jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mirza Manjgo | Srečko Glodež | Gorazd Lojen | Tomaž Vuherer
Source: Journal of Manufacturing and Materials Processing (article, 2025-06-24)
Field: Materials Science — Materials Chemistry

Determination of Fracture Toughness and Resistance Curves by Three Methods on Armoured Steel Parameters of EPFM are used as relevant parameters in structural integrity assessments. In this research, the fracture toughness of armoured steel was determined. The resulting resistance curves and KJIC obtained according to the ASTM E1820 standard with normalization, compliance and multi-specimen methods were compared. Also, the KIC was verified according to the ASTM E399 standard as the most precise method for obtaining the KIC, which also requires a lot of knowledge. For the experiment, the multi-specimen method was used, which is the most expensive and most accurate method, where the least assumption and crack size is measured on the specimen. A fractographic analysis was also presented, and this heat-treated high-strength steel, which is used for anti-ballistic protection, was fully characterized. Fracture toughness ; Materials science ; Fracture (geology) ; Composite material ; Forensic engineering ; Engineering High-Velocity Impact and Material Behavior ; Fatigue and fracture mechanics ; Microstructure and Mechanical Properties of Steels
Authors: Kefeng Gao | Xingdao He | Zengqian Liu | Zhefeng ZHANG
Source: Journal of Inorganic Materials (article, 2025-01-01)
Field: Materials Science — Biomaterials

Bioinspired Nacre-like Ceramic-Polymer Composites with Multiscale Layered and Gradient Structures Due to their intrinsic brittleness and high sensitivity to structural flaws, ceramics face fundamental limitation in applications that require mechanical load-bearing capacity, impact resistance, and reliable performance under complex service conditions.Natural ceramic-based materials, such as nacre, have evolved intricate multiscale structures through long-term evolutionary processes, effectively integrating high strength and fracture toughness, offering valuable inspiration for the design of synthetic ceramics.This study utilized an accumulative rolling technique combined with a layerby-layer assembly process to fabricate bioinspired ceramic-polymer composites featuring nacre-like layered and gradient structures at the multiscale.The composites exhibit a characteristic nacre-like brick-and-mortar architecture at the microscale, as well as periodic or gradient variations in ceramic content at the mesoscale, collectively forming bioinspired multiscale layered and gradient structures.The mechanical properties of the bioinspired composites were systematically investigated and compared with uniform composites with equivalent ceramic content.The relationships between the bioinspired structures, mechanical properties, and damage characteristics were elucidated.The results demonstrate that the bioinspired composites exhibit variations of up to several times in local hardness and elastic modulus along the Composite number ; Double layered ; Nanocomposite ; Biomimetic materials ; Multiscale modeling Calcium Carbonate Crystallization and Inhibition ; Magnesium Oxide Properties and Applications ; Materials Engineering and Processing
Authors: C Li | Ming Cheng | Wei Wang
Source: Energies (article, 2025-05-20)
Field: Materials Science — Electronic, Optical and Magnetic Materials

A New Two-Dimensional Electromagnetic Field Analysis and Loss Calculation Method for High-Frequency Applications Based on Vector Magnetic Circuit Theory Electromagnetic field analysis and loss calculation is crucial for designing and optimizing magnetic components in high-frequency applications. The traditional method of combining eddy current field equations with hysteresis models is often used for low-frequency conditions and has complex applications due to the large number of coefficients. The loss calculation of commercial finite element software is usually in the post-processing phase, making the loss calculation and electromagnetic field analysis irrelevant. In this article, a new two-dimensional electromagnetic field analysis and loss calculation method for high-frequency applications is proposed based on vector magnetic circuit theory. The effects of eddy currents and hysteresis on the magnetic field can be considered simultaneously, and the corresponding eddy current losses and hysteresis losses can also be directly calculated. Two toroidal magnetic cores for high-frequency applications are used to verify the effectiveness and accuracy of the method proposed in this article. The analytical calculation results have higher accuracy compared to the ANSYS simulation results. Eddy current ; Electromagnetic field ; Magnetic field ; Magnetic circuit ; Hysteresis ; Finite element method ; Field (mathematics) ; Vector potential ; Mechanics ; Physics ; Electronic engineering ; Computational physics ; Computer science ; Acoustics ; Electrical engineering ; Engineering ; Mathematics ; Condensed matter physics Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Non-Destructive Testing Techniques
Authors: Airlie J. McCoy | Randy J. Read
Source: Acta Crystallographica Section D Structural Biology (article, 2025-11-19)
Field: Materials Science — Materials Chemistry

