jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jan Piekarczyk | Andrzej Wójtowicz | Marek Wójtowicz | Jarosław Jasiewicz | Katarzyna Sadowska | Natalia Łukaszewska-Skrzypniak | Ilona Świerczyńska | Katarzyna Pieczul
Source: Agronomy (article, 2022-08-20)
Field: Chemistry — Analytical Chemistry

Machine Learning-Based Hyperspectral and RGB Discrimination of Three Polyphagous Fungi Species Grown on Culture Media In this study, three fungi species (Botrytis cinerea, Rhizoctonia solani, Sclerotinia sclerotiorum) were discriminated using hyperspectral and red-green-blue (RGB) data and machine learning methods. The fungi were incubated at 25 °C for 10 days on potato dextrose agar in Petri dishes. The Hyperspectral data were acquired using an ASD spectroradiometer, which measures reflectance with 3 and 10 nm bandwidths over the range 350–1000 nm and the range 1000–2500 nm, respectively. The RGB images were collected using a digital Canon 450D camera equipped with the DIGIC 3 processor. The research showed the possibility of distinguishing the analysed fungi species based on hyperspectral curves and RGB images and assessing this differentiation using machine learning statistical methods (extreme boosting machine with bootstrap simulation). The best results in analysed fungi discrimination based on hyperspectral data were achieved using the Principal Component Analysis method, in which the average values of recognition and accuracy for all three species were 0.96 and 0.93, respectively. The wavelengths of the shortwave infrared (SWIR) wavelength region appeared to be the most effective in distinguishing B. cinerea-R. solani and B. cinerea-S. sclerotiorum, while of the visible range (VIS) of electromagnetic spectrum in discrimination of R. solani-S. sclerotiorum. The hyperspectral reflectance data were strongly correlated with the intensity of the pixels in the visible range (R2 = 0.894–0.984). The RGB images proved to be successfully used primarily for the identification of R. solani (recognition = 0.90, accuracy = 0.79) and S. sclerotiorum (recognition = 0.84, accuracy = 0.76). The greatest differences in the intensity of the pixels between B. cinerea and R. solani as well as R. solani and S. sclerotiorum occurred in the blue band and in distinguishing B. cinerea and S. sclerotiorum in the red band. Sclerotinia sclerotiorum ; Hyperspectral imaging ; RGB color model ; Botrytis cinerea ; Artificial intelligence ; VNIR ; Rhizoctonia solani ; Biology ; Botany ; Mathematics ; Horticulture ; Computer science Spectroscopy and Chemometric Analyses ; Plant Pathogens and Fungal Diseases ; Essential Oils and Antimicrobial Activity
Authors: Sravan Kumar Sikhakolli | Inbarasan Muniraj
Source: Asian Journal of Physics (article, 2022-09-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral imaging and its applications: An Overview Recording of the spectral information together with spatial information is useful for many applications.Hyperspectral Imaging (HSI) is a recently developed imaging modality which is capable of recording both the spatial (x, y) and spectral (λ) information of a scene.Thereafter, several applications have been shown using HSI.In this review, we discussed some of the recent implementations of HSI that aimed at addressing several scientific research problems.We also brief about some of the existing types of HSI cameras.In some of the recent works, Deep Learning (DL) frameworks have been extensively used for several HSI related problems.We have provided insights on some of the recent implementations of DL on HSI based problems. Hyperspectral imaging ; Computer science ; Remote sensing ; Environmental science ; Artificial intelligence ; Geology Spectroscopy and Chemometric Analyses
Authors: Manfred Spitzer
Source: Nervenheilkunde (article, 2022-02-01)
Field: Chemistry — Spectroscopy

