Computational methods for breast cancer diagnosis, prognosis, and treatment prediction

The research presented here develops a robust reliability algorithm for the identification of reliable protein interactions that can be incorporated with a gene expression dataset to improve the algorithm performance, and novel breast cancer based diagnostic, prognostic and treatment prediction algorithms, respectively, which take into account the existing issues in order to provide a fair estimation of their performance.

A computational framework for uncertainty integration in stochastic unit commitment with intermittent renewable energy sources

The penetration of intermittent renewable energy sources (IRESs) into power grids has increased in the last decade. Integration of wind farms and solar systems as the major IRESs have significantly boosted the level of uncertainty in operation of power systems. This paper proposes a comprehensive computational framework for quantification and integration of uncertainties in distributed power systems (DPSs) with IRESs. Different sources of uncertainties in DPSs such as electrical load, wind and solar power forecasts and generator outages are covered by the proposed framework. Load forecast uncertainty is assumed to follow a normal distribution. Wind and solar forecast are implemented by a list of prediction intervals (PIs) ranging from 5% to 95%. Their uncertainties are further represented as scenarios using a scenario generation method. Generator outage uncertainty is modeled as discrete scenarios. The integrated uncertainties are further incorporated into a stochastic security-constrained unit commitment (SCUC) problem and a heuristic genetic algorithm is utilized to solve this stochastic SCUC problem. To demonstrate the effectiveness of the proposed method, five deterministic and four stochastic case studies are implemented. Generation costs as well as different reserve strategies are discussed from the perspectives of system economics and reliability. Comparative results indicate that the planned generation costs and reserves are different from the realized ones. The stochastic models show better robustness than deterministic ones. Power systems run a higher level of risk during peak load hours.

A review on computational intelligence methods for controlling traffic signal timing

Urban traffic as one of the most important challenges in modern city life needs practically effective and efficient solutions. Artificial intelligence methods have gained popularity for optimal traffic light control. In this paper, a review of most important works in the field of controlling traffic signal timing, in particular studies focusing on Q-learning, neural network, and fuzzy logic system are presented. As per existing literature, the intelligent methods show a higher performance compared to traditional controlling methods. However, a study that compares the performance of different learning methods is not published yet. In this paper, the aforementioned computational intelligence methods and a fixed-time method are implemented to set signals times and minimize total delays for an isolated intersection. These methods are developed and compared on a same platform. The intersection is treated as an intelligent agent that learns to propose an appropriate green time for each phase. The appropriate green time for all the intelligent controllers are estimated based on the received traffic information. A comprehensive comparison is made between the performance of Q-learning, neural network, and fuzzy logic system controller for two different scenarios. The three intelligent learning controllers present close performances with multiple replication orders in two scenarios. On average Q-learning has 66%, neural network 71%, and fuzzy logic has 74% higher performance compared to the fixed-time controller.

Computational inverse analysis of static recrystallization kinetics

© 2015 Published by Elsevier Ltd. All rights reserved. Accurate static recrystallization (SRX) models are necessary to improve the properties of austenitic steels by thermo-mechanical operations. This relies heavily on a careful and accurate analysis of "the interrupted test data" and conversion of the heterogeneous deformation data to the flow stress. A "computational-experimental inverse method" was presented and implemented here to analyze the SRX test data, which takes into account the heterogeneous softening of the post-interruption test sample. Conventional and "inverse" methods were used to identify the SRX kinetics for a model austenitic steel deformed at 1273 K (with a strain rate of 1 s-1) using the hot torsion test assess the merits of each method. Typical "static recrystallization distribution maps" in the test sample indicated that, at the onset of the second pass deformation with less than a critical holding time and a given pre-strain, a "partially-recrystallized zone" existed in the cylindrical core of the specimen near its center line. For the investigated scenario, the core was confined in the first half of the gauge radius when the holding time and the maximum pre strain were below 29 s and 0.5, respectively. For maximum pre strains smaller than 0.2, the specimen did not fully recrystallize, even at the gauge surface after holding for 50 s. Under such conditions, the conventional methods produced significant error.

A computational search for lipases that can preferentially hydrolyze long-chain omega-3 fatty acids from fish oil triacylglycerols

Consumption of long-chain omega-3 fatty acids is known to decrease the risk of major cardiovascular events. Lipases, a class of triacylglycerol hydrolases, have been extensively tested to concentrate omega-3 fatty acids from fish oils, under mild enzymatic conditions. However, no lipases with preference for omega-3 fatty acids selectivity have yet been discovered or developed. In this study we performed an exhaustive computational study of substrate-lipase interactions by docking, both covalent and non-covalent, for 38 lipases with a large number of structured triacylglycerols containing omega-3 fatty acids. We identified some lipases that have potential to preferentially hydrolyze omega-3 fatty acids from structured triacylglycerols. However omega-3 fatty acid preferences were found to be modest. Our study provides an explanation for absence of reports of lipases with omega-3 fatty acid hydrolyzing ability and suggests methods for developing these selective lipases.

Building the computational virtual reality environment for anaesthetists' training and practice

Understanding the real world based on visualisation and prediction is essential for the decision-maker. We build a computational virtual reality environment to improve visualisation, understanding and prediction of the physical world and to guide action. It develops a five-dimensional, computer-generated, computational Virtual Reality Environment for Anaesthesia (VREA). Our online prediction will be calculated based on the correlation and composition computing with respect to the three dimensions: horizontal, vertical and individual. The novel musical notes based anesthetic simulator is proposed to identify the abnormality and visualize the online medical time series. The experiments with the online ECG data will present a real-time case to show the effectiveness and efficiency of our proposed system and algorithms.

Building computational virtual reality environment for anesthesia

In this paper, a Computational Virtual Reality Environment for Anesthesia (CVREA) is proposed. Virtual reality, data mining, machine learning techniques will be explored to develop (1) an immersive and interactive training platform for anaesthetists, which can greatly improve their training and learning performance; (2) a knowledge learning environment which collects clinical data with greater richness, process data with more efficacy, and facilitate knowledge discovery in anaesthesiology.

Computational intelligence-based traffic signal timing optimization

 Traffic congestion has explicit effects on productivity and efficiency, as well as side effects on environmental sustainability and health. Controlling traffic flows at intersections is recognized as a beneficial technique, to decrease daily travel times. This thesis applies computational intelligence to optimize traffic signals' timing and reduce urban traffic.

The influence of various seat design parameters: a computational analysis

Nowadays, low back pain becomes a common healthcare problem. Poor or unsuitable seat design is related to the discomfort and other healthcare problems of users. The aim of this study is to investigate the influence of seat design variables on the compressive loadings of lumbar joints. A basis that includes a musculoskeletal human body model and a chair model has been developed using LifeMOD Biomechanics Modeller. Inverse and forward dynamic simulations have been performed for various seat design parameters. The results show that the inclination of backrest and seat pan may or may not decrease the compressive spinal joint forces, depending on other conditions. The medium-level height and depth of seat pan and the medium-level and high-level height of backrest are found to cause the minimum compressive loads on lumbar joints. This work contributes to a better understanding of sitting biomechanics and provides some useful guidelines for seat design.