Surface modification of starch nanocrystals through ring‐opening polymerization of ε‐caprolactone and investigation of their microstructures

Bionanoparticles of starch obtained by submitting native potato starch granules to acid hydrolysis conditions. The resulted starch nanoparticles were used as core or macro initiator for polymerization of ε‐caprolactone (CL). Starch nanoparticle‐g‐polycaprolactone was synthesized through ring‐opening polymerization (ROP) of CL in the presence of Sn(Oct)₂ as initiator. The detailed microstructure of the resulted copolymer was characterized with NMR spectroscopy. Thermal characteristic of the copolymer was investigated using DSC and TGA. By introducing PCL, the range of melting temperature for starch was increased and degradation of copolymer occurred in a broader region. X‐ray diffraction and TEM micrographs confirmed that there was no alteration of starch crystalline structure and morphology of nanoparticles, respectively. © 2008 Wiley Periodicals, Inc. J Appl Polym Sci, 2008     

Direct Measurement of Transformation Zone Strains in Toughened Zirconia

Residual strains responsible for crack tip shielding have been measured within transformation zones surrounding cracks in Mg-PSZ. Two techniques were used for strain measurement: moiré interferometry and high-resolution image matching. Both methods provide maps of differential in-plane displacements within the specimen surface intersected by the crack, the latter method with the higher resolution. The results are compared with finite-element analysis to assess surface relaxation effects, and the measured strains are used to evaluate the crack tip shielding stress intensity factor. Calculations based on the assumption that the unconstrained transformation strain is hydrostatic dilatation yielded results that were significantly smaller than the measured toughness increases.     

Quality Management in Pakistan's Bedwear Industry

Pakistan's bedwear industry contributes significantly to Pakistan's industry in general and to its textile industrial output and exports in particular. The exports of bedwear have increased steadily. However, its key basis of competitive advantage has been low cost. After greater liberalization from 2005 onwards and due to tariffication of quotas, the bedwear industry will not only have to scale higher tariff walls but will be more exposed to the threat of anti-dumping duties as it relies primarily on low prices to penetrate the export markets. To enhance its competitiveness, it will have to combine better quality with low costs and quick response. Research in quality management in Pakistan's bedwear industry was, therefore, undertaken. A postal self-completion questionnaire was mailed to 38 members of Pakistan Bedwear Manufacturers' and Exporters' Association (PBMEA). The response rate was almost 79%. It was found that this industrial segment was in various stages of development. This study demonstrates both the effectiveness and the limits of quality assurance in improving the levels of quality which remain a moving target in a dynamic liberalizing international trade environment that is more competitive since 2005. The bedwear industry, therefore, needs to graduate to advanced practices in quality management in order to remain competitive in rapidly changing times.     

Design of disturbance observer based on adaptive-neural control for large-scale time-delay systems in the presence of actuator fault and unknown dead zone

This article presents an adaptive neural compensation scheme for a class of large-scale time delay nonlinear systems in the presence of unknown dead zone, external disturbances, and actuator faults. In this article, the quadratic Lyapunov–Krasovskii functionals are introduced to tackle the system delays. The unknown functions of the system are estimated by using radial basis function neural networks. Furthermore, a disturbance observer is developed to approximate the external disturbances. The proposed adaptive neural compensation control method is constructed by utilizing a backstepping technique. The boundedness of all the closed-loop signals is guaranteed via Lyapunov analysis and the tracking errors are proved to converge to a small neighborhood of the origin. Simulation results are provided to illustrate the effectiveness of the proposed control approach.     

