Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of Plant leaf diseases using Adaptive Neuro Fuzzy Classification

The Plant Leaf infection attestation tallies gathers on segregating different sorts of highlights from leaf pictures of weakened plants. The Leaf disorders are critical segments, as it can cause colossal abatement in both quality and number of harvests in agribusiness creation. Thusly, area and portrayal of ailments is a huge task. The highlights extricated from the leaf pictures depends on the grouping cycle. The leaf picture-based infection acknowledgment approach comprises of three techniques: locale division of unhealthy leaf pictures utilizing K-implies bunching, extraction of surface and shape highlights from grouped injury pictures, and arranging the ailing leaf pictures utilizing Adaptive Neuro Fuzzy characterization. The framework produced for leaf sicknesses acknowledgment is used to remove the features from tainted leaves and will be used to gather leaf ailments using ANFIS.
Flux Observation of Induction Machine Based on the Enhanced Sensorless Voltage Model

This paper presents the identification of the rotor flux of the induction machine. In the field-oriented control topology, since the terminal stator voltages are a pulsating voltage, so the only way is to calculate the reference values of the voltages from the inverter states phase. At very low speed, the reconstructed voltages are nearly not real, so that, the range of speed rotation of an encoder-less field-oriented control is not quite enough to cover all the speed range. However, the design of a 3-phase inverter which is prepared by silicon carbide (SiC)-MOSFETs with inserting an output filter between the inverter and the machine may supply sinusoidal voltages and currents at the output. The high switching frequency which can be applied with the SiC MOSFETs permits the proposal and implementation of a small low pass LC filter that can be equipped with the inverter output side. With this structure, it is possible to reduce the harmonic of the output voltages in addition to reduce the content of the current harmonics causing in a practically sinusoidal voltage as confirmed in the experimental results of this work. The main goal of this outline is two points, on the first side it can be reduce the winding machine stress, therefore, it is minimizing the harmonic in the voltages at the machine terminals. And on the other hand, is to use the measured voltages as an alternative of the recalculated ones to enhance the voltage model and the performance of the control scheme. It is predictable that the range of machine speed of an encoder-less field-oriented control can be extended.
Coarse-grained molecular dynamics simulation of cis-1,4-polyisoprene with silica nanoparticles under extreme uniaxial tension

Coarse-grained (CG) cis-1,4-polyisoprene (PI) models with multiple silica nanoparticles (NPs) are built to study the effect of NPs and crosslinks in the uniaxial tensile simulation. The potential functions of the CG models are obtained mainly via the iterative Boltzmann inversion method. The tensile simulation results show that the grafted silica NPs and the crosslinked structure play reinforcing roles while the smooth silica NPs do the opposite, which have the similar trends with the experiment results. The differences of mechanical properties for these models are studied from different microscopic aspects, such as the network of NPs, the bond lengths, the free molecular chains, the entanglements, the stress and strain distribution and the microvoid evolution. As a result, the main reasons for the weakening of PI models with smooth silica NPs come from the weak interfacial interaction, the inhomogeneity of structural deformation and the reduction of the number of entanglements. However, if there are graft chains, the interfacial interaction can be enhanced by entangling with the matrix molecular chains. The graft chains can make it possible for the aggregated NPs to separate and can hinder the growth of microvoids at the interface. In addition, the inconsistency of the stress and strain distributions at the microscopic level is verified and the nucleation mechanism of microvoids is believed to be caused by the local violent movement of molecular chains.
Systematic Investigation of Dispersions of Unmodified Inorganic Nanoparticles in Organic Solvents with Focus on the Hansen Solubility Parameters

