jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Noshin Tasnia | Moon Halder | Mst. Jiasmin Ara | Md Rejaul Karim | Shakik Mahmud
Source: Annals of Computer Science and Information Systems (article, 2022-02-20)
Field: Chemistry — Analytical Chemistry

Root Rot Lentil and Healthy Lentil Detection Using Image Processing The hardest thing to do in agriculture is to figure out which leaves are healthy and which ones are damaged.Bangladesh makes 80% of its money from farming.Most farmers cannot read or write.They didn't know how much fertilizer to put on a lentil with root rot or a healthy lentil.They sometimes spray medicine on the plants, which is terrible for them.As a result, agriculture has become much less productive.In this paper, a picture-segmenting algorithm is given that can automatically find and classify plant leaf diseases.Also included are surveys of different ways to classify diseases that can be used to find plant leaf diseases.The Convolution Neural Network model is used to segment images, an essential part of finding plant leaf diseases.Every country's growth is based on its agricultural production.To keep agricultural production at a certain level and keep growing sustainably, scientists need to study how to find and treat diseases.Standard methods in the literature for classifying leaf images involve extracting attributes and training classifier models, which makes them less accurate.The technique suggested gets rid of any unnecessary data from the image collection.Using the mixture model for region growth, we first find the area of interest based on the colors of the leaves in the image.After extracting the features, a deep convolution neural network model is used to classify the leaf photos.A convolutional neural network model can be used with the deep learning model to find different patterns in color photos.Examining the execution strategy of the proposed model using an unauthorized dataset.According to the results of the simulating replica, the suggested model outperforms the well-known current methods in the field, with mean classification accuracy and area under the characteristics curve of 95.35% and 94.7%, respectively. Image processing ; Computer science ; Root (linguistics) ; Image (mathematics) ; Artificial intelligence ; Computer vision ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Jiancheng Li | Chongle Pan | Xuan Guo
Source: 2022 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (article, 2022-12-06)
Field: Chemistry — Spectroscopy

IDIA: An Integrative Signal Extractor for Data-Independent Acquisition Proteomics In proteomics, data-independent acquisition (DIA) has been shown to provide less biased and more reproducible results than data-dependent acquisition. Recently, many researchers have developed a series of methods to identify peptides and proteins by using spectrum libraries for DIA data. However, spectrum libraries are not always available for novel organisms or microbial communities. To detect peptides and proteins without a spectrum library, we developed IDIA, a library-free method using DIA data to generate pseudo-spectra that can be searched using conventional sequence database searching software. IDIA integrates two isotopic trace detection strategies and employs B-spline and Gaussian filters to help extract high-quality pseudo-spectra from the complex DIA data. The experimental results on human and yeast data demonstrated that our approach remarkably produced more peptide and protein identifications than the two state-of-the-art library-free methods, i.e., DIA-Umpire and Group-DIA. IDIA is freely available under the GNU GPL license at https://github.com/Biocomputing-Research-Group/IDIA. Computer science ; Proteomics ; Extractor ; Data acquisition ; Software ; TRACE (psycholinguistics) ; Data mining ; Chemistry ; Programming language Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Identification and Quantification in Food
Authors: Xiaojun Xu | Yue Yu | Hanzhang Wang | Alok Lal | Carl A. Gunter | Bo Li
Source: arXiv (Cornell University) (preprint, 2022-12-27)
Field: Materials Science — Materials Chemistry

