jablonkagroup/rise_ai_scientists huggingface dataset:
Source: Malaysian Journal of Chemistry (article, 2024-02-26)
Field: Chemistry — Organic Chemistry

Agarwood Oil Grade Clustering of Aquilaria malaccensis Species using Extraction by GC-MS Analysis: Efficient KNN Algorithm Based on Patterns Visualization of Two-dimensional Graph Data visualization pattern is an essential task in data analysis.A two-dimensional graph (2D graph) is one of the graphical presentations for data visualization.Over the past decades, Agarwood Oil grade clustering is still at a disadvantage since there is no official standard grading system.Most of the time, an expert grades the agarwood oil manually based on oil appearances such as resin color, smell, texture and intensity.The importance of the agarwood oil grading system will help the seller to stabilize the oil price based on its approximate quality.Besides, Agarwood oil got high requests from big buyers and traders due to its benefits as medicine , cosmetics, perfume and incense.This paper attempts to formulate a better Agarwood oil grading system based on its chemical properties, develops an artificially intelligent k-Nearest Neighbor (KNN) and trained using Matlab version R2015a.The data acquisition process of investigating the chemical compounds was conducted using GC-MS analysis.From 103 chemical compounds extracted, four significant compounds; 10-epi--eudesmol, -agarofuran, -eudesmol and agarofuran were chosen to model the agarwood oil quality.The agarwood oil sample data were categorized into low, medium-low, medium-high and high grades.The findings show that KNN yielded 100% accuracy.Then, 2D graph was applied to plot the sample visualization pattern parallel with KNN accuracy.The KNN 2D plot revealed a distinct separation between the four groups.The accuracy of 100% proved the potential of the KNN model as a good supervised learning classifier towards four different grades of Agarwood oil.In conclusion, the Agarwood oil quality grading technique based on KNN and 2D graph was successful with the ability of KNN to confirm these qualities into 4 grades. Agarwood ; Cluster analysis ; Computer science ; Algorithm ; Pattern recognition (psychology) ; Mathematics ; Data mining ; Artificial intelligence Wood and Agarwood Research ; Tea Polyphenols and Effects ; Identification and Quantification in Food
Authors: Ruoyao Chen
Source: Science and Technology of Engineering Chemistry and Environmental Protection (article, 2024-12-31)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Research on the Latest Technological Advances and Trends in High-Frequency and High-Efficiency Power Electronic Converters Power electronic converters play a vital role in modern power systems. To meet the surging demand for more efficient and compact devices for these applications, high frequency and high efficiency have become two main areas of development in power electronics. Increasing switching frequency in power electronics allows for a significant size reduction in passive components, thus enhancing the power density and reducing overall system size. However, high-frequency operation also causes higher switching losses and poses several design and efficiency challenges. These are further compounded by constraints arising from the harsh operating environment, which dictates the need to pursue new and better materials and circuit approaches to maximize converter efficiency. This study has explored the application of latest wide-bandgap semiconductor materials, including gallium nitride (GaN) and silicon carbide (SiC), as well as ultra-wide-bandgap semiconductor materials such as Ga2O3 and AlN. They present new opportunities for the development of high-frequency and high-efficiency power electronic converters that significantly allow for higher frequency operation, reducing most of the switching losses to achieve overall efficiency. Additionally, vital advancements in soft-switching techniques such as zero-voltage switching (ZVS) and zero-current switching (ZCS), also the development of new circuit topologies like LLC and DAB converters, are evaluated. Further, various gate driver designs, and their comparison are considered key strategies toward minimal energy losses at high frequencies. The review is based on a novel perspective of the bottlenecks left unchallenged so far in power conversion systems, considering some critical technological deficiencies and showing pathways to more efficient, compact, and reliable future solutions. Converters ; Power electronics ; Gallium nitride ; Power (physics) ; Electronic engineering ; Power semiconductor device ; Electrical engineering ; Silicon carbide ; Computer science ; Electronics ; Materials science ; Voltage ; Engineering ; Nanotechnology ; Physics Ga2O3 and related materials ; GaN-based semiconductor devices and materials ; Silicon Carbide Semiconductor Technologies
Authors: Lujie Fan
Source: International Journal of Computer Science and Information Technology (article, 2024-04-26)
Field: Chemistry — Analytical Chemistry

