jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenyang Jia | Alessandro Ferragina | Saskia M. van Ruth | Anastasios Koidis
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Robustness of Hyperspectral Imaging in Food Analysis of Thin Surfaces: A Case Study Using Thin Ham Products Hyperspectral imaging ; Robustness (evolution) ; Computer science ; Remote sensing ; Materials science ; Artificial intelligence ; Computer vision ; Chemistry ; Geology ; Biochemistry Spectroscopy and Chemometric Analyses
Authors: Thomas Poulet | Pouria Behnoudfar
Source:  (preprint, 2024-03-08)
Field: Materials Science — Materials Chemistry

Retrieving stress and elastic properties distributions from stress orientation and satellite data using physics-informed machine learning Traditional geomechanical modelling involves a lot of a priori knowledge regarding, among others, the distribution of material properties and boundary conditions to apply to a model. Setting those parameters is usually time-consuming, starting with extensive literature reviews to get initial estimates, which are then refined by trial and error by calibrating the numerical simulations against observations. In particular, the boundary conditions typically remain poorly constrained since precise data is rarely available.In this contribution, we present a physics-based machine learning approach to infer the current displacements and full stress tensor distribution of an effective 2D linear elastic model, based on stress orientation and Global Navigation Satellite System (GNSS) data. This allows for automatic retrieval of a reasonable approximation of the model's elastic material properties, consistent displacement, and stress values over the whole physical domain, including at the boundaries, which could be used for instance in following forward simulations.We show an application to the Australian continent, for which a rich dataset of stress orientation is available from the World Stress Map project and the GNSS measurements are particularly steady. This allows us to compare various options to account for stress orientation and displacement information as input data. Interestingly, we recover the smoothest stress field compatible with the (very accurate) GNSS observations and consequently identify areas where the resulting stress orientation differs from current estimates. Stress (linguistics) ; Orientation (vector space) ; Satellite ; Physics ; Computer science ; Remote sensing ; Artificial intelligence ; Mathematics ; Astronomy ; Geology ; Geometry Machine Learning in Materials Science ; Mineral Processing and Grinding ; Non-Destructive Testing Techniques
Authors: Alexander Gorfer | Rainer Abart | Christoph Dellago
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Mechanism and Kinetics of Sodium Diffusion in Na-Feldspar from Neural Network Based Atomistic Simulations Kinetics ; Feldspar ; Mechanism (biology) ; Diffusion ; Sodium ; Artificial neural network ; Materials science ; Chemical physics ; Thermodynamics ; Chemistry ; Computer science ; Physics ; Metallurgy ; Quartz ; Artificial intelligence Machine Learning in Materials Science ; Fuel Cells and Related Materials
Authors: Ellie I. James | David W. Baggett | Edcon Chang | Joel B. Schachter | Thomas Nixey | Karoline Choi | Miklós Guttman | Abhinav Nath
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-02-03)
Field: Chemistry — Organic Chemistry

Tryptanthrin Analogs Substoichiometrically Inhibit Seeded and Unseeded Tau4RD Aggregation Microtubule-associated protein tau is an intrinsically disordered protein (IDP) that forms characteristic fibrillar aggregates in several diseases, the most well-known of which is Alzheimer's disease (AD). Despite keen interest in disrupting or inhibiting tau aggregation to treat AD and related dementias, there are currently no FDA-approved tau-targeting drugs. This is due, in part, to the fact that tau and other IDPs do not exhibit a single well-defined conformation but instead populate a fluctuating conformational ensemble that precludes finding a stable "druggable" pocket. Despite this challenge, we previously reported the discovery of two novel families of tau ligands, including a class of aggregation inhibitors, identified through a protocol that combines molecular dynamics, structural analysis, and machine learning. Here we extend our exploration of tau druggability with the identification of tryptanthrin and its analogs as potent, substoichiometric aggregation inhibitors, with the best compounds showing potencies in the low nanomolar range even at a ~100-fold molar excess of tau4RD. Moreover, conservative changes in small molecule structure can have large impacts on inhibitory potency, demonstrating that similar structure-activity relationship (SAR) principles as used for traditional drug development also apply to tau and potentially to other IDPs. Chemistry Quinazolinone synthesis and applications ; Signaling Pathways in Disease ; Synthesis and Biological Evaluation
Authors: Raul Ariel Duran Jimenez | Natchanon Suppaadirek | Tomohiro Shibata
Source:  (article, 2024-08-26)
Field: Materials Science — Polymers and Plastics

Discrimination and prediction of soft surfaces for cloth classification The sense of touch plays an indispensable role in human perception by enabling the discernment of various objects based on their textures, roughness, and shapes. In the realm of robotics, touch sensors play a vital role in the manipulation and control of objects. By providing information on mechanical properties, these sensors facilitate the visualization of grip, shear, and pressure force at contact points. However, handling deformable objects such as clothing poses a significant challenge. While some research relies on visual sensors for the detection and classification of clothing types, this research focuses on leveraging tactile sensors for the discrimination and classification of deformable surfaces in real-time.The aim is centered on soft surfaces, with a specific focus on examining seven different types of materials. The system we present focuses on signal processing and machine learning techniques for cloth recognition, and classification in real time. The system integrates a soft touch sensor fingertip into a robotic arm to achieve these objectives.The current study has developed a classification model leveraging Long Short-Term Memory (LSTM) networks. The objective of this model is to accurately classify seven distinct types of clothing by analyzing their texture features. Computer science ; Artificial intelligence ; Pattern recognition (psychology) Textile materials and evaluations
Authors: Claudia Ardino | Filomena Sannio | Giulio Poli | Salvatore Galati | Elena Dreassi | Lorenzo Botta | Jean‐Denis Docquier | Ilaria D’Agostino
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

An Update on Antibacterial Alkylguanidino Ureas Design of New Derivatives, Synergism with Colistin and Data Analysis of the Whole Library Colistin ; Combinatorial chemistry ; Chemistry ; Computer science ; Organic chemistry ; Antimicrobial Synthesis and Catalytic Reactions ; Synthesis of β-Lactam Compounds ; Phenothiazines and Benzothiazines Synthesis and Activities
Authors: Fei Liu | Arlindo da Silva
Source:  (preprint, 2024-03-08)
Field: Chemistry — Physical and Theoretical Chemistry

Detecting diurnal cycle and lifetime of pyrocumulonimbus using GOES-16 infrared data with a machine learning model&amp;#160; Satellite infrared (IR) cloud imagery has proven valuable in the identification of Pyrocumulonimbus (pyroCb) clouds. The substantial brightness temperature difference observed between warm shortwave IR wavelengths (~4 &amp;#956;m) and window IR wavelengths (~11 &amp;#956;m) has served as a reliable marker for detecting daytime pyroCb. However, this indicator becomes ineffective during nocturnal hours when the enhanced brightness temperature at 4 &amp;#956;m is solely a daytime phenomenon, arising from PyroCb microphysics that increase solar reflectivity of clouds. We have developed a machine learning model designed to detect pyroCb events during nighttime using IR channels from the Advanced Baseline Imager (ABI) aboard GOES-16. The model leverages the distinctive characteristics of daytime IR channels as its training data. We applied the trained model to five intense pyroCb events in western North America during August 2017. Furthermore, we have employed an established cloud-tracking tool known as Tracking and Object-Based Analysis of Clouds (tobac) to analyze the evolution of the clouds plumes and infer their lifetimes. Our research aims to extend this case study on a global scale, with the objective of creating a comprehensive database for the lifetimes of pyroCb events. Such a database will enhance our understanding of pyroCb dynamics, which is helpful for investigating the radiative implications and the potential impact on stratospheric chemistry. Infrared ; Diurnal cycle ; Computer science ; Physics ; Meteorology ; Optics thermodynamics and calorimetric analyses ; Gas Sensing Nanomaterials and Sensors ; Advanced Sensor Technologies Research
Authors: Junju Lai | Wei Liu | Liehong Huang | Wei Wang | Shihua Huang | Zhijun Guo
Source:  (article, 2024-07-14)
Field: Materials Science — Materials Chemistry

Extended Design of Data Return and Equipment Remote Control for SF6 Gas Leak Detectors With the wide application of sulfur hexafluoride (SF6) gas in high-voltage power equipment, its potential leakage risks and environmental impacts have attracted widespread attention. Traditional monitoring methods have limitations in detection sensitivity and spatial coverage capability, while the development of artificial intelligence technology provides new possibilities for SF6 leakage detection. In this paper, we address the shortcomings of the existing monitoring methods and combine infrared imaging technology and convolutional neural network (CNN) to propose a new design for data return and equipment remote control extension of SF6 gas leak detection device. The design captures the thermal image of gas leakage through infrared imaging and processes the image data using CNN in order to realize the automatic detection of SF6 gas leakage. The experimental results show that the system is able to adapt to different light and background conditions and has good robustness, which provides a timely and all-weather supervision means for the power and chemical industry, and effectively improves the safety and environmental protection of the power system. Leak ; Gas leak ; Detector ; Computer science ; Control (management) ; Automotive engineering ; Engineering ; Electrical engineering ; Environmental science ; Nuclear engineering ; Reliability engineering ; Chemistry ; Environmental engineering High voltage insulation and dielectric phenomena ; Thermal Analysis in Power Transmission ; Offshore Engineering and Technologies
Authors: Miriam Franco Guzmán | M. Romero‐Romo | J.A. Cobos-Murcia | Manuel Palomar‐Pardavé | D. Hernández-Ramírez | Giaan Arturo Álvarez‐Romero | Guadalupe Yoselín Aguilar-Lira | Juan Manuel Gutiérrez
Source: Journal of The Electrochemical Society (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Development of a Voltammetric Methodology Based on an Artificial Neural Network for the Quantification of Sodium Diclofenac in Pharmaceutical Samples Sodium diclofenac is a widely used anti-inflammatory drug that can cause heart diseases if consumed constantly in high doses. Consequently, it is essential to have strict control of the amounts of this active principle in pharmaceutical products. The combination of electroanalytical techniques with advanced chemometrics has risen as a viable alternative for the exact and precise determination of active principles even in the presence of chemical interferences. In this research, an artificial neural network (ANN) for the voltammetric quantification of diclofenac in the presence of paracetamol, pyridoxine, and caffeine is presented, using a carbon paste electrode modified with multilayer carbon nanotubes and titanium dioxide nanoparticles. Cyclic voltammetry is performed to study the effect of the interferences on diclofenac response. Subsequently, a set of diclofenac standards and interferents was prepared using a fractional factorial design to build the response model and perform differential pulse voltammetry to produce the data of the input layer of the ANN. The ANN developed was able to predict the concentration of diclofenac even in the presence of the interferences, since multiple correlation coefficients of 0.9917 and 0.8387 were obtained for training and test data in the analysis of pharmaceutical samples with a recovery percentage of 95.9%. Diclofenac Sodium ; Diclofenac ; Voltammetry ; Factorial experiment ; Chemometrics ; Chemistry ; Cyclic voltammetry ; Chromatography ; Materials science ; Electrode ; Computer science ; Electrochemistry ; Machine learning Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies ; Analytical Methods in Pharmaceuticals
Authors: Emmanuel Lorin
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Spectroscopy

Data-Driven Polarization Learning for Attosecond Science Attosecond ; Polarization (electrochemistry) ; Physics ; Computer science ; Data science ; Psychology ; Optics ; Chemistry ; Ultrashort pulse ; Laser Molecular spectroscopy and chirality ; Spectroscopy and Quantum Chemical Studies ; Scientific Computing and Data Management
Authors: Michał Bogdan | Jesús Pineda | Mihir Durve | L. Jurkiewicz | Sauro Succi | Giovanni Volpe | Jan Guzowski
Source: Physical Review Research (article, 2024-08-08)
Field: Materials Science — Materials Chemistry

Crystallization and topology-induced dynamical heterogeneities in soft granular clusters Soft-granular media, such as dense emulsions, foams or tissues, exhibit either fluid- or solidlike properties depending on the applied external stresses. Whereas bulk rheology of such materials has been thoroughly investigated, the internal structural mechanics of finite soft-granular structures with free interfaces is still poorly understood. Here, we report the spontaneous crystallization and melting inside a model soft granular cluster—a densely packed aggregate of <a:math xmlns:a="http://www.w3.org/1998/Math/MathML"><a:mrow><a:mi>N</a:mi><a:mo>∼</a:mo><a:mn>30</a:mn><a:mo>−</a:mo><a:mn>40</a:mn></a:mrow></a:math> droplets engulfed by a fluid film—subject to a varying external flow. We develop machine learning tools to track the internal rearrangements in the quasi-two-dimensional cluster as it transits a sequence of constrictions. As the cluster relaxes from a state of strong mechanical deformations, we find differences in the dynamics of the grains within the interior of the cluster and those at its rim, with the latter experiencing larger deformations and less frequent rearrangements, effectively acting as an elastic membrane around a fluidlike core. We conclude that the observed structural-dynamical heterogeneity results from an interplay of the topological constrains, due to the presence of a closed interface, and the internal solid-fluid transitions. We discuss the universality of such behavior in various types of finite soft granular structures, including biological tissues. Published by the American Physical Society 2024 Rheology ; Crystallization ; Cluster (spacecraft) ; Materials science ; Universality (dynamical systems) ; Chemical physics ; Porous medium ; Porosity ; Fluid dynamics ; Topology (electrical circuits) ; Mechanics ; Physics ; Thermodynamics ; Condensed matter physics ; Composite material ; Computer science ; Mathematics Pickering emulsions and particle stabilization ; Blood properties and coagulation ; Cellular Mechanics and Interactions
Authors: Stepan D. Osipov | Egor V. Zinovev | Arina A. Anuchina | Alexander Kuzmin | Andronika V. Minaeva | Yury L. Ryzhykau | A.V. Vlasov | Ivan Gushchin
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

High-Throughput Algorithm Predicts F-Type Atp Synthase Rotor Ring Stoichiometries of 8 to 27 Protomers ATP synthase ; Stoichiometry ; Rotor (electric) ; Throughput ; Ring (chemistry) ; Chemistry ; Algorithm ; Computer science ; Physics ; Enzyme ; Biochemistry ; Organic chemistry Enzyme Structure and Function ; ATP Synthase and ATPases Research ; Protein Structure and Dynamics
Authors: Chao Sun | N. Liu | Zhenzhen Wu | Lizheng Wang | Jianjun Ding
Source: Photochemical & Photobiological Sciences (article, 2024-11-29)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Detection of pesticide residues using flower-like silver SERS substrates based on flexible sponge Sponge ; Pesticide ; Pesticide residue ; Chemistry ; Nanotechnology ; Materials science ; Biology ; Ecology ; Botany Gold and Silver Nanoparticles Synthesis and Applications ; Advanced biosensing and bioanalysis techniques ; Biosensors and Analytical Detection
Authors: Yongyi Zhang | Zhongqian Sun | Qingxia Kong | zhang junchen
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Real-World Safety of Zonisamide: Mining and Analysis of Adverse Events Related to Zonisamide Based on Faers Database Zonisamide ; Database ; Data mining ; Medicine ; Computer science ; Epilepsy Analytical Methods in Pharmaceuticals ; Pharmacy and Medical Practices
Authors: Longguo Wu | Wencheng Pan | Yao Zhang | Ling Ma | Yu Tian | Yanzhe Zhu | Xingwu Tian | Yiyang Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Antioxidant Enzyme Activities in Melon Leaves Based on Microhyperspectral Imaging Technique Melon ; Antioxidant ; Enzyme ; Chemistry ; Horticulture ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses
Authors: Qilin Jiang | Panpan Niu | Xiangwei Liu | Shuyao Zhang | Jiao Geng | Cheng Wang | Lan He | И. Бабушкин | Liping Shi
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning Optimized Ultrafast Laser Direct Writing Anti-Reflective Alsic Surfaces Ultrashort pulse ; Laser ; Materials science ; Computer science ; Optics ; Nanotechnology ; Physics Optical Coatings and Gratings ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Michael S. Jones | Kirill Shmilovich | Andrew L. Ferguson
Source: The Journal of Physical Chemistry A (article, 2024-11-14)
Field: Materials Science — Materials Chemistry

Tutorial on Molecular Latent Space Simulators (LSSs): Spatially and Temporally Continuous Data-Driven Surrogate Dynamical Models of Molecular Systems The inherently serial nature and requirement for short integration time steps in the numerical integration of molecular dynamics (MD) calculations place strong limitations on the accessible simulation time scales and statistical uncertainties in sampling slowly relaxing dynamical modes and rare events. Molecular latent space simulators (LSSs) are a data-driven approach to learning a surrogate dynamical model of the molecular system from modest MD training trajectories that can generate synthetic trajectories at a fraction of the computational cost. The training data may comprise single long trajectories or multiple short, discontinuous trajectories collected over, for example, distributed computing resources. Provided the training data provide sufficient sampling of the relevant thermodynamic states and dynamical transitions to robustly learn the underlying microscopic propagator, an LSS furnishes a global model of the dynamics capable of producing temporally and spatially continuous molecular trajectories. Trained LSS models have produced simulation trajectories at up to 6 orders of magnitude lower cost than standard MD to enable dense sampling of molecular phase space and large reduction of the statistical errors in structural, thermodynamic, and kinetic observables. The LSS employs three deep learning architectures to solve three independent learning problems over the training data: (i) an encoding of the high-dimensional MD into a low-dimensional slow latent space using state-free reversible VAMPnets (SRVs), (ii) a propagator of the microscopic dynamics within the low-dimensional latent space using mixture density networks (MDNs), and (iii) a generative decoding of the low-dimensional latent coordinates back to the original high-dimensional molecular configuration space using conditional Wasserstein generative adversarial networks (cWGANs) or denoising diffusion probability models (DDPMs). In this software tutorial, we introduce the mathematical and numerical background and theory of LSS and present example applications of a user-friendly Python package software implementation to alanine dipeptide and a 28-residue beta-beta-alpha (BBA) protein within simple Google Colab notebooks. Computer science ; Space (punctuation) ; Molecular dynamics ; Dynamical systems theory ; Physics Machine Learning in Materials Science ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Yan Jing | Yingling Zhang | Hui Liang | Zibin Jiao | Lifan Song | Qingming Zhang | Yaowen Liu | Wen Qin
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Physicochemical Characterization of Thermally Oxidized Rapeseed Oil: An Insight into Combining Acoustic Diagnostic Technique and Chemometrics Chemometrics ; Rapeseed ; Characterization (materials science) ; Materials science ; Process engineering ; Biochemical engineering ; Biological system ; Acoustics ; Chemical engineering ; Analytical Chemistry (journal) ; Environmental science ; Computer science ; Chemistry ; Environmental chemistry ; Chromatography ; Nanotechnology ; Engineering ; Food science ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Edible Oils Quality and Analysis
Authors: Longguo Wu | Yao Zhang | Ling Ma | Yu Tian | Yanzhe Zhu | Xingwu Tian | Yiyang Zhang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Construction of Microhyperspectral Imaging Model for Antioxidant Enzyme Activities In Melon Leaves Melon ; Antioxidant ; Enzyme ; Chemistry ; Botany ; Horticulture ; Biology ; Biochemistry Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Spectroscopy Techniques in Biomedical and Chemical Research