jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenyao Zhu | Frank Benkwitz | Paul A. Kilmartin
Source: ChemRxiv (preprint, 2021-06-07)
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) &lt;p&gt;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. A potential pitfall that would cause inaccurate quantification &lt;i&gt;i.e.&lt;/i&gt;, competitive ionization, is also discussed. 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.&lt;/p&gt; Ion-mobility spectrometry ; Chromatography ; Wine ; Detection limit ; Chemistry ; Repeatability ; Mass spectrometry ; Gas chromatography–mass spectrometry ; Gas chromatography ; Aroma ; Reproducibility ; Analytical Chemistry (journal) Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Fermentation and Sensory Analysis
Authors: Brian Harker
Source: Digital Commons - USU (Utah State University) (article, 2021-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

On the Applicability of Genetic Algorithms to Fast Solar Spectropolarimetric Inversions for Vector Magnetography The measurement of vector magnetic fields on the sun is one of the most important diagnostic tools for characterizing solar activity. The ubiquitous solar wind is guided into interplanetary space by open magnetic field lines in the upper solar atmosphere. Highly-energetic solar flares and Coronal Mass Ejections (CMEs) are triggered in lower layers of the solar atmosphere by the driving forces at the visible ``surface'' of the sun, the photosphere. The driving forces there tangle and interweave the vector magnetic fields, ultimately leading to an unstable field topology with large excess magnetic energy, and this excess energy is suddenly and violently released by magnetic reconnection, emitting intense broadband radiation that spans the electromagnetic spectrum, accelerating billions of metric tons of plasma away from the sun, and finally relaxing the magnetic field to lower-energy states. These eruptive flaring events can have severe impacts on the near-Earth environment and the human technology that inhabits it. This dissertation presents a novel inversion method for inferring the properties of the vector magnetic field from telescopic measurements of the polarization states (Stokes vector) of the light received from the sun, in an effort to develop a method that is fast, accurate, and reliable. One of the long-term goals of this work is to develop such a method that is capable of rapidly-producing characterizations of the magnetic field from time-sequential data, such that near real-time projections of the complexity and flare-productivity of solar active regions can be made. This will be a boon to the field of solar flare forecasting, and should help mitigate the harmful effects of space weather on mankind's space-based endeavors. To this end, I have developed an inversion method based on genetic algorithms (GA) that have the potential for achieving such high-speed analysis. Algorithm ; Genetic algorithm ; Physics ; Computer science ; Mathematics ; Mathematical optimization Magnetic Properties and Applications ; Solar Radiation and Photovoltaics
Authors: РУП «НПЦ НАН Беларуси по механизации сельского хозяйства» | Д. И. Комлач | Александр Сергеевич Воробей | И. А. Барановский | Нина Леонидовна Ракова | Белорусский государственный аграрный технический университет | Жанна Ивановна Пантелеева | Институт математики НАН Беларуси | Михаил Николаевич Трибуналов
Source: Журнал «Агропанорама» (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Обоснование кинематических параметров автоматической установки для удаления некондиционных клубней картофеля струей сжатого воздуха аспирант Института математики Political science Spectroscopy and Chemometric Analyses
Authors: Patricia Cho
Source:  (article, 2021-08-01)
Field: Chemistry — Analytical Chemistry

AGD: Autonomous Gaussian Decomposition for Spectral Line Identification. Abstract not provided. Identification (biology) ; Decomposition ; Gaussian ; Computer science ; Artificial intelligence ; Physics ; Biology ; Ecology Spectroscopy and Chemometric Analyses ; Image Retrieval and Classification Techniques ; Time Series Analysis and Forecasting
Authors: Zul Hasrizal Bohari | Muzamir Isa | Ahmad Zaidi Abdullah | Ping Jack Soh | Mohamad Fani Sulaima
Source: Bulletin of Electrical Engineering and Informatics (article, 2021-03-16)
Field: Materials Science — Materials Chemistry

