jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kiswanto Kiswanto | Hadiyanto Hadiyanto | Eko Sediyono
Source: Ingénierie des systèmes d information (article, 2025-04-30)
Field: Chemistry — Analytical Chemistry

Evaluation of Wavelet and Gray Level Co-Occurrence Matrix Combination Model for Texture Image Feature Extraction of Various Types of Meat The purpose of this research is to evaluate the combination of Wavelet and Gray Level Cooccurrence Matrix (GLCM) methods in extracting texture features of various types of meat, including beef, buffalo, lamb, horse, and pork.This method integrates the advantages of wavelet transform in capturing spatial-frequency features with GLCM's ability to analyse statistical texture patterns.The classification process is carried out using the k-Nearest Neighbors (k-NN) algorithm, and the model accuracy is evaluated using a confusion matrix.The research results show that the combination of Wavelet and GLCM features significantly improves the classification performance, with an average accuracy of 97.2%.Further analysis shows that the integration of these two methods provides better classification results between fresh, frozen, and rotten categories for each type of meat.Although there are some classification errors, the overall results show the reliability and effectiveness of this approach.Further research can explore parameter optimization or the integration of more sophisticated classification algorithms. Co-occurrence matrix ; Pattern recognition (psychology) ; Gray level ; Artificial intelligence ; Texture (cosmology) ; Gray (unit) ; Wavelet ; Feature extraction ; Image texture ; Computer science ; Computer vision ; Image (mathematics) ; Mathematics ; Image processing ; Medicine ; Radiology Spectroscopy and Chemometric Analyses
Authors: Danila Lissovoy | Alina Zakeryanova | Rustem Orazbayev | Tomiris Rakhimzhanova | Michael Lewis | Hüseyin Atakan Varol | Mei‐Yen Chan
Source: Foods (article, 2025-10-22)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging for Quality Assessment of Processed Foods: A Case Study on Sugar Content in Apple Jam Apple jam is a widely used all-season product. The quality of the jam is closely related to its sugar concentration, which affects its taste, texture, shelf life, and legal compliance with production requirements. Although traditional methods for measuring sugar, such as titration, enzymatic methods, and chromatography, are accurate, they are also invasive, destructive, and unsuitable for rapid screening. This study investigates a non-destructive and non-invasive alternative method that uses hyperspectral imaging (HSI) in combination with machine learning to estimate the sugar content in processed apple products. Eight cultivars were selected from the Central Asian region, recognized as the origin of apples and known for its rich diversity of apple cultivars. A total of 88 jam samples were prepared with sugar concentrations ranging from 25% to 75%. For each sample, several hyperspectral images were obtained using a visible-to-near-infrared (VNIR) camera. The acquired spectral data were then processed and analyzed using regression models, including the support vector machine (SVM), eXtreme gradient boosting (XGBoost), and a one-dimensional residual network (1D ResNet). Among them, ResNet achieved the highest prediction accuracy of R<sup>2</sup> = 0.948. The results highlight the potential of HSI and machine learning for a fast, accurate, and non-invasive assessment of the sugar content in processed foods. Hyperspectral imaging ; Sugar ; Boosting (machine learning) ; Residual ; Quality assessment ; Extreme learning machine Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Authors: Ruiyi Murong
Source: Frontiers in Business Economics and Management (article, 2025-07-10)
Field: Chemistry — Analytical Chemistry

