jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Josif Risto | Leomira Osmani-Lataj | Dritan Topi
Source: South Eastern European Journal of Public Health (article, 2025-02-04)
Field: Chemistry — Organic Chemistry

Evaluating Olive oil Quality and Food Safety Compliance in Tirana, Albania, Retail Markets The debate over the significance of the quality of fats in our diet has been present in recent decades. Olive oil (OO) is unique among vegetable oils due to its alleged health benefits. Quality assessment, including the traceability of food products through control of the primary compounds, is a key step. Bottled OOs in retail markets have been subject to deceptive labeling, fraud, and adulteration. Health concerns are associated with mixing them with different vegetable oils. We investigated the fatty acid (FA) composition of several bottled olive oils from popular domestic brands in Tirana retail markets, using gas chromatography with a flame ionization detector and gas chromatography-mass spectrometry.This study's results show that olive oils' fatty acid (FA) composition (OOs) does not reflect adulteration. The levels of trans-fatty acids (trans-FAs) were below the threshold established by European Union regulation 2568/91, as amended by 1989/2003. Specifically, oleic acid content ranged from 64.71% to 75.62%, palmitic acid from 8.43% to 14.26%, and linoleic acid from 6.64% to 13.1%. Furthermore, linolenic acid levels were below 1%, indicating that mixing with other vegetable oils is not evident. The trans-FA levels aligned with the label information, and the linolenic acid values were between 0.38% and 0.94%. The fatty acid profile of these olive oils demonstrated low saturated FA levels and a high concentration of monounsaturated FA. Our findings indicate that bottled olive oils from the Kalinjoti cultivar are appropriate for marketing as mono-cultivar brands. However, this is not advisable for bottled olive oils from Nisioti and Bardhi Tirana cultivars because they exhibit lower oxidative stability. . Food science ; Oleic acid ; Chemistry ; Linoleic acid ; European union ; Palmitic acid ; Fatty acid ; Olive oil ; Linolenic acid ; Vegetable oil ; Business ; Organic chemistry ; Biochemistry Edible Oils Quality and Analysis ; Identification and Quantification in Food ; Spectroscopy and Chemometric Analyses
Authors: Zhen Gao | Yan Meng | Hong-yu Zou | Naifu Zheng | Linyun Yang | Ruo-Yang Zhang | Jingming Chen | Xi Xiang | Bei Yan | Yong Ge | Yi-jun Guan | Shouqi Yuan | Gui-Geng Liu | Zhenxiao Zhu | Hong-xiang Sun | Ce Shang | Hongsheng Chen | Qihang Liu | Yihao Yang
Source: Research Square (preprint, 2025-10-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Gauge-field-induced duality group in metamaterials Duality (order theory) ; Homogeneous space ; Metamaterial ; Group (periodic table) ; Symmetry group ; Lattice (music) ; Symmetry (geometry) ; Invariant (physics) Metamaterials and Metasurfaces Applications ; Advanced Antenna and Metasurface Technologies
Authors: Yi-Yun Chou | Jing-Chen Yang | Tsung‐Heng Tsai | Ching-Tai Chen
Source: Scientific Reports (article, 2025-08-29)
Field: Chemistry — Spectroscopy

IQUP identifies quantitatively unreliable spectra with machine learning for isobaric labeling-based proteomics Mass spectrometry‑based proteomics using isobaric labeling technology has become popular for proteomic quantitation. Existing approaches rely on the mechanism of target-decoy search and false discovery rate control to examine whether a peptide-spectrum match (PSM) is utilized for quantitation. However, some PSMs passing the examination may still exhibit high quantitation errors, which can deteriorate the overall quantitation accuracy. We present IQUP, a machine learning-based method to identify quantitatively unreliable PSMs, termed QUPs. PSMs were characterized by 16 spectral and distance-based features for machine learning. Independent test results reveal that the best-performing models for the three datasets achieve accuracies of 0.883-0.966, AUCs of 0.924-0.963, and MCCs of 0.596-0.691. Notably, the distributions of relative errors for QUPs and quantitatively reliable PSMs (QRPs) exhibit significant differences. By using only the predicted QRPs for peptide-level quantitation, the proportions of peptides with larger relative errors decrease significantly, with a range between 15.3 and 83.3% for the three datasets; in the meantime, the proportions of peptides with smaller relative errors increase by 3.1-25.5%. Our experimental results demonstrate that IQUP provides robust performance and strong generalizability across multiple datasets and has great potential in improving proteomic quantitation accuracy at PSM and peptide levels for isobaric labeling experiments. Isobaric process ; Computer science ; Proteomics ; Isobaric labeling ; Computational biology ; Artificial intelligence ; Machine learning ; Quantitative proteomics ; Chemistry ; Biology ; Biochemistry ; Physics Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Tomoaki Kahyo | Lili Xu | Mitsutoshi Setou
Source: Electrophoresis Letters (article, 2025-01-01)
Field: Chemistry — Spectroscopy

