jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Institut Teknologi Perkebunan Pelalawan Indonesia | Riri Nasirly | Fadli Arsy | Qolby Rohatul
Source: Journal of Ocean Mechanical and Aerospace -science and engineering- (JOMAse) (article, 2025-03-30)
Field: Chemistry — Inorganic Chemistry

Comparison of Machine Learning Algorithms for Palm Oil Fresh Fruit Bunch (FFB) Ripeness Determining the maturity level of FFB is crucial because it directly affects the quality and quantity of palm oil produced. Ripe FFB has high oil content, ensuring better-quality palm oil. Traditional methods include visual inspection, manual sampling, and physical testing, which are labor-intensive and subjective and can result in inconsistencies and errors. Machine learning algorithms can analyze datasets quickly and accurately, while also identifying patterns and features that are not easily visible to humans. Therefore, the aim of this study is to examine and evaluate the effectiveness of machine learning algorithm classifiers in determining oil palm FFB ripeness. The algorithms used in this research for classification analysis are Logistic Regression, Support Vector Machines (SVM), k-Nearest Neighbors (k-NN), Neural Networks (NN), and Naïve Bayes. In this research, the analysis was carried out using the Orange data mining tool, which carried out data analysis and data visualization. The results of performance evaluation was tested, and assessment (cross-validation accuracy estimation), ROC (Receiver Operating Characteristic) analysis, and confusion matrix. The best models were Neural Network, Logistic Regression and SVM. Naïve Bayes appears to have lower performance. The results using the prediction widget show that logistic regressing has the best accuracy. Ripeness ; Palm oil ; Palm ; Mathematics ; Agricultural engineering ; Algorithm ; Horticulture ; Computer science ; Artificial intelligence ; Machine learning ; Environmental science ; Engineering ; Agroforestry ; Biology ; Physics ; Ripening Coconut Research and Applications ; Oil Palm Production and Sustainability ; Spectroscopy and Chemometric Analyses
Authors: R K Rathod | A K Biswas | P K Mandal | Judy Lalthanmawii | Shubham Mandhale
Source: Journal of Meat Science (article, 2025-12-23)
Field: Chemistry — Analytical Chemistry

Applications of Hyperspectral Imaging in Meat Quality and Safety Evaluation Meat products are highly susceptible to safety and quality issues due to their complex structure, diverse processing techniques, and intricate supply chains. Traditional analytical methods for assessing meat quality are often time consuming, labor-intensive, and destructive, making them unsuitable for real time quality control in modern food production environments. Hyperspectral Imaging (HSI), an emerging non-destructive and rapid analytical technology, integrates imaging and spectroscopy to detect both physical and chemical attributes of meat. This review provides a comprehensive overview of the current applications and research developments of HSI in meat quality and safety evaluation. It explores the potential of HSI in detecting microbiological contamination, assessing quality parameters, and enhancing traceability throughout the supply chain. Furthermore, it evaluates the advancements in HSI hardware and software, and their readiness for integration into industrial settings under the framework of Food Industry 4.0. The future prospects of HSI for predictive and real-time quality control, enabling large-scale, automated meat inspection systems, are also discussed. This review highlights HSI as a promising tool that can shift the meat industry from reactive to proactive quality assurance practices. Traceability ; Hyperspectral imaging ; Quality assurance ; Quality (philosophy) ; Food safety ; Meat packing industry ; Food quality Spectroscopy and Chemometric Analyses ; Listeria monocytogenes in Food Safety ; Biopolymer Synthesis and Applications
Authors: Muhammad Kashif | Aziz Ur Rahman | Iqra Ramzan | Madiha Muzammil | Awais Ahmed Uttra | Rida Kainat
Source: Indus journal of bioscience research. (article, 2025-12-30)
Field: Materials Science — Biomaterials

