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PurposeThe purpose of this paper is to overcome the application limitations of other multi-variable regression based on polynomials due to the huge computation room and time cost.Design/methodology/approachIn this paper, based on the idea of feature selection and cascaded regression, two strategies including Laguerre polynomials and manifolds optimization are proposed to enhance the accuracy of multi-variable regression. Laguerre polynomials were combined with the genetic algorithm to enhance the capacity of polynomials approximation and the manifolds optimization method was introduced to solve the co-related optimization problem.FindingsTwo multi-variable Laguerre polynomials regression methods are designed. Firstly, Laguerre polynomials are combined with feature selection method. Secondly, manifolds component analysis is adopted in cascaded Laguerre polynomials regression method. Two methods are brought to enhance the accuracy of multi-variable regression method.Research limitations/implicationsWith the increasing number of variables in regression problem, the stable accuracy performance might not be kept by using manifold-based optimization method. Moreover, the methods mentioned in this paper are not suitable for the classification problem.Originality/valueExperiments are conducted on three types of datasets to evaluate the performance of the proposed regression methods. The best accuracy was achieved by the combination of cascade, manifold optimization and Chebyshev polynomials, which implies that the manifolds optimization has stronger contribution than the genetic algorithm and Laguerre polynomials.

The Ni3Al-based alloy has been considered as a robust catalyst for oxygen evolution reaction (OER) due to its long-term durability and acceptable activity. However, related reports about understanding the catalytic mechanism are rare and desirable. Herein, the effect of γ/γ' phase lattice mismatch on the catalytic performance caused by various cooling rates after solution heat treatment was investigated. With decreasing cooling rate, the morphologies of γ′ precipitates transformed from sphere to cube and the lattice mismatch increased from -0.172% to -0.409%. The increased lattice mismatch facilitated the formation of the active β-NiOOH phase and enhanced the intrinsic catalytic activity, resulting in the optimized OER overpotential of 240 mV at a current density of 10 mA cm−2 with a Tafel slope of 66.1 mV dec−1 and a stability of 200 h in 1 M KOH. This work reveals the lattice mismatch effect on OER and provides a potential candidate for OER.

Diabetes Mellitus (DM) is a significant risk factor for cardiovascular disease (CVD), which is leading cause of deaths in DM patients. However, there are limited effective medical therapies for diabetic CVD. Vascular endothelial injury caused by DM is a critical risk factor for diabetic CVD. Previous study has indicated that Angiotensin-(1-7) (Ang-(1-7)) may prevent diabetic CVD, whereas it is not clear that Ang-(1-7) whether attenuates diabetic CVD through suppressing vascular endothelial injury. In this study, we found that Ang-(1-7) alleviated high glucose (HG)-induced endothelial injury in bEnd3 cells. Moreover, Ang-(1-7) ameliorated HG-induced endothelial injury through downregulating chloride channel 3 (CIC-3) via Mas receptor. Furthermore, HG-induced CIC-3 enhanced reactive oxygen species (ROS) and cytokine production and reduced the level of nitric oxide (NO), while Ang-(1-7) preserved the impact of HG-induced CIC-3 on productions of ROS, cytokine and NO through inhibiting CIC-3 via Mas receptor. Summarily, the present study revealed that Ang-(1-7) alleviated HG-induced vascular endothelial injury through the inhibition of CIC-3, suggested that Ang-(1-7) may preserve diabetic CVD through suppressing HG-induced vascular endothelial injury.

Considering the current global goal of carbon neutrality, the relationship between cultivated land intensive use (CLIU) and carbon emission efficiency (CEE) should be explored to address the global climate crisis and move toward a low-carbon future. However, previous work in this has been conducted at provincial/regional scales and few have identified the spatial correlation between CLIU and CEE at the scale of large river basins. Therefore, this study explored the spatiotemporal characteristics of CLIU, cultivated land carbon emissions (CLCE), and CEE, as well as the spatial correlation between CLIU and CEE in the Yellow River Basin (YRB), China. A comprehensive evaluation model, the Intergovernmental Panel on Climate Change (IPCC) coefficient methodology, existing data envelopment analysis model, and bivariate spatial autocorrelation models were used to analyze statistical data from 2005 to 2017. We found that the overall CLIU and CLCE values in the YRB exhibited a continuous increase; the average carbon emission total efficiency and carbon emission scale efficiency first decreased and then increased, and the average carbon emission pure technical efficiency gradually decreased. Areas of high CLCE were concentrated in eastern areas of the YRB, whereas those of high CLIU, carbon emission total efficiency, carbon emission scale efficiency, and carbon emission pure technical efficiency predominantly appeared in the eastern areas, followed by central and western areas of the YRB. Spatial analysis revealed a significant spatial dependence of CLIU on CEE. From a global perspective, the spatial correlations between CLIU and CEE changed from positive to negative with time. Moreover, the aggregation degree between CLIU and CEE gradually decreases with time, while the dispersion degree increases with time, and the spatial correlation gradually weakens. The local spatial autocorrelation further demonstrates that the number of high-low and low-high clusters between CLIU and CEE gradually increases over time, while the number of high-high and low-low clusters gradually decreased over time. Collectively, these findings can help policymakers formulate feasible low-carbon and efficient CLIU policies to promote win-win cooperation among regions.

