Numerical implementation of drop and tilt method of five-axis tool positioning for tensor product surfaces

The Drop and Tilt tool position method for five-axis machining positions a toroidal tool to have two points of contact with a tensor product Bézier surface. The method results in high order simultaneous non-linear transcendental equations that are slow to solve. In this paper, an efficient numerical implementation of the Drop and Tilt Method of multi-point tool positioning on tensor product surfaces is presented. The method was implemented in C++, and is a direct method that does not convert the tensor product surface into an intermediate representation such as triangles or point clouds. The method is successfully demonstrated on a number of tensor product Bézier surfaces.     

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.     

Low‐temperature plasma processing for the enhancement of surface properties and dyeability of wool fabric

In the present investigation, wool fabric was treated with a low‐temperature air plasma. The plasma discharge power and treatment time were varied. The effect of plasma treatment on various fabric properties such as wettability, wickability, dyeability, crease recovery angle, breaking strength, and elongation at break was investigated. Surface morphology was studied using SEM micrographs. The fabric became substantially hydrophilic even with a short duration of air plasma treatment of 30 s with improvement in dye uptake and in the rate of dyeing when dyed at a lower temperature. Under these treatment conditions, aging was almost nil in a dry environment, even after 45 days, whereas some aging was observed in a humid (75% relative humidity) environment. A 20% increase in the breaking strength and 24% increase in the elongation at break were observed with reduction in wrinkle recovery angle to 133–144° from 169° for untreated fabric. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2016 , 133, 43097.     

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.     

Micellar Growth in Cetylpyridinium Chloride/Alcohol System: Role of Long Chain Alcohol,Electrolyte and Surfactant Head Group

The microstructural transition of aqueous 0.1 M cetylpyridinium chloride (CPC) in the combined presence of salt KBr and long chain alcohol (C₉OH-C₁₂OH) has been studied as a function of alcohol concentration, electrolyte concentration and temperature. The viscosity of the CPC/KBr micellar system showed a peaked behavior with alcohol concentration (C ₀), due to alcohol induced structural transition, which was confirmed by dynamic light scattering (DLS) and rheological analysis. Besides C ₀, the chain length of alcohol (n) was found to show a remarkable effect on the micellization behavior of CPC/KBr system. It was observed that the ability of alcohol to induce micelle growth diminishes with n, which was well supported by viscosity, rheology and DLS measurements. To examine the effect of the electrolyte on the micellar growth, the salt concentration was varied from 0.05 to 0.15 M and it was observed that with increase in [KBr], the peak position shifts towards lower C ₀. The effect of temperature on the micellar system showed interesting phase behavior for CPC/KBr/Decanol. The system exhibited a closed solubility loop with an upper critical solution temperature (UCST) > the lower critical solution temperature (LCST), reminiscence of nicotine-water system. The role of surfactant head group on the structural evolution was revealed by comparing the present results with our previous report for similar micellar system, CTAB/KBr/long chain alcohol.     

Tuning of Cloud Point by the Nature of Surfactant Headgroup: Influence of Counterion and Additives

Work was performed to distinguish the role of sulfonate (–SO₃ ^?) and sulfate (–OSO₃ ^?) with respect to the micellization and clouding phenomenon in ionic surfactant solutions. The clouding phenomenon is a recent addition to the conventional one observed with nonionic surfactants. Three ionic surfactants [sodium dodecylsulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), and sodium dodecylsulfonate (SDSo)] are chosen and the effects of added tetra-n-pentylammonium bromide (TPeAB) and benzyl tributylammonium bromide (BTAC) have been studied on micellization and clouding behaviors in aqueous solution. Based on critical micelle concentration (CMC) and cloud point (CP) measurements, the following order has been observed: SDBS < SDS < SDSo. Though both SDBS and SDSo contain sulfonate groups, they are found at the two ends of the ordering. Therefore, the role of the phenyl ring is also having importance in clouding phenomena. For a typical surfactant, TPeAB was found to be more effective than BTAC. Based on the CP studies, two compositions of SDSo + TPeAB/BTAC were chosen and the effects of different additives (carbohydrate, amino acid, and l-ascorbic acid) on the CP were investigated. Additive may either decrease or increase CP, depending on the structure of the counterion or additive. The present work shows a few novelties: (1) headgroup/counterion dependence of CP and (2) hydrophobicity of counterion/surfactant has an important bearing on the phenomenon. The data can be utilised in improving cloud point extraction methodologies (CPEMs).     

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...