jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiri Konecny | Jaromír Konecny | Kamil Bancik | Miroslav Mikus | Jan Choutka | Jiří Koziorek | Ibrahim A. Hameed | Algimantas Valinevičius | Darius Andriukaitis | Michal Prauzek
Source: Sensors (article, 2025-07-07)
Field: Materials Science — Materials Chemistry

Efficient Soil Temperature Profile Estimation for Thermoelectric Powered Sensors Internet of Things (IoT) sensors designed for environmental and agricultural purposes can offer significant contributions to creating a sustainable and green environment. However, powering these sensors remains a challenge, and exploiting the temperature difference between air and soil appears to be a promising solution. For energy-harvesting technologies, accurate soil temperature profile data are needed. This study uses meteorological and soil temperature profile data collected in the Czech Republic to train machine learning models based on Polynomial Regression (PR), Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) to predict the soil temperature profile. The results of the study indicate an error of 0.79 °C, which is approximately 10.9% lower than the temperature error reported in state-of-the-art studies. Beyond achieving a lower temperature prediction error, the proposed solution simplifies the input parameters of the model to only ambient temperature and solar irradiance. This improvement significantly reduces the computational costs associated with the regression model, offering a more efficient approach to predicting soil temperature for the purpose of optimizing energy harvesting in IoT sensors. Support vector machine ; Polynomial regression ; Regression analysis ; Environmental science ; Regression ; Computer science ; Linear regression ; Soil science ; Remote sensing ; Data mining ; Machine learning ; Mathematics ; Statistics ; Geography Advanced Thermoelectric Materials and Devices ; Greenhouse Technology and Climate Control ; Innovative Energy Harvesting Technologies
Authors: Weidong Xiong | Bangqi Yin | Jiancheng Zhang | Liusuo Wu | Bingang Xu | Jianfei Luo | Xiaoxiao Cheng
Source: Highlights in Science Engineering and Technology (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Influence of Compressed Air Foam Extinguishing System on The Performance of Aqueous Film-Forming Foam Extinguishing Agent According to the national standard, the relationship between the expansion, the drainage time and the gas-liquid ratio of the water-formed film foam extinguishing agent under different mixture ratio was investigated experimentally by using the gas-liquid two-phase foam gun. The results show that with the increase of gas-liquid ratio, the foaming multiple of water-formed foam first increases rapidly and then tends to a stable value. 25% drainage time first increases and then decreases and finally tends to a stable value. With the increase of mixture ratio, the expansion and 25% drainage time of the water-formed film foam extinguishing agent all increase, which has a significant effect on its performance. Materials science ; Foaming agent ; Expansion ratio ; Composite material ; Compressed air ; Aqueous solution ; Drainage ; Chemical engineering ; Chemistry ; Porosity ; Organic chemistry Pickering emulsions and particle stabilization
Authors: Qiang Xu | Yanling Zhang | Aiguo Wang | Guangqing Chen | Xianjie Cai | Shuoye Zhou | Junying Li | Baofeng Jin | Yan Ding | Jiajie Huang | Zuxiao Chen | Heng Zhang | Jianwei Wang | Weimin Guo | Jianjun Liu
Source: Frontiers in Plant Science (article, 2025-07-02)
Field: Chemistry — Analytical Chemistry

Intelligent recognition of tobacco leaves states during curing with deep neural network Our proposed framework effectively enables morphological state recognition in industrial curing, supporting parameter optimization and enhanced tobacco quality. Tobacco leaf ; Automation ; Artificial intelligence ; Deep learning ; Computer science ; Deep neural networks ; Agricultural engineering ; Curing (chemistry) ; Artificial neural network ; Pattern recognition (psychology) ; Machine learning ; Materials science ; Engineering ; Mechanical engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: H. Metzger | Y. Zhang | Andrea Cammarano | Paul Prentice
Source: Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences (article, 2025-07-01)
Field: Materials Science — Materials Chemistry

