jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aufar Mepian Apriliano | Aviv Yuniar Rahman | Rangga Pahlevi Putra
Source: JOURNAL OF SCIENCE AND APPLIED ENGINEERING (article, 2025-09-20)
Field: Chemistry — Analytical Chemistry

CLASSIFICATION OF IMAGE QUALITY OF TUBAN LEGEN BEVERAGES USING CONVOLUTIONAL NEURAL NETWORK (CNN) METHOD BASED ON RANDOM FOREST This study focuses on developing a classification method for the quality of Tuban legen beverage using a combination of Convolutional Neural Network (CNN) and Random Forest. The siwalan plant (Borassus flabellifer L.), commonly found in Tuban Regency, East Java, produces sap that is susceptible to fermentation and microbial damage. Therefore, it is essential to process legen properly to maintain its taste and quality. The dataset used consists of 1280 images of legen beverage categorized into two classes: Steril and Campuran. The research process involves data collection, preprocessing, and splitting the dataset into training, validation, and testing sets with various ratios. CNN, specifically the VGG16 architecture, is used for feature extraction from images, while Random Forest serves as the classification algorithm. The model is optimized using GridSearchCV and evaluated with metricssuch as accuracy, precision, recall, F1-score, and confusion matrix. The results indicate that the combination of CNN and Random Forest can achieve optimal accuracy of up to 100% for certain data configurations, both for images with and without background, with a data ratio of 80% training, 10% validation, and 10% testing. Data augmentation is applied to enhance model variation and generalization, contributing to improved classification accuracy. Although these results demonstrate the model's ability to recognize patterns, the study suggests potential for further improvement. It is recommended to explore other classification methods, conduct deeper hyperparameter optimization, and apply transfer learning to enhance model performance. Additionally, further research is needed to understand the impact of background on classification and how to address it. This study makes a significant contribution to the development of a quality classification system for legen beverages by utilizing more robust and accurate deep learning technology. These findings can serve as an important reference for future research in image classification and food product quality, providing insights for developing more efficient and effective methods. Random forest ; Convolutional neural network ; Pattern recognition (psychology) ; Hyperparameter ; Artificial neural network ; Confusion ; Feature extraction Spectroscopy and Chemometric Analyses
Authors: M Fellahi | Yazid Datoussaid | Tarik Attar | Mohammed El Amine Dib | Abbes Benchadlı
Source: South African Journal of Chemistry (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Experimental Design and Statistical Modelling for Analysis of Inhibition Efficiency Using Daucus crinitus Essential Oil in corrosion Processes This research explores the effectiveness of Daucus crinitus essential oil as a corrosion inhibitor for copper in a 0.5 M sulfuric acid solution, utilizing experimental design and statistical analysis. The study examines how temperature, inhibitor concentration, and immersion duration influence inhibition efficiency (IE) through Response Surface Methodology (RSM). GC/MS analysis identified the essential oil's main components as aliphatic compounds, such as dodecyl acetate (28.5%), undecane (35.2%), and dodecanal (14%).The highest inhibition efficiency (85%) was obtained at an inhibitor concentration of 10.25 (v/v), a temperature of 40 °C, and an immersion period of 3 hours. Elevated temperatures decreased IE, indicating a physisorption-based mechanism, whereas higher concentrations and longer immersion times improved copper protection. Statistical evaluation confirmed the model's reliability, with an of 0.991 and a value above 0.95, reflecting strong predictive capability. These results position D. crinitus essential oil as an eco-friendly and sustainable substitute for traditional synthetic inhibitors, demonstrating its potential for effective corrosion control in industrial settings. The study emphasizes the value of plant-derived inhibitors for reducing copper corrosion in acidic environments and offers insights for enhancing corrosion prevention approaches. Corrosion ; Copper ; Sulfuric acid ; Immersion (mathematics) ; Essential oil ; Undecane Corrosion Behavior and Inhibition
