jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Manon Chossegros | Amelia Hubbard | Megan Burt | Richard J. Harrison | Charlotte F. Nellist | Nastasiya F. Grinberg
Source: Plant Methods (article, 2025-11-07)
Field: Chemistry — Analytical Chemistry

Hyperspectral image analysis for classification of multiple infections in wheat Plant diseases can cause heavy yield losses in arable crops resulting in major economic losses. Effective early disease recognition is paramount for modern large-scale farming. Since plants can be infected with multiple concurrent pathogens, it is important to be able to distinguish and identify each disease to ensure appropriate treatments can be applied. Hyperspectral imaging is a state-of-the art computer vision approach, which can improve plant disease classification, by capturing a wide range of wavelengths before symptoms become visible to the naked eye. Whilst a lot of work has been done applying the technique to identifying single infections, to our knowledge, it has not been used to analyse multiple concurrent infections which presents both practical and scientific challenges. In this study, we investigated three wheat pathogens (yellow rust, mildew and Septoria), cultivating co-occurring infections, resulting in a dataset of 1447 hyperspectral images of single and double infections on wheat leaves. We used this dataset to train four disease classification algorithms (based on four neural network architectures: Inception and EfficientNet with either a 2D or 3D convolutional layer input). The highest accuracy was achieved by EfficientNet with a 2D convolution input with 81% overall classification accuracy, including a 72% accuracy for detecting a combined infection of yellow rust and mildew. Moreover, we found that hyperspectral signatures of a pathogen depended on whether another pathogen was present, raising interesting questions about co-existence of several pathogens on one plant host. Our work demonstrates that the application of hyperspectral imaging and deep learning is promising for classification of multiple infections in wheat, even with a relatively small training dataset, and opens opportunities for further research in this area. However, the limited number of Septoria and yellow rust + Septoria samples highlights the need for larger, more balanced datasets in future studies to further validate and extend our findings under field conditions. Hyperspectral imaging ; Convolutional neural network ; Pattern recognition (psychology) ; Plant disease ; Arable land ; Convolution (computer science) ; Image processing ; Deep learning Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: Amreen Taj | K. N. Bharath | Anupama Hiremath | Madhu Puttegowda | M. R. Sanjay | Suchart Siengchin
Source: Journal of Umm Al-Qura University for Engineering and Architecture (article, 2025-10-20)
Field: Materials Science — Polymers and Plastics

Mechanical enhancement of sustainable natural fiber composites through filler additives: a comprehensive review Filler (materials) ; Composite number ; Work (physics) ; Sustainability ; Natural (archaeology) ; Fiber Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research ; Tribology and Wear Analysis
Authors: Vartika Tripathi | Abhishek Purohit
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2025-04-24)
Field: Chemistry — Analytical Chemistry

