Sohail Anwar

An Evolutionary Firefly Algorithm for the Estimation of Nonlinear Biological Model Parameters

The development of accurate computational models of biological processes is fundamental to computational systems biology. These models are usually represented by mathematical expressions that rely heavily on the system parameters. The measurement of these parameters is often difficult. Therefore, they are commonly estimated by fitting the predicted model to the experimental data using optimization methods. The complexity and nonlinearity of the biological processes pose a significant challenge, however, to the development of accurate and fast optimization methods. We introduce a new hybrid optimization method incorporating the Firefly Algorithm and the evolutionary operation of the Differential Evolution method. The proposed method improves solutions by neighbourhood search using evolutionary procedures. Testing our method on models for the arginine catabolism and the negative feedback loop of the p53 signalling pathway, we found that it estimated the parameters with high accuracy and within a reasonable computation time compared to well-known approaches, including Particle Swarm Optimization, Nelder-Mead, and Firefly Algorithm. We have also verified the reliability of the parameters estimated by the method using an a posteriori practical identifiability test.

An improved swarm optimization for parameter estimation and biological model selection.

One of the key aspects of computational systems biology is the investigation on the dynamic biological processes within cells. Computational models are often required to elucidate the mechanisms and principles driving the processes because of the nonlinearity and complexity. The models usually incorporate a set of parameters that signify the physical properties of the actual biological systems. In most cases, these parameters are estimated by fitting the model outputs with the corresponding experimental data. However, this is a challenging task because the available experimental data are frequently noisy and incomplete. In this paper, a new hybrid optimization method is proposed to estimate these parameters from the noisy and incomplete experimental data. The proposed method, called Swarm-based Chemical Reaction Optimization, integrates the evolutionary searching strategy employed by the Chemical Reaction Optimization, into the neighbouring searching strategy of the Firefly Algorithm method. The effectiveness of the method was evaluated using a simulated nonlinear model and two biological models: synthetic transcriptional oscillators, and extracellular protease production models. The results showed that the accuracy and computational speed of the proposed method were better than the existing Differential Evolution, Firefly Algorithm and Chemical Reaction Optimization methods. The reliability of the estimated parameters was statistically validated, which suggests that the model outputs produced by these parameters were valid even when noisy and incomplete experimental data were used. Additionally, Akaike Information Criterion was employed to evaluate the model selection, which highlighted the capability of the proposed method in choosing a plausible model based on the experimental data. In conclusion, this paper presents the effectiveness of the proposed method for parameter estimation and model selection problems using noisy and incomplete experimental data. This study is hoped to provide a new insight in developing more accurate and reliable biological models based on limited and low quality experimental data.

Use of a Case Study Approach to Teach Engineering Technology Students

This paper describes a case study-based instructional approach used to teach baccalaureate degree electro-mechanical engineering technology (BSEMET) students. The need for an instructional model based on engineering case studies and the development and implementation of this model in selected BSEMET courses are described. Examples of engineering case studies used in these courses are presented. The results of implementation of a case study-based instructional model are described.

Recommended factors for triage categories using Simulation Modelling

Malaysia is in advancement towards the establishment of effective and efficient energy mix plan within its Nuclear Power Programme (NPP) resulting from current governance acceptance and support. Prior precautions have been inculcated in order to comprehend nuclear energy accountability in striving high income economic growth and energy security. This paper is attempting to prescribe nuclear safety recommendations encompassing radiation and nuclear (RN) event recovery phase within integrated disaster operations management (DOM) activities from Soft System Methodology (SSM) perspective by using Simulation Modelling. Possibly, this method is introduced in order to recommend triage categories for vulnerable population groups which are commonly at-risks during RN emergencies, incidents and disasters. Therefore, this paper will proposed some factors of triage categories which may affect these groups whereby representing an initial system map. It is concluded that prioritized suggestions given could possibly ascertain the characteristics of other interventions involved in order to achieve a sustainable and secure nuclear safety in the future.

Development of integrated learning modules for engineering technology students

This paper describes a project currently in progress at Penn State Altoona. The project deals with the development of learning modules which will integrate various concepts from physics, mathematics, and engineering technology to provide students with a clear understanding of the usefulness of physics and mathematics in real-world engineering problems. Case studies derived from real-life situations will constitute a significant part of these modules. These learning modules will be suitable for use in a one-semester physics course for associate degree engineering technology students. Both EET and MET students will benefit from these modules.

Use of engineering case studies to teach associate degree electrical engineering technology students

This paper describes the use of engineering case studies to teach associate degree electrical engineering technology (2EET) students at the Penn State University, Altoona College (Penn State Altoona). Engineering case studies are used in several 2EET courses such as digital electronics (EET 117), semiconductors (EET 210), and electrical machines (EET 213 W). The paper begins with a description of the need for the use of engineering case studies to teach engineering technology students. Development and implementation of the case study based instructional model used in several 2EET courses is described next. The results of implementation of this instructional technique in the 2EET courses are presented in the paper.

A contemporary science and engineering education program for 8th and 9th grade students

This paper describes a 5-day science and engineering education program for 8th and 9th grade students, held at Penn State Altoona in August 1997. The objectives of this program were (a) to create a vision of science and engineering that is process-oriented, motivational, exploratory, and encourages problem solving, and (b) to impart students tools which would be helpful to them in learning more advanced scientific concepts. Participants conducted a series of inquiry-based exercises designed to introduce them to contemporary concepts in engineering design, materials science, lasers/electro-optics/fiber-optics, statistical quality control, and biological sciences. Working collaboratively in teams, 8th and 9th graders who participated in this program designed experiments, manipulated equipment, made measurements, and interpreted and presented data in an appropriate style. Each student created a portfolio of written, graphic, and project materials which was evaluated by the project faculty at the conclusion of the program. The portfolio approach was used to enhance: verbal and written communication skills, interdisciplinary learning, conceptual understanding of science and engineering, student responsibility for learning, self esteem. The paper begins with a description of the reasons for conducting the above mentioned summer school of excellence for 8th and 9th graders. A discussion of portfolio approach is presented. The activities carried out as a part of the above mentioned program are described. Finally, the student assessment and program evaluation are discussed.

The impact of germanium in strained Si/relaxed Si1−xGex on carrier performance in non-degenerate and degenerate regimes

The impact of the fraction of germanium on the carrier performance of two-dimensional strained silicon, which embraces both the non-degenerate and degenerate regimes, is developed. In this model, the Fermi integral of order zero is employed. The impact of the fraction of germanium on the relaxed Si1−xGex substrate (x), carrier concentration and temperature is reported. It is revealed that the effect of x on the hole concentration is dominant for a normalized Fermi energy of more than three, or in other words the non-degenerate regime. On the contrary, the x gradient has less influence in the degenerate regime. Furthermore, by increasing x there is an increase in the intrinsic velocity, particularly with high carrier concentration and temperature.

Energy quantization on the current-voltage characteristic of nanoscale two-dimensional mosfet

The current-voltage characteristic for nanoscale MOSFET is presented based to the velocity saturation and quantum confinement. It has been clarified that the drain velocity which is saturated with an increased drain voltage limits the onset of the current saturation. In the presence of high electric field, the motion of the velocity saturation that is randomly oriented in the equilibrium becomes streamlined and unidirectional. The model presents the current-voltage characteristic from the drift-diffusion regime to the ballistic regime with the presence of the quantum confinement on the charge carrier distribution and the energy quantization. The obtained results are considered in the modeling of the current-voltage characteristics of nanoscale two-dimensional MOSFET and show good agreement with the experimental data without using any artificial parameters.

Developing Laboratory Exercises to Effectively Teach Transformer System Monitoring and Testing

High voltage auto transformers are regarded as one of the most important and expensive electrical power system devices. Thus, it is essential to remotely control and monitor their operating conditions. A key objective of autotransformer monitoring systems is to ensure continuity and reliability of autotransformer operation resulting in a reduction in costs and economic losses associated with the possible failures of autotransformers. This paper presents details regarding the laboratory equipment and the associated lab exercises used to teach transformer monitoring concepts to fifth-year electrical power systems engineering students at Warsaw University of Technology in Poland. These students develop a comprehensive understanding of high voltage autotransformer monitoring and protection through the use of an ADAM5510E/TCP microprocessor-based controller.