jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jiarong Zhong | Zhanlong Zhang | Zijian Dong | Zhixuan Xue | Jiaqi Cheng | Jun Deng | Fan Wang
Source: Applied Sciences (article, 2025-01-05)
Field: Materials Science — Materials Chemistry

Research on Adhesion Characteristic Experiment of Carbon Particles in Transformer Oil on the Surface of Epoxy Resin Under DC Electric Field Transformer oil will inevitably be contaminated by impurity particles in the actual operation process; Carbon particles are the most abundant conductive particles in transformer oil. The adhesion behavior on the internal insulating surface will cause electric field distortion, which will pose a serious threat to the safe and stable operation of oil-immersed power equipment. To this end, this paper builds an experimental platform for simulating the adhesion behavior of carbon particles in transformer oil, studies the adhesion characteristics of carbon particles, and analyzes the influence of electric field, particle size, oil flow velocity, and other factors on the adhesion of carbon particles. The results show that: the DC electric field is the main factor driving the adhesion of carbon particles on the surface of epoxy resin (with the increase in electric field strength, the degree of adhesion of carbon particles firstly rises and then decreases); the smaller the size of the carbon particles, the easier it is to adhere, and the corresponding electric field strength is different for different sizes of carbon particles when the degree of adhesion is the largest; the velocity of the transformer oil will have a significant impact on the adhesion behavior of the carbon particles (with the increase in the flow velocity, the degree of adhesion of carbon particles firstly rises and then decreases). The research conclusion of this article is helpful in guiding the evaluation of insulation performance and the optimization of insulation structure design in the converter transformer valve-side bushing considering the phenomenon of particle adhesion. Epoxy ; Materials science ; Composite material ; Transformer ; Electrical engineering ; Voltage ; Engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation
Authors: Sara León Ecay | Jon Ruiz de Gauna González | Ainara López Maestresalas | Carmen Jarén | Silvia Arazuri Garín
Source: Academica-e (Universidad Pública de Navarra) (article, 2025-01-01)
Field: Chemistry — Analytical Chemistry

On-site identification of esca-affected vines using hyperspectral imaging Esca represents one of the greatest threats to modern viticulture as it causes large annual economic losses. At present, there is a lack of effective strategies for disease control, so a technique capable of detecting affected vines would allow annual monitoring of disease incidence in the vineyard leading to a better crop management and decision making. This study evaluates close-range hyperspectral imaging for the detection of esca naturally infected vines. Images of 11 vines of the Tempranillo variety grown on plots in Bodegas Otazu, in Etxauri (Navarre, Spain) were acquired. A Specim IQ snapshot hyperspectral camera was used to record the images on August, 21 2023 on the field under natural light conditions. The camera has a spectral resolution of 7 nm (204 wavelengths) and a spatial resolution of 512 x 512 in the 400 ¿ 1000 nm spectral range (Vis-NIR). An individual image was acquired for each vine, of which 9 were symptomatic and 2 asymptomatic. Three classes were analysed: asymptomatic leaves of asymptomatic vines (Class 1), asymptomatic leaves of symptomatic vines (Class 2) and asymptomatic areas of symptomatic leaves of symptomatic vines (Class 3). A total of 300 pixels were randomly selected, 100 per class, for further analysis. Partial Least Square Discriminant Analysis (PLSDA) was used to classify the pixels into the three categories. An accuracy of 86% was achieved in the cross-validation dataset. Models were externally validated using an image of an asymptomatic vine and an image of a symptomatic vine. The visualisation of the images showed that the majority of the pixels of the asymptomatic vine image were classified as class 1, while most of the pixels of the symptomatic vine image were classified as either class 2 or class 3. Hence, this study demonstrated the potential of close-range HSI for the on-site detection of esca. Hyperspectral imaging ; Vineyard ; Pixel ; Vine ; Asymptomatic ; Image resolution ; High resolution Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Chuanbing Wang | Yuxia Tang | Jiajia Tang | Jie Zhang | Siqi Wang | Feiyun Wu | Shouju Wang
Source: European Radiology Experimental (article, 2025-01-10)
Field: Materials Science — Materials Chemistry

