Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Epstein-Barr virus Cause Multiple Sclerosis?

MS is a chronic autoimmune disease of the CNS. Until now there is no specific cause for MS, but EBV is the top candidate and this paper tested the theory that MS is caused by EBV. A 20 year old study of over 10 million soldiers shows the risk of MS increased 32 fold after infection with EBV but wasn’t increased with other infections. These findings can’t be explained by any known risk factor for MS and suggest EBV as the leading cause of MS.
Intentions towards Individual vs. Corporate Entrepreneurial Intentions towards Individual vs. Corporate Entrepreneurship

An individual’s intention to behave entrepreneurially will have attitudinal and self-efficacy antecedents. Nascent entrepreneurs also have the choice to behave entrepreneurially in their own new business or to behave entrepreneurially in an existing business (as an intrapreneur). When faced with choice, intentions are driven by attitudes towards elements of the outcomes associated with the choice items. Since the outcomes of self-employment and employment as an intrapreneur typically differ in terms of level of decision-making autonomy, degree of ownership and degree of risk exposure, we might expect the choice between these alternative career paths to depend on the individual’s attitudes to autonomy, ownership and risk. We also expect self-efficacy to moderate the effect of attitudes on intentions, since self-employment involves considerable risk and greater personal decision responsibility. Our empirical tests confirm these expectations, with the intention to engage in individual entrepreneurship or corporate entrepreneurship having different attitudinal antecedents, with one of the attitude variables interacting with entrepreneurial self-efficacy.
A comparative study on supervised and unsupervised techniques of land use and land cover classification

This study reviewed a number of supervised and unsupervised techniques of Land Use and Land Cover classification. Land use and land cover is a basic concept of the Earth's system that is associated with the relationship between humans’ activities and the environment. Frequently updated land use/land cover information is necessitated for many socioeconomic and environmental activities, such as urban and regional planning, natural resource conservation and management, etc (Homer et al ., 2007; Lu and Weng, 2007; Jensen, 2009). Earth observation technology such as remote sensing offers a useful source of data that covers the earth surface at a spatial and spectral resolution that allows easier land use and land cover classification than traditional approaches. LULC map is the most significant remote sensing products, which is achieved by a technique called image classification. Classification is a very important statistical method for obtaining information from satellite images. LULC maps are essential for monitoring of numerous earth observation activities such as land use planning, natural resources inventories, agricultural forecasting, deforestation, environmental disasters monitoring and estimation of soil erosion. As enough platforms are deployed in orbit and new applications arise in various fields, so many researchers in LULC have examined in various sections of subjects during the last two decades. The purpose of the study is to provide consistent information on all-encompassing algorithms of supervised and unsupervised technique for LULC mapping.
ASIC Design for Bitcoin Mining

This paper first gives a brief introduction to the bitcoin and how the SHA-256 hash function is related with the bitcoin mining. We examine how the hash function is implemented on the CPU and GPU. Three ASIC designs (Naïve, Novel Counter-Based, Pipeline) are then given, beating the CPU and GPU in terms of power efficiency and latency. Finally, the costs of all hardware designs are compared. The code can be found at the github repository 1
Design and Evaluation of FFT Based FMCW Radar For Range Detection Under Noisy Environment

: There are many short-range monitoring applications, which require detecting the objects, and radars serve this purpose. There are various design parameters responsible for the efficient working of radars for range measurements. This paper proposed to design the 77 GHz FMCW radar and evaluate the performance to identify the impact of varying the design parameters like range, velocity of object, and FFT size and false alarm rate (CFAR). The velocity of object varied from 100 to 200 m/s for evaluation. This paper also presets extended survey of FMCW radar design for range measurement. The FFT based Doppler shift estimation opted for evaluating the performance of radar. This paper evaluated the radar performance under noisy environment. Paper finally concluded various parametric impacts on range detection.
Perceptual warping exposes categorical representations for speech in human brainstem responses

