jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nelson Vargas | Juan Pablo Villate Diaz | Margarita Habran
Source: Revista Inventum (article, 2021-11-22)
Field: Materials Science — Polymers and Plastics

Caracterización mecánica y determinación de la resistencia a la tracción de fibras de caña brava para uso en construcciones rurales Las técnicas usadas tradicionalmente para la construcción son evaluadas por normas y métodos de diseño en la ingeniería, las cuales se van actualizando diariamente para optimizar las propiedades estructurales y de los materiales. Sin embargo, el uso de materiales naturales está limitado por no contar con estudios necesarios y técnicas de conservación de los mismos y aún más por no contar con normatividad para su utilización en construcciones civiles, esto se ve reflejado en que las comunidades rurales no hacen uso de los materiales propios de su región para mejoramiento y adecuación de sus viviendas, por ello, en el presente estudio se pretende investigar y dar resultados cuantitativos y cualitativos sobre las fibras de caña brava (gynerium sagittatum), esto con el objetivo de determinar si dichas fibras pueden ser utilizadas y tener aplicaciones en el área de la construcción y mejoramiento de viviendas en el sector rural. En este artículo podrá encontrarse la primera fase del proyecto en la cual se realizaron estudios de caracterización física empleando técnicas de microscopía óptica (MO) y microscopía de barredura electrónica (MEB), así como ensayos de resistencia mecánica de tracción y compresión, mediante los cuales fue posible determinar la resistencia del objeto en estudio Humanities ; Physics ; Art ; Geography Natural Fiber Reinforced Composites ; Knowledge Societies in the 21st Century ; Plant and soil sciences
Authors: Hao Yuan | Samuel Britwum Wilson | Emmanuel Asamoah | Jianrong Cia | Xukang Bao
Source: Journal of Food Industry (article, 2021-03-09)
Field: Chemistry — Analytical Chemistry

DETECTION OF LOTUS ROOT CONTAMINANTS USING INTELLIGENT VISUAL MACHINE VISION TECHNIQUES Lotus root, which is a water plant cherished by people in the Asian continent and some other parts of the world, is manually inspected for quality by experts to detect impurities. There is the need to update this inspection process in order to improve the quality and safety of lotus root. Machine vision systems and techniques are used for consistent, efficient, effective, and reliable inspection of images. The lotus root inspection system has been proposed to inspect the lotus roots for impurities. The detection algorithms use the size, shape, texture and color of the lotus root images as parameters to analyze the quality of lotus roots. The lotus root undergoes some processes before image acquisition and image processing. The camera and illumination used, in collaboration with the edge detection, and image segmentation techniques, efficiently and effectively exposed the impurities in the lotus root at a much faster rate. Also, it is less expensive compared to the traditional human inspections. Lotus ; Artificial intelligence ; Computer vision ; Machine vision ; Computer science ; Root (linguistics) ; Process (computing) ; Lotus effect ; Image processing ; Segmentation ; Environmental science ; Image (mathematics) ; Chemistry ; Botany Spectroscopy and Chemometric Analyses ; Traditional Chinese Medicine Analysis ; Chromatography in Natural Products
Authors: Laurent David | Mark C. Wenlock | Patrick Barton | Andreas Ritzén
Source: ChemRxiv (preprint, 2021-06-10)
Field: Chemistry — Spectroscopy

Prediction of Chameleonic Efficiency &lt;p&gt;Prediction of chameleonic properties from environment-dependent conformational ensembles generated by molecular dynamics in explicit solvent is presented. This methodology yields predictive models of overall polarity (experimentally determined EPSA) and hydrogen bond donor exposure (experimentally measured &lt;i&gt;delta&lt;/i&gt; log &lt;i&gt;P&lt;/i&gt;&lt;sub&gt;oct-tol&lt;/sub&gt;) for 24 FDA-approved drug molecules. Two quantitative indices of chameleonic efficiency are introduced to aid drug designers striving to combine sufficient permeability and solubility.&lt;/p&gt; Chemistry ; Solubility ; Computational chemistry ; Thermodynamics ; Physics ; Physical chemistry Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Free Radicals and Antioxidants
Authors: Bao Guo Shen | Jin Dai | Xiao Dong Zhang | Zhao Hui Duan
Source: JOURNAL OF ADVANCES IN AGRICULTURE (article, 2021-06-22)
Field: Chemistry — Analytical Chemistry

