Hasil untuk "Physical geography"

Greg Brown, P. Reed, C. Raymond

Abstract The concept of “place” links people to their environment and is foundational to disciplines such as geography, environmental psychology, and urban studies. With growth in geographic information systems (GIS) in the 1990s, research began to operationalize place concepts using GIS to better inform land use decisions. After two decades, participatory mapping has emerged as an important method to identify place values. This article summarizes lessons from empirical research completed in diverse social and geographic contexts. Specifically, we find that mapped place values: (1) are best understood as relationship values, (2) reflect participant spatial/geographic discounting, (3) are closely related to place attachment and “sense of place” concepts, (4) are correlated with participant attitudes and preferences toward land use, (5) are predictive of land use conflict, (6) are associated with physical landscape features, (7) are generally stable over time, (8) are valid at multiple geographic scales, (9) exhibit greater similarity than differences across geographic areas and populations, and (10) show little evidence of actually influencing land use decisions. Despite research validity and the potential to improve social acceptability of land use decisions, place values will have limited social impact without elevating the importance of broader public participation in current socio-political systems.

C. Milligan, J. Wiles

Yin Hu, Shaoning Lv, Zhijin Li et al.

Soil freeze–thaw (FT) cycles impact soil functions and atmosphere–land interaction, but accurate measurements are very limited. Since surface dielectric properties and microwave emissions are sensitive to the FT state, brightness temperature (TB) measurements at L-band allow retrieval of the FT state. We have demonstrated the potential of a soil FT retrieval algorithm from Soil Moisture Active Passive (SMAP) TB measurements. This retrieval algorithm is formulated regarding Diurnal Amplitude Variation (DAV), which is defined as the difference in TB observations of ascending and descending orbits. The DAV-FT algorithm uses globally fixed parameters. However, parameters should vary regionally considering factors like land cover type, terrain, and climate regions. We introduce Overall Classification Accuracy (OA) to characterize the extraction of DAV annual variation under different parameters. Then, the parameter optimization process, akin to maximum likelihood estimation, selects a combination of parameters to extract the annual variation of the DAV optimally. The DAV-FT algorithm uses optimized parameters, and the results show that compared to using fixed parameters, (a) the area with OA > 0.7 increases from 54.43% to 89.36%; (b) consistency with ERA5-Land and SMAP data has improved in southwestern North America, the Qinghai–Tibet Plateau, and southwestern Eurasia, with regions showing over 0.7 consistency reaching 81.28% for ERA5-Land and 79.54% for SMAP-FT; and (c) in situ stations with higher accuracy outnumber those with lower accuracy (48.11% versus 22.97% for fixed parameters, 35.14% versus 33.51% for SMAP FT). Furthermore, the algorithm achieves the highest median (0.92) and median accuracy (0.88), compared to fixed parameters and SMAP.

Manuel Morales-Alvarado, Daniel Conde, Josh Bendavid et al.

We study the potential of symbolic regression (SR) to derive compact and precise analytic expressions that can improve the accuracy and simplicity of phenomenological analyses at the Large Hadron Collider (LHC). As a benchmark, we apply SR to equation recovery in quantum electrodynamics (QED), where established analytical results from quantum field theory provide a reliable framework for evaluation. This benchmark serves to validate the performance and reliability of SR before extending its application to structure functions in the Drell-Yan process mediated by virtual photons, which lack analytic representations from first principles. By combining the simplicity of analytic expressions with the predictive power of machine learning techniques, SR offers a useful tool for facilitating phenomenological analyses in high energy physics.

Lingchen Yang, Gaspard Zoss, Prashanth Chandran et al.

3D facial animation is often produced by manipulating facial deformation models (or rigs), that are traditionally parameterized by expression controls. A key component that is usually overlooked is expression 'style', as in, how a particular expression is performed. Although it is common to define a semantic basis of expressions that characters can perform, most characters perform each expression in their own style. To date, style is usually entangled with the expression, and it is not possible to transfer the style of one character to another when considering facial animation. We present a new face model, based on a data-driven implicit neural physics model, that can be driven by both expression and style separately. At the core, we present a framework for learning implicit physics-based actuations for multiple subjects simultaneously, trained on a few arbitrary performance capture sequences from a small set of identities. Once trained, our method allows generalized physics-based facial animation for any of the trained identities, extending to unseen performances. Furthermore, it grants control over the animation style, enabling style transfer from one character to another or blending styles of different characters. Lastly, as a physics-based model, it is capable of synthesizing physical effects, such as collision handling, setting our method apart from conventional approaches.

Ratanaporn Poosathit, Benjapon Kunlanit, Frank Rasche et al.

The influence of the quantities and ratios of dissolved organic carbon (DOC) and dissolved nitrogen (DN) generated by different chemical quality classes of organic residues on soil microbial processes in the decomposition process is not well understood. If the DOC-to-DN ratio (hereafter, ratio) of the substrate is close to that of the microbial C-to-N ratio, then the DOC-and-DN stoichiometry of the substrate is balanced, resulting in enhanced microbial processing, i.e., carbon use efficiency (CUE). Uncertainty exists about the influence of DN and the DOC-to-DN ratio on CUE, particularly in high-quality class (high nitrogen) residue-treated soils. A long-term field experiment was used to explore the effect of the annual application of residues of different quality classes on decomposition processes, focusing on the effects of DOC, DN, and the ratio on the microbial metabolic quotient (<i>q</i>CO₂), which is the inverse of CUE. DOC and DN were extracted from soils during the 13th year of the experiment. Soils treated with high-quality class groundnut residue (high-nitrogen) had higher DN (5.4 ± 2.6 mg N kg⁻¹) and a lower ratio (6.8 ± 2.6) than those treated with medium-quality (medium-nitrogen) tamarind (3.0 ± 0.6 and 10.7 ± 2.2, respectively). The positive influence of DN on <i>q</i>CO₂ (R² = 0.49 *) in groundnut-treated soil suggested that the high bioavailability of DN reduced CUE due to imbalanced DOC-and-DN stoichiometry. This contradicted earlier published findings on high-nitrogen residues which had balanced DOC-and-DN stoichiometry. The positive influence of the ratio on <i>q</i>CO₂ under the tamarind-treated soil (R² = 0.60 *) indicated that its balanced DOC-and-DN stoichiometry enhanced CUE. High-quality class organic residues can result in either higher or lower CUE than their lower-quality class counterparts depending on whether the resulting DOC-and-DN stoichiometry is balanced or imbalanced.

Yuanxin Song, Yanjun Zhang, Ningyue Chen et al.

Study region: The Huanggou Hillslope in China Study focus: The study of the threshold behavior and nonlinear characteristics of subsurface stormflow is not only essential for hydrology theory but also for flash flood disaster prevention. However, the formation mechanism and determination method of the thresholds have received little attention. Firstly, this study proposed the three-stage subsurface stormflow mechanism hypothesis. Secondly, based on in-situ observation, this study utilized the piecewise regression method and the soil moisture accounting method to analyze the threshold behavior and verify the three-stage mechanism hypothesis. And found that at the Huanggou Hillslope, the thresholds are related to the soil characteristic water content, and the sum of the thresholds of stratified runoff is approximately equal to the threshold of total runoff. Finally, this study developed the three-stage subsurface stormflow-based model (TSSM) and applied it to the Huanggou Hillslope and the Huanggou Watershed. New hydrological insights for the region: The results show that TSSM performed well, with NSEs of 0.82 and 0.67 in the calibration and verification periods of the Huanggou slope, and NSEs of 0.76 and 0.74 in the calibration and verification periods of the Huanggou Watershed, respectively. This study elucidated the three-stage subsurface stormflow mechanism and developed an effective simulation model, which contributes to increasing our understanding of three-stage subsurface stormflow and is beneficial for hydrologists to develop more realistic hydrological models.

Dani Varghese, Viviana Piermattei, Alice Madonia et al.

Chlorophyll-a (Chl-a) and Total Suspended Matter (TSM) are key health indicators of the coastal ocean and seas. The former is linked to primary productivity, while the latter is associated with water quality; both are influenced by change in climate. Recent studies have highlighted a declining trend in Chl-a levels along the Mediterranean coastal region. River discharge plays an important role in regulating the coastal Chl-a concentration levels. The present research primarily focuses on understanding the significance of Tiber River −driven spatial dynamics of Chl-a and TSM along the central Tyrrhenian Sea coasts. The research also focuses on evaluating the applicability of Sentinel-2 and identifying a suitable method for estimating Chl-a and TSM from Sentinel-2. Neural networks and dark spectrum fitting techniques were applied using multiple algorithms to estimate the dynamic distribution of Chl-a and TSM driven by the Tiber River in the study area. Multiple statistical analyses were performed, and statistically significant relationships were observed. The Case-2 Regional Coast Colour Neural Network (C2RCC-Net) outperformed all other algorithms, with an R2 value of 0.903 for Chl-a and an R2 value of 0.966 for TSM. Furthermore, the present research also identified a positive pixel to pixel spatial correlation between Chl-a and TSM in all four seasons, highlighting the positive impact of Tiber River on maintaining Chl-a levels along the coasts of Tyrrhenian Sea. This stands in contrast with the negative trend seen in the Mediterranean scale.

R. Pain

Marcel Fischbach, Kerstin Weinberg

Extrusion-based 3D printing has become one of the most common additive manufacturing methods and is widely used in engineering. This contribution presents the results of flexural creep experiments on 3D printed PLA specimens, focusing on changes in creep behavior due to physical aging. It is shown experimentally that the creep curves obtained on aged specimens are shifted to each other on the logarithmic time scale in a way that the theory of physical aging can explain. The reason for the physical aging of 3D printed thermoplastics is assumed to be the special heat treatment that the polymer undergoes during extrusion. Additionally, results of a long-term flexural creep experiment are shown, demonstrating that non-negligible creep over long periods can be observed even at temperatures well below the glass transition temperature. Such creep effects should be considered for designing components made of 3D printed thermoplastics.

Mageshwaran Tamilan, Gargi Shaw, Sudip Bhattacharyya et al.

Tidal disruption event (TDE) iPTF16fnl shows a relatively low optical flare with observationally very weak X-ray emission and the spectroscopic property that the helium emission line from the source dominates over the hydrogen emission line at early times. We explore these observed signatures by calculating spectral emission lines with the publicly available code, CLOUDY. We estimate five physical parameters by fitting the observed optical UV spectra on multiple days to a theoretical model of a steady-state, slim disk with a spherical outflow. The resultant key parameters among them are black hole mass $M_{\bullet} \sim 4.43 \times 10^5 M_{\odot}$, stellar mass $M_{\star} \sim 0.46 M_{\odot}$, and wind velocity $v_{\rm w} \sim 5382.7~{\rm km~s^{-1}}$. The disk-wind model also estimates the radiative efficiency to be $0.01\lesssimη\lesssim0.04$ over the observational time, resulting in the disk being radiatively inefficient. In our CLOUDY model, the filling factor of the wind is also estimated to be 0.8, suggesting that the wind is moderately clumpy. We reveal that the helium-to-hydrogen number density ratio of the wind lies between 0.1 and 0.15, which is nearly the same as the solar case, suggesting the tidally disrupted star is originally a main sequence star. Because the optical depth of the helium line is lower than the hydrogen line by two orders of magnitude, the helium line is significantly optically thinner than the hydrogen line. Consequently, our results indicate that the helium line luminosity dominates the hydrogen line luminosity due to the optical depth effect despite a small helium-to-hydrogen number density ratio value.

Young-Jin Ahn, Zuhriddin Juraev

Green spaces have gained increasing urgency due to the global and local challenges resulting from rapid urbanization and environmental issues. This study specifically focuses on the design, management, and crucial significance of urban green spaces in Uzbekistan within the framework of sustainable development. Through the integration of diverse theoretical perspectives and interdisciplinary approaches, the study seeks to enhance biodiversity, facilitate wildlife movement, and promote environmental sustainability within urban green spaces. The research methodology employed in this study is robust, encompassing comprehensive data analysis, which provides compelling evidence of the positive impacts of green spaces on both physical and mental well-being. Moreover, the study establishes a clear correlation between the concept of nature-based solutions and the imperative to leverage the potential of nature to effectively address environmental challenges and achieve socioeconomic advantages. In addition, the study explores the role of geography in effectively addressing the opportunities and challenges associated with urban green spaces in Uzbekistan. Policymakers, researchers, and practitioners engaged in promoting sustainable urban development can draw valuable insights from this study. The findings underscore the critical importance of adopting a multidimensional approach to urban planning to foster the development of sustainable and livable cities. By implementing the recommendations outlined in this study, Uzbekistan has the potential to enhance its green spaces and successfully tackle pressing environmental challenges. Consequently, this study fills a significant research gap by highlighting the pivotal role of geography in comprehending and addressing the importance of urban green spaces. The findings align with the concepts of nature-based solutions and geography, offering actionable guidance for sustainable urban development in Uzbekistan.

Z. Shafeei, S. A. Alavi

Lorentz symmetry is one of the cornerstone of both general relativity and the standard model of particle physics. We study the violation of Lorentz symmetry in some basic phenomena in atomic physics. Using the Green's function, and the source 4-current, the differential equation of 4-vector of electromagnetic potential is solved and the modified coulomb potential is obtained by some researchers. Using modified Coulomb potential, we find the corrections due to LIV on the spectrum of Hydrogen and Helium atoms. We also investigate the consequences of LIV on Stark, Zeeman and Spin orbit effects and obtain some upper bounds for the LIV coefficients.

Rajat Arora, Pratik Kakkar, Biswadip Dey et al.

This work presents a physics-informed neural network (PINN) based framework to model the strain-rate and temperature dependence of the deformation fields in elastic-viscoplastic solids. To avoid unbalanced back-propagated gradients during training, the proposed framework uses a simple strategy with no added computational complexity for selecting scalar weights that balance the interplay between different terms in the physics-based loss function. In addition, we highlight a fundamental challenge involving the selection of appropriate model outputs so that the mechanical problem can be faithfully solved using a PINN-based approach. We demonstrate the effectiveness of this approach by studying two test problems modeling the elastic-viscoplastic deformation in solids at different strain rates and temperatures, respectively. Our results show that the proposed PINN-based approach can accurately predict the spatio-temporal evolution of deformation in elastic-viscoplastic materials.

Micah Bowles, Hongming Tang, Eleni Vardoulaki et al.

We define deriving semantic class targets as a novel multi-modal task. By doing so, we aim to improve classification schemes in the physical sciences which can be severely abstracted and obfuscating. We address this task for upcoming radio astronomy surveys and present the derived semantic radio galaxy morphology class targets.

Saeed Rad, Dai Junfeng, Xu Jingxuan et al.

We analyzed the characteristics of main karstic/non-karst reaches of the Lijiang River to uncover the causes behind different flood behaviors by providing a better understanding of the flood formation. Having 63 years of rainfall-runoff data and applying the HEC-HMS model, geo/hydrological features were investigated. The available reservoir capacity of karts (ARCK) was included through soil moisture accounting loss data to assess its impact. In particular, the expected instantaneous peak discharge rates/times were found largely imbalanced with generated unit hydrographs. Moreover, significant gaps among the floods’ features for different subbasins in terms of required peak modifications (2–4 times larger for mid-upstream, respectively) were mainly associated with the unique karst structure and initial condition due to various ARCK in rainy/dry seasons. Besides, notable dissimilarities between the wedge/prism storage volumes and the hydrograph’s wave traveling/receding time were observed owing to the geomorphological conditions. Although the contribution rates of drivers in karst flood formation cannot be quantitively modeled, based on our results the ARCK emerged to play a substantial role on the forecasted results, comparatively. Our results suggest that since ARCK varies, taking it into account (as initial abstraction) results in a more reliable estimation. This was underpinned by the results in which the unmodified simulations had a qualified rate of 52% accuracy on average and increased to 67.5% after the ARCK inclusion. This work adds to the body of evidence illustrating that in karst hydrology, ignoring the situational circumstances in modeling might lead to inaccuracies in flood forecasting for such dynamic watersheds. HIGHLIGHTS Hydrological models inaccurately forecast flood features in karst basins.; The seasonality of available karst reservoir capacity drives flood peaks.; Initial conditions must be considered in model calibration for karstic areas.;

E. Berntell, Q. Zhang, Q. Li et al.

<p>The mid-Pliocene warm period (mPWP; <span class="inline-formula">∼3.2</span> million years ago) is seen as the most recent time period characterized by a warm climate state, with similar to modern geography and <span class="inline-formula">∼400</span> ppmv atmospheric CO<span class="inline-formula">₂</span> concentration, and is therefore often considered an interesting analogue for near-future climate projections. Paleoenvironmental reconstructions indicate higher surface temperatures, decreasing tropical deserts, and a more humid climate in West Africa characterized by a strengthened West African Monsoon (WAM). Using model results from the second phase of the Pliocene Modelling Intercomparison Project (PlioMIP2) ensemble, we analyse changes of the WAM rainfall during the mPWP by comparing them with the control simulations for the pre-industrial period. The ensemble shows a robust increase in the summer rainfall over West Africa and the Sahara region, with an average increase of 2.5 mm/d, contrasted by a rainfall decrease over the equatorial Atlantic. An anomalous warming of the Sahara and deepening of the Saharan Heat Low, seen in <span class="inline-formula"><i>&gt;</i>90</span> % of the models, leads to a strengthening of the WAM and an increased monsoonal flow into the continent. A similar warming of the Sahara is seen in future projections using both phase 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5). Though previous studies of future projections indicate a west–east drying–wetting contrast over the Sahel, PlioMIP2 simulations indicate a uniform rainfall increase in that region in warm climates characterized by increasing greenhouse gas forcing. We note that this effect will further depend on the long-term response of the vegetation to the CO<span class="inline-formula">₂</span> forcing.</p>

Huiren Tian, Pengxin Wang, Kevin Tansey et al.

The rapid and effective acquisition of crop yield information is critical to the stability of food markets and development and implementation of related policies. It is an important baseline observation that is used for ensuring regional and global food security. In this study, a novel deep learning framework was developed for winter wheat yield estimation using meteorological data and two remotely sensed indices, Vegetation Temperature Condition Index (VTCI) and Leaf Area Index (LAI) at the main growth stages of winter wheat in the Guanzhong Plain. The proposed deep learning model was based on Long Short-Term Memory (LSTM) neural network with an attention mechanism (ALSTM), which the main idea is to assign attention to the key parts of the input sequence that affect the target vectors so that the specific features can be accurately extracted. The ALSTM model provided an improved estimation accuracy (R2 = 0.63, MAPE = 8.20%, RMSE = 502.71 kg/ha, NRMSE = 11.15%) as compared with the LSTM (R2 = 0.55, MAPE = 13.46%, RMSE = 699.92 kg/ha, NRMSE = 15.52%). A validation based on leave-one-year-out-validation further substantiated the robustness of ALSTM with smaller values of NRMSE and MAPE (13.63% and 11.54%). We demonstrated that the ALSTM model provided good generalization ability for sampling sites under different farming systems, including irrigation and rain-fed sampling sites. In addition, we evaluated the relative importance of each input variable in determining yields based on stepwise sensitivity analysis. It was found that LAI at the heading-filling stage and the milk stage as well as VTCI at the jointing stage contributed more than other input feature variables towards the corresponding yield. In conclusion, our findings highlighted that the attention mechanism helped to improve the interpretability of neural networks and the ALSTM model along with remotely sensed biophysical indices can provide a reliable and robust estimation of crop yield. An accurate estimation of wheat yield is not only helping towards informed crop management decisions but it will improve efficiency and sustainability of farming operations.

Abhijeet Sahu, Patrick Wlazlo, Zeyu Mao et al.

A power system is a complex cyber-physical system whose security is critical to its function. A major challenge is to model and analyze its communication pathways with respect to cyber threats. To achieve this, the design and evaluation of a cyber-physical power system (CPPS) testbed called Resilient Energy Systems Lab (RESLab) is presented that captures realistic cyber, physical, and protection system features. RESLab is architected to be a fundamental tool for studying and improving the resilience of complex CPPS to cyber threats. The cyber network is emulated using Common Open Research Emulator (CORE) that acts as a gateway for the physical and protection devices to communicate. The physical grid is simulated in the dynamic time frame using PowerWorld Dynamic Studio (PWDS). The protection components are modeled with both PWDS and physical devices including the SEL Real-Time Automation Controller (RTAC). Distributed Network Protocol 3 (DNP3) is used to monitor and control the grid. Then, exemplifying the design and validation of these tools, this paper presents four case studies on cyber-attack and defense using RESLab, where we demonstrate false data and command injection using Man-in-the-Middle and Denial of Service attacks and validate them on a large-scale synthetic electric grid.

M. Reitner, P. Chalupa, L. Del Re et al.

While the breakdown of the perturbation expansion for the many-electron problem has several formal consequences, here we unveil its physical effect: Flipping the sign of the effective electronic interaction in specific scattering channels. By decomposing local and uniform susceptibilities of the Hubbard model via their spectral representations, we prove how entering the non-perturbative regime causes an enhancement of the charge response, ultimately responsible for the phase-separation instabilities close to the Mott MIT. Our analysis opens a new route for understanding phase-transitions in the non-perturbative regime and clarifies why attractive effects emerging from a strong repulsion can induce phase-separations, but not s-wave pairing or charge-density wave instabilities.