Dispersions of unmodified nanoparticles (titanium dioxide, hydroxyapatite) were prepared by redispersion of nanoparticle powders in organic solvents using an ultrasound treatment. The dispersion quality was judged by dynamic light scattering (DLS) measurements and visual evaluation. Whereas “bad” solvents led to no or unstable dispersions with large particle diameters, dispersions made from the “good” solvents consisted of particles with relatively small diameters and were stable for several days or longer. For titanium dioxide, mixtures from four of the “good” solvents identified after first screening of a large set of solvents were prepared and tested as dispersion agent. Thus obtained dispersions showed superior properties compared to the previous dispersions, with small particles sizes and good long-time stability. Based on a rating of solvent quality and by calculation using the software HSPiP v3, the Hansen solubility parameters of the particles were then determined. Subsequently, entirely new solve.
Speckle Interferometry at the OAN-SPM México: Multiband Astrometry of Double Stars Measured in 2018 and 2019

In this paper we present multiband speckle interferometric measurements of double stars performed at the 2.1 m telescope of the Observatorio Astronómico Nacional, San Pedro Mártir, México. We focused our efforts on objects from the Washington Double Star Catalog that were observed during five runs allocated in 2018 and 2019. We report here 2101 astrometric and 2027 photometric measurements of 631 pairs, with angular separations ranging from 0. ″05 to 6. ″09, including 1622 measurements with separations smaller than 1″, and a magnitude difference up to 5.99 mag through the V filter, 5.46 mag for the R filter, and 5.85 mag for the I filter. We estimated a mean error in separation of 12 mas, 1. °1 in position angle, and 0.14 mag for the magnitude difference. We confirmed 58 double stars discovered by Hipparcos. For the first time we are presenting astrometric measurements for 7 systems listed at the Washington Double Star Catalog as being suspected binaries, which did not have a secondary confirmation, and 15 stars with only one published measurement. We identified a new close companion with a mean angular separation of 0. ″10, detected in a previously known pair. Finally, we present a correction to two previously published orbital solutions of two visual binaries.
Uncertainty in the extreme flood magnitude estimates of large-scale flood hazard models

The growing worldwide impact of flood events has motivated the development and application of global flood hazard models (GFHMs). These models have become useful tools for flood risk assessment and management, especially in regions where little local hazard information is available. One of the key uncertainties associated with GFHMs is the estimation of extreme flood magnitudes to generate flood hazard maps. In this study, the 1-in-100 year flood (Q100) magnitude was estimated using flow outputs from four global hydrological models (GHMs) and two global flood frequency analysis datasets for 1350 gauges across the conterminous US. The annual maximum flows of the observed and modelled timeseries of streamflow were bootstrapped to evaluate the sensitivity of the underlying data to extrapolation. Results show that there are clear spatial patterns of bias associated with each method. GHMs show a general tendency to overpredict Western US gauges and underpredict Eastern US gauges. The GloFAS and HYPE models underpredict Q100 by more than 25% in 68% and 52% of gauges, respectively. The PCR-GLOBWB and CaMa-Flood models overestimate Q100 by more than 25% at 60% and 65% of gauges in West and Central US, respectively. The global frequency analysis datasets have spatial variabilities that differ from the GHMs. We found that river basin area and topographic elevation explain some of the spatial variability in predictive performance found in this study. However, there is no single model or method that performs best everywhere, and therefore we recommend a weighted ensemble of predictions of extreme flood magnitudes should be used for large-scale flood hazard assessment.
Advances on the biosensor based on nanotechnology

Biosensor is a detection instrument which is sensitive to biological substances and convert the signal of biological activity expression into electrical signal in order to determine the characteristic of samples. Besides, the development of nanoparticles greatly promoted the development and progress of biosensors. Biosensors are promising to be widely used in many fields such as food, pharmacy, chemical industry, clinical examination, biomedicine, environmental monitoring, they have gradually become a research focus on recent years hence. This paper presents a review of worldwide researches, particularly summarized and discussed in composition materials, classification, and application areas of biosensors as well. It is expected that this review can provide some help and references for further research on biosensors.
A shape and texture features fusion to retrieve similar Trademark Image Material

