Miracle-dz/UniFAR-FTUs huggingface dataset:
On estimation in the nested case‐control design under nonproportional hazards

Analysis of time‐to‐event data using Cox's proportional hazards (PH) model is ubiquitous in scientific research. A sample is taken from the population of interest and covariate information is collected on everyone. If the event of interest is rare and covariate information is difficult to collect, the nested case‐control (NCC) design reduces costs with minimal impact on inferential precision. Under PH, application of the Cox model to data from a NCC sample provides consistent estimation of the hazard ratio. However, under non‐PH, the finite‐sample estimates corresponding to the Cox estimator depend on the number of controls sampled and the censoring distribution. We propose two estimators based on a binary predictor of interest: one recovers the estimand corresponding to the Cox model under a simple random sample, while the other recovers an estimand that does not depend on the censoring distribution. We derive the asymptotic distribution and provide finite‐sample variance estimators.
Ambidextrous Marketing Capabilities, Exploratory and Exploitative Market-Based Innovation, and Innovation Performance: An Empirical Study on China’s Manufacturing Sector

Despite the acknowledged importance of innovation for enterprises’ sustainable development, in comparison to technological innovation, research on market-based innovation is still in its nascent stage. It is unclear what factors will predict market-based innovation and how market-based innovation boosts innovation performance. Therefore, based on an adaptive marketing capabilities perspective and drawing on insights from the organizational ambidexterity literature, this study explored the relationships among enterprises’ marketing capabilities, market-based innovation and innovation performance. Data collected from 237 manufacturing enterprises in China were analyzed using hierarchical regression analysis and bootstrapping methods. The results showed that: (1) Marketing capabilities and market-based innovation activities are both ambidextrous. Specifically, marketing capabilities include marketing exploration capabilities and marketing exploitation capabilities, and market-based innovation includes exploratory market-based innovation and exploitative market-based innovation. (2) Ambidextrous marketing capabilities can enhance innovation performance significantly, with ambidextrous market-based innovation playing a partial mediating role in this relationship. Specifically, marketing exploration capabilities affect innovation performance through the partial mediating role of exploratory market-based innovation, whereas marketing exploitation capabilities affect innovation performance through the partial mediating role of exploitative market-based innovation. (3) Exploratory market-based innovation negatively moderates the relationship between exploitative market-based innovation and innovation performance, and exploitative market-based innovation also exerts a negative moderating effect on the exploratory market-based innovation–innovation performance association. These findings complement and enrich the ambidextrous capability (innovation) theory, helping manufacturing enterprises to better understand and deploy ambidexterity, and hence to achieve sustainability.
Ultra-short-term PV Power Forecasting Based on Cloud Image Modeling

Due to the impact of many local sudden changes in photovoltaic (PV) output power, its ultra-short-term forecasting is facing great challenges. In a time scale of 1 to 2 minutes, the generation, movement, and ablation of clouds in the sky are the main factors that affect the output power. Therefore, a prediction method based on cloud images is proposed for ultra-short-term prediction of PV output power. First, apply image processing technology to extract image features that affect the changes in PV output power, including cloud coverage rate, direct sunlight rate, and image brightness; second, calculate the extraterrestrial irradiation and air mass at each time point; finally, use the above features as input factors, and use PV output power output data, and a long short-term memory network (LSTM) is used to construct a PV power prediction model to achieve ultra-short-term forecasting of PV output power.
Hepato-nephroprotective Role of Lepidium sativum against Oxidative Stress Induced by Dexamethasone in Rats

