jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Maja Mahnić Naglić | Slavenka Petrak | Antoneta Tomljenović
Source: Polymers (article, 2025-07-23)
Field: Materials Science — Polymers and Plastics

Analysis of Woven Fabric Mechanical Properties in the Context of Sustainable Clothing Development Process This paper presents research in the field of computer-aided 3D clothing design, focusing on an investigation of three methods for determining the mechanical properties of woven fabrics and their impact on 3D clothing simulations in the context of sustainable apparel development. Five mechanical parameters were analyzed: tensile elongation in the warp and weft directions, shear stiffness, bending stiffness, specific weight, and fabric thickness. These parameters were integrated into the CLO3D CAD software v.2025.0.408, using data obtained via the KES-FB system, the Fabric Kit protocol, and the AI-based tool, SEDDI Textura 2024. Simulations of women's blouse and trousers were evaluated using dynamic tests and validated by real prototypes measured with the ARAMIS optical 3D system. Results show average differences between digital and real prototype deformation data up to 6% with an 8% standard deviation, confirming the high accuracy of 3D simulations based on the determined mechanical parameters of the real fabric sample. Notably, the AI-based method demonstrated excellent simulation results compared with real garments, highlighting its potential for accessible, sustainable, and scalable fabric digitization. Presented research is entirely in line with the current trends of digitization and sustainability in the textile industry. It contributes to the advancement of efficient digital prototyping workflows and emphasizes the importance of reliable mechanical characterization for predictive garment modeling. Fast fashion ; Context (archaeology) ; Digitization ; Clothing ; Workflow ; Software ; Stiffness ; Computer science ; Mechanical engineering ; Textile ; Materials science ; Structural engineering ; Engineering ; Composite material Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies ; 3D Shape Modeling and Analysis
Authors: Víctor Tuninetti | Carlos Beecher | Héctor Sepúlveda | Laurent Duchêne | Ángelo Oñate | Gonzalo Pincheira
Source: Procedia Structural Integrity (article, 2025-01-01)
Field: Materials Science — Materials Chemistry

Fracture stress triaxiality of Ti-6Al-4V for computational design of damage tolerant aeroengines Fracture stress triaxiality affects the maximum deformation of materials, and reliable computational design of damage-tolerant aircraft depends on accurate predictions of this state variable. This numerical study analyses the effect of fracture stress triaxiality on the damage behavior of Ti-6Al-4V during aero-engine fan blade-out events using explicit dynamic finite element analysis. Experimental data used in previously reported model calibration are compared with analytical predictions of the Johnson-Cook damage model and with preliminary simulation results of stress-triaxiality at fracture in the shroud due to a fan blade-out. This analysis is necessary to ensure that the structural integrity assessment with the identified model is consistent with the stress-triaxiality range observed in the real case of damaged engine shroud. The main result emphasizes the importance of adequate experimental calibration for accurate modelling of structural assessment of aeroengines. Damage tolerance ; Materials science ; Structural engineering ; Fracture (geology) ; Stress (linguistics) ; Composite material ; Engineering ; Composite number High-Velocity Impact and Material Behavior ; Mechanical Behavior of Composites ; Fatigue and fracture mechanics
Authors: Xueyun Zhang | Li He | Hui Zou | Xianwen Wang
Source: Discover Artificial Intelligence (article, 2025-07-02)
Field: Materials Science — Polymers and Plastics

Evaluation and adjustment of clothing comfort based on fuzzy inference This study proposes a smart clothing comfort evaluation and adjustment system based on fuzzy inference to improve wearability across varying environmental conditions. The system integrates real-time physiological data, environmental parameters, and subjective comfort ratings to dynamically regulate key garment properties, including breathability, thermal resistance, and moisture absorption. Optimized through a genetic algorithm, a fuzzy inference engine enables adaptive and responsive control in complex and variable scenarios. The system's performance was validated through indoor, outdoor, and extreme experiments. Results show that the fuzzy inference approach significantly enhances comfort levels, reduces response time, and consistently outperforms traditional control methods. The system also demonstrates robustness and adaptability, making it suitable for daily wear and challenging environmental conditions. These findings highlight the potential of fuzzy logic-based optimization in advancing intelligent clothing design and personalized comfort regulation in wearable technologies. Clothing ; Fuzzy inference ; Fuzzy logic ; Inference ; Fuzzy inference system ; Computer science ; Architectural engineering ; Artificial intelligence ; Psychology ; Adaptive neuro fuzzy inference system ; Engineering ; Fuzzy control system ; History ; Archaeology Textile materials and evaluations ; Color perception and design ; Consumer Perception and Purchasing Behavior
Authors: René Brands | Lukas Fuchs | Judith Friebel | Naim Bajcinca | Jens Bartsch | Urs A. Peuker | Volker Schmidt | Markus Thommes
Source: Journal of Pharmaceutical Sciences (article, 2025-06-25)
Field: Chemistry — Analytical Chemistry

