Miracle-dz/UniFAR-FTUs huggingface dataset:
Red Rose Pigments Utilities as Dyes at Different pH but Poor Anti-Bacterial

Introduction Ten thousand years old, Atharva Veda illustrates that plants are God carrying many medicines to cure diseases that are due to imbalance of different forces of our body [1]. But there was no concept of microorganisms those we could not see by naked eye [2]. Hippocrates’ ethical code, known as the Hippocratic Oath, was guided the practice of western medicine to unfold. By 100 BC, China had become a major centre of medical research and its best doctors wrote the Neijing, a book about Chinese medicine [3]. Major breakthrough came when microscope was developed to see bacteria and fungi by Anton Van Leeuwenhoek Abstract Active plant extracts for drug development was studied as described in Charaka Samhita and Atharva Veda, and further being illustrated in 11 volumes of Bengali book, Chiranchib Bonoushadhi. Previously, we detected 40% ampicillin and amoxicillin resistant bacteria species but most of them were imipenem sensitive. Further, we described that Suregada multiflora, Cassia fistula as well as extracts of Labanga and Derchini spices are better than Azidirachita indica and Chenopodium album. Because rose and marigold cultivation are abundant in West Bengal, we have checked the possible anti-bacterial activities of flower petals extract. Petals ethanol extracts of rose (Rosa indica) and marigold (Tagetes patula) flowers have some anti-bacterial activities. Marigold ethanol extract seems more active than rose while chloroform extract is less effective. Both water soluble red and yellow pigments are inactive in KirbyBauer assay. Thus, antibacterial potential was not associated with color coded phyto-chemicals. However, we have found that water soluble red rose pigments are non-toxic and such abundant chemicals have utility as food and medicine color additives and in paint industry. We made organic abir and painted cotton fabrics at different pH giving beautiful different colors like red, blue and green. Unique acid-base property of water-soluble red rose dyes was presented which were likely terpenoids or flavo-alkaloids.
IMPLEMENTATION OF SENSORLESS CONTROL OF AN INDUCTION MOTOR ON FPGA USING XILINX SYSTEM GENERATOR

In this paper, we will presented a deterministic observation approach , nonlinear applied to the Induction Motor: this is a sliding mode observer. Indeed, this paper will serve to emphasize the importance of the order without sensor to increase the profitability of our machine. Our sliding mode observer will be applied to the field oriented vector control then to the sliding mode control. The contribution of this paper is the design of sensorless control using XSG order to be implanted on the FPGA.
imulation of biological ion channels with technology omputer-aided design

Computer simulations of realistic ion channel structures have always been challenging and a subject of rigorous study. Simulations based on continuum electrostatics have proven to be computationally cheap and reasonably accurate in predicting a channel’s behavior. In this paper we discuss the use of a device simulator, SILVACO, to build a solid-state model for KcsA channel and study its steady-state response. SILVACO is a well-established program, typically used by electrical engineers to simulate the process flow and electrical characteristics of solid-state devices. By employing this simulation program, we have presented an alternative computing platform for performing ion channel simulations, besides the known
An experimental study on FRP – concrete delamination

The first results of an experimental campaign on FRP – concrete delamination are presented. Four specimens with different bond lengths have been tested. Both strain gages along the FRP plate and LVDT transducers have been used. Starting from experimental data, shear stress – slips data have been computed. A non linear interface law has been calibrated. A numerical bond – slip model has been used, adopting the above mentioned law for the FRP – concrete interface. Numerical results are found to be in good agreement with experimental results.
A Panel Study of the Environmental Kuznets Curve for Carbon Emissions in ASEAN-5 Countries

