jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Wenzhe Ding | Xinhong Li | Hong Yang | Jiping An | Zhibin Zhang
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Materials Chemistry

A Novel Method for Damage Prediction of Stuffed Protective Structure Under Hypervelocity Impact by Stacking Multiple Models Adopting appropriate protective structures to resist the impact of micrometeoroid or space debris on spacecraft is a valuable research content of space engineering. After the single-layer board and Whipple protective structure, the stuffed protective structure has become one of the research focuses in recent years. However, due to the different filling materials and filling modes, the prediction of hypervelocity impact damage of projectile by using the explicit ballistic limit equation of the stuffed protective structure will lead to some deviation between the predicted results and the measured data. In this paper, the problem is transformed into a binary classification problem, which is characterized by projectile impact parameters and protective structure parameters. And a novel method is proposed to predict the hypervelocity impact damage of the stuffed protective structure under the stacking ensemble learning framework. In this method, the hypervelocity impact damage data set of the stuffed protective structure is firstly divided into specific data subsets. Extreme Gradient Boosting (XGBoost), Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) are taken as the base models of the stacking ensemble learning framework. And each base model is trained by using the divided sub-datasets. Thus the meta-features with stronger generalization ability are extracted. Then, the Long Short Term Memory (LSTM) network is used as the meta-model of the stacking ensemble learning framework, and the meta-features extracted before are used to train it. In this way, the high-precision prediction of the impact damage of the stuffed protective structure is realized under the small data set. The experimental results show that the stacking ensemble learning model has a good prediction effect on the impact damage of the stuffed protective structure. The cross-validation under different training sample sizes further proves that the stacking ensemble learning model has excellent robustness and accuracy. Hypervelocity ; Stacking ; Space debris ; Support vector machine ; Projectile ; Computer science ; Ensemble learning ; Boosting (machine learning) ; Artificial intelligence ; Decoy ; Machine learning ; Materials science ; Spacecraft ; Aerospace engineering ; Physics ; Engineering High-Velocity Impact and Material Behavior ; Structural Response to Dynamic Loads ; Advanced Measurement and Detection Methods
Authors: Or Perlman | Hirotaka Ito | Assaf A. Gilad | Michael T. McMahon | E. Antonio Chiocca | Hiroshi Nakashima | Christian T. Farrar
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-04-23)
Field: Materials Science — Materials Chemistry

Redesigned Reporter Gene for Improved Proton Exchange-based Molecular MRI Contrast Abstract Reporter gene imaging allows for non-invasive monitoring of molecular processes in living cells, providing insights on the mechanisms underlying pathology and therapy. A lysine-rich protein (LRP) chemical exchange saturation transfer (CEST) MRI reporter gene has previously been developed and used to image tumor cells, cardiac viral gene transfer, and oncolytic virotherapy. However, the highly repetitive nature of the LRP reporter gene sequence leads to DNA recombination events and the expression of a range of truncated LRP protein fragments, thereby greatly limiting the CEST sensitivity. Here we report the use of a redesigned LRP reporter (rdLRP), aimed to provide excellent stability and CEST sensitivity. The rdLRP contains no DNA repeats or GC rich regions and 30% less positively charged amino-acids. RT-PCR of cell lysates transfected with rdLRP demonstrated a stable reporter gene with a single distinct band corresponding to full-length DNA. A distinct increase in CEST-MRI contrast was obtained in cell lysates of rdLRP transfected cells and in in vivo LRP expressing mouse brain tumors (p=0.0275, n=10). Reporter gene ; Transfection ; Molecular biology ; Gene ; DNA ; Genetic enhancement ; Cell culture ; Chemistry ; Oncolytic virus ; Biology ; Gene expression ; Cancer research ; Biochemistry ; Genetics ; Tumor cells Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Electron Spin Resonance Studies
Authors: A. Milenin | W. Zalecki | Monika Pernach | Łukasz Rauch | Roman Kuziak | Tomasz Zygmunt | Maciej Pietrzyk
Source: Archives of Civil and Mechanical Engineering (article, 2020-09-11)
Field: Materials Science — Materials Chemistry

Numerical simulation of manufacturing process chain for pearlitic and bainitic steel rails Abstract Computer system for the design of technology of the manufacturing of pearlitic and bainitic rails was presented in this paper. The system consists of the FEM simulation module of thermal–mechanical phenomena and microstructure evolution during hot rolling integrated with the module of phase transformation occurring during cooling. Model parameters were identified based on dilatometric tests. Physical simulations, including Gleeble tests, were used for validation and verification of the models. In the case of pearlitic steels, the process of subsequent immersions of the rail head in the polymer solution was numerically simulated. The objective function in the optimization procedure was composed of minimum interlamellar spacing and maximum hardness. Cooling in the air at a cooling bed was simulated for the bainitic steel rails and mechanical properties were predicted. The obtained results allowed us to formulate technological guidelines for the process of accelerated cooling of rails. Materials science ; Microstructure ; Process (computing) ; Thermal ; Finite element method ; Head (geology) ; Metallurgy ; Mechanical engineering ; Computer simulation ; Phase (matter) ; Composite material ; Structural engineering ; Computer science ; Engineering ; Thermodynamics ; Simulation Metal Alloys Wear and Properties ; Metallurgy and Material Forming ; Microstructure and Mechanical Properties of Steels
Authors: Edwin R. Arboleda | Kimberly M. Parazo | Christle M. Pareja
Source: Jurnal Teknologi dan Sistem Komputer (article, 2020-10-20)
Field: Chemistry — Analytical Chemistry

Watermelon ripeness detector using near infrared spectroscopy This study aimed to design and develop a watermelon ripeness detector using Near-Infrared Spectroscopy (NIRS). The research problem being solved in this study is developing a prototype wherein the watermelon ripeness can be detected without the need to open it. This detector will save customers from buying unripe watermelon and the farmers from harvesting an unripe watermelon. The researchers attempted to use the NIRS technique in determining the ripeness level of watermelon as it is widely used in the agricultural sector with high-speed analysis. The project was composed of Raspberry Pi Zero W as the microprocessor unit connected to input and output devices, such as the NIR spectral sensor and the OLED display. It was programmed by Python 3 IDLE. The detector scanned a total of 200 watermelon samples. These samples were grouped as 60 % for the training dataset, 20 % for testing, and another 20 % for evaluation. The data sets were collected and are subjected to the Support Vector Machine (SVM) algorithm. Overall, experimental results showed that the detector could correctly classify both unripe and ripe watermelons with 92.5 % accuracy. Ripeness ; Detector ; Microprocessor ; Raspberry pi ; Computer science ; Support vector machine ; Mathematics ; Artificial intelligence ; Computer hardware ; Embedded system ; Internet of Things ; Chemistry ; Food science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Mércia de Sousa Galvão | K.L. de Santana | Juliete Pedreira Nogueira | Maria Terezinha Santos Leite Neta | Narendra Narain
Source: Journal of Analytical Sciences Methods and Instrumentation (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

Method Optimization Study on Isolation of Volatile Compounds by Headspace Solid-Phase Microextraction (HS-SPME) from Custard Apple (&amp;lt;i&amp;gt;Annona squamosa&amp;lt;/i&amp;gt; L.) Pulp. Aroma is an important quality parameter, which can help in delaying degradative processes and hence understanding the volatile compounds that are part of an aroma is extremely important. Thus, the aim of this study was to isolate the volatile compounds present in custard apple (Annona squamosa L.) pulp by solid-phase microextraction (SPME) technique using 3 different fibers (DVB/CAR/PDMS, PDMS and PDMS/DVB). An experimental design was set up to evaluate the best extraction conditions wherein the variables were extraction temperature (minimum 28°C, maximum 62°C), ionic strength (minimum 0, maximum 30.0%) and sample concentration (min 1, max 99.0%). The separation of volatiles was achieved in chromatographic columns of different polarity (polar and non-polar) while identification of volatile compounds was performed by high resolution gas chromatography system coupled with mass spectrometry. The results obtained using 3 different fibers revealed the isolation of 71 compounds with fiber DVB/CAR/PDMS, while only 30 and 21 compounds were isolated using fibers PDMS/DVB and PDMS, respectively. The major classes of compounds identified were terpenes, esters, alcohols and aldehydes. Optimum conditions for obtaining higher number of volatiles for non-polar column were 45&degC for extraction temperature, 15% of sodium chloride and 50% of pulp amount which resulted in isolation of a maximum of 78 peaks. Chromatography ; Solid-phase microextraction ; Chemistry ; Aroma ; Custard-apple ; Extraction (chemistry) ; Pulp (tooth) ; Gas chromatography–mass spectrometry ; Mass spectrometry ; Food science Analytical chemistry methods development ; Plant-Derived Bioactive Compounds ; Cultural Heritage Materials Analysis
Authors: Fengxia Li | Annette Feuchtinger | Axel Walch | Na Sun
Source: Hormone and Metabolic Research (review, 2020-04-29)
Field: Chemistry — Spectroscopy

