jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Rafael Izbicki | Luben M. C. Cabezas | Fernando A. B. Colugnatti | Rodrigo F. L. Lassance | Altay Alves Lino de Souza | Rafael B. Stern
Source: arXiv (Cornell University) (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

REACT to NHST: Sensible conclusions to meaningful hypotheses While Null Hypothesis Significance Testing (NHST) remains a widely used statistical tool, it suffers from several shortcomings in its common usage, such as conflating statistical and practical significance, the formulation of inappropriate null hypotheses, and the inability to distinguish between accepting the null hypothesis and failing to reject it. Recent efforts have focused on developing alternatives that address these issues. Despite these efforts, conventional NHST remains dominant in scientific research due to its procedural simplicity and mistakenly presumed ease of interpretation. Our work presents an intuitive alternative to conventional NHST designed to bridge the gap between the expectations of researchers and the actual outcomes of hypothesis tests: $\texttt{REACT}$. $\texttt{REACT}$ not only tackles shortcomings of conventional NHST but also offers additional advantages over existing alternatives. For instance, $\texttt{REACT}$ applies to multiparametric hypotheses and does not require stringent significance-level corrections when conducting multiple tests. We illustrate the practical utility of $\texttt{REACT}$ through real-world data examples. Null hypothesis ; Statistical hypothesis testing ; Computer science ; Interpretation (philosophy) ; Alternative hypothesis ; Statistical evidence ; Data science ; Econometrics ; Statistics ; Mathematics Machine Learning in Materials Science ; Explainable Artificial Intelligence (XAI) ; Mental Health Research Topics
Authors: Jéssica Taveira da Rocha | Caroline Soares Rodrigues Neto | Diego Resende Santos | Eric Caetano Maurício Vieira | Quéren Hapuque de Souza Eugenio | Carlos da Mata Campos
Source: Brazilian Journal of Production Engineering (article, 2023-12-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Motores de indução trifásicos para operações industriais Os motores de indução trifásicos pertencem à classe de motores de maior preferência e utilização na área industrial fabril, não somente a nível de Brasil, mas também mundialmente, essencialmente devido às suas características de resistência, acessibilidade e versatilidade na aplicação. A sua aplicação é vasta, com isso, a instalação e funcionamento cada vez mais vem sendo expostos a ambientes e condições de operações impróprias sujeitando-os a diversas falhas incipientes. Ocorrência de falhas inesperadas na indústria resulta em transtornos e podem significar prejuízos, sejam estes financeiros, redução de horas homem produtivas proporcionadas por parada, além de uma redução de produtos produzidos. Neste contexto, a manutenção preditiva é uma possível aliada para a execução de um controle e acompanhamento nos motores de indução trifásicos, pois esta é uma metodologia programática e alinhada com métodos modernos de detecção e diagnósticos de falhas possibilitam que processos e equipamentos tenham uma maior disponibilidade, proporcionando que atinjam uma máxima eficiência, minimizando perdas de produção e acentuando a lucratividade. O objetivo deste trabalho é apresentar uma visão geral dos motores de indução trifásicos para operações industriais, de forma a identificar e evidenciar os pontos relevantes relacionados ao tema. Humanities ; TRIF ; Physics ; Philosophy ; Chemistry Magnetic Properties and Applications ; Advanced Machining and Optimization Techniques ; Non-Destructive Testing Techniques
Authors: Max Greifenstein | Andreas Dreizler
Source: Journal of Raman Spectroscopy (article, 2023-12-29)
Field: Chemistry — Spectroscopy

CARS.jl: Efficient fitting of dual‐pump multi‐species CARS spectra using compressed libraries Abstract In this publication, we demonstrate the extension of our loss‐less library approach previously limited to fitting ro‐vibrational N 2 CARS spectra for thermometry purposes to accurate and efficient fitting of dual‐pump CARS spectra with multiple species. The fundamental reasoning behind the compression approach is to make use of finite resolution effects arising from the laser linewidths, which are typically larger than the transitional linewidths. For accurate reconstruction at runtime of the fit, in addition to the squared modulus and real component, also the imaginary component as well as intra‐region species cross terms have to be compressed and tabulated for dual‐pump multi‐species spectra. The resulting libraries do not require to tabulate combinations of mole fractions, leading to a near linear growth of the library with respect to the number of species. The accuracy and computational efficiency of the method is benchmarked using a reference implementation provided alongside this publication. Library generation times, mainly depending on the underlying spectral model, are typically in the order of seconds to a few minutes. The simulation of a single dual‐pump spectrum containing three species is sped up by a factor of &gt;5000 without significantly affecting the accuracy of reconstruction. In a simulated experiment with Stokes‐noise imposed data, the time required to it temperature, mole fraction of three resonant species and wavenumber shift on a single spectrum required 90 ms. Wavenumber ; Spectral line ; Mole fraction ; Raman spectroscopy ; Computational physics ; Dual (grammatical number) ; Component (thermodynamics) ; Algorithm ; Chemistry ; Analytical Chemistry (journal) ; Physics ; Optics ; Computer science ; Thermodynamics ; Quantum mechanics Spectroscopy and Laser Applications ; Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Andrea Baragetti | Liliana Grigore | Fabio Pellegatta | Elisa Mattavelli | Marco Chierici | Giuseppe Jurman | Alberico L. Catapano
Source: Atherosclerosis (article, 2023-08-01)
Field: Chemistry — Spectroscopy

Targeted proteomics and the presence of carotid plaques in subjects at low cardiovascular disease risk Medicine ; Disease ; Proteomics ; Cardiology ; Internal medicine ; Biology ; Genetics Advanced Proteomics Techniques and Applications ; Nutritional Studies and Diet ; Cerebrovascular and Carotid Artery Diseases
Authors: Jad Beyhum | Jonas Striaukas
Source: arXiv (Cornell University) (preprint, 2023-07-25)
Field: Chemistry — Analytical Chemistry

Testing for sparse idiosyncratic components in factor-augmented regression models We propose a novel bootstrap test of a dense model, namely factor regression, against a sparse plus dense alternative augmenting model with sparse idiosyncratic components. The asymptotic properties of the test are established under time series dependence and polynomial tails. We outline a data-driven rule to select the tuning parameter and prove its theoretical validity. In simulation experiments, our procedure exhibits high power against sparse alternatives and low power against dense deviations from the null. Moreover, we apply our test to various datasets in macroeconomics and finance and often reject the null. This suggests the presence of sparsity -- on top of a dense model -- in commonly studied economic applications. The R package FAS implements our approach. Regression ; Regression analysis ; Computer science ; Factor regression model ; Linear regression ; Regression testing ; Statistics ; Factor analysis ; Factor (programming language) ; Covariance ; Mathematics ; Proper linear model ; Polynomial regression Spectroscopy and Chemometric Analyses ; Gene expression and cancer classification ; Statistical Methods and Inference
Authors: Et al. Digambar Aggayya Jakkan
Source: International Journal on Recent and Innovation Trends in Computing and Communication (article, 2023-11-30)
Field: Chemistry — Analytical Chemistry

Carbon Property of Soil Prediction By VIS/NIR Spectroscopy Using DrSeqANN Carbon(C) levels have a direct impact on plant health and productivity. 200 soil samples from the Indian state of Uttar Pradesh were utilized in this study as a database to assess the efficacy of employing visible/near-infrared (VIS/NIR) spectroscopy data. The samples wavelengths ranged from 350 to 2,500 nm. The spectral features used to predict C were chosen using Ensemble Lasso Ridge Regression (ELRR), Random Forest (RF), and the more complicated Artificial Neural Network. The preprocessing employed the log derivative, Log10x derivative, and inverse derivative to replicate the wavelength of the spectrum. The essential feature wavelengths for C were discovered to be between 350 and 450 nm, according to the results. The recommended Dropout Sequential Artificial Neural Network (DrSeqANN) technique combined with the Log10x pre-processed data produced the most accurate results. Carbon fibers ; Property (philosophy) ; Spectroscopy ; Soil carbon ; Materials science ; Environmental science ; Soil science ; Physics ; Soil water ; Composite material Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Witsarut Sriratana | Wongwit Iamyang | Lerdlekha Sriratana
Source:  (article, 2023-12-14)
Field: Chemistry — Analytical Chemistry

