jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Arailym Serikbay | Venera Nurmanova | Yerbol Akhmetov | Amin Zollanvari | Mehdi Bagheri
Source: International Transactions on Electrical Energy Systems (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

A Systematic YOLO‐Specific Model Selection for Mechanical Fault Identification in High‐Voltage Insulators Regular monitoring of outdoor insulators is crucial to ensure the reliable functioning of the power grid. With recent progress in computer vision technologies, traditional manual and expensive visual inspections can now be replaced by automated analysis using images captured by unmanned aerial vehicles (UAVs). In such applications, a practitioner might opt to choose a state‐of‐the‐art object detection and classification deep learning architecture, including You Look Only Once (YOLO). The variety of existing YOLO architectures per se makes selecting the best application‐dependent YOLO model challenging. However, selecting the best architecture solely based on performance without considering the model complexity limits its deployment on resource‐limited embedded devices. Consequently, we conduct a rigorous, systematic model selection based on the performance–complexity trade‐off across 13 YOLO architectures to determine the most effective model for detecting common mechanical faults in insulators using images captured by UAVs. A dataset comprising 15,000 images of insulators, categorized into normal condition, bird‐pecking damage, cracks, and missing caps, has been compiled for training the models. Specifically, all considered YOLO architectures are compared using model complexity and the mAP@0.5:0.95. During the model selection stage, YOLOv8l proved to be the best model in terms of mAP@0.5:0.95, while YOLOv5n was the model of choice in terms of complexity at the expense of a slight reduction in performance. Alongside YOLOv8l and YOLOv5n, an “optimal” model (OP‐YOLO) was selected using a multicriteria decision‐making approach, balancing detection accuracy and computational efficiency. In particular, in terms of test‐set performance, YOLOv8l, YOLOv5n, and OP‐YOLO achieved 0.919, 0.901, and 0.896 mAP@0.5:0.95, respectively. Although YOLOv8l reported a higher mAP@0.5:0.95, YOLOv5n requires ∼20.9 times less memory and ∼40.2 times less floating‐point operations per second (FLOPs). Also, YOLOv5n outperforms the OP‐YOLO model, still requiring ∼12 times less memory and ∼19 times less FLOPs. Identification (biology) ; Selection (genetic algorithm) ; Fault (geology) ; Reliability engineering ; High voltage ; Voltage ; Computer science ; Electronic engineering ; Engineering ; Electrical engineering ; Biology ; Machine learning High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Jannery Rivas | Omar Rivera‐Caballero | Héctor Poveda | Jorge Alfredo Ardila‐Rey | Carlos Boya‐Lara
Source: High Voltage (article, 2025-05-30)
Field: Materials Science — Materials Chemistry

Separation Maps for Classification of Multiple Partial Discharges: A Comparative Study Focusing on Time and Frequency Characteristics ABSTRACT Electrical insulation faults produce partial discharges (PD), which can be analysed to identify specific types of defects. PD clustering is a widely used method to identify PD sources, although its success depends largely on the feature maps used. In this paper, three widely used feature maps, or separation maps, are compared: chromatic, energy wavelet with principal component analysis (EW‐PCA), and time–frequency (TF). To compare and evaluate, five scenarios with multi‐PD environments with noise were developed. The clustering ability of the maps was evaluated using two performance indicators: intercluster distance and intracluster distance. The results indicate that the EW‐PCA map performed the best in all scenarios, correctly identifying the largest number of data points and producing the clearest and most distinct clusters. The TF map created distinct clusters in several scenarios, but not all. The chromatic map created distinct clusters in all scenarios but was not as well defined as the other two separation maps. Given the results, it is important in fieldwork to use a wide range of PD clustering, accompanied by performance metrics that support a less biased decision tailored to the test object. Separation (statistics) ; Pattern recognition (psychology) ; Statistics ; Artificial intelligence ; Mathematics ; Computer science High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Liang Zhao | Yachun Wang | Fei Xu
Source: Scientific Reports (article, 2025-08-07)
Field: Materials Science — Materials Chemistry