Hyperspectral Technology in Agricultural Soil Heavy Metal Detection: Current Applications and Future Directions Soil heavy metal pollution has become a global environmental issue, posing serious threats to agricultural productivity, food safety, and human health. The emergence of hyperspectral technology provides new approaches for rapid and non-destructive detection of soil heavy metals. This paper reviews the application of hyperspectral technology in identifying heavy metal elements in agricultural soils. First, it introduces the fundamental principles of hyperspectral technology, including the relationship between soil spectral characteristics and heavy metal elements, hyperspectral data acquisition and processing, as well as spectral feature extraction and analysis methods. Then, the research progress in agricultural soil heavy metal identification using hyperspectral technology is detailed from three aspects: laboratory studies, field applications, and integrated applications with other technologies. Studies demonstrate that hyperspectral technology can achieve high-precision prediction of soil heavy metal content through interactions between soil components and heavy metals, utilizing methods such as Continuous Wavelet Transform (CWT) combined with Radial Basis Function (RBF) models. However, practical applications still face challenges including soil background interference, data complexity, and high operational costs. Finally, the paper discusses the advantages and limitations of hyperspectral technology in agricultural soil heavy metal identification, and prospects future development directions including technological improvements and innovations, expansion of application scopes, and establishment of standardization and normalization. Future research should focus on enhancing sensor resolution, optimizing algorithms, and establishing unified spectral databases to improve model generalizability, while promoting widespread application of hyperspectral technology in agricultural soil heavy metal monitoring through multidisciplinary collaboration. Hyperspectral imaging ; Current (fluid) ; Agriculture ; Environmental science ; Remote sensing ; Agricultural engineering ; Engineering ; Geology ; Geography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Beatrice Cipriani | Hender López
Source: ArXiv.org (preprint, 2025-10-09)
Field: Materials Science — Biomaterials

Impact of protein corona morphology on nanoparticle diffusion in biological fluids: insights from a mesoscale approach Nanoparticles (NPs) demonstrate considerable potential in medical applications, including targeted drug delivery and diagnostic probes. However, their efficacy depends on their ability to navigate through the complex biological environments inside living organisms. In such environments, NPs interact with a dense mixture of biomolecules, which can reduce their mobility and hinder diffusion. Understanding the factors influencing NP diffusion in these environments is key to improving nanomedicine design and predicting toxicological effects. In this study, we propose a computational approach to model NP diffusion in crowded environments. We introduce a mesoscale model that accounts for the combined effects of the Protein Corona (PC) and the crowded medium on NP movement. By including volume-exclusion interactions and modelling the PC both explicitly and implicitly, we identify key macromolecular descriptors that affect NP diffusion. Our results show that the morphology of the PC can significantly affect the diffusion of NPs, and the role of the occupied volume fraction and the size ratio between tracers and crowders are analysed. The results also show that approximating large macromolecular assemblies with a hydrodynamic single-sphere model leads to inexact diffusion estimates. To overcome the limitations of single-sphere representations, a strategy for an accurate parametrization of NP-PC systems using a single-sphere model is presented. Mesoscale meteorology ; Diffusion ; Nanomedicine ; Parametrization (atmospheric modeling) ; Nanoparticle ; Macromolecule ; Corona (planetary geology) Nanoparticle-Based Drug Delivery ; Electrostatics and Colloid Interactions ; Molecular Communication and Nanonetworks
Authors: M. S. Thirumalai | Vinod K. Sharma | Kuldeep Tripathi | S. Rajkumar | Dhammaprakash Pandhari Wankhede | Haritha Bollinedi | Tanay Joshi | Rakesh Bhardwaj | Jai Chand Rana | Amritbir Riar
Source: Scientific Reports (article, 2025-11-25)
Field: Chemistry — Analytical Chemistry

NIRS chemometrics for rapid nutritional profiling of vegetable pea (Pisum sativum L.) germplasm Vegetable pea (Pisum sativum L.) is a nutritionally rich food source with a balanced profile of macronutrients and micronutrients, contributing multiple health benefits and plays a crucial role in combating nutritional deficiencies. Its nutritional diversity encompassing high range of protein, starch, soluble sugars, and phenolic content, renders it an ideal candidate for nutritional profiling, which is essential for mining Nutri-dense accessions. Near-infrared reflectance spectroscopy (NIRS) is a valuable alternative to conventional methods for nutritional profiling, offering rapid, accurate, less laborious, cost-effective, and non-destructive analysis with the capability to measure multiple parameters simultaneously for large-scale germplasms. This investigation developed NIRS prediction models based on Modified Partial Least Square (mPLS) regression for moisture content, protein, starch, amylose, total dietary fibre (TDF), phenols, total soluble sugars (TSS), and phytic acid with spectral pre-processing done by standard normal variate (SNV) and detrending (DT) using 90 vegetable pea (both marketable and mature stages) dried seed flour. The best-performing models were developed for moisture content (0.938, 0.469, 3.989), protein (0.931, 0.709, 3.063), starch (0.814, 1.312, 2.317), amylose (0.847, 0.646, 2.556), TDF (0.932, 0.652, 3.473), phenol (0.925, 0.078, 3.538), TSS (0.918, 0.231, 3.494), and phytic acid (0.898, 0.095, 2.358) corresponding to coefficient of determination (RSQ), corrected standard error of prediction (SEP(C)), and ratio of performance to deviation (RPD), respectively. This study presents the first report on the development of NIRS based prediction models using MPLS method for multi-trait assessment across different developmental stages in diverse vegetable pea germplasm, exhibiting high-throughput capability of the models in an economical and precise way. Germplasm ; Sativum ; Phytic acid ; Biplot ; Starch ; Amylose ; Chemometrics ; Coefficient of determination ; Metabolomics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food composition and properties
Authors: Estari Mamidala | Poornima Munipally
Source: Research Square (preprint, 2025-06-03)
Field: Materials Science — Biomaterials

Inhibitory potential of α-Amyrin from Calotropis procera against HIV-1 reverse transcriptase: insights from in silico and in vitro assays Calotropis procera ; In silico ; In vitro ; Reverse transcriptase ; Human immunodeficiency virus (HIV) ; Inhibitory postsynaptic potential ; Chemistry ; Biology ; Computational biology ; Virology ; Biochemistry ; Botany ; Polymerase chain reaction ; Neuroscience Phytochemistry and Bioactive Compounds ; Phytochemical compounds biological activities ; Phytochemistry and Bioactivity Studies
Authors: Vidya A. Patil | Shivraj Jadhav | Sunil Mahajan
Source: Prospects in Pharmaceutical Sciences (article, 2025-10-01)
Field: Chemistry — Physical and Theoretical Chemistry

“DEVELOPMENT AND OPTIMIZATION OF DANAZOL CO-CRYSTAL LOADED TABLET TO TREAT ENDOMETRIOSIS” This study aimed to develop, optimize, and characterize Danazol co-crystal-loaded tablets to enhance solubility and dissolution, thereby improving therapeutic efficacy in the treatment of endometriosis. Danazol co-crystals were prepared using various pharmaceutically acceptable co-formers via the solvent evaporation method and characterized using Fourier-transform infrared spectroscopy (FTIR), differential scanning calorimetry (DSC), and X-ray diffraction (XRD). Among the systems studied, Danazol-malonic acid co-crystals (1:2 molar ratio) demonstrated the greatest solubility enhancement (11.42 ± 0.53 μg/mL), corresponding to a 13.76-fold increase compared to pure Danazol. The formation of a novel crystalline phase was confirmed by distinct XRD peaks at 2θ values of 19° and 21°. Co-crystals were incorporated into tablets using direct compression and optimized using a 3² factorial design, with sodium croscarmellose (8–24 mg) and polyvinylpyrrolidone K-30 (4–20 mg) as independent variables. The formulations were assessed for pre-compression characteristics, tablet quality parameters, disintegration time, in vitro drug release, and stability. Statistical analysis revealed strong predictive models for disintegration time (R² = 0.9971) and drug release (R² = 0.9483). The optimized formulation (VF7) containing 24 mg sodium croscarmellose and 4 mg PVP K-30 exhibited rapid disintegration (74.0 ± 3.2 s) and significantly improved dissolution (95.8 ± 2.0% at 60 min), outperforming the marketed formulation (75.2 ± 2.7%). The optimized tablets remained stable under accelerated conditions (40°C/75% RH) for three months. These findings highlight the potential of co-crystallization and formulation optimization to overcome solubility challenges, offering a promising strategy for improving the clinical performance of Danazol in endometriosis therapy. Croscarmellose sodium ; Danazol ; Solubility ; Polyvinylpyrrolidone ; Differential scanning calorimetry ; Factorial experiment ; Friability ; Dissolution Crystallography and molecular interactions ; Drug Solubulity and Delivery Systems ; Crystallization and Solubility Studies
Authors: S. Chandra | M.K. Samal | Vivek Chavan
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2025-09-09)
Field: Materials Science — Materials Chemistry

Twin formation through repeated dislocation impingement on phase boundary in Ni-based superalloys using atomistic simulations Abstract Formation of stacking faults and deformation twins in the matrix as well as precipitate phase of Ni-based superalloys governs their proficiency in high-temperature service. In view of this phenomena, the present work reports a qualitatively different pathway for the formation of twins in $$\gamma ^\prime$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>γ</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:math> phase during plastic deformation. The involved mechanism relies on the prevalence of multiple matrix dislocations on adjacent slip planes in the vicinity of $$\gamma -\gamma ^\prime$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>γ</mml:mi> <mml:mo>-</mml:mo> <mml:msup> <mml:mi>γ</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:mrow> </mml:math> interface along with sufficient magnitude of glide stress and temperature. An attempt is also made to compare the results with experiments available in the literature. It is envisioned that this work shall open up novel avenues for probing other deformation pathways also that may lead to formation of twins in $$\gamma ^\prime$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mi>γ</mml:mi> <mml:mo>′</mml:mo> </mml:msup> </mml:math> phase of Ni-based superalloys. This may ultimately lead to an advanced understanding of deformation mechanisms in Ni-based superalloys at high temperatures. Graphical Abstract Superalloy ; Crystal twinning ; Deformation (meteorology) ; Dislocation ; Deformation mechanism ; Phase (matter) ; Work (physics) ; Slip (aerodynamics) Microstructure and mechanical properties ; High Temperature Alloys and Creep ; Metallurgy and Material Forming
Authors: Slim Hamdi | Kais Loukil | Adnène Boubaker | Hichem Snoussi | Mohamed Abid
Source: Preprints.org (preprint, 2025-09-23)
Field: Chemistry — Analytical Chemistry

Hybrid AI Pipeline for Industrial Detection of Internal Potato Defects Using 2D RGB Imaging The internal quality assessment of potato tubers is a crucial task in agro-industrial processing. Traditional methods struggle to detect internal defects such as hollow heart, internal bruises, and insect galleries using only surface features. We present a novel, fully modular hybrid AI architecture designed for defect detection using RGB images of potato slices, suitable for integration in industrial sorting lines. Our pipeline combines high-recall multi-threshold YOLO detection, contextual patch validation using ResNet, precise segmentation via the Segment Anything Model (SAM), and skin-contact analysis using VGG16 with a Random Forest classifier. Experimental results on a labeled dataset of over 6000 annotated instances show a recall above 90\% and precision near 100\% for most defect classes. The approach offers both robustness and interpretability, outperforming previous methods that rely on costly hyperspectral or MRI techniques. This system is scalable, explainable, and compatible with existing 2D imaging hardware. RGB color model ; Hyperspectral imaging ; Pipeline (software) ; Robustness (evolution) ; Segmentation ; Modular design ; Object detection ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Tony Sumaryada | Sarah Aribah Miftahulfallah | Sitti Yani
Source: International Journal of Nanoelectronics and Materials (IJNeaM) (article, 2025-11-05)
Field: Chemistry — Organic Chemistry