Penetration depth and effective sample size characterization of UV/Vis radiation into pharmaceutical tablets The pharmaceutical industry is moving from off-line to real-time release testing (RTRT) to enhance quality while reducing costs. UV/Vis spectroscopy has emerged as a promising tool for RTRT given its simplicity, sensitivity and cost-effectiveness. Nevertheless, the effective sample size must be characterized in relation to the penetration depth to justify its representativeness and suitability for RTRT. In this study, bilayer tablets were produced using a hydraulic tablet press. The lower layer contained titanium dioxide and microcrystalline cellulose (MCC), while the upper layer consisted of MCC, lactose or a combination with theophylline. The thickness of the upper layer was stepwise increased. Spectra from 224 to 820 nm were recorded with an orthogonally aligned UV/Vis probe. Thereby, the experimental penetration depth reached up to 0.4 mm, while the Kubelka-Munk model yielded a theoretical maximum penetration depth of 1.38 mm. Based on these values, the effective sample sizes were determined. Considering a parabolic penetration profile, the maximum volume was 2.01 mm³. The results indicated a wavelength and particle size dependency. Micro-CT analysis confirmed the even distribution of the API in the tablets proving the sufficiency of the UV/Vis sample size. Consequently, UV/Vis spectroscopy is a reliable alternative for RTRT in tableting. Microcrystalline cellulose ; Penetration (warfare) ; Tableting ; Penetration depth ; Materials science ; Bilayer ; Analytical Chemistry (journal) ; Chromatography ; Composite material ; Chemistry ; Optics ; Cellulose ; Mathematics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Water Quality Monitoring and Analysis
Authors: Kouki Yamamoto | Kensuke Ogawa | Moeta Tsukamoto | Yuto Ashida | Kento Sasaki | Kensuke Kobayashi
Source: Applied Physics Express (article, 2025-02-01)
Field: Materials Science — Materials Chemistry

Nanodiamond quantum thermometry assisted with machine learning Abstract Nanodiamonds (NDs) are quantum sensors that enable local temperature measurements, taking advantage of their small size. Though model-based analysis methods have been used for ND quantum thermometry, their accuracy has yet to be thoroughly investigated. Here, we apply model-free machine learning with the Gaussian process regression (GPR) to ND quantum thermometry and compare its capabilities with the existing methods. We prove that GPR provides more robust results than them, even for a small number of data points and regardless of the data acquisition methods. This study extends the range of applications of ND quantum thermometry with machine learning. Nanodiamond ; Quantum ; Materials science ; Nanotechnology ; Physics ; Quantum mechanics ; Composite material ; Diamond Diamond and Carbon-based Materials Research ; Carbon Nanotubes in Composites ; Advanced Fiber Laser Technologies
Authors: Chih-Ching Hung | Xinyu Lin | Tung-Lung Wu | Shu-Han Liao | Ho-Sheng Chen | Cheng-Fu Yang
Source: Photonics (article, 2025-01-17)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Investigation of an Ultra-Wideband Optical Absorber with the Bandwidth from Ultraviolet C to Middle Infrared In the development of optical absorption technology, achieving ultra-wideband high absorption structures that span from the 200 nm ultraviolet C region to the 5800 nm mid-infrared range has been a significant challenge in materials science. Previous studies have shown that few optical absorbers can simultaneously achieve an absorption rate above 0.900 and cover such a vast spectral range. This study presents an innovative seven-layer composite structure that successfully addresses this long-standing technical issue. Through a carefully designed layered architecture, the researchers employed COMSOL Multiphysics (version 6.0) for detailed numerical simulations to verify the optical performance of the structure. The structural design features two key innovations. In the layered composition, the bottom (h1), h3, and h5 layers are made of metallic Fe, while the layers above them (h2, h4, and h6) use SiO2. The top layer is composed of a discontinuous cylinder Ti matrix. The first innovation involves the use of an inwardly recessed square design on the metallic Fe planes of the h4 and h6 layers, achieving high absorption across the 600–5800 nm range. The second innovation involves the use of the discontinuous cylinder Ti matrix for the top layer, which successfully enhances absorption performance in the 200–600 nm wavelength range. This structure not only employs relatively low-cost metals and oxide materials but also demonstrates significant optical absorption potential. Through numerical simulations and precise structural design, this study provides new ideas and technological pathways for the development of ultra-wideband optical absorbers. Wideband ; Bandwidth (computing) ; Ultraviolet ; Optoelectronics ; Infrared ; Materials science ; Optics ; Near-infrared spectroscopy ; Telecommunications ; Physics ; Computer science Metamaterials and Metasurfaces Applications ; Advanced Optical Imaging Technologies ; Photorefractive and Nonlinear Optics
Authors: 高明 Gao Ming | 杨瑞芳 Yang Ruifang | 赵南京 Zhao Nanjing | 殷高方 Yin Gaofang | 王亮 Wang Liang | 姜玉喜 Jiang Yuxi | 宋恒鑫 Song Hengxin | 陈晓伟 Chen Xiaowei
Source: Acta Optica Sinica (article, 2025-01-01)
Field: Chemistry — Spectroscopy

基于CNN的地下水多组分多环芳烃3DEEM快速定性定量分析方法 基于紫外诱导荧光法开展地下水多环芳烃定性定量分析方法研究，利用荧光分光光度计获取多环芳烃溶液的三维激发发射矩阵（3DEEM），提出一种适用于地下水多环芳烃多组分检测的卷积神经网络（CNN）荧光成分快速定性定量分析方法，基于3DEEM输入光谱尺寸和多环芳烃预测类别设计了多层CNN定量分析模型，通过改进卷积层间非线性映射构建了多环芳烃定性分析模型，所设计的双阶段CNN模型能够分别实现对多环芳烃3DEEM的定性和定量分析。利用去离子水和化工企业周边地下水模拟多环芳烃污染，用于验证所提方法的有效性。使用去离子水配置的多环芳烃溶液的定性分析准确率为99.8%，定量分析平均相对误差为10.71%。将该方法用于实际地下水多环芳烃污染检测，在地下水未知荧光背景扰动情况下，所提方法表现出良好的定性分析效果，定量分析的相对误差控制在一定范围内，两处地下水的定性分析准确率为100.00%和95.83%，去除受荧光背景影响严重的多环芳烃后两处地下水预测的平均相对误差分别为36.64%和27.53%。研究结果表明，所提方法能够实现地下水多组分多环芳烃污染快速定性定量分析，有望实现对地下水多环芳烃的现场快速检测。 Computer science Mass Spectrometry Techniques and Applications ; Forensic Fingerprint Detection Methods ; Analytical chemistry methods development
Authors: Fei Xue | Tianle Cheng | Yinkai Lei | Richard P. Oleksak | Youhai Wen
Source: Journal of the American Ceramic Society (article, 2025-11-24)
Field: Materials Science — Ceramics and Composites

Phase‐field modeling of crack growth and interface sliding in ceramic matrix composites Abstract Fiber‐reinforced ceramic matrix composites (CMCs) are used in structural components of gas turbines and fusion reactors due to their high toughness and ability to withstand ultra‐high temperatures. The enhanced fracture toughness of CMCs arises from the complex interactions between mechanical damage and their microstructures that involve fiber bridging, fiber pull‐out, crack deflection, and sliding at the fiber‐matrix interfaces. However, there is a lack of microstructure‐resolved models that enable direct simulation of these damage processes. This study introduces a phase‐field model that explicitly accounts for microstructure‐level cracking and interface sliding in CMCs. Simulations are performed to investigate the effects of fiber diameter, orientation, length, density, and interface sliding resistance. Fiber bridging and fiber pull‐out are successfully simulated. In particular, the simulation results demonstrate the critical role of interface sliding in fiber bridging. Parametric studies suggest that thicker fibers, longer fibers, and lower interface sliding resistance can lead to enhanced performance of CMCs when fiber bridging dominates the damage processes. This model has demonstrated its capability to serve as a valuable tool for quantitative understanding of CMC damage processes and for guiding design of the microstructures for next‐generation CMCs. Bridging (networking) ; Ceramic matrix composite ; Microstructure ; Ceramic ; Toughness ; Fiber ; Parametric statistics ; Fracture toughness Advanced ceramic materials synthesis ; Numerical methods in engineering ; Graphite, nuclear technology, radiation studies
Authors: Shuang Ren | Sanjun Liu | Jinzhao Fan | Didi Lu
Source: Scientific Reports (article, 2025-01-15)
Field: Chemistry — Analytical Chemistry