Adaptive neural dynamic surface control of MIMO nonlinear time delay systems with time‐varying actuator failures

In this paper, an adaptive dynamic surface control approach is developed for a class of multi‐input multi‐output nonlinear systems with unknown nonlinearities, bounded time‐varying state delays, and in the presence of time‐varying actuator failures. The type of the considered actuator failure is that some unknown inputs may be stuck at some time‐varying values where the values, times, and patterns of the failures are unknown. The considered actuator failure can cover most failures that may occur in actuators of the systems. With the help of neural networks to approximate the unknown nonlinear functions and combining the dynamic surface control approach with the backstepping design method, a novel control approach is constructed. The proposed design method does not require a priori knowledge of the bounds of the unknown time delays and actuator failures. The boundedness of all the closed‐loop signals is guaranteed, and the tracking errors are proved to converge to a small neighborhood of the origin. The proposed approach is employed for a double inverted pendulums benchmark as well as a chemical reactor system. The simulation results show the effectiveness of the proposed method. Copyright © 2016 John Wiley & Sons, Ltd.     

CuIns2/Cus Nanocomposite: Synthesis via Simple Microwave Approach and Investigation Its Behavior in Solar Cell

CuInS₂/CuS nanocomposite were synthesized by a copper complex, [bis(ethylenediamine)copper(ΙΙ)] sulfate. Eight sulfur sources were used for this experiment. The products were characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy, energy dispersive X-ray analysis spectroscopy, and room temperature photoluminescence spectroscopy. Thin film of nanocomposite powder was fabricated and its feature (V_oc, J_sc and FF) was calculated by current–voltage (I–V) curve.     

Mutual information-based optimization of sparse spatio-spectral filters in brain–computer interface

Recently, neuro-rehabilitation based on brain–computer interface (BCI) has been considered one of the important applications for BCI. A key challenge in this system is the accurate and reliable detection of motor imagery. In motor imagery-based BCIs, the common spatial patterns (CSP) algorithm is widely used to extract discriminative patterns from electroencephalography signals. However, the CSP algorithm is sensitive to noise and artifacts, and its performance depends on the operational frequency band. To address these issues, this paper proposes a novel optimized sparse spatio-spectral filtering (OSSSF) algorithm. The proposed OSSSF algorithm combines a filter bank framework with sparse CSP filters to automatically select subject-specific discriminative frequency bands as well as to robustify against noise and artifacts. The proposed algorithm directly selects the optimal regularization parameters using a novel mutual information-based approach, instead of the cross-validation approach that is computationally intractable in a filter bank framework. The performance of the proposed OSSSF algorithm is evaluated on a dataset from 11 stroke patients performing neuro-rehabilitation, as well as on the publicly available BCI competition III dataset IVa. The results show that the proposed OSSSF algorithm outperforms the existing algorithms based on CSP, stationary CSP, sparse CSP and filter bank CSP in terms of the classification accuracy, and substantially reduce the computational time of selecting the regularization parameters compared with the cross-validation approach.     

Survey of Motion Planning Literature in the Presence of Uncertainty: Considerations for UAV Guidance

This paper provides a survey of motion planning techniques under uncertainty with a focus on their application to autonomous guidance of unmanned aerial vehicles (UAVs). The paper first describes the primary sources of uncertainty arising in UAV guidance and then describes relevant practical techniques that have been reported in the literature. The paper makes a point of distinguishing between contributions from the field of robotics and artificial intelligence, and the field of dynamical systems and controls. Mutual and individual contributions for these fields are highlighted providing a roadmap for tackling the UAV guidance problem.     

Low Temperature Hall Effect Investigation of Conducting Polymer-Carbon Nanotubes Composite Network

Polypyrrole (PPy) and polypyrrole-carboxylic functionalized multi wall carbon nanotube composites (PPy/f-MWCNT) were synthesized by in situ chemical oxidative polymerization of pyrrole on the carbon nanotubes (CNTs). The structure of the resulting complex nanotubes was characterized by transmission electron microscopy (TEM) and X-ray diffraction (XRD). The effects of f-MWCNT concentration on the electrical properties of the resulting composites were studied at temperatures between 100 K and 300 K. The Hall mobility and Hall coefficient of PPy and PPy/f-MWCNT composite samples with different concentrations of f-MWCNT were measured using the van der Pauw technique. The mobility decreased slightly with increasing temperature, while the conductivity was dominated by the gradually increasing carrier density.