Information imaging in imaging mass spectrometry as a spatial omics approach イメージング質量分析（Imaging Mass Spectrometry: IMS）は，空間オミクスの一種であり，質量分析計によって試料表面を二次元的にスキャンすることで，空間情報と質量情報を同時に取得する技術である．この手法では，数千から数万に及ぶ測定点（ピクセル）から質量スペクトルが得られ，そのデータ量は極めて膨大になる．IMSによって得られる大規模データのイメージング解析技術が発展したことで，組織染色や免疫組織化学などの従来の組織学的手法では観察が困難であった生体構造の抽出が可能になってきている．本説では，情報エントロピーと空間情報の粗視化とニューラルネットワークに基づいて，質量スペクトルから特徴量を抽出し“情報イメージング”として紹介する．さらに，抽出した特徴量を活用したIMSの空間分解能向上に関する取り組みについても報告する． Mass spectrometry imaging ; Mass spectrometry ; Medical imaging ; Pattern recognition (psychology) ; Spatial analysis Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Sonja Bumann | Eric Neuscamman
Source: ArXiv.org (preprint, 2025-07-25)
Field: Materials Science — Materials Chemistry

Reducing the Cost of Energy Differences in Variational Monte Carlo with Spotlight Sampling We investigate an approximate sampling scheme that can significantly reduce the cost scaling of variational Monte Carlo when it is employed to predict the energy differences associated with local chemical changes. Inspired by side-chaining and embedding methods, this spotlight sampling approach adopts an approximate fragmented Hamiltonian and correlated sampling to reduce cost scaling to the point that it is essentially linear with system size, with the potential to go sub-linear if certain conditions are met. In tests on bond stretching energies in alcohols, hydrogen dimer chains, and molecules with various degrees of $π$-system delocalization, we observe the anticipated linear scaling as well as an explicit cost crossover with standard variational Monte Carlo. Monte Carlo method ; Scaling ; Crossover ; Sampling (signal processing) ; Hybrid Monte Carlo ; Hamiltonian (control theory) ; Embedding ; Energy (signal processing) ; Monte Carlo method in statistical physics ; Importance sampling Machine Learning in Materials Science ; Protein Structure and Dynamics ; Markov Chains and Monte Carlo Methods
Source: Materialwissenschaft und Werkstofftechnik (article, 2025-03-01)
Field: Materials Science — General Materials Science

Materialwiss. Werkstofftech. 3/2025 This research is an integrated study on the hydromechanical deep drawing processing of AZ31B magnesium alloy at elevated temperatures.Considering the process parameters such as friction coefficient, blank holder force and pressure rate, simulation and experiment were carried out. Microstructure analysis and processing parameter optimization of AZ31B magnesium alloy during hydromechanical deep drawingGefgeanalyse und Optimierung der Verarbeitungsparameter von MgAl3Zn1-Magnesiumlegierung beim hydromechanischen Tiefziehen Materials science Material Properties and Applications ; Engineering and Materials Science Studies
Authors: Lu Zhu (228766) | Cong Lv (2070793) | Wei Hua (127758) | Dechang Huang (15152368) | Yuanyuan Liu (136992)
Source: Figshare (article, 2025-04-14)
Field: Materials Science — Electronic, Optical and Magnetic Materials

PTLOR-Net:Physical Transfer Learning Based OpticalResponse Prediction Network of Metasurfaces Accurate and rapid optical predictions of metasurfaces are essential for assessing their performance. However, traditional data-driven models depend on large-scale data sets and necessitate retraining of parameters for different data set paradigms. Furthermore, these models are often limited in generalization and transfer abilities due to neglecting physical prior knowledge and spatial-physical correlations in data. This paper addresses these challenges by introducing the physical transfer learning based optical response prediction network (PTLOR-Net) of metasurfaces, consisting of the physical representation model (PRM) and the fusion-prediction model (FPM). The encoder of PRM captures physical information applicable across many optical scenarios under the constraints of governing equations, while the FPM integrates multiscale features and maps them to predict optical responses. PTLOR-Net can transfer knowledge across similar and different types of data sets, which facilitates the physical transfer from all-dielectric metasurfaces to metasurfaces or absorbers at different frequency bands. Remarkably, with merely 1800 samples, the PTLOR-Net can effectively predict the absorption spectrum of the absorbers with high degrees of freedom (DOFs)a 10-fold reduction in training data compared to conventional neural networks. Additionally, the generative model integrated with the PTLOR-Net achieves the inverse design of the absorber and further verifies the effectiveness of the prediction. Artificial neural network ; Transfer function ; Generalization ; Set (abstract data type) ; Representation (politics) ; Transfer of learning ; Degrees of freedom (physics and chemistry) ; Data modeling ; Encoder ; Transfer (computing) Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Acoustic Wave Phenomena Research
Authors: Nahoko Kuroki
Source: BUNSEKI KAGAKU (article, 2025-09-05)
Field: Chemistry — Physical and Theoretical Chemistry

