Effect of prior cold work on the mechanical properties of weldments

Heat exchanger units used in steam raising power plant are often manufactured using many metres of austenitic stainless steel tubes that have been plastically formed (bent and swaged) and welded into complex shapes. The amount of plastic deformation (pre-straining) before welding varies greatly. This has a significant effect on the mechanical properties of the welded tubes and on the final residual stress state after welding. The aim of the present work was to measure and understand the combined effects of pre-straining and welding on the properties and residual stress levels in stainless steel tubing weldments. Effects of plastic deformation were simulated by plastically straining three identical stainless steel tubes to different strain levels (0%, 10% and 20%). Then each tube was cut into two halves and welding back together. The variation in mechanical properties across weldments was measured using digital image correlation (DIC) and a series of strain gauges (SG). Residual stresses were measured on the 0% (undeformed) and 20% prestrained and welded tubes by neutron diffraction. It was found that the welding process had a marked effect on the tensile properties of parent material within 25mm of the weld centre-line. Evidence of cyclic strain hardening was observed in the tube that had not been pre-strained, and evidence of softening seen in the 10% and 20% pre-strained tubes. Macroscopic residual stresses were measured to be near zero at distances greater than 25 mm from the weld centre-line, but measurements in the 20% pre-strained tube revealed the presence of micro residual stresses having a magnitude of up to 50 MPa

The Hopf algebra structure of the Z3_3-graded quantum supergroup GLq,j(1∣1)_{q,j}(1|1)

In this work, we give some features of the Z$_3$-graded quantum supergroup

New intelligent heuristic algorithm to mitigate security vulnerabilities in IPv6

Zero day Cyber-attacks created potential impacts on the way information is held and protected, however one of the vital priorities for governments, agencies and organizations is to secure their network businesses, transactions and communications, simultaneously to avoid security policy and privacy violations under any circumstances. Covert Channel is used to in/ex-filtrate classified data secretly, whereas encryption is used merely to protect communication from being decoded by unauthorized access. In this paper, we propose a new Security Model to mitigate security attacks on legitimate targets misusing IPv6 vulnerabilities. The approach analyses, detects and classifies hidden communication channels through implementing an enhanced feature selection algorithm with a coherent Naive Bayesian Classifier. NBC is one of the most prominent classification algorithm defining the highest probability in data mining area. The proposed framework uses Intelligent Heuristic Algorithm (IHA) to analyse and create a novel primary training data, furthermore a modified Decision Tree C4.5 technique is suggested to classify the richest attribute presenting hidden channels in IPv6 network. The results evaluation showed better detection performance, high accuracy in True Positive Rate (TPR) and a low False Negative Rate (FNR) and a clear attribute ranking

Implementation of hybrid artificial intelligence technique to detect covert channels in new generation network protocol IPv6

Intrusion detection systems offer monolithic way to detect attacks through monitoring, searching for abnormal characteristics and malicious behavior in network communications. Cyber-attack is performed through using covert channel which currently, is one of the most sophisticated challenges facing network security systems. Covert channel is used to ex/infiltrate classified information from legitimate targets, consequently, this manipulation violates network security policy and privacy. The New Generation Internet Protocol version 6 (IPv6) has certain security vulnerabilities and need to be addressed using further advanced techniques. Fuzzy rule is implemented to classify different network attacks as an advanced machine learning technique, meanwhile, Genetic algorithm is considered as an optimization technique to obtain the ideal fuzzy rule. This paper suggests a novel hybrid covert channel detection system implementing two Artificial Intelligence (AI) techniques; Fuzzy Logic and Genetic Algorithm (FLGA) to gain sufficient and optimal detection rule against covert channel. Our approach counters sophisticated network unknown attacks through an advanced analysis of deep packet inspection. Results of our suggested system offer high detection rate of 97.7% and a better performance in comparison to previous tested techniques

Numerical Investigation of Ohmic Heating in Channel Flow

Heat generation by direct electric conduction (ohmic heating) in a fully developed channel flow was studied to evaluate interaction between the hydrodynamic, electric and the thermal phenomena involved under the effect of natural convection. The equations governing the system were solved numerically by CFD finite volume code (FLUENT6.1 software package). The velocity profiles accelerate more near the wall than at the center that makes the temperature distribution uniform in the channel span. The numerical model is validated with an earlier experimental study [El Moctar et all. 1996] and yielded good agreement

Instructors’ transformations during early online teaching experiences

AbstractUse of online learning environments became popular through the opportunities that internet technologies provide. However, most of instructors have only some assumptions and beliefs about teaching online in a lack of prior knowledge or experience. Transformative learning occurs when they critically reflect on and revise these, and change their actions. Transformative learning is essential for instructors especially in technology-affiliated learning environments. The study is designed as a qualitative case study. The purpose is analyzing and presenting instructors’ transformational experiences in online learning environments

Lifetimes, branching ratios, and transition probabilities in Co ii

The radiative lifetime of 14 levels in the z^5F, z^5D, and z^5G terms of Co ii have been measured with use of time-resolved laser fluorescence spectroscopy with a Co+-ion beam. Our lifetime values are shorter by 15–50 % than earlier results from beam-foil time-of-flight measurements. The lifetimes were converted to 41 individual transition probabilities with use of branching ratios measured on spectra recorded with the 1-m Fourier-transform spectrometer at the Kitt Peak National Observatory. On average our transition probabilities agree with those of Kurucz and Peytremann; for ΔS=1 transitions their calculated values are lower than our experimental results by a factor of ∼(1/4)