Trademark images or materials such as symbols, text, logos, image, design or phrase are used to unique representation of any organization. Retrieval of trademark material images are important to protect the new trademark image that is to be registered. Therefore, retrieval of similar trademark images is required. In this paper, an approach is presented to extract more similar trademark images so that a unique trademark image can be registered. In this paper, Zernike moment of the query image and dataset images are computed, then most similar images from the dataset are retrieved at the first layer refinement. In the second layer, texture features are extracted of query image and refined dataset images to retrieve most appropriate similar images. Zernike moments is applied to extract global shape features and Scale Invariant Feature Transform (SIFT) and Speeded-Up Robust Feature (SURF) are applied to extract texture features on the basis of a few key-points of the trademark images. A weighted average of both the key-points feature vectors is computed for retrieving the rank1, rank5, rank10, rank15 and rank20 most similar images using Euclidean distance. Experiments have been performed on a proposed dataset to perform the analysis and found that proposed work perform better and improves the accuracy.
Flood hydrograph simulation to estimate peak discharge in Ciliwung river basin

Ciliwung River Basin is one of 13 watersheds located in the Special Capital Region of Jakarta (DKI Jakarta), one of the most vulnerable cities facing flood disaster. Modeling hydrographs in a flood computer model is a crucial process in understanding the hydraulic processes in floods; however, more often than have adequately not studied watersheds do not available observed data. Well-calibrated hydrographs serve as the basis for a basin-wide flood model. In this study, the Snyder, ITB, and SCS Unit Hydrograph are constructed in the study case of flooding in the Ciliwung River Basin, with West Java rainfall distribution method and Alternating Block Method as the adopted rainfall distributions, providing six combinations of approaches. Calibration and validation analysis show a combination of West Java rainfall distribution and SCS UH with a lag time of 5.5 gives the smallest deviation, with an accuracy of 83.3%, therefore it is recommended for use to estimate design future flood hydrograph.
Photocatalytic disinfection of bacteria under visible light irradiation by BiFeO3 photocatalyst

BiFeO3 nanoparticles was synthesized by sol gel auto combustion. The as-synthesized BiFeO3 were characterized by X-ray diffraction (XRD), UV-Vis diffuse reflectance spectra (DRS) and Brunauer-Emmett-Teller (BET) analysis. The disinfection activities towards Gram-positive Staphylococcus aureus (S.aureus) were examined under visible light irradiation. The results showed a complete inactivation of 3 × 106 CFU/mL S.aureus was achieved within 20 min. The disruption of bacterial cell structure was observed by Transmission electron microscope (TEM). The cells were severely damaged after being exposed to BiFeO3 under visible light irradiation. Hence, the results demonstrated the potential application of perovskite-type photocatalyst, BiFeO3 in photocatalytic disinfection of various microorganisms.
Identifying 3D Models by Matching Rendered Image and Depth Image with Using a New Combined Descriptor

This paper presented a 3D model identification method by matching pairs of rendered images and depth images. Firstly, the technique uses the HARRIS detector and PIIFD detector (partial intensity invariant feature descriptor) to detect feature points of rendered-depth image pair of shape and then uses LGHD (Log-Gabor Histogram Descriptor) to descript these feature points. Secondly, the shape identification processing is conducted, including normalizing shape pose, capturing multi-view pairs of rendered images and depth images, and image matching. Finally, 100 pairs of 3D models are used for the experiment. The experiment results show that the proposed method has an efficient performance on rendered-depth image matching and can be used for 3D model identification.
Requirement Acquisition Method of Product Life cycle Based on HLDA Hierarchy Model under the Background of Web Technology

Product requirement is the goal of product development and design activities, which originates from all stages of product life cycle. In the process of acquiring product requirements, there are some problems such as incomplete mining of product requirements and fuzzy structural expression. In view of the above problems, this paper proposes a product life cycle requirements acquisition method based on axiomatic design. Firstly, web technology is applied to collect requirements information, and based on HLDA hierarchy model, the requirements information is classified according to the whole life cycle stages of products; then, the product requirements mining model based on axiomatic design is established to mine the requirements of the whole product life cycle; finally, the product requirements model tree is structured and the product life cycle requirement model is established; This paper takes shield machine segment erector as an example to verify the effectiveness of the proposed product life cycle requirements acquisition method.
Disposition of Taltirelin (1): Absorption, Distribution, Metabolism and Excretion in Rats and Dogs