Environmental degradation has now been one of top concerns for scientists, environmentalists, policymakers and people all around the world alike. Carbon dioxide (CO2) is believed to be one of the major components of greenhouse gases that contribute to air pollution and thus global warming and climate change. This research is conducted to investigate the relationship between economic growth and CO2 emissions in ASEAN-5 countries in the presence of foreign direct investment inflow (FDI) and trade openness for the period between 1981 and 2010. In order to carry out the study, various tests are applied such as panel unit root tests, Pedroni panel cointegration test, the Dynamic Ordinary Least Squares (DOLS) and Fully Modified Ordinary Least Squares (FMOLS) regressions, as well as panel vector error correction model (PVECM). The results of the study indicate that the relationship between economic growth and CO2 emissions exists in inverted-S shape in both shortand long-run for ASEAN-5 countries after controlling two additional explanatory variables, i.e. FDI inflow and trade openness. In addition, the outcome of this study also offers some fundamental policy recommendations to policymakers. Key-Words: Carbon dioxide emissions, economic growth, Environmental Kuznets curve, ASEAN-5 countries
An Improved Multi-Context Trajectory Embedding Model using Parameter Tuning Optimization for Human Trajectory Data Analysis

As the explosion of location-based social networks such as Facebook, etc., a number of ways have been provided for tracing human mobility, including user-generated Geo-tagged contents, check-in-services and mobile applications. For mining human trajectory data, different techniques were proposed over the past decades. However, the issue in many applications was analyzing and mining trajectory data due to the complex characteristics reflected in human mobility which is affected by multiple contextual information. As a result, Multi-Context Trajectory Embedding Model using Convolutional Neural Network (MC-TEM-CNN) was proposed that reduces the computation time during the learning process of contextual features. However, it requires an optimization algorithm to enhance the tuning of parameters which are needed to model the contextual information. Hence in this article, an Improved Multi-Context Trajectory Embedding Model (IMC-TEM) is proposed based on the frogleaping optimization algorithm. In this algorithm, the parameters are tuned according to the frog characteristics. In each iteration, the global best fitness is chosen to adjust the position of worst fitness frogs. Thus, the proposed IMC-TEM tunes parameters in a better manner. Finally, the experimental results are conducted based on three real-world datasets to observe the performance efficiency of the IMC-TEM than MC-TEM-CNN.
14 A . 3 Exploring Impacts of Rapid-scan Radar Data on NWS Warning Decision Making

The development of rapid-scan capabilities with S-band phased-array radar (PAR) at the National Weather Radar Testbed in Norman, Oklahoma (Zrnić et al. 2007; Heinselman and Torres 2011) presents new opportunities for advancement of weather sensing. An important component of the technology development process is assessment of operational benefit(s) of new radar capabilities. This has been done for previous radar upgrades, only during the technology transfer process (e.g., JDOP (Burgess 1979) and JPOLE (Scharfenberg et al. 2005)).
An approach to visualization of knowledge portal content

The process of development of an ontology-based knowledge portal and creation of its content is time-consuming and labor-intensive. It is very desirable to have special analysis facilities for maintenance and development of such a portal. The subject of our paper is a tool for visual analysis of content and ontology of a knowledge portal. The basis of our approach is application of graph generation and graph visualization methods. Browsing of the content of a knowledge portal is organized as a multilevel stepwise process. Appropriate placement algorithms are used at each step of this process. They take into account certain types of ontological relations and their combinations. Radial, circular and layered tree placement algorithms are used for visualization of the inheritance relation. Several force-directed algorithms are used for visualization of role relations between different classes as well as between class instances. Finally, we have two special placement algorithms for visualization of the superposition of partonomy and role relations. A number of useful graph transformations are implemented. They essentially improve understandability of a portal under investigation. This approach provides users with general understanding of ontology and a possibility of visual estimation of the sizes of classes and relations. It allows the user to select and analyze specific relations between classes and between objects as well as to identify errors. The program was tested on real knowledge portals and appeared to be rather helpful.
Tiny Circularly Polarized Printed Slot Antenna for UWB Usage

