Comprehensive review for energy efficient hierarchical routing protocols on wireless sensor networks

Wireless Networks - In recent years, wireless sensor networks (WSNs) have played a major role in applications such as tracking and monitoring in remote environments. Designing energy efficient...     

Biophysicochemical evaluation and micropropagation study of Jatropha curcas and Ricinus communis for biodiesel production

The upcoming energy sources of Jatropha curcas and Ricinus communis promise to mitigate the energy crisis and environmental pollution belonging to the Euphorbiaceae family. Both of them have been researched in terms of availability, cost, and biochemical parameters. The seed oils of various jatropha and castor biotypes were screened out and evaluated for their physiochemical parameters viz. oil content (34–49%), biodiesel yield (30–81%), density (0.875–0.971 g/cm³), viscosity (0.6032–2.004 mm³/s), iodine value (75–450.45 mg/g), free fatty acid value (0.986–3.400 mg/g), saponification value (59.29–93.79 mg/g), flash point (133–218°C), fire point (163–262°C), fatty acid composition, and ash content (0.065–0.398%), and were estimated for comparison between jatropha and castor biotypes. Various combinations of auxins with cytokinins were used for a regeneration study. The best shoot regeneration (70%) was observed in MS medium supplemented with NAA (0.5 ppm) and BAP (2.5 ppm). Root induction (95%) was successfully obtained in plane MS. Acclimatization and hardening was quite successful with a survival rate of 70%.     

Polypropylene random copolymer/MWCNT nanocomposites: Isothermal crystallization kinetics,structural, and morphological interpretations

This article describes the crystallization process of polypropylene random copolymer (PPCP) under isothermal conditions in presence of varying amounts of multiwalled carbon nanotubes (MWCNT) ranging from 0.5 to 4.0% w/w. Increase in the crystallization temperature under dynamic conditions confirmed the nucleating behavior of MWCNTs, which was also corroborated by crystallization studies under isothermal conditions. The crystallization kinetics was analyzed using Avrami equation and the parameters such as Avrami exponent, the equilibrium melting temperature and fold surface energy for the crystallization of PPCP chains in nanocomposites were obtained from the calorimetric data in order to determine the effect of MWCNTs on these parameters. Spherulitic growth of PPCP crystals was also investigated as a function of time and MWCNT content using hot stage polarizing microscope. © 2014 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015 , 132, 41734.     

Occurrence and Source Apportionment of Polycyclic Aromatic Hydrocarbons in Urban Residential Soils from National Capital Region,Uttar Pradesh,India

This study aims to investigate the level of priority polycyclic aromatic hydrocarbons (PAHs) and identification of their potential sources in residential soils. During the study, a total 36 soil samples collected from twelve residential locations at Sahibabad-Ghaziabad area of western Uttar Pradesh, India, a constituted part of the National Capital Region of India. Samples extracted using ultrasonication, cleaned with silica and analyzed by diode array detector–high-performance liquid chromatography using acetonitrile/water as mobile phase. The 25th and 75th percentile concentration of ∑PAHs was 264 μg kg^?1 and 584 μg kg^?1, respectively, with mean and median of 445 μg kg^?1 and 421 μg kg^?1. The detection frequency of PAHs in all samples was lower for low molecular weight PAHs (19%) than high molecular weight PAHs (81%). The concentration of seven probable carcinogenic PAHs accounted for 67% of the ∑PAHs. PAHs toxicity potential as benzo(a)pyrene toxicity equivalent ranged between 2.52–253 μg BaP_TEQ kg^?1. Composition profile of PAHs with different aromatic rings and selected diagnostic molecular ratios suggested the local pyrogenic sources of PAHs from vehicular emissions, diesel engines, biomass combustion, gasoline, and coal combustions.     

Pruning deep convolutional neural networks for efficient edge computing in condition assessment of infrastructures

Health monitoring of civil infrastructures is a key application of Internet of things (IoT), while edge computing is an important component of IoT. In this context, swarms of autonomous inspection robots, which can replace current manual inspections, are examples of edge devices. Incorporation of pretrained deep learning algorithms into these robots for autonomous damage detection is a challenging problem since these devices are typically limited in computing and memory resources. This study introduces a solution based on network pruning using Taylor expansion to utilize pretrained deep convolutional neural networks for efficient edge computing and incorporation into inspection robots. Results from comprehensive experiments on two pretrained networks (i.e., VGG16 and ResNet18) and two types of prevalent surface defects (i.e., crack and corrosion) are presented and discussed in detail with respect to performance, memory demands, and the inference time for damage detection. It is shown that the proposed approach significantly enhances resource efficiency without decreasing damage detection performance.     

An Instant,Green, Microwave Irradiated Process for the Preparation of Advanced,Hybrid, Nanoflower of Thorium Oxide and Thorium Oxalate Hydrate Useful for Broad Application Spectrum

Protection of Metals and Physical Chemistry of Surfaces - We report a facile and reproducible method to synthesize advanced, homogenized, hybrid, nanoflower of thorium oxide and thorium oxalate...