Thermal Imaging and Disease Detection in Hibiscus Plant Abstract: The threat of plant diseases poses a significant challenge to agricultural productivity, especially in developing countries where small-scale farmers are highly vulnerable. To avoid crop loss and guarantee food security, early identification of plant stress and disease is crucial. Even if they work well, traditional diagnostic techniques take a lot of time and effort. This study explores the potential of thermal imaging as a non-invasive and efficient solution for early stress detection in Hibiscus plants. The experiment was conducted on a single potted hibiscus plant at Bikaner Technical University over a period of approximately two months (14th December 2024 to 5th February 2025). Initially kept outdoors with regular watering, the plant was moved to a closed indoor setting without sunlight and water from Day 9, allowing for observation of stress progression and disease emergence. Although the thermal dataset was recorded for 16 days, intermediate day observations confirmed consistent stress behavior. Four visual diseases—Leaf Spot, Rust Disease, Botrytis Blight, and Mosaic Virus—were noted, but due to the limited dataset, the prototype focuses on classifying plant health into four thermal stress categories: healthy, mild, significant, and critical. Thermal images were captured from top and front views, and a deep learning model based on MobileNetV2 was developed using a multi-view classification approach. The model was trained using Leave-One-Out Cross-Validation (LOOCV) to ensure robustness with constrained data. Instead of relying solely on traditional performance metrics, a confidence-based interpretation method was adopted to improve decision reliability. The prototype demonstrates the feasibility of using thermal imaging and deep learning for early, non-destructive plant stress classification, paving the way for smarter and more sustainable agricultural monitoring. Hibiscus ; Disease ; Medicine ; Biology ; Botany ; Pathology Spectroscopy and Chemometric Analyses
Authors: Rimma Ualiyeva | Mariya M. Kaverina | А. В. Осипова | Alina Faurat | Sayan Zhangazin | Nurgul Iksat
Source: Biology (article, 2025-12-01)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging and Machine Learning for Automated Pest Identification in Cereal Crops The spectral characteristics of harmful insect pests in wheat fields were characterised using hyperspectral imaging for the first time. The analysis of spectral profiles revealed that reflectance is determined by the structure of the insect's chitin and the colouration of its body surface. Insects with lighter or more vivid colours (white, yellow, or green) showed higher reflectance values compared to those with predominantly dark pigmentation. Reflectance was also influenced by the presence of wings, surface roughness, and the age of the insect. Each species exhibited distinct spectral patterns that allowed for differentiation not only from other insect species but also from the plant background. A classification model using PLS-DA was developed and demonstrated high accuracy in identifying 12 pest species, confirming the strong potential of hyperspectral imaging for species-level classification. The results validate the PLS-DA method for differentiating insects based on spectral characteristics and underscore the reliability of this approach for automated monitoring systems to detect phytophagous pests in crop fields. This technology could reduce insecticide use by 30-40% through targeted application. The research has both scientific and economic significance, laying the groundwork for integrating machine learning and computer vision into agricultural monitoring. It supports the advancement of precision farming and contributes to improved global food security. Hyperspectral imaging ; Reflectivity ; PEST analysis ; Crop ; Identification (biology) ; Insect pest ; Precision agriculture ; Spectral signature Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Carlos García-Ausencio | Fernando Guzmán‐Chávez | Romina Rodríguez‐Sanoja | Sergio Sánchez
Source: World Journal of Microbiology and Biotechnology (review, 2025-08-01)
Field: Chemistry — Organic Chemistry

Promiscuity of lanthipeptide enzymes: new challenges and applications Lanthipeptides are a group of peptides synthesized by ribosomes that undergo post-translational modifications and have significant potential for medical and biotechnological applications. Various bacterial strains produce these peptides, and their synthesis involves the structural modification of precursor compounds through specialized enzymes present within a biosynthetic gene cluster (BGC) of the producing organisms. These enzymes are particularly notable for their capacity to modify non-cognate substrates, allowing for the installation of lanthionine rings on precursor peptides and enabling further modifications, such as methylation, reduction, and oxidation, to enhance the biological properties of specific peptides. The inherent flexibility of lanthipeptide enzymes-an important characteristic of this class of proteins-can be utilized to create peptides with improved bioactive and physicochemical properties. This review synthesizes recent advances in the application of promiscuous enzymes for the synthesis of bioactive peptides, emphasizing the diverse classes identified to date. Lantibiotics ; Enzyme ; Lanthionine ; Biochemistry ; Computational biology ; Ribosome ; Biology ; Peptide ; Chemistry ; Gene ; Combinatorial chemistry ; Bacteria ; RNA ; Genetics Carbohydrate Chemistry and Synthesis ; Microbial Natural Products and Biosynthesis ; Chemical Synthesis and Analysis
Authors: Zhuozhuo Li | Guangyong Liu
Source: Journal of Applied Polymer Science (article, 2025-01-15)
Field: Materials Science — Polymers and Plastics