A smart partial discharge classification SOM with optimized statistical transformation feature Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal. Partial discharge ; Normalization (sociology) ; Pattern recognition (psychology) ; Transformation (genetics) ; Artificial neural network ; Computer science ; Self-organizing map ; Artificial intelligence ; Database normalization ; MATLAB ; SIGNAL (programming language) ; Data mining ; Feature (linguistics) ; Engineering ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Water Quality Monitoring and Analysis ; Elevator Systems and Control
Authors: Ángel Isaac Burgos Naranjo | Daniel Sebastián Vásquez Játiva | Danny Navarrete
Source: Ingeniería Investigación y Desarrollo (article, 2021-10-18)
Field: Chemistry — Analytical Chemistry

APLICACIONES DE LA INDUSTRIA 4.0 EN LA ESTANDARIZACIÓN DEL PROCESO PRODUCTIVO DE LAS MERMELADAS El presente artículo tiene como objetivo ilustrar una de las tantas aplicaciones de la Industria 4.0 mediante el uso de procedimientos analíticos multivariados y modelos de aprendizaje automático multirrespuesta, como un camino para analizar, modelar y estandarizar las relaciones entre las distintas variables de entrada y de salida que gobiernan la formulación de las mermeladas. Este trabajo de investigación es llevado a cabo en una compañía dedicada a la producción y comercialización de productos agropecuarios, describe la metodología de estudio utilizada que permitió hallar los rangosde valores para los niveles de azúcar (°Bx) y acidez (pH) que satisfacen matemática y estadísticamente los parámetros de liberación de producto terminado definidos por la misma compañía. Humanities ; Geography ; Political science ; Art Spectroscopy and Chemometric Analyses ; Agricultural and Food Production Studies ; Natural Products and Biological Research
Authors: Technomash CSRTI | А. А. Алтухов | К. В. Захарченко | V.A. Kolyubin
Source: Problems of advanced micro- and nanoelectronic systems development (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Simulation of the Operation of a Diamond Detector of Ionizing Radiation Имитационное моделирование работы алмазного детектора ионизирующих излучений Ionizing radiation ; Radiation ; Diamond ; Detector ; Nuclear engineering ; Particle detector ; Physics ; Medical physics ; Environmental science ; Nuclear physics ; Computer science ; Optics ; Materials science ; Engineering ; Irradiation Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Laser-Ablation Synthesis of Nanoparticles
Authors: Marc Creixell | Aaron S. Meyer
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-06-10)
Field: Chemistry — Spectroscopy

Motif-based phosphoproteome clustering improves modeling and interpretation Abstract Cell signaling is orchestrated in part through a network of protein kinases and phosphatases. Dysregulation of kinase signaling is widespread in diseases such as cancer and is readily targetable through inhibitors of kinase enzymatic activity. Mass spectrometry-based analysis of kinase signaling can provide a global view of kinase signaling regulation but making sense of these data is complicated by its stochastic coverage of the proteome, measurement of substrates rather than kinase signaling itself, and the scale of the data collected. Here, we implement a dual data and motif clustering strategy (DDMC) that simultaneously clusters substrate peptides into similarly regulated groups based on their variation within an experiment and their sequence profile. We show that this can help to identify putative upstream kinases and supply more robust clustering. We apply this clustering to large-scale clinical proteomic profiling of lung cancer and identify conserved proteomic signatures of tumorigenicity, genetic mutations, and tumor immune infiltration. We propose that DDMC provides a general and flexible clustering strategy for the analysis of phosphoproteomic data. One-sentence Summary DDMC is a general and flexible strategy for phosphoproteomic analysis by clustering phosphopeptides using both their phosphorylation abundance and sequence motifs. Cluster analysis ; Computational biology ; Phosphorylation ; Kinase ; Biology ; Proteome ; Proteomics ; Phosphoproteomics ; Protein kinase A ; Protein phosphorylation ; Cell biology ; Bioinformatics ; Computer science ; Biochemistry ; Gene ; Artificial intelligence Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Machine Learning in Bioinformatics
Authors: Zhifeng Xie | Xu Su | Siwei Liu | Guisong Zhang | Lizhuang Ma
Source: Journal of Computer-Aided Design & Computer Graphics (article, 2021-05-01)
Field: Materials Science — Polymers and Plastics

Hair Attribute Transfer via Deep Feature Fusion <p indent=0mm>To tackle the problem that existing attribute transfer methods can’t transfer hair attributes effectively, a method of hair attribute transfer based on deep feature fusion is presented. This method includes three subnetworks which are responsible for feature extraction, attribute vector extraction and image synthesis. Firstly, feature extraction network extracts features from original images, and keeps the identity of original images unchanged by adding a reconstruction loss. At the same time, attribute vector extraction network constructs the mapping model of hair features and hair attributes, and generates the attribute vector. Finally, the synthesis network takes the fusion result of image features and the attribute vector as input, and generates final results. Various attribute transfer experiments on FFHQ show that the proposed method can effectively transfer hair attributes and generate high-resolution results. Experiments on Celeba show that the proposed method can achieve better visual quality than existing popular attribute transfer methods. Computer science ; Pattern recognition (psychology) ; Artificial intelligence ; Feature (linguistics) ; Feature extraction ; Fusion ; Transfer (computing) ; Image (mathematics) ; Feature vector Textile materials and evaluations
Authors: Kota Tomioka | Sumitaka Tachikawa | Yuji Nagasaka
Source: Netsu Bussei (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Evaluation of Thermal Control Material using Dielectric Multi-layer Film for Spacecraft 従来の宇宙機用ラジエータ材は太陽光を反射する目的で金属を含む材料を一般に用いていたため，電波透過面に適用することは出来ない．そこで，金属を用いず低太陽光吸収率を示し，かつ，高全半球放射率を示す新しい電波透過型宇宙用熱制御材COSF(Controlled Optical Surface Film)を開発している．本材料は，耐宇宙環境性に優れたポリイミドフィルムを基板とし，その表面には誘電体多層膜が成膜されている．多層膜の膜厚は遺伝的アルゴリズムを用いて設計し，低太陽光吸収率及び高全半球放射率となるように，評価関数を設定した．本論文では，熱光学特性に関する設計方法及び測定結果について報告する． Materials science ; Spacecraft ; Layer (electronics) ; Dielectric ; Thermal ; Optoelectronics ; Composite material ; Aerospace engineering ; Meteorology ; Physics ; Engineering Silicone and Siloxane Chemistry ; Structural Analysis and Optimization ; Spacecraft Design and Technology
Authors: Matteo Croci | Piotr Morawiecki | Valentin Sulzer | Florian Theil
Source:  (preprint, 2021-05-04)
Field: Chemistry — Spectroscopy

Classification of Two-Dimensional Gas Chromatography Data Comprehensive two-dimensional gas chromatography (GCxGC) is often used for analysing complex chemical samples. DSTL want to be able to use data from GCxGC to attribute samples to a particular region or cultivar. However, the nature of the data means that several difficulties must be overcome before being able to do this. In this report, we investigate several methods to overcome such difficulties, and then classify the data. We are very successful in telling apart blanks from vseeds but obtain limited success when trying to classify between seeds. The method that shows the most promise is k-Nearest Neighbours classification by Wasserstein distance. However, this is still quite sensitive to the noise created by the solvent in the sample. Thus, we suggest that more blank runs be obtained, so that the ‘ground truth’ behaviour of the solvent is better understood, allowing us to remove the effect of the solvent from seed data. Sample (material) ; Blank ; Noise (video) ; Gas chromatography ; Pattern recognition (psychology) ; Computer science ; Chromatography ; Data mining ; Artificial intelligence ; Chemistry ; Materials science Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Adsorption, diffusion, and thermodynamic properties of materials
Authors: Moazza Muzzammil Maryam Shahzadi
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2021-01-09)
Field: Materials Science — Materials Chemistry

