Synthesis,structural and optical properties of nanoparticles (Al,V) co-doped zinc oxide

The synthesis by the sol–gel method, structural and optical properties of ZnO, Zn_0.99Al_0.01O (AlZ), Zn_0.9V_0.1O (VZ) and Zn_0.89Al_0.01V_0.1O (AlVZ) nanoparticles was reported. The approach was slow release of water for hydrolysis by esterification reaction followed by a supercritical drying in ethyl alcohol. After thermal treatment at 500^°C in air, the obtained nanopowders were characterized by various techniques such as transmission electron microscopy, X-ray diffraction and photoluminescence (PL) spectroscopy. The structural properties showed that the ZnO nanoparticles with an average particle size of 25 nm exhibit hexagonal wurtzite structure. From the optical studies, it was found that the optical band gap was located between 2.97 and 3.17 eV. The obtained electrical properties showed the potential application of the samples in optoelectronic devices. The powder of AlVZ presented a strong luminescence band in the visible range. The PL band energy position presented a small blue shift with the increase of measurement temperature. Different possible attributions of this emission band will be discussed.     

Ti/TiO<Subscript>2</Subscript> nanotube array electrode as a new sensor to photoelectrocatalytic determination of ethylene glycol

The photoelectrocatalytic oxidation behaviour of ethylene glycol (EG) was studied in the present work using the TiO₂-modified Ti foil (Ti/TiO₂) electrode. The Ti/TiO₂ nanotube array (Ti-NTA) electrode was prepared by anodizing of the Ti foil in the HF aqueous solution (0.2% v/v). The anodization was conducted in the constant 30 V for 2 h, and then the as-prepared Ti-NTA electrode was calcinated at 50 ^° C for 2 h. The surface morphology of Ti- NTA electrode was studied using scanning electron microscopy images. For EG determination, the photocurrent of EG (EG oxidation current in the UV irradiation) was assessed using the hydrodynamic photoamperometric method in the phosphate buffers. Ultimately, the optimum conditions of EG determination were studied in various pH values and applied bias potentials, and the pH = 3.0 and E= 1.0 V (vs. reference electrode) were determined as the optimum conditions. It was found that the photocurrent of EG was linearly dependent on the concentration of EG in the range of 3.0 × 10 ^?5 to 0.88 mol l ^?1 , and the detection limit of EG determination was found to be 7.2 × 10^?6 mol l^?1 (3 σ).     

Spectral studies on CuO in sodium–calcium borophosphate glasses

Transparent borophosphate glasses doped with CuO were prepared by melt quenching technique. X-ray diffraction (XRD), optical and luminescence properties of sodium–calcium borophosphate glasses doped with CuO have been studied. The XRD results showed the amorphous nature of the sample. The introduction of CuO was favourable for the colour changes from light blue to dark bluish green colour. Direct optical energy bandgaps before and after doping with different percents of copper oxide obtained in the range 4.81–2.99 eV indicated the role of copper in the glassy matrix by ultraviolet (UV) spectra. The glasses have more than 80% transparency for emission wavelength range, and strong absorption bands due to the charge transition of the Cu⁺ and Cu²⁺ ions were observed. The emission bands observed in the UV and blue regions are attributed to 3d⁹4s–3d¹⁰ triplet transition in Cu⁺ ion.     

Influence of pulse electrodeposition parameters on microhardness,grain size and surface morphology of Ni–Co/SiO<Subscript>2</Subscript> nanocomposite coating

Ni–Co/SiO ₂ nanocomposite coatings and Ni–Co alloy coatings were prepared on steel substrate using direct and pulse electrodeposition methods. X-ray diffraction (XRD), field emission scanning electron microscope (FESEM), X-ray map and energy dispersive X-ray spectroscopy (EDX) were employed to investigate the phase structure, surface morphology, and elemental analysis of coatings, respectively. In high discharge rates, the surface morphology was rough, disordered and gross globular; on the contrary, in the low rates, it was smoother, more ordered and fine globular. Also, effect of electrodeposition parameters such as average current density, pulse frequency and duty cycle on the microhardness and grain size of nanocomposite coatings that produced through the pulse current electrodeposition method have been investigated. By amplifying both duty cycles up to 50% and average current density from 2 to 6 A dm^?2, microhardness increased, while the grain size decreased. But when duty cycle mounted on more than 50% and the average current density went up to 8 A dm^?2, microhardness lessened, while the grain size rose. The optimum value for pulse frequency was about 25 Hz. Results showed that microhardness of nanocomposite coatings which were produced by pulse current method was higher than that of produced by direct current method.     

Combining stochastic programming and optimal control to decompose multistage stochastic optimization problems

The paper suggests a possible cooperation between stochastic programming and optimal control for the solution of multistage stochastic optimization problems. We propose a decomposition approach for a class of multistage stochastic programming problems in arborescent form (i.e. formulated with implicit non-anticipativity constraints on a scenario tree). The objective function of the problem can be either linear or nonlinear, while we require that the constraints are linear and involve only variables from two adjacent periods (current and lag 1). The approach is built on the following steps. First, reformulate the stochastic programming problem into an optimal control one. Second, apply a discrete version of Pontryagin maximum principle to obtain optimality conditions. Third, discuss and rearrange these conditions to obtain a decomposition that acts both at a time stage level and at a nodal level. To obtain the solution of the original problem we aggregate the solutions of subproblems through an enhanced mean valued fixed point iterative scheme.     

