jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Jin Uk Heo | T. Arsenault | Shujin Sun | Atallah Baydoun | Robert S. Jones | Yi Gu | Yizhang Jiang | Pengjiang Qian | Melanie Traughber | Rose Al Helo | Min Yao | Jennifer A. Dorth | John Nakayama | Steven Waggoner | Tithi Biswas | Eleanor Harris | Kate Sandstrom | Bryan Traughber | Raymond F. Muzic
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Dosimetric Evaluation of Synthetic Ct Used in Radiation Therapy for MRI Guided Liver Sbrt Medicine ; Nuclear medicine ; Radiation therapy ; Radiology ; Medical physics ; Computed tomography Lanthanide and Transition Metal Complexes ; Advanced Radiotherapy Techniques ; Boron Compounds in Chemistry
Authors: Jie You | Zhichao Zhao | Xingwu Zheng
Source:  (article, 2024-04-21)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Model Agnostic Data Enhancement Algorithm in 2D Chiral Metamaterials: From Prediction to Inverse Design In this paper, we introduce a powerful model agnostic data enhancement (MADE) learning algorithm to realize the efficient prediction and rapid inverse design of 2D diffractive chiral metamaterials. In order to vividly demonstrate the performance of our MADE algorithm, multi-shaped chiral structures with various geometrical parameters (e.g., width, separation space, module length, depths) and material components are selected. To generate the training data for MADE model, we utilize the conventional method of rigorous coupled wave analysis (RCWA) to capture the chiroptical response of metamaterials. Moreover, data enhancement and multi-task learning strategy constitute two key sections of MADE algorithm. First, a forward prediction network replying on data enhancement is built to generate large amount of "pseudo data" to reinforce the original training dataset that is much less than previous work, which can also perform fast prediction of the metamaterials’ circular dichroism response. Additionally, multi-task learning strategy is exploited in MADE algorithm to address the issue that various geometric parameters apparently affect the chiroptical response in a different manner, enhancing the model versatility. The comprehensive results demonstrate that our MADE algorithm can not only carry out the forward prediction for metamaterials’ chiroptical response with a significantly-fast computational speed that is 4 orders of magnitude faster than RCWA, but equips with fast inverse design function for on-demand metamaterials, which also can be applied to contrive new structures with desired optical response. This study set out to assess the feasibility of deep learning method in the large-scale, efficient design of 2D diffractive chiral metamaterials and more sophisticated photonic devices on chip. Metamaterial ; Inverse ; Computer science ; Algorithm ; Inverse problem ; Materials science ; Mathematics ; Optoelectronics ; Mathematical analysis ; Geometry Metamaterials and Metasurfaces Applications ; Electromagnetic Scattering and Analysis ; Antenna Design and Analysis
Authors: Zhiyang Hu | Xing Yang | Jiwen Zhao | Haoqi Gao | Haoli Xu | Hua Mu | Yangyang Wang
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Materials Chemistry

Physically Structured Adversarial Patch Inspired by Natural Leaves Multiply Angles Deceives Infrared Detectors Adversarial system ; Natural (archaeology) ; Infrared ; Detector ; Computer science ; Artificial intelligence ; Computer vision ; Optics ; Geology ; Physics ; Telecommunications ; Paleontology High-Velocity Impact and Material Behavior ; Adversarial Robustness in Machine Learning ; Infrared Target Detection Methodologies
Authors: Longguo Wu | Yao Zhang | Ling Ma | Yanzhe Zhu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Fluorescence Hyperspectral in Combination with Deep Learning for Detecting the Crucial Index Fv/Fm of Melon Leaves Hyperspectral imaging ; Artificial intelligence ; Melon ; Index (typography) ; Fluorescence ; Pattern recognition (psychology) ; Computer science ; Biology ; Horticulture ; Physics ; World Wide Web ; Optics Spectroscopy and Chemometric Analyses
Authors: Jun Xu | Xiemeng Zhu | Qiang Liu | Shiyu Du | Yiming Zhang
Source: Materials Research Express (article, 2024-02-01)
Field: Materials Science — Ceramics and Composites

