Miracle-dz/UniFAR-FTUs huggingface dataset:
Bibliometric analysis of venture teams of technology-based firms

Due to its growing impact on the economic development of countries, research on venture teams in new technology-based firms - TBFs has been increasing in recent years, seeking to identify the success and failure factors of this type of firms, given their high mortality rates. This paper analyzes the changes that have occurred in the intellectual structure of this discipline through the bibliometric analysis of research on the theme of venture teams in the new TBFs. The information collected was extracted from the main collection of the Web of Science (WoS) and SCOPUS databases from 1987 to 2020. The Nvivo and VOSviewer softwares are used to perform the initial analyzesas well as the analysis of citations, co-citations , co-authorship, etc. The advances associated with the main authors, sources and countries, the general citation structure and the development of this field are presented. The results show a growing publication trend as of 2009, seeing a higher production of articles between 2014 and 2019. USA is the most influential country, followed by UK and Italy. The"Journal of Business Venturing" and "Technovation" are the most influential sources. The main contribution of this work is to show the evolution of this theme, so that researchers can use it in the future in their theoretical and research frameworks
Intracranial pressure relationships in the protected and unprotected head

This paper represents a continuation of previous research on closed head impact in the human cadaver and an associated mathematical model. Two different sets of experiments were conducted: sequential impacts on a single embalmed helmeted specimen; and impact experiments on individual helmeted unembalmed specimens. Impact parameters and intracranial pressures were measured. A finite element model is presented which can predict the intracranial pressures throughout the brain for the first 8 msec. Language: en
Predicting the production rates of foundation construction using factor and regression analysis

Foundations are often part of the critical path for bridge construction. However, foundation constructions are highly variable due to the fact that construction process faces difficult problems like poor soil conditions, congested traffic and unpredictable weather. Designers are often unable to obtain reliable production rates in order to improve the accuracy of project time estimation that often leads to more delays and disputes. Field construction data related to this research were collected from twenty-five Texas highway projects within a two-year period to develop three models that could predict production rates of drilled shafts and prestressed concrete piles more accurately. Analyses showed that these models are estimate production rates accurately and relatively simple to apply and develop.
Combination of Limited Meteorological Data for Predicting Reference Crop Evapotranspiration Using Artificial Neural Network Method

Reference crop evapotranspiration (ETo) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ETo based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (Tmin), maximum temperature (Tmax), mean temperature (Tmean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (gradmax), minimum global radiation (gradmin), day length, and ETo data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ETo included Tmax, Tmin, H, and SR at hidden layers (4, 3); gradmin, SR, and WS at (6, 4); SR, day length, Ssh, and Tmean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ETo in case of a lack of climate data with high performance. Models using ANN can help promote the decision-making for water managers, designers, and development planners.
Some case studies of cirriform clouds over India during the winter period

A few cases of extensive formations of upper tropospheric clouds over north and central India during the winter of 1969- 70 were studied utilizing satellite pictures in conjunction with synoptic and upper air charts as well as aircraft reports and hourly observations from airport stations. The ground-observed cloud reports are compared with satellite pictures. The clouds were found to be associated with the jet stream and formed when large amplitude troughs extended into India and neighbourhood. The clouds were also organised in transverse as well as parallel modes; prevailing lapse rates and vertical wind shears have been found to be different in the two modes of organizations.
Effect of Potassium Supply and Water Stress on Potato Drought Tolerance and Water Productivity

ABSTRACT Potato (Solanum tuberosum L.) is one of major food crops grown worldwide and in Lebanon. The response of field grown potato to 0, 200, 300 kg of potassium (K) ha−1 was examined under deficit irrigation (imposed water stress). Trials were conducted under Near East-North Africa (NENA) pedo-climatic conditions found in the semiarid central Bekaa of Lebanon. Irrigation water was provided by a drip irrigation system. A mild deficit irrigation, corresponding to 68% of calculated ET0 in 2015 and 85% of it in 2016, was started at the flowering stage and lasted till foliar senescence. Canopy temperature, at maximum development, showed a slight decrease with the increase of potassium levels. During physiological maturity, the midday leaf temperature was significantly lower at the higher K dose, indicating improved leaf water potential. The highest K dose showed a non-significant, higher, chlorophyll content in the leaves, notably in the first year. A significant increase in the weight of one commercial tuber was found in K300 (247.6 g). The commercial yield ranged between 33.05 and 34.78 Mg ha−1 (K300) and 23.31 and 25.53 Mg ha−1 (K0) in 2015 and 2016 respectively. During the 2016 trial, 8 plots out of 16 had an agronomic water yield between 9 and 13 kg.m−3 against 12 out of 16 plots in 2015, competing with the reported values for potato in the region. Optimizing nutrient and water input in the NENA region can contribute to food security with minimal environmental impact.
Device-free detection and localization of people using UWB networks

Ultra-wideband (UWB) transmission is a promising technology for indoor localization, due to its high-resolution ranging and obstacle penetration capabilities. Most practical UWB localization systems rely on targets (e.g., objects, people) to carry an active UWB device. In some scenarios (e.g., intruder detection, elderly care, smart environments, emergency response) it is desirable to have the ability to track people and assets in a passive manner, without requiring them to be equipped with any radio-frequency (RF) device. The high time resolution property of UWB radios can make it possible to resolve the human-body reflected path among all the other multipath reflections. This reflected path changes over time due to minuscule movements, even though the person seemingly stands still. Because of this, the corresponding received signal samples also vary over time. This observation is presented for an experimental UWB system and forms the basis of the device-free person detection and localization technique in this thesis. The technique collects the energy in the variations of the signal and estimates the travelling distance for the human-body reflected path through its delay estimation. Each estimate draws an ellipse around the transmitter and the receiver for the position of the person. By combining multiple ellipses, a unique position estimate is given for many different positions of the person in an indoor environment. The received signal samples show different variations depending on the random movement of each person. This gives us a way to detect a second person in the environment without extending the measurement setup. By quantifying the correlations between the samples of the received signal, we can understand if the samples are affected by the same person or not. The correlation value is higher if samples are affected by the same person and remains low if the samples are affected by different persons. A detection and device-free ranging method is given for the second person. A localization method for multiple persons is also developed by combining the multiple link correlations.
Cellular Uptake of Silica and Gold Nanoparticles Induces Early Activation of Nuclear Receptor NR4A1

The approval of new nanomedicines requires a deeper understanding of the interaction between cells and nanoparticles (NPs). Silica (SiO2) and gold (Au) NPs have shown great potential in biomedical applications, such as the delivery of therapeutic agents, diagnostics, and biosensors. NP-cell interaction and internalization can trigger several cellular responses, including gene expression regulation. The identification of differentially expressed genes in response to NP uptake contributes to a better understanding of the cellular processes involved, including potential side effects. We investigated gene regulation in human macrophages and lung epithelial cells after acute exposure to spherical 60 nm SiO2 NPs. SiO2 NPs uptake did not considerably affect gene expression in epithelial cells, whereas five genes were up-regulated in macrophages. These genes are principally related to inflammation, chemotaxis, and cell adhesion. Nuclear receptor NR4A1, an important modulator of inflammation in macrophages, was found to be up-regulated. The expression of this gene was also increased upon 1 h of macrophage exposure to spherical 50 nm AuNPs and 200 nm spherical SiO2 NPs. NR4A1 can thus be an important immediate regulator of inflammation provoked by NP uptake in macrophages.
The Emergence of New Catalytic Abilities in an Endoxylanase from Family GH10 by Removing an Intrinsically Disordered Region

Endoxylanases belonging to family 10 of the glycoside hydrolases (GH10) are versatile in the use of different substrates. Thus, an understanding of the molecular mechanisms underlying substrate specificities could be very useful in the engineering of GH10 endoxylanases for biotechnological purposes. Herein, we analyzed XynA, an endoxylanase that contains a (β/α)8-barrel domain and an intrinsically disordered region (IDR) of 29 amino acids at its amino end. Enzyme activity assays revealed that the elimination of the IDR resulted in a mutant enzyme (XynAΔ29) in which two new activities emerged: the ability to release xylose from xylan, and the ability to hydrolyze p-nitrophenyl-β-d-xylopyranoside (pNPXyl), a substrate that wild-type enzyme cannot hydrolyze. Circular dichroism and tryptophan fluorescence quenching by acrylamide showed changes in secondary structure and increased flexibility of XynAΔ29. Molecular dynamics simulations revealed that the emergence of the pNPXyl-hydrolyzing activity correlated with a dynamic behavior not previously observed in GH10 endoxylanases: a hinge-bending motion of two symmetric regions within the (β/α)8-barrel domain, whose hinge point is the active cleft. The hinge-bending motion is more intense in XynAΔ29 than in XynA and promotes the formation of a wider active site that allows the accommodation and hydrolysis of pNPXyl. Our results open new avenues for the study of the relationship between IDRs, dynamics and activity of endoxylanases, and other enzymes containing (β/α)8-barrel domain.
Dietary Patterns and Prevalent NAFLD at Year 25 from the Coronary Artery Risk Development in Young Adults (CARDIA) Study

