Dysprosium doped copper oxide (Cu1-xDyxO) nanoparticles enabled bifunctional electrode for overall water splitting

The production of hydrogen, a favourable alternative to an unsustainable fossil fuel remains as a significant hurdle with the pertaining challenge in the design of proficient, highly productive and sustainable electrocatalyst for both oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Herein, the dysprosium (Dy) doped copper oxide (Cu_1-xDy_xO) nanoparticles were synthesized via solution combustion technique and utilized as a non-noble metal based bi-functional electrocatalyst for overall water splitting. Due to the improved surface to volume ratio and conductivity, the optimized Cu_1-xDy_xO (x = 0.01, 0.02) electrocatalysts exhibited impressive HER and OER performance respectively in 1 M KOH delivering a current density of 10 mAcm^?2 at a potential of ?0.18 V vs RHE for HER and 1.53 V vs RHE for OER. Moreover, the Dy doped CuO electrocatalyst used as a bi-functional catalyst for overall water splitting achieved a potential of 1.56 V at a current density 10 mAcm^?2 and relatively high current density of 66 mAcm^?2 at a peak potential of 2 V. A long term stability of 24 h was achieved for a cell voltage of 2.2 V at a constant current density of 30 mAcm^?2 with only 10% of the initial current loss. This showcases the accumulative opportunity of dysprosium as a dopant in CuO nanoparticles for fabricating a highly effective and low-cost bi-functional electrocatalyst for overall water splitting.     

Surface Hardening of High-Strength Low Alloy Steels (HSLA) Dual-Phase Steels by Ball Burnishing Using Factorial Design

Burnishing is used increasingly as a finishing operation which gives additional advantages such as increased hardness, fatigue strength, and wear resistance. Experimental work based on 3⁴ factorial design was carried out to establish the effects of ball burnishing parameters on the surface hardness of high-strength low alloy steels (HSLA) dual-phase (DP) steel specimens. Statistical analysis of the results shows that the speed, feed, lubricant and ball diameter have significant effect on surface hardness.     

Crystal plasticity modeling of deformation and creep in polycrystalline Ti-6242

This paper develops an experimentally validated computational model based on crystal plasticity for the analysis of two-phase α/β Ti-6242 polycrystalline alloys. A rate-dependent elastic-crystal plasticity model is incorporated in this model to accommodate anisotropy in material behavior and tension-compression asymmetry inherent to this alloy. A combination of microtesting, orientation imaging microscopy, computational simulations, and minimization process, involving genetic algorithms, is implemented in this study for careful characterization and calibration of the material parameters. Size effects are considered in this analysis through a simple scaling process. A homogenized equivalent model of the primary α with transformed β colonies is developed for incorporation in the Ti-6242 FE model. The polycrystalline Ti-6242 computational model incorporates accurate phase volume fractions, as well as statistically equivalent orientation distributions to those observed in the orientation imaging microscopy scans. The effects of orientation, misorientations, and microtexture distributions are investigated through simulations by this computational model. The model is used to simulate constant strain rate and creep tests in compression and tension, and the results are compared with experiments. The effects of microstructure and creep-induced load-shedding on the localization of microstructural stresses and strains are studied for potential crack initiation criteria.     

Wavelet based Denial-of-Service detection

Network Denial-of-Service (DoS) attacks that disable network services by flooding them with spurious packets are on the rise. Criminals with large networks (botnets) of compromised nodes (zombies) use the threat of DoS attacks to extort legitimate companies. To fight these threats and ensure network reliability, early detection of these attacks is critical. Many methods have been developed with limited success to date. This paper presents an approach that identifies change points in the time series of network packet arrival rates. The proposed process has two stages: (i) statistical analysis that finds the rate of increase of network traffic, and (ii) wavelet analysis of the network statistics that quickly detects the sudden increases in packet arrival rates characteristic of botnet attacks.Most intrusion detections are tested using data sets from special security testing configurations, which leads to unacceptable false positive rates being found when they are used in the real world. We test our approach using data from both network simulations and a large operational network. The true and false positive detection rates are determined for both data sets, and receiver operating curves use these rates to find optimal parameters for our approach. Evaluation using operational data proves the effectiveness of our approach.