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Abstract Particle geometric is a key parameter that defines the eometric attributes of calcareous sand particles and is intricately related to their mechanical traits, such as compression and shear. The scanning electron microscopy and digital imaging were applied to capture the microscopic properties and geometric projections of calcareous sand. The qualitative analysis, conventional statistical methods and fractal theory were employed to describe the geometric morphology of sand particles. Additionally, we analyzed the structural and physical traits of calcareous sand based on its unique biological genesis. We developed a hypothetical structural-physical model for calcareous sand. Our findings revealed the interwoven reticulation on the surface of calcareous gravel particles, along with an uneven distribution of pores on the external surface. As the particle size increased, the global profile factor decreased and the angularity increased. The critical threshold for the variations in flatness, surface roughness, and circularity was observed at a particle size of 5 mm, with the particle size having a relatively minor effect on these characteristics for particles smaller than 5 mm. The shape of the calcareous sand particles exhibited fractal characteristics, with fractal dimension serving as a measure of surface smoothness, particle breakage, and strength. These experimental results could significantly enhance our understanding of the mechanical behavior of calcareous sand.

Abstract The particle breakage effect and compression characteristics of calcareous sand are related to the water content in the sand material. However, the effects of water content on the particle breakage and compression characteristics of calcareous sand have rarely been investigated. In this work, 50 sets of confined compression tests were conducted on calcareous sand specimens, and the compression characteristics and particle breakage effects of two single-particle-size groups (particle size ranges of 1–0.5 mm and 0.5–0.25 mm) of calcareous sand were investigated under five different water contents. The test results showed that with the increase in the water content, the final compression deformation of calcareous sand was positively correlated with the water content. The final compression deformation decreased when the water content reached a certain value. The water content corresponding to the peak final compression deformation was related to the gradation of the calcareous sand; the specific values were 10% and 15% for particle size ranges of 1–0.5 mm and 0.5–0.25 mm, respectively. With the increase in the water content, the slope of the loading curve of calcareous sand appeared to increase and then decrease, reaching maximum when the water content was 10%. Moreover, the slope of the loading curve was close to twice that of the loading curve of dry sand, whereas the slope of the unloading curve changed little. Under the same water content, the initial gradation had no effect on the compression and unloading characteristics of the specimens beyond a vertical pressure of 1 MPa. The effects of the variation in the water content on the particle breakage of calcareous sand were mainly reflected in the softening effect of water on the specimen particles, which reduced the Mohr strength of the particles.

Excavation in foundation pits can result in serious issues for nearby tunnel structures like deformation, differential settlement, and seepage damage, which profoundly impact project timelines and potentially endanger life and property safety. Therefore, it is imperative to investigate these impacts before and after construction and to facilitate timely adjustments of construction measures and reinforcement where possible. In this study, a foundation pit construction project near a rail transit line is employed as a case to comprehensive study the impact of on-site deep foundation pit excavation on adjacent rail transit structures by numerical simulation. A three-dimensional finite-element model of the foundation pit based on site geological characteristics and construction procedures is established to study the excavation and maintenance processes. Through analysis of key parameters including soil deformation, displacement, shear force, and bending moment of the tunnel structures, the designed protective structure is found to have effectively mitigated soil deformation, ensuring the stability of the foundation pit. As excavation progresses, lateral soil deformation and vertical uplift gradually increase but remain within specified control values. During various excavation stages, the maximum displacement of the tunnel structure gradually increases, with the increase rates of maximum settlement being 29.09%, 20.51%, and 6.45%, respectively. This indicates a gradual enhancement of the stability of the tunnel structure. Additionally, excavation of the foundation pit has a significant impact on the bending moment distribution of the tunnel structure but does not affect the axial force and shear force of the tunnel structure. The findings of this study offer crucial scientific insights for evaluating the safety and stability of construction near tunnel structures.

It is meaningful to analyze the market correlations for stock selection in the field of financial investment. Since it is difficult for existing deep clustering methods to mine the complex and nonlinear features contained in financial time series, in order to deeply mine the features of financial time series and achieve clustering, a new end-to-end deep clustering method for financial time series is proposed. It contains two modules: an autoencoder feature extraction network based on TCN (temporal convolutional neural) networks and a temporal clustering optimization algorithm with a KL (Kullback–Leibler) divergence. The features of financial time series are represented by the causal convolution and the dilated convolution of TCN networks. Then, the pre-training results based on the KL divergence are fine-tuned to make the clustering results discriminative. The experimental results show that the proposed method outperforms existing deep clustering and general clustering algorithms in the CSI 300 and S&P 500 index markets. In addition, the clustering results combined with an inference strategy can be used to select stocks that perform well or poorly, thus guiding actual stock market trades.

Our purpose is to prove the existence of periodic solutions for a competition Lotka-Volterra system on time scales, and one example is given to illustrate our results.

In wireless networks, an attacker can tune a receiver and tap the communication between two nodes. Whether or not some meaningful information is obtained by tapping a wireless connection depends on the transmission scheme. In this paper, we design some secure network coding by combining information-theoretic approaches with cryptographic approaches. It ensures that the wiretapper cannot get any meaningful information no matter how many channels are wiretapped. In addition, if each source packet is augmented with a hash symbol which is computed from a simple nonlinear polynomial function of the data symbols, then the probability of detecting the modification is very high.

Reducing food waste is an important initiative to ensure food security and mitigate climate change. Previous studies have largely ignored the influence of reference points on food waste behavior and the role of human-to-human relational interactions, failing to capture their role in guiding residents' food waste behavior (RFWBs). To address this gap, this study proposes a model that examines the role of reference dependence, normative internalization, and symbolic expectations in RFWBs, while introducing network embeddedness as a moderating variable. Using data collected from 981 respondents online, ordinary least squares were used to test the hypotheses. The results showed that reference dependence and normative internalization had a positive effect on reducing RFWBs with regression coefficients of 0.178 and 0.293 respectively, while symbolic expectations also had a positive effect on RFWBs with a regression coefficient of 0.227. Additionally, Network embeddedness played a significant moderating role in reference dependence, normative internalization, and symbolic expectations with RFWBs. This paper is theoretically innovative and the relevant findings fill the current research gap in the field of food waste, providing guidance and reference for government departments to formulate policies and promote residents to implement food waste reduction behaviors. [ABSTRACT FROM AUTHOR]