jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Richárd Takács | Ibolya Zsoldos | Dániel Szentendrei
Source: SAE International Journal of Engines (article, 2024-04-04)
Field: Materials Science — Surfaces, Coatings and Films

Water Droplet Collison and Erosion on High-Speed Spinning Wheels &lt;div&gt;The water droplet erosion (WDE) on high-speed rotating wheels appears in several engineering fields such as wind turbines, stationary steam turbines, fuel cell turbines, and turbochargers. The main reasons for this phenomenon are the high relative velocity difference between the colliding particles and the rotor, as well as the presence of inadequate material structure and surface parameters. One of the latest challenges in this area is the compressor wheels used in turbochargers, which has a speed up to 300,000 rpm and have typically been made of aluminum alloy for decades, to achieve the lowest possible rotor inertia. However, while in the past this component was only encountered with filtered air, nowadays, due to developments in compliance with tightening emission standards, various fluids also collide with the spinning blades, which can cause mechanical damage. One such fluid is the condensed water in the low-pressure exhaust gas recirculation channel (LP-EGR) formulated at cold starts and low-speed high load conditions. This kind of design has been developed to reduce nitrogen oxide emissions and is used in both gasoline and diesel engines. This article presents a state-of-the-art review of this WDE process, focusing on the formation of the condensed water before the compressor wheel, summarizing the influencing factors of WDE and the effects of the damage including using component testbench experiences and simulation methodologies. Inspection possibilities such as high-speed camera measurement and vibration analysis are also an important part of the document.&lt;/div&gt; Spinning ; Erosion ; Materials science ; Automotive engineering ; Environmental science ; Mechanics ; Physics ; Composite material ; Engineering ; Geology Surface Modification and Superhydrophobicity ; Aerosol Filtration and Electrostatic Precipitation ; Icing and De-icing Technologies
Authors: Dmitry Dobrykh | Anna Mikhailovskaya | Konstantin Grotov | Dmytro Vovchuk | Vladyslav Tkach | Mykola Khobzei | Anton Kharchevskii | Aviel Glam | Pavel Ginzburg
Source: arXiv (Cornell University) (preprint, 2024-08-02)
Field: Materials Science — Electronic, Optical and Magnetic Materials

3D Genetic Metamaterials for Scattering Maximization The rapidly growing volume of drone air traffic demands improved radar surveillance systems and increased detection reliability in challenging conditions. The scattering cross-section, which characterizes a target's radar visibility, is a key element in detection schemes and thus becomes a primary objective in civilian applications. Here, we introduce a concept of genetically designed metamaterials, specifically engineered to enhance scattering for end-fire incidence scenarios. Multi-layer stacks of arrays, encompassing strongly coupled electric and magnetic resonators, demonstrated above 1 m^2 broadband scattering at 10 GHz, despite having an end-fire physical cross-section smaller than one squared wavelength. Those performances, crucial for effective civil radar air traffic monitoring, facilitate exploring highly scattering structures as labels for small airborne targets. This objective has been demonstrated with a set of outdoor experiments with the DJI Mini 2 drone. Lightweight, conformal add-ons with significantly high scattering cross-sections can serve as auxiliary tools to empower passive monitoring systems, thereby providing an additional layer of security in urban airspace. Metamaterial ; Scattering ; Maximization ; Physics ; Computer science ; Optics ; Mathematics ; Mathematical optimization Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: Emre Gülsoylu | Zeynep Çipiloğlu Yıldız
Source: Gazi Journal of Engineering Sciences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

