jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Muqi Sun | Chengyue Xiong | Yu Zhou | Xicheng Zhang | Aoming Yuan | Yan Sun | Yongbing Li
Source: Scientific Reports (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Rapid thickness distribution prediction of superplastic formed parts based on geometry adapted PSO-BP neural network surrogate model Rapid and accurate prediction of thickness distribution remains a critical challenge in achieving real-time process optimization for superplastic forming (SPF) operations. Conventional prediction method based on finite element analysis (FEA) faces constraints in computation efficiency and completely dependency on precisely defined boundary conditions, rendering them unsuitable for real-time control systems. This study proposes a mesh-informed neural network surrogate model based on particle swarm optimized-back propagation (PSO-BP) algorithm to predict the thickness distribution of superplastic forming parts with different geometric feature parameters. A geometric fitness function based on SPF part features is proposed to solve the problem of large local errors in round-corner areas. The mean absolute percentage error of the improved algorithm prediction results has reduced from 1.3% to 0.8% by approximately 38.5% compared to the standard PSO-BP neural network. A rapid prediction of the thickness nephogram of Ti-6Al-4 V box-shaped parts within 0.5 s was achieved with an average deviation from the finite element simulation results less than 1%. This computational advancement enables closed-loop process control by bridging the temporal gap between simulation-based optimization and actual manufacturing cycle times. The developed system shows significant potential for in-process quality monitoring and dynamic parameter adjustment in industrial SPF applications. Particle swarm optimization ; Artificial neural network ; Finite element method ; Computation ; Superplasticity ; Rendering (computer graphics) ; Surrogate model ; Fitness function ; Bridging (networking) Machine Learning in Materials Science ; Metal Forming Simulation Techniques ; Metallurgy and Material Forming
Authors: Anabel A. Hernández‐Cortés | José C. Escobedo‐Bocardo | J.M. Almanza-Robles | Dora A. Cortés‐Hernández
Source: Metals (article, 2025-10-22)
Field: Materials Science — Biomaterials

Effect of Cross- or Unidirectional Rolling on the Microstructure, Corrosion Rate, and Hemolysis of Ternary Magnesium–Zinc–Gallium Alloys The effect of cross- or unidirectional rolling on the microstructure, corrosion rate, texture, and hemolysis of the Mg-0.5Zn-0.25Ga and Mg-1.5Zn-0.375Ga alloys was evaluated. After both rolling processes, the microstructure of the as-cast alloys was considerably refined due to the recrystallization process, obtaining higher grain refinement after cross-rolling. The Mg-1.5Zn-0.375Ga alloy showed a finer microstructure than the Mg-0.5Zn-0.25Mg alloy due to the effect of both the severe plastic deformation obtained after cross-rolling and the higher amount of alloying elements, which act as grain refiners. After unidirectional rolling, the texture intensity of the basal plane increases, while the cross-rolled alloys show lower texture intensity due to the activation of the pyramidal and/or prismatic slip systems. The cross-rolled alloys showed a higher corrosion rate than the unidirectionally rolled alloys due to the basal texture developed. The Mg-1.5Zn-0.375Ga alloy showed a higher corrosion rate than the Mg-0.5Zn-0.25Ga alloy since the voids formed during heat treating were not fully eliminated during rolling. The Mg-0.5Zn-0.25Ga alloy after unidirectional rolling was not hemolytic (4.7%) and showed the lowest corrosion rate (0.8 mm/y). Thus, this alloy may be an excellent candidate for use in the fabrication of biodegradable implants. Alloy ; Microstructure ; Corrosion ; Recrystallization (geology) ; Grain boundary ; Grain size ; Texture (cosmology) ; Ternary operation Magnesium Alloys: Properties and Applications ; Aluminum Alloys Composites Properties ; Aluminum Alloy Microstructure Properties
Authors: Alina Fatianova | Yan Li | В. А. Лавриненко | Оlga I. Yarovaya
Source: NEUROSCIENCE FOR MEDICINE AND PSYCHOLOGY (article, 2025-04-24)
Field: Chemistry — Organic Chemistry

EFFECT OF HETEROCYCLIC DERIVATIVE OF HYDRAZONE (+)-CAMPHOR ON THE BEHAVIOR OF MICE Hydrazone ; Camphor ; Derivative (finance) ; Chemistry ; Computer science ; Stereochemistry ; Organic chemistry ; Business Synthesis and Reactions of Organic Compounds ; Chemical synthesis and pharmacological studies ; Synthesis and biological activity
Authors: A A AlZubi
