Miracle-dz/UniFAR-FTUs huggingface dataset:
Physicochemical and control releasing properties of date pit (Phoenix dactylifera L.) phenolic compounds microencapsulated through fluidized‐bed method

Abstract This study aimed to investigate the effect of different ethanol/water solvents on phenolic compound extraction and microencapsulated extract of date pit powder. The highest and the lowest amounts of total phenolic compounds were 742.37 and 236.07 mg GAE/g dm, respectively, observed in water–ethanol composite solvent (25% W: 75% E) and water solvent (100% W). In this regard, the highest and lowest values of IC50 were 6.83 and 0.90 μg/ml, measured in water solvent (100% W) and water–ethanol solvent (25% W: 75% E), respectively. In the second phase, using maltodextrin (10%, 20%, and 30% W/V) as the first layer, date pit extract was microencapsulated. Alhagi maurorum gum (10%, 20%, and 30% W/V) as the second layer and medium‐chain triglycerides (MCT oil) (15% W/W) as the third layer were used by a fluidized‐bed drying technique. By increasing temperature, the microencapsulated extract powder solubility was increased as well. In contrast, the moisture content, bulk density, tapped density, and compressibility index decreased. By increasing temperature, the maltodextrin and A. maurorum gum concentration, the coating efficiency, and the loading capacity of the samples increased initially and decreased eventually. Moisture content, powder solubility, bulk density, and compressibility index increased, with increasing maltodextrin concentration, however, tapped density decreased. The optimal physicochemical properties of the phenolic compounds’ microcapsules were determined at 45°C and at a concentration of 20% of each of the maltodextrin and A. maurorum gum. According to scanning electron images, the powder particles were spherical and had a relatively smooth surface. Notably, the release rate of phenolic compounds reached its maximum (64%) after 24 h.
Analysis of parametric instability of unstable electrostatic ion cyclotron waves in an ion beam-plasma system

The authors examined the parametric decay instability of the linearly unstable electrostatic ion cyclotron wave in an ion beam-plasma system which decays to an ion acoustic wave and a linearly stable electrostatic ion cyclotron wave. They have derived a set of equations which represent this mechanism, and obtained numerical results of the temporal evolution of the amplitude of the related wave and distortions of the velocity distribution function. The beam distortion begins to occur at the stage of linear instability and becomes large at the stage of nonlinear instability. Ion heating of the plasma occurs only at the stage of nonlinear instability. These large distortions of the ion beam and ion heating of the plasma are caused by the deceleration of the ion beam.
Association of Abnormal Renal Profiles with Subretinal Fluid in Diabetic Macular Edema

Purpose To investigate the risk factors for subretinal fluid (SRF) in diabetic macular edema (DME). Methods We reviewed the records of 66 patients with DME. Systemic parameters, including hypertension, glycosylated hemoglobin, serum fasting glucose, total cholesterol, triglycerides, and diabetic kidney disease, were evaluated. Renal parameters for diabetic kidney disease included serum albumin, serum creatinine, albuminuria, and estimated glomerular filtration rate. Ocular factors included visual acuity and diabetic retinopathy, and the following parameters are evaluated through optical coherence tomography examination: disorganization of the retinal inner layers, loss of ellipsoid zone, central subretinal fluid thickness, central macular thickness, and presence of SRF. Results Higher albuminuria (odds ratio, 3.431; 95% confidence interval, 1.039–11.334; P=0.043) was associated with the presence of SRF in patients with DME. Lower serum albumin levels (beta = −14.028, se = 6.646, P=0.044) were associated with increased SRF thickness. Conclusions Poor kidney function was associated with the presence of SRF in DME. Screening for SRF in DME in patients with higher albuminuria and lower serum albumin levels should be routinely performed.
Multi-Class Logistic Regression Classifier with BFGS Method

The classical multi-class logistic regression classifier uses Newton method to optimize its loss function and suffers the expensive computations and the un-stable iteration process. In our work, we apply the state-of-art optimization techniques BFGS to train multi-class logistic regression and compare them with Newton method on the classification accuracy of 25 datasets experimentally. The results show that BFGS achieves better classification accuracy than the Newton method. Moreover, BFGS have the lower time complexity, in contrast with Newton method. Finally, we also observe that logistic classifier with BFGS method demonstrate comparable performance with the SVM classifier.
X-ray spectra of Orion OB supergiants

