jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Heena Kauser | B.G. Shilpashree | A.R. Praveen | B. P. Pushpa | Himgauri Naik | Parwez Ali
Source: Bhartiya Krishi Anusandhan Patrika (review, 2022-05-16)
Field: Chemistry — Analytical Chemistry

Innovation Techniques to Assess Adulterated Ghee: A Review Adulteration in dairy products is not only serious trouble to mortal health but also causes profitable losses. Ghee, the most precious fat, is vented at a high price over other fats and oil. Unethical vendors take advantage of this by mixing ghee with inexpensive fats or oils. Adulteration of ghee could be estimated through its physical-chemical constants such as Butyro-Refractometer reading, Reichert-Meissl value, Polenske value, Iodine value, saponification value, which are time-consuming, Therefore, researchers nowadays adopted novel techniques. The addition of coconut oil in ghee is detected using Attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR) and chemometrics. Analysis of the adulteration in cow ghee with soybean oil by olfactory machine system, electronic nose (E-nose) tool is used as a rapid technique. Analysis of adulteration in cow ghee with vanaspati by using an image analysis technique called particle analysis and colour measurement and a rapid and accessible protocol based on β-sitosterol using Reversed-phase thin layer chromatography (RP-TLC) is developed to check the purity of milk fat. DPPH (2, 2-diphenyl-1-picrylhydrazyl) based chromogenic assay is a rapid method that has been used to identify the presence of palm oil in ghee. Iodine value ; Saponification value ; Butterfat ; Food science ; Electronic nose ; Saponification ; Chemistry ; Coconut oil ; Chemometrics ; Chromatography ; Mathematics ; Milk fat ; Linseed oil ; Materials science Spectroscopy and Chemometric Analyses ; Coconut Research and Applications ; Fatty Acid Research and Health
Authors: Wan Zakiah Wan Ismail | Irneza Ismail
Source: ASM Science Journal (article, 2022-06-03)
Field: Materials Science — Materials Chemistry

Smart automatic cooling system with reduced humidity effect for pet house during Covid 19 crisis Covid 19 is a pandemic crisis that affects not only human lifestyle but also pet. The crisis halts pets’ social activities and they need to stay at home. Some owners prefer to keep their pets in separate pet enclosure/house due to health issue. The pet enclosure needs to be comfortable as the pets need to stay inside it for quite a long time. Besides, living in middle of the city in Malaysia makes it difficult for people to get a good level of humidity for pets. Thus, this research aims to design and develop an automatic cooling system with reduced humidity effect that can give comfort to the pets for staying many hours inside the pet house. The system uses electronic sensors, peltier, silica gel and Arduino as the system controller. The system relies on Internet of Things (IoT), where the data analysis of the temperature and humidity of pet houses can be done by the users. Particularly, thermoelectric peltier for thermoelectric cooling system, Arduino Uno and NodeMCU ESP8266 Wi-Fi Module are used here. The proposed solution helps to ensure the desired comfort of a pet house, where pet owners can easily and efficiently monitor the temperature and humidity. The system is simple but it still can help maintaining the health and wellbeing of the pets. Coronavirus disease 2019 (COVID-19) ; Arduino ; Humidity ; Internet of Things ; Thermoelectric effect ; Enclosure ; Human health ; Thermoelectric cooling ; Computer science ; Bluetooth ; Architectural engineering ; Business ; Embedded system ; Telecommunications ; Engineering ; Medicine ; Environmental health ; Meteorology ; Geography ; Wireless Advanced Thermoelectric Materials and Devices ; IoT-based Smart Home Systems ; Refrigeration and Air Conditioning Technologies
Authors: George A. Marchant | Lívia B. Pártay
Source:  (article, 2022-11-02)
Field: Materials Science — Materials Chemistry

Nested Sampling of Materials’ Potential Energy Surfaces: Case Study of Zirconium The nested sampling (NS) method was originally proposed by John Skilling to calculate the evidence in Bayesian inference. The method has since been utilised in various research fields, and here we focus on how NS has been adapted to sample the Potential Energy Surface (PES) of atomistic systems, enabling the straightforward estimation of the partition function. Using two interatomic potential models of zirconium, we demonstrate the workflow and advantages of using nested sampling to calculate pressure-temperature phase diagrams. Without any prior knowledge of the stable phases or the phase transitions, we are able to identify the melting line, as well as the transition between the body-centred-cubic and hexagonal-close-packed structures. Zirconium ; Phase diagram ; Sampling (signal processing) ; Workflow ; Interatomic potential ; Statistical physics ; Materials science ; Computer science ; Phase transition ; Phase (matter) ; Thermodynamics ; Molecular dynamics ; Physics ; Chemistry ; Computational chemistry ; Database Machine Learning in Materials Science ; High-pressure geophysics and materials ; Nuclear Materials and Properties
Authors: Lichen QIU
Source: CPSS Transactions on Power Electronics and Applications (article, 2022-03-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on Measurement Technology of Internal Temperature Rise of High Frequency Magnetic Components The measurement of the internal temperature rise of high-frequency magnetic components is of key significance in the design, application, and safety regulation of magnetic components. However, the eddy current loss at the metal end of the thermocouple will bring a large additional temperature rise, which distorts the temperature rise measurement in the part with high frequency magnetic field for the high-frequency magnetic element. Firstly, this paper tests the additional temperature rise of thermocouples with different sizes at different frequencies. Through the analysis of the results, the influencing factors and degree of temperature rise error are clarified, which is helpful to select the appropriate thermocouple according to the working frequency in the actual measurement and evaluate and correct errors. In order to further deduct the unpredictable influence of high-frequency magnetic field environment on thermocouples, this paper proposes to correct the additional temperature rise of thermocouples caused by eddy current loss by using the cooling process curve of magnetic components after power failure and combined with the thermal circuit model of the cooling process. At the same time, the neural network method is proposed to deduce the actual temperature rise of the measured point deducting the additional temperature rise of the thermocouple before the data prediction of the cooling characteristic curve, so as to realize the accurate measurement of the hot spot temperature rise in the magnetic element under the high-frequency magnetic field. Thermocouple ; Eddy current ; Magnetic field ; Temperature measurement ; Materials science ; Field (mathematics) ; Thermal ; Acoustics ; Mechanics ; Electrical engineering ; Electronic engineering ; Engineering ; Physics ; Thermodynamics ; Mathematics Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Advanced Sensor Technologies Research
Authors: Varvara Turova | Andrey Kovtanyuk | Oleg Pykhteev | Irina Sidorenko | Renée Lampe
Source: Bioengineering (article, 2022-09-10)
Field: Chemistry — Physical and Theoretical Chemistry

Glycocalyx Sensing with a Mathematical Model of Acoustic Shear Wave Biosensor The article deals with an idea of exploiting an acoustic shear wave biosensor for investigating the glycocalyx, a polysaccharide polymer molecule layer on the endothelium of blood vessels that, according to recent studies, plays an important role in protecting against diseases. To test this idea, a mathematical model of an acoustic shear wave sensor and corresponding software developed earlier for proteomic applications are used. In this case, the glycocalyx is treated as a layer homogenized over the thin polymer "villi". Its material characteristics depend on the density, thickness, and length of the villi and on the viscous properties of the surrounding liquid (blood plasma). It is proved that the model used has a good sensitivity to the above parameters of the villi and blood plasma. Numerical experiments performed using real data collected retrospectively from premature infants show that the use of acoustic shear wave sensors may be a promising approach to investigate properties of glycocalyx-like structures and their role in prematurity. Glycocalyx ; Shear (geology) ; Materials science ; Biosensor ; Polymer ; Acoustics ; Biomedical engineering ; Composite material ; Chemistry ; Nanotechnology ; Physics ; Engineering Electrostatics and Colloid Interactions
Authors: Parvaneh Karami | Mohsen Zandi | Ali Ganjloo
Source: Food Science and Technology (article, 2022-04-01)
Field: Materials Science — Biomaterials

Investigation and modeling of the release kinetics of yarrow (Achillea millefolium L.) essential oil from gelatin-sodium alginate active film into food simulants Investigation and modeling of the release kinetics of yarrow (Achillea millefolium L.) essential oil from gelatin-sodium alginate active film into food simulants Achillea millefolium ; Gelatin ; Kinetics ; Sodium alginate ; Chemistry ; Essential oil ; Sodium ; Chemical engineering ; Chromatography ; Botany ; Organic chemistry ; Biology ; Engineering Nanocomposite Films for Food Packaging
Authors: Jun Zhang | Ruixin Liang | Newman Lau | Qiwen Emma Lei | Joanne Yip
Source: International Journal of Environmental Research and Public Health (article, 2022-12-27)
Field: Materials Science — Polymers and Plastics

A Systematic Analysis of 3D Deformation of Aging Breasts Based on Artificial Neural Networks The measurement and prediction of breast skin deformation are key research directions in health-related research areas, such as cosmetic and reconstructive surgery and sports biomechanics. However, few studies have provided a systematic analysis on the deformations of aging breasts. Thus, this study has developed a model order reduction approach to predict the real-time strain of the breast skin of seniors during movement. Twenty-two women who are on average 62 years old participated in motion capture experiments, in which eight body variables were first extracted by using the gray relational method. Then, backpropagation artificial neural networks were built to predict the strain of the breast skin. After optimization, the R-value for the neural network model reached 0.99, which is within acceptable accuracy. The computer-aided system of this study is validated as a robust simulation approach for conducting biomechanical analyses and predicting breast deformation. Artificial neural network ; Backpropagation ; Biomechanics ; Artificial intelligence ; Computer science ; Deformation (meteorology) ; Machine learning ; Physical medicine and rehabilitation ; Computer vision ; Pattern recognition (psychology) ; Medicine ; Anatomy ; Materials science Textile materials and evaluations ; Infrared Thermography in Medicine ; 3D Shape Modeling and Analysis
Authors: Praveen Sridhar | Juan Manuel Rodríguez Prieto | Kristin M. de Payrebrune
Source: Journal of Manufacturing and Materials Processing (article, 2022-10-13)
Field: Materials Science — Materials Chemistry

Modeling Grinding Processes—Mesh or Mesh-Free Methods, 2D or 3D Approach? The objectives of this study are mainly two: (1) to validate whether a single grain scratch process can be modeled in two dimensions under the assumption of plane strain, and (2) to select the best discretization approach to model a single grain scratch process. This paper first focuses on the simulation of the orthogonal cutting process (aluminum alloy A2024 T351) using two mesh-based discretization approaches, the pure Lagrangian method (LAG) and the arbitrary Lagrangian–Eulerian method (ALE), and two particle-based approaches, the particle finite element method (PFEM) and smooth particle hydrodynamics (SPH), for both positive and negative rake angles. Benchmarking of the orthogonal cutting models at a rake angle of γ=20∘ is performed with the results of the process forces (cutting and passive forces) of a turning experiment from the literature. It is shown that all models are able to predict the cutting forces, but not the passive force. The orthogonal cutting model is further extended to simulate the cutting mechanism with negative rake tool geometries typically found in grinding and single grit scratching processes. The effects of the negative rake angles on the discretization approaches are studied. The calculated process forces are also compared to the measurements of the single grit scratch process performed at our laboratory. The 2D orthogonal cutting models significantly overestimate the process forces. One of the reasons why the orthogonal 2D cutting model is inadequate is that it cannot describe the complex mechanisms of material removal such as rubbing, plowing, and cutting. To account for these phenomena in LAG, ALE, and SPH discretization approaches, a 3D scratch model is developed. When comparing the process forces of the 3D model with the experimental measurements, all three discretization approaches show good agreement. However, it can be seen that the ALE model most closely matches the process forces with the experimental results. Finally, the ALE 3D scratch model was subjected to sensitivity analysis by changing the cutting speed, the depth of cut and the tool geometry. The results clearly show that the ALE method not only predicts the process forces and form the trends observed in the scratching experiments, but also predicts the scratch topography satisfactorily. Hence, we conclude that a 3D model is necessary to describe a scratch process and that the ALE method is the best discretization method. Discretization ; Rake angle ; Finite element method ; Rubbing ; Smoothed-particle hydrodynamics ; Rake ; Materials science ; Process (computing) ; Mechanics ; Mechanical engineering ; Computer science ; Structural engineering ; Mathematics ; Mathematical analysis ; Engineering ; Physics ; Composite material ; Machining High-Velocity Impact and Material Behavior ; Fluid Dynamics Simulations and Interactions ; Advanced Surface Polishing Techniques
Authors: Amorndej Puttipipatkajorn | Amornrit Puttipipatkajorn
Source: IEEE Access (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Quality Evaluation of Camellia Oleifera Seed Kernel Using a Developed Portable NIR With Optimal Wavelength Selection Moisture content is one of the factors measured to evaluate the quality of Camellia oleifera seeds. High quality <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. oleifera</i> seeds used for trading must have a low moisture content, specifically not more than 15% on a dry basis (db). Moisture content analysis requires a prolonged laboratory investigation so that the development of fast and effective determination methods is helpful. The objective of this paper was to develop a low-cost portable NIR reflectance spectrometer collaborating with an android application for the rapid prediction of the moisture content in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. oleifera</i> seeds. To calibrate the prediction model, an effective chemometric algorithm, based on partial least squares regression was established, and models based on wavelength selection algorithms such as backward interval partial least squares ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bi</i> PLS) and partial least squares coupled with variable importance projection (VIP-PLS) were implemented as an improved version of PLS. Both algorithms ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bi</i> PLS and VIP-PLS) improved the predictive performance and accuracy of the model. The experimental results showed that the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">bi</i> PLS model with the 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> derivative transformation provided the best prediction for measuring the moisture content of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C. oleifera</i> seeds with a coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ) value of 0.927, standard error of prediction (SEP) of 0.848%db, bias of −0.067%db, function slope of 1.005, and ratio of performance deviation (RPD) of 3.696. Finally, the device was tested according to the ISO 12099:2017(E) standard and confirmed the reliability of the device for in-field use. Partial least squares regression ; Camellia oleifera ; Computer science ; Selection (genetic algorithm) ; Mathematics ; Artificial intelligence ; Machine learning ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Leaf Properties and Growth Measurement
Authors: Srinidhi Nagaraja | Harshad M. Paranjape | Christopher P. Cheng
Source: Shape Memory and Superelasticity (article, 2022-06-01)
Field: Materials Science — Materials Chemistry

Durability of Nitinol Cardiovascular Devices Durability ; Materials science ; Medicine ; Composite material Shape Memory Alloy Transformations ; Photoacoustic and Ultrasonic Imaging ; Advanced Memory and Neural Computing
Authors: Theerapat Sangpetch | Tossapon Boongoen | Natthakan Iam-On
Source: Computers, materials & continua/Computers, materials & continua (Print) (article, 2022-09-22)
Field: Chemistry — Analytical Chemistry

Profiling Astronomical Objects Using Unsupervised Learning Approach Attempts to determine characters of astronomical objects have been one of major and vibrant activities in both astronomy and data science fields. Instead of a manual inspection, various automated systems are invented to satisfy the need, including the classification of light curve profiles. A specific Kaggle competition, namely Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC), is launched to gather new ideas of tackling the abovementioned task using the data set collected from the Large Synoptic Survey Telescope (LSST) project. Almost all proposed methods fall into the supervised family with a common aim to categorize each object into one of pre-defined types. As this challenge focuses on developing a predictive model that is robust to classifying unseen data, those previous attempts similarly encounter the lack of discriminate features, since distribution of training and actual test datasets are largely different. As a result, well-known classification algorithms prove to be sub-optimal, while more complicated feature extraction techniques may help to slightly boost the predictive performance. Given such a burden, this research is set to explore an unsupervised alternative to the difficult quest, where common classifiers fail to reach the 50% accuracy mark. A clustering technique is exploited to transform the space of training data, from which a more accurate classifier can be built. In addition to a single clustering framework that provides a comparable accuracy to the front runners of supervised learning, a multiple-clustering alternative is also introduced with improved performance. In fact, it is able to yield a higher accuracy rate of 58.32% from 51.36% that is obtained using a simple clustering. For this difficult problem, it is rather good considering for those achieved by well-known models like support vector machine (SVM) with 51.80% and Naïve Bayes (NB) with only 2.92%. Cluster analysis ; Computer science ; Artificial intelligence ; Classifier (UML) ; Profiling (computer programming) ; Machine learning ; Categorization ; Supervised learning ; Data mining ; Pattern recognition (psychology) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Time Series Analysis and Forecasting ; Astronomical Observations and Instrumentation
Authors: Yutong Jiang | Rongrong Zhu | Bo Yang | Huan Lu | Tianhang Chen | Bin Zheng
Source: Frontiers in Materials (article, 2022-09-15)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Programmable metasurface RCS prediction under obstacles based on DNN Programmable metasurfaces have attracted significant attention in various applications such as radar and 6G communications, owing to their ability freely shape the far-field pattern. However, complex calculations and simulations are always required when designing specific far-field patterns, especially when irregular obstacles are outside the metasurface. In this article, we propose a method using a four-layer artificial neural network to realize the far-field radar cross section (RCS) prediction of programmable metasurfaces in an environment with obstacles, and the prediction value agreed with the simulation data reasonably well. Results show that the proposed prediction model is characterized by better learning and generalization capacity. Our work has broad application prospects and value in complex environment signal transmission, metasurface inverse design, etc. Radar ; Computer science ; Artificial neural network ; Field (mathematics) ; Generalization ; Radar cross-section ; SIGNAL (programming language) ; Inverse ; Electronic engineering ; Artificial intelligence ; Telecommunications ; Engineering ; Mathematics Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Antenna Design and Analysis