Künstliche Intelligenz Von künstlicher Intelligenz im Sinne von selbsttätig lernenden neuronalen Netzwerken war an dieser Stelle schon mehrfach die Rede [33]–[36]. Immer dann, wenn es um die Vorhersage bei einem komplexen System oder Sachverhalt geht und sehr viele Daten vorliegen, kann man versuchen, diese Daten mit einer selbstlernenden Maschine zu analysieren, d. h. eine Maschine verwenden, um Strukturen in den Daten zu finden, die einem Menschen mit seiner begrenzten Lern- und Denkkapazität nicht auffallen würden. Das hat vor 5 Jahren beim Brettspiel Go erstmals weltweit sichtbar so richtig gut funktioniert: Die Aufgabe beim Go, den richtigen nächsten Spielzug von mehr als 10171 möglichen Spielzügen zu finden, ist zwar so groß, dass sich das Ergebnis prinzipiell nicht berechnen lässt (die Anzahl der Atome im gesamten Universum bzw. der beim Schach möglichen Spielzüge liegt jeweils etwa bei vergleichsweise „schlappen“ 1080 Möglichkeiten). Aber eine Maschine (genannt AlphaGo) der britischen Firma DeepMind [ 1 ] konnte anhand vieler vorhandener Go-Partien und anschließendem Weiterspielen gegen sich selbst lernen, das Spiel besser zu spielen als die weltbesten Menschen (März 2016). Im Oktober 2017 wurde gezeigt, dass eine neue Maschine (AlphaGo Zero), die von Anfang an nur gegen sich selbst spielte, bei 100 Spielen gegen AlphaGo 100-mal gewann und weit besser als die Menschen gute Spielzüge finden kann. Seither versuchen die weltbesten Go-Spieler zu verstehen, was die Maschine macht. Sie gewinnt immer. Während der vergangenen 5 Jahre wurde diese Technik in den verschiedensten Bereichen eingesetzt, wie an dieser Stelle in 2 Übersichten im Jahr 2021 dargestellt wurde: In der Medizin können Maschinen Flecken auf der Haut dermatologisch diagnostizieren (Krebs oder nicht?), Röntgenbilder beurteilen (dito), und vieles mehr [36]. Psychology Advanced Proteomics Techniques and Applications ; Machine Learning in Healthcare ; Receptor Mechanisms and Signaling
Authors: S Sandeep | B. Rama Murthy | Sathyashankara Sharma | A. A. Prasanna
Source: Journal of Composites Science (article, 2022-12-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Martensitic Transformation and Magnetic Properties of Ni-Mn Quinary Heusler Alloy Ni-Mn-based quinary Heusler alloys have seldom been investigated with respect to their martensitic transformation and mechanical properties for near room temperature transformation. In the current work, we identified and investigated martensitic transformation near room temperature, and the martensitic properties of Ni-Mn-Sn-Fe-In-based quinary Heusler alloys. Alloys prepared in an argon-rich vacuum arc melting furnace. During X-ray diffraction (XRD) analysis, it was identified that the L21 cubic structure austenite phase of the alloy transforms into L10 orthorhombic martensite phase in the case of alloys with greater Fe substitution. The martensitic transformation zone of the alloy is also shifted to the near-room-temperature range of 15–28 °C by changing the stoichiometry of the alloy composition. Magnetic measurements like field heating (FH), field cooling (FC) and zero field cooling (ZFC) indicate the presence of a dual magnetic phase in the alloy, while magnetic susceptibility testing also helped to establish claims regarding the magnetic measurement results. Quinary ; Materials science ; Diffusionless transformation ; Magnetic shape-memory alloy ; Austenite ; Alloy ; Metallurgy ; Martensite ; Phase (matter) ; Condensed matter physics ; Magnetic field ; Magnetization ; Microstructure ; Magnetic domain ; Chemistry Heusler alloys: electronic and magnetic properties ; Shape Memory Alloy Transformations ; Advanced Welding Techniques Analysis
Authors: Santanu Saha | Simone Di Cataldo | Federico Giannessi | Alessio Cucciari | Wolfgang von der Linden | Lilia Boeri
Source: arXiv (Cornell University) (preprint, 2022-05-05)
Field: Materials Science — Materials Chemistry