e-ksper: A Convolutional Neural Network Based System for Seedless Raisin Quality Grading Seedless raisins are graded according to their quality which is determined based on several features such as color, size, texture, and humidity.Currently, most of the raisin grading process is performed by human experts manually, which is laborious and subjective work.Therefore, an automated system that enables objective evaluation of the raisins would be helpful for both producers and experts during this process.In this study, we propose a simple machinery prototype that takes images of raisins under standard background and illumination conditions and an automated system that performs quality grading of raisins using convolutional neural networks.The proposed model not only targets color classes but also aims to identify foreign matters and defected raisins.The model achieves about 88.2% average classification accuracy on five classes including four color classes and a defected raisin class; whereas the model's accuracy becomes 98.6% if defected raisins are excluded.Hence, the proposed model is very successful in differentiating color classes and has also considerable success in detecting foreign matters and defected raisins.We provide a comprehensive analysis and discussion on these results. Convolutional neural network ; Grading (engineering) ; Computer science ; Artificial intelligence ; Engineering Spectroscopy and Chemometric Analyses
Authors: Yona Davincy R. | Ebenezer Veemaraj | E. Bijolin Edwin | Stewart Kirubakaran S | M. Roshni Thanka | Dafny Neola J.
Source: International Journal of Informatics and Communication Technology (IJ-ICT) (article, 2024-10-22)
Field: Chemistry — Inorganic Chemistry

Revolutionizing agricultural efficiency with advanced coconut harvesting automation &lt;span style="vertical-align: inherit;"&gt;&lt;span style="vertical-align: inherit;"&gt;The precision coconut harvesting system aims to develop an efficient system for accurately detecting coconuts in agricultural landscapes using advanced image processing techniques. Coconut cultivation is vital to many tropical economies and precise monitoring is essential for optimizing yield and resource utilization. Traditional methods of coconut detection are labor-intensive and time-consuming. The proposed computer vision-based approach automates and enhances coconut detection by analyzing high-resolution images of coconut plantations. Pre-processing techniques improve image quality and object detection algorithms such as convolutional neural networks (CNNs) identify coconut clusters. Challenges like lighting variations and background clutter are addressed using feature extraction and pattern recognition. A user-friendly interface visualizes detection results, aiding farmers in timely decision-making. Extensive testing on diverse datasets evaluates system effectiveness. This model aims to advance precision agriculture, enhancing productivity and informing coconut farmers' decision-making processes. Using a CNN model, the accuracy of coconut detection based on its ripeness was 98.8%.&lt;/span&gt;&lt;/span&gt; Automation ; Agriculture ; Agricultural engineering ; Computer science ; Business ; Agricultural economics ; Agricultural science ; Agroforestry ; Environmental science ; Engineering ; Biology ; Economics ; Ecology Coconut Research and Applications
Authors: Yuan‐Wei Ma | Guanfang Wang | Hongyi Chen | Min‐Yeh Tsai
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-03-03)
Field: Chemistry — Organic Chemistry

Exploring Abeta42 Monomer Diffusion Dynamics on Fibril Surfaces through Molecular Simulations Abstract This study provides critical insights into the role of surface-mediated secondary processes in Alzheimer’s disease, particularly regarding the aggregation of Abeta42 peptides. Employing coarse-grained molecular dynamics simulations, we focus on elucidating the molecular intricacies of these secondary processes beyond primary nucleation. Central to our investigation is the analysis of a freely diffusing Abeta42 monomer on pre-formed fibril structures. We conduct detailed calculations of the monomer’s diffusion coefficient on fibril surfaces (as a one-dimensional case), along with various monomer orientations. Our findings reveal a strong and consistent correlation between the monomer’s diffusion coefficient and its orientation on the surface. Further analysis differentiates the effects of parallel and perpendicular alignments with respect to the fibril axis. Additionally, we explore how different fibril surfaces influencèmonomer dynamics by comparing the C-terminal and N-terminal surfaces. We find that the monomer exhibits lower diffusion coefficients on the N-terminal surface. Differences in surface roughness (S R ), quantified using root-mean-square distances, significantly affect monomer dynamics, thereby influencing the secondary aggregation process. Importanly, this study underscores that fibril twisting acts as a regulatory niche, selectively influencing these orientations and their diffusion properties necessary for facilitating fibril growth within biologically relevant time scales. This discovery opens new avenues for targeted therapeutic strategies aimed at manipulating fibril dynamics to mitigate the progression of Alzheimer’s disease. Molecular dynamics ; Monomer ; Fibril ; Dynamics (music) ; Biophysics ; Chemistry ; Chemical physics ; Nanotechnology ; Materials science ; Computational chemistry ; Physics ; Polymer ; Biology Surfactants and Colloidal Systems ; Advanced Polymer Synthesis and Characterization ; Computational Drug Discovery Methods
Authors: Tsiye Tekleyohanis
Source: International Journal of Environmental Sciences & Natural Resources (article, 2024-09-12)
Field: Materials Science — Materials Chemistry

