jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aleksandra D. Chybowska | Spyros I. Vernardis | Daniel L. McCartney | Jure Mur | Josephine Robertson | Hannah M. Smith | A.M. Campbell | Camilla Drake | Hannah Grant | Poppy Adkin | Matthew White | Christoph B. Messner | Arturas Grauslys | Sergej Andrejev | Charles Brigden | David J. Porteous | Caroline Hayward | Jackie F. Price | Kathryn L. Evans | Aleksej Zelezniak | Markus Ralser | Riccardo E. Marioni
Source: medRxiv (preprint, 2025-11-21)
Field: Chemistry — Spectroscopy

Untargeted Proteomic Profiling Identifies Candidate Biomarkers for Early Detection of Cardiovascular Disease and Mortality Abstract Background The serum proteome can provide valuable insights into the development and progression of diseases. This is particularly important for cardiovascular disease (CVD), a leading cause of death worldwide. In this large-scale cohort study, we employ an untargeted mass-spectrometry-based approach to explore associations between highly expressed proteins, incident CVD (analysed as six individual outcomes and one composite outcome) and all-cause mortality. Methods The abundances of 439 proteins and protein groups quantified by mass spectrometry in serum were related to incident outcomes in 8,343 Generation Scotland participants (age 40–69 years), who were free of CVD at baseline (n all_cause_death =618, n composite_CVD =666, follow-up ≤17 years). Cox proportional hazards (PH) models were run before and after adjustment for pre-selected known CVD risk factors. Sex-specific effects were explored. A protein-based risk score for composite CVD outcome was developed using penalised regression. Results Forty-eight high abundance serum proteins and protein groups were significantly associated with incident CVD and death outcomes (P Bonferroni &lt;1.14x10 - 4 ), including 24 associations not reported in the Open Targets database. Proteins involved in immune and oxidative stress responses were associated with composite CVD (Immunoglobulin heavy variable 3/OR16-9, Hazard Ratio per SD (HR)=0.85 [95%CI 0.79,0.92]) and death (Alpha-1-antitrypsin, HR=1.27 [1.17, 1.38]), while heart failure was linked to proteins playing a role in lipid metabolism (Apolipoprotein A-II, HR=0.70 [0.59, 0.84]) and complement cascade (Complement C1q subcomponent subunit B, HR=1.40 [1.18, 1.66]). Applied to the test set, the proteomic risk score improved 17-year incident CVD prediction over models including age, sex, and nine lifestyle and clinical risk factors (ΔAUC = 0.010, ROC P = 0.013). Conclusion The highly abundant serum proteome, readily assessed by mass spectrometry, reveals candidate biomarkers for incident CVD and provides predictive value for early risk stratification. Proteome ; Disease ; Cohort ; Proportional hazards model ; Hazard ratio ; Proteomics ; Cohort study ; Confounding Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; GDF15 and Related Biomarkers
Authors: Weiwei Wang | Hongjun Jiang | Ning Wang | Chao Yang | Yanwei Miao
Source: Brain Conflux (article, 2025-07-18)
Field: Materials Science — Materials Chemistry

Amide Proton Transfer Imaging in the Central Nervous System: Recent Advances and Clinical Applications Amide proton transfer (APT) imaging, as a novel magnetic resonance molecular imaging technique based on the chemical exchange saturation transfer (CEST) effect, indirectly reflects molecular-level information such as tissue protein concentration, pH, and microenvironment by detecting the exchange between intracellular free water and amide protons in proteins/peptides. Since it was first proposed by Professor Jinyuan Zhou in 2003, the technique has achieved significant progress in theoretical modeling, sequence optimization, and clinical translation, particularly in its expanding application to central nervous system diseases. In recent years, APT has been widely used for tumor grading, stroke staging, early identification of hypoxic-ischemic encephalopathy (HIE), and pathological tracking of neurodegenerative diseases. This review systematically summarizes the principles and advantages of APT imaging, with a focus on its recent applications in brain tumors, cerebral infarction, and neurodegenerative diseases. It also discusses signal variation characteristics and underlying mechanisms in different disease states, as well as current clinical applications and technical challenges.Keywords: Amide proton transfer (APT); chemical exchange saturation transfer (CEST); magnetic resonance molecular imaging; central nervous system (CNS); brain tumor; cerebral infarction; neurodegenerative disease Magnetic resonance imaging ; Central nervous system ; Neuroscience ; Encephalopathy ; Cerebral infarction ; Molecular imaging ; Chemistry ; Medicine ; Nuclear magnetic resonance ; Biology ; Ischemia ; Cardiology ; Internal medicine Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: Nathália Gabriele Franca Dias | Gilson Mendonça de Miranda Júnior | Edgar Vladimiro Mantilla Carrasco | Rejane Costa Alves
Source: Caderno Pedagógico (article, 2025-11-10)
Field: Materials Science — Polymers and Plastics

Innovation in the evaluation of wood-plastic composites: non-destructive vibration and ultrasound tests This study examines non-destructive wave propagation tests to determine the modulus of elasticity (MOE) of wood-plastic composites, materials valued for sustainability and versatility. The research compares vibration excitation tests (longitudinal, flexural, and torsional modes) and ultrasonic wave propagation tests against the standard 3-point static bending test. Specimens from national and international manufacturers, with varying polymer and wood fiber ratios, were tested. Results show that excitation frequency and material composition significantly affect test responses. Longitudinal and ultrasonic modes exhibited higher sensitivity to internal defects, while flexural and torsional modes demonstrated greater consistency, with determination coefficients (R²) exceeding 70% relative to static bending tests. The international composite showed superior homogeneity and mechanical performance. The study concludes that non-destructive tests offer a promising, efficient alternative for evaluating these composites. However, factors like anisotropy, internal defects, and manufacturing variations must be considered to enhance method accuracy. The research highlights the need for further investigation into standardizing NDTs for these composites and understanding how processing variables impact material performance. This work contributes to advancing testing methodologies for wood-plastic composites, potentially improving quality control and material development processes. Vibration ; Bending ; Flexural strength ; Nondestructive testing ; Young's modulus ; Ultrasonic sensor ; Homogeneity (statistics) ; Ultrasonic testing Natural Fiber Reinforced Composites ; Wood Treatment and Properties ; Material Properties and Processing
Authors: Tatyana Voblikova | Yulia Yusupova | Nassif Hussein Basma | В. В. Кузнецов
Source: Dairy Industry (article, 2025-12-12)
Field: Chemistry — Analytical Chemistry

Fourier Transform Infrared Spectroscopy Combined with Chemometrics in Goat Milk Authentication Chemometrics improves the efficiency of the Fourier transform infrared (FTIR) spectroscopy as a method for authentication of pasteurized goat's milk in its mixes with cow's milk. The research featured mixes of two brands of goat's and cow's milk in various proportions, from 1 to 90%. The chemometric modeling involved the method of partial least squares regression (PLS) to predict the authenticity percentage based on preprocessed spectral data. A five-fold cross-check was performed to verify the PLS model. The dataset was divided into five groups, each of which was used to train and test the model. The prediction error was quantified using the standard deviation (RMSE), which measured the mean deviation of the predicted values from the actual ones. The RMSE was calculated for value, and the mean RMSE for all values served as an indicator of prediction accuracy. The method of bar graphs visualized the RMSE for each fold; the cumulative variance was explained by the PLS components. The PLS model obtained by the FTIR spectroscopy demonstrated that the mean error remained below 3–5% even in the worst scenario. The IR-Fourier spectroscopy proved to be a fast, cheap, and non-destructive authentication tool for goat's milk. Chemometrics ; Mean squared error ; Partial least squares regression ; Standard deviation ; Fourier transform ; Fourier transform spectroscopy ; Fourier transform infrared spectroscopy ; Standard error Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food ; Milk Quality and Mastitis in Dairy Cows
Authors: З. Инсепов | Bakhytzhan Lesbayev | Sandugash Tanirbergenova | Zhanna Alsar | Aisultan Kalybay | З. А. Мансуров
Source: Preprints.org (preprint, 2025-08-15)
Field: Materials Science — Materials Chemistry

