Towards a Computational Approach for Proactive Robot Behaviour in Assistive Tasks

While most of the current work has been focused on developing adaptive techniques to respond to human-initiated inputs (what behaviour to perform), very few of them have explored how to proactively initiate an interaction (when to perform a given behaviour). The selection of the proper action, its timing and confidence are essential features for the success of proactive behaviour, especially in collaborative and assistive contexts. In this work, we present the initial phase towards the deployment of a robotic system that will be capable of learning what, when, and with what confidence to provide assistance to users playing a sequential memory game

Technology-Enabled Medical IoT System for Drug Management

This study introduces an innovative framework for the storage and administration of pharmaceuticals, which effectively tackles the pressing requirements of maintaining optimal temperature and humidity conditions, monitoring medicine inventory, and processing real-time data in healthcare establishments. By utilizing a comprehensive network of Internet of Things (IoT) sensors strategically positioned within pharmaceutical storage facilities, our technology effectively guarantees the preservation and security of stored drugs. The study conducted in our research demonstrates that low temperature fluctuation effectively protects medicinal substances, hence reducing potential dangers to patients. The real-time inventory management system effectively optimizes medicine control by following expiry criteria and minimizing wasted spending. Furthermore, our study emphasizes the importance of cloud response latency, as the average data transfer time is a rapid 100 milliseconds. The expeditious integration of crucial data enables prompt notifications and alerts, hence augmenting the quality and safety of pharmaceutical products

Precis of neuroconstructivism: how the brain constructs cognition

Neuroconstructivism: How the Brain Constructs Cognition proposes a unifying framework for the study of cognitive development that brings together (1) constructivism (which views development as the progressive elaboration of increasingly complex structures), (2) cognitive neuroscience (which aims to understand the neural mechanisms underlying behavior), and (3) computational modeling (which proposes formal and explicit specifications of information processing). The guiding principle of our approach is context dependence, within and (in contrast to Marr [1982]) between levels of organization. We propose that three mechanisms guide the emergence of representations: competition, cooperation, and chronotopy; which themselves allow for two central processes: proactivity and progressive specialization. We suggest that the main outcome of development is partial representations, distributed across distinct functional circuits. This framework is derived by examining development at the level of single neurons, brain systems, and whole organisms. We use the terms encellment, embrainment, and embodiment to describe the higher-level contextual influences that act at each of these levels of organization. To illustrate these mechanisms in operation we provide case studies in early visual perception, infant habituation, phonological development, and object representations in infancy. Three further case studies are concerned with interactions between levels of explanation: social development, atypical development and within that, developmental dyslexia. We conclude that cognitive development arises from a dynamic, contextual change in embodied neural structures leading to partial representations across multiple brain regions and timescales, in response to proactively specified physical and social environment

INDUSTRY 4.0 TECHNOLOGIES AND ORGANIZATIONAL DESIGN–EVIDENCE FROM 15 ITALIAN CASES

Current literature on Industry 4.0 technologies has mainly explored their relationship to the employment dynamics, or to the required competencies and emerging roles. This paper is complementing current literature with a perspective focused on organizational design. The aim of the paper is to explore how organizations are re-designed when Industry 4.0 technologies are implemented. The paper is based on 15 case studies carried out in Italian manufacturing companies and data was collected from 70 semi-structured interviews to relevant roles involved in the implementation of digital technologies. Results show that, when Industry 4.0 technologies are implemented, organizations are redesigned following an employee control-oriented or following an employee commitment-oriented organizational design. These results show that organizational design is the result of decisions, and is not determined by technology. The implications of our findings are presented and discussed

Spatio-Temporal Patterns act as Computational Mechanisms governing Emergent behavior in Robotic Swarms

open access articleOur goal is to control a robotic swarm without removing its swarm-like nature. In other words, we aim to intrinsically control a robotic swarm emergent behavior. Past attempts at governing robotic swarms or their selfcoordinating emergent behavior, has proven ineffective, largely due to the swarm’s inherent randomness (making it difficult to predict) and utter simplicity (they lack a leader, any kind of centralized control, long-range communication, global knowledge, complex internal models and only operate on a couple of basic, reactive rules). The main problem is that emergent phenomena itself is not fully understood, despite being at the forefront of current research. Research into 1D and 2D Cellular Automata has uncovered a hidden computational layer which bridges the micromacro gap (i.e., how individual behaviors at the micro-level influence the global behaviors on the macro-level). We hypothesize that there also lie embedded computational mechanisms at the heart of a robotic swarm’s emergent behavior. To test this theory, we proceeded to simulate robotic swarms (represented as both particles and dynamic networks) and then designed local rules to induce various types of intelligent, emergent behaviors (as well as designing genetic algorithms to evolve robotic swarms with emergent behaviors). Finally, we analysed these robotic swarms and successfully confirmed our hypothesis; analyzing their developments and interactions over time revealed various forms of embedded spatiotemporal patterns which store, propagate and parallel process information across the swarm according to some internal, collision-based logic (solving the mystery of how simple robots are able to self-coordinate and allow global behaviors to emerge across the swarm)

Sustainability transition of production systems in the digital era - a systems perspective for building resilient and sustainable production systems

Locked-in manufacturing industries with highly structured operations and path dependencies are major contributors to the sustainability challenges currently burdening our planet. The effects of the ongoing pandemic, large-scale environmental impacts due to climate change and constant economic and social downturns are just some examples of these sustainability challenges. Increased digitalisation, awareness, global initiatives and regulations are pressuring manufacturing industries to transition towards sustainable development. However, there exists a multitude of interpretations in implementing sustainability in manufacturing industries. This makes proposing tangible actions to translate global initiatives complicated, thus hindering the sustainability transition process.The purpose of this thesis is to support the advancement of resilient production systems which can overcome sustainability challenges in the Industry 4.0 era. Hence, the thesis aims to investigate: (i) the systemic challenges of manufacturing companies which hinder their sustainability transition process and (ii) the mechanisms by which a systems perspective may be applied to support the transition. A mixed-methods approach was used to carry out the research, using qualitative and quantitative data from three (empirical and theoretical) studies. Applying a systems perspective helped reveal the challenges which hinder the sustainability transition of production systems. Understanding the production ‘system’ as a whole (and the underlying web of intricate dependencies and challenges in production operations) required this holistic perspective. Regarding the challenges, it was observed that manufacturing industries across different domains face three main types of challenge: internal (such as organisational routines, strategies and cultural mindset), external (such as regulations and collaboration with stakeholders) and technological (such as maturity levels and data). Three different enabling mechanisms were explored which may help overcome the above sustainability challenges and support the sustainability transition of manufacturing industries: (1) Industry 4.0 technologies, (2) dynamic capabilities and (3) resilience engineering. It was observed that Industry 4.0 technologies (such as artificial intelligence/machine learning, virtual development tools and sensors) are largely implemented to enable sustainable manufacturing in the form of resource efficiency and waste reduction. The results also revealed five microfoundations of dynamic capabilities – communication, organisation, resources, collaboration and technology. Based on Industry 4.0 opportunities to promote sustainability transitions, the results revealed five industrial resilience factors – robustness, agility, resourcefulness, adaptability and flexibility.This research contributes to theory by studying the convergence of emergent research topics, such as Industry 4.0, dynamic capabilities and resilience engineering in the context of sustainability transitions. In terms of a practical contribution, the sustainability transitions model developed in this thesis may support industrial practitioners in gaining a holistic understanding of the systemic challenges to sustainability, plus corresponding mechanisms to promote the sustainability transition of industries and the building of resilient production systems