Review: Green Energy Alchemy: Converting Industrial Flue Gas into Eco-Friendly Charcoal Charcoal ; Alchemy ; Environmentally friendly ; Flue gas ; Waste management ; Environmental science ; Architectural engineering ; Process engineering ; Engineering ; Materials science ; Art ; Art history ; Metallurgy Thermal and Kinetic Analysis ; Chemistry and Chemical Engineering ; Various Chemistry Research Topics
Authors: Mat Jusoh, Suriani | Zulkifli, Mohammad Fakhratul Ridwan | Abdullah, Samsuri | Shafi, Ayu Rafiqah | Mohd Nasir, Mohd Fadzhel | Maulana, Ummi Nurashira | Mohd Radzi, Fathin Sakinah | Zainulabidin, Syakir Hakimi
Source: Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) (article, 2024-10-30)
Field: Materials Science — Polymers and Plastics

Response surface methodology for optimisation of parameter on water absorption of natural fibre hybrid composite in boat construction Today, the utilisation of natural or green fibre has supplanted engineered fibre as fibreglass has become a pattern in composite boat producing and other gears because of its lightweight, excellent relative mechanical properties, and more significant elements. For example, it is eco-accommodating, has manageable materials, and has lower costs compared to fibreglass. The utilisation of fibreglass is costly and has a high effect on the natural biological system. It also gets over the area of ecological contamination, word-related well-being, and security concerns. This study used the Woven Kenaf/fibreglass as reinforcement and polyester as matric to fabricate hybrid composite coupons. Response Surface Methodology (RSM) of Box-Behnken Design (BBD) was applied to optimise fibre contents (35 to 75 wt%) and water absorption performance for the development of Woven Kenaf/fibreglass to polyester hybrid composite material based on the parameters. The BBD revealed that 45% Woven Kenaf/fibreglass to polyester performed the optimum fibre content and water absorption. Analyses of variance (ANOVA) showed that the model satisfactorily correlated the parameters. After immersion, 45% Woven Kenaf/fibreglass to polyester composite also gained 9.48% weight. Composite number ; Polyester ; Response surface methodology ; Absorption of water ; Natural fiber ; Synthetic fiber ; Central composite design Natural Fiber Reinforced Composites ; Material Selection and Properties ; Mechanical Behavior of Composites
Authors: Kehinde Odelabu | Christianah Racheal
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-02-23)
Field: Materials Science — Polymers and Plastics

Investigating the Intradermal Irritation Test of Hydrogel: A Study on Domestic Production Abstract This study employs the intradermal injection method to assess the skin irritation potential of domestically produced medical hydrogel. Healthy white rabbits received intradermal injections of 0.5% sodium chloride solution (control), 0.5% sodium chloride extract, and olive oil extract as test samples. Erythema and edema were observed at 24 and 72 hours post-injection, and the primary irritation index was determined. Results revealed a primary irritation index of 0 for domestically produced hydrogel, indicating no skin irritation response. Irritation ; Skin irritation ; Production (economics) ; Test (biology) ; Dermatology ; Medicine ; Immunology ; Economics ; Biology Conducting polymers and applications ; Hydrogels: synthesis, properties, applications ; Additive Manufacturing and 3D Printing Technologies
Authors: Jun Li | Meiqi Zhang | Kaixuan Wu | Hengxu Chen | Zhe Ma | Juan Xia | Guangwen Huang
Source: Agriculture (article, 2024-12-14)
Field: Chemistry — Analytical Chemistry