Authors: Y. G. Zhang | Binhui Liao | Mostafa Gouda | Xuelun Luo | Xinbei Song | Yihang Guo | Yingjie Qi | Hui Zeng | Chuangchuang Zhou | Yujie Wang | Jingfei Zhang | Xiaoli Li
Source: Foods (article, 2025-04-28)
Field: Chemistry — Analytical Chemistry

Visualization of Moisture Distribution in Stacked Tea Leaves on Process Flow Line Using Hyperspectral Imaging The distribution of moisture content in stacked tea leaves during processing significantly influences tea quality. Visualizing this moisture distribution is crucial for optimizing processing parameters. In this study, we utilized hyperspectral imaging (HSI) technology combined with machine learning algorithms to evaluate the moisture content and its distribution in the stacked tea leaves in West Lake Longjing and Tencha green tea products during the processing flow line. A spectral quantitative determination model was developed, achieving high accuracy (Rp2 > 0.940) The model demonstrated strong generalization ability, allowing it to predict moisture content in both types of tea. Through hyperspectral imaging, we visualized moisture distribution in seven key processing steps and observed that moisture content was non-uniform, with the leaf tips and petioles having higher moisture levels than the leaf surface. This study offers a novel solution for real-time moisture monitoring of stacked tea leaves in tea production, ensuring consistent product quality. Future research could focus on refining model transfer techniques and exploring additional tea varieties to further enhance the generalization of the approach. Hyperspectral imaging ; Visualization ; Moisture ; Process (computing) ; Line (geometry) ; Environmental science ; Chemical imaging ; Remote sensing ; Soil science ; Materials science ; Computer science ; Artificial intelligence ; Geology ; Mathematics ; Composite material Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing in Agriculture
Authors: F.L. Leokha | V. Khrychikov
Source: Theory and practice of metallurgy (article, 2025-12-28)
Field: Materials Science — Materials Chemistry

Current trends in the production and application of titanium alloys Purpose: Analysis of the state of scientific research in the field of production and application of titanium alloys and trends in their development over the past 20 years. Methodology: Narrative review and analysis of the literature with the most current publications in open sources Google Scholar, Sciencedirect, Researchgate, Scopus. Results: Titanium is distinguished by its strength, low density, corrosion resistance, biocompatibility and heat resistance, which makes it important for industry. More than 100 titanium alloys have been created, of which only 20–30 have reached commercial status. The most common is Ti-6Al-4V (over 50% of applications), another 20–30% are pure titanium alloys. They are used in the aerospace, medical, automotive and chemical sectors. Titanium metallurgy is based on vacuum arc and electron beam remelting, but an active search for alternative technologies continues, such as electroslag remelting of titanium alloys. Additive technologies are also being actively introduced. The scientific novelty lies in the comprehensive generalization of modern research related to the production and application of titanium alloys with an emphasis on modern methods of remelting titanium charge into a finished product. The publication includes the most relevant sources that are not yet available in other reviews. Practical significance. The publication will help scientists, engineers, teachers, and students to quickly navigate the array of existing knowledge, avoid duplication of research, and better plan further activities. Titanium ; Titanium alloy ; Current (fluid) ; Corrosion ; Powder metallurgy ; Production (economics) Titanium Alloys Microstructure and Properties ; Additive Manufacturing Materials and Processes ; Surface Treatment and Coatings
Authors: Rutba Riaz
Source: Lahore Garrison University Research Journal of Computer Science and Information Technology (article, 2025-12-31)
Field: Chemistry — Analytical Chemistry

