Electrospun MgO/Nylon 6 Hybrid Nanofibers for Protective Clothing

Nano-Micro Letters - Magnesia (MgO) nanoparticles were produced from magnesite ore (MgCO3) using ball mill. The crystalline size, morphology and specific SSA were characterized by X-ray diffraction...     

In vitro corrosion behaviour of plasma nitrided Ti–6Al–7Nb orthopaedic alloy in Hanks solution

Titanium alloy (Ti–6Al–7Nb) used for orthopaedic applications was nitrided using a conventional dc plasmatechnique. Load-dependent microhardness measurements exhibit a hardness of 2087 H_v at 25 g load for the alloy nitrided at 900 8C for 8 h. Cyclic polarization measurement in Hanks solution show maximum corrosion rate and minimum areaof repassivation for the alloy nitrided for 8 h at 900 8C. Electrochemical impedance measurements show an increase in charge transfer resistance and decrease in double layer capacitance when compared to untreated alloy.     

A new fluoride mediated synthesis of mesoporous silica and their usefulness in controlled delivery of duloxetine hydrochloride a serotonin re-uptake inhibitor

A new type of mesoporous silica nanoparticle (MSN) was synthesized in fluoride media via sol–gel technique using TritonX 100 and Tween-20. The surface area and pore volume of the MSN particles were modified by varying the concentration of Tween-20. The prepared MSN nanoparticles with large surface area and pore volume (T-2, T-3) were selected to accommodate the model drug duloxetine hydrochloride (DX) for evaluation of their drug-loading and release abilities. Calcined and DX loaded nanoparticles were characterized by Brunauer–Emmett–Teller technique (BET), powder X-ray diffraction (XRD), scanning electron microscopy (SEM), thermogravimetric (TG) analysis and UV-diffuse reflectance (UV-DRS). In vitro release studies proved that the particle displays an initial burst release followed by sustained release for up to 140 h. From the studies it is evident that the synthesized particle may be useful as a carrier for sustained release of active pharmaceutical ingredients (APIs).     

Vision-based action recognition of earthmoving equipment using spatio-temporal features and support vector machine classifiers

Video recordings of earthmoving construction operations provide understandable data that can be used for benchmarking and analyzing their performance. These recordings further support project managers to take corrective actions on performance deviations and in turn improve operational efficiency. Despite these benefits, manual stopwatch studies of previously recorded videos can be labor-intensive, may suffer from biases of the observers, and are impractical after substantial period of observations. This paper presents a new computer vision based algorithm for recognizing single actions of earthmoving construction equipment. This is particularly a challenging task as equipment can be partially occluded in site video streams and usually come in wide variety of sizes and appearances. The scale and pose of the equipment actions can also significantly vary based on the camera configurations. In the proposed method, a video is initially represented as a collection of spatio-temporal visual features by extracting space–time interest points and describing each feature with a Histogram of Oriented Gradients (HOG). The algorithm automatically learns the distributions of the spatio-temporal features and action categories using a multi-class Support Vector Machine (SVM) classifier. This strategy handles noisy feature points arisen from typical dynamic backgrounds. Given a video sequence captured from a fixed camera, the multi-class SVM classifier recognizes and localizes equipment actions. For the purpose of evaluation, a new video dataset is introduced which contains 859 sequences from excavator and truck actions. This dataset contains large variations of equipment pose and scale, and has varied backgrounds and levels of occlusion. The experimental results with average accuracies of 86.33% and 98.33% show that our supervised method outperforms previous algorithms for excavator and truck action recognition. The results hold the promise for applicability of the proposed method for construction activity analysis.     

Tensile stress relaxation in unsaturated granular materials

The mechanics of granular media at low liquid saturation levels remain poorly understood. Macroscopic mechanical properties are affected by microscale forces and processes, such as capillary forces, inter-particle friction, liquid flows, and particle movements. An improved understanding of these microscale mechanisms is important for a range of industrial applications and natural phenomena (e.g. landslides). This study focuses on the transient evolution of the tensile stress of unsaturated granular media under extension. Experimental results suggest that the stress state of the material evolves even after cessation of sample extension. Moreover, we observe that the packing density strongly affects the efficiency of different processes that result in tensile stress relaxation. By comparing the observed relaxation time scales with published data, we conclude that tensile stress relaxation is governed by particle rearrangement and fluid redistribution. An increased packing density inhibits particle rearrangement and only leaves fluid redistribution as the major process that governs tensile stress relaxation.     

Layered reasoning for randomized distributed algorithms

This paper adopts the communication closed layer (CCL) concept of Elrad and Francez to the formal reasoning of randomized distributed algorithms. We do so by enriching probabilistic automata (PA) with a layered composition operator, an intermediate between parallel and sequential composition. Layered composition is used to establish probabilistic counterparts of the CCL laws that exploit independence and/or precedence conditions between the constituent PA. The probabilistic CCL laws enable partial order (po-) equivalence when layered composition is replaced by sequential composition. Such po-equivalence induces a purely syntactic partial-order state space reduction via layered separation in compositions of PA while preserving probabilistic next-free linear-time properties. The feasibility of such layered separation is demonstrated on a randomized mutual exclusion algorithm by Kushilevitz and Rabin, complementing an algebraic approach (for analyzing this algorithm) by McIver, Gonzalia, Cohen, and Morgan.