jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fawzia Rahman | Joy Das | Md. Rasel Al Mamun | Md. Mashurul Haque | Sidratul Muntaha Upoma | Anisur Rahman | Muhammad Ashik-E-Rabbani
Source: Journal of agricultural machinery and bioresources engineering. (article, 2024-09-25)
Field: Chemistry — Analytical Chemistry

Prediction of Potato Volume by Neural Network Regression Model Potatoes are essential for meeting the world's dietary requirements due to their high concentration of carbohydrates, vitamins, and minerals. Potato production is plentiful worldwide, but postharvest processing losses have reduced potato quality. Potatoes can be graded manually, which is time-consuming and labor-intensive. In this study, a neural network regression model was developed to predict the volume of potatoes using 2D images, which can be used as a parameter for grading potatoes automatically. The image processing method was used to extract features like the major axis, minor axis, and roundness of 400 potato samples, and each potato's volume was thoroughly measured using the water displacement method, and a dataset was generated. These attributes were used to create the neural network regression model and predict the volume. The results revealed that the mean squared error (MSE) was 83.93, the root mean squared error (RMSE) was 9.16, and R2 was approximately 0.882. The potatoes were categorized into three groups: small, medium, and large, based on their size. The utilized model demonstrated an overall accuracy rate of 91.4%. Specifically, it achieved 90% and 85% accuracy for small-sized and medium-sized potatoes, respectively, whereas the model displayed a decrease in accuracy from 60% to 70% for large-sized potatoes. To summarize, the suggested neural network regression model provided an efficient approach to volume prediction using 2D images to improve the grading process of agricultural products. Artificial neural network ; Volume (thermodynamics) ; Regression ; Regression analysis ; Statistics ; Artificial intelligence ; Econometrics ; Computer science ; Mathematics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Serkan Akkoyun
Source: Cumhuriyet Science Journal (article, 2024-03-28)
Field: Chemistry — Inorganic Chemistry

Machine Learning Based Classification of the Halos in Light Nuclei Region Experimental and theoretical studies on halo nuclei, whose nucleon binding energies are extremely weak, are among the most interesting topics of nuclear physics studies. By better defining and understanding this unusual behavior of these nuclei, our understanding of nuclear structure can be further improved. Although there are already a few experimentally proven halo nuclei in the literature, many others have found their place in the literature as candidate halo nuclei. In this study, the classification of halo nuclei was carried out using an artificial neural network approach. In the light nuclei region, the properties of nuclei, including halo nuclei, were discussed and the existing halo nuclei were classified. The success of the obtained results indicates that machine learning methods can be used for identifying halo nuclei. Thus, these methods are considered as one of the alternative tools to confirm the existence of new or candidate halo nuclei. Artificial intelligence ; Halo ; Computer science ; Pattern recognition (psychology) ; Physics ; Astrophysics ; Galaxy Inorganic Fluorides and Related Compounds
Authors: Qingxu Li | Hao Li | Renhao Liu | Xiaofeng Dong | Hongzhou Zhang | Wanhuai Zhou
Source: Agriculture (article, 2024-11-29)
Field: Chemistry — Analytical Chemistry

A Rapid Identification Method for Cottonseed Varieties Based on Near-Infrared Spectral and Generative Adversarial Networks China is a major cotton-growing country with numerous cotton varieties, each exhibiting significant differences in yield and fiber quality. However, the current management of cottonseed varieties is disorganized, resulting in severe homogenization and the presence of counterfeit and mislabeled varieties. The detection of cottonseed variety information has become a critical issue for the Chinese cotton industry. In this study, we collected near-infrared (NIR) spectral data from six cottonseed varieties and constructed a GAN for cottonseed NIR data (GAN-CNIRD) model to generate additional cottonseed NIR data. The Euclidean distance method was used to label the generated NIR data according to the characteristics of the true NIR data. We then applied Standard Normal Variate (SNV), Multiplicative Scatter Correction (MSC), and Normalization algorithms to preprocess the combined dataset of generated and real cottonseed NIR data. Feature wavelengths were extracted using Bootstrap Soft Shrinkage (BOSS) and Competitive Adaptive Reweighted Sampling (CARS) algorithms. Subsequently, we developed Linear Discriminant Analysis (LDA), Random subspace method (RSM), and convolutional neural network (CNN) models to classify the cottonseed varieties. The results showed that for the LDA model, the use of feature wavelengths extracted after Normalization-BOSS processing achieved the best performance with an accuracy of 97.00%. For the RSM model, the use of feature wavelengths extracted after SNV-CARS processing achieved the best performance with an accuracy of 98.00%. For the CNN model, the use of feature wavelengths extracted after MSC-CARS processing achieved the best performance with an accuracy of 100.00%. Data augmentation using GAN-CNIRD-generated cottonseed data improved the accuracy of the three optimal models by 6%, 5%, and 6%, respectively. This study provides a crucial reference for the rapid detection of cottonseed variety information and has significant implications for the standardized management of cottonseed varieties. Adversarial system ; Identification (biology) ; Generative grammar ; Cottonseed ; Artificial intelligence ; Cottonseed oil ; Biological system ; Pattern recognition (psychology) ; Computer science ; Botany ; Biology ; Food science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: A. A. Kozma
Source: ГРААЛЬ НАУКИ (article, 2024-07-26)
Field: Chemistry — Inorganic Chemistry

