High temperature tensile properties of 316LN stainless steel investigated using automated ball indentation technique

Type 316LN stainless steel (SS) is the principal structural material for the components of sodium cooled fast reactors operating under elevated temperature conditions. In order to assess the degradation in strength of service exposed components using a small specimen testing technique such as automated ball indentation (ABI), it is necessary to carry out prior detailed ABI studies on the virgin material. In this investigation, the tensile behaviour of as-received 316LN SS were investigated at several temperatures in the range 298–973 K using ABI technique. The load-depth of indentation data measured from ABI tests was analyzed using semi-empirical relationships to obtain the tensile properties. The yield stress and the flow curves were determined by correlating ABI results with corresponding uniaxial tensile test results. Trend curve for tensile strength with temperature, as estimated from ABI tests, exhibited a plateau region in the temperature around 823 K, similar to uniaxial tensile tests. The variations of strength coefficient, strain hardening exponent, yield ratio, hardness and uniform ductility with temperature were evaluated from ABI tests. The ABI technique was found to estimate the influence of temperature on tensile properties sensitively.     

Binary whale optimization algorithm: a new metaheuristic approach for profit-based unit commitment problems in competitive electricity markets

This article presents a metaheuristic approach, the binary whale optimization algorithm (BWOA), to solve complex, constrained, non-convex, binary-nature profit-based unit commitment (PBUC) optimization problems of a price-taking generation company (GenCo) in the electricity market. To simulate the binary-nature PBUC problem, the continuous, real-value whale position/location is mapped into binary search space through various transfer functions. This article introduces three variants of BWOA using tangential hyperbolic, inverse tangent (arctan) and sigmoidal transfer functions. The effectiveness of the BWOA approaches is examined in test systems with different market mechanisms, i.e. an energy-only market, and energy and reserve market participation with different reserve payment methods. The simulation results are presented, discussed and compared with other existing approaches. The convergence characteristics, solution quality and consistency of the results across different BWOA variants are discussed. The superiority and statistical significance of the proposed approaches with respect to existing approaches is also presented.     

Effect of Oxygen Flow Rate on Structural,Electrical and Optical Properties of Zinc Aluminum Oxide Thin Films Deposited by DC Magnetron Sputtering

Semiconductors - Zinc Aluminum Oxide thin films were deposited on glass substrates by reactive DC magnetron sputtering method by varying oxygen flow rates from 1 to 4 sccm. Glancing angle X-ray...     

Effects of SiC,Al2O3, and ZrO2 particles on the LBMed characteristics of Al/SiC,Al/Al2O3, and Al/ZrO2 MMCs prepared by stir casting process

The effects of SiC, Al₂O₃, and ZrO₂ particles on the characteristics of Al/SiC, Al/Al₂O₃, and Al/ZrO₂ metal matrix composites (MMCs) have been studied in the present research work. The comparison of machining characteristics has been done to analyze the behavior of various reinforced particles with the variation of laser machining variables. The output characteristics such as dross height and kerf deviation have been investigated and compared with each MMCs. SEM and XRD have been used for the investigation of morphological changes in the structure and agglomeration of reinforced particles. The crack and recast layer formation has been examined in the specimens of higher quantity of reinforced particles. It was observed that the MMC material reinforced with SiC particles has shown different behavior as compared to other MMC materials.     

Solid lipid nanoparticles of Annona muricata fruit extract: formulation,optimization and in vitro cytotoxicity studies

The present study was aimed to develop Annona muricata fruit extract loaded solid lipid nanoparticles (SLNs) and explore its cytotoxic potential in vitro model of breast cancers. Extract loaded SLNs were successfully prepared by high-pressure homogenization followed by ultrasonication method and optimized using 2³ full factorial design. The extract loaded SLNs were characterized using different parameters such as particle size (PS), % entrapment efficiency (EE), zeta potential (ZP) and % cumulative drug release (CDR). The SLNs formulation was optimized on the basis of software analysis with an overall desirability factor. The PS and %EE of the optimized formulation were found to be 134.8?nm and 83.26%, respectively. The optimized formulation showed a CDR of 79.83% up to 48?h. In vitro cytotoxicity efficacy of extract loaded SLNs was determined using MTT and Apoptosis assay and compared to that of a free extract. The SLNs showed a notable apoptotic effect and better efficacy to kill MCF7 cancer cells as compared to free extract. Thus, extract loaded SLNs could be an alternative dosage form which possibly controls therapeutic action with reducing side effect.     

Applicability of Wireless Sensor Networks in Precision Agriculture: A Review

Wireless Personal Communications - Data security is currently become a serious concern in wireless communication system for both the users and providers. Without a secure medium, the data...     

Sensitivity of Viscosity on Molten Ti Infusion into a B4C-Packed Bed at the Microscale

Metallurgical and Materials Transactions B - The effect of the surface tension–viscosity dissipation driving liquid Ti flow into a B4C packed bed was analyzed at 1941 K and 2573 K. The model...     

Distant Supervised Relation Extraction with Cost-Sensitive Loss

Recently, many researchers have concentrated on distant supervision relation extraction (DSRE). DSRE has solved the problem of the lack of data for supervised learning, however, the data automatically labeled by DSRE has a serious problem, which is class imbalance. The data from the majority class obviously dominates the dataset, in this case, most neural network classifiers will have a strong bias towards the majority class, so they cannot correctly classify the minority class. Studies have shown that the degree of separability between classes greatly determines the performance of imbalanced data. Therefore, in this paper we propose a novel model, which combines class-to-class separability and cost-sensitive learning to adjust the maximum reachable cost of misclassification, thus improving the performance of imbalanced data sets under distant supervision. Experiments have shown that our method is more effective for DSRE than baseline methods.