jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Abdul Majid Soomro | Muhammad Sohaib Asghar | Sanjoy Kumar Debnath | Susama Bagchi | Awad Bin Naeem | M.K.A. Ahamed Khan | Mastaneh Mokayef | Takao Ito
Source: Proceedings of International Conference on Artificial Life and Robotics (article, 2025-02-13)
Field: Chemistry — Analytical Chemistry

An Innovative Deep Learning Technique to Identify Potato Illness Potato cultivation is important for world food security as it itself is attacked by a great number of diseases like early blight and late blight, which cause a lot of damage to the yield and quality of the crop.But deep learning offers a great opportunity to address these disease detections; however, how effective this will be in the potatogrowing environment in Pakistan is still not known.This research is designed to evaluate the convolutional neural network (CNN) by building custom datasets that denote the local disease description.The ultimate intention is to develop a high-accuracy, reliable disease detection model that will suit the particular needs of Pakistan.The project, therefore, tries to address data imbalance with the use of the synthetic minority oversampling technique (SMOTE) and develop a CNN architecture that is optimized to provide high diagnostic accuracy.Acquired through realworld pictures, the assessment of the model's performance shows significant progress in detecting potato diseases.This research can give innovative and productive locally useful solutions, which might transform the management of diseases for Pakistani farmers while improving food security and economic stability.These deep learning systems also need to be context-sensitive and reliable, which in turn would help preserve long-term agricultural productivity in Pakistan and beyond. Computer science ; Artificial intelligence ; Psychology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Wenbo Jin | Zhuo Chen | Kemin Dai | Qing Quan | Zongxiao Ren
Source: Processes (article, 2025-10-16)
Field: Chemistry — Analytical Chemistry

Prediction of Wax Deposition Rate of Waxy Crude Oil Based on Improved Elman Neural Network The influencing factors of wax deposition are numerous and complex, and accurately predicting the wax deposition rate is of great practical significance for the safe operation of pipelines and the formulation of reasonable pigging schemes. On the basis of mastering the prediction steps of the Elman neural network (ENN), the arithmetic optimization algorithm (AOA) was introduced to improve the Elman neural network and an optimization model was established, and the differences in prediction results between improved models (AOA-ENN model, PSO-ENN model, GA-ENN model) and the traditional ENN model were compared and analyzed through examples. The prediction results of three examples showed that the average relative errors of the AOA-ENN model are 2.5470%, 1.4974%, and 2.3819 %, respectively, while the average relative errors of the traditional ENN model are 19.0313%, 9.1568%, and 11.4836%, respectively. Therefore, the arithmetic optimization algorithm used in this paper has good reliability. For the three improved models, the AOA-ENN model has the highest prediction accuracy, followed by the PSO-ENN model and the GA-ENN model. Overall, the Elman neural network improved by an arithmetic optimization algorithm can be used for predicting wax deposition rate, which can provide new ideas for accurate prediction of wax deposition rate. Artificial neural network ; Wax ; Deposition (geology) ; Pigging ; Pipeline transport ; Approximation error Petroleum Processing and Analysis
Authors: Xiao-Qi Han | Zhiyuan Gao | X.-P. Wang | Zhenfeng Ouyang | Peng‐Jie Guo | Zhong-Yi Lu
Source: ArXiv.org (preprint, 2025-06-04)
Field: Materials Science — Materials Chemistry

HTSC-2025: A Benchmark Dataset of Ambient-Pressure High-Temperature Superconductors for AI-Driven Critical Temperature Prediction The discovery of high-temperature superconducting materials holds great significance for human industry and daily life. In recent years, research on predicting superconducting transition temperatures using artificial intelligence~(AI) has gained popularity, with most of these tools claiming to achieve remarkable accuracy. However, the lack of widely accepted benchmark datasets in this field has severely hindered fair comparisons between different AI algorithms and impeded further advancement of these methods. In this work, we present the HTSC-2025, an ambient-pressure high-temperature superconducting benchmark dataset. This comprehensive compilation encompasses theoretically predicted superconducting materials discovered by theoretical physicists from 2023 to 2025 based on BCS superconductivity theory, including the renowned X$_2$YH$_6$ system, perovskite MXH$_3$ system, M$_3$XH$_8$ system, cage-like BCN-doped metal atomic systems derived from LaH$_{10}$ structural evolution, and two-dimensional honeycomb-structured systems evolving from MgB$_2$. The HTSC-2025 benchmark has been open-sourced at https://github.com/xqh19970407/HTSC-2025 and will be continuously updated. This benchmark holds significant importance for accelerating the discovery of superconducting materials using AI-based methods. Benchmark (surveying) ; Superconductivity ; Field (mathematics) ; Superconducting transition temperature ; Transition temperature Machine Learning in Materials Science ; Superconductivity in MgB2 and Alloys ; Inorganic Chemistry and Materials
Authors: Bowen Zhang | Yanlin Yang | Jia Su | Linyu Zeng | Zenghao Kong | Zhijie Li | Zhiping Yang | Fazhan Shi
Source: Biophysics Reports (article, 2025-06-03)
Field: Materials Science — Materials Chemistry

3D positioning and optically detected magnetic resonance of intracellular fluorescent nanodiamonds using a multi-plane microscope Wide-field quantum sensing with fluorescent nanodiamonds (FNDs) in biological systems offers significant potential for understanding intracellular dynamics at the nanoscale. However, current wide-field quantum sensing methods are limited to 2D correlated measurements. 3D correlated quantum sensing remains challenging due to the inherent properties of wide-field microscopy. Here, we have developed a multi-plane wide-field microscope platform that achieves an imaging volume of 50 × 50 × 5 μm³. This is accomplished by simultaneously imaging eight focal planes at varying sample depths using a beam-splitting prism. By employing a Fourier-transform-based fluorescent particle positioning method, the platform attains lateral positioning precision of 9 nm and axial precision of 12 nm. Using this platform, we performed correlated 3D positioning of FNDs in mouse cardiomyocytes and conducted optically detected magnetic resonance on nitrogen-vacancy color centers within intracellular FNDs. Our results demonstrate the potential of this platform for single-particle tracking and highlight its capability to achieve correlated 3D quantum sensing. Microscope ; Fluorescence ; Fluorescence microscope ; Nuclear magnetic resonance ; Resonance fluorescence ; Plane (geometry) ; Magnetic resonance imaging ; Microscopy ; Materials science ; Chemistry ; Optics ; Physics ; Medicine ; Radiology Diamond and Carbon-based Materials Research ; Force Microscopy Techniques and Applications ; Nonlinear Optical Materials Studies
Authors: Chao Zhang | Chunrong Zou | Shaojun Guo | Yanwen Zhao | Tongsheng Shen
Source: Materials (article, 2025-10-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep Learning-Based Inverse Design of Stochastic-Topology Metamaterials for Radar Cross Section Reduction Electromagnetic (EM) metamaterials have a wide range of applications due to their unique properties, but their design is often based on specific topological structures, which come with certain limitations. Designing with stochastic topologies can provide more diverse EM properties. However, this requires experienced designers to search and optimise in a vast design space, which is time-consuming and requires substantial computational resources. In this paper, we employ a deep learning network agent model to replace time-consuming full-wave simulations and quickly establish the mapping relationship between the metamaterial structure and its electromagnetic response. The proposed framework integrates a Convolutional Block Attention Module-enhanced Variational Autoencoder (CBAM-VAE) with a Transformer-based predictor. Incorporating CBAM into the VAE architecture significantly enhances the model's capacity to extract and reconstruct critical structural features of metamaterials. The Transformer predictor utilises an encoder-only configuration that leverages the sequential data characteristics, enabling accurate prediction of electromagnetic responses from latent variables while significantly enhancing computational efficiency. The dataset is randomly generated based on the filling rate of unit cells, requiring only a small fraction of samples compared to the full design space for training. We employ the trained model for the inverse design of metamaterials, enabling the rapid generation of two cells for 1-bit coding metamaterials. Compared to a similarly sized metallic plate, the designed coding metamaterial radar cross-section (RCS) reduces by over 10 dB from 6 to 18 GHz. Simulation and experimental measurement results validate the reliability of this design approach, providing a novel perspective for the design of EM metamaterials. Metamaterial ; Radar ; Inverse ; Radar cross-section ; Convolutional neural network ; Autoencoder ; Coding (social sciences) ; Topology (electrical circuits) ; Network topology Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Electromagnetic Scattering and Analysis
Authors: Wilson Jauhary | Albert Julius Yaphentus | Yennimar Yennimar
Source: SinkrOn (article, 2025-04-14)
Field: Chemistry — Analytical Chemistry

