On the feasibility of community-scale photovoltaic-powered reverse osmosis desalination systems for remote locations

This paper presents a generalized methodology to evaluate the feasibility of photovoltaic-powered reverse osmosis (PVRO) systems for small, remote communities in challenging environments. The economic feasibility is determined by comparing the cost of water from a photovoltaic reverse osmosis system with the cost of water obtained using conventional diesel-powered reverse osmosis. For PVRO systems, the feasibility is a function of location due to variation in solar resource, water type, system demand and local governmental policies. A series of sample cases were analyzed to demonstrate the method. Analysis for community-scale seawater reverse osmosis systems was carried out for various locations using Geographic Information System (GIS) data. A site-specific analysis has been completed for seawater and brackish water reverse osmosis systems. It was found that photovoltaic-powered reverse osmosis is feasible for the majority of remote locations with a large solar resource.     

An Optimized Neural Network with Bat Algorithm for DNA Sequence Classification

Recently, many researchers have used nature inspired metaheuristic algorithms due to their ability to perform optimally on complex problems. To solve problems in a simple way, in the recent era bat algorithm has become famous due to its high tendency towards convergence to the global optimum most of the time. But, still the standard bat with random walk has a problem of getting stuck in local minima. In order to solve this problem, this research proposed bat algorithm with levy flight random walk. Then, the proposed Bat with Levy flight algorithm is further hybridized with three different variants of ANN. The proposed BatLFBP is applied to the problem of insulin DNA sequence classification of healthy homosapien. For classification performance, the proposed models such as Bat levy flight Artificial Neural Network (BatLFANN) and Bat levy Flight Back Propagation (BatLFBP) are compared with the other state-of-the-art algorithms like Bat Artificial Neural Network (BatANN), Bat back propagation (BatBP), Bat Gaussian distribution Artificial Neural Network (BatGDANN). And Bat Gaussian distribution back propagation (BatGDBP), in-terms of means squared error (MSE) and accuracy. From the perspective of simulations results, it is show that the proposed BatLFANN achieved 99.88153% accuracy with MSE of 0.001185, and BatLFBP achieved 99.834185 accuracy with MSE of 0.001658 on WL5. While on WL10 the proposed BatLFANN achieved 99.89899% accuracy with MSE of 0.00101, and BatLFBP achieved 99.84473% accuracy with MSE of 0.004553. Similarly, on WL15 the proposed BatLFANN achieved 99.82853% accuracy with MSE of 0.001715, and BatLFBP achieved 99.3262% accuracy with MSE of 0.006738 which achieve better accuracy as compared to the other hybrid models.

     

Classification of multi-lingual tweets,into multi-class model using Naïve Bayes and semi-supervised learning

Multimedia Tools and Applications - Twitter is a social media platform which has been proven to be a great tool for insights of emotions about products, policies etc. through a 280-character...     

Biological nitrogen and phosphorus removal and changes in microbial community structure in a membrane bioreactor: effect of different carbon sources

Bacterial community structures in four sequencing anoxic/anaerobic-aerobic membrane bioreactors (SAMs) that were fed with synthetic medium composed of different organic compounds in substrate as carbon source; acetate-dominant (acetate/propionate = 4/1), propionate-dominant (acetate/propionate = 1/4), glucose-dominant (glucose/acetate = 4/1) and methanol-dominant (methanol/acetate/propionate = 6/3/1) were analyzed by respiratory quinone profile and fluorescent in situ hybridization (FISH) techniques. The SAMs were operated at controlled pH range 7-8.5 and at constant temperature 25 degrees C. Total nitrogen (TN), total phosphorus (TP) and COD removal performances were also evaluated and compared. In addition, trans-membrane pressure was monitored to observe the impact of substrate composition on membrane fouling. The dominance of the mole fraction of ubiquinone (UQ-8) in the SAMs indicated dominance of the beta-subclass of Proteobacteria; however, its population comparatively decreased when the substrate was glucose dominant or methanol dominant. A relatively higher and stable enhanced biological phosphorus removal performance was observed when methanol-dominant substrate was used concurrently with an increase in the gamma-subclass of Proteobacteria. The population of the alpha-subclass of Proteobacteria slightly increased along with a decrease in phosphate removal activity when the substrate was glucose-dominant. Results from FISH analysis also supported the findings of the quinone profile. The trans-membrane pressure variation in the SAMs indicated that fouling was relatively rapid when propionate-dominant or methanol-dominant substrate was used and most stable when glucose-dominant substrate was used. A combination of methanol and acetate would be a better choice as an external carbon source when nutrients removals, as well as fouling, are considered in the membrane bioreactor- (MBR-) coupled biological nutrients removing (BNR) process.     

