jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Ran Hu | Haisong Xu | Xu Lu | Anzhe Wang | Zhifeng Xu | Yuli Wang | Daning Zhang
Source: IET Generation Transmission & Distribution (article, 2024-08-16)
Field: Materials Science — Materials Chemistry

Research on live detection technology of distribution network cable insulation deterioration state based on harmonic components Abstract Due to the limitations imposed by urban power grid outages for maintenance, on‐line harmonic current detection technology for distribution network cables is expected to become an effective supplement to traditional offline diagnostic methods, enhancing the real‐time diagnosis of distribution network cable insulation conditions. This study established a 10 kV distribution network cable test platform and prepared typical defective cables subjected to moisture and long‐term thermal aging. Using COMSOL finite element electromagnetic simulation, the magnetic flux evolution laws of the cable insulation under typical defects were obtained. Experimental tests provided the harmonic current characteristics and statistical features of cables with typical defects. Based on these data, a method for analysing the degradation degree of distribution network cables was constructed using least absolute shrinkage and selection operator (LASSO) regression analysis. Furthermore, a defect‐type identification method based on cluster analysis was proposed. Results indicate that the odd harmonics and the 4th harmonic of the distribution network cable's harmonic current are closely related to the cable's degradation state. A model integrating principal component analysis (PCA) data dimensionality reduction and expectation‐maximization clustering analysis achieved a recognition accuracy of up to 75.64% in distinguishing between moisture‐affected and normal cable states. The proposed on‐line detection and evaluation methods can effectively identify high‐risk cables with latent defects. Harmonic ; Harmonics ; Power cable ; Engineering ; Cluster analysis ; Electronic engineering ; Harmonic analysis ; Principal component analysis ; Transient (computer programming) ; Computer science ; Reliability engineering ; Voltage ; Electrical engineering ; Acoustics ; Artificial intelligence ; Materials science High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Akash Phillip
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2024-09-15)
Field: Materials Science — Biomaterials

Bioplastics from Waste Biomass: Paving the Way for a Sustainable Future Abstract: Bioplastics originating from waste biomass present a viable remedy to the environmental dilemmas associated with traditional plastics, thereby reducing dependency on fossil fuels and mitigating carbon emissions. This review investigates the utilization of diverse waste biomass sources, encompassing agricultural residues, food waste, industrial by-products, municipal solid waste, and forest biomass residues, in the synthesis of bioplastics. Technological innovations in feedstock pretreatment, bioconversion methodologies, and polymerization techniques have markedly enhanced the efficiency and material characteristics of bioplastics, establishing them as formidable alternatives to petroleum-derived plastics. Notwithstanding these advancements, obstacles such as feedstock inconsistency, scalability issues, technological limitations, and consumer acceptance persistently impede the extensive adoption of bioplastics. The ecological advantages of bioplastics, particularly concerning their diminished carbon footprint and potential for biodegradability, are underscored through life cycle assessments (LCAs). Government policies, the increasing market demand for sustainable products, and advancements in emerging technologies, including synthetic biology and AI-enhanced process optimization, are propelling the commercialization of bioplastics derived from waste biomass. The incorporation of bioplastics within a circular economy framework, coupled with considerations for long-term sustainability, positions them as a pivotal element in fostering a more sustainable and environmentally friendly future. This review offers a thorough examination of the present landscape, challenges, and future prospects of bioplastics production from waste biomass. Bioplastic ; Biomass (ecology) ; Raw material ; Life-cycle assessment ; Sustainability ; Greenhouse gas ; Waste management ; Environmentally friendly ; Carbon footprint ; Food waste ; Natural resource economics ; Environmental science ; Biochemical engineering ; Engineering ; Production (economics) ; Economics ; Ecology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Recycling and Waste Management Techniques
Authors: Xiao Yang | Liangcai Shu | Dongsheng Yang
Source: IEEE Transactions on Power Electronics (article, 2024-11-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Hierarchical Physics-Embedding Neural Network Framework for 3D Magnetic Modeling of Medium-Frequency Transformers Neural network (NN) technology is revolutionizing the modeling paradigm in the engineering arena, simultaneously enhancing model precision and process speed. Nevertheless, a large amount of training data is an unattainable requirement in the magnetic component modeling issue, especially when dealing with the 3D magnetic behaviors in complex geometry. In this work, to establish an accurate 3D magnetic model of medium-frequency transformers with limited data sources, the 2D FEM data, 3D FEM data, and analytical model are structurally integrated into a unified NN training framework. First, the low-cost 2D FEM data is abundantly fed into the inferior 2D section model to capture the winding fringe effect, which is missing in the conventional 1D Dowell's equation. The considerable data information and prior physics knowledge jointly ensure the high accuracy of section modeling. The layer-precision region model transition pipelines are then established between the 2D section model and the 3D region model. These pipelines alleviate the error propagation from the 2D section model, thus the error of partial length in the corner region can be well amended with minor 3D FEM data. Accordingly, this proposed hierarchical physics-embedding neural network (HPENN) framework improves the physical interpretability of NN-based models and relieves the strict computing requirement at the data generation stage. According to the training and experiment results, the computation burden for data generation is reduced to 1/10 with the proposed framework compared with classic NN methods. Besides, the average error of the proposed HPENN framework is only 1%, and the maximum error does not exceed 7%. Transformer ; Embedding ; Artificial neural network ; Electronic engineering ; Computer science ; Physics ; Electrical engineering ; Engineering ; Voltage ; Artificial intelligence Magnetic Properties and Applications ; Magnetic Field Sensors Techniques ; Electric Motor Design and Analysis
Authors: Nicole C. Auvil | Mark E. Bier
Source: Journal of the American Society for Mass Spectrometry (article, 2024-06-25)
Field: Chemistry — Spectroscopy

