jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aneta Lipkiewicz | Edyta Małachowska | Marcin Dubowik | Piotr Przybysz
Source: Scientific Reports (article, 2021-09-01)
Field: Materials Science — Biomaterials

Impact of shredding degree on papermaking potential of recycled waste The properties of paper products depend on the structure of the cellulose fibres therein. Although fibre properties in virgin pulps can be modified by a refining process, this is more difficult in pulp from recovered fibre, particularly waste from office shredders that tend to shorten fibres during shredding. The shorter fibres in shredded paper make it difficult to easily reconstitute them into high-quality paper products. Moreover, because of high energy usage during the recycling process and transportation inefficiencies, there is a need to determine how to responsibly shred paper to alleviate this environmental burden. With this in mind, the influence of initial fibre length on the tensile properties of paper was investigated. Changes in initial fibre length significantly influenced many pulp and paper properties. It was found that cutting the paper into pieces with an area less than 25 mm<sup>2</sup> caused significant changes in the important morphological parameters of the fibres and a sharp decrease in the tensile properties of the reconstituted paper. Papermaking ; Ultimate tensile strength ; Pulp (tooth) ; Pulp and paper industry ; Waste paper ; Cellulose ; Materials science ; Environmental science ; Composite material ; Waste management ; Chemical engineering ; Engineering Advanced Cellulose Research Studies ; Recycling and Waste Management Techniques ; Material Properties and Processing
Authors: Alicia Landeira‐Viñuela | Paula Díez | Pablo Juanes‐Velasco | Quentin Lécrevisse | Alberto Órfão | Javier De Las Rivas | Manuel Fuentes
Source: Biomolecules (article, 2021-11-26)
Field: Chemistry — Spectroscopy

Deepening into Intracellular Signaling Landscape through Integrative Spatial Proteomics and Transcriptomics in a Lymphoma Model Human Proteome Project (HPP) presents a systematic characterization of the protein landscape under different conditions using several complementary-omic techniques (LC-MS/MS proteomics, affinity proteomics, transcriptomics, etc.). In the present study, using a B-cell lymphoma cell line as a model, comprehensive integration of RNA-Seq transcriptomics, MS/MS, and antibody-based affinity proteomics (combined with size-exclusion chromatography) (SEC-MAP) were performed to uncover correlations that could provide insights into protein dynamics at the intracellular level. Here, 5672 unique proteins were systematically identified by MS/MS analysis and subcellular protein extraction strategies (neXtProt release 2020-21, MS/MS data are available via ProteomeXchange with identifier PXD003939). Moreover, RNA deep sequencing analysis of this lymphoma B-cell line identified 19,518 expressed genes and 5707 protein coding genes (mapped to neXtProt). Among these data sets, 162 relevant proteins (targeted by 206 antibodies) were systematically analyzed by the SEC-MAP approach, providing information about PTMs, isoforms, protein complexes, and subcellular localization. Finally, a bioinformatic pipeline has been designed and developed for orthogonal integration of these high-content proteomics and transcriptomics datasets, which might be useful for comprehensive and global characterization of intracellular protein profiles. Proteomics ; Proteome ; Computational biology ; Transcriptome ; Proteogenomics ; Biology ; Human proteome project ; Quantitative proteomics ; Gene ; Bioinformatics ; Biochemistry ; Gene expression Advanced Proteomics Techniques and Applications ; RNA and protein synthesis mechanisms ; Ubiquitin and proteasome pathways
Authors: John Chauvin | Ray Duran | Kouhyar Tavakolian | Alireza Akhbardeh | Nicholas MacKinnon | Jianwei Qin | Diane E. Chan | Chansong Hwang | Insuck Baek | Moon S. Kim | Rachel B. Isaacs | Ayse Gamze Yilmaz | Jiahleen Roungchun | Rosalee S. Hellberg | Fartash Vasefi
Source: Applied Sciences (article, 2021-11-11)
Field: Chemistry — Analytical Chemistry

