bluuebunny/crossref_metadata_2025_split huggingface dataset:
Rejecting the effects of both input disturbance and measurement noise: A second‐order control system example
Authors: Zhu Yang , Zhu Bo , Liu Hugh H. T. , Qin Kaiyu 
Published: August 2020

SummaryThis article originates from the well‐accepted observations in practice: rejection of both input disturbance and measurement noise is practically important for high‐precision tracking control, and the classic estimators, such as the uncertainty and disturbance estimator (UDE) and disturbance observer, are proven to be inherently sensitive to measurement noises. Motivated by these observations, we develop a robust control solution and demonstrate the possibility of unifying the design of noise estimator (NE) and UDE for a class of second‐order systems. Interestingly, the NE and UDE have three important features in common: (i) the designs are based on system model and reliable state measurement; (ii) a first‐order filter is used to ensure that the design is physical realizable, rather than to filter out undesired signals; (iii) the filter parameters are readily determined by an introduced singular perturbation parameter. The performance of UDE is improved when augmented with NE to reject measurement noises. Then, a simple mapping for parameter tuning is presented, by which the estimation performance can be explicitly analyzed using the singular perturbation theory. Comparative simulation and experimental studies show that the proposed NE+UDE‐based solution is not only less sensitive to measurement noise than the classic UDE‐based control, but also able to deliver superior trajectory‐tracking performance over other robust output feedback control approaches.
Stochastic tracking control of multivariable nonlinear systems subject to external disturbances
Authors: Ma Tong 
Published: August 2020

AbstractBy incorporating a disturbance rejection control law with receding horizon optimization technique, this paper synthesizes a stochastic tracking nonlinear model predictive control (STNMPC) framework for multivariable nonlinear systems subject to external disturbances. In the control scheme, a cancellation strategy combined with a feedforward prefilter is adopted to drive the expected value of the output to the reference signal by compensating the undesired components in the feedback loop. Given the statistics of the stochastic disturbances, covariance evolution is involved to describe the propagation of uncertainties through the system model and a time‐varying feedback control law is designed to minimize the covariance. By employing the strong optimizing property of model predictive control (MPC), a stochastic MPC (SMPC) framework is formulated. By using the Schur complement, the nonlinear dependence of covariance evolution on time‐varying feedback control gains is removed and the cost function is reformulated as a linear one with linear matrix inequalities, which resolves the implementation issue existing in the numerical problem. To guarantee the convergence and recursive feasibility of the SMPC framework, terminal constraint with some tricks are applied, for example, Lyapunov equation. The well‐established theories and tricky techniques allow to cast the SMPC framework as a semidefinite programming, which reduces the computational cost and yields a feasible numerical solution. Numerical examples are provided to demonstrate the effectiveness of the proposed control method, comparisons with some adaptive control algorithm shows the superiority of the proposed approach.
A comparison of benthic algal and macroinvertebrate communities in a dammed and undammed Mediterranean river (Eel River watershed, California, USA)
Authors: Jansen Lara Stephanie , O'Dowd Alison , Bouma‐Gregson Keith 
Published: August 2020

AbstractFlow regulation by dams can reduce hydrologic variability, and subsequently, alter river food webs. This study compared the summer algal and macroinvertebrate assemblages from the mainstem Eel River (Mainstem) below Cape Horn Dam to that of the unregulated Middle Fork Eel River (Middle Fork). Despite above average rainfall in 2017, flow regulation produced a steeper spring flow recession limb with only 15.72 cubic meter per second in the dammed Mainstem in late May, which was about half the undammed Middle Fork's discharge of 32.28 cubic meter per second at the same time of year. Peak summer water temperatures in the Mainstem (mid‐August) were later relative to the Middle Fork (late July) based on past years of monitoring. The regulated Mainstem supported less Cladophora with lower mid‐summer algal richness, resulting in a macroinvertebrate community less reliant on Cladophora, with fewer key invertebrates such as Chironominae compared to the unregulated Middle Fork. Dam systems that reduce flow variation in Mediterranean rivers may shift the downstream community away from seasonal proliferation of dense green filamentous algae and invertebrate grazers typical of rivers in this climate, and towards adnate epilithic algae and cyanobacteria and generalist invertebrates, due to the encroachment of riparian vegetation and reduced thermal variation.
Experiments on restoring alluvial cover in straight and meandering rivers using gravel augmentation
Authors: Welber Matilde , Papangelakis Elli , Ashmore Peter , MacVicar Bruce 
Published: August 2020

AbstractSuccessful management of natural and engineered channels with discontinuous alluvial cover requires knowledge of how the cover develops and evolves. We report on physical model experiments designed to compare alluvial cover dynamics in straight and sinuous fixed‐bed channels at a range of gravel‐bed material supply rates and constant discharge conditions. Experiments investigated the formation of alluvial cover from a bare bed, relationships between equilibrium cover characteristics and sediment supply rate, and the evolution of an initial uniform cover of varying thickness. A stable partially‐alluviated state is achieved in both the straight and sinuous channels for a range of sediment supply rates. The areal extent and stored mass of the cover increase progressively with supply rate, and the rate of increase is higher in the straight channel. While alluvial cover develops from isolated patches in the straight channel, cover in the sinuous channel develops as well‐defined bars, with deposition on the inside of bends and expanding outwards along the channel as cover area increases. Artificially emplaced cover quickly adjusts to a cover extent within 4–20% of that formed from a bare bed at the same feed rate, with initial cover thickness only influencing the final cover in the sinuous channel. Neither the sinuous nor the straight channel can sustain an alluvial cover in the absence of upstream sediment supply. This study can inform the management of semi‐alluvial channels because it highlights the primary roles of sediment supply and planform geometry in maintaining an alluvial cover in natural and engineered channels.
Multiclass machine learning classification of functional brain images for Parkinson's disease stage prediction
Authors: Huang Guan‐Hua , Lin Chih‐Hsuan , Cai Yu‐Ren , Chen Tai‐Been , Hsu Shih‐Yen , Lu Nan‐Han , Chen Huei‐Yung , Wu Yi‐Chen 
Published: August 2020

AbstractWe analyzed a data set containing functional brain images from 6 healthy controls and 196 individuals with Parkinson's disease (PD), who were divided into five stages according to illness severity. The goal was to predict patients' PD illness stages by using their functional brain images. We employed the following prediction approaches: multivariate statistical methods (linear discriminant analysis, support vector machine, decision tree, and multilayer perceptron [MLP]), ensemble learning models (random forest [RF] and adaptive boosting), and deep convolutional neural network (CNN). For statistical and ensemble models, various feature extraction approaches (principal component analysis [PCA], multilinear PCA, intensity summary statistics [IStat], and Laws' texture energy measure) were employed to extract features, the synthetic minority over‐sampling technique was used to address imbalanced data, and the optimal combination of hyperparameters was found using a grid search. For CNN modeling, we applied an image augmentation technique to increase and balance data sizes over different disease stages. We adopted transfer learning to incorporate pretrained VGG16 weights and architecture into the model fitting, and we also tested a state‐of‐the‐art machine learning model that could automatically generate an optimal neural architecture. We found that IStat consistently outperformed other feature extraction approaches. MLP and RF were the analytic approaches with the highest prediction accuracy rate for multivariate statistical and ensemble learning models, respectively. Overall, the deep CNN model with pretrained VGG16 weights and architecture outperformed other approaches; it captured critical features from imaging, effectively distinguished between normal controls and patients with PD, and achieved the highest classification accuracy.
Sustainable development: Impacts of consumers' risk aversion on remanufacturing model selection and environmental performance
Authors: Deng Weisheng 
Published: August 2020

AbstractRemanufacturing serves as an efficient way to achieve sustainable development and has become increasingly popular. Various types of services, such as extended warranties and full refunds, are provided to reduce consumers' concerns and promote the sale of remanufactured products. This work is motivated by the fact that consumers are risk‐averse to remanufactured products and prefer new products due to quality, safety, and environmental issues. Taking into account the risk‐averse consumers, we consider four models in which there is no remanufacturing (NR model) and in which remanufacturing is carried out by the supplier (SR model), the manufacturer (MR model), or an independent remanufacturer (IR model); then, we analyze the remanufacturing model choice. We find that consumers' risk aversion and the relative profitability of remanufacturing are two critical factors that drive the optimal results. In particular, depending on the relationship of these two critical factors, the supplier may prefer either the SR model or the IR model, the environment may prefer either the NR model or the SR model, and the manufacturer always prefers the MR model. We further identify the condition in which the SR model may simultaneously benefit the supplier and the environment.
Women and the rhetoric of safety: The impact of policy framing on attitudes toward discriminatory policies
Authors: Wemlinger Elizabeth 
Published: August 2020