Field Grading of Longan SSC via Vis-NIR and Improved BP Neural Network Soluble solids content (SSC) measurements are crucial for managing longan production and post-harvest handling. However, most traditional SSC detection methods are destructive, cumbersome, and unsuitable for field applications. This study proposes a novel field detection model (Brix-back propagation neural network, Brix-BPNN), designed for longan SSC grading based on an improved BP neural network. Initially, nine preprocessing methods were combined with six classification algorithms to develop the longan SSC grading prediction model. Among these, the model preprocessed with Savitzky–Golay smoothing and the first derivative (SG-D1) demonstrated a 7.02% improvement in accuracy compared to the original spectral model. Subsequently, the BP network structure was refined, and the competitive adaptive reweighted sampling (CARS) algorithm was employed for feature wavelength extraction. The results show that the improved Brix-BPNN model, integrated with the CARS, achieves the highest prediction performance, with a 2.84% increase in classification accuracy relative to the original BPNN model. Additionally, the number of wavelengths is reduced by 92% compared to the full spectrum, making this model both lightweight and efficient for rapid field detection. Furthermore, a portable detection device based on visible-near-infrared (Vis-NIR) spectroscopy was developed for longan SSC grading, achieving a prediction accuracy of 83.33% and enabling fast, nondestructive testing in field conditions. Grading (engineering) ; Artificial neural network ; Environmental science ; Botany ; Horticulture ; Biology ; Artificial intelligence ; Computer science ; Ecology Spectroscopy and Chemometric Analyses ; Optical Systems and Laser Technology ; Industrial Vision Systems and Defect Detection
Authors: Boris Kriuk | Kriuk, Fedor
Source: arXiv (Cornell University) (preprint, 2024-01-30)
Field: Materials Science — Materials Chemistry

Deep Learning-Driven Approach for Handwritten Chinese Character Classification Handwritten character recognition (HCR) is a challenging problem for machine learning researchers. Unlike printed text data, handwritten character datasets have more variation due to human-introduced bias. With numerous unique character classes present, some data, such as Logographic Scripts or Sino-Korean character sequences, bring new complications to the HCR problem. The classification task on such datasets requires the model to learn high-complexity details of the images that share similar features. With recent advances in computational resource availability and further computer vision theory development, some research teams have effectively addressed the arising challenges. Although known for achieving high accuracy while keeping the number of parameters small, many common approaches are still not generalizable and use dataset-specific solutions to achieve better results. Due to complex structure, existing methods frequently prevent the solutions from gaining popularity. This paper proposes a highly scalable approach for detailed character image classification by introducing the model architecture, data preprocessing steps, and testing design instructions. We also perform experiments to compare the performance of our method with that of existing ones to show the improvements achieved. Character (mathematics) ; Computer science ; Artificial intelligence ; Natural language processing ; Mathematics Machine Learning in Materials Science
Authors: Pengyuan Li | Qingquan Xu | Cheng Huang | Haoqing Wang | Yisen Wang | Zibo Wang | Yibo Zhang | Ao Wang | Tianci Cui | Xinyue Ni | Yutong Wang | Chen Gong
Source: Journal of Sustainability for Energy (article, 2024-03-30)
Field: Materials Science — Materials Chemistry

