jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Huan Wu | Yongjie Hu
Source: International Journal of Heat and Mass Transfer (article, 2023-12-03)
Field: Materials Science — Materials Chemistry

Ab initio investigations on hydrodynamic phonon transport: From diffusion to convection Diffusion ; Materials science ; Phonon ; Ab initio ; Convection ; Mechanics ; Thermodynamics ; Condensed matter physics ; Physics ; Quantum mechanics Thermal properties of materials ; Gas Dynamics and Kinetic Theory ; Quantum, superfluid, helium dynamics
Authors: Udith Krishnan Vadakkum Vadukkal | Michela Palumbo | G. Attolico
Source: Applied Sciences (article, 2023-06-09)
Field: Chemistry — Analytical Chemistry

Semantic Segmentation of Packaged and Unpackaged Fresh-Cut Apples Using Deep Learning Computer vision systems are often used in industrial quality control to offer fast, objective, non-destructive, and contactless evaluation of fruit. The senescence of fresh-cut apples is strongly related to the browning of the pulp rather than to the properties of the peel. This work addresses the identification and selection of pulp inside images of fresh-cut apples, both packaged and unpackaged; this is a critical step towards a computer vision system that is able to evaluate their quality and internal properties. A DeepLabV3+-based convolutional neural network model (CNN) has been developed for this semantic segmentation task. It has proved to be robust with respect to the similarity of colours between the peel and pulp. Its ability to separate the pulp from the peel and background has been verified on four varieties of apples: Granny Smith (greenish peel), Golden (yellowish peel), Fuji, and Pink Lady (reddish peel). The semantic segmentation achieved an accuracy greater than 99% on all these varieties. The developed approach was able to isolate regions significantly affected by the browning process on both packaged and unpackaged pieces: on these areas, the colour analysis will be studied to evaluate internal quality and senescence of packaged and unpackaged products. Pulp (tooth) ; Browning ; Artificial intelligence ; Segmentation ; Pulp and paper industry ; Computer science ; Food science ; Computer vision ; Pattern recognition (psychology) ; Mathematics ; Chemistry ; Engineering Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Smart Agriculture and AI
Authors: Jixing Sun | Yongzhi Fan | Weifan Qiu | Sibo Song | Xinyu Tan | Yu Tian | Xuewen Jiao | Huili Wang | Nana Wu
Source: IET Science Measurement & Technology (article, 2023-01-03)
Field: Materials Science — Materials Chemistry

Applicability of the acoustic–electrical joint detection method to identify defects in gas insulated system Abstract There are many defects in gas‐insulated metal‐enclosed switchgear (GIS) and gas‐insulated transmission lines (GIL) that may cause accidents, such as floating potentials, metal particles, air gaps, and cracks in support insulators. Nevertheless, to‐this‐date, there is no effective method to recognize them to adjust their operation status. Accordingly, this study compared three typical defect models to explore the partial discharge process, and three typical experimental models were established to identify the characteristic parameters of acoustic and electrical information in the development of defects. The research study shows that that as the voltage increases, the partial discharge of free‐metal‐particle defects and air‐gap defects obtained by the pulse‐current method increase gradually. A sudden increase also occurred in the partial discharge of the floating potential defect. The discharge amount of free conductive particles obtained by an ultrahigh‐frequency detection method was distributed symmetrically on both sides of the applied voltage peak, and the other two defects were on one side of the peak and yielded an obvious phase difference. The particle collision signal obtained by the ultrasonic method was obvious, but had no obvious phase relationship with the applied voltage. However, the obtained floating potential defect information had obvious phase differences, and the ultrasonic method was not sensitive to air‐gap defects. The three typical defects can be identified by the combined method more accurately; this provides a theoretical basis and data support for GIS and GIS voltage tests, detection technologies, and online monitoring methods. Switchgear ; Partial discharge ; Materials science ; Voltage ; Acoustics ; Ultrasonic sensor ; Air gap (plumbing) ; Electric power transmission ; Phase (matter) ; Particle (ecology) ; Electrical conductor ; Electrical engineering ; Composite material ; Engineering ; Chemistry High voltage insulation and dielectric phenomena ; Electrical Fault Detection and Protection ; Power Transformer Diagnostics and Insulation
Authors: Mahmoud Ali Rababah | Faris M. AL‐Oqla
Source: Pertanika journal of science & technology (article, 2023-10-25)
Field: Materials Science — Polymers and Plastics

