jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huma Ali
Source: FARMACIA (article, 2019-07-10)
Field: Chemistry — Analytical Chemistry

DESIGN AND OPTIMIZATION OF FAST DISPERSIBLE FORMULATIONS OF MULTI STRENGTH MELOXICAM TABLETS USING RESPONSE SURFACE METHODOLOGY The purpose of the current study was to design, develop and optimize fast dispersible tablets (FDT) of meloxicam 7.5 mg and 15 mg. For this, CCRD was used with Design Expert software. Nine diverse formulations, each of strength (M A 1 -M A 9; M B 1 -M B 9) (F1 -F9) were designed, with two independent variables, including (X 1 ) Croscarmellose (Ac-Di-Sol) (4 -10%), (X 2 ) Microcrystalline cellulose (Avicel PH-102) (50 -65%) at five different levels (1, -1, 0, , -). Disintegration time (Y 1 ) and hardness (Y 2 ) were chosen as responses variables. Powder blends of these trial compositions were evaluated for micromeritic parameters and finally six formulations from each batch were qualified for compression stage. The selected formulations were compressed by direct compression procedure. Tablet fill was improved by aspartame, incorporated as a sweetener. Different compendial and non-compendial tests were carried out to determine the quality attributes of FDT of Meloxicam and the results were observed within the adequate values. M A 6 and M B 6 were selected as the preeminent optimized products on the basis of micromeritic and physicochemical features. M A 6 and M B 6 have shown quicker disintegration time i.e. 18 sec; 19 sec and optimal percentage release of drug i.e., 99.38 0.86%; 99.86 0.91% and drug recovery (assay) values as 100.76 0.52%; 100.01 0.84% respectively. Correspondingly, predicted and actual values of hardness and disintegration time were found in good correlation. FDTs of Meloxicam were successfully developed using RSM tool, which was found valuable in discovering the consequences of numerous variables on drug processability. Meloxicam ; Response surface methodology ; Materials science ; Chromatography ; Chemistry ; Pharmacology ; Medicine Spectroscopy and Chemometric Analyses ; Injection Molding Process and Properties ; Drug Solubulity and Delivery Systems
Authors: Sachin Sonawane | Mohan Awasthy | Nitin S. Choubey | Mr. Digambar B. Sonawane - | Mukesh Patel | Graham Treece | Adam Erwin | Diah Nevriyanto | Purnamasari | P Chitra | Jintu Manuel | Kuo-Shiuan | Fang-Cheng Peng | Yi-Pai Lin | Huang | D Han-Ping | Shieh | Ashwini Khekade | Kishor Bhoyar | Salman Khan | Mohammed Bennamoun | Y Shedlovska | V Hnatushenko | Dr Manoj K Sabnis | Kumar Shukla | Arti Tiwari | Pradeep Singh | Sobia Amin | Abubakar Yamin | Faisal Imran | Usman Akbar | Hassan Syed | Tanvir | Choubeila Maaoui | Christophe Rosenberger | Bruno Emile | M Reis | R Morais | E Peres | C Pereira | O Contente | S Soares | A Valente | J Baptista | P Ferreira | J Cruz | Zalhan Mohd Zin | Sera Syannila Sameon | Mohd Nazri Ismail | Mohd Taha Ismail | D Surya Prabha | J Kumar | Somya Sharma | Jabal Raval | Bhushan Jagyasi | Qing Yao | Zexin Guan | Yingfeng Zhou | Jian Tang | Yang Hu | Baojun Yang | Panitnat Yimyam | Adrian Clark | Nur Badariah | Ahmad Mustafa | Kumutha Arumugam | Khaleel Syed | Zainul Ahmed | Md Abidin | Sharrif | Ksh | Saurabh Singh | Chaudhury | Takayuki Yamashita | Kohji Mitani | Engr | Dr Zahida Parveen | Engr Muhammad Anzar Alam | Shakir | I Zayas | C Martin | J Steele | Katsevich
Source: International Journal of Innovative Research in Electronics and Communications (review, 2019-01-01)
Field: Chemistry — Analytical Chemistry

