Electrochemical growth and characterization of iron doped cadmium sulfide thin films

Cadmium Sulfide and Ferrous doped Cadmium Sulfide thin films have been prepared on different substrates using an electrodeposition technique. Linear sweep voltammetric analysis has been carried out to determine deposition potential of the prepared films. X-ray diffraction analysis showed that the prepared films possess polycrystalline nature with hexagonal structure. Surface morphology and film composition have been analyzed using Scanning electron microscopy and Energy dispersive analysis by X-rays. Optical absorption analysis showed that the prepared films are found to exhibit Band gap value in the range between 2.3, 2.8 eV for Cadmium Sulfide and Ferrous doped Cadmium Sulfide.     

Performance evaluation of noise reduction method during on-line monitoring of MV switchgear for PD measurements by non-intrusive sensors

Partial Discharge (PD) measurement is a globally accepted method for insulation diagnosis of electrical assets. The consequences of insulation breakdown are well known. The trend is to move from conventional offline testing to online monitoring for insulation life prediction, which results in the inclusion of high frequency noise in the captured signals. Therefore de-noising is of paramount importance in online monitoring to obtain useful information from the signal.In this research, a 20 kV switchgear panel has been subjected to PD faults in the laboratory and measurements have been carried out by using different non-intrusive sensors including a novel sensor, the D-dot sensor and recorded by a high frequency oscilloscope. The measured results show the effective applicability of sensors for switchgear. The Discrete Wavelet Transform (DWT) has been used to de-noise PD signals in this paper. Time domain and frequency domain comparison of original and de-noised PD signals reveals the significance of this technique for online monitoring of Medium Voltage (MV) switchgear. Finally, an adaptive online de-noising concept, based on automatic de-noising is also proposed in this paper.     

Understanding value creation in closed loop supply chains – Past findings and future directions

The topic of value creation through the recovery of returned products in closed loop supply chains is scattered across various bodies of literature. We undertake a systematic literature review of 144 articles in relevant green, reverse and closed loop supply chain literature to synchronize existing knowledge on value creation. Value manifestations of four types of value, namely economic, environmental, information and customer value, are identified. Value adding concepts from the forward- and reverse supply chain may leverage the process of value creation. They are classified into six subclasses, namely partnerships and collaboration, product design characteristics, service concepts, IT solutions, supply chain processes and organizational characteristics. We present a conceptual framework on a strategic level. In this way companies can create competitive advantages by closing the loop. The results of the literature analysis suggest avenues for future research on the operational and strategic level.     

The effect of growth conditions,point defects and hydrogen on the electronic structure and properties of p-type (Al,N) codoped ZnO: A first principles study

The effects of point defects, hydrogen, and growth conditions on the electronic structure and properties of the (Al,N) codoped p-type ZnO have been investigated using the first principles method. The obtained results showed that the Al_Zn–N_O–V_Zn complex is a shallow acceptor that can play an important role in achieving the p-type conductivity in the (Al,N) codoped ZnO films. Our results showed also that the electrical conductivity type in the (Al,N) codoped ZnO films strongly depends on the donor/acceptor concentrations ratio. The codoped ZnO films prepared under both Zn-rich and O-rich growth conditions with a donors/acceptors ratio of 1:2 have a p-type conductivity, while those prepared with a ratio of 1:1 cannot be p-type unless if they are prepared under O-rich conditions. The achieved p-type quality depends also on the used nitrogen doping source. To prepare p-type ZnO film of high quality using the (Al,N) codoping method, the use of NO or NO₂ is recommended. The presence of donor defects such as oxygen vacancies and hydrogen will significantly affect the electronic properties of the (Al,N) codoped ZnO films, and if the concentration of these defects in the sample is high enough, the material can be easily converted to n-type.     

Models for solar radiation prediction based on different measurement sites

The modeling of solar radiation for forecasting its availability is a key tool for managing photovoltaic (PV) plants and, hence, is of primary importance for energy production in a smart grid scenario. However, the variability of the weather phenomena is an unavoidable obstacle in the prediction of the energy produced by the solar radiation conversion. The use of the data collected in the past can be useful to capture the daily and seasonal variability, while measurement of the recent past can be exploited to provide a short term prediction. It is well known that a good measurement of the solar radiation requires not only a high class radiometer, but also a correct management of the instrument. In order to reduce the cost related to the management of the monitoring apparatus, a solution could be to evaluate the PV plant performance using data collected by public weather station installed near the plant. In this paper, two experiments are conducted. In the first, the plausibility of the short term prediction of the solar radiation, based on data collected in the near past on the same site is investigated. In the second experiment, the same prediction is operated using data collected by a public weather station located at ten kilometers from the solar plant. Several prediction techniques belonging from both computational intelligence and statistical fields have been challenged in this task. In particular, Support Vector Machine for Regression, Extreme Learning Machine and Autoregressive models have been used and compared with the persistence and the k-NN predictors. The prediction accuracy achieved in the two experimental conditions are then compared and the results are discussed.     

ECG heart beat classification method based on modified ABC algorithm

Electrocardiogram is the most commonly used tool for the diagnosis of cardiologic diseases. In order to help cardiologists to diagnose the arrhythmias automatically, new methods for automated, computer aided ECG analysis are being developed. In this paper, a Modified Artificial Bee Colony (MABC) algorithm for ECG heart beat classification is introduced. It is applied to ECG data set which is obtained from MITBIH database and the result of MABC is compared with seventeen other classifier's accuracy.In classification problem, some features have higher distinctiveness than others. In this study, in order to find higher distinctive features, a detailed analysis has been done on time domain features. By using the right features in MABC algorithm, high classification success rate (99.30%) is obtained. Other methods generally have high classification accuracy on examined data set, but they have relatively low or even poor sensitivities for some beat types. Different data sets, unbalanced sample numbers in different classes have effect on classification result. When a balanced data set is used, MABC provided the best result as 97.96% among all classifiers.Not only part of the records from examined MITBIH database, but also all data from selected records are used to be able to use developed algorithm on a real time system in the future by using additional software modules and making adaptation on a specific hardware.