Clinical Applications of Nanotechnology in Drug Delivery for Surgical Patients: Advances in Targeting and Controlled Release This quantitative study examined the clinical applications of nanotechnology-based drug delivery systems in surgical patients, with emphasis on advances in targeted delivery and controlled drug release. A prospective comparative research design was employed in the surgical and perioperative units of a tertiary care hospital, involving a total sample of 120 adult surgical patients selected through consecutive sampling. Participants were divided into two equal groups receiving either nanotechnology-based drug delivery systems or conventional drug formulations. Quantitative data were collected on targeted drug delivery effectiveness, therapeutic outcomes, and adverse effect occurrence. Statistical analyses, including chi-square tests and independent samples t tests, were used to evaluate associations and group differences. The findings revealed that nanotechnology-based drug delivery systems achieved significantly higher rates of effective drug targeting and were associated with reduced adverse drug effects compared to conventional methods (p &lt; .01). Controlled drug release through nanotechnology demonstrated improved therapeutic performance and enhanced drug safety in the postoperative period. These results support the clinical effectiveness of nanotechnology-enabled drug delivery in surgical care and highlight its potential role in advancing precision pharmacotherapy and improving patient outcomes. Drug delivery ; Drug ; Targeted drug delivery ; Adverse effect ; Perioperative ; Clinical trial ; Pharmacotherapy ; Controlled release Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoparticles: synthesis and applications
Authors: Yichen Li | Xiaodong Sun | H. Shen | Xinhua Hu
Source: Scientific Reports (article, 2025-05-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Impedance-matched metadevices with a single matrix material Artificial periodic structures including photonic crystals (PCs) and metamaterials have been widely employed to construct metadevices, whereas their effective refractive indices n<sub>e</sub> and relative impedances Z<sub>r</sub> are usually coupled together and hard to design separately. Here, we present a method for flexibly tuning the indices n<sub>e</sub> and impedances Z<sub>r</sub> of PCs. The unit cell of PC consists of a dielectric matrix with multiple air holes symmetrically distributed around the center of the unit cell. We find that the index n<sub>e</sub> of the PC mainly depends on the filling ratio of the dielectric, while its impedance Z<sub>r</sub> relies on the position of the holes. When the holes have an appropriate distance from the center of the unit cell, matched impedances (Z<sub>r</sub> = 1) can be achieved for PCs with different indices n<sub>e</sub>. As a result, impedance-matched metadevices, such as generalized Mikaelian lenses with high efficiency (> 96%), can be constructed even with high refractive indices in their matrices. Computer science ; Matrix (chemical analysis) ; Bone matrix ; Medicine ; Materials science ; Composite material ; Anatomy Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies ; Photonic Crystals and Applications
Authors: Mohamed Eltohamy | Ibrahim M. Etman | Ramadan A. Geioushy | Hanaa A. El Kazazz | Karim Badawy | Osama A. Fouad
Source: Research Square (preprint, 2025-10-10)
Field: Materials Science — Ceramics and Composites

Energy-saving low-E with dual IR-reflective and UV-protective TiO2-ZnO- SiO2 coated glass Nanocrystalline material ; Emissivity ; Infrared ; Amorphous solid ; Low emissivity ; Visible spectrum ; Ultraviolet ; Layer (electronics) Glass properties and applications
Authors: Raluca Cimpean | Daniel Berean | Anca Becze | Nastasia Belc | Sorin Daniel Dan
Source: Current Research in Nutrition and Food Science Journal (article, 2025-11-20)
Field: Chemistry — Analytical Chemistry

Comparative Evaluation of Near-Infrared Spectroscopy and Classical Reference Methods for Nutritional Analysis of Fast-Food Products Nutritional profiling of fast-food products is essential for both consumer transparency and industrial quality control, yet conventional methods such as Kjeldahl, Soxhlet, and gravimetric assays are time consuming and require specialized laboratories. Near-infrared (NIR) spectroscopy offers a rapid and non-destructive alternative, but its reliability in complex food matrices such as burgers and pizzas remains underexplored. In this study, four types of burgers (10 samples each, three replicates) and thirteen types of pizzas (three replicates each) were analyzed using both reference methods and a Tango FT-NIR spectrometer to determine protein, fat, carbohydrates, sugars, ash, dietary fiber, and dry matter. Results indicated excellent agreement between NIR and classical methods for major components, including protein, fat, carbohydrates, and dry matter, with no statistically significant differences (p &gt; 0.05). However, sugars showed systematic deviations, with overestimation in burgers (p &lt; 0.05) and underestimation in pizzas (p &lt; 0.01). Ash content also differed significantly in pizzas (p &lt; 0.05), while dietary fiber showed the largest discrepancy, with NIR consistently underestimating values (p &lt; 0.05). Despite these limitations, NIR displayed high repeatability, with standard deviations below 0.2% for most parameters. These findings confirm NIR as a robust and efficient tool for routine nutritional screening in fast food, though reference methods remain indispensable for sugars and dietary fiber. Gravimetric analysis ; Dietary fiber ; Food products ; Quality assessment ; Relative standard deviation ; Systematic error ; Transparency (behavior) Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Food composition and properties
Authors: Mohamad Danial Hairul Anuar | Thangavel Lakshmipriya | Subash C. B. Gopinath | Tijjani Adam | Mohammed A. Mohamme | Evan Salim | Makram A. Fakhri
Source: International Journal of Nanoelectronics and Materials (IJNeaM) (article, 2025-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Shape and Size Dependent Sensing Enhancement by Gold Nanomaterials Gold nanomaterials (GNs) of different shapes (nanospheres, nanorods, nanostars) and sizes exhibit properties to enhance the surface area. The surface plasmon resonance of GNs can be tuned by changing their shape and size, which affects the binding efficiency of target antigen and antibody interaction. The shape of GNs can also affect the orientation of attached antibodies and antigens, leading to improved specificity in sensing platforms. This study has investigated the morphological and optical properties of GNs, evaluated the impact of different sizes and shapes on ELISA surfaces, and optimized the conditions to enhance the sensitivity. The morphology and optical properties of GNs were studied using a 3D nanoprofiler and, UV-Visible spectrophotometer. Gold nanospheres (GNPs) with 10, 15 and 30 nm, nanorods with 500,700 and 980 nm, and gold nanostars with 60 nm were employed. Smaller GNPs may have higher binding capacities due to their increased surface area to volume ratio. The height and roughness of anti-spike protein antibody morphology increased when 10 nm GNPs were added, demonstrating attachment between spike-antibody molecules and GNPs. The attained limit of detection (LOD) is 1 pM, where the limit of quantification (LOQ) is 10 pM. Gold nanospheres and nanostars behave better than nanorods and provide good LOD and LOQ. Nanorod ; Surface plasmon resonance ; Nanomaterials ; Surface roughness ; Colloidal gold ; Plasmon ; Aspect ratio (aeronautics) ; Detection limit Gold and Silver Nanoparticles Synthesis and Applications ; Advanced Biosensing Techniques and Applications ; Biosensors and Analytical Detection
Authors: Indah Nurpriyanti | Elisa Sulistyorini | Mastuki Mastuki
Source: BULETIN FISIKA (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Magnetic Property Analysis ZnFe2O4 Magnetic Nanomaterial Derived From Iron Sand Using the Coprecipitation Magnetic Method Iron sand is a natural resource rich in iron oxide compounds such as Fe₃O₄, α-Fe₂O₃, and γ-Fe₂O₃, which have the potential to be developed into functional materials, particularly magnetic nanoparticles. This study aims to analyze the magnetic properties of ZnFe₂O₄ nanomaterials synthesized from iron sand using the coprecipitation method. The synthesis was carried out by dissolving iron sand in HCl, mixing with a ZnCl₂ solution and titrating with NH₄OH until a dark brown precipitate was formed. The precipitate was heated at 200 °C for 2 hours. Characterization was conducted using X-Ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Vibrating Sample Magnetometry (VSM). XRD results confirmed the presence of ZnFe₂O₄ phase by JCPDS No. 22-1012, with a crystallite size of 24.9 nm and a lattice parameter of 8.28 Å. SEM images revealed non-uniform particle morphology and agglomeration. VSM results showed a remanent magnetization (Mr) of 0.0016 emu, coercivity (Hc) of 314 Oe, and saturation magnetization (Ms) of 1.540 emu. Based on these results, the material exhibits soft ferromagnetic behavior consistent with the spinel structure of ZnFe₂O₄. Coprecipitation ; Coercivity ; Remanence ; Crystallite ; Iron oxide ; Scanning electron microscope ; Ferromagnetism ; Magnetometer ; Nanomaterials ; Spinel Magnetic Properties and Synthesis of Ferrites ; Magnesium Oxide Properties and Applications ; Nanoparticle-Based Drug Delivery
Authors: M. Aloisi | Gianna Rossi | Sandra Cecconi
Source: Antioxidants (review, 2025-08-29)
Field: Materials Science — Materials Chemistry

