Direct Adaptive Neurocontrol of Structures under Earth Vibration

A direct adaptive neurocontroller is proposed to reduce structure response to earth vibrations by actively creating an equal but opposite force to that of the first mode force of the structure. While earthquake forces are generally considered to be unpredictable, the short-term predictions by the proposed neurocontrol architecture significantly reduce structure vibrations. To demonstrate its general applicability and utility to future earthquakes, the proposed adaptation algorithm is also shown to be asymptotically convergent. The approach is validated by several simulations in which actual time series from the Hachino, Northridge, Kobe, and Bam earthquakes are applied against structures of various heights, three-, five-, and seven-story structures. The simulation results are then compared with those of a conventional linear quadratic regulator. Results indicate a significant and consistent improvement in minimal structure displacement.     

Fractional-order adaptive backstepping control of a class of uncertain systems with external disturbances

Fractional control schemes are powerful tools for fulfilling robust tracking performance of different systems. This paper is the pioneering one in developing a fractional-order adaptive backstepping controller (FOABC) for a general class of integer-order and fractional-order (FO) systems. Model uncertainties and external disturbances can perturb system response and the controller is designed such that it can suppress the performance degradation caused by these factors. Moreover, rigorous mathematical analyses are carried out based on fractional Lyapunov theorems to ensure stability of the controlled systems. To justify the claims, worked-out examples including integer-order and FO systems are simulated. Good tracking performance of the proposed controller as well as robustness against uncertainties and insensitivity to external disturbances make it a good candidate for a broad range of systems. The results of implementing the proposed controller on different systems are compared with some newly proposed control approaches which highlight the outperformance of the FOABC.     

Experimental and numerical investigation of bridge pier scour estimation using ANFIS and teaching–learning-based optimization methods

Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments. The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study, the adaptive network-based fuzzy inference system (ANFIS) and teaching–learning-based optimization (TLBO) method were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18, Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.

     

Water swellable rubber composites: An update review from preparation to properties

In recent years, water swellable rubber composites have been the subject of many scientific and research investigations as well as many industrial programs. Here, we present an updated overview of the developments in the area of water swellable rubber composites with different kinds of fillers, compatibilizers, and cross‐linked agents, in terms of their manufacturing methods, synthesis, chemical, physical, and mechanical properties. Several critical issues and suggestions for future work are detailed, underscoring the roles of material scientists and manufacturing engineers in the bright future of this new material through value addition to enhance its usage and fields of application. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 42786.     

Spectrophotometric and chemometric studies on the simultaneous determination of two benzodiazepines in human plasma

A numerical simple, accurate and precise method based on spectrophotometric data coupled with multivariate calibration methods, PLS and MLR, combined with GA was developed for the simultaneous determination of two benzodiazepines, Clobazam and Flurazepam. A data set of absorption spectra obtained from a calibration set of mixtures containing the compounds was used to build GA-PLS and GA-MLR models. The models were tested using a dataset constructed from the compound synthetic solutions. The better model was also applied to plasma samples. The proposed method requires no preliminary separation steps and can be used for these drugs analysis in quality control laboratories.     

A metadata model for collaborative experiments and simulations in earthquake engineering

Research projects in earthquake engineering yield a very large amount of complex data from experiments and computer simulations. Understanding and exchanging these complicated and voluminous data sets prompted the development of metadata models that document the processes of data generation, and facilitate the collaboration and exchange of information between researchers. The present metadata model was designed to document and exchange a large number of large data files in earthquake engineering, but is applicable to other fields of engineering and science. The model was conceived based on a series of former data models, which were unduly complicated and limited to few types of experiments. Simpler than its predecessors, the present metadata model applies to all kinds of earthquake engineering experiments. It was developed in the object-oriented framework using Protégé. Its applications are illustrated with examples from centrifuge experiments.     

Effects of Kenaf core on properties of poly(lactic acid) bio‐composite

In this study, biodegradable poly(lactic acid) (PLA)/Kenaf core composites with different amount of Kenaf core were prepared using screw extrusion. The Structure, thermal stability, mechanical properties, and biodegradation of bio‐composites are evaluated. FTIR result shows the possible interaction between the Ken core and PLA matrix. The FESEM result showed that Kenaf core was uniformly disperse in PLA matrix. Tensile and flexural strength of PLA was improved Up to the 30%vol of kenaf core content. Young's modulus and hardness properties were improved by adding kenaf core into PLA matrix. Bio‐composite density has been decreased by adding more kenaf core and water absorption of the compound was increased linear. High Kenaf core content was also found to increase the rate of biodegradability of PLA/kenaf core. It can be proven by exposure of the samples to the environment and weight loss in soil burial analysis. POLYM. COMPOS., 35:1220–1227, 2014. © 2013 Society of Plastics Engineers     

Self-assembled targeted nanoparticles: evolution of technologies and bench to bedside translation

Nanoparticles (NPs) have become an important tool in many industries including healthcare. The use of NPs for drug delivery and imaging has introduced exciting opportunities for the improvement of disease diagnosis and treatment. Over the past two decades, several first-generation therapeutic NP products have entered the market. Despite the lack of controlled release and molecular targeting properties in these products, they improved the therapeutic benefit of clinically validated drugs by enhancing drug tolerability and/or efficacy. NP-based imaging agents have also improved the sensitivity and specificity of different diagnostic modalities. The introduction of controlled-release properties and targeting ligands toward the development of next-generation NPs should enable the development of safer and more effective therapeutic NPs and facilitate their application in theranostic nanomedicine. Targeted and controlled-release NPs can drastically alter the pharmacological characteristics of their payload, including their pharmacokinetic and, in some cases, their pharmacodynamic properties. As a result, these NPs can improve drug properties beyond what can be achieved through classic medicinal chemistry. Despite their enormous potential, the translation of targeted NPs into clinical development has faced considerable challenges. One significant problem has been the difficulty in developing targeted NPs with optimal biophysicochemical properties while using robust processes that facilitate scale-up and manufacturing. Recently, efforts have focused on developing NPs through self-assembly or high-throughput processes to facilitate the development and screening of NPs with these distinct properties and the subsequent scale-up of their manufacture. We have also undertaken parallel efforts to integrate additional functionality within therapeutic and imaging NPs, including the ability to carry more than one payload, to respond to environmental triggers, and to provide real-time feedback. In addition, novel targeting approaches are being developed to enhance the tissue-, cell-, or subcellular-specific delivery of NPs for a myriad of important diseases. These include the selection of internalizing ligands for enhanced receptor-mediated NP uptake and the development of extracellular targeting ligands for vascular tissue accumulation of NPs. In this Account, we primarily review the evolution of marketed NP technologies. We also recount our efforts in the design and optimization of NPs for medical applications, which formed the foundation for the clinical translation of the first-in-man targeted and controlled-release NPs (BIND-014) for cancer therapy.