Source: Indian Journal of Geo-Marine Sciences (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Convolutional neural network modelling and image analysis techniques for the detection of fish diseases Fish rearing or pisciculture holds the key to food security and economic well-being for several countries across the globe. Diseases in fish are the biggest threat due to their rapid spread rate and high calamity, leading to a sharp decrease in fish yield. Deep learning techniques such as Convoluted Neural Networks (CNNs) hold an extremely promising impact on disease detection and raising the predictability of production amount. In this paper, the sequential CNN model indicates rigour and high reliability. The experimental setup consists of a model based on TensorFlow, Keras and 8-core TPU to accelerate computational Machine Learning (ML) tasks. The dataset obtained from Kaggle consists of 457 files depicting seven distinct classes (one healthy and six diseased classes), including all major fish diseases. Image data preprocessing is done by resizing and rescaling to train the optimised model. Image augmentation is done to expand the available data set and resolve overfitting issues within the CNN model. Modelling involves multiclass classification with a convolutional layer to extract features, keeping non-linearity in the model by an activation function. By transfer learning, inadequacies of the dataset are minimised. The proposed CNN model architecture efficaciously classifies various fish diseases. Identification and categorisation are done using Python, and the algorithm’s learning efficiency is predicted to be quite high. The model makes reasonably accurate predictions with an accuracy close to 91 %. A good pattern of learning during the training of the model is observed. These observations indicate the model's remarkable capacity to correctly identify the diseases. Convolutional neural network ; Overfitting ; Preprocessor ; Pattern recognition (psychology) ; Deep learning ; Transfer of learning ; Generalization ; Fish <Actinopterygii> ; Contextual image classification Spectroscopy and Chemometric Analyses
Authors: Mingliang Pan | Quan Wang | Yuanzhe Zhang | Chenxu Li | David Li
Source: Journal of Innovative Optical Health Sciences (article, 2025-09-27)
Field: Chemistry — Electrochemistry

Deep learning techniques for diffuse correlation spectroscopy: A review Diffuse correlation spectroscopy (DCS) is an optical technology for extracting blood flow index (BFi) by measuring intensity fluctuations of the back-scattered light emitted from tissues. The remarkable characteristics of DCS, such as its noninvasiveness, deep penetration depth, and cost-effectiveness, have led to its widespread application for human health evaluation. However, traditional DCS data processing utilizes the analytical solution of the correlation diffusion equation to fit the measured autocorrelation function [Formula: see text], which is computationally demanding and susceptible to noise, especially when using the multi-layer analytical models to detect cerebral BFi. These drawbacks limit its further application for human BFi monitoring. To accelerate BFi extraction, deep learning (DL) techniques have been introduced. DL-assisted DCS has demonstrated fast and improved noise robustness for BFi extraction compared to traditional curve-fitting methods. In this review, the development of DL models for DCS data processing, current challenges and outlooks will be discussed, aiming to provide a reference for further promotion of the development of DL-based DCS technology for human health analysis. Deep learning ; Autocorrelation ; Robustness (evolution) ; Pattern recognition (psychology) ; Feature extraction ; Noise (video) ; Correlation ; Limit (mathematics) Electrochemical Analysis and Applications ; NMR spectroscopy and applications ; Advanced MRI Techniques and Applications
Authors: Muhammad Anshori | Pulung Nurtantio Andono | Moch Arief Soeleman
Source: International Journal of Advances in Data and Information Systems (article, 2025-12-01)
Field: Chemistry — Analytical Chemistry

Enhanced Classification of Lombok Pearl Quality Based on Shape and Size Using PSO-Optimized Artificial Neural Network This study aims to develop an intelligent classification model for pearl quality assessment using an integrated approach combining Gray Level Co-occurrence Matrix (GLCM), Particle Swarm Optimization (PSO), and Artificial Neural Network (ANN). Sixteen texture features were extracted from four directional orientations using GLCM. PSO was employed as a feature selection algorithm to reduce dimensionality and enhance classification performance. Two ANN models were compared: a baseline model using all GLCM features and an optimized model utilizing only PSO-selected features. The models were trained and validated using 10-fold cross-validation. Results showed that the PSO-enhanced ANN achieved an accuracy of 94.72%, outperforming the baseline model which reached only 89.17%. Further evaluations using confusion matrix, Receiver Operating Characteristic (ROC) analysis, and Principal Component Analysis (PCA) confirmed the superior discriminative capability and improved class separability of the optimized model. These findings highlight the effectiveness of combining swarm intelligence with neural networks in texture-based classification tasks, offering a robust and scalable solution for automated quality inspection in the pearl industry and related domains. Artificial neural network ; Discriminative model ; Particle swarm optimization ; Pattern recognition (psychology) ; Principal component analysis ; Confusion matrix ; Feature selection ; Curse of dimensionality Spectroscopy and Chemometric Analyses ; Calcium Carbonate Crystallization and Inhibition ; Meat and Animal Product Quality
Authors: Filipe Pereira | Miguel Oliveira | Filomena Soares | Rosa Vasconcelos | Vı́tor Carvalho
Source: EAI Endorsed Transactions on Digital Transformation of Industrial Processes (article, 2025-08-12)
Field: Materials Science — Polymers and Plastics

A Low-Cost Framework for Textile Yarn Characterization Using Image Processing The textile industry increasingly demands innovative and cost-effective solutions for yarn quality assessment, as conventional equipment is costly and occupies substantial space. This work presents a compact, low-cost image processing framework to characterize key yarn parameters, providing a foundation for future automated quality control systems. The framework employs classical image processing techniques—smoothing, thresholding, segmentation, and morphological operations—implemented with open-source tools such as Visual Studio and OpenCV. An experimental setup using low-cost hardware enabled the acquisition of high-quality images under controlled conditions. The system extracted parameters including linear mass, average diameter, specific volume, defect quantification, hairiness coefficient, and twist direction and pitch. Tests on three yarn types (cotton and polyester) produced results comparable to the industrial reference Uster Tester 3, with error rates below 7%. The proposed solution offers an affordable alternative for small industries and research laboratories, with potential for future integration of advanced computer vision and artificial intelligence to enhance defect detection and classification. Yarn ; Image processing ; Textile industry ; Key (lock) ; Textile ; Quality (philosophy) ; Data processing ; Machine vision Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; 3D Shape Modeling and Analysis
Authors: Tae‐Hoon Lee
Source: Materials (article, 2025-08-20)
Field: Materials Science — Materials Chemistry

Chemical Ordering in Liquid and Supercooled Ge2Sb2Te5 Phase-Change Materials The origin of chemical ordering in liquid and supercooled liquid Ge<sub>2</sub>Sb<sub>2</sub>Te<sub>5</sub> (GST) was investigated using ab initio molecular dynamics (AIMD) simulations. Bond dynamics were analyzed via continuous (τcon) and intermittent (τint) lifetimes. The intermittent lifetime (τint) reveals that chemically ordered Ge-Te and Sb-Te bonds are the most stable, although τcon exhibits a stability anomaly. The faster increase of τint for these bonds upon cooling explains the overall chemical ordering. A novel ordering mechanism was identified through the analysis of bond separation dynamics. Te-Te 'wrong' bonds exhibit a unique dynamic instability, breaking and separating much faster than any other bond type, which actively drives the system towards chemical order. A correlation between lifetime and bond strength, as calculated by the Integrated Crystal Orbital Hamilton Population (ICOHP), supports these dynamic findings. Chemical ordering shows a positive correlation with medium-range structural order, evidenced by the instability of 4-fold rings containing wrong bonds. This study provides a detailed dynamic origin for ordering in liquid GST, highlighting the role of Te-Te bond relaxation. Supercooling ; Chemical bond ; Molecular dynamics ; Chemical physics ; Materials science ; Bond order ; Instability ; Population ; Chemical stability ; Relaxation (psychology) ; Thermodynamics ; Crystallography ; Computational chemistry ; Chemistry ; Bond length ; Crystal structure ; Organic chemistry ; Physics Phase-change materials and chalcogenides ; Material Dynamics and Properties ; Liquid Crystal Research Advancements
