jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergiy Reva | Denys Tsybliyev
Source: Eastern-European Journal of Enterprise Technologies (article, 2024-12-30)
Field: Chemistry — Analytical Chemistry

Devising a computer method to recognize and analyze spectrometric signals parameters The object of this study is computerized systems for measuring the parameters of spectrometric signals digitized using special hardware. The task addressed in the research is to improve the process of filtering the usable pulse signal from noise and increase the accuracy of measuring pulse parameters by devising a new method of analysis. In order to verify the performance of the new method in comparison with several already known ones, input data arrays with predetermined parameters were prepared using computer simulation. A special algorithm was also developed to verify each detected pulse. As a result, the main characteristics of the methods, such as signal recognition accuracy and data processing speed, were obtained for several scenarios with different durations of modeling process and different pulse generation intensities. Comparative performance metrics were provided for all described software analysis methods. Ultimately, in the studied scenarios, the devised method showed better recognition ability than the considered alternative methods. The key features of the proposed method are the use of software filters built on the basis of the application of Fast Discrete Fourier Transform (FDFT) algorithms and further computer processing of the signal using a mechanism for correcting the amplitudes of superimposed pulses. This makes it possible to filter the signal from noise without significantly changing the usable component and to more accurately determine the amplitudes in case of their frequent superposition. In practice, the devised method could be used to improve existing and design new computer systems of spectral analysis Computer science ; USable ; Noise (video) ; Filter (signal processing) ; Process (computing) ; Software ; Superposition principle ; SIGNAL (programming language) ; Signal processing ; Basis (linear algebra) ; Key (lock) ; Artificial intelligence ; Algorithm ; Digital signal processing ; Computer vision ; Computer hardware ; Image (mathematics) ; Mathematics Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Fault Detection and Control Systems
Authors: Matthias Beuting | Alex J. Fairhall | Randall H. Goldsmith | Lianyi Chen | Scott T. Sanders
Source:  (preprint, 2024-01-02)
Field: Materials Science — Materials Chemistry

Backscatter absorption spectroscopy for process monitoring in powder bed fusion Laser powder bed fusion is a metal 3D printing technology that involves melting and solidifying a metal powder using a process laser or electron beam. The interaction of metal vapors with the melt pool is known to cause manufacturing defects. Here we use absorption spectroscopy of laser light reflected from the liquid metal surface to measure atomic Ti vapor during processing of Ti-6Al-4V inside the vapor cavity forming in the melt pool. The absorption line strength and shape were found to vary significantly with processing parameters. In addition, laser-induced fluorescence (LIF) was observed, providing further opportunities for in situ diagnostics. Backscatter (email) ; Materials science ; Spectroscopy ; Fusion ; Process (computing) ; Absorption (acoustics) ; Absorption spectroscopy ; Analytical Chemistry (journal) ; Optics ; Chemistry ; Computer science ; Composite material ; Physics ; Chromatography ; Telecommunications Crystallization and Solubility Studies ; Spectroscopy and Chemometric Analyses ; Thermal and Kinetic Analysis
Authors: Torsten Hielscher
Source: Modern Concepts in Material Science (article, 2024-07-23)
Field: Materials Science — Materials Chemistry

Tool Wear Influence on The Fatigue Life of Lathe Processed Specimens of 1.6310 Steel Machining processes result in numerous influencing factors on the fatigue behavior of manufactured specimens or components. In addition to various process parameters, such as feed rate, cutting speed and cutting depth, the wear condition of the cutting tools is a particularly important influencing factor. In the present work, the relationship between the wear condition of turning tools, represented by flank wear and cutting edge rounding, and the surface roughness of machined specimens is presented and their influence on fatigue behavior is investigated. Experiments were carried out on the ferritic-bainitic steel 1.6310 (20MnMoNi5-5), which is mainly used in spray lines in German nuclear power plants. The aim of the presented research is to attribute the scattering of fatigue life to the respective tool wear at given stress amplitudes. Materials science ; Metallurgy ; Engineering ; Engineering drawing Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Aluminum Alloy Microstructure Properties
Authors: Bernardo Duarte | Ângela Carneiro | Pedro Moisés de Sousa
Source:  (article, 2024-11-06)
Field: Chemistry — Analytical Chemistry

