jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Minarni Shiddiq | Saktioto Saktioto | Roni Salambue | Fiqra Wardana | Vicky Vernando Dasta | Ihsan Okta Harmailil | Mohammed Fisal Rabin | Nisa Arpyanti | Dilham Wahyudi
Source: Bulletin of Electrical Engineering and Informatics (article, 2024-09-20)
Field: Chemistry — Analytical Chemistry

Multispectral imaging and deep learning for oil palm fruit bunch ripeness detection Oil palm fresh fruit bunches (FFBs) are the raw material of crude palm oil (CPO) on which ripeness levels of FFBs are essential to obtain good quality CPO. Most palm oil mills use experienced graders to evaluate FFB ripeness levels. Researchers have developed rapid and non-destructive methods for ripeness detection using computer vision (CV) and deep learning. However, most of the experiments used color cameras, such as a webcam or a smartphone, limited to visible wavelengths, and used still FFBs on–trees or on the ground. This study developed a light-emitting diode (LED)-based multispectral imaging system with deep learning for rapid and real-time ripeness detection of oil palm FFBs on a moving conveyor. The ripeness levels used were unripe and ripe. We also evaluated the spectrum of reflectance intensities for the ripeness levels. The ripeness detection system employed a two-class you only look once version 4 (YOLOv4) detection model using a dataset of 2000 annotated unripe and ripe FFB multispectral images and a video of 30 moving FFBs for real-time testing. The results show a promising method to detect oil palm FFB ripeness with an average accuracy of 99.66% and a speed range of 3.32-3.62 frame per second (FPS). Ripeness ; Multispectral image ; Artificial intelligence ; Computer science ; Environmental science ; Computer vision ; Food science ; Chemistry ; Ripening Spectroscopy and Chemometric Analyses ; Oil Palm Production and Sustainability ; Date Palm Research Studies
Authors: Liang Luo | George K. Stylios
Source: Machine Learning and Knowledge Extraction (article, 2024-01-22)
Field: Materials Science — Polymers and Plastics

Algorithmic Information Theory for the Precise Engineering of Flexible Material Mechanics The structure of fibrous assemblies is highly complex, being both random and regular at the same time, which leads to the complexity of its mechanical behaviour. Using algorithms such as machine learning to process complex mechanical property data requires consideration and understanding of its information principles. There are many different methods and instruments for measuring flexible material mechanics, and many different mechanics models exist. There is a need for an evaluation method to determine how close the results they obtain are to the real material mechanical behaviours. This paper considers and investigates measurements, data, models and simulations of fabric’s low-stress mechanics from an information perspective. The simplification of measurements and models will lead to a loss of information and, ultimately, a loss of authenticity in the results. Kolmogorov complexity is used as a tool to analyse and evaluate the algorithmic information content of multivariate nonlinear relationships of fabric stress and strain. The loss of algorithmic information content resulting from simplified approaches to various material measurements, models and simulations is also evaluated. For example, ignoring the friction hysteresis component in the material mechanical data can cause the model and simulation to lose more than 50% of the algorithm information, whilst the average loss of information using uniaxial measurement data can be as high as 75%. The results of this evaluation can be used to determine the authenticity of measurements and models and to identify the direction for new measurement instrument development and material mechanics modelling. It has been shown that a vast number of models, which use unary relationships to describe fabric behaviour and ignore the presence of frictional hysteresis, are inaccurate because they hold less than 12% of real fabric mechanics data. The paper also explores the possibility of compressing the measurement data of fabric mechanical properties. Computational mechanics ; Computer science ; Process (computing) ; Nonlinear system ; Continuum mechanics ; Stress (linguistics) ; Algorithm ; Mechanics ; Engineering ; Finite element method ; Physics ; Structural engineering Textile materials and evaluations ; Manufacturing Process and Optimization ; Industrial Vision Systems and Defect Detection
Authors: Banghao Wu | Bozitao Zhong | Lirong Zheng | Runye Huang | Shudong Jiang | Mingchen Li | Pan Tan | Liang Hong
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-11-15)
Field: Materials Science — Materials Chemistry

