Functional regeneration of brainstem–spinal pathways occurs in the developing chick when the spinal cord is severed prior to embryonic day (E) 13. Functional spinal cord regeneration is not observed in animals injured after E13. This developmental transition from a permissive to a restrictive repair period may be due to the formation of an extrinsic inhibitory environment preventing axonal growth, and/or an intrinsic inability of mature neurons to regenerate. Here, we investigated the capacity of specific populations of brainstem–spinal projection neurons to regrow neurites in vitro from young (E8) versus mature (E17) brainstem explants. A crystal of carbocyanine dye (DiI) was implanted in ovo into the E5 cervical spinal cord to retrogradely label brainstem–spinal projection neurons. Three or 12 days later, discrete regions of the brainstem containing DiI-labeled neurons were dissected to produce explant cultures grown in serum-free media on laminin substrates. The subsequent redistribution of DiI into regenerating processes permitted the study of in vitro neurite outgrowth from identified brainstem–spinal neurons. When explanted on E8, i.e., an age when brainstem–spinal neurons are normally elongating through the spinal cord and are capable of in vivo functional regeneration, robust neurite outgrowth was observed from all brainstem populations, including rubro-, reticulo-, vestibulo-, and raphe–spinal neurons. In contrast, when explanted on E17, robust neurite outgrowth was seen only from raphe-spinal neurons. Neurite outgrowth from raphe-spinal neurons was 5-hydroxy-tryptamine immunoreactive. This study demonstrates that in growth factor-free environments with permissive growth substrates, neurite outgrowth from brainstem–spinal neurons is dependent on both neuronal age and phenotype., Peer-reviewed article, Published. Received 22 February 2000; Accepted 25 May 2000; Available online 25 May 2002.
Proceedings from CIGRÉ Canada Conference, Montreal, Sept. 2012. In recent decade, smart microgrids have raised the feasibility and affordability of adaptive and real-time Volt/VAr optimization (VVO) and Conservation Voltage Reduction (CVR) implementations by their exclusive features such as using smart metering technologies and various types of dispersed generations. Smart distribution networks are presently capable of achieving higher degrees of efficiency and reliability through employing a new integrated Volt/VAr optimization system. For VVO application, two well-known approaches are recommended by different utilities and/or companies: Centralized VVO and Decentralized VVO. In centralized VVO, the processing system is placed in a central controller unit such as DMS in the so called “Utility Back Office”. The DMS uses relevant measurements taken from termination points (i.e. utility subscribers) supplied to it from either field collectors or directly from MDMS, to determine the best possible settings for field-bound VVO/CVR assets to achieve the desired optimization and conservation targets. These settings are then off-loaded to such assets through existing downstream pipes, such as SCADA network In contrast, decentralized VVO utilizes VVO/CVR engines which are located in the field and in close-proximity to the relevant assets to conserve voltage and energy according to local attributes of the distribution network. In this case, local measurements do not need to travel from the field to the back-office, and the new settings for VVO/CVR assets are determined locally, rather than from a centralized controller. Without having any preference between above mentioned VVO techniques, this paper studies an adaptive optimization engine for real-time VVO/CVR in smart microgrids based on Intelligent Agent technology. The optimization algorithm provides the best optimal solution for VVO/CVR problem at each real-time stage through minimizing system loss cost and improves system energy efficiency as well as voltage profile of the relevant distribution system. The algorithm may employ distributed generation sources to address the Volt/VAr optimization problem in real-time. Coordinated VVO/CVR requires real-time data analysis every 15 minutes. It utilizes a distributed command and control architecture to supply the VVO Engine (VVOE) with the required data, and secures real-time configuration from the VVO engine for the VVO control devices such as On-Load Tap Changers (OLTCs), Voltage Regulators (VRs) and Capacitor Banks (CBs). It also has the option of employing distributed generation (DG) as well as modelling load effects in VVO/CVR application. The algorithm minimizes the distribution network power loss cost at each time stage, checks the voltage deviation of distribution buses and distributed generation sources considering different types of constraints such as system power flow, distribution network power factor, system active and reactive power constraints and switching limitations of Volt/VAr control devices. The algorithm receives required real-time data from an intelligent agent. Then, it starts to solve the real-time VVO/CVR problem in order to find the best optimal configuration of the network in real-time. The paper uses British Columbia Institute of Technology (BCIT) distribution network as its case study in order to explore the effectiveness and the accuracy of the optimization engine. Moreover, the VVO/CVR optimization algorithm is implemented in different configurations; a) VVO/CVR confined to the substation and b) VVO/CVR optimization algorithm within the substation and along distribution feeders. The algorithm also checks the availability of DGs to assist VVO/CVR control functions and assesses the impact of new distributed sources such as: Flywheel Energy Storage System (FESS) on real-time VVO/CVR. For this reason, the algorithm classified DGs in a microgrid based on their impacts and instantiates them based on their application feasibility for real-time VVO/CVR., Conference paper, Published.
This paper proposes a new approach for real-time and adaptive Volt/VAr optimization (VVO)/conservation voltage reduction (CVR) system using Intelligent Agents, communicating through IEC 61850 Goose Messaging Protocol. The paper also proposes new real-time adaptive VVO/CVR algorithms tailored for different service level targets and system topologies. The paper argues that each of these variations requires different Intelligent Agent Systems, data structures, and communication requirements. To test the applicability of the VVO/CVR optimization engine, a modified IEEE 34 Node system is used as case study., Article, Published
Accepted in IEEE International Conference on Smart Energy Grid Engineering, May 2015. This paper presents a real-time co-simulated platform for novel voltage and reactive power optimization (VVO) of distribution grids through a real-time digital simulator (RTDS) in presence of a reliable communication platform. The proposed VVO engine is able to capture quasi real-time data from local Advanced Metering Infrastructure (AMI) and optimizes the distribution network for each quasi real-time stage (every 5 minutes) based on system main characteristics (i.e. active/reactive power of nodes). At each time stage, the VVO engine tries to minimize losses in the distribution network as well as to improve the voltage profile of the system. In order to test robustness, performance and the applicability of proposed Volt-VAR Optimization engine, a 33 node distribution network has been modeled and studied in a real-time Co-simulated environment by real-time simulator (RTDS) and a real communication platform with DNP.3 protocol. The preliminary results prove well-performance of proposed AMI-based VVO engine and show that the engine enables system to achieve higher level of loss/operating cost reduction through a sophisticated optimization engine compare with conventional approaches., Conference paper, Published.
This paper presents an implementation of an IEC 61850-based real-time co-simulation platform for verification of the performance of a volt-VAR optimization (VVO) engine for smart distribution networks. The proposed VVO engine is able to minimize grid loss, volt-VAR control asset operational costs, and conservation voltage reduction operational costs through its comprehensive objective functions, weighted by fuzzification using advanced metering infrastructure (AMI) data. The optimization engine receives the AMI data stream through measurement aggregators. Moreover, it sends control commands to volt-VAR control components modeled in real-time digital simulator (RTDS) through DNP.3 protocol. To check the performance and the precision of proposed VVO, a fault scenario is imposed upon the system. IEC 61850 GOOSE messages are generated and sent to change the status of specified breakers, while the VVO engine receives system reconfiguration commands via IEC61850 Manufacturing Message Specification (MMS) protocol. The results of the study on 33-node feeder showed adequate performance of proposed VVO in grid operating scenarios., Article, Published.
Proceeding of IEEE International Conference on Smart Energy Grid Engineering (SEGE), Aug. 2015, Oshawa, ON, Canada. This paper investigates a real-time communication platform for a Smart Grid adaptive Volt-VAR Optimization (VVO) engine. Novel VVO techniques receive inputs from Advanced Metering Infrastructure (AMI) to dynamically optimize distribution networks. As communication platform design and characteristics affect Smart Grid-based VVO performance in terms of control accuracy and response time, VVO ICT studies is essential for grid planners and/or power utilities. Hence, this paper primarily introduces a real-time co-simulated environment comprised of Smart Grid adaptive VVO engine, RTDS model and system communication platform using DNP3 protocol. This platform is built to test and asses the influence of different components included in Smart Grid monitoring and control system; namely the sensors, measurement units, communication infrastructure on the operation and control of VVO. Moreover, this paper uses a real-time platform to check the robustness of the monitoring and control applications for communication network considerations such as delays and packet loss. Next, this paper investigates how such a platform could look into communication issues while taking system requirements into consideration. A 33-node distribution feeder is employed to check system performance through communication parameters such as throughput and response time., Conference paper, Published.
Mason and Birch have developed a direct brain–computer interface for intermittent control of devices such as environmental control systems and neuroprotheses. This EEG-based brain switch, named the LF-ASD, has been used in several off-line studies, but little is known about its usability with real-world devices and computer applications. In this study, able-bodied individuals and people with high-level spinal injury used the LF-ASD brain switch to control a video game in real time. Both subject groups demonstrated switch activations varying from 30% to 78% and false-positive rates in the range of 0.5% to 2.2% over three 1-hour test sessions. These levels correspond to switch classification accuracies greater than 94% for all subjects. The results suggest that subjects with spinal cord injuries can operate the brain switch to the same ability as able-bodied subjects in a real-time control environment. These results support the findings of previous studies., Peer-reviewed article, Published.
Proceedings of the World Congress on Engineering and Computer Science 2007 WCECS 2007, October 24-26, 2007, San Francisco, USA. Gene recruitment or co-option is defined as the placement of a gene under a foreign regulatory system. Such re-arrangement of pre-existing regulatory networks can lead to an increase in genomic complexity. This reorganization is recognized as a major driving force in evolution. We simulated the evolution of gene networks by means of the Genetic Algorithms (GA) technique. We used standard GA methods of (point) mutation and multi-point crossover, as well as our own operators for introducing or withdrawing new genes on the network. The starting point for our computer evolutionary experiments was a minimal 4-gene dynamic model representing the real genetic network controlling segmentation in the fruit fly Drosophila. Model output was fit to experimentally observed gene expression patterns in the early fly embryo. We found that the mutation operator, together with the gene introduction procedure, was sufficient for recruiting new genes into pre-existing networks. Reinforcement of the evolutionary search by crossover operators facilitates this recruitment. Gene recruitment causes outgrowth of an evolving network, resulting in structural and functional redundancy. Such redundancies can affect the robustness and evolvability of networks., Conference paper, Published.
There appears to be an increased risk of cardiovascular disease (CVD) among individuals with spinal cord injury. Quantitative data concerning the risk of heart disease among individuals with other neurological disorders (NDs) are not available. Our aim was to estimate the prevalence of heart disease among individuals with NDs and to compare their risk with a control group., Article, Published. Received: September 19, 2014 ; Accepted: January 05, 2015 ; Published online: February 17, 2015 ; Issue release date: March 2015.
The Prosthetics and Orthotics Department at the British Columbia Institute of Technology (BCIT) in Vancouver, Canada, has recently completed a visioning process which was done as part of a curriculum review. This report presents and discusses the key points emerging from the process. It is anticipated that the results of the visioning process will provide a basis for a major curriculum revision to the BCIT’s prosthetics and orthotics program. The intent of a curriculum review is to determine whether an educational program’s curriculum is current and relevant with respect to providing students with entry-level skills for the workforce. It involves examining the current scope of practice and competencies of the profession in question and then reflecting back on the curriculum to determine whether these competencies are being taught adequately. Visioning attempts to determine not what entry-level skills graduates require but, instead, what knowledge and skills students need to meet the challenges of the workplace approximately 10 to 15 years into the future., Peer-reviewed article, Published.
Prepared for the Atlantic Canada Economic Association Conference, October 19-20, 2012, Halifax Nova Scotia. Both the Canada and United States invest a great deal of resources in the training of their military personal. Many of the skills and experiences accumulated by soldiers are those that are highly valued by civilian employers. Further, these skills are often embodied in academic programs, suggesting soldiers would have a comparative advantage in such programs; however, despite the efforts of government agencies, many soldiers are unable to convert their skills and training into meaningful careers. This paper presents the findings from a pilot program at the BC institute of Technology 2009-2012. The program uses an alternative approach to assessing military training for advanced placement into post-secondary programs. By mapping learning outcomes rather than course equivalences, those from non-traditional education backgrounds are given advanced placement into post-secondary programs. During the pilot period, a cohort of reservists and veterans who have been given advanced placement are tracked and benchmarked against regular students in the same programs over time, measuring academic scores and testing for the development of skills, abilities, and qualities considered important for workplace success., Conference paper, Draft available.
Proceedings of the 2011 International Conference on Evolutionary Computation Theory and Applications. Classical understandings of biological evolution inspired creation of the entire order of Evolutionary Computation (EC) heuristic optimization techniques. In turn, the development of EC has shown how living organisms use biomolecular implementations of these techniques to solve particular problems in survival and adaptation. An example of such a natural Genetic Algorithm (GA) is the way in which a higher organism's adaptive immune system selects antibodies and competes against its complement, the development of antigen variability by pathogenic organisms. In our approach, we use operators that implement the reproduction and diversification of genetic material in a manner inspired by retroviral reproduction and a genetic-engineering technique known as DNA shuffling. We call this approach Retroviral Genetic Algorithms, or retroGA (Spirov and Holloway, 2010). Here, we extend retroGA to include: (1) the utilization of tags in strings; (2) the capability of the Reproduction-Crossover operator to read these tags and interpret them as instructions; and (3), as a consequence, to use more than one reproductive strategy. We validated the efficacy of the extended retroGA technique with benchmark tests on concatenated trap functions and compared these with Royal Road and Royal Staircase functions., Conference paper, Published.