EDoG: Adversarial Edge Detection For Graph Neural Networks Graph Neural Networks (GNNs) have been widely applied to different tasks such as bioinformatics, drug design, and social networks. However, recent studies have shown that GNNs are vulnerable to adversarial attacks which aim to mislead the node or subgraph classification prediction by adding subtle perturbations. Detecting these attacks is challenging due to the small magnitude of perturbation and the discrete nature of graph data. In this paper, we propose a general adversarial edge detection pipeline EDoG without requiring knowledge of the attack strategies based on graph generation. Specifically, we propose a novel graph generation approach combined with link prediction to detect suspicious adversarial edges. To effectively train the graph generative model, we sample several sub-graphs from the given graph data. We show that since the number of adversarial edges is usually low in practice, with low probability the sampled sub-graphs will contain adversarial edges based on the union bound. In addition, considering the strong attacks which perturb a large number of edges, we propose a set of novel features to perform outlier detection as the preprocessing for our detection. Extensive experimental results on three real-world graph datasets including a private transaction rule dataset from a major company and two types of synthetic graphs with controlled properties show that EDoG can achieve above 0.8 AUC against four state-of-the-art unseen attack strategies without requiring any knowledge about the attack type; and around 0.85 with knowledge of the attack type. EDoG significantly outperforms traditional malicious edge detection baselines. We also show that an adaptive attack with full knowledge of our detection pipeline is difficult to bypass it. Adversarial system ; Computer science ; Preprocessor ; Graph ; Artificial intelligence ; Data mining ; Theoretical computer science ; Machine learning Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Adversarial Robustness in Machine Learning
Authors: Jalest Septiano | Amega Yasutra | Silvya Dewi Rahmawati
Source: Scientific Contributions Oil and Gas (article, 2022-04-01)
Field: Chemistry — Analytical Chemistry

Build of Machine Learning Proxy Model for Prediction of Wax Deposition Rate in Two Phase Flow Water-Oil Wax deposit is one of the major fl ow assurance experienced in the process of oil production and transportation from sub- surface to surface. Large amounts of data are required to perform modeling using existing thermodynamic models such as carbon number data from HGTC. In this paper, a machine learning algorithm using unifi ed model approach from Huang (2008). Two types of input are implemented in order to simulate infl uence of feature selection used in training and testing machine learning which are input A consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), and temperature gradient (dT/dR) and input B consists of water volume fraction (fw), shear stress (τw), effective viscosity (μe), wax concentration gradient (dC/dT), temperature gradient (dT/dR), shear stripping variable (SV) dan diffusion variable (DV). The random forest with Ntree = 500 known to be the best machine learning method compared to others. Based on accuracy parameter it achieves error parameter R-squared (R2) for training, testing and total data for input A and B are 0.999, 0.992, 0.9975 and 0.999, 0.993, 0.9977, respectively. Wax ; Shear stress ; Machine learning ; Viscosity ; Volume fraction ; Artificial intelligence ; Materials science ; Shear rate ; Algorithm ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Composite material ; Chromatography Petroleum Processing and Analysis
Authors: Omsalma Alsadig Adam Gadalla | Yeşim BENAL ÖZTEKİN | Geofrey Prudence Baitu
Source: DergiPark (Istanbul University) (article, 2022-05-01)
Field: Chemistry — Analytical Chemistry

Application of Computer Vision and Image Processing Technology in Agro-Product Quality Control- Review Post harvest quality evaluation processes are very important operations and play a significant role in determining the acceptability and marketability of the product. Computer vision and image processing technology has been applied wildly in food industries in order to evaluate the quality of agricultural products such as; sorting, grading, and classification processes due to the performance, low cost, and effectiveness of the technology. In this article, we aim to review to application of computer vision and image processing technology in evaluating the quality of agricultural products representing in; application of this technology in evaluation the quality of fruits, vegetables, and nut products with more attention to hazelnut product. Computer science ; Computer vision ; Image processing ; Product (mathematics) ; Artificial intelligence ; Quality (philosophy) ; Image quality ; Image (mathematics) ; Mathematics Spectroscopy and Chemometric Analyses
Authors: Mohadese Mohammadi | Mohammad Firoz Khan | Ridwan Bin Rashid | Sina Mirzaie Nokhostin | Mohammad Mamun Ur Rashid
Source: Journal of Applied Pharmaceutical Science (article, 2022-05-05)
Field: Chemistry — Organic Chemistry