The Dual Role of Nanomaterials in Ovarian Cancer and Female Fertility as Anti- and Prooxidants Nanomaterials (NMs) are becoming increasingly important in biomedical applications, especially in reproductive biology and oncology. In this review, we examined the "double face" of NMs as prooxidants and antioxidants in relation to ovarian cancer and female fertility. NMs have been shown to reduce oxidative stress pathways in tumors, enhancing the effectiveness of chemotherapy and serving as carriers for drugs and compounds. They are also considered for their protective effects on female fertility by improving oocyte quality, maturation, and survival under various healthy and adverse conditions. However, certain NMs can induce oxidative stress, mitochondrial dysfunction, and ovarian tissue apoptosis when present in high concentrations or after prolonged exposure. These "double face" effects highlight the complex nature of NMs' concentration, shape, and biocompatibility. Although NMs show promise in cancer treatment and fertility preservation, a comprehensive assessment of their prooxidant potential is necessary for successful clinical application. Dual (grammatical number) ; Fertility ; Ovarian cancer ; Oncology ; Medicine ; Cancer ; Internal medicine ; Environmental health ; Population ; Art Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; Selenium in Biological Systems
Authors: Reham M. Aldahasi | Sahar S. Alghamdi | Nouf Albulushi | Hanin Alhuraibi | Rasha Saad Suliman | Arwa Alsubait | Amirah S. Alahmari | Afrah E. Mohammed
Source: Chemical and Biological Technologies in Agriculture (article, 2025-10-09)
Field: Materials Science — Materials Chemistry

Metabolite detection via QTOF-LCMS and computational analysis on Russelia equisetiformis exploring green nanotechnology and antifungal against phytopathogens This study explores Russelia equisetiformis as a sustainable source for the green synthesis of antifungal nanomaterials. In response to increasing phytopathogenic fungi and environmental issues, it focuses on the fabrication of manganese nanoparticles (MnONPs), nitrogen-doped carbon dots (N-CDs), and their nanocomposite (MnO NPs@N-CDs) using R. equisetiformis extract. The synthesized nanomaterials were characterized by UV–Vis spectrophotometry, FTIR, DLS, TEM, and EDX to confirm nanoparticle formation. Their antifungal effectiveness was evaluated against Sclerotinia sclerotiorum, Fusarium equiseti, and Fusarium venenatum. Additionally, bioactive metabolites were identified via LC–QTOF-MS, and their antifungal activity, pharmacokinetic profiles, and toxicity levels were assessed through computational tools. Nanomaterials produced from R. equisetiformis extract significantly reduced fungal growth, highlighting their potential as eco-friendly solutions for plant disease control. Most metabolites exhibited favorable safety profiles with minimal predicted cardiotoxicity and mutagenicity, indicating their promise as safe fungicides. R. equisetiformis provides compounds with effective antifungal activity and low toxicity, offering a promising, environmentally friendly alternative to conventional chemical fungicides for sustainable plant disease management. Further research is essential to expand their application in agriculture. Antifungal ; Fusarium ; Fungicide ; Metabolite ; Environmentally friendly ; Sclerotinia sclerotiorum ; Plant disease ; Nanomaterials Nanoparticles: synthesis and applications ; Advanced Nanomaterials in Catalysis ; Medicinal Plants and Neuroprotection
Authors: Isabel Ramos Galán | Juan José Cubillas | Ricardo de Córdoba | Lidia Ortega
Source: New Medit (article, 2025-10-02)
Field: Chemistry — Organic Chemistry

Olive oil price prediction with machine learning algorithms The main objective of this study is to develop a machine learning model to accurately predict the price at origin of Extra Virgin Olive Oil (EVOO) in Jaén (South Spain), addressing the growing importance of price prediction in the food sector. The methodology used integrates an exhaustive set of predictor variables obtained from official sources between 2011 and 2024. These variables range from historical base price, seasonal factors and production costs, to climatic conditions, economic indicators, global oil production, and early prediction of olive crop production. Several regression algorithms are applied and compared, including Linear Regression, SVM, Neural Networks, Random Forest, Gradient Boosting and K-Nearest Neighbors. The results highlight that ensemble models, particularly Gradient Boosting and Random Forest, show superior predictive ability and are more effective in capturing the complex non-linear relationships and market inflections that affect the price of EVOO. Crossvalidation confirms that the use of the multivariate ensemble consistently improves accuracy over using the base price alone, validating the relevance of the selected attributes and their ability to reflect market dynamics. A pattern of price behaviour is detected that is significantly influenced by weather conditions, crop production, energy costs, speculation and global markets, factors that the ensemble models manage to model effectively. Gradient boosting ; Boosting (machine learning) ; Random forest ; Artificial neural network ; Multivariate statistics ; Ensemble learning ; Ensemble forecasting ; Regression ; Predictive modelling Edible Oils Quality and Analysis ; Wine Industry and Tourism ; Stock Market Forecasting Methods
Authors: G.Alexandar Narkunam
Source: Journal of Information Systems Engineering & Management (article, 2025-05-01)
Field: Chemistry — Analytical Chemistry

Enhancing Agricultural Forecasting with an Ensemble Learning Approach for Broccoli Yield Prediction Accurate yield prediction for broccoli remains a critical challenge due to the complex, non-linear interactions between climatic variables, soil properties, and agronomic practices. Traditional statistical models often fail to capture these interactions, leading to suboptimal decision-making and resource inefficiencies for farmers. To overcome these limitations, this study proposes an advanced machine learning-based approach called the Broccoli Yield Prediction Ensemble Method (BYPEM), designed to improve prediction accuracy and agricultural planning. BYPEM integrates both bagging and boosting ensemble learning techniques for robust broccoli yield forecasting. The study begins with a comprehensive data preprocessing phase, including handling missing values, outlier removal, categorical variable encoding, and normalization. Feature selection is performed using backward elimination to retain the most relevant predictors. The dataset is split into training and test sets through stratified sampling to ensure balanced representation. In the model development phase, BYPEM applies bagging methods such as Random Forest Regressor and Extra Trees Regressor to reduce variance, and boosting methods such as Gradient Boosting Regressor (GBR), XGBoost, LightGBM, and CatBoost to minimize bias by iteratively improving predictions. Hyperparameter tuning further optimizes model performance. The models are evaluated using multiple metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R² score, and overall accuracy. Among all models, XGBoost achieves the highest predictive performance, confirming the effectiveness of the BYPEM framework in capturing complex yield dynamics. This research demonstrates how ensemble learning can support sustainable agriculture by enhancing precision in broccoli yield prediction and guiding data-driven farm management strategies. Yield (engineering) ; Ensemble learning ; Machine learning ; Agricultural engineering ; Computer science ; Agriculture ; Artificial intelligence ; Engineering ; Materials science ; Geography Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Iman Hussein AL-Qinani
Source: International Journal of Scientific Research in Science Engineering and Technology (article, 2025-01-27)
Field: Chemistry — Analytical Chemistry

Automatic Classification of Olive Leaves Disease Based on Adaptive Neuro-Fuzzy Inference System with Hybrid Features Modern economies in many nations owe a great deal to the production of crops. Foliar diseases may cause immense damage to many different types of crops. These crops include wheat, olive, fruit, and many more. New AI breakthroughs require more precise identification of olive leaf diseases. An adaptive neuro-fuzzy inference system (ANFIS) is suggested based on mixed features to determine whether olive leaves are healthy or infected. Images of olive leaves are processed in various ways to enhance them, such as increasing contrast and filtering to remove unwanted noise. Olive leaf segmentation is performed using the k-means method. Several methods are combined to create hybrid features, including statistical moment invariants, a feature histogram, and a grey-level co-occurrence matrix (GLCM). The obtained characteristics are analyzed in this study to measure their potential discriminative ability between healthy and diseased olive leaves. In conclusion, the proposed approach provides a more accurate and efficient result for categorization. The suggested model achieves a remarkable 98.5% accuracy in its predictions.7 Artificial intelligence ; Pattern recognition (psychology) ; Discriminative model ; Segmentation ; Adaptive neuro fuzzy inference system ; Fuzzy logic ; Computer science ; Identification (biology) ; Mathematics ; Machine learning ; Fuzzy control system ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Stéphane Calvez | Antoine Rouxel | Kumar Kinjalk | Antoine Monmayrant | E. Popov | Anne-Laure Fehrembach | Olivier Gauthier‐Lafaye
Source:  (article, 2025-03-19)
Field: Materials Science — Surfaces, Coatings and Films

Review on cavity-resonant integrated grating filters International audience Grating ; Optical filter ; Materials science ; Resonant cavity ; Band-pass filter ; Optics ; Optoelectronics ; Physics ; Laser Optical Coatings and Gratings ; Photonic and Optical Devices ; Advanced Fiber Optic Sensors