Unlocking the strength: the predictions of silicon carbide fracture toughness revealed through data-driven approach Abstract Silicon carbide ceramics are widely used within various applications, including mechanical, chemical, aerospace and military; where the fracture toughness plays a crucial role. From the processing perspectives, the fracture toughness is controlled by the combination of starting phases and sintering conditions (including additives, atmosphere, temperature and pressure). However, the interplay of these factors makes the forward predictions of fracture toughness untreatable neither through experimentation nor physical modeling; not mention to the reverse estimations of optimal processing parameters. In this work, a data-driven strategy was proposed that firstly to predict the fracture toughness from processing parameters; and then to explore certain parameters that have large impacts on the fracture toughness. From running four different machine learning (ML) algorithms on a well-established dataset of SiC sintering recipe, it was found that the eXtreme Gradient Boosting (XGBoost) model possess the best performance with accuracy up to 88%. Further, the feature importance scores revealed that the sintering temperature and the types of sintering additives show their significant influence on fracture toughness. It was found that the sintering temperature is the most critical factor affecting the obtained fracture toughness of SiC, where the optimum temperature range is of 1800 °C–2000 °C; and also, the sintering additives of Al and Al 2 O 3 have great influences on the obtained fracture toughness, where the optimum range of their mass fraction within the whole additives is 3–8 wt%. Finally, the developed model shows its capability to propose sintering strategy for the preparation of SiC ceramics with target fracture toughness. Sintering ; Fracture toughness ; Materials science ; Silicon carbide ; Ceramic ; Composite material ; Toughness Advanced ceramic materials synthesis ; Aluminum Alloys Composites Properties ; Advanced materials and composites
Authors: Zekiyos Abayneh Bochera | Solomon Gizaw | Olivier Clémеnt
Source:  (article, 2024-12-14)
Field: Chemistry — Analytical Chemistry

Automatic Identification of Teff (Eragrostis Tef) Flour Adulteration Using Machine Learning Food adulteration, the act of mixing foodstuffs with lower-quality foodstuffs or other food and non-food items, has become a global issue, particularly in developing countries like Ethiopia. This is a significant problem as it can lead to nutritional deficiencies and in some cases, illnesses, diseases or even death. The identification of adulterated food typically requires physio-chemical laboratory tests that vary depending on the type and level of adulterant. Teff flour, a daily staple food in Ethiopia, is often adulterated with wood flour and gypsum powder. In this research work, we utilized computer vision techniques to extract textural and color features from images of both adulterated and pure Teff flour. We then trained a Support Vector Machine (SVM) based machine learning model to identify adulterated Teff flour and predict the level of adulteration. The presented results show varying levels of prediction accuracy depending on the type of Teff flour, the level of adulteration. The developed model was also found to provide good results with images sizes ranging from <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{128 \times 128}$</tex> to <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\boldsymbol{2048 \times 2048}$</tex> pixels. The results show overall model accuracy was above 90% when the adulteration level of red Teff flour is more than 25% and above 84% when the adulteration level of white wood flour was above 25%. Eragrostis ; Identification (biology) ; Artificial intelligence ; Computer science ; Food science ; Mathematics ; Biology ; Agronomy ; Botany Spectroscopy and Chemometric Analyses
Authors: Yaoxing Wu | Gabriel Sinclair | Raghavendhran Avanasi Narasimhan | Alison M. Pecquet
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Spectroscopy

Physiologically Based Kinetic (PBK) Modeling of Propiconazole Using a Machine Learning Based Read-Across Approach for Interspecies Extrapolation Extrapolation ; Kinetic energy ; Computer science ; Artificial intelligence ; Machine learning ; Biological system ; Mathematics ; Statistics ; Physics ; Biology Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Tuberculosis Research and Epidemiology
Authors: Volodymyr Lytvynenko | S. Olszewski | Andrzej Smolarz | Serhii Kobernik | Eva Zaets | В. Ф. Демченко | Dariia Dontsova | Saule Kumargazhanova
Source:  (article, 2024-12-16)
Field: Chemistry — Analytical Chemistry

