The Impact of Ownership Structure and Capital Structure on Firm Performance: Evidence from China GEM

Purpose: This study researches the relationship between ownership structure, capital structure and corporate performance of the companies listed on the China Growth Enterprise Market (GEM). Methodology: This study collects the panel data for 490 companies from 2016 to 2019. I use the fixed effects regression model to analyse the data. In addition,this research chooses ROA, the largest shareholder's shareholding ratio, the top five largest shareholder's shareholding ratio, management shareholding, leverage, short-term debt ratio, and long-term debt ratio as variables. Results: Empirical analysis presents that there is a significant positive correlation between the fraction of shares held by largest shareholder and firm performance. In addition, the top 5 shareholders also has a positive effective on firm performance. However, the relationship between managerial ownership and firm performance is not significant. In term of capital structure, leverage has negative relationship with firm value, but short term debt rate are positive with company performanc. What is more, the relationship between long term debt and firm performance is not significant. Conclusion:Increasing equity concentration and reducing debt level to a certain extent are beneficial for companies listed on the China GEM. Shareholders should adopt appropriate incentive measures and monitoring mechanism to reduce agency cost and strengthen the alliance effect between management and shareholders. Companies should make reasonable use of debt financing to optimize their own capital structure

Higher-order Knowledge Transfer for Dynamic Community Detection with Great Changes

Network structure evolves with time in the real world, and the discovery of changing communities in dynamic networks is an important research topic that poses challenging tasks. Most existing methods assume that no significant change in the network occurs; namely, the difference between adjacent snapshots is slight. However, great change exists in the real world usually. The great change in the network will result in the community detection algorithms are difficulty obtaining valuable information from the previous snapshot, leading to negative transfer for the next time steps. This paper focuses on dynamic community detection with substantial changes by integrating higher-order knowledge from the previous snapshots to aid the subsequent snapshots. Moreover, to improve search efficiency, a higher-order knowledge transfer strategy is designed to determine first-order and higher-order knowledge by detecting the similarity of the adjacency matrix of snapshots. In this way, our proposal can better keep the advantages of previous community detection results and transfer them to the next task. We conduct the experiments on four real-world networks, including the networks with great or minor changes. Experimental results in the low-similarity datasets demonstrate that higher-order knowledge is more valuable than first-order knowledge when the network changes significantly and keeps the advantage even if handling the high-similarity datasets. Our proposal can also guide other dynamic optimization problems with great changes.Comment: Submitted to IEEE TEV

Design and compressive behavior of controllable irregular porous scaffolds: based on Veronoi-tessellation and for additive manufacturing

Adjustment of the mechanical properties (apparent elastic modulus and compressive strength) in porous scaffolds is important for artificial implants and bone tissue engineering. In this study, a top-down design method based on Voronoi-Tessellation was proposed. This method was successful in obtaining the porous structures with specified and functionally graded porosity. The porous specimens were prepared by selective laser melting technology. Quasi-static compressive tests were conducted as well. The experiment results revealed that the mechanical properties were affected by both porosity and irregularity. The irregularity coefficient proposed in this study can achieve good accommodation and balance of “irregularity” and “controllability”. The method proposed in this study provides an efficient approach for the bionic design and topological optimization of scaffolds

Draft genome sequence of Acidithiobacillus ferrooxidans YQH-1

AbstractAcidithiobacillus ferrooxidans YQH-1 is a moderate acidophilic bacterium isolated from a river in a volcano of Northeast China. Here, we describe the draft genome of strain YQH-1, which was assembled into 123 contigs containing 3,111,222bp with a G+C content of 58.63%. A large number of genes related to carbon dioxide fixation, dinitrogen fixation, pH tolerance, heavy metal detoxification, and oxidative stress defense were detected. The genome sequence can be accessed at DDBJ/EMBL/GenBank under the accession no. LJBT00000000

Identification of a spliced gene from duck enteritis virus encoding a protein homologous to UL15 of herpes simplex virus 1