Long-term effects of linear versus macrocyclic GBCAs on gene expression in the central nervous system of mice Chronic gadolinium retention alters gene expression in the mouse central nervous system. Significant neuroinflammatory pathway changes were observed after linear gadodiamide exposure. MRI showed increased signal intensity in deep cerebellar nuclei after gadodiamide injection. Gadodiamide ; Gadolinium ; Spinal cord ; Medicine ; Central nervous system ; Magnetic resonance imaging ; Real-time polymerase chain reaction ; Saline ; Pathology ; Gene expression ; Chemistry ; Internal medicine ; Gene ; Biochemistry Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Molecular Sensors and Ion Detection
Authors: Yoon Ho Park | Myeong Seon Jeong | Gang San Song | Tak Gwonbaek | Young Kwan Kim | Hyun Suk Jung
Source: Han-guk hyeonmigyeong hakoeji/Applied microscopy (article, 2025-05-28)
Field: Materials Science — Materials Chemistry

Technical approaches of 3D reconstruction from protein complex using the mixture of differently stained images: providing suggestive evidence for improving its resolution Negative staining electron microscopy remains a valuable tool for structural biology, particularly for initial characterization of large protein complexes. However, the limitations of single staining methods often result in incomplete structural information. Here, we present a novel multi-stain approach for negative staining electron microscopy, applied to the structural analysis of the pyruvate dehydrogenase E2 (PDH E2) complex. By integrating data from three distinct staining agents (uranyl acetate, ammonium phosphotungstate, and ammonium molybdate) we demonstrate significant improvements in structural resolution and detail. Our method improved the resolution from a range of approximately 27-31 Å (observed with individual stains) to about 21.7 Å in the combined dataset. This enhancement facilitated a clearer visualization of the complex's icosahedral symmetry and allowed for a more precise determination of the overall shape and domain organization of the PDH E2 complex. The multi-stain approach revealed complementary structural information, with each stain highlighting different aspects of the protein complex. Uranyl acetate provided excellent overall contrast, while ammonium phosphotungstate and molybdate offered enhanced visibility of specific structural elements. The integration of these complementary data sets resulted in a more comprehensive structural model. Our findings suggest that this multi-stain negative staining approach can be a powerful tool for enhancing low-resolution structural information of large protein complexes, bridging the gap between initial characterization and high-resolution studies. This method holds promise for improving our understanding of challenging macromolecular assemblies and may serve as a valuable precursor to more advanced structural biology techniques. Uranyl acetate ; Negative stain ; Stain ; Ammonium molybdate ; Staining ; Resolution (logic) ; Chemistry ; Uranyl ; Structural biology ; Microscopy ; Biophysics ; Computational biology ; Crystallography ; Electron microscope ; Computer science ; Biochemistry ; Biology ; Optics ; Artificial intelligence ; Physics Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; Photosynthetic Processes and Mechanisms
Authors: Imad Eddine Marouf | Enzo Tartaglione | Stéphane Lathuilière | Joost van de Weijer
Source: ArXiv.org (preprint, 2025-02-06)
Field: Materials Science — Materials Chemistry

Ask and Remember: A Questions-Only Replay Strategy for Continual Visual Question Answering Continual Learning in Visual Question Answering (VQACL) requires models to acquire new visual-linguistic skills (plasticity) while preserving previously learned knowledge (stability). The inherent multimodality of VQACL exacerbates this challenge, as models must balance stability across visual and textual domains while adapting to novel objects and reasoning tasks. Existing methods, primarily designed for unimodal settings, often fall short in addressing this dual requirement. In this work, we present QUestion-only replay with Attention Distillation (QUAD), a novel approach for VQACL that leverages only past task questions for regularization. By eliminating the need to store visual data, QUAD not only reduces memory overhead, but also alleviates privacy concerns. Our method introduces a Question-only Replay mechanism that selectively reuses prior task questions to counteract overfitting to the answer space of the current task, addressing the problem out of answer set. Complementing this, we propose Attention Consistency Distillation to enforce both intra-modal and inter-modal attention consistency across tasks, preserving essential visual-linguistic associations. Extensive experiments on VQAv2 and NExT-QA demonstrate that QUAD significantly outperforms state-of-the-art methods, achieving robust performance in continual VQA. Code is available at: https://github.com/IemProg/QUAD. Computer science ; Artificial intelligence ; Knowledge management ; Psychology Machine Learning in Materials Science
Authors: Xianghao Ding | Yatian Wang | Kun Gen | Lei Ma | Tainian Wang | Guopeng Dong
Source: Diagnostyka (article, 2025-12-13)
Field: Materials Science — Materials Chemistry

