jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kostiantyn Medvediev | Anna Kharchenko | Anzhelika Stakhova | Vitalii Tsybulskyi | Yurii Yevseichyk | Adrián Bekő
Source: Preprints.org (preprint, 2023-12-19)
Field: Materials Science — Materials Chemistry

Methodology for Assessing the Technical Condition and Durability of Bridge Structures Запропонована методика спрямована на визначення та прогнозування технічного стану елементів мосту, що може слугувати прогресивним інженерним інструментом для оцінки надійності та довговічності. Розроблено на основі фундаментальних досліджень, які узагальнюють досвід вивчення фізико-механічних і фізико-хімічних властивостей матеріалів мостових конструкцій, що експлуатуються в реальних умовах. Теоретичною основою методики є модель надійності та прогнозування залишкового ресурсу елементів мосту на основі теорії Маркова. Розроблена методика призначена для оцінки технічного стану окремих елементів мосту з подальшою комплексною оцінкою всієї конструкції. За показник технічного стану прийнята надійність при експлуатації. Цей кількісний показник надійності в моделі є критерієм для: оцінки рівня безпеки елементів мосту; ранжування елементів мосту за необхідністю для конкретних видів ремонту, реконструкції або заміни; стратегічне планування витрат на ремонт або реконструкцію в умовах обмеженого фінансування; прогнозування залишкового ресурсу елементів. Для застосування розробленої методики запропоновано алгоритм оцінки та прогнозування технічного стану мостів. Було проведено математичний експеримент розробленої методики, який підтвердив адекватність висунутої гіпотези, тобто використання моделі надійності та прогнозування залишкового ресурсу елементів мосту. По-перше, запропоновано триступеневий механізм уточнення технічного стану мосту, суттєво підвищення точності розрахунків. Тому розроблена методика має практичне значення і може слугувати основою для інформаційно-аналітичних систем управління станом мостів. Durability ; Bridge (graph theory) ; Forensic engineering ; Engineering ; Construction engineering ; Computer science ; Reliability engineering ; Medicine ; Database ; Surgery Material Properties and Failure Mechanisms
Authors: 哲也 稲垣
Source: Journal of the Spectroscopical Society of Japan (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

講座 Quantitative analysis of spectral data using chemometrics and machine learning are explained here.After explaining dierence between chemometrics and machine learning that the author thinks, frequently used algorithms (i.e.CLS, ILS, PCA, PLSR, random forest, support vector machine, neural network) are introduced.After that, the way how to divide the training, validation, and prediction datasets are explained.Finally, the author idea about ``what kind of algorithm should be used in spectral analysis'' are explained. Geology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Machine Learning and ELM
Authors: Zhonghui Li | Chenxi Liu | Weihong Ma | Kaiping Deng | Shanyuan Chen | Zhengxing Lian | Zhang Wenguang | Zhihong Liu | Wenrong Li
Source: Scientia Sinica Vitae (article, 2023-04-01)
Field: Chemistry — Analytical Chemistry

绵羊和山羊肉用性能表型鉴定方法现状与展望 <p indent="0mm">The meat performance of sheep and goats determines the yield and quality of the meat produced. Identifying phenotypic meat performance traits facilitates the development of excellent breeds and promotes genetic improvement in sheep and goats. Current identification methods for meat performance phenotypes mainly depend on manual measurements, which are time-consuming, laborious, and low-throughput. With the progress of machine vision, spectral imaging, artificial intelligence, and other cutting-edge technologies, phenotypic identification methods have gradually shifted to noncontact automatic measurements to achieve accurate high-throughput identifications. However, a large amount of data and complex image algorithm processing have become major challenges to developing intelligent phenotype identification techniques. Here, we focused on identifying the phenotypic traits of meat performance, summarizing advances in phenotype identification methods, and highlighting the emerging trends and future prospects around phenotypic traits identification to provide a reference for selecting a meat performance phenotype identification method for sheep and goats. Geography Spectroscopy and Chemometric Analyses ; Food Supply Chain Traceability ; Identification and Quantification in Food
Authors: Mohamed Rasim | Renjini Girija
Source: Journal of Industrial Product Design Research and Studies (article, 2023-06-30)
Field: Materials Science — Polymers and Plastics

A Product Development Model of Denim Jeans for The Indian Market Product development is an essential process in producing a marketable product. The study proposes a new product development model incorporating customer requirements and market trends for developing a new jeans range. The data on consumer preference of denim jeans was collected by employing a survey. The consumer survey provided insights into fit, colour, washing tone and style preference, wearing patterns, buying behaviour and sustainability requirements. Further, a web scraping algorithm was used to collect data on competitive online products. The features of jeans sold in the online market in India were studied. The data analysis provided the basis for feature selection for the denim jeans samples. These features provided the base for developing a new jeans range for men and women. Potential consumers wore the newly designed jeans, and feedback was collected. The method of competition mapping described in this study is economical and effective for forecasting trends. The proposed product development model has practical applications and is affordable for all brands. Denim ; Product (mathematics) ; New product development ; Marketing ; Business ; Preference ; Competition (biology) ; Advertising ; Computer science ; Mathematics ; Geography ; Statistics Textile materials and evaluations ; Color perception and design ; Fashion and Cultural Textiles
Authors: Charlie G.Y. Lim | Bige Özkan | Yujian Liang | Jiali Yao | Nang Ei Ei Khaing | Mary R. Rooney | Chiadi E. Ndumele | E Shyong Tai | Josef Coresh | Xueling Sim | Rob M. van Dam
Source: medRxiv (preprint, 2023-07-27)
Field: Chemistry — Spectroscopy

Plasma proteomic signatures of adiposity are associated with cardiovascular risk factors and type 2 diabetes risk in a multi-ethnic Asian population Abstract The molecular mechanisms connecting obesity and cardiometabolic diseases are not fully understood. We evaluated the associations between body mass index (BMI), waist circumference (WC), and ∼5,000 plasma proteins in the Singapore Multi-Ethnic Cohort (MEC1). Among 410 BMI-associated and 385 WC-associated proteins, we identified protein signatures of BMI and WC and validated them in an independent dataset across two timepoints and externally in the Atherosclerosis Risk in Communities (ARIC) study. The BMI- and WC-protein signatures were highly correlated with total and visceral body fat, respectively. Furthermore, the protein signatures were significantly associated with cardiometabolic risk factors and were able to differentiate between metabolically healthy and unhealthy obesity. In prospective analyses, the protein signatures were strongly associated with type 2 diabetes risk in MEC1 (odds ratio per SD increment in WC-protein signature = 2.84, 95% CI 2.47 to 3.25) and ARIC (hazard ratio = 1.97, 95% CI 1.87 to 2.07). Pathways related to cell signaling, systemic inflammation, and glucose and fat metabolism were overrepresented in the BMI- and WC-protein signatures. Our protein signatures have potential uses for the monitoring of metabolically unhealthy obesity. Article Highlights We evaluated the associations between ∼5000 plasma proteins and BMI and WC in a multi-ethnic Asian population. We identified 410 proteins associated with BMI and 385 proteins associated with WC and derived protein signatures of BMI and WC, which we validated externally in a US cohort. Both the BMI- and WC-protein signatures were strongly associated with cardiometabolic risk factors and the risk of type 2 diabetes and were enriched in pathways relating to cell signaling, systemic inflammation, and glucose and fat metabolism. Our protein signatures have potential uses for monitoring metabolically unhealthy obesity. Body mass index ; Type 2 diabetes ; Waist ; Medicine ; Internal medicine ; Obesity ; Population ; Cohort ; Endocrinology ; Odds ratio ; Diabetes mellitus ; Environmental health Advanced Proteomics Techniques and Applications ; Adipose Tissue and Metabolism ; Metabolism, Diabetes, and Cancer
Authors: Ziang Zhang | Alex Stringer | Patrick O. Brown | James E. Stafford
Source: arXiv (Cornell University) (preprint, 2023-02-04)
Field: Chemistry — Analytical Chemistry

Model-based Smoothing with Integrated Wiener Processes and Overlapping Splines In many applications that involve the inference of an unknown smooth function, the inference of its derivatives will often be just as important as that of the function itself. To make joint inferences of the function and its derivatives, a class of Gaussian processes called $p^{\text{th}}$ order Integrated Wiener's Process (IWP), is considered. Methods for constructing a finite element (FEM) approximation of an IWP exist but have focused only on the order $p = 2$ case which does not allow appropriate inference for derivatives, and their computational feasibility relies on additional approximation to the FEM itself. In this article, we propose an alternative FEM approximation, called overlapping splines (O-spline), which pursues computational feasibility directly through the choice of test functions, and mirrors the construction of an IWP as the Ospline results from the multiple integrations of these same test functions. The O-spline approximation applies for any order $p \in \mathbb{Z}^+$, is computationally efficient and provides consistent inference for all derivatives up to order $p-1$. It is shown both theoretically, and empirically through simulation, that the O-spline approximation converges to the true IWP as the number of knots increases. We further provide a unified and interpretable way to define priors for the smoothing parameter based on the notion of predictive standard deviation (PSD), which is invariant to the order $p$ and the placement of the knot. Finally, we demonstrate the practical use of the O-spline approximation through simulation studies and an analysis of COVID death rates where the inference is carried on both the function and its derivatives where the latter has an important interpretation in terms of the course of the pandemic. Inference ; Spline (mechanical) ; Smoothing ; Gaussian process ; Applied mathematics ; Finite element method ; Smoothing spline ; Mathematics ; Thin plate spline ; Mathematical optimization ; Gaussian ; Computer science ; Algorithm ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses ; Gaussian Processes and Bayesian Inference ; Control Systems and Identification
Authors: Widya Aprilini | Dian Palupi Rini | Hadipurnawan Satria
Source: Sriwijaya Journal of Informatics and Applications (article, 2023-04-30)
Field: Chemistry — Analytical Chemistry

Automatic Clustering and Fuzzy Logical Relationship to Predict the Volume of Indonesia Natural Rubber Export Natural rubber is one of the pillars of Indonesia's export commodities. However, over the last few years, the export value of natural rubber has decreased due to an oversupply of this commodity in the global market. To overcome this problem, it is possible to predict the volume of Indonesia natural rubber exports. Predicted values can also help the government to compile market intelligence for natural rubber commodities periodically. In this study, the prediction of the export volume of natural rubber was carried out using the Automatic Clustering as an interval maker in the Fuzzy Time Series or usually called Automatic Clustering and Fuzzy Logical Relationship (ACFLR). The data used is 51 data per year from 1970 to 2020. The purpose of this study is to predict the volume of Indonesia natural rubber exports and compare the prediction results between the Automatic Clustering and Fuzzy Logical Relationship (ACFLR) and Chen's Fuzzy Time Series. The results showed that there was a significant difference between the two methods, ACFLR got 0.5316% MAPE with and Chen's Fuzzy Time Series model got 8.009%. Show that the ACFLR method performs better than the pure Fuzzy Time Series in predicting volume of Indonesia natural rubber exports. Natural rubber ; Volume (thermodynamics) ; Cluster analysis ; Fuzzy logic ; Commodity ; Fuzzy clustering ; Series (stratigraphy) ; Computer science ; Data mining ; Econometrics ; Mathematics ; Economics ; Artificial intelligence ; Geology ; Finance ; Materials science Spectroscopy and Chemometric Analyses ; Machine Learning in Bioinformatics
Authors: Programmer Analyst at Thermofisher Scientific, USA | Arvind Uttiramerur
Source: Progress in Medical Sciences (article, 2023-12-31)
Field: Materials Science — Materials Chemistry

SAS in Real-World Evidence (RWE) Generation for Clinical Trials Real-World Evidence (RWE) is increasingly recognized as a vital element in the evaluation of treatment effectiveness and safety, complementing findings from traditional clinical trials. Real world evidence ; Clinical trial ; Medical physics ; Medicine ; Internal medicine Machine Learning in Materials Science ; Meta-analysis and systematic reviews
Authors: Wahyu Kartika | Apriani Soepardi
Source: OPSI (article, 2023-12-30)
Field: Materials Science — Polymers and Plastics

Redesign of the sizing process optimization to produce warp yarn with strong weaving ability The research focuses on optimization of the sizing process parameters for cotton warp yarn material. The sizing process is a crucial step where warp yarn is coated with a starch solution before weaving. The aim is to produce warp yarn with strong weaving ability, thereby facilitating the entire process. To achieve this, several factors are considered, namely the steam bar, and squeeze roll pressure, including machine speed. The techniques used to address this issue include Taguchi in collaboration with Grey Relational Analysis (GRA) to effectively address multi-responses. In this analysis, the larger the better characteristic was applied to S/N Ratio, indicating that the higher the GRG value, the closer it is to the desired optimum condition. Based on the calculations from S/N Ratio response table for the GRG, it can be determined that factor steam bar pressure was selected at 4 Bar, with an average S/N Ratio value of -2.935. factor squeeze roll should be set to level 10 Km/m3, as it yields an average S/N Ratio value of -2.912. Factors squeeze roll) and C (Machine Speed) were selected at levels 2 (10 Km/m3) and 1 (45 M/Min) with average values of -2.912, and -4.771, respectively. Weaving ; Sizing ; Yarn ; Bar (unit) ; Process (computing) ; Engineering ; Taguchi methods ; Engineering drawing ; Mechanical engineering ; Structural engineering ; Computer science ; Mathematics ; Statistics Textile materials and evaluations
Authors: Ryan A. León León | Jair Milton García Saire
Source:  (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Artificial Vision System for the inspection of dates in industrial food products The main objective of this work is to improve the efficiency in the inspection of dates in industrial food products through an artificial vision system using the Python language, which will make it possible to process the images for their subsequent classification between an image of correct or incorrect visualization, contributing to future studies and development of similar systems. It began by identifying the main problems that exist in most of the food industries worldwide. Likewise, based on the conclusions found in previously developed works. Once the theoretical base was obtained, we proceeded to identify the necessary materials to carry out this project and carry out the processing where the correct visualization of the expiration dates of said products is verified. At the end of the control, the approved products go to the dispatch area for marketing, otherwise, it will be rejected as a defective product. Various tests were carried out that conclude that the artificial vision system together with thePython language shows an efficiency of 97.20% reducing human error, proving its effectiveness in the quality process. Computer vision ; Machine vision ; Computer science ; Artificial vision ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Date Palm Research Studies ; Identification and Quantification in Food
Authors: saad zabeeb
Source:  (preprint, 2023-03-29)
Field: Chemistry — Analytical Chemistry

Image Analysis for Mango Fruit Defect Identification and Maturity Detection Image Analysis for Mango Fruit Defect Identification andMaturity Detection Identification (biology) ; Maturity (psychological) ; Horticulture ; Image (mathematics) ; Artificial intelligence ; Biology ; Computer science ; Botany ; Psychology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Industrial Vision Systems and Defect Detection
Authors: Takuma Masuda | Masaki Kobayashi | Koji Yatani
Source: Journal of Synchrotron Radiation (article, 2023-10-27)
Field: Materials Science — Materials Chemistry