Coffee bean quality analysis using convolutional neural networks Coffee grading is the main procedure in its production. One of the processes to grade coffee is done manually, requiring a lot of training and experience from experts. The main objective of this work is to use technologies based on Artificial Intelligence, with Convolutional Neural Network models, and together with the application of image processing techniques, to improve the quality analysis of coffee beans. We performed a comparison of pre-trained models, namely AlexNet and DenseNet, using a coffee bean dataset. A total of 4272 coffee bean images from the USK-COFFEE database were used in this work. The model training, testing and validation processes were performed with an 80/10/10 division of the obtained images. Classification metrics such as Recall, F-1 Score, were used for the detailed analysis of the performance models. ROC curves were used to analyze their distinction. Computer science ; Convolutional neural network ; Quality (philosophy) ; Artificial intelligence ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Currency Recognition and Detection
Authors: Jingfeng Yao | Cheng Wang | Wenyu Liu | Xinggang Wang
Source: arXiv (Cornell University) (preprint, 2024-10-14)
Field: Materials Science — Materials Chemistry

FasterDiT: Towards Faster Diffusion Transformers Training without Architecture Modification Diffusion Transformers (DiT) have attracted significant attention in research. However, they suffer from a slow convergence rate. In this paper, we aim to accelerate DiT training without any architectural modification. We identify the following issues in the training process: firstly, certain training strategies do not consistently perform well across different data. Secondly, the effectiveness of supervision at specific timesteps is limited. In response, we propose the following contributions: (1) We introduce a new perspective for interpreting the failure of the strategies. Specifically, we slightly extend the definition of Signal-to-Noise Ratio (SNR) and suggest observing the Probability Density Function (PDF) of SNR to understand the essence of the data robustness of the strategy. (2) We conduct numerous experiments and report over one hundred experimental results to empirically summarize a unified accelerating strategy from the perspective of PDF. (3) We develop a new supervision method that further accelerates the training process of DiT. Based on them, we propose FasterDiT, an exceedingly simple and practicable design strategy. With few lines of code modifications, it achieves 2.30 FID on ImageNet 256 resolution at 1000k iterations, which is comparable to DiT (2.27 FID) but 7 times faster in training. Architecture ; Transformer ; Training (meteorology) ; Computer science ; Diffusion ; Computer architecture ; Electrical engineering ; Engineering ; Physics ; Geography ; Voltage Electronic and Structural Properties of Oxides ; Ferroelectric and Negative Capacitance Devices ; Advanced Memory and Neural Computing
Authors: Alexander C. Tyner | Alexander V. Balatsky
Source: PLoS ONE (article, 2024-11-08)
Field: Chemistry — Inorganic Chemistry