The absorption, distribution, excretion and metabolism were investigated after a single oral (3 mg/kg) or intravenous (1 mg/kg) administration of 14C-taltirelin to male rats and dogs. 1. The extent of absorption calculated from the ratio of urinary excretion after or al and intravenous administration of 14C-taltirelin was 9 and 21% of the dose in rats and dogs. The intestinal absorption ratio in non-fasted rats decreased by 38% compared with that in fasted rats. 2. After oral administration to rats, the plasma level of radioactivity reached the maximum level of 157 ng eq./ml at 1 hr after dosing and then decreased with a half-life of 119 min. After oral administration to dogs, the plasma level of radioactivity reached the maximum level of 508 ng eq./ml at 1.5 hr and then decreased with a half-life of 146 min. The values of bioavailability in rats and dogs were 3.9 and 18.5%, respectively. 3. The ratios of plasma protein binding of radioactive substances in rats and dogs were below 12% both under the in vivo and in vitro conditions. 4. Radioactivity levels afte r oral administration to rats reached a peak at 30 min to 3 hr in most tissues and were high in the liver, kidney, spleen, lung, blood and skin, except for gastrointestinal tract. But at 24 hr radioactivity levels in these tissues decreased remarkably. Radioactivity level in the brain, a target organ of pharmacological effect, was low and decreased slowly. 5. In both rats and dogs, most of the dose wa s excreted within 48 hr after dosing and the absorbed 14C-taltirelin was excreted mainly in urine. 6. Radioactivity excreted in bile within 24 hr was 1.7 and 5.7% of the dose after oral and intravenous administration to rats, respectively. 7. Two metabolites, (−)-N-[(S)-hexahydro-1-methyl-2, 6-dioxo-4-pyrimidinyl carbonyl]-L-histidyl-L-proline (“Acid”) and (S)-hexahydro-1-methyl-2, 6-dioxo-4-pyrimidinecarboxylic acid (“MDOA”), together with unchanged taltirelin were detected in plasma and urine of both rats and dogs.
Application of Technology of UAV-Mounted Ground Penetrating Radar in the Study of the Thickness of Soil Plow Layer

When the detection field is a large field, the traditional ground penetrating radar system has the problems of low detection efficiency and high labor cost. Aiming at this problem, an integrated system of UAV-mounted ground penetrating radar is designed, and a detection method based on UAV-mounted ground penetrating radar is proposed. Taking a small area of cultivated land in Taiyuan, Shanxi as an example, the detection method of UAV-mounted ground penetrating radar is applied to measure the thickness of soil cultivated layer. After the collected data is extracted and linearly interpolated, the thickness of the cultivated soil layer obtained by image analysis is similar to the measured thickness of the soil cultivated layer, which highlights the advantages of high efficiency and low cost based on the detection method of UAV-mounted ground penetrating radar , making the detection of concealed loose bodies in large sites becomes possible.
Retraction Retraction: A Review on Development of Rare Earth Based Contrast Agents for Dual Modal Imaging of Cancer Cells

The risk of developing cancer is becoming higher due to the genetic and environmental factors. Diagnosing cancer at an early stage is a very big challenge to clinicians and researchers. Several imaging modalities are being used in hospitals for diagnostic purposes. But each imaging modality has some limitations to identify the cancer cells at their early stage. Magnetic resonance imaging can be combined with optical imaging for better diagnosis of cancer. This concept of combining two imaging modalities is termed as ‘dual modal imaging’. In dual modal imaging, the limitation of one technique becomes the advantage of other. This review article focuses on the dual modal imaging which is achieved by using rare earth doped gadolinium oxide nanoparticles. By doping the rare earth ions into the gadolinium oxide matrix, both the optical and magnetic properties of the material are shared.
DNA barcoding of commercial fish products using dual mitochondrial markers exposes evidence for mislabelling and trade of endangered species