The intelligent selenium-enriched tea withering control system This paper addresses the low level of intelligence in tea processing equipment in Enshi Prefecture by designing an intelligent withering control system based on the STMicroelectronics 32-bit Microcontroller (STM32). This control system can achieve real-time monitoring of the withering environment and automate the control of heating and ventilation dehumidification modules. By integrating IoT technology, relevant users can view the tea production process via mobile devices, enabling intelligent and remote production operations. Application results show that the system operates stably, accurately measures temperature and humidity in the withering environment, and achieves a control precision of [Formula: see text]% through a fuzzy control algorithm. It effectively meets the needs of tea processing in Enshi Prefecture. The system not only optimizes traditional processing workflows, enhancing processing efficiency and tea quality, but also provides new technological means for tea processing enterprises, contributing to the development and upgrading of the local tea industry. Selenium ; Computer science ; Chemistry Heavy Metals in Plants ; Tea Polyphenols and Effects ; Metabolomics and Mass Spectrometry Studies
Authors: Garren Hermanus | Tobias M. Louw | Jamie T. Cripwell
Source: ChemRxiv (preprint, 2025-06-01)
Field: Chemistry — Analytical Chemistry

Prediction of excess enthalpy in binary mixtures through probabilistic matrix completion, with a GP enforced smoothness constraint Matrix completion (MC) methods have found success in application to the prediction of scalar valued thermophysical properties, and more recently for mixture data in our own group. In this work we employ MC using the probabilistic matrix factorization (PMF) framework to generate pseudo excess enthalpy data, HE, for binary mixtures using data from established thermophysical property databases. We employ Gaussian processes (GP) to enforce smoothness of the pseudo excess enthalpy data across both composition and temperature. The equivalent kernel for the GP was derived from a modified version of the Redlich-Kister polynomial. We incorporate several thermodynamic considerations to improve the accuracy, robustness and interpretability of the estimates obtained, which we propose as surrogates in the absence of experimental data for fundamental thermodynamic model improvement. Smoothness ; Binary number ; Probabilistic logic ; Constraint (computer-aided design) ; Matrix (chemical analysis) ; Enthalpy ; Mathematics ; Thermodynamics ; Applied mathematics ; Statistics ; Chemistry ; Physics ; Chromatography ; Mathematical analysis ; Arithmetic Spectroscopy and Chemometric Analyses
Authors: Min Li | Suliadi Firdaus Sufahani | Noratikah Abu | Lee Kah Howe
Source: Multidisciplinary Science Journal (article, 2025-08-01)
Field: Chemistry — Analytical Chemistry

Forecasting of rice production in Malaysia using naïve, holt’s linear trend, ARIMA, and feedforward backpropagation neural network Rice is a vital dietary staple across the globe, especially in Asia. In Malaysia, per-capita rice consumption tops the world average of 54.6 kg, given by the OECD–FAO Agricultural Outlook. According to the Malaysian Adult Nutrition Survey (MANS) findings, Malaysian adults consume an average of two and a half plates of rice daily. Continuing population growth brings about an increase in demand, and consequently, national rice consumption is expected to grow steadily, which may further compel the country to resort to increasing rice imports to satisfy future demand. Yet, this situation might prove adverse for imports due to limitations in the domestic rice industry and depreciation in the Malaysian ringgit, therefore further inducing food security issues. Consequently, pertinent to this study is the immediate need to make an accurate forecast on rice production, so as to feed into strategic planning and policy-making. Different forecasting methods are evaluated in this study using various accuracy metrics, namely the Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), Theil’s Inequality Coefficient (TIC), and Holt’s Linear Trend (HLT), where lower values indicate better predictive accuracy. The analysis also compares conventional with more sophisticated models, which include the Naïve approach, the ARIMA (Autoregressive Integrated Moving Average), and the FBNN (Feedforward Backpropagation Neural Network). A comparative analysis presented a set of strengths and weaknesses across models to highlight their appropriateness for the forecasting of rice production at the national level. The findings drive the need to use methods that are well-developed and primarily data-oriented to increase prediction accuracies. Hence, future works can delve into the concept of hybrid modeling and use more accuracy measures to better the certainty of forecasting. In the grand scheme, this study carries a lot of weight in agricultural forecasting in Malaysia, aiding decision-making processes for producers, stakeholders, and policymakers, thereby guaranteeing food security in the long term. Autoregressive integrated moving average ; Backpropagation ; Production (economics) ; Population ; Staple food ; Artificial neural network ; Agriculture ; Food security Spectroscopy and Chemometric Analyses ; Stock Market Forecasting Methods ; Currency Recognition and Detection
Authors: Konstantinos Aouant | Paris Christodoulou | Thalia Tsiaka | Irini F. Strati | Dionisis Cavouras | Vassilia J. Sinanoglou
Source: Foods (article, 2025-12-01)
Field: Chemistry — Analytical Chemistry

Implementation of Instrumental Analytical Methods, Image Analysis and Chemometrics for the Comparative Evaluation of Citrus Fruit Peels Citrus fruit cultivation and processing are constantly rising due to the increasing market demand and diverse utilization potentials. This generates large quantities of residues, predominantly composed of citrus peels. This study aimed to evaluate six different citrus peels using rapid and/or nondestructive instrumental analytical techniques such as ATR-FTIR spectroscopy, spectrophotometric assays, image textural analysis and physicochemical parameter determination. Image textural features managed to discriminate citrus peels based on their structure uniformity, which was found increased in lemon (<i>C. limon</i>) and yellow grapefruit (<i>C. paradisi</i>), whereas clementine (<i>C. clementina)</i> and red grapefruit (<i>C. paradisi)</i> images exhibited an increased non-uniformity of the structure. Physicochemical parameters provided insights into the quality characteristics of citrus peels, while their high ascorbic acid content seems to enhance their antioxidant activity. The obtained results from phenolic and flavonoid content determination indicated a high concentration of polyphenols in the peels, which is aligned with the ATR-FTIR spectra absorption bands. Furthermore, the spectrophotometric assays' strong correlation suggests that the antioxidant activity of citrus peels is mainly attributed to polyphenols. Ultimately, a chemometric model was employed to provide a comprehensive understanding of the analytical methods' interactions. Hence, citrus peels' significant biochemical and, consequently, economic value can be highlighted, underscoring the importance of further research. Chemometrics ; Ascorbic acid ; Polyphenol ; Citrus paradisi ; Citrus fruit ; Clementine (nuclear reactor) ; Antioxidant capacity ; Flavonoid Spectroscopy and Chemometric Analyses ; Phytochemicals and Antioxidant Activities ; Food Drying and Modeling
Authors: Emily Fanning | Graham T. Eyres | Russell Frew | Biniam Kebede
Source: Food and Bioprocess Technology (article, 2025-02-12)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy Combined with Multivariate Analysis for the Geographical Origin Traceability of New Zealand Hops Abstract The increased demand for hops with distinctive aromas by the craft brewing industry has elevated the risk of fraudulent activities linked to their origin. Given the significant rise in food fraud and consumers’ growing attention to origin transparency, there is a need for rapid authentication methods to verify origin. This study employed near-infrared (NIR) spectroscopy combined with multivariate data analysis for the geographical origin traceability of New Zealand hops at the regional and farm levels. Three hop cultivars were collected from eight farms in the Tasman region of New Zealand. Additionally, six cultivar pairs were compared between the Tasman and Central Otago regions. The raw NIR spectra were preprocessed, and partial least squares discriminant analysis (PLS-DA) was employed for classification. The Suderdelic™ cultivar displayed the highest separation between the farms, with each sample forming distinct groups without any overlap. The Nectaron® cultivar displayed three primary clusters, while the Nelson Sauvin™ cultivar illustrated the least variation between farm origins. The regional samples PLS-DA classification model revealed genetics as the dominant factor, where the samples from the same cultivar were positioned close to each other. Interestingly, an apparent location effect emerged in the third dimension of the PLS-DA model. This study demonstrated the potential of NIR spectroscopy combined with multivariate data analysis to rapidly classify hop samples by their geographical origin at different scales (farms and regions), thereby aiding in the prevention and detection of food fraud related to origin. Traceability ; Multivariate statistics ; Multivariate analysis ; Infrared spectroscopy ; Spectroscopy ; Geographical indication ; Near-infrared spectroscopy ; Environmental science ; Analytical Chemistry (journal) ; Geography ; Chemistry ; Statistics ; Environmental chemistry ; Mathematics ; Biology ; Regional science ; Physics ; Organic chemistry Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Fermentation and Sensory Analysis