In situ Metabolite Mass Spectrometry Imaging: New Insights into the Adrenal Gland The adrenal gland integrates catecholamine-producing neuroendocrine cells and steroid-producing cells with mesenchymal origin in a structured manner under one capsule and is a key regulator for vital bioactivity. In addition to adrenal-specific disease, dysregulation of adrenal hormones is associated with systemic effects, leading to undesirable metabolic and cardiovascular consequences. Mass spectrometry imaging (MSI) technique can simultaneously measure a broad range of biomolecules, including metabolites and hormones, which has enabled the study of tissue metabolic and hormone alterations in adrenal and adrenal-related diseases. Furthermore, this technique coupled with labeled immunohistochemistry staining has enabled the study of the pathophysiological adaptation of the adrenal gland under normal and abnormal conditions at different molecular levels. This review discusses the recent applications of in situ MSI in the adrenal gland. For example, the combination of formalin-fixed paraffin-embedded tissue microarray and MSI to tissues from patient cohorts has facilitated the discovery of clinically relevant prognostic biomolecules and generated promising hypotheses for new sights into physiology and pathophysiology of adrenal gland. MSI also has enabled the discovery of clinically significant tissue molecular (i. e., biomarker) and pathway changes in adrenal disease, particularly in adrenal tumors. In addition, MSI has advanced the ability to optimally identify and detect adrenal gland specific molecules. Thus, as a novel analytical methodology, MSI has provided unprecedented capabilities for in situ tissue study. Adrenal gland ; Mass spectrometry imaging ; Hormone ; Biology ; Adrenocortical carcinoma ; Endocrinology ; Internal medicine ; Pathology ; Medicine ; Chemistry ; Mass spectrometry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Flórián Garai
Source: International Journal of Engineering and Management Sciences (article, 2020-04-15)
Field: Materials Science — Materials Chemistry