<i>Xtricorder</i> : a likelihood-enhanced self-rotation function and application to a machine learning-enhanced Matthews prediction of asymmetric unit copy number Analysis of crystallographic diffraction data after collection and integration but before phasing gives the crystallographer a `first-look' assessment of data quality and flags potential challenges in subsequent structure determination. We here report the development of Xtricorder, a `first-look' application specifically targeted at likelihood-based phasing. Xtricorder incorporates the full array of analyses previously available in the Phaser codebase, with some enhancements and updates, in a more streamlined and accessible implementation. In addition, Xtricorder offers a likelihood-enhanced self-rotation function. A novel graphical representation of the self-rotation function, the `composite-section diagram', presents the results for user inspection and has the added advantage that, in an adapted form, it is appropriate for training a convolutional neural network to enhance the standard Matthews analysis and double the accuracy of asymmetric unit copy-number prediction. We investigate the usefulness of the likelihood-enhanced self-rotation function in `first-look' analyses, exploring the circumstances under which the self-rotation function results are useful, and discuss the application to AI-generated structure prediction. Phaser ; Convolutional neural network ; Representation (politics) ; Function (biology) ; Unit (ring theory) ; Function representation ; Data structure Enzyme Structure and Function ; Advanced Electron Microscopy Techniques and Applications ; X-ray Diffraction in Crystallography
Authors: Siva Avudaiappan | René Gómez | Lucas Daza-Badilla | Manuel Chávez-Delgado | Fernando Betancourt | Erick Saavedra Flores | Krishna Prakash Arunachalam | Pablo Guindos
Source: REVIEWS ON ADVANCED MATERIALS SCIENCE (article, 2025-01-01)
Field: Materials Science — Polymers and Plastics

Comparative study on the effect of natural and synthetic fibers on the production of sustainable concrete Abstract The increased concern with the use of environmentally friendly products in construction has led to the search for natural fiber replacements to synthetic fibers used in concrete. This research attempts to understand the trends that result from the addition of natural (hemp and luffa sponge) and synthetic fibers to concrete and its mechanical properties at the micro level. Concrete samples were prepared with fiber volume fractions of 0.4%, 0.8%, and 1.2%, and their compressive and flexural strengths were subsequently determined. Scanning electron microscopy (SEM) and energy dispersive spectroscopy were employed for assessing the fiber pattern and characteristics of the cementitious matrix–fiber interface. It was found that all fibers improved the compressive and flexural strengths of concrete in each blend relative to the reference mix. Most importantly, it was observed that the highest compressive strength of 28.15 MPa was obtained with luffa sponge fibers at 1.2% volume fraction accompanied by an enhancement of flexural strength. This was further supported by SEM analysis, which showed the evidence of the natural fibers’ better distribution on the concrete matrix than the synthetic ones, thus improving the structure. The results of this research underscore the versatility of natural fibers, especially luffa sponge, to be used as a construction material with long-term value. This article offers specific recommendations for civil engineers and construction practitioners who wish to improve concrete performance as well as undertake environmentally conscious construction practices. Flexural strength ; Compressive strength ; Cementitious ; Fiber ; Environmentally friendly ; Synthetic fiber ; Volume (thermodynamics) ; Natural fiber Natural Fiber Reinforced Composites ; Innovations in Concrete and Construction Materials ; Innovative concrete reinforcement materials
Authors: Jingjing Wang | Weiqi Bian | Xian Li | Yanli Zhu | Gang Wang
Source: Journal of Physics Conference Series (article, 2025-05-01)
Field: Materials Science — Materials Chemistry