The current research was prepared to explore the hepato-nephroprotective effect of Lepidium sativum seedaquatic extract against oxidative stress stimulated by dexamethasone in rats. Animals were classified asfollows; group one was used as control while groups 2, 3 and 4 were treated orally with Lepidium sativumseed extract (LSSE; 20 mg/kg, daily), interperitoneally with dexamethasone (DEX; 1 mg/kg BW) andLSSE plus DEX for 14 days, respectively. Administration of DEX elevated thiobarbituric acid reactivesubstances (TBARS), hydrogen peroxide (H2O2), and kidney and liver function biomarkers level, andlactate dehydrogenase activity. While enzymatic (SOD, CAT, GPx, GR, GST) and non-enzymatic (GSH)antioxidants, protein content, and alkaline phosphatase activity were significantly decreased. Otherwise, ratssupplemented with LSSE singly declined lipid peroxidation and improved most of the studied parameters.Moreover, rats pretreated with LSSE then received DEX showed significant alleviation in lipid peroxidation,antioxidant status and biochemical indices with respect to DEX treated group. Conclusively, LSSE hasbeneficial protective effects and has the capability to counteract the harmful influence of DEX. So, Lepidiumsativum might represent a novel approach in the therapy of dexamethasone because of its antioxidantproperties.
Evaluation of different mathematical models in the CFD-DEM simulation of conical spouted bed fluid dynamics

The input parameters, empirical, and semi-empirical models significantly influence the responses obtained by CFD-DEM simulations. In this work, the effect of three turbulence models, three conditions of the particle rotation, and five drag models on the fluid dynamic behavior of a conical spout bed applied to the drying of sorghum grains were evaluated. Experimental data on the solids pressure drop, height, and shape of the fountain were used to validate the simulations. Results showed the importance of including the particle rotation in the model to approximate the results simulated with the experimental behavior. Compared with experimental data, considering the particle rotation by Dennis et al. model, the deviation was of the 2% for the fountain height and 9.18% for the pressure drop. While for the model without the particle rotation, the deviations were 106.33% and 42.31% for the fountain height and pressure drop, respectively. For the analyzed case, the standard k-? turbulence model showed a greater agreement with the experimental data. For the drag models evaluated, the best fit with the experimental data was obtained by the Koch-Hill drag model, followed by the Gidaspow model, with deviations less than 10%.
A Hybrid Deep Network Framework for Android Malware Detection

Android is a growing target for malicious software (malware) because of its popularity and functionality. Malware poses a serious threat to users’ privacy, money, equipment and file integrity. A series of data-driven malware detection methods were proposed. However, there exist two key challenges for these methods: (1) how to learn effective feature representation from raw data; (2) how to reduce the dependence on the prior knowledge or human labors in feature learning. Inspired by the success of deep learning methods in the feature representation learning community, we propose a malware detection framework which starts with learning rich-features by a novel unsupervised feature learning algorithm Merged Sparse Auto-Encoder (MSAE). In order to extract more compact and discriminative feature from the rich-features to further boost the malware detection capability, a hybrid deep network learning algorithm Stacked Hybrid Learning MSAE and SDAE (SHLMD) is established by further incorporating a classical deep learning method Stacked Denoising Auto-encoders (SDAE). After that, we feed the feature learned by MSAE and SHLMD respectively to classification algorithms, e.g., Support Vector Machine (SVM) or K-NearestNeighbor (KNN), to train a malware detection model. Evaluation results on two real-world datasets demonstrate that SHLMD achieves 94.46 and 90.57 percent accuracy respectively, which outperforms the classical unsupervised feature representation learning Sparse Auto-encoder (SAE). MSAE performs similarly to SAE. SHLMD can further improve the performance of MSAE and the supervised fine-tuned method SDAE. Besides, we compare the performance of our methods with that of state-of-the-art detection approaches, including classical deep-learning-based methods. Extensive experiments show that our proposed methods are effective enough to detect Android malware.
Nanoscale Topographies for Corneal Endothelial Regeneration