Authors: Thomas Kapapa | Ralph König | Jan Coburger | Benjamin Mayer | Kornelia Kreiser | Volker Rasche
Source: Current Oncology (review, 2025-10-24)
Field: Chemistry — Spectroscopy

Metabolic Imaging as Future Technology and Innovation in Brain-Tumour Surgery: A Systematic Review <b>Background:</b> Standard imaging in neurosurgery often fails to visualize infiltrative tumor regions that extend beyond contrast enhancement. Metabolic imaging using hyperpolarized 13C-MRI may offer new intraoperative insights into tumor biology. <b>Objective:</b> To systematically assess the clinical and technical evidence on hyperpolarized MRI for metabolic tumour characterization in patients with malignant brain tumors. <b>Eligibility criteria:</b> We included original human studies reporting on hyperpolarized 13C-MRI for perioperative and diagnostic use in brain tumor patients. Reviews, animal studies, and technical-only reports were excluded. <b>Information sources:</b> Searches were conducted in PubMed, Embase, and Web of Science on 26 December 2024. <b>Risk of bias:</b> Methodological quality was assessed using the QUADAS-2 tool. <b>Synthesis of results:</b> A qualitative synthesis was performed, and where feasible, random-effects meta-analysis was used to calculate standardized mean differences (SMDs) and heterogeneity statistics. <b>Results:</b> Three studies (n = 15 patients) met inclusion criteria. The bicarbonate-to-pyruvate ratio showed a significant difference between tumor and non-tumour brain (SMD = 1.34, <i>p</i> = 0.002), whereas pyruvate-to-lactate ratio (kPL) values showed minimal difference (SMD = 0.06, <i>p</i> = 0.730). Asmall effect was observed for kPL between tumor and normal-appearing white matter (SMD = -0.33). One study provided qualitative data only. Overall heterogeneity was high (I<sup>2</sup> = 69.4%). <b>Limitations:</b> Limitations include small sample sizes, heterogeneous methodologies, and limited availability of patient-level data. <b>Interpretation:</b> Hyperpolarized 13C-MRI shows metabolic differentiation between tumor and healthy tissue in certain parameters, especially bicarbonate metabolism. While promising, the technology requires further clinical validation before routine intraoperative application. White matter ; Brain tumor ; Neuroimaging ; Neurosurgery ; Clinical imaging ; Magnetic resonance imaging ; Clinical Practice ; Medical imaging Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; Cancer, Hypoxia, and Metabolism
Authors: Peng Xu
Source: Advances in Engineering Research Possibilities and Challenges (article, 2025-11-04)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Data-Driven Inverse Design of THz Metamaterials: A Deep Learning Approach for Small-Data Regimes Inverse design based on deep learning offers a revolutionary paradigm for ac-celerating the development of novel terahertz (THz) metamaterials. Howev-er, its application is often constrained by the high cost of acquiring large-scale sim-ulation datasets. This work focuses on the high-accuracy inverse design of THz electromagnetically induced transparency (EIT) metamaterials under small da-taset conditions, systematically evaluating the effectiveness of different deep learning architectures. To achieve precise inversion from a target spectrum to its physical structure, we constructed a representative dataset through parametric electromagnetic si-mu-lation. On this foundation, we built, trained, and compared three neural network models: a Multi-Layer Perceptron (MLP) as the baseline, a residual ful-ly con-nected network (FC-ResNet) for enhanced deep network training, and a one-dimensional convolutional neural network (1D-CNN) designed for se-quen-tial data. The results reveal a key finding: for this inverse design task, the FC-ResNet demonstrated superior predictive performance, achieving a coefficient of de-termination (R²) of 0.9794 on an independent test set. This significantly out-performed the baseline MLP (0.9438) and surprisingly surpassed the theo-reti-cally more suitable 1D-CNN. Further analysis suggests that for the EIT in-verse problem investigated here, the prediction relies more on the global mor-phology and correlation features of the spectrum than on localized characteris-tics. The FC-ResNet, with its deep architecture and effective residual learning mecha-nism, successfully captured this complex, non-local mapping relation-ship. The core contribution of this work is demonstrating that for complex physical prob-lems, a deep, general-purpose model with sufficient expressive power and stable trainability can outperform a more specialized architecture that is prone to in-formation loss. This finding provides a crucial guideline for model selection in the intelligent design of physical devices, particularly in re-source-constrained scenarios Deep learning ; Residual ; Parametric statistics ; Artificial neural network ; Perceptron ; Inversion (geology) ; Convolutional neural network ; Terahertz radiation ; Inverse problem Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Terahertz technology and applications
