jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Khallikkunaisa | SIDDHAM SHETTEY SHIVANI | Verella Sai Spandana | S Sudhiksha | K C Vidhya
Source: Zenodo (CERN European Organization for Nuclear Research) (article, 2023-03-03)
Field: Chemistry — Analytical Chemistry

Machine Learning-Based Adulteration Detection in Fruits <em>A fruit adulteration technique based on the Internet-of-things (IoT) is created to identify the concentration of the formalin using the machine learning approaches. Various methods of the machine learning were used to classify the fruits based on their extracted attributes. A Volatile compound sensor coupled to an Arduino Uno 3 is used to obtain the concentration of formalin as the function of the output voltage of any fruit. Our system uses Machine learning techniques to precisely predict the right formalin level at all temperature and can distinguish between naturally occurring and artificially added formalin. This System’s objective is to replace the manual inspection system. This system captures images from the camera that are mounted on the belt conveyors. The necessary fruit attributes such as color and size are then obtained by image processing. Based on picture pixels, contaminated fruit is identified. Sorting is done according to size and color.</em> Computer science ; Artificial intelligence ; Traditional medicine ; Food science ; Machine learning ; Chemistry ; Medicine Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Wei Jiang | Yao Liu | Fu Qiao | Zhongyan Liu | Jianfang Xiong | Shaogeng Zeng
Source: Acta Scientiarum Polonorum Technologia Alimentaria (article, 2023-06-30)
Field: Chemistry — Analytical Chemistry

Phase diagram combined with improved fuzzy support vector machine for rapid and nondestructive detection of Diarrhetic shellfish poisoning Background. The diarrhoeal shellfish poisoning (DSP) toxin is a powerful marine biological toxin. Eating DSP toxin-contaminated mussels will lead to serious gastrointestinal diseases. To this end, a method for the detection of DSP toxins using near-infrared reflectance spectroscopy combined with pattern recognition is proposed. Material and methods. In the range from 950−1700 nm, spectral data of healthy mussels and DSP-contaminated mussels were obtained. To select the optimal band subsets, a band selection algorithm based on model cluster analysis was applied. As distinguishing DSP toxin-contaminated mussels from healthy mussels is a classification problem of imbalanced data, an improved fuzzy support vector machine-based recognition method was proposed. The influence of the parameters of the band selection algorithm and the fuzzy support vector machine on the model performance was analyzed. Results. Compared with the traditional support vector machine, the proposed model has better performance in detecting DSP toxins and is not affected by the imbalance ratio. Its geometric mean value can reach 0.9886 and the detection accuracy can reach 98.83%. Conclusion. The results show that as an innovative, fast and convenient analytical method, near-infrared spectroscopy is feasible for the detection of DSP toxins in mussels. Support vector machine ; Digital signal processing ; Artificial intelligence ; Fuzzy logic ; Pattern recognition (psychology) ; Shellfish ; Computer science ; Biology ; Fishery Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Madona Mekhael | Timo Stolt | Anna Vesala | Heikki Rekola | Tommi K. Hakala | Robert Fickler | Mikko J. Huttunen
Source: ArXiv.org (preprint, 2023-10-06)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Phase-Matched Second-Harmonic Generation from Metasurfaces Inside Multipass Cells We demonstrate a simple and scalable approach to increase conversion efficiencies of nonlinear metasurfaces by incorporating them into multipass cells and by letting the pump beam to interact with the metasurfaces multiple times. We experimentally show that by metasurface design, the associated phase-matching criteria can be fulfilled. As a proof of principle, we achieve phase matching of second-harmonic generation (SHG) using a metasurface consisting of aluminium nanoparticles deposited on a glass substrate. The phase-matching condition is verified to be achieved by measuring superlinear dependence of the detected SHG as a function of number of passes. We measure an order of magnitude enhancement in the SHG signal when the incident pump traverses the metasurface up to 9 passes. Results are found to agree well with a simple model developed to estimate the generated SHG signals. We also discuss strategies to further scale-up the nonlinear signal generation. Our approach provides a clear pathway to enhance nonlinear optical responses of metasurface-based devices. The generic nature of our approach holds promise for diverse applications in nonlinear optics and photonics. Second-harmonic generation ; SIGNAL (programming language) ; Nonlinear system ; Signal beam ; Optics ; Phase (matter) ; Nonlinear optics ; Photonics ; Harmonic ; Proof of concept ; Phase matching ; Materials science ; Matching (statistics) ; Quasi-phase-matching ; Physics ; Optoelectronics ; Beam (structure) ; Computer science ; Acoustics ; Mathematics ; Laser Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Plasmonic and Surface Plasmon Research
Authors: Muhammad Naufal Mansor | Sofi Yahya | Wan Azani Mustafa | Habibah Mokhtaruddin | Syahrul Affandi Saidi | Ilham Shafini Ahmad Mahyudin | Mohd Aminudin Jamlos | Noor Anida Abu Talib | Mohd Zamri Hasan
Source: Journal of Advanced Research in Applied Sciences and Engineering Technology (article, 2023-08-04)
Field: Chemistry — Analytical Chemistry

