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.     

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.     

Stepwise-Graded Si3N4–SiC Ceramics with Improved Wear Properties

The processing of stepwise graded Si₃N₄/SiC ceramics by pressureless co-sintering is described. Here, SiC (high elastic modulus, high thermal expansion coefficient) forms the substrate and Si₃N₄ (low elastic modulus, low thermal expansion coefficient) forms the top contact surface, with a stepwise gradient in composition existing between the two over a depth of ∼1.7 mm. The resulting Si₃N₄ contact surface is fine-grained and dense, and it contains only 2 vol% yttrium aluminum garnet (YAG) additive. This graded ceramic shows resistance to cone-crack formation under Hertzian indentation, which is attributed to a combined effect of the elastic-modulus gradient and the compressive thermal-expansion-mismatch residual stress present at the contact surface. The presence of the residual stress is corroborated and quantified using Vickers indentation tests. The graded ceramic also possesses wear properties that are significantly improved compared with dense, monolithic Si₃N₄ containing 2 vol% YAG additive. The improved wear resistance is attributed solely to the large compressive stress present at the contact surface. A modification of the simple wear model by Lawn and co-workers is used to rationalize the wear results. Results from this work clearly show that the introduction of surface compressive residual stresses can significantly improve the wear resistance of polycrystalline ceramics, which may have important implications for the design of contact-damage-resistant ceramics.     

Space and time efficient parallel algorithms and software for EST clustering

Expressed sequence tags, abbreviated as ESTs, are DNA molecules experimentally derived from expressed portions of genes. Clustering of ESTs is essential for gene recognition and for understanding important genetic variations such as those resulting in diseases. We present the algorithmic foundations and implementation of PaCE, a parallel software system we developed for large-scale EST clustering. The novel features of our approach include 1) design of space-efficient algorithms to limit the space required to linear in the size of the input data set, 2) a combination of algorithmic techniques to reduce the total work without sacrificing the quality of EST clustering, and 3) use of parallel processing to reduce runtime and facilitate clustering of large data sets. Using a combination of these techniques, we report the clustering of 327,632 rat ESTs in 47 minutes, and 420,694 Triticum aestivum ESTs in 3 hours and 15 minutes, using a 60-processor IBM xSeries cluster. These problems are well beyond the capabilities of state-of-the-art sequential software. We also present thorough experimental evaluation of our software including quality assessment using benchmark Arabidopsis EST data.