The prevalence of nonalcoholic fatty liver disease is rapidly rising. We aimed to investigate associations of diet quality and dietary patterns with nonalcoholic fatty liver disease (NAFLD) in Black and White adults. We included 1726 participants who attended the Year 20 Exam of the Coronary Artery Risk Development in Young Adults (CARDIA) study and had their liver attenuation (LA) measured using computed tomography at Year 25 (2010–2011). NAFLD was defined as an LA of ≤51 Hounsfield units after the exclusion of other causes of liver fat. The a priori diet-quality score (APDQS) was used to assess diet quality, and dietary patterns were derived from principal components analysis. Univariate and multivariable logistic regression models were used to evaluate the association between the APDQS, dietary patterns, and NAFLD, and were adjusted for Year 20 covariates. NAFLD prevalence at Year 25 was 23.6%. In a model adjusted for age, race, sex, education, alcohol use, physical activity, smoking, and center at Year 25, the APDQS was inversely associated (p = 0.004) and meat dietary pattern was positively associated (p < 0.0001) with NAFLD, while the fruit-vegetable dietary pattern was not significantly associated (p = 0.40). These associations remained significant when additionally adjusting for comorbidities (type 2 diabetes mellitus, dyslipidemia, hypertension), however, significant associations were diminished after additionally adjusting for body mass index (BMI). Overall, this study finds that the APDQS and meat dietary patterns are associated with prevalent NAFLD in mid-life. The associations appear to be partially mediated through higher BMI.
Attributes of effective EFL teachers in Vietnamese context as perceived by students and teachers

Identifying characteristics of effective teachers has always been a topic of paramount importance. Accordingly, an extensive volume of theoretical endeavours and empirical research has been devoted to the investigation of typical attributes that effective teachers possess in diverse contexts. Yet, current understanding of the issue in Vietnamese setting remains limited. Given that perceptions about effective teachers’ qualities have been typically found to be highly context-bounded, gaining an insight into these attributes in this under-researched context is expected to provide a solid foundation for further attempts to improve the quality of teaching and learning. The present study responds to this pressing need by exploring the perceptions held by 200 learners and 22 teachers in a language school in Vietnam, employing questionnaires for collecting both quantitative and qualitative data. A theoretical framework adapted from Shulman’s (1986, 1987) model of teachers’ knowledge base was employed. Findings from the study revealed both convergence and mismatches in the perceptions held by the teacher and learner groups. As a whole, teachers, and learners convergently prioritised teachers’ subject matter content knowledge as most important, compared to pedagogical content knowledge and teachers’ identity and interactions with learners. Both groups also voted teachers’ ability to provide clear instructions and explanations as the most important pedagogical feature of effective teachers and uniformly downplayed the role of sociocultural knowledge and the ability to effectively use technology. More in-depth analyses, however, showed that teachers placed more importance over the pedagogical aspects of subject matter knowledge, language proficiency, and teaching methodology, whereas learners more appreciated the psychological and motivational aspects. In particular, the teacher participants highly valued the teachers’ ability of classroom management, material adaptation, and facilitating students’ engagement in learning activities. In contrast, qualities most desired by learners included a positive attitude towards the teaching career and teachers’ willingness to care, understand learners’ problems, encourage, and treat all students on a fair basis.
Strain Imaging for the Early Detection of Cardiac Remodeling and Dysfunction in Primary Aldosteronism

Speckle tracking echocardiography is a novel technique to quantify cardiac function and deformation. It has been applied in a series of cardiovascular diseases for the evaluation of early cardiac impairment. We recently used this technique to investigate cardiac structure and function in patients with primary aldosteronism. Cardiac damage usually occurs earlier in patients with primary aldosteronism than those with primary hypertension, probably because aldosterone hypersecretion is more commonly observed in the former than the latter patients. In this article, we will review the imaging studies, especially with speckle tracking echocardiography, for the detection of early cardiac dysfunction in primary aldosteronism as a disease model.
Diffracted X-ray Tracking Method for Measuring Intramolecular Dynamics of Membrane Proteins

Membrane proteins change their conformations in response to chemical and physical stimuli and transmit extracellular signals inside cells. Several approaches have been developed for solving the structures of proteins. However, few techniques can monitor real-time protein dynamics. The diffracted X-ray tracking method (DXT) is an X-ray-based single-molecule technique that monitors the internal motion of biomolecules in an aqueous solution. DXT analyzes trajectories of Laue spots generated from the attached gold nanocrystals with a two-dimensional axis by tilting (θ) and twisting (χ). Furthermore, high-intensity X-rays from synchrotron radiation facilities enable measurements with microsecond-timescale and picometer-spatial-scale intramolecular information. The technique has been applied to various membrane proteins due to its superior spatiotemporal resolution. In this review, we introduce basic principles of DXT, reviewing its recent and extended applications to membrane proteins and living cells, respectively.
Label Noise Cleaning Using Support Vector Machines

