Research on the Financial Sustainable Growth of the Listed Companies on GEM

Currently high risk assumed by a listed company on GEM makes numerous operators, investors and creditors pay close attention to the sustainable growth ability of an enterprise. In view of this, this paper makes an empirical analysis on the current status of the financial sustainable growth of the listed companies on GEM and the main factors that might affect their sustainable growth separately through the Wilcoxon signed ranks test and the factor analysis. The research result turns out that the listed companies on GEM have failed to achieve the financial sustainable growth with its actual growth rate greater than the sustainable growth rate to indicate an excessive growth. Meanwhile since the factors that influence the enterprise sustainable growth include the profitability, the cash-generating ability, the debt paying ability, the operation capacity and the growth ability, then the managers should take the following measures to achieve the financial sustainable growth of the enterprise through the cost control, the reinforcement of cash management, the cultivation of core competence, the optimization of the capital structure and the enhancement of the management level etc. Key words: Listed companies on GEM; Financial sustainable growth; Empirical analysi

A centering correction method for GNSS antenna diversity theory and implementation using a software receiver

GPS is performing well in open sky situation. However, severe attenuation or blockage of signals by high buildings may leads to an insufficient number of received satellites. Antenna diversity scheme is viewed as a method to alleviate signal attenuation and enhance the performance of GNSS positioning in the harsh environments. This paper introduces an antenna diversity system, composed of two spatially separated antennas. If relative geometry of two antennas is known, the carrier phase measurement outputs from these two antennas can be combined with Centering Correction Method (CCM). Even each antenna may not able to acquire more than four satellites this antenna diversity system can still precisely estimate each antenna’s location with centimeter-level accuracy, as long as the sum of the captured satellites by two separate antennas is no less than four

The R Protein of SARS-CoV: Analyses of Structure and Function Based on Four Complete Genome Sequences of Isolates BJ01-BJ04

The R (replicase) protein is the uniquely defined non-structural protein (NSP) responsible for RNA replication, mutation rate or fidelity, regulation of transcription in coronaviruses and many other ssRNA viruses. Based on our complete genome sequences of four isolates (BJ01-BJ04) of SARS-CoV from Beijing, China, we analyzed the structure and predicted functions of the R protein in comparison with 13 other isolates of SARS-CoV and 6 other coronaviruses. The entire ORF (open-reading frame) encodes for two major enzyme activities, RNA-dependent RNA polymerase (RdRp) and proteinase activities. The R polyprotein undergoes a complex proteolytic process to produce 15 function-related peptides. A hydrophobic domain (HOD) and a hydrophilic domain (HID) are newly identified within NSP1. The substitution rate of the R protein is close to the average of the SARS-CoV genome. The functional domains in all NSPs of the R protein give different phylogenetic results that suggest their different mutation rate under selective pressure. Eleven highly conserved regions in RdRp and twelve cleavage sites by 3CLP (chymotrypsin-like protein) have been identified as potential drug targets. Findings suggest that it is possible to obtain information about the phylogeny of SARS-CoV, as well as potential tools for drug design, genotyping and diagnostics of SARS

A Novel Solid-Phase Site-Specific PEGylation Enhances the In Vitro and In Vivo Biostabilty of Recombinant Human Keratinocyte Growth Factor 1

Keratinocyte growth factor 1 (KGF-1) has proven useful in the treatment of pathologies associated with dermal adnexae, liver, lung, and the gastrointestinal tract diseases. However, poor stability and short plasma half-life of the protein have restricted its therapeutic applications. While it is possible to improve the stability and extend the circulating half-life of recombinant human KGF-1 (rhKGF-1) using solution-phase PEGylation, such preparations have heterogeneous structures and often low specific activities due to multiple and/or uncontrolled PEGylation. In the present study, a novel solid-phase PEGylation strategy was employed to produce homogenous mono-PEGylated rhKGF-1. RhKGF-1 protein was immobilized on a Heparin-Sepharose column and then a site-selective PEGylation reaction was carried out by a reductive alkylation at the N-terminal amino acid of the protein. The mono-PEGylated rhKGF-1, which accounted for over 40% of the total rhKGF-1 used in the PEGylation reaction, was purified to homogeneity by SP Sepharose ion-exchange chromatography. Our biophysical and biochemical studies demonstrated that the solid-phase PEGylation significantly enhanced the in vitro and in vivo biostability without affecting the over all structure of the protein. Furthermore, pharmacokinetic analysis showed that modified rhKGF-1 had considerably longer plasma half-life than its intact counterpart. Our cell-based analysis showed that, similar to rhKGF-1, PEGylated rhKGF-1 induced proliferation in NIH 3T3 cells through the activation of MAPK/Erk pathway. Notably, PEGylated rhKGF-1 exhibited a greater hepatoprotection against CCl4-induced injury in rats compared to rhKGF-1

Research on the construction of a “full-chain” rapid response system for power emergencies

A crucial industry for improving society's sustainable development is the power sector. To address issues with the ineffectiveness of electric power emergency response during emergencies and the unclear division of duty among emergency subjects. A prefecture-level city power supply company to respond to the “In-Fa” typhoon, for example, to build a “1 + N” two-level emergency rapid response unit. Furthermore, it is proposed from the emergency response, emergency coordination, emergency material reserves, etc., to build a “full-chain” type of power emergency quick reaction system. Case studies have revealed that the quick response system's emergency combat capability, catastrophe preventive and mitigation capability, and emergency security capability have all improved. The construction of a “full-chain” type of power emergency rapid response system specialized and standardized the power emergency response system and provided a reference basis for the power industry's emergency response

Deep Transfer Learning Method Based on Automatic Domain Alignment and Moment Matching

Domain discrepancy is a key research problem in the field of deep domain adaptation. Two main strategies are used to reduce the discrepancy: the parametric method and the nonparametric method. Both methods have achieved good results in practical applications. However, research on whether the combination of the two can further reduce domain discrepancy has not been conducted. Therefore, in this paper, a deep transfer learning method based on automatic domain alignment and moment matching (DA-MM) is proposed. First, an automatic domain alignment layer is embedded in the front of each domain-specific layer of a neural network structure to preliminarily align the source and target domains. Then, a moment matching measure (such as MMD distance) is added between every domain-specific layer to map the source and target domain features output by the alignment layer to a common reproduced Hilbert space. The results of an extensive experimental analysis over several public benchmarks show that DA-MM can reduce the distribution discrepancy between the two domains and improve the domain adaptation performance

A Comprehensive Evaluation and Analysis of Ground Surface Damage Due to Mining under Villages Based on GIS

This paper aims to evaluate the severities and causes of ground surface building and cropland damages after coal mining in a better way, and to clarify the correlation between the damage assessment indexes that influence mining. Against the backdrop of multi-seam mining in certain coal mines in China, the estimated results of each displacement and deformation were analyzed using GIS technology. The damage range determined for each deformation index is divided according to the displacement and deformation combined with the virtue of damage judgment threshold. The damage ranges on the ground surface based on the comprehensive value of each displacement and deformation index were obtained through superimposing those ranges delineated by each displacement and deformation index, and the law on influence from displacement indexes upon various levels of damage was analyzed in a quantitative manner accordingly. The results showed that coal mining destroyed 14 buildings and a cropland area of 11.96 hm2; among them, building damage was only associated with displacement indexes E (horizontal deformation) and T (inclined deformation). Seven buildings were solely destroyed by T alone; five buildings were solely damaged by E; two buildings were damaged jointly by E and T; and, moreover, with the aggravation in building damage level, the proportion of building damage due to E decreased while the proportion of building damage under the same level due to T increased. Regarding cropland destruction, the damage due to T accounted for 33.48% while the damage jointly caused by W (Subsidence), E and T accounted for 30.45%. Moreover, the proportion of damaged cropland area due to inclined deformation T was positively correlated with cropland damage level. These findings can provide a reference for rational judgment regarding civilian building and cropland destruction on the ground surface after coal mining

Predicting the Risk of Incident Type 2 Diabetes Mellitus in Chinese Elderly Using Machine Learning Techniques

Early identification of individuals at high risk of diabetes is crucial for implementing early intervention strategies. However, algorithms specific to elderly Chinese adults are lacking. The aim of this study is to build effective prediction models based on machine learning (ML) for the risk of type 2 diabetes mellitus (T2DM) in Chinese elderly. A retrospective cohort study was conducted using the health screening data of adults older than 65 years in Wuhan, China from 2018 to 2020. With a strict data filtration, 127,031 records from the eligible participants were utilized. Overall, 8298 participants were diagnosed with incident T2DM during the 2-year follow-up (2019–2020). The dataset was randomly split into training set (n = 101,625) and test set (n = 25,406). We developed prediction models based on four ML algorithms: logistic regression (LR), decision tree (DT), random forest (RF), and extreme gradient boosting (XGBoost). Using LASSO regression, 21 prediction features were selected. The Random under-sampling (RUS) was applied to address the class imbalance, and the Shapley Additive Explanations (SHAP) was used to calculate and visualize feature importance. Model performance was evaluated by the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. The XGBoost model achieved the best performance (AUC = 0.7805, sensitivity = 0.6452, specificity = 0.7577, accuracy = 0.7503). Fasting plasma glucose (FPG), education, exercise, gender, and waist circumference (WC) were the top five important predictors. This study showed that XGBoost model can be applied to screen individuals at high risk of T2DM in the early phrase, which has the strong potential for intelligent prevention and control of diabetes. The key features could also be useful for developing targeted diabetes prevention interventions