AbstractThe expansion of the rights for many in the LGBT community across the US along with theObergefell v. HodgesSupreme Court ruling has created a backlash in many states against the LGBT community. This backlash has recently been furthered by even more restrictive policy, reflected in North Carolina's HB2 act in 2016 eliminating the power of localities to provide more inclusive environments for the LGBT community through multiple policy mechanisms. The rhetoric surrounding these policies focus on the safety of women and children. The purpose of this study is to analyze the relationship between gender and attitudes toward discriminatory policies. What impact does this framing have on a mother's attitudes toward LGBT policies? Utilizing US individual level survey data this study evaluates whether there are gender differences regarding attitudes toward restrictive gay and lesbian policy and a change in the success of these frames from 2008 to 2014.
Engineered Ferritin Nanoparticles for the Bioluminescence Tracking of Nanodrug Delivery in Cancer
Authors: Bellini Michela , Riva Benedetta , Tinelli Veronica , Rizzuto Maria Antonietta , Salvioni Lucia , Colombo Miriam , Mingozzi Francesca , Visioli Alberto , Marongiu Laura , Frascotti Gianni , Christodoulou Michael S. , Passarella Daniele , Prosperi Davide , Fiandra Luisa 
Published: August 2020

AbstractThe identification of a highly sensitive method to check the delivery of administered nanodrugs into the tumor cells is a crucial step of preclinical studies aimed to develop new nanoformulated cures, since it allows the real therapeutic potential of these devices to be forecast. In the present work, the ability of an H‐ferritin (HFn) nanocage, already investigated as a powerful tool for cancer therapy thanks to its ability to actively interact with the transferrin receptor 1, to act as an efficient probe for the monitoring of nanodrug delivery to tumors is demonstrated. The final formulation is a bioluminescent nanoparticle, where the luciferin probe is conjugated on nanoparticle surface by means of a disulfide containing linker (Luc‐linker@HFn) which is subjected to glutathione‐induced cyclization in tumor cell cytoplasm. The prolonged imaging of luciferase+ tumor models, demonstrated by an in vitro and an in vivo approach, associated with the prolonged release of luciferin into cancer cells by disulfide bridge reduction, clearly indicates the high efficiency of Luc‐linker@HFn for drug delivery to the tumor tissues.
Multiphase Assembly of Small Molecule Microcrystalline Peptide Hydrogel Allows Immunomodulatory Combination Therapy for Long‐Term Heart Transplant Survival
Authors: Majumder Poulami , Zhang Yichuan , Iglesias Marcos , Fan Lixin , Kelley James A. , Andrews Caroline , Patel Nimit , Stagno Jason R. , Oh Byoung Chol , Furtmüller Georg J. , Lai Christopher C. , Wang Yun‐Xing , Brandacher Gerald , Raimondi Giorgio , Schneider Joel P. 
Published: August 2020

AbstractCombination therapies that target multiple pathways involved in immune rejection of transplants hold promise for patients in need of restorative surgery. Herein, a noninteracting multiphase molecular assembly approach is developed to crystallize tofacitinib, a potent JAK1/3 inhibitor, within a shear‐thinning self‐assembled fibrillar peptide hydrogel network. The resulting microcrystalline tofacitinib hydrogel (MTH) can be syringe‐injected directly to the grafting site during surgery to locally deliver the small molecule. The rate of drug delivered from MTH is largely controlled by the dissolution of the encapsulated microcrystals. A single application of MTH, in combination with systemically delivered CTLA4‐Ig, a co‐stimulation inhibitor, affords significant graft survival in mice receiving heterotopic heart transplants. Locoregional studies indicate that the local delivery of tofacitinib at the graft site enabled by MTH is required for the observed enhanced graft survival.
ZnO/Nanocarbons‐Modified Fibrous Scaffolds for Stem Cell‐Based Osteogenic Differentiation
Authors: Xia Yi , Fan Xin , Yang Hua , Li Ling , He Chao , Cheng Chong , Haag Rainer 
Published: August 2020

AbstractCurrently, mesenchymal stem cells (MSCs)‐based therapies for bone regeneration and treatments have gained significant attention in clinical research. Though many chemical and physical cues which influence the osteogenic differentiation of MSCs have been explored, scaffolds combining the benefits of Zn2+ ions and unique nanostructures may become an ideal interface to enhance osteogenic and anti‐infective capabilities simultaneously. In this work, motivated by the enormous advantages of Zn‐based metal–organic framework‐derived nanocarbons, C‐ZnO nanocarbons‐modified fibrous scaffolds for stem cell‐based osteogenic differentiation are constructed. The modified scaffolds show enhanced expression of alkaline phosphatase, bone sialoprotein, vinculin, and a larger cell spreading area. Meanwhile, the caging of ZnO nanoparticles can allow the slow release of Zn2+ ions, which not only activate various signaling pathways to guide osteogenic differentiation but also prevent the potential bacterial infection of implantable scaffolds. Overall, this study may provide new insight for designing stem cell‐based nanostructured fibrous scaffolds with simultaneously enhanced osteogenic and anti‐infective capabilities.
An Activatable Nano‐Prodrug for Treating Tyrosine‐Kinase‐Inhibitor‐Resistant Non‐Small Cell Lung Cancer and for Optoacoustic and Fluorescent Imaging
Authors: Xie Xin , Zhan Chenyue , Wang Jie , Zeng Fang , Wu Shuizhu 
Published: August 2020

AbstractNon‐small cell lung cancer (NSCLC) is the most common type of lung cancer and the cause of high rate of mortality. The epidermal growth factor receptor (EGFR)‐targeted tyrosine kinase inhibitors are used to treat NSCLC, yet their curative effects are usually compromised by drug resistance. This study demonstrates a nanodrug for treating tyrosine‐kinase‐inhibitor‐resistant NSCLC through inhibiting upstream and downstream EGFR signaling pathways. The main molecule of the nanodrug is synthesized by linking a tyrosine kinase inhibitor gefitinib and a near‐infrared dye (NIR) on each side of a disulfide via carbonate bonds, and the nanodrug is then obtained through nanoparticle formation of the main molecule in aqueous medium and concomitant encapsulation of a serine threonine protein kinase (Akt) inhibitor celastrol. Upon administration, the nanodrug accumulates at the tumor region of NSCLC‐bearing mice and releases the drugs for tumor inhibition, and the dye for fluorescence and optoacoustic imaging. Through suppressing the phosphorylation of upstream EGFR and downstream Akt in the EGFR pathway by gefitinib and celastrol, respectively, the nanodrug exhibits high inhibition efficacy against orthotopic NSCLC in mouse models.
Simultaneous identification and estimation of glycyrrhizin, glabridin, and 18β‐glycyrrhetinic acid in de‐glycyrrhized Ayurvedic lipid‐based formulation of Glycyrrhiza glabra using dual wavelength reverse phase‐high‐performance liquid chromatography
Authors: Kumbhalkar Bhagyashri , Walunj Tanhaji , Chavan Sandeep , Gupta Vidya , Deshmukh Vineeta , Sardeshmukh Sadanand 
Published: August 2020

AbstractPlant‐based Ayurvedic formulations such as medicated oils, confectioneries, etc. are developed with a rationale of selecting specific compounds for targeted action and minimal side effects. It is imperative to develop an analytical method to simultaneously identify and quantify the targeted compounds for good resolution with low retention time. The present assay using reverse phase‐high‐performance liquid chromatography is optimized to resolve glycyrrhizin, glabridin, and 18β‐glycyrrhetinic acid simultaneously at retention times of 6.6, 8.1, and 10.2 min, respectively, using acidified mobile phase from Glycyrrhiza glabra utilized in Ayurvedic lipid (cow's ghee and sesame oil) based formulations. Raw material, its decoction, and residues formed during preparation steps were extracted in methanol while lipid formulations were extracted using a binary solvent system of methanol and n‐hexane. The separation was performed on Hypersil gold column maintained at 40°C using 0.2% ortho‐phosphoric acid with pH 3.5 in water and acetonitrile as binary gradient mobile phase. The compounds were detected at wavelengths 230 (glabridin) and 254 (glycyrrhizin and 18β‐glycyrrhetinic acid) nm. The method revealed de‐glycyrrhized finished products containing glabridin and 18β‐glycyrrhetinic acid having medicinal value.
An orthogonal approach for method development and validation of three potential halo alkyl alcohol genotoxic impurities in miglitol drug substance by fast gas chromatography–mass spectrometry
Authors: Rajana Nagaraju , Ramana D.V. , Ganta Rama Rao , Devi Dharamasoth Rama , Deshpande Amol A. , Babu J. Moses , Basavaiah K. 
Published: August 2020

