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2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Aluisius Hery Pratono

Purpose This study aims to understand the culture of excellence by examining the role of entrepreneurial culture in shaping how firms achieve sustainable competitive advantage (CA). This study takes into consideration the firms’ capability to transform the entrepreneurial culture into a sustainable CA by generating product development and adapting the information technological turbulence. Design/methodology/approach This study first gathers evidence from literature then carries out a detailed study to propose a structural equation model followed by an online survey that supports empirical evidence. This empirical test involves a data set with 782 usable responses following the 4,000 emails sent to the respondents and removed data due to the missing values. The population data are taken from the firm directory in Surabaya City that the Indonesian Ministry of Trade and Industry published. Findings There is a strong tendency that entrepreneurial culture is imperative for firms to attain sustainable CA by supporting new product development. The results show that product development provides a partial mediating effect, which indicates that entrepreneurial culture may affect the sustainable CA directly and with the product development support. This study also touches on dynamic capability by proposing a scenario approach that suggests that firms should refine the entrepreneurial culture to adapt to the information technological turbulence. Originality/value This study extends the understanding of the culture of excellence by underpinning the dynamic capability theory, which argues that entrepreneurial culture is a valuable resource, which helps firms achieve sustainable CA by promoting product development.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yaw Owusu-Agyeman ◽  
Enna Moroeroe

PurposeScholarly studies on student engagement are mostly focused on the perceptions of students and academic staff of higher education institutions (HEIs) with a few studies concentrating on the perspectives of professional staff. To address this knowledge gap, this paper aims to examine how professional staff who are members of a professional community perceive their contributions to enhancing student engagement in a university.Design/methodology/approachData for the current study were gathered using semi-structured face-to-face interviews among 41 professional staff who were purposively sampled from a public university in South Africa. The data gathered were analysed using thematic analysis that involved a process of identifying, analysing, organising, describing and reporting the themes that emerged from the data set.FindingsAn analysis of the narrative data revealed that when professional staff provide students with prompt feedback, support the development of their social and cultural capital and provide professional services in the area of teaching and learning, they foster student engagement in the university. However, the results showed that poor communication flow and delays in addressing students’ concerns could lead to student disengagement. The study further argues that through continuous interaction and shared norms and values among members of a professional community, a service culture can be developed to address possible professional knowledge and skills gaps that constrain quality service delivery.Originality/valueThe current paper contributes to the scholarly discourse on student engagement and professional community by showing that a service culture of engagement is developed among professional staff when they share ideas, collaborate and build competencies to enhance student engagement. Furthermore, the collaboration between professional staff and academics is important to addressing the academic issues that confront students in the university.


2021 ◽  
Author(s):  
Yuhang Liu ◽  
Changjiang Liu ◽  
Aixi Yu

Abstract Background: Soft tissue sarcoma is relatively rare and highly heterogeneous, which brings great difficulties to treatment. Long non-coding RNA acts a vital role in the occurrence and progression of soft tissue sarcoma, especially in the tumor-related immune process, which has become a hot spot of current research. Therefore, we are committed to developing lncRNA markers related to immunity to promote the treatment and prognosis of patients with soft tissue sarcoma.Methods:Based on the TCGA-SARC and GTEx data set, we screened out 8 prognostic-related immune lncRNAs and constructed a nomogram, which was verified in the test set. Furthermore, immune infiltration analysis was carried out on patients of high and low risk.Results: Based on the results of Pearson's correlation coefficient, we obtained 859 immune-related lncRNAs. After difference analysis, we finally determined 54 different lncRNAs. Univariate and multivariate cox regression analysis finally determined 8 immune-related lncRNAs to construct prognostic models and nomograms to predict the prognosis of STS patients. The above results have been verified in external data sets, indicating that this model has good predictive ability. Gene Set Enrichment Analysis and ESTIMATE analysis showed obviously differences exist in the immune infiltration status and immune cell subtypes of high- and low-risk patients.Conclusion: We constructed an immune-related lncRNA pattern to predict the survival status of soft tissue sarcoma patients.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ajab Khan

Purpose This study aims to investigate the impact of ownership structure and board characteristics on dividend policy in the listed Turkish firms between 2013 and 2019. Design/methodology/approach This study uses the probability of paying dividends, dividend payout ratio and dividend yield measures. The suitable regression procedures (logit, probit and Tobit models) are used to examine the research hypotheses by focusing on a panel data set drawn from the Borsa Istanbul (BIST) 100 index, excluding financial and utility firms. Findings The empirical findings indicate that institutional and concentrated ownerships are significant and positively associated with dividend payouts, whereas family ownership does not influence dividend policy. On the other end, board size is positive, while chief executive officer duality is negatively related to dividend policy. Additionally, the female directors and board independence are insignificant in influencing firms to pay high dividends. Research limitations/implications Future researchers can validate this paper’s findings by considering the stock dividends as well. Additionally, future researchers may investigate the relationship between these constructs by extending the sample size of firms listed on BIST or in other emerging markets. Practical implications This study’s findings may serve policymakers, regulators, investors and academic researchers to get valuable guidance from relevant literature. The Turkish firms may improve dividend policy by implementing the regulatory framework introduced by the Capital Markets Law in 2012 for effective monitoring and protecting the minority shareholders’ rights. The controlling shareholders may alleviate principal-principal conflicts by ensuring the independence of directors and increasing the number of female directors according to the critical mass of at least 30% of board members. Originality/value This study contributes to agency theory and signaling theory by considering ownership structure and board attributes among Turkish firms related to dividend payments.


2021 ◽  
Vol 45 (4) ◽  
pp. 233-238
Author(s):  
Lazar Kats ◽  
Marilena Vered ◽  
Johnny Kharouba ◽  
Sigalit Blumer

Objective: To apply the technique of transfer deep learning on a small data set for automatic classification of X-ray modalities in dentistry. Study design: For solving the problem of classification, the convolution neural networks based on VGG16, NASNetLarge and Xception architectures were used, which received pre-training on ImageNet subset. In this research, we used an in-house dataset created within the School of Dental Medicine, Tel Aviv University. The training dataset contained anonymized 496 digital Panoramic and Cephalometric X-ray images for orthodontic examinations from CS 8100 Digital Panoramic System (Carestream Dental LLC, Atlanta, USA). The models were trained using NVIDIA GeForce GTX 1080 Ti GPU. The study was approved by the ethical committee of Tel Aviv University. Results: The test dataset contained 124 X-ray images from 2 different devices: CS 8100 Digital Panoramic System and Planmeca ProMax 2D (Planmeca, Helsinki, Finland). X-ray images in the test database were not pre-processed. The accuracy of all neural network architectures was 100%. Following a result of almost absolute accuracy, the other statistical metrics were not relevant. Conclusions: In this study, good results have been obtained for the automatic classification of different modalities of X-ray images used in dentistry. The most promising direction for the development of this kind of application is the transfer deep learning. Further studies on automatic classification of modalities, as well as sub-modalities, can maximally reduce occasional difficulties arising in this field in the daily practice of the dentist and, eventually, improve the quality of diagnosis and treatment.


PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257338
Author(s):  
Peggy L. Brady ◽  
Mark S. Springer

Pseudoextinction analyses, which simulate extinction in extant taxa, use molecular phylogenetics to assess the accuracy of morphological phylogenetics. Previous pseudoextinction analyses have shown a failure of morphological phylogenetics to place some individual placental orders in the correct superordinal clade. Recent work suggests that the inclusion of hypothetical ancestors of extant placental clades, estimated by ancestral state reconstructions of morphological characters, may increase the accuracy of morphological phylogenetic analyses. However, these studies reconstructed direct hypothetical ancestors for each extant taxon based on a well-corroborated molecular phylogeny, which is not possible for extinct taxa that lack molecular data. It remains to be determined if pseudoextinct taxa, and by proxy extinct taxa, can be accurately placed when their immediate hypothetical ancestors are unknown. To investigate this, we employed molecular scaffolds with the largest available morphological data set for placental mammals. Each placental order was sequentially treated as pseudoextinct by exempting it from the molecular scaffold and recoding soft morphological characters as missing for all its constituent species. For each pseudoextinct data set, we omitted the pseudoextinct taxon and performed a parsimony ancestral state reconstruction to obtain hypothetical predicted ancestors. Each pseudoextinct order was then evaluated in seven parsimony analyses that employed combinations of fossil taxa, hypothetical predicted ancestors, and a molecular scaffold. In treatments that included fossils, hypothetical predicted ancestors, and a molecular scaffold, only 8 of 19 pseudoextinct placental orders (42%) retained the same interordinal placement as on the molecular scaffold. In treatments that included hypothetical predicted ancestors but not fossils or a scaffold, only four placental orders (21%) were recovered in positions that are congruent with the scaffold. These results indicate that hypothetical predicted ancestors do not increase the accuracy of pseudoextinct taxon placement when the immediate hypothetical ancestor of the taxon is unknown. Hypothetical predicted ancestors are not a panacea for morphological phylogenetics.