Assessment of quality grading in orange fruit using deep learning approach The quality of oranges is necessary to improve sales growth in the market. Oranges are widely grown and mostly used in the food processing industry. Each year, Pakistan exports millions of tons of oranges to national and international markets. However, these areas of the economy face a shortage of standards in quality assurance, more time for grading, and poor-quality production. The quality assessment of orange fruit is usually determined by humans. Because of the limitation ofindividual perceptions, different challenges occur, such as time-consuming, inaccuracy, and inconsistency. The main problem is that only trained personnel can do this job, and it requires a huge amount of effort and time to inspect each piece of fruit. To overcome these drawbacks, we developed an automated system for quality grading of oranges using Deep Learning. This paper proposed a classification model that categorized images of oranges using deep learning and machine learning algorithms. The model was trained on the VGG-19 convolutional neural network structure, which is a 19-layer deep network, through the application of transfer learning, to optimize feature extraction on the orange image dataset. A dataset of 4500 images of oranges was captured under controlled lighting conditions using a DSLR camera. For the classification, three classes of oranges, namely Grade-A, Grade-B, and Grade-C, are categorized based on color, shape, texture, and size of the orange. Theproposed classification method for quality grading of oranges, VGG-19, based on a transfer learning approach, achieved 98.00% accuracy. Orange (colour) ; Grading (engineering) ; Deep learning ; Convolutional neural network ; Economic shortage ; Transfer of learning ; Feature extraction Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Innovations in Aquaponics and Hydroponics Systems
Authors: Крымский федеральный университет им. В.И. Вернадского | А.С. Тютюник | A. S. Mazinov | Павел А. Варагушин | Александр Е. Лисичников | Е.В. Брага | А.Н. Гусев
Source: Radioelectronics Nanosystems Information Technologies (article, 2025-10-21)
Field: Materials Science — Materials Chemistry

Study of Luminescent and Resistive Properties of Thin Films Based on Copper(I) Iodide Представлено исследование электрофизических и люминесцентных свойств тонких пленок 2-(4-передил)-5-фенил-1,3,4-оксадиазолий (трифенилфоснин) иодид меди (Cu(I)-PPh3) с точки зрения оценки его практического применения в полупроводниковой индустрии. Спектры фотолюминесценции Cu(I)-PPh3 регистрировались в желто-оранжевой области в диапазоне длин волн от 510 до 740 нм, при этом квантовый выход составил 55.84%. Исследованы зависимости тока, проходящего через резистивные структуры на основе полученных тонких пленок, от относительной влажности воздуха и проведена оценка быстродействия этих структур. Показано, что при увеличении относительной влажности с 30% до 90% проводимость увеличилась с 1.5 мСм до 5.2 мСм при быстродействии не более 20 с. Thin film ; Resistive touchscreen ; Luminescence ; Iodide ; Deposition (geology) Luminescence Properties of Advanced Materials
Authors: Alka Yadav | Sonali Gangwar | Udai Bhan Singh Rathore | Neeraj Sharma | Praveen Sharma
Source: Journal of Neonatal Surgery (article, 2025-07-18)
Field: Chemistry — Organic Chemistry

Design, In Vitro Antimicrobial Evaluation, and In Silico Drug-Likeness Assessment of Novel Pyrazoline Derivatives The present study reports the synthesis, In vitro antimicrobial evaluation, and in silico drug-likeness prediction of a novel series of pyrazoline derivatives (4a–4h). Antimicrobial activity was evaluated using the cup plate method against a panel of bacterial and fungal pathogens. The zone of inhibition was measured to assess antibacterial and antifungal efficacy. In silico studies were performed using a Python-based RDKit tool to determine key physicochemical parameters, ADME properties, and toxicity predictions. Compound 4h exhibited the most potent activity against all tested strains. All compounds showed favorable drug-likeness and high gastrointestinal absorption, with no AMES toxicity flags ADME ; In silico ; Antimicrobial ; Moxifloxacin ; Drug ; In vitro ; Pharmacology ; Chemistry ; Biology ; Microbiology ; Antibiotics ; Biochemistry Synthesis and biological activity ; Computational Drug Discovery Methods ; Diverse Scientific Research Studies
Authors: Kadambini Das | Arpana Katiyar | Ritika Kumari | Niti Kirai | Deepak Kumar | Poonam Kumari | Neelam Kumari | Ranjana Kumari
Source: International Journal of Agriculture and Nutrition (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Nano-pesticides: Emerging Opportunities for Sustainable Agriculture and Food Security The excessive application of conventional pesticides has detrimental effects on soil health and the environment. Chemical remnants of these pesticides damage the soil environment by impacting nontargeted organisms and, with time, decreasing soil fertility. Thus, to effectively manage insect pests in agricultural fields without deterioration of the surrounding biota, nanotechnology has proven to be a significant substitute for conventional chemical pesticides. Compared with conventional pesticides, nanopesticides reduce and control excess agricultural inputs and maintain environmental balance in addition to improving the shelf life and nutritional content of produce. This technology offers more potential for sustainably enhancing worldwide food production. Currently employed conventional pest management strategies are inadequate and have several harmful impacts on the environment and humankind. There is a serious need for the development of safe and promising formulations and the implementation of a regulatory framework for the safe use of nanopesticides. This chapter addresses the need for nanopesticides for sustainable agriculture, their environmental efficacy, their associated benefits and disadvantages, and the prospects of nanopesticides. Pesticide ; Agriculture ; Food security ; Sustainable agriculture ; Food systems ; Business ; Nano- ; Natural resource economics ; Environmental planning ; Agricultural engineering ; Environmental science ; Engineering ; Agronomy ; Economics ; Geography ; Biology Nanoparticles: synthesis and applications
Authors: Chunhua Feng | Hui Ye
Source: Journal of Artificial Intelligence Practice (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Case Analysis in AI Practice Courses: A Comparative Study of Tool Wear Prediction Methods Artificial intelligence is anticipated to play a crucial role in shaping the future of education and academic research. Using a mechanical testing course as a case study, this paper illustrates how AI can be effectively integrated into engineering education by examining various AI methodologies through the application of tool wear prediction. In predictive modelling, commonly used intelligent algorithms mainly come from traditional machine learning techniques, such as support vector machines (SVM) and random forests, as well as deep learning methods like long short-term memory (LSTM) and gated recurrent units (GRU). Traditional models generally rely on manual feature extraction, which provides a degree of interpretability but often falls short in capturing the dynamic characteristics of time-series data. This study assesses the performance of several deep learning algorithms in predicting tool wear. The CNN-LSTM hybrid model consistently outperformed other models across all evaluation metrics. Specifically, compared to the GRU model, it reduced RMSE by 42.07% and MAE by 52.06%, while improving R² by 4.43%. When compared to the standalone LSTM model, the CNN-LSTM model achieved a 17.72% reduction in RMSE and a 42.25% decrease in MAE, along with a 2.97% increase in R². These results indicate that the CNN-LSTM architecture successfully combines CNN's proficiency in automatic local feature extraction with LSTM's capacity to model long-term temporal dependencies, thereby providing a highly effective and accurate method for tool wear prediction. Interpretability ; Feature engineering ; Feature (linguistics) ; Support vector machine ; Artificial neural network ; Deep learning ; Mean squared error Metal Alloys Wear and Properties ; Additive Manufacturing Materials and Processes
Authors: Aamna Shah
Source: EKSPLORIUM (article, 2025-01-01)
Field: Materials Science — Biomaterials

Targeted Drug Delivery Systems for Cancer Management: Advancements, Challenges, and Future Directions Background: Cancer malignancy management remained a critical challenge despite therapeutic advancements attributed to systemic toxicity, non-specificity &amp;drug targeting issues, reduced drug bioavailability and drug resistance problems associated with available chemotherapeutic approaches. Current limitations underline prime requirement for development of highly efficacious patient oriented drug delivery systems. Objective: Current review is focused to discuss cancer-specific targeted drug delivery systems (TDDS), its&nbsp; types and formulation&nbsp; modifications brought about to shape cancer therapeutics with ultimate benefited therapeutic outcomes and precise controlled-release with minimal possible side effects. Methods: Current review discussed the development, possible mode/mechanism of action, and effectiveness of various TDDS, including nanoparticles, liposomes, dendrimers, antibody-drug conjugates, ligand-based biomimetic and, stimuli-responsive drug delivery systems thereby improving targeted drug release