Revisiting the subharmonic route to acoustic chaos: broadband noise clearing via cavitation bubble synchronization The cavitation activity within a tube transducer subject to a linearly increasing drive amplitude is studied experimentally, via high-speed imaging and parallel acoustic detection. A spectrogram of the cavitation emission signal collected over the duration of the ramped sonication, confirms a subharmonic route to chaos; the progression from harmonic emissions to broadband noise via a period-doubling bifurcation. Stroboscopic mapping demonstrates that the emergence of broadband signal is due to increasingly asynchronous bubble collapse across the population. Particular attention is given to a region of sudden broadband clearing in the spectrogram, coincident with strong subharmonic lines, approximately halfway through the sonication. The clearing is due to phase synchronization of the bubble oscillations. Broadband noise gradually re-emerges, often with further bifurcation to include period-quadrupled emissions, as oscillations dephase again following synchronization. Numerical modelling investigates the influence of amplitude ramping, inter-bubble distance and bubble dispersity on the oscillations and emissions from a 12-bubble system, based on an experimental observation. For a polydisperse system, it is found that a single bubble responding with period-2 oscillations initiates period-2 synchronization for the population, offering explanation for the consistent observation that prominent subharmonic emissions at half the driving frequency coincide with broadband clearing. Bubble ; Broadband ; Amplitude ; Noise (video) ; Cavitation ; Harmonic ; SIGNAL (programming language) ; Transducer Ultrasound and Cavitation Phenomena ; Underwater Acoustics Research ; Microfluidic and Bio-sensing Technologies
Authors: Pouya Sohrabipour | Chaitanya Pallerla | Ammirreza Davar | Siavash Mahmoudi | Philip G. Crandall | Wan Shou | Yu She | Dongyi Wang
Source: Preprints.org (preprint, 2025-01-26)
Field: Chemistry — Analytical Chemistry

Cost Effective Active Laser Scanning System for Depth-Aware Deep-Learning-Based Instance Segmentation for Poultry Processing The poultry industry plays a pivotal role in global agriculture, serving as a major source of protein and contributing significantly to economic growth. However, the sector faces challenges associated with labor-intensive tasks that are repetitive and physically demanding. Automation has emerged as a critical solution to enhance operational efficiency and improve working conditions. Specifically, robot manipulation and handling of objects is becoming ubiquitous in factories. However, challenges exist to precisely identify and guide the robot to handle a pile of objects with similar textures and colors. This paper focuses on the development of a vision system for a robotic solution aimed at automating the chicken rehanging process, a fundamental yet physically strenuous activity in poultry processing. To address the limitation of the generic instance segmentation model in identifying overlapped objects, a cost-effective dual-active laser scanning system was developed to generate precise depth data of objects. The generated well-registered depth data integrated with the RGB images were sent into the instance segmentation model for individual chicken detection and identification. This enhanced approach significantly improved the model’s performance in handling complex scenarios involving overlapping chickens. Specifically, the integration of RGB-D data increased the model’s mean Average Precision (mAP) detection accuracy by 4.9% and significantly improved the center offset—a customized metric introduced in this study to quantify the distance between the ground truth mask center and the predicted mask center. Precise center detection is crucial for the development of future robotic control solutions, as it ensures accurate grasping during the chicken rehanging process. The center offset was reduced from 22.09 pixels (7.30 mm) to 8.09 pixels (2.65 mm), demonstrating the approach’s effectiveness in mitigating occlusion challenges and enhancing the reliability of the vision system. Segmentation ; Artificial intelligence ; Computer science ; Laser scanning ; Laser ; Computer vision ; Optics Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Industrial Vision Systems and Defect Detection
Authors: Yudong Zhang | Puzhen Shao | Yangguang Liu | Guijing Dou | Xianrui Zou
Source:  (article, 2025-09-09)
Field: Materials Science — Metals and Alloys