<p>Abstract</p> <p>Background</p> <p>In herpesviruses, UL15 homologue is a subunit of terminase complex responsible for cleavage and packaging of the viral genome into pre-assembled capsids. However, for duck enteritis virus (DEV), the causative agent of duck viral enteritis (DVE), the genomic sequence was not completely determined until most recently. There is limited information of this putative spliced gene and its encoding protein.</p> <p>Results</p> <p>DEV UL15 consists of two exons with a 3.5 kilobases (kb) inron and transcribes into two transcripts: the full-length UL15 and an N-terminally truncated UL15.5. The 2.9 kb UL15 transcript encodes a protein of 739 amino acids with an approximate molecular mass of 82 kiloDaltons (kDa), whereas the UL15.5 transcript is 1.3 kb in length, containing a putative 888 base pairs (bp) ORF that encodes a 32 kDa product. We also demonstrated that UL15 gene belonged to the late kinetic class as its expression was sensitive to cycloheximide and phosphonoacetic acid. UL15 is highly conserved within the <it>Herpesviridae</it>, and contains Walker A and B motifs homologous to the catalytic subunit of the bacteriophage terminase as revealed by sequence analysis. Phylogenetic tree constructed with the amino acid sequences of 23 herpesvirus UL15 homologues suggests a close relationship of DEV to the <it>Mardivirus </it>genus within the <it>Alphaherpesvirinae</it>. Further, the UL15 and UL15.5 proteins can be detected in the infected cell lysate but not in the sucrose density gradient-purified virion when reacting with the antiserum against UL15. Within the CEF cells, the UL15 and/or UL15.5 localize(s) in the cytoplasm at 6 h post infection (h p. i.) and mainly in the nucleus at 12 h p. i. and at 24 h p. i., while accumulate(s) in the cytoplasm in the absence of any other viral protein.</p> <p>Conclusions</p> <p>DEV UL15 is a spliced gene that encodes two products encoded by 2.9 and 1.3 kb transcripts respectively. The UL15 is expressed late during infection. The coding sequences of DEV UL15 are very similar to those of alphaherpesviruses and most similar to the genus <it>Mardivirus</it>. The UL15 and/or UL15.5 accumulate(s) in the cytoplasm during early times post-infection and then are translocated to the nucleus at late times.</p

Q-YOLO: Efficient Inference for Real-time Object Detection

Real-time object detection plays a vital role in various computer vision applications. However, deploying real-time object detectors on resource-constrained platforms poses challenges due to high computational and memory requirements. This paper describes a low-bit quantization method to build a highly efficient one-stage detector, dubbed as Q-YOLO, which can effectively address the performance degradation problem caused by activation distribution imbalance in traditional quantized YOLO models. Q-YOLO introduces a fully end-to-end Post-Training Quantization (PTQ) pipeline with a well-designed Unilateral Histogram-based (UH) activation quantization scheme, which determines the maximum truncation values through histogram analysis by minimizing the Mean Squared Error (MSE) quantization errors. Extensive experiments on the COCO dataset demonstrate the effectiveness of Q-YOLO, outperforming other PTQ methods while achieving a more favorable balance between accuracy and computational cost. This research contributes to advancing the efficient deployment of object detection models on resource-limited edge devices, enabling real-time detection with reduced computational and memory overhead

Quality Prediction and Control of Reducing Pipe Based on EOS-ELM-RPLS Mathematics Modeling Method

The inspection of inhomogeneous transverse and longitudinal wall thicknesses, which determines the quality of reducing pipe during the production of seamless steel reducing pipe, is lags and difficult to establish its mechanism model. Aiming at the problems, we proposed the quality prediction model of reducing pipe based on EOS-ELM-RPLS algorithm, which taking into account the production characteristics of its time-varying, nonlinearity, rapid intermission, and data echelon distribution. Key contents such as analysis of data time interval, solving of mean value, establishment of regression model, and model online prediction were introduced and the established prediction model was used in the quality prediction and iteration control of reducing pipe. It is shown through experiment and simulation that the prediction and iteration control method based on EOS-ELM-RPLS model can effectively improve the quality of steel reducing pipe, and, moreover, its maintenance cost was low and it has good characteristics of real time, reliability, and high accuracy