Mesoscopic view of spike glycoprotein inhibition by various size of fullerenes Fullerene molecules, a class of allotropic carbon nanomaterials, were investigated for their potential to inhibit SARS-CoV-2 by targeting its spike glycoprotein through molecular docking simulations. Both blind and targeted docking methods were employed to evaluate interactions between various fullerene sizes (C20, C28, C60, C78, C84) and the spike glycoprotein. In the blind docking method, with the exception of C84, all fullerene interactions are distributed over the entire surface of the spike glycoprotein. In the targeted docking method, C78 and C84 interactions are closer to the actual binding sites of angiotensin-converting enzyme 2 (ACE2) on the spike glycoprotein. The most negative binding affinity value was found for fullerene C84, with a value of -15.9 kcal/mol, primarily via hydrophobic interactions. Binding affinity correlated positively with fullerene size; larger fullerenes demonstrated a greater capacity to obstruct the ACE2 binding site. Smaller fullerenes (C20, C28) were ineffective, binding at unrelated regions. C60 showed moderate potential, with 85% of its binding occurring at the ACE2 site. In contrast, C78 and C84 exhibited 100% of their docking directly at the ACE2 binding site, indicating stronger inhibition potential. These findings underscore the significance of fullerene size in enhancing spike protein interaction and suggest that larger fullerenes, especially C84, may serve as promising candidates for SARS-CoV-2 entry inhibition. Fullerene ; Docking (animal) ; Binding site ; Plasma protein binding ; Molecular dynamics ; Spike (software development) ; Molecular binding ; Intramolecular force Fullerene Chemistry and Applications ; Graphene and Nanomaterials Applications ; Boron and Carbon Nanomaterials Research
Authors: Saeed Heshmatian | M. Aligholami | S. Shafiei | I G Madiba | Shohreh Azizi | Ahmed A. Hussein | Malik Maaza
Source: RSC Advances (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Size effect on thermal conductivity and stability of TiO <sub>2</sub> /MWCNT-based hybrid nanofluids synthesized <i>via</i> probe ultrasonication This study reports the enhancement of thermal conductivity in hybrid TiO<sub>2</sub> grafted onto multi-wall carbon nanotubes (MWCNTs) dispersed in an ethylene glycol nanofluid synthesized by a scalable probe-ultrasonication process. The hybrid nanofluids were formulated at ultra-low loadings; MWCNT = 0.001 wt% (fixed) and TiO<sub>2</sub> = 0.001-0.01 wt% (15 nm and 30 nm). The 15 nm TiO<sub>2</sub> sample at 0.01 wt% achieved 16.7% thermal conductivity enhancement at 70 °C while maintaining >4 weeks stability. To the best of our knowledge, this is the first report achieving double-digit conductivity improvement at ≤0.01 wt% solids using a surfactant-free, scalable probe-ultrasonication route. Homogeneous and stable TiO<sub>2</sub>/MWCNT nanofluids were produced using a surfactant-free approach, and their performance was validated through Raman spectroscopy, Zetasizer, TEM, and UV-Vis analyses. Formulations with ultra-low loadings, MWCNT = 0.001 wt% (fixed) and TiO<sub>2</sub> = 0.001-0.01 wt% (15 or 30 nm), were investigated. The sample containing 15 nm TiO<sub>2</sub> at 0.01 wt% exhibited a reproducible 16.7% thermal-conductivity enhancement at 70 °C and maintained colloidal stability for over four weeks. Such a high enhancement at extremely low solid content in an ethylene glycol matrix, achieved through a surfactant-free and scalable ultrasonication route, has not been previously reported. Nanofluid ; Sonication ; Ethylene glycol ; Thermal conductivity ; Carbon nanotube ; Raman spectroscopy ; Thermal stability Thermal properties of materials ; Nanofluid Flow and Heat Transfer ; Solar-Powered Water Purification Methods
Authors: Li Zhang | Meng Song | Huaping Guo | Yange Sun | Xinxia Wang
Source: Materials (article, 2025-04-10)
Field: Materials Science — Materials Chemistry

Insulator Defect Detection via a Residual Denoising Diffusion Mechanism Insulators are critical components of transmission lines, and defective insulators pose a serious threat to the safety of power supply systems. Timely detection of these defects is crucial to prevent catastrophic consequences for human lives and property. However, insulator defects are often small and easily affected by the noise of rain, fog, sunlight, dirt, and other pollutants, making detection challenging. We observe that diffusion models learn data distribution by progressively introducing noise and subsequently performing denoising. The progressive denoising mechanism can naturally simulate the randomness of environmental noise. Based on this observation, we treat the localization of insulator defects as a denoising-based recovery process, where the true defect bounding boxes are progressively reconstructed from noisy representations. To this end, we propose a novel diffusion-based Insulator Defect Detector (IDDet) that is specifically designed to handle complex environmental noise. IDDet introduces noise to the true bounding boxes to generate noisy target boxes with random distributions and is then trained to recover the true bounding boxes from these noisy representations through a residual denoising diffusion mechanism. For the inference stage, IDDet refines the defect location from a random noise bounding box by gradually removing the noise, ultimately achieving the task of precisely locating the defect in the image. Experimental results show that IDDet significantly improves detection capability in noisy environments, achieving the best mean average precision (mAP) of 92.3%, confirming the feasibility and effectiveness of our approach. Noise reduction ; Insulator (electricity) ; Randomness ; Noise (video) ; Computer science ; Bounding overwatch ; Artificial intelligence ; Residual ; Step detection ; Minimum bounding box ; Computer vision ; Algorithm ; Engineering ; Mathematics ; Statistics ; Electrical engineering High voltage insulation and dielectric phenomena ; Advanced Neural Network Applications ; Infrastructure Maintenance and Monitoring