Mislabeled examples affect the performance of supervised learning algorithms. Two novel approaches to this problem are presented in this Thesis. Both methods build on the hypothesis that the large margin and the soft margin principles of support vector machines provide the characteristics to select mislabeled examples. Extensive experimental results on several datasets support this hypothesis. The support vectors of the one-class and two-class SVM classifiers captures around 85% and 99% of the randomly generated label noise examples (10% of the training data) on two character recognition datasets. The numbers of examples that need to be reviewed can be reduced by creating a two-class SVM classifier with the non-support vector examples, and then by only reviewing the support vector examples based on their classification score from the classifier. Experimental results on four datasets show that this method removes around 95% of the mislabeled examples by reviewing only around about 14% of the training data. The parameter independence of this method is also verified through the experiments. All the experimental results show that most of the label noise examples can be removed by (re-)examining the selective support vector examples. This property can be very useful while building large labeled datasets.
Improved Space Charge Modeling for Simulation and Design of Photoinjectors

Photoinjectors in advanced high-energy accelerators reduce beam energy spreads and enhance undulator photon fluxes. Photoinjector design is difficult because of the substantial differences in time and spatial scales. This Phase I program explored an innovative technique, the local Taylor polynomial (LTP) formulation, for improving finite difference analysis of photoinjectors. This included improved weighting techniques, systematic formula for high order interpolation and electric field computation, and improved handling of space charge. The Phase I program demonstrated that the approach was powerful, accurate, and efficient. It handles space charge gradients better than currently available technology.
Deep Neural Network Approach for Pose, Illumination, and Occlusion Invariant Driver Emotion Detection

Monitoring drivers’ emotions is the key aspect of designing advanced driver assistance systems (ADAS) in intelligent vehicles. To ensure safety and track the possibility of vehicles’ road accidents, emotional monitoring will play a key role in justifying the mental status of the driver while driving the vehicle. However, the pose variations, illumination conditions, and occlusions are the factors that affect the detection of driver emotions from proper monitoring. To overcome these challenges, two novel approaches using machine learning methods and deep neural networks are proposed to monitor various drivers’ expressions in different pose variations, illuminations, and occlusions. We obtained the remarkable accuracy of 93.41%, 83.68%, 98.47%, and 98.18% for CK+, FER 2013, KDEF, and KMU-FED datasets, respectively, for the first approach and improved accuracy of 96.15%, 84.58%, 99.18%, and 99.09% for CK+, FER 2013, KDEF, and KMU-FED datasets respectively in the second approach, compared to the existing state-of-the-art methods.
aridec: an open database of litter mass loss from aridlands worldwide with recommendations on suitable model applications

Abstract. Plant litter decomposition in terrestrial ecosystems involves the physical and chemical breakdown of organic matter. Development of databases is a promising tool for achieving a predictive understanding of organic matter degradation at regional and global scales. In this paper we present aridec: a comprehensive open database con-taining litter-mass loss data from aridlands across the world. We describe in detail the structure of the database and discuss general patterns in the data. Then, we explore what are the most appropriate model structures to inte-grate with data on litter decomposition from the database by conducting a collinearity analysis. The database includes 184 entries from aridlands across the world, representing a wide range of climates. For the majority of the data gathered in aridec it is possible to fit models of litter decomposition that consider initial organic matter as a homogenous reservoir (one pool models), as well as models with two distinct types of organic compounds that decompose at different speeds (two pool models). Moreover, these two carbon pools can either decompose without interaction (parallel models), or with matter transfer from a labile pool to a slow-decomposing pool after transformation (series models). Although most entries in the database can be used to fit these models, we suggest potential users of this database to test identifiability for each individual case as well as the number of degrees of freedom. Other model applications that were not discussed in this publication might also be suitable for use with this database. Lastly, we give some recommendations for future decomposition studies to be potentially added to this database. The extent of the information included in aridec in addition to its open-science approach makes it a great platform for future collaborative efforts in the field of aridland biogeochemistry. The aridec version 1.0. 1 is archived and publicly available at https://doi.org/10.5281/zenodo.6025969 (Sarquis et al., 2022), and the database is managed under a version-controlled system and centrally stored in GitHub (https://github.com/AgustinSarquis/aridec, last access: 9 February 2022).