Authors: Kanwal Preet Singh Attwal
Source: International Journal of Agriculture and Food Science (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Integrated machine learning model for wheat yield prediction using agronomic and meteorological factors: A case study from Punjab, India The study aims to develop a Prediction Model for the wheat yield based on meteorological and agronomic factors using Data Mining techniques. The target area of the Research is the cultivators in the Patiala district of Punjab, India. The researcher has applied the knowledge of the wheat morphology and phenology to build the model. Crop yield is affected by several agronomic factors such as soil type and date of sowing, and meteorological factors such as temperature and rainfall. The existing models apply classification or regression techniques to all the identified factors for the prediction of crop yield. This study divides the set of factors into two categories - the factors which are responsible for year-wise variation in yield, and the factors which are responsible for the individual variation of yield for a particular year among various cultivators. It is found that the year-wise yield variation for a particular cultivator (or a particular region) may be attributed to meteorological factors, whereas the agronomic factors are responsible for variation in yield among different cultivators. So, two models have been proposed; the first model - henceforth known as Block-wise Average Yield Prediction model (BAY model) predicts the Block-wise Average Yield based on temperature, rainfall and the yield data, and the second model - henceforth known as Yield Class Prediction model (YC model) predicts the Yield Class based on soil, management practices and yield data. Finally, these models, i.e., BAY model and YC model are integrated into the final model - Final Yield Prediction model (FYP model) to predict the final yield of a particular cultivator. Yield (engineering) ; Agricultural engineering ; Agronomy ; Environmental science ; Engineering ; Biology ; Materials science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Genetics and Plant Breeding
Authors: Deni Kurnia | Agus Sutanto | Hanif Fakhrurroja | Lovely Son
Source: Mechanical Engineering for Society and Industry (article, 2025-12-28)
Field: Materials Science — Polymers and Plastics

Smart vibration sensing and predictive analytics for intelligent textile manufacturing: An IoT-edge and machine learning method This study aimed to propose an IoT-Edge method for detecting vibration abnormalities in Textile Manufacturing, specifically on Draw Texturing Yarn (DTY) machines using an ADXL345 sensor and a Machine Learning Algorithm. The proposed system incorporated wireless sensor nodes, the MQTT protocol, Fast Fourier Transform (FFT) analysis, and a tuned Random Forest (RF) classifier to enable real-time monitoring as well as predictive maintenance. During the analysis, vibration data were collected from 13 spindles, with features extracted in both time as well as frequency domains to distinguish between normal and abnormal machine conditions. The RF model, optimized through hyperparameter tuning, achieved an accuracy of 97%, significantly outperforming the Support Vector Machine (SVM) baseline, which reached 71%. Major results showed the effectiveness of energy and centroid features in fault detection, with the Z-axis vibration proving to be a good indicator of yarn defects. The system presented low latency (average 20.37 ms) in data transmission using the MQTT protocol, ensuring practical deployability. This study offered a scalable and cost-effective solution for industrial vibration monitoring, bridging gaps in real-time processing and seamless IoT incorporation to support predictive maintenance in textile manufacturing. Predictive maintenance ; Support vector machine ; Random forest ; Vibration ; Hyperparameter ; Analytics ; Fault detection and isolation ; Classifier (UML) ; Predictive analytics Textile materials and evaluations ; Advanced Sensor and Energy Harvesting Materials ; Advanced Fiber Optic Sensors