Business incubators have become a popular policy option and economic development intervention tool. However, recent research shows that incubated firms may not benefit significantly from their incubator relationships, and may even be more vulnerable to failure post departure (graduation) from an incubator. These findings suggest that the impact of business incubation on new venture viability may be contingent on the type of support offered by an incubator and attributes of business environments within which incubation services are provided. Incubation services that protect and isolate ventures from key resource dependencies may hinder venture development and increase subsequent vulnerability to environmental demands. Alternatively, incubation services that help ventures connect and align with key resource dependencies are likely to promote firm survival. We propose that incubators vary in the services and resources they offer, and that university incubators typically provide greater connectivity and legitimacy with respect to important contingencies associated with key industry and community stakeholders. This leads us to propose that university affiliation is an important contingency that affects the relationship between firms' participation in incubators and their subsequent performance. The purpose of this study is to evaluate this contingency by examining whether firms graduating from university incubators attain higher levels of post-incubation performance than firms participating in non-university affiliated incubators. We test this by evaluating the performance of a sample of graduated firms associated with the population of university-based incubators in the US contrasted against the performance of a matched cohort of non-incubated firms. The analysis uses an enhanced dataset that tracks the number of employees, sales, and the entry and graduation (departure) points of incubated firms from a university incubation program, so as to delineate the scope of influence of the incubator.& Vernet Lasrado
In this paper, we give a review of recent transition path search methods for nanoscale phase transition simulation A potential energy surface (PES) characterizes detailed information about phase transitions where the transition path is related to a minimum energy path on the PES. The minimum energy path connects reactant to product via saddle point(s) on the PES. Once the minimum energy path is generated, the activation energy required for transitions can be determined. Using transition state theory, one can estimate the rate constant of the transition. The rate constant is critical to accurately simulate the transition process with sampling algorithms such as kinetic Monte Carlo. NOMENCLATURE PESPotential energy surface MEP Minimum energy path Q O Vector of the molecular conformation of the reactant in reaction coordinates on the PES with respect to its time (t) in the reaction i.e. t=0 Q F Vector of the molecular conformation of the product in reaction coordinates on the PES with respect to its time (t) in the reaction i.e. t=F Q X Vector of the molecular conformation of a point in reaction coordinates on the PES with respect to its time (t) in the reaction i.e. t=X V Potential Energy ∇ Gradient INTRODUCTIONIn this paper, we give a review of recent transition path search methods for nanoscale phase transition simulation. It is not in the scope of this review to compare the methods against each other. Rather, we aim to provide the reader with the essence of each method reviewed. A phase transition is a geometric and topological transformation process of materials from one phase to another, each of which has a unique and homogeneous physical property. The most important step involved in modeling phase transition is the knowledge of the activation energy barrier and rate constant involved in the transition.In 1931, Erying and Polanyi proposed the transition state theory (TST) as a means to calculate the activation energy and rate constants [1,2] for characterizing reactions. An activation energy barrier always exists between phases. This activation energy characterizes the transition state. The methods reviewed are built on the theory prescribed by TST or some variants of TST (Variational Transition State Theory [3] and Reaction Path Hamiltonian [4])In an effort to simulate a reaction or transition, a potential energy surface (PES) that characterizes the process is first generated. Then, a minimum energy path (MEP) is computed which represents the transition pathway in the reaction coordinate space. Finally, the activation energy and rate constant that define the speed of the process (the rate of the reaction) can be calculated using TST and information about the saddle point(s).
In this paper, we give a review of recent transition path search methods for nanoscale phase transition simulation A potential energy surface (PES) characterizes detailed information about phase transitions where the transition path is related to a minimum energy path on the PES. The minimum energy path connects reactant to product via saddle point(s) on the PES. Once the minimum energy path is generated, the activation energy required for transitions can be determined. Using transition state theory, one can estimate the rate constant of the transition. The rate constant is critical to accurately simulate the transition process with sampling algorithms such as kinetic Monte Carlo. NOMENCLATURE PESPotential energy surface MEP Minimum energy path Q O Vector of the molecular conformation of the reactant in reaction coordinates on the PES with respect to its time (t) in the reaction i.e. t=0 Q F Vector of the molecular conformation of the product in reaction coordinates on the PES with respect to its time (t) in the reaction i.e. t=F Q X Vector of the molecular conformation of a point in reaction coordinates on the PES with respect to its time (t) in the reaction i.e. t=X V Potential Energy ∇ Gradient INTRODUCTIONIn this paper, we give a review of recent transition path search methods for nanoscale phase transition simulation. It is not in the scope of this review to compare the methods against each other. Rather, we aim to provide the reader with the essence of each method reviewed. A phase transition is a geometric and topological transformation process of materials from one phase to another, each of which has a unique and homogeneous physical property. The most important step involved in modeling phase transition is the knowledge of the activation energy barrier and rate constant involved in the transition.In 1931, Erying and Polanyi proposed the transition state theory (TST) as a means to calculate the activation energy and rate constants [1,2] for characterizing reactions. An activation energy barrier always exists between phases. This activation energy characterizes the transition state. The methods reviewed are built on the theory prescribed by TST or some variants of TST (Variational Transition State Theory [3] and Reaction Path Hamiltonian [4])In an effort to simulate a reaction or transition, a potential energy surface (PES) that characterizes the process is first generated. Then, a minimum energy path (MEP) is computed which represents the transition pathway in the reaction coordinate space. Finally, the activation energy and rate constant that define the speed of the process (the rate of the reaction) can be calculated using TST and information about the saddle point(s).
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