High-Precision Tomato Maturity Detection Using a Genetic Algorithm Optimized Swin-YOLO Network In the realm of precision agriculture, the determination of tomato maturity stages plays a crucial role in optimizing harvest timings, ensuring produce quality, and maximizing yields. Traditional approaches, largely reliant on manual inspection, are not only labor-intensive but also subject to human error, making them unsuitable for modern, large-scale agricultural operations. Addressing these challenges, this study pioneers the use of a Genetic Algorithm (GA) optimized Swin-YOLO (You Only Look Once) network, aiming to automate and enhance the precision of tomato maturity detection. By integrating the advanced capabilities of the Swin Transformer for feature extraction with the efficiency of the YOLO object detection framework, the proposed Swin-YOLO model is meticulously designed to identify varying stages of tomato maturity from complex agricultural imagery. The introduction of a GA facilitates the systematic optimization of the network's architecture and hyperparameters, focusing on improving accuracy and computational efficiency across diverse environmental conditions. Our research involved compiling a comprehensive dataset of annotated tomato images, reflecting a wide array of maturity stages under different lighting and occlusional challenges. The findings from our study indicate a significant leap in performance, with the GA-optimized Swin-YOLO network outshining existing deep learning models and conventional manual methods in terms of both detection accuracy and processing speed. Notably, the model exhibits exceptional adeptness at handling images affected by variable lighting and partial occlusions, marking a substantial advancement in automated tomato maturity detection. The implications of this research are profound, offering a scalable, efficient, and highly accurate solution to one of agriculture's longstanding challenges. This breakthrough not only stands to reduce labor costs and enhance operational efficiency but also contributes to the sustainability of tomato farming practices by facilitating more informed and timely harvesting decisions. Furthermore, the successful application of GA for network optimization underscores the potential of combining deep learning with evolutionary algorithms to tackle complex agricultural challenges, setting the stage for future innovations in precision agriculture and beyond. Computer science ; Maturity (psychological) ; Genetic algorithm ; Algorithm ; Artificial intelligence ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Sebastian V. Pios | Jiaji Zhang | Maxim F. Gelin | Hong-Guang Duan | Lipeng Chen
Source: arXiv (Cornell University) (preprint, 2024-08-28)
Field: Chemistry — Physical and Theoretical Chemistry

Tracking the Electron Density Changes in Excited States -- A Computational Study on Pyrazine The development of X-ray free-electron lasers (XFELs) has enabled ultrafast X-ray diffraction (XRD) experiments, which are capable of resolving electronic/vibrational transitions and structural changes in molecules, or capturing molecular movies. While time-resolved XRD has received increasing attention, the extraction of information content from signals is challenging and requires theoretical support. In this work, we combined X-ray scattering theory and trajectory surface hopping approach to resolve dynamical changes in the electronic structure of photo-excited molecules by studying time evolution of electron density changes between electronic excited states and ground state. Using pyrazine molecule as an example, we show that key features of reaction pathways can be identified, enabling the capture of structural changes associated with electronic transitions for a photo-excited molecule. Pyrazine ; Excited state ; Tracking (education) ; Electron ; Physics ; Atomic physics ; Quantum mechanics ; Chemistry ; Psychology Photochemistry and Electron Transfer Studies ; GaN-based semiconductor devices and materials ; bioluminescence and chemiluminescence research
Authors: Ivan Gilardoni | Giovanni Bussi | Valerio Piomponi | Thorben Fröhlking
Source: arXiv (Cornell University) (preprint, 2024-11-12)
Field: Chemistry — Physical and Theoretical Chemistry

Datasets for tutorials of MDRefine Python package Datasets for tutorials of MDRefine Python package. See https://github.com/bussilab/MDRefine and https://pypi.org/project/MDRefine/. These datasets are derived from the analysis of some Molecular Dynamics (MD) simulations, as described in the jupyter notebooks load_data_alchemical.ipynb (for DATA_alchemical, corresponding to methylated RNA systems) and load_data_oligomers.ipynb (for DATA, corresponding to RNA oligomers) in the GitHub directory. They can be used to run the Tutorial*.ipynb notebooks in the GitHub directory, which show some examples of refinement of molecular systems as described in the corresponding paper "MDRefine: a Python package for refining Molecular Dynamics trajectories with experimental data". Python (programming language) ; Computer science ; Refining (metallurgy) ; R package ; Programming language ; Computer graphics (images) ; Chemistry Various Chemistry Research Topics
Authors: D. Lee | Jungmin Park | Donghyeon Han | Ippei Suzuki | Takahashi Yukiko | Sujung Noh | Jisung Lee | Joonhyun Kwon | Hansaem Lee | Sanghoon Kim
Source: Physical Review Applied (article, 2024-02-13)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Statistical approach based on the Stoner-Wohlfarth model for the switching field in a misoriented magnet array The Stoner-Wohlfarth model (SW model) is used to explain the relationship between the external magnetic field and the magnetization reversal of a single magnetic domain. However, it is inappropriate for inhomogeneous magnetic films or bulk materials, such as granular and defective films, because the magnetization reversal of the films is affected by magnetic particles or grains, which could have a different anisotropy for uniaxial magnetic anisotropy (UMA) of the films. In this paper, we propose a model based on the SW model to understand the magnetization switching behavior of magnetic films with misoriented grains. Our model considers the distribution effect of different anisotropies originating from magnetic defects or domains against the UMA of a magnetic thin film, and a Gaussian function is employed for the probability distribution of tilted anisotropy. Finally, we experimentally demonstrate that our model describes the magnetic switching behavior of a granular magnetic film ($\mathrm{Fe}\mathrm{Pt}$-$\mathrm{C}$) with perpendicular magnetic anisotropy. This study provides an alternative analytical method for the magnetization reversal in magnetic devices. Magnet ; Stoner–Wohlfarth model ; Field (mathematics) ; Magnetic field ; Condensed matter physics ; Physics ; Materials science ; Magnetic anisotropy ; Mathematics ; Quantum mechanics ; Magnetization Magnetic Properties and Applications ; Magnetic properties of thin films ; Magnetic Properties of Alloys
Authors: Dept. of ECE GNITS Hyderabad, India
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (article, 2024-06-29)
Field: Chemistry — Analytical Chemistry

Apple Pesticide Detection and Alert System Many modern techniques were developed to produce more quantity of food for growing population. Now a day fruits and vegetables have become the major source of nutrients and energy. Many chemicals are used in the production of fruits and vegetables, which are dangerous for consumers. To identify pesticides in organic fruits and vegetables, it is necessary to build low-cost, portable, sensitive, and selective biosensing platform. Many sensors like nano-sensors, pH sensors, humidity and temperature sensors and gas sensors are used to detect the pesticides on the fruits and vegetables. The input data is taken from sensors and send to the microcontroller where the program is written in such a way that it calculates the amount of pesticide present on fruits and display the output. The software for this project is written in Arduino c program. The safe value of pesticides in fruits or vegetables which can be consumed by humans and animals are mentioned in the Arduino c program. If the pesticide content is above or below the threshold level then the sample contains pesticides. Developing an alert system that triggers messages when pesticide levels surpass predefined thresholds. The message is sent through proper communication channels such as SMS to the stakeholders such as farmers, distributors, retailers, and consumers by using GSM modem. Keywords: Pesticides, nano-sensor, pH sensor, GSM Modem, SMS, Arduino C Pesticide ; Environmental science ; Computer science ; Biology ; Ecology Spectroscopy and Chemometric Analyses
Authors: Yuji Shirakami | Ken‐ichi Ikeda | Seiji Miura | Koji Morita | T. Suzuki | Yoshio Sakka
Source: Journal of the Japan Institute of Metals and Materials (article, 2024-04-25)
Field: Materials Science — Materials Chemistry

Orientation Dependence of High Temperature Compressive Behavior of Textured Ti&lt;sub&gt;3&lt;/sub&gt;SiC&lt;sub&gt;2&lt;/sub&gt; In order to clarify the orientation dependent deformation behavior of the MAX phase ceramics, compressive deformation behavior was examined in a textured Ti3SiC2 (TSC) at a high temperature of 1200℃. Depending on the relationship between the texture and loading directions, both the deformation behavior and microstructure were strongly influenced, and the resultant basal slip, kink formation and delamination affected the compression behavior of the textured TSC. When the stress was loaded parallel or perpendicular to the basal plane (0TSC and 90TSC), the stress-strain (S-S) curves showed higher peak stresses followed by the reduction in the flow stress. When the stress was loaded 45° to the basal plane (45TSC), the S-S curve showed strain hardening after yielding, but did not show peak stress. Although the strength was higher both in 0TSC and 90TSC than in 45TSC, both 0TSC and 90TSC showed the formation of cracks and delamination, resulting to the large drop in the flow stress. In contrast to 0TSC and 90TSC, although 45TSC did not exhibit the peak stress, it exhibited work hardening due to the kink boundary formation, irrespective of the formation of delamination. It is reasonable to conclude from the deformation behavior and the deformed microstructures that for the TSC, the kink boundary plays an important role for attaining both deformability and strength. Materials science ; Microstructure ; Flow stress ; Composite material ; Slip (aerodynamics) ; Hardening (computing) ; Work hardening ; Drop (telecommunication) ; Strain hardening exponent ; Deformation (meteorology) ; Physics ; Thermodynamics MXene and MAX Phase Materials ; Aluminum Alloys Composites Properties ; Advanced ceramic materials synthesis
Authors: Zhaoqi Xie | Siqing Fan | Junyu Xu | Haijing Xiao | Jiaxin Yang | Min Guo | Chunsong Cheng
Source: Research Square (preprint, 2024-04-01)
Field: Chemistry — Organic Chemistry

Origin and diversification of Aquilaria (Thymelaeaceae): inferences from a phylogenetic study based on matK sequences <title>Abstract</title> In the realm of <italic>Aquilaria</italic> classification and grading, a persistent market uncertainty persists, questioning whether the basis should be geographical distribution or biological origin. In this study, our goal is to shed light on the origin and diversification of the <italic>Aquilaria</italic> genera. We collected and analyzed a total of 320 specimens belonging to 25 species within the <italic>Aquilaria</italic> genera. Noteworthy findings include observed leaf similarities among three distinct species—<italic>A. yunnanensis</italic>, <italic>A. sinensis</italic>, and <italic>A. beccariana</italic>. Further results emphasize the effectiveness of matK molecular markers in differentiating <italic>Aquilaria</italic> species, leveraging 8 stable polymorphic loci. The integration of matK and trnL-trnF not only validates this efficacy but also streamlines the systematic categorization of 34 agarwood products. Delving into the evolutionary status and genetic background of <italic>Aquilaria</italic>, this study employs molecular clock analyses, revealing four distinct pedigrees: the Chinese pedigree 1 and 2, the Indonesian pedigree, and the Indochina pedigree, aligning with <italic>A. sinensis</italic>, <italic>A. malaccensis</italic>, and <italic>A. cumingiana</italic>, respectively. Notably, we identified <italic>A. sinensis</italic> and <italic>A. malacca</italic> as part of the youngest branch of <italic>Aquilaria</italic> evolution, with <italic>A. hirta</italic> emerging as the oldest member dating back to 6.78 million years ago. Furthermore, the research challenges previous assumptions by reevaluating <italic>G. walla</italic>, placing it at 5.75 million years ago, and suggesting its incorporation within the <italic>Aquilaria</italic> genus rather than considering it as an early divergent species. Thymelaeaceae ; Phylogenetic tree ; Biology ; Diversification (marketing strategy) ; Evolutionary biology ; Botany ; Genetics ; Gene ; Business Wood and Agarwood Research ; Identification and Quantification in Food ; Plant and Fungal Species Descriptions
Authors: Eric Lewis | Mehrdad Shahnam | MaryAnn Clarke | Omer Dogan | Dushyant Shekhawat | Eric J. Popczun | Pranjali Muley | Ping Wang | Ranjani Siriwardane | Robert Stevens | Gavin Pickenpaugh | Matthew Jamieson | Benjamin Chorpening | Samuel Bayham
Source:  (article, 2024-05-07)
Field: Chemistry — Inorganic Chemistry

Advanced Reaction Systems An overview of NETL&#39;s Gasification Systems R&amp;D was provided at DOE&#39;s Hydrogen and Fuel Cells Technology Office (HFTO) Annual Merit Review (AMR) on May 7, 2024, in Arlington, VA. The objective of the Advanced Reaction Systems Portfolio is to design, develop, and analyze technologies to support the mission of the Gasification Program to enable the use of diverse feedstocks to produce hydrogen and other value-added products with net-zero greenhouse gas emissions. Computer science Zeolite Catalysis and Synthesis
Authors: Azizah Ahmad | Mohd Nasir Taib | Hasnain Abdullah | Noor Azila Ismail | Ahmad Ihsan Mohd Yassin | Linda Mohd Kasim | Norhayati Mohamad Noor
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2024-11-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Artificial neural networks classification of s-band absorption performance in eco-friendly microwave absorbers Microwave absorbers are essential for applications such as radar stealth and electromagnetic compatibility. Nevertheless, traditional materials encounter obstacles related to cost and sustainability, which has led to the exploration of new options such as materials derived from agricultural waste. This study focuses on the classification challenge of evaluating the absorption performance of eco-friendly microwave absorbers in the S-band (2 to 4 GHz) frequency. Three multilayer perceptron (MLP) algorithms, namely levenberg marquardt (LM), resilient backpropagation (RBP) and scale conjugate gradient (SCG) are assessed for classification accuracy. The dataset consists of 135 absorption performance values of microwave absorbers that were taken from experimental measurements using the naval research laboratory (NRL) arch free. The MLP algorithms will be divided into three divisions, which are training, validation and testing, evaluating important criteria such as accuracy, precision, sensitivity and specificity. The performance of three types of algorithms will be compared using two basic inputs: the absorption values and the single slot sizes. The RBP algorithm achieved 100% accuracy, and a lower mean squared error (MSE) of 0.02500 compared to the LM and SCG. This study provides valuable insights for designing better microwave absorbers and highlights the commercial potential of agricultural waste materials in such applications. Microwave ; Artificial neural network ; Multilayer perceptron ; Radar ; Computer science ; Backpropagation ; Absorption (acoustics) ; Rprop ; Mean squared error ; Electromagnetic compatibility ; X band ; Conjugate gradient method ; Algorithm ; Machine learning ; Electronic engineering ; Engineering ; Mathematics ; Acoustics ; Telecommunications ; Physics ; Statistics Electromagnetic wave absorption materials ; Microwave and Dielectric Measurement Techniques ; Advanced Antenna and Metasurface Technologies