Nanoelectrode Atmospheric Pressure Chemical Ionization Mass Spectrometry A small ionization needle with an ultrasharp, ultrafine tip is introduced. It is lab-fabricated from tungsten wire and serves as a corona discharge emitter in nanoelectrode atmospheric pressure chemical ionization mass spectrometry (nAPCI-MS). Tip radii ranged from 8 to 44 nm, up to 44× smaller than the sharpest previously reported corona needle. Because of this, nAPCI was able to operate at +1.0 kV with no auxiliary counter electrode. Alternatively, at +1.2 kV, nAPCI could be enclosed in a small plastic assembly for headspace analysis with a sampling tube attachment as long as 15 m. No added heat or gas flow was necessary. The efficacy of nAPCI-MS was demonstrated through needle durability studies and direct analysis of vapors from real-world samples. Provisional identifications include ibuprofen from a pharmaceutical tablet, albuterol aerosol sprayed from a medical inhaler, cocaine from paper currency, caffeine from a fingertip, and bisphenol E from a paper receipt. Chemistry ; Atmospheric pressure ; Chemical ionization ; Ionization ; Mass spectrometry ; Analytical Chemistry (journal) ; Aerosol ; Corona discharge ; Atmospheric-pressure chemical ionization ; Vaporizer ; Electrode ; Nanotechnology ; Chromatography ; Ion ; Organic chemistry ; Materials science Mass Spectrometry Techniques and Applications ; Advanced Chemical Sensor Technologies ; Analytical chemistry methods development
Authors: Lucky Ogheneakpobo Ejeta
Source: Journal of Polymer Science and Engineering (review, 2024-01-23)
Field: Materials Science — Polymers and Plastics

Modeling and formulation/process parameters design methodological approaches for improving the performance of biocomposite materials for building, construction, and automotive applications: A state-of-the-art review In today’s manufacturing sector, high-quality materials that satisfy customers’ needs at a reduced cost are drawing attention in the global market. Also, as new applications are emerging, high-performance biocomposite products that complement them are required. The production of such high-performance materials requires suitable optimization techniques in the formulation/process design, not simply mixing natural fibre/filler, additives, and plastics, and characterization of the resulting biocomposites. However, a comprehensive review of the optimization strategies in biocomposite production intended for infrastructural applications is lacking. This study, therefore, presents a detailed discussion of the various optimization approaches, their strengths, and weaknesses in the formulation/process parameters of biocomposite manufacturing. The report explores the recent progress in optimization techniques in biocomposite material production to provide baseline information to researchers and industrialists in this field. Therefore, this review consolidates prior studies to explore new areas. Biocomposite ; Automotive industry ; Engineering ; Construction engineering ; Manufacturing engineering ; Process (computing) ; Systems engineering ; Mechanical engineering ; Computer science ; Materials science ; Composite material ; Aerospace engineering Natural Fiber Reinforced Composites ; Additive Manufacturing and 3D Printing Technologies ; Innovations in Concrete and Construction Materials
Authors: Weijie Zhao
Source: National Science Review (article, 2024-03-26)
Field: Materials Science — Materials Chemistry

A panel discussion on AI for science: the opportunities, challenges and reflections Artificial intelligence (AI) tools are changing the way we do science. AlphaFold basically solved the conundrum of protein structure prediction; DeepMD greatly improved the efficiency and accuracy of molecular simulations; and the emerging large language models such as ChatGPT are opening up more possibilities for scientific applications. In this panel, five experts from China and the US discussed the concept, development, bottlenecks and opportunities of AI for Science (AI4S), as well as their understanding of the relationship between AI and science. Roberto Car Professor at Department of Chemistry, Princeton University, USA Weinan E Professor at School of Mathematical Sciences, Peking University, China; AI for Science Institute, Beijing, China David Srolovitz Professor at Department of Mechanical Engineering, University of Hong Kong, China Han Wang Professor at the Institute of Applied Physics and Computational Mathematics, Chinese Academy of Sciences, China Linfeng Zhang (Chair) Chief scientific officer of DP Technology, China; AI for Science Institute, Beijing, China. Beijing ; China ; Chinese academy of sciences ; Zhàng ; Science and engineering ; Officer ; Library science ; Engineering physics ; Engineering ethics ; Political science ; Engineering ; Computer science ; Law Machine Learning in Materials Science ; Scientific Computing and Data Management
Authors: Apichat Suratanee | Panita Chutimanukul | Tanapon Saelao | Supachitra Chadchawan | Teerapong Buaboocha | Kitiporn Plaimas
Source: PLoS ONE (article, 2024-10-02)
Field: Chemistry — Analytical Chemistry

Phenolic content discrimination in Thai holy basil using hyperspectral data analysis and machine learning techniques Hyperspectral imaging has emerged as a powerful tool for the non-destructive assessment of plant properties, including the quantification of phytochemical contents. Traditional methods for antioxidant analysis in holy basil (Ocimum tenuiflorum L.) are time-consuming, while hyperspectral imaging has the potential to rapidly observe holy basil. In this study, we employed hyperspectral imaging combined with machine learning techniques to determine the levels of total phenolic contents in Thai holy basil. Spectral data were acquired from 26 holy basil cultivars at different growth stages, and the total phenolic contents of the samples were measured. To extract the characteristics of the spectral data, we used 22 statistical features in both time and frequency domains. Relevant features were selected and combined with the corresponding total phenolic content values to develop a neural network model for classifying the phenolic content levels into 'low' and 'normal-to-high' categories. The neural network model demonstrated high performance, achieving an area under the receiver operating characteristic curve of 0.8113, highlighting its effectiveness in predicting phenolic content levels based on the spectral data. Comparative analysis with other machine learning techniques confirmed the superior performance of the neural network approach. Further investigation revealed that the model exhibited increased confidence in predicting the phenolic content levels of older holy basil samples. This study exhibits the potential of integrating hyperspectral imaging, feature extraction, and machine learning techniques for the rapid and non-destructive assessment of phenolic content levels in holy basil. The demonstrated effectiveness of this approach opens new possibilities for screening antioxidant properties in plants, facilitating efficient decision-making processes for researchers based on comprehensive spectral data. Hyperspectral imaging ; Ocimum ; Artificial intelligence ; Artificial neural network ; Phytochemical ; Computer science ; Content (measure theory) ; Machine learning ; Pattern recognition (psychology) ; Mathematics ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Essential Oils and Antimicrobial Activity ; Medicinal Plants and Neuroprotection
Authors: Hanting Zou | Tianmeng Lan | Yongwen Jiang | Xiaolan Yu | Haibo Yuan
Source: Foods (article, 2024-11-21)
Field: Chemistry — Analytical Chemistry

