Study and Integrative Evaluation on the development of Circular Economy of Shaanxi Province

AbstractA kind of index system used for evaluating the circular economy development of Shaanxi province was built in this paper based on the circular economy development's content and some basic principles that should be followed by the system, along with the current status of the circular economy development of Shaanxi province. This system includes five aspects that are social and economic development, resource efficiency, resource recycling and reuse, environment protection, pollution reduction. By using this system and principal component analysis as well as analytic hierarchy process, we studied the circular economy development of Shaanxi Province. The results show that circular economy development in this province is in steady upward developing. Finally, some advices for accelerating the circular economy development of Shaanxi were prompted

Enhancing Deep Knowledge Tracing with Auxiliary Tasks

Knowledge tracing (KT) is the problem of predicting students' future performance based on their historical interactions with intelligent tutoring systems. Recent studies have applied multiple types of deep neural networks to solve the KT problem. However, there are two important factors in real-world educational data that are not well represented. First, most existing works augment input representations with the co-occurrence matrix of questions and knowledge components\footnote{\label{ft:kc}A KC is a generalization of everyday terms like concept, principle, fact, or skill.} (KCs) but fail to explicitly integrate such intrinsic relations into the final response prediction task. Second, the individualized historical performance of students has not been well captured. In this paper, we proposed \emph{AT-DKT} to improve the prediction performance of the original deep knowledge tracing model with two auxiliary learning tasks, i.e., \emph{question tagging (QT) prediction task} and \emph{individualized prior knowledge (IK) prediction task}. Specifically, the QT task helps learn better question representations by predicting whether questions contain specific KCs. The IK task captures students' global historical performance by progressively predicting student-level prior knowledge that is hidden in students' historical learning interactions. We conduct comprehensive experiments on three real-world educational datasets and compare the proposed approach to both deep sequential KT models and non-sequential models. Experimental results show that \emph{AT-DKT} outperforms all sequential models with more than 0.9\% improvements of AUC for all datasets, and is almost the second best compared to non-sequential models. Furthermore, we conduct both ablation studies and quantitative analysis to show the effectiveness of auxiliary tasks and the superior prediction outcomes of \emph{AT-DKT}.Comment: Accepted at WWW'23: The 2023 ACM Web Conference, 202

Clinical Advanced in Early-stage ALK-positive Non-small Cell Lung Cancer Patients

Lung cancer is the leading cause of cancer death in China. Non-small cell lung cancer (NSCLC) accounts for 85% of lung cancer cases, with the majority of the cases diagnosed at the advanced stage. Molecular targeted therapy is becoming the focus attention for advanced NSCLC. Echinoderm microtubule-associated protein-like 4 gene and the anaplastic lymphoma kinase gene (EML4-ALK) is among the most common molecular targets of NSCLC; its specific small-molecule tyrosine kinase inhibitors (TKIs) are approved for use in advanced NSCLC cases of ALK-positive. However, the influence of EML4-ALK fusion gene on the outcome of early-stage NSCLC cases and the necessity of application of TKIs for early-stage ALK-positive NSCLC patients are still uncertain. In this paper, we summarized the progression of testing methods for ALK-positive NSCLC patients as well as clinicopathological implication, outcome, and necessity of application of TKIs for early-stage ALK-positive NSCLC patients

High temperature and salinity enhance soil nitrogen mineralization in a tidal freshwater marsh.

Soil nitrogen (N) mineralization in wetlands is sensitive to various environmental factors. To compare the effects of salinity and temperature on N mineralization, wetland soils from a tidal freshwater marsh locating in the Yellow River Delta was incubated over a 48-d anaerobic incubation period under four salinity concentrations (0, 10, 20 and 35‰) and four temperature levels (10, 20, 30 and 40°C). The results suggested that accumulated ammonium nitrogen (NH4+-N) increased with increasing incubation time under all salinity concentrations. Higher temperatures and salinities significantly enhanced soil N mineralization except for a short-term (≈10 days) inhibiting effect found under 35‰ salinity. The incubation time, temperature, salinity and their interactions exhibited significant effects on N mineralization (P<0.001) except the interactive effect of salinity and temperature (P>0.05), while temperature exhibited the greatest effect (P<0.001). Meanwhile, N mineralization processes were simulated using both an effective accumulated temperature model and a one-pool model. Both models fit well with the simulation of soil N mineralization process in the coastal freshwater wetlands under a range of 30 to 40°C (R2 = 0.88-0.99, P<0.01). Our results indicated that an enhanced NH4+-N release with increasing temperature and salinity deriving from the projected global warming could have profound effects on nutrient cycling in coastal wetland ecosystems