phenomenon and ultimate benefited clinical outcomes however certain limitations including&nbsp; tumor heterogeneity, immune system clearance, scalability and manufacturing cost, together with&nbsp; limited penetration to solid tumors and unpredictable Bio distribution ultimately acquire innovative &nbsp;drug delivery platforms for global. Current review is also focused on recent advancements in TDDS over the past decade together with evaluation of its limitations and ascertaining potential future directions for renovating cancer management. Results: Management of cancer malignancies has been remarkably reformed via TDDS, allowing the potential delivery of anticancer medicaments to the targeted site and preventing systemic side effects. Liposome’s, polymeric nanoparticles, dendrimers, Exosomes, stimuli-responsive systems and&nbsp; antibody-drug conjugates&nbsp; presenting significant advancements together with promising clinical outcomes. Currently precision nano-medicine, CRISPR-based carriers, AI integration, and microenvironment-responsive systems are under consideration for efficacious management of cancer malignancies. Conclusion: TDDS provide a breakthrough approach for cancer management by augmenting treatment efficacy and meanwhile reducing drug induced toxicity. Continued research and interdisciplinary collaboration is required to address intervening hurdles and completely analyzing the potential of TDDS in clinical oncology. Drug delivery ; Cancer drugs ; Targeted drug delivery ; Drug ; Medicine ; Nanotechnology ; Pharmacology ; Materials science Nanoparticle-Based Drug Delivery ; Graphene and Nanomaterials Applications ; Nanoplatforms for cancer theranostics
Authors: Panagiotis Krokidas | Vassilis Gkatsis | J. Theocharis | George Giannakopoulos
Source: Digital Discovery (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Navigating materials design spaces with efficient Bayesian optimization: a case study in functionalized nanoporous materials Bayesian optimization with non-random initialization, coupled with an XGBoost regressor trained on BO-acquired samples, reduces experimental cost while efficiently identifying top-performing instances in large design spaces. Bayesian optimization ; Bayesian probability ; Design of experiments ; Nanoporous ; Key (lock) Machine Learning in Materials Science ; Advanced Multi-Objective Optimization Algorithms ; Stochastic Gradient Optimization Techniques
Authors: Sławomir Kozieł | Anna Pietrenko‐Dabrowska
Source: Scientific Reports (article, 2025-11-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Machine learning for microwave optimization using simplex surrogates, dual-resolution computational models and local tuning with sparse sensitivity updates Numerical optimization procedures are now an integral part of the microwave design process. Ensuring reliability requires conducting parameter tuning at the electromagnetic (EM) analysis level. This, however, entails considerable computational costs. Additionally, global optimization is often necessary (e.g., multimodal problems, large-scale operating frequency re-design, design of metasurfaces), which is incomparably more expensive when using conventional techniques. In this work, we introduce a novel approach to the fast globalized optimization of microwave structures. Our methodology is founded on processing the operating parameters of the circuit rather than its complete frequency characteristics, and the utilization of simplex-based regressors. Both permit regularizing the objective function, which facilitates and speeds up the identification of the optimum design. Further acceleration is enabled by employing dual-fidelity EM simulations and restricted sensitivity updates at the final parameter tuning stage. The introduced algorithm has been comprehensively demonstrated using several microstrip components and proved to be superior over several benchmark approaches. Apart from reliability, its attractive features include remarkable computational efficiency (the average optimization cost corresponding to fewer than fifty EM simulations of the circuit), as well as simple implementation and handling with a small number of control parameters that do not have to be tuned to a specific problem at hand. Benchmark (surveying) ; Sensitivity (control systems) ; Acceleration ; Microwave ; Optimization problem ; Identification (biology) ; Reliability (semiconductor) ; Simplex Metamaterials and Metasurfaces Applications ; Antenna Design and Optimization ; Advanced Antenna and Metasurface Technologies