A Literature Review on Image Processing and Classification Techniques for Agriculture Produce and Modeling of Quality Assessment system for Soybean industry Sample Soybean is the most admired and favored food in the world. It is readily available in all the countries. Because of richness in protein, it is recommended and consumed in the daily food. Variety of valueadded edible products can be prepared from Soybean including Soya-Milk, Oil, Pulses etc. for human as well as animals. The countries having major share in Soybean production are United States, Brazil, Argentina, China and India In international food market, the quality of Soybean is a major concerning order to assure the production of best tertiary product by food industry. To ease the trading process the countries like US, Canada have recommended certain criteria and standards to buyers for grading Soybean quality. Thus, the measurement of Soybean quality is essential as well as an equally important requirement in today"s scenario to protect the buyers from substandard produce Accordingly, if the Soybean quality assessment is done manually then the chance of error in measurement is significant and in case it is done with the help of a perfectly trained automatic machine then the chance of error would be minimum. On these aspects, this research work is directed towards the modeling of an automated system for the quality measurements of Soybean Sample Quality (philosophy) ; Sample (material) ; Image processing ; Agriculture ; Computer science ; Agricultural engineering ; Artificial intelligence ; Process engineering ; Image (mathematics) ; Engineering ; Geography ; Chromatography ; Chemistry Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Food Supply Chain Traceability
Authors: S. S. Negi | Navneet Yadav | Rahul Rawat | Rishabh Singh | A Danti | J Kulkarni | P Hiremath | N Yadav | M Salazar | L Suarez | D .-Marie | I Pike | S Joshi | I Navotas | C Santos | E Balderrama | F Candido | A Issac | M Dutta | B Sarkar | R Burget | G Xue | W Yu | L Yutong | Z Qiang | G lafsdttir | E Martinsdottir | J Oehlenschlger | P Dalgaard | B Jensen | Z Chen | Y Lin | X Ma | L Guo | B Qiu | G Chen | Z Lin | Navneet Yadav Born | Kulbir Singh | Sudan Jha | Raghvendra Kumar | Le Hoang Son | Jyotir Moy Chatterjee | Navneet Manjukhari | Florentinsmarandache Yadav | B
Source: International Journal of Innovative Technology and Exploring Engineering (article, 2019-08-23)
Field: Chemistry — Analytical Chemistry

An Effective Technique for Determining Fish Freshness using Image Processing This paper presents an efficient image processing based algorithm to determine the freshness of a fish. The determination is made after judging the condition of the eyes and the gills of the sample. The method is applicable for most edible fishes and can be easily reconfigured by changing the effect that the two determinants have on the final outcome. The novelty of the method lies in the ability of the algorithm to easily adapt to new samples which is a result of the universal applicability of the parameters selected. The method shows highly accurate results when compared to the ground truth. Fish <Actinopterygii> ; Novelty ; Image (mathematics) ; Computer science ; Outcome (game theory) ; Image processing ; Sample (material) ; Artificial intelligence ; Ground truth ; Computer vision ; Pattern recognition (psychology) ; Mathematics ; Fishery ; Biology ; Chemistry ; Chromatography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring Technologies ; Identification and Quantification in Food
Authors: Ahmed Rady | Daniel E. Guyer | W. W. Kirk | Irwin R. Donis-González
Source: Journal of Imaging (article, 2019-01-09)
Field: Chemistry — Analytical Chemistry

Prediction of the Leaf Primordia of Potato Tubers Using Sensor Fusion and Wavelength Selection The sprouting of potato tubers during storage is a significant problem that suppresses obtaining high quality seeds or fried products. In this study, the potential of fusing data obtained from visible (VIS)/near-infrared (NIR) spectroscopic and hyperspectral imaging systems was investigated, to improve the prediction of primordial leaf count as a significant sign for tubers sprouting. Electronic and lab measurements were conducted on whole tubers of Frito Lay 1879 (FL1879) and Russet Norkotah (R.Norkotah) potato cultivars. The interval partial least squares (IPLS) technique was adopted to extract the most effective wavelengths for both systems. Linear regression was utilized using partial least squares regression (PLSR), and the best calibration model was chosen using four-fold cross-validation. Then the prediction models were obtained using separate test data sets. Prediction results were enhanced compared with those obtained from individual systems' models. The values of the correlation coefficient (the ratio between performance to deviation, or <i>r</i>(RPD)) were 0.95(3.01) and 0.9s6(3.55) for FL1879 and R.Norkotah, respectively, which represented a feasible improvement by 6.7%(35.6%) and 24.7%(136.7%) for FL1879 and R.Norkotah, respectively. The proposed study shows the possibility of building a rapid, noninvasive, and accurate system or device that requires minimal or no sample preparation to track the sprouting activity of stored potato tubers. Partial least squares regression ; Hyperspectral imaging ; Sprouting ; Mathematics ; Horticulture ; Computer science ; Biology ; Statistics ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Leaf Properties and Growth Measurement
Authors: Sami Sainio | Niklas Wester | Charles J. Titus | Dennis Nordlund | Sang‐Jun Lee | Jari Koskinen | Tomi Laurila
Source: AIP Advances (article, 2019-08-01)
Field: Materials Science — Materials Chemistry