The head–discharge relationship of an overflow weir is a prerequisite for flow measurement. Conventionally, it is determined by regression methods. With machine learning techniques, data-driven modelling becomes an alternative. However, a standalone model may be inadequate to generate satisfactory results, particularly for a complex system. With the intention of improving the performance of standard gene expression programming (GEP), a hybrid evolutionary scheme is proposed, which is coupled with grey system theory and probabilistic technique. As a gene filter, grey relational analysis (GRA) eliminates noise and simulated annealing (SA) reduces overfitting by optimising the gene weights. The proposed GEP-based model was developed and validated using experimental data of a submerged pivot weir. Compared with standalone GEP, the GRA–GEP–SA model was found to generate more accurate results. Its coefficients of determination and correlation were improved by 3.6% and 1.7%, respectively. The root mean square error was lowered by 24.8%, which is significant. The number of datasets with an error of less than 10% and 20% was increased by 15% and 12%, respectively. The proposed approach outperforms classic genetic programming and shows a comparative error level with the empirical formula. The hybrid procedure also provides a reference for applications in other hydraulic issues.

With the rapid development of high tech, Internet of Things (IoT) and artificial intelligence (AI) achieve a series of achievements in the healthcare industry. Among them, automatic glaucoma diagnosis is one of them. Glaucoma is second leading cause of blindness in the world. Although many automatic glaucoma diagnosis approaches have been proposed, they still face the following two challenges. First, the data acquisition of diseased images is extremely expensive, especially for disease with low occurrence, leading to the class imbalance. Second, large-scale labeled data are hard to obtain in medical image domain. The aforementioned challenges limit the practical application of these approaches in glaucoma diagnosis. To address these disadvantages, this paper proposes an unsupervised anomaly detection framework based on sparse principal component analysis (SPCA) for glaucoma diagnosis. In the proposed approach, we just employ the one-class normal (nonglaucoma) images for training, so the class imbalance problem can be avoided. Then, to distinguish the glaucoma (abnormal) images from the normal images, a feature set consisting of segmentation-based features and image-based features is extracted, which can capture the shape and textural changes. Next, SPCA is adopted to select the effective features from the feature set. Finally, with the usage of the extracted effective features, glaucoma diagnosis can be automatically accomplished via introducing the T 2 statistic and the control limit, overcoming the issue of insufficient labeled samples. Extensive experiments are carried out on the two public databases, and the experimental results verify the effectiveness of the proposed approach.

Protected areas play significant roles in protecting biodiversity by mitigating human activities. As an indicator for human activities, the human footprint (HF) can be used to assess the effectiveness of protected areas. We developed a HF dataset for the Sanjiangyuan region in China after localizing the global HF model. Then we used it to assess the effectiveness of Sanjiangyuan nature reserve (SNR) in mitigating human pressures. Our results suggest that the HF value for the Sanjiangyuan region was generally low from 1995 to 2015, with a fluctuating increasing trend. The SNR is partially effective in mitigating human pressures. For 2005–2010, the HF values decreased both within and outside the reserve with more decreases for the outside. For the three functional zones of the SNR, our results show that the HF values decreased during 2005–2010 for all of them, with the largest decrease in the experimental area, and increased during 2010–2015, with the lowest increase in the core areas. The results we obtained are consistent with ecological indicator-based assessments. It is a challenge for the government to balance conservation and development in establishing Sanjiangyuan National Park.

With the development of social economy and the improvement of learning quality and quality education requirements, the social attention to the psychological health of college students and social adaptability needs is also more and more high. The research shows that, due to the influence of various factors such as region, economy and culture, the social service needs of minority and Han university students will also be different at different levels. As the hope and future of all ethnic groups, minority college students are the backbone of inheriting, developing and innovating the traditional culture of ethnic groups. It is an important aspect of the development of ethnic education to pay attention to the psychological status of such a special group. At present, there are a lot of researches on the psychological health service needs of college students at home and abroad, but the researches on the psychological health service needs of minority college students are less, and mainly focus on the application. Therefore, the research on the needs of minority college students for group counseling and group counseling is of great theoretical significance.