CS-SegFormer: a dual-path attention enhanced segmentation network for metallographic grain size evaluation Grain size evaluation is a key factor in assessing the mechanical properties and service performance of stainless steel. However, conventional manual analysis of metallographic images is time-consuming, subjective, and often lacks reproducibility, limiting its industrial applicability. To overcome these challenges, this paper proposes CS-SegFormer (Channel and Spatial), an improved semantic segmentation framework tailored for metallographic image analysis of austenitic stainless steel. Built upon SegFormer, the proposed model integrates channel and spatial attention modules, which enhance the perception of grain boundaries, fine-scale textures, and complex microstructural variations.Experiments were conducted on a self-constructed dataset containing 120 ultra-high-resolution metallographic images, annotated into six categories including grains, twins, precipitates, and defects. Comparative results demonstrate that CS-SegFormer outperforms representative segmentation networks—UNet, KNet, SegFormer, and FastSCNN—achieving improvements of approximately 10% in both mIoU and mDice. These findings validate the superior accuracy and robustness of CSSegFormer and highlight its potential for reliable automated grain size grading in metallographic evaluation and industrial applications. Segmentation ; Robustness (evolution) ; Automation ; Limiting ; Grading (engineering) ; Grain size ; Image segmentation Hydrogen embrittlement and corrosion behaviors in metals ; Welding Techniques and Residual Stresses ; Microstructure and Mechanical Properties of Steels
Authors: Yueyun Yu | Huang Xin | Danjv Lv | Benjamin K. Ng | Chan‐Tong Lam
Source: Mathematics (article, 2025-06-18)
Field: Chemistry — Analytical Chemistry

A Novel Approach to Pine Nut Classification: Combining Near-Infrared Spectroscopy and Image Shape Features with Soft Voting-Based Ensemble Learning Pine nuts hold significant economic value due to their rich plant protein and healthy fats, yet precise variety classification has long been hindered by limitations of traditional techniques such as chemical analysis and machine vision. This study proposes a novel near-infrared (NIR) spectral feature selection algorithm, termed the improved binary equilibrium optimizer with selection probability (IBiEO-SP), which incorporates a dynamic probability adjustment mechanism to achieve efficient feature dimensionality reduction. Experimental validation on a dataset comprising seven pine nut varieties demonstrated that, compared to particle swarm optimization (PSO) and the genetic algorithm (GA), the IBiEO-SP algorithm improved average classification accuracy by 5.7% (p &lt; 0.01, Student’s t-test) under four spectral preprocessing methods (MSC, SNV, SG1, and SG2). Remarkably, only 2–3 features were required to achieve optimal performance (MSC + random forest: 99.05% accuracy, 100% F1/precision; SNV + KNN: 97.14% accuracy, 100% F1/precision). Furthermore, a multimodal data synergy strategy integrating NIR spectroscopy with morphological features was proposed, and a classification model was constructed using a soft voting ensemble. The final classification accuracy reached 99.95%, representing a 2.9% improvement over single-spectral-mode analysis. The results indicate that the IBiEO-SP algorithm effectively balances feature discriminative power and model generalization needs, overcoming the contradiction between high-dimensional data redundancy and low-dimensional information loss. This work provides a high-precision, low-complexity solution for rapid quality detection of pine nuts, with broad implications for agricultural product inspection and food safety. Pattern recognition (psychology) ; Feature selection ; Random forest ; Artificial intelligence ; Computer science ; Weighted voting ; Preprocessor ; Ensemble learning ; Discriminative model ; Mathematics ; Voting Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote Sensing in Agriculture
Authors: Wenxian Hu | Xudong Sun | Yating Qin
Source: Energies (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Simulation of a Natural Gas Solid Oxide Fuel Cell System Based on Rated Current Density Input Solid Oxide Fuel Cells (SOFCs) offer high-efficiency electrochemical conversion of fuels like natural gas, yet detailed modeling is crucial for optimization. This paper presents a simulation study of a natural gas-fueled SOFC system, developed using Aspen Plus with Fortran integration. Distinct from prevalent paradigms assuming rated power output, this work adopts rated current density as the primary input, enabling a more direct investigation of the cell’s electrochemical behavior. We conducted a comprehensive sensitivity analysis of key parameters, including fuel utilization, water-carbon ratio, and current density, and further investigated the impact of different interconnection configurations on overall module performance. Results demonstrate that a single unit operating at a current density of 180 mA/cm2, a fuel utilization of 0.75, and a water-carbon ratio of 1.5 can achieve a maximum net stack-level electrical efficiency of 54%. Furthermore, optimizing the interconnection of a 400 kW module by combining series and parallel units boosts the overall net system-level electrical efficiency to 59%, a 5-percentage-point increase over traditional parallel setups. This is achieved by utilizing a bottoming cycle for exhaust heat recovery. This research validates the rated current density approach for SOFC modeling, offering novel insights into performance optimization and modular design for integrated energy systems. Natural gas ; Current (fluid) ; Solid oxide fuel cell ; Fuel cells ; Oxide ; Materials science ; Nuclear engineering ; Environmental science ; Process engineering ; Chemical engineering ; Waste management ; Chemistry ; Electrical engineering ; Engineering ; Electrode ; Anode ; Metallurgy ; Physical chemistry Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Chemical Looping and Thermochemical Processes
Authors: Argentina. Universidad Nacional Arturo Jauretche | Laureano Sabatier | Universidad Provincial de Ezeiza; Argentina. | María Joselevich | Carina Lion | Argentina Universidad de Buenos Aires
Source: Educación Lenguaje y Sociedad (article, 2025-12-22)
Field: Chemistry — Physical and Theoretical Chemistry

