Photoactive behavior of polyacrylonitrile fibers based on silver and zirconium co‐doped titania nanocomposites: Synthesis,characterization, and comparative study of solid‐phase photocatalytic self‐cleaning

Silver and zirconium co‐doped and mono‐doped titania nanocomposites were synthesized and deposited onto polyacrylonitrile fibers via sol–gel dip‐coating method. The resulted coated‐fibers were characterized by X‐ray diffraction (XRD), scanning electron microscopy, energy dispersive spectroscopy, transmission electron microscopy, diffuse reflectance spectroscopy, thermogravimetric analysis, and BET surface area measurement. Photocatalytic activity of the TiO₂‐coated and TiO₂‐doped coated fibers were determined by photomineralization of methylene blue and Eosin Y under UV–vis light. The progress of photodegradation of dyes was monitored by diffuse reflectance spectroscopy. The XRD results of samples indicate that the TiO₂, Ag‐TiO₂, Zr‐TiO₂, and Ag‐Zr‐TiO₂ consist of anatase phase. All samples demonstrated photo‐assisted self‐cleaning properties when exposed to UV–vis irradiation. Evaluated by decomposing dyes, photocatalytic activity of Ag–Zr co‐doped TiO₂ coated fiber was obviously higher than that of pure TiO₂ and mono‐doped TiO₂. Our results showed that the synergistic action between the silver and zirconium species in the Ag‐Zr TiO₂ nanocomposite is due to both the structural and electronic properties of the photoactive anatase phase. These results clearly indicate that modification of semiconductor photocatalyst by co‐doping process is an effective method for increasing the photocatalytic activity. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013     

A new hybrid correction method for short-term load forecasting based on ARIMA,SVR and CSA

Accurate load-forecasting problem is a significant and vital issue, especially in the new competitive electricity market. The models that are employed for forecasting purposes would determine how reliable the last forecasted results are. Therefore, this paper proposes a new hybrid correction method based on autoregressive integrated moving average (ARIMA) model, support vector regression (SVR) and cuckoo search algorithm (CSA) to achieve a more reliable forecasting model. The proposed method gets use of the autocorrelation function (ACF) and the partial ACF to search the stationary or non-stationary behaviour of the investigated time series. In the case of non-stationary data, it will be differenced one or more times to become stationary. After that, Akaike information criterion is utilised to find the appropriate ARIMA model such that the linear component of the data would be captured. Therefore, the ARIMA residuals would contain the non-linear components that should be modelled by use of the SVR model. The role of CSA as a successful optimisation algorithm is to find the optimal SVR parameters for more accurate forecasting. Meanwhile, a novel self-adaptive modification method based on CSA is proposed to empower the total search ability of the algorithm effectively. The proposed method is applied to the empirical peak load data of Fars Electrical Power Company in Iran.     

Experimental investigation and flow analysis of clear-water scour around pier and abutment in proximity

Local scour around bridge piers and abutments is one of the most significant causes of bridge failure. Despite a plethora of studies on scour around individual bridge piers or abutments, few studies have focused on the joint impact of a pier and an abutment in proximity to one another on scour. This study conducted laboratory experiments and flow analyses to examine the interaction of piers and abutments and their effect on clear-water scour. The experiments were conducted in a rectangular laboratory flume. They included 18 main tests (with a combination of different types of piers and abutments) and five control tests (with individual piers or abutments). Three pier types (a rectangular pier with a rounded edge, a group of three cylindrical piers, and a single cylindrical pier) and two abutment types (a wing–wall abutment and a semi-circular abutment) were used. An acoustic Doppler velocimeter was used to measure the three-dimensional flow velocity for analyses of streamline, velocity magnitude, vertical velocity, and bed shear stress. The results showed that the velocity near the pier and abutment increased by up to 80%. The maximum scour depth around the abutment increased by up to 19%. In contrast, the maximum scour depth around the pier increased significantly by up to l71%. The presence of the pier in the vicinity of the abutment led to an increase in the scour hole volume by up to 87% relative to the case with a solitary abutment. Empirical equations were also derived to accurately estimate the maximum scour depth at the pier adjacent to the abutment.     

Sustainability of Chinese airlines: A modified slack-based measure model for CO2 emissions

Sustainable airline operations have become an increasingly important issue in recent years. With this respect, several initiatives for reducing pollutant emissions—such as carbon dioxide (CO₂)—in the airline industry are now under consideration by regulators, policymakers, and companies. The impact of these initiatives upon efficiency levels of airline operations is still being analysed by different authors. This article is focused on the efficiency assessment of 13 major Chinese airlines from 2008 to 2015, applying a modified slack-based measure model to account for CO₂ emissions. The impact of contextual variables related to the airline's age, fleet mix, stock market governance, ownership type, network span, and whether or not it has undergone a previous merger and acquisition process is tested by means of a stochastic non-linear robust regression approach. Findings suggest that sustainability in Chinese airline operations is dependent upon a number of economic factors such as learning curve, economies of scale, technology type, and network management. Policy implications are derived for Chinese airlines.     