Numerical analysis and modeling of shape memory alloy with stochastic structural variations Abstract Shape memory alloy (SMA) textiles provide a promising soft actuator strategy for handling fragile or shaped objects without causing damage, showing great potential in wearable and medical devices. The spatial randomness in the geometric structure of SMA-based textiles, induced by mechanical forces during manufacturing, introduces displacement deviations, which challenge the accurate prediction of mechanical behaviors. This study develops an innovative stochastic modeling framework for high-fidelity simulations, facilitating the targeted design and optimization of SMA textile actuators. The framework incorporates: (1) quantitative analysis of randomness in material weaving structures using deviation-based Pearson correlation, combined with PCA for dimensionality reduction, (2) node-based SMA structural modeling utilizing series expansion methods, and (3) validation with experimental data using correlation length and RMSD demonstrates model accuracy, with a precision of 94.8% and 98.1%, respectively. This approach lays the foundation for the future optimization of high-performance textile-based soft actuators. Shape-memory alloy ; Numerical analysis ; Alloy ; Computer science ; Materials science ; Mathematics ; Metallurgy ; Mathematical analysis Shape Memory Alloy Transformations ; Topology Optimization in Engineering ; Aeroelasticity and Vibration Control
Authors: Achmad Fauzan | Anang Kurnia
Source: JOIV International Journal on Informatics Visualization (article, 2025-09-30)
Field: Chemistry — Analytical Chemistry

A Machine Learning Approach to Spatial Analysis of Paddy Field Conversion Using Multispectral Sentinel-2A Imagery The expanse of rice fields is a critical metric as it is intimately linked to agricultural productivity in a given locale. This study investigates the application of satellite imagery to quantify trice fields' acreage and temporal variations. The data utilized was acquired by the Sentinel-2A multispectral satellite. The variables employed are the image's baseband and spectral index. The research area encompasses the Sukamakmur sub-district in Bogor Regency, Indonesia. The types of machine learning models include Extreme Gradient Boosting (XGBoost), Multi-Layer Perceptron (MLP), and k-Nearest Neighbor (kNN). The simulation of class numbers is conducted to achieve the most stable and precise evaluation metric values. The XGBoost algorithm is used for the overall classification process of the region based on the optimal metric score. The model's accuracy, precision, recall, and F1-score are 92.37%, 92.3%, 92.38%, and 92.33%, respectively, indicating a very good performance. The model successfully captures a decline in rice field area between 2020 and 2023. Using the Modified Moran’s Index (MMI), the study reveals a positive spatial autocorrelation, indicating a clustered pattern in land-use change. Regions that experience either substantial or minor changes in land use are commonly situated near areas exhibiting similar characteristics. This study presents a spatially aware machine learning framework that enables the effective monitoring of agricultural land-use dynamics. In the future, this framework can be enhanced by integrating time-series forecasting and socio-economic data, supporting more informed decision-making in food security planning and agricultural policy development. Multispectral image ; Metric (unit) ; Boosting (machine learning) ; Satellite imagery ; Multilayer perceptron ; Field (mathematics) ; Multispectral pattern recognition ; Random forest Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Smart Agriculture and AI
Authors: 证威 司
Source: Applied Physics (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Research on the Measurement of the Effective Thermal Conductivity of LDPE Powder by the Hot Needle Probe Method 本文基于热探针法，通过设计新的测量仪器和算法，研究了粒径为(500 μm, 800 μm)和(800 μm, 1000 μm)的低密度聚乙烯(LDPE)粉末体积分数与导热系数的关系，并与各种理论预测进行了对比。实验表明，在不同体积分数下的LDPE粉末的热导率位于Bruggeman模型与Maxwell-Eucken模型之间，并通过线性拟合对体积分数在(0.35, 0.58)的LDPE粉末的有效热导率进行了拟合。This study investigates the relationship between volume fraction and thermal conductivity of low-density polyethylene (LDPE) powders with particle size ranges of (500~800) μm and (800~1000) μm using an improved thermal probe methodology. A novel measurement apparatus and cor- responding algorithm were developed to enhance measurement accuracy. Experimental results demonstrate that the effective thermal conductivity of LDPE powders at different volume fractions falls within the bounds predicted by the Bruggeman model and Maxwell-Eucken model. Specifically, for powders with volume fractions in the range of [0.35, 0.59], a linear regression model was established to characterize the effective thermal conductivity, showing good agreement with experimental data. Low-density polyethylene ; Materials science ; Thermal conductivity ; Thermal conductivity measurement ; Composite material ; Conductivity ; Thermal ; Analytical Chemistry (journal) ; Polyethylene ; Thermodynamics ; Chromatography ; Chemistry ; Physics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Dielectric materials and actuators
Authors: В. Н. Романюк | A. M. Niyakovskii | N. V. Strutsky | A. L. Svistun
Source: ENERGETIKA Proceedings of CIS higher education institutions and power engineering associations (article, 2025-05-29)
Field: Materials Science — Materials Chemistry

