Photocatalytic degradation of methyl orange using heteropolytungstic acid-encapsulated TiSBA-15

Heteropolyphosphotungstic acid (H₃PW₁₂O₄₀) (HPA)-encapsulated titanium-supported SBA15 nanoporous materials (HPA-TiSBA-15) were synthesized as a new photocatalyst, and they were characterized by FT-IR and ³¹P NMR MAS spectroscopic methods. Their photocatalytic effects on degradation of methyl orange in water were investigated under natural conditions, and it was found that a 4-fold increase in the photodegradation rate is observed in the presence of HPA-TiSBA-15 as compared to the rate observed in the presence of titanium-supported SBA15 nanoporous materials (TiSBA-15) without HPA.     

Halogen-free acylation of toluene over FeSBA-1 molecular sieves

Three-dimensional cage type iron substituted mesoporous silica with different iron contents (FeSBA-1) was synthesized in a highly acidic media using cetyltriethylammonium bromide and tetraethylorthosilicate as a template and a silica source, respectively. Acylation of toluene with acetic anhydride (AA) was carried out over FeSBA-1 mesoporous catalysts with different n_Si/n_Fe ratios in the temperature range 80–180 °C for a time-on-stream of 1–6 h under liquid phase conditions. The important factors affecting the conversion and the selectivity of the reaction, such as the reaction temperature, feed ratio, catalyst weight and time-on-stream were studied and the results are discussed in detail. The reaction conditions were optimized and the n_AA/n_Toluene ratio of 2 and catalyst weight of 0.1 g (3.3 wt% of total reaction mixture) were maintained for all catalytic runs. It was found that the catalytic activity is strongly influenced by the amount of tetrahedral iron in the catalysts. Among the catalysts used in the present study, FeSBA-1(36) showed a high toluene conversion and selectivity to p-methylacetophenone (p-MAP) under the optimized reaction conditions. It was also found that the selectivity for p-MAP was always higher than m-MAP and o-MAP for all the catalysts and the activity of the catalysts changes in the following order: FeSBA-1(36) > FeSBA-1(90) > FeSBA-1(120).     

Wideband reflecting frequency-selective surface polarizer for X-band applications

This paper proposed the design of a broadband linear-to-circular reflecting polarizer for X-band applications. The polarizer is designed with three-arm dipole-shaped unit cells with a full ground plane to get wideband performance. The incident linear polarized wave is converted to a right-handed circularly polarized wave upon reflection from the polarizer. The footprint of the unit cell is 9 × 9 mm, which is printed on a FR-4 substrate. Fabricated prototype is measured for its performances like polarization conversion ratio (PCR), fractional bandwidth, and aperture efficiency. The PCR is about 97%, and the fractional bandwidth is 64% with the aperture efficiency of more than 80%. Polarizer performance is analyzed in transverse electric and transverse magnetic modes, and they are stable up to 40° of various incident angles. The polarizer exhibits the 3-dB axial ratio bandwidth from 8 to 12 GHz, which is suitable for satellite applications.     

An energy flow analysis in a paper-based industry

Industrial sector is the largest user of energy in India and many parts of the world. More than half of the total energy is used in industries to operate various energy-using machineries. Energy conservation is a cost-effective approach in any energy optimization program that can be implemented in industrial activities. Energy-intensive industries in developing economies offer significant energy-saving potential owing to the technology gap as well as lower operating efficiency. A detailed energy flow analysis was conducted in a paper carton manufacturing unit in India to quantify the energy-saving potential. The study revealed that the compressed air leakage is responsible for about 50% of the total energy loss. The specific electrical and thermal energy consumption was estimated and found to be 91.85?kWh/ton and 1,619?MJ/ton of biomass, respectively. Annual energy saving potential was found to be 5.9% of the total annual energy consumption. The cost of DG power is found to be 389% higher than the grid power. The share of self-generated DG power is estimated to be 41% of the total power consumed. Electrical energy intensity of the plant could be reduced by 6.36% implementing energy conservation measures proposed in this study.     

Expression characteristics of the maeA and maeB genes by extracellular malate and pyruvate in Escherichia coli

The malate-pyruvate conversion pathway is catalyzed by two malic enzyme isomers, MaeA and MaeB. qRT-PCR was carried out under malate and pyruvate supplemented conditions to understand the dynamics of maeA and maeB gene expression. maeA expression was elevated by malate, and maeB expression was inhibited by levels of both malate and pyruvate higher than 0.1 mM. Green fluorescent protein (GFP) reporter plasmids were also constructed by integration of the maeA/maeB promoter with the gfp gene. We showed that maeA driven GFP expression was positively and negatively correlated with extracellular malate and pyruvate induction. In contrast, no significant changes in maeB driven GFP expression were observed under both malate and pyruvate supplemented conditions.     