Fish fraud has been extensively reported in world fish trade. The fraud includes IUCN Red List and CITES-listed species. Hence, there is a growing need to identify the trade of endangered and threatened species that has been misused to satisfy consumer needs. Here, we apply DNA barcoding by using dual mitochondrial marker; cytochrome b (Cytb) polymorphic fragment and cytochrome c oxidase subunit I (COI) to authenticate 50 commercial fish products collected from the Malaysian market. The dual marker system improves species detection in tested fish products even in highly processed food and exposes the trade of one critically endangered (also CITES-listed) and three endangered or near threatened species under the IUCN red-list status. Our result also indicates that 36% of fish products in the Malaysian market is mislabelled and might cause concern for food safety. The newly developed Cytb primer pair also shows a higher success rate by identifying 92% of the tested samples compared to 40% for COI primer. This work suggests the dual-marker DNA barcoding approach is more effective in detecting food mislabelling and is indeed a promising tool to help regulatory bodies obtain a clearer standpoint for monitoring endangered fish trade to prevent further biodiversity loss.
Rolling verification of the performance of real estate investment funds

Currently, there is an urgent need to develop an institutional rental housing market. It is an obvious fact that this issue cannot be resolved without the formation of available investment resources. This article discusses aspects of one of these promising forms – real estate investment funds. The article uses historical data on the dynamics of profitability of the 21st real estate fund for 2013 -2020 to test the method of assessing the risks of investing funds for potential investors – the method of sliding verification of groups of leaders synthesized using the “Yield-risk” model. The model is based on the basic principles of portfolio analysis. The results obtained indicate the suitability of the proposed method. Testing was carried out on current data from 2017-2020, which were not used to build the model. The constructed groups of leading funds demonstrate their continuity at all stages of rolling verification. The conducted correlation analysis showed a fairly close mutual correlation between the dynamics of profitability of the studied real estate funds, which significantly weakens by 2020. The proposed method can be used when making decisions about investing money in investment real estate, including rental housing of various categories.
Region of Interest Coding Based on Convolutional Neural Network

The traditional region of interest coding method mainly uses low-level features to detect the Region of Interest (ROI). The ROI detected by it is poor in stability and is not easily interfered by noise. In this paper, ROI detection is performed on the image through a deep convolutional network to obtain a stable ROI based on the high-level feature extraction of the image, and then the discrete cosine transform (DCT) is performed on the image and divided into coding units. According to whether the coding unit is an ROI, To determine the quantization matrix used when encoding it. This article uses fine quantization for coding units that belong to ROI, and coarse quantization for non-ROI coding units. In this way, it can be ensured that the compression rate is greatly reduced without affecting the subjective perception of the image. Experiments show that the compression rate of this method can reach about 84%, and the weighted peak signal-to-noise ratio is improved by about 0. 99dB on average compared with JPEG encoding.
A Survey on Image Captioning datasets and Evaluation Metrics

In the task of image captioning, a natural language explanation is generated for a given image. It uses the subfields of artificial intelligence: computer vision and language generation. Convolutional Neural Network (CNN) is generally applied to capture image features and language processing models such as Recurrent Neural Network for sentence generation. In this paper, various datasets and evaluation metrics which are useful for image captioning task are discussed. Also, the datasets and evaluation metrics applied by the state-of-the-art image captioning models is summarized.
The Comparison between Bulk RNA-seq and Ssingle-cell RNA-seq

Bulk RNA-seq and single cell RNA-seq (Sc-RNA) seq are two well-known methods and are broadly used in biology areas. Even though the two ways are all starting from the mRNA level to do the transcriptional analysis, many differences still show in them, but the differences which are critical for researchers to judge and consider were not addressed clearly in the past studies. Here we summarized the principle, workflow and data presentation of the two methods and compared them comprehensively, which shed a light on the application of high throughput sequencing technique for researchers.