Soil and Ground Factors Affecting the Steel Underground Pipelines Corrosion Process and their Computer Simulation Corrosion of various gas pipelines,especially such widespreadtype of it aselectrochemical ones,isthemainthreattosteelundergroundpipelines during the operational phase. The negativeeffect of corrosionisthedirectloss of metal,increasedmaterialandlaborcostsformaintenance,repairandreplacement of pipelines,equipmentdowntime,deterioration of safetyandenvironmentalsituationincase of release of the pumpedproduct(especiallyifit is combustiblegasoroilandpetroleum products)into the environment.Thearticleprovides an analysis of the complex of soil-and-soilfactorsaffectingtheintensity of corrosionprocessesoccurringonundergroundsteelpipelines, and highlightscurrentresearchapproachestothisissue as well asdomesticandforeign relevant literature sources. Thekeyparametersdetermining the corrosiveaggressiveness of the soil,suchashumidity,soil aeration,its physical-and-mechanical properties, the chemicalcomposition of the soilelectrolyte(including the content of chloridesandsulfates), the presence of a number of specificsoilmicroorganisms, areconsidered. The possibility and expediency of using the computer simulation method to assess their impact using modern software tools, in particular, the SOLID and ANSYS software environments, as well as neural network modeling technologies, are considered and demonstrated. Computer simulation provides ample opportunities for analyzing and predicting corrosion processes depending on changes in environmental conditions, which can be of great practical importance for a better understanding of these processes and their impact on the reliability and durability of steel underground pipelines inthesespecificconditions. Pipeline transport ; Reliability (semiconductor) ; Process (computing) ; Corrosion ; Durability ; Software Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Corrosion Behavior and Inhibition
Authors: G. Senthil Kumar | Himaja | Jella Siddartha | Mat Nayan | L. Bhadru
Source: International Journal of Data Science and IoT Management System (article, 2025-08-30)
Field: Chemistry — Analytical Chemistry