Fragment Theory-based &lt;i&gt;Ab Initio&lt;/i&gt; Evaluation of Intermolecular Interactions in Solution 溶液中で形成される分子間相互作用ネットワークは，化学反応の進行や物性の発現を支配している．ゆらぎを伴った相互作用ネットワークの構造を定量的に理解することは，新たな溶液の設計や機能の予測において重要な鍵となる．本研究では，量子化学に根差したフラグメント理論に基づき，溶液内の相互作用を高速かつ高精度に評価するいくつかの手法を提案した．イオン液体や超臨界流体，オスモライト水溶液を対象として，溶液を構成する分子の幾何的特徴・電子的特徴から，溶液の構造や熱力学・動力学に関する物性を評価した．提案手法は，いずれも経験的パラメーターに依存しないため，未知の化学現象の予測にも適用可能な汎用性を有する．機能溶液物理化学の深化に向けた，新たなアプローチを提供する． Intermolecular force ; Fragment (logic) ; Intermolecular interaction ; Chemical solution Various Chemistry Research Topics ; Computational Drug Discovery Methods ; Chemical Thermodynamics and Molecular Structure
Authors: Singanallur Venkatakrishnan | Obaidullah Rahman | Lynda Amichi | Jose Arregui Mena | Haoran Yu | D. M. Cullen | Amirkoushyar Ziabari
Source: Research Square (preprint, 2025-01-13)
Field: Materials Science — Surfaces, Coatings and Films

Model-based iterative reconstruction with adaptive regularization for artifact reduction in electron tomography Regularization (linguistics) ; Tomography ; Reduction (mathematics) ; Iterative reconstruction ; Artifact (error) ; Computer science ; Artificial intelligence ; Computer vision ; Algorithm ; Physics ; Mathematics ; Optics ; Geometry Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Non-Destructive Testing Techniques
Authors: Razaqri Razaqri | Deri Kaswandi | Hamid Nasrullah | Agus Haris Abadi | Bahtiar Wilantara
Source: JASATEC Journal of Students of Automotive Electronic and Computer (article, 2025-07-29)
Field: Materials Science — Materials Chemistry

Rancang Bangun Bumper Bus Listrik dengan Teknik Pengelasan GMAW di PT. X Penelitian ini membahas rancang bangun bumper bus listrik menggunakan teknik pengelasan Gas Metal Arc Welding (GMAW) di PT.X, Republik Ceko. GMAW dipilih karena efisien untuk baja karbon rendah (AISI 1020), namun tetap memiliki tantangan seperti porositas dan kurangnya penetrasi. Penelitian dilakukan secara kualitatif melalui observasi, wawancara, dan studi dokumentasi. Hasil menunjukkan pentingnya pemilihan parameter pengelasan yang tepat (arus, voltase, kecepatan) untuk menghasilkan sambungan yang kuat dan bebas cacat. Integrasi sistem hidrolis elektrik juga meningkatkan kemampuan bumper menghadapi berbagai medan. Gas metal arc welding ; Engineering ; Welding ; Computer science ; Physics ; Mechanical engineering ; Arc welding Material Selection and Properties ; Engineering and Technology Innovations ; Engineering Structural Analysis Methods
Authors: Zhen Zhang | Jun Qiu | Jun Wei Zhang | Huidong Li | Dong Tang | Qiang Cheng | Wei Lin
Source: ArXiv.org (preprint, 2025-01-20)
Field: Materials Science — Materials Chemistry

Physics-Informed Machine Learning for Efficient Reconfigurable Intelligent Surface Design Reconfigurable intelligent surface (RIS) is a two-dimensional periodic structure integrated with a large number of reflective elements, which can manipulate electromagnetic waves in a digital way, offering great potentials for wireless communication and radar detection applications. However, conventional RIS designs highly rely on extensive full-wave EM simulations that are extremely time-consuming. To address this challenge, we propose a machine-learning-assisted approach for efficient RIS design. An accurate and fast model to predict the reflection coefficient of RIS element is developed by combining a multi-layer perceptron neural network (MLP) and a dual-port network, which can significantly reduce tedious EM simulations in the network training. A RIS has been practically designed based on the proposed method. To verify the proposed method, the RIS has also been fabricated and measured. The experimental results are in good agreement with the simulation results, which validates the efficacy of the proposed method in RIS design. Machine design ; Computer science ; Surface (topology) ; Artificial intelligence ; Human–computer interaction ; Engineering ; Mechanical engineering ; Mathematics Machine Learning in Materials Science ; Manufacturing Process and Optimization
Authors: Sarah L. C. Giering | Filipa Carvalho | Morten Hvitfeldt Iversen | Zonghua Liu | Océane Merchiers | C. Mark Moore | Michael Ockwell | Uta Passow | Alex J. Poulton | Elisa Romanelli | Thangavel Thevar | Richard Sanders
Source: Limnology and Oceanography (article, 2025-03-22)
Field: Materials Science — Biomaterials

Species‐specific diatom dynamics shape their vertical distribution and export during bloom decline Abstract The Southern Ocean is a critical region for global biogeochemical cycles, particularly for carbon dioxide uptake and organic carbon export, partly driven by extensive phytoplankton blooms. In naturally iron‐fertilized regions of this otherwise iron‐limited ocean, these blooms are primarily driven by diatoms. Variability in bloom occurrences on spatiotemporal scales makes it challenging to track and understand a bloom's decline and associated carbon export. Moreover, species‐specific processes may be critical yet are typically not well resolved. Here, we use state‐of‐the‐art in situ imaging, producing high‐resolution vertical profiles of phytoplankton and particles, combined with water sampling, to resolve the decline of a Southern Ocean spring bloom. We observed significant vertical and temporal changes in particle and diatom composition and abundance. Fragilariopsis kerguelensis showed deepening abundance peaks correlating with silicic acid gradients and water density changes, while their chain lengths decreased likely due to nutrient stress and physical breakage. High downward fluxes of F. kerguelensis and concentrations below the mixed later suggest they were a key contributor to carbon export and transfer. Conversely, Eucampia antarctica exhibited a rapid decline in surface abundance with minimal vertical redistribution and low fluxes, potentially linked to changing environmental conditions. These findings highlight species‐specific responses to environmental conditions and implications for carbon processes. Measured values of the apparent photosynthetic efficiency (Fv/Fm) suggests that photosynthetically viable diatoms were a significant contributor to export and particle biomass in the upper mesopelagic zone. Our results underscore the importance of understanding individual species dynamics for oceanic carbon and nutrient cycles. Bloom ; Diatom ; Oceanography ; Distribution (mathematics) ; Dynamics (music) ; Algal bloom ; Geology ; Environmental science ; Biology ; Ecology ; Phytoplankton ; Mathematics ; Physics ; Nutrient Diatoms and Algae Research ; Aquatic Ecosystems and Phytoplankton Dynamics ; Marine and coastal ecosystems
Authors: О. О. Іванов | В. В. Шкатуляк | С. Р. Зубко
Source: METHODS AND DEVICES OF QUALITY CONTROL (article, 2025-06-27)
Field: Materials Science — Materials Chemistry

МОЖЛИВОСТІ ТА ПЕРСПЕКТИВИ МОДИФІКАЦІЇ МЕТОДИКИ ТРИВИМІРНОГО МОДЕЛЮВАННЯ МІКРОСТРУКТУРИ Запропонована методика тривимірного моделювання мікроструктури матеріалів базується на серійному аналізі зображень, отриманих у процесі пошарового полірування. Метод дозволяє створювати реалістичні 3D-моделі мікроструктури, усуваючи обмеження традиційної металографії та доповнюючи можливості чисельного моделювання. У статті здійснено порівняння існуючих методів прогнозування фазового складу та механічних властивостей, зокрема CALPHAD, FEM та Molecular Dynamics, висвітлено їхні переваги та недоліки. Запропонований підхід дозволяє отримувати точну просторову інформацію про частинки матеріалу, що особливо актуально для аналізу структурних дефектів, розподілу фаз та контролю якості. Визначено перспективні напрями вдосконалення: автоматизація алгоритму, розширення функціональності за допомогою машинного навчання та розрахунок похибок вимірювання. Одержані результати демонструють потенціал запропонованого методу для дослідження реальних матеріалів, їхньої структури та експлуатаційних характеристик. Process (computing) ; Identification (biology) ; Finite element method ; Work (physics) Material Properties and Failure Mechanisms ; Military Technology and Strategies ; Environmental and Industrial Safety
Authors: Song Thanh Quynh Le | Thuy My Nguyen Thi
Source: EAI Endorsed Transactions on Sustainable Manufacturing and Renewable Energy (article, 2025-09-16)
Field: Materials Science — Polymers and Plastics

