Measurements and transient multistep outflow simulation of soil-water characteristic curve for soils modified with biopolymers

In recent decades, biopolymers have shown promising applications in soil modification due to its environmental friendly nature. Most of the studies, however, focused on mechanical properties at saturated or unsaturated conditions. The study on unsaturated soil behaviors under controlled pore air and pore water pressures were limited. Soil-water characteristic curve (SWCC), relating water content with matric suction is a key property to evaluate unsaturated soils. With SWCC, other soil properties, such as hydraulic conductivity and shear strength can be estimated. In this study, SWCC of sands modified with different biopolymers were measured with both Tempe cell and Fredlund SWCC device. An elevation-controlled low suction (0.01 to 5 kPa) horizontal tube was developed to accurately measure SWCC of sands. Corrections for air diffusion and evaporation were performed. The results were fitted by both Fredlund and Xing and van Genutchen equations. In addition, inverse simulation of SWCC based on one-step or multistep SWCC measurements were carried out with software Hydrus 1D, finite element software. The measured SWCC results of mine tailing were used as an example. The inverse model can significantly reduce the time to measure a SWCC curve, especially for soils with low hydraulic conductivity (clay and silt). Three different input outflow methods were used, namely multiple single-step outflow method (MSOM), one-step outflow method (OOM), and multiple-step outflow method (MOM). Their performance was evaluated by both SWCC results and outflow vs. time curves. It was found that MOM provided the most accurate SWCC, while MSOM yielded the most accurate Flux -Time results --Abstract, page iii

An Analysis of the Anti-Japanese National United Front and Its Formation Process from the Perspective of Game Theory

The united front is a magic weapon for the victory of the revolution, construction and reform of the Communist Party of China. The formation process of the united front is a kind of game, starting from the perspective of game theory, using the assets exclusiveness theory of economics, combining the Nash equilibrium and historical events, building a game model, analyzing the Anti-Japanese National United Front and its formation process, are of great significance to improve the scientific cognition level of historical research work and united front work

Sedimentation Behavior of Fly Ash-Kaolinite Mixtures

The sedimentation behavior of fine grained soil is largely dependent on its pore fluid chemistry. Different ionic concentration will lead to different fabrics of soil suspension, such as dispersion, aggregation, and flocculation. The ionic concentration also influences the thickness of the Diffuse Double Layer (DDL), which leads to the change in the final sediment volumes. Besides the ionic concentration influence, adding different amount of fly ash (FA) can also cause different sedimentation behaviors. The objective of this research was to quantify the interaction between fly ash and fine grained soils by comparing the influence of ionic concentration with that of fly ash on the sedimentation behavior of kaolinite. It was found that an increase in the percentage of fly ash in FA-kaolinite mixture could cause an increase in the settling speed. The final sedimentation volume decreased as the ionic concentration increased. The addition of fly ash was found more efficiency than the ionic concentration, because the fly ash could not only interact with kaolinite particles but also increase the ionic concentration in the dissolution by precipitation of Calcium hydroxide and pozzolanic reaction products

Land Cover Information Extraction Based on Daily NDVI Time Series and Multiclassifier Combination

A timely and accurate understanding of land cover change has great significance in management of area resources. To explore the application of a daily normalized difference vegetation index (NDVI) time series in land cover classification, the present study used HJ-1 data to derive a daily NDVI time series by pretreatment. Different classifiers were then applied to classify the daily NDVI time series. Finally, the daily NDVI time series were classified based on multiclassifier combination. The results indicate that support vector machine (SVM), spectral angle mapper, and classification and regression tree classifiers can be used to classify daily NDVI time series, with SVM providing the optimal classification. The classifiers of K-means and Mahalanobis distance are not suited for classification because of their classification accuracy and mechanism, respectively. This study proposes a method of dimensionality reduction based on the statistical features of daily NDVI time series for classification. The method can be applied to land resource information extraction. In addition, an improved multiclassifier combination is proposed. The classification results indicate that the improved multiclassifier combination is superior to different single classifier combinations, particularly regarding subclassifiers with greater differences

RAEDiff: Denoising Diffusion Probabilistic Models Based Reversible Adversarial Examples Self-Generation and Self-Recovery

Collected and annotated datasets, which are obtained through extensive efforts, are effective for training Deep Neural Network (DNN) models. However, these datasets are susceptible to be misused by unauthorized users, resulting in infringement of Intellectual Property (IP) rights owned by the dataset creators. Reversible Adversarial Exsamples (RAE) can help to solve the issues of IP protection for datasets. RAEs are adversarial perturbed images that can be restored to the original. As a cutting-edge approach, RAE scheme can serve the purposes of preventing unauthorized users from engaging in malicious model training, as well as ensuring the legitimate usage of authorized users. Nevertheless, in the existing work, RAEs still rely on the embedded auxiliary information for restoration, which may compromise their adversarial abilities. In this paper, a novel self-generation and self-recovery method, named as RAEDiff, is introduced for generating RAEs based on a Denoising Diffusion Probabilistic Models (DDPM). It diffuses datasets into a Biased Gaussian Distribution (BGD) and utilizes the prior knowledge of the DDPM for generating and recovering RAEs. The experimental results demonstrate that RAEDiff effectively self-generates adversarial perturbations for DNN models, including Artificial Intelligence Generated Content (AIGC) models, while also exhibiting significant self-recovery capabilities

The DKU-MSXF Speaker Verification System for the VoxCeleb Speaker Recognition Challenge 2023