Reinventing the Chemistry Class: Between Narratives, Artificial Intelligence, and collaboration Work (physics) ; Context (archaeology) ; Quality (philosophy) ; Feature (linguistics) Various Chemistry Research Topics ; Machine Learning in Materials Science ; Science Education and Pedagogy
Authors: Niravkumar J. Patel | Jalpa Thakkar | Kalpesh K Dudani
Source: Journal of Engineering and Applied Science (article, 2025-12-01)
Field: Materials Science — Materials Chemistry

Particle filter- based localization approach for PD in power transformers This study presents a new and well-validated method using a particle filter for detecting Partial Discharge (PD) in power transformers. Recognizing the limitations of traditional methods in noisy settings, our creative approach effectively merges multi-modal acoustic and Ultra - High Frequency (UHF) sensor data, enhanced by advanced signal processing and improved feature extraction techniques. At first, our basic algorithm showed higher error rates than earlier research, highlighting the necessity for major enhancements. We tackled this issue by creating an optimized particle filter algorithm, inspired by the precise Monte Carlo localization methods used in satellite navigation and deep-space tracking. This improved algorithm, supported by comprehensive MATLAB simulations and detailed quantitative analysis, significantly lowered localization errors, achieving outstanding performance gains with sub-millimeter accuracy in ideal conditions and consistently maintaining sub-6 mm precision across various testing scenarios. A key contribution is the combined use of advanced Time Difference of Arrival (TDOA) techniques with our dependable Particle Filter framework, which is improved by adaptive mechanisms tailored for transformer environments. This strong combination greatly boosts fault detection and noise immunity, while also offering outstanding resistance to environmental changes that are often faced in transformer diagnostics. When compared to traditional methods, our integrated, sensor-driven framework consistently shows better performance in terms of detection precision and localization accuracy. This improved ability is crucial for enabling dependable predictive maintenance plans, avoiding costly breakdowns, and extending the lifespan of high-voltage equipment. The proven practicality and success of this combined method emphasize its potential to revolutionize transformer condition monitoring. Future work will focus on enhancing computational efficiency for real-time uses and expanding the framework’s applicability to a broader range of high-voltage devices, thus solidifying its importance as a flexible and vital tool in modern electrical asset management. Particle filter ; Transformer ; Monte Carlo method ; MATLAB ; Filter (signal processing) ; Fault detection and isolation ; Signal processing High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Aerosol Filtration and Electrostatic Precipitation
Authors: Sonia Singh
Source: ASIDE Health Sciences (article, 2025-01-01)
Field: Chemistry — Organic Chemistry

