29 research outputs found
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Building Bridges between People with Stroke, Families, and Health Professionals: Development of a Blended Care Program for Self-Management
Evidence-informed interventions for stroke self-management support can influence functional capability and social participation. People with stroke should be offered self-management support after hospital discharge. However, in Portugal, there are no known programs of this nature. This study aimed to develop a person-centered and tailored blended care program for post-stroke self-management, taking into account the existing evidence-informed interventions and the perspectives of Portuguese people with stroke, caregivers, and health professionals. An exploratory sequential mixed methods approach was used, including qualitative methods during stakeholder consultation (stage 1) and co-production (stage 2) and quantitative assessment during prototyping (stage 3). After ethical approval, recruitment occurred in three health units. Results from a literature search led to the adaptation of the Bridges Stroke Self-Management Program. In stage one, 47 participants were interviewed, with two themes emerging: (i) Personalized support and (ii) Building Bridges through small steps. In stage two, the ComVida program was developed, combining in-person and digital approaches, supported by a workbook and a mobile app. In stage three, 56 participants evaluated prototypes, demonstrating a strong level of quality. Understandability and actionability of the developed tools obtained high scores (91–100%). The app also showed good usability (A-grade) and high levels of recommendation (5 stars)
Energy Prediction for Cloud Workload Patterns
The excessive use of energy consumption in Cloud infrastructures has become one of the major cost factors for Cloud providers to maintain. In order to enhance the energy efficiency of Cloud resources, proactive and reactive management tools are used. However, these tools need to be supported with energy-awareness not only at the physical machine (PM) level but also at virtual machine (VM) level in order to enhance decision-making. This paper introduces an energy-aware profiling model to identify energy consumption for heterogeneous and homogeneous VMs running on the same PM and presents an energy-aware prediction framework to forecast future VMs energy consumption. This framework first predicts the VMs’ workload based on historical workload patterns using Autoregressive Integrated Moving Average (ARIMA) model. The predicted VM workload is then correlated to the physical resources within this framework in order to get the predicted VM energy consumption. Compared with actual results obtained in a real Cloud testbed, the predicted results show that this energy-aware prediction framework can get up to 2.58 Mean Percentage Error (MPE) for the VM workload prediction, and up to −4.47 MPE for the VM energy prediction based on periodic workload pattern
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The amazon dense gnss meteorological network a new approach for examining water vapor and deep convection interactions in the tropics
The Amazon Dense Global Navigational Satellite System (GNSS) Meteorological Network ((ADGMN) provides high spatiotemporal resolution, all-weather precipitable water vapor for studying the evolution of continental tropical and sea-breeze convective regimes of Amazonia. The ADGMN campaign consisted of two experiments: a 6-week campaign in and around Belem, which coincided with the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA) and a 1-yr campaign in and around Manaus. The Belem network was composed of 15 GNSS/meteorological stations that provided high-frequency (5 min) PWV data as well as surface meteorological variables For the 6-week duration of the Belem experiment, days were categorized as convective (22 days) or nonconvective (19 days) based solely on a minimum cloud-top temperature of 240 K or below over the central portion of the network and a report of precipitation at at least one site during the afternoon or evening. The Manaus network commenced in April 2011 with 12 GNSS meteorological stations. Local circulations in Manaus driven by anthropogenic deforestation have, in particular, received attention
Recommended from our members
The amazon dense gnss meteorological network a new approach for examining water vapor and deep convection interactions in the tropics
The Amazon Dense Global Navigational Satellite System (GNSS) Meteorological Network ((ADGMN) provides high spatiotemporal resolution, all-weather precipitable water vapor for studying the evolution of continental tropical and sea-breeze convective regimes of Amazonia. The ADGMN campaign consisted of two experiments: a 6-week campaign in and around Belem, which coincided with the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA) and a 1-yr campaign in and around Manaus. The Belem network was composed of 15 GNSS/meteorological stations that provided high-frequency (5 min) PWV data as well as surface meteorological variables For the 6-week duration of the Belem experiment, days were categorized as convective (22 days) or nonconvective (19 days) based solely on a minimum cloud-top temperature of 240 K or below over the central portion of the network and a report of precipitation at at least one site during the afternoon or evening. The Manaus network commenced in April 2011 with 12 GNSS meteorological stations. Local circulations in Manaus driven by anthropogenic deforestation have, in particular, received attention