jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Feifei Qin | Zhenzhen Liu | Q. Zhang | H. Zhang | Jun‐Jun Xiao
Source: Scientific Reports (article, 2018-09-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Mantle Cloaks Based on the Frequency Selective Metasurfaces Designed by Bayesian Optimization We propose a full optimization procedure for designing mantle cloaks enclosing arbitrary objects, using sub-wavelength conformal frequency selective surface (FSS). Rely on the scattering cancellation principle of mantle cloak characterized by an average surface reactance, a personal computer can achieve this design procedure. By combing a Bayesian optimization (BO) with an electromagnetic solver, we can automatically find the optimal parameters of a conformal mantle cloak which can nearly cancel the scattering from the enclosed objects. It is shown that the results obtained by our method coincide with those from a rigorous analytical model and the numerical results by full parametric scanning. The proposed methodology opens up a new route for realizing ultra-wideband illusion scattering of electromagnetic wave, which is important for stealth and microwave applications. Cloak ; Conformal map ; Cloaking ; Reactance ; Computer science ; Scattering ; Solver ; Metamaterial ; Broadband ; Optics ; Physics ; Acoustics ; Algorithm ; Mathematics ; Mathematical analysis Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis
Authors: Emma Serrano-Pallicer | Marta Muñoz-Albero | Clara Pérez-Fuster | Rafael Masot | Nicolás Laguarda-Miró
Source: Sensors (article, 2018-12-19)
Field: Chemistry — Analytical Chemistry

Early Detection of Freeze Damage in Navelate Oranges with Electrochemical Impedance Spectroscopy The early detection of freeze damage in Navelate oranges (Citrus sinensis L. Osbeck) was studied using electrochemical impedance spectroscopy (EIS), which is associated with a specific double-needle sensor. The objective was to identify this problem early in order to help to determine when a freeze phenomenon occurs. Thus, we selected a set of Navelate oranges without external defects, belonging to the same batch. Next, an intense cold process was simulated to analyze the oranges before and after freezing. The results of the spectroscopy analysis revealed different signals for oranges depending on whether they had experienced freezing or not. Principal Component Analysis (PCA) and Partial Least Squares-Discriminant Analysis (PLS-DA) of the obtained data demonstrated that it is possible to discriminate the samples, explaining 88.5% of the total variability (PCA) and being able to design a mathematical model with a prediction sensitivity of 80% (PLS-DA). Additionally, a designed artificial neural network (ANN) prediction model managed to correctly classify 100% of the studied samples. Therefore, EIS together with ANN-based data treatment is proposed as a viable alternative to the traditional techniques for the early detection of freeze damage in oranges. Dielectric spectroscopy ; Principal component analysis ; Partial least squares regression ; Linear discriminant analysis ; Biological system ; Artificial neural network ; Sensitivity (control systems) ; Citrus × sinensis ; Pattern recognition (psychology) ; Artificial intelligence ; Analytical Chemistry (journal) ; Materials science ; Computer science ; Mathematics ; Chemistry ; Machine learning ; Orange (colour) ; Chromatography ; Engineering ; Electrode ; Electrochemistry ; Biology ; Electronic engineering ; Food science Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Meat and Animal Product Quality
Authors: Shan Lu | Yong Cao | Sheng‐Bo Fan | Zhen-Lin Chen | Run-Qian Fang | Si‐Min He | Meng‐Qiu Dong
Source: Biophysics Reports (article, 2018-04-01)
Field: Chemistry — Spectroscopy