Mapping Superconductivity in High-Pressure Hydrides: The $Superhydra$ Project The discovery of high-$T_c$ conventional superconductivity in high-pressure hydrides has helped establish computational methods as a formidable tool to guide material discoveries in a field traditionally dominated by serendipitous experimental search. This paves the way to an ever-increasing use of data-driven approaches to the study and design of superconductors. In this work, we propose a new method to generate meaningful datasets of superconductors, based on element substitution into a small set of representative structural templates, generated by crystal structure prediction methods (MultiTemplate-HighThroughput approach). Our approach realizes an optimal compromise between structural variety and computational efficiency, and can be easily generalized to other elements and compositions. As a first application, we apply it to binary hydrides at high pressure, realizing a database of 880 hypothetical structures, characterized with a set of electronic, vibrational and chemical descriptors. 139 structures of our $Superhydra$ Database are superconducting according to the McMillan-Allen-Dynes approximation. Studying the distribution of $T_c$ and other properties across the database with advanced statistical and visualization techniques, we are able to obtain comprehensive material maps of the phase space of binary hydrides. The $Superhydra$ database can be thought as a first step of a generalized effort to map conventional superconductivity. Superconductivity ; Binary number ; Set (abstract data type) ; Computer science ; Field (mathematics) ; Visualization ; Data mining ; Database ; Computational science ; Statistical physics ; Physics ; Mathematics ; Quantum mechanics Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; High-pressure geophysics and materials
Authors: Anatoly Sorokin | Denis S. Bormotov | Denis S. Zavorotnyuk | Vasily A. Eliferov | Konstantin V. Bocharov | Stanislav I. Pekov | Е. Н. Николаев | Igor Popov
Source: Data (article, 2022-12-27)
Field: Chemistry — Spectroscopy

Aggregation of Multimodal ICE-MS Data into Joint Classifier Increases Quality of Brain Cancer Tissue Classification Mass spectrometry fingerprinting combined with multidimensional data analysis has been proposed in surgery to determine if a biopsy sample is a tumor. In the specific case of brain tumors, it is complicated to obtain control samples, leading to model overfitting due to unbalanced sample cohorts. Usually, classifiers are trained using a single measurement regime, most notably single ion polarity, but mass range and spectral resolution could also be varied. It is known that lipid groups differ significantly in their ability to produce positive or negative ions; hence, using only one polarity significantly restricts the chemical space available for sample discrimination purposes. In this work, we have developed an approach employing mass spectrometry data obtained by eight different regimes of measurement simultaneously. Regime-specific classifiers are trained, then a mixture of experts techniques based on voting or mean probability is used to aggregate predictions of all trained classifiers and assign a class to the whole sample. The aggregated classifiers have shown a much better performance than any of the single-regime classifiers and help significantly reduce the effect of an unbalanced dataset without any augmentation. Overfitting ; Pattern recognition (psychology) ; Artificial intelligence ; Computer science ; Classifier (UML) ; Sample (material) ; Chemistry ; Chromatography ; Artificial neural network Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Nadiah Syafiqah Shaharuddin | Zalizawati Abdullah | Farah Saleena Taip
Source: Malaysian Journal of Chemical Engineering and Technology (article, 2022-10-31)
Field: Chemistry — Analytical Chemistry

Optimisation of neural network topology for predicting moisture content of spray dried coconut milk powder Moisture content is an important parameter to be controlled in an agricultural product to prevent rapid degradation and promote stabilisation of product and longer shelf life. There are various techniques on predicting the moisture content of spray dried powder that had been used by past researchers. In this study artificial neural network (ANN) is proposed to be used for its well-known benefits of simplicity and accuracy. The aim of this research is to evaluate the effect of hidden layer and hidden neuron in ANN in predicting moisture content of spray dried coconut milk. The effect of training algorithm, e.g., Gradient Descent (GD) back propagation and Levenberg-Marquart (LM) back propagation, and activation functions, e.g., hyperbolic tangent sigmoid (tansig) and log sigmoid (logsig) functions are also studied. Based on the result of correlation coefficient of determination (R2) value of 0.9951 and root mean square error (RMSE) value of 0.0145 that was used to evaluate the ANN performance, it can be concluded that the best ANN topology is 2-10-1 with Levenberg-Marquart for learning algorithm, and tangent sigmoid as activation function. Sigmoid function ; Artificial neural network ; Hyperbolic function ; Mean squared error ; Activation function ; Water content ; Mathematics ; Backpropagation ; Topology (electrical circuits) ; Algorithm ; Artificial intelligence ; Computer science ; Statistics ; Mathematical analysis ; Engineering ; Geotechnical engineering Spectroscopy and Chemometric Analyses ; Coconut Research and Applications ; Food Drying and Modeling
Authors: Seon-Chil Kim
Source: Coatings (article, 2022-10-26)
Field: Materials Science — Materials Chemistry

