bluuebunny/crossref_metadata_2025_split huggingface dataset:
Formation of luminescent states in polybenzimidazole‐based films
Authors: Akovantseva Anastasiya , Kotova Svetlana , Gromov Oleg , Burdukovskii Vitaliy , Kholkhoev Bato , Timashev Peter , Yusupov Vladimir , Rybaltovsky Аleksey 
Published: September 2020

AbstractHere we present the results of experimental studies and theoretical computations of the spectroscopic parameters of polybenzimidazole (PBI) films formed by casting from a formic acid solution. The kinetics of the polymer luminescence is described, depending on the amount of the residual solvent. We demonstrate the changes in the intensity of the film luminescence with the formic acid release from the film, which is confirmed by the IR data. Based on the quantum chemistry computations, a mechanism is suggested for the formation of absorption and luminescence bands characteristic of the PBI matrix.
Modular, synthetic, thiol‐ene mediated hydrogel networks as potential scaffolds for 3D cell cultures and tissue regeneration
Authors: Lüchow Mads , Fortuin Lisa , Malkoch Michael 
Published: September 2020

AbstractNatural polymers such as collagen are popular materials for tissue engineering scaffolds due to their innate bioactivity and biocompatibility. Being derived from animal sources, however, means that batch‐to‐batch consistency is often low and the extraction of collagen is costly. This conundrum facilitates the need for synthetic alternatives as scaffolding materials. In this study, a system of poly(ethylene glycol) (PEG)‐based thiol‐ene coupled (TEC) hydrogel scaffolds is presented for tissue engineering purposes. The platform includes several necessary features, namely cytocompatibility, high swelling ability, biodegradability, tunable stiffness, and fast, straightforward fabrication. The swelling ability is provided by the hydrophilicity of the ether‐links of PEG, which facilitated the formation of high water content hydrogels that match the water content of soft tissues for the proper diffusion of nutrients and waste compounds. TEC ensures fast and facile fabrication, with cross‐linking moieties that allow for the biodegradation of the hydrogel network through hydrolytic cleavage. The mechanical properties of the scaffolds are made tunable in the range of storage moduli spanning <1 kPa to >100 kPa. It is also shown that despite the synthetic nature of the hydrogels, human dermal fibroblasts and murine macrophages, Raw 264.7, were able to survive and produce extracellular protein excretions while embedded in the 3D hydrogels.
Cystic fibrosis year in review 2019: Section 2 pulmonary disease and infections
Authors: Savant Adrienne P. , McColley Susanna A. 
Published: September 2020

AbstractDuring the year 2019, research and case reports or series in the field of cystic fibrosis (CF) were in abundance. To adequately address the large body of CF research published during 2019, the CF year in review will be divided into three sections. This report is the second section, focusing specifically on new research related to pulmonary disease and infections. Additional sections will concentrate on CF transmembrane conductance regulator modulators and the multisystem effects of CF. It is an exciting time to be providing care for patients and their families with CF with all the exciting new discoveries that will be shared in these reviews.
Chaotic neural network model for SMISs reliability prediction based on interdependent network SMISs reliability prediction by chaotic neural network
Authors: Zhu Jianhua , Gong Zhuping , Sun Yanming , Dou Zixin 
Published: September 2020

AbstractWith the development of industrial Internet, smart manufacturing information systems (SMISs) are faced with more uncertainties, dynamics, and complexity. These problems bring more challenges to the reliability operation of SMISs. To solve the above problem, a prediction model based on phase space reconstruction, chaos analysis, and back propagation (BP) neural network is proposed to predict SMISs reliability. First, we decompose failure data series into some subdata series components with strong regularity by using C‐C algorithm and Cao algorithm. On this basis, we use the maximum Lyapunov index to identify chaotic characteristics of failure data series. And then, we establish BP neural network prediction model by using reconstructing failure data to predict SMISs failure behaviors. Finally, we use two groups of failure data series to verify the effectiveness of chaotic BP neural network model, and the experiment results verify that chaotic BP neural network model has more accurate prediction results compared with BP network, support vector machine, long short term memory networks (LSTM), and autoregressive model (AR).LEAD PARAGRAPHSMISs are open and complex systems, human error and external environment cause the uncertainty of reliability. The threat of the external environment mainly comes from malicious attacks and the threat of the human error mainly comes from the wrong operation of the operator. Human error and external environment often cause frequent failures of software and hardware of the system or physical failures of devices. As an open complex system, the reliability operation of SMISs is very important for manufacturing enterprises. However, in the daily use of SMISs, the most common failures are time failure caused by human error and external environment. Therefore, it is very important to study the time failure of SMISs.Main points of this paper: (1) SMISs are complex and open systems, so we establish an interdependent network based on the characteristics of SMISs, and use the cascade effect of the complex network to point out that when SMISs fail, the system will easily fall into failure. (2) The phase space reconstruction method is used to restore the real data characteristics of failure data. (3) By using the reconstructed data and the neural network, the failure behavior can be predicted accurately. Compared with other popular prediction methods, it is found that general machine learning methods cannot predict data with chaotic characteristics.The research results of this paper find that when SMISs fail, the failure behavior can easily lead SMISs into chaos through the propagation of interdependent network. Therefore, when future scholars conduct fault analysis on SMISs, they should consider the chaos of the data, otherwise the systems fault analysis and diagnosis cannot be carried out accurately.
Totally robotic caudate lobe liver resection: Bridge over troubled water
Authors: Cañada Trofo Surjan Rodrigo , do Prado Silveira Sergio 
Published: September 2020

AbstractBackgroundMinimally invasive hepatectomy has well‐known advantages over the traditional open approach. Inherent limitations of laparoscopy make major hepatectomies and the resection of upper and posterior segments a great technical challenge. The robotic approach overcomes most of these limitations, and this technology is most useful in the resection of the deeply located caudate lobe.MethodsWe describe the robotic caudate lobe resection technical aspects, using the first robotic resection of the caudate lobe to treat a biphenotypic hepatocholangiocarcinoma to illustrate the procedure. We also performed a literature review on the current status of the robotic approach to segment (Sg) 1.ResultsTechnical approach to the robotic caudate lobe resection is described in a patient with uneventful post‐operative recovery. Literature review demonstrated only four previous authors reporting the use of this technique.ConclusionWe present a step‐by‐step approach to the hepatic Sg 1 resection by robotic approach.
pH variation impacts molecular pathways associated with somatic cell reprogramming and differentiation of pluripotent stem cells
Authors: Kim Narae 
Published: September 2020

AbstractRationaleThe study of somatic cell reprogramming and cell differentiation is essential for the application of recent techniques in regenerative medicine. It is, specifically, necessary to determine the appropriate conditions required for the induction of reprogramming and cell differentiation.MethodsBased on a comprehensive literature review, the effects of pH fluctuation on alternative splicing, mitochondria, plasma membrane, and phase separation, in several cell types are discussed. Additionally, the associated molecular pathways important for the induction of differentiation and reprogramming are reviewed.ResultsWhile cells change their state, several factors such as cytokines and physical parameters affect cellular reprogramming and differentiation. As the extracellular and intracellular pH affects biophysical phenomena in a cell, the effects of pH fluctuation can ultimately decide the cell fate through molecular pathways. Though few studies have reported on the direct effects of culture pH on cell state, there is substantial information on the pathways related to stem cell differentiation and somatic cell reprogramming that can be stimulated by environmental pH.ConclusionEnvironmental pH fluctuations may decide cell fate through the molecular pathways associated with somatic cell reprogramming and cell differentiation.
Data‐driven plant‐model mismatch estimation for dynamic matrix control systems
Authors: Xu Xiaodong , Simkoff Jodie M. , Baldea Michael , Chiang Leo H. , Castillo Ivan , Bindlish Rahul , Ashcraft Brian 
Published: September 2020

SummaryThis article addresses the plant‐model mismatch estimation problem for linear multiple‐input and multiple‐output systems operating under the dynamic matrix control (DMC) implementation of model predictive control. An autocovariance‐based method is proposed, aiming to identify parameter values that minimize the discrepancy between the theoretical autocovariance matrices derived from implementing the (explicit) DMC control law and the sampled autocovariance matrices calculated from operating data. We provide proof that the method results in unbiased estimates. A means for dealing with potential overfitting issues caused by the finite step response models used in DMC in practice is proposed. Several examples are presented to illustrated the theoretical developments.
Model predictive emissions control of a diesel engine airpath: Design and experimental evaluation
Authors: Liao‐McPherson Dominic , Huang Mike , Kim Shinhoon , Shimada Masanori , Butts Ken , Kolmanovsky Ilya 
Published: September 2020

SummaryThis article presents the development and experimental validation of an emissions oriented model predictive controller for a diesel engine. The control objective is to minimize cumulative NOx and hydrocarbon emissions while limiting visible smoke production and without compromising fuel economy or torque response. This is accomplished by using a supervisory model predictive controller (SMPC) and nonlinear model predictive controller (NMPC) in tandem. The SMPC controller coordinates the exhaust gas recirculation (EGR) rate target and fuel supplied to the engine in real time to satisfy combustion quality constraints, while the NMPC controller tracks the EGR rate target by manipulating the EGR throttle, EGR valve, and variable geometry turbine. The NMPC controller uses MPC for feedforward and feedback in a novel configuration to simultaneously achieve fast tracking performance, disturbance rejection, and robustness. We demonstrate that the proposed diesel engine MPC controller is able to reduce cumulative emissions by 10% to 15% relative to a state of the art benchmark strategy when placed in closed loop with an engine on a transient dynamometer.
Sustainable development: The colors of sustainable leadership in learning organization
Authors: Iqbal Qaisar , Ahmad Noor Hazlina 
Published: September 2020

AbstractSustainable development has emerged as a field of applied research across multi‐discipline. Sustainable leadership is a new domain of effective leadership, which has been established recently to cope with issues related to sustainable development. On the subject of natural resource‐based view (NRBV) and dynamic capability theory, this study examined methods of embedding sustainable leadership into sustainable performance and investigated the mediating effect of organizational learning to sustainable leadership and sustainable performance. The data in this study were collected from a total of 369 small and medium‐sized enterprises in Pakistan using a purposive sampling technique. Partial least squares structural equation modeling was used to analyze the data. The results revealed (a) a significant positive effect of sustainable leadership on organizational learning; (b) organizational learning significantly influences sustainable performance; (c) organizational learning partially mediates the relationship between sustainable leadership and sustainable performance. The present study offered an in‐depth theory of NRBV, dynamic capability, and sustainable development. Effective management advices are proposed for organizations encountering sustainable development challenges.
An efficient variance estimator of AUC and its applications to binary classification
Authors: Wang Qing , Guo Alexandria 
Published: September 2020

AbstractThe area under the ROC (receiver operating characteristic) curve, AUC, is one of the most commonly used measures to evaluate the performance of a binary classifier. Due to sampling variation, the model with the largest observed AUC score is not necessarily optimal, so it is crucial to assess the variation of AUC estimate. We extend the proposal by Wang and Lindsay and devise an unbiased variance estimator of AUC estimate that is of a two‐sample U‐statistic form. The proposal can be easily generalized to estimate the variance of a K‐sample U‐statistic (K ≥ 2). To make our developed variance estimator more applicable, we employ a partition‐resampling scheme that is computationally efficient. Simulation studies suggest that the developed AUC variance estimator yields much better or comparable performance to jackknife and bootstrap variance estimators, and computational times that are about 10 to 30 times faster than the times of its counterparts. In practice, the proposal can be used in the one‐standard‐error rule for model selection, or to construct an asymptotic confidence interval of AUC in binary classification. In addition to conducting simulation studies, we illustrate its practical applications using two real datasets in medical sciences.
Effective approaches to combining lexical and syntactical information for code summarization
Authors: Zhou Ziyi , Yu Huiqun , Fan Guisheng 
Published: September 2020

SummaryNatural language summaries of source codes are important during software development and maintenance. Recently, deep learning based models have achieved good performance on the task of automatic code summarization, which encode token sequence or abstract syntax tree (AST) of code with neural networks. However, there has been little work on the efficient combination of lexical and syntactical information of code for better summarization quality. In this paper, we propose two general and effective approaches to leveraging both types of information: a convolutional neural network that aims to better extract vector representation of AST node for downstream models; and a Switch Network that learns an adaptive weight vector to combine different code representations for summary generation. We integrate these approaches into a comprehensive code summarization model, which includes a sequential encoder for token sequence of code and a tree based encoder for its AST. We evaluate our model on a large Java dataset. The experimental results show that our model outperforms several state‐of‐the‐art models on various metrics, and the proposed approaches contribute a lot to the improvements.
Structure and Morphological Properties of Starch Macromolecule Using Biophysical Techniques
Authors: Govindaraju Indira , Chakraborty Ishita , Baruah Vishwa Jyoti , Sarmah Bhaswati , Mahato Krishna Kishore , Mazumder Nirmal 
Published: September 2020

AbstractStarch is an abundant plant polysaccharide that occurs naturally and the main source of carbohydrate and fuel in the human diet. Several biochemical and physicochemical studies have been performed to understand the molecular structure of starch of which microscopy, spectroscopy, and thermal analysis are the most popular. The ultrastructure and birefringence of starch granules have been observed using optical microscopy and polarization microscopy. Scanning electron microscopy (SEM) and X‐ray diffraction (XRD) have been used for spatially resolved micro to nanostructures as well as surface topography and crystallinity in the starch granules. Fourier Transform Infrared (FTIR) spectroscopy has been utilized to elucidate the chemical composition and the changes that occur in its chemical compositions on various modifications. Differential scanning calorimetry (DSC) has also been used to study the thermal characteristics of starch. This review deals with starch characterization using optical microscopy including polarization microscopy, SEM, XRD, FTIR, and DSC to understand the structural and functional characteristics of starch subjected to different processing conditions.
Resistance of concrete to carbonation and chloride penetration assessed on site through nondestructive test
Authors: Torrent Roberto , Imamoto Kei‐ichi , Neves Rui 
Published: September 2020

AbstractThe paper describes the main mechanisms of carbonation and chloride penetration into concrete and how the rate of penetration can be measured with laboratory tests. It is also discussed why nondestructive test (NDT) methods for measuring directly the resistance of aged field concrete to carbonation or chloride penetration are not available. Only destructive tests are applicable which, given the very high‐spatial variability encountered, become prohibitive in terms of costs and damage. The only way to assess the resistance of aged field concrete to carbonation or chloride penetration is through surrogate tests measuring variables that are linked to it. There are two standardized test methods with potential as surrogate tests, as they are fast and entirely nondestructive: the coefficient of air permeability kT (“Torrent” method) and the electrical resistivity ρ (“Wenner” method). Data obtained on‐site with both methods are presented, confirming their potential to assess the resistance of aged field concrete to carbonation or chloride penetration.
Biosensors and sensors for dopamine detection
Authors: Liu Xixia , Liu Juewen 
Published: September 2020

AbstractDopamine is a key catecholamine neurotransmitter and it has critical roles in the function of the human central nervous system. Abnormal release of dopamine is related to neurological diseases and depression. Therefore, it is necessary to monitor dopamine levels in vivo and in real time to understand its physiological roles. In this review, we discuss dopamine detection focusing on the molecular recognition methods including enzymes, antibodies, and aptamers, as well as new advances based on nanomaterials and molecularly imprinted polymers (MIPs). A large fraction of these sensors rely on electrochemical detection to fulfill the requirement of fast, in situ, and in vivo detection with a high spatial and temporal resolution. These methods need to overcome interferences from molecules with a similar redox potential. In addition, fluorescent and colorimetric sensors based on aptamers are also quite popular, and care needs to be taken to validate specific dopamine binding. Combining aptamers or MIPs with electrochemistry promises to achieve rapid detection and increased selectivity. In this article, we pay more attention to the molecular recognition mechanism and critically review the sensor designs. In the end, some future directions are discussed.
Changes in water vapor adsorption and water film thickness in clayey materials as a function of relative humidity
Authors: Lin Xiang , Hu Qinhong , Chen Zhihua , Wang Qiming , Zhang Tao , Sun Mengdi 
Published: September 2020

AbstractSoil water, with adsorbed water being an important component, plays an important role in fluid flow and chemical movement in the unsaturated zone. The adsorbed water consists of water film and interlayer water, and its content is related to the relative humidity (RH). This study theoretically and experimentally focuses on the variation in adsorbed water content in clayey materials as the RH changes. Based on a slit‐pore model, three types of water (water film, interlayer water, and capillary water) could be present in the water vapor adsorption process. The variation of water film is obtained from the analysis of interfacial forces, and then the total water content can be quantitatively subdivided into these three types of water for different RH values. The slit‐pore adsorption model was used with experimental measurements (water vapor adsorption and N2 physisorption) to quantify the amounts and interrelationships of these three different types of water to six clayey materials, namely three clay minerals (kaolinite, montmorillonite, and illite–smectite mixed layer) and three clay‐rich sediments from the Jianghan Plain in China. The main results are that (a) the volumes of water vapor adsorption are much greater than those of N2 adsorption except for kaolinite; (b) interlayer water largely dominates the growth of total water content in montmorillonite and results in a concentration of 0.090 ml g−1 at 95% RH; and (c) the minimum thickness of water film is calculated to be 0.23 nm, and the maximum value is one‐third of the pore width by considering the interfacial forces.