Miracle-dz/UniFAR-FTUs huggingface dataset:
The Safety and Efficacy of Botanicals with Nootropic Effects

Recent estimates for the global brain health supplement category, i.e. nootropic market size, will grow to nearly $5.8 billion by 2023. Overall, nearly one-quarter (23%) of adults currently take a supplement to maintain or improve brain health or delay and reverse dementia. Not surprisingly, the use of such supplements increases with age - more than one-third of the oldest generation (ages 74 and older) takes a supplement for brain health. This widespread use is being driven by a strong desire both in the younger and older generations to enhance cognitive performance and achieve healthy aging. The most prevalent botanicals currently dominating the nootropic marketplace include Gingko biloba, American ginseng, and Bacopa monnieri. However, other botanicals that affect stress, focus, attention, and sleep have also been procured by dietary supplement companies developing products for improving both, short and long-term brain health. This review focuses on efficacy data for neuroactive botanicals targeted at improving cognitive function, stress reduction, memory, mood, attention, concentration, focus, and alertness, including Bacopa monnieri, Ginkgo biloba, Holy basil, American ginseng, Gotu kola, Lemon balm, Common and Spanish sages and spearmint. Botanicals are discussed in terms of available clinical efficacy data and current safety profiles. Data gaps are highlighted for both efficacy and safety to bring attention to unmet needs and future research.
USING OPENQUAKE TO DEFINE SEISMIC RISK AND REAL TIME DAMAGE SCENARIO IN ITALY

Web-based GIS tools have been developed in order to calculate seismic risk and real time damage scenario for Italian structures and infrastructures. The goal of seismic risk definition is to guide decision makers in identifying the most critical conditions and to manage fund flows to plan mitigation interventions. On the other hand, the target of the definition of real time damage scenario is to provide decision makers with specific tools to plan/handle emergencies in case of an earthquake. The GIS tools described in this paper manage data and results for residential units, school buildings and infrastructures such as the road system, harbors and the airports. For real time damage scenario, the tools also adopt OpenQuake as calculation engine. OpenQuake comes also handy to define seismic risk maps of residential buildings. The implementation and the use of OpenQuake were extremely user friendly for decision makers. The writing of input files, the management of the messages and the output are all automatized through dialog windows. Finally, the implementation has also been tested in terms of damage scenario by means of comparison with observation damage data.
Deep Learning based Buy Predictions with Sequence Models

Identifying whether a clicked item in an e-commerce session will end in a purchase is a current research inquiry. This task was also set at the RecSys Challenge in 2015. The competition was won with a two-stage approach by Romov and Sokolov. They first recognized the buyers with the help of session data and then determined the purchased items on the recognized purchase sessions. However, in the features of their models, there is no information about the exact click sequences of the items that were viewed in a session. Therefore, in this paper, I examine the question of whether the purchase of products depends on the click stream of the items that are viewed in a session. For this purpose, I train sequence models on the item clicks of the sessions with purchases to check whether they can help predict purchased items. Here, I use the two sequence models of sequence classification and sequence labeling. I combine my sequence models with those of Romov and Sokolov to determine if their approach improves with sequence information. This research demonstrates that buying an item depends on the click order of the items that are viewed and can improve the predictive results of a feature-based approach, such as that of Romov and Sokolov. In addition, the research illustrates that data preparation is less time-consuming for this approach than for a feature-based approach.
Synthetic Data Augmentation to Aid Small Training Datasets

This is an experimental investigation into using Generative Adversarial Networks (GANs) to enhance the effectiveness of training deep neural network classifiers, thus extending the reach of these classifiers in situations where labeled data is scarce or expensive. We are employing a GAN to create artificial images with a known label. We then combine these images with a small number of actual labeled images to satisfy a classifier network’s training needs, while using only the smaller set of the actual labeled data. Thus, the classifier, ingesting the enhanced input data (actual and generated images) will be able to classify unseen data effectively.
Label-free SEM Content Recognition with Cycle-consistent Generative Adversarial Networks (Computer Vision)

Deep learning-based approaches for image segmentation have been widely applied in a range of ﬁelds. In material science, segmenting scanning electron microscope (SEM) images is of particular interest, given its importance in feature detection and characterization. However, traditional neural networks perform on image segmentation requires laborious labeling efforts. Inspired by CycleGAN networks’ ability to perform style transfer with unpaired examples, we propose a label-free method for SEM image segmentation. The SEM images can be trained with original lithographic designs or nanostructures’ labels to achieve style transfer. The results benchmark against the supervised ResNet approach shows that this method is competitive in SEM segmentation and can be further used in synthetic SEM image generation.
Latent Space Regression in GANs for Invertible Image Generation

