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In this study, changes in the basic physical properties, mineral composition, mass, and microstructure of layered sandstone were evaluated following heat treatment at 200–800 °C. Dynamic impact compression tests were performed using a split-Hopkinson pressure bar test system (SHPB), and digital image correlation (DIC) was used to monitor the dynamic failure processes of the involved specimens. Results indicate that high-temperature treatment reduces the mass, wave velocity and peak stress of layered sandstone; increases the porosity, pore length, and pore aperture. The rates of decrease in the wave velocity and peak stress considerably increase with increasing temperature above a threshold of 400 °C. This is because at temperatures above 400 °C, thermal cracks will form both between and within particles. As the number of cracks increases, they will propagate and connect with each other, forming a network of cracks. DIC results show that as the heat treatment temperature rises, the range of the strain-concentration areas, which are formed by sandstone failures, substantially expands. However, the increase in the heat treatment temperature only negligibly influences the propagation direction of primary sandstone cracks, which mainly propagate along the weak bedding planes. [ABSTRACT FROM AUTHOR]

Background: Suaeda glauca and Suaeda salsa have obvious morphological features and strongly tolerate saline–alkali environments. However, the mechanisms that lead to the differences in saline–alkali tolerance between them remain unclear. Methods: In this study, we employed comparative transcriptome analysis to investigate S. glauca and S. salsa under saline–alkali stress. Results: Our sequencing efforts resulted in the identification of 99,868 unigenes. We obtained 12,021 and 6227 differentially expressed genes (DEGs) from the S. glauca and S. salsa under salt stress compared with plants in the control. Notably, 1189 and 1864 were specifically upregulated DEGs in the roots and leaves of S. salsa under saline–alkali conditions, respectively. These genes were enriched in pathways such as "Plant hormone signal transduction", "Carbon metabolism" and "Starch and sucrose metabolism". Further analysis of stress-related pathways and gene expression levels revealed that key genes involved in abscisic acid (ABA) and jasmonic acid (JA) biosynthesis, ABA signal transduction, and their downstream transcription factors were upregulated in the roots of S. salsa under saline–alkali conditions. Additionally, 24 DEGs associated with stress response were identified in the roots and leaves of both species. The expression levels of these pathways and related genes were higher in S. salsa than in S. glauca, suggesting that S. salsa enhances its saline–alkali tolerance by elevating the expression of these genes. Conclusions: This study provides a new research perspective for revealing the differences in saline–alkali tolerance mechanisms between S. glauca and S. salsa, bringing forth important candidate genes for studying their saline–alkali tolerance. [ABSTRACT FROM AUTHOR]

This paper is intended to study noise-to-state stability in probability (NSSP) for random coupled Kuramoto oscillators with input control (RCKOIC). A feedback control is designed, which makes us give the existence and uniqueness of a solution for RCKOIC. Based on Kirchhoff's matrix tree theorem in graph theory, an original and appropriate Lyapunov function for RCKOIC is established. With the help of the Lyapunov method and by resorting to some analysis skills, NSSP for RCKOIC with an arbitrarily coupled topological structure and second-order moment process stochastic disturbance is acquired. Finally, the effectiveness of the obtained results is verified by a numerical test and its simulation process. [ABSTRACT FROM AUTHOR]

The aim of this study was to investigate the effect of ultrasonic treatment on the physicochemical properties and bioactivities of polysaccharides from Sargassum samples (SPs) extracted with different solvents. The alkali-assisted extraction of polysaccharide (SPA), acid-assisted extraction of polysaccharides from (SPB), and hot water extraction of polysaccharides (SPCs) were perofrmed on Sargassum. Ultrasonic treatment was performed with the SPA, SPB, and SPC in turn, and named USPA, USPB, and UPSC, respectively. The results showed that SPs mainly consisted of mannose, glucose, xylose, rhamnose, galactose, fucose, glucuronic acid, mannuronic acid and guluronic acid. The molecular weight of SPA (434.590 kDa) was the lowest under different solvent extractions, and the molecular weights of SPA, SPB, and SPC were reduced after sonication. SPA had a high carbohydrate content of (52.59 ± 5.16)%, and SPC possessed a high sulfate content of (3.90 ± 0.33)%. After ultrasonic treatment, the biological activities of SPs were significantly increased. The α-glucosidase inhibition assay reflected that the IC50 values of the ultrasonic treatment SPs were significantly reduced, and USPA showed the best activity, with an IC50 of (0.058 ± 0.05) mg/mL. Antioxidant assays demonstrated that USPC exhibited greater DPPH- and ABTS-scavenging capacity. In the anti-glycosylation assay, SPs after sonication demonstrated excellent inhibition of glycosylation products and protein oxidation products, with USPA showing the highest inhibition rate. In conclusion, the biological activities of SPs were enhanced after ultrasonic treatment. This study provides a theoretical reference for their use in food and medicines. [ABSTRACT FROM AUTHOR]

This paper investigates the stability of predator–prey models within multi-patch environments, with a particular focus on the influence of cross-dispersion across patches. We apply Kirchhoff's matrix tree theorem and Liapunov's method to derive criteria related to the cross-dispersion topology, thus solving the challenge of determining global asymptotic stability conditions. The method incorporates realistic ecological interactions and spatial heterogeneity, offering a framework for stability analysis. Our findings demonstrate that an appropriate level of cross-dispersion can effectively mitigate oscillations and foster convergence toward equilibrium. Two numerical examples validate these theoretical results and demonstrate the feasibility and effectiveness of the model across multiple patches. [ABSTRACT FROM AUTHOR]

In recent years, data mining technology has become increasingly popular, evolving into an independent discipline as research deepens. This study constructs and optimizes an association rule algorithm based on the Boolean vector (BV) inner product and ant colony optimization to enhance data mining efficiency. Frequent itemsets are extracted from the database by establishing BV and performing vector inner product operations. These frequent itemsets form the problem space for the ant colony algorithm, which generates the maximum frequent itemset. Initially, data from the total scores of players during the 2022–2024 regular season was analyzed to obtain the optimal lineup. The results obtained from the Apriori algorithm (AA) were used as a standard for comparison with the Confidence-Debiased Adversarial Fuzzy Apriori Method (CDAFAM), the AA based on deep learning (DL), and the proposed algorithm regarding their results and required time. A dataset of disease symptoms was then used to determine diseases based on symptoms, comparing accuracy and time against the original database as a standard. Finally, simulations were conducted using five batches of radar data from the observation platform to compare the time and accuracy of the four algorithms. The results indicate that both the proposed algorithm and the AA based on DL achieve approximately 10% higher accuracy compared with the traditional AA. Additionally, the proposed algorithm requires only about 25% of the time needed by the traditional AA and the AA based on DL for target recognition. Although the CDAFAM has a similar processing time to the proposed algorithm, its accuracy is lower. These findings demonstrate that the proposed algorithm significantly improves the accuracy and speed of target recognition. [ABSTRACT FROM AUTHOR]