Background Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the leading major histological phenotypes of all non-small cell lung cancer (NSCLC). In this study, the candidate genes and the potential tumorigenesis distinguishing between LUAD and LUSC were analyzed. Methods The present study investigated two microarray datasets (GSE28571 and GSE10245) downloaded from the Gene Expression Omnibus (GEO) database. A protein-protein interaction (PPI) network was applied to screen out the candidate genes. In addition, differently expressed genes (DEGs) between lung adenocarcinoma and lung squamous cell carcinoma of the two datasets were functionally analyzed by Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. R 4.0.2 was used to perform Kaplan-Meier analysis of DSG3 (desmoglein 3) and KRT14 (keratin 14) by analyzing the expression and clinical data from The Cancer Genome Atlas (TCGA) database. Results The results revealed that 47 DEGs of the two datasets were ascertained in our study. It was found that the DEGs were mainly involved in pathways related to p63 transcription factor network and validated transcriptional factor targeting TAp63, etc. Based on the analysis, we finally identified DSG3 and KRT14 as potential biomarkers for distinguishing between LUAD and LUSC. These results suggested that DSG3 and KRT14 could have the potential to play an important role in NSCLC patients, as diagnostic markers. At the same time, DSG3 or KRT14 indicated a worse prognosis in LUSC patients, which were associated with pathways relevant to the TRAIL signaling pathway and TNF receptor signaling pathway according to bioinformatic analysis. Conclusion The DSG3 and KRT14 have the potential to be used as diagnostic markers, which presented here may facilitate improvements in distinguishing between LUAD and LUSC in advanced NSCLC patients.

As an important part of the North China Craton extension system, the southwest of Shandong Province comprises several uplift and fault depression structures. However, the tectonic activity there is controversial, and more studies are required for the upper crustal velocity structure. In the present study, the three-dimensional upper crustal P wave velocity structure in southwestern Shandong Province was obtained using double-difference seismic tomography. The findings show that southwestern Shandong Province has many vertical high-velocity structures, indicating that mantle-magma upwelling was strong there and formed the uplift structures. It is reasoned that Taishan Mountain is still undergoing uplift caused by magma upwelling that is concentrated mainly below a depth of 20 km. The shallow–middle low-velocity structures in southwestern Shandong Province correspond strongly to the uplift depression structures. The tectonic activity in southwestern Shandong Province can be divided mainly into (i) brittle-ductile deformation of uplift depression structures at depths of 0–10 km and (ii) mantle-magma upwelling under the uplift structures. The formation of uplift structures promoted the formation of the fault depression structures, and the fault depression and uplift structures are closely related in genesis.

This study used the double-difference seismic tomography method to obtain the upper crustal velocity structure in Laizhou Bay and its adjacent areas (LBAA). We found that the horizontal and vertical distributions of the earthquakes after relocation in earthquake swarms are more concentrative than that before relocation. The lateral heterogeneity of crustal velocity structure near the surface in the LBAA is weak, and the low-velocity sedimentary caprocks are developed in most areas except for the Jiaobei uplift. The velocity structure in 5–25 km depth range is highly heterogeneity, which is closely related to different tectonic units. The local uplift tectonic units in Jiyang depression show high-velocity anomalies and should have deep crustal media properties. The high-velocity anomalies under the crust of Tancheng–Lujiang fault zone and the northern Laizhou Bay indicate the properties of upwelling mantle material media. Earthquake swarms activity in the study area are mainly concentrated in the high-velocity interlayer of the upper crust and its periphery. Compared with the relatively stable high-velocity body at the bottom crust, the location of high-velocity interlayer in the study area is more prone to earthquakes under the combined action of gravity and tectonic stress.

Global historical land use scenarios are widely used to simulate the climatic and ecological effects of changes in land cover; however, reliability evaluation of these scenarios for data on China’s forests is missing. By using a historical document-derived Chinese forest dataset (CHFD) for the years 1700–2000, we evaluated the reliability of data on forests in China over three global scenarios—SAGE (Center for Sustainability and the Global Environment), PJ (Pongratz Julia), and KK10 (Kaplan and Krumhardt 2010)—through trend-related, quantitative, and spatial comparisons. The results show the following: (1) Although the area occupied by forests in China in the SAGE, PJ, KK10, and CHFD datasets decreased over the past 300 years, there were large differences between global scenarios and CHFD. The area occupied by forests in China in the SAGE scenario for 1700–1990 was 20%–40% more than that according to CHFD, and that occupied by forests in the KK10 from 1700 to 1850 was 32%–46% greater than that in CHFD. The difference between the PJ and CHFD was lower than 20% for most years. (2) Large differences were detected at the provincial and grid cell scales, where the PJ scenario was closer to CHFD in terms of total forested area. Provinces with large differences in terms of trend and quantity were 84% and 92% of all provinces, respectively. Grid cells with relative differences greater than 70% accounted for 60%–80% of all grids. (3) These global historical land use scenarios do not accurately reveal the spatiotemporal pattern of Chinese forests due to differences in the data sources, methods of reconstruction, and spatial scales.

Considering that the utilization of fly ash in the removal of flue gas pollutants not only provide a way of high value-added utilization of fly ash, but also greatly reduce the cost of removing flue gas pollutant, the synthesis of silica sulfuric acid catalyst from fly ash and its application in simultaneous removal of NOx and SO2 with H2O2 were investigated in this work. Circulating fluidized bed boiler (CFB) fly ash and pulverized coal boiler (PC) fly ash were selected as raw material to prepare silica sulfuric acid catalyst by H2SO4 activation. PC fly ash was difficult to be activated by H2SO4 due to its dense structure, while CFB fly ash could be treated with H2SO4 to promote dealumination, thereby increasing the silica content. Moreover, the -SO3H withdrawing groups were detected on the silica surface by XPS and Py-FTIR technologies, indicating the formation of silica sulfuric acid. Silica sulfuric acid showed higher activity in catalyzing the NO oxidation by H2O2, and a possible reaction mechanism was proposed. Combined with alkali absorption, 99% SO2 and 92% NOx removal efficiencies can be achieved. The effects of activation conditions such as activation temperature, activation time and calcination temperature and removal experimental parameters such as H2O2 concentration, SO2 concentration and simulated flue gas temperature on the catalytic performance were studied. Finally, the catalyst was not found to be deactivated for ten hours in the stability test.

BACKGROUND Phenotype information in electronic health records (EHRs) is mainly recorded in unstructured free text, which cannot be directly used for clinical research. EHR-based deep-phenotyping methods can structure phenotype information in EHRs with high fidelity, making it the focus of medical informatics. However, developing a deep-phenotyping method for non-English EHRs (ie, Chinese EHRs) is challenging. Although numerous EHR resources exist in China, fine-grained annotation data that are suitable for developing deep-phenotyping methods are limited. It is challenging to develop a deep-phenotyping method for Chinese EHRs in such a low-resource scenario. OBJECTIVE In this study, we aimed to develop a deep-phenotyping method with good generalization ability for Chinese EHRs based on limited fine-grained annotation data. METHODS The core of the methodology was to identify linguistic patterns of phenotype descriptions in Chinese EHRs with a sequence motif discovery tool and perform deep phenotyping of Chinese EHRs by recognizing linguistic patterns in free text. Specifically, 1000 Chinese EHRs were manually annotated based on a fine-grained information model, PhenoSSU (Semantic Structured Unit of Phenotypes). The annotation data set was randomly divided into a training set (n=700, 70%) and a testing set (n=300, 30%). The process for mining linguistic patterns was divided into three steps. First, free text in the training set was encoded as single-letter sequences (P: phenotype, A: attribute). Second, a biological sequence analysis tool—MEME (Multiple Expectation Maximums for Motif Elicitation)—was used to identify motifs in the single-letter sequences. Finally, the identified motifs were reduced to a series of regular expressions representing linguistic patterns of PhenoSSU instances in Chinese EHRs. Based on the discovered linguistic patterns, we developed a deep-phenotyping method for Chinese EHRs, including a deep learning–based method for named entity recognition and a pattern recognition–based method for attribute prediction. RESULTS In total, 51 sequence motifs with statistical significance were mined from 700 Chinese EHRs in the training set and were combined into six regular expressions. It was found that these six regular expressions could be learned from a mean of 134 (SD 9.7) annotated EHRs in the training set. The deep-phenotyping algorithm for Chinese EHRs could recognize PhenoSSU instances with an overall accuracy of 0.844 on the test set. For the subtask of entity recognition, the algorithm achieved an F1 score of 0.898 with the Bidirectional Encoder Representations from Transformers–bidirectional long short-term memory and conditional random field model; for the subtask of attribute prediction, the algorithm achieved a weighted accuracy of 0.940 with the linguistic pattern–based method. CONCLUSIONS We developed a simple but effective strategy to perform deep phenotyping of Chinese EHRs with limited fine-grained annotation data. Our work will promote the second use of Chinese EHRs and give inspiration to other non–English-speaking countries.