Small Modular Nuclear Power Reactors as a New Vector of Development of Nuclear Technologies Small Modular Reactors (SMRs) are becoming one of the key trends in the development of nuclear technology, offering a flexible, safe and cost-effective alternative to large nuclear power plants. This article provides a comprehensive overview of the main classes of SMRs, categorised by fuel type and application, ranging from low enriched uranium (LEU) and HALEU reactors to thorium, metallic fuel and reprocessed nuclear materials. The key technical advantages of SMRs are discussed: passive safety systems, extended fuel cycles, modular production and compactness, which make such reactors particularly suitable for use in hard-to-reach regions, military facilities, in space and as part of hybrid power systems. Special attention is paid to the prospects of advanced fuel cycles, including the conversion of thorium to uranium-233 and the reuse of actinides, which contributes to waste reduction and brings closer the realisation of a closed nuclear cycle. The current status of SMR projects around the world is also analysed, highlighting the most promising solutions and discussing regulatory, infrastructure readiness and geopolitical factors. It concludes that SMRs are well positioned to play an important role in the future of low-carbon and decentralised energy due to their diversity and adaptability. Modular design ; Nuclear power ; Nuclear engineering ; Computer science ; Engineering ; Physics ; Nuclear physics ; Operating system Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Nuclear reactor physics and engineering
Authors: Xun Zou | Lulu Wang | Yulu Chen | Hang Fu | Yuan Gao | Bin Liu | Minjia Tan | Linhui Zhai
Source: Research Square (preprint, 2025-09-11)
Field: Chemistry — Spectroscopy

In-Depth Analysis of Data Characteristics and Comparative Evaluation of DDA and DIA Accuracy in Label-Free Quantitative Proteomics of Biological Samples Quantitative proteomics ; Proteomics ; Data acquisition ; Quantitative analysis (chemistry) ; Label-free quantification ; Identification (biology) Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Hendi | Muhammad Ikhsan Maulana | Suci Patria | Adam Zuyyinal Adib | Yudi Setiawan
Source: Journal of Mechanical Engineering and Applied Technology (article, 2025-11-30)
Field: Materials Science — Materials Chemistry

ANALISIS PERBANDINGAN KARAKTERISTIK FISIK MELALUI UJI KEKERASAN VICKERS PADA MATERIAL KOMPOSIT BERBASIS SERAT ALAMI DAN SERAT SINTETIS Composite materials have become highly important in the manufacturing industry. However, the development of composites often faces the challenge of finding effective, economical, and environmentally friendly reinforcing materials. This study aims to analyze the comparison of physical characteristics in composite materials reinforced with natural fibers through Vickers hardness testing and then compare these hardness values with composite materials reinforced with synthetic fibers. The Vickers hardness testing method adheres to the ASTM E348 microhardness testing standard. The results show that the addition of fibers to composite materials tends to increase the likelihood of voids or trapped air bubbles within the composite material, which increases its porosity value. Furthermore, the use of synthetic fibers, especially carbon fiber, is still superior to pineapple leaf fiber (PALF) because the hardness value of carbon fiber is indeed superior to other fibers. However, pineapple leaf fiber (PALF) has advantages in its stability, is more environmentally friendly, and has a lower production cost compared to synthetic fibers. Indentation hardness ; Vickers hardness test ; Composite number ; Fiber ; Porosity ; Environmentally friendly Material Selection and Properties ; Natural Fiber Reinforced Composites ; Aluminum Alloys Composites Properties
Authors: С. В. Батаєв | Ольга Смагіна | Надія Васильєва
Source: Таврійський науковий вісник Серія Технічні науки (article, 2025-06-26)
Field: Materials Science — Materials Chemistry