Automatic pruning and lightweight deployment design of discharge depth diagnosis model based on reinforcement learning Reinforcement learning ; Pruning ; Software deployment ; Key (lock) High voltage insulation and dielectric phenomena ; Advanced Sensor and Control Systems ; Elevator Systems and Control
Authors: Chanchan Xu | Shuai Dong | Xiaojie Wang
Source: ArXiv.org (article, 2025-12-23)
Field: Materials Science — Materials Chemistry

Design and Modeling of a Simple-Structured Continuously Variable Transmission Utilizing Shape Memory Alloy Superelasticity for Twisted String Actuator Twisted String Actuators (TSAs) are widely used in robotics but suffer from a limited range of Transmission Ratio (TR) variation, restricting their efficiency under varying loads.To overcome this, we propose a novel lightweight, simple-structured Continuously Variable Transmission (CVT) mechanism for TSA utilizing Shape Memory Alloy (SMA) superelasticity. The CVT mechanism consists solely of a pair of highly lightweight superelastic SMA rods connecting the ends of twisted strings. These rods deform under external loads, adjusting the inter-string distance to enable continuous TR variation.We develop a comprehensive theoretical model that integrates three critical nonlinearities Shape-memory alloy ; Actuator ; SMA* ; Pseudoelasticity ; Continuously variable transmission ; Rod ; String (physics) Shape Memory Alloy Transformations ; Soft Robotics and Applications ; Prosthetics and Rehabilitation Robotics
Authors: Саид Шаумаров | Sanjar Kandakhorov | Zafar Okilov | Aziza Gulomova
Source: Международный научный журнал «Инженер» (article, 2025-03-31)
Field: Materials Science — Materials Chemistry

Improvement of pavement concrete by industrial waste microfillers Determination of the composition of pavement concrete using industrial waste in the most optimal options, determination of its flow limit, surface activity, and other properties of the composition, such as water separation, are presented. Environmental science ; Industrial waste ; Composition (language) ; Waste management ; Civil engineering ; Engineering Structural mechanics and materials ; Advanced Energy Technologies and Civil Engineering Innovations ; Concrete and Cement Materials Research
Authors: Habib Hamam
Source: Journal of Artificial Intelligence & Cloud Computing (article, 2025-05-14)
Field: Materials Science — Materials Chemistry

Connecting the Dots: Exploring the Fundamental Underpinnings of Deep Learning Deep learning has transformed various sectors, introducing new applications and opportunities. However, the underlying physical mechanisms or mathematical theories responsible for its success remain fundamental questions. This inquiry explores the connection between deep learning algorithms and established scientific principles with the aim of uncovering the mysteries behind their remarkable capabilities. By bridging the gap between deep learning, neural networks, and scientific knowledge, we can develop robust and interpretable models with enhanced capabilities. This ongoing research involves collaboration across diverse fields to unveil the hidden intricacies of deep learning algorithms and their links to physical phenomena. The ultimate goal is to contribute to the potential of the journal by examining the theory, design and application of neural networks and machine learning, focusing on the effectiveness of neural network paradigms for deep learning and their connections to physical events. By examining the intersection of deep learning, neural networks, and physical phenomena, we aim to advance our understanding and use of neural networks and machine learning in many areas of space, pushing the boundaries of excellence in science and engineering Deep learning ; Psychology ; Cognitive science ; Cognitive psychology ; Computer science ; Artificial intelligence Machine Learning in Materials Science ; Computational Physics and Python Applications ; Knowledge Management and Technology
Authors: Bradley L. Edgar | Val Hyginus Udoka Eze | Saadelnour Abdueljabbar Adam Saadelnour | Sanusi Yinka | Pius Erheyovwe Bubu
Source: Frontiers in Mechanical Engineering (article, 2025-12-02)
Field: Materials Science — Biomaterials