Ultra trace mercury(II) detection by a highly selective new optical sensor

This paper presents the development and construction of a sensitive new optical sensor that is highly selective to Hg²⁺ ion in aqueous solution. The sensing element, the newly synthesized (1Z,2Z)-N′¹,N′²-dihydroxy-N¹,N²-dipyridin-2-ylethanediimidamide, incorporated into a plasticized poly(vinyl chloride) membrane, is capable of determining mercury(II) with a high selectivity over a wide dynamic range from 5.78 × 10⁻⁹ to 1.05 × 10⁻³ M at pH 4.0 with a lower detection limit of 1.71 × 10⁻⁹ M. The optode membrane's response to Hg²⁺ is fully reversible and reveals a very good selectivity towards Hg²⁺ ion over a wide variety of other metal ions in solution. Performance characteristics of the sensor evaluated as good reversibility, wide dynamic range, long life span, long-term response stability, and high reproducibility. The proposed optical sensor gives good results for applications in direct determination of mercury(II) in environmental real samples that are satisfactorily comparable with corresponding data from cold-vapor atomic absorption spectrometry.     

Stochastic sampling design using a multi-objective genetic algorithm and adaptive neural networks

This paper presents a novel multi-objective genetic algorithm (MOGA) based on the NSGA-II algorithm, which uses metamodels to determine optimal sampling locations for installing pressure loggers in a water distribution system (WDS) when parameter uncertainty is considered. The new algorithm combines the multi-objective genetic algorithm with adaptive neural networks (MOGA–ANN) to locate pressure loggers. The purpose of pressure logger installation is to collect data for hydraulic model calibration. Sampling design is formulated as a two-objective optimization problem in this study. The objectives are to maximize the calibrated model accuracy and to minimize the number of sampling devices as a surrogate of sampling design cost. Calibrated model accuracy is defined as the average of normalized traces of model prediction covariance matrices, each of which is constructed from a randomly generated sampling set of calibration parameter values. This method of calculating model accuracy is called the ‘full’ fitness model. Within the genetic algorithm search process, the full fitness model is progressively replaced with the periodically (re)trained adaptive neural network metamodel where (re)training is done using the data collected by calling the full model. The methodology was first tested on a hypothetical (benchmark) problem to configure the setting requirement. Then the model was applied to a real case study. The results show that significant computational savings can be achieved by using the MOGA–ANN when compared to the approach where MOGA is linked to the full fitness model. When applied to the real case study, optimal solutions identified by MOGA–ANN are obtained 25 times faster than those identified by the full model without significant decrease in the accuracy of the final solution.     

Optimal direct mailing modelling based on data envelopment analysis

Data envelopment analysis (DEA) is a mathematical programming technique that is frequently used for measuring and benchmarking efficiency of the homogenous decision‐making units (DMUs). This paper proposes a new use of DEA for customers scoring and particularly their direct mailing modelling. Moreover, because DEA models suffer from some weaknesses, that is, unrealistic weighting scheme of the inputs and outputs and incomplete ranking among efficient DMUs, the present paper compares different ways of solving these problems and concludes that common set of weights method, as a result of some advantages, outperforms other procedures.     

The detailed kinetics of the desorption of hydrogen from polycrystalline copper catalysts

The kinetics of desorption of hydrogen from the copper component of an alumina-supported polycrystalline copper catalyst has been studied in detail by temperature-programmed desorption (TPD). Line-shape analysis of the hydrogen TPD spectra shows: (i) that the desorption is second order, (ii) that the desorption activation energy is in the range 64–68 kJ mol^–1 in the coverage range 7–44% of a monolayer, and (iii) that the desorption pre-exponential term has a value 10^–5 cm² s^–1 atom^–1 consistent with the desorption being second order, involving mobile adsorbates and a mobile desorption transition state.     

Robust process capability indices for multiple linear profiles

In some statistical process control applications, the quality of a process is described by a linear relationship between the response variable(s) and the independent variable(s), which is called a linear profile. Process capability is a significant issue in statistical process control. The ability of a process to meet customer specifications or standards is measured by the process capability indices (PCIs). There are several attempts for studying the process capability in linear profiles. In this research, two robust PCIs for multiple linear profiles are proposed. In the suggested robust PCIs, the process capability is estimated using the M-estimator and the Fast-τ-estimator. Performances of the proposed robust PCIs in comparison with the classical PCIs in the absence and presence of contamination are evaluated. The results show that the robust PCIs proposed in this research perform as well as the classical PCIs in the absence of contamination and much better in the presence of contamination. The proposed PCIs, using Fast-τ-estimator, perform better in small shifts, and the proposed PCIs, using M-estimator, perform better in large shifts. Introduction of robust indices for multivariate multiple linear profiles is an area for further research.