01810nas a2200241   4500008004100000022001400041245010400055210006900159260004200228300001000270490000700280520107100287653001501358653002101373653002301394653001501417653002101432100001801453700002101471700002601492700002001518856003001538       2021                        eng d  a1927-032100aSimulating a Biorefinery Ecosystem to Manage and Motivate Sustainable Regional Nutrient Circulation0 aSimulating a Biorefinery Ecosystem to Manage and Motivate Sustai  aOttawabTalent First Networkc02/2021  a33-430 v113 aCreating an ecologically sustainable circulation of nutrients requires local solutions with commitment from all participating parties. Due to vast differences between various regions, it is very complicated, if not impossible, to create fair, simple, and applicable legislation that can consider all of these differences in a meaningful way. Thus, there is a need for clear and easy ways of developing sustainable and viable solutions locally, as well as communicating them with local community and all the way up to the supervising governmental representatives. To meet this need, we developed a simulation tool that allows the user to explore the effectiveness and impact of a local biorefinery in waste management. As an iterative model based on state machine agents, it can easily be modified for a multitude of scenarios with changes taking place over time, while considering the viewing points of all involved. In this article, we report the first version of this tool and demonstrate its usefulness in estimating suitable biogas reactor size in a biorefinery.10aBioreactor10acircular economy10anutrient recycling10asimulation10awaste management1 aKoskela, Olli1 aDempers, Clemens1 aKymäläinen, Maritta1 aNummela, Jarkko  utimreview.ca/article/142100534nas a2200181   4500008004100000022001400041245006400055210006000119260004200179300001000221490000600231653001800237653001200255653001400267653001500281100002000296856003600316       2014                        eng d  a1927-032100aTIM Lecture Series – Cybersecurity Metrics and Simulation0 aTIM Lecture Series Cybersecurity Metrics and Simulation  aOttawabTalent First Networkc10/2014  a43-450 v410acybersecurity10ametrics10amodelling10asimulation1 aCybenko, George  uhttp://timreview.ca/article/83901660nas a2200241   4500008004100000022001400041245008300055210006900138260004200207300001000249490000600259520093900265653002801204653001801232653001901250653001401269653001801283653001801301653001501319653002601334100002201360856003601382       2013                        eng d  a1927-032100aProtecting Critical Infrastructure by Identifying Pathways of Exposure to Risk0 aProtecting Critical Infrastructure by Identifying Pathways of Ex  aOttawabTalent First Networkc08/2013  a34-400 v33 aIncreasingly, our critical infrastructure is managed and controlled by computers and the information networks that connect them. Cyber-terrorists and other malicious actors understand the economic and social impact that a successful attack on these systems could have. While it is imperative that we defend against such attacks, it is equally imperative that we realize how best to react to them. This article presents the strongest-path method of analyzing all potential pathways of exposure to risk – no matter how indirect or circuitous they may be – in a network model of infrastructure and operations. The method makes direct use of expert knowledge about entities and dependency relationships without the need for any simulation or any other models. By using path analysis in a directed graph model of critical infrastructure, planners can model and assess the effects of a potential attack and develop resilient responses. 10acritical infrastructure10acybersecurity10adirected graph10amodelling10apath analysis10arisk analysis10asimulation10astrongest-path method1 aO’Neill, Philip  uhttp://timreview.ca/article/71400705nas a2200253   4500008004100000022001400041245004000055210003700095260004200132300000800174490000600182653002600188653002800214653002100242653001500263653002500278653002200303653002600325653001500351653001300366653001800379100001800397856003600415       2012                        eng d  a1927-032100aEditorial: Insights (November 2012)0 aEditorial Insights November 2012  aOttawabTalent First Networkc11/2012  a3-30 v210acompetitive advantage10aglobal entrepreneurship10agreen innovation10ainnovation10aprocess adaptability10aprocess alignment10aprocess ambidexterity10asimulation10astartups10asupply chains1 aMcPhee, Chris  uhttp://timreview.ca/article/62301792nas a2200217   4500008004100000022001400041245012900055210006900184260004200253300001000295490000600305520107900311653002601390653001501416653001501431653003901446653000801485653002501493100002001518856003601538       2012                        eng d  a1927-032100aSimulation of Supply-Chain Networks: A Source of Innovation and Competitive Advantage for Small and Medium-Sized Enterprises0 aSimulation of SupplyChain Networks A Source of Innovation and Co  aOttawabTalent First Networkc11/2012  a13-200 v23 aOn a daily basis, enterprises of all sizes cope with the turbulence and volatility of market demands, cost variability, and severe pressure from globally distributed competitors. Managing uncertainty about future demand requirements and volumes in supply-chain networks has become a priority. One of the ways to deal with uncertainty is the utilization of simulation techniques and tools, which provide greater predictability of decision-making outcomes. For example, simulation has been widely applied in decision-making processes related to global logistics and production networks at the strategic, tactical, and operational levels, where it is used to predict the impact of decisions before their implementation in complex and uncertain environments. Large enterprises are inclined to use simulation tools whereas small and medium-sized enterprises seem to underestimate its advantages. The objective of this article is to emphasize the relevance of simulation for the design and management of supply-chain networks from the perspective of small and medium-sized firms. 10acompetitive advantage10ainnovation10asimulation10asmall and medium-sized enterprises10aSME10asupply-chain network1 aLiotta, Giacomo  uhttp://timreview.ca/article/625