€ Harnessing Protein Language Model for Structure-Based Discovery of Highly Efficient and Robust PET Hydrolases Abstract Plastic waste, particularly polyethylene terephthalate (PET), presents significant environmental challenges, prompting extensive research into enzymatic biodegradation. Existing PET hydrolases are limited to a narrow sequence space and demonstrate insufficient performance for biodegradation. This study introduces a novel discovery pipeline that combines protein language models (PLMs) with a structural representation tree to identify enzymes based on structural similarity. Using the crystal structure of Is PETase as a template, we employed PLMs and a representation tree to efficiently search and cluster target proteins. Screening of candidate proteins was further refined using PLM-based assessments of solubility and thermal stability. Biochemical experiments showed that 14 of 34 candidates exhibited PET degradation activity across a temperature range of 30–60 °C. Notably, we identified a PET hydrolase - Kb PETase, which possesses a melting temperature 32 °C higher than that of Is PETase and exhibits the highest PET degradation activity within 30–50 °C compared to other wild-type PETases. Kb PETase also shows higher catalytic efficiency than FastPETase. X-ray crystallography and molecular dynamics simulations further revealed that Kb PETase has a conserved catalytic domain and enhanced intramolecular interactions. This work develops a novel deep learning approach to discover natural PETases with enhanced functions. Computational biology ; Computer science ; Data science ; Biology Enzyme Structure and Function ; Advanced Proteomics Techniques and Applications ; Protein Structure and Dynamics
Authors: Jiaqian Zhu | Guohao Sun | Linfeng Ding | Lianjun Wang
Source: Journal of Non-Crystalline Solids (article, 2024-10-05)
Field: Materials Science — Ceramics and Composites

Advancing the prediction of crystalline phases in glass-ceramics via machine learning Materials science ; Ceramic ; Glass-ceramic ; Composite material ; Mineralogy ; Geology Glass properties and applications ; Pigment Synthesis and Properties
Authors: Qiuhui Chang | Ruhan Wang | Leying Qing | Jian Jiang
Source: Chinese Journal of Polymer Science (article, 2024-10-21)
Field: Chemistry — Physical and Theoretical Chemistry

Trends in Sequence-Defined Polyelectrolyte Systems: A Perspective Polyelectrolyte ; Perspective (graphical) ; Sequence (biology) ; Materials science ; Computer science ; Chemistry ; Artificial intelligence ; Polymer ; Biochemistry ; Composite material Electrostatics and Colloid Interactions ; Polymer Surface Interaction Studies ; Surfactants and Colloidal Systems
Authors: Wei Li
Source: Preprints.org (preprint, 2024-02-27)
Field: Chemistry — Spectroscopy

High-Throughput Extraction of Interfacial Electrostatic Features from GLP-1-GLP-1R Complex Structures: A GLP-1-GLP-1R-Based Mini GIBAC Perspective From a structural point of view, understanding interfacial electrostatic features of ligand-receptor complexes is crucial for biophysics-based design of therapeutics with improved efficacy and safety. Here, with a high-throughput structural feature extraction approach, this article reports a set of electrostatic structural features from glucagon-like peptide-1 (GLP-1) and GLP-1 receptor (GLP-1R) complex structures determined using experimental tools. Leveraging a set of computational structural biophysical analyses, this article systematically characterized the electrostatic interactions within the GLP-1-GLP-1R complexes with two sets of criteria to identify interfacial salt bridges and hydrogen bonds. Overall, the results here reveal intricate details of electrostatic interactions critical for GLP-1 binding to its receptor GLP-1R, providing biophysical insights into the stabilization of the GLP-1-GLP-1R complex structure and the molecular basis of GLP-1/GLP-1R signaling. Furthermore, the electrostatic structural features extracted from GLP-1 and GLP-1R complex structures are useful in the development of a machine learning-based ligand-receptor binding affinity calculating model, i.e., a GLP-1-GLP-1R-specific general intermolecular binding affinity calculator (GIBAC), paving the way for accelerated discovery and structure-based design of drug candidates targeting the GLP-1/GLP-1R system with improved efficacy and safety. Perspective (graphical) ; Throughput ; Extraction (chemistry) ; Materials science ; Computer science ; Chemistry ; Chromatography ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced biosensing and bioanalysis techniques
Authors: Florian Kiehas | Martin Reiter | Juan Pablo Torres | Michael Jerabek | Zoltán Major
Source: Polymer (article, 2024-02-15)
Field: Materials Science — Polymers and Plastics