Generative Adversarial Networks (GANs) are one of the most popular generative architectures, yet how the generator convert unstructured latent code into high ﬁdelity output is still an open question. In this paper, we aim to better understand and manipulate the concepts GANs learns about the world by probing the latent vector space. Specifically, we aim to take a step towards achieving an invert-ible representation from the image manifold output of GANs to the latent code input. Our approach combines a regres-sor model composed of a simple encoder with a pre-trained GAN generator. The underlying assumption is that a pre-trained generator, without any additional interventions, already represents certain compositional properties in its latent code space. In order to quantify the effects of our in-vertible representation, we conduct experiments with three types of generators (DCGAN, GANsformer, and ProGAN) in combination with three regressor models. Given each ﬁxed pre-trained generator, we train the respective regres-sor network to predict the latent code from an input image, while adding various masking modiﬁcations. We investigate the compositional properties of the pre-trained generators across the CelebA-HQ, COCO2017, and FFHQ datasets, and ultimately show that the combination of the regres-sor and generator forms a strong image prior. Our results demonstrate that the regressor model is capable of projecting the given masked image into a reasonable part of the latent code space, which the generator is then able to map onto the output image manifold.
STUDY OF THE SPATIAL CORRELATION OF EARTHQUAKE GROUND MOTION BY MEANS OF PHYSICS-BASED NUMERICAL SCENARIOS

Spatial correlation of the ground motion plays a key role in all those applications requiring the definition of the joint occurrence of ground motion intensities at several locations during the same earthquakes, such as in seismic risk assessment of portfolios of buildings or spatially distributed infrastructure systems in large urban areas. In spite the increasing amount of strong-motion records in recent years, the overall number of near source records (i.e. Rrup < 5-10 km) remains extremely limited. For this reason, the strong ground motion in this region may be insufficiently constrained in spite of its importance for a proper assessment of the potential losses. Therefore, in this study we decided to take advantage of the 3D physics-based numerical simulations (3DPBNSs) demonstrating their capability to reproduce the spatial correlation of the recorded earthquake ground motion. Geostatistical tools are used to this end. Four different case studies worldwide have been considered: Po Plain (Northern Italy), Thessaloniki (Greece), Beijing (China) and Istanbul (Turkey). The locations share the following features: (i) proximity to a well-known mapped fault capable to trigger a severe earthquake, (ii) a relatively good description of the geotechnical characterization of the soil and (iii) a reliable reconstruction of the deep alluvial structure. The results obtained are compared against the real ones computed using the large set of stations which recorded the 2012 Po Plain earthquake and against some previously published studies on this topic. The results 3DPBNSsbased were found consistent with the empirical ones demonstrating that numerical simulations can realistically represent the real correlation structures at long and short periods.
What do central banks talk about? A European perspective on central bank communication

In this paper, we apply a structural topic model (STM) to analyze over 7,000 speeches delivered by European central bankers and ECB staff over the period from 1996 to 2019. Our findings indicate that neither the size of an economy nor its monetary policy regime appear to be related to how frequently a country’s central bank communicates through speeches with the public. We moreover find that the following four topics dominate in central bank speeches: (1) European integration, (2) monetary policy and price stability, (3) f inancial stability, and (4) “outside the box” content, subsuming rhetoric on issues beyond central banks’ core responsibilities. While coverage of monetary policy topics has been stable over time, European integration has been discussed less in central bank speeches since the early days of the euro and at least up until the Brexit referendum. Speeches on financial stability surged in the aftermath of the global financial crisis. When examining the regional distribution of topic prevalence, we find that speeches given by central bankers from non-euro area countries broadly follow the trends described above. Interestingly, many speeches delivered by central bank staff from Central, Eastern and Southeastern Europe (CESEE) fall under the “outside the box” category, suggesting that CESEE central banks cover a broader range of topics than the rest of their European counterparts.
Modal Analysis in a Full-wave Electromagnetic Simulator for Dual-Band Microstrip Antenna Design

The analysis of resonant modes simplifies the optimization process required in the design of microstrip antennas. This approach is bolstered by the incorporation of characteristic-mode analysis (CMA) tools in more recent versions of numerical electromagnetic solvers. However, in cases where automated CMA tools are unavailable, physical insights into the antenna behavior can still be obtained by a modal analysis of full-wave electromagnetic simulation results. This paper demonstrates an approach to optimize a simulation model of a dual-band microstrip antenna by analysing the resonant eigenmodes excited on the antenna structure. The analysis enables the selection of appropriate antenna geometries to be optimized in order to achieve the required performance. The optimized antenna structure is simulated in two different numerical electromagnetic solvers, and comparable results are obtained in each case.
Kidney Injury in Ordinary Adult Cases of Coronavirus Disease 2019