The spectra are at a higher energy resolution than earlier X-ray observations of OB supergiants, and this allows stronger constraints to be placed on the location of the emission source in the outer atmosphere. The spectra of all three stars are found to be consistent with thermal sources which are only slightly attenuated by stellar wind opacity and have temperatures of approximately 10 to the (6.5 + or - 0.2) power K. A number of explanations of the soft X-ray flux distributions of early-type stars are tested. A model with the X rays coming only from a corona in a slab at the base of the wind does not seem a possibility unless the ionization or density structure of the overlying material is significantly different from that usually assumed for a cool wind. A model where the X rays are produced by radiatively driven shocks, some of which must be strong, can explain the X-ray luminosity and satisfy upper limits on the absorption by oxygen derived from the data. A high-energy (greater than 1 keV) contribution from a base coronal zone is considered possible. The spectrum of Zeta Ori shows emission near 2 keV, wich can be interpreted as line emission of Si XIII and S XV from a very hot source component with T = 1.5 x 10 to the 7th K.
Analysis of University Social Media User Engagement by Topic

Social media is currently one of the primary platforms for spreading information about organizations or companies, including universities. To reach more users, we need to analyze user engagement. Topic is one of essential feature to attract user interest on a post. Therefore, by understanding the topic preference, we can increase the user engagement. In this study, we identify topic posts using Latent Dirichlet Algorithm (LDA) method. Furthermore, we predict the engagement scores (number of likes and comments) using decision tree and random forest method. The experimental results show that there are 11 topics identified by LDA, and after analyzing further at three different engagement levels (views, likes, and comments), the most engaging topic is achievement. The experimental results on engagement prediction reveal that random forest performs better than decision tree, with the MAE score at 1074
Schrödinger equations with an external magnetic field: Spectral problems and semiclassical states

In this thesis, we study Schrodinger equations with an external magnetic field. In the first part, we are interested in an eigenvalue problem. We work in an open, bounded and simply connected domain in dimension two. We consider a magnetic potential singular at one point in the domain, and related to the magnetic field being a multiple of a Dirac delta. Those two objects are related to the Bohm-Aharonov effect, in which a charged particle is influenced by the presence of the magnetic potential although it remains in a region where the magnetic field is zero. We consider the Schrodinger magnetic operator appearing in the Schrodinger equation in presence of an external magnetic field. We want to study the spectrum of this operator, and more particularly how it varies when the singular point moves in the domain. We prove some results of continuity and differentiability of the eigenvalues when the singular point moves in the domain or approaches its boundary. Finally, in case of half-integer circulation of the magnetic potential, we study some asymptotic behaviour of the eigenvalues close to their critical points. In the second part, we study nonlinear Schrodinger equations in a cylindrically setting. We are interested in the semiclassical limit of the equation. We prove the existence of a semiclassical solution concentrating on a circle. Moreover, the radius of that circle is determined by the electric potential, but also by the magnetic potential. This result is totally new with respect to the ones before, in which the concentration is driven only by the electric potential.
Reliability optimization using probabilistic sufficiency factor and correction response surface

Reliability-based design optimization for low failure probability often requires millions of function analyses. Response surface approximation of the response functions (analysis response surface(ARS)) is often used to reduce the cost of failure probability calculations. Failure probabilities obtained from numerical sampling schemes are noisy and unsuitable for gradient-based optimization. To overcome this, response surfaces have been fitted to the failure probability of the designs (design response surface (DRS)) as a function of the design variables and used in optimization. Two shortcomings of the approach are that (i) the ARS fitting is extremely expensive for a large number of variables, especially for the high accuracy required to obtain very accurate reliability estimates, and (ii) DRS introduces fitting errors which affect the tails of the distributions which are significant for low failure probabilities. An approach to obtaining high-accuracy reliability estimates using the probabilistic sufficiency factor and correction response surface is investigated in this article. The method is demonstrated using a thin-walled box beam structure designed for minimum weight with failure probability constraints. The design is subjected to buckling, strength, and displacement constraints. Two methods of correcting low-fidelity analyses are compared for accuracy and efficiency. It is shown that correction to the response function is more accurate than the correction fitted to the probabilistic sufficiency factor.
A static birefringent interferometer for the measurement of upper atmospheric winds

An instrument concept called the Birefringent Imaging Doppler Wind Interferometer (BIDWIN) is being validated in the Atmospheric and Space Physics Lab at the University of New Brunswick in collaboration with COM DEV Ltd (Ottawa, Canada) to determine its capabilities for measuring Doppler wind fields in the Earth’s nightglow. The instrument is adapted from a similar approach used to obtain two dimensional images of high speed (~1000 m/s) flow fields in plasmas at the Australian National University. For that application the precision of the wind measurements was not explored in detail. With BIDWIN, the intent is to obtain ~ 5 m/s precision on each bin of a CCD image of the wind field. An examination of the instrument concept and sensitivity of the wind measurements made using this approach is undertaken to determine the feasibility of this criterion. The BIDWIN has the advantage over other instruments that can be used for a similar purpose (such as the field widened Michelson interferometer and Fabry-Perot interferometer) in that it has no moving parts, has a large throughput, is light weight and is relatively cheap to construct. In this paper, the instrument concept is presented and the ideal and non-ideal instrument effects are explored. Calibration measurements conducted using a proto-type of the instrument are used to verify the instrument concept and confirm the feasibility of the approach for making atmospheric wind measurements.
Bayesian Estimation of Log-Normal Means with Finite Quadratic Expected Loss