ОКСИДНІ ФОТОКАТАЛІЗАТОРИ ДЛЯ ЗАХИСТУ ТА ВІДНОВЛЕННЯ НАВКОЛИШНЬОГО СЕРЕДОВИЩА Наразі навколишнє середовище (атмосферне повітря, природні водойми, ґрунти) інтенсивно забруднюється багатьма шкідливими речовинами: небезпечними газами, антибіотиками, барвниками тощо. За допомогою фотокаталізаторів зазначені забруднювачі можна ефективно розкладати на простіші та безпечніші для навколишнього середовища компоненти. Medicine Pigment Synthesis and Properties ; Advanced Photocatalysis Techniques ; TiO2 Photocatalysis and Solar Cells
Authors: Yabing Liu
Source: Theoretical and Natural Science (article, 2024-06-21)
Field: Chemistry — Spectroscopy

Enhancing drug analysis through advanced mass spectrometry techniques and quantitative modeling The integration of advanced mass spectrometry (MS) techniques with quantitative modeling has revolutionized the field of drug analysis, offering unprecedented accuracy, sensitivity, and specificity in the detection and quantification of pharmaceutical compounds and their metabolites. High-Resolution Mass Spectrometry (HRMS) and Tandem Mass Spectrometry (MS/MS) have emerged as pivotal tools, capable of distinguishing isobaric species and providing detailed structural information, respectively. Ambient ionization techniques such as Desorption Electrospray Ionization (DESI) and Direct Analysis in Real Time (DART) have further simplified sample preparation, enabling rapid, direct analyses under ambient conditions. This review discusses the significant strides made in enhancing drug analysis through these innovations, alongside the application of calibration curves and mathematical models for quantitation and pharmacokinetic modeling. Additionally, it addresses the challenges inherent in quantitative MS, including matrix effects, analyte stability, and the need for improved sensitivity and specificity. Through a comprehensive overview, this article underscores the transformative impact of these advancements on drug analysis, paving the way for more accurate and efficient pharmaceutical research and diagnostics. Mass spectrometry ; DART ion source ; Analyte ; Chemistry ; Chromatography ; Tandem mass spectrometry ; Quantitative analysis (chemistry) ; Bioanalysis ; Computer science ; Ionization Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies
Authors: Andi Febrisiantosa | Rina Wahyuningsih | Teguh Wahyono | Diah Pratiwi | Tri Ujilestari | Selma Noor Permadi | Muhammad Fajri | Lukman Hakim
Source: International Food Research Journal (article, 2024-08-01)
Field: Materials Science — Biomaterials

