The Impact of Strategic Orientation on Digital Transformation: Empirical Evidence Based on Chinese Listed Manufacturing Firms

In examining the antecedents of digital transformation, few studies have focused on how a firm’s extant strategic orientation influences its digital transformation intensity, and how this relationship is affected by strategic leaders and the firm’s life cycle. This work empirically tested these relationships based on the panel data of Chinese-listed manufacturing firms from 2007 to 2021. The findings suggest that a prospector orientation enhances, while a defender orientation weakens digital transformation intensity, and that the match between CEO background and strategic orientation amplifies the effects of both strategic orientations. Moreover, the relationships between the two strategic orientations and digital transformation intensity differ significantly at different stages of the firm\u27s life cycle. This work enriches research on the driving factors of digital transformation at the strategic level. It inspires firms to understand the impact of their existing strategic orientation on new strategic change, choosing strategic leaders, and timing the transition

Federated Learning for Short Text Clustering

Short text clustering has been popularly studied for its significance in mining valuable insights from many short texts. In this paper, we focus on the federated short text clustering (FSTC) problem, i.e., clustering short texts that are distributed in different clients, which is a realistic problem under privacy requirements. Compared with the centralized short text clustering problem that short texts are stored on a central server, the FSTC problem has not been explored yet. To fill this gap, we propose a Federated Robust Short Text Clustering (FSTC) framework. FSTC includes two main modules, i.e., robust short text clustering module and federated cluster center aggregation module. The robust short text clustering module aims to train an effective short text clustering model with local data in each client. We innovatively combine optimal transport to generate pseudo-labels with Gaussian-uniform mixture model to ensure the reliability of the pseudo-supervised data. The federated cluster center aggregation module aims to exchange knowledge across clients without sharing local raw data in an efficient way. The server aggregates the local cluster centers from different clients and then sends the global centers back to all clients in each communication round. Our empirical studies on three short text clustering datasets demonstrate that FSTC significantly outperforms the federated short text clustering baselines

“Teacher-Student Multiple Assessment” Mode for Ideological and Political Construction Based on the “Scenario-Action” Teaching Mode in the Major of Foreign Languages

This paper refers to the assessment mode in the field of Ideological and political construction of curriculum in the major of foreign languages, the authors will firstly analyse the background of our exploration of the new mode of teaching and assessment, and then, present the main cores of this mode, especially its “teacher-student multiple” principle, in order to establish a mechanism for the ideological and political construction in curriculum of foreign languages

An empirical comparison of several recent epistatic interactions detection methods

ABSTRACT Motivation: Many new methods have recently been proposed for detecting epistatic interactions in GWAS data. There is however no in-depth independent comparison of these methods yet. Results: Five recent methods-TEAM, BOOST, SNPHarvester, SNPRuler, and Screen and Clean (SC)-are evaluated here in terms of power, type-1 error rate, scalability, and completeness. In terms of power, TEAM performs best on data with main effect and BOOST performs best on data without main effect. In terms of type-1 error rate, TEAM and BOOST have higher type-1 error rates than SNPRuler and SNPHarvester. SC does not control type-1 error rate well. In terms of scalability, we tested the five methods using a dataset with 100,000 SNPs on a 64-bit Ubuntu system, with Intel (R) Xeon(R) CPU 2.66GHz, 16G memory. TEAM takes ∼36 days to finish and SNPRuler reports heap allocation problems. BOOST scales up to 100,000 SNPs and the cost is much lower than that of TEAM. SC and SNPHarvester are the most scalable. In terms of completeness, we study how frequently the pruning techniques employed by these methods incorrectly prune away the most significant epistatic interactions. We find that, on average, 20% of datasets without main effect and 60% of datasets with main effect are pruned incorrectly by BOOST, SNPRuler, and SNPHarvester

Sample Size Calculation for Controlling False Discovery Proportion

The false discovery proportion (FDP), the proportion of incorrect rejections among all rejections, is a direct measure of abundance of false positive findings in multiple testing. Many methods have been proposed to control FDP, but they are too conservative to be useful for power analysis. Study designs for controlling the mean of FDP, which is false discovery rate, have been commonly used. However, there has been little attempt to design study with direct FDP control to achieve certain level of efficiency. We provide a sample size calculation method using the variance formula of the FDP under weak-dependence assumptions to achieve the desired overall power. The relationship between design parameters and sample size is explored. The adequacy of the procedure is assessed by simulation. We illustrate the method using estimated correlations from a prostate cancer dataset

Study on particle plugging in propagating fractures based on CFD-DEM

In the drilling and completion process of fractured formations, wellbore stability is a key factor affecting the safety of drilling and completing engineering. Previous studies have demonstrated that propping moderately and plugging fractures with soluble particles can improve formation fracture pressure. When it comes to particle transport in 3D rough propagation fractures, the interactions between particle-fracture-fluid need to be considered. Meanwhile, size-exclusion, particle bridging/strain effects all influence particle transport behavior and ultimately particle plugging effectiveness. However, adequate literature review shows that fracture plugging, and fracture propagation have not been considered together. In this study, a coupled CFD-DEM method was put forward to simulate the particle plugging process of propagating fracture, and the effects of positive pressure difference, fracture roughness, particle concentration, and particle shape on the plugging mechanism were examined. It is concluded through the study that: 1) Positive pressure difference too large will lead to excessive fracture aperture, making the particles unable to form effective plugging in the middle of the fracture; positive pressure difference too small will lead to fracture aperture too small, making particles unable to enter into and plug the fracture. 2) No matter how the concentration, particle size and friction coefficient change, they mainly affect the thickness of the plugging layer, while the front end of the particle is still dominated by single-particle bridging, and double-particles bridging and multiple-particles bridging are hardly ever seen. For the wellbore strengthening approaches, such as stress cages, fracture tip sealing, etc., specific analysis should be carried out according to the occurrence of extended fractures. For example, for fractures with low roughness, the particles rarely form effective tight plugging in the middle of the fracture, so it is more suitable for fracture tip sealing; For the fracture with high roughness, if the positive pressure difference is controlled properly to ensure reasonable fracture extension, the particle plugging effect will be good, and the stress cage method is recommended for borehole strengthening