The log-normal distribution is a popular model in biostatistics and other fields of statistics. Bayesian inference on the mean and median of the dis- tribution is problematic because, for many popular choices of the prior for the variance (on the log-scale) parameter, the posterior distribution has no finite mo- ments, leading to Bayes estimators with infinite expected loss for the most common choices of the loss function. We propose a generalized inverse Gaussian prior for the variance parameter, that leads to a log-generalized hyperbolic posterior, for which it is easy to calculate quantiles and moments, provided that they exist. We derive the constraints on the prior parameters that yield finite posterior moments of or- der r. We investigate the choice of prior parameters leading to Bayes estimators with optimal frequentist mean square error. For the estimation of the lognormal mean we show, using simulation, that the Bayes estimator under quadratic loss compares favorably in terms of frequentist mean square error to known estimators.
State-of-the-Art Review of the Key Factors Affecting Electric Vehicle Adoption by Consumers

The dependence of road transport on fossil fuels and its contribution to greenhouse gas (GHG) and pollutant emissions are main concerns leading to the need for shifting toward alternative energy sources and, namely, electromobility. The current paper aims to identify the key determinants affecting the consumer adoption of electric vehicles (EVs), focusing on private passenger cars. Toward this purpose, a systematic review of recent international literature is conducted in order to identify motivators and barriers, which are then categorized following the PESTLE (Political–Economic–Social–Technological–Legal–Environmental) approach. Based on the review results, main policy implications and recommendations are discussed. A main conclusion is that the recent literature highlights a wide array of determinants, without converging as to which ones are the most influential regarding EV adoption by consumers. Another conclusion is that the environmental aspects are less important for consumers than anticipated, despite the concerns about climate change and renewable energy transition.
Current status, application, and challenges of the interpretability of generative adversarial network models

The generative adversarial network (GAN) is one of the most promising methods in the field of unsupervised learning. Model developers, users, and other interested people are highly concerned about the GAN mechanism where the generative model and the discriminative model learn from each other in a gameplay manner, which generates a causal relationship among output features, internal network structure, feature extraction process, and output results. Through the study of the interpretability of GANs, the validity, reliability, and robustness of the application of GANs can be verified, and the weaknesses of the GANs in specific applications can be diagnosed, which can provide support for designing better network structures. It can also improve security and reduce the decision‐making and prediction risks brought by GANs. In this article, the study of the interpretability of GANs is explored, and ways of the evaluation of the application effect of GAN interpretability techniques are analyzed. Besides, the effect of interpretable GANs in fields such as medical treatment and military is discussed, and current limitations and future challenges are demonstrated.
Embedded hyperlink structures as a feature for fake news classification

Since 2016 fake news classification has risen from an obscure concept to a prominent and dynamic field of research. Much attention has been given to the very natural idea of classifying articles based on their textual contents, but as we push against the limits of this technique other features to continue improving machine learning models are being investigated. One technique that has been looked at in particular has been the use of network graph analysis to examine the spread of fake news across social media platforms. Twitter in particular has been a popular target of this research. However online news articles already have a built in propagation technique in the form of hyperlinks embedded within the article. This feature could potentially hold the same kind of classification capabilities as social media networks, however until now this has went uninvestigated. This research project attempts to fill this hole in the research by proposing a technique to develop a data set for this purpose. It proposes a web scraping process that can be applied to almost any website with minimal adjustments. It also describes an algorithm for converting these websites into network graphs. It then applies the popular PageRank algorithm to these graphs and uses this feature to compare a purely linguistic model to a novel linguistic-network hybrid model. This research contributes its novel data set and model to the literature in the hope of making future research in this area easier going forwards.
Real-time 3D MRI reconstruction from cine-MRI using unsupervised network in MRI-guided radiotherapy for liver cancer.