The brain transforms continuous acoustic events into discrete category representations to downsample the speech signal for our perceptual-cognitive systems. Such phonetic categories are highly malleable and heir percepts can change depending on surrounding stimulus context. Previous work suggests these acoustic-phonetic mapping and perceptual warping of speech emerge in the brain no earlier than auditory cortex. Here, we examined whether these auditory-category phenomena inherent to speech perception occur even earlier in the human brain, at the level of auditory brainstem. We recorded speech-evoked frequency following responses (FFRs) during a task designed to induce more/less warping of listeners’ perceptual categories depending on stimulus presentation order of a speech continuum (random, forward, backward directions). We used a novel clustered stimulus paradigm to rapidly record the high trial counts needed for FFRs concurrent with active behavioral tasks. We found serial stimulus order caused perceptual shifts (hysteresis) near listeners’ category boundary confirming identical speech tokens are perceived differentially depending on stimulus context. Critically, we further show neural FFRs during active (but not passive) listening are enhanced for prototypical vs. category-ambiguous tokens and are biased in the direction of listeners’ phonetic label even for acoustically-identical speech stimuli. Our data expose FFRs carry category-level information and suggest top-down processing actively shapes the neural encoding and categorization of speech at subcortical levels. These findings suggest the acoustic-phonetic mapping and perceptual warping in speech perception occur surprisingly early along the auditory neuroaxis, which might aid understanding by reducing ambiguity inherent to the speech signal.
SiO2 and ZnO Hybrid Nano llers Modi ed Natural Rubber Latex: Excellent Tensile Properties and Antibacterial Properties

The modification of polymer with nanoparticles has been proved to have good results, but the key of this method is that nanoparticles can maintain uniform dispersion in the polymer. Herein, silica (SiO2) and zinc oxide (ZnO) hybrid nanofillers were prepared by self-made dispersion equipment to modify natural rubber latex (NRL). The results show that the uniformly dispersed nano-SiO2 fillers increase the number of entanglements in the molecular chain of NRL, thereby improving the tensile properties. The tensile strength and elongation at break of LZ1S4.2 were increased to 32.61 MPa and 957%, respectively. The increase of the number of entanglements also makes the structure of NRL more compact, and improves the barrier performance and aging resistance. The uniformly dispersed nano-ZnO fillers endow the NRL with excellent antibacterial properties, and the antibacterial rate (For Escherichia coli and Staphylococcus aureus) is greater than 99.9%. In addition, NRL condoms and gloves incorporating hybrid nanofillers have been proved to have good biocompatibility, and this NRL nanocomposite is expected to be applied to other NRL products.
MOVIE RECOMMENDATION SYSTEM APPROACH USING CLASSIFICATION TECHNIQUES

Movie recommendation is one of the hottest topics in the domain of Recommender Systems. In order to improve user experience and reduce movie searching / exploring time, a better movie recommendation is preferred. Websites like Netflix, Amazon Prime, etc. use movie recommendation to improve their revenue or profits. Some recommender systems use collaborative filtering technique whereas some recommender systems use content filtering technique. Major factor for decision making is the ratings given by the users to the movies which they have watched. The project proposes to get the movie recommendations based on binary classification techniques. Users can get user personalized movie recommendations by using this technique. Multiple Machine Learning and/or Deep Learning algorithms have been used in order to get recommendations. A lot of analysis is done in order to see if the recommendations are appropriate or not
Measurement of the energy flow in a large eta range and forward jets at LHC at sqrt{s} = 0.9 TeV, 2.36 TeV and 7 TeV

A measurement is presented for the energy flow of minimum bias events in the forward region (3.15 <|\eta |< 4.9, where \eta is the pseudorapidity) of the CMS detector at the LHC for center-of-mass energies sqrt(s) = 0.9 TeV, 2.36 TeV and 7 TeV. The measurement is compared to Monte Carlo simulations, which use a model of multiparton interactions for the underlying event. In addition, production of forward jets was studied for the very first pp collisions at sqrt(s) = 0.9 TeV at LHC.
Factors Influencing Green Brand Loyalty and Green Purchase Behaviour

This study discusses the influences of green perceived value (GPV), green trust (GT), green brand loyalty (GBL) on green purchase behavior (GPB) of Indian consumers. Structural equation modelling (SEM) is used to obtain the empirical results of this study. The results are as follows: First, this study finds that GPV has a positive effect on GBL and GPB. Second, this study points out that GT has positive effects on GBL and GPB. Third, this study observes that GBL has a positive effect on GPB. Fourth, this study verified that GBL mediates the positive relationship between GPV and GPB as well as GT and GPB.
Memristor Compact Model

This document serves as a user guide for the Memristor Compact model initially proposed in [1]. The proposed model is validated against experimental data of HfOx devices fabricated in-house at SUNY Polytechnic Institute [2]. While the model can be generally applied to any memristive device with sufficient accuracy, its structure and parameters are ideally suited for Transition Metal Oxide (TMO) (i.e. RRAM) devices. Several models have been developed since 2008 that vary from physics-based compact models that are specific to some particular switching mechanism to behavioral models that hinge on the fundamental memristor equations proposed by Chua. The difficulty in modeling and understanding memristive dynamics rests in their dynamic behavior which circuit designers are not accustomed to. Most memristor models rely on defining a state variable controlled by either voltage or current. The state variable can be a physical quantity such as the gap distance in physical models or abstract quantity in behavioral models. The state variable is then translated into resistance via an equation which similar to the state variable might describe a physical phenomenon such as tunneling in physical models or a behavioral equation in behavioral models. The challenge, however, is that both approaches do not necessarily reflect measurable parameters which experimentalists report. Experimental data is usually presented in the form of V-I relationship from which the effective device resistance can be extracted. However, making the connection between the extracted resistance and the state variable, be it physical or abstract, is left to the modeler to interpret which is usually challenging! Copyrights Copyright of the model is maintained by the developers. This model is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International Public License
What AI Practitioners Say about Human-AI Trust: Its Role, Importance, and Factors That Affect It

Trust has become a priority when designing and deploying AI-embedded systems alongside with other Human-Centered AI values, i.e. explainability, transparency, and fairness. However, due to their multifaceted and multidisciplinary nature, these terms can have various context-dependent meanings. Thus, translating these values into design can be a challenge [6]. Trust is not an exception. in real-world products and services. AI practitioners , people involved in different aspects of system design and deployment in the field, with roles ranging from AI developers to project managers and policy makers, can shed light on the role of Human-AI trust and what Human-AI trust factors are considered in real organiza-tional settings. Their insights can better detail the needs, challenges, and experiences of different stakeholders when it comes to Human-AI trust. trust risk-sensitive Human-AI
Acoustic Source Localization Based on Time Difference of Arrivals using Generalize Cross-Correlation Method

This paper describes how to detect the location of a sound source using a microphone array by determining the Time Delay of Arrivals (TDOA), which is a powerful technique to obtain the position of the sound source depending on the position of the microphone array. The mathematical model represents the sound propagation, which affects the sensors. To estimate the location of the sound, a digital signal processor-educational kit DSK6713-was used to collect the sound signals from the microphones through four channels audio Daughter Card, the Code composer studio software is used for the configuration of the DSK6713 and Daughter Card, and MATLAB is used for analyzing the previously collected sound signals. The estimated position was coming properly and the error minimized to less than two centimetres.
ROS interrupts blood-brain barrier integrity by inhibiting TET2-dependent ZO-1 expression in vascular endothelial cells

Lan Wang Fudan University School of Basic Medical Sciences Bei Mao Fudan University School of Basic Medical Sciences Keyang Fan Fudan University School of Basic Medical Sciences Renqiang Sun Fudan University School of Basic Medical Sciences Jialong Zhang Fudan University School of Basic Medical Sciences Huazheng Liang (  andyliang2018@126.com ) Tongji University https://orcid.org/0000-0001-9097-7357 Ying Liu Fudan University School of Basic Medical Sciences
Anatomical adaptations of four Astragalus L . species under salt stress

: Four Astragalus species have been evaluated for their tolerance and adaptability potential to salt stress. The adaptive components of salt tolerance were assessed on the basis of biomass production and anatomical changes. A. tenuifolius was the most tolerant among the species under study, with increased leaf water content, stable shoot dry weight, stable root thickness, thick epidermis and increased cortical cell area, increased root cortex, thick stele, stable xylem tissue. Also, observed intensive density of thick-walled vessel elements. Moreover, thicker leaves and mesophyll tissue, thick upper epidermis, increased vascular bundle area, intensive density of thick-walled vessel elements and larger stomata with unchanged density were recorded in the leaves of this species with an increase in salinity levels. A. armatus the second tolerant species relied on the maintenance of epidermis and stele thickness and xylem vessel diameter under salinity. Also, root and cortex thickness decreased only at 300 mM NaCl, and increased density of xylem vessels, thicker leaves and mesophyll tissue, thick upper epidermis, increased mesophyll cells size, intensive density of thick-walled vessel elements was observed. In addition, the maintenance of water content and the lower (14.2 %) reduction in biomass production. A. mareoticus appeared to be the most salt sensitive when the decrease in biomass production reached 56.8% with anatomical parameters severely affected. For A. caprinus (salt sensitive species) has shown better adaptation than A. mareoticus relied on increased cortical cell area as well as stable root xylem vessel diameter, also, thick upper epidermis and increased leaf vascular bundle area. evaluate the difference in the responses to different salinity levels to the degree of tolerance they to salt the that,
Finite strain analysis of nonuniform deformation inside shear bands in sands