Indonesian native chicken eggshell membranes as source for collagen: Optimum conditions and general characteristics High quantity of Indonesian native chicken eggshell membrane is available as a by-product of the hatchery industry. The eggshell has a membrane-like interior, and still contains approximately 10% of the membrane’s total protein. In the present work, membranes from Indonesian chicken eggs sourced from a hatchery could have the potential to be a source of collagen. Based on the interaction of two variables (enzyme concentration and hydrolysis duration), response surface methodology (RSM) with a central composite design was utilised to identify the optimum collagen extraction conditions. Using 5 M acetic acid and pepsin (85, 90, and 95 U/mg defatted skin) at 4°C for 72, 96, and 120 h, the collagen was extracted. The optimal conditions yielded 31% collagen with a hydrolysis duration of 120 h, and an enzyme concentration of 90 U/mg. The optimal yield was characterised using colour analysis, soluble protein, and FTIR. Collagen from the Indonesian native chicken eggshell membrane showed similar properties with standard collagen sources. Eggshell membrane ; Eggshell ; Membrane ; Chemistry ; Food science ; Biochemistry ; Biology ; Ecology Collagen: Extraction and Characterization ; Protein Hydrolysis and Bioactive Peptides ; Bone and Dental Protein Studies
Authors: Mohammad Ashiqur Rahman | Mst Nigar Sultana | Daud Sharif | Sultan Mahmud | Justin Legleiter | Peng Li | Blake Mertz | Stephen J. Valentine
Source: ACS Physical Chemistry Au (article, 2024-11-13)
Field: Chemistry — Spectroscopy

Structure Characterization of a Disordered Peptide Using In-Droplet Hydrogen/Deuterium Exchange Mass Spectrometry and Molecular Dynamics In-droplet hydrogen/deuterium exchange (HDX)-mass spectrometry (MS) experiments have been conducted for peptides of highly varied conformational type. A new model is presented that combines the use of protection factors (PF) from molecular dynamics (MD) simulations with intrinsic HDX rates (<i>k</i> <sub>int</sub>) to obtain a structure-to-reactivity calibration curve. Using the model, the relationship of peptide structural flexibility and HDX reactivity for different peptides is elucidated. Additionally, the model is used to describe the degree of conformational flexibility and structural bias for the disease-relevant Nt17 peptide; although highly flexible, intrinsically primed for facile conversion to α-helical conformation upon binding with molecular partners imparts significant in-droplet HDX protection for this peptide. In the future, a scale may be developed whereby HDX reactivity is predictive of the degree of structural flexibility and bias (propensity to form 2° structural elements such as α-helix, β-sheet, and β-turn) for intrinsically disordered regions (IDRs). Such structural resolution may ultimately be used for high-throughput screening of IDR structural transformation(s) upon binding of ligands such as drug candidates. Hydrogen–deuterium exchange ; Chemistry ; Peptide ; Molecular dynamics ; Reactivity (psychology) ; Mass spectrometry ; Molecular model ; Flexibility (engineering) ; Protein structure ; Biophysics ; Computational chemistry ; Stereochemistry ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Chemical Synthesis and Analysis ; RNA and protein synthesis mechanisms
Authors: Yeachan Kim | Jun-Hyung Park | Sungho Kim | Juhyeong Park | SangYun Kim | SangKeun Lee
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

SEED: Semantic Knowledge Transfer for Language Model Adaptation to Materials Science Materials science is an interdisciplinary field focused on studying and discovering materials around us.However, due to the vast space of materials, downstream datasets in this field are typically scarce and have limited coverage.This inherent limitation poses challenges when adapting pre-trained language models (PLMs) to materials science, as existing methods rely heavily on frequency information from these limited datasets.In this paper, we propose Semantic Knowledge Transfer (SEED), a novel vocabulary expansion method designed to adapt pre-trained language models (PLMs) for materials science.The core strategy of SEED is to transfer materials knowledge from lightweight embeddings into PLMs.To achieve this, we introduce knowledge bridge networks, which learn to transfer the latent knowledge embedded in materials-specific embeddings into representations compatible with PLMs.By expanding the embedding layer of PLMs with these transformed embeddings, the models can comprehensively understand the complex terminology associated with materials science.We conduct extensive experiments across a broad range of materials-related benchmarks.The comprehensive evaluation results convincingly demonstrate that SEED mitigates the limitations of previous adaptation methods, showcasing the efficacy of embedding knowledge transfer into PLMs. 1 Computer science ; Adaptation (eye) ; Natural language processing ; Knowledge transfer ; Artificial intelligence ; Knowledge management ; Psychology Machine Learning in Materials Science
Authors: Keunsoo Ko | Changgyun Kim
Source: Applied Sciences (article, 2024-11-10)
Field: Materials Science — Materials Chemistry

Attention-Based Hydrogen Refueling Imputation Model for Efficient Hydrogen Refueling Stations During hydrogen refueling, the data values determining the state of charge (SoC) of a vehicle can be missing due to internal and external factors. This causes inaccurate SoC estimation, resulting in oversupply or undersupply. To overcome this issue, an attention-based hydrogen refueling imputation (AHRI) model, which restores missing values, is proposed in this paper. In particular, considering that data variables can vary depending on the environmental conditions and equipment in a hydrogen refueling station (HRS), we use the attention mechanism. It determines the primary features, which improves the predictive performance and helps adapt to new conditions. Using the observed data during hydrogen refueling, we train the proposed AHRI model and verify its efficacy. Experimental results show that the proposed AHRI model outperforms existing imputation models significantly. Here, AHRI achieves 0.95 and 0.82 in terms of R2 when 20% and 40% of the values are missing, respectively. These results indicate that the proposed model can be used to solve the data missing problems in HSRs. Computer science ; Environmental science Hydrogen Storage and Materials ; Graphite, nuclear technology, radiation studies
Authors: Mahtem Teweldemedhin Mengstu | Alper Taner | Hüseyin Duran
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Spectroscopy and machine learning in food processing survey For food safety, quality control from the foodstuff production to the tasting of foods is needed and should be simple and non-destructive. Recent and notable non-destructive measurements of food and agricultural products are based on optical and spectroscopic techniques. Spectroscopy, meets the requirements of industrial applications for continuous quality control and process monitoring. Hence, this article covers a survey of recent research works, highlighting the application of spectroscopy and machine learning in food processing from bibliographic database. The survey was based on relevant articles, obtained from scientific database and evaluated selected research works based on survey inquires, the assessment included food processing problem addressed (varieties classification, origin identification, adulteration and quality control), types of spectroscopy used, machine learning models applied to solve the particular problem and keyword analysis to show the perspective of the research. Quality (philosophy) ; Wine tasting ; Food processing ; Food quality ; Computer science ; Control (management) ; Perspective (graphical) ; Identification (biology) ; Artificial intelligence ; Agriculture ; Machine learning ; Data science ; Chemistry ; Food science ; Geography ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Identification and Quantification in Food
Authors: Christian Mänken | Dominik Schäfer | Rüdiger‐A. Eichel
Source: Journal of The Electrochemical Society (article, 2024-10-21)
Field: Materials Science — Materials Chemistry

Communication—Prediction of Area Specific Resistance of Solid Oxide Cell Stacks from Electrochemical Impedance Spectra A support vector regression model was trained to predict the area specific resistance of solid oxide cell stacks from electrochemical impedance spectroscopy measurements. In particular, the minimum necessary frequency range required for accurate predictions was investigated. Therefore, 711 pairs of DC polarization and electrochemical impedance spectroscopy measurements, conducted at the same stage of stack degradation and under similar measurement conditions, were evaluated. Prediction accuracies of less than 8% mean absolute percentage error and coefficients of determination greater than 93% were achieved. The 10 1 –10 2 Hz range is sufficient for accurate predictions, allowing an efficient and non-destructive etimation of DC polarization measurements. Electrochemistry ; Oxide ; Electrical impedance ; Dielectric spectroscopy ; Materials science ; Spectral line ; Analytical Chemistry (journal) ; Chemical engineering ; Chemistry ; Electrode ; Chromatography ; Physical chemistry ; Metallurgy ; Electrical engineering ; Physics ; Engineering Advancements in Solid Oxide Fuel Cells ; Machine Learning and ELM ; Industrial Vision Systems and Defect Detection
Authors: Capucine Laprais | Clément Zrounba | Julien Bouvier | Nicholas Blanchard | Matthieu Bugnet | Yael Gutiérrez | Saúl Vázquez-Miranda | Shirly Espinoza | Peter Thiesen | Romain Bourrellier | Aziz Benamrouche | Nicolas Baboux | Guillaume Saint‐Girons | Lotfi Berguiga | Sébastien Cueff
Source: arXiv (Cornell University) (preprint, 2024-05-03)
Field: Materials Science — Materials Chemistry