ГЕНЕРАТИВНІ ЗМАГАЛЬНІ МЕРЕЖІ (GAN): НОВІТНІ ПІДХОДИ ТА ПРАКТИЧНІ ЗАСТОСУВАННЯ Дослідження спрямоване на забезпечення стабільності навчання, оптимізації продуктивності моделей і адаптації їх до освітніх потреб, зокрема для створення синтетичних даних, персоналізованих навчальних матеріалів і інтерактивних симуляційних середовищ на базі Unity. Мета статті. Ця стаття присвячена впровадженню GAN в освітні технології з використанням безкоштовних фреймворків PyTorch і TensorFlow, а також новітніх моделей, таких як StyleGAN4, CycleGAN, Pix2Pix, оскільки вони стають частиною ефективних мікросервісних систем з відповідним управлінням даними та наглядом за експериментами. Наукова новизна. Інноваційним є те, що ця робота об’єднує GAN в рамках мікросервісів з FastAPI, Docker, PostgreSQL-розширеннями pgvector і Neo4j, а також Weights &amp; Biases і MLflow для відстеження. Запропоновано адаптувати моделі StyleGAN4, CycleGAN та Pix2Pix для освітніх ІТ, а використання різних алгоритмів може покращити якість та стабільність їхніх результатів. Представлено аналіз застосування GAN для створення інтерактивних симуляцій в Unity, що відкриває нові можливості для викладання. Результати. Досліджено технічні аспекти реалізації GAN, включаючи використання PyTorch і TensorFlow для розробки моделей StyleGAN4, CycleGAN і Pix2Pix. StyleGAN4 виявилася оптимальною для генерації високоякісних зображень для навчальних матеріалів, тоді як CycleGAN і Pix2Pix ефективні для трансформації даних у симуляційних середовищах. Оптимізація через AdamW і Lookahead забезпечила стабільність навчання, а Wasserstein loss зменшила зникнення градієнтів. Інтеграція GAN у мікросервісні системи з FastAPI і Docker дозволила створити масштабовані рішення, а PostgreSQL із pgvector і Neo4j оптимізували управління даними. Моніторинг через Weights &amp; Biases і MLflow забезпечив контроль за метриками в реальному часі. Застосування GAN у освітніх проєктах продемонструвало їхню здатність генерувати синтетичні дані для курсів комп’ютерного зору, створювати персоналізовані навчальні матеріали та розробляти реалістичні віртуальні середовища в Unity. Висновки. Генеративні змагальні мережі є перспективним інструментом для освітніх ІТ-проєктів, забезпечуючи створення високоякісних синтетичних даних, адаптивних навчальних матеріалів і симуляційних середовищ. Використання PyTorch, TensorFlow, StyleGAN4, CycleGAN, Pix2Pix із оптимізацією через AdamW, Lookahead і Wasserstein loss гарантує стабільність і ефективність моделей. Інтеграція з FastAPI, Docker, PostgreSQL із pgvector і Neo4j забезпечує масштабованість і управління даними, а моніторинг через Weights &amp; Biases і MLflow підвищує надійність експериментів. Застосування GAN у поєднанні з Unity відкриває нові можливості для інновацій в освіті, сприяючи персоналізації та інтерактивності навчання. Materials science Machine Learning in Materials Science ; Data Visualization and Analytics ; Software System Performance and Reliability
Authors: Stanka Baycheva | Zlatin Zlatev | Neli Grozeva | T. Kolev | Milena Tzanova | Zornitsa Zherkova
Source: Sensors (article, 2025-12-12)
Field: Chemistry — Analytical Chemistry

Sensor-Based Evaluation of Purslane-Enriched Biscuits Using Multivariate Feature Selection and Spectral Analysis This study presents a sensor-integrated framework for evaluating purslane (<i>Portulaca oleracea</i> L.) stalk flour as a functional ingredient in butter biscuits. A Design of Experiments (DoEs) approach was applied using multisensor probes (electrical conductivity, pH, TDS, ORP) and digital imaging sensors (visible reflectance spectra) for real-time, non-destructive quality assessment. Multivariate analysis with Repeated Relief Feature Selection (RReliefF) and Principal Component Analysis (PCA) reduced 54 initial measurements to 19 informative features, with the first two principal components explaining over 96% of the variance related to flour concentration. Regression modeling combined with linear programming identified an optimal substitution level of 9.62%. Biscuits at this level showed improved texture, enhanced elemental composition (Ca, Mg, Fe, Zn), stable color, and maintained sensory acceptability. The methodology demonstrates a reliable, low-cost sensing and chemometric approach for data-driven, non-destructive quality monitoring and product optimization in food manufacturing. Principal component analysis ; Pattern recognition (psychology) ; Feature selection ; Multivariate statistics ; Chemometrics ; Ingredient ; Feature (linguistics) ; Principal component regression ; Selection (genetic algorithm) ; Multivariate analysis Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Nutritional Studies and Diet
Authors: Houxing Teng | Xiao Li | Liangyao Li | Tianpeng Chen
Source: Processes (article, 2025-04-25)
Field: Chemistry — Analytical Chemistry

