jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shagufta Rizwana | Manuj Kumar Hazarika
Source: Multidisciplinary Research Journal (article, 2025-04-28)
Field: Chemistry — Analytical Chemistry

Near Infrared Spectroscopy and Machine Learning for Non-Destructive Estimation of Ageing of Komal Chaul Komal Chaul is a traditional form of a parboiled rice product from rice varieties indigenous to the state of Assam and it constitutes a culturally significant dish served during the auspicious occasions. Ageing impacts its rehydration properties diminishing its value as a no-cooking rice product. To empower the consumers in selecting Komal Chaul of desired rehydration qualities, this study focused on developing a non-destructive tool based on near-infrared spectral data coupled with machine learning (ML) algorithm for distinguishing the aged Komal Chaul. An NIR spectral library of KomalChaul samples was created, covering the spectral range of 740–1050 nm for samples stored for a period up to one year under ambient conditions. The methodology involved spectral preprocessing to enhance data quality, followed by partial least squares (PLS) regression modeling to predict storage time. Statistical metrics, including regression coefficient (R²), relative error percentage (REP), and root mean squared error (RMSE), were used to validate the model. Feature selection based on coefficient weightage was performed to identify key wavelengths contributing to time prediction. Classification models, including LDA, KNN, CART, Naïve Bayes, SVM, and Random Forest, were employed to categorize samples into aging periods of 1, 3, and 6 months. Partial Least Squares (PLS) regression models predicted the ageing time with a validation score R2 of 0.897 and RMSE of 19.41 days. Optimized with wavelength selection, the PLS regression model achieved significant accuracy in estimating the ageing time, with a prediction score R2 of 0.89 and RMSE of 2.01 days. Similarly, using the same approach for cooking quality prediction resulted in satisfactory performance, achieving a validation R² of 0.79. Classification models further enhanced prediction accuracy, with the Random Forest model attaining the highest accuracy of 92% for six-month interval classifications. These results underscore the potential of integrating NIR spectroscopy and machine learning for efficient, non-destructive quality assessment of Komal Chaul, supporting its commercialization as a value-added traditional food product Ageing ; Spectroscopy ; Infrared ; Infrared spectroscopy ; Functional near-infrared spectroscopy ; Artificial intelligence ; Materials science ; Computer science ; Physics ; Optics ; Psychology ; Biology ; Astronomy ; Cognition ; Neuroscience Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Francesco Vicino | Francesco Paciolla | Tommaso Quartarella | Simone Pascuzzi
Source: Engineering for Rural Development (article, 2025-05-21)
Field: Chemistry — Analytical Chemistry

Analysis of images of vineyard with computer vision models for data understanding interpretation Abstract: The wine-growing sector represents one of the most important sectors of the agri-food system in the Italian region of Apulia. Suffice it to say that wine-growing alone has about 11 thousand farms and 600 wine cellars. The regional area allocated to this sector is about 89,000 hectares, which represents about 10% of the national figure. In addition, the data indicate that in recent years the production of wine from Puglia has followed an increasing trend, covering about 20% of the national total. However, it is the production of table grapes in this region that boasts 60% of all Italian production. The latest ISTAT data available show that, with over 24,000 hectares, Puglia is the first region for area destined to the cultivation of table grapes. To support this large agri-food chain there are investments related to the purchase of tangible and intangible assets aimed at improving product quality, greater organizational efficiency to increase market demand, and increased competitiveness. To achieve these objectives, agricultural practices related to agriculture 4.0 and precision farming are increasingly used. In this study we want to highlight how, starting from RGB images that can be captured by different devices (drone, robot or camera), it is possible to create a dataset to train computer vision models and obtain potentially usable data at an agronomic level, as a decision support. In particular, the study proposes a computer vision model trained with images of table grapes captured in a vineyard in Apulia (Italy). The objective is to have a greater understanding of the data available in the field from different devices, obtaining a more complete frame of the state of the art of the vineyard for more efficient agronomic management. Vineyard ; Computer science ; Interpretation (philosophy) ; Computer vision ; Artificial intelligence ; Computer graphics (images) ; Geography ; Archaeology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Horticultural and Viticultural Research
Authors: Muhammad Mubashir Rasheed | Javeria Javed | Nabgha Zafar | Irum Asif | Riad Kouzeiha | Ifra Ghori
Source: Insights-Journal of Health and Rehabilitation (article, 2025-12-15)
Field: Materials Science — Biomaterials