The ecological conservation of nature reserves has garnered considerable attention and is subject to stringent management in China. However, the majority of these areas have a history of underdeveloped economies and require urgent improvements in the well-being of local communities. Effectively coupling and harmonizing the dynamic relationship between ecosystem services and socio-economic development has emerged as a crucial concern for nature reserves. Therefore, further exploration is needed to achieve a spatio-temporal balance and alignment between the supply and demand of ESs in nature reserves in China. Utilizing multiple datasets, RULSE, and bivariate autocorrelation methods, this study investigated the spatio-temporal evolution of the ecosystem services supply–demand ratio (ESDR) and supply–demand spatial matches for soil conservation services (SCSs) in the Qinling Mountains (QMs) from 2000 to 2020. The results indicated the following: (1) Over the years, the supply of SCSs exhibited a consistently high level, with an upward trend observed in 63.10% of the QMs, while the demand for SCSs is generally low, with a decreasing trend observed in 82.68% of the QMs. (2) The supply and demand of SCSs remained favorable, with a positive ESDR reaching 82.19% of the QMs. From 2000 to 2010, there was a significant decline in ESDR; however, a substantial rebound was observed in the ESDR across the region from 2010 to 2020. (3) Over years, the majority of counties and districts exhibited positive values in ESDR. When examining cities, Weinan, Xi'an, and Ankang demonstrated relatively consistent patterns with higher ESDR values over time. In 2000, the ESDR on the northern slope exhibited lower values than that on the southern slope; however, this situation subsequently underwent a reversal. (4) The spatial distribution of SCS supply and demand was predominantly characterized by matching regions exhibiting either High Supply–High Demand or Low Supply–Low Demand for years. This study suggests that the supply and demand dynamics of SCSs in the Qinling Mountains have been favorable in recent years, with consistent spatial supply–demand matches. These findings can provide valuable insights for similar nature reserves aiming to implement ecological environmental protection and achieve sustainable development. The future research endeavors, however, should strive to expand upon these findings by exploring the supply and demand patterns associated with other ESs across diverse nature reserves, while considering their unique geographical characteristics, in order to promote more rational ecological management strategies. [ABSTRACT FROM AUTHOR]

Filamentous bacteria are one of the main components of activated sludge microorganisms, which not only support flocculent bacteria in forming sludge flocs but also easily lead to sludge bulking. This article summarizes the high-throughput sequencing technology and database construction that integrates the FISH probe and 16s rRNA gene of filamentous bacteria. The detection frequency and abundance were statistically analyzed to determine the core filamentous bacteria. The filamentous bacteria searchable in the NCBI database belong to six phyla and 42 genera, with a total of 94 species. The core filamentous bacteria in the expanded activated sludge include Microthrix parvicella (M. parvicella), type 0092, Mycobacterium fortuitum, etc. The physiological ecology of the core filamentous bacteria is discussed in detail, aiming to establish targeted sludge expansion control strategies, reduce the incidence and harm of sludge expansion, and propose that future research needs to promote further development in the field of environmental microbiology. [ABSTRACT FROM AUTHOR]

Microreactors are essential for microchemical reactions owing to their high mass transfer efficiency, precise control of reaction time, easy amplification, and good safety performance. These characteristics provide several advantages, including shortened reaction times and enhanced chemical reaction conversion rates, rendering microreactors particularly significant in chemical production. In this study, a finite-rate model was developed for the esterification of monobutyl chlorophosphate (MCP) and n-butanol in a microchannel reactor. This study investigates the impact of the microchannel's length-to-diameter ratio, the mass ratio of n-butanol to MCP at the inlet, and the inlet flow ratio on the entire reaction system through numerical simulations. The findings indicate that increasing the length-to-diameter ratio and reducing the inlet flow rate effectively prolongs the residence time of materials in the microreactor, thereby enhancing the conversion rate of the reactants. Optimal results are achieved with a moderate n-butanol/MCP mass ratio, which facilitates MCP transformation. Moreover, this study employs response surface analysis to investigate the influence of independent factors, such as the microchannel's length-to-diameter ratio, component ratio, and inlet velocity ratio, on MCP conversion rates. A prediction formula with conversion rate as the dependent variable and microchannel length-to-diameter ratio, component ratio, and inlet velocity ratio as independent variables was established. [ABSTRACT FROM AUTHOR]

Chrysanthemum morifolium cv. Fubaiju (CMF) is regarded as one of the three most renowned varieties of white Chrysanthemum in China, and different extraction methods have significant effects on its composition and activities. Therefore, six extractions were used in this study to assess the effects on extracts. The basic chemical composition showed that hot water extract (Hw) had the highest total phenolic content, alkali water immersion-assisted hot water extract (Al) had the highest content of protein, and enzyme-assisted hot water extract (Enz) had the highest content of carbohydrate. The UPLC-Q-Exactive-MS results evinced the presence of 19 small-molecule compounds, including chlorogenic acid, caffeic acid, tuberonic acid glucoside, luteolin-7-O-rutinoside, and other substances. In addition, the antioxidant test found that the Hw exhibited the best 1,1-diphenyl-2-picrylhydrazyl (DPPH) (82.05 ± 1.59 mM TE/mg) and 2,2'-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) (61.91 ± 0.27 mM TE/mg) scavenging ability. The anti-glycation test demonstrated that Enz possessed the most pronounced inhibitory effect on glycation products, including fructosamine and advanced glycation end products (AGEs). Additionally, the Enz also exhibited the most significant inhibitory effect on the protein oxidation product N'-formylkynurenine. The correlation analysis revealed that there was a close relationship between antioxidant properties and glycation resistance of extracts, and tuberonic acid glucoside, 1,3-di-O-caffeoylquinic acid, 1,4-Dicaffeoylquinic acid, quercetin-7-O-β-D-glucopyranoside, and isochlorogenic acid B were key small molecule components that affected activities. In summary, the extracts of CMF can be regarded as an excellent antioxidant and anti-glycosylation agent. [ABSTRACT FROM AUTHOR]

Personalized tag recommendation algorithms generate personalized tag lists for users by learning the tagging preferences of users. Traditional personalized tag recommendation systems are limited by the problem of data sparsity, making the personalized tag recommendation models unable to accurately learn the embeddings of users, items, and tags. To address this issue, we propose a contrastive learning-based personalized tag recommendation algorithm, namely CLPTR. Specifically, CLPTR generates augmented views of user–tag and item–tag interaction graphs by injecting noises into implicit feature representations rather than dropping nodes and edges. Hence, CLPTR is able to greatly preserve the underlying semantics of the original user–tag or the item–tag interaction graphs and avoid destroying their structural information. In addition, we integrate the contrastive learning module into a graph neural network-based personalized tag recommendation model, which enables the model to extract self-supervised signals from user–tag and item–tag interaction graphs. We conduct extensive experiments on real-world datasets, and the experimental results demonstrate the state-of-the-art performance of our proposed CLPTR compared with traditional personalized tag recommendation models. [ABSTRACT FROM AUTHOR]