A systematic review of thermodynamic modeling and machine learning integration for optimizing plate heat exchanger performance in Uganda’s brewing industry Introduction Plate Heat Exchangers (PHEs) play a crucial role in industrial thermal processes, particularly in the brewing industry, where precise temperature regulation influences fermentation efficiency and product quality. In Uganda, PHE performance is constrained by fouling, variable thermal loads, and resource limitations. These challenges highlight the need for advanced optimization approaches tailored to tropical climates and resource-limited settings. Methods A systematic review was conducted to evaluate the use of thermodynamic modeling and machine learning (ML) for optimizing PHE operation in industrial applications. A total of 199 studies were screened, of which 112 met predefined methodological and quality criteria. Extracted data were synthesized to compare traditional approaches with hybrid physical-ML models, including Artificial Neural Networks (ANN), Particle Swarm Optimization (PSO), and Genetic Algorithms (GA). Performance indicators assessed included predictive accuracy, energy efficiency, fouling behavior, and operational responsiveness. Results Hybrid models integrating thermodynamic principles with ML techniques consistently outperformed conventional modeling approaches. Significant gains were observed in predictive accuracy across included studies, although effect sizes varied due to dataset diversity and differing evaluation metrics. Real-time fouling prediction using ML contributed to a 22% reduction in maintenance costs and a 15% decrease in operational downtime. Implementations of digital twin architectures and adaptive control algorithms achieved an 18% improvement in energy efficiency and enhanced system responsiveness by up to 30% under dynamic thermal load conditions. Discussion Findings demonstrate the strong potential of combining thermodynamic modeling with AI-driven methodologies to enhance PHE performance in the brewing sector and related industries. While substantial technological improvements have been reported, context-specific barriers persist, particularly the adaptation of advanced models to tropical environmental conditions and the cost-effective integration of renewable energy sources. Addressing these challenges will be essential for unlocking the full potential of self-optimizing PHE systems that promote energy efficiency, product quality, and sustainable industrial growth in regions such as Uganda. Particle swarm optimization ; Artificial neural network ; Heat exchanger ; Brewing ; Predictive modelling ; Fouling ; Resource efficiency ; Efficient energy use Calcium Carbonate Crystallization and Inhibition ; Freezing and Crystallization Processes ; Heat Transfer and Optimization
Authors: Yixuan Wang | Hong Wang | X. Wang
Source: Remote Sensing (article, 2025-09-01)
Field: Chemistry — Analytical Chemistry

A Method for the Extraction of Apocynum venetum L. Spatial Distribution in Yuli County, Xinjiang, via an Improved SegFormer Network Efficient and accurate acquisition of spatial distribution information for Apocynum venetum L. is highly important for the sustainable development of agriculture in Yuli County, Xinjiang. As an important cash crop, Apocynum relies on specific natural conditions for growth, and its survival environment is currently under severe threat. Therefore, accurately quantifying its spatial distribution information is crucial. This research takes Yuli County in Xinjiang as the study area and proposes an enhanced SegFormer model based on deep learning, aiming to realize the effective identification and extraction of Apocynum. The study indicates the following. (1) The improved SegFormer model adds smaller-scale feature layers in the encoder stage, allowing the improved model’s encoder to extract features at five scales: 1/4, 1/8, 1/16, 1/32, and 1/64; meanwhile, integrating the T2T-ViT backbone network into the encoder significantly enhances the precision and efficiency of Apocynum’s spatial distribution extraction. (2) Compared with Unet, TransUNet, and the original SegFormer, the improved SegFormer model outperforms the other models in terms of the mIoU, OA, and mPA metrics, achieving values of 88.22%, 93.98%, and 89.66%, respectively. (3) Ablation experiments show that the T2T_vit_14 model performs best among all the T2T-ViT configurations, with superior extraction effects on fragmented small plots compared with the other models. Therefore, the T2T_vit_14 model is integrated into the SegFormer model. This work improves the extraction accuracy and efficiency of the spatial distribution of Apocynum via an improved SegFormer model, which has strong stability and robustness and offers scientific evidence for resource protection, restoration planting, and germplasm breeding in Yuli County, Xinjiang. Extraction (chemistry) ; Spatial distribution ; Forestry ; Geography ; Environmental science ; Remote sensing ; Chromatography ; Chemistry Chromatography in Natural Products ; Traditional Chinese Medicine Analysis ; Plant-based Medicinal Research
Authors: Bilge BERKHAN KASTACI
Source: DergiPark (Istanbul University) (article, 2025-05-12)
Field: Materials Science — Polymers and Plastics

