‘Project performance’ is the metric of delivering project objectives. This research is motivated by levels of project failure and the purpose of the research is to investigate improved project performance. The scientific spectrum is considered; arguing project management as a sub-field of management science based in ‘design science’. Despite research since the 1950s, there is no established community paradigm for project management, illustrated by multiple ‘schools of thought’ failing to stimulate performance improvement. This is investigated with respect to the changing nature of projects and their management; application in numerous industrial sectors, across increasing scope of the product lifecycle (including service projects), and the changing role of project managers as value adding ‘implementers’ rather than status ‘reporters’. Methodology examines the community paradigm and identifies the lack of community paradigm and argues that gap spotting is not appropriate. Conducting research that fills knowledge gaps does not identify underlying issues and reinforces fundamental failings. Underlying assumptions are identified and challenged. Key characteristics are examined in the context of requirements of the community paradigm. The purpose of theory is to describe, explain and predict. Some techniques describe and explain. Few, if any, predict. This locates ‘performance prediction’ as the research issue and suggests it is a missing function for performance improvement. The research focus considers single tasks within a project network. A research model of early stage deviation from plan is developed from the literature on project pathogens and incubation processes. ‘Deviation lifecycle’ as a project function is identified as having no previous evidence in literature. This is developed into a practice model extending the role of failure modes and effects analysis (FMEA) and integrating weak signals and tipping point theory to test performance. Case studies examine representative application of the model and build on the previous cases to illustrate potential for practice. The case studies were reviewed by industrial experts. The changing role of project managers to value added implementers implies a need to improve performance. Research found potential to understand and predict early stage deviation and develops the deviation lifecycle and research model. Across the case studies the research model illustrated potential application. Practical implications indicate potential contribution of project management techniques based on prediction rather than traditional reporting. Developing the community paradigm based on design science is discussed as further work. The originality of the research challenges the lack of theoretical foundation for project management by discussion of the community paradigm and proposes design science as a candidate. The work identifies ‘prediction’ as a relevant but missing function from the project management ‘toolbox’, and introduces the concept of the deviation lifecycle and note no previous literature. The research develops an industrial research model that extends the application of FMEA to examine ‘performance’ and integrates weak signals and tipping point analysis to manage the resolution.

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.

The mystery behind the origin of the pain and the difficulty to propose methodologies for its quantitative characterization fascinated philosophers (and then scientists) from the dawn of our modern society. Nowadays, studying patterns of information flow in mesoscale activity of brain networks is a valuable strategy to offer answers in computational neuroscience. In this paper, complex network analysis was performed on the time-varying brain functional connectomes of a rat model of persistent peripheral neuropathic pain, obtained by means of local field potential and spike train analysis. A wide range of topological network measures (14 in total, the code is publicly released at: https://github.com/biomedical-cybernetics/topological_measures_wide_analysis) was employed to quantitatively investigate the rewiring mechanisms of the brain regions responsible for development and upkeep of pain along time, from three hours to 16 days after nerve injury. The time trend (across the days) of each network measure was correlated with a behavioural test for rat pain, and surprisingly we found that the rewiring mechanisms associated with two local topological measure, the local-community-paradigm and the power-lawness, showed very high statistical correlations (higher than 0.9, being the maximum value 1) with the behavioural test. We also disclosed clear functional connectivity patterns that emerged in association with chronic pain in the primary somatosensory cortex (S1) and ventral posterolateral (VPL) nuclei of thalamus. This study represents a pioneering attempt to exploit network science models in order to elucidate the mechanisms of brain region re-wiring and engram formations that are associated with chronic pain in mammalians. We conclude that the local-community-paradigm is a model of complex network organization that triggers a local learning rule, which seems associated to processing, learning and memorization of chronic pain in the brain functional connectivity. This rule is based exclusively on the network topology, hence was named epitopological learning.