jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Lee Margetts | William P. Smith | Raska Soemantoro | Adam Barker | Oliver Woolland | Miao Zheng | Ji Li | Philip D. Edmondson
Source: Civil-comp conferences (article, 2025-08-21)
Field: Materials Science — Materials Chemistry

Development of a Digital Twin Platform for the Lithium-Based Breeder and Reactor Integrated Test Installation (LIBRTI) Project Significant international efforts are underway to advance fusion technology from scientific demonstration to a practical source of electricity.One of the key challenges is the fuel cycle, in particular the supply of deuterium-tritium fuel.While deuterium is readily obtained from seawater, tritium is scarce in nature and cannot sustain a power plant without on-site production.The accepted strategy is to breed tritium inside the reactor using lithium-based breeding blankets, which generate tritium when exposed to high-energy neutrons from the fusion reaction.Engineers are designing and testing mock-ups of these systems to demonstrate reliable tritium generation, storage, and handling.To support this effort, a new high-power neutron test facility, LIBRTI, is being established to evaluate candidate tritium breeding technologies.Our contribution is to develop a digital twin of LIBRTI, enabling experimental and simulation data to be integrated and used to train AI models that accelerate design and evaluation.We present preliminary work on a low-code digital twin architecture that leverages Nvidia Omniverse and open-source tools.As a proof of concept, we demonstrate a prototype digital twin of a gas-driven permeation system.This work will interest the fusion community and engineers engaged in hydrogen technologies. Breeder (animal) ; Lithium (medication) ; Computer science ; Systems engineering ; Nuclear engineering ; Reliability engineering ; Blanket ; Engineering ; Materials science Graphite, nuclear technology, radiation studies
Authors: Rudiati Evi Masithoh | Aridatuz Zahroh | Muhammad Fahri Reza Pahlawan | Laila Rahmawati
Source: Pertanika journal of science & technology (article, 2025-07-10)
Field: Chemistry — Analytical Chemistry

Pork Adulteration in Beef and Mutton Detection Using Visible Near-Infrared (Vis-NIR) Spectroscopy and Chemometrics The addition of pork to beef and mutton is a potentially fraudulent practice. This study aims to detect adulteration of pork in beef and mutton using visible near-infrared (Vis-NIR) spectroscopy. Classification models for Beef+Pork and Mutton+Pork were developed using principal component analysis (PCA) and linear discriminant analysis (LDA). PCA demonstrated that respiratory pigments and the Soret band influenced the classification of meat based on Vis-NIR spectra. The most effective model was achieved by combining PCA with linear discriminant analysis (PCA-LDA), utilizing the original Vis-NIR spectra. The PCA-LDA model achieved a calibration accuracy of 99% for Beef+Pork and 82.4% for Mutton+Pork, with prediction accuracies of 91.3% and 73.7%, respectively. These results demonstrate that Vis-NIR spectroscopy can be utilized to authenticate minced meat, providing a promising approach for on-site screening or halal verification. Chemometrics ; Near-infrared spectroscopy ; Food science ; Spectroscopy ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Optics Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Identification and Quantification in Food
Source: Al-Noor Journal for Oil and Gas Studies (article, 2025-07-01)
Field: Chemistry — Spectroscopy

` Improved normal gasoline specifications by adding high-octane number gasoline at (NRC) Baiji The aim of this work was to the improvement of regular gasoline specifications by adding high-octane gasoline, then found the mixing ratio to ensure accuracy in the desired octane number. Three samples of gasoline were used (regular gasoline, premium gasoline, super gasoline). The tests were conducted of them by finding its octane number, density, and distillation after and before the addition processes. Moreover, laboratory test results showed that the octane number results before addition were reached 79, 97 and 82 normal gasoline, super gasoline, and premium gasoline, respectively. The results also showed that adding 7500 m3 of super to 30000 m3 of regular resulted in a mixture with an octane number of 82 and an amount of 37000 m3, which indicates the effectiveness of the mixing process. The results recommend the NRC Baiji to adding these amounts to make sure gasoline quality Gasoline ; Octane rating ; Octane ; Environmental science ; Waste management ; Engineering ; Chemistry ; Organic chemistry Analytical Chemistry and Chromatography ; Various Chemistry Research Topics
Authors: Ali Nursamsi Dahlan | Bambang Agus Kironoto | Adam Pamudji Raharjo
Source: Semesta Teknika (article, 2025-05-20)
Field: Materials Science — Materials Chemistry

