Effects of Prior Annealing on the Spark Plasma Sintering of Nanostructured Y2O3 Powders

The effects produced by annealing Y₂O₃ nanopowders on their spark plasma sintering (SPS) behavior are systematically investigated in this work. It is found that the annealed powders display higher sinterability with respect to the as‐received ones. Indeed, the maximum densification level reached from pristine powders is about 97.5%, whereas density decreases when further increasing either the sintering temperature or the dwell time. In contrast, the density of SPS products obtained from pretreated powder monotonically increases with temperature and processing time, thus leading to fully dense materials in 30 min at 1050°C and 60 MPa. Correspondingly, it is found that the annealing treatment markedly inhibits grain coarsening during SPS. Thus, dense translucent samples with grain size below 100 nm can be attained from annealed powders. On the other hand, white‐opaque specimens with significantly coarser microstructures (up to 1‐μm‐sized grains) are obtained when pristine powders are directly processed under the same sintering conditions. Furthermore, it is observed that the annealing treatment of SPS samples in air allows for graphite contamination removal, whereas no improvement in term of light transmittance is produced.     

Thermal and thermoelastic properties of fast extrusion furnace (FEF) carbon black loaded SBR vulcanizates

The effect of FEF carbon black as filler on the thermal capacity c, diffusivity a, and thermal conductivity λ, of styrene butadiene rubber (SBR) composites in the temperature range 300–420 K was studied. The filler strongly increases the thermal diffusivity, whilst strongly decreasing the thermal capacity and the thermal conductivity (except at high FEF content ≥80 phr). The influence of the filler on the thermoelastic behaviour of the same composites was also investigated. It was found that the thermoelastic temperature change (ΔT) increased with carbon black concentration as well as the entropy change per unit extension.     

An efficient IoT based smart farming system using machine learning algorithms

Multimedia Tools and Applications - This paper suggests an IoT based smart farming system along with an efficient prediction method called WPART based on machine learning techniques to predict crop...     

Power and Wheel Wear for Grinding Nickel Alloy with Plated CBN Wheels

Electroplated CBN grinding wheels are manufactured with a single layer of abrasive grains. The grinding performance of these plated wheels changes significantly as the wheel wears down. The present investigation was undertaken to understand the transient grinding behavior with electroplated CBN wheels in order to provide a logical basis for process control. In this paper, particular attention is directed to the effect of wheel wear and operating parameters on grinding of a nickel alloy. Wheels were worn to various stages and then used to perform grinding tests under various grinding conditions to measure grinding forces and power and to produce ground specimens. Based on models for grinding with conventional aluminum oxide wheels, a power model for grinding of a nickel alloy with plated CBN wheels was established and validated. Microscopic observations of the ground specimens reveal that thermal damage in the form of a White Etch Layer (WEL) appears only when grinding with a worn wheel under conditions that lead to high temperatures.     

Maximal limited similarity-based rough set model

Non-symmetric similarity relation-based rough set model (NS-RSM) is viewed as mathematical tool to deal with the analysis of imprecise and uncertain information in incomplete information systems with “?” values. NS-RSM relies on the concept of non-symmetric similarity relation to group equivalent objects and generate knowledge granules that are then used to approximate the target set. However, NS-RSM results in unpromising approximation space when addressing inconsistent data sets that have lots of boundary objects. This is because objects in the same similarity classes are not necessarily similar to each other and may belong to different target classes. To enhance NS-RSM capability, we introduce the maximal limited similarity-based rough set model (MLS-RSM) which describes the maximal collection of indistinguishable objects that are limited tolerance to each other in similarity classes. This allows accurate computation to be done for the approximation space. Furthermore, approximation accuracy comparisons have been conducted among NS-RSM and MLS-RSM. The results demonstrate that MLS-RSM model outperforms NS-RSM and can approximate the target set more efficiently.     

Analysis of viscoelastic Bernoulli–Euler nanobeams incorporating nonlocal and microstructure effects

The present study introduces an analytical–computational model to simulate the effects of different simultaneous aspects on the behavior of nanobeams. The first one deals with the space nonlocality interaction and taking into account the microstructure effects, which has been formulated by using the nonlocal couple-stress elasticity. The second factor deals with the memory-dependent effect and has been investigated in the framework of linear viscoelasticity theory. It is the first time to apply the coupled effects of the microstructure and long-range interactions between the particles, to reflect the size-dependency of viscoelastic structures. Bernoulli–Euler nanobeam is taken as a vehicle to present the details of the proposed model. Eringen nonlocal elasticity and the modified couple-stress theory are used to formulate the two phenomena of long-range cohesive interaction and the microstructure local rotation effects, respectively. Boltzmann superposition viscoelastic model, endowed by Wiechert series, is used to simulate the linear behavior of isotropic, homogeneous and non-aging viscoelastic materials. The extended Hamilton’s principle is applied to formulate the analytical model of mechanical behavior of the nonlocal couple-stress nanobeam. The model has been verified and some results are compared with those published in the literature and a good agreement has been obtained. It is shown that the material-length scale parameter, nonlocal parameter, viscoelastic relaxation time and length-to-thickness ratio have a significant effect on the bending response of viscoelastic nanobeams with various boundary conditions.     

Anticonvulsant Profiles of Certain New 6-Aryl-9-substituted-6,9-diazaspiro-[4.5]decane-8,10-diones and 1-Aryl-4-substituted-1,4-diazaspiro[5.5]undecane-3,5-diones

Synthesis and anticonvulsant potential of certain new 6-aryl-9-substituted-6,9-diazaspiro[4.5]decane-8,10-diones (6a–l) and 1-aryl-4-substituted-1,4-diazaspiro[5.5]undecane-3,5-diones (6m–x) are reported. The intermediates 1-[(aryl)(cyanomethyl)amino]cycloalkanecarboxamides (3a–f) were prepared via adopting Strecker synthesis on the proper cycloalkanone followed by partial hydrolysis of the obtained nitrile functionality and subsequent N-cyanomethylation. Compounds 3a–f were subjected to complete nitrile hydrolysis to give the respective carboxylic acid derivatives 4a–f which were cyclized under mild conditions to give the spiro compounds 5a–f. Ultimately, compounds 5a–f were alkylated or aralkylated to give the target compounds 6a–i and 6m–u. On the other hand, compounds 6j–l and 6v–x were synthesized from the intermediates 5a–f through alkylation, dehydration and finally tetrazole ring formation. Anticonvulsant screening of the target compounds 6a–x revealed that compound 6g showed an ED₅₀ of 0.0043 mmol/kg in the scPTZ screen, being about 14 and 214 fold more potent than the reference drugs, Phenobarbital (ED₅₀ = 0.06 mmol/kg) and Ethosuximide (ED₅₀ = 0.92 mmol/kg), respectively. Compound 6e exhibited an ED₅₀ of 0.019 mmol/kg, being about 1.8 fold more potent than that of the reference drug, Diphenylhydantoin (ED₅₀ = 0.034 mmol/kg) in the MES screen. Interestingly, all the test compounds 6a–x did not show any minimal motor impairment at the maximum administered dose in the neurotoxicity screen.     

Multistage bayesian autonomy for high‐precision operation in a large field

This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high‐precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high‐precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real‐world problems. A successful demonstration using the UAV is also presented.