In order to solve the problems of existing spiral fertilizer apparatuses, such as the variation in cavity filling rate with rotational speed, fluctuation of fertilizer discharge flow, and inability to discharge fertilizer precisely, a triple-head gradually reducing spiral fertilizer apparatus is designed, which gradually compresses fertilizer particles through the triple-head reducing fertilizer spiral structure to achieve complete cavity filling and uniform fertilizer discharge. The main factors that affect the particle motion state and the structural size of the spiral fertilizer through theoretical analysis are determined, and its theoretical fertilizer discharge amount and rotational speed are calculated. Using EDEM (Discrete Element Method Software 2022) to establish a simulation model of a single-head gradually reducing fertilizer apparatus, the spiral lead reduction percentage x1, spiral diameter reduction percentage x2, and rotational speed x3 are determined as experimental factors, and the filling rate μ and spiral torque Yaverage are used as experimental indicators to conduct a simulation study on the secondary universal rotation combination design experiment. The results show that when the rotational speed is 95 r/min, the spiral lead reduction percentage is 60.00~73.21%, the spiral diameter reduction percentage is 86.55~97.05%, the filling rate μ is greater than 95%, and the spiral torque Yaverage is less than 16 N·m. In order to further improve the uniformity of fertilizer discharge and ensure the controllable accuracy of fertilizer discharge, comparative verification experiments are conducted on single-, double-, and triple-head gradually reducing spiral fertilizer discharge devices and ordinary spiral fertilizer discharge devices. The results show that the precision of the gradually reducing spiral fertilizer apparatus is better than that of the ordinary spiral fertilizer apparatus. Moreover, it is determined that the three-head style performed best. The triple-head gradually reducing spiral fertilizer apparatus is also validated by randomly adjusting six rotational speeds. The experiment results show that the average deviation of the fertilizer discharge flow rate of the fertilizer apparatus from the preset value is 3.16%. The two have a minor deviation, and the fertilizer precision is high. Precise control of the amount of fertilizer discharged can be achieved by adjusting the rotational speed, and the research can provide a specific reference for the improved design and precise control of the spiral fertilizer apparatus. [ABSTRACT FROM AUTHOR]

The complementary combination of emphasizing target objects in infrared images and rich texture details in visible images can effectively enhance the information entropy of fused images, thereby providing substantial assistance for downstream composite high-level vision tasks, such as nighttime vehicle intelligent driving. However, mainstream fusion algorithms lack specific research on the contradiction between the low information entropy and high pixel intensity of visible images under harsh light nighttime road environments. As a result, fusion algorithms that perform well in normal conditions can only produce low information entropy fusion images similar to the information distribution of visible images under harsh light interference. In response to these problems, we designed an image fusion network resilient to harsh light environment interference, incorporating entropy and information theory principles to enhance robustness and information retention. Specifically, an edge feature extraction module was designed to extract key edge features of salient targets to optimize fusion information entropy. Additionally, a harsh light environment aware (HLEA) module was proposed to avoid the decrease in fusion image quality caused by the contradiction between low information entropy and high pixel intensity based on the information distribution characteristics of harsh light visible images. Finally, an edge-guided hierarchical fusion (EGHF) module was designed to achieve robust feature fusion, minimizing irrelevant noise entropy and maximizing useful information entropy. Extensive experiments demonstrate that, compared to other advanced algorithms, the method proposed fusion results contain more useful information and have significant advantages in high-level vision tasks under harsh nighttime lighting conditions. [ABSTRACT FROM AUTHOR]

In order to solve the traditional single-spiral fertilizer discharger issue of the fluctuation of fertilizer-discharge flow and the problem of precise fertilizer discharge, the innovative design of a cantilevered oblique placement of a fertilizer-discharging spiral structure in the form of an inclined spiral fertilizer discharger was realized, in which, through the fertilizer spiral's full end-filled extrusion, uniform delivery of the discharge was achieved. Discrete element simulation was used to compare the fertilizer-discharge characteristics of inclined and traditional single-spiral fertilizer dischargers, and the results proved that the inclined spiral fertilizer discharger effectively reduced the fluctuation of the fertilizer-discharge flow rate. Through a theoretical analysis preformed to determine the theoretical fertilizer discharge and the main parameters affecting the uniformity of fertilizer discharge, we identified the tilting angle of the fertilizer discharger (θ) and the distance from the termination spiral blade to the fertilizer outlet (l). A two-factor, five-level quadratic generalized rotary combination experiment was conducted with two parameters (θ and l) as the experiment factors and the variation coefficient of fertilizer-discharge uniformity (σ) as the experiment indicator. The experimental results showed that for σ, θ was a highly significant effect, l was a significant effect, and σ was less than 8.5%; when θ was 35.02° and l was 16.87 mm, the fertilizer-discharge performance was better. A bench experiment was used to compare the traditional and inclined spiral fertilizer dischargers, and the results showed that the relative error of the variation coefficient between the bench and the simulation experiment under this combination was 2.28%. And compared with the traditional spiral fertilizer discharger's σ average increase of 80.79%, the effect of fertilizer discharge was better than the traditional spiral fertilizer discharger. A fertilizer application controller was developed, and the bench performance was tested based on the measured fertilizer-discharge flow rate fitting equation of this combined inclined spiral fertilizer discharger. The results show that the electronically controlled inclined spiral fertilizer discharger has an average deviation of 3.12% from the preset value, which can be used to regulate the flow of fertilizer discharged through the fertilizer controller to realize precise fertilizer application, and this study can provide a reference for the optimal design of the spiral fertilizer discharger. [ABSTRACT FROM AUTHOR]

Improving crop yield and resource utilization efficiency is essential for agricultural productivity. In the Huang-Huai-Hai maize region of China, optimizing planting density, nitrogen (N) application, and fertilization methods are key strategies for enhancing maize yield and N use efficiency. However, traditional approaches have often hindered these improvements. To address this issue, we conducted a study in Baoding, Hebei, from 2022 to 2023, focusing on planting density, the N application rate, and the fertilization method on grain yield, N use efficiency, water use efficiency (WUE), and economic benefits. The trial involved two planting densities: 6.0 × 104 plants ha−1 (D1, typical local density) and 9.0 × 104 plants ha−1 (D2). Five N application rates were tested: 0 (N0), 120 kg ha−1 (N1), 180 kg ha−1 (N2), 240 kg ha−1 (N3), and 300 kg ha−1 (N4). The control treatment (D1N4) utilized the local planting density and traditional fertilization methods. Our findings revealed a positive correlation between the maize yield and N application rate, with the maximum yields (13.78–13.88 t ha−1), high WUE (24.42–29.85 kg m−3), agronomic efficiency of N (AEN) (18.11–19.00 kg kg−1), and economic benefits (2.44 × 104–2.47 × 104 CNY ha−1) observed with D2N3 and surface drip fertilization. This was significantly higher than the yield and resource efficiency of traditional fertilization methods and saved fertilizer and production costs. Therefore, adopting surface drip fertilization, adjusting planting density, and optimizing N application rates proved effective in enhancing maize yield and resource utilization efficiency in the Huang-Huai-Hai maize region. [ABSTRACT FROM AUTHOR]

Seeking novel high-performance elastocaloric materials with low critical stress plays a crucial role in advancing the development of elastocaloric refrigeration technology. Here, as a first attempt, the elastocaloric effect of TiZrNbAl shape memory alloy at both room temperature and finite temperatures ranging from 245 K to 405 K, is studied systematically. Composition optimization shows that Ti-19Zr-14Nb-1Al (at.%), possessing excellent room-temperature superelasticity with a critical stress of around 100 MPa and a small stress hysteresis of around 70 MPa and outstanding fracture resistance with a compressive strain of 20% and stress of 1.7 GPa, demonstrates a substantial advantage as an elastocaloric refrigerant. At room temperature, a large adiabatic temperature change ( Δ T ad ) of −6.7 K is detected, which is comparable to the highest value reported in the Ti-based alloys. A high elastocaloric cyclic stability, with almost no degradation of Δ T ad after 4000 cycles, is observed. Furthermore, the sizeable elastocaloric effect can be steadily expanded from 255 K to 395 K with a temperature window of as large as 140 K. A maximum Δ T ad of −7.9 K appears at 355 K. The present work demonstrates a promising potential of TiZrNbAl as a low critical stress and low hysteresis elastocaloric refrigerant. [ABSTRACT FROM AUTHOR]

As a new concept for systematically evaluating ecosystem services, Gross Ecosystem Product (GEP) provides an effective means to comprehensively reveal the overall status of the ecosystem, the impact of economic activities on the ecological environment, and the effectiveness of ecological protection efforts. GEP accounting has been conducted in various regions; however, GEP's application in natural reserves still requires further exploration. Taking the Qinling Mountains as the research area, this paper aims to assess the relationship between GEP and economic development on the basis of the GEP accounting system. The results indicated that: (1) From 2010 to 2020, GEP tended to increase continuously and exhibited a distribution pattern with high value regions in the east and west, and low value regions in the north and south. (2) Over the years, the coupling coordination degree between GEP and GDP was in a consistent upward trend. In 2020, a good coupling coordination state between GEP and GDP was achieved in most districts and counties. (3) With the relative development between GEP and GDP, the social economy of most districts and counties lagged behind GEP in 2010. The number of districts and counties lagging in GEP in 2020 increased, while the number of regions with a balanced development of GEP and GDP was still relatively discouraging. (4) In general, elevation, contagion, temperature, population density, and precipitation were the main drivers of coupling coordination degree between GEP and GDP. If the relationship between economic development and ecological environmental protection can be reasonably balanced, it will further promote the sustainable development of nature reserves, and provide a scientific basis for sustainable policy-making in other similar areas. [ABSTRACT FROM AUTHOR]

The intricate regulatory process governing rice immunity against the blast fungus Magnaporthe oryzae remains a central focus in plant–pathogen interactions. In this study, we investigated the important role of Osa-miR11117, an intergenic microRNA, in regulating rice defense mechanisms. Stem-loop qRT-PCR analysis showed that Osa-miR11117 is responsive to M. oryzae infection, and overexpression of Osa-miR11117 compromises blast resistance. Green fluorescent protein (GFP)-based reporter assay indicated OsPAO4 is one direct target of Osa-miR11117. Furthermore, qRT-PCR analysis showed that OsPAO4 reacts to M. oryzae infection and polyamine (PA) treatment. In addition, OsPAO4 regulates rice resistance to M. oryzae through the regulation of PA accumulation and the expression of the ethylene (ETH) signaling genes. Taken together, these results suggest that Osa-miR11117 is targeting OsPAO4 to regulate blast resistance by adjusting PA metabolism and ETH signaling pathways. [ABSTRACT FROM AUTHOR]

Taking a certain coastal area of Jiangsu province as the research background, this study scientifically predicts the runoff on the medium and long-term time scale according to the changes of various climate factors such as atmospheric circulation, sea surface temperature, and solar activity in the first half of the year. A lag correlation is established between various related climate factors and the monthly runoff process in the research area for the previous 1–6 months. Selecting advantageous factors and constructing a significant factor set. Using the improved BP (Back-Propagation) artificial neural network model and combining it with the sensitivity analysis method, a specific number of 8-factor combinations are selected from the set of significant factors for medium and long-term runoff prediction. After that, the prediction results are compared with the forecasting effects of two multi-factor combination runoff simulation schemes formed by stepwise regression and Spearman rank correlation methods. The study concluded that the multi-factor combination simulation effect formed through sensitivity analysis was the best. The 20% standard forecast qualification rate of the three schemes is not significantly different. The Mean Absolute Relative Error of the multi-factor combination training and validation periods simulated through sensitivity analysis is the smallest among the three schemes, which are 36.61% and 38.01%, respectively. The Nash Efficiency Coefficient in the validation period is 0.45, which is far better than other schemes and has better generalization ability. The Standard Deviation of Relative Error in the training and validation periods is much smaller than other schemes, and the dispersion of relative errors is the smallest. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAVs) have received widespread attention due to their flexible deployment characteristics. Automated airports equipped with UAVs are expected to become important equipment for improving quality and reducing costs in many inspection scenarios. This paper focuses on the automated inspection business of UAVs dispatched by automated airports in highway scenarios. On the basis of considering the shape of highway curves, inspection targets, and the energy consumption characteristics of UAVs, planning the flight parameters of UAVs is of great significance for ensuring the effectiveness of the inspection process. This paper first sets the inspection path points for the UAV based on highway curves, and then proposes an efficient heuristic method for the nonlinear non-convex parameter optimization problem, through which the parameters of the UAV's inspection altitude, hovering altitude, and flight speed are planned. Simulation and analysis show that the proposed method possesses good parameter planning efficiency. By combining several existing trajectory planning methods, e.g., the traversal method, the deep Q-network based method, and the genetic method, it can be concluded that the proposed method in this paper has better overall planning performance including planning efficiency and inspection effectiveness. [ABSTRACT FROM AUTHOR]