Synthesis, thermal stability and properties of [(Fe1−xCox)72Mo4B24]94Dy6 bulk metallic glasses

A series of [(Fe_1−xCo_x)₇₂Mo₄B₂₄]₉₄Dy₆ (x = 0.1, 0.2, 0.3, 0.4 and 0.5 at.%) bulk metallic glasses (BMGs) in rod geometries with critical diameter up to 3 mm were fabricated by copper mold casting method. This alloy system exhibited good thermal stability with high glass transition temperature (T_g) 860 K and crystallization temperature (T_x) 945 K. The addition of Co was found to be effective in adjusting the alloy composition deeper to eutectic, leading to lower liquidus temperature (T_l). The [(Fe_0.8Co_0.2)₇₂Mo₄B₂₄]₉₄Dy₆ alloy showed the largest supercooled liquid region (ΔT_x = T_x − T_g = 92 K), reduced glass transition temperature (T_rg = T_g/T_l = 0.622) and gamma parameter (γ = T_x/(T_g + T_l) = 0.424) among the present system. Maximum compressive fracture strength of 3540 MPa and micro-Vickers hardness of 1185 kg/mm² was achieved, resulting from the strong bonding structure among the alloy constituents. The alloy system possessed soft magnetic properties with high saturation magnetization of 56.61-61.78 A m²/kg and coercivity in the range of 222-264.2 A/m, which might be suitable for application in power electronics devices.     

Low temperature pulsed laser deposition of textured γ′-Fe4N films on Si (1 0 0)

Low-temperature reactive pulsed laser deposition (PLD) was used to prepare iron nitride films. The textured γ′-Fe₄N films with (0 0 1)-orientation were deposited on Si (1 0 0) substrate with Fe buffer layer at a substrate temperature as low as 150 °C. The (0 0 1)-oriented γ′-Fe₄N film grew on the Fe buffer layer with a 3.5-nm thick amorphous interlayer, which eliminated the lattice mismatch stress between them. The films showed a columnar granular morphology with an average lateral grain size of approximately 110 nm. The films exhibited good soft magnetic properties with a high in-plane M_r/M_s value of 0.84. The magneto-optic Kerr effect results indicated an in-plane magnetic isotropy and confirmed the high remnant ratio of the γ′-Fe₄N films.     

The magnetic, structure and mechanical properties of rapidly solidified (Nd7Y2.5)-(Fe64.5Nb3)-B23 nanocomposite permanent magnet

The Nd₇Y_2.5Fe_64.5Nb₃B₂₃ nanocomposite permanent magnets in the form of rods with 2 mm in diameter have been developed by annealing the amorphous precursors produced by copper mold casting technique. The phase evolution, structure, magnetic and mechanical properties were investigated with X-ray diffractometry, differential scanning calorimetry, electron microscopy, magnetometry and universal uniaxial compression strength techniques. The heat treatment conditions under which the magnets attained maximum magnetic and mechanical properties have been established. The results indicate that magnet properties are sensitive to grain size and volume content of the magnetic phases present in the microstructure. The composite microstructure was mainly composed of soft α-Fe (20-30 nm) and hard Nd₂Fe₁₄B (45-65 nm) magnetic phase grains. The maximum coercivity of 959.18 kA/m was achieved with the magnets annealed at 760 °C whereas the highest remanence of 0.57 T was obtained with the magnets treated at 710 °C. The optimally annealed magnets possessed promising magnetic properties such as _jH_c of 891.52 kA/m, B_r of 0.57 T, M_r/M_s = 0.68, (BH)_max of 56.8 kJ/m³ as well as the micro-Vickers hardness (H_v) of 1138 ± 20 and compressive stress (σ_f) of 239 ± 10 MPa.     

On the Genesis of the Marshall-Olkin Family of Distributions via the T-<i>X</i> Family Approach: Statistical Modeling

In the last couple of years, there Has been an increased interest among the statisticians to define new families of distributions by adding one or more additional parameter(s) to the baseline distribution. In this regard, a number of families have been introduced and studied. One such example is the Marshall-Olkin family of distributions that is one of the most prominent approaches used to generalize the existing distributions. Whenever, we see a new method, the natural questions come in to mind are (i) what are the genesis of the newly proposed method and (ii) how did the proposed method is obtained. No doubt, the Marshall-Olkin family is a very useful method and has attracted the researchers. But, unfortunately, the authors failed to provide the explanation about the genesis of the method that how this family of distributions is obtained. To address this issue, in this article, an attempt Has been made to provide a straight forward computation about the genesis of the Marshall-Olkin family that somehow completes its derivation. The genesis of the Marshall-Olkin family is based on the T-X family approach. Furthermore, we have showed that other extensions of the Marshall-Olkin family can also be obtained via the T-X family method. Finally, a real-life application form insurance science is presented to illustrate the newly proposed extension of the Marshall-Olkin family.     

Efficiency and optimization of an annular fin with combined heat and mass transfer – An analytical solution

An analysis was carried out to study the efficiency of annular fin when subjected to simultaneous heat and mass transfer mechanisms. The temperature and humidity ratio differences are the driving forces for the heat and mass transfer, respectively. Analytical solutions are obtained for the temperature distribution over the fin surface when the fin is fully wet. The effect of the atmospheric pressure on the fin efficiency was also studied, in addition to fin optimum dimensions. It is demonstrated that the closed-form solutions for a dry-fin case presented in many text books are special cases for the solutions presented in this paper.     

Emotion Recognition from Occluded Facial Images Using Deep EnsembleModel

Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications.