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This study assessed the interactive effect of selenium (Se) and farmyard manure (FYM) on soil microbial activities, growth, yield, and Se accumulation by wheat grains. Preliminarily, the effect of Se (0–250 µg kg−1 soil) and FYM (0–12.5 g kg−1 soil) was assessed on soil microflora. Selenium exhibited an adverse impact on soil microflora; respiration was decreased at ≥ 10 µg kg−1 soil while dehydrogenase and urease activities were decreased at ≥ 125 µg kg−1 soil. At 250 µg Se kg−1 soil, respiration, dehydrogenase and urease activities were decreased by 81, 40 and 35%, respectively, on unamended soil, and by 9, 47 and 22%, respectively, on FYM-amended soil. The subsequent plant experiments were conducted with same Se and FYM rates; one was harvested 42 days after sowing and other at crop maturity. The application of 125 µg Se kg−1 and 12.5 g FYM kg−1 soil improved seedling biomass by 12.6 and 22%, respectively, while their combined use lacked synergistic effect. Similarly, at maturity Se and FYM increased grain yield while their combined effect was not synergistic. The Se-induced suppression in microbial activities was not related to yield which was improved (11% at the highest rate in unamended soil) by Se application. Selenium application increased grain Se content in a rate-dependent manner, it increased from 0 to 1025 µg kg−1 by applying 250 µg Se kg−1 soil. Moreover, FYM application decreased Se accumulation in grains. It is concluded that FYM application increased soil microbial activities and yield but reduced grain Se accumulation in wheat on Se-applied soil.

Objective: To determine prevalence of Typhi dot IgM positive results in COVID-19 patients at primary and secondary health care hospitals in Pakistan. Study Design: Descriptive Cross-sectional study. Setting: Tehsil Head Quarter Hospital of Burewala and District Head Quarter Hospital of Vehari. Period: 1st March 2020 to 30th June 2020. Material & Methods: Study was done among diagnosed cases of COVID-19 after taking ethical approval from ethical review board. Results were assessed by using SPSS version-24. Results: Males were predominant (71.30%) in our study as COVID-19 positives than the females. Most of the patients (51.70%) were from middle aged group, i.e. 31-60 years. Only 7.40% patients were from elder age group (more than 60 years). More than half of the patients (51.90%) were found to be positive with IgM typhi dot test. We repeated the tests and found the same results. We also calculated the prevalence of typhoid positive cases in COVID patients according to different demographic variables. As far as gender is concerned, our result was highly statistically significant when associated with typhoid positive results among COVID patients (P=0.0001). Males were predominantly having high rate of typhi dot IgM positives (71.30%) than females. Middle aged patients were found to be more affected by typhi dot positive results. Conclusion: It is concluded that there is a high and significant ratio of typhi dot positive results for IgM in COVID patients at Vihari district.

Optimal reactive power dispatch (RPD) for reducing the real power losses of the transmission system is one of the paramount concerns for the research community to investigate the efficiency of power systems. In this paper, strength of meta-heuristic computing paradigm based on fractional-order Darwinian particle swarm optimization (FO-DPSO) is exploited for optimization of RPD problems in energy sector. The fitness functions including line loss minimization and voltage deviation (voltage profile index) are constructed to find the optimal reactive power flow for IEEE 30- and 57-bus test systems. The rich heritage of fractional evolutionary computing through variants of FO-DPSO is applied to minimization problem of optimal power flow by determination of control variables in terms of VAR compensators, bus voltages and transformer tap settings. Comparison of the results shows that fractional swarming intelligence outperformed the state-of-the-art counterparts by means of both line loss minimization and voltage deviation. Superiority of the proposed scheme is also validated for different degrees of freedom in the optimal RPD problems.

An optimal strategy is derived for robust performance of feedforward and feedback noise controllers in active noise control systems with a bounded narrowband disturbance. The designed recursive algorithm updates the weighting factor to make sure that controller updates are performed when the current measurement data contain new information to improve the estimation quality. This significantly reduces the computational complexity by allowing fewer updates in the steady state and persistent updates otherwise. The presented algorithm can guarantee non-increasing and positive-definite covariance matrices for feedforward and feedback filters, and this results in a bounded and non-increasing estimation error. Moreover, the proposed algorithm achieves fast convergence with improved performance in steady-state noise reduction. In simulations, the comparison of the proposed method with the established methods under benchmark conditions demonstrates the improvement in the overall performance.

A differential evolution based methodology is introduced for the solution of elliptic partial differential equations (PDEs) with Dirichlet and/or Neumann boundary conditions. The solutions evolve over bounded domains throughout the interior nodes by minimization of nodal deviations among the population. The elliptic PDEs are replaced by the corresponding system of finite difference approximation, yielding an expression for nodal residues. The global residue is declared as the root-mean-square value of the nodal residues and taken as the cost function. The standard differential evolution is then used for the solution of elliptic PDEs by conversion to a minimization problem of the global residue. A set of benchmark problems consisting of both linear and nonlinear elliptic PDEs has been considered for validation, proving the effectiveness of the proposed algorithm. To demonstrate its robustness, sensitivity analysis has been carried out for various differential evolution operators and parameters. Comparison of the differential evolution based computed nodal values with the corresponding data obtained using the exact analytical expressions shows the accuracy and convergence of the proposed methodology.

In this study, adaptive filtering paradigm-based kernel least mean square (KLMS) algorithm is developed for feed-forwarded active noise control (ANC) systems by exploiting the strength of activation functions of neural network (NN) as kernels. The transfer functions NN based on logistic, tan-sigmoid and inverse-tan kernels are introduced as a variant of KLMS, normalized KLMS and affine projection KLMS algorithms. All three proposed adaptive filtering strategies are implemented for optimization of design parameters of ANC system of a headset with nonlinear noise interference under several scenarios based on tonal, narrowband, broadband and varying acoustic path. Comparison studies on the basis of detailed numerical experimentation are conducted to establish the worth of the proposed methodologies.

Fractional calculus plays a fundamental role in understanding the physics of nonlinear systems due to its heritage of uncertainty, nonlocality and complexity. In this study, novel sign fractional least mean square (F-LMS) algorithms are designed for ease in hardware implementation by applying sign function to input data and estimation error corresponding to first and fractional-order derivative terms in weight update mechanism of the standard F-LMS method. Theoretical expressions are derived for proposed sign F-LMS and its variants; strength of methods for different fractional orders is evaluated numerically through computer simulations for parameter estimation problem based on nonlinear Hammerstein system for low and high signal–noise variations. Comparison of the results from true parameters of the model illustrates the worth of the scheme in terms of accuracy, convergence and robustness. The stability and viability of design methodologies are examined through statistical observations on sufficiently large number of independent runs through mean square deviation and Nash–Sutcliffe efficiency performance indices.

In the present study, a novel application of nature-inspired heuristics is presented for problems in nonlinear circuit analysis using neural networks, particle swarm optimization (PSO), and interior-point algorithm (IPA) as well as integrated approach PSO–IPA. The governing system models of resistor–capacitor circuits with nonlinear capacitance as well as resistor–inductor circuits with nonlinear inductance are mathematically modeled through competency of neural networks and weights of these networks are trained for global search with PSO hybrid with IPA for speedy refinements. The designed technique is applied on a number of scenarios by taking different values of resistance, current, voltage inductance, and capacitance parameters in nonlinear electrical circuit models. Comparative study with Adams numerical solvers having matching of the order 10−04–10−07 and consistently attaining near-optimal gauges of performance indices based on root-mean-squared error, Theil’s inequality coefficient, and Nash–Sutcliffe efficiency metrics validate and verify the efficacy of the scheme.

This article presents a methodology to solve a one-dimensional steady-state nonlinear reactive transport model (RTM) that is meant for fluid and solute transport model of soft tissues and microvessels. The methodology integrates the artificial neural network (ANN), genetic algorithms (GAs), and pattern search (PS) aided by active-set technique (AST) and interior-point technique (IPT). The RTM is represented with nonlinear second-order system based on the boundary value problem of ordinary differential equation. The ANN modeling is used for governing expression of RTM to form a fitness function in mean square sense, and optimization solvers based on the GA, PS, GA-AST, GA-IPT, PS-AST, PS-IPT are used for viable learning of weights. Proposed techniques are applied to different nonlinear RTMs based on variation in the characteristic reaction rate and half-saturation concentration. The proposed stochastic numerical solutions are compared with state-of-the-art solvers in order to check the accuracy and convergence based on sufficient large multiple runs of the algorithms.

Selective updating (SU)-based adaptive algorithms are proposed for secondary path estimation and noise control in active noise control systems. Use of the proposed variable threshold (VT) for noise control filter aids in reducing the computational complexity of filtered-x LMS-Newton algorithm to achieve fast convergence and improved noise reduction irrespective of the eigen-spread of input signal correlation matrix. A VT-SU least mean square algorithm is presented for the online estimation of the secondary path that reduces computational cost while maintaining estimation accuracy and convergence speed. A power scheduling scheme is presented that requires only one tunable parameter and discontinues auxiliary noise for power level below a predefined limit to reduce the residual error signal. The proposed VT-SU algorithms allow frequent updates in the transition phase and fewer updates in the steady state. Simulations are performed under benchmark conditions to validate the improved performance of the proposed method in comparison with established state-of-the-art methods in terms of estimation accuracy, steady-state residual error, convergence speed, number of tunable parameters, tracking capability, and computational complexity.

In the present study, strength of stochastic computational paradigms is investigated for parameter estimation of Hammerstein control autoregressive (HCAR) model by exploiting differential evolution, genetic algorithms and pattern search methods. Multidimensional and nonlinear nature of the problem emerging in digital signal systems along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of stochastic solvers to ensure convergence and avoid trapping in local minima. The performance of meta-heuristic approaches is validated through statistical performance indices based on absolute error, weight deviations and mean squared error. Comparative studies of HCAR system identification established efficacy of the designed methodology based on differential evolution over its counterparts.

Aim of this research is to explore the strength of evolutionary and swarm intelligence techniques for parameter identification of control autoregressive moving average (CARMA) systems. The fitness function for CARMA system identification problem is formulated through error function created in mean square sense, and learning of unknown parameters of the system model is carried out with an effective global search techniques based on genetic algorithms and particle swarm optimization algorithm. Comparative study of the design methodology is conducted from actual parameters of the systems for different values of noise variance and degree of freedom in CARMA identification model. The correctness of the proposed scheme is validated through the results of various performance measures based on mean absolute error, mean weight deviation, variance account for and Theil’s inequality coefficient, and their global variants for sufficiently large number of independent runs.

In this study, hybrid computational frameworks are developed for active noise control (ANC) systems using an evolutionary computing technique based on genetic algorithms (GAs) and interior-point method (IPM), following an integrated approach, GA-IPM. Standard ANC systems are usually implemented with the filtered extended least mean square algorithm for optimization of coefficients for the linear finite-impulse response filter, but are likely to become trapped in local minima (LM). This issue is addressed with the proposed GA-IPM computing approach which is considerably less prone to the LM problem. Also, there is no requirement to identify a secondary path for the ANC system used in the scheme. The design method is evaluated using an ANC model of a headset with sinusoidal, random, and complex random noise interferences under several scenarios based on linear and nonlinear primary and secondary paths. The accuracy and convergence of the proposed scheme are validated based on the results of statistical analysis of a large number of independent runs of the algorithm.

In this study, nature-inspired computational intelligence is exploited for active noise control (ANC) systems using variants of particle swarm optimization (PSO) algorithm and its memetic combination with efficient local search technique based on active-set (AS), interior-point (IP), Nelder–Mead (NM) and sequential quadratic programming (SQP) algorithms. In ANC, filtered extended least mean square algorithm is normally used for finding the optimal parameters of the linear finite-impulse response filter, which is more likely to trap in local minima (LM). The issue of LM problem is effectively handled with competence of nature-inspired heuristics by developing four variants of memetic computing approaches based on PSO-NM, PSO-AS, PSO-IP, and PSO-SQP in order to adapt the design variables of ANC with linear and nonlinear primary and secondary paths by taking input noise interferences of pure sinusoidal, random and complex random types. The comparative studies of proposed schemes through statistical performance indices have established the worth of the schemes in terms of accuracy, convergence and complexity parameters.

In this study, a new non-linear recursive mechanism for Volterra least mean square (VLMS) algorithm is proposed in the domain of non-linear adaptive signal processing and control. The proposed adaptive scheme is developed by applying concepts and theories of fractional calculus in weight adaptation structure of standard VLMS approach. The design scheme based on fractional VLMS (F-VLMS) algorithm is applied to parameter estimation problem of non-linear Hammerstein Box-Jenkins system for different noise and step size variations. The adaptive variables of F-VLMS are compared from actual parameters of the system as well as with the results of conventional VLMS for each case to verify its correctness. Comprehensive statistical analyses are conducted based on sufficient large number of independent runs and performance indices in terms of mean square error, variance account for and Nash–Sutcliffe efficiency establish the worth and effectiveness of the scheme.

Fractional calculus extends the scope of adaptive algorithms supporting the design of novel fractional methods that outperform standard strategies in various applications arising in applied physics and engineering. In this study, a momentum fractional least-mean-square (M-FLMS) algorithm for nonlinear system identification using a first and fractional-order gradient information is proposed. The M-FLMS avoids being trapped in local minima and provides faster convergence than the standard FLMS. The convergence and complexity analysis of the M-FLMS are given along with simulation results of a benchmark nonlinear system identification problem. The M-FLMS accuracy is verified through a parameter estimation problem for a nonlinear Hammerstein structure, modeling an electrically stimulated muscle (ESM) for rehabilitation of paralyzed muscles. The proposed method is studied in detail for different levels of noise variance, fractional orders and proportion of gradients used in the current update.

Self-adapting variable step size (SAVSS) normalized least mean square (NLMS) algorithms are derived and analysed for the active noise control systems with acoustic feedback path. The objective of the proposed SAVSS scheme is to resolve the conflicting requirements of rapid convergence and low mis-adjustment. A tuningless power scheduling scheme is proposed that varies the gain according to the output of the predictor filter, and switches to a low gain on the basis of a minimum criteria. In the steady state, a low gain auxiliary noise significantly improves noise reduction performance, and the update of the feedback compensation filter and predictor filter is discontinued to reduce computations. Simulations are performed with narrowband noises to validate the improved performance of the proposed method in comparison with established state-of-the-art methods.

In this study, strength of evolutionary computational intelligence based on genetic algorithms (GAs) is exploited for parameter identification of nonlinear Hammerstein controlled autoregressive (NHCAR) systems. The fitness function is constructed for the NHCAR system by defining an error function in the mean square sense. Unknown adjustable weights of the system are optimized with GAs, used as an effective tool for effective global search. Comparative analysis of the proposed scheme is made from true parameters of the systems for a number of scenarios based on different levels of signal-to-noise ratios. The validation of the performance is made through statistics based on sufficiently large number of runs using indices of mean absolute error, variance account for, and Thiel's inequality coefficient as well as their global versions.

This paper presents a sliding-window approximation-based fractional least mean square (FLMS) algorithm for parameter estimation of Hammerstein nonlinear autoregressive moving average system with exogenous noise. The FLMS algorithm available in the literature makes use of data available at the current iteration only (or memory-less algorithm). This results in poor convergence rate of the algorithm, and the presence of immeasurable noise terms in the information vector makes identification a difficult task. The sliding-window approximation-based fractional LMS (SW-FLMS) algorithm uses not only the current data but also the past data at each iteration. The proposed algorithm uses sliding-window approximation of the expectation where the length of data used by SW-FLMS algorithm determines the size of sliding window. Moreover, a variable convergence approach is also proposed for fast convergence of SW-FLMS algorithm. Compared with the standard FLMS algorithm, the proposed SW-FLMS algorithms can converge at a fast rate to highly accurate parameter estimates. Estimation accuracy and convergence rate of the standard FLMS algorithm and proposed methods are evaluated for 200 independent runs. Simulation results confirm that performance of standard FLMS algorithm can be improved by the use of proposed modifications.

Maximum likelihood methods are significant for parameter estimation and system modeling. This paper derives a maximum likelihood principle based least squares identification algorithm for online secondary path modeling in feed-forward active noise control systems with autoregressive moving average noise. This derivation proves that minimizing the cost function of least squares is equivalent to the maximum of likelihood function. Proposed method requires tuning of only one parameter in comparison with other recognized methods. Simulation tests show that proposed algorithm has better estimation accuracy and noise reduction capability than the current state-of-the-art methods in the presence and absence of disturbance at the error microphone.

This note points out some errors in the identification model and implementation issues of adaptive strategies presented by Chaudhary and Raja for estimation of input nonlinear Box-Jenkins (INBJ) systems (Chaudhary and Raja, 2015 1). This note proves that identification model presented in above mentioned paper is incorrect and in stepwise manner suggests some suitable corrections. Authors claim to propose two algorithms for INBJ systems in above mentioned paper: fractional least mean square (F-LMS) algorithm and auxiliary model fractional least mean square (AM-FLMS) algorithm. AM-FLMS algorithm is not presented in this paper, but results of this algorithm are provided. The adaptive algorithms presented by authors are not implementable due to the presence of unknown noise terms in information vector. Authors have ignored these critical issues. Finally, corrections on the basis of established techniques are also mentioned that solve the problems in the above mentioned paper. This note proves that identification model presented in 1 is incorrect.AM-FLMS algorithm is not presented in 1, but results of this algorithm are provided.The algorithms presented in 1 are not implementable due to unknown noise terms.Noise v ( t ) is treated as known signal. This is totally wrong.Solutions of issues are presented on the basis of established results.

In this paper, a new adaptive strategy based on fractional signal processing is proposed using multi-directional step size fractional least mean square algorithm for online secondary path modeling, which is a fundamental problem in practical active noise control systems, as opposed to the generally-employed increasing step size strategy that compromises model accuracy for faster convergence. The design approach presents step size strategy in relation with disturbance signal in the desired response of modeling filter which is not available directly so an indirect approach is used to track its variations. Comparative results for narrowband and broadband noise signals show that the proposed technique outperforms other state-of-the-art methods in terms of model accuracy and convergence rate. A new scheme is designed for noise control as shown in Fig. 3.The fractional LMS method is employed the ANC system.The scheme uses variable step sizes to achieve fast convergence.The proposed method achieves better performance in terms of accuracy and convergence speed.

Small angle X-ray scattering (SAXS) has been performed on porous anodic alumina prepared at 10, 20 and 30 V. These were prepared by two-step anodizing to have uniform through-thickness structure. The thickness of the membranes was of the order of 100–200 μm. The scattering has been explained on the basis of structure factor; the magnitude of scattering vectors (q) corresponding to well-established peaks being multiples of 2π/d, where d is the inter-pore distance. The usually studied form factor has not been found to play any profound role to determine the scattering response of anodic alumina. A large number of experiments have been performed with various geometries of the anodic alumina with respect to the incident beam. The scattering has been extremely anisotropic, where q always lies at about 90° with respect to the sample's normal. A deviation from this angle which is limited to about ±2° has been due to scatter of the pore axis with respect to sample's normal, which is caused by occasional pore turning, branching and annihilation events. The analysis and methodology adopted in this study has also been suggested as extremely useful for determining the orientation of high-aspect ratio objects in an anisotropic sample along with assessing the degree of misalignment.

We report a novel technique for the formation of metal nanoparticles, based on electrolysis of the alcogels containing metal chlorides. The alcogel was formed from TEOS, water, ethanol, and nickel chloride, and subjected to galvanostatic electrolysis. This resulted in successful formation of Ni nanoparticles inside the silica gel. Average particle size of FCC Ni lies between 18 and 20 nm. The formation of tetragonal nickel (a sub-oxide of nickel) as well as NiO were also detected by XRD and SAED. The resistivity measurements showed that the nickel nanoparticles were separated from each other by Ni(O) present between them. Magnetic studies based on ZFC and FC measurements below room temperature (up to 5 K) and above room temperature (up to 700 K) were conducted using SQUID and Magnetic TGA, respectively, which showed strong magnetic irreversibility as attributable to exchange interaction between metallic and oxide phases and mutual interactions among metallic particles in the network structure. The blocking temperature (~600 K) of the samples was above room temperature. M–H studies based on VSM showed an increase in magnetic coercivity with the formation of NiO. A magnetic transition associated with tetragonal nickel was seen at 10 K.

Photoluminescence (PL) of the anodic alumina has been studied and related with quality of hexagonal ordering of the pores. The photoluminescence excitation (PLE) spectra have been successfully de-convoluted into primarily two sub-bands with peak positions at about 355 and 395 nm and maximum emission at about 450 and 500 nm, respectively; the former being assigned to F+ centers and the latter to the F-centers. A red shift in the PLE takes place, at a given anodizing voltage, when the quality of the hexagonal pore ordering deteriorates with an increase in number density of defects, i.e., pentagons and hexagons with missing pores. The metallic hills at these defects change the curvatures of the metal–oxide and the oxide–electrolyte interfaces that could affect the field distribution and hence the stress-state and other characteristics of the oxide at the defects. This allows a comparatively larger concentration of F centers (395 nm band), causing a red shift in the PLE with increase in defect density.

In situ formation of tungsten carbide in the matrix of FCC nickel has been achieved by carburizing of the electrodeposited Ni-W alloy coatings. The size of the carbide particles ranges between 100 and 500 nm. The carbide phase is also present in the form of very small precipitates inside the nickel grains. The size of such precipitates is between 10 and 40 nm. The carburizing environment was created by introducing a flowing mixture of vaporized 95.5% alcohol (0.25 ml/min, liquid) and argon (0.5 L/min, gas) into the carburizing furnace. Supersaturated nature of electrodeposited amorphous and nanocrystalline alloys, in addition to high diffusivity, have been attributed for the formation of carbide phase in the deposits at a temperature range of 700-850 degrees C. The carbide-metal interface is clean and the composite coatings are compact. Hardness values up to about 1100 KHN are achieved. Hardness increases with tungsten content and carburizing temperature. (C) 2009 Elsevier B.V. All rights reserved.

Multilayer TiO(2)-Ge thin films have been deposited using electron beam evaporation and resistive heating. The thickness of the TiO(2) layers is 20 nm, while the thickness of the Ge layers varies from 2 to 20 nm with a step of 2 nm away from the substrate. These films were characterized by studying their optical, electrical, and structural properties. The films were annealed at various temperatures up to 500 degrees C for 2 h. The films are amorphous up to an annealing temperature of 400 degrees C, although Raman spectra suggest short-range ordering (and adjustments). The films annealed at 450 and 500 degrees C exhibit X-ray reflections of Ge and anatase TiO(2). Illumination in sunlight increases the conductivity of the as-deposited and annealed films. The band gap of the amorphous films changes from 1.27 to 1.41 eV up to 400 degrees C; the major contribution is possibly through direct transition. Two band gap regimes are clearly seen after 450 and 500 degrees C, which have been assigned to an indirect band gap at about 1.2 eV and a direct band gap at about 1.8 eV. Conductivity of the multilayer films has been higher than that of pure Ge film. The conductivity increases with annealing temperature with abrupt increase at about 380 degrees C. The results imply that the TiO(2)-Ge multilayer films may be employed as heterojunctions with tunable band gap energy as related to quantum confinement effects.

In this work, biologically-inspired computing framework is developed for HIV infection of CD4[Formula: see text] T-cell model using feed-forward artificial neural networks (ANNs), genetic algorithms (GAs), sequential quadratic programming (SQP) and hybrid approach based on GA-SQP. The mathematical model for HIV infection of CD4[Formula: see text] T-cells is represented with the help of initial value problems (IVPs) based on the system of ordinary differential equations (ODEs). The ANN model for the system is constructed by exploiting its strength of universal approximation. An objective function is developed for the system through unsupervised error using ANNs in the mean square sense. Training with weights of ANNs is carried out with GAs for effective global search supported with SQP for efficient local search. The proposed scheme is evaluated on a number of scenarios for the HIV infection model by taking the different levels for infected cells, natural substitution rates of uninfected cells, and virus particles. Comparisons of the approximate solutions are made with results of Adams numerical solver to establish the correctness of the proposed scheme. Accuracy and convergence of the approach are validated through the results of statistical analysis based on the sufficient large number of independent runs.

A series of porous anodic alumina has been prepared by anodizing aluminum surface in 0.3 M oxalic acid at different voltages. Prior to anodizing, the surface was pretreated in two different electropolishing electrolytes. One was Brytal solution (15% Na2CO3 and 5% Na3PO4) at 80 °C in which the electropolishing was performed at 2 V. This resulted in about 100–150 nm apart random features of 4–5 nm height. The other was the commonly employed perchloric acid–alcohol solution (1:4 ratio by volume), in which the electropolishing was performed at 20 V. The resulting surface comprised nanostripes of 1–2 nm amplitude with a wavelength of about 50 nm. The former pretreatment proved better for self-ordering of the pores at the anodizing voltage of 50–60 V, while the latter pretreatment was found better at the anodizing voltage of 40 V. The improved pore ordering at a given voltage was attributed to the higher pore density as associated with greater repulsive interactions among the pores.

Through-thickness anodic oxidation of aluminum sheet has been possible to form 100–400 μm thick transparent alumina in 0.3 M oxalic acid at 30–70 V. Transmission at 900–1100 nm was 20–60%, the higher being at lower anodizing voltages. Two-step anodizing after electropolishing in Brytal solution resulted in best hexagonal order at 50 V. The alumina layers with ordered porosity growing from the opposite faces met at the middle of the sample without changing the orientation of the pores, except at the last stage when a number of pores meet to form Y-, S- and I-type junctions. Three-dimensional compressive stresses around aluminum nanowires (nanoparticles) seem to be responsible for re-orienting the pores. In case of thicker barrier layers formed at higher anodizing voltage, the pores growing from opposite side do not meet and are separated by a barrier type compact oxide at the middle of the sample.

Nanocrystalline zinc-nickel alloy coatings were electro-deposited in chloride bath containing ZuCl 2 (50-200 gL -1 ), NiCl 2 .6H 2 O (50-200 gL -1 ) and H 3 BO 3 (40 gL -1 ) at45°C. The initial alloy composition, as investigated by voltammetry, chronopotentiometry or EDX analysis, exhibited a pronounced dependence on the composition of plating bath. However, the alloy composition tended to approach an upper limit of 18 atomic percent when deposits grew thicker, for most of the plating bath compositions. The process was clearly the anomalous codeposition, which was considered to be due to the formation of an adsorbed layer of Zn(OH) 2 containing nickel cations, as an intermediate product, whose composition determines the composition of the electro-deposited alloy. The electro-deposited coatings comprised of single-phase structure of γ-phase (Nι 5 Zn 21 ) for about 12-18 atomic percent nickel and a mixture of γ- and η-phases (hcp) for nickel atom percent below 8. The grain size decreased from about 50nm to 20nm with decrease in nickel content away from the stoichiometric composition of Ni 5 Zn 21 along with a decrease in hardness from about 450KHN to 150KHN. The decrease in hardness with a decrease in grain size seems attributable to the grain boundary sliding as predominant deformation mechanism. Anodic dissolution of the alloys involved pronounced dezincification, forming α-phase (fcc) that dissolved at the end.

This note points out some errors in the identification algorithms presented by M. A. Z. Raja and N. I. Chaudhary for estimation of controlled autoregressive moving average systems "Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems", Signal Processing 107 (2015), 327-339.. Corrections on the basis of established techniques are also mentioned that solve the problems of the above mentioned paper. In addition, lack of any technical innovation between single stage and two-stage algorithms is highlighted. No technical difference between single stage and two-stage algorithms of 1.Noise v ( t ) is treated as known signal. This is totally wrong.Solutions of issues are presented on the basis of established results.

A novel algorithm is developed based on fractional signal processing approach for parameter estimation of input nonlinear control autoregressive (INCAR) models. The design scheme consists of parameterization of INCAR systems to obtain linear-in-parameter models and to use fractional least mean square algorithm (FLMS) for adaptation of unknown parameter vectors. The performance analyses of the proposed scheme are carried out with third-order Volterra least mean square (VLMS) and kernel least mean square (KLMS) algorithms based on convergence to the true values of INCAR systems. It is found that the proposed FLMS algorithm provides most accurate and convergent results than those of VLMS and KLMS under different scenarios and by taking the low-to-high signal-to-noise ratio.

A study on the radiological hazard in Portland cement due to the presence of naturally occurring radioactive materials is being carried out. The Portland cement manufactured in the Islamabad/Rawalpindi region of Pakistan, intermediate products (clinker) and the various raw materials which compose the product have been analysed for (226)Ra, (232)Th and (40)K using a gamma spectrometry system with a N-type high-purity germanium detector of 80 % relative efficiency. From the measured gamma ray spectra, specific activities were determined. The mean values of the total specific activity of (226)Ra, (232)Th and (40)K are 34.2±11.9, 29.1±3.6 and 295.1±66.9 Bq kg(-1), respectively in Portland cement, 28.4±8.7, 11.3±1.7 and 63.1±17.3 Bq kg(-1), respectively in lime stone, 8.2±1.9, 16.2±3.9 and 187.7±53.2 Bq kg(-1), respectively in gypsum, 34.7±13.1, 41.2±6.7 and 187.6±17.2 Bq kg(-1), respectively in clay, 41.1±11.8, 39.3±6.9 and 195.1±29.2 Bq kg(-1), respectively in latrite and 51.1±18.2, 23.2±1.2 and 258.4±15.3 Bq kg(-1), respectively in clinker. The radium equivalent activities (Ra(eq)), external hazard index (H(ex)), internal hazard index (H(in)), absorbed dose rate in air (D) and annual effective dose rate (E(eff)) were also determined. The measured activity concentrations for these radio nuclides and radiological indices were compared with the reported national and international data. All these measured values are comparable with the worldwide data reported in UNSCEAR publications.