MODELING AND PREDICTION OF THE BURSTING STRENGTH OF KNITTED FABRICS WITH DIFFERENT STRUCTURAL PROPERTIES USING MACHINE LEARNING METHODS Örme kumaşların patlama mukavemeti, çok eksenli ve ani basınçlara karşı dayanımı belirleyen kritik bir mekanik özelliktir ve spor–performans, tıbbi tekstil ve koruyucu giyim uygulamalarında önemli bir rol oynar. Ancak geleneksel test yöntemleri zaman ve maliyet açısından sınırlayıcıdır. Bu nedenle, kumaşın yapısal ve fiziksel özelliklerine dayalı tahmin modellerinin geliştirilmesi üretim verimliliği ve kalite kontrol açısından önemli avantajlar sunmaktadır. Bu çalışmada, 56 farklı konstrüksiyona sahip örme kumaştan elde edilen deneysel veriler kullanılarak çeşitli makine öğrenmesi algoritmalarıyla patlama mukavemeti tahmin modelleri geliştirilmiştir. Girdi parametreleri literatür bulguları, korelasyon analizi ve Varyans Şişirme Faktörü (VIF) sonuçlarına göre belirlenmiştir. Doğrusal regresyon (LR), çok değişkenli ayarlanabilir regresyon eğrileri (MLR) ve destek vektör makinelerinde regresyon (SMOreg) modelleri, k-katlı çapraz doğrulamada en yüksek tahmin performansını göstermiştir. Duyarlılık analizi, LR ve SMOreg modellerinde çubuk sıklığının, MLR modelinde ise iplik mukavemetinin tahmin performansını en az etkileyen parametre olduğunu ortaya koymuştur. Bu bulgular, makine öğrenmesi tabanlı yaklaşımların örme kumaşların mekanik özelliklerinin öngörülmesinde etkili ve uygulanabilir bir alternatif sunduğunu göstermektedir. Artificial neural network ; Pattern recognition (psychology) ; Quality (philosophy) ; Neutral network Textile materials and evaluations ; 3D Shape Modeling and Analysis ; Advanced Sensor and Energy Harvesting Materials
Authors: Shaotong Pei | Xu Tian | Wang Wei-qi | Keyu Li | Hu Chenlong
Source: Energy Science & Engineering (article, 2025-10-16)
Field: Materials Science — Materials Chemistry

PSH‐YOLO: A Detection Method for Small‐Target Thermal Defects in Porcelain Insulators ABSTRACT In recent years, infrared image‐based insulator defect detection technology has been widely applied in the field of online monitoring for power equipment due to its noncontact and high‐efficiency characteristics; however, existing algorithms still face issues such as insufficient detection accuracy and low computational efficiency in multistate insulator classification tasks, making it difficult to meet practical engineering requirements; to address these challenges, this paper proposes an improved small‐target multidefect detection algorithm Porcelain insulator Small‐target Heating defect detection You Only Look Once (PSH‐YOLO): based on YOLOv8, it employs a hybrid model of self‐attention and convolution to aggregate both convolutional and self‐attention features; then applies the MobileViT network to enhance the model's training speed and parameter efficiency, ensuring the overall lightweight nature of the model; additionally incorporates a bidirectional feature pyramid network to improve accuracy through multilevel feature pyramids and bidirectional information flow; finally, utilizes the Inner‐WIoU loss function to effectively reduce oscillations during training while further enhancing the model's accuracy; to obtain test data, this paper conducted infrared imaging experiments on defective insulators to capture images under varying conditions; experimental validation confirms that the proposed multidefect small‐target YOLO algorithm, PSH‐YOLO, achieves an average accuracy improvement of 6.17%, with Giga Floating‐point Operations Per Second reduced to 7.1, fulfilling the requirements for identifying small‐target insulator defects, while ablation and comparative studies demonstrate the effectiveness and superiority of the proposed algorithm. Insulator (electricity) ; Convolution (computer science) ; Convolutional neural network ; Pyramid (geometry) ; Feature (linguistics) ; Deep learning ; Thermal ; Feature extraction High voltage insulation and dielectric phenomena ; Industrial Vision Systems and Defect Detection ; Advanced Neural Network Applications