Miracle-dz/UniFAR-FTUs huggingface dataset:
Depression Symptoms Mediate Mismatch Between Perceived Severity of the COVID-19 Pandemic and Preventive Motives

The present study monitored changes in beliefs about the coronavirus disease 2019 (COVID-19) pandemic, depressive symptoms, and preventive motives between the first and second waves in South Korea using an online survey administered to 1,144 individuals nationally representative for age, gender, and areas of residence. While participants correctly updated their beliefs about the worsening pandemic situations, the perceived importance of social distancing did not change, and their motives to follow prevention measures shifted toward compulsory rather than voluntary motives. This inconsistency appeared to be mediated by depressive symptoms, such that negative belief changes followed by increased depressive symptoms were associated with the decreased perceived importance of social distancing and decreased voluntary motives. Our data highlights the importance of psychological responses to the dynamically evolving pandemic situations in promoting preventive behaviors.
White Matter Microstructure Alterations Associated With Paroxetine Treatment Response in Major Depression

More than one-third of depressive patients do not achieve remission after the first antidepressant treatment. The “watch and wait” approach used to find the most effective antidepressant leads to an increased personal, social, and economic burden in society. In order to overcome this challenge, there has been a focus on studying neural biomarkers associated with antidepressant response. Diffusion tensor imaging measures have shown a promising role as predictors of antidepressant response by pointing to pretreatment differences in the white matter microstructural integrity between future responders and non-responders to different pharmacotherapies. Therefore, the aim of the present study was to explore whether response to paroxetine treatment was associated with differences in the white matter microstructure at baseline. Twenty drug-naive patients diagnosed with major depressive disorder followed a 6- to 12-week treatment with paroxetine. All patients completed magnetic resonance brain imaging and a clinical assessment at baseline and 6–12 weeks after treatment. Whole-brain tract-based spatial statistics was used to explore differences in white matter microstructural properties estimated from diffusion magnetic resonance imaging. Voxel-wise statistical analysis revealed a significant increase in fractional anisotropy and a decrease in radial diffusivity in forceps minor and superior longitudinal fasciculus in responders compared to non-responders. Thus, alterations in white matter integrity, specifically in forceps minor and the superior longitudinal fasciculus, are associated with paroxetine treatment response. These findings pave the way for personalized treatment strategies in major depression.
NMR Diffusion Studies on the Binding of Hyaluronate - Methotrexate Conjugates with Bovine Serum Albumin in Aqueous Solution

The study of the interactions of drugs with plasmatic proteins is of fundamental importance for the understanding of their biological applications and effects. The classical analytical techniques employed for the characterization of the degree of binding in such ligand/receptor systems are mainly based on separation method and spectroscopic characterizations. In the present study, we show that diffusion-based NMR spectroscopy is a useful tool for the fast and reliable determination of the quantitative binding between a polymer-drug bioconjugate and a model protein in aqueous solution. The dissociation constant (Kd) related to the binding event was calculated and compared with the results obtained from a classical fluorescence-based assessment, showing good match between these two methods.
Study on extraction of tea saponin by microwave-assisted method

Tea saponin was extracted by ethanol as solvent from Camellia shell under microwave radiation.The effects of the factors,including ethanol concentration,the microwave power,the ratio of solid to liquid and the extraction time,on the yield rate were investigated.The optimal process parameters obtained are as follows:ethanol concentration 50%,microwave power 400 W,the ratio of solid to liquid 1:3,the extraction time 8 min,the yield rate of tea saponin from Camellia shell reached 12.16%. Compared with routine methods,the microwave-assisted extraction offers a short extracting time,a high yield rate,and application prospect good.
Studying Relationship between Exchange Rate and Dividend : Case Study : Iran Khodro Company