Research on Rapid Detection Methods of Tea Pigments Content During Rolling of Black Tea Based on Machine Vision Technology As a crucial stage in the processing of black tea, rolling plays a significant role in both the color transformation and the quality development of the tea. In this process, the production of theaflavins, thearubigins, and theabrownins is a primary factor contributing to the alteration in color of rolled leaves. Herein, tea pigments are selected as the key quality indicators during rolling of black tea, aiming to establish rapid detection methods for them. A machine vision system is employed to extract nine color feature variables from the images of samples subjected to varying rolling times. Then, the tea pigment content in the corresponding samples is determined using a UV-visible spectrophotometer. In the meantime, the correlation between color variables and tea pigments is discussed. Additionally, Z-score and PCA are used to eliminate the magnitude difference and redundant information in original data. Finally, the quantitative prediction models of tea pigments based on the images' color features are established by using PLSR, SVR, and ELM. The data show that the Z-score-PCA-ELM model has the best prediction effect for tea pigments. The Rp values for the model prediction sets are all over 0.96, and the RPD values are all greater than 3.50. In this study, rapid determination methods for tea pigments during rolling of black tea are established. These methods offer significant technical support for the digital production of black tea. Black tea ; Pigment ; Food science ; Machine vision ; Chemistry ; Artificial intelligence ; Traditional medicine ; Computer science ; Medicine Spectroscopy and Chemometric Analyses ; Tea Polyphenols and Effects ; Remote Sensing and Land Use
Authors: Syed Masroor Hassan Rizvi
Source: International Journal of Scientific Research and Management (IJSRM) (article, 2024-06-03)
Field: Chemistry — Spectroscopy

Advanced Analytical Techniques for Characterizing Petroleum-Derived Contaminants in the Environment The characterization of petroleum-derived contaminants in the environment is crucial for understanding their impact on ecosystems and human health. Traditional analytical techniques such as Gas Chromatography (GC), Mass Spectrometry (MS), and High-Performance Liquid Chromatography (HPLC) have been instrumental in identifying and quantifying these contaminants. However, the complexity and diversity of petroleum-derived compounds necessitate the development and application of advanced analytical techniques for more comprehensive analysis. This paper reviews the most cutting-edge methods currently employed in environmental analysis, including Comprehensive Two-Dimensional Gas Chromatography (GC×GC), Fourier Transform Ion Cyclotron Resonance Mass Spectrometry (FT-ICR MS), Nuclear Magnetic Resonance (NMR) Spectroscopy, and Synchrotron Radiation-Based Techniques such as X-ray Absorption Spectroscopy (XAS) and X-ray Fluorescence (XRF), as well as Laser-Induced Breakdown Spectroscopy (LIBS). Each technique's principles, capabilities, and applications are discussed, highlighting their roles in detecting and characterizing hydrocarbons, polycyclic aromatic hydrocarbons (PAHs), heavy metals, and volatile organic compounds (VOCs). Case studies demonstrate the practical applications of these advanced techniques in real-world scenarios, such as oil spill analysis and the identification of complex contaminant mixtures. The paper also addresses the advantages and limitations of these advanced techniques, considering factors like sensitivity, selectivity, complexity, and cost. Finally, future directions and emerging technologies, including nanotechnology, biosensors, and machine learning, are explored for their potential to enhance environmental monitoring and remediation efforts. This comprehensive review underscores the importance of continued innovation in analytical methods to effectively address the challenges posed by petroleum-derived contaminants in the environment. Environmental analysis ; Mass spectrometry ; Analytical technique ; Contamination ; Environmental remediation ; Petroleum ; Fourier transform ion cyclotron resonance ; Environmental chemistry ; Gas chromatography ; Chemistry ; Biochemical engineering ; Environmental science ; Process engineering ; Chromatography ; Engineering Mass Spectrometry Techniques and Applications ; Atmospheric and Environmental Gas Dynamics ; Air Quality Monitoring and Forecasting
Authors: Zhixin Yu | Hongxin Fu | Jufang Wang
Source: Fermentation (article, 2024-03-06)
Field: Chemistry — Spectroscopy

Modeling and Optimization of the Culture Medium for Efficient 4′-N-Demethyl-Vicenistatin Production by Streptomyces parvus Using Response Surface Methodology and Artificial-Neural-Network-Genetic-Algorithm 4′-N-demethyl-vicenistatin is a vicenistatin analogue that has better antitumor activity with promising applications in the pharmaceuticals industry. The harnessing of the complete potential of this compound necessitates a systematic optimization of the culture medium to enable the cost-effective production of 4′-N-demethyl-vicenistatin by Streptomyces parvus SCSIO Mla-L010/ΔvicG. Therefore, in this study, a sequential approach was employed to screen the significant medium compositions, as follows: one-factor-at-a-time (OFAT) and Plackett–Burman designs (PBD) were initially utilized. Cassava starch, glycerol, and seawater salt were identified as the pivotal components influencing 4′-N-demethyl-vicenistatin production. To further investigate the direct and interactive effects of these key components, a three-factor, five-level central composite design (CCD) was implemented. Finally, response surface methodology (RSM) and an artificial-neural-network-genetic-algorithm (ANN-GA) were employed for the modeling and optimization of the medium components to enhance efficient 4′-N-demethyl-vicenistatin production. The ANN-GA model showed superior reliability, achieving the most 4′-N-demethyl-vicenistatin, at 0.1921 g/L, which was 17% and 283% higher than the RSM-optimized and initial medium approaches, respectively. This study represents pioneering work on statistically guided optimization strategies for enhancing 4′-N-demethyl-vicenistatin production through medium optimization. Response surface methodology ; Artificial neural network ; Production (economics) ; Genetic algorithm ; Computer science ; Artificial intelligence ; Algorithm ; Biological system ; Machine learning ; Biotechnology ; Biochemical engineering ; Biology ; Engineering ; Economics Analytical Chemistry and Chromatography ; Chromatography in Natural Products
Authors: Tony Trent | Justin J. Miller | Gregory R. Bowman
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-11-28)
Field: Chemistry — Spectroscopy

The G protein inhibitor YM-254890 is an allosteric glue Given the prominence of G protein coupled receptors (GPCRs) as drug targets, targeting their immediate downstream effectors, G proteins, could be of immense therapeutic value. The discovery that the natural product YM-254890 (YM) can arrest uveal melanoma by specifically inhibiting constitutively active Gq/11without impacting other G protein families demonstrates the potential of this approach. However, efforts to find other G protein family-specific inhibitors have had limited success. Better understanding the mechanism of YM could facilitate efforts to develop other highly specific G protein inhibitors. We hypothesized that differences between the conformational distributions of various G proteins play an important role in determining he specificity of inhibitors like YM. To explore this hypothesis, we built Markov state models (MSMs) from molecular dynamics simulations of the Gα subunits of three different G proteins, as YM predominantly contacts Gα. We also modeled the heterotrimeric versions of these proteins where Gα is bound to the Gβγ heterodimer. We find that YM-sensitive Gα proteins have a higher probability of adopting YM-bound-like conformations than insensitive variants. There is also strong allosteric coupling between the YM- and Gβγ-binding interfaces of Gα. This allostery gives rise to positive cooperativity, wherein the presence of Gβγ enhances preorganization for YM binding. We predict that YM acts as an "allosteric" glue that allosterically stabilizes the complex between Gα and Gβγ despite the minimal contacts between YM and Gβγ. GLUE ; Allosteric regulation ; Chemistry ; Biochemistry ; Receptor ; Materials science ; Composite material Advanced Proteomics Techniques and Applications ; Growth Hormone and Insulin-like Growth Factors ; Protein Kinase Regulation and GTPase Signaling