Optimizing the Mechanical Performance of Green Composite Materials Using Muti-Integrated Optimization Solvers Natural fiber composites are potential alternatives for synthetic materials due to environmental issues. The overall performance of the fiber composites depends on the reinforcement conditions. Thus, this work aimed to optimize the reinforcement conditions of the natural fiber composites to improve their mechanical performance via applying an integrated scheme of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and differential evolution (DE) methods considering various reinforcement conditions including fiber length, fiber loading, and treatment time for optimal characteristics of the composite mechanical performance. The B-Spline approximation function was adopted to predict the experimental performance of green composites. The B-Spline approximation function demonstrated incomparable accuracy compared to linear or quadratic regressions. The function is then optimized using an integrated optimization method. Results have demonstrated that optimal reinforcement conditions for the maximized desired mechanical performance of the composite were achieved with high accuracy. The robustness of the proposed approach was approved using various surface plots of the considered input-output parameter relations. Pareto front or the non-dominated solutions of the desired output mechanical properties were also obtained to demonstrate the interaction between the desired properties to facilitate finding the optimal reinforcement conditions of the composite materials. Composite number ; Reinforcement ; Particle swarm optimization ; Robustness (evolution) ; Mathematical optimization ; Fiber-reinforced composite ; Materials science ; Natural fiber ; Computer science ; Composite material ; Mathematics Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research
Authors: Weichao Liu | Hongyuan Huo | Zhou Ping | Mingyue Li | Yuzhen Wang
Source: Remote Sensing (article, 2023-09-24)
Field: Chemistry — Analytical Chemistry

Research on Hyperspectral Modeling of Total Iron Content in Soil Applying LSSVR and CNN Based on Shannon Entropy Wavelet Packet Transform The influence of some seemingly anomalous samples on modeling is often ignored in the quantitative prediction of soil composition modeling with hyperspectral data. Soil spectral transformation based on wavelet packet technology only performs pruning and threshold filtering based on experience. The feature bands selected by the Pearson correlation coefficient method often have high redundancy. To solve these problems, this paper carried out a study of the prediction of soil total iron composition based on a new method. First, regarding the problem of abnormal samples, the Monte Carlo method based on particle swarm optimization (PSO) is used to screen abnormal samples. Second, feature representation based on Shannon entropy is adopted for wavelet packet processing. The amount of information held by the wavelet packet node is used to decide whether to cut the node. Third, the feature bands selected based on the correlation coefficient and the competitive adaptive reweighted sampling (CARS) algorithm using the least squares support vector regression (LSSVR) are applied to the soil spectra before and after wavelet packet processing. Finally, the Fe content was calculated based on a 1D convolutional neural network (1D-CNN). The results show that: (1) The Monte Carlo method based on particle swarm optimization and modeling multiple times was able to handle the abnormal samples. (2) Based on the Shannon entropy wavelet packet transformation, simple operations could simultaneously preserve the spectral information while removing high-frequency noise from the spectrum, effectively improving the correlation between soil spectra and content. (3) The 1D-CNN with added residual blocks could also achieve better results in soil hyperspectral modeling with few samples. Wavelet ; Computer science ; Hyperspectral imaging ; Particle swarm optimization ; Wavelet packet decomposition ; Pattern recognition (psychology) ; Algorithm ; Artificial intelligence ; Wavelet transform Spectroscopy and Chemometric Analyses ; Remote-Sensing Image Classification ; Soil Geostatistics and Mapping
Authors: Mahdi Forouharshad | Andrea Raspa | Amanda Marchini | Maria Gessica Ciulla | Alice Magnoni | Fabrizio Gelain
Source: Pharmaceutics (article, 2023-08-31)
Field: Materials Science — Biomaterials

