jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Phan Minh Nhat | Ngo Le Huy Hien | Minh Dinh | Le Viet Hung | Phan Van Binh | P. T. N. Anh | Nguyen Thi Hoang Phuong | Nguyễn Văn Hiệu
Source: ZooKeys (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

EBAR: A Novel Machine Learning Model for Quantifying Chemical Concentrations using NIR Spectroscopy The examination of Near Infrared Reflectance Spectroscopy (NIR) in cattle and poultry fertilizers provides a viable solution for determining optimal fertilizer composition for crop growth while mitigating adverse impacts on soil and groundwater quality. In recent studies, conventional machine learning models combined with spectral analysis have been used to ascertain cattle and poultry fertilizer concentrations. However, these traditional machine learning models encounter challenges in achieving data generalization, resulting in suboptimal prediction accuracy. To address this issue, this study proposes a synthesized machine learning model named EBAR (Error Based Accumulation Regression), which exhibits a commendable coefficient of determination, with an average R2 = 0.865 across 7 chemical substances, surpassing the performance of existing traditional machine learning models. Additionally, a Backward Elimination technique is designed to identify crucial wavelength ranges for monitoring component concentrations. The research&amp;nbsp;outcome is promising and acts as a novel benchmark for later models in determining component concentrations through NIR spectroscopy. Future research gears toward expanding datasets and increasing samples of fertilizers, extending examined wavelength, and improving the model&amp;rsquo;s efficiency to apply to various types of foods, including seafood, vegetables, and fruits. Spectroscopy ; Computer science ; Machine learning ; Biological system ; Materials science ; Artificial intelligence ; Physics ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Gagana Sindhu S | G. V. Shwetha | Kaveri Aramani | Nikita Kankanawadi | V Shravanilakshmi | M K Veenita
Source: Annual Research & Review in Biology (article, 2025-08-02)
Field: Materials Science — Biomaterials

Tissue Culture and its Applications in Mulberry Propagation Mulberry is an economically important crop in sericulture industry, as it provides the only food source for the silkworm (Bombyx mori L.). Despite its importance, traditional methods of propagation like stem cuttings and grafting have several limitations including low success rates, seasonal dependency and vulnerability to diseases. To overcome these challenges, tissue culture offers a reliable and efficient alternative for mass multiplication, genetic improvement, and long-term preservation of mulberry varieties. Techniques such as micropropagation, somatic embryogenesis and organogenesis make it possible to produce healthy, high-quality planting material on a large scale. In addition, tissue culture plays a crucial role in conserving rare or valuable germplasm under controlled environments, helping to safeguard genetic diversity for breeding and research purposes. This review explores how tissue culture is contributing to the advancement and preservation of mulberry and discusses the latest developments and future potential of these biotechnological approaches in promoting sustainable sericulture. Tissue culture ; Biology ; Genetics ; In vitro Silk-based biomaterials and applications ; Silkworms and Sericulture Research ; Viral Infectious Diseases and Gene Expression in Insects
Authors: Neha Srikanth | Rachel Rudinger
Source:  (article, 2025-01-01)
Field: Chemistry — Electrochemistry

NLI under the Microscope: What Atomic Hypothesis Decomposition Reveals Neha Srikanth, Rachel Rudinger. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025. Decomposition ; Microscope ; Materials science ; Computer science ; Nanotechnology ; Chemistry ; Optics ; Physics Electrochemical Analysis and Applications
Authors: Tejendra Bhushal | Rashedul Haque | Md. Rejwanul Islam | Hussain Al Mohmud
Source: Journal of Engineering Advancements (article, 2025-12-24)
Field: Materials Science — Polymers and Plastics

Investigation of the Mechanical Properties and Durability Performance of Jute Fiber Concrete Concrete, inherently weak in tension and shows brittleness, can be strengthened and made more ductile through the addition of continuous reinforcement like fibers. However, conventional reinforced concrete is costly due to production and transportation expenses. This study explores the feasibility of enhancing concrete properties like mechanical and durability performances using locally sourced jute fibers. Mechanical properties like compressive strength, splitting tensile strength, unit weight of jute fiber concrete (JFC) was observed as well as water absorption capacity was observed as durability performance of JFC. Experiments involved preparing concrete cylinders with 0.6%, 0.8%, and 1.0% of volumetric concentrations of 10 mm and 12 mm jute fibers. Results indicated a significant increase in compressive strength from 14.54% to 30.10% and splitting tensile strength of 12.64% with 0.6%, 12 mm jute fibers concrete compared to plain concrete after 28 days. An increasing trend of water absorption capacity of JFC was observed for larger size jute fiber in the jute fiber concrete. The findings are for jute fiber-reinforced concrete as a cost-effective alternative for sustainable construction. Durability ; Ultimate tensile strength ; Absorption of water ; Compressive strength ; Fiber ; Tension (geology) Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Bamboo properties and applications
Authors: Xinze Li | Wenyue Wang | Bing Pan | Siyu Zhu | Junhui Zhang | Yunzhao Ma | Hongpeng Guo | Zhe Liu | Wenfu Wu | Yan Xu
Source: Agriculture (article, 2025-08-29)
Field: Chemistry — Analytical Chemistry