Mapping disulfide bonds from sub-micrograms of purified proteins or micrograms of complex protein mixtures Disulfide bonds are vital for protein functions, but locating the linkage sites has been a challenge in protein chemistry, especially when the quantity of a sample is small or the complexity is high. In 2015, our laboratory developed a sensitive and efficient method for mapping protein disulfide bonds from simple or complex samples (Lu <i>et al</i>. in Nat Methods 12:329, 2015). This method is based on liquid chromatography-mass spectrometry (LC-MS) and a powerful data analysis software tool named pLink. To facilitate application of this method, we present step-by-step disulfide mapping protocols for three types of samples-purified proteins in solution, proteins in SDS-PAGE gels, and complex protein mixtures in solution. The minimum amount of protein required for this method can be as low as several hundred nanograms for purified proteins, or tens of micrograms for a mixture of hundreds of proteins. The entire workflow-from sample preparation to LC-MS and data analysis-is described in great detail. We believe that this protocol can be easily implemented in any laboratory with access to a fast-scanning, high-resolution, and accurate-mass LC-MS system. Disulfide bond ; Chemistry ; Chromatography ; Stereochemistry ; Biochemistry Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics ; Mass Spectrometry Techniques and Applications
Authors: Lifeng Wang | Pengwei Xing | Cong Wang | Xiaomao Zhou | Zhijun Dai | Lianyang Bai
Source: Journal of the Brazilian Chemical Society (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Maximal Information Coefficient and Support Vector Regression Based Nonlinear Feature Selection and QSAR Modeling on Toxicity of Alcohol Compounds to Tadpoles of Rana temporaria Efficient evaluation of biotoxicity of organics is of vital significance to resource utilization and environmental protection. In this study, toxicity of 110 alcohol compounds to tadpoles of Rana temporaria is adopted as the dependent variable and 1388 physiochemical parameters (features) calculated by PCLIENT are used for representing each compound. A feature selection pipeline with three steps is developed to refine the feature subset: 282 features that significantly correlated with biotoxicity of chemical compounds are preliminarily selected via the maximum information coefficient (MIC); 138 descriptors that have positive contribution to the model's performance are reserved after a support vector regression (SVR) based backward elimination; 18 descriptors are finally selected via a forward selection process that integrated minimal redundancy maximal relevance (mRMR), MIC and SVR. In terms of feature subsets with different numbers of variables, quantitative structure activity relationship (QSAR) models are built using multiple linear regression (MLR), partial least square regression (PLS) and SVR, respectively. The independent prediction evaluation index, Q 2 , increases from -74.787, 0.824 and 0.868 to 0.892, 0.878 and 0.940, for the three regression models, respectively. Results suggest that nonlinear feature selection methods involved in MIC and SVR can effectively eliminate irrelevant descriptors. SVR outperforms classical statistical models to QSAR modeling on high-dimensional data containing nonlinear relationship between features. The methods proposed in this study have a potential application in the QSAR research field such as biotoxicity compounds. Quantitative structure–activity relationship ; Nonlinear regression ; Rana ; Nonlinear system ; Feature selection ; Support vector machine ; Selection (genetic algorithm) ; Stepwise regression ; Regression ; Biological system ; Artificial intelligence ; Chemistry ; Computer science ; Regression analysis ; Mathematics ; Biology ; Statistics ; Machine learning ; Anatomy ; Physics Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Advanced Chemical Sensor Technologies
Authors: Ali AlSuwaidi | Bruce Grieve | Hujun Yin
Source: Measurement Science and Technology (article, 2018-07-27)
Field: Chemistry — Analytical Chemistry

Combining spectral and texture features in hyperspectral image analysis for plant monitoring A texture enhanced spectral analysis framework is proposed for classifying hyperspectral images of plants of different conditions. Differentiating different plant conditions is important to precision agriculture as it helps detect diseases and stresses and optimise growth control. Advanced machine learning techniques are used to identify distinctive features in the spectral domain of hyperspectral images. In addition, texture properties are explored in the subband images. The framework integrates these two levels of properties at both feature extraction and classifying decision stages. The main crux of the work lies in the use of the significant spectral and texture features and a decision fusion mechanism to enhance the image properties, thus improving classification accuracy. Two hyperspectral datasets, originated from proximal hyperspectral systems, were used in the evaluation and significant improvements in classification accuracy achieved. Hyperspectral imaging ; Artificial intelligence ; Texture (cosmology) ; Computer science ; Pattern recognition (psychology) ; Feature extraction ; Precision agriculture ; Feature (linguistics) ; Image (mathematics) ; Spectral signature ; Computer vision ; Remote sensing ; Agriculture ; Geology ; Geography Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Smart Agriculture and AI
Authors: Arun Kumar R | Vijay S. Rajpurohit | Bhairu J. Jirage
Source: Journal of Horticultural Research (article, 2018-06-01)
Field: Chemistry — Analytical Chemistry

Pomegranate Fruit Quality Assessment Using Machine Intelligence and Wavelet Features Abstract Quality assessment is an important concern in the post-harvest marketing of fruits. Manual quality assessment of pomegranate fruits poses various problems because of human operators. In the present paper, an efficient machine vision system is designed and implemented in order to assess the quality of pomegranate fruits. The main objectives of the present study are (1) to adopt a best pre-processing module, (2) to select best class of features and (3) to develop an efficient machine learning technique for quality assessment of pomegranates. The sample images of pomegranate fruits are captured using a custom-made image acquisition system. Two sets of features, namely, spatial domain feature set and wavelet feature set are extracted for all of the sample images. Experiments are conducted by training both artificial neural networks (ANNs) and support vector machines (SVMs) using both sets of features. The results of the experiments illustrated that ANNs outperform SVMs with a difference in the accuracy of 12.65%. Further, the selection of wavelet featureset for training yielded more accurate results against spatial domain feature set. Artificial intelligence ; Support vector machine ; Pattern recognition (psychology) ; Wavelet ; Feature selection ; Machine learning ; Computer science ; Artificial neural network ; Feature (linguistics) ; Set (abstract data type) ; Quality (philosophy) ; Sample (material) ; Wavelet transform ; Data mining Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Piotr M. Pieczywek | Małgorzata Nowacka | Magdalena Dadan | Artur Wiktor | Katarzyna Rybak | Dorota Witrowa‐Rajchert | Artur Zdunek
Source: Sensors (article, 2018-04-04)
Field: Chemistry — Analytical Chemistry

Postharvest Monitoring of Tomato Ripening Using the Dynamic Laser Speckle The dynamic laser speckle (biospeckle) method was tested as a potential tool for the assessment and monitoring of the maturity stage of tomatoes. Two tomato cultivars&mdash;Admiro and Starbuck&mdash;were tested. The process of climacteric maturation of tomatoes was monitored during a shelf life storage experiment. The biospeckle phenomena were captured using 640 nm and 830 nm laser light wavelength, and analysed using two activity descriptors based on biospeckle pattern decorrelation&mdash;C4 and &epsilon;. The well-established optical parameters of tomatoes skin were used as a reference method (luminosity, a*/b*, chroma). Both methods were tested with respect to their prediction capabilities of the maturity and destructive indicators of tomatoes&mdash;firmness, chlorophyll and carotenoids content. The statistical significance of the tested relationships were investigated by means of linear regression models. The climacteric maturation of tomato fruit was associated with an increase in biospckle activity. Compared to the 830 nm laser wavelength the biospeckle activity measured at 640 nm enabled more accurate predictions of firmness, chlorophyll and carotenoids content. At 640 nm laser wavelength both activity descriptors (C4 and &epsilon;) provided similar results, while at 830 nm the &epsilon; showed slightly better performance. The linear regression models showed that biospeckle activity descriptors had a higher correlation with chlorophyll and carotenoids content than the a*/b* ratio and luminosity. The results for chroma were comparable with the results for both biospeckle activity indicators. The biospeckle method showed very good results in terms of maturation monitoring and the prediction of the maturity indices of tomatoes, proving the possibility of practical implementation of this method for the determination of the maturity stage of tomatoes. Ripening ; Postharvest ; Speckle pattern ; Climacteric ; Horticulture ; Carotenoid ; Maturity (psychological) ; Chlorophyll ; Linear regression ; Chemistry ; Food science ; Mathematics ; Optics ; Biology ; Physics ; Statistics Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Postharvest Quality and Shelf Life Management
Authors: Siddharth Chaudhary | Sarawut Ninsawat | Tai Nakamura
Source: Sensors (article, 2018-12-28)
Field: Chemistry — Analytical Chemistry

Non-Destructive Trace Detection of Explosives Using Pushbroom Scanning Hyperspectral Imaging System The aim of this study was to investigate the potential of the non-destructive hyperspectral imaging system (HSI) and accuracy of the model developed using Support Vector Machine (SVM) for determining trace detection of explosives. Raman spectroscopy has been used in similar studies, but no study has been published which is based on measurement of reflectance from hyperspectral sensor for trace detection of explosives. HSI used in this study has an advantage over existing techniques due to its combination of imaging system and spectroscopy, along with being contactless and non-destructive in nature. Hyperspectral images of the chemical were collected using the BaySpec hyperspectral sensor which operated in the spectral range of 400⁻1000 nm (144 bands). Image processing was applied on the acquired hyperspectral image to select the region of interest (ROI) and to extract the spectral reflectance of the chemicals which were stored as spectral library. Principal Component Analysis (PCA) and first derivative was applied to reduce the high dimensionality of the image and to determine the optimal wavelengths between 400 and 1000 nm. In total, 22 out of 144 wavelengths were selected by analysing the loadings of principal components (PC). SVM was used to develop the classification model. SVM model established on the whole spectrum from 400 to 1000 nm achieved an accuracy of 81.11%, whereas an accuracy of 77.17% with less computational load was achieved when SVM model was established on the optimal wavelengths selected. The results of the study demonstrate that the hyperspectral imaging system along with SVM is a promising tool for trace detection of explosives. Hyperspectral imaging ; Support vector machine ; Principal component analysis ; Artificial intelligence ; Remote sensing ; Computer science ; Imaging spectroscopy ; Pattern recognition (psychology) ; Chemical imaging ; Explosive material ; Computer vision ; Chemistry ; Geology Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Computational Drug Discovery Methods
Authors: Madhuri V. Kakde
Source: Trends in Textile Engineering & Fashion Technology (article, 2018-08-07)
Field: Materials Science — Polymers and Plastics

Effect of Sewing Parameters on Seam Strength and Seam Efficiency Seam quality is determined by the correlation between types of fabric, structures of fabric, sewing threads and selection of stitches and seams. The performance of seam also depends on the sewing conditions like size of needle, sewing thread tension, stitches per inch and lastly on the proper working and maintenance of the stitching machine. The objective of present study is to investigate and scrutinize the impact of weft knitted fabric structures, sewing thread types and stitch types on seam strength and efficiency of superimposed type of seam for cotton apparels. An experimental design was employed for the study which includes two types of sewing thread and four classes of stitch. The seam strength and efficiency of the fabric were determined by using the tensile testing machine. Thread (computing) ; Image stitching ; Ultimate tensile strength ; Engineering ; Breaking strength ; Sewing machine ; Structural engineering ; Engineering drawing ; Mechanical engineering ; Computer science ; Materials science ; Composite material ; Artificial intelligence Textile materials and evaluations
Authors: Baohong Sun | Ming Zhang | Ninglin Zhou | Xiaohong Chu | Ping Yuan | Cheng Chi | Fan Wu | Jian Shen
Source: RSC Advances (article, 2018-01-01)
Field: Materials Science — Polymers and Plastics

Study on montmorillonite–chlorhexidine acetate–terbinafine hydrochloride intercalation composites as drug release systems This paper focuses on the intercalation of chlorhexidine acetate (CA) and terbinafine hydrochloride (TBH) into montmorillonite as sustained release drug carriers. The intercalation compounds were characterized by X-ray diffraction (XRD), Fourier transform infrared (FT-IR) spectroscopy, and thermogravimetric analysis (TGA). The basal spacing of montmorillonite increased from 1.23 to 2.97 nm. It was confirmed that CA and TBH molecules were well-stabilized in the interlayer space of clay <i>via</i> mono-, double or triplicate layer stacking. The adsorption amounts and molecular structures of CA and TBH appeared to depend on the cation exchange capacity of MMT, which in turn, tailored the drug release patterns. <i>In vitro</i> release tests of MMT-CA-TBH in 0.9 wt% NaCl solution at 37 °C show a biphasic and sustained profile of CA and TBH ion release. After release, dissolution-diffusion kinetic models were fitted. The mechanism of MMT-CA-TBH release is probably due to surface diffusion and bulk diffusion <i>via</i> ionic exchange of MMT ions on or in the MMT with ions in the NaCl solution. The <i>in vitro</i> release experiments revealed that CA and TBH were released from MMT steadily, depending on the cooperation between the drugs themselves and the electrostatic interactions between the drugs and MMT. It was found that the cross-linking ratio increased due to a decrease in the free volume available for diffusion. Montmorillonite ; Intercalation (chemistry) ; Chlorhexidine ; Hydrochloride ; Chemistry ; Drug ; Composite material ; Nuclear chemistry ; Materials science ; Dentistry ; Pharmacology ; Organic chemistry ; Medicine Conducting polymers and applications ; Bone Tissue Engineering Materials ; Graphene and Nanomaterials Applications
Authors: Eugene Mamontov
Source: Scientific Reports (article, 2018-03-02)
Field: Chemistry — Physical and Theoretical Chemistry

Microscopic diffusion processes measured in living planarians Living planarian flatworms were probed using quasielastic neutron scattering to measure, on the pico-to-nanosecond time scale and nanometer length scale, microscopic diffusion of water and cell constituents in the planarians. Measurable microscopic diffusivities were surprisingly well defined in such a complex system as living animals. The overall variation in the microscopic diffusivity of cell constituents was found to be far lower than the variation in the microscopic diffusivity of water in planarians in a temperature range of 284.5 to 304.1 K. Planarian ; Thermal diffusivity ; Diffusion ; Biophysics ; Living cell ; Chemistry ; Materials science ; Biology ; Physics ; Biological system ; Thermodynamics thermodynamics and calorimetric analyses ; Magnetic and Electromagnetic Effects ; Advanced Thermodynamics and Statistical Mechanics
Authors: T. Ahlgren | Pasi Jalkanen | Kenichiro Mizohata | Vladimir Tuboltsev | J. Räisänen | K. Heinola | P. Tikkanen
Source: Nuclear Fusion (article, 2018-12-06)
Field: Materials Science — Materials Chemistry

Hydrogen isotope exchange in tungsten during annealing in hydrogen atmosphere The radiological safety of the future thermonuclear fusion devices depends critically on the total tritium inventory in the plasma-facing components. The planned method to remove tritium from the ITER reactor tungsten divertor is to perform vacuum baking. We show that tritium removal from tungsten can be enhanced by the isotope exchange mechanism by doing the baking in H-2 atmosphere. The results show that the retained deuterium from 30 keV implantation can be expected to drop almost to zero after 24h annealing at 250 degrees C in H-2 atmosphere. Annealing in vacuum requires temperatures above 400 degrees C for close to zero retention. Thermonuclear fusion ; Tungsten ; Tritium ; Divertor ; Deuterium ; Hydrogen ; Materials science ; Annealing (glass) ; Fusion power ; Atmosphere (unit) ; Radiochemistry ; Isotope ; Hydrogen isotope ; Reducing atmosphere ; Plasma ; Nuclear physics ; Chemistry ; Metallurgy ; Physics ; Tokamak ; Thermodynamics Fusion materials and technologies ; Nuclear Materials and Properties
Authors: Elena Simone | J. McVeigh | Nuno M. Reis | Zoltán K. Nagy
Source: Lab on a Chip (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

A high-throughput multi-microfluidic crystal generator (MMicroCryGen) platform for facile screening of polymorphism and crystal morphology for pharmaceutical compounds In this work, a novel multi-microfluidic crystallization platform called MMicroCryGen is presented, offering a facile methodology for generating individual crystals for fast and easy screening of the polymorphism and crystal habit of solid compounds. The MMicroCryGen device is capable of performing 8 × 10 cooling crystallization experiments in parallel using 8 disposable microcapillary film strips, each requiring less than 25 μL of solution. Compared to traditional microfluidic systems, the MMicroCryGen platform does not require complex fluid handling; it can be directly integrated with a 96-well microplate and it can also work in a "dipstick" mode. The produced crystals can be safely and directly observed inside the capillaries by optical and spectroscopic techniques. The platform was validated by performing a number of independent experimental runs for: (1) polymorph and hydrate screening of ortho-aminobenzoic acid, succinic acid and piroxicam; (2) co-crystal form screening of the p-toluenesulfonamide/triphenylphosphine oxide system; (3) studying the effect of o-toluic acid on ortho-aminobenzoic cooling crystallization (effect of structurally related additives). In all three cases, all known solid forms were identified with a single experiment using ∼200 μL of solvent and just a few micrograms of the solid material. The MMicroCryGen is simple to use, inexpensive and it provides increased flexibility compared to traditional crystallization techniques, being an effective new microfluidic solution for solid form screening in pharmaceutical, fine chemicals, food and agrochemical industries. Microfluidics ; High-throughput screening ; Polymorphism (computer science) ; Solvent ; Materials science ; Dipstick ; Nanotechnology ; Crystal (programming language) ; Generator (circuit theory) ; Chemistry ; Computer science ; Organic chemistry ; Allele ; Physics ; Biochemistry ; Thermodynamics Crystallization and Solubility Studies ; Innovative Microfluidic and Catalytic Techniques Innovation ; Analytical Chemistry and Chromatography