Attracting and Enabling Women For Future Technical Roles in Aviation: Opportunities, Challenges and Recommendations

Agenda Phenomenon Opportunities Issues Barriers Initiatives Enabler

A Circuit-Based Neural Network with Hybrid Learning of Backpropagation and Random Weight Change Algorithms.

A hybrid learning method of a software-based backpropagation learning and a hardware-based RWC learning is proposed for the development of circuit-based neural networks. The backpropagation is known as one of the most efficient learning algorithms. A weak point is that its hardware implementation is extremely difficult. The RWC algorithm, which is very easy to implement with respect to its hardware circuits, takes too many iterations for learning. The proposed learning algorithm is a hybrid one of these two. The main learning is performed with a software version of the BP algorithm, firstly, and then, learned weights are transplanted on a hardware version of a neural circuit. At the time of the weight transplantation, a significant amount of output error would occur due to the characteristic difference between the software and the hardware. In the proposed method, such error is reduced via a complementary learning of the RWC algorithm, which is implemented in a simple hardware. The usefulness of the proposed hybrid learning system is verified via simulations upon several classical learning problems

Impact Of Noise And Hearing On Task And Academic Performance Of Primary School Children In Kuala Lumpur

Noise poses a serious threat to children's hearing, health, learning and behavior. This study was done to determine the effects of noise and hearing on task and academic performance of primary school children in Kuala Lumpur. A total of 110 Standard One Malay children aged from 6 1/2 to 7 1/2 years were recruited in this study according to stratified random sampling. Environmental noise levels and personal noise exposures were measured by using sound level meter and noise dosimeter, respectively. A personal questionnaire and audiometric tests was administered on all the respondents. Seven tests in the McCarthy Scales of Children's Abilities constituted the tests in the Task Performance. Task Performance was carried out twice on the same respondents in quiet and noise condition. The child's academic performance was determined by his latest examination result in the school. Environmental noise measurement indicated that a mean equivalent continuous sound level (LEQ), maximum level (LMAX) and minimum level (LMIN) of at least 60 dB (A) was found inside and outside the classrooms irrespective of school days or holidays

A cyberciege traffic analysis extension for teaching network security

CyberCIEGE is an interactive game simulating realistic scenarios that teaches the players Information Assurance (IA) concepts. The existing game scenarios only provide a high-level abstraction of the networked environment, e.g., nodes do not have Internet protocol (IP) addresses or belong to proper subnets, and there is no packet-level network simulation. This research explored endowing the game with network level traffic analysis, and implementing a game scenario to take advantage of this new capability. Traffic analysis is presented to players in a format similar to existing tools such that learned skills may be easily transferred to future real-world situations. A network traffic analysis tool simulation within CyberCIEGE was developed and this new tool provides the player with traffic analysis capability. Using existing taxonomies of cyber-attacks, the research identified a subset of network-based attacks most amenable to modeling and representation within CyberCIEGE. From the attacks identified, a complementary CyberCIEGE scenario was developed to provide the player with new educational opportunities for network analysis and threat identification. From the attack scenario, players also learn about the effects of these cyber-attacks and glean a more informed understanding of appropriate mitigation measures.http://archive.org/details/acyberciegetraff109451057

Strategies in Developing an Aviation & Aerospace Skill Ecosystem for the State of Telangana, India – Case Study of TASK -Telangana Academy for Skill and Knowledge

The aviation & aerospace sector in India has been expanding and growing through partnerships and new research facilities, thereby encouraging more players to invest and optimize the manufacturing process. Improving green and fuel-efficient technologies have contributed to a steady rise in fuelling the demand for aerospace engineers. The state of Telangana is fast emerging as a new aviation cluster with some of the country’s best-qualified engineers and technicians. The potential of the aerospace industry is immense as it continuously requires skilled technicians and engineers. Considering the precision of skill required, it is imperative that rigorous training be imparted to all those who would be directly working on the components. With a plethora of job opportunities available in this industry, from engineering technicians, template makers, numeric control tool programmers, die makers, to technical writers and graphic artists, TASK has identified the gaps in the current learning landscape. It has taken the onus of working with technology partners and service providers across the aviation, aerospace and avionics value chain to train youth/professionals to be ‘reskilled’ to fit the aerospace and aviation sector. This paper thus explores the strategies to enhance synergies between industry, government and academia

A Voltage Mode Memristor Bridge Synaptic Circuit with Memristor Emulators

A memristor bridge neural circuit which is able to perform signed synaptic weighting was proposed in our previous study, where the synaptic operation was verified via software simulation of the mathematical model of the HP memristor. This study is an extension of the previous work advancing toward the circuit implementation where the architecture of the memristor bridge synapse is built with memristor emulator circuits. In addition, a simple neural network which performs both synaptic weighting and summation is built by combining memristor emulators-based synapses and differential amplifier circuits. The feasibility of the memristor bridge neural circuit is verified via SPICE simulations

Regulation of Cullin RING E3 Ubiquitin Ligases by CAND1 In Vivo

Cullin RING ligases are multi-subunit complexes consisting of a cullin protein which forms a scaffold onto which the RING protein Rbx1/2 and substrate receptor subunits assemble. CAND1, which binds to cullins that are not conjugated with Nedd8 and not associated with substrate receptors, has been shown to function as a positive regulator of Cullin ligases in vivo. Two models have been proposed to explain this requirement: (i) CAND1 sequesters cullin proteins and thus prevents autoubiquitination of substrate receptors, and (ii) CAND1 is required to promote the exchange of bound substrate receptors. Using mammalian cells, we show that CAND1 is predominantly cytoplasmically localized and that cullins are the major CAND1 interacting proteins. However, only small amounts of CAND1 bind to Cul1 in cells, despite low basal levels of Cul1 neddylation and approximately equal cytoplasmic endogenous protein concentrations of CAND1 and Cul1. Compared to F-box protein substrate receptors, binding of CAND1 to Cul1 in vivo is weak. Furthermore, preventing binding of F-box substrate receptors to Cul1 does not increase CAND1 binding. In conclusion, our study suggests that CAND1 does not function by sequestering cullins in vivo to prevent substrate receptor autoubiquitination and is likely to regulate cullin RING ligase activity via alternative mechanisms