Author(s):  
Sèna Kimm Gnangnon

This article explores the effect of poverty on tax revenue performance (tax revenue share), using an unbalanced panel data set of 102 developing countries over the period from 1996 to 2015. Based on the two-step system generalized methods of moments (GMM) approach, the empirical analysis shows that higher poverty rates significantly reduce tax revenue performance in developing countries. However, the magnitude of this negative effect is lower in least developed countries (LDCs) than in other countries of the sample. The analysis has also revealed that the tax revenue performance effect of poverty depends on the level of household consumption as well as the prevailing unemployment rate in the economy. Finally, development aid inflows help to mitigate the negative effect of poverty on tax revenue performance in developing countries. These findings not only highlight the importance of poverty for tax revenue performance in developing countries, but they additionally show that the provision of higher amounts of development aid to these countries could help them mitigate the adverse tax revenue effect of poverty, and even allow them to enjoy higher tax revenue performance, which is key for attaining their development objectives. JEL Classification: I30, I32, H20


2021 ◽  
Author(s):  
Ziming Zheng ◽  
Qilin Zhang ◽  
Yong Han ◽  
Tingting Wu ◽  
Yu Zhang

Abstract Background: The influential factors of chemotherapy-induced myelosuppression in esophageal cancer in central China are unclear. This study aimed to develop a model for prediction of incidence of myelosuppression during chemotherapy among patients with esophageal cancer. Methods: A total of 1446 patients with esophageal cancer who underwent five different chemotherapy regimens between 2013 and 2020 at our institute were randomly assigned in a 7:3 ratio to training and validation data sets. Clinical and drug-related variables were used to develop the prediction model from the training data set by the machine learning method of random forest. Finally, this model were tested in the validation data set.Results: The prediction model were established with 16 indispensable variables selected from 46 variables. The model obtained an area under the receiver-operating characteristic curve of 0.883 and accompanied by prediction accuracy of 80.0%, sensitivity of 77.8% and specificity of 81.8%. Conclusion: This new prediction model showed excellent predictive ability of incidence of myelosuppression in turn providing preventative measures for patients with esophageal cancer during chemotherapy.Trial registration: This study protocol was approved by the institutional ethics board of the Union Hospital of Huazhong University of Science and Technology (retrospectively registered No. 2018S333). This study was performed in accordance with the ethical guidelines of the Declaration of Helsinki and Strengthening the Reporting of Observational Studies in Epidemiology (STROBE).


2021 ◽  
Author(s):  
Matthias W. Wagner ◽  
Khashayar Namdar ◽  
Abdullah Alqabbani ◽  
Nicolin Hainc ◽  
Liana Nobre Figuereido ◽  
...  

Abstract Machine learning (ML) approaches can predict BRAF status of pediatric low-grade gliomas (pLGG) on pre-therapeutic brain MRI. The impact of training data sample size and type of ML model is not established. In this bi-institutional retrospective study, 251 pLGG FLAIR MRI datasets from 2 children’s hospitals were included. Radiomics features were extracted from tumor segmentations and five models (Random Forest, XGBoost, Neural Network (NN) 1 (100:20:2), NN2 (50:10:2), NN3 (50:20:10:2)) were tested to classify them. Classifiers were cross-validated on data from institution 1 and validated on data from institution 2. Starting with 10% of the training data, models were cross-validated using a 4-fold approach at every step with an additional 2.25% increase in sample size. Two-hundred-twenty patients (mean age 8.53 ± 4.94 years, 114 males, 67% BRAF fusion) were included in the training dataset, and 31 patients (mean age 7.97±6.20 years, 18 males, 77% BRAF fusion) in the independent test dataset. NN1 (100:20:2) yielded the highest area under the receiver operating characteristic curve (AUC). It predicted BRAF status with a mean AUC of 0.85, 95% CI [0.83, 0.87] using 60% of the training data and with mean AUC of 0.83, 95% CI [0.82, 0.84] on the independent validation data set.


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