STUDY TO DETERMINE THE PRESENCE OF IRON DEFICIENCY ANEMIA AND BETA THALASSEMIA TRAIT SIMULTANEOUSLY <strong><em>Introduction:</em></strong><em> The feature of beta-thalassemia (β-TT) is common in this subcontinent as well as in Pakistan. 3% of our total population has been documented to have β-TT. Iron deficiency anemia (IDA) remains one of the most serious and important eating disorders in the world, especially in Pakistan. Microcytic hypochromic blood pictures are a common symptom of both disorders. The purpose of this study was to see if the two could coexist.</em> <strong><em>Methods:</em></strong><em> This was a cross-sectional study conducted at the Department of Internal Medicine of Mayo Hospital Lahore for one-year duration from July 2019 to July 2020. A total of 54 participants were selected from the Medicine department. All of them had β-TT confirmed by capillary hemoglobin electrophoresis. Appropriate advice was given and informed consent was given. Relevant history was collected and a study was conducted. 5 ml of venous blood was collected and sent to the Biochemistry department to measure iron, ferritin, and serum iron binding capacity (TIBC). The transferrin saturation (T.Sat) was calculated from the formula (iron / TIBC) X100.</em> <strong><em>Results:</em></strong><em> Finally, 54 patients who met the recruitment criteria were analyzed. Among them, 85% (46) of patients were women and 15% (8) were men. The median age was 30 years (range 11-80 years). Most of the 78% (42) patients belong to the 15 to 44 age group. Mean (± SD) HbA2 (%) was 5.35 (± 1.39), Hb (%) 9.04 ± (1.39), MCV (fl) - 67.02 (± 10.39), MCH (pg) - 21.06 (± 3.99), WFD (%) - 17.77 (± 3.85). Among them, 44.4% (24) had low iron levels (&lt;40 μg / dl), 29.6% (16) had low ferritin (15 μgm / l), 33.3% (18) had high TIBC (&gt; 407 μg / dl), and 50% (27) had low transferrin saturation (&lt;16%). Iron deficiency was found in 13% (7) of patients across all parameters, and 63% (34) of patients were iron deficient in at least one parameter.</em> <strong><em>Conclusions:</em></strong><em> In this study, the frequent occurrence of iron deficiency (29.6%) in people with β-TT, which is a potentially correctable clinical condition, was demonstrated. Iron levels among β-TT should be properly assessed, and people with iron deficiency should be treated promptly, which may improve their overall well-being.</em> <strong><em>Key words:</em></strong><em> feature of β-thalassemia, iron deficiency anemia.</em> Trait ; Beta thalassemia ; Thalassemia ; BETA (programming language) ; Anemia ; Iron-deficiency anemia ; Iron deficiency ; Medicine ; Internal medicine ; Computer science Graphite, nuclear technology, radiation studies
Authors: Ahmet YÜCEL
Source: Uluslararası Yönetim Bilişim Sistemleri ve Bilgisayar Bilimleri Dergisi (article, 2021-06-30)
Field: Chemistry — Analytical Chemistry

COMPONENT ANALYSIS FOR INTERESTING PATTERN DETECTION IN MULTI-VARIABLE DATA SETS In recent years, great advances have been made on the concept of data, which has become the new power source of our age. Thanks to new methods and techniques at both coding and mechanical level, tremendous speeds have been achieved in the transfering, storing, and processing of data. Thanks to those digital developments, storing even the smallest information on digital platforms has become a natural part of daily life. From family photos to health history, from commercial records to academic publications, from a comment shared on Twitter to a video shared on Youtube, data in almost every field is stored instantly in different sizes. Interesting patterns and information in stored data waiting to be revealed are the main goals of data mining. In data mining studies, the size of data is one of the biggest problems encountered. Some of the problems encountered in large-scale data are the length of the processes of structuring such data and the jams that may occur during the execution of a model to be created afterward. Many dimension reduction algorithms have been developed to overcome the problems arising from large data sizes. In this study, a new dimension reduction approach has been developed on multivariate data. This approach generally consists of pattern recognition steps based on Principal Component Analysis (PCA). The created models were applied on disjoint and balanced sub-datasets and all produced significant results at the 0.05 confidence level. Explanatory performances of the models; They are in the range of [0.819, 0.888] on the multiple R-Square scale and in the range of [0.804, 0.878] on the R-Square scale. Computer science ; Scalability ; Disjoint sets ; Dimensionality reduction ; Data mining ; Principal component analysis ; Dimension (graph theory) ; Field (mathematics) ; Data science ; Artificial intelligence ; Database ; Mathematics Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Yancie Gagnon
Source: SPIRE - Sciences Po Institutional REpository (preprint, 2021-12-17)
Field: Chemistry — Organic Chemistry

Étude de l’extraction des huiles végétales en milieu aqueux assistée par des tensioactifs One of the major current concerns in the crushing industry is to identify an alternative to the processthat uses hexane because of its impact on the environment, product quality, and human health. Theuse of water as a substitute solvent has several advantages, but this path has long been set apart dueto the immiscibility of water and oils. The proposed solution suggests using an aqueous solution ofsurfactants allowing the reduction of the interfacial tension between water and oil below 10-2 mN/m(i.e., the formation of microemulsion). This thesis, therefore, aims to study the extraction of vegetable oils from oilseeds in aqueous media in the presence of surfactants. The main objective is to acquire a better understanding of the mechanisms involved, in particular, the physicochemical interactions between the different compounds of the system. Different questions have been addressed in this thesis to consider step by step the role and the impact of each constituent of the system. First, the physicochemical characterization of different types of surfactants, in particular, the so-called extended surfactants and bio-based surfactants that are largely less studied in the literature, which made it possible to select those capable of solubilizing a maximum of vegetable oil in water. Then, the studied systems become more complex by considering the oilseeds, therefore the solid-liquid interface, which has never been studied inthe literature. Hypotheses as to the mechanisms of action of surfactants on the wettabilitymodification of oleaginous surfaces have been proposed. Finally, the study of the extractionprocess was approached. The influence of the operating parameters on the extraction yields hasbeen apprehended. The results obtained offer prospects for further action to consider the emergenceof this technology on an industrial scale. Extraction (chemistry) ; Aqueous medium ; Aqueous solution ; Chemistry ; Pulp and paper industry ; Chemical engineering ; Environmental science ; Petroleum engineering ; Chromatography ; Engineering ; Organic chemistry Surfactants and Colloidal Systems ; Analytical Chemistry and Chromatography
Authors: Shou Dahua
Source: Journal of Textile Science & Fashion Technology (article, 2021-06-29)
Field: Materials Science — Polymers and Plastics

Thermal Comfort Analysis of Moisture Management Treated Cotton Fabrics for Sportwear The COVID-19 pandemic has posed great challenges to the learning and teaching in universities. While addressing this crisis, the teachers and students have recognized the coexistence of the challenge and opportunity. The pandemic has not only changed education dramatically, with the distinctive rise of e-learning and remote teaching, but also brings the latest fashion technologies such as intelligent wearables and advanced textiles to citizens including the university students. Following this trend, we provide a case study in fashion and wearable technology subjects by putting the COVID-19 in a context of teaching and learning. We have proposed varied assignments and encourage students to work on teamwork and group projects related to the COVID-19. Particularly, development of a wearable cooling mask is demonstrated and discussed as a study case, which has significantly improved interest of the students and their understanding of knowledge in fashion technologies. Teamwork ; Context (archaeology) ; Wearable computer ; Coronavirus disease 2019 (COVID-19) ; Pandemic ; Wearable technology ; Work (physics) ; Computer science ; Medical education ; Knowledge management ; Engineering ; Political science ; Medicine ; Geography ; Mechanical engineering Textile materials and evaluations