Synergistic Treatment of Congo Red Dye with Heat Treated Low Rank Coal and Micro-Nano Bubbles

In this study, the adsorption method and micro-nano bubble (MNB) technology were combined to improve the efficiency of organic pollutant removal from dye wastewater. The adsorption properties of Congo red (CR) on raw coal and semi-coke (SC) with and without MNBs were studied. The mesoporosity of the coal strongly increased after the heat treatment, which was conducive to the adsorption of macromolecular organics, such as CR, and the specific surface area increased greatly from 2.787 m2/g to 80.512 m2/g. MNBs could improve the adsorption of both raw coal and SC under different pH levels, temperatures and dosages. With the use of MNBs, the adsorption capacity of SC reached 169.49 mg/g, which was much larger than that of the raw coal at 15.75 mg/g. The MNBs effectively reduced the adsorption time from 240 to 20 min. In addition, the MNBs could ensure the adsorbent maintained a good adsorption effect across a wide pH range. The removal rate was above 90% in an acidic environment and above 70% in an alkaline environment. MBs can effectively improve the rate of adsorption of pollutants by adsorbents. SC was obtained from low-rank coal through a rapid one-step heating treatment and was used as a kind of cheap adsorbent. The method is thus simple and easy to implement in the industrial context and has the potential for industrial promotion

Performance Evaluation of Missing-Value Imputation Clustering Based on a Multivariate Gaussian Mixture Model.

BACKGROUND:It is challenging to deal with mixture models when missing values occur in clustering datasets. METHODS AND RESULTS:We propose a dynamic clustering algorithm based on a multivariate Gaussian mixture model that efficiently imputes missing values to generate a "pseudo-complete" dataset. Parameters from different clusters and missing values are estimated according to the maximum likelihood implemented with an expectation-maximization algorithm, and multivariate individuals are clustered with Bayesian posterior probability. A simulation showed that our proposed method has a fast convergence speed and it accurately estimates missing values. Our proposed algorithm was further validated with Fisher's Iris dataset, the Yeast Cell-cycle Gene-expression dataset, and the CIFAR-10 images dataset. The results indicate that our algorithm offers highly accurate clustering, comparable to that using a complete dataset without missing values. Furthermore, our algorithm resulted in a lower misjudgment rate than both clustering algorithms with missing data deleted and with missing-value imputation by mean replacement. CONCLUSION:We demonstrate that our missing-value imputation clustering algorithm is feasible and superior to both of these other clustering algorithms in certain situations

Numerical simulations on shear behaviour of rock joint network under constant normal stiffness conditions.

In this study, the numerical direct shear tests were conducted to investigate the shear mechanical properties of joint networks under constant normal stiffness (CNS) boundary conditions. The influence of random joint number on shear stress (τ), dilation (normal displacement, δv) and normal stress (σn) of rock mass were studied quantitatively with fixed main slip surface. At the same time, the internal stress evolution process and failure process were analyzed. The results reveal that the number of random joints (γ) has little effect on the shear and normal stresses. The normal displacement of the sample generally decreases as the number of random joints increases. In addition, the normal displacement of the specimen is absorbed by the random joints when the number of random joints in the specimen increases to a certain level: when γ is greater than 6 and the shear displacement (μ) reaching 10 mm, the specimen exhibits shear contraction. Therefore, the internal random joints mainly control the failure mode and dilatancy performance of the specimen, while the main joint of the rock controls the shear stress of the specimen