Accurate segmentation of localized fuel cladding chemical interaction layers in SEM micrographs with deep learning method U-Zr metallic fuels are promising fuel candidates for fast reactor applications. Fuel/cladding chemical interaction (FCCI) is a random, localized, complex interdiffusion phenomenon occurring at the fuel cladding interface under irradiation, thinning the cladding wall. This interaction has been recognized as a limiting factor in deploying metal fuels to achieve higher burnup under steady state operations. The post irradiation examination of Experimental Breeder Reactor II and Fast Flux Test Facility fuel pins with different irradiation conditions have been the primary method for investigating FCCI in metal fuels, utilizing various characterization techniques, including scanning electron microscopy (SEM). This study compared several computer vision and deep learning approaches for the automated segmentation of FCCI layers in SEM micrographs. We deployed and compared state-of-the-art deep learning models for the task of FCCI layer segmentation in SEM micrographs. A deep learning based end-to-end method proved its capability to enable rapid and accurate segmentation of FCCI in as-collected SEM micrographs, making it highly suitable for real-time applications to automate data analysis. The average segmentation mAP achieves a high performance of 90.8% for dozens of FCCI layers. Furthermore, the method reported in this study is extendable to segmentation tasks for other materials with similar resolution, texture, and contrast characteristics, paving the way for accelerated and automated analysis in characterization analysis and beyond. Cladding (metalworking) ; Segmentation ; Artificial intelligence ; Computer science ; Deep learning ; Computer vision ; Materials science ; Pattern recognition (psychology) ; Composite material Nuclear Materials and Properties ; Machine Learning in Materials Science ; Nuclear reactor physics and engineering
Authors: B. Neelampary | R.L. Samaraweera
Source: Sri Lankan Journal of Applied Sciences (article, 2025-02-28)
Field: Materials Science — Materials Chemistry

