LINK BETWEEN EXECUTIVE STOCK OWNERSHIP AND CORPORATE FINANCIAL PERFORMANCE

This study examined the relationship between executive stock ownership and the financial performance of firms in the hospitality industry. The study sample included 30 public hospitality companies listed on NASDAQ, all of which had 14 years of complete financial data. The study used the Pearson correlation and linear regression analysis to test the relationship in the hotel segment, the restaurant segment, and the combined hospitality segment. The results show there is no statistically significant positive relationship between executive stock ownership and firm profit in the hotel segment, whereas in the restaurant segment, there is a negative linear relationship. Furthermore, the combined 30 hospitality companies show a slight negative linear relationship. The findings neither support the “Agency Theory,” nor reveal a clear correlation between executive stock ownership and the profit performance of firms in the hospitality group

An Experimental Study on Attribute Validity of Code Quality Evaluation Model

Regarding the practicality of the quality evaluation model, the lack of quantitative experimental evaluation affects the effective use of the quality model, and also a lack of effective guidance for choosing the model. Aiming at this problem, based on the sensitivity of the quality evaluation model to code defects, a machine learning-based quality evaluation attribute validity verification method is proposed. This method conducts comparative experiments by controlling variables. First, extract the basic metric elements; then, convert them into quality attributes of the software; finally, to verify the quality evaluation model and the effectiveness of medium quality attributes, this paper compares machine learning methods based on quality attributes with those based on text features, and conducts experimental evaluation in two data sets. The result shows that the effectiveness of quality attributes under control variables is better, and leads by 15% in AdaBoostClassifier; when the text feature extraction method is increased to 50 - 150 dimensions, the performance of the text feature in the four machine learning algorithms overtakes the quality attributes; but when the peak is reached, quality attributes are more stable. This also provides a direction for the optimization of the quality model and the use of quality assessment in different situations

In situ phase transition of microemulsions for parenteral injection yielding lyotropic liquid crystalline carriers of the antitumor drug bufalin

International audienc

Formation of ceramic microstructures: honeycomb patterned polymer films as structure-directing

Here, we show a facile and versatile method preparing highly ordered ceramic microstructures on solid substrates by pyrolyzing UV cross-linked copolymer films to circumvent the expensive lithographic technique. The employed block copolymer, poly(dimethylsiloxane)-block-polystyrene (PDMS-b-PS), in this study was synthesized by controlling radical polymerization. The highly ordered microporous polymer films were formed using a static breath figure process. After 4 h UV irradiation, the PS composition was effectively cross-linked. The cross-linked microporous polymer matrix served as a structure-directing agent in the following pyrolysis process, in which the PDMS composition was converted into silica to form honeycomb structured micropatterns on the substrate. The chemical components of the ceramic microstructures were adjusted by simply mixing different functional precursors. Moreover, the ceramic microstructures on substrate could be replicated to prepare textured PDMS stamps. This simple technique offers new prospects in the fields of micropatterns, soft lithography and templates

Transfusion-dependent non-severe aplastic anemia: characteristics and outcomes in the clinic

Transfusion-dependent non-severe aplastic anemia (TD-NSAA) is a rare condition of bone marrow failure that can persist for a long time or develop into severe aplastic anemia (SAA). Little is known about the clinical and laboratory characteristics, and disease prognosis and outcomes in TD-NSAA patients. The clinical and laboratory data of 124 consecutive TD-NSAA patients in the Chinese Eastern Collaboration Group of Anemia from December 2013 and January 2017 were analyzed retrospectively. In 124 TD-NSAA patients, the median age was 32 years (range: 3-80) and the median disease course was 38 months (range: 3-363). Common complications were iron overload (53/101, 52.5%), liver and kidney dysfunction (42/124, 33.9%), diabetes mellitus/impaired glucose tolerance (24/124, 19.4%), and severe infection (29 cases, 23.4%). 58% of patients (57/124) developed severe aplastic anemia with a median progression time of 24 months (range: 3-216). Patients with absolute neutrophil count (ANC) <0.5×109/L, severe infection, or iron overload had a higher probability of progression to SAA (P=0.022, P=0.025, P=0.001). Patients receiving antithymocyte globulin (ATG) plus Cyclosporin A (CsA) had a higher overall response rate compared to those receiving CsA alone (56.7% vs 19.3%, P < 0.001). The addition of ATG was the favorable factor for efficacy (P=0.003). Fourteen patients developed secondary clonal hematologic disease: eleven patients with paroxysmal nocturnal hemoglobinuria, two patients with myelodysplastic syndromes, and one patient with acute myeloid leukemia, respectively. Ten patients (8.1%) died with a median follow-up of 12 months (range: 3- 36 months). Patients with TD-NSAA usually have a prolonged course of disease, and are prone to be complicated with important organ damage and disease progression to SAA. Intensive immunosuppressive therapy based on ATG might be an appropriate approach for TD-NSAA.Clinical trial registration:http://www.chictr.org.cn/edit.aspx?pid=125480&htm=4, identifier ChiCTR2100045895

A microencapsulation approach to design microbial seed coatings to boost wheat seed germination and seedling growth under salt stress

