Can Teacher Participation in School Governance Improve Student Academic Performance?

This study published in Modern Education Management uses the data of 9,841 students from the Program for International Student Assessment (PISA) 2015 in four provincial administrative regions of China as sample to analyze the impact of teacher participation in school governance on student academic performance. Since there are significant differences in student academic performance among various schools, the multi-layer regression model is adopted to address the question "whether teacher participation in school governance can improve stu-dent academic performance"

Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net

Models applied on real time response task, like click-through rate (CTR) prediction model, require high accuracy and rigorous response time. Therefore, top-performing deep models of high depth and complexity are not well suited for these applications with the limitations on the inference time. In order to further improve the neural networks' performance given the time and computational limitations, we propose an approach that exploits a cumbersome net to help train the lightweight net for prediction. We dub the whole process rocket launching, where the cumbersome booster net is used to guide the learning of the target light net throughout the whole training process. We analyze different loss functions aiming at pushing the light net to behave similarly to the booster net, and adopt the loss with best performance in our experiments. We use one technique called gradient block to improve the performance of the light net and booster net further. Experiments on benchmark datasets and real-life industrial advertisement data present that our light model can get performance only previously achievable with more complex models.Comment: 10 pages, AAAI201

Machine learning-based prediction models for patients no-show in online outpatient appointments

With the development of information and communication technologies, all public tertiary hospitals in China began to use online outpatient appointment systems. However, the phenomenon of patient no-shows in online outpatient appointments is becoming more serious. The objective of this study is to design a prediction model for patient no-shows, thereby assisting hospitals in making relevant decisions, and reducing the probability of patient no-show behavior. We used 382,004 original online outpatient appointment records, and divided the data set into a training set (N1 = 286,503), and a validation set (N2 = 95,501). We used machine learning algorithms such as logistic regression, k-nearest neighbor (KNN), boosting, decision tree (DT), random forest (RF) and bagging to design prediction models for patient no-show in online outpatient appointments. The patient no-show rate of online outpatient appointment was 11.1% (N = 42,224). From the validation set, bagging had the highest area under the ROC curve and AUC value, which was 0.990, followed by random forest and boosting models, which were 0.987 and 0.976, respectively. In contrast, compared with the previous prediction models, the area under ROC and AUC values of the logistic regression, decision tree, and k-nearest neighbors were lower at 0.597, 0.499 and 0.843, respectively. This study demonstrates the possibility of using data from multiple sources to predict patient no-shows. The prediction model results can provide decision basis for hospitals to reduce medical resource waste, develop effective outpatient appointment policies, and optimize operations

Deep Interest Evolution Network for Click-Through Rate Prediction

Click-through rate~(CTR) prediction, whose goal is to estimate the probability of the user clicks, has become one of the core tasks in advertising systems. For CTR prediction model, it is necessary to capture the latent user interest behind the user behavior data. Besides, considering the changing of the external environment and the internal cognition, user interest evolves over time dynamically. There are several CTR prediction methods for interest modeling, while most of them regard the representation of behavior as the interest directly, and lack specially modeling for latent interest behind the concrete behavior. Moreover, few work consider the changing trend of interest. In this paper, we propose a novel model, named Deep Interest Evolution Network~(DIEN), for CTR prediction. Specifically, we design interest extractor layer to capture temporal interests from history behavior sequence. At this layer, we introduce an auxiliary loss to supervise interest extracting at each step. As user interests are diverse, especially in the e-commerce system, we propose interest evolving layer to capture interest evolving process that is relative to the target item. At interest evolving layer, attention mechanism is embedded into the sequential structure novelly, and the effects of relative interests are strengthened during interest evolution. In the experiments on both public and industrial datasets, DIEN significantly outperforms the state-of-the-art solutions. Notably, DIEN has been deployed in the display advertisement system of Taobao, and obtained 20.7\% improvement on CTR.Comment: 9 pages. Accepted by AAAI 201

Failure characteristics and reasonable width of water-resisting pillar under the coupling effect of mining and seepage