Optimizing the Needle - Thread - Fabric Interaction for Enhanced Seam Performance in Clothing Manufacturing In apparel industry, product quality is always the key factor determining the success of a business. Not only does this create customer satisfaction, but product quality also directly affects the business's reputation, competitiveness, and market expansion opportunities. In this, seam appearance is one of the most important quality requirements, directly determining customer satisfaction. This paper examines the relationship and impact of three most important specifications, including needle-thread compatibility, fabric weight on the quality of seam performance. Through the experiment according to the full factorial design, this paper has determined the influence of each factor, in which the fabric weight is the factor with the most significant impact on the variation of the seam length, followed by the needle size and thread size. Determining the relationship between material parameters (fabric, thread) and equipment parameters (needle) not only helps to minimize errors but also creates the premise for building standardization processes for technical parameters according to each specific product group. These results will support the operator to adjust the machine faster, more accurately and less dependent on manual experience, thereby improving productivity and efficiency of the whole system. Clothing ; Thread (computing) ; Quality (philosophy) ; Clothing industry ; Standardization ; Customer satisfaction ; Product (mathematics) Textile materials and evaluations ; Color perception and design ; Ergonomics and Musculoskeletal Disorders
Source:  (article, 2025-01-28)
Field: Chemistry — Inorganic Chemistry

[REVISO 351311300720158066] CLCULO DA TEMPERATURA INICIAL DE APARECIMENTO DE CRISTAIS (TIAC) COM UM MODELO DE WON MODIFICADO Problems related to paraffin precipitation and crystallization during production, transportation and processing of crude oils cause major difficulties in these processes.The wax appearance temperature (WAT) is an important parameter to evaluate the occurrence of oil fractions precipitation.Among the various methods for WAT prediction it is possible to mention Won method whose conceptual structure is well grounded and has inspired many other authors who have introduced small changes without modifying the original basic concept.In this work improvements of Won model are presented, comprising: i) altering the characterization of the oil with respect to the molecular weight; ii) considering the solubility parameter and molar volume of the fractions as functions of temperature.The evaluation of these changes is performed using a literature database consisting of 18 oils from various geographical regions, and the solid phase is considered as an ideal solution.For the new approach, the average error in the calculation of WAT for the 18 oils is 1.94%.This result demonstrated that the dependence of the properties regarding the molecular weight is as pronounced as with respect to the temperature.It can be seen that good results can be achieved by a simple calculation without the necessity of very detailed characterization, nor many experimental data. Physics ; Humanities ; Art Pigment Synthesis and Properties
Authors: Suyog Pundlikrao Jungare | P.V. Joshi | Munish Sharma
Source: International Journal of Reconfigurable and Embedded Systems (IJRES) (article, 2025-11-01)
Field: Materials Science — Polymers and Plastics

Calibration and measurement of cotton moisture using real time system with statistical analysis Accurate moisture measurement in cotton is essential for maintaining fibre quality, ensuring safe storage, and supporting efficient processing. Improper moisture levels can result in microbial growth, fibre degradation, or mechanical damage during ginning and spinning operations. This study presents the development of a real-time moisture measurement system for cotton used in the ginning industry. The system operates on the principle of electrical resistance change to detect varying moisture levels. Cotton samples were categorized into four types: wet, new, old, and dry. The system is designed for use on moving or in-process cotton. To evaluate system performance, linear discriminant analysis (LDA), and hierarchical clustering analysis (HCA) were employed for classification. Partial least squares (PLS) regression was used to calibrate the system against the standard oven-drying method (ASTM D2495-07). Further, artificial neural network (ANN) modelling was applied for moisture prediction. The system successfully discriminated between the cotton types, achieving over 85% explained variance in classification. ANN-based prediction aligned closely with the standard reference method. The developed system provides a low-cost, fast, and real-time solution for moisture measurement in cotton, with strong potential for industrial application. Moisture ; Calibration ; Partial least squares regression ; Linear regression ; System of measurement ; Linear discriminant analysis ; Statistical analysis Textile materials and evaluations ; Research in Cotton Cultivation ; Microwave and Dielectric Measurement Techniques