Topological fractal networks introduced by mixed degree distribution

Several fundamental properties of real complex networks, such as the small-world effect, the scale-free degree distribution, and recently discovered topological fractal structure, have presented the possibility of a unique growth mechanism and allow for uncovering universal origins of collective behaviors. However, highly clustered scale-free network, with power-law degree distribution, or small-world network models, with exponential degree distribution, are not self-similarity. We investigate networks growth mechanism of the branching-deactivated geographical attachment preference that learned from certain empirical evidence of social behaviors. It yields high clustering and spectrums of degree distribution ranging from algebraic to exponential, average shortest path length ranging from linear to logarithmic. We observe that the present networks fit well with small-world graphs and scale-free networks in both limit cases (exponential and algebraic degree distribution respectively), obviously lacking self-similar property under a length-scale transformation. Interestingly, we find perfect topological fractal structure emerges by a mixture of both algebraic and exponential degree distributions in a wide range of parameter values. The results present a reliable connection among small-world graphs, scale-free networks and topological fractal networks, and promise a natural way to investigate universal origins of collective behaviors.Comment: 14 pages, 6 figure

Ameliorative Effect of D-α-Tocopherol Acetate Complexes on D-Galactose-Induced Aging in Mice

To investigate the ameliorative effect of the D-α-tocopheryl acetate compound on D-galactose-induced aging in mice, the in vitro antioxidant capacity of the compound of natural oils+phytosterols (VEO), the compound of D-α-tocopheryl acetate+phytosterol (VEZ), and the compound of D-α-tocopheryl acetat+phytosterol+astaxanthin (VEX) were measured. The aging model was established using mice injected with D-galactose on the back of the neck, while the intervention was carried out with different compounds. The results showed that all three groups of compounds had strong antioxidant effects, with the VEZ group showing better in vitro antioxidant effects. Compared with the aging model mice, the intervention of the three compounds increased glutathione peroxidase (GSH-Px) and total antioxidant capacity (T-AOC), decreased malondialdehyde (MDA) (P<0.01), and a decrease in the serum inflammatory factors interleukin-1β (IL-1β), interleukin-6 (IL-6), tumor necrosis factor (TNF-α) and liver function indicators alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were significantly reduced (P<0.01). After the intervention, the mRNA and protein expression of nuclear factor-erythroid 2-related factor 2 (Nrf2), quinone oxidoreductase (NQO-1) and heme oxygenase-1 (HO-1) in mice were significantly enhanced (P<0.0001). This indicated that the different combinations exerted their antioxidant effects through up-regulating the expression of Nrf2, NQO-1 and HO-1, thus achieving anti-aging effects, with the VEZ group showing the best expression effect. In conclusion, D-α-Tocopheryl acetate complex achieved their anti-aging effects by increasing the expression of antioxidant-related mRNAs and proteins, thus enhancing the levels of downstream antioxidant enzymes, among which D-α-tocopheryl acetate was more effective when combined with phytosterols

Secure Communication Scheme Based on Asymptotic Model of Deterministic Randomness

We propose a new cryptosystem by combing the Lissajous map, which is the asymptotic model of deterministic randomness, with the one-way coupled map lattice (OCML) system. The key space, the encryption efficiency, and the security are investigated. We find that the parameter sensitivity can reach the computational precision when the system size is only three, all the lattice outputs can be treated as key stream parallelly, and the system is resistible against various attacks including the differential-like chosen cipher attack. The findings of this paper are a strong indication of the importance of deterministic randomness in secure communications.Comment: 16 pages, 7 figure

A deep learning-based approach for automated yellow rust disease detection from high resolution hyperspectral UAV images

Yellow rust in winter wheat is a widespread and serious fungal disease, resulting in significant yield losses globally. Effective monitoring and accurate detection of yellow rust are crucial to ensure stable and reliable wheat production and food security. The existing standard methods often rely on manual inspection of disease symptoms in a small crop area by agronomists or trained surveyors. This is costly, time consuming and prone to error due to the subjectivity of surveyors. Recent advances in Unmanned Aerial Vehicles (UAVs) mounted with hyperspectral image sensors have the potential to address these issues with low cost and high efficiency. This work proposed a new deep convolutional neural network (DCNN) based approach for automated crop disease detection using very high spatial resolution hyperspectral images captured with UAVs. The proposed model introduced multiple Inception-Resnet layers for feature extraction and was optimized to establish the most suitable depth and width of the network. Benefiting from the ability of convolution layers to handle three-dimensional data, the model used both spatial and spectral information for yellow rust detection. The model was calibrated with hyperspectral imagery collected by UAVs in five different dates across a whole crop cycle over a well-controlled field experiment with healthy and rust infected wheat plots. Its performance was compared across sampling dates and with random forest, a representative of traditional classification methods in which only spectral information was used. It was found that the method has high performance across all the growing cycle, particularly at late stages of the disease spread. The overall accuracy of the proposed model (0.85) was higher than that of the random forest classifier (0.77). These results showed that combining both spectral and spatial information is a suitable approach to improving the accuracy of crop disease detection with high resolution UAV hyperspectral images

Spatiotemporal Variation and Future Predictions of Soil Salinization in the Werigan–Kuqa River Delta Oasis of China

Soil salinization is a serious global issue; by 2050, without intervention, 50% of the cultivated land area will be affected by salinization. Therefore, estimating and predicting future soil salinity is crucial for preventing soil salinization and investigating potential arable land resources. In this study, several machine learning methods (random forest (RF), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Decision Tree (GBDT), and eXtreme Gradient Boosting (XGBoost)) were used to estimate the soil salinity in the Werigan–Kuqa River Delta Oasis region of China from 2001 to 2021. The cellular automata (CA)–Markov model was used to predict soil salinity types from 2020 to 2050. The LightGBM method exhibited the highest accuracy, and the overall prediction accuracy of the methods had the following order: LightGBM > RF > GBRT > XGBoost. Moderately saline, severely saline, and saline soils were dominant in the east and south of the research area, while non-saline and mildly saline soils were widely distributed in the inner oasis area. A marked decreasing trend in the soil salt content was observed from 2001 to 2021, with a decreasing rate of 4.28 g/kg·10 a−1. The primary change included the conversion of mildly and severely saline soil types to non-saline soil. The generalized difference vegetation index (51%), Bio (30%), and temperature vegetation drought index (27%) had the greatest influence, followed by variables associated with soil attributes (soil organic carbon and soil organic carbon stock) and terrain (topographic wetness index, slope, aspect, curvature, and topographic relief index). Overall, the CA–Markov simulation resulted exhibited suitable accuracy (kappa = 0.6736). Furthermore, areas with non-saline and mildly saline soils will increase while areas with other salinity levels will continue to decrease from 2020 to 2050. From 2046 to 2050, numerous areas with saline soil will be converted to non-saline soil. These results can provide support for salinization control, agricultural production, and soil investigations in the future. The gradual decline in soil salinization in the research area in the past 20 years may have resulted from large-scale land reclamation, which has turned saline alkali land into arable land and is also related to effective measures taken by the local government to control salinization

Effects of the Rare Earth Y on the Structural and Tensile Properties of Mg-based Alloy: A First-Principles Study

In order to investigate the effect of the rare earth element Y on the strengthening potency of magnesium alloys and its strengthening mechanism under tension. In this paper, the solid solution structures with Y atom content of 1.8 at.% and 3.7 at.% were built, respectively, and their cohesive energies and stress-strain curve were calculated in the strain range of 0–20%. The calculation results of the cohesive energies showed that the structure of element Y is more stable with the increase of strains. The calculation results of stress and strain showed that Y element can improve the yield strength and tensile strength of the Mg-based alloy, and the strengthening effect is better when the Y content is 3.7 at.%