This letter offers a wideband circularly polarized square slot antenna (CPSSA) fed by coplanar waveguide (CPW), with a compact size of only 25 mm (length) ×25mm (width) × 0.8 mm (height). The proposed antenna provides impedance bandwidth of 2.86–10.95 GHz (117%) with VSWR ≤ 2, and its CP bandwidth is larger than 47.5% that covers 5.05-8.20 GHz which is obtained by embedding two inverted-L grounded strips around two opposite corners whereas the impedance bandwidth can be greatly improved through the tuning stubs. The simulated and measured results of the proposed antenna have been discussed to verify its characteristics. [Majid Shokri, Ch. Ghobadi and J.Nourinia, Hamed Shirzad, S.Asiaban and Zh. Amiri. Tiny Circularly Polarized Printed Slot Antenna for UWB Usage. Life Sci J 2012;9(3):2288-2291] (ISSN:1097-8135). http://www.lifesciencesite. com. 400
Effects of fishmeal quality on growth performance , protein digestibility and trypsin gene expression in pacific white shrimp ( Litopenaeus vannamei )

Fishmeal quality is of great importance for production of aquatic animals particularly shrimp; however it varies widely depending on raw materials and processing methods. A seven week feeding trial was conducted using five diets to examine effects of fishmeal quality on growth performance, protein digestibility and expression of gene regulating trypsin in Litopenaeus vannamei. Each test diet was fed four times daily to four groups of shrimp with an average initial weight of 2.2 g shrimp. The shrimp fed S1-premium grade fish meal (S1-PGFM) diet attained the highest weight gain, specific growth rate with the best feed conversion ratio (FCR) and protein utilization efficiency. Growth of shrimp fed S2-PGFM, grade 1 FM and grade 2 FM containing diets was inferior to those of S1-PGFM fed group. The imported FM diet fed group showed the slowest growth and feed utilization efficiency. FCR of shrimp fed grade 2 FM and imported FM was significantly the poorest (p<0.05). In-vitro protein digestibility using crude enzyme extract from shrimp fed reciprocal diet was not significantly different among treatments but in-vivo protein digestibility of the S1-PGFM fed group was the highest (92.06+0.42%) in concordance with the highest trypsin gene expression.
Study of brain imaging correlates of Mild Cognitive Impairment and Alzheimer ' s Disease with machine learning

Accurate diagnosis in the early stages is an important challenge for the prevention and effective treatment of Alzheimer’s Disease (AD). This work proposes a method of analysis of the correlation of Mild Cognitive Impairment (MCI) subtypes and its progression to AD using neuroimages such as structural magnetic resonance imaging (MRI) scans. Basic data pre-processing such as the extraction of brain-tissue related parts of the image, image registration and standardization to the mean and deviation is applied. A convolutional autoencoder (CAE) is used to reduce data dimensionality and learn generic features capturing AD biomarkers, followed by various clustering techniques in order to detect different patterns on MCI data. In addition, six MCI patient clusters are generated based on AD progression information provided by ADNI. The method is evaluated on a total of 1069 structural MRI scans (522 MCI scans, 243 AD scans and, 304 CN scans) on the baseline from ADNI database. No clearly separable clusters are found after using CAE model trained on MCI data. Therefore, it is difficult to confirm a strong correlation between different subtypes of MCI patients and its progression to AD. Nevertheless, a significant correlation within the baseline images of the respective six groups identified based on AD progression is reported. It is hypothesized that lack of domain-specific MRI processing, planned in this work, could be a deciding factor about the findings in this research.
Functional Role of WT 1 in Prostate Cancer

Although initial discoveries of Wilms tumor 1 (WT1) expression in extrarenal disease generated controversy, we and others have examined WT1 expression in non-Wilms cancers and have demonstrated that the WT1(A) isoform, lacking the lysine-threonine-serine tripeptide (KTS) insertion, transcriptionally regulates the expression of growth control genes in other cancer types. Here, we review our evidence that WT1 is expressed in prostate cancer (PC) epithelial cells and regulates PC critical genes. That WT1 may promote metastatic disease is consistent with previous findings that WT1 suppressed E-cadherin and enhanced motility of PC cells with low migratory and metastatic potential. Recent findings led us to ask
Design oriented simulation of the behavior of PAC consumables and experimental validation of results