Study on the Prediction Model of Swelling Response of <scp>EPDM</scp> in Liquid Mixture by Using the Modified Flory–Huggins Interaction Parameter (<i>χ</i><scp><sub>HSP</sub></scp>) ABSTRACT The swelling responses of ethylene propylene diene monomers (EPDM) in cyclohexane/xylene (CYH/X) and cyclohexane/cyclohexanone (CYH/C) binary systems were studied by using equilibrium swelling method, and the co‐solvent effect was further analyzed. The results show that the swelling responses of EPDM in CYH/X and CYH/C binary systems are significantly different. EPDM exhibits more obvious co‐solvent effect in CYH/C binary system which may be generated by the self‐association of cyclohexanone. In addition, the modified Flory–Huggins interaction parameter ( χ HSP ) calculated by three‐dimensional solubility parameters (HSP) was used to establish the prediction model from the correlation with the swelling ratio ( q m ) of EPDM. The prediction model can be represented by an exponential equation with high degree of fitting, and the calculated q m values from this model are comparable with the experimental values. The prediction model has been employed to evaluate the swelling responses ( q ‐fitting) of EPDM in IRM 903 test oil/ethanol mixtures theoretically. It reveals that lower χ HSP values result in higher swelling responses of EPDM, following the same rule of q – χ HSP . Potentially, the prediction model based on χ HSP provides a new way to determine the swelling responses of EPDM based rubber parts in any possible fluids. Swelling ; Flory–Huggins solution theory ; Materials science ; Chemical engineering ; Polymer chemistry ; Composite material ; Thermodynamics ; Polymer ; Physics ; Engineering Synthesis and properties of polymers ; Polymer Nanocomposites and Properties ; Advanced Polymer Synthesis and Characterization
Authors: Anyang Li | Shengjun Guo | Jianwen Deng | Hong Chen | Jinxuan Laura Wu | Ru Jiang | Jing Tan | Lihua Cheng | Libo Zhang | Qinzhen Fan
Source: Journal of Marine Science and Engineering (article, 2025-09-04)
Field: Chemistry — Analytical Chemistry

The Mechanism of the Effect of FCC Slurry Oil Blending Ratio on the Colloidal Stability and Asphaltene Aggregation Behavior of Low-Sulfur Marine Fuel Oil Addressing IMO 2020 compliance, this study investigates marine fuel oil production from hydrotreated residues, focusing on mitigating excessive total sediment potential (TSP) caused by over-hydrotreatment. This study systematically investigates the impact of blending ratios of Fluid Catalytic Cracking (FCC) slurry oil with Residue Desulfurization (RDS) heavy oil on TSP, colloidal stability, and asphaltene structure evolution. Techniques such as XRD, SEM, and XPS were employed to analyze the structural changes in asphaltenes during the TSP exceeding process. The results indicate that as the FCC slurry oil blending ratio increases, TSP in the blended oil initially rises and then decreases. The peak TSP value of 0.41% occurs at a 10% FCC slurry oil blending ratio, primarily due to high-saturation hydrocarbons in RDS heavy oil disrupting the colloidal stability of asphaltenes in FCC slurry oil. When the blending ratio reaches 25%, TSP significantly decreases to 0.09%, attributed to the solubilizing effect of high aromatic compounds in the FCC slurry oil on the asphaltenes. The ω(Asp)/ω(Res) ratio mirrors the TSP trend, and the colloidal solubilizing capacity of asphaltenes increases with the blending ratio. Asphaltenes in RDS heavy oil exhibit a spherical structure, whereas those in FCC slurry oil show a layered structure. The precipitated asphaltenes in the blends primarily result from the aggregation of asphaltenes in FCC slurry oil, with heteroatoms (N, S, O) mainly originating from RDS heavy oil asphaltenes. During the early stage of blending, TSP formation is dominated by FCC slurry oil asphaltenes, but increasing the aromatic content in the system can significantly reduce TSP. This work provides theoretical and technical support for optimizing marine fuel blending processes in petrochemical enterprises. Asphaltene ; Slurry ; Chemical engineering ; Colloid ; Sulfur ; Fuel oil ; Mechanism (biology) ; Materials science ; Petroleum engineering ; Chemistry ; Waste management ; Metallurgy ; Geology ; Composite material ; Engineering Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Catalysis and Hydrodesulfurization Studies
Authors: Kun Zhang | Chuyan Zhang | Zhenan Zhou | Zheyuan Liu | Yu Deng | Chen Gu | Songsong Zhou | Dongxu Sun | Hou-gang Liu | Xinzhe Yu
Source: Polymers (article, 2025-11-10)
Field: Materials Science — Materials Chemistry

An Ensemble Learning Model for Aging Assessment of Silicone Rubber Considering Multifunctional Group Comprehensive Analysis With the widespread deployment of high-voltage and ultra-high-voltage transmission lines, composite insulators play a vital role in modern power systems. However, prolonged service leads to material aging, and the current lack of standardized, quantitative methods for evaluating silicone rubber degradation poses significant challenges for condition-based maintenance. To address this measurement gap, we propose a novel aging assessment framework that integrates Fourier Transform Infrared (FTIR) spectroscopy with a measurement-oriented ensemble learning model. FTIR is utilized to extract absorbance peak areas from multiple aging-sensitive functional groups, forming the basis for quantitative evaluation. This work establishes a measurement-driven framework for aging assessment, supported by information-theoretic feature selection to enhance spectral relevance. The dataset is augmented to 4847 samples using linear interpolation to improve generalization. The proposed model employs k-nearest neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Gradient-Boosting Decision Tree (GBDT) within a two-tier ensemble architecture featuring dynamic weight allocation and a class-balanced weighted cross-entropy loss. The model achieves 96.17% accuracy and demonstrates strong robustness under noise and anomaly disturbances. SHAP analysis confirms the resistance to overfitting. This work provides a scalable and reliable method for assessing silicone rubber aging, contributing to the development of intelligent, data-driven diagnostic tools for electrical insulation systems. Silicone rubber ; Ensemble learning ; Robustness (evolution) ; Random forest ; Decision tree ; Support vector machine ; Feature selection ; Noise (video) High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Xue Li | Wenwen Liu | Lu Xiao | Zhao Jie | Yan Chen | Liangxiao Zhang | Peiwu Li | Dolores Pérez‐Marín | Xu Wang
Source: Food Chemistry X (review, 2025-01-01)
Field: Chemistry — Analytical Chemistry

The application of emerging technologies for the quality and safety evaluation of oilseeds and edible oils Oilseeds and edible oils are an indispensable part for the human diet and provide nutritional support for the human health. It has been reported a total of above 170 million tons per annum of edible oils consumption were consumed worldwide. Safety and quality of oilseeds and edible oils cannot be ignored, which can pose risk to human health and cause agro-economic loss. Classical techniques widely used to detect the safety and quality attributes of oilseeds and edible oils often involve time-consuming and tedious operation; therefore, the development of low cost, rapid and non-destructive detection method is necessary. This review presents applications of four emerging spectroscopic techniques in recent ten years, such as Raman spectroscopy, fluorescence spectroscopy, fourier transform infrared spectroscopy and near-infrared spectroscopy for determining the quality and safety of oilseeds and edible oils. Meanwhile, the technical challenges and future prospects of these non-destructive spectroscopic technologies are also discussed. Quality (philosophy) ; Business ; Environmental science ; Engineering ; Physics Spectroscopy and Chemometric Analyses
Authors: Tzu-Ching Tai | S. Bao | Bharath Kumar Boyanapalli | Cheng‐Chien Kuo | Chien‐Kuo Chang
Source: Applied Sciences (article, 2025-02-17)
Field: Materials Science — Materials Chemistry

Partial Discharge Inception and Breakdown Voltage Analysis in PCB-Based Electrode Structures This paper investigates partial discharge (PD) characteristics and breakdown behavior in five electrode configurations fabricated from fiberglass circuit boards, including parallel electrode (PE), triangular electrode (TE), right-angled electrode (RE), and two floating electrode (FE) designs with 0.3 and 0.5 mm insulation gaps. The electrodes are tested according to IEC 60270:2000 using a commercial device with eight samples per configuration. Key parameters such as the PD inception voltage (PDIV) and breakdown voltage are measured. The TE configuration exhibited the highest breakdown voltage of 14.9 kV, with a PDIV of 6.13 kV and RPDIV of 8.04 kV, indicating strong dielectric properties. The RE configuration showed a PDIV of 7 kV, RPDIV of 8.5 kV, and a breakdown voltage of 13.3 kV. The FE_0.5 mm sample exhibited surface discharges, whereas the FE_0.3 mm sample experienced breakdown at 18 kV with an average breakdown voltage of 15.3 kV. The results indicate that the electrode geometry and insulation spacing strongly influence PD behavior and breakdown resilience. The phase-resolved partial discharge (PRPD) patterns at PDIV and breakdown provide further understanding of the dielectric stability. These findings offer critical insights into designing insulation systems under electrical stress. Partial discharge ; Materials science ; Electrical engineering ; Voltage ; Engineering High voltage insulation and dielectric phenomena ; Electrostatic Discharge in Electronics ; Electromagnetic Compatibility and Noise Suppression
Authors: Amr S. Abou El‐Ela | Chao Zhang | Asim Munawar | Xuan Chen | Yixin Zhang | Eric Siaw Ntiri | Modhi O. Alotaibi | Amr Elkelish | Suhailah S. Al-Jameel | Wenwu Zhou | Zeng‐Rong Zhu
Source: Chemical and Biological Technologies in Agriculture (article, 2025-05-10)
Field: Materials Science — Materials Chemistry

Physiological, biochemical and histological effects of abscisic acid-synthesized silver and copper oxide nanoparticles on the potato tuber moth, Phthorimaea operculella The potato tuber moth (PTM), Phthorimaea operculella, poses a significant threat to potato cultivation in tropical and subtropical regions. Chemical control, though widely used, poses risks to human and environmental health, necessitating safer alternatives. This study investigated eco-friendly alternatives by synthesizing silver (AgNPs) and copper oxide nanoparticles (CuONPs) using abscisic acid (ABA). Characterization through UV–vis spectroscopy, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray, X-ray diffraction, Fourier transform infrared spectroscopy, and zeta potential analysis confirmed the successful synthesis of AgNPs with a uniform size of 35.8 nm and a plasmon resonance at 430 nm. CuONPs displayed a porous, rugby sheet-like structure, with a thickness of 68.5 nm and a 290 nm absorption peak. The effectiveness of the nanoparticles against PTM larvae was assessed through larval spraying and leaf-dipping. Larval spraying outperformed leaf-dipping, with median lethal concentration (LC50) values of 670 mg/L for AgNPs and 1320 mg/L for CuONPs. AgNPs and CuONPs significantly altered digestive enzyme activities in treated PTM larvae, reducing α-amylase, β-glucosidase, lipase, protease, and trypsin, and increasing acid phosphatase activity. Histological studies revealed damage to the larvae's midgut epithelial layer and peritrophic membrane, resulting in nuclei dispersion. Our study shows a cost-effective, minimal-impact strategy for synthesizing AgNPs and CuONPs, which can serve as a potential approach for managing PTM. Phthorimaea operculella ; Abscisic acid ; Plant physiology ; Gelechiidae ; Copper ; Biology ; Chemistry ; Botany ; Agronomy ; PEST analysis ; Biochemistry Nanoparticles: synthesis and applications ; Insect Pest Control Strategies ; Moringa oleifera research and applications
Authors: Chris Jones | Jenny W. Fothergill | Rainier Barrett | L. Ghanbari | Nicholas R. Enos | Olivia D. McNair | Jeffrey S. Wiggins | Eric Jankowski
Source: Polymers (article, 2025-01-05)
Field: Materials Science — Polymers and Plastics

Representing Structural Isomer Effects in a Coarse-Grain Model of Poly(Ether Ketone Ketone) Carbon-fiber composites with thermoplastic matrices offer many processing and performance benefits in aerospace applications, but the long relaxation times of polymers make it difficult to predict how the structure of the matrix depends on its chemistry and how it was processed. Coarse-grained models of polymers can enable access to these long-time dynamics, but can have limited applicability outside the systems and state points that they are validated against. Here we develop and validate a minimal coarse-grained model of the aerospace thermoplastic poly(etherketoneketone) (PEKK). We use multistate iterative Boltzmann inversion to learn potentials with transferability across thermodynamic states relevant to PEKK processing. We introduce tabulated EKK angle potentials to represent the ratio of terephthalic (T) and isophthalic (I) acid precursor amounts, and validate against rheological experiments: The glass transition temperature is independent to T/I, but chain relaxation and melting temperature is. In sum we demonstrate a simple, validated model of PEKK that offers 15× performance speedups over united atom representations that enables studying thermoplastic processing-structure-property-performance relationships. Materials science ; Thermoplastic ; Glass transition ; Polymer ; Welding ; Thermosetting polymer ; Aerospace ; Relaxation (psychology) ; Polyether ether ketone ; Ether ; Crystallization ; Stress relaxation ; Ketone ; Composite material ; Chemical engineering ; Creep ; Chemistry ; Organic chemistry ; Aerospace engineering Polymer crystallization and properties ; Composite Material Mechanics ; Graphene research and applications