From Social Simulation to Integrative System Design

As the recent financial crisis showed, today there is a strong need to gain "ecological perspective" of all relevant interactions in socio-economic-techno-environmental systems. For this, we suggested to set-up a network of Centers for integrative systems design, which shall be able to run all potentially relevant scenarios, identify causality chains, explore feedback and cascading effects for a number of model variants, and determine the reliability of their implications (given the validity of the underlying models). They will be able to detect possible negative side effect of policy decisions, before they occur. The Centers belonging to this network of Integrative Systems Design Centers would be focused on a particular field, but they would be part of an attempt to eventually cover all relevant areas of society and economy and integrate them within a "Living Earth Simulator". The results of all research activities of such Centers would be turned into informative input for political Decision Arenas. For example, Crisis Observatories (for financial instabilities, shortages of resources, environmental change, conflict, spreading of diseases, etc.) would be connected with such Decision Arenas for the purpose of visualization, in order to make complex interdependencies understandable to scientists, decision-makers, and the general public.Comment: 34 pages, Visioneer White Paper, see http://www.visioneer.ethz.c

Adopting Jaya Algorithm for Team Formation Problem

This paper presents a simple and mighty metaheuristic algorithm, Jaya, which is applied to solve the team formation (TF) problem and it is a very fundamental problem in many databases and expert collaboration networks or web applications. The Jaya does not need any distinctive parameters that require comprehensive tuning, which is usually troublesome and inefficient. Among several optimization methods, Jaya is chosen for TFP because of its simplicity and it always avoids the worst solutions and moving towards the global best solution. This victorious nature makes Jaya Algorithm more powerful and significant as compared to any other contemporary optimization algorithms. To evaluate the efficiency of the Jaya Algorithm (JA) against another metaheuristic algorithm, Sine-Cosine Algorithm (SCA), both algorithms are tested and assessed for the TF problem solution using an ACM dataset containing experts and their skills. The experimental results validate the improved performance of the optimization solutions and the potential of JA with fast convergence for solving TF problems which are better than SCA

Investigation of Team Formation in Dynamic Social Networks

Team Formation Problem (TFP) in Social Networks (SN) is to collect the group of individuals who match the requirements of given tasks under some constraints. It has several applications, including academic collaborations, healthcare, and human resource management. These types of problems are highly challenging because each individual has his or her own demands and objectives that might conflict with team objectives. The major contribution of this dissertation is to model a computational framework to discover teams of experts in various applications and predict the potential for collaboration in the future from a given SN. Inspired by an evolutionary search technique using a higher-order cultural evolution, a framework is proposed using Knowledge-Based Cultural Algorithms to identify teams from co-authorship and industrial settings. This model reduces the search domain while guiding the search direction by extracting situational knowledge and updating it in each evolution. Motivated from the above results, this research examines the palliative care multidisciplinary networks to identify and measure the performance of the optimal team of care providers in a highly dynamic and unbalanced SN of volunteer, community, and professional caregivers. Thereafter, a visualization framework is designed to explore and monitor the evolution in the structure of the care networks. It helps to identify isolated patients, imbalanced resource allocation, and uneven service distribution in the network. This contribution is recognized by Hospice and the Windsor Essex Compassion Care Community in partnership with the Faculty of Nursing. In each setting, several cost functions are attempted to measure the performance of the teams. To support this study, the temporal nature of two important evaluation metrics is analyzed in Dynamic Social Networks (DSN): dynamic communication cost and dynamic expertise level. Afterward, a novel generic framework for TFP is designed by incorporating essential cost functions, including the above dynamic cost functions. The Multi-Objective Cultural Algorithms (MOCA) is used for this purpose. In each generation, it keeps track of the best solutions and enhances exploration by driving mutation direction towards unexplored areas. The experimental results reach closest to the exact algorithm and outperform well-known searching methods. Subsequently, this research focuses on predicting suitable members for the teams in the future, which is typically a real-time application of Link Prediction. Learning temporal behavior of each vertex in a given DSN can be used to decide the future connections of the individual with the teams. A probability function is introduced based on the activeness of the individual. To quantify the activeness score, this study examines each vertex as to how actively it interacts with new and existing vertices in DSN. It incorporates two more objective functions: the weighted shortest distance and the weighted common neighbor index. Because it is technically a classification problem, deep learning methods have been observed as the most effective solution. The model is trained and tested with Multilayer Perceptron. The AUC achieves above 93%. Besides this, analyzing common neighbors with any two vertices, which are expected to connect, have a high impact on predicting the links. A new method is introduced that extracts subgraph of common neighbors and examines features of each vertex in the subgraph to predict the future links. The sequence of subgraphs\u27 adjacency matrices of DSN can be ordered temporally and treated as a video. It is tested with Convolutional Neural Networks and Long Short Term Memory Networks for the prediction. The obtained results are compared against heuristic and state-of-the-art methods, where the results reach above 96% of AUC. In conclusion, the knowledge-based evolutionary approach performs well in searching through SN and recommending effective teams of experts to complete given tasks successfully in terms of time and accuracy. However, it does not support the prediction problem. Deep learning methods, however, perform well in predicting the future collaboration of the teams

Expert-based development of a standard in CO2 sequestration monitoring technology

Bureau of Economic Geolog

The role of Computer Aided Process Engineering in physiology and clinical medicine

This paper discusses the potential role for Computer Aided Process Engineering (CAPE) in developing engineering analysis and design approaches to biological systems across multiple levels—cell signalling networks, gene, protein and metabolic networks, cellular systems, through to physiological systems. The 21st Century challenge in the Life Sciences is to bring together widely dispersed models and knowledge in order to enable a system-wide understanding of these complex systems. This systems level understanding should have broad clinical benefits. Computer Aided Process Engineering can bring systems approaches to (i) improving understanding of these complex chemical and physical (particularly molecular transport in complex flow regimes) interactions at multiple scales in living systems, (ii) analysis of these models to help to identify critical missing information and to explore the consequences on major output variables resulting from disturbances to the system, and (iii) ‘design’ potential interventions in in vivo systems which can have significant beneficial, or potentially harmful, effects which need to be understood. This paper develops these three themes drawing on recent projects at UCL. The first project has modeled the effects of blood flow on endothelial cells lining arteries, taking into account cell shape change resulting in changes in the cell skeleton which cause consequent chemical changes. A second is a project which is building an in silico model of the human liver, tieing together models from the molecular level to the liver. The composite model models glucose regulation in the liver and associated organs. Both projects involve molecular transport, chemical reactions, and complex multiscale systems, tackled by approaches from CAPE. Chemical Engineers solve multiple scale problems in manufacturing processes – from molecular scale through unit operations scale to plant-wide and enterprise wide systems – so have an appropriate skill set for tackling problems in physiology and clinical medicine, in collaboration with life and clinical scientists

Training of Crisis Mappers and Map Production from Multi-sensor Data: Vernazza Case Study (Cinque Terre National Park, Italy)

This aim of paper is to presents the development of a multidisciplinary project carried out by the cooperation between Politecnico di Torino and ITHACA (Information Technology for Humanitarian Assistance, Cooperation and Action). The goal of the project was the training in geospatial data acquiring and processing for students attending Architecture and Engineering Courses, in order to start up a team of "volunteer mappers". Indeed, the project is aimed to document the environmental and built heritage subject to disaster; the purpose is to improve the capabilities of the actors involved in the activities connected in geospatial data collection, integration and sharing. The proposed area for testing the training activities is the Cinque Terre National Park, registered in the World Heritage List since 1997. The area was affected by flood on the 25th of October 2011. According to other international experiences, the group is expected to be active after emergencies in order to upgrade maps, using data acquired by typical geomatic methods and techniques such as terrestrial and aerial Lidar, close-range and aerial photogrammetry, topographic and GNSS instruments etc.; or by non conventional systems and instruments such us UAV, mobile mapping etc. The ultimate goal is to implement a WebGIS platform to share all the data collected with local authorities and the Civil Protectio