jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Gamze Süpüren Mengüç | Gonca Özçelik Kayseri | Nіlgün Özdіl
Source: Tekstil ve Mühendis (article, 2019-12-31)
Field: Materials Science — Polymers and Plastics

Yün İçeren Çeşitli Kumaşların Pürüzlülük Özelliğinin İncelenmesi parametreler olduka geni kapsaml olarak incelenmi olmasna ramen, kuma yzeyindeki tylenmenin lm ile ilgili snrl sayda almann bulunduu grlmektedir Animal science ; Biology Textile materials and evaluations
Authors: Jennifer Tabi Sefaah | Eric Apau Asante | Kwadwo Fosu Duako
Source: Leather and Footwear Journal (article, 2019-06-30)
Field: Materials Science — Biomaterials

Methods used to manage defects related to vegetable tanned leather Leather surface quality is vital when it comes to leather artifact production. Users of the locally produced vegetable tanned leather adopt various methods to manage and improve on the poor surface quality of leather produced in Ghana. Students from the various secondary schools and local craftsmen who often depend on the vegetable tanned leather for it inherits unique properties such as flexibility, resilience, breathability, perspiration, durability and mechanical strength adopt various methods to improve on the surface quality and also meet the market standard. The necessity for quality leather surface in leather artefact production has called for the need for corrective measures to manage defects associated with vegetable tanned leathers produced in Ghana. Stains, cuts, holes, grain loosing, creases, wrinkles, scratch marks, bacterial and viral infections are common defects which affect the quality of vegetable tanned leathers produced in Ghana. Defect managing methods such as dyeing, painting, screen printing, stamping, appliqu, and scorching adopted by leather users to manage surface defects were examined for their viability in this current study. Pulp and paper industry ; Business ; Waste management ; Engineering Collagen: Extraction and Characterization
Authors: Maxime Metz | Matthieu Lesnoff | Jean‐Michel Roger
Source: Agritrop (Cirad) (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Un algorithme big-data pour la local-PLS La spectrométrie proche infrarouge peut fournir d'énormes quantités de données à l'agriculture numérique. Un des outils principaux utilisé pour analyser les spectres est la PLS. La méthode a prouvé sa pertinence pour les petites bases de données homogènes. Son extension aux bases de taille moyenne est la "local-PLS" : elle détermine un voisinage de l'individu à prédire, puis réalise une PLS sur ce voisinage. Cette méthode combine la puissance de la méthode des k plus proches voisins et de la PLS. Cependant, cette méthode n'est pas capable de traiter de grandes bases de données qui apparaîtront dans un futur proche. Les algorithmes local-PLS actuels utilisent tous des algorithmes k-NN (méthode force brute) pour lesquels les temps de calcul deviennent irréalistes ; d'autres algorithmes doivent être considérés. Nous proposons d'étudier un algorithme de réduction de dimensionnalité et de recherche rapide du voisinage utilisé pour traiter de grands ensembles de séries chronologiques (ayant une structure de données similaire aux spectres NIR) : ParSketch. Dans cette présentation, nous allons étudier la méthode ParSketch et la comparer à la méthode k-nn. Pour cela, nous avons traité un problème de classification et étudié les résultats des méthodes ParSketch-PLS-DA, Knn(force brute)-PLS-DA et PLS-DA. Humanities ; Physics ; Philosophy Spectroscopy and Chemometric Analyses
Authors: Karima Meghar | Christian Mestres | Fabrice Davrieux | Julien Ricci | Léa Ollier | Fifamè Laurenda Honfozo | Noël Akissoé
Source: Agritrop (Cirad) (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Détection de la répartition longitudinale et radiale de matière sèche, polyphénols et sucres dans l'igname frais par SPIR L'igame, Dioscorea sp., est un tubercule multi-espèces cultivé en Afrique, Asie, Amérique du Sud, Caraïbes et îles du Pacifique Sud. C'est un aliment de base en Afrique de l'Ouest où il est cultivé largement. Jusqu'à maintenant, le suivie de la répartition spatiale des composés majeurs de l'igname se fait par les méthodes physico-chimiques traditionnelles (Graham-Acquaah et al., 2014). Cependant, la caractérisation par ces dernières demandes beaucoup de temps et d'argent. C'est pour cette raison que nous avons décidé avec nos partenaires du Bénin de mettre en place une méthode d'analyse rapide et non destructive qui détecte la distribution spatiale des composés dans l'igname. Dans la présente étude, 4 variétés d'origine du Bénin ont été utilisées. Les tubercules ont été découpés longitudinalement en deux tranches, ensuite une seule tranche a été utilisée pour découpé 5 cubes de la zone proximale à la zone distale. Chaque cube a été découpé à son tour en tranches fines comme le montre le schéma ci-dessous. Enfin 3 spectres ont été acquis en mode réflectance sur chaque tranche de chaque cube à l'aide d'un spectromètre ASD LabSpec (350-2500 nm). Une fois les données spectrales collectées, une ACP a été appliquée afin de discriminer les différentes zones du tubercule. Ensuite, des modèles prédictifs ont été développés pour évaluer les performances de quantification de MS, polyphénols et sucres sur des tranches d'igname frais. Les résultats obtenus ont montré une différence significative entre les zones proximale, centrale et distale mais pas de différence significative entre les tanches radiales. Humanities ; Forestry ; Geography ; Art Spectroscopy and Chemometric Analyses
Authors: Masahiko Ishida
Source: Journal of The Japan Institute of Electronics Packaging (article, 2019-07-31)
Field: Materials Science — Materials Chemistry