Study of Nano Metal Oxide Incorporated Zinc Phosphate Coatings as Corrosion Inhibitors on Mild Steel The existing corrosion prevention processes and methods are incapable of predicting all environmental conditions. On the other hand, the local metal coating and paint industries continue to suffer from a lack of appropriate advanced phosphating technology as a surface pre-coating technique. The incorporation of ceramic nanoparticles into the phosphate coating has improved the properties such as high corrosion resistance and secondary coating adhesion. As a result, the goal of this research was to create and improve a corrosion prevention method suitable for the metal coating industries by incorporating nano TiO2 into a phosphate precoating. To facilitate the coating via chemical conversion, the cleaned mild steel specimens were dipped in the prepared phosphating bath containing the nano TiO2. Under the given conditions, the optimum coating time was determined to be 90 minutes. Significant changes in coating weight, adhesion quality, surface porosity, and corrosion resistance were observed with the addition of nano TiO2 (average particle size ~ 25 nm). X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and a gemmological microscope were used to investigate the crystal phases and surface morphology of zinc phosphate coatings. XRD data clearly showed that nano TiO2 has successfully incorporated into the phosphate layer. The addition of nano TiO2 to the zinc phosphate coating improved its properties, such as coating thickness and surface area, promising an improvement in corrosion resistance. With minor modifications aiming to lower the coating time, the findings of this study could be easily implemented in the metal coating industries and extended to other metals such as steel, aluminium, and alloys. Coating ; Corrosion ; Zinc phosphate ; Zinc ; Scanning electron microscope ; Conversion coating ; Phosphate Corrosion Behavior and Inhibition ; Magnesium Oxide Properties and Applications ; Pigment Synthesis and Properties
Authors: Hyun Jung Kim | S. Yoo
Source: Tehnički glasnik (article, 2025-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Replacing Backpropagation with the Forward-Forward (FF) Algorithm in Transformer Models This study proposes a novel integration of the Forward-Forward (FF) algorithm into Transformer architectures as an efficient and gradient-free alternative to Backpropagation (BP). Motivated by the computational limitations of BP-such as high memory usage and gradient instability-we aim to examine whether FF can maintain comparable model performance while improving training efficiency. We present both theoretical justifications and empirical evaluations on the IMDB sentiment analysis dataset. Our experiments show that FF reduces training time by approximately 20% and memory usage by 30%, with only a marginal decrease in BLEU score (27.8 vs. 28.3) and slight increase in Perplexity (13.2 vs. 12.5). Furthermore, we extend our evaluation across varying model depths and hardware platforms (desktop GPU, cloud GPU, SoC-based laptop), and perform statistical testing and ablation studies to investigate FF’s behavior within Transformer components. These results highlight the viability of FF for scalable, rethereforeurce-efficient Transformer training and provide a foundation for future research in hybrid and distributed deep learning frameworks. Backpropagation ; Computer science ; Algorithm ; Transformer ; Artificial neural network ; Artificial intelligence ; Electrical engineering ; Engineering ; Voltage Magnetic Properties and Applications ; Power Systems Fault Detection
Authors: Lei Chen | Yingjie Cheng | Fujian Qin
Source: Journal of Composites Science (article, 2025-12-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Influence of FeSiB Layer Thickness on Magnetoelectric Response of Asymmetric and Symmetric Structures of Magnetostrictive/Piezoelectric Composites Asymmetric and symmetric magnetoelectric (ME)-laminated composites with magnetostrictive layer FeNi and piezoelectric layer PZT are prepared. The longitudinal resonance ME voltage coefficient in the symmetric composite is approximately 1.57 times that in the asymmetric composite with same constituents due to the flexural deformation and asymmetric stress distribution in the asymmetric structure. By bonding an additional high-permeability FeSiB, combining FeSiB with FeNi forms magnetization-graded ferromagnetic materials. A stronger maximum ME voltage coefficient, a dual-peak phenomenon, and a self-bias ME effect are observed. The maximum ME voltage coefficients for asymmetric and symmetric composites reach 3.10 V/Oe and 5.67 V/Oe by adjusting the thickness of the FeCuNbSiB layer. The maximum zero-bias ME voltage coefficients for asymmetrical and symmetrical composite materials reach 2.19 V/Oe at 25 µm thickness of FeSiB and 2.87 V/Oe at 75 µm thickness of FeSiB. Such high performances enable the ME composites to possess ideal sensing and make them promising for self-bias current sensor applications. Magnetostriction ; Composite number ; Voltage ; Layer (electronics) ; Piezoelectricity ; Deformation (meteorology) Multiferroics and related materials ; Magnetic Properties and Applications ; Metallic Glasses and Amorphous Alloys
Authors: H A Abhishek
Source: Journal of Information Systems Engineering & Management (article, 2025-05-27)
Field: Materials Science — Polymers and Plastics

Investigation on Impact of Thermo Physical Properties of Graphene Water and Al2O3 According to multiple scholarly and professional publications, introducing tiny fluids or operating waters with nanometer particulate size has significantly improved the thermal transmission characteristics of thermal exchange mechanisms. Prospective uses of tiny fluids in oil extraction and medication administration are drawing more interest. Reliability, along with effective productivity, is an obstacle, though. Stable enhancement and comprehension of tiny fluid dynamics are essential to ensure thermo physical qualities and exploit such enhanced fluids over time. This experimental work outlines upcoming exploration prospects to fill the divide between in-lab and production. It analyzes the state of the industry within tiny liquids studies, covering synthesis processes, stabilization assessment, and thermo physical features. Graphene water and Al2O3 are the two nanofluids considered in the experimental investigation. The outcomes of the research state that with an increase in temperature, the nanofluid density decreased, while all other properties, dynamic viscosity, specific heat capacity, and thermal conductivity values increased. Al2O3 has shown better performance in terms of dynamic viscosity, thermal conductivity, and specific heat capacity properties. XRD patterns are compared; an Al2O3 spike is high with 2θ = 30.1. Graphene ; Materials science ; Composite material ; Environmental science ; Nanotechnology Polymer Nanocomposite Synthesis and Irradiation ; Electrohydrodynamics and Fluid Dynamics ; Graphene research and applications
Authors: Takuma Tsukakoshi | Tamon Miyake | Tetsuya Ogata | Yushi Wang | Takumi Akaishi | Shigeki Sugano
Source: ArXiv.org (preprint, 2025-05-06)
Field: Materials Science — Polymers and Plastics

Close-Fitting Dressing Assistance Based on State Estimation of Feet and Garments with Semantic-based Visual Attention As the population continues to age, a shortage of caregivers is expected in the future. Dressing assistance, in particular, is crucial for opportunities for social participation. Especially dressing close-fitting garments, such as socks, remains challenging due to the need for fine force adjustments to handle the friction or snagging against the skin, while considering the shape and position of the garment. This study introduces a method uses multi-modal information including not only robot's camera images, joint angles, joint torques, but also tactile forces for proper force interaction that can adapt to individual differences in humans. Furthermore, by introducing semantic information based on object concepts, rather than relying solely on RGB data, it can be generalized to unseen feet and background. In addition, incorporating depth data helps infer relative spatial relationship between the sock and the foot. To validate its capability for semantic object conceptualization and to ensure safety, training data were collected using a mannequin, and subsequent experiments were conducted with human subjects. In experiments, the robot successfully adapted to previously unseen human feet and was able to put socks on 10 participants, achieving a higher success rate than Action Chunking with Transformer and Diffusion Policy. These results demonstrate that the proposed model can estimate the state of both the garment and the foot, enabling precise dressing assistance for close-fitting garments. RGB color model ; SOCKS ; Population ; Clothing ; Object (grammar) ; Robot ; Joint (building) Textile materials and evaluations ; Color perception and design ; Ergonomics and Musculoskeletal Disorders
Authors: Ângela Fernandes | Bandhu Prasad | Anand Prakash | Sadananda B Naik
Source:  (review, 2025-07-29)
Field: Materials Science — Polymers and Plastics

Anterior and Posterior Matrix Systems: A Comprehensive Review Establishment of an ideal contact and contour between adjacent and opposing teeth are of paramount clinical importance. It ensures the correct deflection of food and debris from the interproximal spaces called embrasures. This prevents food impaction, restoration overhanging, interproximal caries, and subsequent periodontal disease. Establishing an ideal contact is and has always been a challenging task for the clinician. However, a close-to-ideal contact can be recreated using matricing and matrix systems. This review article presents the various types of anterior and posterior matrix systems available, its classifications, indications, contraindications, advantages and disadvantages. It also indicates and explains the type of matrix to be used according to different clinical situations based on the category of caries. Matrix (chemical analysis) ; Computer science ; Materials science ; Composite material Textile materials and evaluations
Authors: ALEX FERNANDES DE SOUZA | Filipe Alves Neto Verri | Pedro Paulo Balestrassi
Source: Anais ... Encontro Nacional de Engenharia de Produção/Anais do Encontro Nacional de Engenharia de Produção (article, 2025-11-04)
Field: Materials Science — Materials Chemistry

