Organizational impact of evidence-informed decision making training initiatives : a case study comparison of two approaches

Background The impact of efforts by healthcare organizations to enhance the use of evidence to improve organizational processes through training programs has seldom been assessed. We therefore endeavored to assess whether and how the training of mid- and senior-level healthcare managers could lead to organizational change. Methods We conducted a theory-driven evaluation of the organizational impact of healthcare leaders’ participation in two training programs using a logic model based on Nonaka’s theory of knowledge conversion. We analyzed six case studies nested within the two programs using three embedded units of analysis (individual, group and organization). Interviews were conducted during intensive one-week data collection site visits. A total of 84 people were interviewed. Results We found that the impact of training could primarily be felt in trainees’ immediate work environments. The conversion of attitudes was found to be easier to achieve than the conversion of skills. Our results show that, although socialization and externalization were common in all cases, a lack of combination impeded the conversion of skills. We also identified several individual, organizational and program design factors that facilitated and/or impeded the dissemination of the attitudes and skills gained by trainees to other organizational members. Conclusions Our theory-driven evaluation showed that factors before, during and after training can influence the extent of skills and knowledge transfer. Our evaluation went further than previous research by revealing the influence—both positive and negative—of specific organizational factors on extending the impact of training programs

CLIMB-COVID: continuous integration supporting decentralised sequencing for SARS-CoV-2 genomic surveillance.

Funder: Wellcome TrustIn response to the ongoing SARS-CoV-2 pandemic in the UK, the COVID-19 Genomics UK (COG-UK) consortium was formed to rapidly sequence SARS-CoV-2 genomes as part of a national-scale genomic surveillance strategy. The network consists of universities, academic institutes, regional sequencing centres and the four UK Public Health Agencies. We describe the development and deployment of CLIMB-COVID, an encompassing digital infrastructure to address the challenge of collecting and integrating both genomic sequencing data and sample-associated metadata produced across the COG-UK network

Influence of measured radio map interpolation on indoor positioning algorithms

Indoor positioning and navigation increasingly has become popular and there are many different approaches, using different technologies. In nearly all of the approaches the locational accuracy depends on signal propagation characteristics of the environment. What makes many of these approaches similar is the requirement of creating a signal propagation Radio Map (RM) by analysing the environment. As this is usually done on a regular grid, the collection of Received Signal Strength Indicator (RSSI) data at every Reference Point (RP) of a RM is a time consuming task. With indoor positioning being in the focus of the research community, the reduction in time required for collection of RMs is very useful as it allows researchers to spend more time with research instead of data collection. In this paper we analyse the options for reducing the time required for the acquisition of RSSI information. We approach this by collecting initial RMs of Wi-Fi signal strength using 5 ESP32 micro controllers working in monitoring mode and placed around our office. We then analyse the influence the approximation of RSSI values in unreachable places has, by using linear interpolation and Gaussian Process Regression (GPR) to find balance between final positioning accuracy, computing complexity, and time requirements for the initial data collection. We conclude that the computational requirements can be significantly lowered, while not affecting the positioning error, by using RM with a single sample per RP generated considering many measurements.- (undefined

Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook

UIDB/00066/2020The advent of the Industry 4.0 initiative has made it so that manufacturing environments are becoming more and more dynamic, connected but also inherently more complex, with additional inter-dependencies, uncertainties and large volumes of data being generated. Recent advances in Industrial Artificial Intelligence have showcased the potential of this technology to assist manufacturers in tackling the challenges associated with this digital transformation of Cyber-Physical Systems, through its data-driven predictive analytics and capacity to assist decision-making in highly complex, non-linear and often multistage environments. However, the industrial adoption of such solutions is still relatively low beyond the experimental pilot stage, as real environments provide unique and difficult challenges for which organizations are still unprepared. The aim of this paper is thus two-fold. First, a systematic review of current Industrial Artificial Intelligence literature is presented, focusing on its application in real manufacturing environments to identify the main enabling technologies and core design principles. Then, a set of key challenges and opportunities to be addressed by future research efforts are formulated along with a conceptual framework to bridge the gap between research in this field and the manufacturing industry, with the goal of promoting industrial adoption through a successful transition towards a digitized and data-driven company-wide culture. This paper is among the first to provide a clear definition and holistic view of Industrial Artificial Intelligence in the Industry 4.0 landscape, identifying and analysing its fundamental building blocks and ongoing trends. Its findings are expected to assist and empower researchers and manufacturers alike to better understand the requirements and steps necessary for a successful transition into Industry 4.0 supported by AI, as well as the challenges that may arise during this process.publishersversionepub_ahead_of_prin

An Industrial Data Analysis and Supervision Framework for Predictive Manufacturing Systems