Thin-layer chromatography image segmentation for toxicological studies The solution to TLC (Thin-Layer Chromatography) image segmentation in organochlorine pesticides (OCPs) toxicology research is crucial for scientific advancement and industrial applications. OCPs are found in minute concentrations in biological and environmental samples, necessitating precise segmentation for detection. Accurate segmentation is essential due to the potential multiple OCPs in samples, ensuring reliable component separation. Automated segmentation minimizes subjective errors inherent in manual methods, maintaining accuracy in toxicological studies. Standardized, automated processes ensure reproducible results, accelerating analysis for large-scale studies like environmental monitoring. Rapid TLC image segmentation reduces turnaround time critical for time-sensitive studies such as environmental assessments. Integration with analytical techniques like chromatography enhances comprehensive OCP analysis. Challenges include sample complexity with overlapping chemicals on TLC plates, and low OCP concentrations necessitating sensitive detection amidst noise and artifacts. Sophisticated algorithms are required for accurate analysis, sensitive to trace OCP signals and robust against experimental variations. Developing machine learning algorithms involves creating diverse training samples, a resource-intensive but essential task for reliable segmentation. This paper reviews segmentation methods, emphasizing threshold segmentation for its simplicity, speed, and accuracy. Algorithms for signal correction and peak detection enhance clarity, with trapezoidal integration accurately measuring peak areas. Developed in R, the program processes chromatographic images, providing tools for analysis, method refinement, and validation. These advancements are pivotal for ensuring the accuracy, efficiency, and reliability of OCP toxicology studies, crucial for environmental protection, public health, and regulatory compliance. Segmentation ; Computer science ; Reliability (semiconductor) ; Artificial intelligence ; Image segmentation ; Noise (video) ; Pattern recognition (psychology) ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies
Authors: З. А. Годжаев | Sergey Senkevich | I. Malahov | S. Yu. UYUTOV | V. Е. Kuz’min
Source:  (article, 2024-12-25)
Field: Materials Science — Polymers and Plastics

JUSTIFICATION, DEVELOPMENT AND CREATION OF A TEST BENCH FOR ELEMENTS OF ADAPTIVE RUNNING SYSTEMS AGRICULTURAL MEAS WITH THE USE OF ARTIFICIAL NEURAL NETWORK Studies show that the number of traction and transport agricultural energy facilities aged 15 years and more account for 70% of the total fleet of machines, then increasing the durability of the MEA will allow it to be operated for a longer period. The use of an adaptive running system of the MES with elements of artificial intelligence involves reducing the pressure on the soil by 25% and reduc-ing the slipping of the propulsor to 35%., as well as reducing the level of vibration activity of the MES structural elements, which will significantly improve the working conditions of the operator; protect the MES from the effects of vibrations, increase durability by reducing dynamic loads, and subsequently increase fatigue resistance and reliability of the MES. To do this, it is necessary to develop algorithms for controlling the impact and reaction of the control system to possible event scenarios during the operation of the MEA in the fields. Before in-stalling the control system on the MES, it is also necessary to debug the control system, identify er-rors in operation and eliminate them. To do this, it is necessary to create a specialized stationary stand that simulates the real operating conditions of an adaptive springing system and develop op-timal control of the actuator in the active suspension system in order to most effectively dampen vibrations arising from road irregularities. This article develops and substantiates the technical characteristics of a test bench for adap-tive springing systems for mobile energy facilities (MES) for agricultural purposes with a pneumatic springing system. The principles of simulation of the road surface using the program code of the controller with an on-board computer, which contains the algorithm of the trained neural network for controlling the pneumatic system, have been developed. The purpose of this stand is to provide (simulate) the modes of operation of an adaptive springing system (in online mode) for mobile agri-cultural energy facilities. Test bench ; Artificial neural network ; Computer science ; Test (biology) ; Artificial intelligence ; Reliability engineering ; Engineering ; Embedded system ; Biology Textile materials and evaluations
Authors: Jiao Zhang | Xixi He | Xuhui Guo | Jia Wang | Xilong Gong | Dechuang Jiao | Haibin Chen | Zhenzhen Liu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Spectroscopy

Identification Potential Biomarkers for Diagnosis, and Progress of Breast Cancer by Using High-Pressure Photon Ionization Time-of-Flight Mass Spectrometry Mass spectrometry ; Breast cancer ; Ionization ; Identification (biology) ; Time-of-flight mass spectrometry ; Chromatography ; Medicine ; Chemistry ; Cancer ; Internal medicine ; Ion ; Biology Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Metabolomics and Mass Spectrometry Studies
Authors: 贺 qiang | Tai-Fu Li
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Quantitative Analysis of Ethyl Acetate Content in Base Liquor of Chinese Baijiu Using Ftir Combined with Uve-Lstm Calibration ; Near-infrared spectroscopy ; Computer science ; Artificial intelligence ; Chemistry ; Biological system ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Fermentation and Sensory Analysis ; Food Quality and Safety Studies
Authors: Fumiko Kimura | Shuma Tanaka | Kazuyoshi Ogasawara
Source: ECS Meeting Abstracts (article, 2024-11-22)
Field: Materials Science — Materials Chemistry

Prediction of Emission Energy of d<sup>3</sup> Ions in Oxides Based on Local Structures Using First-Principles Calculations and Machine Learning Red phosphors activated with Mn 4+ are attracting attention as potential phosphor materials that can improve the color rendering properties of white LEDs. However, since Mn 4+ : KSF which is currently in practical use is unstable and toxic, a special surface treatment is required in actual usage. Therefore, in order to theoretically design stable and inexpensive red phosphors, we focused on oxide phosphors activated with Mn 4+ and other d 3 ions. Since emission energies of Mn 4+ in oxides are always lower than the desired value, it is important to explore local structures which realize higher emission energies. For this purpose, we have recently created emission energy maps based on local structures using systematic first-principles calculations for CrO 6 and MnO 6 clusters with D 4h and C 4v symmetries. In this work, in order to investigate the influence of the ionic species, we created the emission energy maps of the other d 3 ions such as V 2+ and Fe 5+ . We constructed VO 6 clusters and FeO 6 clusters with gradually changed local structures and performed systematic first-principles calculations using the DV-Xα method and the DVME method. The emission energy maps of VO 6 and FeO 6 based on local structures were created and compared to those of CrO 6 and MnO 6 . By using the obtained multiplet energies as the training data, we also created predictive models of emission energies of d 3 ions in oxides by machine learning. Ion ; Energy (signal processing) ; Atomic physics ; Materials science ; Computer science ; Engineering physics ; Physics ; Quantum mechanics Catalytic Processes in Materials Science ; Luminescence Properties of Advanced Materials ; Machine Learning in Materials Science
Authors: Eugen Mircea Anitas
Source: The European Physical Journal E (article, 2024-06-01)
Field: Materials Science — Materials Chemistry

Integrating machine learning with $$\alpha $$-SAS for enhanced structural analysis in small-angle scattering: applications in biological and artificial macromolecular complexes Alpha (finance) ; Macromolecule ; Computer science ; Nanotechnology ; Materials science ; Chemistry ; Mathematics ; Biochemistry ; Statistics Enzyme Structure and Function ; Protein Structure and Dynamics ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Natalia Kireeva | А. Yu. Tsivadze
Source: ChemRxiv (preprint, 2024-08-26)
Field: Materials Science — Materials Chemistry

Rational Screening of Mn(II) Metal-Ligand Complexes for Using as MRI Contrast Agents Using Machine Learning Recent decades have shown a good progress in all the fields of the advanced diagnostic capabilities and personalized medicine. The smart contrast agents (SCAs) that may appear the important element of the proper diagnosis specification should become the standard in a normal clinical practice. Most widely used contrast agents are the kinetically inert and thermodynamically stable complexes of organic ligands with Gd(III) metal ion. However, known safety concerns associated with high toxicity and severe side effects due to demetallization reactions leading to the formation of free Gd(III) ion necessitate the demand in the contrast agents of new generation. It is expected that one of the alternatives will be ML complexes with essential paramagnetic metals such as the Mn(II), Mn(III) and Fe(III). In this study, the experimental data on Mn(II)-based contrast agents (CAs) including the key functional characteristics such as the relaxation enhancement characteristics, kinetic inertness and thermodynamic stability were analyzed. The machine learning methods were used in a combination with the physicochemical and substructure(topology)-based features to estimate the missed experimental data concerned the thermodynamic stability and relaxivity characteristics evaluated at certain conditions. The analysis of the experimental data has shown that the bispidine scaffold can be considered as the reference point for the ligand search as providing with a sufficient compromise for all the investigated functional characteristics of CAs. The analysis of the literature suggests using iodine and fluorine substituents for the electronic and steric hindrance thus preventing the cleavage of the bonds while the histidine residue can be recommended for the pH sensitivity of MRI CAs while targeting the OATPs (organic anion transporting polypeptides) to achieve the hepatobiliary clearance as well as for the enhanced contrast for hepatobiliary system thus personalizing the MRI investigation. We assume that the "proton sponge" effects may contribute to the enhanced kinetic inertness characteristics of the ligands. Contrast (vision) ; Ligand (biochemistry) ; Metal ; Computer science ; Chemistry ; Artificial intelligence ; Biochemistry ; Organic chemistry Machine Learning in Materials Science
Authors: Youwei He | Zhao Guo-qing | Yong Tang | Zhenhua Rui | Jiazheng Qin | Wei Yu | Shirish Patil | Kamy Sepehrnoori
Source: SPE Annual Technical Conference and Exhibition (article, 2024-09-20)
Field: Chemistry — Analytical Chemistry

Prediction of Minimum Miscibility Pressure Between CO2 and Crude Oil by Integrating Improved Grey Wolf Optimization into SVM Algorithm Abstract CO2 injection can enhance oil recovery and achieve geological carbon sequestration. The miscibility between CO2 and crude oil significantly impacts the CO2 EOR performance. Although the minimum miscible pressure (MMP) can be obtained by slim-tube experiment or slim-tube modeling, it is time-consuming, inconvenient, and complicated. This work aims to enhance the prediction efficiency and accuracy of MMP between CO2 and crude oil under reservoir conditions by improved and integrated machine-learning approaches. A novel method is proposed to improve the forecasting accuracy and efficiency of the MMP by integrating Grey Wolf optimization (GWO) and improved GWO (IGWO) into the Support Vector Machine (SVM) algorithm. Firstly, data sets are collected and data preprocessing is performed to improve the quality of data sets. Secondly, K-fold cross-validation is applied to enhance the generalization of the model. The MMP is predicted by the SVM algorithm. Thirdly, the MMP prediction can be enhanced by introducing GWO and IGWO algorithms, and the optimal model is investigated to evaluate the effect and convergence of the SVM-GWO and SVM-IGWO algorithms. Fourthly, the predicted MMP and evaluation index (MAE, MAPE) are compared. Finally, the field case study is performed to show the practical potential of the approach. The dominant factors of the MMP include formation temperature (TR), MwC5+ (Molecular weight of pentane plus), MwC7+ (Molecular weight of heptane plus), Volatile (mole fraction of volatile components including N2 and CH4), and Intermediate (mole fraction of intermediate components including CO2, H2S, and C2–C4). The data set is formed by filling 87 groups of missing values using the K-Nearest Neighbor (KNN) algorithm and removing 19 groups of outliers based on the Box-plot detection method. The accuracy is improved by 37.45% and 40.79% using GWO and IGWO based on the MAE compared to SVM. The calculated MAPE shows that the accuracy can be enhanced by 37.79% and 41.29% after adding GWO and IGWO. The SVM-GWO and SVM-IGWO improved the accuracy by 54.16% and 57.12%. The proposed method can accurately determine the MMP between CO2 and crude oil. The field case study highlights the reliability of the proposed method. The developed method can forecast the MMP between CO2 and crude oil more efficiently and economically. Support vector machine ; Crude oil ; Miscibility ; Optimization algorithm ; Computer science ; Algorithm ; Mathematical optimization ; Artificial intelligence ; Mathematics ; Petroleum engineering ; Engineering ; Chemistry ; Polymer Petroleum Processing and Analysis ; Enhanced Oil Recovery Techniques ; Hydrocarbon exploration and reservoir analysis