The coupling failure induced by the influence of mining practice and the water immersion softening of the water-resisting coal pillars in old goaf is one of the common causes of water inrush accidents in the same seam working face. Exploring the failure characteristics and reasonable width of the water-resisting coal pillars in the old goaf is of great significance for the prevention and control of mine water damage. The safe width of the water-resisting coal pillars in the closed overlying goaf in the same layer of the No.250209 working face of the Yanbei Coal Mine in Huating, Gansu Province is taken as the research object. The theoretical analysis and the FLAC3D numerical simulation are conducted to analyze the whole instability disaster process of water-resisting coal pillars as the occurrence of partial failure of the immersed coal blocks, collapse, and insufficient total width. The coupling characteristics of the stress field, plastic area and seepage field in the water-resisting coal pillars under the complex influence of water immersion weakening, mining and seepage have been revealed and the water blocking capacity and stability evolution law of water-resisting coal pillars with different coal pillar widths are obtained. Accordingly, a method for determining the width of the water-resisting coal pillars of three-zone combined type of “water seepage zone + elastic compaction water resisting zone + plastic zone” is proposed. The results show that ① the plastic failure firstly occurs in the lower coal at the immersed side of the water-resisting coal pillar under the superimposed action of overburden load and water pressure in the goaf. And with the expansion of the seepage scope of water erosion and the gradual immersion-induced weakening, the deterioration and collapse of the bearing capacity of the coal in this area are induced, which ultimately leads to the eccentric axial compression and collapse of the water-resisting coal pillar. ② In the three stages, the development width of the plastic zone at the upper, middle and lower parts of the water-resisting coal pillar is different, showing a gradual increase from top to bottom along the increasing height of the coal pillar. That is, the extension range of the plastic zone at the lower part of the water-resisting coal pillar at the soaking side is larger than that at the middle and upper parts, indicating that the bottom area of the water-resisting coal pillar is more likely to form a water channel. This practical condition is consistent with the theoretical analysis. ③ The volume of plastic zone of water-resisting coal pillar, accounting for more than 83% of the total volume of water seepage zone, is the main area for water transmission of water-resisting coal pillar. Although the volume of elastic zone only accounts for 17%, a relatively small proportion, of the total volume of water seepage zone, it determines the maximum expansion boundary of the range of water seepage zone. Also, the seepage coupling characteristics of “small range, high stress, and low permeability” and “large range, low stress, and high permeability” have been shown in the elastic water seepage zone. ④ The water blocking capacity of the water-resisting coal pillar depends on the range and connectivity between the water seepage area and the plastic area on the mining side. When the width of the water-resisting coal pillar is 110 m and 120 m, the water seepage area and the plastic area are completely connected. And when the width of water-resisting coal pillar become 130, 140 and 150 m, the water seepage area and plastic area are not connected, and the width of elastic compaction water resisting area between them is 5.5, 11.5 and 23.5 m, respectively. Based on this, the connection between the seepage zone and the mining plastic zone is regarded as the critical condition for the mine water to break through the water-resisting pillar. And a method for determining the width of the three zones combined water-resisting coal pillar of “water seepage zone + elastic compaction water resisting zone + plastic zone” with the condition that width of the elastic compaction zone is no less than 20 m is proposed. It is pointed out that not only the conventional stability index of support coal pillar, such as more than 31% proportion of the elastic core zone and the unconnected plastic zone, but also the water resisting performance of the water-resisting coal pillar should be taken as the criteria to determine the stability of the water-resisting coal pillar

Telomere maintenance-related genes are important for survival prediction and subtype identification in bladder cancer

Background: Bladder cancer ranks among the top three in the urology field for both morbidity and mortality. Telomere maintenance-related genes are closely related to the development and progression of bladder cancer, and approximately 60%–80% of mutated telomere maintenance genes can usually be found in patients with bladder cancer.Methods: Telomere maintenance-related gene expression profiles were obtained through limma R packages. Of the 359 differential genes screened, 17 prognostically relevant ones were obtained by univariate independent prognostic analysis, and then analysed by LASSO regression. The best result was selected to output the model formula, and 11 model-related genes were obtained. The TCGA cohort was used as the internal group and the GEO dataset as the external group, to externally validate the model. Then, the HPA database was used to query the immunohistochemistry of the 11 model genes. Integrating model scoring with clinical information, we drew a nomogram. Concomitantly, we conducted an in-depth analysis of the immune profile and drug sensitivity of the bladder cancer. Referring to the matrix heatmap, delta area plot, consistency cumulative distribution function plot, and tracking plot, we further divided the sample into two subtypes and delved into both.Results: Using bioinformatics, we obtained a prognostic model of telomere maintenance-related genes. Through verification with the internal and the external groups, we believe that the model can steadily predict the survival of patients with bladder cancer. Through the HPA database, we found that three genes, namely ABCC9, AHNAK, and DIP2C, had low expression in patients with tumours, and eight other genes—PLOD1, SLC3A2, RUNX2, RAD9A, CHMP4C, DARS2, CLIC3, and POU5F1—were highly expressed in patients with tumours. The model had accurate predictive power for populations with different clinicopathological features. Through the nomogram, we could easily assess the survival rate of patients. Clinicians can formulate targeted diagnosis and treatment plans for patients based on the prediction results of patient survival, immunoassays, and drug susceptibility analysis. Different subtypes help to further subdivide patients for better treatment purposes.Conclusion: According to the results obtained by the nomogram in this study, combined with the results of patient immune-analysis and drug susceptibility analysis, clinicians can formulate diagnosis and personalized treatment plans for patients. Different subtypes can be used to further subdivide the patient for a more precise treatment plan

