Miracle-dz/UniFAR-FTUs huggingface dataset:
Board Interlock Tenure and Firm Internationalization

ABSTRACT To overcome liabilities of foreignness and outsidership during internationalization, board interlock is an effective conduit of foreign knowledge inflows and organizational learning that firms require. We focus on the time dimension of such influence and hypothesize that the tenure of board interlocks with firms with experience in outward foreign direct investment (OFDI) in a country promotes the OFDI decision of the focal firm to that particular country. However, such an effect diminishes as the tenure of interlock ties increases. Moreover, as an alternative knowledge source, OFDI knowledge from the focal firm's neighboring region may weaken the baseline effect. Based on longitudinal data of listed firms in China, our empirical results support the hypotheses. This study enriches the literature on social network learning by identifying its temporal nature and the substitution between different knowledge sources. It also demonstrates the importance of rotating a firm's board members, so that knowledge acquisition and learning remain fresh. 摘要 为了克服企业在国际化过程中遇到的外来者劣势或局外人劣势，企业常常通过建立连锁董事进行组织学习并获取国际知识。 本研究关注连锁董事任职时间长短的效应，假设虽然与其他拥有对某一国家的对外直接投资经验企业建立连锁董事会促进本企业对同一国家的对外直接投资决策，但这种影响会随着连锁董事任期的增长而减弱。 与此同时，企业拥有的对所在区域的对外直接投资知识可以削弱连锁董事任期对企业国际化决策的影响。 本研究采用中国上市公司的面板数据进行了回归分析，结果证实了所提假设。 本研究表明，企业在社会网络中学习的时间维度和不同知识来源之间的替代都会影响企业的决策，因此丰富了社会网络学习的相关文献。 同时，本研究也阐述了企业董事会成员轮换的重要性，因为只有这样才能获取知识并保持习得知识的新颖性。
Direct parametric inference for the cumulative incidence function

Summary. In survival data that are collected from phase III clinical trials on breast cancer, a patient may experience more than one event, including recurrence of the original cancer, new primary cancer and death. Radiation oncologists are often interested in comparing patterns of local or regional recurrences alone as first events to identify a subgroup of patients who need to be treated by radiation therapy after surgery. The cumulative incidence function provides estimates of the cumulative probability of locoregional recurrences in the presence of other competing events. A simple version of the Gompertz distribution is proposed to parameterize the cumulative incidence function directly. The model interpretation for the cumulative incidence function is more natural than it is with the usual cause‐specific hazard parameterization. Maximum likelihood analysis is used to estimate simultaneously parametric models for cumulative incidence functions of all causes. The parametric cumulative incidence approach is applied to a data set from the National Surgical Adjuvant Breast and Bowel Project and compared with analyses that are based on parametric cause‐specific hazard models and nonparametric cumulative incidence estimation.
A Hyper-Personalized Product Recommendation System Focused on Customer Segmentation: An Application in the Fashion Retail Industry

Providing the right products, at the right place and time, according to their customer’s preferences, is a problem-seeking solution, especially for companies operating in the retail industry. This study presents an integrated product RS that combines various data mining techniques with this motivation. The proposed approach consists of the following steps: (1) customer segmentation; (2) adding the location dimension and determining the association rules; (3) the creation of product recommendations. We used the RFM technique for customer segmentation and the k-means clustering algorithm to create customer segments with customer-based RFM values. Then, the Apriori algorithm, one of the association rule mining algorithms, is used to create accurate rules. In this way, cluster-based association rules are created. Finally, product recommendations are presented with a rule-based heuristic algorithm. This is the first system that considers customers’ demographic data in the fashion retail industry in the literature. Furthermore, the customer location information is used as a parameter for the first time for the clustering phase of a fashion retail product RS. The proposed systematic approach is aimed at producing hyper-personalized product recommendations for customers. The proposed system is implemented on real-world e-commerce data and compared with the current RSs used according to well-known metrics and the average sales information. The results show that the proposed system provides better values.
Expression of the H2O2 Biosensor roGFP-Tpx1.C160S in Fission and Budding Yeasts and Jurkat Cells to Compare Intracellular H2O2 Levels, Transmembrane Gradients, and Response to Metals