Strain engineering is a vital way to manipulate the electronic properties of two-dimensional (2D) materials. As a typical representative of transition metal mono-chalcogenides (TMMs), a honeycomb CuSe monolayer features with one-dimensional (1D) moiré patterns owing to the uniaxial strain along one of three equivalent orientations of Cu(111) substrates. Here, by combining low-temperature scanning tunneling microscopy/spectroscopy (STM/S) experiments and density functional theory (DFT) calculations, we systematically investigate the electronic properties of the strained CuSe monolayer on the Cu(111) substrate. Our results show the semiconducting feature of CuSe monolayer with a band gap of 1.28 eV and the 1D periodical modulation of electronic properties by the 1D moiré patterns. Except for the uniaxially strained CuSe monolayer, we observed domain boundary and line defects in the CuSe monolayer, where the biaxial-strain and strain-free conditions can be investigated respectively. STS measurements for the three different strain regions show that the first peak in conduction band will move downward with the increasing strain. DFT calculations based on the three CuSe atomic models with different strain inside reproduced the peak movement. The present findings not only enrich the fundamental comprehension toward the influence of strain on electronic properties at 2D limit, but also offer the benchmark for the development of 2D semiconductor materials., 14 pages, 12 figures, 25 reference

The time series is a kind of complex structure data, which contains some special characteristics such as high dimension, dynamic, and high noise. Moreover, multivariate time series (MTS) has become a crucial study in data mining. The MTS utilizes the historical data to forecast its variation trend and has turned into one of the hotspots. In the era of rapid information development and big data, accurate prediction of MTS has attracted much attention. In this paper, a novel deep learning architecture based on the encoder-decoder framework is proposed for MTS forecasting. In this architecture, firstly, the gated recurrent unit (GRU) is taken as the main unit structure of both the procedures in encoding and decoding to extract the useful successive feature information. Then, different from the existing models, the attention mechanism (AM) is introduced to exploit the importance of different historical data for reconstruction at the decoding stage. Meanwhile, feature reuse is realized by skip connections based on the residual network for alleviating the influence of previous features on data reconstruction. Finally, in order to enhance the performance and the discriminative ability of the new MTS, the convolutional structure and fully connected module are established. Furthermore, to better validate the effectiveness of MTS forecasting, extensive experiments are executed on two different types of MTS such as stock data and shared bicycle data, respectively. The experimental results adequately demonstrate the effectiveness and the feasibility of the proposed method.

Scientific estimation of carbon emissions induced by historical land use and land cover change (LUCC) can improve the accuracy of terrestrial ecosystem carbon budget estimates and deepen understanding of the future carbon-sink potential of terrestrial ecosystems. The present study, using historical-document-based data for provincial cropland, forest, and grassland area in China, and experimental-data-based information for provincial vegetation and soil organic carbon density, re-estimates China's LUCC-induced carbon emissions for 1700-1980 using a bookkeeping model in which we updated tabulated functions for carbon losses and gains. The past 300 years have witnessed a dramatic LUCC in China. The cropland area has increased by 67.11 million ha, while the forest and grassland areas have decreased by 127.96 million ha and 16.72 million ha, respectively. Accordingly, the net carbon emissions for 1700-1980 are 6.17-12.35 Pg C, with 8.55 Pg C in the moderate scenario. Among the contributing factors, deforestation was the largest carbon source, accounting for over 90 % of the total carbon emissions. According to our estimates, over 70 % of carbon emissions were caused by harvesting wood, while30 % were from converting forest and grassland to cropland. Spatially, for the whole period, carbon emissions in southwestern China (Chuan-Yu, Yunnan, and Guangxi), northeastern China (Liaoning, Jilin, and Heilongjiang), and parts of northwestern China (Gan-Ning, Qinghai, and Xinjiang) were as high as 6.03 Pg C, accounting for 70 % of the total carbon emissions. Extending previous studies, we updated the historical LUCC data, carbon density data, and tabulated functions for carbon losses and gains. The estimation results objectively reveal the historical spatiotemporal changes in LUCC-induced emissions.

This study deals with a bottom outlet that causes significant surges at both the inlet and exit. The bottom outlet comprises an intake, a vertical shaft and a tunnel underneath the rockfill dam and a submerged exit. In the intake tower, a cylinder gate controls hexagonal openings near the reservoir bottom. Operations during the dam construction show that discharges at a low reservoir water level result in upsurges of air-water mixture within the hollow gate cylinder and blowouts in the tailwater. To understand the flow behaviors, 3D CFD modeling is performed to examine air entrainment at the intake and transport down the waterway. Both low and high-water levels are simulated, and the air-water flow phenomenon is reproduced. In both cases, air pockets are generated, which undergo accumulation and breakup process. When moving downstream, they could cause severe surface fluctuations, even blowouts. Consequently, engineering solutions are required to address this issue. The aim of this study is to provide basis for risk assessment of outlet operations and potential rehabilitation measures.