Volume 2 | Issue 1 Kidney Injury in Ordinary Adult Cases of Coronavirus Disease 2019 Yang H1, Yu H2, Li H1, Pan K3, Zhang G3, Chen G4 and Lu M*1 1Department of Nephrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, China 2Department of General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, China 3Department of Critical Care, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, China 4Department of Anesthesiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, China *Corresponding author: Lu M, Department of Nephrology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, 3 Qingchun East Road, Hangzhou, Zhejiang province, China, Tel: +86-15267035918, E-mail: lumingxi@zju.edu. cn Research Article Open Access
Training A Tractor Agent with Deep Reinforcement Learning

Board games present a unique challenge for AI agents. Tractor, a popular playing cards game, is challenging because it requires players to make decisions based uncertainty due to not knowing other players’ cards at hand. We model the problem as a Markov Decision Process with unknown transition functions and trained one AI agent playing this game reasonably well using deep reinforcement learning. Our results show that the trained agent performs very well against both random agents and agents using our designed strategy.
Quality of Life of the Post-Menopausal Women: A Study among the Bengalee Middle Class Women in Kolkata, West Bengal, India

Introduction: Menopause brings about the transition in a woman’s life leading to changes in both the physical and psychological aspects affecting the quality of life (QOL). The average age of attainment of menopause is around 48 years. In India, women attain menopause at an average age of 45 to 50 years. Hence menopausal health demands attention in India. Aim: This study aims to study the QOL of menopausal women in Kolkata. Materials: The present study was conducted among 285 women residing in Middle Income Group of Kalindi Housing Estate, Kolkata. A quantitative approach was adapted with cross-sectional survey design. Data was collected by interview using demographic questionnaire specific on QOL during menopause.QOL was assessed in four domains of Vasomotor Psychosocial Physical and Sexual. Cervantes Scale (BEN/CS) was assessed adapting the Bengali version. Pearson’s correlation was used for evaluation of BEN/CS with WHQ and WHOQoL-bref. Results: The mean age of women in this study was 46.7 + 0.46 years. CS was observed to be >0.7. Presence of vasomotor symptoms and lack of physical activity were associated with lower QOL. Sexuality and couple relationship however did not show any association to QOL. Discussion: There is paucity of works on QOL in menopausal women from India. Addressing the issues of menopausal women in India is the need of time. There is ample scope to improve the QOL in Indian menopausal women through health education on the symptoms of menopause, its treatment by modifications in their present lifestyle, asking them to avoid sedentary lifestyle and encouraging them for mild exercises and the like. Conclusion: Health care professionals have a great role in addressing these issues to prepare women to face the challenges of reproductive health in India.
Evaluating Fall Prevention for Patient at Nam Dinh Hospital in Vietnam

Purpose: Assessing the reality of nursing knowledge and practice about falls prevention for patients at Nam Dinh General Hospital. Method: Descriptive study cross-section with the participation of 170 nurses directly taking care of people in clinical departments of Nam Dinh General Hospital from December 2019 to June 2020. Results: The percentage of nurses with general knowledge is 56.5%. Of these, 51.8% of nurses said that there was no risk of falling for the patient in the hospital, 91.2% of the nurses said that the hospital should not develop a fall risk assessment form and only 3, 5% of nurses said that the patient's risk of falling due to physiological problems could be prevented by providing a safe environment; for example: clear road to the bathroom, no clutter room, good shoes. The proportion of nurses who self-assessed practice for general fall prevention for patients was 48.8%. Conclusion: The level of nursing knowledge and practice of falls prevention is not high in the nursing home. There is a relationship between knowledge and practice of falling prevention in the patient. The research results show the need to strengthen nursing knowledge about fall prevention in patients.
Generating Artistic Portraits with Different GANs

Recent years, many powerful photo/video apps obtain various ﬁlters for people to generate different styles of portraits. Yet many of them are too expensive or only provided temporarily. The goal of this project is to learn how the portraits can be translated into cartoons with pix2pix and CycleGAN methods. Modiﬁcations are made to the model structures and the generators to see the behavior of each part of the models. We use FID to evaluate the generated results
Inexpensive Fluid Flow Visualization in a Microfluidic Channel Experiment Overview

