jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Siddhartha Kundu
Source: Acta Biotheoretica (article, 2018-04-26)
Field: Chemistry — Spectroscopy

Mathematical Basis of Predicting Dominant Function in Protein Sequences by a Generic HMM–ANN Algorithm The accurate annotation of an unknown protein sequence depends on extant data of template sequences. This could be empirical or sets of reference sequences, and provides an exhaustive pool of probable functions. Individual methods of predicting dominant function possess shortcomings such as varying degrees of inter-sequence redundancy, arbitrary domain inclusion thresholds, heterogeneous parameterization protocols, and ill-conditioned input channels. Here, I present a rigorous theoretical derivation of various steps of a generic algorithm that integrates and utilizes several statistical methods to predict the dominant function in unknown protein sequences. The accompanying mathematical proofs, interval definitions, analysis, and numerical computations presented are meant to offer insights not only into the specificity and accuracy of predictions, but also provide details of the operatic mechanisms involved in the integration and its ensuing rigor. The algorithm uses numerically modified raw hidden markov model scores of well defined sets of training sequences and clusters them on the basis of known function. The results are then fed into an artificial neural network, the predictions of which can be refined using the available data. This pipeline is trained recursively and can be used to discern the dominant principal function, and thereby, annotate an unknown protein sequence. Whilst, the approach is complex, the specificity of the final predictions can benefit laboratory workers design their experiments with greater confidence. Computer science ; Algorithm ; Sequence (biology) ; Hidden Markov model ; Function (biology) ; Basis (linear algebra) ; Protein function prediction ; Computation ; Artificial intelligence ; Mathematics ; Protein function Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Machine Learning in Bioinformatics
Authors: Ruicong Zhi | Mingxuan Gao | Zhengling Liu | Yongping Yang | Zhixin Zheng | Shi Bolin
Source: Sensors and Materials (article, 2018-12-10)
Field: Chemistry — Analytical Chemistry

Color Chart Development by Computer Vision for Flue-cured Tobacco Leaf Chart ; Curing of tobacco ; Tobacco leaf ; Computer science ; Artificial intelligence ; Computer vision ; Horticulture ; Mathematics ; Agricultural engineering ; Engineering ; Statistics ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Arthur Vrijdag | Yuanrui Sang
Source: Proceedings of the International Ship Control Systems Symposium (iSCSS) (article, 2018-10-03)
Field: Chemistry — Spectroscopy

Fingerprinting the ship propulsion system: low hanging fruit or mission impossible? In this paper the concept of ship propulsion system “fingerprinting” is explored as an alternative for data driven models that require extensive measured datasets collected over long periods of ship operation. As a first exploratory step a model of a ship in bollard pull conditions is linearised and its transfer functions are determined. Subsequently limited experimental data, involving sinusoidal excitation of the system input at a wide range of frequencies, is used to determine the system parameters. The resulting parameter estimates compare well against previously determined values. Although the developed ideas are far from ready to be used on full scale, the authors believe that the approach is promising enough to be developed further towards full scale application. Propulsion ; Marine engineering ; Range (aeronautics) ; Computer science ; Aerospace engineering ; Scale (ratio) ; Transfer function ; Engineering ; Physics ; Electrical engineering Mass Spectrometry Techniques and Applications ; Structural Health Monitoring Techniques ; Target Tracking and Data Fusion in Sensor Networks
Authors: Bünyamin Demır
Source: DergiPark (Istanbul University) (article, 2018-05-29)
Field: Chemistry — Analytical Chemistry

Application of data mining and adaptive neuro-fuzzy structure to predict color parameters of walnuts (Juglans regia L.) Quality is the primary factor designating consumer satisfaction and the market price of agricultural commodities. Colorand general appearance are the basic quality indicators for agricultural products. Surface colors are assessed through colorimetricmeasurements including L*, a*, and b* color parameters. In the present study, L*, a*, and b* color parameters of Bilecik, Fernette, Fernor,Kaman-1, Maraş-12, Maraş-18, Sunland, Şen-2, Yalova-1, and Yalova-3 walnut cultivars (color parameters of 100 randomly selectedwalnuts from each cultivar) were measured with a chroma meter (CR-5 Konica Minolta). Based on L*, a*, and b* measurements,equations from which color index (CI), chroma (C*), and hue (h*) angle parameters could be calculated were developed with the FindLaws algorithm of PolyAnalyst. The color parameters obtained from these newly developed equations were used in training of adaptiveneuro-fuzzy structure. Then color index (CI), chroma (C*), and hue (h*) angle parameters were predicted by adaptive neuro-fuzzyapproach. Root mean square error values of the adaptive neuro-fuzzy-based approach were respectively identified as 0.02 for Bilecik,0.01 for Fernette, 0.02 for Fernor, 0.01 for Kaman-1, 0.01 for Maraş-12, 0.01 for Maraş-18, 0.01 for Sunland, 0.01 for Şen-2, 0.01 forYalova-1, and 0.01 for Yalova-3 walnuts. The obtained equations can be used as a viable alternative instead of equations that varydepending on whether a* and b* are negative or positive. Juglans ; Juglandaceae ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Botany ; Biology Spectroscopy and Chemometric Analyses
Authors: Mehmet Bağmancı | Muharrem Karaaslan | Emın Ünal | Faruk Karadağ
Source: Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi (article, 2018-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Microstrip Patch Antennas Covered with Chiral Metamaterial Structures In this working we present gain characteristic of microstrip patch antennas covered with chiral metamaterial. In order to determine gain of antennas covered with chiral metamaterial structure, S11 parameters and radiation pattern of antennas with chiral metamaterial and without chiral metamaterial are plotted and compared each other. The simulation results show that antennas covered with chiral metamaterial structure increase either gain or radiation pattern or both at operation frequency. Metamaterial ; Metamaterial antenna ; Microstrip antenna ; Microstrip ; Directional antenna ; Physics ; Optics ; Materials science ; Optoelectronics ; Antenna (radio) ; Computer science ; Telecommunications ; Slot antenna Metamaterials and Metasurfaces Applications ; Antenna Design and Analysis ; Advanced Antenna and Metasurface Technologies
Authors: İlkan Özkan | Pınar Duru Baykal | Halil Özdemir
Source:  (article, 2018-12-31)
Field: Materials Science — Polymers and Plastics

Effects of Intermingled Yarn Surface Characteristics on Knitted Fabric’s Color Parameters In this research,it is aimed to investigate the effects of intermingled yarns surface structure,caused by difference in number of nips, on the knitted fabrics’ colorparameters such as lightness (L*), reflectance (R), absorption coefficient / scatteringcoefficient (K/S) and color difference (ΔE*). For this aim, filament yarnshaving different number of nips with 283 dtex linear density and 100 filamentswere used for producing the single jersey knitted fabrics. Fabrics were dyedand colors parameters of samples were measured. Results were analyzed by usingRealcolor 1.3 software package. As a result of study, it was determined thatthe R and L* values effected by the changing in nips value and fabric’sconstruction.&amp;amp;nbsp; Yarn ; Composite material ; Materials science ; Surface (topology) ; Mathematics ; Geometry Textile materials and evaluations ; Consumer Perception and Purchasing Behavior ; Dyeing and Modifying Textile Fibers
Authors: Yao Dong | He Jiang
Source: Complexity (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

A Two‐Stage Regularization Method for Variable Selection and Forecasting in High‐Order Interaction Model Forecasting models with high‐order interaction has become popular in many applications since researchers gradually notice that an additive linear model is not adequate for accurate forecasting. However, the excessive number of variables with low sample size in the model poses critically challenges to predication accuracy. To enhance the forecasting accuracy and training speed simultaneously, an interpretable model is essential in knowledge recovery. To deal with ultra‐high dimensionality, this paper investigates and studies a two‐stage procedure to demand sparsity within high‐order interaction model. In each stage, square root hard ridge (SRHR) method is applied to discover the relevant variables. The application of square root loss function facilitates the parameter tuning work. On the other hand, hard ridge penalty function is able to handle both the high multicollinearity and selection inconsistency. The real data experiments reveal the superior performances to other comparing approaches. Multicollinearity ; Feature selection ; Computer science ; Curse of dimensionality ; Regularization (linguistics) ; Model selection ; Selection (genetic algorithm) ; Variable (mathematics) ; Mean squared error ; Function (biology) ; Machine learning ; Artificial intelligence ; Econometrics ; Mathematical optimization ; Regression analysis ; Mathematics ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Statistical Methods and Models ; Grey System Theory Applications
Authors: IOAN PAVEL OANA | Oana Dorina | Simona Tripa
Source: Industria Textila (article, 2018-09-01)
Field: Materials Science — Polymers and Plastics

Influencing factors analysis of tensile properties of wool yarns with different proportions of polyamide blend This paper presents a comparative analysis of three batches of wool yarns with different fineness, twists and compositions and the way in which these characteristics influence the tensile properties of the yarns. We performed the tensile strength tests and the values for the following tensile characteristics were determined: breaking force, elongation at break, tenacity and the mechanical breaking work and were made the diagrams. Stroke for strength and elongation at break – the diagrams give us an idea on the distribution of weak sections along the yarn tested. In order to make this analysis we used the machine USTER® TENSOJET 4. The main conclusions drawn from this analysis are following: the breaking force of the yarns is mainly determined by the value of length density and only after that by the percentage of polyamide and the twisting value, elongation at break is primarily influenced by the percentage of polyamide from the yarns composition and only then by the yarns twisting degree, the toughest yarns are the ones with the smallest fineness, the mechanical work created when stretching the yarns depends mainly by the percentage of polyamide from the yarns composition, by the yarns fineness and only then by their twisting. Fineness ; Polyamide ; Ultimate tensile strength ; Elongation ; Wool ; Materials science ; Composite material ; Tenacity (mineralogy) ; Yarn ; Breaking strength Textile materials and evaluations
Authors: Amy M. Kwon
Source: BMC Medical Informatics and Decision Making (article, 2018-05-31)
Field: Chemistry — Spectroscopy

A rank weighted classification for plasma proteomic profiles based on case-based reasoning RWCBR approach showed potential as a robust classifier in predicting the clinical status of the subjects for plasma proteomic profiles. Classifier (UML) ; Pattern recognition (psychology) ; Support vector machine ; Computer science ; Artificial intelligence ; Machine learning Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Data Mining Algorithms and Applications
Authors: Sunita Boruah | B. Kalita
Source: Journal of Applied and Natural Science (article, 2018-09-01)
Field: Materials Science — Polymers and Plastics

Evaluation of physical properties of Errandi-acrylic union fabrics Errandi silk has a potential of emerging as “A silk of the new millennium”. The experimental finding provides information on physical properties as well as subjective evaluations of Errandi silk -Acrylic union fabrics with twill and herringbone weave. The count and diameter of errandi silk fibre were higher than acrylic fibre and in case of woven fabrics, herringbone weave were found to have maximum value in cloth covers, weight and thickness. A comparative subjective evaluation was done based on fabric quality by the panel of 100 respondents. 100 per cent respondent opined that all the woven samples have good appearance, soft in hand and smooth in texture. It was observed that cent percent of respondents found errandi-acrylic blended union fabrics with twill and herringbone weaves were high in lustre. The costs of blended yarns and the cost of union fabrics of different proportion were got reduced in comparison to that of 100 per cent errandi-silk yarn (Rs.3000.00/kg) and controlled errandi silk fabrics (Rs.985.00/mt.). Based on the aesthetic properties such as handle, appearance, texture and lustre and considering the cost of twill and herringbone errandi-acrylic fabrics were suitable for production of fashionable wear. Thus¸ the newly designed Errandi silk with Acrylic union fabrics are unique, a new venture for the textile industry and Ericulturists. Lustre (file system) ; SILK ; Materials science ; Composite material ; Acrylic resin ; Computer science Textile materials and evaluations
Authors: Hüseyin Ankara | Fatma Çiçek
Source: Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi (article, 2018-04-06)
Field: Chemistry — Analytical Chemistry

Suda dağılmaya karşı duraylılık indeksi (SDI) deneylerinde istatistiksel ortalama kontrol grafiklerinin kullanımı Standartların ve literatürün önerileri yorumladığı zaman, ideale yakın SDI değerine ulaşabilmek için SDI deneylerinde kullanılan temsili deney örneklerinin eş boyut/kütleli ve pürüzsüz yüzeyli kürelerden meydana gelmesi istenmektedir. Bu çalışmada, masif ve laminalı marn kaya örneklerinden hazırlanan eş boyutlu/kütleli ve pürüzsüz yüzeyli temsili küre deney örnekler üzerinde çok çevrimli SDI deneyleri yapılmış ve her bir çevrimdeki SDI değerlerindeki değişim belirlenmiştir. SDI indeks değerlerindeki değişim uyumlu görülmesine rağmen, çevrimlerdeki kütle kaybı miktarlarının düzensiz olduğu gözlenmiştir. Bunun için çevrimlerdeki kütle kaybı miktarları istatistiksel ortalama kontrol grafikleri üzerinde işaretlenmiş ve değişkenlikler yorumlanmıştır. İstatistiksel yaklaşım ve ortalama kontrol grafiklerinin yorumlanması sonucunda, kütle kaybı miktarlarındaki değişkenlik sınırları ve bu değişkenliğe bağlı olarak tahmini çevrim sayıları tekrar belirlenmiştir . SDI deneylerinde kullanılan kaya örnekleri için yeni bir yaklaşımla alt ve üst SDI indeks değerleri hesaplanmıştır. Physics ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Fenghua Yu | Shuang Xiang | Zhonghui Guo | Honggang Zhang | Juchi Bai | Tongyu Xu
Source: International Journal of Precision Agricultural Aviation (article, 2018-01-01)
Field: Chemistry — Analytical Chemistry

Collaborative inverse modeling of nitrogen and phosphorus content in rice based on WOA-ELM Nitrogen and phosphorus play an important role in the growth and development of crops, and the accurate acquisition of information on crop nitrogen and phosphorus nutrient levels is of great significance in terms of accurate crop management and saving planting costs. To address the problem that previous single-element monitoring has led to difficulties in synergy between inversion models, this paper proposes a method based on a whale algorithm with an extreme learning machine to achieve synergistic inversion of nitrogen and phosphorus content in rice, and demonstrates the feasibility of using spectral data to invert nitrogen and phosphorus simultaneously. In this paper, an unmanned aircraft hyperspectral remote sensing platform was used to acquire hyperspectral remote sensing images of the canopy of japonica rice at key fertility stages, and agronomic information was sampled simultaneously on ground. The hyperspectral data were downscaled by principal component analysis (PCA) and discrete wavelet multiscale decomposition (DWT), and the filtered feature vectors were used as input and the measured nitrogen and phosphorus content as output. Two models, the limit learning machine and the limit learning machine based on the whale algorithm, were used to collaboratively estimate the nitrogen and phosphorus content of japonica rice at critical fertility stages, and the following conclusions were drawn: 1) The inversion accuracy of both models for nitrogen The R 2 of the training set was above 0.64 and the R 2 of the validation set was above 0.56. 2) The dimensionality reduction method using wavelet decomposition was more representative than that of principal component analysis in filtering feature vectors, and it was the best for phosphorus inversion. 3) Overall, the WOA-ELM model was better than the ELM model in estimation, with the R 2 of nitrogen inversion reaching up to this model has greatly improved the efficiency of obtaining the nutrient content of rice leaves. Keywords: rice, nitrogen, phosphorus, hyperspectral, whale algorithm, extreme learning machine DOI: 10.33440/j.ijpaa.20220501.184 Citation: Yu F H, Xiang S, Guo Z H, Zhang H G, Bai J C, Xu T Y. Collaborative inverse modeling of nitrogen and phosphorus content in rice based on WOA-ELM. Int J Precis Agric Aviat, 2022; 5(1): 1–9. Inverse ; Phosphorus ; Nitrogen ; Mathematics ; Environmental science ; Chemistry Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Remote Sensing and Land Use
Authors: Efe Astarlioglu
Source: Theseus (Ammattikorkeakoulujen) (article, 2018-01-01)
Field: Materials Science — Materials Chemistry

Simulation of carbon nanostructures with a vacancy present Carbon is a very special element given it can take part in millions of different chemical compounds due to its 4 valence electrons and its compact size to fit in larger molecules. Further, carbon forms allotropes. Most familiar allotropes of carbon are diamond and graphite. Graphene and carbon nanotube will be the two allotropes of carbon which will be the subject in this project.&#13;\n&#13;\nBoth graphene and carbon nanotubes have unique mechanical, thermal, optic and electrical properties. Therefore, they are either in use, or can potentially be used, in myriad different applications in industries or research. Consequently, it is important for future development to understand the behavior of these allotropes in diverse settings.&#13;\n&#13;\nMain task of this project was to investigate the behavior and effect of vacancy defects (point defects) in graphene sheet and carbon nanotube of different sizes as a function of temperature. In order to study these behavior and effects, computer simulations on molecular dynamics were employed. This report will introduce the necessary chemistry and material science concepts as well as tools used in simulation of the structures.&#13;\n&#13;\nAs a result of the simulations, diffusion of vacancies in the structures was observed. The simulations ran with a resolution of 1 femtoseconds in which the structures were heated from 0K to upwards of 4500K.The diffusions took place at high temperatures for both graphene and carbon nanotube. Vacancy defect ; Carbon fibers ; Materials science ; Computer science ; Chemistry ; Crystallography ; Algorithm Graphene research and applications ; Carbon Nanotubes in Composites ; Fiber-reinforced polymer composites
Authors: Albert Konijnenberg | Jinyu Li | Johny Habchi | Marion Dosnon | Giulia Rossetti | Rita Grandori | Sonia Longhi | Paolo Carloni | Frank Sobott
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2018-03-16)
Field: Chemistry — Spectroscopy

Conformational footprinting of proteins using a combination of top-down electron transfer dissociation and ion mobility Abstract In recent years native mass spectrometry has been increasingly employed to study protein structure. As such a thorough understanding of the effect of the gas-phase on protein structure is becoming increasingly important. We show how a combination of top-down ETD and ion mobility can be used to probe the gas-phase structure of heterogeneous protein ensembles. By applying collisional activation to the non-covalently bound ETD products after IM separation, the peptide fragments can be released while maintaining the conformational information of the protein ion. We studied the unknown gas-phase structures of the measles virus (MeV) phosphoprotein X domain (P XD ), which shows a wide range of different conformations in the gas-phase. We then generated structural models by state-of-the-art gas-phase steered molecular dynamics, which we verified using restraints from ion mobility and the fragment patterns observed. Our findings illustrate the applicability of ETD for obtaining conformational specific structural information on heterogeneous protein ensembles. Electron-transfer dissociation ; Ion-mobility spectrometry ; Footprinting ; Phosphoprotein ; Chemistry ; Gas phase ; Chemical physics ; Ion ; Dissociation (chemistry) ; Measles virus ; Protein structure ; Biophysics ; Mass spectrometry ; Tandem mass spectrometry ; Biochemistry ; Physical chemistry ; Chromatography ; Phosphorylation ; DNA ; Biology Mass Spectrometry Techniques and Applications ; Protein Structure and Dynamics ; Advanced Proteomics Techniques and Applications
Authors: Chuang Zhang | Songze Chen | Zhaoli Guo
Source: arXiv (Cornell University) (preprint, 2018-06-07)
Field: Materials Science — Materials Chemistry

An implicit kinetic scheme for multiscale heat transfer problem accounting for phonon dispersion and polarization An efficient implicit kinetic scheme is developed to solve the stationary phonon Boltzmann transport equation (BTE) based on the non-gray model including the phonon dispersion and polarization. Due to the wide range of the dispersed phonon mean free paths, the phonon transport under the non-gray model is essentially multiscale, and has to be solved differently and appropriately for varied phonon frequencies and branches. The proposed implicit kinetic scheme is composed of a microscopic iteration and a macroscopic iteration. The microscopic iteration is capable of automatically adapting with varied phonon mean free path of each phonon frequency and branch through solving the phonon BTE. The energy transfer of all phonons is gathered together by the microscopic iteration to evaluate the heat flux. The temperature field is predicted through a macroscopic heat transfer equation according to the heat flux, and the equilibrium state in the phonon BTE is also updated. The combination of the phonon BTE solver and the macroscopic equation makes the present method very efficient in a wide length scale. Three numerical tests, including the cross-plane, in-plane and nano-porous heat transfer in silicon, validate that the present scheme can handle with the phonon dispersion and polarization correctly and predict the multiscale heat transfer phenomena efficiently in a wide range. The present method could be tens of times faster than the typical implicit DOM and keeps the same amount of the memory requirements as the Fourier solver for multiscale heat transfer problem. Phonon ; Boltzmann equation ; Heat transfer ; Heat flux ; Physics ; Statistical physics ; Computational physics ; Mechanics ; Condensed matter physics ; Thermodynamics Thermal properties of materials ; Thermal Radiation and Cooling Technologies ; Heat Transfer and Optimization