A high-performance computing method for data allocation in distributed database systems

Enhancing the performance of the DDBs (Distributed Database system) can be done by speeding up the computation of the data allocation, leading to higher speed allocation decisions and resulting in smaller data redundancy and shorter processing time. This paper deals with an integrated method for grouping the distributed sites into clusters and customizing the database fragments allocation to the clusters and their sites. We design a high speed clustering and allocating method to determine which fragments would be allocated to which cluster and site so as to maintain data availability and a constant systemic reliability, and evaluate the performance achieved by this method and demonstrate its efficiency by means of tabular and graphical representation. We tested our method over different network sites and found it reduces the data transferred between the sites during the execution time, minimizes the communication cost needed for processing applications, and handles the database queries and meets their future needs.     

Quasi‐Continuous Wave Near‐Infrared Excitation of Upconversion Nanoparticles for Optogenetic Manipulation of C. elegans

Optogenetics is an emerging powerful tool to investigate workings of the nervous system. However, the use of low tissue penetrating visible light limits its therapeutic potential. Employing deep penetrating near‐infrared (NIR) light for optogenetics would be beneficial but it cannot be used directly. This issue can be tackled with upconversion nanoparticles (UCNs) acting as nanotransducers emitting at shorter wavelengths extending to the UV range upon NIR light excitation. Although attractive, implementation of such NIR‐optogenetics is hindered by the low UCN emission intensity that necessitates high NIR excitation intensities, resulting in overheating issues. A novel quasi‐continuous wave (quasi‐CW) excitation approach is developed that significantly enhances multiphoton emissions from UCNs, and for the first time NIR light‐triggered optogenetic manipulations are implemented in vitro and in C. elegans. The approach developed here enables the activation of channelrhodopsin‐2 with a significantly lower excitation power and UCN concentration along with negligible phototoxicity as seen with CW excitation, paving the way for therapeutic optogenetics.     

State-of-the-art nanoplatform-integrated MALDI-MS impacting resolutions in urinary proteomics

Urine proteomics has become a subject of interest, since it has led to a number of breakthroughs in disease diagnostics. Urine contains information not only from the kidney and the urinary tract but also from other organs, thus urinary proteome analysis allows for identification of biomarkers for both urogenital and systemic diseases. The following review gives a brief overview of the analytical techniques that have been in practice for urinary proteomics. MALDI-MS technique and its current application status in this area of clinical research have been discussed. The review comments on the challenges facing the conventional MALDI-MS technique and the upgradation of this technique with the introduction of nanotechnology. This review projects nano-based techniques such as nano-MALDI-MS, surface-assisted laser desorption/ionization, and nanostructure-initiator MS as the platforms that have the potential in trafficking MALDI-MS from the lab to the bedside.     

Resilience of super users’ mental models of enterprise-wide systems

Research on information system training has focused primarily on methods, while neglecting the effects of interruption intervals between training and system implementation. This empirical study examines the resilience of accurate mental models of an enterprise-wide system in a large health care facility. Accurate mental models were shown to withstand the passage of time and resulted in superior field performance following a crash conversion. Consistent with prior experimental research, certain types of learners were more likely to acquire these sounder mental models. In large organisations where the simultaneous training of users on a complex system is virtually impossible, scheduling is an important facet of the training paradigm. The main practical implications of this study involve the development of: (1) training programmes emphasising performance on far-transfer tasks, (2) training schedules designed to minimise knowledge erosion, and (3) criteria for selecting highly capable super users.     

Palladium‐nanoparticle intercalated vermiculite for selective hydrogenation of α,β‐unsaturated aldehydes

Palladium nanoparticles were generated in the interlamellar region of swelling 2:1 type vermiculite clay using an adsorption excess isotherm. An adsorption excess isotherm was constructed for vermiculite after exchanging the exchangeable Na⁺ ions in the interlamellar region using a binary liquid mixture (ethanol:toluene). Based on adsorption excess isotherm, 1% w/w palladium was intercalated into the interlamellar space of vermiculite. The Pd‐intercalated vermiculite was characterized by X‐ray diffraction, transmission electron microscopy and atomic absorption spectroscopy. The 1% w/w Pd‐intercalated vermiculite was tested for its catalytic activity towards selective hydrogenation reactions involving some α,β‐unsaturated carbonyl compounds using a pulse reactor and its catalytic activity was compared with commercial 1% Pd/C. The intercalated catalyst was found to be very selective towards the desired product of unsaturated alcohols. Copyright © 2007 Society of Chemical Industry     

Microstructural,physico-chemical and mechanical characterisation of Sansevieria cylindrica fibres – An exploratory investigation

The microstructural, physical, chemical and mechanical properties of Sansevieria cylindrica fibres are described for the first time in this work. A microstructural analysis of S. cylindrica leaves showed the presence of structural fibres and arch fibres. Polarised light microscopy and scanning electron microscopy of these fibres revealed a hierarchical cell structure that consisted of a primary wall, a secondary wall, a fibre lumen and middle lamellae. The cross-sectional area and porosity fraction of the fibre were estimated to be approximately 0.0245 mm² and 37%, respectively. The fibre density and fineness were approximately 0.915 ± 0.005 g/cm³ and 9 Tex, respectively. An X-ray diffraction and Fourier transform infrared analysis of the fibres showed the presence of cellulose I_β with a crystallinity index of 60%. Tensile tests showed that the corrected Young’s modulus was approximately 7 GPa, the tensile strength was 658 MPa, and the total elongation was between 10% and 12%.     

Red, green, and blue LEDs for white light illumination

The rapid improvement of the white light efficacy achievable with light-emitting diodes (LEDs) opens up new opportunities in the general illumination market. An LED light source made of red, green, and blue LEDs (RGB-LEDs) can provide the unique feature of color variability, allowing the user to select the desired color point of the lamp. The white light color accuracy required in the general illumination market is a challenge for LEDs. The variation in lumen output and wavelength for nominally identical LEDs and the change in these parameters with temperature and time result in an unacceptably high variability in the color point of white light from RGB-LEDs. In this paper, we show that these problems can be overcome with suitable feedback control schemes that can be implemented in a practical LED lamp. We present results of experiment and theoretical modeling that shows the performance that can be achieved with a number of different control schemes     

Antlion Algorithm Optimized Fuzzy PID Supervised On-line Recurrent Fuzzy Neural Network Based Controller for Brushless DC Motor

In this paper, Antlion algorithm optimized Fuzzy PID supervised on-line Recurrent Fuzzy Neural Network based controller is proposed for the speed control of Brushless DC motor. Learning parameters of the supervised on-line recurrent fuzzy neural network controller, i.e., learning rate (η), dynamic factor (α), and number nodes (N_i) are optimized using Genetic algorithm, Particle Swarm optimization, Ant colony optimization, Bat algorithm, and Antlion algorithm. The proposed controller is tested with different operating conditions of the Brushless DC motor, such as varying load conditions and varying set speed conditions. The time domain specifications such as rise time, overshoot, undershoot, settling time, recovery time, and steady state error and also integral performance indices such as root mean square error, integral of absolute error, integral of squared error, and integral of time multiplied absolute error are measured and compared for above optimized controller. Simulation results show Antlion algorithm optimized Fuzzy PID supervised on-line recurrent fuzzy neural network based controller has proved to be superior than other considered controllers in all aspects. In addition, the experimental verification of proposed control system is presented to test the effectiveness of the proposed controller with different operating conditions of the Brushless DC motor.