Nondestructive Testing of Lettuce Nitrogen Stress Based on Multidimensional Image Visible light near infrared (VS-NIR) hyperspectral combined with three-dimensional laser scanning was applied to extract the VS-NIR features of lettuce nitrogen between 400-1700 nm and 3D morphological features of the plants. Such combination realizes the rapid quantitative detection of lettuce nitrogen. This study is based on the hyperspectral image data cube achieved from lettuce leaves with different nitrogen levels. Stepwise regression sensitive area was used and adaptive band selection method was combined to extract the characteristic spectrum and feature image of lettuce nitrogen and characterize the average image intensity. Also; the error caused by moisture variation content in lettuce nitrogen image features was compensated. Then a model of lettuce nitrogen hyperspectral image diagnosis was built. The reverse engineering software Geomagic Qualify was used to repair and smooth interference noise and discontinuous range which are based on the 3D laser scanning data of lettuce. Accordingly, the stem diameter, plant height, leaf area, and biomass features of different nitrogen levels of lettuce are obtained and the model of nitrogen detection about lettuce growth features was built based on reverse engineering and integral method. Multi-scale fusion lettuce nitrogen detection model is built by using the acquired hyperspectral images with growing features of lettuce nitrogen and adopting genetic algorithm combined with partial least squares regression. Results show the correlation coefficient R of the built model is 0.95; the model precision is much better than single feature of hyperspectral images and 3D laser scanning model. The feature extraction algorithm and the eigenvectors provide the reference for development of facilities for online monitoring system of crop growth information. Hyperspectral imaging ; Artificial intelligence ; Partial least squares regression ; Feature (linguistics) ; Remote sensing ; Nitrogen ; Biological system ; Mathematics ; Pattern recognition (psychology) ; Environmental science ; Computer science ; Chemistry ; Statistics ; Biology ; Geography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Smart Agriculture and AI
Authors: Ziyue Wang | Adam Cryar | Oliver Lemke | Daniela Ludwig | Pinkus Tober‐Lau | Elisa Theresa Helbig | Daniel Blake | Catherine Lane | Rebekah Sayers | Christoph Mueller | Johannes Zeiser | StJohn Townsend | Vadim Demichev | Michael Mülleder | Florian Kurth | Ernestas Sirka | Johannes Hartl | Markus Ralser
Source: medRxiv (preprint, 2021-12-05)
Field: Chemistry — Spectroscopy

A multiplex protein panel assay determines disease severity and is prognostic about outcome in COVID-19 patients Abstract Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can both help to optimize resource allocation and accelerate the development and evaluation of new therapies. Here, we present a multiplex proteomic panel assay for the assessment of disease severity and outcome prediction in COVID-19. The assay quantifies 50 peptides derived from 30 COVID-19 severity markers in a single measurement using analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM), on equipment that is broadly available in routine and regulated analytical laboratories. We demonstrate accurate classification of COVID-19 severity in patients from two cohorts. Furthermore, the assay outperforms established risk assessments such as SOFA and APACHE II in predicting survival in a longitudinal COVID-19 cohort. The prognostic value implies its use for support of clinical decisions in settings with overstrained healthcare resources e.g. to optimally allocate resources to severely ill individuals with high chance of survival. It can furthermore be helpful for monitoring of novel therapies in clinical trials. Multiplex ; Coronavirus disease 2019 (COVID-19) ; Medicine ; Disease ; Pandemic ; Cohort ; Intensive care medicine ; Severity of illness ; Clinical trial ; Internal medicine ; Oncology ; Bioinformatics ; Biology ; Infectious disease (medical specialty) Advanced Proteomics Techniques and Applications ; SARS-CoV-2 detection and testing ; SARS-CoV-2 and COVID-19 Research
Authors: Georgios Batsios | Pavithra Viswanath | Céline Taglang | Robert R. Flavell | J Costello | Russell O. Pieper | Peder E. Z. Larson | Sabrina M. Ronen
Source: Neuro-Oncology Advances (article, 2021-03-01)
Field: Chemistry — Spectroscopy