Modern Applications of Aluminium Foams The implementation of aluminium alloy foam has more and more attention. Application of closed cell aluminium foam has made an impact in automobile and aerospace applications where crash energy absorption, vibration and weight reduction are obligatory [1,2,3]. The aluminium alloy foam is an advanced lightweight material providing high strength and stiffness at relatively low density. The technological use of aluminium alloy foam is difficult with the currently available technologies. In the case of open cell aluminium foams, the most common research areas for application are heat exchanger components, filters and sound damping elements [3]. The manuscript focuses on the manufacturing techniques of the aluminium alloy foams according to the application areas. First step is the investigation of the requirements for the application: what are the loads and the circumstances and why can we use aluminium foams. Second step is the knowledge of the producing methods of the foam or the component. And the last step is the investigation of the possible testing methods. Aluminium ; Aluminium foam sandwich ; Materials science ; Metal foam ; Aluminium alloy ; Aerospace ; Specific strength ; Alloy ; 5005 aluminium alloy ; Heat exchanger ; 6063 aluminium alloy ; Crash ; Composite material ; Mechanical engineering ; Computer science ; Engineering Material Selection and Properties ; Additive Manufacturing and 3D Printing Technologies ; Material Properties and Applications
Authors: Lianqiang Chi | Dianhai Zhang | Mengfan Jia | Ziyan Ren
Source: IEEE Access (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Implementation of Vector Hysteresis Model Utilizing Enhanced Neural Network Based on Collaborative Algorithm A hysteresis model, based on the enhanced neural network with parallel strategy, is put forward for the prediction of the accurate magnetic behavior of electrical steel sheets (ESSs). Aimed at overcoming the drawbacks such as low convergence rate and convenient to trap into local optimum in the conventional back-propagation neural network (BPNN), a novel collaborative BPNN learning algorithm is introduced according to the error back propagation mechanism and particle swarm optimization (PSO). The reasonable selection of the test point set by the uniform design of experiment methodology, has the potential of lowering the measurement cost, together with guaranteeing the accuracy of the hysteresis modeling. A parallel strategy, which is based on the fast Fourier transformation (FFT), is applied for enhancing the train efficiency of BPNNs. The proposed algorithm is applied for the purpose of modeling the vector hysteresis behavior of ESS. Together, the comparison of the measured and predicted results of H-locus and core loss is discussed as well. Particle swarm optimization ; Computer science ; Artificial neural network ; Backpropagation ; Fast Fourier transform ; Algorithm ; Hysteresis ; Convergence (economics) ; Control theory (sociology) ; Artificial intelligence Magnetic Properties and Applications ; Non-Destructive Testing Techniques ; Grey System Theory Applications
Authors: Fernando Feijoo Garcia | Francisco Javier Gómez-Gil | J. Gil
Source: Sensors (article, 2020-09-26)
Field: Chemistry — Analytical Chemistry

Application of Composite Spectrum in Agricultural Machines Composite spectrum (CS) is a data-fusion technique that reduces the number of spectra to be analyzed, simplifying the analysis process for machine monitoring and fault detection. In this work, vibration signals from five components of a combine harvester (thresher, chopper, straw walkers, sieve box, and engine) are obtained by placing four accelerometers along the combine-harvester chassis in non-optimal locations. Four individual spectra (one from each accelerometer) and three CS (non-coherent, coherent and poly-coherent spectra) from 18 cases are analyzed. The different cases result from the combination of three working conditions of the components-deactivated (off), balanced (healthy), and unbalanced (faulty)-and two speeds-idle and maximum revolutions per minute (RPM). The results showed that (i) the peaks can be identified in the four individual spectra that correspond to the rotational speeds of the five components in the analysis; (ii) the three formulations of the CS retain the relevant information from the individual spectra, thereby reducing the number of spectra required for monitoring and detecting rotating unbalances within a combine harvester; and, (iii) data noise reduction is observed in coherent and poly-coherent CS with respect to the non-coherent CS and the individual spectra. This study demonstrates that the rotating unbalances of various components within agricultural machines, can be detected with a reduced number of accelerometers located in non-optimal positions, and that it is feasible to simplify the monitoring with CS. Overall, the coherent CS may be the best composite spectra formulation in order to monitor and detect rotating unbalances in agricultural machines. Accelerometer ; Chopper ; Spectral line ; Chassis ; Vibration ; Noise (video) ; Acoustics ; Combine harvester ; Reduction (mathematics) ; Computer science ; Engineering ; Structural engineering ; Mathematics ; Electrical engineering ; Physics ; Mechanical engineering ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Machine Fault Diagnosis Techniques ; Agricultural Engineering and Mechanization
Authors: Luthfi Maulana | Yusuf Gladiensyah Bihanda | Yuita Arum Sari
Source: Register Jurnal Ilmiah Teknologi Sistem Informasi (article, 2020-09-01)
Field: Chemistry — Analytical Chemistry

Color space and color channel selection on image segmentation of food images Image segmentation is a predefined process of image processing to determine a specific object. One of the problems in food recognition and food estimation is the lack of quality of the result of image segmentation. This paper presents a comparative study of different color space and color channel selection in image segmentation of food images. Based on previous research regarding image segmentation used in food leftover estimation, this paper proposed a different approach to selecting color space and color channel based on the score of Intersection Over Union (IOU) and Dice from the whole dataset. The color transformation is required, and five color spaces were used: CIELAB, HSV, YUV, YCbCr, and HLS. The result shows that A in LAB and H in HLS are better to produce segmentation than other color channels, with the Dice score of both is 5 (the highest score). It concludes that this color channel selection is applicable to be embedded in the Automatic Food Leftover Estimation (AFLE) algorithm. Artificial intelligence ; Color space ; Computer vision ; Color image ; Image segmentation ; Computer science ; YCbCr ; HSL and HSV ; Pattern recognition (psychology) ; Channel (broadcasting) ; Segmentation ; Scale-space segmentation ; Color histogram ; Mathematics ; Image processing ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Yoshio Makino | Yumi Kousaka
Source: Foods (article, 2020-05-02)
Field: Chemistry — Analytical Chemistry

Prediction of Degreening Velocity of Broccoli Buds Using Hyperspectral Camera Combined with Artificial Neural Networks Developing a noninvasive technique to estimate the degreening (loss of green color) velocity of harvested broccoli was attempted. Loss of green color on a harvested broccoli head occurs heterogeneously. Therefore, hyperspectral imaging technique that stores spectral reflectance with spatial information was used in the present research. Using artificial neural networks (ANNs), we demonstrated that the reduction velocity of chlorophyll at a site on a broccoli head was related to the second derivative of spectral reflectance data at 15 wavelengths from 405 to 960 nm. The reduction velocity was predicted using the ANNs model with a correlative coefficient of 0.995 and a standard error of prediction of 5.37 × 10<sup>-5</sup> mg·g<sup>-1</sup>·d<sup>-1</sup>. The estimated reduction velocity was effective for predicting the chlorophyll concentration of broccoli buds until 7 d of storage, which was established as the maximum time for maintaining marketability. This technique may be useful for nondestructive prediction of the shelf life of broccoli heads. Hyperspectral imaging ; Artificial neural network ; Artificial intelligence ; Computer science ; Environmental science ; Computer vision Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Greenhouse Technology and Climate Control
Authors: Lindsay K. Pino | Josue Baeza | Richard Lauman | Birgit Schilling | Benjamin A. García
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-11-23)
Field: Chemistry — Spectroscopy

Improved SILAC quantification with data independent acquisition to investigate bortezomib-induced protein degradation ABSTRACT Stable isotope labeling by amino acids in cell culture (SILAC) coupled to data-dependent acquisition (DDA) is a common approach to quantitative proteomics with the desirable benefit of reducing batch effects during sample processing and data acquisition. More recently, using data-independent acquisition (DIA/SWATH) to systematically measure peptides has gained popularity for its comprehensiveness, reproducibility, and accuracy of quantification. The complementary advantages of these two techniques logically suggests combining them. Here, we develop a SILAC-DIA-MS workflow using free, open-source software. We determine empirically that using DIA achieves similar peptide detection numbers as DDA and that DIA improves the quantitative accuracy and precision of SILAC by an order of magnitude. Finally, we apply SILAC-DIA-MS to determine protein turnover rates of cells treated with bortezomib, a 26S proteasome inhibitor FDA-approved for multiple myeloma and mantle cell lymphoma. We observe that SILAC-DIA produces more sensitive protein turnover models. Of the proteins determined differentially degraded by both acquisition methods, we find known ubiquitin-proteasome degrands such as HNRNPK, EIF3A, and IF4A1/EIF4A-1, and a slower turnover for CATD, a protein implicated in invasive breast cancer. With improved quantification from DIA, we anticipate this workflow making SILAC-based experiments like protein turnover more sensitive. Stable isotope labeling by amino acids in cell culture ; Quantitative proteomics ; Bortezomib ; Protein turnover ; Computational biology ; Protein degradation ; Workflow ; Proteomics ; Proteasome ; Multiple myeloma ; Chemistry ; Biology ; Computer science ; Biochemistry ; Protein biosynthesis ; Immunology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Ubiquitin and proteasome pathways