jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Sergey V. Baranovski | Konstantin V. Mikhaylovskiy
Source: RUDN Journal of Engineering Researches (article, 2020-12-15)
Field: Materials Science — General Materials Science

Assessment of wing deformation influence on airload determination at the initial design stages At designing aircraft, much attention is given to the wing, as one of the most critical elements of the airframe. It is necessary to have a clear-eyed outlook at response sequence, efficiency of various factors and calculation features on the receipt timing and quality of the result. In addition, the design of a structural element is a complex multidisciplinary task affecting various fields of science, which is complicated by the use of polymer composite materials. In the furtherance of solving the urgent task of the methods of designing of a polymer composite wing, it is necessary to determine the influence of wing deformation on the airload used in the calculation and determination of product parameters. Methods of designing of a polymer composite wing used at the initial stages and taking into account the choice of the external appearance, justification of the structural arrangement and load-bearing elements. The paper considers the flow of air over the wing of a passenger airliner and analyzes the pressure values for various flight modes. A comparison is made of the initial theoretical wing surface and deformed during flight, and the difference in loading of the considered options is determined. A future methodology of polymer composite wing design based on parametric modelling will take these results into account and make use of them. Wing ; Deformation (meteorology) ; Parametric statistics ; Airframe ; Computer science ; Airplane ; Mechanical engineering ; Structural engineering ; Aerospace engineering ; Engineering ; Materials science ; Mathematics Material Properties and Applications ; Aerospace, Electronics, Mathematical Modeling ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Robbin Bouwmeester | Lennart Martens | Sven Degroeve
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-01-14)
Field: Chemistry — Spectroscopy

Generalized calibration across LC-setups for generic prediction of small molecule retention times Abstract Motivation Accurate prediction of liquid chromatographic retention times from small molecule structures is useful for reducing experimental measurements and for improved identification in targeted and untargeted MS. However, different experimental setups (e.g. differences in columns, gradients, solvents, or stationary phase) have given rise to a multitude of prediction models that only predict accurate retention times for a specific experimental setup. In practice this typically results in the fitting of a new predictive model for each specific type of setup, which is not only inefficient but also requires substantial prior data to be accumulated on each such setup. Results Here we introduce the concept of generalized calibration, which is capable of the straightforward mapping of retention time models between different experimental setups. This concept builds on the database-controlled calibration approach implemented in PredRet, and fits calibration curves on predicted retention times instead of only on observed retention times. We show that this approach results in significantly higher accuracy of elution peak prediction than is achieved by setup-specific models. Calibration ; Retention time ; Identification (biology) ; Computer science ; Calibration curve ; Biological system ; Algorithm ; Chromatography ; Chemistry ; Mathematics ; Statistics Analytical Chemistry and Chromatography ; Metabolomics and Mass Spectrometry Studies ; Computational Drug Discovery Methods
Authors: Keiichi Torimitsu
Source: Oleoscience (article, 2020-01-01)
Field: Materials Science — Polymers and Plastics

Flexible Silk Electrode: Fiber Electrode バイタル等の生体信号を取得する上で電極の生体適合性はもちろんながら，その肌感触や風合いは極めて重要である。計測していることを意識させないさりげなさは，計測によるストレスを軽減させる。シルクなどの繊維素材は，この様な点で優れており，導電性化することでストレスの少ないフレキシブルな電極としてバイタル計測に最適であると考える。本稿では，この繊維電極についてその技術と可能性について紹介する。 Electrode ; SILK ; Fiber ; Materials science ; Composite material ; Polymer science ; Chemistry Conducting polymers and applications ; Advanced Sensor and Energy Harvesting Materials ; Neuroscience and Neural Engineering
Authors: Şükran Katmer | Çetin Karataş
Source: DergiPark (Istanbul University) (article, 2020-02-05)
Field: Materials Science — Polymers and Plastics

Part quality in injection moulding of shape memory polyurethane Akıllı malzemenin bir türü olan şekil hafızalı polimerler (ŞHP) son yıllarda endüstriyel uygulamalarda yaygınlaşmaktadır. Şekil hafızalı polimerlerin bir türü olan termoplastik şekil hafızalı poliüretan (ŞHPU) kolay kalıplanabilme ve tekrar kullanılabilme özellikleri ile ilgi çekmektedir. Mevcut termoplastiklerde olduğu gibi ŞHPU’dan üretilen parçaların da kalıcı şekillerinin verilmesinde enjeksiyon kalıplamanın yaygın kullanılması beklenmektedir. Bu çalışmada, enjeksiyon kalıplamayla kalıcı şekilleri verilmiş ŞHPU numunelerde parça kalitesi; eksik baskı, hava sıkışması ve parça ağırlığı üzerinden araştırılmıştır. Enjeksiyon basıncı, enjeksiyon sıcaklığı, kalıp sıcaklığı, ütüleme basıncı ve süresi, soğutma süresi parametreleri ile kalıpta eksik baskı, hava sıkışması ve numune ağırlığı arasındaki ilişkiler deneysel olarak incelenmiştir. Deney tasarımı, Taguchi L27 ortogonal dizilim ile hazırlanmış, deneysel veriler varyans analiziyle değerlendirilmiştir. Eksik baskı oluşumuna enjeksiyon sıcaklığı ve basıncının etkileri istatistiksel yönden anlamlı bulunurken, hava sıkışması ise enjeksiyon basıncı ve kalıp sıcaklığından etkilenmiştir. Numune ağırlığı üzerinde en etkili parametrelerin ise sırasıyla enjeksiyon basıncı ve sıcaklığı, kalıp sıcaklığı ve ütüleme basıncı olduğu bulunmuştur. Polyurethane ; Injection moulding ; Quality (philosophy) ; Composite material ; Materials science ; Shape-memory alloy ; Engineering drawing ; Computer science ; Engineering ; Physics Polymer composites and self-healing
Authors: Alexander R. Pelletier | Yun-En Chung | Zhibin Ning | Nora Wong | Daniel Figeys | Mathieu Lavallée‐Adam
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-05-23)
Field: Chemistry — Spectroscopy

MealTime-MS: A Machine Learning-Guided Real-Time Mass Spectrometry Analysis for Protein Identification and Efficient Dynamic Exclusion ABSTRACT Mass spectrometry-based proteomics technologies are the prime methods for the high-throughput identification of proteins in complex biological samples. Nevertheless, there are still technical limitations that hinder the ability of mass spectrometry to identify low abundance proteins in complex samples. Characterizing such proteins is essential to provide a comprehensive understanding of the biological processes taking place in cells and tissues. Still today, most mass spectrometry-based proteomics approaches use a data-dependent acquisition strategy, which favors the collection of mass spectra from proteins of higher abundance. Since the computational identification of proteins from proteomics data is typically performed after mass spectrometry analysis, large numbers of mass spectra are typically redundantly acquired from the same abundant proteins, and little to no mass spectra are acquired for proteins of lower abundance. We therefore propose a novel supervised learning algorithm that identifies proteins in real-time as mass spectrometry data are acquired and prevents further data collection from confidently identified proteins to ultimately free mass spectrometry resources to improve the identification sensitivity of low abundance proteins. We use real-time simulations of a previously performed mass spectrometry analysis of a HEK293 cell lysate to show that our approach can identify 92.1% of the proteins detected in the experiment using 66.2% of the MS2 spectra. We also demonstrate that our approach outperforms a previously proposed method, is sufficiently fast for real-time mass spectrometry analysis, and is flexible. Finally, MealTime-MS’ efficient usage of mass spectrometry resources will provide a more comprehensive characterization of proteomes in complex samples. Mass spectrometry ; Proteomics ; Proteome ; Identification (biology) ; Chemistry ; Computational biology ; Mass spectrum ; Computer science ; Chromatography ; Biology ; Biochemistry Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Lauri Tiitinen
Source: Aaltodoc (Aalto University) (article, 2020-05-18)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Standstill identification of the magnetizing inductance of an induction motor This thesis deals with standstill identification of the induction motor parameters. The main goal of the work is to find parameter identification methods suitable for a fully self-commissioning motor drive. The thesis focuses on identification of the saturable magnetizing inductance. Following a review of the literature, two different identification methods are chosen for further study. The first method estimates the rotor time constant by iteratively searching for a magnetization time that produces a known flux linkage. The second method estimates the stator flux linkage using a robust, two-part integration of the stator voltage equation. Both methods are able to estimate the magnetizing inductance in the linear range of the magnetizing curve without explicit knowledge of the actual stator voltage or resistance. However, the iterative magnetizing time search method is found to be problematic in the saturation range due to the assumed linearity of the system. The feasibility of the robust flux integration method is further investigated with laboratory measurements. The identified magnetizing curve matches well with the reference curve derived using a conventional no-load test when the stator resistance remains constant during the identification. However, if the stator resistance slightly changes during the identification, the accuracy of identified magnetizing curve is significantly deteriorated. This thesis suggests a simple compensation method, greatly improving the accuracy of the identification when a voltage drift is present. Inductance ; Induction motor ; Identification (biology) ; Control theory (sociology) ; Computer science ; Electrical engineering ; Engineering ; Voltage ; Artificial intelligence ; Biology ; Control (management) Magnetic Properties and Applications ; Electric Motor Design and Analysis ; Electric Power Systems and Control
Authors: Zoe Leigh Newman
Source: NCSU Libraries Repository (North Carolina State University Libraries) (article, 2020-10-21)
Field: Materials Science — Polymers and Plastics

A Comparison of Natural and Synthetic Yarns in Common Knit Footwear Structures. A Comparison of Natural and Synthetic Yarns in Common Knit Footwear Structures.(Under the direction of Dr. Andre West Natural (archaeology) ; Synthetic fiber ; Composite material ; Geology ; Materials science ; Fiber Textile materials and evaluations
Authors: Anna Tamulewicz | Ewaryst Tkacz | Ivo Provazník
Source:  (preprint, 2020-06-18)
Field: Chemistry — Analytical Chemistry

Hot spot identification by means of classification methods employing wavelet transform-based features <title>Abstract</title> Background: Proteins can interact with one another by an interface composed of two proteins. Some of the interface residues - called hot spots - have the greatest impact on binding energy in a protein complex. This paper introduces the application of continuous wavelet transform based on the fast Fourier transform (CWTFT) to the analysis of hot spots in proteins.Results: The algorithm was evaluated by using data sets containing 30 proteins. From the number of tested classifiers the best 10 models were preferred. The classifiers achieved sensitivity of 52% - 71%, specificity of 70% - 83% and accuracy of 70% - 74%.Conclusion: The analyses show that the method combining CWTFT and classification algorithms is able to identify hot spot residues with valuable results.Methods: The basis of the algorithm is extraction of features from spectra obtained by using CWTFT with different wavelet functions including Morlet, m^th order derivative of Gaussian, Paul and Bump wavelets. Then, the classifiers that are able to separate hot spot from non-hot spot residues according to these features are applied. Pattern recognition (psychology) ; Identification (biology) ; Artificial intelligence ; Wavelet transform ; Hot spot (computer programming) ; Wavelet ; Computer science ; Biology Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Image Processing Techniques and Applications
Authors: William Crowe | Dawen Zhao | Zhongwei Zhang | Lulu Wang | J. Daniel Bourland
Source: Neuro-Oncology (article, 2020-11-01)
Field: Materials Science — Materials Chemistry

NIMG-04. MRI EVALUATION OF THE EFFECT OF WHOLE BRAIN RADIOTHERAPY ON BRAIN METASTASIS Abstract Poor prognosis of breast cancer brain metastasis partially results from the inability of therapeutics to penetrate the Blood-Brain-Barrier (BBB). Whole Brain Radiotherapy (WBRT) is standard-of-care for brain metastasis, however the early effects of fractionated WBRT on brain metastases have not been evaluated. Here, we applied longitudinal MRI to study WBRT’s effect on tumor volume, BBB permeability, and tumor-cell killing using anatomic, Dynamic Contrast Enhanced (DCE), and Diffusion-weighted (DW) MRI, evaluating tumor volume, permeability, and cytotoxic effect, respectively. The BCBM mouse model was established with left ventricle MDA-MB231-Br cell injection. MRI occurred before and 24h after 3 daily fractionated 4Gy WBRT (2.4Gy/min) or sham irradiation. In addition to anatomic MRI, DW MRI measured tissue ADC to study cytotoxic effect, and DCE MRI measured BBB permeability, quantified as the permeability variable Ktrans. Before treatment, T2-weighted images revealed hyperintense multifocal lesions in brains, and many had intact BBB indicated by no T1-w contrast-enhancement. WBRT tumors were smaller than sham irradiated tumors after 3 days (p&amp;lt; 0.05), while no difference in the number of new lesions was observed. DW MRI demonstrated increased ADC in WBRT tumors (p&amp;lt; 0.05), correlating well with elevated Caspase-3 staining of apoptotic cells. T1-w contrast enhanced images of tumors after treatment revealed no differences in the population of contrast-enhanced tumors between the WBRT and sham radiation groups. However, quantitative DCE MRI showed higher Ktrans in WBRT treated tumors relative to sham treated lesions, and an increase in Ktrans of WBRT tumors (p&amp;lt; 0.05), despite high permeability heterogeneity both inter- and intra-tumorally, indicating remaining impermeable BBB. In addition to monitoring tumor volume, longitudinal MRI provided non-invasive assessments of temporal and spatial changes in cellularity and vascular permeability of brain metastasis in response to WBRT, which may prove to be useful for defining an ideal treatment window for potential combination BCBM treatments. Medicine ; Brain metastasis ; Radiation therapy ; Magnetic resonance imaging ; Metastasis ; Nuclear medicine ; Dynamic contrast-enhanced MRI ; Pathology ; Cancer ; Radiology ; Internal medicine Lanthanide and Transition Metal Complexes ; Brain Metastases and Treatment ; Radiopharmaceutical Chemistry and Applications
Authors: David C. L. Handler | Paul A. Haynes
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2020-08-21)
Field: Chemistry — Spectroscopy

PeptideMind – applying machine learning algorithms to assess replicate quality in shotgun proteomic data Abstract Assessment of replicate quality is an important process for any shotgun proteomics experiment. One fundamental question in proteomics data analysis is whether any specific replicates in a set of analyses are biasing the downstream comparative quantitation. In this paper, we present an experimental method to address such a concern. PeptideMind uses a series of clustering Machine Learning algorithms to assess outliers when comparing proteomics data from two states with six replicates each. The program is a JVM native application written in the Kotlin language with Python sub-process calls to scikit-learn. By permuting the six data replicates provided into four hundred triplet non redundant pairwise comparisons, PeptideMind determines if any one replicate is biasing the downstream quantitation of the states. In addition, PeptideMind generates useful visual representations of the spread of the significance measures, allowing researchers a rapid, effective way to monitor the quality of those identified proteins found to be differentially expressed between sample states. Replicate ; Python (programming language) ; Shotgun proteomics ; Computer science ; Cluster analysis ; Pairwise comparison ; Machine learning ; Artificial intelligence ; Data quality ; Data mining ; Data set ; Algorithm ; Proteomics ; Programming language ; Metric (unit) ; Statistics ; Mathematics ; Engineering ; Biology Advanced Proteomics Techniques and Applications ; Advanced Biosensing Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Yongxiang Zhou | Haifeng Yang | Jianghui Cai | Xujun Zhao | Yaling Xun | Cai-Xia Qu
Source: IEEE Access (article, 2020-01-01)
Field: Chemistry — Analytical Chemistry

WEDA: A Weak Emission-Line Detection Algorithm Based on the Weighted Ranking The Hα emission line in rest wavelength frame of optical spectra is valuable characteristics for nebulae detection. Searching and recognizing the spectra with Hα emission line from massive data are necessary for the further study, while the most of methods existed currently do not adapt to such spectral data, especially for the spectra with weak Hα emission line. To address this issue, a new algorithm (named WEDA) for detection of spectra with Hα emission line is provided in this paper. Firstly, the difference factor μ between the line characteristics of the specific data is defined as its weight in recognizing of the whole lines table. Secondly, a tuning functionf(τ, δ) based on the momentum formula is defined to update the weights during the process. In this step, the spectra with Hα emission line are analysed and classified as 3 different situations. The amount of spectra with Hα emission line is different in 3 different situations, so the speed of weight of update is different in 3 different situations. The weight of update helps us detect the data containing weak Hα emission line in the 3 situations. Based on this, a new integrated algorithm especially for the detection of the spectra with Hα is provided. In the end, by using several spectral datasets from the DR5 of LAMOST survey, experiments results indicate that the WEDA shows higher accuracy basically unaffected by the dataset size and the signal to noise ratio(SNR) than the other similar algorithms. Spectral line ; Line (geometry) ; Emission spectrum ; Algorithm ; Ranking (information retrieval) ; Computer science ; Physics ; Table (database) ; Mathematics ; Artificial intelligence ; Data mining Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Anomaly Detection Techniques and Applications
Authors: Gi Ren Liu | Yuan Chung Sheu | Hau Tieng Wu
Source: arXiv (Cornell University) (preprint, 2020-11-21)
Field: Chemistry — Analytical Chemistry

Central and Non-central Limit Theorems arising from the Scattering Transform and its Neural Activation Generalization Motivated by analyzing complicated and non-stationary time series, we study a generalization of the scattering transform (ST) that includes broad neural activation functions, which is called neural activation ST (NAST). On the whole, NAST is a transform that comprises a sequence of ``neural processing units'', each of which applies a high pass filter to the input from the previous layer followed by a composition with a nonlinear function as the output to the next neuron. Here, the nonlinear function models how a neuron gets excited by the input signal. In addition to showing properties like non-expansion, horizontal translational invariability and insensitivity to local deformation, the statistical properties of the second order NAST of a Gaussian process with various dependence and (non-)stationarity structure and its interaction with the chosen high pass filters and activation functions are explored and central limit theorem (CLT) and non-CLT results are provided. Numerical simulations are also provided. The results explain how NAST processes complicated and non-stationary time series, and pave a way towards statistical inference based on NAST under the non-null case. Generalization ; Central limit theorem ; Limit (mathematics) ; Filter (signal processing) ; Series (stratigraphy) ; Nonlinear system ; Artificial neural network ; Activation function ; Computer science ; Gaussian ; Function (biology) ; Applied mathematics ; Mathematics ; Algorithm ; Mathematical analysis ; Artificial intelligence ; Physics ; Statistics Spectroscopy and Chemometric Analyses ; Neural Networks and Applications ; Blind Source Separation Techniques
Authors: Vijay Kumar Mandava
Source: Digital Scholarship - UNLV (University of Nevada Reno) (article, 2020-03-31)
Field: Materials Science — Materials Chemistry

Extents and limits of radioscopic detection of nuclear materials in cargo containers with two megavoltage energy barriers The megavoltage X-ray technology is utilized for detecting nuclear materials in cargo containers. Interlaced response is obtained by switching rapidly between 6MeV and 9 MeV beams. It is known that the ratio of penetration levels of cargo contents taken at nominal and dual energies provides the information about atomic numbers of materials, and thus can also indicate the threat group. However, the identification is not straightforward if combinations of materials are present. The latter can lead to misdetections. It is imperative to know what are the extent and the limit of the currently employed technology, and how to carry out the inspection in real-time by balancing the human involvement and the computer assistance. We have performed experiments with Linatron K9, analyze data and conclude on an efficient system configuration. The following are addressed: (a) visualization the contents to produce an image suitable for the visual analysis, and (b) prompting the custom personnel on the presence and the location of suspicious objects. Nuclear engineering ; Engineering Graphite, nuclear technology, radiation studies ; Nuclear and radioactivity studies ; Radioactive contamination and transfer