Performance Evaluation According to Polymer Encapsulation Characteristics of Eco-Friendly Plastic Gamma-Ray Shield To eliminate the exposure of medical staff to radiopharmaceuticals during nuclear medicine tests, a new process technology was proposed for manufacturing gamma-ray shields used in nuclear medicine. In the case of manufacturing the existing gamma-ray shield, a method of increasing the content of the shielding material in the mixed material is used to improve the shielding performance. However, it is impossible to improve the shielding performance by simply increasing the content of the shielding material. Therefore, this study aims to present the optimal conditions for improving the miscibility between composite materials. Following the additional mixing of barium sulfate and bismuth oxide with tungsten, a syringe shield was developed via a plastic injection mold process. When tungsten was solely used or in combination with other shielding materials, polymer encapsulation occurred, and miscibility between composite materials was observed. Based on these results, the optimal conditions in terms of eco-friendly materials, economic feasibility, and improvement in shielding performance were determined. The findings of this study reveal that when tungsten and the polymers are combined, the polymer encapsulation is optimal, the particles are uniformly dispersed, and the shielding performance is significantly improved. With a 99mTc source, a 6.9% improvement in the shielding performance is achieved compared with the use of lead. Electromagnetic shielding ; Materials science ; Tungsten ; Polymer ; Composite material ; Miscibility ; Shield ; Fabrication ; Bismuth ; Composite number ; Metallurgy Radiation Shielding Materials Analysis ; Graphite, nuclear technology, radiation studies ; Nuclear materials and radiation effects
Authors: Zofia Tillman | Edward J. Wolfrum
Source: Frontiers in Energy Research (article, 2022-06-03)
Field: Chemistry — Analytical Chemistry

Comparing Calibration Algorithms for the Rapid Characterization of Pretreated Corn Stover Using Near-Infrared Spectroscopy Rapid characterization of biomass composition is a key enabling technology for biorefineries—the ability to measure the chemical composition of biomass materials entering the biorefinery as well as the composition of key process intermediate streams would allow real-time process control and the development of robust models to predict process performance. The utility of near-infrared (NIR) spectroscopy for rapid characterization requires multivariate algorithms for building calibration models. The most prevalent algorithm used for building calibration models using NIR spectra is the linear modeling algorithm Partial Least Squares Regression (PLS). Nonlinear regression algorithms (which are typically more computationally intensive than linear modeling approaches) have gained popularity in recent years due to their ability to solve a wide variety of classification and regression problems and the dramatic increase in available computational resources. In this work, we demonstrate that a calibration model can predict the composition of corn stover process intermediate samples pretreated with three different treatments—hot water (HW), dilute acid (DA), and deacetylation followed by dilute acid (DDA). We quantitatively compare three different algorithms for building prediction models based on near-infrared spectroscopy—partial least squares (PLS), support vector machines (SVM), and random forests (RF). We demonstrate the utility of improving model performance by accounting for instrument performance variability using repeated measurements of standard materials (e.g., the “repeatability file” strategy) and investigate its performance with nonlinear regression techniques, and we discuss methods for quantifying the uncertainties of specific predictions among the three methods. Partial least squares regression ; Calibration ; Algorithm ; Support vector machine ; Biorefinery ; Biological system ; Computer science ; Linear model ; Corn stover ; Process analytical technology ; Machine learning ; Process engineering ; Mathematics ; Chemistry ; Statistics ; Engineering ; Raw material ; Work in process Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Jordão Cabral Moulin | Dercílio Júnior Verly Lopes | Lucas Braga Mulin | Gabrielly dos Santos Bobadilha | Ramon Ferreira Oliveira
Source: CERNE (article, 2022-01-01)
Field: Chemistry — Organic Chemistry

Microscopic identification of brazilian commercial wood species via machine-learning Background: Multiple challenges are faced by industry and certification agencies when commercializing tropical species. Anatomical similarities of tropical hardwoods impair identification. Deep learning models can facilitate microscopic identification of wood by using sophisticated techniques such as deep convolutional networks (DCNN). Our objective was to microscopically identify 23 commercially available Brazilian wood species using a custom DCNN model. Results: Photographs from microscopic slides of each wood species were processed, and the final data set contained 2,448 images. We applied stratified k-fold cross-validation technique during training to increase model’s robustness and trustworthiness. Thus, the dataset was divided into approximately 80% training (1,958 images) and 20% validation (490 images) for each fold. A series of augmentations were performed only on training data to include variations in rotation, zoom, and perspective. Image augmentation was performed on-the-fly. The network consisted of convolutions, max pooling, global average pooling, and fully connected layers. We tested the performance of the DCNN against accuracy, precision, recall, and F1-score on the validation set for each fold. Conclusion: The custom machine learned model accuracy was higher than 0.90. The model’s worst performance was identified in distinguishing between Toona ciliate and Khaya ivorensis, which was due more to wood variability than to a machine learning deficiency. Future studies should focus on integration, verification/monitoring, and updating of current models for end user manipulation, trust, ethics, and security. Identification (biology) ; Botany ; Biology ; Mathematics ; Forestry ; Agroforestry ; Geography Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Erny Haslina Abd Latib | Nurlaila Ismail | Saıful Nizam Tajuddin | Jasmin Jamil | Zakiah Mohd Yusoff
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2022-03-14)
Field: Chemistry — Organic Chemistry

k-nearest neighbor modelling of agarwood oil samples available in capital of Malaysia market &lt;span&gt;Agarwood oil is consumed during traditional ceremonies and even in medicinal purposes due to its effective therapeutic characteristic. As a part of ongoing research on agarwood oil, this paper presented a k-nearest neighbor (k-NN) modelling of agarwood oil samples available in the capital of Malaysia market. The work involved agarwood oil samples from three sources which are lab, local manufacturer and market. The inputs are the chemical compounds and the output is the oil’s resources. The input-output was divided into training and testing dataset with the ratio of 80% to 20%, respectively, before they were fed to the k-NN for model development as well as model validation. During the model development, the k-value was varied from 1 to 5, and their accuracy was observed. The result showed that the k=1 and k=2 shared the similar accuracy for training and testing datasets, which are 98.63% and 100.00%, respectively. This study revealed the capabilities of the k-NN model in classifying the agarwood oil samples to the three sources: lab, local manufacturer and market. It was a significant study and contributed to further work especially those related to agarwood oil research area.&lt;/span&gt; Agarwood ; Mathematics ; Span (engineering) ; Work (physics) ; Computer science ; Statistics ; Engineering ; Mechanical engineering ; Medicine ; Structural engineering Wood and Agarwood Research ; Image Processing and 3D Reconstruction ; Forensic Fingerprint Detection Methods
Authors: Reno Georgiou | Renata Kostovska
Source: Maso International (article, 2022-10-01)
Field: Materials Science — Biomaterials

Physicochemical and mechanical properties of edible films based on WPI and a novel hydrocolloid: Persian gum Abstract In this study, the effect of Persian gum (PG) as a novel gum to be used in edible films was examined. Sorbitol-plasticized whey protein isolate (WPI) films with incorporation of PG in different ratios (100:0, 80:20, 60:40, 50:50, 40:60 and 20:80 WPI: PG) were prepared. The effects of PG concentration on the physicochemical properties; water vapour permeability (WVP), moisture sorption isotherms and mechanical properties; tensile strength (σ max ) and elongation at break (%EB) were observed. Incorporation of PG in the film matrix significantly increased WVP (p&lt;0.05) when added in ratios 60:40, 50:50, 40:60 and further increased in the ratio of 20:80 (WPI: PG). The moisture uptake of the films was not greatly influenced by the increment of PG. Addition of PG in the lowest concentration of 80:20 (WPI: PG) highly decreased the tensile strength of the films; however further addition of PG did not cause any great decrement. A significantly higher elongation was observed especially when PG was present in ratios 40:60 and 20:80 (WPI: PG). Overall, incorporation of PG in sorbitol-plasticized WPI films affected positively the elongation characteristics. However, improvement is needed for WVP to retain more stable values. This study suggests further examination of the soluble and insoluble fractions of PG using different polymers and plasticizers. A study on the antimicrobial properties of PG in edible films could also be examined since PG is traditionally used as a remedy. Whey protein isolate ; Ultimate tensile strength ; Plasticizer ; Elongation ; Sorbitol ; Chemistry ; Polymer ; Sorption ; Chemical engineering ; Materials science ; Food science ; Whey protein ; Composite material ; Adsorption ; Organic chemistry Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution ; biodegradable polymer synthesis and properties