Variable Slope Research Articles

Multimodel ensemble estimation of Landsat-like global terrestrial latent heat flux using a generalized deep CNN-LSTM integration algorithm

Accurate estimates of high-spatial-resolution global terrestrial latent heat flux (LE) from Landsat data are crucial for many basic and applied research. Yet current Landsat-derived LE products were developed using single algorithm with large uncertainties and discrepancies. Here we proposed a convolutional neural network-long short-term memory (CNN-LSTM)-based integrated LE (CNN-LSTM-ILE) framework that integrates five Landsat-derived physical LE algorithms, topography-related variables (elevation, slope and aspect) and eddy covariance (EC) observations to estimate 30-m global terrestrial LE. CNN-LSTM-ILE not only conserves good performance of LE estimation from pure deep learning (DL) algorithm, but partially inherits physical mechanism of the individual physical algorithms for improving the generalization of the integration algorithms for extreme cases. CNN-LSTM is an algorithm that combines two deep learning structures (CNN and LSTM) to effectively utilize the spatial and temporal information contained in the forcing inputs. The data were collected from 190 globally distributed EC observations from 2000 to 2015 and were provided by FLUXNET. The cross-validation results indicated that the CNN-LSTM integration algorithm improved the LE estimates by reducing the root mean square error (RMSE) of 5–8 W/m2 and increasing Kling-Gupta efficiency (KGE) of 0.05–0.16 when compared with the individual LE algorithms and the results of three other machine learning integration algorithms (multiple linear regression, MLR; random forest, RF; and deep neural networks, DNN). The CNN-LSTM integration algorithm had highest KGE (0.81) and R2 (0.66) compared to ground-measured and was applied to generate the Landsat-like regional and global terrestrial LE. An innovation of our strategy is that the CNN-LSTM-ILE model integrates pixel proximity effects and daily LE variations to enhance the accuracy of 16-day LE estimations. This approach can produce a more reliable Landsat-like global terrestrial LE product to improve the representativeness of heterogeneous regions for monitoring hydrological variables.

Effects of root traits on soil detachment capacity driven by farmland abandonment

Vegetation restoration significantly affected root traits during farmland abandonment, thereby considerably altering the soil detachment process. However, few studies have investigated the effects of root traits on soil detachment capacity under different ages of farmland abandonment. To examine the effects of root trait changes caused by vegetation restoration on soil detachment capacity by overland flow (Dc) and soil resistance to erosion under different ages of farmland abandonment, flow scouring tests under five shear stress levels (2.80–12.95 Pa) were conducted on 125 undisturbed soil samples collected using an indoor variable slope flume. The results showed that, compared to slope farmland, the root traits of abandoned farmland (5, 12, 24, and 36 years) increased considerably, and the Dc and erodibility decreased by 51.3–93.9 % and 61.8–94.1 %, respectively. The critical shear stress of abandoned farmland gradually increased with the age of farmland abandonment, but remained less than that of slope farmland. Among hydraulic parameters, stream power was the optimal index for predicting Dc, while among root morphological traits, root surface area density (RSAD) was the best indicator for predicting Dc. The median soil particle size (D50) and cohesion are important factors that affect soil detachment. According to the Water Erosion Prediction Project (WEPP) model, Dc can be predicted using the RSAD and soil cohesion (R2 = 0.95, NSE = 0.95). In addition, a new model was established to estimate the soil detachment capacity based on D50 (R2 = 0.97, NSE = 0.97). In the present study, the performance of the model was improved compared with that of previous studies.

Deciphering the effect of variation in slope on flow characteristics in a vertical slot fishway

The effects of a vertical slot fishway slope (with slope values from 1.5% to 6%) on a flow field are numerically investigated, using a re-normalization group k – ε model. The distribution of the velocity, turbulence kinetic energy (TKE), average energy dissipation rate per unit volume (E), and vorticity for different slopes are systemically explored. The results indicate that, with an increase in slope, the appearance of downward flow in conjunction with an increase in vertical velocity results in three-dimensional flow characteristics. The recirculation region in Hs, at a 6.0% slope, was 20.9% less than that at a 1.5% slope. Meanwhile, the flow velocity in the vertical slot region grew with increasing slope, which would limit the passage of fish with burst speed lower than the velocity in the vertical slot region. The TKE and E may locally exceed the threshold at larger slopes. Furthermore, vorticity distribution shows little variability with increasing slope, but may interfere with the equilibrium of the fish in the vertical slot region. In addition, the change in water level has little effect on the flow field, which is changed by the increase in slope. These findings can aid vertical slot fishway designs especially in terms of the efficiency of fish passage.

