jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: L.Vargas | R.Rengel | Julio J. Caparrós | D.Tejada-Guzmán | E. López
Source: Renewable Energy and Power Quality Journal (article, 2023-06-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Technical evaluation of hybrid super capacitor technologies for local energy communities Renewable energy sources have demonstrated in recent years their ability to reduce the dependence on fossil fuels, contribute to the security of energy supply and play an important role in reducing greenhouse gas emissions. To benefit from the local use of renewable sources and energy efficiency in distributed systems, the concept of local energy communities is established. In local energy communities, new innovative technologies are combined, highlighting the role of energy storage, to provide greater autonomy to communities that adopt this model. Among energy storage, the use of batteries stands out, while lithium batteries are currently the most widely used for domestic and residential applications, as well as in local energy communities. This work describes the experimental evaluation of an innovative energy storage system for energy communities based on hybrid supercapacitors (HSC), focused on local energy communities, and its comparison with other well established storage technologies, such as lithium-ion batteries. Renewable energy ; Energy storage ; Work (physics) ; Environmental economics ; Greenhouse gas ; Fossil fuel ; Supercapacitor ; Energy supply ; Energy engineering ; Energy security ; Business ; Energy (signal processing) ; Environmental science ; Engineering ; Waste management ; Electrical engineering ; Power (physics) ; Ecology ; Mechanical engineering ; Economics Supercapacitor Materials and Fabrication ; Microgrid Control and Optimization ; Advanced Battery Technologies Research
Authors: Dr.Roopa K.V
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2023-04-10)
Field: Materials Science — Polymers and Plastics

A comparative Study of Weather Resistant Jacket to Investigate the Assessment of Weather Resistant Jacket Performance in Extreme Environmental Conditions This study paper's goal is to assess how well coats that are weatherproof perform in harsh environmental circumstances. Weather-resistant coats are made to shield wearers from inclement weather like rain, heat, and cold. In this study, we assessed the jackets' capacity to fend off moisture, withstand cold, and keep warmth under harsh conditions. Cold weather ; Environmental science ; Meteorology ; Hot weather ; Shield ; Extreme weather ; Extreme Cold ; Extreme heat ; Cold climate ; Climatology ; Climate change ; Geology ; Geography Textile materials and evaluations
Authors: Jéssica de Oliveira Gaspar
Source: LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (article, 2023-01-01)
Field: Chemistry — Organic Chemistry

Extraction and Determination of Phenolic Compounds in Nutritional Microalgae Using Digital Images Neste estudo, foi utilizado um método baseado em colorimetria de imagens digitais para determinação do teor de fenólicos totais em microalgas. A extração dos compostos fenólicos, utilizando como solvente extrator o metanol puro, foi otimizada usando metodologia multivariada e estabelecidas as melhores condições de extração. A reação colorimétrica foi estabelecida de acordo com dados relatados na literatura, utilizando o método Folin-Denis. As imagens digitais das soluções cromogênicas foram capturadas com câmera microscópica HD, armazenadas no formato JPEG e analisadas com base no sistema de cores RBG e a forma de processamento dos dados da imagem foi Sinal analítico = (255 - Red), os limites de detecção e quantificação foram 59,1 e 90 mg 100 g -1 , respectivamente. A precisão foi de 1,03% para uma amostra de spirulina. Os ensaios de recuperação com ácido gálico para adição de 2,0, 4,0 e 6,0 μg ml-1 à amostra foram de 98,07%, 105,61% e 101,73%, respectivamente. O método proposto foi aplicado para analisar diferentes amostras de microalgas (spirulina e chlorella formuladas em cápsulas e em pó) e o teor de fenólicos totais variou de 111,68 a 662,15 mg 100 g -1 . As comparações foram concordantes mediante a comparação entre as médias. Nesses resultados não houve diferenças significativas daqueles obtidos por espectrofotometria UV-Vis, com nível de confiança de 95%. Extraction (chemistry) ; Digital image ; Sample (material) ; Decantation ; Image processing Plant-Derived Bioactive Compounds ; Edible Oils Quality and Analysis ; Phytochemistry Medicinal Plant Applications
Authors: Hua Wang
Source: International Journal of New Technology and Research (article, 2023-05-07)
Field: Chemistry — Analytical Chemistry

Origin Traceability of Tobacco Leaves by Multi-elements Analysis Using Inductively Coupled Plasma Atomic Emission Spectroscopy and Cluster Analysis An Inductively Coupled Plasma Atomic Emission Spectroscopy (ICP-AES) method was developed for determination of 16 elements (As, Se, Zn, Pb, Cd, Ba, Fe, B, Mn, Cr, Mg, V, Ca, Cu, Al, K) in tobacco leaves. Collected 36 samples from Peru, Zimbabwe, and China were analyzed. The results showed that the content of Cd, Pb, As, Cr and other heavy metals in all samples did not exceed the limit of China national regulation. Principal component factor (PCA) analysis indicated that the studied 16 elements are not well correlated. The variations of elements in tobacco leaves was attributed to the difference of environmental soil rather than the result of tobacco secondary metabolism. 36 samples can be approximately divided into 10 groups. This mathematical model can serve as a traceability tool for tobacco leaf products. This method may facilitate the tobacco selection and quality identification. Traceability ; Inductively coupled plasma ; Inductively coupled plasma atomic emission spectroscopy ; Cluster (spacecraft) ; Analytical Chemistry (journal) ; Chemistry ; Materials science ; Plasma ; Environmental chemistry ; Physics ; Computer science ; Mathematics ; Statistics ; Nuclear physics Analytical chemistry methods development
Authors: Abhijith Jayakumar | Marc Vuffray | Andrey Y. Lokhov
Source: arXiv (Cornell University) (preprint, 2023-04-08)
Field: Materials Science — Materials Chemistry

Learning Energy-Based Representations of Quantum Many-Body States Efficient representation of quantum many-body states on classical computers is a problem of enormous practical interest. An ideal representation of a quantum state combines a succinct characterization informed by the system's structure and symmetries, along with the ability to predict the physical observables of interest. A number of machine learning approaches have been recently used to construct such classical representations [1-6] which enable predictions of observables [7] and account for physical symmetries [8]. However, the structure of a quantum state gets typically lost unless a specialized ansatz is employed based on prior knowledge of the system [9-12]. Moreover, most such approaches give no information about what states are easier to learn in comparison to others. Here, we propose a new generative energy-based representation of quantum many-body states derived from Gibbs distributions used for modeling the thermal states of classical spin systems. Based on the prior information on a family of quantum states, the energy function can be specified by a small number of parameters using an explicit low-degree polynomial or a generic parametric family such as neural nets, and can naturally include the known symmetries of the system. Our results show that such a representation can be efficiently learned from data using exact algorithms in a form that enables the prediction of expectation values of physical observables. Importantly, the structure of the learned energy function provides a natural explanation for the hardness of learning for a given class of quantum states. Observable ; Physical system ; Quantum state ; Quantum ; Representation (politics) ; Computer science ; Ansatz ; Quantum system ; Quantum machine learning ; Statistical physics ; Theoretical computer science ; Quantum algorithm ; Theoretical physics ; Physics ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Quantum Computing Algorithms and Architecture
Authors: Abiola J. Adeyi | Oladayo Adeyi | Olusola Oke | Akinola David Ogunsola | Oluwaseun Ajayi | John Adebayo Otolorin | Silvester Areghan | Anderson Owoloja | Bose Folashade Isola | Oladapo Akinyemi
Source: Journal of the Ghana Institution of Engineering (JGhIE) (article, 2023-01-27)
Field: Chemistry — Analytical Chemistry

Convective drying of unripe plantain: a comparative response surface methodology and genetic algorithm optimization study, certainty and sensitivity analysis The requirement for reduction of post-harvest losses increased production, and cost-effectiveness of foods are driving continuous food process investigations. In this study, Response Surface Methodology (RSM) was utilized to understand, model, and optimize the effect of selected process factors on the moisture content (MC) of convectively dried unripe plantain fruit. For technical accuracies, Multi Gene Genetic Programming (MGGP) was also used to model the process and both MGGP and RSM models were statistically compared. Furthermore, Monte Carlo Simulation (MCS) was used to conduct sensitivity analysis of unripe plantain’s MC to each selected process factor. Results showed that increased sample thickness increased the MC while increased drying temperature and drying time decreased the MC of unripe plantain. RSM model had Chi-square, MBE, t-value, RMSE and R2 values of 15.2131, 0.7531, 7.6170, 0.9193 and 0.9674, respectively; while MGGP model had 3.0415, 0.2563, 2.6871, 0.4111 and 0.9956, respectively. Sensitivity analysis showed that sample thickness, drying temperature and drying time had +89.5 %, -10.2 % and -0.3 % contribution to the variances of MC, respectively. These results are useful in unripe plantain drying process prediction, optimization, and product standardization. Response surface methodology ; Sensitivity (control systems) ; Mathematics ; Water content ; Broyden–Fletcher–Goldfarb–Shanno algorithm ; Food science ; Environmental science ; Statistics ; Chemistry ; Computer science ; Engineering Spectroscopy and Chemometric Analyses
Authors: Giuseppe Tradigo | Patrizia Vizza | Federica Aracri | Marco Gaspari | Pietro Hiram Guzzi | Pierangelo Veltri
Source: Research Square (preprint, 2023-08-30)
Field: Chemistry — Spectroscopy

Machine Learning Pipeline to analyze Clinical and Proteomics Data: experiences on a prostate cancer case Abstract Proteomic-based analysis is used to identify biomarkers in blood samples and tissues. Data produced by devices such as mass spectrometry, requires platforms to identify and quantify proteins (or peptides). Clinical information can be related with mass spectrometry data to identify diseases at an early stage. Machine learning techniques can be used to support physicians and biologists for studying and classifying pathologies. We present the application of machine learning techniques to define a pipeline aimed to study and classify protemics data enriched by means of clinical information. The pipeline allows users to relate established blood biomarkers with clinical parameters and proteomics data. The proposed pipeline entails three main phases: (i) feature selection, (ii) models training and (iii) models ensembling. We report the experience of applying such a pipeline on prostate related diseases. Models have been trained on a datasets which results from the integration of clinical and mass spectrometry based data. The pipeline receives as input data from blood analytes, tissue samples, proteomic analysis and urine biomarkers. It then learns different models for feature selection and classification.The presented pipeline has been applied on two datasets onbtained a 2 years research project which aimed to extract hidden information from mass spectrometry, serum and urine samples from hundreds of patients. We report results on analyzing prostate datasets with 163 samples, including 79 patient, and a urine dataset with 121 patients. As results pipeline allowed to identify interesting peptides in the two datasets, 6 for the first one and 2 for the second one. The best model for both prostate (AUC=0.815, F1=0.8, Specificity=0.75, Sensitivity=0.88) and urine (AUC=0.810, F1=0.824, Specificity=0.805, Sensitivity=0.814) datasets showed good predictive performances.We are confident that the pipeline can be successfully adopted in similar clinical setups. Pipeline (software) ; Computer science ; Feature selection ; Proteomics ; Prostate cancer ; Artificial intelligence ; Feature (linguistics) ; Machine learning ; Data mining ; Medicine ; Cancer ; Internal medicine ; Chemistry Advanced Proteomics Techniques and Applications ; Prostate Cancer Treatment and Research ; Mass Spectrometry Techniques and Applications
Authors: Venusa Abriandika | Achmad As’ad Sonief | Yudy Surya Irawan
Source: Jurnal Rekayasa Mesin (article, 2023-08-15)
Field: Materials Science — Materials Chemistry

PENGARUH PAHAT ENDMILL NORMAL DAN VARIABEL HELIX ANGLE PADA METODE POCKET ZIG-ZAG TERHADAP CHATTER The manufacturing industry mostly uses CNC Milling machines in processing products that require high precision values. This machine is capable of a wide variety of feeding methods, inclinations, feed speeds, various tool styles, and much more. The purpose of this research is to analyze one method of infeed direction with the Zig-Zag effect by using 2 types of normal and variable tool types combined with variations in spindle speed and DOC to find out which variable is effective and produce a smoother comparison of surface roughness values. The type of material tested was SS 304 because this material has good advantages against corrosion. From the results of research conducted on this material for the use of a variable tool, it has an average roughness value of 0.327 µm and for the results of the roughness test on the use of a normal tool, it has an average roughness value of 0.470 µm. From the results of this research, it can be concluded that the value of the Depth Of Cut (DOC), the roughness value are directly proportional, and the faster the spindle rotation, the frictional force generated also increases. The highest Ra value from the research results occurred in DOC 1.6 mm with a spindle speed of 2750 rpm using a variable tool of 0.361 µm and a roughness value of 0.516 µm on a normal tool. The conclusion of this research is that the variable tool produces a lower Ra value than the normal tool so that the type of material defects caused by chatter vibration can be reduced. Surface roughness ; Surface finish ; Variable (mathematics) ; Rotational speed ; Materials science ; Machine tool ; Value (mathematics) ; Mathematics ; Geometry ; Composite material ; Mechanical engineering ; Mathematical analysis ; Engineering ; Statistics ; Metallurgy Material Selection and Properties ; Arduino and IoT Applications ; Occupational Health and Safety Management
Authors: Mason Jones | Jean‐Pierre Delplanque | Daniel Moser | Theron Rodgers | Brian Weston
Source:  (article, 2023-07-01)
Field: Materials Science — Materials Chemistry

Analyzing and Tuning a Fast Machine Learning Based Surrogate Microstructure Model for LPBF Computer science ; Artificial intelligence ; Microstructure ; Machine learning ; Materials science ; Metallurgy Machine Learning in Materials Science ; Injection Molding Process and Properties ; Drilling and Well Engineering
Authors: Suprapto Suprapto | Yatim Lailun Ni’mah | Rifda Alifah Hisana
Source: Malaysian Journal of Fundamental and Applied Sciences (article, 2023-08-27)
Field: Chemistry — Analytical Chemistry

Robust Regression Analysis of Full Overlapping Caffeine and Pyridoxine HCl UV-Vis Spectra in Pharmaceutical Tablet The determination of caffeine and pyridoxine HCl in medicinal tablets has been successfully carried out. The mixture of caffeine and pyridoxine solution produces UV-Vis with full overlapping spectra. The full overlapping spectra can not be analyzed using conventional multicomponent analysis, as they do not have a distinct maximum wavelength. This research proposed a full overlapping spectra analysis using Robust regression. The regression models used in this research were based on Huber, RANSAC, and Theil-Sen Regression. Robust regression is a regression method that was not sensitive to the presence of outliers from the input or output data. Robust Regression models were trained using 25 standard solutions of caffeine and pyridoxine HCl at varied ratios. The models were validated using test solutions with known concentration ratios. The validated models were applied to determine the concentration of medicinal tablets. From this study, the recovery values of medicinal tablets obtained using Huber, RANSAC, and Theil-Sen Regression methods for caffeine were 96.94%, 97.19%, and 96.16% respectively, while the recoveries of pyridoxine HCl were 122.65%, 104.89%, and 107.48%. Pyridoxine ; Caffeine ; Outlier ; Regression analysis ; Linear regression ; Mathematics ; Robust regression ; Statistics ; Regression ; Chemistry ; Chromatography ; Medicine ; Biochemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: K. Parliński
Source: arXiv (Cornell University) (preprint, 2023-01-04)
Field: Materials Science — Materials Chemistry

Ab initio calculations of thermal conductivities in crystals The calculations of thermal conductivity requires to know anharmonic properties of the crystal. For this purpose a non-perturbative anharmonic theory is applied, which do not make use of the potential energy expansion over atomic displacements, but instead, runs ab initio calculations of Hellmann-Feynman forces for atomic patterns of atoms with specific displacements to rebuild the anharmonic phonon frequencies, and group velocities. see [K.Parlinski, Phys.Rev. B 98, 054305 (2018),] The Green-Kubo equation for the thermal conductivity needs to know the above quantities and the phonon relaxation times, which are related to the 4th-order phonon correlation function expressed in terms of phonon anihilation and creation Bose operators. In currect formulation of anharmonic theory the relaxation times can be derived as analitical expression. The Green-Kubo formulae was succesfully applied to find thermal conductivity of Si and conductivities, related to the phonon and elastic waves, respectivily, were computed. Anharmonicity ; Phonon ; Thermal conductivity ; Ab initio ; Relaxation (psychology) ; Condensed matter physics ; Feynman diagram ; Crystal (programming language) ; Physics ; Chemistry ; Quantum mechanics Thermal properties of materials ; Advanced Thermoelectric Materials and Devices ; Thermography and Photoacoustic Techniques
Authors: Shaimaa Sait | Isoif Vaisman
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-01-04)
Field: Materials Science — Materials Chemistry

Computational mutagenesis by using AUTO-MUTE 2.0 to examine X-ray crystallography structures for p53 and the impact of its resolution on predictions ABSTRACT The mutation of P53 is found in 50% of all human cancers, affecting the protein structure conformation, which further impacts its function. Identifying the appropriate protein structure for future research has been challenging. Many X-ray crystallography structures exist in the Protein Data Bank (PDB) for a given protein. We compared two proteins with different resolutions to determine whether the differences between the two structures were statistically significant. The present study used mutagenesis to study two structures of the protein P53 with various resolutions to assess how the conformation of the protein changes upon mutation. We used an AUTO-MUTE 2.0, which is a computational geometry method based on the Delaunay tessellation for predicting protein structures. The current study compares the prediction results obtained from different predictive methods. We used logistic regression models and corresponding receiver operating characteristic (ROC) curves to model the differences between structures using the variables available at PDB. We conclude from this study that x-ray crystallography is a sensitive and advanced technique, and the resulting proteins at different resolutions are similar. Protein Data Bank ; Protein Data Bank (RCSB PDB) ; Mutagenesis ; Protein structure ; Receiver operating characteristic ; Computer science ; Resolution (logic) ; Computational biology ; Mutation ; Algorithm ; Biological system ; Crystallography ; Biology ; Artificial intelligence ; Chemistry ; Machine learning ; Genetics ; Biochemistry Enzyme Structure and Function ; Molecular Biology Techniques and Applications ; Cancer-related Molecular Pathways
Authors: Zu-Xing Yang | Xiao-Hua Fan | Z. P. Li | Haozhao Liang
Source: arXiv (Cornell University) (preprint, 2023-10-17)
Field: Materials Science — Materials Chemistry

A Data-Driven Density Functional Model for Nuclear Systems Through ensemble learning with multitasking and complex connection neural networks, we aggregated nuclear properties, including ground state charge radii, binding energies, and single-particle state information obtained from the Kohn-Sham auxiliary single-particle systems. Compared to traditional density functional theory, our model can more accurately characterize nuclear ground state information. Aiming at binding energy, the root mean square error is reduced to 450 keV. Although the complexity involving the nuclear interaction is skipped, the model has not completely devolved into a black box. Leveraging the correlation between densities and binding energies, we calculate the neutron skin thickness of $^{208}$Pb to be 0.223 fm. This model will advance our understanding of nuclear properties and accelerate the integration of machine learning into modern nuclear physics. Binding energy ; Neutron ; Physics ; Nuclear structure ; State (computer science) ; Density functional theory ; Nuclear drip line ; Statistical physics ; Computer science ; Nuclear physics ; Quantum mechanics ; Algorithm Machine Learning in Materials Science ; Nuclear Physics and Applications