ObjectiveThe purpose of this study was to predict elevated TSH levels by developing an effective machine learning model based on large-scale physical examination results.MethodsSubjects who underwent general physical examinations from January 2015 to December 2019 were enrolled in this study. A total of 21 clinical parameters were analyzed, including six demographic parameters (sex, age, etc.) and 15 laboratory parameters (thyroid peroxidase antibody (TPO-Ab), thyroglobulin antibody (TG-Ab), etc.). The risk factors for elevated TSH levels in the univariate and multivariate Logistic analyses were used to construct machine learning models. Four machine learning models were trained to predict the outcome of elevated TSH levels one year/two years after patient enrollment, including decision tree (DT), linear regression (LR), eXtreme Gradient boosting (XGBoost), and support vector machine (SVM). Feature importance was calculated in the machine learning models to show which parameter plays a vital role in predicting elevated TSH levels.ResultsA total of 12,735 individuals were enrolled in this study. Univariate and multivariate Logistic regression analyses showed that elevated TSH levels were significantly correlated with gender, FT3/FT4, total cholesterol (TC), TPO-Ab, Tg-Ab, creatinine (Cr), and triglycerides (TG). Among the four machine learning models, XGBoost performed best in the one-year task of predicting elevated TSH levels (AUC (0.87(+/- 0.03))). The most critical feature in this model was FT3/FT4, followed by TPO-Ab and other clinical parameters. In the two-year task of predicting TSH levels, none of the four models performed well.ConclusionsIn this study, we trained an effective XGBoost model for predicting elevated TSH levels one year after patient enrollment. The measurement of FT3 and FT4 could provide an early warning of elevated TSH levels to prevent relative thyroid diseases.

Fractures are the most common type of unintentional injury in children, with traumatic upper limb fractures accounting for approximately 80% of all childhood fractures. Many epidemiological investigations of upper limb fractures in children have been conducted, but with the development of society, the patterns of childhood fractures may have changed. This study aimed to analyze the epidemiology and economic cost factors of upper limb fractures in Chinese children.We retrospectively reviewed children with upper limb fractures or old upper limb fractures hospitalized between December 1, 2015, and December 31, 2019, in 22 tertiary children's hospitals, under China's Futang Research Center of Pediatric Development. We used the ICD10 codes on the front sheet of their medical records to identify cases and extracted data on age, sex, injury cause, fracture site, treatment, the year of admission and discharge, visiting time, and various costs during hospitalization from the medical record.A total of 32,439 children (21,478 boys and 10,961 girls) were identified, of whom 32,080 had fresh fractures and 359 had old fractures. The peak age was 3-6 years in both sexes. A total of 4788 were infants, 14,320 were preschoolers, 10,499 were in of primary school age, and 2832 were adolescent. Fractures were most frequent in autumn (August to October). Admissions peaked at 0 o'clock. Among the 32,080 children with fresh upper limb fractures, the most common fracture site was the distal humerus, with a total of 20,090 fracture events including 13,134 humeral supracondylar fractures and 4914 lateral humeral condyle fractures. The most common cause of injuries was falling over. The most common joint dislocation accompanying upper limb fractures occurred in the elbow, involving 254 cases. Surgery was performed in 31,274 children, and 806 did not receive surgery. Among those with clear operative records, 10,962 children were treated with open reduction and 18,066 with closed reduction. The number of cases was largest in the East China region (Anhui Province, Shandong Province, Jiangsu Province, Zhejiang Province, and Fujian Province), with 12,065 cases overall. Among the 359 children with old fractures, 118 were admitted with a diagnosis of "old humerus fracture," accounting for the highest proportion; 244 underwent surgical open reduction, 16.16% of whom had osteotomy. For the children with fresh fractures, the average total hospital cost was 10,994 yuan, and the highest average total hospital cost was 14,053 yuan, for humeral shaft fractures. For the children with old fractures, the average total hospital cost was 15,151 yuan, and the highest average total hospital cost was 20,698 yuan, for old ulna fractures. Cost of materials was the principle factor affecting total hospital cost, followed by surgery and anesthesia costs, both in children with fresh fractures and those with old fractures. Significant differences were observed in all hospital costs (P 0.001) except treatment costs (P = 0.702), between children with fresh fractures and those with old fractures. Among the 32,439 children, full self-payment accounted for the highest proportion of all payment methods, involving 17,088 cases, with an average cost of 11,111 yuan.Information on the epidemiological characteristics of childhood fractures suggests that health and safety education and protective measures should be strengthened to prevent upper limb fractures in children. For both fresh and old fractures, the cost of materials was the principal factor affecting total hospital cost, followed by surgery and anesthesia costs. The overall average total hospital cost is higher in children with old fractures than in children with fresh fractures. Among all children, full self-payment, at 53% of children, accounted for the highest proportion of all payment methods. Hospital costs are a headache for those families who will pay on their own. It can lead to a delayed treatment and unhealed fractures or malunion in some children. Therefore, the child trauma care system and training on fractures need to be improved, to reduce the late presentation of fractures. These combined measures will improve children's quality of life, reduce the expenditure of families, and decrease the public health burden. To provide better medical services for children, authorities must improve the allocation of health resources, establish a comprehensive medical security system for children, and set up more child trauma centers.

