This study tries to understand the power of knowledge within collaborative care networks to provide insights for designing successful collaboration within care networks by combining intersectionality and epistemic (in)justice. Becoming an informal carer for someone with an acquired brain injury (ABI) causes a dramatic disruption of daily life. Collaboration between professionals and carers with a migration background may result in unjust and unfair situations within care networks. Carer experiences are shaped by aspects of diversity which are subject to power structures and processes of social (in)justice in care networks. In this study, intersectionality was used to both generate complex in-depth insights into the different active layers of carer experiences and focus on within-group differences. Intersectionality was combined with the theoretical concept of epistemic (in)justice to unravel underlying dynamics in collaborative care networks contributing to the understanding that carers with a migration background are often not seen as ‘knowers of reality.’ This qualitative study conducted in the Netherlands between 2019 and 2022 incorporated three informal group conversations (N = 32), semi-structured interviews (N = 21), and three dialogue sessions (N = 7) with carers caring for someone with an ABI. A critical friend and a community of practice, with carers, professionals, and care recipients (N = 8), contributed to the analysis. Three interrelated themes were identified as constituting different layers of the carer experience: (a) I need to keep going, focusing on carers' personal experiences and how experiences were related to carers social positioning; (b) the struggle of caring together, showing how expectations of family members towards carers added to carer burden; and (c) trust is a balancing act, centering on how support from professionals shaped carers' experiences, in which trusting professionals' support proved challenging for carers, and how this trust was influenced by contextual factors at organizational and policy levels. Overall, the need for diversity-responsive policies within care organizations is apparent. Carers with a migration background need to feel heard so they can meaningfully tailor care to meet recipients' needs.
This paper presents a method and mock-up design for evaluating the heat-island mitigation effect of porous/water-retentive blocks in a climatic environmental chamber using ambient temperature measurements. To create the proposed method, the heat circulation mechanism of blocks was considered. From this, we specified the climatic chamber design requirements, determined the required components and equipment for the mock-up, and developed the proposed method for evaluating heat-island mitigation performance based on ambient temperature. Using the proposed mock-up design and method, we confirmed that both surface and air temperatures were lower when porous/water-retentive blocks were installed compared to conventional blocks. This method can be used to analyze the difference between surface and ambient temperatures under various conditions to quantify the heat-island mitigation performance of different materials according to ambient temperature.
District heating (DH) has a major potential to increase the efficiency, security, and sustainability of energy management at the community scale. However, there is a huge challenge for decision makers due to the lack of knowledge about thermal energy demand during a year. https://www.mdpi.com/1996-1073/14/17/5462
MULTIFILE
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The integration of renewable energy resources, controllable devices and energy storage into electricity distribution grids requires Decentralized Energy Management to ensure a stable distribution process. This demands the full integration of information and communication technology into the control of distribution grids. Supervisory Control and Data Acquisition (SCADA) is used to communicate measurements and commands between individual components and the control server. In the future this control is especially needed at medium voltage and probably also at the low voltage. This leads to an increased connectivity and thereby makes the system more vulnerable to cyber-attacks. According to the research agenda NCSRA III, the energy domain is becoming a prime target for cyber-attacks, e.g., abusing control protocol vulnerabilities. Detection of such attacks in SCADA networks is challenging when only relying on existing network Intrusion Detection Systems (IDSs). Although these systems were designed specifically for SCADA, they do not necessarily detect malicious control commands sent in legitimate format. However, analyzing each command in the context of the physical system has the potential to reveal certain inconsistencies. We propose to use dedicated intrusion detection mechanisms, which are fundamentally different from existing techniques used in the Internet. Up to now distribution grids are monitored and controlled centrally, whereby measurements are taken at field stations and send to the control room, which then issues commands back to actuators. In future smart grids, communication with and remote control of field stations is required. Attackers, who gain access to the corresponding communication links to substations can intercept and even exchange commands, which would not be detected by central security mechanisms. We argue that centralized SCADA systems should be enhanced by a distributed intrusion-detection approach to meet the new security challenges. Recently, as a first step a process-aware monitoring approach has been proposed as an additional layer that can be applied directly at Remote Terminal Units (RTUs). However, this allows purely local consistency checks. Instead, we propose a distributed and integrated approach for process-aware monitoring, which includes knowledge about the grid topology and measurements from neighboring RTUs to detect malicious incoming commands. The proposed approach requires a near real-time model of the relevant physical process, direct and secure communication between adjacent RTUs, and synchronized sensor measurements in trustable real-time, labeled with accurate global time-stamps. We investigate, to which extend the grid topology can be integrated into the IDS, while maintaining near real-time performance. Based on topology information and efficient solving of power flow equation we aim to detect e.g. non-consistent voltage drops or the occurrence of over/under-voltage and -current. By this, centrally requested switching commands and transformer tap change commands can be checked on consistency and safety based on the current state of the physical system. The developed concepts are not only relevant to increase the security of the distribution grids but are also crucial to deal with future developments like e.g. the safe integration of microgrids in the distribution networks or the operation of decentralized heat or biogas networks.