Predicting Ductile–Brittle transition temperatures for polyolefins using convolutional neural networks and instrumented notched Charpy experiments For polyolefins, a transition from ductile- to brittle fracture behavior can be observed at decreasing temperatures, which is typically characterized by the Ductile-Brittle Transition Temperature (DBTT). For each material, the DBTT needs to be determined by performing multiple impact tests across wide temperature ranges. In this study, we utilize Convolutional Neural Networks (CNN) to predict the DBTT from single Instrumented Notched Charpy (INC) experiments instead. Our dataset consists of over 6732 INCs encompassing 132 Polyethylene (PE) and Polypropylene (PP) compounds. For each of these compounds, we examine 51 impact tests at temperatures ranging from -40 °C to 60 °C. We train models on combinations of instrumentation signals and fracture surface images. We test the trained networks on 1683 INCs, where we achieve a R2-score of 0.96 and an average error below 5 °C of predictions compared to experimentally determined DBTTs. The presented methodology provides a flexible way of predicting DBTTs in case of non-instrumented testing or if thermal specimen conditioning is not possible. This allows for fast screening of new materials. Charpy impact test ; Brittleness ; Materials science ; Composite material ; Toughness Polymer crystallization and properties ; Composite Material Mechanics ; Epoxy Resin Curing Processes
Authors: Lukas Cvitkovich | Franz Fehringer | Christoph Wilhelmer | Diego Milardovich | Dominic Waldhör | Tibor Grasser
Source: The Journal of Chemical Physics (article, 2024-10-10)
Field: Materials Science — Materials Chemistry

Machine learning force field for thermal oxidation of silicon Looking back at seven decades of highly extensive application in the semiconductor industry, silicon and its native oxide SiO2 are still at the heart of several technological developments. Recently, the fabrication of ultra-thin oxide layers has become essential for keeping up with trends in the down-scaling of nanoelectronic devices and for the realization of novel device technologies. With this comes a need for better understanding of the atomic configuration at the Si/SiO2 interface. Classical force fields offer flexible application and relatively low computational costs, however, suffer from limited accuracy. Ab initio methods give much better results but are extremely costly. Machine learning force fields (MLFF) offer the possibility to combine the benefits of both worlds. We train a MLFF for the simulation of the dry thermal oxidation process of a Si substrate. The training data are generated by density functional theory calculations. The obtained structures are in line with ab initio simulations and with experimental observations. Compared to a classical force field, the most recent reactive force field, the resulting configurations are vastly improved. Our potential is publicly available in an open-access repository. Silicon ; Computer science ; Ab initio ; Field (mathematics) ; Realization (probability) ; Substrate (aquarium) ; Force field (fiction) ; Nanotechnology ; Scaling ; Process (computing) ; Engineering physics ; Materials science ; Artificial intelligence ; Physics ; Optoelectronics Machine Learning in Materials Science ; Semiconductor materials and devices ; Advancements in Semiconductor Devices and Circuit Design
Authors: Mohammed Al-Zamzami | Adel Al‐Gheethi | Shehab Abdulhabib Alzaeemi | Mohammed Al-Sahari | Qais Ali Al‐Maqtari | Efaq Ali Noman
Source: Chemosphere (article, 2024-07-05)
Field: Chemistry — Analytical Chemistry