Achievement of dynamic tablet defect detection mechanism using biaxial slope symmetry algorithm

An inspection in tablet appearance integrity before bottling is regarded as a routine task in a pharmaceutical factory. Although some methods such as automated optical instrument, video or artificial intelligence (AI) are currently available in industry, it usually pays for a complex computational process as well as high cost. Based on the symmetry of tablet appearance in reality, this study develops a biaxial scanning slope symmetry algorithm to realize a dynamic real-time tablet defect detection with a simple arithmetic operation. First, the tablet is discretely scanned using image sensor in two axes, i.e. X and Y directions, simultaneously. Second, the analogy output signals generated from the sensor during the scanning process is discretely digitized and stored in an array. Third, the coordinate of center point in the series data array is identified from every line scanning. Fourth, every section slope between two nearby center points from the first to last lines is formulated and calculated sequentially. Finally, the square mean error (SME) is used to evaluate the shape defect situation according to all accumulated errors from every slope variation. The experimental results verify that the proposed algorithm can achieve both fast and accurate detection performance.

Local projections in unstable environments

This paper develops a local projection estimator for estimating impulse responses in the presence of time variation. Importantly, we allow local instabilities in both slope coefficients and variances. Monte Carlo simulations illustrate that the method performs well in practice. Using our proposed estimator, we shed new light on the effects of fiscal policy shocks and the size of government spending multipliers. Our analysis uncovers the existence of instabilities that were unaccounted for in previous studies, and links time variation in the multipliers to the size of government debt.

Rainfall-Induced Probability of Failure in Spatially Variable Soil Slopes and a Case Study of the Konkan Railway Slope Failure

Rainfall-Induced Probability of Failure in Spatially Variable Soil Slopes and a Case Study of the Konkan Railway Slope Failure

Digital solutions in the determination of bearing capacity for straight and stepped mat foundations on sloping ground

On sloped sites where a mat foundation is to be used, because of the limited bearing capacity of the ground, it is necessary to cut into the sloped surface to provide a level base for the mat foundation. To reduce the amount of excavation and backfill a stepped mat may be used down the slope. Traditionally, determining the bearing capacity values for spread foundations on sloping ground has been a complex and time-consuming process. Several factors must be considered, including various variables (soil type, soil profile, slope angle and geometrical configuration of the infrastructure). The digital solution used in this study involves the use of advanced software that takes into account various input parameters and performs complex calculations to determine the bearing capacity of the foundation on horizontal and sloped ground. For this research, the analyses were done for three different slope angles (β = 5°, 10°, 15°) and a comparison is made between the bearing capacity of the ground for straight and stepped mat foundations using both analytical methods and finite element method.

Geographic patterns and environmental correlates of taxonomic, phylogenetic and functional β-diversity of wetland plants in the Qinghai-Tibet Plateau

The Qinghai-Tibet Plateau (QTP) harbours the largest wetland complex in China, with a relatively high diversity of wetland plants. While there has been extensive research on the α-diversity of various plant groups in the QTP, the study of β-diversity specifically in wetland plants has been limited. In this study, we quantified the geographic pattern of wetland plant β-diversity from three dimensions: taxonomic, phylogenetic, and functional. We assessed the relative importance of geographic distance, climatic distance, environmental distance and topographic distance on the pattern of β-diversity. The results indicate a high consistency in the geographic pattern of wetland plant β-diversity across the three dimensions. β-diversity is high in the mountainous regions at the edge of the plateau and relatively low in the plateau platform. The turnover component contributes more to taxonomic β-diversity than nestedness, whereas the opposite is true for phylogenetic and functional β-diversity. Furthermore, taxonomic β-diversity is higher than phylogenetic and functional β-diversity. The geographic patterns of taxonomic, phylogenetic and functional diversity of wetland plants in the QTP are jointly influenced by environmental filtering and dispersal limitation. Climatic variables (temperature seasonality, annual precipitation, precipitation seasonality and annual mean temperature) and topographic variables (elevation variation coefficient, elevation and slope) were identified as the main factors influencing wetland plant β-diversity in the QTP. Overall, this study contributes to elucidating the evolutionary history and formation mechanisms of wetland plant communities in the QTP, and reveals the large-scale patterns of wetland plant diversity.