In silico screening of hispolon and its analogs: Pharmacokinetics and molecular docking studies with cyclooxygenase-2 enzyme Hispolon is a phenolic compound with diverse biological activities. The analgesic action of hispolon is due to the inhibition of prostaglandins biosynthesis. However, the molecular basis of this inhibition has not been explored yet. Therefore, we have carried out theoretical investigations to evaluate the molecular basis of analgesic action. Furthermore, we have conducted high throughput in silico screening of a library of compounds to get novel cyclooxygenase 2 (COX-2) inhibitors with better pharmacokinetic and analgesic properties. The docking study was conducted using PyRx and the drug-like properties were calculated by MarvinSketch. Furthermore, the pharmacokinetic properties were computed on the online server PreADMET. In this study, our virtual screening based on structure similarity search using hispolon as reference structure afforded 1,699 compounds. These compounds were subjected to molecular docking with COX-2. These were then filtered based on binding affinity, binding poses, and drug-like properties which yielded seven compounds. The in silico pharmacokinetic study revealed that these compounds possess good human intestinal absorption and moderate permeability. Moreover, molecular docking of these compounds revealed that all the ligands possess moderate to good binding affinity (-7.6 to -8.9 Kcal/mol). Our computed properties may assist in developing hispolon derivatives with better pharmacokinetic and COX-2 inhibitory activity. Pharmacophore ; Docking (animal) ; In silico ; Chemistry ; Stereochemistry ; Cytotoxicity ; Biochemistry ; Medicine ; In vitro Synthesis and biological activity ; Computational Drug Discovery Methods
Authors: Jesse A. Jones | Michael P. Andreas | Tobias W. Giessen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-07-13)
Field: Materials Science — Materials Chemistry

Exploring the Extreme Acid Tolerance of a Dynamic Protein Nanocage Abstract Encapsulins are protein nanocages capable of efficient self-assembly and cargo enzyme encapsulation. They are found in a wide variety of bacteria and archaea, including many extremophiles, and are involved in iron and sulfur homeostasis, oxidative stress resistance, and secondary metabolite production. Resistance against physicochemical extremes like high temperature and low pH is a key adaptation of many extremophiles and also represents a highly desirable feature for many biotechnological applications. However, no systematic characterization of acid stable encapsulins has been carried out, while the influence of pH on encapsulin shells has so far not been thoroughly explored. Here, we report on a newly identified encapsulin nanocage (AaEnc) from the acid-tolerant bacterium Acidipropionibacterium acidipropionici. Using transmission electron microscopy, dynamic light scattering, and proteolytic assays, we demonstrate its extreme acid tolerance and resilience against proteases. We structurally characterize the novel nanocage using cryo-electron microscopy, revealing a dynamic five-fold pore that displays distinct “closed” and “open” states at neutral pH, but only a singular “closed” state under strongly acidic conditions. Further, the “open” state exhibits the largest pore in an encapsulin shell reported to date. Non-native protein encapsulation capabilities are demonstrated, and the influence of external pH on internalized cargo is explored. AaEnc is the first characterized highly acid stable encapsulin with a unique pH-dependent dynamic pore and its molecular characterization provides novel mechanistic details underlying the pH stability of large dynamic protein complexes. Nanocages ; Proteases ; Archaea ; Chemistry ; Extremophile ; Bacteria ; Biophysics ; Biochemistry ; Nanotechnology ; Enzyme ; Thermophile ; Materials science ; Biology ; Catalysis Enzyme Structure and Function ; Protein Structure and Dynamics ; Bacteriophages and microbial interactions
Authors: Panuphong Mankeed | Thossaporn Onsree | Nakorn Tippayawong
Source: AIP conference proceedings (article, 2022-01-01)
Field: Materials Science — Materials Chemistry

Analysis of reaction kinetics for thermal degradation of hemp residues Hemp is becoming one of the high-value economic crops in Thailand and many countries, in which it is cultivated for specific industrial applications, such as seeds, fibers, and medicinal products. After production processing, hemp hurds or residues obtained were approximately 2/3 w/w of starting raw material. They may be used as a renewable material or fuel via thermo-chemical conversion. Therefore, the objective of this work was to investigate thermo-chemical degradation and kinetic behaviors of hemp residues using thermogravimetry with non-isothermal conditions of 10-50 °C/min heating rates under an inert environment. From the results, it was found that hemp residues appeared to have three zones of thermal decomposition. With devolatilization, the residues were highly decomposed at temperatures between 200 and 400 °C for about 60% w/w of mass loss. By Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS), and Kissinger free-model methods, the kinetic parameters were evaluated. The activation energies for the FWO and KAS were found to be an inverse function of conversion fractions, varying from 250 to 370 kJ/mol with the average values of 265.5 and 291.5 kJ/mol, respectively. While, for Kissinger, the activation energy was a constant of 282.3 kJ/mol. Materials science ; Thermogravimetry ; Activation energy ; Degradation (telecommunications) ; Isothermal process ; Decomposition ; Raw material ; Thermal decomposition ; Inert ; Kinetics ; Renewable energy ; Inert gas ; Chemical engineering ; Composite material ; Thermodynamics ; Physical chemistry ; Organic chemistry ; Chemistry Thermal and Kinetic Analysis ; Thermochemical Biomass Conversion Processes ; Insect Pest Control Strategies
Authors: Anis Hazirah ‘Izzati Hasnu Al-Hadi | Aqib Fawwaz Mohd Amidon | Siti Mariatul Hazwa Mohd Huzir | Nurlaila Ismail | Zakiah Mohd Yusoff | Saıful Nizam Tajuddin | Mohd Nasir Taib
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2022-11-18)
Field: Chemistry — Organic Chemistry