New Materials Exploration Using Materials Informatics Informatics ; Materials informatics ; Computer science ; Engineering ; Materials science ; Health informatics ; Medicine ; Engineering informatics ; Electrical engineering Machine Learning in Materials Science ; Electronic and Structural Properties of Oxides ; Advanced Materials Characterization Techniques
Authors: Denis Cornet | L. Desfontaines | Fabien Cormier | Carine Marie-Magdeleine | Gemma Arnau | Karima Meghar | Fabrice Davrieux | Grégory Beurier
Source: Agritrop (Cirad) (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Assembler la diversité des modèles classiques et " deep learning " pour développer un pipeline de calibration SPIR performant et générique International audience Humanities ; Philosophy ; Art ; Political science Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Kai Cheng | Zhibin Ning | Xu Zhang | Leyuan Li | Bo Liao | Janice Mayne | Daniel Figeys
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2019-09-03)
Field: Chemistry — Spectroscopy

MetaLab 2.0 enables accurate post-translational modifications profiling in metaproteomics Abstract Studying the structure and function of microbiomes is an emerging research field. Metaproteomic approaches focusing on the characterization of expressed proteins and post-translational modifications (PTMs) provide a deeper understanding of microbial communities. Previous research has highlighted the value of examining microbiome-wide protein expression in studying the roles of the microbiome in human diseases. Nevertheless, the regulation of protein functions in complex microbiomes remains under-explored. This is mainly due to the lack of efficient bioinformatics tools to identify and quantify PTMs in the microbiome. We have developed a comprehensive software termed MetaLab for the data analysis of metaproteomic datasets. Here we build an open search workflow within MetaLab for unbiased identification and quantification of PTMs from microbiome samples. This bioinformatics platform provides information about proteins, PTMs, taxa, functions, and pathways of microbial communities. The performance of the workflow was evaluated using conventional proteomics, metaproteomics from mouse and human gut microbiomes, and modification-specific enriched datasets. Superior accuracy and sensitivity were obtained simultaneously by using our method comparing with the traditional closed search strategy. Metaproteomics ; Microbiome ; Computational biology ; Workflow ; Profiling (computer programming) ; Posttranslational modification ; Proteomics ; Identification (biology) ; Biology ; Human microbiome ; Human Microbiome Project ; Computer science ; Function (biology) ; Metagenomics ; Bioinformatics ; Data science ; Genetics ; Ecology ; Biochemistry ; Gene Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Machine Learning in Bioinformatics
Authors: Владимир Лебедев | Sergey Loy
Source: Bulletin of the National Technical University «KhPI» Series New solutions in modern technologies (article, 2019-05-31)
Field: Materials Science — General Materials Science