This paper is the system description of the DKU-MSXF System for the track1, track2 and track3 of the VoxCeleb Speaker Recognition Challenge 2023 (VoxSRC-23). For Track 1, we utilize a network structure based on ResNet for training. By constructing a cross-age QMF training set, we achieve a substantial improvement in system performance. For Track 2, we inherite the pre-trained model from Track 1 and conducte mixed training by incorporating the VoxBlink-clean dataset. In comparison to Track 1, the models incorporating VoxBlink-clean data exhibit a performance improvement by more than 10% relatively. For Track3, the semi-supervised domain adaptation task, a novel pseudo-labeling method based on triple thresholds and sub-center purification is adopted to make domain adaptation. The final submission achieves mDCF of 0.1243 in task1, mDCF of 0.1165 in Track 2 and EER of 4.952% in Track 3.Comment: arXiv admin note: text overlap with arXiv:2210.0509

Blue-Sky Albedo Reduction and Associated Influencing Factors of Stable Land Cover Types in the Middle-High Latitudes of the Northern Hemisphere during 1982–2015

Land surface albedo (LSA) directly affects the radiation balance and the surface heat budget. LSA is a key variable for local and global climate research. The complexity of LSA variations and the driving factors highlight the importance of continuous spatial and temporal monitoring. Snow, vegetation and soil are the main underlying surface factors affecting LSA dynamics. In this study, we combined Global Land Surface Satellite (GLASS) products and ERA5 reanalysis products to analyze the spatiotemporal variation and drivers of annual mean blue-sky albedo for stable land cover types in the middle-high latitudes of the Northern Hemisphere (30~90°N) from 1982 to 2015. Snow cover (SC) exhibited a decreasing trend in 99.59% of all pixels (23.73% significant), with a rate of −0.0813. Soil moisture (SM) exhibited a decreasing trend in 85.66% of all pixels (22.27% significant), with a rate of −0.0002. The leaf area index (LAI) exhibited a greening trend in 74.38% of all pixels (25.23% significant), with a rate of 0.0014. Blue-sky albedo exhibited a decreasing trend in 98.97% of all pixels (65.12% significant), with a rate of −0.0008 (OLS slope). Approximately 98.16% of all pixels (57.01% significant) exhibited a positive correlation between blue-sky albedo and SC. Approximately 47.78% and 67.38% of all pixels (17.13% and 25.3% significant, respectively) exhibited a negative correlation between blue-sky albedo and SM and LAI, respectively. Approximately 10.31%, 20.81% and 68.88% of the pixel blue-sky albedo reduction was mainly controlled by SC, SM and LAI, respectively. The decrease in blue-sky albedo north of 40°N was mainly caused by the decrease in SC. The decrease in blue-sky albedo south of 40°N was mainly caused by SM reduction and vegetation greening. The decrease in blue-sky albedo in the western Tibetan Plateau was caused by vegetation greening, SM increase and SC reduction. The results have important scientific significance for the study of surface processes and global climate change

Assessment of Night-Time Lighting for Global Terrestrial Protected and Wilderness Areas

Protected areas (PAs) play an important role in biodiversity conservation and ecosystem integrity. However, human development has threatened and affected the function and effectiveness of PAs. The Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) night-time stable light (NTL) data have proven to be an effective indicator of the intensity and change of human-induced urban development over a long time span and at a larger spatial scale. We used the NTL data from 1992 to 2013 to characterize the human-induced urban development and studied the spatial and temporal variation of the NTL of global terrestrial PAs. We selected seven types of PAs defined by the International Union for Conversation of Nature (IUCN), including strict nature reserve (Ia), wilderness area (Ib), national park (II), natural monument or feature (III), habitat/species management area (IV), protected landscape/seascape (V), and protected area with sustainable use of natural resources (VI). We evaluated the NTL digital number (DN) in PAs and their surrounding buffer zones, i.e., 0–1 km, 1–5 km, 5–10 km, 10–25 km, 25–50 km, and 50–100 km. The results revealed the level, growth rate, trend, and distribution pattern of NTL in PAs. Within PAs, areas of types V and Ib had the highest and lowest NTL levels, respectively. In the surrounding 1–100 km buffer zones, type V PAs also had the highest NTL level, but type VI PAs had the lowest NTL level. The NTL level in the areas surrounding PAs was higher than that within PAs. Types Ia and III PAs showed the highest and lowest NTL growth rate from 1992 to 2013, respectively, both inside and outside of PAs. The NTL distributions surrounding the Ib and VI PAs were different from other types. The areas close to Ib and VI boundaries, i.e., in the 0–25 km buffer zones, showed lower NTL levels, for which the highest NTL level was observed within the 25–100 km buffer zone. However, other types of PAs showed the opposite NTL patterns. The NTL level was lower in the distant buffer zones, and the lowest night light was within the 1–25 km buffer zones. Globally, 6.9% of PAs are being affected by NTL. Conditions of wilderness areas, e.g., high latitude regions, Tibetan Plateau, Amazon, and Caribbean, are the least affected by NTL. The PAs in Europe, Asia, and North America are more affected by NTL than South America, Africa, and Oceania

Group Decision Support Systems for Emergency Management and Resilience: CoastalProtectSIM

This paper introduces the concept of Group Decision Support Systems (GDSS) as a tool to support emergency management in coastal cities. As an illustration of the potential value of GDSS, we discuss the use of CoastalProtectSIM, a simulation model that can be a valuable GDSS tool, particularly in the mitigation stages of the emergency management cycle. We present preliminary results from the use of the simulation environment in a graduate course. We finish the paper by presenting our experience as a framework for building more efficient and secure emergency management systems through the use of GDSS