Liquid Composition Identification and Characteristic Measurement Using Ultrasonic Transmission Technique via Neural Network This project is to determine the composition of liquids solvent by using the ultrasonic frequency signal from echoscope scan machine. The transmission technique of ultrasonic signal is focused. On the research experiment, studies on mixing of distilled water with control sodium chloride (Kitchen Salt), kitchen sugar and monosodium glutamate (MSG). The Parameters such as Fast Fourier Transform (FFT) which is the parameters are using to identify the ratio of composition of liquid solvent. The feature extraction of median, average and root mean square (RMS) from FFT is represented with different result analysis such as sensitivity, specificity, accuracy, Area under curve, kappa, F-measure and precision. The results performed more than 90% with Neural Network. Fast Fourier transform ; Ultrasonic sensor ; SIGNAL (programming language) ; Distilled water ; Mixing (physics) ; Artificial neural network ; Root mean square ; Materials science ; Biological system ; Analytical Chemistry (journal) ; Acoustics ; Chromatography ; Chemistry ; Mathematics ; Computer science ; Artificial intelligence ; Physics ; Algorithm Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Yuxiang Wei | Zhilong Ji | Xiaohe Wu | Jinfeng Bai | Lei Zhang | Wangmeng Zuo
Source: arXiv (Cornell University) (preprint, 2023-05-31)
Field: Materials Science — Materials Chemistry

Inferring and Leveraging Parts from Object Shape for Improving Semantic Image Synthesis Despite the progress in semantic image synthesis, it remains a challenging problem to generate photo-realistic parts from input semantic map. Integrating part segmentation map can undoubtedly benefit image synthesis, but is bothersome and inconvenient to be provided by users. To improve part synthesis, this paper presents to infer Parts from Object ShapE (iPOSE) and leverage it for improving semantic image synthesis. However, albeit several part segmentation datasets are available, part annotations are still not provided for many object categories in semantic image synthesis. To circumvent it, we resort to few-shot regime to learn a PartNet for predicting the object part map with the guidance of pre-defined support part maps. PartNet can be readily generalized to handle a new object category when a small number (e.g., 3) of support part maps for this category are provided. Furthermore, part semantic modulation is presented to incorporate both inferred part map and semantic map for image synthesis. Experiments show that our iPOSE not only generates objects with rich part details, but also enables to control the image synthesis flexibly. And our iPOSE performs favorably against the state-of-the-art methods in terms of quantitative and qualitative evaluation. Our code will be publicly available at https://github.com/csyxwei/iPOSE. Computer science ; Leverage (statistics) ; Segmentation ; Object (grammar) ; Image (mathematics) ; Artificial intelligence ; Information retrieval Machine Learning in Materials Science ; Advanced Neural Network Applications ; Handwritten Text Recognition Techniques
Authors: Julian Arnold | Niels Lörch | Flemming Holtorf | Frank Schäfer
Source: arXiv (Cornell University) (preprint, 2023-11-17)
Field: Materials Science — Materials Chemistry

Machine learning phase transitions: Connections to the Fisher information Despite the widespread use and success of machine-learning techniques for detecting phase transitions from data, their working principle and fundamental limits remain elusive. Here, we explain the inner workings and identify potential failure modes of these techniques by rooting popular machine-learning indicators of phase transitions in information-theoretic concepts. Using tools from information geometry, we prove that several machine-learning indicators of phase transitions approximate the square root of the system's (quantum) Fisher information from below -- a quantity that is known to indicate phase transitions but is often difficult to compute from data. We numerically demonstrate the quality of these bounds for phase transitions in classical and quantum systems. Phase transition ; Computer science ; Phase (matter) ; Quality (philosophy) ; Statistical physics ; Quantum ; Artificial intelligence ; Information geometry ; Quantum phase transition ; Square root ; Machine learning ; Theoretical physics ; Algorithm ; Mathematics ; Physics ; Quantum mechanics ; Geometry Machine Learning in Materials Science ; Statistical Mechanics and Entropy
Authors: Hasan Öztürk
Source: Drvna industrija (article, 2023-09-29)
Field: Materials Science — Polymers and Plastics

Prediction of Optimum Veneer Drying Parameters with Artifi cial Neural Networks for Production of Plywood with High Mechanical Properties Veneer drying is the manufacturing process in the plywood industry that most affects energy consumption and panel properties such as bonding and bending. Therefore, the veneer drying temperature and moisture content should be accurately adjusted. Moreover, the determination of veneer thermal conductivity is as important as these two parameters and the thermal conductivity values should also be specifi ed when forming the drying programs. This study aimed to predict the optimum values of the veneer drying temperatures, moisture content and thermal conductivity, which gave the best mechanical properties, by artifi cial neural network (ANN) analysis. Poplar (Populus deltoidesI-77/51) and spruce (Picea orientalis L.) veneers and urea formaldehyde (UF) resin were used in the production of plywood. The thermal conductivity of veneer and the bonding, bending strength and elasticity modulus of the panels were tested by the relevant standards. The most accurate and reliable prediction models were obtained by analyzing the experimental data with ANN. The optimum veneer drying temperature, moisture content and thermal conductivity values that gave the best values for all three mechanical properties were 149 &amp;deg;C, 6.2 % and 0.02668 W/mK for poplar and 116 &amp;deg;C, 4.4 % and 0.02534 W/mK for spruce. Veneer ; Materials science ; Composite material ; Urea-formaldehyde ; Thermal conductivity ; Water content ; Moisture ; Flexural strength ; Young's modulus ; Adhesive ; Geotechnical engineering Textile materials and evaluations ; Wood Treatment and Properties ; Additive Manufacturing and 3D Printing Technologies
Authors: Ningning Shen | Huijun Yang | Xuzhi Wang | Juan Du | Zhiqing Yang | Lei Miao | Siying Liu | Lifang Gao | Wenxia Ma | Chen Wang
Source: Research Square (preprint, 2023-10-12)
Field: Chemistry — Spectroscopy

Pan-cancer Analysis of a Cytoskeleton Mobility related ARHGAP44 gene with Potential Implications in Cancer Prognosis Risk Prediction and Immune Landscape Modulation GTPase ; Biology ; Gene ; Cancer ; Gene expression ; Cytoskeleton ; Cancer research ; Cancer cell ; Genetics ; Cell biology ; Cell Advanced Proteomics Techniques and Applications ; Endoplasmic Reticulum Stress and Disease ; Enzyme Structure and Function
Authors: Trần Văn Quân | Nguyễn Hoàng Giang | Ngoc Tan Nguyen
Source: Journal of Science and Technology in Civil Engineering (JSTCE) - HUCE (article, 2023-05-26)
Field: Materials Science — Materials Chemistry

Đánh giá các yếu tố ảnh hưởng đến cường độ nén và mô đun đàn hồi của bê tông cốt liệu cao su bằng mô hình học máy Nghiên cứu này tập trung vào phát triển mô hình học máy để thiết kế thành phần bê tông sử dụng cốt liệu cao su. Mô hình tăng cường độ dốc (GB) dự đoán cường độ chịu nén và mô đun đàn hồi với độ chính xác cao trên tập dữ liệu kiểm chứng với hệ số xác định R2 = 0,9983 và sai số căn quân phương RMSE = 0,8947 MPa khidự đoán cường độ chịu nén và R2 = 0,9971 và RMSE = 0,7473 GPa cho dự đoán mô đun đàn hồi. Các giá trị SHAP toàn cục và tuyệt đối được sử dụng để đánh giá độ ảnh hưởng của tám tham số đầu vào đến cường độchịu nén và mô đun đàn hồi. Ảnh hưởng của các yếu tố đến cường độ chịu nén được sắp xếp theo thứ tự là: Vụn cao su cắt nhỏ (CR) &gt; Vụn cao su (TR) &gt; Xi măng (C) &gt; Nước (W) &gt; Muội silic (SF) &gt; Cốt liệu thô (CA) &gt; Cát(S) và với mô đun đàn hồi theo thứ tự là CR &gt; TR &gt; W &gt; C &gt; CA &gt; S &gt; SF. Hàm lượng nước, TR và CR làm giảm cường độ và mô đun đàn hồi của bê tông cao su khi tăng hàm lượng, trong khi các yếu tố còn lại đều có lợi cho việc cải thiện cường độ chịu nén và mô đun đàn hồi khi tăng hàm lượng. Chemistry ; Stereochemistry Structural mechanics and materials ; Research studies in Vietnam ; Geodetic Measurements and Engineering Structures
Authors: Aryan Singh | Pepijn Van de Ven | Ciarán Eising | Patrick Denny
Source: arXiv (Cornell University) (preprint, 2023-07-24)
Field: Materials Science — Materials Chemistry