Modernization of the plasmatron for dusting on air and in controlled atmosphere (vacuum) One of the most productive, technological and efficient ways to obtain protective coatings on gas turbine engine elements, plants and other types of equipment from exposure to significant dynamic loads, corrosive media, high temperatures, etc., is plasma spraying. The main element that provides the necessary characteristics of the sprayed particles is the plasma torch. To date, a large number of plasma torches of various designs, power, cooling methods have been developed in the world, each of which has its own advantages and disadvantages. In most of the plasma torch designs, the material that is sprayed is fed into the plasma stream radially through the channel close to the nozzle section and this negatively affects the quality of the coating (poor adhesion, increased porosity, etc.). In addition, the utilization rate of the powder material due to uneven heating is quite low. To increase the sufficient heating, the power of the plasmatron is increased, which leads to a decrease in its service life. In this paper, the directions of modernization of the existing designs of plasmatrons, which are aimed at improving the quality of the sprayed layer, increasing the power of the process, increasing the service life of the equipment, are proposed. In this case, the constructive solutions associated with the cooling of the main components of the plasma torch, which reduce the thermal load from the device as a whole, will stand out. Considered technical solutions to ensure more efficient and uniform heating of the sprayed material, which also leads to better coating of various applications, an increase in the utilization rate of the powder. Attention is paid to nozzles that are structurally related to the plasma torch itself. Nozzles significantly improve the performance properties of plasma torches. Powder particles in such a warm up better and have greater speed of movement accelerated, which also improves the quality of the coating. Experimental studies allow to determine the most effective working sizes of nozzles. The proposed and tested design of the nozzle has a cooling system and is made of certain materials including stainless and insulating. Laboratory tests of the plasma torch of the developed design during modernization showed that the time of its continuous operation is at least 30 hours at a power of about 50 kW with high quality of spraying. Plasmatron ; Nozzle ; Materials science ; Coating ; Service life ; Thermal spraying ; Plasma torch ; Plasma ; Process engineering ; Mechanical engineering ; Metallurgy ; Composite material ; Engineering Material Properties and Applications ; Surface Treatment and Coatings ; High-Temperature Coating Behaviors
Authors: Mahmut Kayar
Source: DergiPark (Istanbul University) (article, 2019-06-25)
Field: Materials Science — Polymers and Plastics

Investigation of Capacity Utilization Rates of Cutting Room Departments of Apparel Companies The cutting process is one of the most important steps for apparel production and it creates the actual production stages with the sewing process. Because of this importance, the capacity utilisation rate of cutting room department affects the whole production. In this study, a survey was conducted to apparel companies for investigating the capacity utilization rates and problems on the low capacity utilization rate of cutting room departments of apparel companies and the data obtained were evaluated. Research results have shown that the capacity utilization rate of the companies varies between 75-100% and the companies have 4 different problems on low capacity utilization rate. Clothing ; Business ; Production (economics) ; Capacity utilization ; Production rate ; Process (computing) ; Operations management ; Manufacturing engineering ; Engineering ; Computer science ; Economics Textile materials and evaluations ; Assembly Line Balancing Optimization ; Manufacturing Process and Optimization
Authors: Bixin Li | Rong Fan | Bing Yang | Shigang Lin
Source: Revue d intelligence artificielle (article, 2019-10-30)
Field: Chemistry — Analytical Chemistry