A non-autonomous optimal control model of renewable energy production under the aspect of fluctuating supply and learning by doing

Given the constantly raising world-wide energy demand and the accompanying increase in greenhouse gas emissions that pushes the progression of climate change, the possibly most important task in future is to find a carbon-low energy supply that finds the right balance between sustainability and energy security. For renewable energy generation, however, especially the second aspect turns out to be difficult as the supply of renewable sources underlies strong volatility. Further on, investment costs for new technologies are so high that competitiveness with conventional energy forms is hard to achieve. To address this issue, we analyze in this paper a non-autonomous optimal control model considering the optimal composition of a portfolio that consists of fossil and renewable energy and which is used to cover the energy demand of a small country. While fossil energy is assumed to be constantly available, the supply of the renewable resource fluctuates seasonally. We further on include learning effects for the renewable energy technology, which will underline the importance of considering the whole life span of such a technology for long-term energy planning decisions.     

A single phase photovoltaic inverter control for grid connected system

This paper presents a control scheme for single phase grid connected photovoltaic (PV) system operating under both grid connected and isolated grid mode. The control techniques include voltage and current control of grid-tie PV inverter. During grid connected mode, grid controls the amplitude and frequency of the PV inverter output voltage, and the inverter operates in a current controlled mode. The current controller for grid connected mode fulfills two requirements – namely, (i) during light load condition the excess energy generated from the PV inverter is fed to the grid and (ii) during an overload condition or in case of unfavorable atmospheric conditions the load demand is met by both PV inverter and the grid. In order to synchronize the PV inverter with the grid a dual transport delay based phase locked loop (PLL) is used. On the other hand, during isolated grid operation the PV inverter operates in voltage-controlled mode to maintain a constant amplitude and frequency of the voltage across the load. For the optimum use of the PV module, a modified P&O based maximum power point tracking (MPPT) controller is used which enables the maximum power extraction under varying irradiation and temperature conditions. The validity of the proposed system is verified through simulation as well as hardware implementation.     

Block truncation coding with color clumps: A novel feature extraction technique for content based image classification

The paper has explored principle of block truncation coding (BTC) as a means to perform feature extraction for content based image classification. A variation of block truncation coding, named BTC with color clumps has been implemented in this work to generate feature vectors. Classification performance with the proposed technique of feature extraction has been compared to existing techniques. Two widely used public dataset named Wang dataset and Caltech dataset have been used for analyses and comparisons of classification performances based on four different metrics. The study has established BTC with color clumps as an effective alternative for feature extraction compared to existing methods. The experiments were carried out in RGB color space. Two different categories of classifiers viz. K Nearest Neighbor (KNN) Classifier and RIDOR Classifier were used to measure the classification performances. A paired t test was conducted to establish the statistical significance of the findings. Evaluation of classifier algorithms were done in receiver operating characteristic (ROC) space.     

Automated test data generation for branch testing using incremental genetic algorithm

Cost of software testing can be reduced by automated test data generation to find a minimal set of data that has maximum coverage. Search-based software testing (SBST) is one of the techniques recently used for automated testing task. SBST makes use of control flow graph (CFG) and meta-heuristic search algorithms to accomplish the process. This paper focuses on test data generation for branch coverage. A major drawback in using meta-heuristic techniques is that the CFG paths have to be traversed from the starting node to end node for each automated test data. This kind of traversal could be improved by branch ordering, together with elitism. But still the population size and the number of iterations are maintained as the same to keep all the branches alive. In this paper, we present an incremental genetic algorithm (IGA) for branch coverage testing. Initially, a classical genetic algorithm (GA) is used to construct the population with the best parents for each branch node, and the IGA is started with these parents as the initial population. Hence, it is not necessary to maintain a huge population size and large number of iterations to cover all the branches. The performance is analyzed with five benchmark programs studied from the literature. The experimental results indicate that the proposed IGA search technique outperforms the other meta-heuristic search techniques in terms of memory usage and scalability.     

Object Recognition in X-ray Testing Using Adaptive Sparse Representations

In recent years, X-ray screening systems have been used to safeguard environments in which access control is of paramount importance. Security checkpoints have been placed at the entrances to many public places to detect prohibited items such as handguns and explosives. Human operators complete these tasks because automated recognition in baggage inspection is far from perfect. Research and development on X-ray testing is, however, ongoing into new approaches that can be used to aid human operators. This paper attempts to make a contribution to the field of object recognition by proposing a new approach called Adaptive Sparse Representation (XASR+). It consists of two stages: learning and testing. In the learning stage, for each object of training dataset, several patches are extracted from its X-ray images in order to construct representative dictionaries. A stop-list is used to remove very common words of the dictionaries. In the testing stage, test patches of the test image are extracted, and for each test patch a dictionary is built concatenating the ‘best’ representative dictionary of each object. Using this adapted dictionary, each test patch is classified following the Sparse Representation Classification methodology. Finally, the test image is classified by patch voting. Thus, our approach is able to deal with less constrained conditions including some contrast variability, pose, intra-class variability, size of the image and focal distance. We tested the effectiveness of our method for the detection of four different objects. In our experiments, the recognition rate was more than 97 % in each class, and more than 94 % if the object is occluded less than 15 %. Results show that XASR+ deals well with unconstrained conditions, outperforming various representative methods in the literature.