An EPQ model with promotional demand in random planning horizon: population varying genetic algorithm approach

One of the economic production quantity problems that have been of interest to researchers is the production with reworking of the imperfect items including waste most disposal form and vending the units. The available models in the literature assumed that the decay rate of the items is satisfied from three different points of view: (i) minimum demands of the customer’s requirement, (ii) demands to be enhanced for lower selling price and (iii) demands of the customers who are motivated by the advertisement. The model is developed over a finite random planning horizon, which is assumed to follow the exponential distribution with known parameters. The model has been illustrated with a numerical example, whose parametric inputs are estimated from market survey. Here the model is optimized by using a population varying genetic algorithm.     

An efficient algorithm for mass detection and shape analysis of different masses present in digital mammograms

The present study introduces an efficient algorithm for automatic segmentation and detection of mass present in the mammograms. The problem of over and under-segmentation of low-contrast mammographic images has been solved by applying preprocessing on original mammograms. Subtraction operation performed between enhanced and enhanced inverted mammogram significantly highlights the suspicious mass region in mammograms. The segmentation accuracy of suspicious region has been improved by combining wavelet transform and fast fuzzy c-means clustering algorithm. The accuracy of mass segmentation has been quantified by means of Jaccard coefficients. Better sensitivity, specificity, accuracy, and area under the curve (AUC) are observed with support vector machine using radial basis kernel function. The proposed algorithm is validated on Mini-Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets. Highest 91.76% sensitivity, 96.26% specificity, 95.46% accuracy, and 96.29% AUC on DDSM dataset and 94.63% sensitivity, 92.74% specificity, 92.02% accuracy, and 95.33% AUC on MIAS dataset are observed. Also, shape analysis of mass is performed by using moment invariant and Radon transform based features. The best results are obtained with Radon based features and achieved accuracies for round, oval, lobulated, and irregular shape of mass are 100%, 70%, 64%, and 96%, respectively.     

Impacts of CO2 emission constraints on technology selection and energy resources for power generation in Bangladesh

This paper examines the impacts of CO₂ emission reduction target and carbon tax on future technologies selection and energy use in Bangladesh power sector during 2005–2035. The analyses are based on a long-term energy system model of Bangladesh using the MARKAL framework. The analysis shows that Bangladesh will not be able to meet the future energy demand without importing energy. However, alternative policies on CO₂ emission constraints reduce the burden of imported fuel, improve energy security and reduce environmental impacts. The results show that the introduction of the CO₂ emission reduction targets and carbon taxes directly affect the shift of technologies from high carbon content fossil-based to low carbon content fossil-based and clean renewable energy-based technologies compared to the base scenario. With the cumulative CO₂ emission reduction target of 10–20% and carbon tax of 2500 Taka/ton, the cumulative net energy imports during 2005–2035 would be reduced in the range of 39–65% and 37%, respectively, compared to the base scenario emission level. The total primary energy requirement would be reduced in the range of 4.5–22.3% in the CO₂ emission reduction targets and carbon tax 2500 Taka/ton scenarios and the primary energy supply system would be diversified compared to the base scenario.     

Darcy–Forchheimer electromagnetic flow of entropy optimized microrotating Casson–Carreau nanomaterials

It is apparent that non-Newtonian nanofluids (especially, Casson and Carreau) find their ubiquitous utilization in diverse industrial processes. The magnetohydrodynamics concept is significantly implemented in the engineering design process. Darcy–Forchheimer's effect characterized by inertia and boundary effects ameliorates the rate of heat transportation outstandingly in association with the flow of nanofluids. Entropy optimization analysis is accentuated as its minimization is the best measure to enhance the efficiency of thermal systems. In view of this, the present article is intended to investigate electromagnetic flow and thermal characteristics of Casson and Carreau nanofluids over the exponential stretched surface. Microrotation facets are entailed. Arrhenius pre-exponential factor law and Robin's condition are implemented. The nondimensional governing equations are solved by the spectral quasi-linearization method. The major outcomes of this study are that axial and transverse flow velocities and heat transfer rate get controlled due to strengthening Casson and microinertia density parameters. More thermal stratification augments the rate of heat transportation efficaciously. Amplification of the Weissenberg parameter intensifies the axial and transverse flow velocities and the associated boundary layer widths. Axial and transverse surface viscous drag enervate due to the rise in porosity, inertia, and magnetic parameters. The entropy generation rate is regulated by the varied Reynolds number.     

A comparative study of walking‐induced dust resuspension using a consistent test mechanism

Human walking influences indoor air quality mainly by resuspending dust particles settled on the floor. This study characterized walking‐induced particle resuspension as a function of flooring type, relative humidity (RH), surface dust loading, and particle size using a consistent resuspension mechanism. Five types of flooring, including hardwood, vinyl, high‐density cut pile carpet, low‐density cut pile carpet, and high‐density loop carpet, were tested with two levels of RH (40% and 70%) and surface dust loading (2 and 8 g/m²), respectively. Resuspension fraction r_a (fraction of surface dust resuspended per step) for house dust was found to be varied from 10^?7 to 10^?4 (particle size: 0.4–10 µm). Results showed that for particles at 0.4–3.0 µm, the difference in resuspension fraction between carpets and hard floorings was not significant. For particles at 3.0–10.0 µm, carpets exhibited higher resuspension fractions compared with hard floorings. Increased RH level enhanced resuspension on high‐density cut pile carpet, whereas the opposite effect was observed on hard floorings. Higher surface dust loading was associated with lower resuspension fractions on carpets, while on hard floorings the effect of surface dust loading varied with different RH levels.     

A production-recycling-inventory model with learning effect

This paper develops an integrated production-recycling system over a finite time horizon. Here, the dynamic demand is satisfied by production and recycling. The used units are bought back and then either recycled or disposed of which are not repairable. The used units are collected continuously from the customers. Recycling products can be used as new products which are sold again. The rate of production and disposal are assumed to be function of time. The setup cost is reduced over time due to “Learning curve” effect. The optimum results are presented both in tabular form and graphically.     

Bootstrapping a weighted linear estimator of the ARCH parameters

Abstract.  A standard assumption while deriving the asymptotic distribution of the quasi maximum likelihood estimator in ARCH models is that all ARCH parameters must be strictly positive. This assumption is also crucial in deriving the limit distribution of appropriate linear estimators (LE). We propose a weighted linear estimator (WLE) of the ARCH parameters in the classical ARCH model and show that its limit distribution is multivariate normal even when some of the ARCH coefficients are zero. The asymptotic dispersion matrix involves unknown quantities. We consider appropriate bootstrapped version of this WLE and prove that it is asymptotically valid in the sense that the bootstrapped distribution (given the data) is a consistent estimate (in probability) of the distribution of the WLE. Although we do not show theoretically that the bootstrap outperforms the normal approximation, our simulations demonstrate that it yields better approximations than the limiting normal.     

Temperature dependent growth and optical properties of SnO<Subscript>2</Subscript> nanowires and nanobelts

SnO₂ nanowires and nanobelts have been grown by the thermal evaporation of Sn powders. The growth of nanowires and nanobelts has been investigated at different temperatures (750–1000°C). The field emission scanning electron microscopic and transmission electron microscopic studies revealed the growth of nanowires and nano-belts at different growth temperatures. The growth mechanisms of the formation of the nanostructures have also been discussed. X-ray diffraction patterns showed that the nanowires and nanobelts are highly crystalline with tetragonal rutile phase. UV-visible absorption spectrum showed the bulk bandgap value (∼ 3–6 eV) of SnO₂. Photoluminescence spectra demonstrated a Stokes-shifted emission in the wavelength range 558–588 nm. The Raman and Fourier transform infrared spectra revealed the formation of stoichiometric SnO₂ at different growth temperatures.     

A neural-network-based space-vector PWM controller for athree-level voltage-fed inverter induction motor drive

A neural-network-based implementation of space-vector modulation (SVM) of a three-level voltage-fed inverter is proposed in this paper that fully covers the linear undermodulation region. A neural network has the advantage of very fast implementation of an SVM algorithm, particularly when a dedicated application-specific IC chip is used instead of a digital signal processor (DSP). A three-level inverter has a large number of switching states compared to a two-level inverter and, therefore, the SVM algorithm to be implemented in a neural network is considerably more complex. In the proposed scheme, a three-layer feedforward neural network receives the command voltage and angle information at the input and generates symmetrical pulsewidth modulation waves for the three phases with the help of a single timer and simple logic circuits. The artificial-neural-network (ANN)-based modulator distributes switching states such that neutral-point voltage is balanced in an open-loop manner. The frequency and voltage can be varied from zero to full value in the whole undermodulation range. A simulated DSP-based modulator generates the data which are used to train the network by a backpropagation algorithm in the MATLAB Neural Network Toolbox. The performance of an open-loop volts/Hz speed-controlled induction motor drive has been evaluated with the ANN-based modulator and compared with that of a conventional DSP-based modulator, and shows excellent performance. The modulator can be easily applied to a vector-controlled drive, and its performance can be extended to the overmodulation region