Preliminary study on agarwood essential oil and its classification techniques using machine learning Using essential oils derived from trees for pharmaceutical purposes, incense, aromatherapy, and other areas has expanded its popularity on the international market. However, since human sensory evaluation is still the primary technique used to grade essential oils in Malaysia, the classification technique for determining their grade is still below standard. Nonetheless, prior studies established new approaches for classifying the grade of essential oils by studying their chemical compounds. Therefore, agarwood essential oil was selected for the suggested model due to the increasing demand and the high cost of the world's natural raw materials. The support vector machine (SVM) using one versus all (OVA) approach was selected as the classifier for agarwood essential oil. This study provides an overview of essential oils and their prior research techniques. In addition, a review of SVM is conducted to demonstrate that the technique is appropriate for future studies. Agarwood ; Incense ; Support vector machine ; Essential oil ; Aromatherapy ; Machine learning ; Artificial intelligence ; Computer science ; Biochemical engineering ; Traditional medicine ; Engineering ; Chemistry ; Food science ; Geography ; Medicine Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Spectroscopy and Chemometric Analyses
Authors: Aiyeshah Alhodaib | Suzy Abd El All | Omnia Ibrahim | Fatthy Ezzeldin
Source: Preprints.org (preprint, 2022-01-31)
Field: Materials Science — Materials Chemistry

Preparation of Bioglasses Developed from Bypass Cement Dust for Bone Regeneration and Comparing their Radiation Damage Prediction with Natural Bone Selected glasses with 10 Na2O + (90-x) P2O5 + x BCD where bypass cement dust (BCD) x value =10, 20, 30 in mol% were synthesized by recognizable melting annealing technique. Cooperative characterization of the prepared glasses were carried out through FTIR and SEM analysis before and after immersed in simulated body fluid (SBF) solution for 13 and 23 days at 37 oC. After immersion in SBF, apatite layer is produced on the glass surface after 13 day and increase after 23 day, showing good bioactivity after immersion in the SBF especially for bioglass sample with 30% BCD. A porous hydroxy apatite layer produced on the surface of SBF-glass composite and this layer became denser after more soaking time, periods were extended from 13 to 23 days. Atomic absorption spectroscopy explained the early period of soaking that cause release of both Si and Ca ions through the glass beside decreasing of phosphrous ions. Bioglass (BCD-30) is studied as shielding for gamma, protons and alphas by using Phy-X software, SRIM Monte Carlo simulation code and its subroutine TRIM. The gamma shielding parameters, mass stopping power (MSP), range for both proton (H- ions) and alpha (He- ions) in bioglass- BCD-30 and human bone tissue have estimated. Also, comparison between them is calculated in predicting the radiation damage and atomic displacements per atom (dpa). Simulated body fluid ; Materials science ; Apatite ; Ion ; Composite material ; Bioactive glass ; Electromagnetic shielding ; Cement ; Radiochemistry ; Analytical Chemistry (journal) ; Mineralogy ; Scanning electron microscope ; Chemistry ; Chromatography Radiation Shielding Materials Analysis ; Nuclear Physics and Applications ; Bone Tissue Engineering Materials
Authors: Marco Clemente | Heloisa Silva | Julia Campos | Nathan Silva | Eduardo Branco de Sousa | Ana Carolina Gomes Mantovani | Karina Gomes Angilelli | Dionísio Borsato
Source: Journal of the Brazilian Chemical Society (article, 2022-11-08)
Field: Chemistry — Analytical Chemistry