Hafnium cathode erosion phenomena are among the most critical issues in the development of plasma arc cutting technology, especially for high current level. In this work, the correlation between the distributions of different quantities inside the chamber of a plasma arc cutting (PAC) torch and the electrode and nozzle erosion has been investigated, by means of a 2-D FLUENT based numerical model. The model has been developed in the design phase of a new Cebora mono-gas plasma torch, in order to address critical issues, experimentally detected. Modelling and numerical simulation have allowed a better understanding of the physical phenomena experimentally observed during the prototype development and have suggested successful design solutions for consumables (in particular nozzle, electrode and primary gas diffusers). Direct correlation was found between modelling results and experimental evidences; numerical simulation has been used to predict electrode and nozzle expected life, proving useful to overcome the critical issues initially pointed out and to significantly improve the expected lifetime of consumables.
Exosomes derived from microRNA-1013 p-overexpressing human bone marrow mesenchymal stem cells suppress oral cancer cell proliferation , invasion , and migration

Dysregulation of microRNAs (miRNAs) has been found to disrupt the progression of oral cancer. However, which miRNAs are most effective against oral cancer and how these miRNAs should be delivered are major unanswered problems. We aimed at investigating if human bone marrow mesenchymal stem cells (hBMSCs)-derived exosomes affect oral cancer development, and the potential regulatory mechanism associated with COL10A1 and miR-101-3p. COL10A1 was upregulated, while miR101-3p was downregulated in oral cancer, and miR-101-3p targeted COL10A1 as verified by dual-luciferase reporter gene assay. Meanwhile, exosomes derived from hBMSCs were isolated and then co-cultured with oral cancer cells to identify the role of exosomes, and the results suggested that hBMSCs-derived exosomes overexpressing miR-101-3p inhibited oral cancer progression. Furthermore, tumorigenicity assay in nude mice further confirmed the inhibitory effects of hBMSCs-derived exosomes, loaded with miR-101-3p, on oral cancer, which provides a new theoretical basis in the treatment of oral cancer.
Integrating regional and local modeling to create a high-resolution air quality forecasting system for Hong Kong

The air quality in urban areas shows complex variations in both time and space, due to influences on many scales from regional pollutant transport to individual road emissions and street canyon geometries. Access to street-scale air quality forecasting data allows residents to make informed choices about transport modes and routes in order to minimise their exposure to air pollutants as they travel around the urban area. The only air quality data currently publicly available for Hong Kong is an air quality health index value for each of 16 monitoring stations, based on real-time measurements. The Personalised Real time Air quality Informatics System for Exposure (PRAISE-HK) project aims to develop a mobile app that will provide highresolution forecast pollution data, which will be combined with route planning and personal health alerts. Several empirical forecasting approaches have previously been described for Hong Kong, all limited to relatively few specific pollutants and none run as publicly available operational forecasts. The synoptic climatological approach (Cheng and Lam 2000) cannot account for local variations of emissions or fine-scale meteorology. Statistical approaches, for example fuzzy logic (Pokrovsky et al. 2002), neural networks (Lu et al. 2004) and regressions (Goyal et al. 2006) all require large volumes of input observational data and are limited to specific output locations. For more general forecasting applications, it is preferable to use models which take into account the transport and chemistry of emitted pollutants. Regional photochemical models such as CMAQ (Byun and Schere 2006) can be used to forecast average pollutant concentrations on neighbourhood scales, but this does not give an accurate representation of the local
Structural , Magnetic and Electrical Properties of La 1x Ba x MnO 3 ( x = 0 – 0 . 7 ) for Absorber Electromagnetic Wave