University teachers selectively disclosing inventions leads to a serious loss of high-value inventions in colleges, which seriously hinders the transformation process of university patents. To promote achievement transformation, Sichuan Province took the lead in carrying out the pilot reform of the mixed ownership of employee inventions in universities in China and encouraged professional inventors to transform achievements with property rights. However, whether university teachers are willing to disclose high-quality inventions to universities is unknown, and the stimulating effect of reforms on college teachers remains to be examined. From the perspective of the current situation of university teachers' willingness to disclose inventions in the process of the transformation of university patents, this paper selects teachers from seven universities in Sichuan Province, China, as the objects of the questionnaire, including Sichuan University and Southwest Jiaotong University. The study uses a structural equation model and draws the following conclusions: university teachers' characteristics, incentive mechanisms, and invention characteristics are positively related to their willingness to disclose inventions; enterprise activities are negatively related to university teachers' willingness to disclose inventions; and the influence of patent examination on the intention of invention disclosure is weak.

Owing to effective aeration and energy dissipation, a stepped spillway is commonly used in a roller-compacted concrete (RCC) dam. However, its complex air-water flow features are far from being ful ...

Background Circular RNA has been revealed as a potential biomarker in multiple malignancies. However, few studies have focused on its potential to be prognostic markers in lung squamous cell carcinoma (LSCC). In this work, we aimed to build a prognostic model of resected LSCC based on circular RNA pyruvate dehydrogenase kinase 1 (circPDK1) and other clinicopathological factors. Methods circPDK1 was identified via next-generation sequencing. Three hundred two cases of LSCC tissue and their adjacent normal lung tissues were obtained from multiple medical centers and divided into study cohort (n=232) and validation cohort (n=70). The expression of circPDK1 was detected for analyzing its potential prognostic value for recurrence-free survival (RFS) and overall survival (OS) in LSCC. Finally, combined with circPDK1, T staging, lymph nodes (LN) metastasis status, age, and serum squamous cell Carcinoma Antigen (SCCAg), we built a prognostic model by nomograms method and confirmed it in the validation cohort. Results CircPDK1 was identified to be overexpressed (P

The laser ultrasonic technique with a pulse-echo mode has been investigated to detect disbonds in a multi-layer bonded structure and a quantitative method has been proposed to evaluate the defect sizes. The simulations were carried out to analyze influences of spot sizes on the characteristics of laser ultrasonic reflected waves, the interaction of laser ultrasonic reflected waves with disbonds, and quantitative characterization on disbonds. A noncontact laser ultrasonic inspection system has been established to perform a series of experiments to verify the theoretical results. Laser ultrasonic C-scans based on reflected shear waves can clearly redraw appearances of disbonds in the adhesive bond. The proposed quantitative method can evaluate the sizes of disbonds within a reasonable error range. Moreover, the experimental data are in good agreement with the simulation results. Therefore, a combination of the laser ultrasonic pulse-echo mode and the proposed quantitative strategy is practical for accurate detection of disbonds in multi-layer bonded structures.

Polyimide (PI) film is widely used as the key component of the capacitive humidity sensor, whose diffusion coefficient has a significant impact on the sensor’s dynamic characteristics, but is rarely discussed. This paper provides a test method and processes for effective diffusion coefficients of water molecules in self-synthesis PI films. The films were formed by four ingredients (PMDA-ODA, BPDA-ODA and BPDA-BAPP, PMDA-BAPP) with PI acid concentrations of 23%, 20%, 17% and 15%, and tested in temperatures of 20 °C, 35 °C and 50 °C, respectively. The results indicated that BPDA-BAPP film was good as a moisture sensitive film, whose average effective diffusion coefficient was 2.709 × 10−14 m2/s. The temperature of the environment had a significant effect on the humidity-sensitive properties, but the PI acid concentration effect could be indirect.

An aerator, installed to prevent cavitation damages in spillways and outlet works, generates a typical two-phase flow affected by its configuration and flow conditions. The major parameters of concern include air demand, jet trajectory and streamwise changes of air concentration near chute bottom. This study reviews the theoretical basis and deals with several aspects of physical scale modeling, supported exclusively by field measurements. Analyses reveal that the sub-atmospheric pressure generated in the air cavity should also be scaled in physical modeling, which is seldom the case in the laboratory environment. Thus, the conventional approach to upscale the air demand is controversial. With the data from both fields and laboratories, it is demonstrated that a direct conversion of air flow from model to prototype is justified only if the approach flow velocity in the model exceeds 7.0–7.5 m/s or the Reynolds number exceeds 1.58 × 106. Failing to meet this premise would bring about errors for prototype predictions; the error extent depends on both model scale and flow magnitude. In terms of cavity pressure drop, the prototype differs by a factor of less than 10 from its scale model with sufficient air supply. In a model, the air concentration along the chute bottom drops considerably within one to two trajectory lengths. The prototype differs from its model in such a way that the air concentration decay is much slower, with a higher level that is maintained over a longer distance downstream of the impact location. This study is intended to provide insight for laboratory studies and engineering design.