Comparative Antimicrobial Efficacy of Cetrimide, Dettol, and Lizol Against Six Different Microbial Species on Epoxy-Coated Pharmaceutical Surfaces Using Surface Challenge Method Introduction: Disinfectants are vital in the pharmaceutical industry's sanitization process and contamination control programs. However, many pharmaceutical companies lack systematic policies for selecting appropriate disinfectants, often relying solely on manufacturer claims, which may not always be reliable. The complexity of existing disinfectant testing methods further complicates proper evaluation, highlighting the need for practical, efficient approaches. Methods: This study used a simple surface challenge method to mimic real-world pharmaceutical conditions to test disinfectant efficacy. Three disinfectants, 1% Cetrimide, 2.5% Dettol, and 2% Lizol, were evaluated for antimicrobial activity. The test organisms included E. coli ATCC 8739, Salmonella typhimurium ATCC 14028, Pseudomonas aeruginosa ATCC 25619, Bacillus subtilis ATCC 6633, Candida albicans ATCC 10231, and an environmental isolate (Bacillus spp.). All testing was conducted on epoxy-coated floors within pharmaceutical industry premises. Results: All three disinfectants demonstrated excellent antimicrobial activity against the tested organisms. After a 20-minute contact time, each disinfectant achieved a ≥6-log reduction in test organisms. The comparative evaluation indicated that 1% Cetrimide exhibited superior antimicrobial effectiveness compared to 2.5% Dettol and 2% Lizol. Conclusions: The surface challenge method offers a practical approach for assessing disinfectant efficacy under pharmaceutical conditions. Among the disinfectants tested, 1% Cetrimide provided the most effective microbial reduction, suggesting its suitability for contamination control in pharmaceutical environments. Cetrimide ; Antimicrobial ; Epoxy ; Traditional medicine ; Materials science ; Medicine ; Biology ; Dentistry ; Composite material ; Microbiology Antimicrobial agents and applications ; Pharmacology and Nanomedicine Research ; Drug Solubulity and Delivery Systems
Authors: Haoran Ma | Guochuan Zhao | Bei Peng | Bo Chen | Yi Rong | Yibo Li
Source: Journal of Food Processing and Preservation (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

Rapid Detection System of Paddy Impurities Based on Machine Vision Aiming at the problems of poor imaging effect and low impurity classification accuracy of existing paddy quality detection systems, we propose a rapid paddy impurity detection system based on machine vision. The system consists of an image acquisition subsystem and an impurity classification subsystem, which aim to accurately evaluate the quality of paddy grains. The image acquisition subsystem is composed of a multi‐degree‐of‐freedom manipulator, a flexible vibrating disc, an industrial camera, a light source, and an embedded computing platform. The multisensor fusion technology is used to realize the two‐dimensional imaging of the target paddy. Moreover, the impurity classification subsystem innovatively adopts the feature extraction method based on convolutional neural network to realize the fast and accurate classification of paddy impurity images. To balance the classification accuracy and computational complexity, we use GhostNet as the backbone network for efficient high‐dimensional semantic feature extraction from paddy images. Furthermore, the visual attention mechanism and multiscale feature aggregation strategy are introduced to enhance the extracted features and improve the accuracy of impurity classification. The image acquisition subsystem collects 256 raw images with a resolution of 5120 × 5120. After image preprocessing, a total of 5000 paddy image blocks are obtained, and each block has different resolutions. These image blocks include lesion paddy images, moldy paddy images, sprout paddy images, immature paddy images, and normal paddy images. The trained impurity classification model achieves 83.64% precision, 83.60% recall, 83.54% F 1 score, and 93.44% accuracy. These results not only show the efficiency and accuracy of the system in practical applications but also provide important reference value for future research and application in a wider range of agricultural detection fields. Convolutional neural network ; Feature (linguistics) ; Feature extraction ; Block (permutation group theory) ; Machine vision ; Pattern recognition (psychology) ; Artificial neural network ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use