Analysis of Eggs Quality by Using the NIR In this study, fresh chicken eggs that had hatched between 11 to 22 days prior were used to examine and devise a method for assessing chicken egg quality [1][9]. The samples were separated into three sizes: medium, large, and extra-large, and stored in an environment with controlled temperature and humidity. The eggs were then exposed to near-infrared spectra projected onto them by a spectrometer in the wavelength range of 610 to 880 nm [2][4][6]. The water molecules within the eggs absorb near-infrared thermal radiation. The unabsorbed portion of the thermal energy or near-infrared spectrum reflects some of the spectrometer's light and generates the spectrum's output in terms of radiation intensity. The Haugh unit and yolk index [1] were then utilized to compare the mean values [3]. This study determined, that chicken eggs aged 11 to 22 days had radiation intensities between 16.43 and 48.19, yolk indices between 0.1994 and 0.2601, and Haugh unit values between 54.7345 and 67.2087. The analysis of the data revealed that elder eggs had a greater effect on egg quality. Each of the three variables (radiation intensity reflected, Haugh units, and egg yolk index) there was a direct correlation with egg quality. Quality (philosophy) ; Computer science ; Physics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Animal Nutrition and Physiology
Authors: Stoyan Stoynev
Source:  (article, 2023-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Introduction to quench detection In the zone ("spot") where quenching starts all the current flows through resistive conductor called "stabilizer"; often copper, Cu (because superconductors are usually very bad normal conductors) From energy conservation it follows that Joule heating must be equal to heat absorption: Then one arrives at an expression like this:Hot spot 6/2/2023 5MIITs-hot-spot-temperature relation Fault detection and isolation ; Electromagnetic coil ; Computer science ; Magnet ; Component (thermodynamics) ; Schedule ; Voltage ; Reliability engineering ; Engineering ; Electronic engineering ; Electrical engineering ; Artificial intelligence ; Physics ; Actuator Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Superconducting Materials and Applications
Authors: Akshay Agarwal | Minxu Peng | Vivek K Goyal
Source: arXiv (Cornell University) (preprint, 2023-03-07)
Field: Materials Science — Surfaces, Coatings and Films

Continuous-Time Modeling and Analysis of Particle Beam Metrology Particle beam microscopy (PBM) performs nanoscale imaging by pixelwise capture of scalar values representing noisy measurements of the response from secondary electrons (SEs) integrated over a dwell time. Extended to metrology, goals include estimating SE yield at each pixel and detecting differences in SE yield across pixels; obstacles include shot noise in the particle source as well as lack of knowledge of and variability in the instrument response to single SEs. A recently introduced time-resolved measurement paradigm promises mitigation of source shot noise, but its analysis and development have been largely limited to estimation problems under an idealization in which SE bursts are directly and perfectly counted. Here, analyses are extended to error exponents in feature detection problems and to degraded measurements that are representative of actual instrument behavior for estimation problems. For estimation from idealized SE counts, insights on existing estimators and a superior estimator are also provided. For estimation in a realistic PBM imaging scenario, extensions to the idealized model are introduced, methods for model parameter extraction are discussed, and large improvements from time-resolved data are presented. Estimator ; Metrology ; Shot noise ; Pixel ; Computer science ; Noise (video) ; Algorithm ; Optics ; Physics ; Artificial intelligence ; Mathematics ; Statistics ; Image (mathematics) Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Advancements in Photolithography Techniques
Authors: Bogdan Budnik | Hossein Amirkhani | Klaus M. Weinberger | Karine Sargsyan | Mohammad H. Forouzanfar | Ashkan Afshin
Source: medRxiv (preprint, 2023-10-28)
Field: Chemistry — Spectroscopy

Landscaping of Urine Proteome: Unlocking Diagnostic Potential and Overcoming Unique Challenges Abstract This study explores the application of deep proteomic profiling to extract disease-specific features from urine. Early detection of cancer and other chronic disorders is crucial for better outcomes, but traditional diagnostics as well as emerging genomic-based diagnostics are expensive and invasive. Our research reveals that a select group of urinary proteins can accurately detect early-stage diseases with high sensitivity, surpassing current tests. While urine-based protein panels could offer cost-effective and accurate alternatives to current screening methods, kidney factors and blood urine barrier pathologies could pose significant challenges. New diagnostic technologies may emerge because of these findings, ushering in an era of early detection for cancer and chronic diseases. One-Sentence Summary Urine-based protein panels show distinct patterns in early disease detection, promising opportunities for advancing diagnostic tests Urine ; Diagnostic test ; Kidney disease ; Proteome ; Disease ; Medicine ; Intensive care medicine ; Bioinformatics ; Biology ; Pathology ; Internal medicine Advanced Proteomics Techniques and Applications ; Molecular Biology Techniques and Applications ; Bladder and Urothelial Cancer Treatments
Authors: Stefan Boettcher
Source: arXiv (Cornell University) (preprint, 2023-02-21)
Field: Materials Science — Materials Chemistry