Evaluating Flow Resistance in Straight Channels Using 2-Point Velocity Measurements Accurately measuring Manning’s roughness coefficient is crucial for enhancing the precision of hydraulic models and supporting informed decision-making in water resource management. Traditionally, this coefficient is determined using the cross-sectional mean velocity, which may introduce inaccuracies due to its approximate nature. However, estimating Manning’s roughness coefficient through flow velocity distribution analysis remains a relatively underexplored approach. This study aims to improve the accuracy of estimating Manning’s roughness coefficient by analyzing the velocity distribution at two vertical positions within the flow: z/H = 0.2 and z/H = 0.8. Secondary velocity data were obtained from four locations to support this investigation: a laboratory flume, the Selokan Mataram irrigation channel, the Kuning River, and the Opak River. The research methodology involves simplifying and applying analytical equations needed to determine Manning’s roughness coefficient based on the velocity profile. The results indicate that, in natural river systems, Manning’s roughness coefficient ranges from 0.035 to 0.095 at z/H = 0.8 and z/H = 0.2 and from 0.032 to 0.085 at z/H = 0.4 and z/H = 0.2. Flow resistance ; Point (geometry) ; Flow (mathematics) ; Geology ; Mechanics ; Geometry ; Mathematics ; Physics Fusion materials and technologies ; Erosion and Abrasive Machining ; Plasma and Flow Control in Aerodynamics
Authors: Md Asaduzzaman
Source: ChemRxiv (preprint, 2025-07-22)
Field: Chemistry — Physical and Theoretical Chemistry

The Tareq Index (TI): A Novel Graph-Based Descriptor for Inorganic Acids Topological indices play a significant role in chemical graph theory by providing quantitative measures of molecular structures. Most existing indices are focused on organic compounds, particularly hydrocarbons, with limited applicability to inorganic acids. In this work, we propose a novel graph-theoretical index, the Tareq Index (TI), specifically designed to reflect bonding patterns in inorganic acid molecules. This index incorporates bond multiplicity and molecular connectivity, capturing characteristics often overlooked by traditional indices like the Zagreb or Randić indices. A dataset of over 20 inorganic acids was analyzed, with the TI compared against pKa values and other topological descriptors. Preliminary trends indicate a potential correlation between TI values and acid strength. This contribution offers a new avenue for the structural interpretation of inorganic compounds and lays the foundation for further expansion into quantitative structure–activity/property relationships (QSAR/QSPR) involving inorganic systems. Index (typography) ; Graph ; Computer science ; Chemistry ; Theoretical computer science ; World Wide Web History and advancements in chemistry ; Computational Drug Discovery Methods
Authors: Monika Janaszek-Mańkowska | Dariusz R. Mańkowski
Source: Preprints.org (preprint, 2025-09-15)
Field: Chemistry — Analytical Chemistry