An Interpretable Stacked Ensemble Learning Framework for Wheat Storage Quality Prediction Accurate prediction of wheat storage quality is essential for ensuring storage safety and providing early warnings of quality deterioration. However, existing methods focus solely on storage environmental conditions, neglecting the spatial distribution of temperature within grain piles, lacking interpretability, and generally failing to provide reliable forecasts of future quality changes. To overcome these challenges, an interpretable prediction framework for wheat storage quality based on stacked ensemble learning is proposed. Three key features, Effective Accumulated Temperature (EAT), Cumulative High Temperature Deviation (CHTD), and Cumulative Temperature Gradient (CTG), were derived from grain temperature data to capture the spatiotemporal dynamics of the internal temperature field. These features were then input into the stacked ensemble learning model to accurately predict historical quality changes. In addition, future grain temperatures were predicted with high precision using a Graph Convolutional Network-Temporal Fusion Transformer (GCN-TFT) model. The temperature prediction results were then employed to construct features and were fed into the stacked ensemble learning model to enable future quality change prediction. Baseline experiments indicated that the stacked model significantly outperformed individual models, achieving R2 = 0.94, MAE = 0.44 mg KOH/100 g, and RMSE = 0.59 mg KOH/100 g. SHAP interpretability analysis revealed that EAT constituted the primary driver of wheat quality deterioration, followed by CHTD and CTG. Moreover, in future quality prediction experiments, the GCN-TFT model demonstrated high accuracy in 60-day grain temperature forecasts, and although the prediction accuracy of fatty acid value changes based on features derived from predicted temperatures slightly declined compared to features based on actual temperature data, it remained within an acceptable precision range, achieving an MAE of 0.28 mg KOH/100 g and an RMSE of 0.33 mg KOH/100 g. The experiments validated that the overall technical route from grain temperature prediction to quality prediction exhibited good accuracy and feasibility, providing an efficient, stable, and interpretable quality monitoring and early warning tool for grain storage management, which assists managers in making scientific decisions and interventions to ensure storage safety. Ensemble learning ; Quality (philosophy) ; Environmental science ; Agricultural engineering ; Artificial intelligence ; Computer science ; Engineering ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Food Supply Chain Traceability
Authors: Marc Joshua Prudente | Edwin R. Arboleda | J. Gutierrez
Source: Control Systems and Optimization Letters (article, 2025-06-11)
Field: Materials Science — Polymers and Plastics

Advancements in AI-driven Cotton Fiber Quality Assessment Through Image Processing: A Comprehensive Review The integration of artificial intelligence (AI) and image processing techniques has emerged as a transformative solution to address the limitations of traditional cotton fiber quality assessment methods, particularly the High-Volume Instrument (HVI) and Advanced Fiber Information System (AFIS), which require time-consuming manual labor. This comprehensive review examines the convergence of three key technological domains: image processing, AI/machine learning, and IoT/edge computing, in revolutionizing cotton fiber quality assessment. The review focuses on three primary image processing techniques—feature extraction, segmentation, and classification—that enable precise analysis of critical fiber properties including length, fineness, strength, and maturity. Advanced AI algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable success in automating the assessment process, achieving accuracy rates of 82-98% in fiber classification tasks. The integration of Internet of Things (IoT) devices and edge computing has further enhanced the system's capabilities, enabled real-time quality assessments and reduced processing time by up to 60% compared to traditional methods. However, several significant challenges persist, including limited availability of high-quality annotated datasets, variability in image quality due to environmental factors, model generalization across different cotton varieties, and real-time processing constraints in industrial settings. The combination of image data with additional sensor inputs, such as spectral analysis and environmental monitoring, offers potential to further enhance assessment accuracy and robustness. This review emphasizes the transformative potential of AI-driven image processing systems in revolutionizing cotton fiber quality assessment, while also identifying critical areas requiring further research for successful industrial implementation. The findings suggest that continued advancements in AI algorithms, coupled with improved IoT integration and edge computing capabilities, will be crucial for developing more robust and efficient quality assessment systems in the cotton industry. Image processing ; Image quality ; Transformative learning ; Convolutional neural network ; Quality (philosophy) ; Key (lock) ; Digital image processing ; Quality assessment Textile materials and evaluations ; Research in Cotton Cultivation ; Spectroscopy and Chemometric Analyses
Authors: Jung–Woo Hwang | Y Jeong | Jung-Hyeon Kim
Source: Asia-pacific Journal of Convergent Research Interchange (article, 2025-08-29)
Field: Materials Science — Materials Chemistry

Research on the Use of TRIZ to Improve the Quality of Phosphate Coatings TRIZ methodology, which companies are continuously using to solve complex problems occurring in the field, is being used as a representative tool of Problem Based Learning (PBL) in university education.This study is a PBL case that uses TRIZ (Theory of Inventive Problem Solving) to solve problems that occurred in the phosphate coating process.The phosphate coating process is a widely used surface treatment technology applied as a pretreatment method to enhance the corrosion resistance of steel products or to improve adhesion properties.Typically, to maintain a stable coating quality, various management parameters are established with predefined criteria.Key control parameters include the accelerator concentration, total acid, free acid, and immersion time of the workpiece, as well as the temperature of the phosphating bath.However, despite maintaining these parameters within the designated conditions, unexplained occurrences of rust formation continue to be reported.To address this issue, stricter control of post-treatment processes, such as drying time and drying temperature, has been necessitated.This situation has led to excessive equipment specifications and increased energy consumption.In this study, we systematically analyze the factors influencing rust formation and evaluate the functionality of existing equipment within the process using the TRIZ methodology.Through this approach, we aim to identify the fundamental causes of rust occurrence and propose optimal solutions to mitigate this issue.The ultimate goal of this study is to establish new management standards that can improve both the stability and efficiency of the phosphating process, thereby contributing to the overall enhancement of coating quality. TRIZ ; Phosphate ; Quality (philosophy) ; Environmental science ; Engineering ; Manufacturing engineering ; Chemistry ; Physics ; Biochemistry Magnesium Oxide Properties and Applications ; Material Properties and Applications ; Pigment Synthesis and Properties
Authors: Igor V. Minin | O.V. Minin
Source:  (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

ANTI-METEORITE PROTECTION OF THE VEGA-HALLEY SPACECRAFT: HOW IT WAS DONE This paper briefly reviews the history of modeling the anti-meteorite shielding for the Vega spacecraft. Apparently, anti-meteorite shielding based on two and three spatially separated shields was first proposed in the works of R. Z. Sagdeev. The role of a computational experiment in studying high-speed interaction processes between the target and the impactor is demonstrated. The successful development and understanding of the physics of anti-meteorite shielding was facilitated by the active collaboration between V. F. Minin and the team from the Institute of Applied Physics and V. E. Fortov and his team from the Institute of Chemical Physics. The research allowed us to develop a physical model for the destruction of the Vega spacecraft’s protective shields upon interaction with micrometeorites and determine complex protection parameters, including the dynamic characteristics of spall elements in the first shield and their impact on the second shield. We also assessed the parameters of the resulting plasma jets and the phase composition of the destruction products upon impact at various angles to the shield normal. The opinion of American colleagues on this project regarding modeling is presented. Shields ; Shield ; Electromagnetic shielding ; Vega ; Spall High-Velocity Impact and Material Behavior ; Astro and Planetary Science ; Gas Dynamics and Kinetic Theory
Authors: Shaokang Chen | Yanfeng Hu | Yile Chen | Junming Chen | Si Yuan Cheng
Source: Coatings (article, 2025-11-12)
Field: Chemistry — Organic Chemistry

Weed Detection on Architectural Heritage Surfaces in Penang City via YOLOv11 George Town, the capital of Penang, Malaysia, was inscribed as a UNESCO World Heritage Site in 2008 and is renowned for its multicultural architectural surfaces. However, these historic façades face significant deterioration challenges, particularly biodeterioration caused by weed growth on wall surfaces under hot and humid equatorial conditions. Root penetration is a critical surface defect, accelerating mortar decay and threatening structural integrity. To address this issue, this study proposes YOLOv11-SWDS (Surface Weed Detection System), a lightweight and interpretable deep learning framework tailored for surface defect detection in the form of weed intrusion on heritage buildings. The backbone network was redesigned to enhance the extraction of fine-grained features from visually cluttered surfaces, while attention modules improved discrimination between weed patterns and complex textures such as shadows, stains, and decorative reliefs. For practical deployment, the model was optimized through quantization and knowledge distillation, significantly reducing computational cost while preserving detection accuracy. Experimental results show that YOLOv11-SWDS achieved an F1 score of 86.0% and a mAP@50 of 89.7%, surpassing baseline models while maintaining inference latency below 200 ms on edge devices. These findings demonstrate the potential of deep learning-based non-destructive detection for monitoring surface defects in heritage conservation, offering both a reliable tool for sustaining George Town’s cultural assets and a transferable solution for other UNESCO heritage sites. Weed ; Cultural heritage ; Aerial image ; Categorical variable ; World heritage ; Aerial imagery ; Inference Wood and Agarwood Research ; 3D Surveying and Cultural Heritage ; Wood Treatment and Properties
Authors: Sara León Ecay | Ainara López Maestresalas | Irantzu Goenaga Uceda | José Antonio Mendizábal Aizpuru | Kizkitza Insausti Barrenetxea
Source: Academica-e (Universidad Pública de Navarra) (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Authentication of cattle finishing diets (conventional vs. vegetable by-products) using near-infrared spectroscopy This study explored the potential of a portable near-infrared (NIR) spectrophotometer (1200 - 2200 nm; 2-nm bandwidth) to discriminate meat and carcasses from cattle fed two different diets: a control of 90% barley and 10% straw (C, n = 12) and, a ration including 37.5% of vegetable by-products (VBPR, n = 11). At 24 h postmortem spectra were collected on the exterior surface of the left carcass, first, between the 5th and 6th ribs, and afterwards, on the 12th and 13th ribs. After fabrication and 7 d of aging, spectral data was acquired from the intact steaks while keeping muscle integrity. When spectra were collected from the carcasses, partial least squares-discriminant analysis (PLS-DA) correctly classified (%CC) &gt;66.67% in both Train and cross-validation (CV) whereas radial basis function-support vector machine (RBF-SVM) discriminated 100 - 83.33% in Train and CV, respectively, using the full spectrum. Reducing the initial matrix ((λ=501) by interval-PLS (iPLS) led into a &gt;75% of well-sorted carcasses by finishing diet while RBFSVM increased the %CC up to &gt;83.33%. Using both discriminant approaches, carcasses were authenticated with a subtle improvement over intact meat (&gt;90% vs. &gt;75% in Train and &gt;80% vs. &gt;65% in CV for C and VBPR, respectively). Variable importance in projection (VIP) scores showed how variables &gt;1592 nm had higher weight in the discrimination process. The results achieved showed the potential of NIR technology as a sustainable, fast and chemical-free tool to assist the integration of the meat industry into the digital age of connectivity and digitization. Linear discriminant analysis ; Straw ; Principal component analysis ; Authentication (law) ; Pattern recognition (psychology) ; Projection (relational algebra) ; Analytical Chemistry (journal) Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Randolph Linderman | Yiran Chen | Scott W. Linderman
Source: ArXiv.org (preprint, 2025-02-12)
Field: Chemistry — Analytical Chemistry

A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-trained or learned embeddings of data points. Here we show a formal relationship between Bayesian nonparametric models and the relative Mahalanobis distance score (RMDS), a commonly used method for OOD detection. Building on this connection, we propose Bayesian nonparametric mixture models with hierarchical priors that generalize the RMDS. We evaluate these models on the OpenOOD detection benchmark and show that Bayesian nonparametric methods can improve upon existing OOD methods, especially in regimes where training classes differ in their covariance structure and where there are relatively few data points per class. Mahalanobis distance ; Bayesian probability ; Nonparametric statistics ; Perspective (graphical) ; Statistics ; Computer science ; Artificial intelligence ; Mathematics ; Pattern recognition (psychology) ; Econometrics Spectroscopy and Chemometric Analyses ; Bayesian Methods and Mixture Models ; Advanced Statistical Methods and Models
Authors: Vijila Kasthuri J | V. Narayani
Source: International Journal of Latest Technology in Engineering Management & Applied Science (article, 2025-11-15)
Field: Chemistry — Analytical Chemistry

Effectiveness of Random Forest Classifier in Food Adulteration Detection Abstract - Food adulteration has become one of the most pressing issues in public health, as it compromises food safety, nutrition, and consumer trust. Various real-time examples highlight how adulterants such as synthetic colors, chemical preservatives, and harmful substitutes are intentionally added to food products for profit, posing serious health risks. To address this challenge, advanced computational techniques, including Natural Language Processing (NLP) and Deep Learning, have been increasingly explored for the detection, prediction, and prevention of food adulteration. By leveraging machine learning models, datasets related to food quality and adulteration patterns can be analyzed to identify hidden correlations and risk indicators. The study further incorporates supervised learning algorithms and confusion matrix evaluation to measure the classification performance in predicting adulterated versus non-adulterated samples. The presented confusion matrix illustrates the predictive accuracy and misclassifications across multiple classes, providing a clear performance insight into the model. Overall, this research emphasizes how Artificial Intelligence (AI) and Machine Learning (ML) techniques can significantly strengthen food adulteration detection, thereby ensure public safety and contribute to a healthier society. Confusion matrix ; Confusion ; Classifier (UML) ; Random forest ; Food safety ; Quality (philosophy) ; Supervised learning ; Food quality Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Melamine detection and toxicity