Implementation of the Dual Channel Convolution Neural Network Method for Detecting Rice Plant Diseases Rice is a strategic and important food crop for the economy in Indonesia. Rice can be infected with diseases caused by fungi, bacteria and viruses. The disease that attacks rice plants goes unnoticed by farmers and farmers often do not understand the diseases that attack rice plants so that it is too late in treating them to diagnose the symptoms, causing rice production to decrease. To solve this problem, it is necessary to carry out a disease detection process in rice plants. In this research, the Dual-Channel Convolutional Neural Network (DCCNN) method will be used. This DCCNN method consists of two channels, namely deep channel and shallow channel. The process of detecting grape plant diseases using the DCCNN method will start from the process of extracting leaf parts from the input image using the Gabor Filter method. After that, the Segmentation Based Fractal Co-Occurrence Texture Analysis method will be used to carry out the process of extracting characteristics, color and texture from the extracted leaf parts. Finally, the DCCNN method will be applied to carry out the process of classifying and detecting types of grape plant diseases. The results of this research are that the DCCNN method can be used to detect types of leaf diseases in rice plants. The accuracy of disease detection results using the DCCNN method depends on the number of datasets contained in the system with an accuracy level of up to 85%. However, more datasets will cause the execution process to take longer. Convolution (computer science) ; Dual (grammatical number) ; Rice plant ; Computer science ; Artificial neural network ; Channel (broadcasting) ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Agricultural engineering ; Agronomy ; Biology ; Engineering ; Telecommunications Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Ridwan Raafi’udin | Yohanes Aris Purwanto | Imas Sukaesih Sitanggang | Dewi Apri Astuti
Source: Simetris Jurnal Teknik Mesin Elektro dan Ilmu Komputer (article, 2025-11-30)
Field: Chemistry — Analytical Chemistry

Effects of Semi-Automated Preprocessing in The Beef Freshness Prediction based on Near Infrared Spectroscopy This study investigates the application of near-infrared spectroscopy (NIR) within the wavelength range of 1350â€“2550 nm to predict key quality parameters of beef, specifically focusing on tenderloin cuts. The quality indicators assessed include drip loss, color, pH, moisture content, storage duration, and total plate count (TPC) as a measure of microbial load. Predictive modeling was conducted using three machine learning algorithms: Partial Least Squares (PLS), Support Vector Regression (SVR), and Random Forest Regressor (RFR). To enhance model accuracy, a semi-automated preprocessing pipeline was employed utilizing the Nippy library. This library integrates several spectral preprocessing techniques including Savitzky-Golay filtering, Standard Normal Variate (SNV), Robust Normal Variate (RNV), Local Standard Normal Variate (LSNV), as well as clipping, resampling, baseline correction, and smoothing.Â Among the models developed using raw spectral data, the RFR model exhibited the highest performance, achieving coefficient of determination (RÂ²) values of 0.82 for drip loss, 0.65 for color, 0.67 for pH, 0.61 for moisture content, 0.81 for storage duration, and 0.76 for TPC. Post preprocessing, the predictive accuracy improved significantly with RÂ² values increasing to 0.89, 0.82, 0.87, 0.85, 0.91, and 0.90 respectively for the same parameters. These findings underscore the potential of combining advanced machine learning techniques with robust preprocessing methods to enhance the non-destructive, rapid assessment of beef quality parameters. This approach offers a promising tool for quality control in the meat processing industry, facilitating more efficient and accurate monitoring of product standards. Partial least squares regression ; Preprocessor ; Data pre-processing ; Random forest ; Near-infrared spectroscopy ; Support vector machine ; Pattern recognition (psychology) ; Pipeline (software) ; Artificial neural network Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Listeria monocytogenes in Food Safety
Authors: Juan Manuel Conde Garrido | Jone Ugarte Valdivielso | Jose I. Aizpurua | Manex Barrenetxea Iñarra | Josefina María Silveyra
Source: IEEE Transactions on Magnetics (article, 2025-11-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Blind Efficient Method for Optimizing Jiles–Atherton Model Parameters We introduce a novel blind optimization method for determining the parameters of the Jiles-Atherton model of hysteresis, eliminating the need for user-provided initial guesses or search spaces. A carefully designed initialization procedure combined with a standard optimizer yields a high-performing, practical method for parameter estimation. Validation against a theoretical benchmark recovers ground-truth parameters in under half a minute with negligible error (relative error < 4×10⁻⁸). When applied to the TEAM32 electrical steel experimental benchmark, our method achieved superior accuracy than previously reported fittings, also converging in under half a minute. Consistently robust performance is further demonstrated across diverse systems, including soft ferrites, nanocrystalline alloys, and magnetostrictive compounds. The presented blind approach offers new insights into magnetic material characterization and is deployed as an automated tool for hysteresis analysis. It advances both fundamental understanding and practical applications by demonstrating the Jiles-Atherton model’s capability to describe anisotropic materials and by revealing its inherent limitations. Initialization ; Benchmark (surveying) ; Magnetostriction ; Magnetic hysteresis ; Hysteresis ; Experimental data ; Anisotropy Magnetic Properties and Applications ; Magnetic properties of thin films ; Microstructure and Mechanical Properties of Steels
Authors: Charles David Warren | Noah Yardeny | Siyang Peng | Colin Burdette | Jacob B. Geri
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2025-07-06)
Field: Chemistry — Organic Chemistry

Global Protein-Ligand Binding Affinity Profiling via Photocatalytic Labeling Protein-ligand binding, selectivity, and affinity dictate the effects of drugs and endogenous molecules in cells. Currently, potential protein-ligand interactions are identified by qualitative interpretation of proteomic, transcriptomic, or genomic data, then binding affinities of hits are measured using purified proteins or engineered reporter systems to validate and quantify the strength of individual interactions. Few methods enable simultaneous target identification and biophysical affinity measurement, and these either apply to specific enzyme classes or proteins with ligand-dependent shifts in stability. Here we describe a general platform, termed Affinity Map, which leverages competitive binding analysis, high fidelity photocatalytic labeling, and high throughput proteomics for global quantitative binding affinity profiling. We show that this method is applicable to major classes of ligands, including small molecules, linear peptides, cyclic peptides, and proteins, and can measure affinities between unmodified ligands and proteins in cell lysates, organ extracts, and live cell surfaces. Affinities ; Chemistry ; Ligand (biochemistry) ; Computational biology ; Proteomics ; Ligand binding assay ; Small molecule ; Plasma protein binding ; Protein ligand ; Biochemistry ; Profiling (computer programming) ; Biophysics ; Biology ; Receptor ; Gene Click Chemistry and Applications ; Chemical Synthesis and Analysis ; Biotin and Related Studies
Authors: Betsy Agustina Naomi Pinaria | Charles R. Ngangi | Saartje Sompotan
Source: Agricultural Science Digest - A Research Journal (article, 2025-10-16)
Field: Chemistry — Analytical Chemistry