Hyperspectral Classification of Kiwiberry Ripeness for Postharvest Sorting Using PLS-DA and SVM: From Baseline Models to Meta-Inspired Stacked SVM The accurate and non-destructive assessment of fruit ripeness is essential for post-harvest sorting and quality management. This study evaluated a meta-inspired classification framework integrating partial least squares discriminant analysis (PLS-DA) with support vector machines (SVMs) trained on latent variables (sSVM) or on class probabilities (pSVM) derived from multiple PLS-DA components. Two kiwi-berry varieties, ‘Geneva’ and ‘Weiki’, were analyzed using variety-specific and combined datasets. Performance was assessed in calibration and prediction using accuracy, F05, Cohen’s kappa, precision, sensitivity, specificity, and likelihood ratios. Conventional PLS-DA provided reasonably good classification, but pSVM models, particularly those with an RBF kernel (pSVM_R), consistently outperformed other approaches and ensured higher stability across all datasets. Unlike sSVMs, which were prone to over-fitting, pSVM_R models achieved the highest accuracy of 92.4–96.9%, Cohen’s kappa of 84.8–93.9%, and precision of 89.1–94.2%, clearly surpassing both score-based SVM and PLS-DA. Contrasting tendencies were observed between cultivars: ‘Geneva’ models improved during prediction, while ‘Weiki’ models declined, especially in specificity. Combined datasets provided greater stability but slightly reduced peak performance than single-variety models. These findings highlight the value of probability-enriched stacking models for non-invasive ripeness discrimination, suggesting that adaptive or hybrid strategies may further enhance generalization across diverse cultivars. Ripeness ; Support vector machine ; Pattern recognition (psychology) ; Sorting ; Stability (learning theory) ; Hyperspectral imaging ; Kernel (algebra) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Федор А. Федулов | Dmitrii V. Savelev | Екатерина В. Болотина | Михаил В. Джапаридзе | Y. K. Fetisov
Source: Radioelectronics Nanosystems Information Technologies (article, 2025-06-26)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Synaptic behavior of a composite multiferroic heterostructure with remanent magnetization at resonant excitation Recent progress in deep learning extends the capabilities of artificial intelligence for various practical tasks, making neural networks an extremely popular and useful tool. However, even the most powerful and modern neural networks operate on chips built on the classical von Neumann architecture. At present, extensive research is being conducted to create devices, based on non-von Neumann architecture to radically improve the efficiency of training and operation of neural networks. This area of research includes the development of neuromorphic computing systems and synaptic devices. The paper demonstrates the synaptic behavior of a composite multiferroic heterostructure based on lead zirconate titanate piezoceramics and nickel at resonant excitation of the magnetoelectric (ME) effect and by applying magnetic field pulses of different amplitudes and polarity. The ME coefficient αE was considered as an analogue of the synaptic weight, and the output electrical voltage generated by the heterostructure was interpreted as a postsynaptic potential. The controlling the ME coefficient by changing the remanent magnetization of nickel when applying magnetic field pulses is shown. The possibility of simulating long-term potentiation and depression in the ME heterostructure, as well as spike-timing-dependent plasticity, is demonstrated. Multiferroics ; Excitation ; Materials science ; Remanence ; Condensed matter physics ; Composite number ; Magnetization ; Nuclear magnetic resonance ; Composite material ; Physics ; Optoelectronics ; Ferroelectricity ; Magnetic field ; Quantum mechanics Multiferroics and related materials ; Theoretical and Computational Physics
Authors: Klarisa Sabila | Mazaya Amajida | Hisbullah Hisbullah | Syaifullah Muhammad | Hesti Meilina
Source: Jurnal Kimia Sains dan Aplikasi (article, 2025-11-25)
Field: Chemistry — Analytical Chemistry

The Employment of ATR-FTIR Spectroscopy for Quantification of Turpentine in Cajuput Oil Cajuput oil is widely used for its therapeutic properties; however, its high economic value makes it vulnerable to adulteration with cheaper solvents, such as turpentine. This study employed ATR-FTIR spectroscopy combined with chemometric methods to qualitatively classify and quantitatively predict turpentine adulteration in cajuput oil. Cajuput oil samples were adulterated with turpentine at concentrations ranging from 0% to 10% (v/v) in 0.5% increments and analyzed in the mid-infrared region (4000–400 cm−1). Spectral pretreatments, including multiplicative scatter correction (MSC), smoothing, baseline correction, second derivative, and standard normal variate (SNV), were applied prior to chemometrics analysis. FTIR spectra revealed the appearance of C=C stretching bands at 1647 and 1508 cm−1 only in the adulterated samples. In contrast, Principal Component Analysis (PCA) with SNV and MSC pretreatments provided clear clustering of samples according to turpentine concentration, with cumulative variances reaching up to 90%. Partial Least Squares (PLS) using MSC, smoothing, baseline correction, and SNV pretreatments yielded excellent calibration and cross-validation performance, with R2 values of 0.98–0.99, and low SEC/SECV and RMSEC/RMSECV values. These results demonstrate that ATR-FTIR spectroscopy combined with appropriate chemometric pretreatments offers a rapid, solvent-free, and reliable approach for authentication and quality control of cajuput oil adulterated with turpentine. Chemometrics ; Turpentine ; Partial least squares regression ; Principal component analysis ; Spectroscopy ; Calibration ; Analytical Chemistry (journal) Spectroscopy and Chemometric Analyses ; Edible Oils Quality and Analysis ; Analytical Chemistry and Chromatography
Authors: Haiyan Lü | Hongsheng Lu | Hongbo Qiao | Jian Wang | Xiaoyun Sun | Shufeng Xiong | Fei Yin | Lei Shi
Source: Research Square (preprint, 2025-08-26)
Field: Chemistry — Analytical Chemistry

Wheat Fusarium head blight monitoring based on Bayesian optimization machine learning with the fusion of RGB and multispectral sensors from UAV Multispectral image ; Fusarium ; Head (geology) ; RGB color model ; Artificial intelligence ; Computer science ; Bayesian probability ; Sensor fusion ; Computer vision ; Environmental science ; Horticulture ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Emine Nakilcioğlu | Gizem Tiryaki | Semih Ötleş
Source: Yüzüncü Yıl Üniversitesi Fen Bilimleri Enstitüsü Dergisi (article, 2025-04-28)
Field: Chemistry — Analytical Chemistry

Glisemik İndeksin Azaltılmasında Geleneksel ve Teknolojik Yöntemler Diyabet ve diğer kronik hastalıkların yaygınlaşması, bu hastalıkların önlenmesi ve yönetimi için çeşitli yöntemlerin araştırılmasını teşvik etmiştir. Bu yöntemler arasında karbonhidrat alımını azaltarak düşük karbonhidratlı diyet uygulamak bulunmaktadır. Diyet müdahaleleri, diyabetin önlenmesi ve yönetiminde önemli bir rol oynayarak yemek sonrası kan şekeri seviyelerini düşük ve stabil tutmayı hedeflemektedir. Glisemik indeks (GI), besinlerin kan şekeri seviyeleri üzerindeki etkilerini değerlendiren ve kan şekeri düzenlenmesine katkıda bulunan önemli bir ölçüttür. Düşük GI değerine sahip olan gıdalar (55 ve altı), kan şekerini yavaşça yükseltirken, yüksek GI değerine sahip olan gıdalar (70 ve üzeri) hızla yükseltmektedir. GI değerinin düşük olması, diyabet, obezite ve kardiyovasküler hastalıklar gibi kronik rahatsızlıkların önlenmesinde kritik bir rol oynamaktadır. GI değerini azaltmak için haşlama, pişirme ve kurutma gibi geleneksel yöntemlerin yanı sıra kızılötesi enerji, mikrodalga teknolojisi ve ultrasonikasyon gibi modern teknolojiler de kullanılmaktadır. Bu yöntemler, yiyeceklerin yapısını değiştirerek ve sindirim sürelerini uzatarak GI değerini düşürmeye yardımcı olmaktadır. Derlemenin amacı, GI değerinin sağlıklı beslenme ve hastalık yönetimindeki rolünü açıklamak ve düşük GI değerine sahip gıdaların üretiminde kullanılan geleneksel ve teknolojik yöntemleri incelemektir. Ayrıca, bu alandaki bilimsel çalışmalardan örnekler sunularak sağlıklı gıda tercihlerine yönelik bilinçlendirmeye katkı sağlanması hedeflenmektedir. Psychology ; Humanities ; Philosophy Dye analysis and toxicity
Authors: Adriano Reis José da Silva | Caroline Pais de Carvalho | Iasmim Lopes de Lima | Felipe Raposo Passos Mansoldo | Alane Beatriz Vermelho | Rosineide C. Simas | Lívia S. Eberlin | Marcos N. Eberlin
Source: Journal of Mass Spectrometry (article, 2025-05-29)
Field: Chemistry — Organic Chemistry

“Touch&amp;Play” Wood Typification by the MasSpec Pen We have investigated the ability of a MasSpec Pen (MSPen) "three-in-one" (extraction, transfer, and ionization) device coupled to a mass spectrometer to provide instantaneous chemical profiles that could promptly characterize wood samples from the mahogany (Meliaceae) family. For that, we selected a set of five representative wood species, that is, Brazilian mahogany (Swietenia macrophylla, also known as Honduran mahogany), two African mahoganies (Khaya ivorensis and Khaya senegalensis), and two "nongenuine" ("fake") mahogany woods: cedar (Cedrela odorata) and andiroba (Carapa guianensis). By simply touching the superficially polished wood surface and after 3 s of automatic extraction, profiles of highly characteristic markers that effectively discriminated all five mahoganies were detected. The superficial surface of a wood Brazilian mahogany sample as compared with internal wood accessed via deep sanding showed minor profile changes mainly by shifts in the relative abundances of the wood markers, indicating that aging only marginally changes MSPen wood signatures. The direct "touch&play" analysis offered by MSPen was therefore found to provide nondestructive, fast, sample-preparation-free, and reliable typification of woods. This "spatially free" device also allows broad screening because multiple points on the whole surface of any small or large-size intricate wood sample can be rapidly analyzed, demonstrating its high potential for forensic investigations, particularly for endangered species such as the Brazilian mahogany. Meliaceae ; Khaya ; Swietenia macrophylla ; Chemistry ; Typification ; Forestry ; Botany ; Geography ; Taxonomy (biology) ; Biology Wood and Agarwood Research ; Cultural Heritage Materials Analysis ; Forensic Fingerprint Detection Methods
Authors: Sergey Kucheryavskiy | Sergei Zhilin
Source: Analytica Chimica Acta (article, 2025-03-15)
Field: Chemistry — Analytical Chemistry

Collinear datasets augmentation using Procrustes validation sets the proposed method provides a fast and simple solution for augmenting collinear datasets, significantly improving model performance without requiring extensive parameter tuning. It is versatile and can be applied to a range of datasets, offering a practical alternative to more complex augmentation techniques. Overfitting ; Collinearity ; Computer science ; Artificial neural network ; Data mining ; Artificial intelligence ; Outlier ; Pattern recognition (psychology) ; Machine learning ; Resampling ; Data set ; Latent variable ; Statistics ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality
Authors: Andressa Gilioli | Alessio Sacco | Andrea Mario Giovannozzi | Simone Giacosa | Antonella Bosso | Loretta Panero | L Ferrero | Silvia Raffaela Barera | Stefano Messina | Marco Lagori | Silvia Motta | Massimo Guaita | E. Vittone | Andrea Mario Rossi
Source: Beverages (article, 2025-11-17)
Field: Chemistry — Analytical Chemistry

Determination of Total Anthocyanin Concentration in Barbera Red Wines by Raman Spectroscopy and Multivariate Statistical Methods The quantity of anthocyanins plays a crucial role in wine quality, since these phenolic compounds significantly influence the color, mouthfeel and organoleptic properties of red wines. It is therefore important to define accurate and precise methodologies to monitor the concentration of total anthocyanins in wine. Currently, this analysis is carried out using Ultraviolet-Visible (UV–visible) spectrophotometry. This work aims to determine an alternative methodology that is equally fast, accurate and allows in situ measurements while opening the measurement of the concentrations of other molecules of interest. The method presented consists of Raman analysis of Barbera wine samples using a portable Raman spectroscopy system. Subsequently, the collected spectra were processed using an algorithm that applies partial least squares (PLS) regression, making it possible to determine the concentration of total anthocyanins for each sample. This approach is characterized by an accuracy and precision comparable to the methodology currently in use, i.e., UV–visible spectrophotometry. It is indeed characterized by an RMSE (root mean square error) and R2 (the coefficient of determination) on the validation set of 0.010 g/L and 0.88 and on the test data of 0.007 g/L and 0.93, respectively. Wine ; Anthocyanin ; Organoleptic ; Raman spectroscopy ; Partial least squares regression ; Analytical Chemistry (journal) ; Chemometrics ; Mouthfeel Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Edible Oils Quality and Analysis
Authors: Imranul Islam | Hasan Mazharul Haq | Md. Farhad Hossain | H. Biswas | Md. Bashar Uddin
Source: Materials Circular Economy (article, 2025-11-24)
Field: Materials Science — Polymers and Plastics

Innovative Solutions for Sustainable Fashion: QR Code-Driven Pre-Consumer Waste Sorting in Garment Manufacturing Abstract Increasing waste generation in the textile industry, particularly pre-consumer waste during garment production, poses significant environmental and economic challenges. The global textile industry produces an estimated 92–110 million tons of waste annually, with 25–40% of the fabric used in garment production becoming pre-consumer waste. This study introduces a QR code-based sorting system designed to enhance waste management practices during the garment cutting process. The system embeds QR codes with fiber-type information on waste bins, ensuring accurate waste categorization, reducing contamination, and facilitating recycling. Researchers tested the system in garment manufacturing facilities in Bangladesh, where stakeholders reported an average sorting efficiency of 94.3% and significant improvements in waste recovery processes. These results demonstrate substantial advancements in waste management practices and sustainability. The study highlights the potential of digital solutions in fostering a circular economy and proposes several avenues for further research, including testing the system in other industries such as food or electronics, scaling it for larger garment manufacturing facilities, and exploring alternative digital technologies, such as machine learning or blockchain, to optimize waste sorting and traceability. Sorting ; Textile ; Textile industry ; Production (economics) ; Clothing ; Sustainability ; Sustainable development ; Waste material Textile materials and evaluations ; Sustainable Supply Chain Management ; Food Waste Reduction and Sustainability