Inference of the optimal probability distribution model for geotechnical parameters by using Weibull and NID distributions

To obtain the optimal probability distribution models of geotechnical parameters, the goodness of fit of the normal information diffusion (NID) distribution and Weibull distribution were investigated and compared for actual engineering samples and Monte Carlo (MC) simulated samples. Two datasets from actual engineering parameters (the strength of a rock mass and the average wind speed) were used to test the fitting abilities of these two distributions. The results show that the parameters of the NID distribution are easily estimated, the Kolmogorov-Smirnov (K-S) test results of the NID distribution are smaller than those of the Weibull distribution, and the NID distribution curves can perfectly reflect the stochastic volatility of geotechnical parameters with small sample sizes. The sample size effects on the fitting accuracy of the NID distribution and Weibull distribution were also investigated in this paper. Eight simulated samples with different sample sizes, namely, 15, 20, 30, 50, 100, 200, 500, and 1000, were produced by using the MC method based on two known Weibull distributions. The results show that with an increase in the sample size, the K-S test results of the NID distribution gradually decrease and tend to converge, while the chi-square test results of the NID distribution remain low and are always lower than those of the Weibull distribution. The cumulative probability values of the NID distribution are larger than those of the Weibull distribution and are always equal to 1.0000. Finally, the comparison of the fitting accuracy between the NID distribution and normalized Weibull distribution was also analyzed

Nonparametric approaches for analyzing carbon emission: from statistical and machine learning perspectives

Linear regression models, especially the extended STIRPAT model, are routinely-applied for analyzing carbon emissions data. However, since the relationship between carbon emissions and the influencing factors is complex, fitting a simple parametric model may not be an ideal solution. This paper investigated various nonparametric approaches in statistics and machine learning (ML) for modeling carbon emissions data, including kernel regression, random forest and neural network. We selected data from ten Chinese cities from 2005 to 2019 for modeling studies. We found that neural network had the best performance in both fitting and prediction accuracy, which implies its capability of expressing the complex relationships between carbon emissions and the influencing factors. This study provides a new means for quantitative modeling of carbon emissions research that helps to understand how to characterize urban carbon emissions and to propose policy recommendations for "carbon reduction". In addition, we used the carbon emissions data of Wuhu city as an example to illustrate how to use this new approach

Vertical stress and stability of interburden over an abandoned pillar working before upward mining: a case study

Upward mining of the residual coal seam over an abandoned pillar working is one of the effective measures to alleviate the contradiction between limited resources and increased consumption. Interburden stability over an abandoned pillar working plays a significant role in guaranteeing the safety of upward mining; however, it has not yet been extensively studied and understood. In this study, the vertical stress of the interburden over an abandoned pillar working was first investigated. The mechanical model of the interburden was established and the damage conditions were analysed. Then, the stability of the interburden over 38502 abandoned workings in Baijiazhuang coal mine was determined by mechanical analysis and field monitoring. The results show that: (i) Vertical stress of the interburden over abandoned mining zones is clearly lower than the initial stress, indicating the existence of a de-stressed effect. Moreover, vertical stress of the interburden over residual coal pillars is greater than the initial stress, which is the evidence of a stress 2 concentration effect. (ii) The interburden over an abandoned pillar working should be regarded as an elastic rectangular plate supported by generalized Kelvin bodies in mechanical modelling. (iii) The interburden over abandoned mining zones may experience two damage stages. In the first stage, initial plastic damage appears at the central region of interburden. In the second stage, the plastic damage evolves from the central point to the surrounding areas. (iv) The mechanical analysis and field monitoring both indicate the initial damage occurred at the central region over 38502 abandoned workings in Baijiazhuang coal mine before upward mining. Related rock control measures should be implemented in that region to guarantee the safe mining of the residual coal seam

Gujin Dan is a Chinese medicine formulation that stimulates cell proliferation and differentiation by controlling multiple genes involved in MC3T3-E1 cells

Background: With the development of Traditional Chinese medicine (TCM) in recent years, the use of TCM in the treatment of osteoporosis has received much attention and research. Gujin Dan (GJD) is one of the representative Chinese medicine formulations that work synergistically with 19 herbs and has been used for decades to treat cervical spondylosis, lumbar disc herniation, osteoarthritis and osteoporosis. However, the exact molecular mechanism by which GJD is used to strengthen bones in the treatment of osteoporosis remains largely unknown. / Methods: In this study, an aqueous extract of GJD was prepared and its components were identified by high-performance liquid chromatography (HPLC). The effect of GJD aqueous extract on MC3T3-E1 cells was determined by Cell Counting Kit-8 (CCK-8) assay, alkaline phosphatase (ALP), and alizarin red S staining (ARS), combined with RNA sequencing (RNA-seq) and qRT-PCR. / Results: Our study showed that GJD significantly promoted the proliferation of MC3T3-E1 cells, as well as the synthesis and mineralisation of the extracellular matrix. GJD significantly increased the expression levels of genes that promote cell proliferation such as Adamts1, Mcam, Cyr61, Fos, Cebpd, Fosl2, Sirt1, Nipbl, Sema3c and Kcnq1ot1, up-regulated genes that inhibit apoptosis such as Gadd45a, Birc3, up-regulated genes that inhibit osteoclastogenesis such as Bcl6, Nfkbiz, Clcf1, Bcl3, Lgals3, Wisp1, Dusp1 and Fblim1, up-regulated genes that promote MC3T3-E1 cell differentiation such as Junb, Egr1, Klf10, Atf6, Malat1, Btg2, Sertad4, Zfyve16, Tet2, Creb5, Snai2, Fam46a, Calcrl and Pdzrn3. In addition, GJD mildly upregulated the expression levels of gene markers such as Atf4, Fn1, Usp7, Sox4, Col16a1, Spp1, Bmp1, Runx2, Bglap, Col12a1, and Alpl in osteoblasts. / Conclusions: Our results show that GJD promotes the differentiation and proliferation of MC3T3-E1 cells, inhibits osteoclast formation, and prevents osteoblast apoptosis. The present study significantly improves the current understanding of the molecular effects of GJD on MC3T3-E1 cells. This study also provides a new strategy for the further use of Chinese medicinal preparations against bone metabolism-related diseases

Gazelle: A Low Latency Framework for Secure Neural Network Inference

The growing popularity of cloud-based machine learning raises a natural question about the privacy guarantees that can be provided in such a setting. Our work tackles this problem in the context where a client wishes to classify private images using a convolutional neural network (CNN) trained by a server. Our goal is to build efficient protocols whereby the client can acquire the classification result without revealing their input to the server, while guaranteeing the privacy of the server's neural network. To this end, we design Gazelle, a scalable and low-latency system for secure neural network inference, using an intricate combination of homomorphic encryption and traditional two-party computation techniques (such as garbled circuits). Gazelle makes three contributions. First, we design the Gazelle homomorphic encryption library which provides fast algorithms for basic homomorphic operations such as SIMD (single instruction multiple data) addition, SIMD multiplication and ciphertext permutation. Second, we implement the Gazelle homomorphic linear algebra kernels which map neural network layers to optimized homomorphic matrix-vector multiplication and convolution routines. Third, we design optimized encryption switching protocols which seamlessly convert between homomorphic and garbled circuit encodings to enable implementation of complete neural network inference. We evaluate our protocols on benchmark neural networks trained on the MNIST and CIFAR-10 datasets and show that Gazelle outperforms the best existing systems such as MiniONN (ACM CCS 2017) by 20 times and Chameleon (Crypto Eprint 2017/1164) by 30 times in online runtime. Similarly when compared with fully homomorphic approaches like CryptoNets (ICML 2016) we demonstrate three orders of magnitude faster online run-time

Highly Stable Pickering Emulsions with Xylan Hydrate Nanocrystals

Xylan is a highly abundant plant-based biopolymer. Original xylans in plants are in an amorphous state, but deacetylated and low-branched xylan can form a crystalline structure with water molecules. The utilizations of xylan have been limited to bulk applications either with inconsistency and uncertainty or with extensive chemical derivatization due to the insufficient studies on its crystallization. The applications of xylan could be greatly broadened in advanced green materials if xylan crystals are effectively utilized. In this paper, we show a completely green production of nano-sized xylan crystals and propose their application in forming Pickering emulsions. The branches of xylan were regulated during the separation step to controllably induce the formation of xylan hydrate crystals. Xylan hydrate nanocrystals (XNCs) with a uniform size were successfully produced solely by a mild ultrasonic treatment. XNCs can be adsorbed onto oil–water interfaces at a high density to form highly stable Pickering emulsions. The emulsifying properties of XNCs were comparable to some synthetic emulsifiers and better than some other common biopolymer nanocrystals, demonstrating that XNCs have great potential in industrial emulsification

Horizontal spatial correlation of reverberation for rough sea-bottom interface

Correlation sonar, which estimates the velocity of vessel utilizing the principle of waveform invariance, can achieve the sampling of the horizontal spatial correlation of sea-bottom reverberation. The horizontal spatial correlation can be expressed as a correlation function and is affected by sea-bottom characteristics. The expression of the correlation function of the sea-bottom reverberation is derived, which is written as the convolution of the autocorrelation function of transmitted signal, the cross-correlation function of the backscattered impulse response from a plane interface, and the autocorrelation function of the probability density function of the sea-bottom roughness. The isotropic interface roughness of the sea-bottom leads to a circular planform of the correlation function whose width varies with roughness. The anisotropic interface roughness of the sea-bottom leads to an elliptical planform of the correlation function whose major axis is in the direction of weaker roughness. Simulation of submarine reverberation and correlation function verifies this conclusion. The model for the spatially covariant field is used to estimate the backscattering cross section which varies with azimuth angle under the condition of anisotropic seafloor roughness. It should be noted that the horizontal spatial correlation of reverberation is also related to sonar parameters and other sea-bottom characteristics

Antecedents of Consumers’ Intention to Purchase Energy-Efficient Appliances: An Empirical Study Based on the Technology Acceptance Model and Theory of Planned Behavior

Personal consumption behavior has negative impacts on the environment, such as climate change and wasted resources. To eliminate the adverse effects, more manufacturers are producing environmentally friendly products and governments are encouraging residents to adopt energy-saving products. Among these products, energy-efficient appliances are designed to save energy in everyday life. In this research, we focused on examining the antecedents of consumers’ acceptance of energy-efficient appliances. A combined framework of the technology acceptance model and the theory of planned behavior was used. The research was empirically tested using an online survey of 280 consumers. The study indicates that perceived ease of use had a significant impact on perceived usefulness; moreover, it positively influenced consumers’ attitudes. Subjective norms, perceived behavioral control, and attitude significantly affected consumers’ purchasing intention. However, perceived usefulness did not have direct significant effect on consumers’ purchasing intention. Furthermore, we conducted a comparative analysis to further analyze the effect of consumers’ awareness of the China Energy Label on their purchasing intentions. Finally, insights and suggestions are discussed

Mine water inrush forecasting during the mining under waters

Stress and deformation of the rock masses around the mine seam induced by mining can affect the water system and the aquifuge within the mining fields region and then often causes coal mine water hazards during mining under waters. In this study, based on the geological and hydrogeological condition of the mining fields and considering the water flowing fracture zone and the caving zone, fine model describing the rock structures, rock properties and effects of mining activities is established. The groundwater seepage evolution is analyzed after mining and the water inrush is forecasted on roof of working faces, adopting the seepage mechanics theory. According to the water inrush forecasting results in different mining depth, the safely mining depth under waters is decided. The research results indicate that strip-partial mining with coal pillars in coal seams under the waters is effective method during mining under waters and after mining to a certain depth, the coal seam can be mined fully safely. The study provides a theoretical basis and practical guidance for decision-making of “three-underground” mining, coal mine water hazards prediction and prevention