The Near-infrared Extinction Law at High and Low Galactic Latitudes

The Milky Way dust extinction curve in the near-infrared (NIR) follows a power-law form, but the value of the slope, β NIR, is debated. Systematic variations in the slope of the Milky Way UV extinction curve are known to be correlated with variations in the optical slope (through R V ), but whether such a dependence extends to the NIR is unclear. Finally, because of low dust column densities, the NIR extinction law is poorly understood at high Galactic latitudes where most extragalactic work takes place. In this paper, we construct extinction curves from 56,649 stars with Sloan Digital Sky Survey (SDSS) and Two Micron All Sky Survey photometry, based on stellar parameters from SDSS spectra. We use dust maps to identify dust-free stars, from which we calibrate the relation between stellar parameters and intrinsic colors. Furthermore, to probe the low-dust regime at high latitudes, we use aggregate curves based on many stars. We find no systematic variation of β NIR across low-to-moderate dust columns (0.02 < E(B − V) ≲ 1), and report average β NIR = 1.85 ± 0.01, in agreement with the law in the 2019 Fitzpatrick et al. study, but steeper than the Cardelli et al. and 1999 Fitzpatrick laws. Star-to-star scatter in β NIR is relatively small (σ(β NIR) = 0.13). We also find no intrinsic correlation between β NIR and R V (there is an apparent correlation that is the result of the correlated uncertainties in the two values). These results hold for typical sightlines; we do not probe very dusty regions near the Galactic Center, nor rare sightlines with R V > 4. Finally, we find R H = 0.345 ± 0.007 and comment on its bearing on Cepheid calibrations and the determination of H 0.

Associations between the resting EEG aperiodic slope and broad domains of cognitive ability.

Recent studies suggest that the EEG aperiodic exponent (often represented as a slope in log-log space) is sensitive to individual differences in momentary cognitive skills such as selective attention and information processing speed. However, findings are mixed, and most of the studies have focused on just a narrow range of cognitive domains. This study used an archival dataset to help clarify associations between resting aperiodic features and broad domains of cognitive ability, which vary in their demands on momentary processing. Undergraduates (N = 166) of age 18-52 years completed a resting EEG session as well as a standardized, individually administered assessment of cognitive ability that included measures of processing speed, working memory, and higher-order visuospatial and verbal skills. A subsample (n = 110) also completed a computerized reaction time task with three difficulty levels. Data reduction analyses revealed strong correlations between the aperiodic offset and slope across electrodes, and a single component accounted for ~60% of variance in slopes across the scalp, in both eyes-closed and eyes-open conditions. Structural equation models did not support relations between the slope and specific domains tapping momentary processes. However, secondary analyses indicated that the eyes-open slope was related to higher overall performance, as represented by a single general ability factor. A latent reaction time variable was significantly inversely related to both eyes-closed and eyes-open resting exponents, such that faster reaction times were associated with steeper slopes. These findings support and help clarify the relation of the resting EEG exponent to individual differences in cognitive skills.

A Staged Real-Time Ground Segmentation Algorithm of 3D LiDAR Point Cloud

Ground segmentation is a crucial task in the field of 3D LiDAR perception for autonomous driving. It is commonly used as a preprocessing step for tasks such as object detection and road extraction. However, the existing ground segmentation algorithms often struggle to meet the requirements of robustness and real-time performance due to significant variations in ground slopes and flatness across different scenes, as well as the influence of objects such as grass, flowerbeds, and trees in the environment. To address these challenges, this paper proposes a staged real-time ground segmentation algorithm. The proposed algorithm not only achieves high real-time performance but also exhibits improved robustness. Based on a concentric zone model, the algorithm filters out reflected noise points and vertical non-ground points in the first stage, improving the validity of the fitted ground plane. In the second stage, the algorithm effectively addresses the issue of undersegmentation of ground points through three steps: ground plane fitting, ground plane validity judgment, and ground plane repair. The experimental results on the SemanticKITTI dataset demonstrate that the proposed algorithm outperforms the existing methods in terms of segmentation results.

Fast surface signal extraction method for photon point clouds with strong background noise without prior altitude information.

It is extremely challenging to rapidly and accurately extract target echo photon signals from massive photon point clouds with strong background noise without any prior geographic information. Herein, we propose a fast surface detection method realized by combining the improved density-dimension algorithm (DDA) and Kalman filtering (KF), termed the DDA-KF algorithm, for photon signals with a high background noise rate (BNR) to improve the extraction of surface photon signals from spacecraft platforms. The results showed that the algorithm exhibited good adaptability to strong background noise and terrain slope variations, and had real-time processing capabilities for massive photon point clouds in large-scale detection range without prior altitude information of target. Our research provides a practical technical solution for single-photon lidar applications in deep space navigation and can help improve the performance in environments characterized by strong background noise.

Land-Use Change Prediction in Dam Catchment Using Logistic Regression-CA, ANN-CA and Random Forest Regression and Implications for Sustainable Land–Water Nexus

For sustainable water resource management within dam catchments, accurate knowledge of land-use and land-cover change (LULCC) and the relationships with dam water variability is necessary. To improve LULCC prediction, this study proposes the use of a random forest regression (RFR) model, in comparison with logistic regression–cellular automata (LR-CA) and artificial neural network–cellular automata (ANN-CA), for the prediction of LULCC (2019–2030) in the Gaborone dam catchment (Botswana). RFR is proposed as it is able to capture the existing and potential interactions between the LULC intensity and their nonlinear interactions with the change-driving factors. For LULCC forecasting, the driving factors comprised physiographic variables (elevation, slope and aspect) and proximity-neighborhood factors (distances to water bodies, roads and urban areas). In simulating the historical LULC (1986–2019) at 5-year time steps, RFR outperformed ANN-CA and LR-CA models with respective percentage accuracies of 84.9%, 62.1% and 60.7%. Using the RFR model, the predicted LULCCs were determined as vegetation (−8.9%), bare soil (+8.9%), built-up (+2.49%) and cropland (−2.8%), with water bodies exhibiting insignificant change. The correlation between land use (built-up areas) and water depicted an increasing population against decreasing dam water capacity. The study approach has the potential for deriving the catchment land–water nexus, which can aid in the formulation of sustainable catchment monitoring and development strategies.

Quantitative characterization of the contribution of electrogenerated active species for NH+ 4-N oxidation on Ti/RuO2-IrO2 anode

The contribution of active species for ammonia nitrogen (NH4+-N) oxidation has received much attention in recent decades through electrocatalytic oxidation (EO) technology. Here we describe the strong NH4+-N removal capacity (more than 98 %) with NaCl as electrolyte of Ti/RuO2-IrO2 anode. Electrochemical tests were used to characterize the excellent NH4+-N degradation performance of Ti/RuO2-IrO2 anode in presence of chlorides. The values of electron transfer number (n = 1.02) and transfer coefficient (α = 0.42) show that the reaction is in the diffusion control stage, which is more conducive to the degradation of NH4+-N. Additionally, three active species (•OH, Cl•, and ClO•) formation is related to the adsorption energy (obtained by density functional theory (DFT) calculation) during the electrolysis process. The difference charge density and density of state (DOS) calculation indicate that the O − in ClO• is more likely to break and react with pollutants in water, which is consistent with the results of active species detection experiments. The combination of kinetics (Tafel) and thermodynamics (adsorption energy) shows that the variation of Tafel slope is affected by the coverage of active species on the electrode surface.

Long Downhill Braking and Energy Recovery of Pure Electric Commercial Vehicles

The thermal decay of the brake has a great impact on the long downhill braking stability of pure electric commercial vehicles. Based on the road slope and using the fuzzy control method, the motor regenerative braking force and friction braking force distribution strategies were designed to reduce the friction braking force, improve the braking stability and recover the braking energy. By establishing road driving conditions with different slopes, numerical analysis methods are used to verify the proposed control strategy. The results show that the vehicle maintains a constant speed downhill at 30 km/h under the condition of 6% constant slope driving, and the braking energy recovery rate reaches 50.93% under 60% initial battery SOC, 50.89% under 70% initial battery SOC, and 50.81% under 80% initial battery SOC. The speed of the vehicle fluctuates slightly under the driving condition of an 18 km long variable slope distance, but the power torque of the electric mechanism can still be maintained at a constant speed of 30 km/h by adjusting the electric mechanism, and the braking energy recovery rate reaches 49.96%. During the downhill driving at a constant speed, the friction braking force does not participate in braking, and the recuperation rate of braking is determined by the slope and the magnitude of braking deceleration.

Forest demography and biomass accumulation rates are associated with transient mean tree size vs. density scaling relations.

Linking individual and stand-level dynamics during forest development reveals a scaling relationship between mean tree size and tree density in forest stands, which integrates forest structure and function. However, the nature of this so-called scaling law and its variation across broad spatial scales remain unquantified, and its linkage with forest demographic processes and carbon dynamics remains elusive. In this study, we develop a theoretical framework and compile a broad-scale dataset of long-term sample forest stands (n = 1,433) from largely undisturbed forests to examine the association of temporal mean tree size vs. density scaling trajectories (slopes) with biomass accumulation rates and the sensitivity of scaling slopes to environmental and demographic drivers. The results empirically demonstrate a large variation of scaling slopes, ranging from -4 to -0.2, across forest stands in tropical, temperate, and boreal forest biomes. Steeper scaling slopes are associated with higher rates of biomass accumulation, resulting from a lower offset of forest growth by biomass loss from mortality. In North America, scaling slopes are positively correlated with forest stand age and rainfall seasonality, thus suggesting a higher rate of biomass accumulation in younger forests with lower rainfall seasonality. These results demonstrate the strong association of the transient mean tree size vs. density scaling trajectories with forest demography and biomass accumulation rates, thus highlighting the potential of leveraging forest structure properties to predict forest demography, carbon fluxes, and dynamics at broad spatial scales.

Data aggregation can lead to biased inferences in Bayesian linear mixed models and Bayesian analysis of variance.

Bayesian linear mixed-effects models (LMMs) and Bayesian analysis of variance (ANOVA) are increasingly being used in the cognitive sciences to perform null hypothesis tests, where a null hypothesis that an effect is zero is compared with an alternative hypothesis that the effect exists and is different from zero. While software tools for Bayes factor null hypothesis tests are easily accessible, how to specify the data and the model correctly is often not clear. In Bayesian approaches, many authors use data aggregation at the by-subject level and estimate Bayes factors on aggregated data. Here, we use simulation-based calibration for model inference applied to several example experimental designs to demonstrate that, as with frequentist analysis, such null hypothesis tests on aggregated data can be problematic in Bayesian analysis. Specifically, when random slope variances differ (i.e., violated sphericity assumption), Bayes factors are too conservative for contrasts where the variance is small and they are too liberal for contrasts where the variance is large. Running Bayesian ANOVA on aggregated data can-if the sphericity assumption is violated-likewise lead to biased Bayes factor results. Moreover, Bayes factors for by-subject aggregated data are biased (too liberal) when random item slope variance is present but ignored in the analysis. These problems can be circumvented or reduced by running Bayesian LMMs on nonaggregated data such as on individual trials, and by explicitly modeling the full random effects structure. Reproducible code is available from https://osf.io/mjf47/. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Downscaled GRACE/GRACE-FO observations for spatial and temporal monitoring of groundwater storage variations at the local scale using machine learning

Groundwater utilization for several purposes such as irrigation in agriculture, industry, and domestic use substantially impacts water storage. Groundwater Storage Anomaly (GWSA) estimates have improved owing to the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (GRACE-FO) advancements. However, the characterization of GWSA fluctuation hotspots has been hindered by the coarse resolution of GRACE data. To better measure groundwater storage and depletion variations throughout an area and identify GWSA variation hotspots, a fine spatial resolution of GWSA estimations is required. Therefore, due to the coarse resolution of GRACE measurements, the eXtreme Gradient Boosting (XGBoost) model was developed to simulate fine resolution 0.1° GWSA combining climatic variables (soil moisture storage, evapotranspiration, temperature, surface runoff, and rainfall) from improved spatial high resolution FLDAS (Famine Early Warning Systems Network Land Data Assimilation System) model derived data and geospatial variables (elevation, slope, and aspect) extracted from Digital Elevation Model (DEM). A correlation of 0.98 demonstrated that the XGBoost model successfully simulated groundwater storage at a finer scale over the Upper Indus Plain Aquifer (UIPA). The findings suggested that the UIPA's groundwater storage has been depleted at an annual rate of 0.44 km3/yr which was 7.94 km3 in total between 2003 and 2020. According to the results, there seems to be consistency between the downscaled and original GWSA regarding temporal and spatial variability. The results were verified to show an improved correlation of 0.77 between the downscaled and the in-situ GWSA, compared to 0.75 between the GRACE-derived and the in-situ GWSA.

Is eGFR Slope a Novel Predictor of Chronic Complications of Type 2 Diabetes Mellitus? A Systematic Review and Meta-Analysis.

Diabetic kidney disease affects approximately 40% of patients with type 2 diabetes mellitus (T2DM) and is associated with an increased risk of end-stage kidney disease (ESKD) and cardiovascular (CV) events, as well as increased mortality. Among the indicators of decline in renal function, the eGFR slope is acquiring an increasing clinical interest. The aim of this study was to evaluate, through a systematic review of the literature and meta-analysis of the collected data, the association between the decline of the eGFR slope, chronic complications, and mortality of T2DM patients, in order to understand whether or not the eGFR slope can be defined as a predictive indicator of complications in T2DM. The review and meta-analysis were conducted according to PRISMA guidelines considering published studies on patients with T2DM. A scientific literature search was carried out on PubMed from January 2003 to April 2023 with subsequent selection of scientific papers according to the inclusion criteria. Fifteen studies were selected for meta-analysis. Risk analysis as hazard ratio (HR) indicated a significant association between all events considered (all-cause mortality, CV events, ESKD, and microvascular events) for patients with steeper eGFR slope decline than subjects with stable eGFR. Calculated HRs (with 95% CI) were as follows: for all-cause mortality, 2.31 (1.70-3.15); for CV events, 1.73 (1.43-2.08); for ESKD, 1.54 (1.45-1.64); and for microvascular events, 2.07 (1.57-2.73). Overall HR was 1.82 (1.72-1.92). An association between rapid eGFR decline and chronic diabetes complications was demonstrated, suggesting that eGFR slope variability significantly impacts the course of T2DM and that eGFR slope should be considered as a predictor for chronic complications in patients with T2DM. According to the obtained results, the therapeutic management of the patient with diabetes should not focus exclusively on glycaemic control, and particular attention should be paid to preserve renal function.

The Influence of Slope Gradient on Termite Assemblages and Soil Physico-Chemical Properties in Koudi Secondary Forest Patches of the Central Region of Cameroon

Soil is the Cameroon’s primary economic driver and an indispensable natural resource for agriculture. Various factors including physical, chemical and biological directly affect agricultural production by enhancing soil fertility. The influence of relief on termites’ diversity was extensively studied. However, the effect of the slope gradient on termite diversity and functional groups remains less studied. This study evaluated termite assemblages in relation to some edaphic parameters, with emphasis on gradual changes in soil slope. The study was conducted in Koudi, in the semi-deciduous forest zone of Southern Cameroon. The study aimed to evaluate the effect of slope gradient on termite diversity and some soil physico-chemical factors; the influence of some physico-chemical soil properties on termites. To achieve these, three slope gradients: gentle slope: A [0 - 15°]; moderate slope: B [15.1°- 31°]; and steeper slope: C [x &gt; 31°[were chosen. The experimental design was composed of 66 rectangular quadrants of 5m x 2m in order to determine the presence or absence of termites. Two soil samples were taken per slope gradient for the determination of physico-chemical parameters. Termite community was characterized using species richness, Shannon diversity, Pielou Evenness and Simpson dominance indexes. Data obtained were subjected to Spearman correlation test and ANOVA followed by the Tukey HSD test at a threshold p = 0.05. Our findings suggested thirty-five(35) termite species belonging to eighteen (18) genders, six (6)subfamilies that were sampled and identified. Termites’ diversity and species richness decreased as slope gradient increased; slope gradient displayed a significant detrimental correlation with sand (%) and total organic (g/kg) content, with a positive significant correlation with clay (%) content, according to Spearman's test. It appeared that variations of slope at soil surface have been identified as one of the factors controlling nutrients and termite diversity.