Flow visualization is vital to describe the motion of a fluid in microfluidic channels. This paper summarizes and lists systems based on two common visualization technique categories: particle-based techniques and scalar-based techniques. For this experiment, microfluidic channels were created using PDMS (polydimethylsiloxane). Based on our results and many fabrication trials, it was difficult to say whether this experiment was easy or inexpensive. If the first channel made by the researchers had successfully worked, it would have been clear that utilizing PDMS to fabricate a microfluidic channel for flow visualization was cheap and easy. However, no fully successful microfluidic chips were made after twenty attempted trials. Although, it should be stated that some of the microfluidic chip were semi-successful, meaning that the microfluidic chips worked to an extent or they worked initially before failing. Since so much PDMS was used to make the microfluidic chips, it is not necessarily inexpensive to achieve flow visualization in comparison with other experimental methods. After fabricating the channels, different mixtures were pushed through the channels to analyze the flow and visualize the velocity profile. This experiment is potentially suitable for undergraduate researchers to complete because it is safe in a laboratory. This paper also demonstrates the use of two tools, PhysMo, which is used for video analysis, and ANSYS, which is used for ideal condition analysis and complex calculations. Both scalar-based techniques and particle-based techniques were used to characterize the velocity of the laminar flow within the microchannels, the speed of which will be greatest in the center of the channel and close to zero near the walls.
Effect of heating on structure and leaching characteristics of a zinc carbonate ore

In this work, the structural changes occurred in a zinc carbonate (smithsonite) ore sample following heating at temperatures between 523 K and 1173 K were investigated in detail using X-ray diffraction (XRD), thermal (TG/DTA) and Fourier-transform infrared (FT-IR) spectroscopy analyses. Afterwards, the leaching characteristics of zinc from the ore sample and the heated ore samples in sodium hydroxide solutions were determined. While heating at 523 K did not cause any structural change in the ore sample, heating at 723 K completely converted smithsonite (ZnCO3) in the ore sample to zinc oxide (ZnO), which resulted lower zinc leaching efficiencies of 40.6% and 62.0% for 3 and 4 mol/dm3 NaOH concentrations, respectively, in comparison to zinc leaching efficiencies (67.2% and 70.7%) obtained for the unheated ore sample. On the other hand, due to neoformation of dissolution resistant ZnFe2O4, Ca2ZnSi2O7 and Zn2SiO4 phases during heating and formation of CaZn2(OH)6∙2H2O phase during leaching, the leaching efficiency of zinc further decreased to 22.2% and 31.3%, respectively, in 3 and 4 mol/dm3 NaOH solutions for the ore sample heated at 1173 K. The formation of zinc-containing dissolution resistant phases by high-temperature heating was observed to be the only reason for the reduction in the zinc leaching efficiency (49.4% at 1173 K) at the highest NaOH concentration (8 mol/dm3) studied. In this work, the comparative precipitation studies were also conducted and crystalline zinc oxides with different morphologies could be precipitated at considerably high efficiencies from the selected pregnant solutions obtained following leaching.
Google-Landmark Recognition with Deep Learning

Our problem is a 6,151 class landmark classiﬁcation problem on a very large-scale dataset, Google-Landmarks. This dataset has been recently released to public by Google featuring millions of images on thousands of distinct landmarks captured at various locations and uploaded on-line by users over the world. This dataset, in spite of its extensive number of landmarks, is extremely unbalanced, posing a key problem in developing powerful models. In this paper we show how to use transfer learning along with data augmentation to obtain a model giving Top-5 accuracy of 82.03% on a derivative version of the original Google-Landmarks dataset, which contains images from 6,151 unique landmarks. Additionally, we explore how a Generative Adversarial Network can be used to alleviate the problems posed by an extremely unbalanced dataset.
Drivers of Restoration Trajectory of a Community of Regenerant Plants: Natural Regeneration or Tree Seedling?

Tree plantation is the most used technique for ecological restoration, although passive restoration requires a lower investment of resources. This research aimed to compare the composition and structure of the community of regenerant plants in areas under restoration through Assisted Natural Regeneration and mixed native Tree Planting, in the same area. We randomly placed 12 plots of 100 m2 each and sampled all the regenerating individuals found within them. We compared both restoration methods regarding growth pattern, dispersal syndromes, and successional groups. In both methodologies, trees and shrubs were the most abundant growth forms. The pioneer successional class was the most abundant in number of individuals for both treatments. We concluded that the two restoration methods allowed the regeneration of native species. We propose that Assisted Natural Regeneration is a viable restoration strategy, capable of boosting the initial processes of the community, especially in a matrix surrounded by remaining forests.