Improvement the Preparation of C4 Oolefin Through Ethanol Coupling and Optimization the Gray Correlation Degree Algorithm C4 olefin is an important chemical raw material, but traditional production methods use limited and polluting fossil energy as raw materials. Ethanol stands out from many alternative energy sources because of its wide sources, easy conversion, and low pollution. The preparation of C4 olefins from ethanol has become an effective alternative route for olefin production, which has great environmental and economic value. Metal oxides are the main catalysts for the preparation of C4 olefins from ethanol. In this paper, a $\mathrm{SiO}_2$-$\mathrm{HAP}$ catalyst with both acid and base active sites was designed, and Co metal with dehydrogenation activity was supported on its surface. To improve the catalytic activity and improve the conversion of ethanol and the selectivity of C4 olefin, experiments were carried out by changing the process parameters such as $\mathrm{Co}$ loading (weight ratio of $\mathrm{Co}$ to $\mathrm{SiO}_2$), HAP (hydroxyapatite) mass, ethanol concentration, and reaction temperature. The improved gray relational degree algorithm was used to analyze the relational degree of process parameters with ethanol conversion and C4 olefin selectivity. Experimental results show that the detection accuracy of this algorithm for C4 olefin selectivity is better than that of the traditional algorithm without considering the difference in change rate between data, the detection accuracy is improved by 50%, and the detection accuracy of ethanol conversion is improved by 2%. Olefin fiber ; Selectivity ; Raw material ; Catalysis ; Dehydrogenation ; Ethanol fuel ; Ethanol ; Materials science ; Algorithm ; Chemical engineering ; Chemistry ; Organic chemistry ; Computer science ; Engineering Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Catalytic Processes in Materials Science
Authors: Yuan Huang | Yiying Liang | Yaquan Wang | Gang Li
Source: Biomedical testing & sensing (article, 2024-01-01)
Field: Chemistry — Spectroscopy

A New Era of Chromatographic Technology: Integration and Application of Intelligent, Automated, and Green Analytical Methods Chromatographic technology, as a vital tool in modern analytical chemistry, is widely applied in fields such as environmental monitoring, food safety, and pharmaceutical testing. With the advancement of artificial intelligence (AI) and the promotion of green development concepts, chromatographic technology is progressively evolving towards intelligence, automation, and sustainability. Intelligence enhances analytical precision and efficiency, automation meets the demands for high-throughput detection, and sustainability drives forward environmentally-friendly practices. This paper, considering China's national context, explores the forefront developments and application challenges of chromatographic technology, proposing strategies to foster technological innovation and industrialization, thereby supporting global green development. Computer science ; Process engineering ; Chromatography ; Biochemical engineering ; Engineering ; Systems engineering ; Chemistry Analytical Chemistry and Chromatography ; Spectroscopy and Chemometric Analyses
Authors: Priyabrata Karmakar | Manzur Murshed | Shyh Wei Teng
Source: arXiv (Cornell University) (preprint, 2024-11-17)
Field: Chemistry — Analytical Chemistry

Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data Recently hyperspectral imaging (HSI)-based grain quality assessment has gained research attention. However, unlike other imaging modalities, HSI data lacks sufficient labelled samples required to effectively train deep convolutional neural network (DCNN)-based classifiers. In this paper, we present a novel approach to grain quality assessment using HSI combined with few-shot learning (FSL) techniques. Traditional methods for grain quality evaluation, while reliable, are invasive, time-consuming, and costly. HSI offers a non-invasive, real-time alternative by capturing both spatial and spectral information. However, a significant challenge in applying DCNNs for HSI-based grain classification is the need for large labelled databases, which are often difficult to obtain. To address this, we explore the use of FSL, which enables models to perform well with limited labelled data, making it a practical solution for real-world applications where rapid deployment is required. We also explored the application of FSL for the classification of hyperspectral images of bulk grains to enable rapid quality assessment at various receival points in the grain supply chain. We evaluated the performance of few-shot classifiers in two scenarios: first, classification of grain types seen during training, and second, generalisation to unseen grain types, a crucial feature for real-world applications. In the first scenario, we introduce a novel approach using pre-computed collective class prototypes (CCPs) to enhance inference efficiency and robustness. In the second scenario, we assess the model's ability to classify novel grain types using limited support examples. Our experimental results show that despite using very limited labelled data for training, our FSL classifiers accuracy is comparable to that of a fully trained classifier trained using a significantly larger labelled database. Hyperspectral imaging ; Quality (philosophy) ; Remote sensing ; Quality assessment ; Environmental science ; Computer science ; Geography ; Engineering ; Reliability engineering ; Evaluation methods ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality