jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Meilong Zhu | Mingda Li | Kangwei Hou | Zhaohui Wang | Long Xian-jun
Source: PLoS ONE (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

A multi-dimensional fusion strategy similarity measure method for patent application technology disclosure document Patent application technology disclosure document is one of the important bases for judging patent novelty and uniqueness. Automated evaluation can effectively solve the problems of long time and strong subjectivity of human evaluation. The text similarity evaluation algorithm based on corpus and deep learning technology has problems such as insufficient amount of cross-library learning data and insufficient core content tendency in the similarity judgment of patent application technology disclosure document, which limits their performance and practical application. In this paper, we propose a similarity evaluation method of patent application technology disclosure document based on multi-dimensional fusion strategy to realize the similarity measurement of patents. Firstly, in the text preprocessing section, word segmentation reconstruction and similarity evaluation optimization strategies based on word frequency and part-of-speech score weighted fusion are proposed. Then, a similarity calculation method of patent application technology disclosure document based on two new mapping spaces of dot matrix and image is proposed to achieve a more diversified comprehensive evaluation. The algorithm was evaluated by using four published text similarity matching datasets (containing 0-5 or 0/1 labels) and a set of patent application technology disclosure documents. Experimental results show that on the published text similarity matching datasets, the similarity evaluation method under the multi-dimensional fusion strategy proposed in this paper has a discrimination accuracy improvement of about 10% compared to traditional vector semantic model, and can match the discriminative ability of lightweight deep learning models without the need for training. At the same time, the discrimination accuracy of the proposed method on the sample dataset of patent application technology disclosure document is superior to traditional vector semantic model (20%) and various deep learning models (1%-8%), and the precision and recall rate are relatively balanced. The visual analysis results on the dataset of the patent application technology disclosure documents also prove the effectiveness and reliability of the similarity calculation method proposed in the dot matrix and image space, which provide a new idea and method for the similarity evaluation between patent application technology disclosure document. Computer science ; Similarity (geometry) ; Artificial intelligence ; Preprocessor ; Information retrieval ; Matching (statistics) ; Set (abstract data type) ; Data mining ; Pattern recognition (psychology) ; Natural language processing ; Image (mathematics) ; Mathematics Machine Learning in Materials Science
Authors: Amara Kamate | Pénétjiligué Adama Soro | Emma Georgina Zoro-Diama | K. Diomandé | A. Adohi-Krou
Source: Optics and Photonics Journal (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Detection of Rice Yellow Mottle at the Asymptomatic Stage by Hyperspectral Fluorescence and Reflectance Spectroscopies Rice yellow mottle is considered the most destructive disease threatening rice production in Africa. Early detection of this infection in rice is essential to limit its expansion and proliferation. However, there is no research devoted to the spectral detection of rice yellow mottle virus (RYMV) infection, especially in the asymptomatic or early stages. This work proposes the use of hyperspectral fluorescence and reflectance data at leaf level for the detection of this disease in asymptomatic stages. A greenhouse experiment was therefore conducted to collect hyperspectral fluorescence and reflectance data at different stages of infection. These data allowed to calculate nine vegetation indices: one from fluorescence spectra and eight from reflectance spectra. A t-test made it possible to identify, from the second day after infection, four relevant reflectance vegetation indices to discriminate healthy leaves from those infected: these are Photochemical Reflectance Index (PRI), Transformed Chlorophyll Absorption in Reflectance Index (TCARI), Structure Intensive Pigment Index (SIPI) and Simple Ratio Pigment Index (SRPI). The fluorescence index was less sensitive in detecting infection. The four significant vegetation indices for the detection of RYMV were then used to build and evaluate models for discriminating plants according to their health status by the supervised classification of support vector machine (SVM) at different stages of infection. The maximum overall accuracy is 92.5% six days after inoculation (6 DAI). The sixth day after inoculation would be the adequate day to detect RYMV. This plants discrimination was validated by the mean reflectance spectra and by the histograms showing the differences between the average reflectance vegetation indices values of the two types of plants. Our results demonstrate the feasibility of differentiating RYMV-infected samples. They suggest that support vector machine learning models could be developed to diagnose RYMV-infected plants based on vegetation indices derived from spectral profiles at early stages of disease development. Hyperspectral imaging ; Remote sensing ; Enhanced vegetation index ; Reflectivity ; Vegetation (pathology) ; Mottle ; Photochemical Reflectance Index ; Sampling (signal processing) ; Environmental science ; Biology ; Veterinary medicine ; Vegetation Index ; Normalized Difference Vegetation Index ; Horticulture ; Chlorophyll ; Agronomy ; Chlorophyll fluorescence ; Medicine ; Plant virus ; Pathology ; Virology ; Optics ; Leaf area index ; Virus ; Geology ; Physics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Plant Virus Research Studies
Authors: Vasyl Loginov | Yevgen Ukrainets | Andrіi Humennyi | Olexandr Yelans'ky | Dmytro Konyshev | Yevhen Spirkin | Vitalii Bezdielnyi
Source: Eastern-European Journal of Enterprise Technologies (article, 2023-08-31)
Field: Materials Science — General Materials Science

Design of the parametric appearance of the power plant for modifications of the regional passenger aircraft An-158 The object of research is the process of remotorization of a regional passenger aircraft to increase its fuel efficiency. Based on the conceptual requirements for the remotorization of the An-158 aircraft with turbojet bypass engines, a parametric appearance of three modifications of this aircraft with turboprop engines for 80, 100, and 120 passenger seats was formed. The study was carried out on the basis of the well-known modular software systems «Integration 2.1» and «Air propeller 2.2» for typical flight profiles of the An-158 aircraft. Improved procedures of weight design and determination of the takeoff characteristics of aircraft with different types of power plant engines have made it possible to identify the most advantageous flight speeds of aircraft modifications with a turboprop engine corresponding to different flight masses. The results of the study of flight performance for optimal and «non-optimal» modifications of the aircraft are reported. The parametric appearance of the propeller was formed, the shape of the propeller blade for the cruising flight mode was determined for modifying the aircraft with a maximum number of passengers – 120 people. It is shown that the propeller for this modification of the aircraft cannot have less than 8 blades since with a smaller number of blades, the maximum chord of the propeller blade increases. The inductive power costs increase significantly due to the small elongation of the blades and, as a result, the flight efficiency of the propeller decreases. It is shown that the total fuel consumption for the entire typical flight of all modifications of the aircraft with turboprop engine at all studied flight speeds is less than the total fuel consumption of the An-158 base aircraft Turboprop ; Takeoff ; Propeller ; Takeoff and landing ; Fuel efficiency ; Airplane ; Parametric statistics ; Turbofan ; Thrust specific fuel consumption ; Engineering ; Propulsion ; Automotive engineering ; Aerospace engineering ; Turbojet ; Drivetrain ; Aircraft flight mechanics ; Wing ; Marine engineering ; Torque Material Properties and Applications ; Aerospace, Electronics, Mathematical Modeling ; Advanced Power Generation Technologies
Authors: Zijiang Wu | Yunlong Shi | Xiaoming Qian | Haiyang Lei
Source: Processes (article, 2023-07-31)
Field: Materials Science — Polymers and Plastics

Evaluation and Prediction of the Effect of Fabric Wetting on Coolness As an important parameter of garment comfort, the thermal sensation of fabrics changes with factors such as sweat-induced humidity, making it a crucial area of research. To explore the coolness sensation of fabrics under different humidities, we tested heat transfer between fabrics and skin for 20 different fabrics with varying thermal absorption rates using fuzzy comprehensive evaluation to objectively assess their coolness levels. Subjective evaluation was then obtained by having subjects touch the fabrics and provide feedback, resulting in a subjective evaluation of their coolness levels. We compared the objective and subjective evaluations and found them to be highly consistent (R2 = 0.909), indicating accurate objective classification of fabric coolness levels. Currently, random forest regression models are widely used in the textile industry for classification, identification, and performance predictions. These models enable the prediction of fabric coolness levels by simultaneously considering the impact of all fabric parameters. We established a random forest regression model for predicting the coolness of wet fabrics, obtaining a high accuracy between predicted and tested thermal absorption coefficients (R2 = 0.872, RMSE = 0.305). Therefore, our random forest regression model can successfully predict the coolness of wet fabrics. Random forest ; Textile ; Environmental science ; Regression analysis ; Linear regression ; Computer science ; Artificial intelligence ; Mathematics ; Machine learning ; Materials science ; Composite material Textile materials and evaluations ; Thermoregulation and physiological responses ; Color perception and design
Authors: Jiwei Jia | Young Ju Lee | Iván Ojeda-Ruiz
Source:  (preprint, 2023-04-12)
Field: Materials Science — Surfaces, Coatings and Films

An Efficient K-way Constrained Normalized Cut and its Connection to Algebraic Multigrid Method &lt;p&gt;Normalized Cut (NCut) discourages the isolated segmentation that may result from the standard minimum Cut by adding a volume constraint. Such a volume constraint introduces a significant computational challenge. In this paper, we propose the K-way constrained Normalized Cut (K-way CNCut). It is formulated as the minimum Cut with a priori chosen constraints or representatives for the cluster. We provide a measure that can assess if the selected constraints can replace the volume constraint as well. For a special case when a single constraint is given as a representative of a single cluster, the K-way CNCut is discovered to have a link with the construction of the prolongation operator in the algebraic multigrid method for the normalized Graph Laplacian. We show how successful multiscale image segmentation can be understood in the framework of the K-way CNCut as well. In particular, we show how the multiscale graph coarsening algorithm can be used to construct a set of constraints for the K-way CNCut. A number of numerical experiments are presented to demonstrate the effectiveness of the proposed framework.&lt;/p&gt; Multigrid method ; Constraint (computer-aided design) ; Mathematics ; A priori and a posteriori ; Measure (data warehouse) ; Laplace operator ; Algebraic number ; Graph ; Segmentation ; Connection (principal bundle) ; Mathematical optimization ; Algorithm ; Computer science ; Discrete mathematics ; Artificial intelligence ; Data mining ; Geometry ; Mathematical analysis Electron and X-Ray Spectroscopy Techniques ; Machine Learning in Materials Science ; Medical Image Segmentation Techniques
Authors: Miranda Khajishvili | Jaba Shainidze | Kakha Makharadze | Nugzar Gomidze
Source: The Eurasia Proceedings of Science Technology Engineering and Mathematics (article, 2023-10-16)
Field: Chemistry — Analytical Chemistry

On the Development of the Fluorescence Excitation-Emission Etalon Matrix Algorithm of Wine Our research provides for the analysis of different types of Georgian wine based on 3D fluorescence spectroscopy (3DF) using the Black Comet (200-950 nm) spectrometer manufactured by StellarNet. In this method, the 3D fluorescence signal is divided into a fixed number of statistical components. For each type of wine, a 3D database is strictly defined, which we conventionally call references. The etalon describe the excitation/emission spectra in detail. The advantage of the 3DF method compared to other statistical methods, such as peak component analysis (PCA), lies in the uniqueness of the unfolding of the spectra. The fluorescence spectra of the wine will be further analyzed by peak component analysis (PCA). After performing the PCA analysis, in order to reduce the number of tolerant etalon, we used the tolerant etalon sample (TES) comparison analysis, thus determining how tolerant the researched wine sample is to this or that specific etalon. Wine ; Fabry–Pérot interferometer ; Fluorescence ; Analytical Chemistry (journal) ; Principal component analysis ; Chemistry ; Spectral line ; Emission spectrum ; Fluorescence spectroscopy ; Matrix (chemical analysis) ; Spectroscopy ; Spectrometer ; Optics ; Algorithm ; Mathematics ; Wavelength ; Physics ; Statistics ; Chromatography Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Tommaso Fontanot | Ujjayanta Bhaumik | R. Kishore | Youri Meuret
Source: Optics Continuum (article, 2023-03-10)
Field: Materials Science — Surfaces, Coatings and Films

POCAL: a Python-based library to perform optical coating analysis and design In this paper a novel Python library denominated POCAL is presented. POCAL (Python Optical Coating Analysis Library) allows to easily design optical coatings both as single layer and as multilayer stack, monitoring a wide variety of optical properties and, moreover, giving the possibility to automatically refine the multilayer design to achieve the desired optical goals. The library is completely open source, and it can be easily coupled to several Python-based ray tracers or libraries to work on more complex simulations. The results have been extensively tested and are comparable with the ones obtained using commercial software. Python (programming language) ; Computer science ; Software ; Coating ; Open source ; Materials science ; Programming language ; Nanotechnology Optical Coatings and Gratings ; Surface Roughness and Optical Measurements ; Advanced Fiber Optic Sensors
Authors: Fitri Yuwita | Ifmalinda Ifmalinda | Muhammad Makky
Source: Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) (article, 2023-12-01)
Field: Chemistry — Inorganic Chemistry

Prediction of Caffeine and Protein of Arabica Coffee Beans Using Near Infrared Spectroscopy (NIRS) Testing the chemical components of coffee beans has so far been carried out conventionally with laboratory analysis which requires a long time and is expensive. Technological advances allow testing of chemical components to be carried out quickly and accurately using the NIRS (Near Infra Red Spectroscopy) method. This research aims to develop a prediction model for caffeine and protein in Solok Radjo coffee beans using the NIRS method. Solok Radjo coffee is a type of Arabica with specialty grade because of its very strong character, aroma and taste. A total of 30 samples with a weight of 6 g per sample were used in this study. This research uses NIRS Type FT-IR IPTEK T-1516 with a wavelength of 1000 - 2500 nm. The partial least squares (PLS) method was used to process the data with several SNV, MN, and MSC pretreatments to improve the model. The research results show that caffeine is found at wavelengths of 1456 - 1475 nm, 1937 - 1974 nm. Proteins 1455 - 1475 nm, and 1935 - 1974 nm. MSC pretreatment is able to improve PLS performance results. Caffeine calibration values are R2 = 0.996 and SEC = 0.002%, validation values R2 = 0.989, SEP = 0.002%, and RPD 11.869 while protein calibration R2 = 0.999 and SEC = 0.004%, Validation values R2 = 0.999, SEP = 0.010%, and RPD 19,943. NIRS can be used to predict the chemical components of Solok Radjo coffee non-destructively using the PLS method. Key work: Caffeine, NIRS, PLS, predict, protein Caffeine ; Aroma ; Partial least squares regression ; Coffea arabica ; Chemistry ; Chromatography ; Calibration ; Near-infrared spectroscopy ; Food science ; Mathematics ; Botany ; Biology ; Statistics Coconut Research and Applications ; Food Chemistry and Fat Analysis ; Food and Agricultural Sciences
Authors: «Vietsovpetro» JV, Vung Tau, Vietnam | Thi Khanh Ly Nguyen | Malik G. Abdullayev | Khasan S. Karimov
Source: Proceedings of OilGasScientificResearchProjects Institute SOCAR (article, 2023-12-30)
Field: Chemistry — Analytical Chemistry

To the question of increasing oil recovery of formations with isolation of water influctions of the foundation of the «White Tiger» field The article deals with the problem of enhanced oil recovery (EOR), which arises in connection with the steady decline in oil production and active depletion of reserves. The water cut of production wells leads to a decrease in oil production and a decrease in the rate of recovery of recoverable reserves. Among the factors influencing this process, both an increase in the water cut of the productive formation itself and the presence of cracks in the layers below the productive formation lead to a decrease in the effectiveness of the third methods of treatment. In this case, water ingress into the reservoir from fractured reservoirs below the reservoir or inefficient use of a certain part of the water injected into the reservoir to displace oil from the reservoir through fractures leads to a decrease in the oil recovery factor. Therefore, creating a screen at the bottom of the productive formation can ensure the efficiency of work. For this purpose, experiments were carried out in the direction of preparing a composition for waterproofing works using certain reagents. Here, the mechanism of interaction of aluminum chloride with alkaline reagents was studied and their optimal ratio in EOR technologies was evaluated. Water-insoluble precipitates are formed as a result of the interaction of aluminum chloride and alkaline reagents. Keywords: field; oil recovery; reaction; alkali; reagents; temperature; foundation. Reagent ; Oil field ; Petroleum engineering ; Waterproofing ; Oil shale ; Chloride ; Oil production ; Foundation (evidence) ; Produced water ; Environmental science ; Chemistry ; Pulp and paper industry ; Geology ; Materials science ; Waste management ; Engineering ; Composite material ; Organic chemistry Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Enhanced Oil Recovery Techniques
Authors: Yongle Jia | Sufen Guo | Chunhua Zhu | Lin Li | Mei Ma | Guoliang Lv
Source: arXiv (Cornell University) (preprint, 2023-07-16)
Field: Chemistry — Spectroscopy

Identifying symbiotic stars with machine learning Symbiotic stars are interacting binary systems, making them valuable for studying various astronomical phenomena, such as stellar evolution, mass transfer, and accretion processes. Despite recent progress in the discovery of symbiotic stars, a significant discrepancy between the observed population of symbiotic stars and the number predicted by theoretical models. To bridge this gap, this study utilized machine learning techniques to efficiently identify new symbiotic stars candidates. Three algorithms (XGBoost, LightGBM, and Decision Tree) were applied to a dataset of 198 confirmed symbiotic stars and the resulting model was then used to analyze data from the LAMOST survey, leading to the identification of 11,709 potential symbiotic stars candidates. Out of the these potential symbiotic stars candidates listed in the catalog, 15 have spectra available in the SDSS survey. Among these 15 candidates, two candidates, namely V* V603 Ori and V* GN Tau, have been confirmed as symbiotic stars. The remaining 11 candidates have been classified as accreting-only symbiotic star candidates. The other two candidates, one of which has been identified as a galaxy by both SDSS and LAMOST surveys, and the other identified as a quasar by SDSS survey and as a galaxy by LAMOST survey. LAMOST ; Stars ; Physics ; Symbiotic star ; Galaxy ; Astrophysics ; Astronomy ; Spectral line ; Emission spectrum Spectroscopy and Laser Applications ; Astronomy and Astrophysical Research ; Astronomical Observations and Instrumentation
Source: Journal of Food Chemistry and Nanotechnology (article, 2023-12-05)
Field: Chemistry — Analytical Chemistry

Modeling the Moisture Loss Kinetics for Different Cultivars of Pistachio Kernels During the Infrared Roasting and its Effect on the Aflatoxin and Microbial Contamination Afsaneh Morshedi, Toktam Mohammadi-Moghaddam and Seyyed Mohammad Ali Razavi This aim of this research was to model the moisture loss kinetic of four pistachio kernels cultivars (Akbari, Ahmadaghaei, Kaleghouchi, and Fandoghi) during the IR (Infrared)-roasting and to study the effect of IR-roasting on the microbial and aflatoxin contamination. The IR-roasting process applied in the optimum point of each variety. For all pistachio cultivars, the best model to predict the moisture ratio during the process was the Weibull model. Aflatoxin ; Roasting ; Moisture ; Contamination ; Cultivar ; Chemistry ; Food science ; Horticulture ; Infrared heater ; Infrared ; Biology ; Ecology ; Organic chemistry Spectroscopy and Chemometric Analyses ; Food Drying and Modeling