REAL TIME SHELF LIFE PREDICTION OF FRESH PRODUCE USING CNN BASED TEMPERATURE ANALYTICS India is one of the largest producers of fruits and vegetables globally, contributing about 14% of the world’s total production. However, a significant portion of this produce—approximately 30–40%—is lost due to inefficient storage and transportation systems, resulting in an economic loss of ₹92,651 crores. The objective of this project is to develop a deep learning-based model that uses temperature simulation data to accurately predict the shelf life of fresh fruits and vegetables, thereby enabling optimized transport and storage conditions. The proposed system employs deep Convolutional Neural Networks (CNNs) to predict how long produce will remain viable based on temperature data collected during storage and transit. This approach supports decision-making aimed at reducing spoilage and enhancing logistics. Traditionally, shelf-life prediction relied on static temperature guidelines, manual inspections, and general estimations based on historical data—methods that often led to inaccuracies and higher wastage. These conventional systems lack the precision and adaptability needed for efficient management, resulting in significant post-harvest losses. Reducing these losses is critical to ensuring food security, especially in a country like India. Machine learning models offer the potential to make precise shelf-life predictions by incorporating real-time environmental data, thereby minimizing waste, improving economic returns, and ensuring fresher produce reaches consumers. A deep learning model trained on temperature patterns can provide real-time shelf-life estimates, allowing stakeholders to make informed decisions regarding storage adjustments and route optimization. Additionally, the AI system can generate alerts for optimal consumption periods and transportation planning. This solution stands to significantly enhance efficiency and reduce the costs associated with spoilage Shelf life ; Analytics ; Computer science ; Off the shelf ; Artificial intelligence ; Real-time computing ; Data mining ; Chemistry ; Food science Spectroscopy and Chemometric Analyses
Authors: Emmanuel Owoicho Abah | Mark L. Isselhardt | Mark Cannella
Source: Foods (article, 2025-12-08)
Field: Chemistry — Organic Chemistry

Maple Quality Testing: An Evaluation of Maple Syrup Compliance with Grade A Standards This study assesses the quality of pure maple syrup sold online to consumers. It aims to determine how likely consumers are to receive syrup that meets legal grading standards. Grade A clarity, color, density, and flavor tests were performed using visual color comparison to known standards, refractometry, and sensory analysis, respectively. The lowest compliance rates were observed in the color test for Golden syrup samples, with 1.7% in 2021, 26.4% in 2023, and 21.9% in 2024. The 2021 test was delayed due to COVID-19, which increased cases of color darkening. Flavor compliance was the second lowest, with Dark samples exhibiting compliance rates of 79.6% in 2021, 79.5% in 2023, and 73.9% in 2024, due to their ability to mask flavor, making it difficult for producers to detect. Syrup color often fails Grade A standards, especially for Golden syrup, likely due to producer grading issues and post-packaging darkening. The producer's ability to accurately evaluate flavor differs between Golden and Dark syrup, with higher observed flavor compliance for Golden syrup and lower compliance due to off-flavors in Dark syrup, as observed in this study. Using proper instruments to verify defects, accuracy, and limitations is essential, and consistent staff training and unbiased feedback are vital in preventing human error. Flavor ; Grading (engineering) ; Quality (philosophy) ; Compliance (psychology) ; Sensory analysis Plant-Derived Bioactive Compounds ; Nuts composition and effects ; Botanical Research and Chemistry
Authors: Olli-Pekka Aro | Victor Casula | Nina Hänninen | Jouni Karjalainen | Mikko J. Nissi | Miika T. Nieminen | Henning Henschel
Source: Heliyon (article, 2025-01-01)
Field: Materials Science — Biomaterials

Effects of collagen and chondroitin sulfate on relaxation at multiple magnetic field strengths Our results suggest that <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub><mml:mrow><mml:mi>R</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn></mml:mrow> </mml:msub> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub><mml:mrow><mml:mi>R</mml:mi></mml:mrow> <mml:mrow><mml:mn>2</mml:mn></mml:mrow> </mml:msub> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub><mml:mrow><mml:mi>R</mml:mi></mml:mrow> <mml:mrow><mml:mn>1</mml:mn> <mml:mi>ρ</mml:mi></mml:mrow> </mml:msub> </mml:math> are modulated by collagen and CS concentrations, with collagen likely dominating at physiological concentrations. Chondroitin sulfate ; Field (mathematics) ; Relaxation (psychology) ; Nuclear magnetic resonance ; Magnetic field ; Materials science ; Chemistry ; Physics ; Medicine ; Mathematics ; Biochemistry ; Internal medicine ; Glycosaminoglycan Collagen: Extraction and Characterization ; Biochemical effects in animals ; Meat and Animal Product Quality