<i>synapse</i>: interactive support on photoemission spectroscopy measurement and analysis for non-expert users Photoemission spectroscopy, an experimental method based on the photoelectric effect, is now an indispensable technique used in various fields such as materials science, life science, medicine and nanotechnology. However, part of the experimental process of photoemission spectroscopy relies on experience and intuition, which is obviously a problem for novice users. In particular, photoemission spectroscopy experiments using high-brilliance synchrotron radiation as a light source are not easy for novice users because measurements must be performed quickly and accurately as scheduled within a limited experimental period. In addition, research on the application of information science methods to quantum data measurement, such as photoemission spectroscopy, is mainly aimed at the development of analysis methods, and few attempts have been made to clarify the problems faced by users who lack experience. In this study, the problems faced by novice users of photoemission spectroscopy are identified, and a native application named synapse with functions to solve these problems is implemented and evaluated qualitatively and quantitatively. This paper describes the contents of an interview survey, the functional design and the implementation of the application synapse based on the interview survey, and results and discussion of the evaluation experiment. Photoemission spectroscopy ; Spectroscopy ; Photoelectric effect ; Angle-resolved photoemission spectroscopy ; Synchrotron radiation ; Inverse photoemission spectroscopy ; X-ray photoelectron spectroscopy ; Computer science ; Physics ; Engineering physics ; Optoelectronics ; Electronic structure ; Optics ; Quantum mechanics ; Nuclear magnetic resonance Machine Learning in Materials Science ; Advanced Semiconductor Detectors and Materials ; Electron and X-Ray Spectroscopy Techniques
Authors: Ran Huang | Longyan Wu | Lisheng Tang | Xinyi Ning | Xiang Geng | Li Chen | Xiaoyan He | Yuanyuan Jin | Jun Zhu | Xin Ma
Source: Research Square (preprint, 2023-10-02)
Field: Materials Science — Polymers and Plastics

A Pioneering Investigation of a Novel in-situ Dynamic Characterization of the Stress-Strain Mechanism on Human Plantar Soft Tissue In situ ; Mechanism (biology) ; Characterization (materials science) ; Strain (injury) ; Soft tissue ; Stress (linguistics) ; Materials science ; Nanotechnology ; Chemistry ; Anatomy ; Medicine ; Philosophy ; Pathology ; Epistemology Textile materials and evaluations ; Lower Extremity Biomechanics and Pathologies ; Tendon Structure and Treatment
Authors: A. Longobardi | Matteo Fossati | Michele Fumagalli | Bhaskar Agarwal | Emma K. Lofthouse | Marta Galbiati | Rajeshwari Dutta | Trystyn A. M. Berg | Louise Welsh
Source: arXiv (Cornell University) (preprint, 2023-07-12)
Field: Chemistry — Analytical Chemistry

Towards an automatic approach to modelling the circumgalactic medium: new tools for mock making and fitting of metal profiles in large surveys We present two new tools for studying and modelling metal absorption lines in the circumgalactic medium. The first tool, dubbed ``NMF Profile Maker'' (NMF$-$PM), uses a non-negative matrix factorization (NMF) method and provides a robust means to generate large libraries of realistic metal absorption profiles. The method is trained and tested on 650 unsaturated metal absorbers in the redshift interval $z=0.9-4.2$ with column densities between $11.2 \le \log{(\mathrm{N/cm^{-2}})} \le 16.3$, obtained from high-resolution ($R&gt; 4000$) and high signal-to-noise ratio ($S/N \ge 10$) quasar spectroscopy. To avoid spurious features, we train on infinite $S/N$ Voigt models of the observed line profiles derived using the code ``Monte-Carlo Absorption Line Fitter'' (MC$-$ALF), a novel automatic Bayesian fitting code that is the second tool we present in this work. MC$-$ALF is a Monte Carlo code based on nested sampling that, without the need for any prior guess or human intervention, can decompose metal lines into individual Voigt components. Both MC$-$ALF and NMF$-$PM are made publicly available to allow the community to produce large libraries of synthetic metal profiles and to reconstruct Voigt models of absorption lines in an automatic fashion. Both tools contribute to the scientific effort of simulating and analysing metal absorbers in very large spectroscopic surveys of quasars like the ongoing Dark Energy Spectroscopic Instrument (DESI), the 4-meter Multi-Object Spectroscopic Telescope (4MOST), and the WHT Enhanced Area Velocity Explorer (WEAVE) surveys. Quasar ; Spurious relationship ; Non-negative matrix factorization ; Redshift ; Monte Carlo method ; Absorption (acoustics) ; Line (geometry) ; Voigt profile ; Physics ; Computer science ; Spectral line ; Computational physics ; Astrophysics ; Algorithm ; Optics ; Galaxy ; Matrix decomposition ; Statistics ; Mathematics ; Astronomy ; Geometry ; Machine learning Spectroscopy and Chemometric Analyses ; SAS software applications and methods ; Scientific Research and Discoveries