Reversible single-pulse laser-induced phase change of Sb$_2$S$_3$ thin films: multi-physics modeling and experimental demonstrations Phase change materials (PCMs) have gained a tremendous interest as a means to actively tune nanophotonic devices through the large optical modulation produced by their amorphous to crystalline reversible transition. Recently, materials such as Sb$_2$S$_3$ emerged as particularly promising low loss PCMs, with both large refractive index modulations and transparency in the visible and NIR. Controlling the local and reversible phase transition in this material is of major importance for future applications, and an appealing method to do so is to exploit pulsed lasers. Yet, the physics and limits involved in the optical switching of Sb$_2$S$_3$ are not yet well understood. Here, we investigate the reversible laser-induced phase transition of Sb$_2$S$_3$, focusing specifically on the mechanisms that drive the optically induced amorphization, with multi-physics considerations including the optical and thermal properties of the PCM and its environment. We theoretically and experimentally determine the laser energy threshold for reversibly changing the phase of the PCM, not only between fully amorphous and crystalline states but also between partially recrystallized states. We then reveal the non-negligible impact of the material's polycrystallinity and anisotropy on the power thresholds for optical switching. Finally, we address the challenges related to laser amorphization of thick Sb$_2$S$_3$ layers, as well as strategies to overcome them. These results enable a qualitative and quantitative understanding of the physics behind the optically-induced reversible change of phase in Sb$_2$S$_3$ layers. Laser ; Thin film ; Pulse (music) ; Phase change ; Materials science ; Physics ; Optoelectronics ; Optics ; Engineering physics ; Nanotechnology Phase-change materials and chalcogenides ; Chalcogenide Semiconductor Thin Films ; Optical and Acousto-Optic Technologies
Authors: Muhammad Ali | Saman Hina | Sheraz Hussain Siddique | RUKSHAN TAUFIQ LODHI
Source: Industria Textila (article, 2024-04-30)
Field: Materials Science — Polymers and Plastics

Prediction of yarn sales price using data mining techniques – a case of yarnmanufacturing industry Data-driven knowledge is required for businesses to make better decisions that result in profit maximisation. In this study, it has been attempted to develop a model to predict yarn sales prices against cotton prices and other parameters. For this purpose, four different data mining techniques namely ARIMA (Autoregressive Integrated Moving Average), Multivariate regression, K-Nearest Neighbor (KNN) and Neural Networks (NN), were considered. The entire analysis was performed on thirty months of data that was collected from the ERP system of a yarn manufacturing industry. The unique aspect of this study is that before separately deploying data mining techniques, significant parameters that impact yarn sales prices were identified through Adjusted R-squared values. Seasonal and trend patterns were checked on yarn sales data, and seasonal adjustments were obtained through data mining algorithms. The performance of all four models was evaluated using Mean Absolute Error and Root Mean Square Error. The analysis shows that the KNN model, in the stated settings, is the most accurate as evident from MAE and RMSE values of 222.85 and 285.082, respectively. This study’s unique combination of features and machine learning algorithms is envisaged to be valuable for decision-makers in the textile yarn manufacturing industry. Yarn ; Engineering ; Manufacturing engineering ; Engineering drawing ; Computer science ; Industrial engineering ; Mechanical engineering Textile materials and evaluations ; Food Supply Chain Traceability ; Industrial Vision Systems and Defect Detection
Authors: Krishna K. Agarwal
Source: Journal of Electrical Systems (article, 2024-04-30)
Field: Chemistry — Analytical Chemistry

IOT Based Monitoring Model to Identify and Classify the Grading of Fruits and Vegetables Thirty to forty percent of the fruits and vegetables harvested are discarded at this time due to the large number of employees with no training. A growing problem throughout reaping is fruit and vegetable waste because human judgment is subjective when it comes to crop recognition, categorization, and assessment. The fruit and vegetable business is in serious require of the launch and deployment of a robotic system in order to classify and grade fruits and vegetables based on their level of ripeness. Machine learning techniques can produce a sophisticated machine learning framework that can differentiate among fruits and vegetables based upon their category, variation, development, and preservation using relevant and suitable computational imaging ideas. We analyzed, contrasted, and proposed an Internet of Things-based monitoring approach in this work for identifying, classifying, and grading fruits and vegetables. Grading (engineering) ; Internet of Things ; Computer science ; Artificial intelligence ; Engineering ; Embedded system ; Civil engineering Spectroscopy and Chemometric Analyses