Off-axis parabolic mirrors have extensive applications in X-ray optics, with the precision of their curvature directly impacting grazing-incidence focusing performance. Notably, the off-axis parabolic surface has non-rotating and non-symmetrical characteristics. Ultra-precision raster grinding utilizing a diamond wheel is a common method. Crucially, establishing an optimal wheel path stands as the key to ensuring surface accuracy during off-axis paraboloid grinding. In this study, according to the double curvature property of an off-axis parabolic surface, two different wheel paths were compared: one tracing the meridian direction (parabolic generatrix) and the other following the arc vector direction (arc). The results showed that the wheel path in raster grinding stepping along the arc vector direction can obtain a smaller scallop height and higher surface accuracy. The surface accuracy of one step along the arc vector direction is 9.6 μm, and that of the other step along the meridian direction is 32.6 μm. A model of the scallop height was established based on the relative relationship between adjacent wheel paths, and the error is within 5%. According to the correlation between scallop height and shape error, we conducted an analysis of the spatial distribution of shape errors under varying wheel paths. The wheel path that steps along the arc vector is more suitable for raster grinding of the off-axis paraboloid. The above study can provide theoretical guidance for the wheel path planning of off-axis parabolic mirrors with high surface accuracy. [ABSTRACT FROM AUTHOR]

Understanding the water status of specific organs can be helpful in evaluating the life activities and growth conditions of maize. To accurately judge organ growth conditions and thus design appropriate interventions, it is necessary to clarify the true water dynamics of each maize organ. Using multiple maize cultivars with different growth periods, spatio-temporal water dynamics were analyzed here in the leaves, stalks, and ear components. Leaf water content was found to gradually decrease from both the bottom and top of the plant to the middle, whereas stalk water content decreased sequentially from the top to the bottom. Each successively higher node from the bottom of the plant was associated with decreases of 0.99% and 1.27% water content in the leaves and stalks, respectively. The water dynamics in leaves and internodes showed three clear stages: the slow loss, rapid loss, and balance stage. A water content of 60% appeared to be an irreversible turning point for initiation of senescence. Using normalized growth period as a measure, each of the tested cultivars could be assigned into one of two types based on their water dynamics: stay-water or general type. General-type cultivars had a shorter duration with a high water content and a water loss rate approximately twice as high as that of the stay-water type. This may have been related to the leaf senescence characteristics. However, the stay-water trait did not interfere with water dynamics of the ear components. Therefore, it may not be robust to evaluate the kernel dehydration of maize according to leaf senescence conditions due to the weak correlation between kernel water content and leaf senescence characteristics. [ABSTRACT FROM AUTHOR]

Due to the characteristics of multibody (MB) and finite element (FE) digital human body models (HBMs), the reconstruction of running pedestrians (RPs) remains a major challenge in traffic accidents (TAs) and new innovative methods are needed. This study presents a novel approach for reconstructing moving pedestrian TAs based on a humanoid robot method to improve the accuracy of analyzing dynamic vehicle–pedestrian collision accidents. Firstly, we applied the theory of humanoid robots to the corresponding joints and centroids of the TNO HBM and implemented the pedestrian running process. Secondly, we used rigid–flexible coupling HBMs to build pedestrians, which can not only simulate running but also analyze human injuries. Then, we validated the feasibility of the RP reconstruction method by comparing the simulated dynamics with the pedestrian in the accident. Next, we extracted the velocity and posture of the pedestrian at the moment of collision and further validated the modeling method through a comparison of human injuries and forensic autopsy results. Finally, by comparing two other cases, we can conclude that there are relative errors in both the pedestrian injury results and the rest position. This comparative analysis is helpful for understanding the differences in injury characteristics between the running pedestrian and the other two cases in TAs. [ABSTRACT FROM AUTHOR]

Alkali-activated persulfate (PS) is widely used in situ in chemical oxidation processes; however, studies on the innovation of the alkali activation process are very limited. Two supported solid superbases, namely KNO3/γ-Al2O3 (KAl) and KNO3/SBA-15/MgO (KSM), respectively, were prepared and used to activate persulfate to degrade DCF in this work. The results showed that the superbases elevated the solution pH once added and thus could catalyze persulfate to degrade diclofenac efficiently above pH 10.5. The catalytic efficiency of KAl was close to that of sodium hydroxide, and that of KSM was the highest. The mechanism might be that, in addition to raising the solution pH, some potassium existed as K2O2, which had a strong oxidizing effect and was conducive to DCF removal. Hydroxyl, sulfate and superoxide radicals were all found in the reaction system, among which hydroxyl might play the most important role. The material composition ratio, common anion and humic acid all had some influences on the catalytic efficiency. A total of five intermediates were found in the KSM/PS oxidation system, and six oxidation pathways, which were hydroxylation, dehydrogen, dechlorination, dehydration, decarboxylation, and C-N bond breakage, might be involved in the reaction process. Several highly toxic oxidation products that should be paid attention to were also proposed. [ABSTRACT FROM AUTHOR]

Digital microfluidic platforms have been extensively studied in biology. However, achieving efficient mixing of macromolecules in microscale, low Reynolds number fluids remains a major challenge. To address this challenge, this study presents a novel design solution based on dielectric electro-wetting (EWOD) by optimizing the geometry of the transport electrode. The new design integrates micro-barriers on the electrodes to generate vortex currents that promote mixing during droplet transport. This design solution requires only two activation signals, minimizing the number of pins required. The mixing performance of the new design was evaluated by analyzing the degree of mixing inside the droplet and quantifying the motion of the internal particles. In addition, the rapid mixing capability of the new platform was demonstrated by successfully mixing the sorbitol solution with the detection solution and detecting the resulting reaction products. The experimental results show that the transfer electrode with a micro-barrier enables rapid mixing of liquids with a six-fold increase in mixing efficiency, making it ideal for the development of EWOD devices. [ABSTRACT FROM AUTHOR]

As a key component of motion support, the rolling bearing is currently a popular research topic for accurate diagnosis of bearing faults and prediction of remaining bearing life. However, most existing methods still have difficulties in learning representative features from the raw data. In this paper, the Xi'an Jiaotong University (XJTU-SY) rolling bearing dataset is taken as the research object, and a deep learning technique is applied to carry out the bearing fault diagnosis research. The root mean square (RMS), kurtosis, and sum of frequency energy per unit acquisition period of the short-time Fourier transform are used as health factor indicators to divide the whole life cycle of bearings into two phases: the health phase and the fault phase. This division not only expands the bearing dataset but also improves the fault diagnosis efficiency. The Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) network model is improved by introducing multi-scale large convolutional kernels and Gate Recurrent Unit (GRU) networks. The bearing signals with classified health states are trained and tested, and the training and testing process is visualized, then finally the experimental validation is performed for four failure locations in the dataset. The experimental results show that the proposed network model has excellent fault diagnosis and noise immunity, and can achieve the diagnosis of bearing faults under complex working conditions, with greater diagnostic accuracy and efficiency. [ABSTRACT FROM AUTHOR]