This paper extends the concept of psychological ownership to the general natural environment, clarifies the concept of environmental psychological ownership, and analyzes the formation mechanism of environmental psychological ownership from three dimensions. According to the results of structural equation model based on data obtained from the questionnaire survey, pro-environment investment, environmental self-efficacy, and environmental knowledge are all positively associated with the individual’s environmental psychological ownership, among which the correlation between environmental knowledge and environmental psychological ownership is the strongest. In addition, the environmental psychological ownership positively predicts the individual’s pro-environmental intentions. The conclusion of the study can guide how to strengthen the environmental psychological ownership, thus providing a new perspective for pro-environmental behavior intervention.

Anomaly detection (AD) aims to distinguish the data points that are inconsistent with the overall pattern of the data. Recently, unsupervised anomaly detection methods have aroused huge attention. Among these methods, feature representation (FR) plays an important role, which can directly affect the performance of anomaly detection. Sparse representation (SR) can be regarded as one of matrix factorization (MF) methods, which is a powerful tool for FR. However, there are some limitations in the original SR. On the one hand, it just learns the shallow feature representations, which leads to the poor performance for anomaly detection. On the other hand, the local geometry structure information of data is ignored. To address these shortcomings, a graph regularized deep sparse representation (GRDSR) approach is proposed for unsupervised anomaly detection in this work. In GRDSR, a deep representation framework is first designed by extending the single layer MF to a multilayer MF for extracting hierarchical structure from the original data. Next, a graph regularization term is introduced to capture the intrinsic local geometric structure information of the original data during the process of FR, making the deep features preserve the neighborhood relationship well. Then, a L1-norm-based sparsity constraint is added to enhance the discriminant ability of the deep features. Finally, a reconstruction error is applied to distinguish anomalies. In order to demonstrate the effectiveness of the proposed approach, we conduct extensive experiments on ten datasets. Compared with the state-of-the-art methods, the proposed approach can achieve the best performance.

The demand forecast of shared bicycles directly determines the utilization rate of vehicles and projects operation benefits. Accurate prediction based on the existing operating data can reduce unnecessary delivery. Since the use of shared bicycles is susceptible to time dependence and external factors, most of the existing works only consider some of the attributes of shared bicycles, resulting in insufficient modeling and unsatisfactory prediction performance. In order to address the aforementioned limitations, this paper establishes a novelty prediction model based on convolutional recurrent neural network with the attention mechanism named as CNN-GRU-AM. There are four parts in the proposed CNN-GRU-AM model. First, a convolutional neural network (CNN) with two layers is used to extract local features from the multiple sources data. Second, the gated recurrent unit (GRU) is employed to capture the time-series relationships of the output data of CNN. Third, the attention mechanism (AM) is introduced to mining the potential relationships of the series features, in which different weights will be assigned to the corresponding features according to their importance. At last, a fully connected layer with three layers is added to learn features and output the prediction results. To evaluate the performance of the proposed method, we conducted massive experiments on two datasets including a real mobile bicycle data and a public shared bicycle data. The experimental results show that the prediction performance of the proposed model is better than other prediction models, indicating the significance of the social benefits.

The optimization of energy control strategy is one of the key technologies of plug-in hybrid vehicles (PHEVs) to improve the capabilities of energy saving and emission reduction. In order to improve fuel economy of PHEV, adaptive equivalent minimum fuel consumption strategy (A-ECMS) is proposed. Firstly, optimization methods of different energy control strategies are analyzed, and the Pontryagin’s Minimum Principle (PMP) and the equivalent fuel consumption theory are selected to optimize energy control strategy of the PHEV. Secondly, the configuration of PHEV and research objectives of the power control system are determined. Thirdly, the energy control problem is analyzed by the PMP theory, and the improvement measures for the energy control problem are put forward by the equivalent minimum fuel consumption strategy (ECMS). Fourthly, after analyzing the relationship between the equivalent factor and reference SOC, adaptive equivalent minimum fuel consumption strategy (A-ECMS) model is established by MATLAB/Simulink. Finally, combined with Cruise software, the PHEV simulation model is simulated, and the simulation results are analyzed. The results show that compared with the CD/CS energy control strategy, the A-ECMS energy control strategy reduced the 100 km fuel consumption of the vehicle by 7% under three times WLTC driving condition.