PURPOSE Respiration has a major impact on the accuracy of radiation treatment for thorax and abdominal tumours. Instantaneous volumetric imaging could provide precise knowledge of tumour and normal organs' three-dimensional (3D) movement, which is the key to reducing the negative effect of breathing motion. Therefore, this study proposed a real-time 3D MRI reconstruction method from cine-MRI using an unsupervised network. METHODS AND MATERIALS Cine-MRI and setup 3D-MRI from eight patients with liver cancer were utilized to establish and validate the deep learning network for 3D-MRI reconstruction. Unlike previous methods that required 4D-MRI for network training, the proposed method utilized a reference 3D-MRI and cine-MRI to generate the training data. Then, a network was trained in an unsupervised manner to estimate the relationship between the cine-MRI acquired on coronal plane and deformation vector field (DVF) that describes the patient's breathing motion. After the training process, the coronal cine-MRI were inputted into the network, and the corresponding DVF was obtained. By wrapping the reference 3D-MRI with the generated DVF, the 3D-MRI could be reconstructed. RESULTS The reconstructed 3D-MRI slices were compared with the corresponding phase-sorted cine-MRI using dice similarity coefficients (DSCs) of liver contours and blood vessel localization error. In all patients, the liver DSC had mean value >96.1% and standard deviation < 1.3%; the blood vessel localization error had mean value <2.6 mm, and standard deviation was <2.0 mm. Moreover, the time for 3D-MRI reconstruction was approximately 100 ms. These results indicated that the proposed method could accurately reconstruct the 3D-MRI in real time. CONCLUSIONS The proposed method could accurately reconstruct the 3D-MRI from cine-MRI in real time. This method has great potential in improving the accuracy of radiotherapy for moving tumours.
Einstein's Materialism and Modern Tests of Quantum Mechanics

After a presentation of Einstein's and Bohr's antagonistic point of view on the interpretation of Quantum Mechanics an illustration of their conflicting positions in the particular case of Young's double slit experiment is presented. It is then shown that in their most recent form (i. e. time dependent neutron interferometry) these experiments suggest (if one accepts absolute energymomentum conservation in all individual microprocesses) that Einstein was right in the Bohr-Einstein controversy. Einsteins Materialismus und heutige Tests der Quantenmechanik Nach einer Darstellung von Einsteins und Bohrs antagonistischen Standpunkten in der Interpretation der Quantenmechanik werden ihre widerspruchlichen Positionen im speziellen Fall des Youngschen Doppelspaltexperiments dargestellt. Es wird dann gezeigt, das diese Experimente in ihrer neuesten Form (d. h. zeitabhangige Neutroneninterferometrie) Einstein in der Bohr-Einsteinkontroverse recht gaben (wenn man absolute Energie-Impulserhaltung bei allen individuellen Mikroprozessen annimmt).
Silenciamiento génico en insectos plaga que afectan la industria agrícola usando ARN de interferencia

Los insectos plaga, son especies de organismos vivos que en forma constante se encuentran en poblaciones altas, ocasionando daños económicos en los cultivos. Generalmente, suele tratarse de especies puntuales, por lo general, sólo una o dos, que pueden causar gran afectación económica en el sector de la agricultura. En las últimas 3 décadas se ha venido desarrollando el concepto de un proceso biológico, detectado en eucariotas ampliamente, mediante el que se pueden silenciar genes, a partir de ARN de doble cadena (ARNdc). Esta maquinaria se ha investigado para conocer su funcionamiento y buscar potenciales aplicaciones que podrían tener en el campo de la biotecnología. En varios estudios se encontró que el silenciamiento de genes se debe a las interacciones enzimáticas intracelulares citoplasmáticas con moléculas de ARN pequeñas (ARNsi), que actúan sobre el ARN mensajero (ARNm) intracelular, impidiendo que este se traduzca a proteína. Mediante este mecanismo se busca silenciar genes específicos en insectos plaga, que sean esenciales para que el insecto pueda vivir y de esa manera evitar la proliferación de la plaga. Este artículo recopila los estudios realizados acerca del ARN de interferencia, referidos al mecanismo genético de los insectos, como alternativa para su control.
Genetic Biomarkers of Antipsychotic-Induced Prolongation of the QT Interval in Patients with Schizophrenia

Antipsychotics (AP) induced prolongation of the QT interval in patients with schizophrenia (Sch) is an actual interdisciplinary problem as it increases the risk of sudden death syndrome. Long QT syndrome (LQTS) as a cardiac adverse drug reaction is a multifactorial symptomatic disorder, the development of which is influenced by modifying factors (APs’ dose, duration of APs therapy, APs polytherapy, and monotherapy, etc.) and non-modifying factors (genetic predisposition, gender, age, etc.). The genetic predisposition to AP-induced LQTS may be due to several causes, including causal mutations in the genes responsible for monoheme forms of LQTS, single nucleotide variants (SNVs) of the candidate genes encoding voltage-dependent ion channels expressed both in the brain and in the heart, and SNVs of candidate genes encoding key enzymes of APs metabolism. This narrative review summarizes the results of genetic studies on AP-induced LQTS and proposes a new personalized approach to assessing the risk of its development (low, moderate, high). We recommend implementation in protocols of primary diagnosis of AP-induced LQTS and medication dispensary additional observations of the risk category of patients receiving APs, deoxyribonucleic acid profiling, regular electrocardiogram monitoring, and regular therapeutic drug monitoring of the blood APs levels.