Compact &amp; Capable: Harnessing Graph Neural Networks and Edge Convolution for Medical Image Classification Graph-based neural network models are gaining traction in the field of representation learning due to their ability to uncover latent topological relationships between entities that are otherwise challenging to identify. These models have been employed across a diverse range of domains, encompassing drug discovery, protein interactions, semantic segmentation, and fluid dynamics research. In this study, we investigate the potential of Graph Neural Networks (GNNs) for medical image classification. We introduce a novel model that combines GNNs and edge convolution, leveraging the interconnectedness of RGB channel feature values to strongly represent connections between crucial graph nodes. Our proposed model not only performs on par with state-of-the-art Deep Neural Networks (DNNs) but does so with 1000 times fewer parameters, resulting in reduced training time and data requirements. We compare our Graph Convolutional Neural Network (GCNN) to pre-trained DNNs for classifying MedMNIST dataset classes, revealing promising prospects for GNNs in medical image analysis. Our results also encourage further exploration of advanced graph-based models such as Graph Attention Networks (GAT) and Graph Auto-Encoders in the medical imaging domain. The proposed model yields more reliable, interpretable, and accurate outcomes for tasks like semantic segmentation and image classification compared to simpler GCNNs Computer science ; Convolutional neural network ; Segmentation ; Feature learning ; Artificial intelligence ; Graph ; Deep learning ; Pattern recognition (psychology) ; Encoder ; Machine learning ; Theoretical computer science Machine Learning in Materials Science ; Advanced Graph Neural Networks ; Computational Drug Discovery Methods
Authors: Haftom Kahsay Tekia | Tibebe Beshah | Fisha Haileslassie | Kibrom Gidey
Source:  (preprint, 2023-02-01)
Field: Chemistry — Analytical Chemistry

Moisture content prediction model for pharmaceutical granules using machine learning techniques The aim of this study was to develop a prediction model and identifying relative important factors in the evaluation of moisture content of pharmaceutical granules using Artificial Neural Network (ANN) and Support Vector Machine (SVM) techniques for the data sets of Addis Pharmaceutical Factory (APF). Optimal models of ANN and SVM models were developed and compared, utilizing Matlab16.0 as a software tool. The performance of the models is evaluated using a quantitative error metric; mean squared error (MSE), Regression(R) and Confusion Matrix (CM). This study reveals that the ANN model is an optimal model for predicting moisture content of pharmaceutical granules for the datasets of APF. The model of ANN, with MSE of 0.016941 and classification accuracy of 98.7% is built and accepted as optimal model for predicting moisture content of pharmaceutical granules. Temperature, Mixer time, Initial moisture, air flow rate and drying time respectively are the most important factors in determining the moisture content of the granules. Confusion matrix ; Support vector machine ; Artificial neural network ; Water content ; Mean squared error ; Machine learning ; Approximation error ; Metric (unit) ; Artificial intelligence ; Computer science ; Mathematics ; Engineering ; Statistics ; Algorithm Spectroscopy and Chemometric Analyses ; Analytical Methods in Pharmaceuticals
Authors: Henning Schlömer | Annabelle Bohrdt
Source: arXiv (Cornell University) (preprint, 2023-01-01)
Field: Materials Science — Materials Chemistry

Fluctuation based interpretable analysis scheme for quantum many-body snapshots Microscopically understanding and classifying phases of matter is at the heart of strongly-correlated quantum physics. With quantum simulations, genuine projective measurements (snapshots) of the many-body state can be taken, which include the full information of correlations in the system. The rise of deep neural networks has made it possible to routinely solve abstract processing and classification tasks of large datasets, which can act as a guiding hand for quantum data analysis. However, though proven to be successful in differentiating between different phases of matter, conventional neural networks mostly lack interpretability on a physical footing. Here, we combine confusion learning with correlation convolutional neural networks, which yields fully interpretable phase detection in terms of correlation functions. In particular, we study thermodynamic properties of the 2D Heisenberg model, whereby the trained network is shown to pick up qualitative changes in the snapshots above and below a characteristic temperature where magnetic correlations become significantly long-range. We identify the full counting statistics of nearest neighbor spin correlations as the most important quantity for the decision process of the neural network, which go beyond averages of local observables. With access to the fluctuations of second-order correlations -- which indirectly include contributions from higher order, long-range correlations -- the network is able to detect changes of the specific heat and spin susceptibility, the latter being in analogy to magnetic properties of the pseudogap phase in high-temperature superconductors. By combining the confusion learning scheme with transformer neural networks, our work opens new directions in interpretable quantum image processing being sensible to long-range order. Artificial neural network ; Interpretability ; Artificial intelligence ; Observable ; Computer science ; Statistical physics ; Convolutional neural network ; Quantum ; Physics ; Pattern recognition (psychology) ; Quantum mechanics Machine Learning in Materials Science ; Quantum many-body systems ; Advanced Thermodynamics and Statistical Mechanics
Authors: A. S. Shankar | D. O. Oriekhov | Andrew K. Mitchell | L. Fritz
Source: arXiv (Cornell University) (preprint, 2023-03-04)
Field: Materials Science — Materials Chemistry

Kondo effect in twisted bilayer graphene The emergence of flat bands in twisted bilayer graphene at the magic angle can be understood in terms of a vanishing Fermi velocity of the Dirac cone. This is associated with van Hove singularities approaching the Fermi energy and becoming higher-order. In the density of states this is reflected by flanking logarithmic van Hove divergences pinching off the central Dirac cone in energy space. The low-energy pseudogap of the Dirac cone away from the magic angle is replaced by a power-law divergence due to the higher-order van Hove singularity at the magic angle. This plays an important role in the exotic phenomena observed in this material, such as superconductivity and magnetism, by amplifying electronic correlation effects. Here we investigate one such correlation effect -- the Kondo effect due to a magnetic impurity embedded in twisted bilayer graphene. We use the Bistritzer-MacDonald model to extract the low-energy density of states of the material as a function of twist angle, and study the resulting quantum impurity physics using perturbative and numerical renormalization group methods. Although at zero temperature the impurity is only Kondo screened precisely at the magic angle, we find highly nontrivial behavior at finite temperatures relevant to experiment, due to the complex interplay between Dirac, van Hove, and Kondo physics. Van Hove singularity ; Physics ; Condensed matter physics ; Bilayer graphene ; Pseudogap ; Density of states ; Fermi energy ; Anderson impurity model ; Kondo effect ; Fermi level ; Dirac fermion ; Quantum mechanics ; Superconductivity ; Graphene ; Impurity ; Cuprate ; Electron Graphene research and applications ; Quantum and electron transport phenomena ; Topological Materials and Phenomena
Source: Angewandte Chemie (article, 2023-05-16)
Field: Chemistry — Spectroscopy

CeMOS Mannheim Connects High‐Impact Science, Co‐development, and Transfer through Collaborations Abstract This invited Team Profile was created by the Applied Mass Spectrometry Team at the Center for Mass Spectrometry and Optical Spectroscopy (CeMOS Mannheim) in Germany. They recently published an article together with Sirius Fine Chemicals SiChem GmbH and Bruker Daltonics . The work presents a novel concept for vacuum‐stable‐by‐design MALDI matrices that enable long MALDI mass spectrometry measurements (e.g., for imaging) of at least 72 hours. Through the use of a photo‐removable group, organic synthesis transformed the widely used, but unfortunately very volatile MALDI matrix 2 , 5 ‐dihydroxyacetophenone (2,5‐DHAP) into a vacuum‐stable one. The protecting group can be uncaged by the MALDI laser in the ion source and then the matrix performs in the same way as the common matrix 2,5‐DHAP. “ A Caged In‐Source Laser‐Cleavable MALDI Matrix with High Vacuum Stability for Extended MALDI‐MS Imaging ”, Q. Zhou, S. Rizzo, J. Oetjen, A. Fülöp, M. Rittner, H. Gillandt, C. Hopf, Angew. Chem. Int. Ed . 2023 , e202217047 . Mass spectrometry ; Accelerator mass spectrometry ; Matrix (chemical analysis) ; Chemistry ; Spectroscopy ; Analytical Chemistry (journal) ; Chromatography ; Physics Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography