Optimal multi-reservoir network control by two-phase neural network

An approach based on two-phase neural network (TPNN) is proposed for the optimal operation of multi-reservoir network control problems. The advantage of the proposed technique is that it takes into account the concurrent interaction among all the water release variables of the problem. Here, the main objective of this work is to figure out the optimal amounts of water releases from each hydro-plant during each interval in the interconnected system and to minimize and distribute uniformly the energy deficit if any. This TPNN approach is basically a two-stage solution method. In stage 1, the neural network is developed to bring the solution trajectory close to the boundary of the feasible region. In stage 2, the directional vector of the constraints is slowly shifted to the corresponding Lagrange multipliers and this moves the solution trajectory to the feasible region which satisfies all practical constraints. Application of this technique to a 10-reservoir network demonstrates efficacy of the proposed algorithm. It is concluded from the results that the proposed method with proper selection of network control parameters is very effective in providing a good optimal solution.     

The efficacy of sand-immobilized organoclays as filtration bed materials for bacteria

Previous research has shown that montmorillonite clays exchanged with cationic surfactants including cetylpyridinium (CP), hexadecyltrimethylammonium (HDTMA), and cetyldimethylethylammonium (CDEA) were effective in reducing Salmonella enteritidis colony counts in bulk antibacterial assays. In order to increase the porosity (and hydraulic conductivity) of these materials for use in filtration beds, organoclays were tightly adhered and immobilized onto the surface of sand. Bulk antibacterial assays performed with 10 mg of the sand-immobilized organoclay (S/IOC) showed that the composite materials retained their antibacterial activity after processing. Reductions in plate colony counts ranged from 16.5% for sand-immobilized CP-exchanged sodium montmorillonite to 83.9% for immobilized CDEA-exchanged low pH montmorillonite. The sand/adhesive matrix and sand-immobilized activated charcoal failed to produce any significant decreases. Pilot studies with columns containing 1.0 g of S/IOC showed that maximal filtration efficiency was obtained at a filtration rate of 1.0 ml/min. Initial column studies with Salmonella suspensions at a concentration of 3.0×10⁸ cfu/ml, produced reductions in colony plate counts varying from 28.5% for immobilized CP-exchanged calcium montmorillonite to 59.5% for immobilized CDEA-exchanged low pH montmorillonite. The sand/adhesive matrix and immobilized activated charcoal yielded much lower reductions (7.6% and 10.4%, respectively). Studies with Escherichia coli suspensions (3.0×10⁷ cfu/ml) indicated reductions varying from 59.3% for immobilized CP-exchanged calcium montmorillonite to 99.9% for immobilized CDEA-exchanged low pH montmorillonite. These initial results suggest that S/IOC can be effective in reducing microbial numbers in wastewater following some modifications.     

Analysis of multi-layered composite cylinders under hygrothermal loading

In this study, a general stress analysis is developed for thick or thin multi-layered composite cylinders under hygrothermal loadings. The layers are oriented symmetrically and antisymmetrically for [0°/90°]₂, [30°/−30°]₂, [45°/−45°]₂ and [60°/−60°]₂ orientations. The solution is carried out on composite cylinders for plane-strain, open end and closed end conditions. Uniform and parabolic temperature distributions are chosen for the thermal loads. All the integration constants are found from the radial stress and displacement in the normal direction of layers. The hygrothermal and other mechanical properties are measured on a glass-epoxy composite layer. Some analytical solutions are compared with the finite element solutions, in which commercial software ANSYS 7.0 is utilized, and close results are obtained between them.     

Green synthetic route of carbon quantum dot-reinforced graphene oxide-poly(vinylidene fluoride-co-hexa fluoropropylene) nanocomposites: Toward high dielectric constant and suppressed loss

A simple and green method was developed to fabricate carbon quantum dot@ graphene oxide filled poly (vinylidene fluoride-co-hexa-fluoropropylene) [CQD@GO-P(VDF-HFP)] nanocomposite films via solution casting technique. The synthetic approach was to bring CQD from bilva leafs, a renewable and sustainable resource. The effect of CQD on dielectric properties, dielectric constant, and dielectric loss in the presence of GO along with P (VDF-HFP) matrix were investigated. The result showed that the nanocomposites having 1.5 wt % of CQD@GO-P(VDF-HFP) with higher dielectric constant (≈144) at 100 Hz and suppressed loss (<1) at 1000 Hz, which is well supported by field emission scanning electron microscopy (FESEM) study. The FESEM study shows a river iceland morphology with a channel-like structure along with voids and pores that may provide a conducting network, which tends to have Maxwell Wagner-Sillars or interfacial polarization results in a high-end properties outcome. Furthermore, the suppressed loss enhanced the possibility of end use performance of CQD@GO-P(VDF-HFP) matrix with a referral memorandum of percolation theory. Thus, the present work demonstrated a new approach to develop high dielectric constant and negligible loss materials in the field of embedded devices for electronic industries through green synthetic approach. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47850.     

Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices

Efficient electricity price forecasting plays a significant role in our society. In this paper, a novel influencer-defaulter mutation (IDM) mutation operator has been proposed. The IDM operator has been combined with six well-known optimization algorithms to create mutated optimization algorithms whose performance has been tested on twenty-four standard benchmark functions. Further, the artificial neural network is integrated with mutated optimization algorithms to solve the electricity price prediction problem. The policymakers can identify appropriate variables based on the predicted prices to help future market planning. The statistical results prove the efficacy of the IDM operator on the recent optimization algorithms.     

Fabrication of composites from different carbon content ferrous powders adopting thermomechanical processes

In this research, deep consolidation technique is utilized for processing of low and high carbon content ferrous powders to manufacture composite material. High carbon content ferrous powders were used as reinforcing material. The powder particles were allowed to mix with low carbon content ferrous powders in 50:50 weight percent for fabrication of composites. The composites were heat treated at three different temperatures (i.e., 800°C, 900°C, and 1000°C). The changes in microstructure, microhardness, the grain size, and bonding among powder particles were studied. Refinement of grains was observed and this led to improved hardness enabling the processed material to be used as a suitable composite. Abrasive wear tests were carried out using a laboratory tribometer in dry reciprocating sliding contact against Sic abrasive paper. The outcomes demonstrate that the abrasive wear resistance was notably affected by the treatment temperature and hardness of the composites.     

A survey on region based image fusion methods

Image fusion has been emerging as an important area of research. It has attracted many applications such as surveillance, photography, medical diagnosis, etc. Image fusion techniques are developed at three levels: pixel, feature and decision. Region based image fusion is one of the methods of feature level. It possesses certain advantages – less sensitive to noise, more robust and avoids misregistration. This paper presents a review of region based fusion approaches. A first hand classification of region based fusion methods is carried out. A comprehensive list of objective fusion evaluation metrics is highlighted to compare the existing methods. A detailed analysis is carried out and results are presented in tabular form. This may attract researchers to further explore the research in this direction.     

Multiobjective design of viscoelastic laminated composite sandwich panels

The optimal design of laminated sandwich panels with viscoelastic core is addressed in this paper, with the objective of simultaneously minimizing weight and material cost and maximizing modal damping. The design variables are the number of layers in the laminated sandwich panel, the layer constituent materials and orientation angles and the viscoelastic layer thickness. The problem is solved using the Direct MultiSearch (DMS) solver for multiobjective optimization problems which does not use any derivatives of the objective functions. A finite element model for sandwich plates with transversely compressible viscoelastic core and anisotropic laminated face layers is used. Trade-off Pareto optimal fronts are obtained and the results are analyzed and discussed.     

A novel region-based multimodal image fusion technique using improved dictionary learning

Recently, the sparse representation (SR) based algorithms have gained much attention from the researchers in the area of image fusion (IF). The building of a compact discriminative dictionary plays a vital role in the sparse-based IF techniques. In this context, an efficient multimodal IF method based on improved dictionary learning is investigated. The key contributions of this paper are: (a) An improved KSVD algorithm is suggested for the dictionary learning process, (b) to reduce the computational time, only the informative patches are selected using energy feature, and (c) a novel region-based fusion scheme is suggested for the first time for the problem on hand. The suggested technique is tested with a number of multimodal images from Harvard Medical School brain database. The results are compared with state-of-the-art multiscale transform-based methods and modified SR-based methods. Unlike earlier methods, our proposed technique generates an adaptive dictionary through selection of informative patches only. This results in a compact dictionary with improved computational efficiency. The experimental results reveal that our approach outperforms other methods. The potential application of the suggested method could be in pathological images for follow-up study and better treatment planning.     

Inhibited grain growth in hydroxyapatite–graphene nanocomposites during high temperature treatment and their enhanced mechanical properties

Nanostructured hydroxyapatite (HA)–graphene nanosheet (GN) composites have been fabricated by spark plasma sintering consolidation. Nanostructual evolution of the bioceramic-based composites during further high temperature heat treatment is characterized and enhanced mechanical strength is assessed. GN keeps intact after the treatment and its presence at HA grain boundaries effectively inhibits HA grain growth by impeding interconnection of individual HA grains. Microstructural characterization discloses strong coherent interfaces between GN and the (300) plane of HA crystals. This particular matching state in the composites agrees well with the competitive theoretical pull-out energy for single graphene sheet being departed from HA matrix. The toughening regimes that operate in HA–GN composites at high temperatures give clear insight into potential applications of GN for ceramic matrix composites.