Biomimetic Electrospun Self-Assembling Peptide Scaffolds for Neural Stem Cell Transplantation in Neural Tissue Engineering Spinal cord regeneration using stem cell transplantation is a promising strategy for regenerative therapy. Stem cells transplanted onto scaffolds that can mimic natural extracellular matrix (ECM) have the potential to significantly improve outcomes. In this study, we strived to develop a cell carrier by culturing neural stem cells (NSCs) onto electrospun 2D and 3D constructs made up of specific crosslinked functionalized self-assembling peptides (SAPs) featuring enhanced biomimetic and biomechanical properties. Morphology, architecture, and secondary structures of electrospun scaffolds in the solid-state and electrospinning solution were studied step by step. Morphological studies showed the benefit of mixed peptides and surfactants as additives to form thinner, uniform, and defect-free fibers. It has been observed that β-sheet conformation as evidence of self-assembling has been predominant throughout the process except for the electrospinning solution. In vitro NSCs seeded on electrospun SAP scaffolds in 2D and 3D conditions displayed desirable proliferation, viability, and differentiation in comparison to the gold standard. In vivo biocompatibility assay confirmed the permissibility of implanted fibrous channels by foreign body reaction. The results of this study demonstrated that fibrous 2D/3D electrospun SAP scaffolds, when shaped as micro-channels, can be suitable to support NSC transplantation for regeneration following spinal cord injury. Neural tissue engineering ; Electrospinning ; Neural stem cell ; Transplantation ; Regeneration (biology) ; Biocompatibility ; Tissue engineering ; Extracellular matrix ; Nanofiber ; Stem cell ; Self-assembling peptide ; Nanotechnology ; Biomedical engineering ; Regenerative medicine ; In vivo ; Materials science ; Chemistry ; Cell ; Cell biology ; Surgery ; Polymer ; Biochemistry ; Biology ; Biotechnology ; Medicine ; Organic chemistry Supramolecular Self-Assembly in Materials ; Electrospun Nanofibers in Biomedical Applications ; Graphene and Nanomaterials Applications
Authors: Jing Tu | Md Azimul Haque | Derya Baran | Wee‐Liat Ong
Source: Fundamental Research (article, 2023-02-22)
Field: Materials Science — Materials Chemistry

Logarithmic sensitivity ratio elucidates thermal transport physics in multivariate thermoreflectance experiments We mathematically derived a sensitivity-based method that identifies the thermal transport physics and parameters suitable for multivariate nonlinear fits in a frequency-domain thermoreflectance (FDTR) experiment. Modern electronic devices often consist of heterogeneous nanolayers with multiple unknown thermal transport properties. However, simultaneous fitting in a single experiment for these unknown parameters will produce unreliable results if they are correlated. Current methods to identify such correlations are unreliable. This unreliability has impeded the accuracy and speed of characterizing the unknown thermal properties of such multilayer stacks. Our proposed logarithmic sensitivity ratio (LSR) analysis can evaluate the feasibility of fitting a pair of unknown parameters and clarify the governing thermal transport physics. The effectiveness and convenience of this analysis were studied using Monte Carlo simulations and actual FDTR experiments for fitting up to three unknown parameters. The principle behind this method can be extended to other techniques where multivariate fits are needed. Logarithm ; Sensitivity (control systems) ; Multivariate statistics ; Monte Carlo method ; Thermal ; Statistical physics ; Nonlinear system ; Computational physics ; Physics ; Materials science ; Biological system ; Mathematics ; Statistics ; Electronic engineering ; Thermodynamics ; Engineering ; Mathematical analysis ; Quantum mechanics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Advanced Thermoelectric Materials and Devices
Authors: Yanxin Wang | Jing Yan | Zhou Yang | Zhanbei Wang | Jianhua Wang | Yingsan Geng
Source: Measurement Science and Technology (article, 2023-03-07)
Field: Materials Science — Materials Chemistry