Simulated Annealing-Based Hyperspectral Data Optimization for Fish Species Classification: Can the Number of Measured Wavelengths Be Reduced? Relative to standard red/green/blue (RGB) imaging systems, hyperspectral imaging systems offer superior capabilities but tend to be expensive and complex, requiring either a mechanically complex push-broom line scanning method, a tunable filter, or a large set of light emitting diodes (LEDs) to collect images in multiple wavelengths. This paper proposes a new methodology to support the design of a hypothesized system that uses three imaging modes—fluorescence, visible/near-infrared (VNIR) reflectance, and shortwave infrared (SWIR) reflectance—to capture narrow-band spectral data at only three to seven narrow wavelengths. Simulated annealing is applied to identify the optimal wavelengths for sparse spectral measurement with a cost function based on the accuracy provided by a weighted k-nearest neighbors (WKNN) classifier, a common and relatively robust machine learning classifier. Two separate classification approaches are presented, the first using a multi-layer perceptron (MLP) artificial neural network trained on sparse data from the three individual spectra and the second using a fusion of the data from all three spectra. The results are compared with those from four alternative classifiers based on common machine learning algorithms. To validate the proposed methodology, reflectance and fluorescence spectra in these three spectroscopic modes were collected from fish fillets and used to classify the fillets by species. Accuracies determined from the two classification approaches are compared with benchmark values derived by training the classifiers with the full resolution spectral data. The results of the single-layer classification study show accuracies ranging from ~68% for SWIR reflectance to ~90% for fluorescence with just seven wavelengths. The results of the fusion classification study show accuracies of about 95% with seven wavelengths and more than 90% even with just three wavelengths. Reducing the number of required wavelengths facilitates the creation of rapid and cost-effective spectral imaging systems that can be used for widespread analysis in food monitoring/food fraud, agricultural, and biomedical applications. VNIR ; Hyperspectral imaging ; Artificial intelligence ; Remote sensing ; Computer science ; Pattern recognition (psychology) ; RGB color model ; Artificial neural network ; Classifier (UML) ; Spectral bands ; Geography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote-Sensing Image Classification
Authors: Jens Kastenhofer | Julian Libiseller-Egger | Vignesh Rajamanickam | Oliver Spadiut
Source: Processes (article, 2021-02-26)
Field: Chemistry — Analytical Chemistry

Monitoring E. coli Cell Integrity by ATR-FTIR Spectroscopy and Chemometrics: Opportunities and Caveats During recombinant protein production with E. coli, the integrity of the inner and outer membrane changes, which leads to product leakage (loss of outer membrane integrity) or lysis (loss of inner membrane integrity). Motivated by current Quality by Design guidelines, there is a need for monitoring tools to determine leakiness and lysis in real-time. In this work, we assessed a novel approach to monitoring E. coli cell integrity by attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectroscopy. Various preprocessing strategies were tested in combination with regression (partial least squares, random forest) or classification models (partial least squares discriminant analysis, linear discriminant analysis, random forest, artificial neural network). Models were validated using standard procedures, and well-performing methods were additionally scrutinized by removing putatively important features and assessing the decrease in performance. Whereas the prediction of target compound concentration via regression was unsuccessful, possibly due to a lack of samples and low sensitivity, random forest classifiers achieved prediction accuracies of over 90% within the datasets tested in this study. However, strong correlations with untargeted spectral regions were revealed by feature selection, thereby demonstrating the need to rigorously validate chemometric models for bioprocesses, including the evaluation of feature importance. Chemometrics ; Partial least squares regression ; Random forest ; Linear discriminant analysis ; Biological system ; Feature selection ; Artificial intelligence ; Preprocessor ; Pattern recognition (psychology) ; Computer science ; Variable elimination ; Machine learning ; Biology ; Inference Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Kateryna Maiorova | Юрій Воробйов | Maksym Boiko | Valeriia Suponina | Oleh Komisarov
Source: Eastern-European Journal of Enterprise Technologies (article, 2021-12-29)
Field: Materials Science — General Materials Science

Implementation of reengineering technology to ensure the predefined geometric accuracy of a light aircraft keel The subject of this research is the technology of reengineering and control of parts of aircraft objects (AOs) and technological equipment for their manufacture. The predefined accuracy of the keel of a light aircraft and molding surfaces of technological equipment for its manufacture has been ensured by using reengineering technology and CAD systems. A portrait of the actual physically existing keel of a light aircraft was built in the *.stl file format using the software Artec Studio (USA). The control and comparison of the geometry of the shapes of the analytical standard with the actual physically existing keel of a light aircraft based on its portrait have been implemented. The methods used are the analysis and synthesis of the experimental geometry of shapes, the method of expert evaluations. The following results were obtained: based on the analysis and synthesis, the presence of significant errors in the accuracy of the manufacture of the keel for a light aircraft in the range from −5.26 mm to +5.39 mm was detected. It has been shown that the key factor is the keel's relative plane indicator, which is outside the tolerance margin and is 85 %. It was decided to fabricate new technological equipment from another material – organic plastics. Control of the technological equipment made from organic plastics for the keel of a light aircraft showed that the shape-forming surfaces of the equipment have appropriate shapes and sizes corresponding to the existing analytical standard and are devoid of inaccuracies that occurred in the previous version. The range of keel margins that was made using the new technological equipment from organic plastics is from –0.51 mm to +0.34 mm while the relative plane of the keel outside the tolerance margin does not exceed 15 %. The study results showed the adequacy of the decisions taken, ensuring the predefined accuracy for the keel of a light aircraft and molding surfaces of technological equipment for its manufacture. Keel ; Molding (decorative) ; Mechanical engineering ; Range (aeronautics) ; Engineering ; Computer science ; Engineering drawing ; Structural engineering ; Aerospace engineering Material Properties and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry ; Engineering Technology and Methodologies
Authors: Diego Aleixo da Silva | Ana Larissa Santiago Hansted | Gabriela Tami Nakashima | Elías Ricardo Durango Padilla | Júlio César Pereira | Fábio Minoru Yamaji
Source: Research Society and Development (article, 2021-10-02)
Field: Materials Science — Polymers and Plastics

Volatile matter values change according to the standard utilized? In lignocellulosic biomass, the volatile matter can vary from 65 to 85%. Different standards are described in the literature for obtaining this parameter. However, it is observed that some studies of regression models have not considered these differences. They create a volatile matter content database, where the standards for obtaining the same parameter are different. Thus, the objective of the study was to verify whether different standards for volatile matter present statistically equal values. That is if they can be compared with each other, without danger of bias. For this, three types of biomasses of Brazil used were used (eucalyptus chips, pine chips, and sugarcane bagasse). The samples were collected, size reduction, size separation, and stored in the laboratory. Three standards of the American Society for Testing and Materials (ASTM D1762, ASTM E872, ASTM D3175) and one standard of the International Organization for Standardization (ISO 18123) were tested. The experimental design was completely randomized, consisting of four treatments and five replications. The central limit theorem was tested in some literature databases of the volatile matter. The results showed statistical differences when changing the type of standard used. For eucalyptus sawdust, the four standards resulted in methodologies with different averages. Still, the central limit theorem was not observed in some databases of different articles. This was explained by the non-standardization of a single standard when grouping data from different works. Therefore, different volatile content standards produce different results and when comparing values, it is important to take this assumption into account. Standardization ; Mathematics ; Environmental science ; Standard deviation ; Biomass (ecology) ; Statistics ; Pulp and paper industry ; Sawdust ; Process engineering ; Computer science ; Engineering ; Agronomy Natural Fiber Reinforced Composites ; Forest Biomass Utilization and Management ; Thermochemical Biomass Conversion Processes
Authors: Eirini Siozou | Vasilios Sakkas | N. Kourkoumelis
Source: Pharmaceuticals (article, 2021-05-07)
Field: Chemistry — Analytical Chemistry

Quantification and Classification of Diclofenac Sodium Content in Dispersed Commercially Available Tablets by Attenuated Total Reflection Infrared Spectroscopy and Multivariate Data Analysis A new methodology, based on Fourier transform infrared spectroscopy equipped with an attenuated total reflectance accessory (ATR FT-IR), was developed for the determination of diclofenac sodium (DS) in dispersed commercially available tablets using chemometric tools such as partial least squares (PLS) coupled with discriminant analysis (PLS-DA). The results of PLS-DA depicted a perfect classification of the tablets into three different groups based on their DS concentrations, while the developed model with PLS had a sufficiently low root mean square error (RMSE) for the prediction of the samples' concentration (~5%) and therefore can be practically used for any tablet with an unknown concentration of DS. Comparison with ultraviolet/visible (UV/Vis) spectrophotometry as the reference method revealed no significant difference between the two methods. The proposed methodology exhibited satisfactory results in terms of both accuracy and precision while being rapid, simple and of low cost. Attenuated total reflection ; Partial least squares regression ; Diclofenac Sodium ; Near-infrared spectroscopy ; Mean squared error ; Spectroscopy ; Analytical Chemistry (journal) ; Ultraviolet visible spectroscopy ; Linear discriminant analysis ; Chromatography ; Spectrophotometry ; Multivariate statistics ; Chemistry ; Infrared spectroscopy ; Materials science ; Mathematics ; Statistics ; Optics ; Physics Analytical Methods in Pharmaceuticals ; Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography
Authors: Tae Young Kim
Source: Energies (article, 2021-04-24)
Field: Materials Science — Materials Chemistry

Prediction of System-Level Energy Harvesting Characteristics of a Thermoelectric Generator Operating in a Diesel Engine Using Artificial Neural Networks This study evaluated the potential of artificial neural networks (ANNs) to predict the system-level performance of a thermoelectric generator (TEG), whose performance depends on various variables including engine load, engine rotation speed, and external load resistance. Therefore, a Python code was developed to determine an optimal ANN structure by tracking the training/prediction errors of the ANN as a function of the number of hidden layers and nodes of hidden layers. The optimal ANN was trained using 484 output current (I)–load resistance (R) datasets obtained under three different engine rotation speeds and five different engine loads. The prediction accuracy of the ANN was validated by comparing 88 I–R datasets reproduced by the ANN using experimental data that were not used for training. In the validation procedure, differences of only 3.49% and 2.59% were observed in the experimental and ANN-predicted output power obtained for the 1000 rpm–0.8 MPa brake mean effective pressure (BMEP) and 1500 rpm–0.4 MPa BMEP scenarios, respectively. The exhaust gas flow characteristics were used for training and validation to predict the pumping loss caused by the installation of the TEG in the middle of the exhaust tailpipe with high accuracy. The results demonstrated that the ANN effectively reproduced datasets to fill the gaps between the discretized experimental results for all the experimental scenarios without any noticeable overfitting and underfitting. The net power gain obtained by the ANN exhibited a clear peak point for the engine rotation speed of 2000 rpm, which is difficult to obtain using experimental data. Artificial neural network ; Mean effective pressure ; Automotive engineering ; Diesel engine ; Overfitting ; Thermoelectric generator ; Computer science ; Control theory (sociology) ; Engineering ; Thermoelectric effect ; Artificial intelligence ; Internal combustion engine Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Refrigeration and Air Conditioning Technologies
Authors: Zhuozhuo Zhu | Xinhua Zhu | Wenchuan Guo
Source: Journal of Dairy Science (article, 2021-09-30)
Field: Chemistry — Analytical Chemistry

