Proof of impact and pipeline planning: directions and challenges for social audit in the health sector

Social audits are typically observational studies, combining qualitative and quantitative uptake of evidence with consultative interpretation of results. This often falters on issues of causality because their cross-sectional design limits interpretation of time relations and separation out of other indirect associations

Studying Emotions at Work Using Agent-Based Modeling and Simulation

Part 11: Multi Agent - IoTInternational audienceEmotions in workplace is a topic that has increasingly attracted attention of both organizational practitioners and academics. This is due to the fundamental role emotions play in shaping human resources behaviors, performance, productivity, interpersonal relationships and engagement at work. In the current research, a computational social simulation approach is adopted to replicate and study the emotional experiences of employees in organizations. More specifically, an emotional agent-based model of an employee at work is proposed. The developed model is used in a computer simulator WEMOS (Workers EMotions in Organizations Simulator) to conduct certain analyzes in relation to the most likely emotions-evoking stimuli as well as the emotional content of several work-related stimuli. Simulation results can be employed to gain deeper understanding about emotions in the work life

CyreumE: A real-time situational awareness and decision making blockchain-based architecture for the energy Internet

Providing reliable and adequate electricity supply has been a persistent concern in the power sector of the less developed countries. While there has been an increasing interest in the use of blockchain in power grid for energy trading around the developed economies, technical challenges such as identifying and preventing operational inefficiency and nontechnical challenges such as inefficient and disintegrated decision-making across the power grid value chain have continuously hindered the overall efficiency of the sector. In this chapter, we present CyreumE, a blockchain-based real-time situational awareness and decision-making architecture with distributed value chain framework (DVC) for the Energy Internet which uses a combination of advanced lightweight cryptography, knowledge representation model, and decision-making process. Despite huge computational costs of blockchains which involve high delays that are not suitable for the power grid, CyreumE proposes CyreumE-CP for efficient data communications which eliminates the delays of blockchains. CyreumE-CP uses shared secret session keys to prevent security attacks from disrupting and weakening the communications. Furthermore, CyreumE prevents operational inefficiency in the power grid via a real-time situational awareness process (RSA) and efficiently utilizes a real-time decision-making process (RDM) for decision-making across the power grid value chain which enhances funding and investment opportunities. We validate CyreumE-CP on ultralow-power IEEE 802.15.4 wireless sensor module and CyreumE on a real-world dataset collected from power generation and distribution facilities. We apply our architecture to two challenges such as unavailability of real-time operational data/reliability issues and losses in the power grid as well as disputes across the value chain. Our results show that CyreumE provides real-time operational efficiency and decision-making guarantees

Fuzzy cognitive modeling: Theoretical and practical considerations

Capítulo del libro "Czarnowski I., Howlett R., Jain L. (eds) Intelligent Decision Technologies 2019. Smart Innovation, Systems and Technologies, vol 142. Springer, Singapore"Fuzzy cognitive maps (FCMs) are knowledge-based neural systems comprised of causal relations and well-defined neural concepts Since their inception three decades ago, FCMs have been used to model a myriad of problems Despite the research progress achieved in this field, FCMs are still surrounded by important misconceptions that hamper their competitiveness in several scenarios In this paper, we discuss some theoretical and practical issues to be taken into account when modeling FCM-based systems Such issues range from the causality fallacy and the timing component to limited prediction horizon imposed by the network structure The conclusion of this paper is that the FCM’s theoretical underpinnings need to be revamped in order to overcome these limitations Closing the gap between FCMs and other neural network models seems to be the right path in that journey