The Wheel of Business Model Reinvention: How to Reshape Your Business Model and Organizational Fitness to Leapfrog Competitors

In today's rapidly changing business landscapes, new sources of sustainable competitive advantage can often only be attained from business model reinvention, based on disruptive innovation and not incremental change or continuous improvement. Extant literature indicates that business models and their reinvention have recently been the focus of scholarly investigations in the field of strategic management, especially focusing on the search for new bases of building strategic competitive advantage, not only to outperform competitors but to especially leapfrog them into new areas of competitive advantage. While the available results indicate that progress is being made on clarifying the nature and key dimensions of business models, relatively little guidance of how to reshape business models and its organizational fitness dimensions have emerged. This article presents a systemic framework for business model reinvention, illustrates its key dimensions, and proposes a systemic operationalization process. Moreover, it provides a tool that helps organizations to evaluate both existing and proposed new business models.

Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process.

To understand the impact of epigenetics on human misfolding disease, we apply Gaussian-process regression (GPR) based machine learning (ML) (GPR-ML) through variation spatial profiling (VSP). VSP generates population-based matrices describing the spatial covariance (SCV) relationships that link genetic diversity to fitness of the individual in response to histone deacetylases inhibitors (HDACi). Niemann-Pick C1 (NPC1) is a Mendelian disorder caused by >300 variants in the NPC1 gene that disrupt cholesterol homeostasis leading to the rapid onset and progression of neurodegenerative disease. We determine the sequence-to-function-to-structure relationships of the NPC1 polypeptide fold required for membrane trafficking and generation of a tunnel that mediates cholesterol flux in late endosomal/lysosomal (LE/Ly) compartments. HDACi treatment reveals unanticipated epigenomic plasticity in SCV relationships that restore NPC1 functionality. GPR-ML based matrices capture the epigenetic processes impacting information flow through central dogma, providing a framework for quantifying the effect of the environment on the healthspan of the individual

On the Growth Rate of Non-Enzymatic Molecular Replicators

It is well known that non-enzymatic template directed molecular replicators X + nO ---> 2X exhibit parabolic growth d[X]/dt = k [X]^{1/2}. Here, we analyze the dependence of the effective replication rate constant k on hybridization energies, temperature, strand length, and sequence composition. First we derive analytical criteria for the replication rate k based on simple thermodynamic arguments. Second we present a Brownian dynamics model for oligonucleotides that allows us to simulate their diffusion and hybridization behavior. The simulation is used to generate and analyze the effect of strand length, temperature, and to some extent sequence composition, on the hybridization rates and the resulting optimal overall rate constant k. Combining the two approaches allows us to semi-analytically depict a fitness landscape for template directed replicators. The results indicate a clear replication advantage for longer strands at low temperatures.Comment: Submitted to: Entrop

A Framework for Meta-heuristic Parameter Performance Prediction Using Fitness Landscape Analysis and Machine Learning

The behaviour of an optimization algorithm when attempting to solve a problem depends on the values assigned to its control parameters. For an algorithm to obtain desirable performance, its control parameter values must be chosen based on the current problem. Despite being necessary for optimal performance, selecting appropriate control parameter values is time-consuming, computationally expensive, and challenging. As the quantity of control parameters increases, so does the time complexity associated with searching for practical values, which often overshadows addressing the problem at hand, limiting the efficiency of an algorithm. As primarily recognized by the no free lunch theorem, there is no one-size-fits-all to problem-solving; hence from understanding a problem, a tailored approach can substantially help solve it. To predict the performance of control parameter configurations in unseen environments, this thesis crafts an intelligent generalizable framework leveraging machine learning classification and quantitative characteristics about the problem in question. The proposed parameter performance classifier (PPC) framework is extensively explored by training 84 high-accuracy classifiers comprised of multiple sampling methods, fitness types, and binning strategies. Furthermore, the novel framework is utilized in constructing a new parameter-free particle swarm optimization (PSO) variant called PPC-PSO that effectively eliminates the computational cost of parameter tuning, yields competitive performance amongst other leading methodologies across 99 benchmark functions, and is highly accessible to researchers and practitioners. The success of PPC-PSO shows excellent promise for the applicability of the PPC framework in making many more robust parameter-free meta-heuristic algorithms in the future with incredible generalization capabilities