Validity of zinc oxide nanoparticles biosynthesized in food wastes extract in treating real samples of printing ink wastewater; prediction models using feed-forward neural network (FFNN) In the present study, biosynthesized ZnO nanoparticles in food wastewater extract (FWEZnO NPs) was used in the photocatalytic degradation of real samples of printing ink wastewater. FWEZnO NPs were prepared using green synthesis methods using a composite food waste sample (2 kg) consisted of rice 30%, bread 20 %, fruits 10 %, chicken 10 %, lamb 10%, and vegetable 20%. The photocatalysis process was optimized using response surface methodology (RSM) as a function of time (15-180 min), pH 2-10 and FWEZnO NP (20-120 mg/100 mL), while the print ink effluent after each treatment process was evaluated using UV-Vis-spectrophotometer. The behaviour of printing ink wastewater samples for photocatalytic degradation and responses for independent factors were simulated using feed-forward neural network (FFNN). FWEZnO NPs having 62.48 % of the purity with size between 18 and 25 nm semicrystalline nature. The main functional groups were -CH, CH<sub>2</sub>, and -OH, while lipid, carbon-hydrogen stretching, and amino acids were the main component in FWEZnO NP, which contributed to the adsorption of ink in the initial stage of photocatalysis. The optimal conditions for printing ink wastewater were recorded after 17 min, at pH 9 and with 20 mg/100 mL of FWEZnO NPs, at which the decolorization was 85.62 vs. 82.13% of the predicted and actual results, respectively, with R<sup>2</sup> of 0.7777. The most significant factor in the photocatalytic degradation was time and FWEZnO NPs. The FFNN models revealed that FWEZnO NPs exhibit consistency in the next generation of data (large-scale application) with an low errors (R<sup>2</sup> 0.8693 with accuracy of 82.89%). The findings showing a small amount of catalyst is needed for effective breakdown of dyes in real samples of printing ink wastewater. Photocatalysis ; Wastewater ; Response surface methodology ; Adsorption ; Materials science ; Nanoparticle ; Central composite design ; Chemistry ; Nuclear chemistry ; Chemical engineering ; Pulp and paper industry ; Chromatography ; Nanotechnology ; Organic chemistry ; Environmental engineering ; Environmental science ; Catalysis Dye analysis and toxicity ; Nanoparticles: synthesis and applications ; Pigment Synthesis and Properties
Authors: Liu Yang | Jianjun Meng | Tailong Li
Source: Actuators (article, 2024-08-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Structural Optimization of a Giant Magnetostrictive Actuator Based on BP-NSGA-II Algorithm This study introduces an integrated structural optimization design method based on a BP neural network and NSGA-II multi-objective genetic algorithm. Initially, a two-dimensional axisymmetric finite element model of the Giant Magnetostrictive Actuator (GMA) was established, and the coupling simulation of the electromagnetic field, structural field, and temperature field was conducted to obtain the GMA’s performance parameters. Subsequently, the structural parameters of the GMA magnetic circuit, including the magnetic conducting ring, magnetic conducting sidewall, magnetic conducting body, and coil, were used as inputs, and the axial magnetic induction intensity, uniformity of axial magnetic induction intensity, and coil loss on the Giant Magnetostrictive Material (GMM) rod were used as outputs to establish a back propagation (BP) neural network model. This model delineated the nonlinear relationship between structural parameters and performance parameters. Then, the BP-NSGA-II algorithm was applied to perform multi-objective optimization on the actuator’s structural parameters, resulting in a set of Pareto optimal non-dominated solutions, from which a set of optimal solutions was obtained using the entropy weight method. Finally, simulation analysis of this optimal solution was conducted, indicating that under a 5 A power supply excitation, the maximum axial magnetic induction intensity on the optimized GMM rod increased from 0.87 T to 1.12 T; the uniformity of axial magnetic induction intensity improved from 93.1% to 96.5%; and the coil loss decreased from 7.79 × 104 W/m3 to 4.97 × 104 W/m3. Based on the optimization results, a prototype actuator was produced, and the test results of the prototype’s output characteristics proved the feasibility of this optimization design method. Electromagnetic coil ; Magnetostriction ; Actuator ; Finite element method ; Magnetic field ; Genetic algorithm ; Electromagnetic induction ; Multi-objective optimization ; Materials science ; Excitation ; Control theory (sociology) ; Magnetic circuit ; Acoustics ; Engineering ; Structural engineering ; Computer science ; Physics ; Mathematics ; Mathematical optimization ; Electrical engineering Magnetic Properties and Applications ; Shape Memory Alloy Transformations ; Piezoelectric Actuators and Control
Authors: Farhaan Hussain | Kinjarapu Sai Manikanta | Naved Wasim Ahmed | A. Vinoth | Sandipan Roy | Shubhabrata Datta
Source: International Journal on Interactive Design and Manufacturing (IJIDeM) (article, 2024-05-15)
Field: Materials Science — General Materials Science

Design optimization of polymer composites for lower suspension arms of automotive vehicles Automotive industry ; Composite material ; Suspension (topology) ; Materials science ; Polymer ; Automotive engineering ; Engineering ; Aerospace engineering ; Mathematics Material Properties and Applications ; Mechanical Systems and Engineering ; Manufacturing Process and Optimization
Authors: Ao Li | Jin-Shi Cui
Source: Optical Engineering (article, 2024-07-02)
Field: Chemistry — Analytical Chemistry

Non-destructive detection of specific enzyme activities in tomato plants using visible-near infrared spectroscopy Tomato is an important vegetable that is rich in antioxidants, vitamins, and minerals and has significant economic and health value. In this study, hyperspectral images in the wavelength range of 370 to 1715 nm were first preprocessed to improve the data quality and comparability. Subsequently, the tomatoes were chemically destroyed, and the average activities of peroxidase enzyme, phenylalanine ammonia-lyase enzyme, and α-amylase enzyme were 5.108 mU/g, 7.347 U/g, and 35.856 U/g, respectively. Then, two spectral selection algorithms, the genetic algorithm (GA) and the successive projection algorithm, were used to extract effective wavelength bands from high dimensional spectral data. And the extracted effective wavelength variables were combined with partial least squares (PLS) regression to build the optimal spectral selection model GA-PLS. Finally, three additional spectral prediction models were created by combining the GA-selected spectra with three other algorithms: support vector machine, particle swarm optimization-backpropagation neural network, and random forest. After comparing the predictive performance of four models, it was found that the GA-PLS model had the highest prediction accuracy and stability. Furthermore, compared with tomato stems, the near infrared (NIR) bands of tomato leaves were more accurate in predicting the enzyme content of tomato plants. It was found that the GA-PLS model had a better prediction performance for the three enzymes in the NIR band of leaves with R¯p (average coefficient of determination for the three enzymes) and RMSEP¯ (average root means square error of the three enzymes) of 0.815 and 1.659, respectively. This provides an effective method for phytochemical composition analysis using hyperspectral imaging. Spectroscopy ; Infrared ; Materials science ; Near-infrared spectroscopy ; Visible spectrum ; Infrared spectroscopy ; Remote sensing ; Functional near-infrared spectroscopy ; Optics ; Optoelectronics ; Analytical Chemistry (journal) ; Computer science ; Chemistry ; Physics ; Astronomy ; Environmental chemistry ; Biology ; Geology Spectroscopy and Chemometric Analyses
Authors: Yunzhen Du | Kunling Peng | Jizheng Duan | Meiling Qi | Yanwei Chen | Changwei Hao | Wen-Shan Duan | Lei Yang | Sheng Zhang | Ping Lin
Source: International Communications in Heat and Mass Transfer (article, 2024-11-17)
Field: Materials Science — Materials Chemistry

Thermal conductivity in MoSi₂N₄(MoN)ₙ: Insights into phonon scattering and transport Thermal conductivity ; Phonon ; Materials science ; Scattering ; Condensed matter physics ; Phonon scattering ; Conductivity ; Physics ; Optics ; Composite material ; Quantum mechanics Thermal properties of materials ; Machine Learning in Materials Science ; MXene and MAX Phase Materials
Authors: Hajar Sharifi | Morteza Vadood | Aminoddin Hajı
Source: Textile & Leather Review (article, 2024-06-10)
Field: Materials Science — Polymers and Plastics

Predicting the Bending Rigidity and Formability of Plasma-Treated Spunbond Nonwoven Fabrics Using Artificial Intelligence Nonwoven fabrics are used in many industries, and surface treatment by plasma can significantly change their physical and mechanical properties by changing the surface chemistry and morphology. In this paper, oxygen/argon plasma has been applied to the spunbond nonwoven fabrics to predict the obtained properties. Therefore, by using the central composite design and considering 4 independent factors including the fabric weight, fabric direction, plasma treatment duration and oxygen ratio, 51 various samples were prepared and their bending rigidity and formability were measured. SEM images showed that the surface roughness increases due to the plasma treatment. Statistical analysis revealed that all the mentioned independent factors have a significant effect on the measured parameters directly or reciprocally. Also, the use of a neural network model with two hidden layers optimized with a method according to the genetic algorithm can predict the bending rigidity and formability based on the independent factors with errors of less than 7% and 9%, respectively. The errors resulting from the surface response method for the same parameters with the same order are about 19% and 17%. The introduced method can be used as a tool to help researchers in the field of mechanical properties. Formability ; Materials science ; Flexural rigidity ; Composite material ; Rigidity (electromagnetism) ; Bending ; Surface roughness ; Plasma ; Surface finish ; Structural engineering ; Engineering Textile materials and evaluations ; Surface Modification and Superhydrophobicity ; Advanced Sensor and Energy Harvesting Materials