NPS Australia Submission System
  • 0

    No products in the cart.

Paper Type: Digest

A FEATURE-INTEGRATED MACHINE LEARNING FRAMEWORK FOR LYSINE PTM SITE CLASSIFICATION

Post-translational modification (PTM) increases the functional diversity of proteins by
introducing new functional groups to the side chain of amino acid of a protein. The structural and
functional diversities of proteins as well as plasticity and dynamics of living cells are significantly
dominated by the post-translational modifications (PTMs). It plays an important role in diversity,
structure, plasticity, active cells even in human diseases and so on. PTMs are also responsible for
expanding the genetic code and for regulating cellular physiology. Like other PTMs, PTM of
lysine residues have proven to be major regulators of gene expression, protein-protein interactions,
and protein processing and degradation. So far so many computational methods have been
developed to identify PTM of lysine but most of them are binary classifier. It can predict various
single-label PTM sites, and a very few have been developed to solve multi-label PTM of lysine.
Our expectation is to design a simple and efficient predictor for predicting multiple lysine PTM
sites. Our goal is to get higher success rates in comparison with the existing predictors in this area.

Smell-Augmented Symbolic Regression for Explainable Cross-Project Defect Prediction

Cross-Project Defect Prediction (CPDP) remains a critical challenge in software engineering, primarily due to severe dataset shift and systemic class imbalance between source and target repositories. Furthermore, the state-of-the-art black-box machine learning models have an inherent lack of interpretability and out-of-distribution generalization. This paper presents an empirical framework leveraging Smell-Enhanced Symbolic Regression (SR) to derive explicit, human-readable mathematical formulations for defect prediction, capable of robust extrapolation. The proposed framework is rigorously evaluated across eight heterogeneous software project transitions against 23 baseline configurations using five standardized performance metrics and non-parametric significance analysis. The empirical results demonstrate that Symbolic Regression delivers complete interpretability without sacrificing the predictive performance of complex, top-tier models.

Analysis of Skin Effect and Transient Behavior in Transformer Bushings with Realistic Material Conductivities

This is important for power system

Modeling of RC Snubber, Ferrite Bead and Gate Drive Impedance for Optimal EMI Suppression and Switching Loss Trade-Off in SiC MOSFET Power Converters

The relentless drive towards extremely fast switching frequency, elevated operating voltage, increased thermal capabilities, and reduced switching losses have positioned Silicon Carbide (SiC) MOSFETs based converters at the forefront in high-performance power electronics applications. This brings in the benefits of enhanced switching frequencies, improved power density, and enhanced dynamic response. Unfortunately, this is critically constrained by severe Electromagnetic Interference (EMI), high-frequency ringing and undesirable switching oscillations. These challenges are addressed through a systemic modelling and analysis mitigation measures, the holistic co-optimizing ferrite bead on the gate loop, RC snubber, and gate drive impedance simultaneously. An LTspice simulation framework was developed, incorporating the manufacturer’s spice models to accurately model parasitics and quantify losses. The proposed methodology shows that the addition of the mitigation technique offers a practical trade-off between EMI suppression and switching performance, without increasing the switching losses.

Modeling, Analysis, and Design of a Solar-Assisted Light Electric Vehicle Drive System Using a Triple Active Bridge Converter

Incorporating a dynamic energy routing system on a light electric vehicle using TAB converter and efficient utilization of solar and electrical energy which will help reduce grid dependence and lower emissions, contributing to sustainable transportation.

Learning through Research: The Impact of Pattern Extraction on Neural Networks Architectures

Meeting today’s learners learning styles. Demonstrate how research influences learning new concepts to the level of mastering.

Design and Simulation of Advanced Patch Antenna and Analysis using High Frequency Structural Simulator (HFSS)

Design a Patch Antenna, Simulation of an optimized Antenna using HFSS, A a good quality antenna modeled with good return loss

Limiting the Pollution of Batteries used in Ultra-Low Power Consumers. A Comprehensive Short Review

Detailed review of battery pollution in ultra-low power consumers

Purification of Exhaust Gas from the Marine Fuels Applied to Next-generation Ships
Community Battery System Sizing To Maximize Financial Returns to the Prosumers in PV-Rich Neighborhood

The paper extends the knowledge on Community battery systems and sustainable energy.

North Atlantic Offshore Wind Characteristics: Modeling and Comparison with Field Measurements and Industry Standards

Wind characteristics are critical to offshore wind resource development. The power output from a wind turbine is very sensitive to the local wind speed. Wind speed measurement is often limited to surface area close to Lidar buoys or meteorological stations and up to 200m due to the range of remote sensing devices. On the other hand, wind fields from ground level and up to 20000m above ground level can be simulated using Weather Research & Forecasting (WRF) model. In this study, WRF simulations are performed for the North Atlantic offshore waters to obtain wind speed time and spatial properties. Statistics of wind speeds for selected sites are derived and validated with field measurements. Wind vertical profiles are compared with ISO and IEC standards, and a power law profile is further derived to find the best fit. It is also demonstrated that the WRF model is reliable to forecast wind data, optimize prediction and improve reliability for coastal and offshore energy development. The wind modeling and characterizing can be extended to global regions to identify prospects with the most renewable energy potentials.