Detection of Abnormal Oil Data Based on Feature Selection To achieve effective oil management, it is critical to disclose the laws of oil supply, consumption, and natural loss through data analysis. However, the accuracy of data analysis is often suppressed by the mistakes and irrelevance of the input data, which are inevitable due to the large size and diversity of the data collected from the oil depots. To solve the problem, this paper proposes an abnormal oil data detection approach based on feature selection (AODDFS). In the AODDFS, the format of the input data was preprocessed to satisfy the requirements of feature selection; the fisher score was then employed to compute the relevance of each entry with normal features; finally, the abnormal entries were located based on the relevance values. Then, the AODDFS results were analyzed with boxplot and standard deviation. Finally, the AODDFS was verified through a case study on the data collected from several large oil depots. The results show that the AODDFS can effectively detect abnormal oil data with a precision of 85.00% and a recall of 80.94%. Feature selection ; Selection (genetic algorithm) ; Feature (linguistics) ; Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Data mining Petroleum Processing and Analysis ; Drilling and Well Engineering ; Advanced Data Processing Techniques
Authors: Zheqi Wu
Source: eScholarship (California Digital Library) (article, 2019-01-01)
Field: Chemistry — Analytical Chemistry

Analysis and Modeling for Ganoderma Data It is fundamentally challenging to learn from small data sets. In this paper, we analyze ganoderma data, also called Lingzhi, which has a tiny dataset with quite a lot of chemical substances. It is quite a challenge to not only build suitable models tting the small data but also do the feature extraction to identify the critical subgroup of the chemical substances that are eective to the cancer treatment. This paper does data preprocessing first to adjust the response variable, eliminate outliers and deal with multicollinearity problem. Secondly, we use four datasets with both linear and non-linear models to experiment. It shows that XGboost model has the best tness of dataset. Also, Principal Component Analysis and Partial Least Square transformation techniques are suitable for our feature dimension reduction purpose that it can reduce the features from 24 dimensions to 5. In the discussion part, we analyze the feature importance between the model with the best performance and the original features. Multicollinearity ; Outlier ; Principal component analysis ; Computer science ; Preprocessor ; Data mining ; Feature (linguistics) ; Artificial intelligence ; Data pre-processing ; Dimensionality reduction ; Pattern recognition (psychology) ; Dimension (graph theory) ; Linear model ; Transformation (genetics) ; Linear regression ; Machine learning ; Mathematics Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Studies ; Remote Sensing and Land Use
Authors: Tetsuro Ueno | Hideitsu Hino | Kanta Ono
Source: Vacuum and Surface Science (article, 2019-03-09)
Field: Materials Science — Materials Chemistry

Efficiency Improvement of X-Ray Spectroscopy Experiment by Machine Learning We present an adaptive design of experiment (DoE) by machine learning for X-ray spectroscopy to improve its efficiency. One of the machine learning techniques, Gaussian process regression predicts a spectrum from the experimental data and determines the optimal energy points to measure. Adaptive DoE successfully reduces total energy points to measure as compared to an X-ray magnetic circular dichroism spectroscopy experiment by a conventional DoE. This method has potential applicability to various measurements and reduces the time and cost of experiments. Measure (data warehouse) ; Spectroscopy ; Design of experiments ; Energy (signal processing) ; Computer science ; Process (computing) ; Gaussian ; Artificial intelligence ; Biological system ; Physics ; Mathematics ; Statistics ; Data mining Machine Learning in Materials Science ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: S. Vishnu Shankar | G. Padmalakshmi | Mustafa Radha
Source: International Journal of Current Microbiology and Applied Sciences (article, 2019-02-10)
Field: Chemistry — Analytical Chemistry

Estimation and Comparison of Support Vector Regression with Least Square Method Regression is one among the most used vital machine learning and statistical tool. Regression is a method of modeling a target value based on independent predictors. It allows making predictions from data by understanding the relationship between features of data and observed continuous-valued response. Support Vector Regression (SVR) is one of the useful and flexible techniques, helping the user to deal with the limitations pertaining to distributional properties of underlying variables, the geometry of the data and the common problem of model overfitting. In this paper an attempt has been made to establish the significance of SVR through the numerical study. A 34 years of Metrological data is used here to compare Support Vector Regression with Least Square Regression. Based on the numerical study SVR model is identified as best fit by using Relative Mean Square Error (RMSE). Overfitting ; Support vector machine ; Regression ; Mean squared error ; Regression analysis ; Regression diagnostic ; Statistics ; Computer science ; Mathematics ; Linear regression ; Machine learning ; Data mining ; Artificial intelligence ; Polynomial regression ; Artificial neural network Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Neural Networks and Applications
Authors: Christophe Demazière | Diana Cuervo | Eugene Shwageraus
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2019-10-15)
Field: Materials Science — Materials Chemistry

New pedagogical approach to teaching about Generation-IV fast reactors This report gives an account of new pedagogical approaches being introduced in tertiary engineering education and aimed at improving student learning. Such methods are often subsumed under the term of active learning methods, based on the observation that students learn much better if they are engaged in well-designed activities under the teacher's supervision. In most cases, active learning methods heavily rely on the use of digital technologies to support learning and even to complement the synchronous interactive sessions with asynchronous sessions. In this report, some examples of active learning methods are presented, as well as some examples of digital technologies to support or complement learning. Some illustrations of the use of such new pedagogical approaches in nuclear engineering are given. How to use active learning methods in existing curricula is also discussed. A more widespread use of active learning methods and digital tools in nuclear engineering would allow making the field more attractive, pedagogy-wise, as well as better educating tomorrow's nuclear engineers. Mathematics education ; Computer science ; Pedagogy ; Sociology ; Psychology Graphite, nuclear technology, radiation studies ; Nuclear reactor physics and engineering
Authors: Javier Villanueva‐Meyer | Adam Autry | Jeremy W. Gordon | Hsin‐Yu Chen | Marisa Lafontaine | Susan M. Chang | Duan Xu | Peder E. Z. Larson | Daniel B. Vigneron | Yan Li
Source: Neuro-Oncology (article, 2019-11-01)
Field: Chemistry — Spectroscopy

NIMG-57. SERIAL HYPERPOLARIZED 13C PYRUVATE AND 1H METABOLIC IMAGING IN RECURRENT HIGH-GRADE GLIOMA Abstract INTRODUCTION Hyperpolarized (HP) 13C MRI is a novel metabolic imaging that allows for real time in vivo probing of pathway-specific metabolic processes relevant to gliomas. The first human brain application reports were published recently. In this study, we characterized longitudinal dynamic and static brain metabolism changes from serial HP 13C and 1H metabolic imaging in a patient with multiply recurrent glioma. METHODS Eight dynamic HP 13C imaging, 1H MRI and MRSI examinations were acquired over a period of 15 months in a patient with multiply recurrent glioma that underwent malignant transformation. The patient was imaged after initial tumor debulking, after chemoradiotherapy, at first recurrence, after re-irradiation and anti-angiogentic therapy, and at second recurrence. Ratios of pyruvate-to-lactate and pyruvate-to-bicarbonate were calculated from HP 13C imaging and choline-to-NAA index (CNI) was derived from 1H MRSI. RESULTS/ DISCUSSION Successful therapies are usually associated with a drop in pyruvate-to-lactate conversion, mediated by different mechanisms from various treatments. We identified three key findings over repeat HP 13C MRIs: 1) a marked response to therapy with a reduction in pyruvate-to-lactate conversion corresponding to associated imaging response to therapy at the primary site of disease. Following chemoradiotherapy, the volume of T2 hyperintensity decreased from 28.21cc to 15.29 cc, and the volume of metabolic abnormality (CNI &gt;3) decreased from 27.84 cc to 10.36 cc. 2) increasing pyruvate-to-lactate conversion in a region of recurrence before morphologic signal abnormality, and 3) a reduction in pyruvate-to-lactate conversion in a recurrent lesion following anti-angiogenic therapy. CONCLUSION Here we show that one can reliably image glioma patients using HP 13C MRI alongside standard of care morphologic and physiologic MRI including diffusion, perfusion, and 1H MRSI. Serial HP 13C MRI can be performed in the neuro-oncologic clinical setting and pyruvate-to-lactate metabolism may be useful to monitor treatment response. Glioma ; Medicine ; Nuclear medicine ; Chemoradiotherapy ; Magnetic resonance imaging ; Radiology ; Radiation therapy ; Cancer research Advanced NMR Techniques and Applications ; Amino Acid Enzymes and Metabolism ; Medical Imaging Techniques and Applications