The synthesis and characterization of La1-xBaxMnO3 magnetic materials by mechanical alloying method have been performed. This magnetic material is prepared by oxides, namely La2O3, BaCO3 and MnCO3. The mixture was milled for 10 h then sintered at temperature of 1000 °C for 10 h. The refinement results of x-ray diffraction pattern showed that the sample La1-xBaxMnO3 (x = 0.2) is the best composition with monoclinic structure, space group of I 1 2/a 1, the lattice parameters of a = 5.5182(8) Å, b = 5.5442(8) Å and c = 7.822(1) Å,  = 90 and  89.63(1), V = 239.68(3) Å and  6.559 gr.cm. The microstructure analyses showed that the particle shapes was aggregate with the particle sizes around 500 nm and distributed homogeneously on the sample surface. The elementary analysis results indicated that the sample has a composition according to expected. The hysteresis curve showed that the presence of barium caused the change magnetic ordering of the sample from antiferomagnetic to ferromagnetic. The electric and magnetic properties of this material is affected by the very complex interaction between charge, spin-ordering and orbital ordering of the electron spin. The results of the electromagnetic wave absorption curve analysis by using a vector network analyzer indicated that that there has been absorption of EM waves in the frequency range between 9-15 GHz with two peak frequency at 11.1 and 14.2 GHz with absorption of ~ 4.8 and 6.8 dB, respectively. The EM wave absorption is calculated reaches 55% with a thickness of 1.5 mm. We concluded that the material lanthanum manganite perovskite system can be used as preliminary study development new materials for electromagnetic wave absorber.
RECOGNITION OF PATHOGENS USING IMAGE CLASSIFICATION BASED ON IMPROVED RECURRENT NEURAL NETWORK WITH LSTM

In this paper, a technique is proposed on Recurrent Neural Network (RNN) with the end goal to group pathogen with five Deep learning stages: preparing dataset images, RNN training, testing the RNN model with collected images, Apply RNN created show on testing information lastly and evaluate the performance of the proposed method. RNN can enhance the precision in pathogens determination that are centered around hand-tuned include extraction suggesting some human oversights. For our examination, we consider cholera affected images i.e. Vibrio cholera pathogen image for minute images classification with a significant RNN. Image classification is the responsibility of consideration the image information and obtaining perfect likelihood of classes that best portrays the image. In spite of the fact that this archive tends to the order of pandemic pathogen Images utilizing a RNN demonstrate, the hidden standards apply to alternate fields of science and innovation, as a result of its execution and its capacity to deal with a larger number of layers than the past customary neural networks.
1 SOIL LIQUEFACTION – INFLUENCE OF NON-PLASTIC FINES IN LIQUEFACTION RESISTANCE

It has been understood since the 1960's that the presence of silt and clay particles will in some manner affect the resistance of a sand to liquefaction. However, a review of studies published in the late literature shows that no clear conclusions can be drawn as to in what manner altering the fines content affects the liquefaction resistance of a sand under cyclic loading. This is particularly true for soils containing non-plastic, i.e. silty fines. An attempt of clarification of the effects of non-plastic fines on the liquefaction susceptibility of sandy soils is presented here in light of a recent study conducted by Martin and Polito (2001), and a recent conceptual framework proposed by Thevanayagam (2000). The assessment of triggering potential is also focused in this document with a review of the latest updates of in situ liquefaction hazard evaluation methods. Since the influence of nonplastic fines on liquefaction susceptibility is the main subject of this work, a detailed presentation of the corrections factors, often used in simplified liquefaction evaluation methods to take into account the affect of fines content on the soil, is made for each in situ test presented. Finally, the influence of the presence of nonplastic fines might have on the methods for liquefaction analysis for sands currently used in engineering practice is analyzed and some suggestions will be made on how to evaluate liquefaction potential in these cases, based on the latest studies on the matter.
Interaction between GA and Ethrel in Inducing Female Flowers in Jatropha Curcas

Jatropha curcas is a plant with tremendous economic potential as a source of biodiesel. It is a monoecious plant with staminate and pistillate flowers on same inflorescence. The number of staminate flowers is higher than pistillate which results in low fruit yield. Phytohormones such as Gibberellin and Ethylene play an important role in modulating flower development. Gibberellin and Ethylene play different role in sex alteration in different plant species. There exists a crosstalk between Gibberellin, Ethylene and Auxin during growth and development. In this study, we report the exogenous application of GA and Ethrel and its impact on femaleness. A correlation between endogenous level of Auxin, Gibberellin to flowering and fruit yield in Jatropha curcas has been attempted. There was considerable decrease in flowering and fruit yield after the application of Ethrel (15 ppm) while GA 100 ppm significantly increases flowering and fruit yield compared to other treatments. The changes seen in endogenous level of Auxin and GA in response to GA and Ethrel application in various stages of inflorescence and its effect on number of female flowersis discussed.