Screening the organic materials database for superconducting metal-organic frameworks The increasing financial and environmental cost of many inorganic materials has motivated study into organic and "green" alternatives. However, most organic compounds contain a large number of atoms in the primitive unit cell, posing a significant barrier to high-throughput screening for functional properties. In this work, we attempt to overcome this challenge and identify superconducting candidates among the metal-organic-frameworks in the organic materials database using a recently proposed proxy for the electron-phonon coupling. We then isolate the most promising candidate for in-depth analysis, C9H8Mn2O11, providing evidence for superconductivity below 100mK. Metal-organic framework ; Superconductivity ; Materials science ; Chemistry ; Physics ; Organic chemistry ; Condensed matter physics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Yaru Huang | Yangxu Wang
Source: International Journal of Advanced Computer Science and Applications (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Exploring Enhanced Object Detection and Classification Methods for Alstroemeria Genus Morado As an important ornamental plant, the automatic detection and classification of the maturity of Alstroemeria Genus Morado flowers hold significant importance in precision agriculture. However, this task faces numerous challenges due to the diversity of morphological characteristics, complex growth environments, and factors such as occlusion and lighting variations. Currently, this field is relatively unexplored, necessitating innovative methods to overcome existing difficulties. To fill this research gap, this study developed a deep learning-based object detection framework, the Alstroemeria Genus Morado Network (AGMNet), specifically optimized for the detection and classification of Alstroemeria Genus Morado flowers. This convolutional neural network utilizes multi-scale feature fusion techniques and spatial attention mechanisms, along with a dual-path detection structure, significantly enhancing its capability for automatic maturity classification and detection of flowers. Notably, AGMNet addresses the issue of class imbalance in its design and employs advanced data augmentation techniques to enhance the model's generalization ability. In comparative experiments on the morado_5may dataset, AGMNet demonstrated superior performance in Precision, Recall, and F1-score, with a 3.8% improvement in the mAP metric over the latest YOLOv9 model, showcasing stronger generalization capabilities. AGMNet is expected to play a more significant role in enhancing agricultural production efficiency and automation levels. Genus ; Object (grammar) ; Computer science ; Botany ; Biology ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Identification and Quantification in Food
Authors: Jihua Zhang | Jinyong Ma | Neuton Li | Shaun Lung | Andrey A. Sukhorukov
Source: arXiv (Cornell University) (preprint, 2024-01-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single-shot characterization of photon indistinguishability with dielectric metasurfaces Characterizing the indistinguishability of photons is a key task in quantum photonics, underpinning the tuning and stabilization of the photon sources and thereby increasing the accuracy of quantum operations. The protocols for measuring the degree of indistinguishability conventionally require photon-coincidence measurements at several different time or phase delays, which is a fundamental bottleneck towards the fast measurements and real-time monitoring of indistinguishability. Here, we develop a static dielectric metasurface grating without any reconfigurable elements that realizes a tailored multiport transformation in the free-space configuration without the need for phase locking and enables single-shot characterization of the indistinguishability between two photons in multiple degrees of freedom including time, spectrum, spatial modes, and polarization. Topology optimization is employed to design a silicon metasurface with polarization independence, high transmission, and high tolerance to measurement noise. We fabricate the metasurface and experimentally quantify the indistinguishability of photons in the time domain with fidelity over 98.4\%. We anticipate that the developed framework based on ultrathin metasurfaces can be further extended for multi-photon states and additional degrees of freedom associated with spatial modalities. Photon ; Photonics ; Polarization (electrochemistry) ; Physics ; Topology (electrical circuits) ; Computer science ; Optics ; Mathematics Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Orbital Angular Momentum in Optics
Authors: John Carlos | Clarisse Gosset‐Erard | Jean‐Yves Salpin | Salomé Poyer
Source: Rapid Communications in Mass Spectrometry (article, 2024-12-02)
Field: Chemistry — Spectroscopy

Electron Transfer Dissociation of Alkali‐Cationized Phosphatidylcholines Allows Easy Double Bonds and <i>sn‐</i> 1/ <i>sn‐</i> 2 Localizations The dissociation rules established for ETD-MS<sup>2</sup> and ETD-CID-MS<sup>3</sup> experiments on PC isomers enable their annotation at the isomeric level without instrumental modification or complex sample preparation. This method could be easily coupled to LC separation using post-column introduction of an alkali salt for complex mixture analysis. Chemistry ; Electron-transfer dissociation ; Dissociation (chemistry) ; Mass spectrometry ; Fragmentation (computing) ; Tandem mass spectrometry ; Collision-induced dissociation ; Alkali metal ; Quadrupole ion trap ; Ion trap ; Analytical Chemistry (journal) ; Chromatography ; Physical chemistry ; Organic chemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Analytical Chemistry and Chromatography
Authors: Zahra Elhamraoui | Eva Borràs | Mathias Wilhelm | Eduard Sabidó
Source: Bioinformatics Advances (article, 2024-12-26)
Field: Chemistry — Spectroscopy

MSCI: an open-source Python package for information content assessment of peptide fragmentation spectra MSCI is implemented in Python and it is released under a permissive MIT license. The source code and the installers are available on GitHub at https://github.com/proteomicsunitcrg/MSCI. Python (programming language) ; Fragmentation (computing) ; Computer science ; Workflow ; Peptide ; Open source ; Proteome ; Proteomics ; Mass spectrometry ; Computational biology ; Bioinformatics ; Chemistry ; Database ; Programming language ; Software ; Biology ; Chromatography ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Jamaladdin Nuraddin Aslanov | Zenfira Seydi Huseynli
Source:  (article, 2024-12-13)
Field: Materials Science — General Materials Science

DETERMINATION OF THE DESIGN PARAMETERS OF SEALS WITH REQUIRED PROPERTIES The article is devoted to the problems of Computer science Material Properties and Applications ; Tribology and Lubrication Engineering ; Engineering Technology and Methodologies
Authors: Tiankai Yao
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

Quantitative Insight to Fission Gas Pores Distribution in Irradiated Annular U-10Zr Metallic Fuel Using Machine Learning Metallic fuels, particularly U-10Zr and its performance in reactor irradiation conditions, have been thoroughly investigated and are a promising candidate for next-generation sodium-cooled fast spectrum nuclear reactors. Irradiation in reactors can lead to the formation of fission gas and increased pore formation which can significantly impact fuel performance. Due to the large number of pores and various phases formed in metallic fuel during irradiation, a quantitative description of fission gas pores as a function of irradiation conditions is not yet available, undermining the fidelity of fuel performance modeling to support fuel qualification. It has been difficult to clearly detect pore boundaries and distinguish matrix phases from fission gas pores using optical microscopy by using simple threshold methods working with low magnification images. The pre-trained deep learning model for fission gas pore detection was applied to ~10,260 high magnification scanning electron microscopy images. The model increased the accuracy of fission gas pore segmentation to obtain statistical features, which cannot be processed manually. A pre-trained decision tree model was used to classify pores as isolated or connected pores, providing new insight into the correlation between the movement of lanthanides, solid fission products, and the radial temperature gradient developed in fuel irradiation conditions. This paper emphasizes the potential that artificial intelligence-based machine learning models have to accelerate qualification and support nuclear fuel development. Fission ; Irradiation ; Distribution (mathematics) ; Computer science ; Nuclear engineering ; Materials science ; Nuclear physics ; Engineering ; Physics ; Mathematics ; Neutron Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Heat transfer and supercritical fluids
Authors: Yuxi Li | Jiafu Wang | Sai Sui | Ruichao Zhu | Yajuan Han | Hongya Chen | Xinmin Fu | Shaojie Wang | Cunqian Feng | Shaobo Qu
Source:  (preprint, 2024-01-27)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Simplistic framework of single-pixel-programmable metasurfaces integrated with capsuled LED array Coding metasurfaces can manipulate electromagnetic wave in real time with high degree of freedom, the fascinating properties of which enrich the metasurface design with a wide range of application prospects. However, most of the coding metasurfaces are designed based on external excitation framework with the wired electrical or wireless light control devices, thus inevitably causing the interference with electromagnetic wave transmission and increasing the complexity of the metasurface design. In this work, a simplistic framework of single-pixel-programmable metasurfaces integrated with capsuled LED array is proposed to dynamically control electromagnetic wave. The framework fully embeds the photoresistor in the meta-atom, controls LED array to directly illuminate the photoresistor to modulate the phase response. With this manner, the complex biasing network is transformed to the universal LED array, which means physical control framework can be transformed to software framework, and thus the functions of the metasurface can be freely manipulated by encoding the capsuled LED array avoiding mutual coupling of adjacent meta-atoms in real time. All the results verify that the far-field scattering pattern can be customized with this single-pixel-programmable metasurface. Encouragingly, this work provides an universal framework for coding metasurface design, which lays the foundation for metasurface intelligent perception and adaptive modulation. Pixel ; Computer science ; Computer hardware ; Artificial intelligence Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: S. M. Afonin
Source: American Journal of Biomedical Science & Research (article, 2024-01-23)
Field: Materials Science — Materials Chemistry

Structural Model of a Nano Drive for Biomedical Science The structural model of a nano drive is determined for biomedical science. The structural scheme of the piezo drive is obtained. The matrix equation is constructed for a nano drive. Nano- ; Nanotechnology ; Computer science ; Engineering ; Materials science ; Chemical engineering Carbon Nanotubes in Composites ; Force Microscopy Techniques and Applications ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Reem Khaled | Rafah Razooq Hameed | Ahmed Zuhair Abdullhameed
Source: Samarra Journal of Pure and Applied Science (article, 2024-12-30)
Field: Chemistry — Organic Chemistry