MULTIFUNCTIONAL NANOCARRIERS BASED TYROSINE KINASE INHIBITORS FOR OSTEOSARCOMA -- CURRENT TRENDS AND FUTURE PERSPECTIVES. A NARRATIVE REVIEW Background: Bone cancer, particularly osteosarcoma, is a highly aggressive malignancy affecting children and adolescents, often associated with poor prognosis due to resistance to conventional therapies. Recent advancements in targeted therapy, particularly the use of tyrosine kinase inhibitors (TKIs), offer new hope. However, systemic toxicity and poor tumor specificity remain significant clinical challenges. Objective: This narrative review aims to explore the potential of nanocarrier-based delivery systems to enhance the precision and efficacy of TKIs in the treatment of bone cancer. Main Discussion Points: The review discusses various classes of nanocarriers—including liposomes, polymeric nanoparticles, micelles, dendrimers, and metal-based systems—that have been investigated for TKI delivery. Special focus is given to stimuli-responsive and biomimetic nanocarriers, which are designed to respond to the bone tumor microenvironment, as well as to pediatric osteosarcoma applications. The role of artificial intelligence in optimizing nanocarrier design is also highlighted. Additionally, the influence of nanoparticle size on cellular uptake, as well as the promise of protein-conjugated and inorganic nanocarriers like gold and silver, are examined. Conclusion: Nanocarriers have demonstrated significant potential in improving the therapeutic index of TKIs by enhancing tumor targeting and minimizing systemic toxicity. Despite ongoing progress, further research is essential to overcome translational barriers and ensure clinical applicability in diverse patient populations. Nanocarriers ; Narrative review ; Osteosarcoma ; Malignancy ; Tyrosine kinase ; Nanomedicine ; Drug delivery ; Review article Nanoparticle-Based Drug Delivery ; Dendrimers and Hyperbranched Polymers ; Graphene and Nanomaterials Applications
Authors: André Pereira | Douglas José Baquião Ribeiro
Source: Revista de Engenharia e Pesquisa Aplicada (article, 2025-02-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine Learning na Predição da Temperatura de Isolação de Motores de Indução A operação de um motor com temperatura acima do limite da sua classe térmica pode reduzir significativamente a vida útil da isolação do enrolamento e consequentemente a vida útil do próprio motor. Com as redes comunicação industriais aliadas ao processo de transformação digital foi possível coletar uma série de dados de operação e funcionamento dos motores de indução, direcionando-os a algoritmos de Machine Learning para produção de resultados como a predição da temperatura de isolação do enrolamento de um motor de indução trifásico. Este processo de predição levará os departamentos de manutenção a agirem antes que o valor de temperatura ultrapasse um determinado limite. Humanities ; Geology ; Art Magnetic Properties and Applications ; Power Transformer Diagnostics and Insulation ; Power Quality and Harmonics
Authors: Yan Xie | Xinming Ma | Bao Jing | Peng Qian | Yutong Qiu | Hui Chen
Source: Applied Ecology and Environmental Research (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

HYPERSPECTRAL MONITORING OF PLANT WATER CONTENT IN WINTER WHEAT (TRITICUM AESTIVUM L.) AFTER DROUGHT STRESS Winter wheat (Triticum aestivum L.) is an important food crop with high economic and social value.Frequent drought disasters in northern China, have a negative influence on the growth and development of winter wheat, thus the risk of diseases and pests is increased, and yield and quality are reduced.We took winter wheat treated with different water stress as the research object, collected the plant water content (PWC) at different growth stages and hyperspectral remote sensing data, and carried out logarithmic (Lg R), first derivative (R') and logarithmic first derivative (Lg R') transformation of the raw spectrum (R) of winter wheat.We established the spectral prediction model of PWC by analyzing the quantitative relationship between the spectral data of winter wheat under different mathematical transformations and the PWC in the range of 350 -2500 nm.The results showed that the prediction model of PWC based on lg R' had the highest accuracy, which can provide an effective basis for rapid and non-destructive monitoring of winter wheat moisture. Hyperspectral imaging ; Water stress ; Drought stress ; Winter wheat ; Agronomy ; Environmental science ; Water content ; Biology ; Remote sensing ; Geography ; Geology Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement
Authors: Xiuzhong Liu
Source:  (article, 2025-03-14)
Field: Chemistry — Organic Chemistry

Rapid Identification of Adulterated Camellia Oil Based on Mass Spectrometry Features and Logistic Regression To achieve the rapid identification of adulterated camellia oil, this paper proposes a mass spectrometry-based method. The Pearson correlation coefficient method and principal component analysis are used to extract the mass spectrometry features, and combined with logistic regression to establish an adulteration identification model for camellia oil adulterated with 20% rapeseed oil, rice bran oil, peanut oil, and sesame oil. The leave-one-out cross-validation and an independent test set are used for double verification to evaluate the generalization ability of the model. The experimental results show that the accuracy of the model in the leave-one-out cross-validation reaches 96.10% (F1 value: 97.30%), and the accuracy on the independent test set is 94.12% (F1 value: 94.00%). The difference between the two types of verification results is less than 2%, indicating that the model has good generalization and stability under different data distributions. Compared with KNN, decision tree, and SVM, logistic regression achieved higher accuracy rates by 8.83, 2.94, and 2.94 percentage points, respectively. This study provides reliable technical support for the quality and safety control of camellia oil. In the future, the robustness of the model can be further improved through data expansion or regularization optimization. Logistic regression ; Identification (biology) ; Mass spectrometry ; Regression analysis ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Chromatography ; Statistics ; Chemistry ; Mathematics ; Biology ; Botany Edible Oils Quality and Analysis ; Identification and Quantification in Food ; Tea Polyphenols and Effects
Authors: Dabira Ogunbiyi | Joshua Olanrewaju Olaoye
Source: International Journal of Research Publication and Reviews (article, 2025-10-01)
Field: Chemistry — Inorganic Chemistry

Fabrication and performance evaluation of a cost-effective coconut dehusking machine integrating mechanical efficiency, durability, and user-centered design. Coconut dehusking remains one of the most labor-intensive and hazardous operations in coconut processing, traditionally performed using sharp manual tools that pose high risks of injury.While mechanized alternatives exist, many are expensive, energy-intensive, and inaccessible to small-and medium-scale farmers.This creates an urgent need for a cost-effective, efficient, and safe solution that balances affordability with robust performance.Addressing this challenge requires innovations that integrate mechanical efficiency, long-term durability, and user-centered design principles to ensure both functionality and operator acceptance.This study focuses on the fabrication and performance evaluation of a coconut dehusking machine developed using locally available materials and simplified design concepts.The machine incorporates optimized dehusking blades, a reinforced structural frame, and a reliable power transmission system engineered to reduce energy losses.Emphasis was placed on durability through material selection and structural integrity, ensuring the machine withstands repetitive high-load operations common in coconut processing environments.User-centered considerations such as ergonomic handling, ease of maintenance, and operator safety were integrated into the design to encourage adoption in rural communities.Performance evaluation assessed throughput capacity, dehusking efficiency, structural resilience, energy consumption, and user safety under varied test conditions.Results revealed the machine to be highly effective, dehusking coconuts at faster rates while consuming less energy compared to conventional semi-mechanized systems.Its robust construction delivered reliable performance with minimal wear, while ergonomic features significantly reduced operator fatigue and risk.The fabricated machine demonstrates that lowcost mechanization, when guided by efficiency, durability, and user-focused design, can transform coconut processing, improving productivity, safety, and profitability in resource-constrained farming communities. Fabrication ; Set (abstract data type) ; Class (philosophy) ; Key (lock) Coconut Research and Applications
Authors: Md Samdani Azad | Donghwi Eum | Se-Yun Kim | Shuji Moriguchi | Tong‐Seok Han
Source: International Journal of Concrete Structures and Materials (article, 2025-11-25)
Field: Materials Science — Materials Chemistry

Evaluation of Stiffness and Tensile Strength in Hardened Cement Paste Using Bayesian Updating The objective of this study is to propose a framework to evaluate the mechanical properties of hardened cement paste, such as stiffness and tensile strength, using Bayesian updating. To supplement time-consuming experiments conducted for the evaluation of material properties, virtual experiments conducted using simulations have been found to be effective and promising. However, even with the help of simulations, accurate and reliable evaluation of the mechanical properties of cement paste is challenging. Owing to the heterogeneous and complex nature of the microstructure of cement paste, considerable time and effort are required to reduce the uncertainties in its responses. The need for a large number of samples from the frequentist approach induces uncertainties and precludes accurate probabilistic assessments. Therefore, this study proposes a framework for a Bayesian approach to assess the mechanical properties of hardened cement pastes using a reduced number of data sets. Mechanical properties evaluated from actual microstructures obtained using micro-computed tomography were adopted as priors, and properties from reconstructed microstructures using generative artificial intelligence were selected as the observed data set. The conjugate prior distribution was then updated using the observed data set, resulting in a posterior distribution. From the updated statistical measures (mean and standard deviation) of the stiffness and tensile strength, it was found that the mechanical properties of cement paste could be estimated with reduced uncertainty using the Bayesian framework. Ultimate tensile strength ; Stiffness ; Bayesian probability ; Cement ; Microstructure ; Frequentist inference ; Bayesian inference ; Probabilistic logic Machine Learning in Materials Science ; Innovative concrete reinforcement materials ; Composite Material Mechanics
Authors: Nazife Gözde Ayter | Zekiye Budak Başçiftçi | Murat Olgun
Source: INTERNATIONAL JOURNAL OF APPLIED BIOLOGY AND ENVIRONMENTAL SCIENCE (IJABES) (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Multivariate Dissection Of Seed Yield Determinants In Bread Wheat: Integrating Path Analysis, K-Means Clustering And Machine-Learning Approaches This study evaluated twelve commercial wheat varieties using major agronomic and quality-related yield components, including seed yield (Seed Y), heading date (Heading D), plant height (Plant H), seed number per spike (Seed N/Sp), seed weight per spike (Seed W/Sp), thousand seed weight (Thou SW), and test weight (Test W).Descriptive statistics revealed considerable phenotypic variation among genotypes, indicating strong genetic diversity in both yield potential and kernel quality characteristics.Correlation analysis showed that Seed N/Sp and Seed W/Sp were positively and strongly associated with Seed Y, supporting the widely recognized "seed number seed weight" principle of wheat yield formation.Path analysis identified Seed W/Sp and Seed N/Sp as the most influential components, showing the highest direct effects on yield.Thousand seed weight and test weight contributed mainly through indirect effects, highlighting their secondary but supportive roles in shaping final productivity.Plant height also exhibited a meaningful direct effect, demonstrating the contribution of biomass production and source-sink relationships to seed filling.K-means clustering (k = 3) effectively separated the twelve varieties into high-yield/high-quality, low-moderate-yield, and morphologically distinct late-heading groups.High-performing varieties such as Yunus, Tosunbey, Bezostaja, and Nacibey clustered together due to superior seed number, seed weight, and kernel density traits.Random Forest regression, while limited in predictive accuracy due to dataset size, reinforced the central importance of Seed N/Sp and Seed W/Sp as key predictors of yield variability.Overall, the integrated statistical and multivariate framework clearly demonstrates that seed yield in bread wheat is predominantly governed by seed number and seed weight, while kernel quality traits provide additional indirect support.These results offer valuable insights for breeding programs aiming to identify superior parents and design ideotypes with improved yield performance Cluster analysis ; Multivariate statistics ; Yield (engineering) ; Path (computing) ; Multivariate analysis ; Path analysis (statistics) Spectroscopy and Chemometric Analyses ; Genetics and Plant Breeding ; Statistical Methods and Applications
Authors: Samuel Verdú-Amat | María del Carmen Granados-Nevárez | Nina G. Heredia-Sandoval | Alma Rosa Islas-Rubio | Francisco Vásquez-Lara
Source: Revista Mexicana de Ciencias Agrícolas (article, 2025-07-21)
Field: Chemistry — Analytical Chemistry

Análisis de imagen aplicada a propiedades morfológicas en galleta de trigo y avena El presente estudio se realizó en el Centro de Investigación en Alimentación y Desarrollo en 2024. El análisis de imagen es una herramienta esencial que puede ser utilizada en el control de procesos alimentarios. Este estudio tuvo como objetivo analizar, mediante técnicas de imagen, las propiedades morfológicas de galletas elaboradas con trigo y avena en diferentes porcentajes y su relación con la firmeza de la galleta. Se determinó el perfil de textura en la masa obtenida de las diferentes formulaciones, así como el peso, diámetro, altura y firmeza de las galletas. Se realizó una caracterización morfológica de las galletas mediante análisis de imagen. Estas mediciones se enfocaron en el color de la superficie de forma global, así como las características de la textura superficial e interna de las galletas a través del análisis de la miga. Los resultados mostraron disminución de la firmeza (151.96 N a 103.17 N) a medida que aumentó el porcentaje de sustitución, un comportamiento similar se observó en el peso y la altura de la galleta, mientras que el diámetro no se vio afectado. En cuanto al análisis de imagen, se observó un mayor agrietamiento de la superficie de la galleta a medida que aumentó el porcentaje de sustitución. Se observaron cambios de color en la miga hacia zonas más claras, lo que indica una disminución de burbujas en la miga de galleta. El análisis de imágenes puede ser una alternativa importante para determinar la calidad de un producto terminado, en este caso, una galleta. Avena ; Biology ; Agronomy Spectroscopy and Chemometric Analyses
Authors: Chiara Ferrari | Marco Puglia | Nicolò Morselli | Simone Pedrazzi | Giulio Allesina | Mauro A. Corticelli | Paolo Tartarini | Alberto Muscio
Source: Power Engineering and Engineering Thermophysics (article, 2025-08-14)
Field: Materials Science — Materials Chemistry

Characterization and Durability of Cool Materials: Standard Methodologies for the Evaluation of Thermal Performance of New and Aged Products This study highlights how the analysis of the long-term performance of solar reflective materials used both as roofing and flooring solutions to mitigate urban overheating has recently become more important.European and Italian legislations focus just on initial properties, but actual energy efficiency depends on durability over time.The objective is the development and the validation of characterization methodologies that consider performance degradation due to aging, providing reliable guidance for designers and policy makers.This study integrates standard thermophysical characterization methods with natural and accelerated aging protocols.Solar reflectance (SR) measurements can be performed by using ultraviolet-visible-near-infrared spectrophotometer, solar spectrum reflectometer and pyranometer/albedometer, while thermal emissivity can be measured with infrared emissometers.Natural aging was implemented at the Energy Efficiency Laboratory structure of the University of Modena and Reggio Emilia, operational since 2017.Accelerated methods include the standard protocol for surface soiling and an innovative method for biological growth.The analysis reveals significant degradations in SR, with reductions of between 10% and 40% after three years of natural exposure.Bituminous membranes show the most marked degradation, while ceramic materials present the best stability.Accelerated methods show interesting correlations with natural aging.The doubling of the standard accelerated cycle, designed on North American climatic conditions, is more representative of European climatic conditions characterized by greater air pollution.It must be recalled that evaluation based just on initial performance significantly underestimates long-term behaviour.The results suggest the need to update regulations by introducing requirements based on post-ageing performance.New materials design should be focus on durability by integrating into new materials both self-cleaning properties and improved stability over time.While, during design process, materials with certified long-term stability, analyzed through durability and degradation analysis should be considered in energy and economic assessments. Durability ; Characterization (materials science) ; Thermal ; Quality (philosophy) Thermal properties of materials ; Chemical Thermodynamics and Molecular Structure ; Material Selection and Properties