Meta-autoencoder-based zero-shot learning for insulation defect diagnosis in gas-insulated switchgear Abstract Data-driven methods are the primary methods of training models for the diagnosis of insulation defects in gas-insulated switchgear (GIS). Due to complicated operating environments, target samples are not available for training sometimes, leading to insufficient feature learning. Therefore, a meta-autoencoder-based zero-shot learning (MAZL) method is proposed for the diagnosis of GIS insulation defects. First, the visual features of insulation defects’ signals are extracted by a convolutional neural network. Next, the mapping between visual and semantic spaces is learned by semantic autoencoder (SAE). For SAE, the triplet loss is adapted to align semantic information, and unknown class semantic constraints are used to alleviate the domain offset. Finally, a nearest-neighbor algorithm is used to achieve zero-sample diagnosis. In model training, episode training is introduced to make the MAZL model learn a more discriminative semantic prototype, which alleviates classification deviations. The MAZL achieved a diagnostic accuracy for single and multi-source GIS insulation defects of 96.215% and 90.41% without using test classes for training, respectively. This finding provides ideas for the diagnosis of new GIS insulation defects. Autoencoder ; Switchgear ; Computer science ; Artificial intelligence ; Convolutional neural network ; Pattern recognition (psychology) ; Offset (computer science) ; Discriminative model ; Generator (circuit theory) ; Machine learning ; Deep learning ; Engineering ; Mechanical engineering ; Physics ; Power (physics) High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Power Transformer Diagnostics and Insulation
Authors: Jose Isagani B. Janairo
Source: RSC Advances (article, 2023-01-01)
Field: Chemistry — Spectroscopy

Sequence rules for gold-binding peptides Metal-binding peptides play a central role in bionanotechnology, wherein they are responsible for directing growth and influencing the resulting properties of inorganic nanomaterials. One of the key advantages of using peptides to create nanomaterials is their versatility, wherein subtle changes in the sequence can have a dramatic effect on the structure and properties of the nanomaterial. However, precisely knowing which position and which amino acid should be modified within a given sequence to enhance a specific property can be a daunting challenge owing to combinatorial complexity. In this study, classification based on association rules was performed using 860 gold-binding peptides. Using a minimum support threshold of 0.035 and confidence of 0.9, 30 rules with confidence and lift values greater than 0.9 and 1, respectively, were extracted that can differentiate high-binding from low-binding peptides. The test performance of these rules for categorizing the peptides was found to be satisfactory, as characterized by accuracy = 0.942, <i>F</i><sub>1</sub> = 0.941, MCC = 0.884. What stands out from the extracted rules are the importance of tryptophan and arginine residues in differentiating peptides with high binding affinity from those with low affinity. In addition, the association rules revealed that positions 2 and 4 within a decapeptide are frequently involved in the rules, thus suggesting their importance in influencing peptide binding affinity to AuNPs. Collectively, this study identified sequence rules that may be used to design peptides with high binding affinity. Peptide ; Chemistry ; Sequence (biology) ; Computational biology ; Combinatorial chemistry ; Amino acid ; Binding site ; Amino acid residue ; Binding affinities ; Affinities ; Peptide sequence ; Biochemistry ; Stereochemistry ; Biology ; Receptor Advanced Proteomics Techniques and Applications ; Advanced biosensing and bioanalysis techniques ; Diatoms and Algae Research
Authors: Bharath Sampath Kumar
Source: Mass Spectrometry (review, 2023-09-19)
Field: Chemistry — Spectroscopy

Recent Advances and Applications of Ambient Mass Spectrometry Imaging in Cancer Research: An Overview Cancer metabolic variability has a significant impact on both diagnosis and treatment outcomes. The discovery of novel biological indicators and metabolic dysregulation, can significantly rely on comprehension of the modified metabolism in cancer, is a research focus. Tissue histology is a critical feature in the diagnostic testing of many ailments, such as cancer. To assess the surgical margin of the tumour on patients, frozen section histology is a tedious, laborious, and typically arbitrary method. Concurrent monitoring of ion images in tissues facilitated by the latest advancements in mass spectrometry imaging (MSI) is far more efficient than optical tissue image analysis utilized in conventional histopathology examination. This article focuses on the "desorption electrospray ionization (DESI)-MSI" technique's most recent advancements and uses in cancer research. DESI-MSI can provide wealthy information based on the variances in metabolites and lipids in normal and cancerous tissues by acquiring ion images of the lipid and metabolite variances on biopsy samples. As opposed to a systematic review, this article offers a synopsis of the most widely employed cutting-edge DESI-MSI techniques in cancer research. Desorption electrospray ionization ; Mass spectrometry imaging ; Cancer ; Computer science ; Biomarker discovery ; Metabolomics ; Computational biology ; Cancer biomarkers ; Medical physics ; Biopsy ; Mass spectrometry ; Pathology ; Bioinformatics ; Medicine ; Biology ; Chemistry ; Proteomics ; Internal medicine ; Biochemistry ; Chromatography Mass Spectrometry Techniques and Applications ; Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Ella Mahlamäki | Inge Schlapp‐Hackl | Marja Rissanen | Michael Hummel | Mikko Mäkelä
Source: Resources Conservation and Recycling (article, 2023-03-31)
Field: Materials Science — Biomaterials

Discriminating the viscoelastic properties of cellulose textile fibers for recycling The viscoelastic properties of cellulose fibers play an important role in chemical recycling of textiles. Here we discriminated the intrinsic viscosity of cotton roll towels and bed linens using near-infrared imaging spectroscopy and supervised pattern recognition. The classification results showed training and test set accuracies of 84–97% and indicated that the relevant spectral features were related to water, cellulose, and cellulose crystallinity. We hypothesized that the decreasing intrinsic viscosity of cotton was associated with changes in cellulose crystallinity and water adsorption, which was supported by additional X-ray and sorption measurements. These results are important as they indicate the potential to non-invasively estimate the degree of polymerization and the suitability of different cotton materials for chemical recycling. We propose that changes in the degree of polymerization and cellulose crystallinity could be used as an indicator of the chemical quality of cellulose fibers, which would have wider impacts for textile recycling. Crystallinity ; Cellulose ; Degree of polymerization ; Viscoelasticity ; Sorption ; Polymerization ; Textile ; Materials science ; Chemical engineering ; Viscosity ; Degree (music) ; Composite material ; Cellulose fiber ; Intrinsic viscosity ; Adsorption ; Pulp and paper industry ; Polymer science ; Polymer ; Chemistry ; Organic chemistry Advanced Cellulose Research Studies ; Dyeing and Modifying Textile Fibers ; Agricultural and Food Sciences
Authors: Dao Thi Cam Minh | Le Thi Bao Tram | Nguyen Hai Phong | Hoang Thi Lan Huong | Le Van Vu | Le Anh Thi | Nguyễn Thị Kiều Anh | Phạm Thị Thanh Hà
Source: Molecules (article, 2023-07-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Single versus Double Coffee-Ring Effect Patterns in Thin-Layer Chromatography Coupled with Surface-Enhanced Raman Spectroscopic Analysis of Anti-Diabetic Drugs Adulterated in Herbal Products In thin-layer chromatography coupled with surface-enhanced Raman spectroscopy (TLC-SERS), the coffee ring effect (CRE) describes the formation of a ring-shape spot (blank in the middle and darker on the edge) caused by the aggregation of silver nanoparticles (Ag NPs), alone (single CRE) or with the analytes (double CRE). In this work, the SCRE and DCRE were investigated in two anti-diabetic drugs, hydrophobic glibenclamide (GLB) and more hydrophilic metformin (MET). The SCRE occurred in GLB analysis, as opposed to the DCRE that occurred in MET. It was proven that for optimization of the TLC-SERS analytical procedure, it is necessary to distinguish the CRE patterns of analytes. Additionally, MET and GLB were analyzed with the developed TLC-SERS method and confirmed by another validated method using high-performance liquid chromatography. Four herbal products collected on the market were found to be adulterated with GLB or/and MET; among those, one product was adulterated with both MET and GLB, and two products were adulterated with GLB at a higher concentration than the usual GLB prescription dose. The TLC-SERS method provided a useful tool for the simultaneous detection of adulterated anti-diabetic herbal products, and the comparison of the SCRE and DCRE provided more evidence to predict CRE patterns in TLC-SERS. Analyte ; Thin-layer chromatography ; Chromatography ; Chemistry ; Raman spectroscopy ; Spots Gold and Silver Nanoparticles Synthesis and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Tea Polyphenols and Effects