Intracellular hydrogen peroxide (H2O2) levels can oscillate from low, physiological concentrations, to intermediate, signaling ones, and can participate in toxic reactions when overcoming certain thresholds. Fluorescent protein-based reporters to measure intracellular H2O2 have been developed in recent decades. In particular, the redox-sensitive green fluorescent protein (roGFP)-based proteins fused to peroxiredoxins are among the most sensitive H2O2 biosensors. Using fission yeast as a model system, we recently demonstrated that the gradient of extracellular-to-intracellular peroxides through the plasma membrane is around 300:1, and that the concentration of physiological H2O2 is in the low nanomolar range. Here, we have expressed the very sensitive probe roGFP2-Tpx1.C169S in two other model systems, budding yeast and human Jurkat cells. As in fission yeast, the biosensor is ~40–50% oxidized in these cell types, suggesting similar peroxide steady-state levels. Furthermore, probe oxidation upon the addition of extracellular peroxides is also quantitatively similar, suggesting comparable plasma membrane H2O2 gradients. Finally, as a proof of concept, we have applied different concentrations of zinc to all three model systems and have detected probe oxidation, demonstrating that an excess of this metal can cause fluctuations of peroxides, which are moderate in yeasts and severe in mammalian cells. We conclude that the principles governing H2O2 fluxes are very similar in different model organisms.
A Visual SLAM Algorithm Based on Dual Attention Semantic Segmentation

Aiming at the problem that visual SLAM algorithms have difficulty in rejecting occluded dynamic objects in dynamic environments, proposed a Dual Attention semantic Segmentation algorithm for visual SLAM (DAS-SLAM). The algorithm enhances feature extraction by improving the deep backbone network, and introduces spatial attention mechanism and channel attention mechanism to build the scene semantic dependency of features to accurately identify occluded objects. The region marked by the instance segmentation uses a combination of multi-view geometry and dynamic thresholding to determine the motion state of objects in the region, eliminating the influence of dynamic targets in the scene on SLAM localization accuracy. The results show that the rootmean-square error of the trajectory of the algorithm is reduced by 36% compared with the DS-SLAM algorithm, which effectively improves the accuracy of camera pose estimation and map construction and demonstrates more robustness.
Evaluation of the Antioxidant Activity of Levetiracetam in a Temporal Lobe Epilepsy Model

Epilepsy is a neurological disorder in which it has been shown that the presence of oxidative stress (OS) is implicated in epileptogenesis. The literature has shown that some antiseizure drugs (ASD) have neuroprotective properties. Levetiracetam (LEV) is a drug commonly used as an ASD, and in some studies, it has been found to possess antioxidant properties. Because the antioxidant effects of LEV have not been demonstrated in the chronic phase of epilepsy, the objective of this study was to evaluate, for the first time, the effects of LEV on the oxidant–antioxidant status in the hippocampus of rats with temporal lobe epilepsy (TLE). The in vitro scavenging capacity of LEV was evaluated. LEV administration in rats with TLE significantly increased superoxide dismutase (SOD) activity, increased catalase (CAT) activity, but did not change glutathione peroxidase (GPx) activity, and significantly decreased glutathione reductase (GR) activity in comparison with epileptic rats. LEV administration in rats with TLE significantly reduced hydrogen peroxide (H2O2) levels but did not change lipoperoxidation and carbonylated protein levels in comparison with epileptic rats. In addition, LEV showed in vitro scavenging activity against hydroxyl radical (HO•). LEV showed significant antioxidant effects in relation to restoring the redox balance in the hippocampus of rats with TLE. In vitro, LEV demonstrated direct antioxidant activity against HO•.
Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6dof Inertial Measurement Units}

This paper presents a novel end-to-end deep learning framework for real-time inertial attitude estimation using 6DoF IMU measurements. Inertial Measurement Units are widely used in various applications, including engineering and medical sciences. However, traditional filters used for attitude estimation suffer from poor generalization over different motion patterns and environmental disturbances. To address this problem, we propose two deep learning models that incorporate accelerometer and gyroscope readings as inputs. These models are designed to be generalized to different motion patterns, sampling rates, and environmental disturbances. Our models consist of convolutional neural network layers combined with Bi-Directional Long-Short Term Memory followed by a Fully Forward Neural Network to estimate the quaternion. We evaluate the proposed method on seven publicly available datasets, totaling more than 120 hours and 200 kilometers of IMU measurements. Our results show that the proposed method outperforms state-of-the-art methods in terms of accuracy and robustness. Additionally, our framework demonstrates superior generalization over various motion characteristics and sensor sampling rates. Overall, this paper provides a comprehensive and reliable solution for real-time inertial attitude estimation using 6DoF IMUs, which has significant implications for a wide range of applications.
The Distribution Regularity and Flotation Study of Niobium-Bearing Minerals in Baiyun Obo

The characteristics of Baiyun Obo niobium-bearing minerals are complex physicochemical properties that make the beneficiation of niobium minerals extremely difficult. In this paper, X-ray diffraction, X-ray fluorescence and mineral liberation analyzer (MLA) systems were used to study the niobium occurrence state and distribution of niobium-bearing minerals in the samples from Baiyun Obo. The results show that the chemical and mineral compositions of the sample are complex, with a Nb2O5 grading of 0.24%. There are many kinds of niobium minerals, including ilmenorutile, nioboaeschynite-Nd, baotite, latrappite, euxenite-Y, fergusonite and columbite-Mn, and the highest mass fraction of 0.55% is achieved with Nb in nioboaeschynite-Nd, followed by the mass fraction of ilmenorutile (0.33%). All of the niobium-containing minerals demonstrate a low degree of dissociation. Flotation experiments explored the optimal flotation conditions for HOBA (1-hydroxyoctyl-1,1-bisphosphonic acid) as a flotation collector for Baiyun Obo niobium minerals, which is able to increase the grade of Nb2O5 in the concentrate to 1.31%. The optimal use conditions of the reagent are pH 3.5–4.5, and the amount of the collector is 1000 g/t. By further optimizing the beneficiation process and reagent system, ilmenorutile and nioboaeschynite-Nd were significantly enriched in the concentrate, which suggested that HOBA can efficiently increase the grade of Nb2O5 in the concentrate.
MangoBase: A Genomics Portal and Gene Expression Atlas for Mangifera indica

Mango (Mangifera indica L.) (2n = 40) is a member of the Anacardiaceae family, which was domesticated at least 4000 years ago in Asia. Mangoes are delicious fruits with great nutritional value. They are one of the major fruit crops worldwide, cultivated in more than 100 countries, with a production of more than 40 million tons. Recently the genome sequences of several mango varieties have been released, but there are no bioinformatics platforms dedicated to mango genomics and breeding to host mango omics data. Here, we present MangoBase, a web portal dedicated to mango genomics, which provides multiple interactive bioinformatics tools, sequences, and annotations to analyze, visualize, and download omics data related to mango. Additionally, MangoBase includes a gene expression atlas with 12 datasets and 80 experiments representing some of the most significant mango RNA-seq experiments published to this date. These experiments study mango fruit ripening in several cultivars with different pulp firmness and sweetness or peel coloration, and other experiments also study hot water postharvest treatment, infection with C. gloeosporioides, and the main mango tree organ tissues.
The impact of entrepreneurial team psychological capital on innovation performance: The mediating role of knowledge sharing and knowledge hiding

Introduction The important role of psychological capital on corporate innovation has attracted the attention of academics, more and more scholars have conducted related studies. Although most scholars have explored the mechanisms and paths of psychological capital on innovation performance, few scholars have explored the inner relationship between them from the perspective of knowledge management. Based on the knowledge management perspective, We explore the influence effect of the psychological capital of entrepreneurial teams on the innovation performance of startups in the entrepreneurial situation. Methods We conducted hypothesis testing using data from 113 Chinese entrepreneurial teams, and conducted reliability analysis, correlation analysis, and regression analysis on the questionnaire data with the help of SPSS software and AMOS software. Results The results showed that (1) entrepreneurial team psychological capital has a significant positive effect on innovation performance of startups; (2) entrepreneurial team psychological capital positively promotes their knowledge sharing behavior and reduces knowledge hiding behavior; (3) entrepreneurial team knowledge sharing and knowledge hiding play a partially mediating role between entrepreneurial team psychological capital and innovation performance of startups; (4)organizational innovation climate plays a moderating role in the influence of entrepreneurial team knowledge sharing and knowledge hiding on the innovation performance of startups. Discussion The findings are consistent with the hypothesis model proposed in this paper, indicating that as the psychological capital of entrepreneurial teams increases, the innovation performance of startups can benefit from higher levels of knowledge sharing and lower levels of knowledge hiding.
Gaussian Parsimonious Clustering Models Scale Invariant and Stable by Projection

Gaussian mixture model-based clustering is now a standard tool to determine an hypothetical underlying structure into continuous data. However many usual parsimonious models, despite their appealing geometrical interpretation, suffer from major drawbacks as scale dependence or unsustainability of the constraints by projection. In this work we present a new family of parsimonious Gaussian models based on a variance-correlation decomposition of the covariance matrices. These new models are stable by projection into the canonical planes and, so, faithfully representable in low dimension. They are also stable by modification of the measurement units of the data and such a modification does not change the model selection based on likelihood criteria. We highlight all these stability properties by a specific geometrical representation of each model. A detailed GEM algorithm is also provided for every model inference. Then, on biological and geological data, we compare our stable models to standard geometrical ones.
A Tensor Solution for Health Indicator Construction of Metro Wheelset Degradation with Irregular Noise

During the operation of metro vehicles, wheel flange and wheel diameter continue to be abrased, so wheel degradation is inevitable. Influenced by the states of welded rail, subgrade and turnout, applied load, speed of running and other components, the degradation process of wheelset is vulnerable to irregular noise interference, which leads to abnormal fluctuation of vibration signal. As a result, the degradation process is hard to be accurately described. To address this problem, this paper proposes a new health indicator construction method for metro wheelset based on tensor reconstruction. First, tensor Tucker decomposition is utilized to obtain the core tensor of original signal, and then tensor reconstruction is applied to transform the signal into a new degradation sequence with noise reduction. Second, the Savitzky-Golay filter is employed to remove the irregular trend from the obtained degradation sequence. Finally, deep autoencoder network is used to extract deep degradation features, and after dimension reduction, a health indicator of wheelset degradation process can be obtained. The effectiveness of the proposed method is verified with Beijing subway wheelset degradation dataset in 2020. The results show that the constructed health indicator can accurately describe the whole degradation process with good trendability and monotonicity. More importantly, the proposed method possess good practicability since the key changing parts in the health indicator can correspond well to the actual maintenance records.
A Fast Design Method of Anisotropic Dielectric Lens for Vortex Electromagnetic Wave Based on Deep Learning

Orbital angular momentum (OAM) has made it possible to regulate classical waves in novel ways, which is more energy- or information-efficient than conventional plane wave technology. This work aims to realize the transition of antenna radiation mode through the rapid design of an anisotropic dielectric lens. The deep learning neural network (DNN) is used to train the electromagnetic properties of dielectric cell structures. Nine variable parameters for changing the dielectric unit structure are present in the input layer of the DNN network. The trained network can predict the transmission phase of the unit cell structure with greater than 98% accuracy within a specific range. Then, to build the corresponding relationship between the phase and the parameters, the gray wolf optimization algorithm is applied. In less than 0.3 s, the trained network can predict the transmission coefficients of the 31 × 31 unit structure in the arrays with great accuracy. Finally, we provide two examples of neural network-based rapid anisotropic dielectric lens design. Dielectric lenses produce the OAM modes +1, −1, and −1, +2 under TE and TM wave irradiation, respectively. This approach resolves the difficult phase matching and time-consuming design issues associated with producing a dielectric lens.
Control of Oscillations in Second-Order Differential Equation

Abstract The article deals with the control of oscillations in a specific type of second-order differential equations. The purpose of the research is to prove the possibility of oscillation frequency control based on a change in the value of a singular perturbation parameter placed into a mathematical model of a nonlinear dynamical system at the highest derivative. The oscillation frequency change caused by a different value of the parameter is verified by numerically modelling the system.
Facilitation by Haloxylon persicum Shrubs Enhances Density and Richness of Soil Seed Bank of Annual Plants in a Hyper-Arid Ecosystem

The soil seed bank is an essential functional component of plant communities. In arid ecosystems, the island-like distribution of shrubs influences the spatial distribution of the soil seed bank. Very little is known about seed banks in deserts of the Middle East. The present study aimed to evaluate the facilitative effects of Haloxylon persicum shrubs on the soil seed bank of annual plants in a sandy desert region in northwestern Saudi Arabia during two consecutive growing seasons (2017–2018 and 2018–2019) with contrasting rainfall. A total of 480 soil samples at 12 stands were collected from two microhabitats, under shrubs and in open areas, soon after the two growing seasons. The germinable seed bank of annual plants was estimated by controlled seedling emergence method. Shrubs significantly facilitated the accumulation of seed bank beneath their canopies after the two growing seasons. In both microhabitats, the size and species richness of soil seed bank were significantly greater after the wet growing season (2018–2019) than following the dry season (2017–2018). The facilitative effects of shrubs were greater following the moister growing season than after the dry season. The effect of shrubs on seed bank–annual vegetation similarity varied between growing seasons, being greater in shrub interspaces than beneath shrub canopies for the dry growing season, while during the wet season, the similarity of the seed bank with standing annual vegetation was greater in sub-canopy microhabitat than in bare soil.
Examining Clinical Opinion and Experience Regarding Utilization of Plain Radiography of the Spine: Evidence from Surveying the Chiropractic Profession

Plain Radiography of the spine (PROTS) is utilized in many forms of healthcare including the chiropractic profession; however, the literature reflects conflicting opinions regarding utilization and value. Despite being an essential part of Evidence-Based Practice (EBP), few studies assess Doctors of Chiropractic (DCs) clinical opinions and experience regarding the utilization of (PROTS) in practice. In this study, DCs were surveyed regarding utilization of PROTS in practice. The survey was administered to an estimated 50,000 licensed DCs by email. A total of 4301 surveys were completed, of which 3641 were United States (US) DCs. The Clinician Opinion and Experience on Chiropractic Radiography (COECR) scale was designed to analyze survey responses. This valid and reliable scale demonstrated good internal consistency using confirmatory factor analysis and the Rasch model. Survey responses show that 73.3% of respondents utilize PROTS in practice and 26.7% refer patients out for PROTS. Survey responses show that, among US DCs, 91.9% indicate PROTS has value beyond identification of pathology, 86.7% indicate that PROTS is important regarding biomechanical analysis of the spine, 82.9% indicate that PROTS is vital to practice, 67.4% indicate that PROTS aids in measuring outcomes, 98.6% indicate the opinion that PROTS presents very low to no risk to patients, and 93.0% indicate that sharing clinical findings from PROTS studies with patients is beneficial to clinical outcomes. The results of the study indicated that based on clinical experience, the majority of DCs find PROTS to be vital to practice and valuable beyond the identification of red flags.