Background: Although hundreds of studies have been conducted, our understanding of the pathogenesis, indications for surgical intervention, and disease markers of Takayasu arteritis (TAK) are still limited. Collection of biological specimens, clinical data and imaging data will facilitate translational research and clinical studies. In this study, we aim to introduce the design and protocol for the Beijing Hospital Takayasu Arteritis (BeTA) Biobank. Methods: Based in the Department of Vascular Surgery of Beijing Hospital and Beijing Hospital Clinical Biological Sample Management Center, the BeTA Biobank is composed of clinical data and sample data from patients with TAK requiring surgical treatment. All clinical data of participants are collected, including demographic characteristics, laboratory tests, imaging results, operation information, perioperative complications, follow-up data, etc. Both blood samples including plasma, serum and cells, and vascular tissues or perivascular adipose tissue are collected and stored. These samples will promote the establishment of a multiomic database for TAK and help to identify disease markers and to explore potential targets for specific future drugs for TAK. [ABSTRACT FROM AUTHOR]

Polyetheretherketone (PEEK) and its fiber-reinforced materials are thermoplastic polymer materials with broad application prospects. Depositing Ni-P alloy on them can improve their poor conductivity and electromagnetic shielding performance, and further expand their application field. The application effect of the plated parts is significantly impacted by the bonding strength between PEEK and coating. The bonding strength between non-metallic substrate and coating is largely influenced by the surface characteristics of the substrate. Therefore, it is significant to study how the surface roughness of PEEK materials and the modified fibers in materials affect the adhesion of the coating. In this study, Ni-P alloy was electroless deposited on PEEK, 30% carbon-fiber-reinforced PEEK (CF30/PEEK), and 30% glass-fiber-reinforced PEEK (GF30/PEEK) with varying surface roughness. The influence of surface roughness and modified fibers on the coating adhesion was studied. Additionally, the effect of the concentrations of nickel sulfate, sodium hypophosphite, pH, and temperature on the deposition rate of the coating was investigated for the three materials. Based on the highest deposition rate, the process parameters were then optimized. The results demonstrated that as surface roughness increased, adhesion between substrate and coating first increased and then decreased. The surface roughness Ra of 0.4 μm produced the highest coating adhesion. Additionally, fiber-reinforced PEEK adhered to coatings more effectively than PEEK did. The mechanism of the difference in bonding strength between different PEEK-modified materials and coatings was revealed. The optimal process parameters were: nickel sulfate: 25 g/L, sodium hypophosphite: 30 g/L, pH: 5.0, and temperature: 70 °C. [ABSTRACT FROM AUTHOR]

Localizing stroke may be critical for elucidating underlying pathophysiology. This study proposes a ridge regression–meanshift (RRMS) framework using electromagnetic signals obtained from 16 antennas placed around the anthropomorphic head phantom. A total of 608 intracranial haemorrhage (ICH) and ischemic (IS) signals are collected and evaluated for RRMS, where each type of signal contains two different diameters of stroke phantoms. Subsequently, multiple ridge regression predictors then give the target distances from the antennas and mean shift is used to cluster the predicted stroke location based on these distances. The test results show that the training time and economic cost are significantly reduced as the average prediction time only takes 0.61 s to achieve an accurate result (average position error = 0.74 cm) using a conventional laptop. It has great potential to be used as an auxiliary standard medical method, or rapid diagnosis of stroke patients in underdeveloped areas, due to its rapidity, good deployability, and low hardware cost. [ABSTRACT FROM AUTHOR]

The interplay between finance and technology with the use of the internet triggered the emergence of digital payment technologies. Such technological innovation in the payment industry is the foundation for financial inclusion. However, despite the continuous progress and potential of moving the payment landscape towards digital payments and connecting the population to the ubiquitous digital environment, some critical issues need to be addressed to achieve a more harmonious inclusive and sustainable cashless society. The study aims to provide a comprehensive literature review on the emerging digital payment technologies and associated challenges. By systematically reviewing existing empirical studies, this study puts forward the state-of-the-art classification of digital payment technologies and presents four categories of digital payment technologies: card payment, e-payment,mobile payment and cryptocurrencies. Subsequently, the paper presents the key challenges in digital payment technologies categorized into broad themes: social, economic, technical, awareness and legal. The classification and categorization of payment technologies and associated challenges can be useful to both researchers and practitioners to understand, elucidate and develop a coherent digital payment strategy. [ABSTRACT FROM AUTHOR]

A singular graph G, defined when its adjacency matrix is singular, has important applications in mathematics, natural sciences and engineering. The chemical importance of singular graphs lies in the fact that if the molecular graph is singular, the nullity (the number of the zero eigenvalue) is greater than 0, then the corresponding chemical compound is highly reactive or unstable. By this reasoning, chemists have a great interest in this problem. Thus, the problem of characterization singular graphs was proposed and raised extensive studies on this challenging problem thereafter. The graph obtained by conglutinating the starting vertices of three paths P s 1 , P s 2 , P s 3 into a vertex, and three end vertices into a vertex on the cycle C a 1 , C a 2 , C a 3 , respectively, is denoted as γ (a 1 , a 2 , a 3 , s 1 , s 2 , s 3) . Note that δ (a 1 , a 2 , a 3 , s 1 , s 2) = γ (a 1 , a 2 , a 3 , s 1 , 1 , s 2) , ζ (a 1 , a 2 , a 3 , s) = γ (a 1 , a 2 , a 3 , 1 , 1 , s) , φ (a 1 , a 2 , a 3) = γ (a 1 , a 2 , a 3 , 1 , 1 , 1) . In this paper, we give the necessity and sufficiency that the γ − graph, δ − graph, ζ − graph and φ − graph are singular and prove that the probability that a randomly given γ − graph, δ − graph, ζ − graph or φ − graph being singular is equal to 325 512 , 165 256 , 43 64 , 21 32 , respectively. From our main results, we can conclude that such a γ − graph( δ − graph, ζ − graph, φ − graph) is singular if at least one cycle is a multiple of 4 in length, and surprisingly, the theoretical probability of these graphs being singular is more than half. This result promotes the understanding of a singular graph and may be promising to propel the solutions to relevant application problems. [ABSTRACT FROM AUTHOR]