Due to the advancements in the Information and Communication Technologies field in the modern interconnected world, the manufacturing industry is becoming a more and more data rich environment, with large volumes of data being generated on a daily basis, thus presenting a new set of opportunities to be explored towards improving the efficiency and quality of production processes. This can be done through the development of the so called Predictive Manufacturing Systems. These systems aim to improve manufacturing processes through a combination of concepts such as Cyber-Physical Production Systems, Machine Learning and real-time Data Analytics in order to predict future states and events in production. This can be used in a wide array of applications, including predictive maintenance policies, improving quality control through the early detection of faults and defects or optimize energy consumption, to name a few. Therefore, the research efforts presented in this document focus on the design and development of a generic framework to guide the implementation of predictive manufacturing systems through a set of common requirements and components. This approach aims to enable manufacturers to extract, analyse, interpret and transform their data into actionable knowledge that can be leveraged into a business advantage. To this end a list of goals, functional and non-functional requirements is defined for these systems based on a thorough literature review and empirical knowledge. Subsequently the Intelligent Data Analysis and Real-Time Supervision (IDARTS) framework is proposed, along with a detailed description of each of its main components. Finally, a pilot implementation is presented for each of this components, followed by the demonstration of the proposed framework in three different scenarios including several use cases in varied real-world industrial areas. In this way the proposed work aims to provide a common foundation for the full realization of Predictive Manufacturing Systems

Machine Learning for Microcontroller-Class Hardware -- A Review

The advancements in machine learning opened a new opportunity to bring intelligence to the low-end Internet-of-Things nodes such as microcontrollers. Conventional machine learning deployment has high memory and compute footprint hindering their direct deployment on ultra resource-constrained microcontrollers. This paper highlights the unique requirements of enabling onboard machine learning for microcontroller class devices. Researchers use a specialized model development workflow for resource-limited applications to ensure the compute and latency budget is within the device limits while still maintaining the desired performance. We characterize a closed-loop widely applicable workflow of machine learning model development for microcontroller class devices and show that several classes of applications adopt a specific instance of it. We present both qualitative and numerical insights into different stages of model development by showcasing several use cases. Finally, we identify the open research challenges and unsolved questions demanding careful considerations moving forward.Comment: Accepted for publication at IEEE Sensors Journa

Living with Chronic Illness Scale: International validation through the classic test theory and Rasch analysis among Spanish-speaking populations with long-term conditions

Background: The Living with Chronic Illness (LW-CI) Scale is a comprehensive patient-reported outcome measure that evaluates the complex process of living with long-term conditions. Objective: This study aimed to analyse the psychometric properties of the LW-CI scale according to the classic test theory and the Rasch model among individuals living with different long-term conditions. Design: This was an observational, international and cross-sectional study. Methods: A total of 2753 people from six Spanish-speaking countries living with type 2 diabetes mellitus, chronic obstructive pulmonary disease, chronic heart failure, Parkinson's disease, hypertension and osteoarthritis were included. The acceptability, internal consistency and validity of the LW-CI scale were analysed using the classical test theory, and fit to the model, unidimensionality, person separation index, item local independency and differential item functioning were analysed using the Rasch model. Results: Cronbach's α for the LW-CI scale was .91, and correlation values for all domains of the LW-CI scale ranged from .62 to .68, except for Domain 1, which showed correlation coefficients less than .30. The LW-CI domains showed a good fit to the Rasch model, with unidimensionality, item local independency and moderate reliability providing scores in a true interval scale. Except for two items, the LW-CI scale was free from bias by long-term condition type. Discussion: After some adjustments, the LW-CI scale is a reliable and valid measure showing a good fit to the Rasch model and is ready for use in research and clinical practice. Future implementation studies are suggested. Patient and Public Contribution: Patient and public involvement was conducted before this validation study - in the pilot study phase.Ministry of Science, Innovation and University, Spanish Government; FEDER/ Ministerio de Ciencia, Innovación y Universidades – Agencia Estatal de Investigación/ Proyecto, Grant/Award Number: CSO2017–82691‐RS

Self-Aware resource management in embedded systems

Resource management for modern embedded systems is challenging in the presence of dynamic workloads, limited energy and power budgets, and application and user requirements. These diverse and dynamic requirements often result in conflicting objectives that need to be handled by intelligent and self-aware resource management. State-of-the-art resource management approaches leverage offline and online machine learning techniques for handling such complexity. However, these approaches focus on fixed objectives, limiting their adaptability to dynamically evolving requirements at run-time. In this dissertation, we first propose resource management approaches with fixed objectives for handling concurrent dynamic workload scenarios, mixed-sensitivity workloads, and user requirements and battery constraints. Then, we propose comprehensive self-aware resource management for handling multiple dynamic objectives at run-time. The proposed resource management approaches in this dissertation use machine learning techniques for offline modeling and online controlling. In each resource management approach, we consider a dynamic set of requirements that had not been considered in the state-of-the-art approaches and improve the selfawareness of resource management by learning applications characteristics, users’ habits, and battery patterns. We characterize the applications by offline data collection for handling the conflicting requirements of multiple concurrent applications. Further, we consider user’s activities and battery patterns for user and battery-aware resource management. Finally, we propose a comprehensive resource management approach which considers dynamic variation in embedded systems and formulate a goal for resource management based on that. The approaches presented in this dissertation focus on dynamic variation in the embedded systems and responding to the variation efficiently. The approaches consider minimizing energy consumption, satisfying performance requirements of the applications, respecting power constraints, satisfying user requirements, and maximizing battery cycle life. Each resource management approach is evaluated and compared against the relevant state-of-the-art resource management frameworks