Quantitatively determining the somatic cell count of raw milk using dielectric spectra and support vector regression To investigate the potential of dielectric spectroscopy in quantitatively determining the somatic cell count (SCC) of raw milk, the dielectric spectra of 301 raw milk samples at different SCC were collected using coaxial probe technology in the frequency range of 20 to 4,500 MHz. Standard normal variate, Mahalanobis distance, and joint x-y distances sample division were used to pretreat spectra, detect outliers, and divide samples, respectively. Principal component analysis and variable importance in projection (VIP) methods were used to reduce data dimension and select characteristic variables (CVR), respectively. The full spectra, 16 principal components obtained by principal component analysis, and 86 CVR selected by VIP were used as inputs, respectively, to establish different support vector regression models. The results showed that the nonlinear support vector regression models based on the full spectra and selected CVR using VIP had the best prediction performance, with the standard error of prediction and residual predictive deviation of 0.19 log SCC/mL and 2.37, respectively. The study provided a novel method for online or in situ detection of the SCC of raw milk in production, processing, and consumption. Mahalanobis distance ; Principal component analysis ; Mathematics ; Statistics ; Somatic cell count ; Standard deviation ; Analytical Chemistry (journal) ; Outlier ; Linear regression ; Regression analysis ; Chemistry ; Chromatography ; Biology Spectroscopy and Chemometric Analyses ; Milk Quality and Mastitis in Dairy Cows ; Listeria monocytogenes in Food Safety
Authors: Marco Baity‐Jesi | Giulio Biroli | David R. Reichman
Source: The European Physical Journal E (article, 2021-06-01)
Field: Materials Science — Materials Chemistry

Revisiting the concept of activation in supercooled liquids In this work, we revisit the description of dynamics based on the concepts of metabasins and activation in mildly supercooled liquids via the analysis of the dynamics of a paradigmatic glass former between its onset temperature [Formula: see text] and mode-coupling temperature [Formula: see text]. First, we provide measures that demonstrate that the onset of glassiness is indeed connected to the landscape, and that metabasin waiting time distributions are so broad that the system can remain stuck in a metabasin for times that exceed [Formula: see text] by orders of magnitude. We then reanalyze the transitions between metabasins, providing several indications that the standard picture of activated dynamics in terms of traps does not hold in this regime. Instead, we propose that here activation is principally driven by entropic instead of energetic barriers. In particular, we illustrate that activation is not controlled by the hopping of high energetic barriers and should more properly be interpreted as the entropic selection of nearly barrierless but rare pathways connecting metabasins on the landscape. Supercooling ; Algorithm ; Computer science ; Thermodynamics ; Physics Material Dynamics and Properties ; Theoretical and Computational Physics ; Liquid Crystal Research Advancements
Authors: Heng Xie | Lijun Xu | Yutian Tan | Guangyu Hou | Zhang Cao
Source: IEEE photonics journal (article, 2021-05-25)
Field: Chemistry — Spectroscopy

Ultra-Low Sampled and High Precision TDLAS Thermometry Via Artificial Neural Network Water vapor temperatures in a heating device were measured by ultra-low sampled and high-precision tunable diode laser spectroscopy (TDLAS) Bichromatic distributed feedback (DFB) lasers were used as light sources. Sampled data at an ultra-low rate was collected after laser passing through a low-pass filter and served as the inputs of an artificial neural network. Classical direct absorption spectroscopy using the line-shape fitting method provided the training dataset, i.e., the integrated absorbances. The proposed method required an ultra-low sampling rate, i.e., only 1/50 of the classical method, but its calculation speed was nearly 14,000 times faster. Also, the proposed method yielded satisfying estimates at temperatures uncovered by the training dataset and was insensitive to random noises. Tunable diode laser absorption spectroscopy ; Artificial neural network ; Laser ; Filter (signal processing) ; Spectroscopy ; Sampling (signal processing) ; Diode ; Materials science ; Water vapor ; Tunable laser ; Optics ; Semiconductor laser theory ; Computer science ; Line (geometry) ; Artificial intelligence ; Optoelectronics ; Physics ; Computer vision ; Mathematics Spectroscopy and Laser Applications ; Laser Design and Applications ; Atmospheric Ozone and Climate
Authors: Chinnasami Sivaji | M Ramachandran | Vidhya Prasanth
Source:  (article, 2021-12-01)
Field: Materials Science — Materials Chemistry

Study of Evaluation Based on Distance from Average Solution on Moyamoya Disease and Energy application set up, conference leaders can add and remove other chairs, change the conference configuration, add and edit tracks, correspondence templates, review forms, and sessions. Moyamoya disease ; Energy (signal processing) ; Medicine ; Computer science ; Mathematics ; Statistics ; Internal medicine Polyoxometalates: Synthesis and Applications ; Moyamoya disease diagnosis and treatment