A New Bayesian Inversion Method for Thixotropic Model Parameters of Waxy Crude Oil Waxy crude oil displays complex rheological behaviors, including viscoelasticity, thixotropy, and yield stress, under the gelling point temperature. To characterize these complex rheological behaviors, numerous models have been proposed, all of which typically involve extensive unknown parameters to be determined. Traditional least squares methods for determining model parameters have problems such as difficulty in determining the initial values for parameter fitting, susceptibility to falling into local optimal solutions, and instability of the fitting results. For this purpose, this paper proposes a parameter inversion method for determining thixotropic model parameters by integrating Adaptive Differential Evolution (ADE) with Bayesian inversion. By leveraging the global search capability of the ADE algorithm and the probabilistic uncertainty quantification advantages of the Bayesian method, the accuracy and stability of model parameter fitting are significantly enhanced. This paper conducted a comparative analysis between the proposed thixotropic model parameter inversion method and traditional methods based on the Houska model, using the thixotropic test data of Daqing and Xianhe waxy crude oil. The results show that the fitting errors of the new method are significantly smaller than those of the least squares method. Furthermore, the new method can invert to obtain superior thixotropic model parameters even under suboptimal initial parameter conditions while maintaining robust optimization capability. The novel method for inverting thixotropic model parameters outperforms the least squares method in terms of algorithm convergence, stability, robustness, and model fitting accuracy, solving the problem of difficult fitting of unknown parameters in thixotropic models. Crude oil ; Thixotropy ; Inversion (geology) ; Bayesian probability ; Econometrics ; Petroleum engineering ; Environmental science ; Pulp and paper industry ; Mathematics ; Process engineering ; Statistics ; Geology ; Engineering ; Geotechnical engineering Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; NMR spectroscopy and applications
Authors: Wanzi Yan | Hao Lu | Yu Tang | Zhencai Zhu | Fengbin Ren
Source: Lubricants (article, 2025-08-26)
Field: Materials Science — Materials Chemistry

Surface Property and Braking Reliability Analyses of YSZ Thermal Barrier-Coated Brake Disc of Kilometer-Deep Well Hoist A significant amount of heat is generated during the braking process of a kilometer-deep well hoist, which causes a large temperature rise and then thermal deformation and cracks in the brake disc. Thus, improving the surface performance of the brake disc is necessary to ensure reliable braking under high-speed and heavy-load conditions. In this paper, thermal barrier coating technology is applied to a brake disc, and the friction and wear characteristics of a yttria-stabilized zirconia (YSZ) thermal barrier-coated brake disc is studied. A coupled thermomechanical model of the hoist disc brake is established, and a temperature field simulation analysis of uncoated and coated brake discs under emergency braking conditions is carried out. Then, a surrogate model of the maximum temperature of the brake disc surface with respect to the random parameters of the brake disc is constructed based on a Latin hypercube experimental design and the Kriging method. The reliability of the brake disc under emergency braking conditions is estimated based on saddlepoint approximation (SPA), and the feasibility of applying a YSZ thermal barrier coating to a hoist disc brake is verified. Kilometer ; Hoist (device) ; Disc brake ; Automotive engineering ; Materials science ; Reliability (semiconductor) ; Road surface ; Thermal ; Brake ; Engineering ; Composite material ; Mechanical engineering ; Physics ; Transport engineering Metal Alloys Wear and Properties ; Surface Treatment and Coatings ; Gear and Bearing Dynamics Analysis
Authors: Antonín Hlaváček | Kateřina Uhrová | Julie Weisová | Hana Brožková | Naděžda Pizúrová
Source: Analytical Chemistry (article, 2025-01-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Optical Microscopy and Deep Learning for Absolute Quantification of Nanoparticles on a Macroscopic Scale and Estimating Their Number Concentration We present a simplistic and absolute method for estimating the number concentration of nanoparticles. Macroscopic volumes of a nanoparticle dispersion (several μL) are dropped on a glass surface and the solvent is evaporated. The optical microscope scans the entire surface of the dried droplet (several mm<sup>2</sup>), micrographs are stitched together (several tens), and all nanoparticles are counted (several thousand per droplet) by using an artificial neural network. We call this method evaporated volume analysis (EVA) because nanoparticles are counted after droplet volume evaporation. As a model, the concentration of ∼60 nm Tm<sup>3+</sup>-doped photon-upconversion nanoparticles coated in carboxylated silica shells is estimated with a combined relative standard uncertainty of 2.7%. Two reference methods provided comparable concentration values. A wider applicability is tested by imaging ∼80 nm Nile red-doped polystyrene and ∼90 nm silver nanoparticles. Theoretical limits of EVA such as the limit of detection, limit of quantification, and optimal working range are discussed. Nanoparticle ; Polystyrene ; Chemistry ; Microscopy ; Detection limit ; Dispersion (optics) ; Volume (thermodynamics) ; Optical microscope ; Evaporation ; Analytical Chemistry (journal) ; Nanotechnology ; Optics ; Chromatography ; Scanning electron microscope ; Materials science ; Composite material ; Thermodynamics Gold and Silver Nanoparticles Synthesis and Applications ; Biosensors and Analytical Detection ; Photoacoustic and Ultrasonic Imaging