AbstractMiglitol is an azasugar. It is the oral antidiabetic drug that inhibits the glucose generation from the polysaccharides and oligosaccharides. The miglitol structure includes 1‐deoxynojirimycin and ethyl alcohol, the alkyl alcohol part is a key starting material for miglitol. The moiety adds by halo alkyl alcohols such as 2‐bromo ethanol, 2‐chloro ethanol, and 2‐iodo ethanol to 1‐deoxynojirimycin. Miglitol's maximum dosage per day is 300 mg, hence any genotoxic impurity in the miglitol should control below 5 ppm. A fast gas chromatography and mass spectrometry analytical method was developed and validated for 2‐bromo ethanol, 2‐chloro ethanol, and 2‐iodo ethanol in miglitol drug substance by using DB‐1, 15 m, 0.25 mm, and 1 µm column, which constitutes the poly‐dimethyl siloxane stationary phase. The limits of detection and quantitation were achieved at 0.7 and 2.0 ppm, respectively. The recoveries were from 81.7 to 118.8% for all impurities. The method was linear from 2.0 to 15 ppm for all impurities and their correlation coefficient values were in the range of 0.992–0.996. The method was precise at limit of quantitation (2.0 ppm), 2.5, 5.0, and 7.5 ppm levels, the %RSD values were <13.5%, and analytical solutions were stable at 25˚C.
A stakeholder framework for evaluating the ‐ilities of autonomous behaviors in complex adaptive systems
Authors: Douglas Andre , Mazzuchi Thomas , Sarkani Shahram 
Published: August 2020

AbstractComplex adaptive systems (CAS) exhibit emergent behaviors caused by the nonlinear actions between individual components within the system and their environment. These emergent behaviors are difficult to predict and measure making it very challenging to initially design from the start. Developing CASs, such as an autonomous swarming unmanned system, is a relevant key task today for solving hard problems with limited resources in a demanding economy. Swarming unmanned systems are valued by their unique capabilities, but how do stakeholders ensure their needs are considered during development? Certain stakeholders have interests that go beyond the initial delivery of the system, yet developers seem to focus efforts on designing autonomous behaviors to satisfy immediate mission needs. To close this gap, we propose a stakeholder focused framework for evaluating CASs over a full system life cycle for swarming unmanned systems. A goal‐oriented requirements engineering modeling methodology, Knowledge Acquisition in AutOmated Specification, is used to map requirements between stakeholder goals and autonomous behaviors. Behaviors are analyzed in an agent‐based simulation using NetLogo for quantifying stakeholder‐driven performance qualities (‐ilities). A case study was performed for a swarm of unmanned surface vessels called wave gliders to evaluate performance over the entire system life cycle.
An interdisciplinary strategy to advance systems engineering theory: The case of abstraction and elaboration
Authors: Topcu Taylan G. , Triantis Konstantinos , Malak Richard , Collopy Paul 
Published: August 2020

AbstractPrior advances in systems engineering (SE) theory were instrumental in defining the discipline and its tools, but are limited in perspective. The SE community needs new theoretical advances to address its existing and emerging sociotechnical challenges. This communication paper is a product of an NSF/SERC/INCOSE funded workshop on theory building in SE with a focus on the use of abstraction and elaboration. The overarching goals of the workshop were twofold. First, to illustrate the nuances and complexities of the theory building process with an emphasis on developing theory about the SE discipline rather than about a particular system of interest. The second goal was to stimulate a new wave of SE theory by providing a set of frames that could be used for formal theory articulation in follow‐up research. The workshop focused on abstraction and elaboration due to their richness and pervasiveness in SE and investigated the concept in two interrelated contexts: (a) the social coordination of design teams and (b) its strategic use during system design. For each research context, we offer a structured guideline for theorizing, and provide an understanding and framing of some of the generalized abstraction and elaboration uses in SE. Research frames articulated in this workshop could serve as the first step for future research on SE theory, which necessitates hypotheses generation, data collection, validation, and refinement.