Predicting Protein Kinase Specificity: Predikin Update and Performance in the DREAM4 Challenge

Predikin is a system for making predictions about protein kinase specificity. It was declared the “best performer” in the protein kinase section of the Peptide Recognition Domain specificity prediction category of the recent DREAM4 challenge (an independent test using unpublished data). In this article we discuss some recent improvements to the Predikin web server — including a more streamlined approach to substrate-to-kinase predictions and whole-proteome predictions — and give an analysis of Predikin's performance in the DREAM4 challenge. We also evaluate these improvements using a data set of yeast kinases that have been experimentally characterised, and we discuss the usefulness of Frobenius distance in assessing the predictive power of position weight matrices

Transformer Risk Assessment

Abstract: This paper describes computational techniques for computing risk associated with voltage insecurity, where risk is assessed as the product of probability and consequence of under-voltage and voltage collapse. In contrast to deterministic assessment of voltage security, our approach directly accounts for uncertainties in the analysis. An approach for operational assessment is provided that uses continuation power flow methods. In addition, a planning approach is described which utilizes an interior point optimization method to identify maximum loading conditions over a sequential trajectory of operating conditions. Analysis of the IEEE Reliability Test System illustrates results that are obtained from the approaches

Co-Design of an active suspension using simultaneous dynamic optimization",

Design of physical systems and associated control systems are coupled tasks; design methods that manage this interaction explicitly can produce system-optimal designs, whereas conventional sequential processes may not. Here, we explore a new technique for combined physical and control system design (co-design) based on a simultaneous dynamic optimization approach known as direct transcription, which transforms infinitedimensional control design problems into finite-dimensional nonlinear programming problems. While direct transcription problem dimension is often large, sparse problem structures and fine-grained parallelism (among other advantageous properties) can be exploited to yield computationally efficient implementations. Extension of direct transcription to co-design gives rise to new problem structures and new challenges. Here, we illustrate direct transcription for co-design using a new automotive active suspension design example developed specifically for testing co-design methods. This example builds on prior active suspension problems by incorporating a more realistic physical design component that includes independent design variables and a broad set of physical design constraints, while maintaining linearity of the associated differential equations. A simultaneous co-design approach was implemented using direct transcription, and numerical results were compared with conventional sequential optimization. The simultaneous optimization approach achieves better performance than sequential design across a range of design studies. The dynamics of the active system were analyzed with varied level of control authority to investigate how dynamic systems should be designed differently when active control is introduced

The Use of Sweep and Dihedral in Multistage Axial Flow Compressor Blading - Part II: Low and High Speed Designs and Test Verification”.

This paper describes the introduction of 3-D blade designs int