jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Pieter Verboven | Ujjwal Verma | I.M. Riley | Bratislav Lukić | Ludovic Broche | Jan A. Delcour | Bart Nicolaı̈
Source: Research Square (preprint, 2024-07-02)
Field: Chemistry — Organic Chemistry

High-speed 4D computed tomography for uncovering the dynamics of oil uptake in deep-fried food products Computed tomography ; Dynamics (music) ; Food science ; Chemistry ; Physics ; Medicine ; Radiology ; Acoustics Edible Oils Quality and Analysis ; Spectroscopy and Chemometric Analyses ; Cardiovascular Health and Disease Prevention
Authors: Rajat Sainju | M.I. Patino | M.J. Baldwin | Osman El‐Atwani | Yuanyuan Zhu
Source: Microscopy and Microanalysis (article, 2024-07-01)
Field: Materials Science — Materials Chemistry

Tracking Metal/Oxide Interface Evolution and Reaction Kinetics of Fusion Energy Nanostructured Tungsten Material Using In Situ ETEM Tungsten ; Materials science ; Fusion ; Kinetics ; Oxide ; In situ ; Metal ; Tungsten oxide ; Nanotechnology ; Metallurgy ; Chemistry Fusion materials and technologies ; Nuclear Materials and Properties ; Electronic and Structural Properties of Oxides
Authors: Piyanun Ruangurai | Nattabut Tanasansurapong | Sirakupt Prasitsanha | Rewat Bunchan | Wiput Tuvayanond | Thana Chotchuangchutchaval | Chaiyaporn Silawatchananai
Source: Chemical Industry and Chemical Engineering Quarterly (article, 2024-08-02)
Field: Chemistry — Analytical Chemistry

Utilizing machine vision and artificial neural networks for dried grape sorting during production This study introduces a machine vision technique that utilizes an artificial neural network (ANN) to develop a predictive model for classifying dried grapes during the drying process. The primary objective of this model is to mitigate the burden placed on the operator and minimize the occurrence of over-dried items. The present study involves the development of a model that is constructed using the characteristics of grape color and shape. There exist two distinct categories of labels for grapes: fully desiccated grapes, commonly referred to as raisins, and grapes that have undergone partial drying. Image processing is utilized to collect and observe five significant characteristics of grapes during the drying process. The findings indicate a significant decrease in the levels of red, green, and blue colors (RGB) during the initial 15-hour drying period. The predictive model extracts properties such as RGB color, roundness, and shrinkage from the image while it undergoes the drying process. The artificial neural network (ANN) model achieved a level of accuracy performance of 78%. In this work, the dehydration apparatus will cease operation in an automated manner whenever the entirety of the grapes situated on the tray has been projected to transform raisins. Sorting ; Artificial intelligence ; Artificial neural network ; Machine vision ; Computer science ; Artificial vision Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Shery L. Y. Chang | Haotian Wen | Christian Dwyer | Huan‐Cheng Chang
Source: BIO Web of Conferences (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Correlative transmission electron microscopy and photoluminescence microscopy revealing enhanced fluorescence in nitrogen vacancy containing nanodiamond Nanodiamond ; Vacancy defect ; Nitrogen-vacancy center ; Photoluminescence ; Microscopy ; Transmission electron microscopy ; Correlative ; Materials science ; Electron microscope ; Fluorescence ; Fluorescence microscope ; Nitrogen ; Nanotechnology ; Optoelectronics ; Chemistry ; Optics ; Crystallography ; Diamond ; Physics Diamond and Carbon-based Materials Research ; Graphene research and applications ; Nonlinear Optical Materials Studies
Authors: Miriam Medina‐García | Esteban A. Roca-Nasser | Miguel Ángel Martínez‐Domingo | Eva M. Valero | Alejandra Arroyo-Cerezo | Ana M. Jiménez–Carvelo
Source: Actualidad analítica. (article, 2024-12-01)
Field: Chemistry — Analytical Chemistry

VERIFICACIÓN DEL CONTENIDO DE HARINA INTEGRAL EN PAN UTILIZANDO TÉCNICAS BASADAS EN IMÁGENES HIPERESPECTRALES (HSI) Y UN MÉTODO DE CUANTIFIACIÓN BASADO EN EL RECUENTO DE PÍXELES PREVIAMENTE CLASIFICADOS Los productos alimenticios integrales han experimentado una creciente demanda en el mercado, fruto de sus numerosos beneficios para la salud humana. Entre ellos, destaca el pan integral siendo uno de los productos más consumidos. Por este motivo, se hace imperativo establecer controles de calidad para este producto, con el fin de proteger al consumidor de posibles fraudes alimentarios. En esta línea, el estudio presentado desarrolló un método analítico rápido y respetuoso con el medio ambiente que permitió evaluar el contenido de harina integral en pan, convirtiéndose así, en la única solución analítica hasta el momento, que permite evaluar la calidad de este producto tras la etapa de producción del mismo. Dicho método está basado en la aplicación de técnicas basadas en imágenes hiperespectrales (HSI), junto con herramientas quimiométricas. Además, se desarrolló la metodología innovadora denominada "cuantificación basada en el recuento de píxeles previamente clasificados" (QPC) Los resultados del método permitieron predecir el porcentaje de harina integral en 48 panes con una desviación máxima de un 8% del valor estimado. Humanities ; Art Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Kévin Jehasse | Jean-Sébastien Jouhanneau | Sophie Wetz | Alexander Schwedt | James F.A. Poulet | Peter Neumann-Raizel | Björn M. Kampa
Source: Scientific Reports (article, 2024-01-18)
Field: Materials Science — Materials Chemistry

Immediate reuse of patch-clamp pipettes after ultrasonic cleaning The patch-clamp technique has revolutionized neurophysiology by allowing to study single neuronal excitability, synaptic connectivity, morphology, and the transcriptomic profile. However, the throughput in recordings is limited because of the manual replacement of patch-pipettes after each attempt which are often also unsuccessful. This has been overcome by automated cleaning the tips in detergent solutions, allowing to reuse the pipette for further recordings. Here, we developed a novel method of automated cleaning by sonicating the tips within the bath solution wherein the cells are placed, reducing the risk of contaminating the bath solution or internal solution of the recording pipette by any detergent and avoiding the necessity of a separate chamber for cleaning. We showed that the patch-pipettes can be used consecutively at least ten times and that the cleaning process does not negatively impact neither the brain slices nor other patched neurons. This method, combined with automated patch-clamp, highly improves the throughput for single and especially multiple recordings. Pipette ; Reuse ; Ultrasonic sensor ; Computer science ; Clamp ; Biomedical engineering ; Medicine ; Biology ; Chemistry ; Computer graphics (images) ; Radiology Ultrasound and Cavitation Phenomena ; Conducting polymers and applications ; bioluminescence and chemiluminescence research
Authors: Geeta Biradar Pavithra | C Swaminathan2 | P. Kathirvelan | SP Sangeetha
Source: Research Square (preprint, 2024-03-15)
Field: Materials Science — Polymers and Plastics

A Bibliometric Analysis of Indian Journal of Fibre and Textile Research (IJFTR) Textile ; Bibliometrics ; Regional science ; Geography ; Library science ; Computer science ; Archaeology Textile materials and evaluations
Authors: Rishov Sarkar | Rachel Paul | Cong Hao
Source: arXiv (Cornell University) (preprint, 2024-04-15)
Field: Materials Science — Materials Chemistry

LightningSimV2: Faster and Scalable Simulation for High-Level Synthesis via Graph Compilation and Optimization High-Level Synthesis (HLS) enables rapid prototyping of complex hardware designs by translating C or C++ code to low-level RTL code. However, the testing and evaluation of HLS designs still typically rely on slow RTL-level simulators that can take hours to provide feedback, especially for complex designs. A recent work, LightningSim, helps to solve this problem by providing a simulation workflow one to two orders of magnitude faster than RTL simulation. However, it still exhibits inefficiencies due to several types of redundant computation, making it slow for large design simulation and design space exploration. Addressing these inefficiencies, we introduce LightningSimV2, a much faster and scalable simulation tool. LightningSimV2 features three main innovations. First, we perform compile-time static analysis, exploiting the repetitive structures in HLS designs, e.g., loops, to reduce the simulation workload. Second, we propose a novel graph-based simulation approach, with decoupled simulation graph construction step and graph traversal step, significantly reducing repeated computation. Third, benefiting from the decoupled approach, LightningSimV2 can perform incremental stall analysis extremely fast, enabling highly efficient design space exploration of large numbers of complex hardware parameters, e.g., optimal FIFO depths. Moreover, the DSE is well-suited for parallel computing, further improving the DSE efficiency. Compared with LightningSim, LightningSimV2 achieves up to 3.5x speedup in full simulation and up to 577x speed up for incremental DSE. Our code is open-source on GitHub at https://github.com/sharc-lab/LightningSim/tree/v0.2.0. Scalability ; Computer science ; Graph ; Parallel computing ; High-level synthesis ; Computer architecture ; Theoretical computer science ; Embedded system ; Operating system ; Field-programmable gate array Machine Learning in Materials Science ; Scientific Computing and Data Management ; Graph Theory and Algorithms
Authors: Wanhai Xu | Zhichao Tong | Di Wang | Yubo Zhao | Guoxi Xie | Hongjian Song | Yaowei Li | Qing Shi | Hang Su | Qian Zhang | Fang Fan | Zongzheng Yang | Feng Wen | Hongxue Meng | Haonan Li | Pengyu Guo | Da‐Yong Hou | Zhenwei Zhang | Zhihao Yin | Ziyi Liu | Jianwei Wang | Peng Zhang | Peng Dai | Changfu Li | Lichen Teng | Yangyang Xu | Xin Hu | Wentao Liu | Benedikt Ebner | Roman Nawroth | Ziqing Deng | Ziqi Wang | Liang Wu | Guibo Li
Source: Research Square (preprint, 2024-10-25)
Field: Chemistry — Spectroscopy

A spatially resolved whole-layers landscape of bladder cancer deciphers dynamic invasive progression Bladder cancer ; Cancer ; Medicine ; Internal medicine Advanced Proteomics Techniques and Applications ; Receptor Mechanisms and Signaling ; Bladder and Urothelial Cancer Treatments
Authors: Fu-Hsien Chen | Horng-Lin Shieh
Source: Preprints.org (preprint, 2024-01-16)
Field: Materials Science — Materials Chemistry

An Operating Condition Monitoring and Fault Diagnosis for Transformer Based on Partial Discharge and Artificial Neural Networks The operation of transformers suffers from natural or man-made disasters, subjecting the equipment to prolonged exposure to temperature, lightning strikes, transient over-voltages, and over-current, gradually producing insulation aging phenomenon, which results in partial discharges within the transformer. Without dealing with it beforehand properly, it may lead to insulation breakdown. Nowadays, with the mature development of microchip control technology and wireless communication technology, which is helpful for realizing the research for online real-time and long-term monitoring capabilities for power equipment. A transformer operating condition monitoring and diagnostic system that can rapidly and effectively prevent the breakdown of transformer insulation, which can impact the overall power supply system is developed in this paper. The system is divided into two parts based on its function. The first part is the operating condition monitoring and real-time alert unit for the transformer, primarily composed of an embedded computing module, a sensor module, and a transmission module. Its work content is to capture real-time online partial discharge ultrasonic signals and high frequency current comparator pulse signals, and analyze and determine the condition of the internal insulation and operational status whether it is fault or not. The second part is the central control unit, consisting mainly of an industrial computer and a transmission module. Its work content is to store the ultrasonic and high frequency current comparator pulse signals from the transformer operation monitoring and real-time alert unit. It uses the software to eliminate signal noise and interference, and then utilizes artificial neural networks to identify the type of fault. In this paper, the research for the phenomenon of insulation degradation in transformer equipment aims to establish aging trend profiles through the dual malfunctioning alert and aging process analysis, as well as the long-term measurement of ultrasonic signals from partial discharges and high-frequency current comparator pulse signals. This approach provides crucial reference data and a foundation for disposable on-site measurement, assessing the phenomenon of insulation aging stages, defects and damage of the transformers effectively. It allows for timely replacement or maintenance in advance, achieving the prevention of malfunctioning and predictive diagnosis of aging. Partial discharge ; Transformer ; Electrical engineering ; Current transformer ; Ultrasonic sensor ; Engineering ; Electronic engineering ; High voltage ; Voltage ; Computer science ; Automotive engineering ; Acoustics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation
Authors: K. Deeba | Amutha Balakrishnan | K. Mekala Devi | Nagendra Singh | P. Angelin Kiruba | R. Ramkumar | D. Karthikeyan
Source: Bulletin of Electrical Engineering and Informatics (article, 2024-04-05)
Field: Chemistry — Analytical Chemistry

Hybrid rater to quantify and measure the severity of infection and spread of infection in muskmelon Disease severity index (DIS) is a way of calculating the percentage of infection spread across the field. The percentage of infection in each leaf has been considered at a time stamp is being calculated and based on that disease, severity of disease spread is analyzed. With the advancement in machine learning and deep learning algorithms in the field of computer vision, identification and classification of diseases is effortless. Percentage of infection in a particular leaf, disease index (DI) is calculated using image processing techniques like Otsu threshold method. With this DI and scales, grading the severity of the infection across the field can be achieved. In this paper various scales used for grading severity of infection namely Horsfall-Barratt (H-B scale) quantitative ordinal scale, Amended 20% ordinal scale, and nearest percent estimates (NPEs) in muskmelon is explored, and based on the empirical results Amended 20% ordinal scale is most efficient method of estimating the DIS is to use the midpoint of the severity scope for each class with twenty percent adjusted to ordinal scale. The results show that the density of leaves is directly proportional to spread of diseases in muskmelon plant. Ordinal Scale ; Ordinal data ; Scale (ratio) ; Grading (engineering) ; Statistics ; Grading scale ; Correlation ; Mathematics ; Artificial intelligence ; Computer science ; Medicine ; Biology ; Cartography ; Ecology ; Geography ; Surgery ; Geometry Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Bintang Vieshe Mone | Yampi R Kaesmetan | Meliana O. Meo
Source: Indonesian Journal of Artificial Intelligence and Data Mining (article, 2024-04-27)
Field: Materials Science — Polymers and Plastics

Digital Image Processing to Detect Sumba Woven Fabric Contour Using Gray Level Co-occurrence Matrix and Self Organizing Map Sumba woven cloth is one of the cultural heritages of the island of Sumba. Based on its manufacture, the classification process for Sumba woven fabrics is based on the identification of colors or motifs. However, the classification process is not an easy process. In addition to the classification process, the wider community also does not get much information about Sumba woven fabrics clearly, therefore digital image processing technology is needed to build a system that can overcome the problems faced. The image of the Sumba woven fabric sample is converted to grayscale and resized, then segmented using Sobel detection. Then extracted using Gray level co-occurrence matrix (GLCM). After extraction, it will be classified using a Self Organizing Map (SOM). Based on the results of this study, it was concluded that the accuracy of the validation test was 80%, and the program was successful. Co-occurrence matrix ; Gray level ; Artificial intelligence ; Computer vision ; Gray (unit) ; Image processing ; Pattern recognition (psychology) ; Computer science ; Digital image ; Matrix (chemical analysis) ; Image (mathematics) ; Computer graphics (images) ; Materials science ; Composite material ; Image texture ; Medicine Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: David Richard
Source: The Journal of Chemical Physics (article, 2024-03-05)
Field: Materials Science — Materials Chemistry

Connecting microscopic and mesoscopic mechanics in model structural glasses We present a novel formalism to characterize elastic heterogeneities in amorphous solids. In particular, we derive high-order strain-energy expansions for pairwise energies under athermal quasistatic dynamics. We then use the presented formalism to study the statistical properties of pairwise expansion coefficients and their link with the statistics of soft, quasilocalized modes, for a wide range of formation histories in both two- and three-dimensional systems. We further exploit the presented framework to access local yield stress maps by performing a non-linear stress-strain expansion within a cavity embedded in a frozen matrix. We show that our "bond micromechanics" compare well with the original "frozen matrix" method, with the caveat of overestimating large stress activations. We additionally show how local yield rules can be used as an input for a scalar elasto-plastic model (EPM) to predict the stress response of materials ranging from ductile to brittle. Finally, we highlight some of the limits of simple mesoscale models in capturing the aging dynamics of post-yielding systems. Intriguingly, we observe subdiffusive and diffusive shear band growths for particle-based simulations and EPMs, respectively. Mesoscopic physics ; Mechanics ; Classical mechanics ; Physics ; Materials science ; Condensed matter physics Material Dynamics and Properties ; Glass properties and applications ; Building materials and conservation
Authors: James Kribs | J. Brown | Angelique Shackleford | Darius Mcklin
Source:  (article, 2024-09-25)
Field: Materials Science — General Materials Science

Time-Dependent Effects of Moisture Content in Polymer Matrix Materials in Composite Material Strength: Results from an Engineering Technology CURE Project Materials science ; Composite material ; Composite number ; Water content ; Polymer ; Matrix (chemical analysis) ; Engineering ; Geotechnical engineering Material Properties and Applications
Authors: Gurpreet Singh | Trishala Mitra | S. Madsen | Aurélien Dantan
Source: arXiv (Cornell University) (preprint, 2024-04-09)
Field: Materials Science — Surfaces, Coatings and Films

Highly reflective and high-$Q$ thin resonant subwavelength gratings We theoretically investigate the design of thin subwavelength gratings possessing high-reflectivity and high-$Q$ resonances when illuminated at normal incidence by a Gaussian beam. We compare the performances of single-period and dual-period rectangular gratings using Finite Element Method-based optimization and predict one to two orders of magnitude improvement in their transmission loss-linewidth product, which is the relevant figure of merit for e.g. resonant mirror-based microcavity applications. Materials science ; Optoelectronics ; Optics ; Physics Optical Coatings and Gratings ; Advanced Fiber Optic Sensors ; Photonic and Optical Devices
Authors: Qi Zhengyang | Liu Zijing | Jiying Zhang | He Cao | Yu Li
Source: arXiv (Cornell University) (preprint, 2024-04-22)
Field: Chemistry — Spectroscopy

ControlMol: Adding Substructure Control To Molecule Diffusion Models Due to the vast design space of molecules, generating molecules conditioned on a specific sub-structure relevant to a particular function or therapeutic target is a crucial task in computer-aided drug design. Existing works mainly focus on specific tasks, such as linker design or scaffold hopping, each task requires training a model from scratch, and many well-pretrained De Novo molecule generation model parameters are not effectively utilized. To this end, we propose a two-stage training approach, consisting of condition learning and condition optimization. In the condition learning stage, we adopt the idea of ControlNet and design some meaningful adjustments to make the unconditional generative model learn sub-structure conditioned generation. In the condition optimization stage, by using human preference learning, we further enhance the stability and robustness of sub-structure control. In our experiments, only trained on randomly partitioned sub-structure data, the proposed method outperforms previous techniques by generating more valid and diverse molecules. Our method is easy to implement and can be quickly applied to various pre-trained molecule generation models. Diffusion ; Molecule ; Control (management) ; Computer science ; Chemistry ; Physics ; Thermodynamics ; Artificial intelligence ; Organic chemistry Analytical Chemistry and Chromatography ; Analytical Chemistry and Sensors ; Radiopharmaceutical Chemistry and Applications
Authors: Ahmed R Khlefha | Abeer Majeed Jasim
Source: CFD letters (article, 2024-08-31)
Field: Materials Science — Polymers and Plastics

Improve Analytical Solutions for Trapezoidal, Rectangular and Dovetail Shapes of Inclined Longitudinal Porous Fin This study improved the approximate analytical solutions of the heat distribution and transport of inclined longitudinal porous fin in the presence of radiative and convective environments with rectangular, trapezoidal, and dovetail profiles. The model of Darcy, which mimics the interaction of fluids and solids, is utilized to obtain the equation of governing the heat transfer of the porous fin. To investigate the rectangular, trapezoidal, and dovetail profiles, a single equation has been solved through analysis of the mathematical model by using the optimal differential transform method (ODTM) which consist least squares differential transform method (LSDTM), and the Galerkin differential transform method (GDTM) while the BVP4c presents the numerical solution. A comparison is made between the approximate analytical and numerical solutions for different parameters. It results in that the solutions produced from LSDTM and GDTM are closer to the numerical solution than the solutions of DTM, nonlinear autoregressive exogenous-levenberg marquardt algorithm (NARX-LMA) and cascade feedforward backpropagated-levenberg marquardt algorithm (CFB-LMA). A comprehensive graphic analysis was conducted to examine the effect of variation in inclination angles, tapering at the tip, wet porous parameters, internal heat generation, progressive natural convection parameters, and dimensionless radiation parameters on the thermal profile and thermal transfer rate of the porous longitudinal fin. The split fin design achieves the greatest heat transfer rate, trailed by rectangular and trapezoidal fin profiles, assuming that internal heat generation is maintained to a minimum. Fin ; Porosity ; Porous medium ; Mechanics ; Materials science ; Structural engineering ; Geometry ; Mathematics ; Engineering ; Physics ; Composite material Textile materials and evaluations ; Heat and Mass Transfer in Porous Media ; Granular flow and fluidized beds