A methodology has been developed to extend the incremental (Eulerian) Digital Image Correlation (DIC) technique to enable a Lagrangian‐based large‐strain analysis framework to examine the nature of strain and kinematic nonuniformity within shear bands in sands. Plane strain compression tests are performed on dense sands in an apparatus that promotes unconstrained persistent shear band formation. DIC is used to capture incremental, grain‐scale displacements in and around shear bands. The performance of the developed accumulation algorithm is validated by comparing accumulated displacements with two sources of reference measurements. A comparison between large and infinitesimal rotation is performed, demonstrating the nature of straining within shear bands in sands and the necessity of using a finite strain formulation to characterize ensuing behavior. Volumetric strain variation along the shear band is analyzed throughout macroscopic postpeak deformation. During softening, volumetric activity within the shear band is purely dilative. During the global critical state, the shear band material is seen on the average to deform at zero volumetric strain; however, locally, the sand is seen to exhibit significant nonzero volumetric strain, putting into question the current definition of critical state. At the softening‐critical state transition, a spatially periodic pattern of alternating contraction and dilation along the shear band is evidenced, and a preliminary evaluation indicates that the periodicity appears to be a physical phenomenon dictated only in part by median grain size. Copyright © 2011 John Wiley & Sons, Ltd.
DETECTION OF DIABETIC RETINOPATHY USING DEEP LEARNING

Diabetic retinopathy is a disease caused due to untreated diabetes for a long period of time. This disease is increasing world-wide and is a major reason for blindness. The effective way of cure for this disease is to detect it at an early state. Even though there are different types of screening techniques but there is still scope for improvements. One of the way to improve the accuracy is by concentrating more on the factors present in the eye which helps in determining the stages of diabetic retinopathy, and secondly by using pre-trained deep convolution deep neural network which can reduce the cost of training. In this paper we have experimented with different types of pre-processing techniques and their effects on enhancing the model performance. Finally the enhanced images were classified using pre-trained models. Total six pre-trained models were used out of which three were fine-tuned namely ResNet-50, Inception-v3 and InceptionResNet-v2 and the rest were used as conventional transfer learning models namely Inception-v4, DenseNet-169 and DenseNet-201. A brief comparison was drawn between the fine-tuned models and the conventional models, using the evaluation metrics like accuracy, validation accuracy and model loss. From the above mentioned experimentation we can conclude that pre-processed images along with fine-tuned pre-trained models are the best combination for diabetic retinopathy classification.
Diagnostic Instrument for Mild Aphasia: adaptation and standardization in French A preliminary study

The Diagnostic Instrument for Mild Aphasia (DIMA) (Satoer et al., 2019) is a valid tool developed in Dutch to evaluate the language of patients with suspected mild aphasia at production and perception levels in the most important linguistic domains: phonology, semantics and (morpho-) syntax. It was developed for pre- and postoperative use on patients with low-grade gliomas. Due to the brain tumor’s slow growth, neural networks undergo reorganization and mild gradual degradation can be observed in higher functions (Duffau, 2014). However, there is a lack of short and sensitive diagnostic instruments (Satoer et al., 2013). No such battery exists in French either (Le Rhun et al., 2009). Therefore, the purpose of this research was to adapt the Dutch battery in terms of linguistic and psycholinguistic variables into French. Two healthy French-speaking populations were compared (Belgians and French participants, total n=67) and other potential effects of demographic variables such as age, gender, level of education (and early and late bilingualism) were explored. Ceiling results in accuracy rates were registered for all tasks. Although no effect of nationality was found, there was an effect of age for most of the tasks, and education and gender also influenced performance in some tasks. Bilingualism did not affect performance. We can thus consider that the final version of the French DIMA as presented here is a valid instrument and that the standardization and clinical validation can be pursued. The application can also be extended to patients with other neurological diseases (such as strokes or traumatic brain injury) with suspected aphasia. In addition to enhancing the reliability of the protocol, creating an internationally valid battery enables more patients to be included in anatomo-clinical studies. inhibition. Participants are presented with ten combinations of three pictures belonging to closely related semantic categories, two of which belong to the same category (e.g. snake, dog, cat; all three of them are animals but the snake is not a pet). The semantic categories were made closer than in the DuLIP. The frequency of the items and the random order of the target item are checked. All images are black and white pictures (Snodgrass & Vanderwart, 1980). Reaction time is limited to 4 seconds (automatically timed slides on PowerPoint) and accuracy is registered.