Objective: This paper applies graph methods to distinguish major depression disorder (MDD) and healthy (H) subjects using the graph features of single-channel electroencephalogram (EEG) signals. Methods: Four network features—graph entropy, mean degree, degree two, and degree three—were extracted from the 19-channel EEG signals of 64 subjects (26 females and 38 males), and then these features were forwarded to a support vector machine to conduct depression classification based on the eyes-open and eyes-closed statuses, respectively. Results: Statistical analysis showed that graph features with degree of two and three, the graph entropy of MDD was significantly lower than that for H (p < 0.0001). Additionally, the accuracy of detecting MDD using single-channel T4 EEG with leave-one-out cross-validation from H was 89.2% and 92.0% for the eyes-open and eyes-closed statuses, respectively. Conclusion: This study shows that the graph features of a short-term EEG can help assess and evaluate MDD. Thus, single-channel EEG signals can be used to detect depression in subjects. Significance: Graph feature analysis discovered that MDD is more related to the temporal lobe than the frontal lobe. [ABSTRACT FROM AUTHOR]

Titanium-based implants are often utilized in oral implantology and craniofacial reconstructions. However, the biological inertness of machined titanium commonly results in unsatisfactory osseointegration. To improve the osseointegration properties, we modified the titanium implants with nanotubular/superhydrophilic surfaces through anodic oxidation and thermal hydrogenation and evaluated the effects of the machined surfaces (M), nanotubular surfaces (Nano), and hydrogenated nanotubes (H-Nano) on osteogenesis and osseointegration in vitro and in vivo. After incubation of mouse bone marrow mesenchymal stem cells on the samples, we observed improved cell adhesion, alkaline phosphatase activity, osteogenesis-related gene expression, and extracellular matrix mineralization in the H-Nano group compared to the other groups. Subsequent in vivo studies indicated that H-Nano implants promoted rapid new bone regeneration and osseointegration at 4 weeks, which may be attributed to the active osteoblasts adhering to the nanotubular/superhydrophilic surfaces. Additionally, the Nano group displayed enhanced osteogenesis in vitro and in vivo at later stages, especially at 8 weeks. Therefore, we report that hydrogenated superhydrophilic nanotubes can significantly accelerate osteogenesis and osseointegration at an early stage, revealing the considerable potential of this implant modification for clinical applications. [ABSTRACT FROM AUTHOR]

Polyetheretherketone (PEEK) is a promising thermo-plastic polymer material due to its excellent mechanical properties. To further improve the mechanical properties of PEEK, different kinds of short fibers are added into the PEEK matrix. The grinding machinability of short-fiber-reinforced PEEK varies with the effect of fiber type and content. Therefore, it is crucial to investigate the surface quality and removal mechanism of fiber-reinforced PEEK in ultra-precision grinding. In this paper, different fiber types and mass fractions of short-fiber-reinforced PEEK, including carbon-fiber-reinforced PEEK (CF/PEEK) and glass-fiber-reinforced PEEK (GF/PEEK), are employed. The grinding machinability of short-fiber-reinforced PEEK was investigated using grinding experiments with grinding wheels of different grit sizes. The effects of the fiber type and mass fraction on the surface quality and removal mechanism during grinding were discussed. The results showed that the brittle–ductile transition depth of carbon fiber was much larger than that of glass fiber, so it was easier to achieve ductile removal in grinding with the carbon fiber. Therefore, the ground surface roughness of CF/PEEK was smaller than that of GF/PEEK under the same grinding conditions. With the increase in carbon fiber mass fraction, the ground surface roughness of CF/PEEK decreased due to the higher hardness. The brittle–ductile transition depth of glass fiber was small, and it was easy to achieve brittle removal when grinding. When the glass fiber removal mode was brittle removal, the GF/PEEK surface roughness increased with the increase in glass fiber content. [ABSTRACT FROM AUTHOR]

Precipitation estimate is important for earth science studies and applications, and it is one of the most difficult meteorological quantities to estimate accurately. For regions such as Peru, reliable gridded precipitation products are lacking due to complex terrains and large portions of remote lands that limit the accuracy of satellite precipitation estimation and in situ measurement density. This study evaluates and cross-examines two high-resolution satellite-based precipitation products, a global rain-gauge interpolated precipitation product, and a Weather Research and Forecast (WRF) model that simulated precipitation for a ten-year period from 2010 to 2019 in the Peruvian Andes region across the Pacific coast, Andes, and in the Amazon. The precipitation estimates examined in this study are the Integrated Multi-SatellitE Retrievals for GPM (IMERG), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Global Precipitation Climatology Center product (GPCC), and a 3 km grid spacing WRF-based regional climate model (RCM) simulation. The evaluation and cross-examination were performed at sub-daily (6 h), daily, and monthly time scales, and at various spatial resolutions. The results show that the WRF simulation performs as well as, if not better than, GPM IMERG in the low precipitation and dry regions but becomes inaccurate in wet regions. GPM IMERG is more suitable for higher precipitation and wet regions, and MSWEP shows a systematic overestimation over the study area. It is therefore important to choose the most suitable precipitation product based on research needs and climate condition of the study for the challenging Peruvian Andes region. [ABSTRACT FROM AUTHOR]

This paper investigates the stabilization for stochastic coupled Kuramoto oscillators (SCKOs) via nonlinear distributed feedback control. An original nonlinear distributed feedback control with the advantages of fast response, no steady-state deviation, and easy implementation is designed to stabilize SCKOs. With the help of the Lyapunov method and stochastic analysis skills, some novel sufficient conditions guaranteeing the stochastic stability for SCKOs are provided by constructing a new and suitable Lyapunov function for SCKOs. Finally, a numerical example is given to illustrate the effectiveness and applicability of the theoretical result. [ABSTRACT FROM AUTHOR]

In recent years, although resin composite has played an important role in the restoration of tooth defects, it still has several disadvantages, including being biodegraded by saliva, bacteria and other enzymes in the oral cavity, which may result in repair failure. This factor is not conducive to the long-term survival of the prosthesis in the mouth. In this article, we review the causes, influencing factors and prevention methods of resin biodegradation. Biodegradation is mainly caused by esterase in saliva and bacteria, which breaks the ester bond in resin and causes the release of monomers. The mechanical properties of the prosthesis can then be affected. Meanwhile, cathepsin and MMPs are activated on the bonding surface, which may decompose the dentin collagen. In addition, neutrophils and residual water on the bonding surface can also aggravate biodegradation. Currently, the primary methods to prevent biodegradation involve adding antibacterial agents to resin, inhibiting the activity of MMPs and enhancing the crosslinking of collagen fibers. All of the above indicates that in the preparation and adhesion of resin materials, attention should be paid to the influence of biodegradation to improve the prosthesis's service life in the complex environment of the oral cavity. [ABSTRACT FROM AUTHOR]