Green synthesis of silver nanoparticles using Zea mays and exploration of its biological applications

The biosynthesis of silver nanoparticles (AgNPs) has been proved to be a cost effective and environmental friendly approach toward chemical and physical methods. In the present study, biosynthesis of AgNPs was carried out using aqueous extract of Zea mays (Zm) husk. The initial colour change from golden yellow to orange was observed between 410 and 450 nm which confirmed the synthesis of AgNPs. Also, dynamic light scattering‐particle size analysis confirmed the average size to be 113 nm and zeta potential value of −28 kV. The morphology of synthesised Zm AgNPs displayed flower‐shaped structure, X‐ray diffraction pattern revealed the strongest peaks at 2θ = 38.6° and 64° which proved that the nanoparticle has the face centred crystalline structure. The Fourier transform infrared spectroscopy results showed strong absorption bands at 1394.53, 2980.02 and 2980.02 cm⁻¹ due to the presence of alkynes, carboxylic acids, alcoholic and phenolic groups. The maximum zone of inhibition was observed against Salmonella typhi (22 mm) and Candida albicans (18 mm). The synthesised nanoparticles exhibited more free radical scavenging activity than the aqueous plant extract. This is the first report on the synthesis of AgNP from Zm husk, delivers the efficient and stable Zm AgNPs through simple feasible approach toward green biotechnology.Inspec keywords: silver, nanoparticles, nanofabrication, light scattering, particle size, X‐ray diffraction, crystal structure, Fourier transform infrared spectra, absorption coefficients, free radicalsOther keywords: green synthesis, silver nanoparticles, biosynthesis, environmental friendly approach, aqueous extract, Zea mays husk, colour change, golden yellow, dynamic light scattering‐particle size analysis, average size, zeta potential value, flower‐shaped structure, X‐ray diffraction pattern, face centred crystalline structure, Fourier transform infrared spectroscopy, absorption bands, alkynes, carboxylic acids, alcoholic groups, phenolic groups, Salmonella typhi, Candida albicans, free radical scavenging activity, aqueous plant extraction, green biotechnology, size 113 nm, wavelength 410 nm to 450 nm     

Efficient heart disease prediction‐based on optimal feature selection using DFCSS and classification by improved Elman‐SFO

Prediction of cardiovascular disease (CVD) is a critical challenge in the area of clinical data analysis. In this study, an efficient heart disease prediction is developed based on optimal feature selection. Initially, the data pre‐processing process is performed using data cleaning, data transformation, missing values imputation, and data normalisation. Then the decision function‐based chaotic salp swarm (DFCSS) algorithm is used to select the optimal features in the feature selection process. Then the chosen attributes are given to the improved Elman neural network (IENN) for data classification. Here, the sailfish optimisation (SFO) algorithm is used to compute the optimal weight value of IENN. The combination of DFCSS–IENN‐based SFO (IESFO) algorithm effectively predicts heart disease. The proposed (DFCSS–IESFO) approach is implemented in the Python environment using two different datasets such as the University of California Irvine (UCI) Cleveland heart disease dataset and CVD dataset. The simulation results proved that the proposed scheme achieved a high‐classification accuracy of 98.7% for the CVD dataset and 98% for the UCI dataset compared to other classifiers, such as support vector machine, K‐nearest neighbour, Elman neural network, Gaussian Naive Bayes, logistic regression, random forest, and decision tree.Inspec keywords: cardiovascular system, medical diagnostic computing, feature extraction, regression analysis, data mining, learning (artificial intelligence), Bayes methods, neural nets, support vector machines, diseases, pattern classification, data handling, decision trees, cardiology, data analysis, feature selectionOther keywords: efficient heart disease prediction‐based, optimal feature selection, improved Elman‐SFO, cardiovascular disease, clinical data analysis, data pre‐processing process, data cleaning, data transformation, values imputation, data normalisation, decision function‐based chaotic salp swarm algorithm, optimal features, feature selection process, improved Elman neural network, data classification, sailfish optimisation algorithm, optimal weight value, DFCSS–IENN‐based SFO algorithm, DFCSS–IESFO, California Irvine Cleveland heart disease dataset, CVD dataset, high‐classification accuracy     

Multi-Valued Performance Metrics for Real-Time Embedded Systems

To rapidly explore the design space of a real-time embedded system, it is essential to be able to efficiently analyze the timing behaviors of different system architectures. This includes not only determining if a design can satisfy all the timing constraints but also comparing the timing performance of different designs for tradeoff purposes. Understanding the exact timing behavior of a large system can be computationally prohibitive. Previous work in this area has mostly focused on producing a yes/no answer to the schedulability of a system architecture under the worst-case scenario. This not only often leads to overly pessimistic designs, but also provides no insight as how to rank different architectural designs with respect to their timing performance. In this paper, we present several metrics that may be used to measure the timing performance of a design. The metrics were analyzed using workloads from both real-world task systems and randomly generated task systems. A superior metric has been identified through analysis of large sets of experiments. We also show, through an example, how this metric can be used effectively during a design exploration process.