One of the characteristics of developed countries is the presence of efficient markets and financial institutions which cause development of these countries in addition to their important role in economy of these countries. Since share market value is affected by macroeconomic variables, the present research was conducted to determine long-term relationship between foreign currency variations and dividend of Iran Khodro Company and explanatory variables of oil price and consumer price index have been used as explanatory variables. In this research, data are studied every year for time period of 1991 to 2012. To study time series statics, augmented Dicky Fuller test has been used. Results of this test showed that variable of foreign currency was static in the level and other variables were static in the first order difference. In this research, relations between variables have been determined based on exchange rate fluctuations have been determined using Auto Regressive With Distributed Lags(ARDL) and results of this test indicate the presence of positive and significant relationship between exchange rate and dividend and between exchange rate and consumer price index and negative and significant relationship between exchange rate and oil price.
Extractability Profiling and Antioxidant Activity of Flavonoids in Sorghum Grain and Non-grain Materials

Extractability Profiling and Antioxidant Activity of Flavonoids in Sorghum Grain and Non-grain Materials. (May 2009) Nenge Lynda Azefor Njongmeta, B.Sc. (Hons), Obafemi Awolowo University, Ile-Ife Nigeria; M.Sc. University of Ibadan, Nigeria Chair of Advisory Committee: Dr. Lloyd W. Rooney Grains, leaves, sheaths, glumes and stalks of sorghum varieties were analyzed for total phenols, condensed tannins, flavan-4-ols, anthocyanins and in vitro antioxidant activity. Black sorghum bran was used to evaluate the effectiveness of organic acids and enzymes on extractability of phenols. Flavonoid profiles of grains and non-grain tissues were determined and characterized using HPLC-PDA and HPLC-ESI-MS. The presence of a pigmented testa and spreader genes (B1B2S) is a predictor for polymeric flavonoids (tannins) but not for simple phenols such as flavan-4-ols, 3-deoxyanthocyanins, flavones and flavanones. Simple flavonoids increased antioxidant capacity of sorghum, and were present in all sorghum except for the white pericarp sorghums that did not have flavanones. The “red turning into black” gene increased phenols in Type I sorghum. The leaves, sheath and glumes of sorghum had higher levels of phenols
AMMONIUM IONS REMOVAL FROM AQUEOUS SOLUTIONS USING MESOPOROUS (Al)Si-MCM-41

The batch removal of ammonium ions from aqueous solutions under different experimental conditions using (Al)Si-MCM-41 mesoporous molecular sieve in comparison with LTA microporous zeolite was investigated in this study. The ammonium uptake was dependent on the initial ammonium concentration. The equilibrium sorption data were fitted using Freundlich and Langmuir isotherm models. The Langmuir model (monolayer sorption) gave the best fit (R 2 = 0.9965 for (Al)Si-MCM-41 and R 2 = 0.9995 for LTA). The saturation capacity (monolayer coverage) was found of 33.33 mg NH4 + /g (Al)Si-MCM-41 and 55.55 mg NH4 + /g LTA. Equilibrium sorption was reached at about 25 min on (Al)Si-MCM-41 and at about 60-90 min in the case of LTA molecular sieves. The kinetic data showed that the sorption rate could be better described by pseudo-second order model.
Experimental Measurement and Thermodynamic Modeling of Hydrate Dissociation Conditions for the Argon + TBAB + Water System

Gas hydrate (clathrate hydrate) dissociation data at low to moderate pressures are necessary for practical applications, e.g., for gas separation. However, it is often known or recognized that the separation conditions encountered exist at high pressures and low temperatures. One solution to this problem is the application of the quaternary ammonium salts (QAS) semiclathrate hydrates such as tetra-n-butyl ammonium bromide (TBAB) which can moderate the hydrate dissociation conditions. The addition of TBAB to the solution enables a shift in the pressure-temperature diagrams to lower pressures and higher temperatures. In this study, the isochoric pressure search method1,4,13,28,34−37 was used to measure hydrate dissociation conditions for the argon + TBAB + water system. The measurement of hydrate dissociation conditions were performed at various TBAB concentrations, namely, (0, 0.05, 0.10, 0.20, and 0.30) mass fraction. The experiments were performed in the temperature range of (274 to 293.6) K and pressure.
Controlled Crystallization of Sodium Chloride Nanocrystals in Microdrop­lets Produced by Electrospray from an Ultra-Dilute Solution

Controlled crystallization is very important for crystal growth. The electrospray (ES) technique has attracted great interest thanks to its advantages of facile manipulation of micrometer droplets and the generation of fine and charged droplets with very narrow size dispersion. In this work, nanocrystals with cubic shapes with 32 nm edges were successfully grown from ultra-dilute NaCl solution (1 µg mL–1) on a millisecond timescale by an ES technique. Dispersed and charged microdroplets were created by ES. In the process, an increase in ion strength and built-in electric field in microdroplets promoted nucleation and growth. Moreover, solvent evaporation increased solute and charge concentration and decreased the size of the microdroplets, which had a further effect on nucleation and growth. The morphologies of crystals were the competitive results of ion strength and evaporation. Fortunately, these physicochemical properties can be controlled facilely by adjusting the ES parameters. This report suggests potential application of ES technology for controllable crystallization of pharmaceuticals, proteins, etc.
Comparative profiles of SARS-CoV-2 Spike-specific milk antibodies elicited by COVID-19 vaccines currently authorized in the USA

Three COVID-19 vaccines are licensed for emergency use in the USA: the Pfizer/BioNTech and Moderna/NIH mRNA-based vaccines, and the Johnson & Johnson (J&J)/Janssen human adenovirus (Ad26) vaccine. These vaccines have immunized ~160 million people nationwide, comprising ~53% Pfizer, 39% Moderna, and 8% J&J recipients [1]. Although at varying stages of clinical investigation for use in children, no COVID-19 vaccines are yet available for this population, posing a significant public health concern. One method to protect infants and young children may be passive immunization via antibodies (Abs) provided in the milk of a lactating vaccinated person. Our early work [2] and other recent reports [3-5] examining the milk Ab response to mRNA-based COVID-19 vaccination have demonstrated that unlike the post-SARS-CoV-2 infection milk Ab profile, which is rich in specific secretory (s)IgA, the vaccine response is highly IgG-dominant. In this report, we present a comparative assessment of the milk Ab response elicited by not only the Pfizer and Moderna vaccines, but importantly, the J&J vaccine as well. This analysis revealed that compared to mRNA vaccine recipients, 49% - 63% fewer J&J vaccine recipient milk samples were positive for Spike-specific IgG, with positive samples exhibiting significantly lower mean IgG titers. J&J recipient milk samples contained significantly less specific IgA than Moderna recipient milk samples, which exhibited significantly greater relative IgA increases compared to both Pfizer and J&J recipients. Absolute and relative vaccine-induced secretory Ab titers were similarly low for all groups, though ~25% more Moderna recipients exhibited a relative increase compared to Pfizer and J&J recipients. These data indicate that J&J vaccine poorly elicits Spike-specific Ab in milk compared to mRNA-based vaccines and that this vaccine should be considered a last choice for immunizing those intending to elicit a strong Ab response in their milk. These data also suggest that Moderna vaccine elicits a superior, albeit moderate, milk (s)IgA response, and highlight the need to design vaccines with optimal protection of the breastfeeding infant in mind.
Air travel-related outbreak of multiple SARS-CoV-2 variants

Background. A large cluster of 59 cases were linked to a single flight with 146 passengers from New Delhi to Hong Kong in April 2021. This outbreak coincided with early reports of exponential pandemic growth in New Delhi, which reached a peak of >400,000 newly confirmed cases on 7 May 2021. Methods. Epidemiological information including date of symptom onset, date of positive-sample detection, and travel and contact history for individual cases from this flight were collected. Whole genome sequencing was performed, and sequences were classified based on the dynamic Pango nomenclature system. Maximum-likelihood phylogenetic analysis compared sequences from this flight alongside other cases imported from India to Hong Kong on 26 flights between June 2020 and April 2021, as well as sequences from India or associated with India-related travel from February to April 2021, and 1,217 reference sequences. Results. Sequence analysis identified six lineages of SARS-CoV-2 belonging to two variants of concern (Alpha and Delta) and one variant of public health interest (Kappa) involved in this outbreak. Phylogenetic analysis confirmed at least three independent sub-lineages of Alpha with limited onward transmission, a superspreading event comprising 37 cases of Kappa, and transmission of Delta to only one passenger. Additional analysis of another 26 flights from India to Hong Kong confirmed widespread circulation of all three variants in India since early March 2021. Conclusions. The broad spectrum of disease severity and long incubation period of SARS-CoV-2 pose a challenge for surveillance and control. As illustrated by this particular outbreak, opportunistic infections of SARS-CoV-2 can occur irrespective of variant lineage, and requiring a nucleic acid test within 72 hours of departure may be insufficient to prevent importation or in-flight transmission.
A High-Precision Analytical Method for Vibration Calculation of Slotted Motor Based on Tooth Modeling

The analytical methods are widely used to predict the vibration and noise of the permanent magnet motors in industry applications. In the traditional analytical method, the influence of the teeth is ignored and a large error of vibration calculation will occur in the slotted motor. In this article, a high-precision analytical method is proposed for vibration calculation of motors based on tooth modeling. First, the electromagnetic forces on each tooth of stator of the motor under no load and load are calculated. Then, the transfer function from the force acting on the teeth to the vibration on the surface of the shell is derived and verified by finite-element method (FEM). Next, the superposition method is introduced and used to superimpose the vibration excited by the force of all teeth to calculate the total vibration acceleration of the motor surface. Finally, the FEM and experiments are conducted to verify the effectiveness of the proposed method.
RIS-Aided Physical Layer Security With Full-Duplex Jamming in Underlay D2D Networks

This paper investigates the physical layer security and data transmission for the underlay device-to-device (D2D) networks, and considers a combination of the reconfigurable intelligent surface (RIS) and full-duplex (FD) jamming receiver for the robustness and security enhancements of the system. In the demonstrated spectrum sharing setup, the total power of the D2D networks is conceived to the transmitter and receiver to transmit a private message and emit the artificial noise (AN) signals. To prevent information leakage, a beamforming design is presented for a multi-antenna FD D2D receiver in order to suppress and inject the AN signals in the direction of legitimate users and eavesdropper, respectively. The statistical characterization of end-to-end RIS-assisted wireless channels is presented, and the achievable ergodic secrecy rate of the system is derived in novel approximate expressions. The numerical and simulation results confirm the accuracy and effectiveness of the proposed analytical framework. The results demonstrate an optimal selection of the D2D power allocations for different number of reflecting elements in terms of achievable ergodic secrecy rates of the system.
Measuring Software Obfuscation Quality–A Systematic Literature Review

Software obfuscation techniques are increasingly being used to prevent attackers from exploiting security flaws and launching successful attacks. With research on software obfuscation techniques rapidly growing, many software obfuscation techniques with varying quality and strength have been proposed in the literature. However, the literature on obfuscation techniques has not yet been coherently collated and reviewed. This research paper aims to present an overview of state-of-the-art software obfuscation techniques, focusing on quality and strength. A systematic analysis and synthesis of literature published between 2010 and April 2021 has been performed to identify the common measures to quantify obfuscation and their measures, the publication venue, and the home country of the researchers. We have identified the obfuscation quality attributes, such as potency, resilience, cost, stealth, and similarity, that are the most widely used metrics to evaluate the quality of obfuscation techniques. In addition, different measures have been used to quantify these qualities, such as complexity (to measure potency), human effort (to measure resilience), efficiency (to estimate cost), and multiclass performance metrics, distance measures, and matching method (to quantify similarity). These measures were then categorized into sub-measures. The literature lacks research in the following two areas: empirical research using a case study strategy, i.e., real-world datasets, and measurements of obfuscation stealth. Researchers did not address stealth as clearly as they addressed potency, cost, and similarity.
Radar Odometry on $SE(3)$ With Constant Velocity Motion Prior

This letter presents an approach to estimating vehicle odometry on <inline-formula><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> using a high resolution automotive radar sensor. We combine a constant velocity <inline-formula><tex-math notation="LaTeX">$SE(3)$</tex-math></inline-formula> motion prior with a 3D radar point cloud measurement model in a sliding window optimization scheme. We leverage radar's highly precise radial velocity measurement to compensate for point cloud sparsity and improve data association. Our approach is tested using real-world measurements from a prototype high-resolution radar sensor. We demonstrate the results of this approach for 6D motion estimation. In addition, we show that our approach is comparable to state-of-the-art <inline-formula><tex-math notation="LaTeX">$SE(2)$</tex-math></inline-formula> radar odometry estimates while running a full order of magnitude faster.
Authorship Classification in a Resource Constraint Language Using Convolutional Neural Networks

Authorship classification is a method of automatically determining the appropriate author of an unknown linguistic text. Although research on authorship classification has significantly progressed in high-resource languages, it is at a primitive stage in the realm of resource-constraint languages like Bengali. This paper presents an authorship classification approach made of Convolution Neural Networks (CNN) comprising four modules: embedding model generation, feature representation, classifier training and classifier testing. For this purpose, this work develops a new embedding corpus (named WEC) and a Bengali authorship classification corpus (called BACC-18), which are more robust in terms of authors’ classes and unique words. Using three text embedding techniques (Word2Vec, GloVe and FastText) and combinations of different hyperparameters, 90 embedding models are created in this study. All the embedding models are assessed by intrinsic evaluators and those selected are the 9 best performing models out of 90 for the authorship classification. In total 36 classification models, including four classification models (CNN, LSTM, SVM, SGD) and three embedding techniques with 100, 200 and 250 embedding dimensions, are trained with optimized hyperparameters and tested on three benchmark datasets (BACC-18, BAAD16 and LD). Among the models, the optimized CNN with GloVe model achieved the highest classification accuracies of 93.45%, 95.02%, and 98.67% for the datasets BACC-18, BAAD16, and LD, respectively.
A Video Prediction Method Based on Optical Flow Estimation and Pixel Generation

Video prediction has developed rapidly after the booming of deep learning. As an important part of unsupervised representation learning, it plays an important role in anomalous behavior detection, autonomous driving, video games, and other fields. However, the prediction method based on optical flow estimation is susceptible to brightness change and camera shake, and it is difficult to predict occluded objects. While the prediction method based on pixel generation is difficult to fit ambiguous and complex scenes, which leads to a blurry prediction. In this work, we proposed an end-to-end video prediction framework that combines the optical flow estimation module with the pixel generation module by a learnable mask weight to predict high-fidelity videos. To further improve the prediction effect, we introduce adversarial training to the framework. We introduced a frame discriminator and a sequence discriminator to ensure the consistency of the spatio-temporal distribution of predicted video frames and real video frames. The results of experiments on challenging datasets demonstrate the practicability and effectiveness of our proposed video prediction framework. On the one hand, our proposed framework has achieved an equal quality compared with the current latest model, which requires fewer parameters and has a faster prediction speed. On the other hand, the results of ablation experiments demonstrate the effect of fusing different modules and the effectiveness of adversarial training.
Toward Vision-Based High Sampling Interaction Force Estimation With Master Position and Orientation for Teleoperation

In this study, a vision-based high sampling rate interaction force estimation method is proposed for teleoperation systems that uses master position and orientation information without using physical sensors such as force/torque (F/T) or tactile sensors. The proposed method uses red-green-blue (RGB) images, six-axis robot pose and motor current data, gripper position and current data, as well as master position and orientation information as inputs without requiring force sensors. To estimate the interaction forces, a deep neural network composed of densely connected convolutional network (DenseNet) and long short-term memory (LSTM) is proposed. The database was created by operators using grip and picking motions to interact with 10 objects over a teleoperation system. In addition, we compared the proposed method with different deep learning networks that used different sets of inputs. The results show that the proposed model can estimate 1 kHz interaction force based on 60 Hz images and 1 kHz master inputs. Moreover, the results indicate that the master position and orientation information are useful in estimating the interaction force at a high sampling rate through the result of the change in the network input.