Knowledge management in construction health and safety is an intensive process involving different stakeholders. However, this domain's information is still fragmented and stored in various disordered formats that require systematic structure for reusing and sharing. This study aims to develop a domain ontology, HSM-Onto (Health and Safety Management-Ontology), to construct health and safety knowledge and improve health and safety management decision making. The HSM-Onto could implement the organization, storage and reuse of construction health and safety knowledge. It comprises two primary domain knowledge contexts, including construction project context and risk context. Based on the conducted analyses, the findings show that the HSM-Onto's health and safety knowledge sharing is effective and equips health and safety employees with sound recommendations for decision making. [ABSTRACT FROM AUTHOR]

The hazards caused by drug-resistant bacteria are rocketing along with the indiscriminate use of antibiotics. The development of new non-antibiotic antibacterial drugs is urgent. The excellent biocompatibility and diverse multifunctionalities of liquid metal have stimulated the studies of antibacterial application. Several gallium-based antimicrobial agents have been developed based on the mechanism that gallium (a type of liquid metal) ions disorder the normal metabolism of iron ions. Other emerging strategies, such as physical sterilization by directly using LM microparticles to destroy the biofilm of bacteria or thermal destruction via infrared laser irradiation, are gaining increasing attention. Different from traditional antibacterial agents of gallium compounds, the pronounced property of gallium-based liquid metal materials would bring innovation to the antibacterial field. Here, LM-based antimicrobial mechanisms, including iron metabolism disorder, production of reactive oxygen species, thermal injury, and mechanical destruction, are highlighted. Antimicrobial applications of LM-based materials are summarized and divided into five categories, including liquid metal motors, antibacterial fabrics, magnetic field-responsive microparticles, liquid metal films, and liquid metal polymer composites. In addition, future opportunities and challenges towards the development and application of LM-based antimicrobial materials are presented. [ABSTRACT FROM AUTHOR]

The traffic-induced soil compaction in the field has gradually become an important constraint to sustainable agricultural development. A field experiment was conducted to acquire the stress transmitted caused by multiple passes with different types of tractors and to investigate the impact of these stresses on soil bulk density and crop growth. The experiment applied two tractors with different masses: LOVOL M904 (HC) and John Deer 280 (LC), and six different treatments of 0 (C0), 1 (C1), 3 (C3), 5 (C5), 7 (C7), and 9 (C9) tractor passes for each tractor. The results showed that at each number of passes, tractors with small axle-loads at 0–20 cm depth generated higher soil additional stress, while tractors with large axle-loads at 20–80 cm depth generated higher soil additional stress. In the 0–20 cm soil layer, when the number of continuous passes is less than 7 times, the passes of a small axle-load tractor lead to larger soil bulk density, and when the number of continuous passes is more than 7, the compaction of large axle-load tractor leads to larger soil bulk density. At depths of 20–80 cm, compaction by large axle-load tractors results in larger soil bulk density. In this study, different levels of tractor compaction inhibited key growth indicators of maize, resulting in yield reductions. The effect of different tractor load compaction on maize yield increased significantly with the number of compaction passes, with 1–5 tractor passes having no significant effect on yield and 7–9 passes producing greater yield reductions for HC. This study will provide a theoretical basis and technical support for the selection of agricultural machinery and reasonable tillage technology. [ABSTRACT FROM AUTHOR]

Exploring the processes of interspecific relationships is crucial to understanding the mechanisms of biodiversity maintenance. Visually detecting interspecies relationships of large mammals is limited by the reconstruction accuracy of the environmental structure and the timely detection of animal behavior. Hence, we used backpack laser scanning (BLS) to reconstruct the high-resolution three-dimensional environmental structure to simulate the process of a predator approaching its prey, indicating that predator tigers would reduce their visibility by changing their behavior. Wild boars will nibble off about 5m of branches around the nest in order to create better visibility around the nest, adopting an anti-predation strategy to detect possible predators in advance. Our study not only points out how predator–prey relationships are affected by visibility as the environment mediates it, but also provides an operable framework for exploring interspecific relationships from a more complex dimension. Finally, this study provides a new perspective for exploring the mechanisms of biodiversity maintenance. [ABSTRACT FROM AUTHOR]

Silicon carbide (SiC) is a promising semiconductor material for making high-performance power electronics with higher withstand voltage and lower loss. The development of cost-effective machining technology for fabricating SiC wafers requires a complete understanding of the deformation and removal mechanism. In this study, molecular dynamics (MD) simulations were carried out to investigate the origins of the differences in elastic–plastic deformation characteristics of the SiC polytypes, including 3C-SiC, 4H-SiC and 6H-SiC, during nanoindentation. The atomic structures, pair correlation function and dislocation distribution during nanoindentation were extracted and analyzed. The main factors that cause elastic–plastic deformation have been revealed. The simulation results show that the deformation mechanisms of SiC polytypes are all dominated by amorphous phase transformation and dislocation behaviors. Most of the amorphous atoms recovered after completed unload. Dislocation analysis shows that the dislocations of 3C-SiC are mainly perfect dislocations during loading, while the perfect dislocations in 4H-SiC and 6H-SiC are relatively few. In addition, 4H-SiC also formed two types of stacking faults. [ABSTRACT FROM AUTHOR]

This paper studies noise-to-state stability in probability (NSSP) for random complex dynamical systems on networks (RCDSN). On the basis of Kirchhoff's matrix theorem in graph theory, an appropriate Lyapunov function which combines with every subsystem for RCDSN is established. Moreover, some sufficient criteria closely related to the topological structure of RCDSN are given to guarantee RCDSN to meet NSSP by means of the Lyapunov method and stochastic analysis techniques. Finally, to show the usefulness and feasibility of theoretical findings, we apply them to random coupled oscillators on networks (RCON), and some numerical tests are given. [ABSTRACT FROM AUTHOR]

Fusarium crown rot (FCR) is one of the most damaging cereal diseases in semi-arid regions worldwide. Genetic studies on FCR resistance have mainly focused on disease symptoms measured by the browning of either leaf sheaths in seedlings or stems of mature plants. Two major QTLs conferring FCR resistance in barley, Qcsr.cpi-1H and Qcrs.cpi-4H, were previously identified in the growth room. They could explain up to 33.4 and 45.3% of phenotypic variance, respectively. This is the first study where the possible effects of FCR-resistant loci identified in the previous studies based on seedling assay are tested for their abilities to reduce grain yield loss. Near isogenic lines (NILs) and backcross (BC) lines targeting these two loci were assessed in the 2017 and 2018 crop seasons. Results from the NILs showed that the presence of a resistance allele at either the 1HL or 4HL locus reduced grain yield loss by an average of 12.0% and 10.7%, respectively. Grain yields of the top BC lines containing resistance alleles at both loci were 34.4% higher than the average of the commercial varieties under FCR inoculation. These lines will be highly valuable in breeding barley varieties with enhanced resistance to FCR. [ABSTRACT FROM AUTHOR]