bluuebunny/crossref_metadata_2025_split huggingface dataset:
Properties of natural rubber filled with untreated and treated spent coffee grounds
Authors: Siriwong Chomsri , Boopasiri Supparoek , Jantarapibun Vanichaya , Kongsook Boonsong , Pattanawanidchai Sirichai , Sae‐Oui Pongdhorn 
Published: November 2017

ABSTRACTThis work studied the properties of spent coffee ground (SCG) filled natural rubber (NR). The SCG was initially characterized by various techniques, prior to being added into rubber. Results revealed that SCG had relatively large particle size with very low specific surface area. It is mainly composed of organic compounds (such as protein, fatty acid, cellulose, hemicellulose, and lignin) with small quantity of inorganic substances (oxides of potassium, silicon, magnesium, calcium, and phosphorous). The incorporation of SCG in NR gave relatively low reinforcement and tended to retard vulcanization due to the presence of hydroxyl groups on the SCG surface. In addition to untreated SCG, reinforcement of SCG treated by liquid epoxidized natural rubber (LENR) and bis‐(3‐triethoxysilylpropyl) tetrasulfide (TESPT) was investigated. Improvement of rubber properties was observed when SCG surface was treated. Overall, TESPT‐treated SCG gave the rubber with the highest mechanical properties, followed by LENR‐treated SCG and untreated SCG, respectively. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46060.
Cu(OAc)2/TEMPO Cooperative Promoted Hydroamination Cyclization and Oxidative Dehydrogenation Cascade Reaction of Homopropargylic Amines
Authors: Kong Yuanfang , Wumaier Kediliya , Liu Yingze , Jiang Chunhui , Wang Shuai , Liu Lingyan , Chang Weixing , Li Jing 
Published: November 2017

AbstractA novel and efficient Cu(OAc)2‐catalyzed hydroamination cyclization and 2,2,6,6‐tetramethylpiperidine‐1‐oxyl (TEMPO)‐mediated oxidative dehydrogenation cascade reaction of homopropargylic amines has been developed. A library of 1,2‐disubstituted pyrrole derivatives were obtained in good‐to‐high yields in one pot with no step‐by‐step feeding process. This reaction involved TEMPO playing dual roles as both an oxidative dehydrogenation reagent and a ligand. An insight into the reaction mechanism was obtained by using several analytical determination methods.
Lyapunov Functional Approach to Stability Analysis of Riemann‐Liouville Fractional Neural Networks with Time‐Varying Delays
Authors: Zhang Hai , Ye Renyu , Cao Jinde , Ahmed Alsaedi , Li Xiaodi , Wan Ying 
Published: November 2017

AbstractThis paper is concerned with the globally asymptotic stability of the Riemann‐Liouville fractional‐order neural networks with time‐varying delays. The Lyapunov functional approach to stability analysis for nonlinear fractional‐order functional differential equations is discussed. By constructing an appropriate Lyapunov functional associated with the Riemann‐Liouville fractional integral and derivative, the asymptotic stability criteria of fractional‐order neural networks with time‐varying delays and constant delays are derived. The advantage of our proposed method is that one may directly calculate the first‐order derivative of the Lyapunov functional. Two numerical examples are also presented to illustrate the validity and feasibility of the theoretical results. With the increasing of the order of fractional derivatives, the state trajectories of neural networks show that the speeds of converging toward zero solution are faster and faster.
On the Complexity of SOS Programming and Applications in Control Systems
Authors: Chesi Graziano 
Published: November 2017

AbstractThe minimization of a linear cost function subject to the condition that some matrix polynomials depending linearly on the decision variables are sums of squares of matrix polynomials (SOS) is known as SOS programming. This paper proposes an analysis of the complexity of SOS programming, in particular of the number of linear matrix inequality (LMI) scalar variables required for establishing whether a matrix polynomial is SOS. This number is analyzed for real and complex matrix polynomials, in the general case and in the case of some exact reductions achievable for some classes of matrix polynomials. An analytical formula is proposed in each case in order to provide this number as a function of the number of variables, degree and size of the matrix polynomials. Some tables reporting this number are also provided as reference for the reader. Two applications in control systems are presented in order to show the usefulness of the proposed results.
Renewable hydrocarbon fuels from hydrothermal liquefaction: A techno‐economic analysis
Authors: Pedersen Thomas Helmer , Hansen Nick Høy , Pérez Oscar Miralles , Cabezas Daniel Esteban Villamar , Rosendahl Lasse A. 
Published: November 2017

AbstractThis study demonstrates the economic feasibility of producing renewable transportation drop‐in fuels from lignocellulosic biomass through hydrothermal liquefaction and upgrading. An Aspen Plus® process model is developed based on extensive experimental data to document a techno‐economic assessment of a hydrothermal liquefaction process scheme. Based on a 1000 tonnes organic matter per day plant size capacity, three different scenarios are analyzed to identify key economic parameters and minimum fuel selling prices (MFSP). Scenario I, the baseline scenario, is based on wood‐glycerol co‐liquefaction, followed by thermal cracking and hydroprocessing. Results show that a minimum fuel selling price (MFSP) of 1.14 $ per liter of gasoline equivalent (LGE) can be obtained. In Scenario II, only wood is used as feedstock, which reduces the MFSP to 0.82 $/LGE. Scenario III is also based on a pure wood feedstock, but investigates a full saturation situation (a maximum hydrogen consumption scenario), resulting in a slightly higher MFSP of 0.94 $/LGE. A sensitivity analysis is performed identifying biocrude yield, hydrogen, and feedstock prices as key cost factors affecting the MFSP. In conclusion, the study shows that renewable fuels, via HTL and upgrading, can be highly cost competitive to other alternative fuel processes. © 2017 The Authors. Biofuels, Bioproducts and Biorefining published by Society of Chemical Industry and John Wiley & Sons, Ltd.
Luminescent studies of binuclear ternary europium(III) pyridineoxide tetrazolate complexes containing bis‐phosphine oxide as auxiliary co‐ligands
Authors: Mal Suraj , Pietraszkiewicz Marek , Pietraszkiewicz Oksana 
Published: November 2017

AbstractA new class of antenna chromophores so called ‘tetrazolates’ have not been explored much for lanthanide luminescencent complexes. However, we have already published several articles considering pyridineoxide tetrazolates as sensitizer with lanthanide ions. Although this class of antenna attracted much less attention because of its poor photoluminescence quantum yields (tris‐pyridineoxide tetrazolate europium complex = 13% in solution) we tried and successfully achieved to improve the photoluminescence quantum yields for this particular antenna molecule by replacing coordinated water from the inner coordination sphere of europium ion by introducing phosphine oxides as additional chromophore. In the present article the two bis‐phosphine oxides attach two molecules of tris‐pyridineoxide tetrazolate europium(III) complex which leads to the improvement of the overall molar absorption coefficients as well as photo‐physical properties of the complexes. We found more than two‐fold increase (31% in solution) in photoluminescence quantum yield with one of the coordinated phosphine oxides comparing with that of tris‐pyridineoxide tetrazolate europium(III) complex.
Photoluminescent properties of Eu3+‐Eu2+ activated MAl2SixO2x + 4 (M = Mg, Ca, Sr, Ba) phosphors prepared in air
Authors: Li Songchu , Yu Lixin , Man Xiaoqin , Zhong Jianlin , Sun Wei 
Published: November 2017

AbstractIn this paper, MAl2SixO2x+4:Eu2+/Eu3+ (Eu2+ + Eu3+ = 2%, molar ratio; M = Mg, Ca, Sr, Ba; x = 0, 0.5, 1, 1.5, 2) phosphors with different SiO2 concentrations (the ratio of SiO2 to MAl2O4 is n%, n = 0, 50, 100, 150, 200, respectively) were prepared by high‐temperature solid‐state reaction under atmospheric air conditions. Their structures and photoluminescent properties were systematically researched. The results indicate that Eu3+ ions have been reduced and Eu2+ ions are obtained in air through the self‐reduction mechanism. The alkaline earth metal ions and doping SiO2 strongly affect the crystalline phase and photoluminescent properties of samples, including microstructures, relative intensity of Eu2+ to Eu3+, location of emission lines/bands. It is interesting and important that the emission color and intensities of europium‐doped various phosphors which consist of aluminosilicate matrices prepared under atmospheric air conditions could be modulated by changing the kinds of alkaline earth metal and the content of SiO2.
Enzymatic degradation of hyaluronan hydrogels with different capacity for in situ bio‐mineralization
Authors: Shi Liyang , Zhang Yu , Ossipov Dmitri 
Published: November 2017

AbstractIn situ cross‐linked hyaluronan (HA) hydrogels with different capacities for biomineralization were prepared and their enzymatic degradation was monitored. Covalent incorporation of bisphosphonates (BPs) into HA hydrogel results in the increased stiffness of the hydrogel in comparison with the unmodified HA hydrogel of the same cross‐linking density. The rate of enzymatic degradation of HABP hydrogel was significantly lower than the rate of degradation of control HA hydrogel in vitro. This effect is observed only in the presence of calcium ions that strongly bind to the matrix‐anchored BP groups and promote further mineralization of the matrix. The degradation of the hydrogels was followed by noninvasive fluorescence measurements enabled after mild and chemoselective labeling of cross‐linkable HA derivatives with a fluorescent tag.
A cell‐free platform for rapid synthesis and testing of active oligosaccharyltransferases
Authors: Schoborg Jennifer A. , Hershewe Jasmine M. , Stark Jessica C. , Kightlinger Weston , Kath James E. , Jaroentomeechai Thapakorn , Natarajan Aravind , DeLisa Matthew P. , Jewett Michael C. 
Published: November 2017

AbstractProtein glycosylation, or the attachment of sugar moieties (glycans) to proteins, is important for protein stability, activity, and immunogenicity. However, understanding the roles and regulations of site‐specific glycosylation events remains a significant challenge due to several technological limitations. These limitations include a lack of available tools for biochemical characterization of enzymes involved in glycosylation. A particular challenge is the synthesis of oligosaccharyltransferases (OSTs), which catalyze the attachment of glycans to specific amino acid residues in target proteins. The difficulty arises from the fact that canonical OSTs are large (>70 kDa) and possess multiple transmembrane helices, making them difficult to overexpress in living cells. Here, we address this challenge by establishing a bacterial cell‐free protein synthesis platform that enables rapid production of a variety of OSTs in their active conformations. Specifically, by using lipid nanodiscs as cellular membrane mimics, we obtained yields of up to 420 μg/ml for the single‐subunit OST enzyme, “Protein glycosylation B” (PglB) from Campylobacter jejuni, as well as for three additional PglB homologs from Campylobacter coli, Campylobacter lari, and Desulfovibrio gigas. Importantly, all of these enzymes catalyzed N‐glycosylation reactions in vitro with no purification or processing needed. Furthermore, we demonstrate the ability of cell‐free synthesized OSTs to glycosylate multiple target proteins with varying N‐glycosylation acceptor sequons. We anticipate that this broadly applicable production method will advance glycoengineering efforts by enabling preparative expression of membrane‐embedded OSTs from all kingdoms of life.
Pharmacokinetics and tissue distribution of pefloxacin mesylate in chickens
Authors: Lin Shuqian , Li Guiming , Zhao Zengcheng , Fu Jian , Feng Minyan , Song Minxun , Huang Zhongli , Yang Shifa , Wang Shuli , Wan Renzhong 
Published: November 2017

AbstractA specific, sensitive and stable high‐performance liquid chromatography (HPLC)‐based analytical method was established to determine the level of pefloxacin mesylate (PM) in the plasma and various tissues of chickens. Chickens were randomly assigned to 12 equal experiment groups, including 11 treatment groups and one control group. The chickens in the treatment groups received oral administration of PM and were sacrificed at different pre‐determined time points, with their blood and various organs harvested, extracted and analyzed by HPLC to quantify the level of the residual antibiotic. Method validation studies indicated that the HPLC measurement showed excellent precision, reproducibility, stability and robustness. The obtained pharmacokinetic parameters suggested that PM reached peak levels in various tissues within 1–2 h after its oral administration, and was mainly concentrated in liver and kidney. The antibiotic was also found to be cleared from chicken crureus, brain, testes, ovaries and pancreas at higher rates compared with other organs. Overall, the rapid accumulation of PM could at least be partially attributed to its relatively slow organ clearance. These results could serve as a useful guidance for the rational use of PM and other quinolone‐derived antimicrobials in the treatment of infectious diseases in chickens and other animals.
A low‐complexity virtual reality modeling method based on ontology grid sparsity
Authors: Wan Neng , Deng Zhongliang , Yang Guangyong 
Published: November 2017

AbstractThis paper focuses on an innovative low‐complexity modeling method for virtual reality. Voxels are the basic unit of pixels in 3D (three dimensions), and when stored and processed in a computer, they contain more data redundancy than the demand for actual human vision presentations. This excess is known as voxel redundancy. The reason for the redundancy is that infinite‐dimensional expansion and heterogeneous voxel features have merged in modeling methods. The major voxel compressions and human factors correlations are important to consider. Therefore, we propose a novel low‐complexity virtual reality based on ontology grid sparsity. The redundancy performance of the existing virtual reality modeling methods is first analyzed. The high‐dimension modeling method is designed to describe the base elements that are needed for humans to distinguish what is actually perceived. Based on the ontology theory, grid sparsity is exploited to construct the correlation model. We set up the relevant degree and sensitivity level calculations to classify the correlations between ontology features and other features. The features with weak sensitivity are ignored or merged, which successfully reduces the dimensions and redundancy that are needed to represent virtual items. Visual feature integration combines human factors into a voxel model. Finally, we build the virtual laboratory as a test case in the Unity 3D and 3DMax environment. The tests demonstrate that the new modeling method offers 21.2% and 34.7% of presentation delay and redundancy improvement, respectively.
Supported Single Atom and Pseudo‐Single Atom of Metals as Sustainable Heterogeneous Nanocatalysts
Authors: Dhiman Mahak , Polshettiwar Vivek 
Published: November 2017

AbstractThe field of heterogeneous nanocatalysis has developed catalysts with excellent activities, superior selectivities and high stabilities. Their properties can be easily tuned by tailoring the size, shape and morphology of the nanomaterial. Thus, nanocatalysis is no longer just a field of academic curiosity, but an evolving field for industries to develop green and sustainable processes with very high turnover numbers, rates, and stabilities. The key to increase the efficiencies of these catalysts is to maximize the number of active centers on the surfaces, which can be achieved by reducing the size of metal NPs to single atom and pseudo‐single atom sizes. This field is growing exponentially, and several reports have described the syntheses, characterizations, and applications of these catalysts for a variety of reactions. This Review discusses the recent advances in the synthesis and catalytic performance of single and pseudo‐single atoms/sub‐nanometer (<1 nm) catalysts. We have reviewed experimental and theoretical studies performed for different reactions and, in the last section a brief outlook on the future prospects.
Additive Manufacturing Technologies: 3D Printing in Organic Synthesis
Authors: Rossi Sergio , Puglisi Alessandra , Benaglia Maurizio 
Published: November 2017

AbstractThe manufacturing of a three‐dimensional product from a computer‐driven digital model (3D printing) has found extensive applications in several fields. Additive manufacturing technologies offer the possibility to fabricate ad hoc tailored products on demand, at affordable prices, and have been employed to make customized and complex items for actual sale. However, despite the great progress and the countless opportunities offered by the 3D printing technology, surprisingly a relatively limited number of applications have been documented in organic chemistry. This Minireview will focus specifically on the exploitation of additive manufacturing technologies in the synthesis of organic compounds, and, in particular, on the use of 3D‐printed catalysts and 3D printed reactors, and on the fabrication and use of 3D printed flow reactors.
A multiobjective approach in constructing a predictive model for Fischer‐Tropsch synthesis
Authors: Dehghanian Effat , Gheshlaghi Saman Zare 
Published: November 2017

AbstractFischer‐Tropsch synthesis (FTS) is an important chemical process that produces a wide range of hydrocarbons. The exact mechanism of FTS is not yet fully understood, so prediction of the FTS products distribution is a not a trivial task. So far, artificial neural network (ANN) has been successfully applied for modeling varieties of chemical processes whenever sufficient and well‐distributed training patterns are available. However, for most chemical processes such as FTS, acquiring such amount of data is very time‐consuming and expensive. In such cases, neural network ensemble (NNE) has shown a significant generalization ability. An NNE is a set of diverse and accurate ANNs trained for the same task, and its output is a combination of outputs of these ANNs. This paper proposes a new NNE approach called NNE‐NSGA‐II that tries to prune this set by a modified nondominated sorting genetic algorithm to achieve an optimum subset according to 2 conflicting objectives, which are minimizing root‐mean‐square error in training and unseen data sets. Finally, a comparative study is performed on a single best ANN, a regular NNE, NNE‐NSGA, and 3 popular ensemble of decision trees called random forest, stochastic gradient boosting, and AdaBoost.R2. The results show that in training data set, stochastic gradient boosting and AdaBoost.R2 have better fitted the samples; however, for the predicted FTS products in unseen data set, NNEs methods specially NNE‐NSGA‐II have considerably improved the generalization ability in comparison with the other competing approaches.
Hydration of hydrogels studied by near‐infrared hyperspectral imaging
Authors: Caponigro Vicky , Marini Federico , Gowen Aoife 
Published: November 2017

AbstractHydrogels are an important class of biomaterials that can absorb large quantities of water. In this study, changes in hydration of natural hydrogels (agar, chitosan, gelatin, starch, and blends of each with chitosan) during storage and rehydration were studied by using near‐infrared hyperspectral imaging (NIR‐HSI). Moisture content was calculated based on changes in sample weight during hydration. The NIR‐HSI data were acquired by using a push‐broom system operating in diffuse reflectance in the wavelength range 943 to 1650 nm. A novel synthesis method was developed to enable common preparation of each hydrogel. Mean spectra obtained from the hyperspectral images were analyzed, and predictive models for moisture content were developed by using partial least squares regression. Models were compared in predictive performance by using an independent validation set of data. The optimal model in predictive performance was a 1 latent variable partial least squares regression model developed on second derivative and mean centered pseudo‐absorbance data in the wavelength range 943 to 1272 nm. This model was applied to pixel spectra from samples in the validation set to inspect spatial variations during dehydration and rehydration. Challenges associated with NIR‐HSI of hydrogels with a large variation in moisture content are discussed.
Machine learning‐based genetic evolution of antitumor proteins containing unnatural amino acids by integrating chemometric modeling and cytotoxicity analysis
Authors: He Yongkang , Gao Luoluo 
Published: November 2017

AbstractAntitumor proteins (ATPs) are small oligoproteins or peptides that have been recognized as new and promising therapeutics against a variety of human tumors and cancers. In order to extend the structural diversity space of ATPs, the unnatural amino acids were incorporated into naturally occurring ATPs by using a chemometrics‐based genetic evolution strategy. Based on hundreds of ATPs derived from animals, plant and microbes statistical regression models were developed, optimized, and validated with a systematic combination of 5 widely used machine learning methods and 3 sophisticated unnatural amino acid descriptors. The best regression predictor was employed to guide genetic evolution of a large oligoprotein population. In the evolution procedure, a number of unnatural amino acids with desired physicochemical properties were introduced, resulting in an evolution‐improved population, from which few oligoprotein candidates with top scores, containing 1 to 3 unnatural amino acids, and having diverse structures were successfully prepared, and their antitumor potency against 2 cancer cell lines was analyzed with biological assays. It was found that the high‐activity ATPs are preferentially structured in partial α‐helix or β‐sheet with an alternative sequence pattern of polar, charged, and hydrophobic amino acids, while the intrinsically disordering oligoproteins usually have low or no antitumor activity against tested cancer cell lines.
One‐dimensional convolutional neural networks for spectroscopic signal regression
Authors: Malek Salim , Melgani Farid , Bazi Yakoub 
Published: November 2017

AbstractThis paper proposes a novel approach for driving chemometric analyses from spectroscopic data and based on a convolutional neural network (CNN) architecture. For such purpose, the well‐known 2‐D CNN is adapted to the monodimensional nature of spectroscopic data. In particular, filtering and pooling operations as well as equations for training are revisited. We also propose an alternative to train the resulting 1D‐CNN by means of particle swarm optimization. The resulting trained CNN architecture is successively exploited to extract features from a given 1D spectral signature to feed any regression method. In this work, we resorted to 2 advanced and effective methods, which are support vector machine regression and Gaussian process regression. Experimental results conducted on 3 real spectroscopic datasets show the interesting capabilities of the proposed 1D‐CNN methods.