The corneal endothelium is the innermost layer of the cornea that selectively pumps ions and metabolites and regulates the hydration level of the cornea, ensuring its transparency. Trauma or disease affecting human corneal endothelial cells (hCECs) can result in major imbalances of such transport activity with consequent deterioration or loss of vision. Since tissue transplantation from deceased donors is only available to a fraction of patients worldwide, alternative solutions are urgently needed. Cell therapy approaches, in particular by attempting to expand primary culture of hCECs in vitro, aim to tackle this issue. However, existing cell culture protocols result in limited expansion of this cell type. Recent studies in this field have shown that topographical features with specific dimensions and shapes could improve the efficacy of hCEC expansion. Therefore, potential solutions to overcome the limitation of the conventional culture of hCECs may include recreating nanometer scale topographies (nanotopographies) that mimic essential biophysical cues present in their native environment. In this review, we summarize the current knowledge and understanding of the effect of substrate topographies on the response of hCECs. Moreover, we also review the latest developments for the nanofabrication of such bio-instructive cell substrates.
Starch–PVA Nanocomposite Film Incorporated with Cellulose Nanocrystals and MMT: A Comparative Study

Abstract The goal of this work was to compare the barrier, mechanical, and thermal properties of two types of starch–polyvinyl alcohol (PVA) nanocomposites. Sodium montmorillonite (MMT) and nanocrystalline cellulose were chosen as nanoreinforcements. X-ray diffraction (XRD) test showed well-distributed MMT in the starch–PVA matrix, possibly implying that the clay nanolayers formed an exfoliated structure. The moisture sorption, solubility and water vapor permeability (WVP) studies revealed that the addition of MMT and nanocrystalline cellulose reduced the moisture affinity of starch–PVA biocomposite. At the level of 7 % MMT, the nanocomposite films showed the highest ultimate tensile strength (UTS) (4.93 MPa) and the lowest strain to break (SB) (57.65 %). The differential scanning calorimetry (DSC) results showed an improvement in thermal properties for the starch–PVA–MMT nanocomposites, but not for the starch–PVA–NCC nanocomposites. Results of this study demonstrated that the use of MMT in the fabrication of starch–PVA nanocomposites is more favorable than that of nanocrystalline cellulose to produce a desirable biodegradable film for food packaging applications.
Primary school pre-service teachers' competence level of computational concepts in programming using Dr. Scratch

This article outlines the results of research on students' perceptions of online learning at the beginning of the Covid-19 pandemic. The method used was a survey with a questionnaire distributed online to active students of even semester 2019/2020. Obtained respondents as many as 91 people. 38% of males and 62% female. About 46% were first-year students and the remaining 54% were second and third-year students. Nearly 90% of respondents attended more than five online learning courses through the Google Classroom application, Google Meet, Zoom, Edmodo, campus e-learning, or Whatsapp Group. The exploration of perception results showed that students still find some obstacles in participating in online learning, argue that the students' assignment was more, they were difficult to receive about the material, still very important to meet and get an explanation from the lecturer. Improving online learning effectiveness could be done and prepared by lecturers and students. Specifically for students, preparing the online learning tools and applications, and improving mental preparation namely adaptable, self-regulated learning, tough, and responsible are the efforts to achieve maximum learning outcomes.
A Milroy Disease Family Caused by FLT4 Gene Mutation of c.2774 T>A with Phenotypes Heterogeneity

Background: Milroy disease is a rare, autosomal dominant disorder. Mutations of FLT4 (Fms Related Tyrosine Kinase 4) gene impaired tyrosine kinase signaling, and further cause symptoms of Milroy disease. In this research, we found a large Chinese MD family with phenotype heterogeneities. And we conducted Next Generation Sequencing analysis to explore possible genetic causative factors might be related to clinical heterogeneities among family members.Methods: Sanger sequencing was conducted on the 17-26 exons of FLT4 (NM_182925.4) gene. Primers were as follows: Froward: 5' CTTCATCAGCGTCGAGTGG 3'; Reverse: 5' ATTATGGGCGGGTTCCTT 3'; Next-generation sequencing was conducted to explore pathogenic mutation might lead to phenotype heterogeneities. Then we conducted Sanger sequencing of the possible related genes. The GIMAP7 gene amplification primers as follows: Forward primer: 5’ ACCACCTGCAAGGAAATCAGCCGCT3’; Reverse primer: 5’GTTAGAGAAATACCTCCTTCCCCTT3’. The amplification system for two genes are as follows: 2×Biotech Power PCR Mix: 10µl; forward primer: 0. 8µl (10µM); reverse primer: 0. 8µl (10µM); DNA template: 1µl (50ng/µl); ddH20: 13. 4µl. The effects of the mutations on the gene functions were evaluated with mutation taster and/or SIFT, PolyPhen.Results: A heterozygous substitution mutation was detected in all patients (FLT4 gene: c.2774 T>A, p.V925E). Meanwhile, a G deletion (c.826delG, p.Val276Phefs*29) of GIMAP7 gene was detected in all patients but two patients with phenotype heterogeneities (I1, and II1). Both the two mutations were predicted to be pathogenic.Conclusions: In this report, we described a large Milroy disease family caused by a missense mutation of the FLT4 gene (c.2774 T>A, p.V925E). Meanwhile, a frame-shift mutation (c.826delG, p.Val276Phefs*29) of GIMAP7 gene might be related to the clinical phenotype heterogeneities of the family.
A Multi-Site Collaborative Study of the Hostile Priming Effect

In a now-classic study by Srull and Wyer (1979), people who were exposed to phrases with hostile content subsequently judged a man as being more hostile. And this “hostile priming effect” has had a significant influence on the field of social cognition over the subsequent decades. However, a recent multi-lab collaborative study (McCarthy et al., 2018) that closely followed the methods described by Srull and Wyer (1979) found a hostile priming effect that was nearly zero, which casts doubt on whether these methods reliably produce an effect. To address some limitations with McCarthy et al. (2018), the current multi-site collaborative study included data collected from 29 labs. Each lab conducted a close replication (total N = 2,123) and a conceptual replication (total N = 2,579) of Srull and Wyer’s methods. The hostile priming effect for both the close replication (d = 0.09, 95% CI [-0.04, 0.22], z = 1.34, p = .16) and the conceptual replication (d = 0.05, 95% CI [-0.04, 0.15], z = 1.15, p = .58) were not significantly different from zero and, if the true effects are non-zero, were smaller than what most labs could feasibly and routinely detect. Despite our best efforts to produce favorable conditions for the effect to emerge, we did not detect a hostile priming effect. We suggest that researchers should not invest more resources into trying to detect a hostile priming effect using methods like those described in Srull and Wyer (1979).
Current-Based Open-Circuit Fault Diagnosis for PMSM Drives With Model Predictive Control

Fault diagnosis is currently considered a crucial method to enhance the reliability of permanent magnet synchronous motor (PMSM) drives. In this article, an open-circuit fault (OCF) diagnosis method of a voltage source inverter-fed PMSM with model predictive control is proposed. The developed approach mainly consists of fault detection and localization. Besides, the normalized cost function variation is designed as a fault detection indicator according to the impact of OCFs on current signatures. It can not only monitor drive system conditions, but also discriminate fault categories. Afterward, the polarities of normalized αβ-current average values and phase angles of residual current vectors are utilized to locate the exact faulty switch in each fault type. Consequently, a total of 21 possible combinations of faulty switches can be correctly detected and located. Finally, the effectiveness in fault diagnoses and the robustness against operating point variations are verified by the experimental results.
A Study On Assessment Of Autonomic Nervous System Function In Patients With Type 2 Diabetes Mellitus

The autonomic nervous system (ANS) innervates the entire neuraxis and influences the functions of all organs. This study was undertaken for evaluating the autonomic dysfunction in diabetic patients using clinical autonomic tests and neuro- electrophysiology. A prospective study was carried out in 66 patients with type II diabetes mellitus in a tertiary care hospital for one year. Systemic examination, necessary investigations, nerve conduction study and clinical testing for the autonomic nervous system were done. The results were noted and analyzed. 65.2% were females, whereas 34.8% were males. Mean duration of diabetes was found to be 9.06 years (SD 4.121). 80.3% population was known to have type 2 diabetes for 5-10 years duration, 13.6% had diabetes for 10-15 years, and only 3.5% had diabetes for more than 15 years. Mean FBS was found to be 196.12(mg/dl) ±77.180 SD and mean PPBS was 303.26(mg/dl) ± 115.385 SD. Mean HbA1c levels were 10.95 ± 2.36 SD. 33.3% showed early parasympathetic involvement for cardiac autonomic neuropathy, 9% had definite parasympathetic involvement, and only 6% had both parasympathetic and sympathetic involvement. 62.12% showed abnormal responses in nerve conduction study, of which 48.78% had autonomic dysfunction. The main factor responsible for the development and progression of autonomic dysfunction is poor glycaemic status. If contributing factors can be detected, early identification of cardiac autonomic neuropathy (CAN) and appropriate management would halt its progression. Aggressive glycaemic monitoring and treatment shall bring down the progression and prolong the time interval in showing abnormal responses in autonomic function testing.
Design and Accuracy Analysis of a Micromachine Tool with a Co-Planar Driving Mechanism

Due to the requirements of manufacturing miniaturized high-tech products, micromachining with micromachine tools has come to be regarded as an important technology. The main goal of this study is to build up the key technologies, including optimal structure and configuration design, synchronous driving control, analysis of optimal accuracy, in order to develop a low-cost and high-accuracy micromachine tool with a multi-degrees of freedom (DOF) platform with a co-plane synchronous driving mechanism. Due to the advantages of such a mechanism, the machine is able to possess a high feed resolution and high accuracy without the use of expensive drive components and high-end CNC controllers. Because of the no pile-up structure, the machine has less movement inertia effect, as well as the merits of light weight, high stiffness, and increased stability. Furthermore, the machine has more DOF, resulting in a better cutting performance than that of 3-DOF machine tools. To better understand the characteristics of major error sources of the machine in order to further enhance its accuracy, hybrid error analysis, kinematics analysis, and a volumetric error model were conducted. Finally, a prototype of the designed micromachine tool was built, and cutting experiments for accuracy calibration and verification were carried out using this machine. The results showed that the machine was able to effectively execute 4-DOF microcutting with positioning accuracy of 800 nm.
Retrieval of UV-Visible aerosol absorption using AERONET and
OMI-MODIS synergy: Spatial and temporal variability across major
aerosol environments

Abstract. Measuring spectral aerosol absorption remains a challenging task in aerosol studies, especially in the UV region, where the ground and airborne measurements are sparse. In this research, we introduce an algorithm that synergizes ground measurements with satellite observations for the derivation of spectral single scattering albedo (SSA, ωo) of aerosols in the UV to visible range (340–670 nm). The approach consists in explaining satellite measured near-UV radiances (340, 354 and 388 nm) by the Ozone Monitoring Instrument (OMI), and visible radiances (466 and 646 nm) by MODerate Imaging Spectrometer (MODIS), in terms of ground-based Aerosol Robotic Network (AERONET) measurements of total column extinction aerosol optical depth (AOD, τ), and retrieved total column wavelength dependent SSA using radiative transfer calculations. Required information on aerosol particle size distribution is taken from an AERONET-based climatology specifically developed for this project. This inversion procedure is applied over 110 AERONET sites distributed worldwide, for which continuous, long-term AERONET measurements are available. Using the derived data set we present seasonal and regional climatology of ωo(λ) for carbonaceous, dust and urban/industrial aerosol types. The UV-Visible spectral dependence of ωo obtained for the three major aerosol types from the synergy algorithm is found to be consistent with the in situ measurements reported in the literature. A comparison to standard AERONET SSA product at 646 nm shows absolute differences within 0.03 (0.05) for 40 % (59 %) of the compared observations. The derived aerosol ωo(λ) data set provides a valuable addition to the existing aerosol absorption record from AERONET by extending the absorption retrieval capability to the near-UV region. The combined UV-Visible data set, in addition to improving our understanding of spectral aerosol absorption properties, also offers wavelength-dependent dynamic aerosol absorption models for use in the satellite-based aerosol retrieval algorithms.