In-situ functionalization of tetrahedral amorphous carbon by filtered cathodic arc deposition Modification of the surface chemistry of carbon-based nanomaterials is often necessary in order to embrace their full potential. A wide variety of different post-fabrication treatments, such as acid, oxidizing plasma and heat treatments have been described in the literature. However, their specific effects on the materials surface chemistry is typically only vaguely disclosed. Here we report an in-situ method to functionalize tetrahedral amorphous carbon (ta-C) thin films by introducing high purity oxygen into the vacuum chamber during the film fabrication. Additionally, we analyze and compare the material properties of the resulting thin films to those of nitric acid and oxygen plasma treated as well as those with no treatment at all. Using x-ray absorption spectroscopy (XAS), we show that in-situ functionalizing decreases the sp2 content of the surface and increases the amount of carboxyl-like functionalities. Subsequent oxygen plasma treatment further decreases the sp2 fraction and ketone/aldehyde content as well as increases the amount of carboxyl groups. The same trends are observed with the reference ta-C exposed to oxygen plasma treatment. For both materials, a concentrated nitric acid treatment has only a subtle effect on the surface chemistry. Capitalizing on this knowledge, we can selectively produce materials with higher surface loading of specific functional groups, paving the way for application specific material fabrication. Surface modification ; Amorphous solid ; Oxidizing agent ; Amorphous carbon ; Carbon fibers ; Nitric acid ; Fabrication ; Materials science ; Chemical engineering ; Oxygen ; Deposition (geology) ; Chemistry ; Inorganic chemistry ; Nanotechnology ; Organic chemistry ; Composite material ; Composite number Diamond and Carbon-based Materials Research ; Metal and Thin Film Mechanics ; Semiconductor materials and devices
Authors: Yunyun Pi | Xiaolong Yin | Wenjun Deng | Wei Xia
Source: Advances in Materials Science and Engineering (article, 2019-07-08)
Field: Materials Science — Materials Chemistry

Study on Surface Hardness and Microstructure of Pure Copper Chip Strips Prepared by LSEM Large strain extrusion machining (LSEM) is one of the severe plastic deformation (SPD) methods that can improve the mechanical properties of materials. The purpose of this experiment is to study the surface hardness and microstructure of the pure copper chip strips. It was found that most of the grains of the chip strips had been refined to the ultrafine grain grade. Finite element analysis (FEA) simulations were conducted to predict the von Mises equivalent strains. Based on the analysis of variance (ANOVA), further study indicated that the surface hardness of the chip strips was decided by several key parameters including the chip thickness compression ratio, rake angle, and uncut chip thickness during LSEM. Through this analysis, a set of parameters which have the greatest impact on the properties of the material can be found. This set of parameters helps us to achieve the strip with the best performance. Materials science ; STRIPS ; Microstructure ; Extrusion ; Finite element method ; Copper ; Chip ; Composite material ; Compression (physics) ; Chip formation ; Machining ; von Mises yield criterion ; Metallurgy ; Deformation (meteorology) ; Indentation hardness ; Structural engineering ; Tool wear Microstructure and mechanical properties ; Metallurgy and Material Forming ; Aluminum Alloys Composites Properties
Authors: William E. Fondrie | William Stafford Noble
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-11-20)
Field: Chemistry — Spectroscopy

A machine learning strategy that leverages large datasets to boost statistical power in small-scale experiments Abstract Machine learning methods have proven invaluable for increasing the sensitivity of peptide detection in proteomics experiments. Most modern tools, such as Percolator and PeptideProphet, use semi-supervised algorithms to learn models directly from the datasets that they analyze. Although these methods are effective for many proteomics experiments, we suspected that they may be suboptimal for experiments of smaller scale. In this work, we found that the power and consistency of Percolator results was reduced as the size of the experiment was decreased. As an alternative, we propose a different operating mode for Percolator: learn a model with Per-colator from a large dataset and use the learned model to evaluate the small-scale experiment. We call this a “static modeling” approach, in contrast to Percolator’s usual “dynamic model” that is trained anew for each dataset. We applied this static modeling approach to two settings: small, gel-based experiments and single-cell proteomics. In both cases, static models increased the yield of detected peptides and eliminated the model-induced variability of the standard dynamic approach. These results suggest that static models are a powerful tool for bringing the full benefits of Percolator and other semi-supervised algorithms to small-scale experiments. Abstract Figure Computer science ; Consistency (knowledge bases) ; Machine learning ; Scale (ratio) ; Artificial intelligence ; Power (physics) ; Data mining Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Selahattin Bardak | Timuçin Bardak
Source: Acta Scientiarum Polonorum Silvarum Colendarum Ratio et Industria Lignaria (article, 2019-03-30)
Field: Materials Science — Polymers and Plastics

MOISTURE CONTENT PREDICTION OF WOOD DRYING PROCESS USING FUZZY LOGIC The fuzzy logic method might be used with various applications, for example, timber production, air conditioners, washing machines, and molecular biology.Fuzzy logic systems are trusted.At the same time easy to use and flexible.Temperatures and relative humidity vary depending on the room where the wood is used.Moisture in wood-based products is one of the key points.Because it directly affects the usage area and production.In the present study, the effects of temperature and drying time on moisture content were modeled and predicted in the wood drying process.For this purpose, the wood test materials were prepared, and their moisture contents were measured.The fuzzy logic model was established by taking references of the observed values.With this fuzzy logic model, the moisture contents of the wood were predicted in relation to drying temperature and time.Then, experimental results were compared with modeling data.97.16% accuracy was observed with the fuzzy logic model. Wood drying ; Water content ; Process (computing) ; Fuzzy logic ; Process engineering ; Environmental science ; Pulp and paper industry ; Mathematics ; Agricultural engineering ; Computer science ; Artificial intelligence ; Geotechnical engineering ; Engineering ; Programming language Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; Advanced Numerical Analysis Techniques
Authors: Shijun Chen | Hao Zhu | Mengmeng Zhang | Wenwu Xu | Yucai Wang | Zhi-Fei Zhang
Source: Zeitschrift für Kristallographie - New Crystal Structures (article, 2019-02-01)
Field: Chemistry — Organic Chemistry

Crystal structure of 1-benzyl-3-cyano-6-phenyl-1,2-dihydropyridine, C<sub>19</sub>H<sub>16</sub>N<sub>2</sub> Abstract C 19 H 16 N 2 , orthorhombic, P 2 1 2 1 2 1 (no. 19), a = 7.5177(10) Å, b = 12.4761(16) Å, c = 16.118(2) Å, V = 1511.7(3) Å 3 , Z = 4, R gt ( F ) = 0.0424, wR ref ( F 2 ) = 0.0967, T = 293(2) K. Crystal structure ; Dihydropyridine ; Crystal (programming language) ; Chemistry ; Crystallography ; Stereochemistry ; Organic chemistry ; Computer science ; Calcium Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles ; Chemical Synthesis and Analysis
Authors: Arshiah Yusuf Mirza
Source: OpenCommons - UConn (University of Connecticut) (article, 2019-01-01)
Field: Materials Science — Materials Chemistry

Model-based Medium-voltage Induction Machine Insulation Material Characterization The voltage and frequency handling capability of power converter modules have tremendously increased with the penetration of wide-band-gap (WBG) semiconductor technology in power electronics. However, very little is known about the effect of the high-voltage high-frequency transients exerted by WBG power electronics on insulation materials. This thesis investigates the effect of high-voltage, high-frequency transients on aging and breakdown characteristics of insulation materials. The aim of this research was to analyze this breakdown and develop the behavioral model of the breakdown time when such insulation materials are used in inverter-fed medium-voltage induction machines. As a part of this research, a series of breakdown tests have been performed on high temperature insulation films using high-voltage high-frequency pulses with a large slew rate (dV/dt) of around 4GV/sec. Behavioral models of breakdown characteristics were modeled as a function of film gauge (insulation material thickness), pulse voltage (breakdown voltage) and frequency under simulated pulse-width-modulated wave forms. The testing methodology and the modeling methodology have also been developed. Characterization (materials science) ; Voltage ; Computer science ; Materials science ; Mechanical engineering ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation
Authors: Jonas Pfab | Dong Si
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-06-02)
Field: Materials Science — Materials Chemistry

Automated Threshold Selection for Cryo-EM Density Maps Abstract Recent advances in cryo-EM have made it possible to create protein density maps with a near-atomic resolution. This has contributed to its wide popularity, resulting in a rapidly growing number of available cryo-EM density maps. In order to computationally process them, an electron density threshold level is required which defines a lower bound for density values. In the context of this paper the threshold level is required in a pre-processing step of the backbone structure prediction project which predicts the location of Cα atoms of the backbone of a protein based on its cryo-EM density map using deep learning techniques. A custom threshold level has to be selected for each prediction in order to reduce noise that could irritate the deep learning model. Automatizing this threshold selection process makes it easier to run predictions as well as it removes the dependency of the prediction accuracy to the ability of someone to choose the right threshold value. This paper presents a method to automatize the threshold selection for the previously mentioned project as well as for other problems which require a density threshold level. The method uses the surface area to volume ratio and the ratio of voxels that lie above the threshold level to non-zero voxels as metrics to derive characteristics about suitable threshold levels based on a training dataset. The threshold level selection was tested by integrating it in the backbone prediction project and evaluating the accuracy of predictions using automatically as well as manually selected thresholds. We found that there was no loss in accuracy using the automatically selected threshold levels indicating that they are equally good as manually selected ones. The source code related to this paper can be found at https://github.com/DrDongSi/Auto-Thresholding . Selection (genetic algorithm) ; Computer science ; Voxel ; Context (archaeology) ; Threshold limit value ; Artificial intelligence ; Process (computing) ; Pattern recognition (psychology) ; Algorithm ; Chemistry Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Protein Structure and Dynamics
Authors: Joseph F. Rudzinski | Marc Radu | Tristan Bereau
Source: The Journal of Chemical Physics (article, 2019-01-08)
Field: Materials Science — Materials Chemistry

Automated detection of many-particle solvation states for accurate characterizations of diffusion kinetics Discrete-space kinetic models, i.e., Markov state models, have emerged as powerful tools for reducing the complexity of trajectories generated from molecular dynamics simulations. These models require configuration-space representations that accurately characterize the relevant dynamics. Well-established, low-dimensional order parameters for constructing this representation have led to widespread application of Markov state models to study conformational dynamics in biomolecular systems. On the contrary, applications to characterize single-molecule diffusion processes have been scarce and typically employ system-specific, higher-dimensional order parameters to characterize the local solvation state of the molecule. In this work, we propose an automated method for generating a coarse configuration-space representation, using generic features of the solvation structure-the coordination numbers about each particle. To overcome the inherent noisy behavior of these low-dimensional observables, we treat the features as indicators of an underlying, latent Markov process. The resulting hidden Markov models filter the trajectories of each feature into the most likely latent solvation state at each time step. The filtered trajectories are then used to construct a configuration-space discretization, which accurately describes the diffusion kinetics. The method is validated on a standard model for glassy liquids, where particle jumps between local cages determine the diffusion properties of the system. Not only do the resulting models provide quantitatively accurate characterizations of the diffusion constant, but they also reveal a mechanistic description of diffusive jumps, quantifying the heterogeneity of local diffusion. Solvation ; Kinetics ; Diffusion ; Particle (ecology) ; Statistical physics ; Materials science ; Chemistry ; Thermodynamics ; Physics ; Organic chemistry ; Geology Material Dynamics and Properties ; Phase Equilibria and Thermodynamics ; Advanced NMR Techniques and Applications