IntroductionSalt stress in seed germination and early seedling growth is the greatest cause of crop loss in saline-alkali soils. Microbial seed coating is an effective way to promote plant growth and salt resistance, but these coatings suffer from poor seed adhesion and low survival rates under typical storage conditions.MethodsIn this study, the marine bacterium Pontibacter actiniarum DSM 19842 from kelp was isolated and microencapsulated with calcium alginate using the emulsion and internal gelation method.ResultsCompared to unencapsulated seeds, the spherical microcapsules demonstrated a bacterial encapsulation rate of 65.4% and survival rate increased by 22.4% at 25°C for 60 days. Under salt stress conditions, the seed germination percentage of microcapsule-embedded bacteria (M-Embed) was 90%, which was significantly increased by 17% compared to the germination percentage (73%) of no coating treatment (CK). Root growth was also significantly increased by coating with M-Embed. Chlorophyll, peroxidase, superoxide dismutase, catalase, proline, hydrogen peroxide and malondialdehyde levels indicated that the M-Embed had the best positive effects under salt stress conditions.DiscussionTherefore, embedding microorganisms in suitable capsule materials provides effective protection for the survival of the microorganism and this seed coating can alleviate salt stress in wheat. This process will benefit the development of sustainable agriculture in coastal regions with saline soils

Fabrication of multi-level carbon nanotube arrays with adjustable patterns

通讯作者地址: Li, L (通讯作者),Xiamen Univ, Coll Mat, Xiamen 361005, Peoples R China 地址: 1. Xiamen Univ, Coll Mat, Xiamen 361005, Peoples R China 2. Xiamen Univ, Coll Chem & Chem Engn, Xiamen, Peoples R China 3. Natl Inst Adv Ind Sci & Technol, Next Generat Device Team, Res Ctr Photovolta Technol, Tsukuba, Ibaraki 3058568, Japan 电子邮件地址: [email protected] carbon nanotube (CNT) arrays with adjustable patterns were prepared by a combination of the breath figure (BF) process and chemical vapor deposition. Polystyrene-b-poly(acrylic acid)/ferrocene was dissolved in carbon disulfide and cast onto a Si substrate covered with a transmission electron microscope grid in saturated relative humidity. A two-level microporous hybrid film with a block copolymer skeleton formed on the substrate after evaporation of the organic solvent and water. One level of ordered surface features originates from the contour of the hard templates; while the other level originates from the condensation of water droplets (BF arrays). Ultraviolet irradiation effectively cross-linked the polymer matrix and endowed the hybrid film with improved thermal stability. In the subsequent pyrolysis, the incorporated ferrocene in the hybrid film was oxidized and turned the polymer skeleton into the ferrous inorganic micropatterns. Either the cross-linked hybrid film or the ferrous inorganic micropatterns could act as a template to grow the multi-level CNT patterns, e. g. isolated and honeycomb-structured CNT bundle arrays perpendicular to the substrate.National Natural Science Foundation of China 50703032 51035002 20974089 Ministry of Education of China NCET-08-0475 Natural Science Foundation of Fujian Province 2009J0602

A Model for Predicting Statement Mutation Scores

A test suite plays a key role in software testing. Mutation testing is a powerful approach to measure the fault-detection ability of a test suite. The mutation testing process requires a large number of mutants to be generated and executed. Hence, mutation testing is also computationally expensive. To solve this problem, predictive mutation testing builds a classification model to predict the test result of each mutant. However, the existing predictive mutation testing methods only can be used to estimate the overall mutation scores of object-oriented programs. To overcome the shortcomings of the existing methods, we propose a new method to directly predict the mutation score for each statement in process-oriented programs. Compared with the existing predictive mutation testing methods, our method uses more dynamic program execution features, which more adequately reflect dynamic dependency relationships among the statements and more accurately reflects information propagation during the execution of test cases. By comparing the prediction effects of logistic regression, artificial neural network, random forest, support vector machine, and symbolic regression, we finally decide to use a single hidden layer feedforward neural network as the predictive model to predict the statement mutation scores. In our two experiments, the mean absolute errors between the statement mutation scores predicted by the neural network and the real statement mutation scores both approximately reach 0.12

Statement-Grained Hierarchy Enhanced Code Summarization

Code summarization plays a vital role in aiding developers with program comprehension by generating corresponding textual descriptions for code snippets. While recent approaches have concentrated on encoding the textual and structural characteristics of source code, they often neglect the global hierarchical features, causing limited code representation. Addressing this gap, our paper introduces the statement-grained hierarchy enhanced Transformer model (SHT), a novel framework that integrates global hierarchy, syntax, and token sequences to automatically generate summaries for code snippets. SHT is distinctively designed with two encoders to learn both hierarchical and sequential features of code. One relational attention encoder processes the statement-grained hierarchical graph, producing hierarchical embeddings. Subsequently, another sequence encoder integrates these hierarchical structures with token sequences. The resulting enriched representation is then fed into a vanilla Transformer decoder, which effectively generates concise and informative summarizations. Our extensive experiments demonstrate that SHT significantly outperforms state-of-the-art approaches on two widely used Java benchmarks. This underscores the effectiveness of incorporating global hierarchical information in enhancing the quality of code summarizations

Constructing Traceability Links between Software Requirements and Source Code Based on Neural Networks

Software requirement changes, code changes, software reuse, and testing are important activities in software engineering that involve the traceability links between software requirements and code. Software requirement documents, design documents, code documents, and test case documents are the intermediate products of software development. The lack of interrelationship between these documents can make it extremely difficult to change and maintain the software. Frequent requirements and code changes are inevitable in software development. Software reuse, change impact analysis, and testing also require the relationship between software requirements and code. Using these traceability links can improve the efficiency and quality of related software activities. Existing methods for constructing these links need to be better automated and accurate. To address these problems, we propose to embed software requirements and source code into feature vectors containing their semantic information based on four neural networks (NBOW, RNN, CNN, and self-attention). Accurate traceability links from requirements to code are established by comparing the similarity between these vectors. We develop a prototype tool RCT based on this method. These four networks’ performances in constructing links are explored on 18 open-source projects. The experimental results show that the self-attention network performs best, with an average Recall@50 value of 0.687 on the 18 projects, which is higher than the other three neural network models and much higher than previous approaches using information retrieval and machine learning