TBMT-01. HYPERPOLARIZED δ-[1-13C]GLUCONOLACTONE MONITORS TERT-INDUCED ELEVATION IN PENTOSE PHOSPHATE PATHWAY FLUX IN BRAIN TUMORS IN VIVO Abstract Telomerase reverse transcriptase (TERT) expression is essential for tumor proliferation and is an attractive therapeutic target for gliomas. TERT expression has previously been shown to enhance glucose flux via the pentose phosphate pathway (PPP) in low grade gliomas expressing TERT. Hyperpolarized δ-[1-13C]gluconolactone has been used to detect flux via the PPP by monitoring its conversion to 6-phospho-[1-13C]gluconate (6PG) in isolated perfused liver. The goal of our study was to evaluate whether hyperpolarized δ-[1-13C]gluconolactone can be used to monitor elevated PPP flux induced by TERT expression in low grade gliomas, thereby providing a non-invasive method of assessing TERT expression in vivo. Immortalized normal human astrocytes without (NHApre) and with TERT expression (NHApost) were used in cell bioreactor experiments. In vivo experiment with rats bearing orthotopic NHApost or patient-derived low-grade oligodendroglioma (SF10417) tumors were contacted. Dynamic 13C MR spectra were acquired at 14T (cells) or 3T (rats) following injection of hyperpolarized δ-[1-13C]gluconolactone. NHApost cells showed significantly higher flux through the PPP compared to NHApre. This finding was in agreement with previous results indicating that TERT expression elevates PPP flux. In all rats δ-[1-13C]gluconolactone and 6PG were observed indicating that δ-[1-13C]gluconolactone crosses the blood-brain barrier and is rapidly metabolized. Furthermore, both models presented homogeneous distribution of δ-[1-13C]gluconolactone in the brain and higher ratio of 6PG-to-δ-[1-13C]gluconolactone in the tumor area. In summary we show in vivo that hyperpolarized δ-[1-13C]gluconolactone metabolism to 6-phospho-[1-13C]gluconate is significantly higher in tumor compared to contralateral normal brain in TERT-expressing genetically-engineered and patient-derived low-grade oligodendrogliomas. Due to its fundamental role in tumor proliferation, TERT is both a tumor biomarker and a therapeutic target. Monitoring HP δ-[1-13C]gluconolactone metabolism, therefore, has the potential to inform on tumor burden and response to therapy in the clinic. In vivo ; Chemistry ; Pentose phosphate pathway ; Flux (metallurgy) ; Cancer research ; Metabolism ; Biochemistry ; Glycolysis ; Medicine ; Biology Advanced NMR Techniques and Applications ; Amino Acid Enzymes and Metabolism ; Electron Spin Resonance Studies
Authors: Rasul Abdusalamov | Prakul Pandit | Mikhail Itskov | Barbara Milow | Ameya Rege
Source: PAMM (article, 2021-12-01)
Field: Chemistry — Spectroscopy

Predictive modeling and simulation of silica aerogels by using aggregation algorithms Abstract Silica aerogels are highly porous solids with very low densities and thermal conductivities. Their high porosity results in a fractal morphology which has a strong influence on their mechanical properties. The geometric structure of silica aerogels can be described by diffusion‐limited cluster‐cluster aggregation (DLCA) models. In this work, the DLCA method is implemented to model silica aerogel networks and investigate the influence of different input parameters, as for example, varying particle sizes on their fractal properties. The resulting model networks are characterized for their fractal properties and compared with the small angle X‐ray scattering (SAXS) results of silica aerogels. Furthermore, their mechanical properties are simulated using the finite element method. There, the effect of varying densities on their mechanical properties is examined. In addition, an artificial neural network (ANN) is trained based on the input parameters of the DLCA algorithm to predict the fractal properties of the silica aerogel model. By inverting the ANN it is possible to identify the necessary inputs to generate desired fractal morphologies with specific mechanical properties. Aerogel ; Small-angle X-ray scattering ; Fractal ; Materials science ; Porosity ; Work (physics) ; Scattering ; Particle (ecology) ; Cluster (spacecraft) ; Diffusion ; Thermal ; Biological system ; Artificial neural network ; Composite material ; Optics ; Computer science ; Thermodynamics ; Mathematics ; Artificial intelligence ; Physics ; Mathematical analysis Aerogels and thermal insulation
Authors: J.A. Ramírez-Rincón | Universidad del Valle | Manuel Palencia | Natalia Afanasjeva | L.N. Benítez | Enrique M Combatt | MINDTECH S.A.S | Universidad de Cordoba
Source: Journal of Science with Technological Applications (article, 2021-08-01)
Field: Chemistry — Analytical Chemistry

Fraccionamiento de las Propiedades Hiperespectrales de Sistemas Edafológicos para el Diagnóstico de la Fertilidad de los Suelos Caracterizar la respuesta hiperespectral de fases constitutivas del suelo: solucin del suelo, fase slida y fase humificada Humanities ; Art Spectroscopy and Chemometric Analyses
Authors: Noratikah Zawani Mahabob | Aqib Fawwaz Mohd Amidon | Zakiah Mohd Yusoff | Nurlaila Ismail | Saıful Nizam Tajuddin | NorAzah Mohd Ali | Mohd Nasir Taib
Source: UiTM Institutional Repositories (Universiti Teknologi MARA) (article, 2021-10-01)
Field: Chemistry — Organic Chemistry

Comparison of ANN performance towards agarwood oil compounds pre-processing based on principal component analysis (PCA) and stepwise regression selection method / Noratikah Zawani Mahabob … [et al.] This paper presents the performance of Artificial Neural Network (ANN) application towards the agar wood oil quality classification. The works involved the uses of agarwood oil compounds based on two different feature selection techniques. The compounds were are selected based on using Principal Component Analysis (PCA) and Stepwise Regression. The compounds identified by PCA (three compounds) were β-agarofuran, α-agarofuran, and 10-epi-ϒ-eudesmol while the compounds identified by stepwise regression (four compounds) were β-agarofuran, ϒ-Eudesmol, Longifolol, and Eudesmol. These compounds were fed into ANN separately as input features and the output was the quality of the oil either high and low. The Resilient Back propagation as classifier algorithm was used and 1 to 10 hidden neuron in the hidden layer were varied. The performance of ANN using three and four compounds was measured and compared using confusion matrix, mean square error (mse) value and number of epoch. The work was done using software application, Matlab R2017a by using ‘patternet’ network. The finding showed that the ANN using four compounds of agar wood oil as input feature obtained greater performance with good accuracy, lower mse value and lower number of epoch in one hidden neuron. Principal component analysis ; Agarwood ; Pattern recognition (psychology) ; Artificial neural network ; Feature selection ; Artificial intelligence ; Mathematics ; Stepwise regression ; Backpropagation ; Mean squared error ; Classifier (UML) ; Computer science ; Statistics Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Image Processing and 3D Reconstruction
Authors: Ahmed El-Tantawi
Source: التصميم الدولية (article, 2021-05-01)
Field: Materials Science — Polymers and Plastics

Methods of increasing the stretchiness of cotton fabrics In response to getting more stretch and elasticity from 100% cotton fibers as woven fabric not made from lycra or spandex yarns and so on. In this survey, the researcher examines the relationship between different spinning methods for Egyptian cotton using twill fabric structure under a variety of weave factors on different weft densities to study the stretch and recovery from cotton fiber. The dependencies of the weft yarn spinning method and weft density were established. The effect of weave structure was determined. It founded that stress on the yarn will relax as a function of time due to the viscoelastic character of textile materials.The maximum wanted stretch direction will be limited to jamming in the anther direction. The max elasticity rate we get by increasing weft per cm for combed cotton but max resilience we can get at minimum weft per cm for carded cotton, Both max (elasticity& resilience) rates at same average float.The woven samples presented in this paper share three common design goals: low cost, stretchability, and long-running by comparing with their peers. Yarn ; Spinning ; Composite material ; Textile ; Materials science ; Viscoelasticity ; Elasticity (physics) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Fashion and Cultural Textiles
Authors: Zhan Zhang | Rong Huang | Zhenglong Li
Source: Scholars Journal of Physics Mathematics and Statistics (article, 2021-06-04)
Field: Materials Science — Materials Chemistry

Energy Prediction Model of PSO-BP Neural Network Three-dimensional Clusters based on Atomic Coordinates Energy prediction for different cluster structures is the basis for finding and predicting the global optimal structure of clusters. The current methods for predicting the energy of the ground state structures of different clusters include theoretical prediction methods and optimized simplified potential energy function methods. The accuracy of the theoretical prediction method is high, but its calculation amount is too large. Therefore, this paper proposes a PSO-BP neural network three-dimensional cluster energy prediction model based on atomic coordinates, and uses different types of Euclidean distances between atoms as input variables, and the energy of clusters with different structures as output variables. Select gold cluster Au20 and boron cluster B45-part of the sample data as the training set to build the model, and predict the rest of the samples, and finally get: the prediction accuracy of the PSO-BP neural network model is higher than that of the traditional BP neural network model. The cluster energy prediction model is feasible. Artificial neural network ; Cluster (spacecraft) ; Computer science ; Energy (signal processing) ; Euclidean distance ; Radial basis function ; Function (biology) ; Set (abstract data type) ; Algorithm ; Artificial intelligence ; Data mining ; Mathematics ; Statistics Machine Learning in Materials Science
Authors: Eylem Odabaş | Hülya Çakmak
Source: Harran Üniversitesi Mühendislik Dergisi (article, 2021-04-29)
Field: Chemistry — Analytical Chemistry

Gıda Analizlerinde Hasarsız Akustik Yöntemlerin Kullanımı Gıda ürünlerin kalitesinin değerlendirilmesinde hasar veren gıda analiz yöntemlerine alternatif olarak geliştirilen ve hasar vermeyen spektroskopik ve kromatografik tekniklerin yanı sıra, mekanik yöntemlerin kullanımının son yıllarda arttığı görülmektedir. Bu teknikler arasında yer alan akustik ve ultrasonik tekniklerin özellikle büyük hacimli gıdaların kalitesinin değerlendirilmesinde kullanımı ile analiz sırasında örneklerin kaybının engellenebileceği ve bu şekilde ekonomik bir kazanç sağlanacağı öngörülmektedir. Akustik teknikler, büyüklük, hacim, şekil ve doku fark etmeksizin birçok gıdanın sertlik, iç kusur, olgunluğunun belirlenmesinde ve gıdaların seçilen kalite özelliklerine göre sınıflandırılmasında başarıyla kullanılabilmektedir. Bu derleme çalışmasında ağırlıklı olarak meyve ve sebzelerin kalitesinin incelenmesinde kullanılan akustik teknikler, lazer Doppler titreşim ve ultrasonik yöntemler incelenmiş olup, farklı gıda gruplarında bu tekniklerin kullanımıyla elde edilen sonuçlar detaylı olarak kıyaslanmıştır. Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Sensory Analysis and Statistical Methods
Authors: Wenyao Zhu | Frank Benkwitz | Bahareh Sarmadi | Paul A. Kilmartin
Source: ChemRxiv (preprint, 2021-10-11)
Field: Chemistry — Spectroscopy

A Validation Study on the Simultaneous Quantification of Multiple Wine Aroma Compounds with Static Headspace-Gas Chromatography-Ion Mobility Spectrometry (SHS-GC-IMS) A new quantitative method based on static headspace−gas chromatography−ion mobility spectrometry (SHS−GC−IMS) is proposed, which enables the simultaneous quantification of multiple aroma compounds in wine. The method was first evaluated for its stability and the necessity of using internal standards as a quality control measure. The two major hurdles in applying GC-IMS in quantification studies, namely, non-linearity and multiple ion species, were also investigated using the Boltzmann function and generalized additive model (GAM) as potential solutions. Metrics characterizing the model performance, including root mean squared error, bias, limit of detection, limit of quantification, repeatability, reproducibility, and recovery were investigated. Both non-linear fitting methods, Boltzmann function and GAM, were able to return desirable analytical outcomes with an acceptable range of error. Potential pitfalls that would cause inaccurate quantification i.e., effects of ethanol content and competitive ionization, were also discussed. The performance of the SHS-GC-IMS method was subsequently compared against a currently established method, namely, GC-MS, using actual wine samples. These findings provide an initial validation of a GC-IMS-based quantification method, as well as a starting point for further enhancing the analytical scope of GC-IMS. Ion-mobility spectrometry ; Wine ; Repeatability ; Detection limit ; Chromatography ; Chemistry ; Gas chromatography–mass spectrometry ; Mass spectrometry ; Gas chromatography ; Reproducibility ; Analytical Chemistry (journal) Analytical Chemistry and Chromatography ; Fermentation and Sensory Analysis ; Advanced Chemical Sensor Technologies