Evaluate the Levels of Fas‐associated Factor 1 and the Oxidation State of Breast Cancer Women The current study aims to evaluate the levels of Human FAS-Associated Factor 1-FAF1, Uric Acid-UC, Glutathione Peroxidase-GPX and Malondialdehyde-MDA in sera of women with breast cancer-BC, ninety serum samples were collected from women, It was divided into 3 groups, the control group-C includes 30 samples of healthy women with no history of breast cancer, 30 samples of women newly diagnosed with breast cancer (as first group-G1), and 30 samples of women with breast cancer who underwent chemotherapy (as second group-G2). The study includes the determination of the level of serum FAF1, UA, GPX and MDA. The results indicate that the value of body mass index-BMI and UA level were significantly (P ≤0.05) increased in G1 as compared with G2 and C. While the levels of FAF1 and MDA were significantly(P ≤0.05) elevated in the patients' group as compared with C, with no significant (P ≤0.05) difference for serum GPX activity between patients and control group. We can conclude from the results of current study, that the FAF1, and MDA were associated with an increased risk of breast cancer. Also UA plays an important role in the development of breast cancer Breast cancer ; Risk factor ; Uric acid ; Glutathione ; Case-control study ; Body mass index Free Radicals and Antioxidants
Authors: Beyza Türköz | Muhammed Mustafa Özçelik | Sebahattin Serhat Turgut
Source: Turkish Journal of Agriculture - Food Science and Technology (article, 2024-12-12)
Field: Chemistry — Analytical Chemistry

Antep Fıstığında Bezelye ve Ispanak Tağşişinin Düşük Maliyetli Spektral Sensör Kullanılarak Tespiti Antep fıstığı, başta baklava olmak üzere birçok tatlıda sıkça kullanılan bir üründür. Özellikle öğütülmüş Antep fıstığı, talebin yüksek olması ve yüksek maliyeti nedeniyle sıklıkla tağşişe maruz kalmaktadır. Yeşil bezelye ve ıspanak, organoleptik özellikleri, renk benzerliği ve ucuzluğu nedeniyle Antep fıstığı ile karıştırılarak en çok sahtecilik gerçekleştirilen ürünler arasındadır. Ancak bu ürünlerin öğütülmüş Antep fıstığına belirli oranlarda karıştırılması durumunda sahteciliğin hızlı ve yerinde tespiti, gözle muayene gibi yöntemlerle çoğu zaman mümkün olmamaktadır. Bu nedenle mevcut çalışma, Antep fıstığındaki yeşil bezelye ve ıspanak tağşişinin, düşük maliyetli bir spektral sensör teknolojisi ve kemometrik yöntemler kullanılarak tespitini amaçlamaktadır. Bu kapsamda yeşil bezelye ve ıspanak örnekleri Antep fıstığı ile %5-50 (a/a) arasındaki konsantrasyonlarda %5’lik artışlarla karıştırılmıştır. Saf Antep fıstığı ve karışık numunelerin reflektans spektrumları 410-940 nm arasında elde edilmiştir. Numunelerdeki sahtecilik oranlarının tespiti amacıyla geliştirilen modelin eğitilmesinde derin öğrenme yöntemi kullanılmıştır. Modele beslenen verilerde boyut indirgeme amacıyla Temel Bileşenler Analizinden faydalanılmış olup, regresyon probleminin uyum iyiliğini test etmek amacıyla belirleme katsayısı (R2), hata kareler ortalamasının karekökü (RMSE) ve artıklık tahminsel sapma (RPD) istatistikleri kullanılmıştır. Sırasıyla eğitim, doğrulama ve test verileri için R2 0,85, 0,83 ve 0,80; RMSE 5,81, 6,13 ve 6,71; RPD 2,55, 2,44 ve 2,21 olarak tespit edilmiştir. Elde edilen bulgular neticesinde, söz konusu ekonomik spektral sensör ve geliştirilen kemometrik yöntemin Antep fıstığında özellikle %10 üzerindeki yeşil bezelye ve ıspanak tağşiş oranının belirlenmesi için tahribatsız, hızlı ve kolay bir yöntem olarak kullanılma potansiyeline sahip olduğu ortaya konmuştur. Physics ; Mathematics ; Molecular biology ; Biology Spectroscopy and Chemometric Analyses