Deep reinforced learning heuristic tested on spin-glass ground states: The larger picture In Changjun Fan et al. [Nature Communications https://doi.org/10.1038/s41467-023-36363-w (2023)], the authors present a deep reinforced learning approach to augment combinatorial optimization heuristics. In particular, they present results for several spin glass ground state problems, for which instances on non-planar networks are generally NP-hard, in comparison with several Monte Carlo based methods, such as simulated annealing (SA) or parallel tempering (PT). Indeed, those results demonstrate that the reinforced learning improves the results over those obtained with SA or PT, or at least allows for reduced runtimes for the heuristics before results of comparable quality have been obtained relative to those other methods. To facilitate the conclusion that their method is ''superior'', the authors pursue two basic strategies: (1) A commercial GUROBI solver is called on to procure a sample of exact ground states as a testbed to compare with, and (2) a head-to-head comparison between the heuristics is given for a sample of larger instances where exact ground states are hard to ascertain. Here, we put these studies into a larger context, showing that the claimed superiority is at best marginal for smaller samples and becomes essentially irrelevant with respect to any sensible approximation of true ground states in the larger samples. For example, this method becomes irrelevant as a means to determine stiffness exponents $θ$ in $d&gt;2$, as mentioned by the authors, where the problem is not only NP-hard but requires the subtraction of two almost equal ground-state energies and systemic errors in each of $\approx 1\%$ found here are unacceptable. This larger picture on the method arises from a straightforward finite-size corrections study over the spin glass ensembles the authors employ, using data that has been available for decades. Heuristics ; Ground state ; Simulated annealing ; Parallel tempering ; Solver ; Computer science ; Heuristic ; Algorithm ; Deep learning ; Mathematical optimization ; Artificial intelligence ; Statistical physics ; Mathematics ; Markov chain Monte Carlo ; Physics ; Quantum mechanics ; Hybrid Monte Carlo Machine Learning in Materials Science ; Theoretical and Computational Physics ; Complex Network Analysis Techniques
Authors: K. Suban | S. N. Hettiarachchi | S. Himaanthri | S. D. F. Aththachchi | C. N. Wickramarachchi | T. S. G. Peiris
Source:  (article, 2023-12-14)
Field: Chemistry — Inorganic Chemistry

Time Series Model to Forecast Fresh Coconut Exports from Sri Lanka Coconut accounts for approximately 12% of all agricultural produce in Sri Lanka with the total land area under cultivation covering 409, 244 hectares ranking second to rice production. The primary regions for coconut cultivation are the Puttalam and Kurunegala districts in North-Western Province and Gampaha district in the Western Province, forming what is known as the Coconut Triangle. This region accounts for 232,270 hectares (50.94%) of the overall coconut cultivation area. The remaining coconut cultivation areas are found in the Southern Province, specifically in the districts of Galle (13,833 hectares), Matara (14,946 hectares), and Hambantota (25,837 hectares), and in non-traditional regions of the Eastern and Northern provinces. The annual coconut production varies between 2,800 to 3,000 million nuts. Having advanced knowledge of exporting coconuts offers numerous advantages to Sri Lanka, particularly in terms of establishing forward contracts with other countries. Based on secondary data of annual fresh coconut exports from 1981 to 2020 obtained from the Coconut Development Authority (CDA) of Sri Lanka, the paper developed ARIMA (2,1,0) model to forecast export. The model was selected out of three parsimonious models which were identified from the Sample Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF) of the stationary series and a comparison of significant parameters and lowest values of Akaike Information Criterion (AIC), Schwarz Bayesian Information Criterion (SBIC) and Hannan-Quinn Information Criterion (HQIC). The errors of the fitted model were found to be random and constant variance. The model was validated using 2021 and 2022 data. The percentage errors for 2021 and 2022 are 20.23% and -29.57% respectively. The predictions for 2023 and 2024 are 14696 and 15052 respectively. The model can be used effectively by the Coconut Development Authority for decision-making. However, it is suggested to develop the model further to reduce the percentage error. Keywords: ARIMA model, Forecast, Fresh Coconut Exports and Validate. Sri lanka ; Series (stratigraphy) ; Time series ; Computer science ; Economics ; Machine learning ; Biology ; Socioeconomics Coconut Research and Applications ; Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research
Authors: Boris Enrique Oblitas-Gastelo | Ingrid Isabel Medina Cardozo | Àngel Quintana | Claudia Sofía Jacinto-Huamanchumo
Source: ITECKNE Innovación e Investigación en Ingeniería (article, 2023-07-11)
Field: Materials Science — Polymers and Plastics

Influencia de las cenizas de cascarilla de arroz como reemplazo parcial del cemento utilizado para la elaboración del concreto: una revisión de literatura La inclusión de cenizas vegetales dentro de una mezcla de concreto ha ganado una gran aceptación, producto del incremento en las propiedades físicas y mecánicas que ofrece. Ante ello, la presente investigación tuvo como objetivo ofrecer una revisión sistemática de literatura sobre la influencia de las cenizas de cascarilla de arroz (CCA) como reemplazo parcial del cemento utilizado para la elaboración del concreto durante los años 2007-2022. En esta revisión de literatura se utilizaron 40 fuentes debidamente seleccionadas con rugosidad académica y científica obtenidas de bases de datos confiables; por lo que se ordenaron según las propiedades intervenidas y sus respectivos resultados, la relación agua/cemento+CCA y el comportamiento del concreto al aumentar el porcentaje de las CCA. En el análisis de la resistencia a la compresión y tracción, se obtuvo que la proporción adecuada de reemplazo es de 15% con una relación agua:cemento+CCA al 0.50, la cual fue añadida de forma molida y sin elementos puzolánicos extra. En torno a la trabajabilidad, la proporción de reemplazo continúa en ese mismo rango (5%-15%), controlando a su vez el descenso del valor del slump con un adecuado uso de aditivos superplastificantes y una correcta granulometría del agregado. En términos generales, la incorporación de las CCA como reemplazo parcial del cemento debe ir acompañada de una buena caracterización y preparación de los especímenes, un correcto diseño de mezcla y procedimientos de ensayos de laboratorio que permitan tener una aceptable confiabilidad y certeza en la interpretación de los resultados. Humanities ; Philosophy ; Physics Natural Fiber Reinforced Composites ; Recycled Aggregate Concrete Performance ; Soil Science and Environmental Management
Authors: Dorian Daimer | Susanne Still
Source: arXiv (Cornell University) (preprint, 2023-09-19)
Field: Materials Science — Materials Chemistry

The physical observer in a Szilard engine with uncertainty Information engines model ``Maxwell's demon" mechanistically. However, the demon's strategy is pre-described by an external experimenter, and information engines are conveniently designed such that observables contain complete information about variables pertinent to work extraction. In real world scenarios, it is more realistic to encounter partial observability, which forces the physical observer, an integral part of the information engine, to make inferences from incomplete knowledge. Here, we use the fact that an algorithm for computing optimal strategies can be directly derived from maximizing overall engine work output. For a simple binary decision problem, we discover interesting optimal strategies that differ notably from naive coarse graining. They inspire a model class of simple, yet compelling, parameterized soft partitionings of the observable. Observability ; Parameterized complexity ; Observer (physics) ; Computer science ; Simple (philosophy) ; Observable ; Maxwell's demon ; Granularity ; Mathematical optimization ; Mathematics ; Algorithm ; Applied mathematics ; Physics Pickering emulsions and particle stabilization ; Block Copolymer Self-Assembly ; Surface Chemistry and Catalysis
Authors: Jianxun Su | Meijun Qu | Hang Yu | Wen Yu Li | Hong Yin | Tie Jun Cui
Source: Research Square (preprint, 2023-07-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Full-band Electromagnetic Stealth Physics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis