representing an important contribution to achieving carbon neutrality. This potential is spatially imbalanced, with the largest restorable carbon potential being located in the southwest (29.5%), followed by the northeast (17.2%) and northwest (16.8%). Our study highlights the need to align tree restoration areas with the uneven distribution of carbon sequestration potential. In addition to being a biological mitigation strategy to partially offset carbon dioxide emissions from fossil fuel burning, reforestation should provide other environmental services such as the restoration of degraded soils, conservation of biological diversity, revitalization of hydrological integrity, localized cooling, and improvement in air quality. Because of the collective benefits of forest restoration, we encourage that such activities be ecosystem focused as opposed to solely focusing on tree planting.

Yen-Ming Chiang, Ruo-Nan Hao, Yue-Ping Xu et al.

Study region: The Shihmen reservoir, the second largest reservoir in northern Taiwan, is flooded frequently, and featured by short period of flood peak due to uneven distribution of rainfall and mountainous topography. Study focus: We proposed a novel streamflow-oriented ensemble recurrent neural networks (ERNN) method to merge three precipitation products, namely gauge measurements, radar and satellite rainfall products. We analyzed (1) the effect of artificial intelligence method for bias correction of precipitation products with reference to streamflow, (2) the comparison of two widely used arithmetic average (AA) and Bayesian model averaging (BMA) methods with ERNN, (3) the performance of merged rainfalls and the original three precipitation products in inflow forecasting during 13 typhoon events. New hydrological insights: We found that all precipitation products are biased and can be appropriately adjusted with an improvement in inflow forecasting of about 2.5 %, 21.8 % and 60.7 % for gauge, radar and satellite rainfall products in terms of RMSE. After rainfall merging, RMSE for radar and satellite are further improved by 14% and 36% at least. ERNN merging method indicates that the optimal merging weights for gauge, radar and satellite are within the range of 0.52–0.56, 0.35–0.39 and 0.08–0.09, respectively according to 95 % confidence interval. The merged rainfall product skillfully predicts the inflow with a lead time of five hours, and ERNN merging method is superior to traditional AA and BMA methods, with NS up to 0.85 and CRPS reduced by near 50 % compared to BMA. Additionally, ERNN is found to capture the peak times and peak flows with the smallest error. Overall, this study provides a potential approach to merge multiple rainfall products and to obtain the effective rainfall by fitting the hydrological responses over mountainous watersheds, where observational biases may frequently occur in gauge, radar and satellite measurements.

Shreyas Ramakrishna, Zahra Rahiminasab, Gabor Karsai et al.

Deep Neural Networks are actively being used in the design of autonomous Cyber-Physical Systems (CPSs). The advantage of these models is their ability to handle high-dimensional state-space and learn compact surrogate representations of the operational state spaces. However, the problem is that the sampled observations used for training the model may never cover the entire state space of the physical environment, and as a result, the system will likely operate in conditions that do not belong to the training distribution. These conditions that do not belong to training distribution are referred to as Out-of-Distribution (OOD). Detecting OOD conditions at runtime is critical for the safety of CPS. In addition, it is also desirable to identify the context or the feature(s) that are the source of OOD to select an appropriate control action to mitigate the consequences that may arise because of the OOD condition. In this paper, we study this problem as a multi-labeled time series OOD detection problem over images, where the OOD is defined both sequentially across short time windows (change points) as well as across the training data distribution. A common approach to solving this problem is the use of multi-chained one-class classifiers. However, this approach is expensive for CPSs that have limited computational resources and require short inference times. Our contribution is an approach to design and train a single $β$-Variational Autoencoder detector with a partially disentangled latent space sensitive to variations in image features. We use the feature sensitive latent variables in the latent space to detect OOD images and identify the most likely feature(s) responsible for the OOD. We demonstrate our approach using an Autonomous Vehicle in the CARLA simulator and a real-world automotive dataset called nuImages.

Matheus E. Leusin, Bjoern Jindra, Daniel S. Hain

This paper draws upon the evolutionary concepts of technological relatedness and knowledge complexity to enhance our understanding of the long-term evolution of Artificial Intelligence (AI). We reveal corresponding patterns in the emergence of AI - globally and in the context of specific geographies of the US, Japan, South Korea, and China. We argue that AI emergence is associated with increasing related variety due to knowledge commonalities as well as increasing complexity. We use patent-based indicators for the period between 1974-2018 to analyse the evolution of AI's global technological space, to identify its technological core as well as changes to its overall relatedness and knowledge complexity. At the national level, we also measure countries' overall specialisations against AI-specific ones. At the global level, we find increasing overall relatedness and complexity of AI. However, for the technological core of AI, which has been stable over time, we find decreasing related variety and increasing complexity. This evidence points out that AI innovations related to core technologies are becoming increasingly distinct from each other. At the country level, we find that the US and Japan have been increasing the overall relatedness of their innovations. The opposite is the case for China and South Korea, which we associate with the fact that these countries are overall less technologically developed than the US and Japan. Finally, we observe a stable increasing overall complexity for all countries apart from China, which we explain by the focus of this country in technologies not strongly linked to AI.

Pedro Henrique de Oliveira Silva, Ismael Soares de Oliveira, Vanessa Carla Neves

No Brasil, o futebol não é um jogo qualquer, sendo capaz de proporcionar uma série de transformações espaciais e socioculturais marcadas nas paisagens urbanas, na economia e na linguagem do povo. O esporte corre na história e se propaga no espaço, merecendo a atenção de estudiosos dos mais variados campos do conhecimento, podendo ser explorado, também, sob a ótica da Geografia. O livro “Estradas e bandeiras: a conquista do Brasil pelo futebol”, escrito pelo geógrafo, professor e futebolista fanático Gilmar Mascarenhas (UERJ), explora, numa perspectiva interdisciplinar, as dimensões espaciais do esporte entre o final do século XIX e o ano de 2014, quando ocorreu a Copa do Mundo no Brasil. A partir de um olhar atento às questões da geografia, o livro traz uma crítica consistente sobre os aspectos políticos e culturais desse esporte que moldou e continua moldando a nossa sociedade.

Scott W. Bailey, Robert P. Long, Stephen B. Horsley

Reductions in exchangeable calcium and magnesium and increase in exchangeable aluminum concentrations have been shown in soils impacted by acid deposition, including at four sites on the Allegheny Plateau, PA, USA, sampled in 1967 and 1997 during a period of peak deposition. We repeated sampling at these sites in 2017 to evaluate changes in soils during the more recent period when there has been a strong decline in acid deposition. The uppermost horizons, including the Oa and A horizons where humified organic matter transitions to mineral soil, were thicker, had higher concentrations of organic carbon and exchangeable calcium and magnesium, and lower concentrations of exchangeable aluminum in 2017 compared to 1997, approximating values measured in 1967. Below the Oa/A horizons, 2017 soil chemistry was more similar to the 1997 results, with some reduction of Ca in the recent measurements. These results suggest recovery of base cation–aluminum balance in surface horizons and may indicate a reduction of aluminum mobilization and increased efficiency of vegetation recycling of nutrients with decreased acid anion concentrations. These changes are consistent with a partial recovery from acid deposition. However, the increase in humified soil organic matter may also be affected by coincident increases in temperature and soil moisture.