Mathematical Mapping Study of Synthetic Antioxidants Behavior in Biodiesel: Application of the Self-Organized Feature Map (SOFM) and Multilayer Perceptron (MLP) The objective of the present work was to apply self-organizing feature maps (SOFM) and multilayer perceptron (MLP) to evaluate the protective capacity of tert-butylhydroquinone (TBHQ), butylhydroxyanisole (BHA), and butylhydroxytoluene (BHT) antioxidants against the biodiesel oxidation reaction. For this, the antioxidant concentration and the biodiesel compliance parameters were used as continuous input variables and the type of antioxidant as a categorical. The SOFM proved to be an adequate tool for the classification of biodiesel samples containing antioxidants. The performance of the 5 most active networks of the model ranged from 89 to 99% for training, testing, and validation with an error below 1.42 × 10-4. Statistical tests applied to validate the model showed no significant difference between predicted and experimental values. The global sensitivity analysis showed that the relative protection factor (RPF) contributed with 34.89%, the antioxidant with 31.49%, the induction period with 10.69%, the water content with 6.00%, and the others all together with 16.93% in the construction of the regression models by MLP. Biodiesel ; Categorical variable ; Multilayer perceptron ; Artificial intelligence ; Feature (linguistics) ; Perceptron ; Antioxidant ; Pattern recognition (psychology) ; Computer science ; Regression analysis ; Biological system ; Machine learning ; Artificial neural network ; Mathematics ; Process engineering ; Chemistry ; Biochemical engineering ; Engineering ; Organic chemistry ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Amine Riad | Mouna Ben Zohra | Abdelilah Alhamany | Mohamed Mansouri
Source: Preprints.org (preprint, 2022-09-08)
Field: Materials Science — Materials Chemistry

Radiation Effect Modeling for Controlling Heat Transfer in the Shape Memory Alloy The shape memory alloys belong to the smart materials thanks to their thermomechanical proprieties' reply to thermal or to mechanical loading. These materials can change shape, stiffness, displacement, natural frequency, and many mechanical characteristics in response to stress or to heat such as conduction, convection or radiation. However, heating by convection or conduction are the most useful and studied methods unlike radiation. Therefore, this paper aims to study the radiation effect on the shape memory alloy behavior Thermal conduction ; Shape-memory alloy ; Materials science ; Heat transfer ; Convection ; Thermal radiation ; Displacement (psychology) ; Radiation ; Stiffness ; Natural convection ; Thermal ; Stress (linguistics) ; Mechanics ; Alloy ; Composite material ; Optics ; Thermodynamics ; Physics Shape Memory Alloy Transformations
Authors: Shinji Iida | Tomoshi Kameda
Source: ChemRxiv (preprint, 2022-11-17)
Field: Chemistry — Spectroscopy

Dissociation Rate Calculation via Constant-force Steered Molecular Dynamics Simulation Steered molecular dynamics (SMD) simulations have been applied to molecular dissociation events by adding a harmonic force to molecules. Further, molecules are pulled at a constant velocity. However, instead of the constant-velocity pulling, we use a constant force: the constant-force SMD (CF-SMD) simulation. The CF-SMD simulation employs a constant force to reduce the activation barrier of molecular dissociation, thereby enhancing the dissociation event. Here, we present the capability of the CF-SMD simulation to estimate the dissociation rate at equilibrium. We performed all-atom CF-SMD simulations for NaCl and protein–ligand systems, producing dissociation rates at various forces. We extrapolated these values to the dissociation rate without a constant force using the Dudko–Hummer–Szabo model. We demonstrate that the CF-SMD simulations and the model predicted the dissociation rate in equilibrium. A CF-SMD simulation is a powerful tool for estimating the dissociation rate in a direct and computationally efficient manner. Dissociation (chemistry) ; Molecular dynamics ; Dissociation rate ; Reaction rate constant ; Chemistry ; Force constant ; Molecule ; Dissociation constant ; Thermodynamics ; Chemical physics ; Physical chemistry ; Computational chemistry ; Physics ; Kinetics ; Classical mechanics ; Organic chemistry Mass Spectrometry Techniques and Applications ; Spectroscopy and Quantum Chemical Studies ; Molecular Junctions and Nanostructures