A CARBON FOOTPRINT ASSESSMENT MODEL FOR SUS-TAINABLE ARTIFICIAL INTELLIGENCE APPLICATIONS Although artificial intelligence (AI) has transformed sectors such as healthcare, finance, and transportation, it also generates significant environmental impacts.The increasing complexity of AI models demands high computational power, leading to greater energy consumption, water use, and carbon emissions.This scenario raises concerns about the sustainability of technological development. In response, the concept of Green AI promotes sustainable practices in the design, training, and deployment of AI systems. This study aims to analyze sustainable practices in AI and propose a mathematical model to calculate the carbon footprint associated with AI applications. The model incorporates variables such as energy consumption during training and operation, emission factors from the energy matrix, and emission reductions achieved through AI-driven optimizations. The methodology involved structuring a model to separately quantify emissions generated during different AI phases and to account for compensations resulting from resource savings. A classification system was also proposed to organize applications into sustainability tiers: Green, Yellow, and Red, according to their final carbon balance. The results highlight that AI technologies can significantly impact carbon emissions. The model enables detailed measurement of emissions, helping organizationsidentify critical areas for improvement.The classification system offers a clear method to assess sustainability performance and guide strategic decisions toward emission reductions.In conclusion, the study presents a practical model for evaluating the environmental impact of AI.Applying this tool can assist organizations in aligning AI development with sustainability goals and promoting responsible innovation. Carbon footprint ; Applications of artificial intelligence ; Footprint ; Automation ; Expert system Material Selection and Properties ; Military Strategy and Technology ; Knowledge Management and Technology
Authors: Yiyi Li | Wangzhe Zhou | Yuqing Zhang | Xiaoyan Huang | Yutai Chen | Man Yuan | Junbo Yang
Source: Nanophotonics (article, 2025-12-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Large‐gap cascaded Moiré metasurfaces enabling switchable bright‐field and phase‐contrast imaging compatible with coherent and incoherent light Bright-field and phase-contrast imaging represent two of the most essential modes for target recognition and feature extraction, offering broad applicability in fields such as biomedicine and autonomous driving. In this work, we propose a cascaded Moiré metasurfaces system with a large interlayer spacing, which enables switchable bright-field and phase-contrast imaging at a wavelength of 532 nm by adjusting the illumination conditions between coherent and incoherent light sources. By employing an optimized phase-iterative algorithm, the stringent spacing requirement of conventional cascaded Moiré metasurfaces is relaxed from the subwavelength scale (∼100 nm) to beyond 1 mm, while maintaining robust imaging performance under spacing deviations of ±0.1 mm. Through controlled relative rotation of the two metasurfaces by an angle <i>θ</i>, the system dynamically switches between a focused solid Airy disk (<i>θ</i> = 0°) and vortex beams with tunable topological charges ranging from -5 to +5 (<i>θ</i> = ±20° to ±100°). The design achieves a focusing efficiency of 82 % and vortex beam purities up to 99 %. Owing to its versatile switching capability, the system supports multi-order edge extraction for both phase-type and amplitude-type objects, reaching a spatial frequency of 228 lp/mm. This approach overcomes the limitation of existing edge-detection metasurfaces, which operate only under either coherent or incoherent illumination. Our findings provide a new technical pathway toward compact, multifunctional, and integrated imaging devices. Rotation (mathematics) ; Spatial frequency ; Wavelength ; Ranging ; Spatial filter ; Enhanced Data Rates for GSM Evolution ; Beam (structure) Metamaterials and Metasurfaces Applications ; Orbital Angular Momentum in Optics ; Advanced Antenna and Metasurface Technologies
Authors: Huda Abdullah | Mojahid Najim | Ban A. Yousif
Source: Experimental and Theoretical NANOTECHNOLOGY (article, 2025-02-15)
Field: Materials Science — Materials Chemistry

Improving physical properties for insulator ceramics using local materials Kaolinitic claystone, silica sand, and Bauxite are used to produce the raw materials, with additional locally available materials being added. Also utilized are Pura Silica, Rice husk ash produces Silica, as well as MgCo3, and Mg(OH)2. The raw materials listed earlier are pulverized and crushed into a size of lower than 45 microns. The raw materials underwent physical and chemical analysis. The samples are dryers add fired in 1300,1350,1400 and 1450°C according fire program 50°C /hour in socking time 2 hours. Physical evaluated tests are done to the samples. From the results of these testes, we saw that 1300°C firing temperature is not enough to get the sintering between mullite and the addition of cordierite. The evaluations given by the samples fired in 1400°C are the most favorable. Samples that fired in 1450°C have not good physical, specifications because the samples containing 50%, 60% and 70% cordierite are melted. Samples containing 30% and 40% cordierite are the best in our current study because they have good physical specifications, their mixtures could be used in production Ceramic insulators. Ceramic ; Materials science ; Insulator (electricity) ; Engineering physics ; Composite material ; Physics Magnesium Oxide Properties and Applications ; Advanced ceramic materials synthesis ; High voltage insulation and dielectric phenomena