Fracture propagation law of hydraulic fracturing of rock-like materials based on discrete element method

Hydraulic fracturing is an important technical means to relieve the pressure of coal seam roof. Better understanding of fracture propagation mechanism is of great significance to the safe mining of coal seam. In order to further explore the law of hydraulic fracture propagation, aiming at rock-like specimens commonly used in the laboratory, MatDEM, a particle discrete element numerical simulation software, was used to establish a two-dimensional discrete element numerical model of hydraulic fracturing, and various hydraulic fracturing tests with different injection pressure increments were carried out. The effect of injection pressure increment on the propagation of hydraulic fractures was studied, and the mechanism of model initiation was revealed. The law of fracture generation and propagation was analyzed from mesoscale, and the propagation characteristics of hydraulic fractures were discussed. The results show that ① the effect of injection pressure increment on the model initiation pressure and initiation time presents an opposite trend. With the increase of injection pressure, the increase trend of initiation pressure is slow and gradually approaches to 5.6 MPa. The initiation time decreases with the increase of injection pressure, and the decreasing trend slows down gradually. ② The cumulative number of fractures increases exponentially with time. The hydraulic fracturing process can be divided into four stages (Ⅰ−Ⅳ): no fracture stage, slow fracture growth stage, steady fracture growth stage and rapid fracture growth stage, which correspond to the pre-crack initiation, pre-crack formation, primary fracture propagation and secondary fracture propagation processes respectively. As the injection pressure increment increases, the durations of stage Ⅰ, Ⅱ and Ⅲ decrease, while the duration of stage Ⅳ increases in a fluctuating manner. The number of cracks in each stage is the highest in stage Ⅳ, followed by stage Ⅲ and stage Ⅱ. ③ As the injection pressure increment increases, the number of secondary fractures increases from 8 to 16, and the growth rate of fractures gradually slows down before the stage Ⅲ, and increases rapidly after the stage Ⅳ. When the injection pressure increment increases from 0.03 MPa to 0.70 MPa, the final fracture length increases by 1.79 times. ④ The internal energy of the model increases with the increase of the injection pressure increment, and the energy input speed gradually becomes faster. After the model initiation, high-pressure water forms stress concentration at the crack tip, which promotes the crack to continue to extend. At higher injection pressure increment, the fracture propagation speed becomes faster, and the particle displacement decreases gradually from the pressure hole to the outside of the model. The increment of injection pressure makes the secondary fracture forming position close to the pressure hole, which inhibits the formation and expansion of the primary fracture and promotes the formation and expansion of the secondary fracture. All fracture types are tensile fractures

Genetic Evaluation of 114 Chinese Short Stature Children in the Next Generation Era: a Single Center Study

Background/Aims: The genetics of human height is a frequently studied and complex issue. However, there is limited genetic research of short stature. To uncover the subgroup of patients to have higher yield and to propose a simplified diagnostic algorithm in the next generation era. Methods: This study included 114 Chinese children with height SDS ≤ -2.5 and unknown etiology from 2014 to 2015. Target/whole exome sequencing (referred as NGS) and chromosomal microarray analysis (CMA) were performed on the enrolled patients sequentially to identify potential genetic etiologies. The samples solved by NGS and CMA were retrospectively studied to evaluate the clinical pathway of the patients following a standard diagnostic algorithm. Results: In total, a potential genetic etiology was identified in 41 (36%) patients: 38 by NGS (33.3%), two by CMA (1.8%), and an additional one by both (0.9%). There were 46 different variants in 29 genes and 2 pathogenic CNVs identified. The diagnostic yield was significantly higher in patients with facial dysmorphism or skeletal abnormalities than those without the corresponding phenotype (P=0.006 and P=0.009, respectively, Pearson’s χ2 test). Retrospectively study the cohort indicate 83.3% patients eventually would be evaluated by NGS/CMA. Conclusion: This study confirms the utility of high-throughput molecular detection techniques for the etiological diagnosis of undiagnosed short stature and suggests that NGS could be used as a primary diagnostic strategy. Patients with facial dysmorphism and/or skeletal abnormalities are more likely to have a known genetic etiology. Moving NGS forward would simplified the diagnostic algorithm