Early Detection of Fungal Diseases in Tomatoes using Convolutional Neural Networks Background: Tomatoes play a pivotal role in global agriculture fostering economic growth while also significantly enhancing food security. This paper introduces a data-based approach to improve the early detection of fungal infections in tomatoes. Methods: The study utilises a dataset comprised of 4,362 high-resolution images obtained from Mendeley, showing healthy tomato plants alongside those damaged by leaf mold, early blight and late blight diseases. Deep learning techniques have been applied to develop a Convolutional Neural Network (CNN) for disease classification based on these images. Result: The CNN model exhibits an overall accuracy rate of 90.83%, underscoring its efficacy in identifying fungal growth in tomato plants. The study proposes the use of machine learning in the detection and treatment of fungal diseases affecting tomatoes, which consequently leads to increased crop yield and quality preservation. It recommends the development of automated tools for farmers to detect and respond to disease outbreaks, thus improving their agricultural practices. Convolutional neural network ; Blight ; Deep learning ; Plant disease ; Agriculture ; Artificial neural network ; Pattern recognition (psychology) ; Crop Spectroscopy and Chemometric Analyses
Authors: Amirhadi Alesadi | Zhaofan Li | Amara Arshad | Wenjie Xia
Source: Chemical Communications (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Predicting the glass transition temperature of conjugated polymers <i>via</i> cheminformatics from monomer structures We present a cheminformatics model for predicting the glass-transition temperature (<i>T</i><sub>g</sub>) of conjugated polymers using four interpretable molecular descriptors. The model achieves high predictive accuracy (<i>R</i><sup>2</sup> ≈ 0.85), and molecular dynamics simulations validate the descriptor-<i>T</i><sub>g</sub> relationships. This integrated framework enables rational design of conjugated polymers with tailored glass-transition properties. Glass transition ; Cheminformatics ; Monomer ; Polymer ; Conjugated system ; Polymer chemistry ; Chemistry ; Chemical engineering ; Materials science ; Polymer science ; Organic chemistry ; Computational chemistry ; Engineering Photopolymerization techniques and applications ; Synthesis and properties of polymers ; Epoxy Resin Curing Processes
Authors: MOSHOOD KEHINDE ABDLATEEF | GUILHERME JOSE DE CASTILHO | WILLIAM DENNER PIRES FONSECA
Source:  (review, 2025-01-28)
Field: Materials Science — Materials Chemistry

EMULSION FORMATION, CHARACTERIZATION AND DEMULSIFICATION TECHNIQUES - A REVIEW Emulses de gua em leo so geradas durante atividades de explorao e refino de petrleo bruto.Eles representam riscos operacionais para as instalaes devido corroso e exigem maior comprometimento econmico devido ao custo da separao.Assim, separar a fase aquosa da emulso do petrleo bruto antes dos processos de refino e transporte uma rea de interesse de pesquisa.Para desestabilizar uma emulso, diversas tcnicas so relevantes, como biolgicas, qumicas, mecnicas e trmicas.Esses mtodos podem alterar as propriedades paramtricas da emulsoviscosidade, pH, concentrao, teor de gua, concentrao do emulsificante e tamanhos de gotculaspara efetuar a separao.Alm disso, diversas tcnicas instrumentais so utilizadas para monitorar o processo de separao no sistema de emulso.A tcnica de teste de garrafa uma das tcnicas mais utilizadas para estudar a separao de emulses; outros so disperso de luz, microscopia, mtodos ultrassnicos, medio de condutividade e tcnicas de RMN, que mostraram diferentes nveis de preciso de monitoramento de coalescncia, formao de creme e sedimentao durante a separao de fases.Este estudo uma reviso de trabalhos sobre emulses, seus Characterization (materials science) ; Emulsion ; Computer science ; Materials science ; Chemical engineering ; Nanotechnology ; Engineering Pickering emulsions and particle stabilization ; Surfactants and Colloidal Systems
Authors: Abd. Mizwar A. Rahim | Theopilus Bayu Sasongko
Source: Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer (article, 2025-03-06)
Field: Chemistry — Analytical Chemistry

Identify the Condition of Corn Plants Using Gray Level Co-occurrence Matrix and Bacpropagation This research aims to increase the accuracy of identifying the condition of corn plants based on leaf features using the GLCM and ANN Backpropagation methods. The GLCM method is used to extract features from corn leaf images, while Backpropagation ANN is used to classify the condition of corn plants based on these features. This classification was carried out using a dataset of corn leaves from four different conditions, namely healthy, leaf-spot, leaf-blight, and leaf-rust. Next, leaf features are extracted using the GLCM method. After that, data normalization was carried out, balancing the dataset, and training was carried out on the Backpropagation ANN model to classify the condition of the corn plants. After training the model, the next model evaluation is carried out using the confusion matrix method. The research results show that the method used can produce quite high accuracy when identifying the condition of corn plants, with an accuracy of 99%. This shows that the use of GLCM and ANN Backpropagation can be a good alternative in identifying the condition of corn plants. This research provides benefits in making it easier to accurately identify the condition of corn plants. Co-occurrence matrix ; Gray (unit) ; Gray level ; Matrix (chemical analysis) ; Environmental science ; Mathematics ; Artificial intelligence ; Materials science ; Computer science ; Composite material ; Medicine Spectroscopy and Chemometric Analyses