European Institute of Innovation and Technology

Rooftop solar photovoltaic (PV) systems can make a significant contribution to Europe's energy transition. Realising this potential raises challenges at policy and electricity system planning level. To address this, the authors have developed a geospatially explicit methodology using up-to-date spatial information of the EU building stock to quantify the available rooftop area for PV systems. To do this, it combines satellite-based and statistical data sources with machine learning to provide a reliable assessment of the technical potential for rooftop PV electricity production with a spatial resolution of 100 m across the European Union (EU). It estimates the levelised cost of electricity (LCOE) using country-specific parameters and compares it to the latest household electricity prices. The results show that the EU rooftops could potentially produce 680 TWh of solar electricity annually (representing 24.4% of current electricity consumption), two thirds of which at a cost lower than the current residential tariffs. Country aggregated results illustrate existing barriers for cost-effective rooftop systems in countries with low electricity prices and high investment interest rates, as well as provide indications on how to address these.

Taking the region of Flanders in Belgium as a case study, this article reflects on how smart cities initiated a grassroots initiative on data interoperability. We observe that cities are struggling due to the fragmentation of data and services across different governmental levels. This may cause frustrations in the everyday life of citizens as they expect a coherent user experience. Our research question considers the relationship between individual characteristics of decision makers and their intention to use data standards. We identified criteria for implementing data standards in the public sector by analysing the factors that affect the adoption of data governance, based on the Technology Readiness and Acceptance Model (TRAM), by conducting an online survey (n = 205). Results indicate that respondents who score high on innovativeness have a higher intention to use data standards. However, we conclude that personality characteristics as described in the TRAM-model are not significant predictors of the perceived usefulness and perceived ease of use of data standards. Therefore, we suggest exploring the effects of network governance and organisational impediments to speed-up the adoption of open standards and raise interoperability in complex ecosystems.

In this paper a small-scale energy system called emergency container is presented. This container has lots of applications and can be designed as stationary solution in remote areas such as rural electrification and a mobile solution for disaster situation, military purposes and exploration teams. In this study the container is a hybrid PV/wind/engine energy system that is designed to provide electricity and drinkable water for 1000 person in disaster situations. A transient model implemented in Transient Simulation System (TRNSYS) program is developed and performance of the system during one-year operation for two locations (Nairobi in Kenya and Nyala in Sudan) with relatively high solar insolation is analyzed. The result of the model is significantly important in order to choose the right size of the different components. Due to the fluctuations of solar and wind energy as well as the importance of the battery life cycle, there is a need to have a smart power management and an appropriate fast response control system. In order to achieve it and to fulfill the energy demand as much as possible through renewable energies, a dispatch strategy is introduced and a control algorithm is applied to the model. This control algorithm has increased system reliability and power availability. The transient simulation shows that the share of power generation by solar energy is 63% and 80% and the share of wind power is 27% and 12% in Nairobi and Nyala respectively. It means that most of the energy demand (around 90%) can be covered by renewable energy. This results in significant mitigation of environmental issues compared to using only diesel engine that is a common solution in disaster situations.

Abstract Lack of access to modern forms of energy hampers efforts to reduce poverty. The provision of electricity to off-grid communities is therefore a long-standing developmental goal. Yet, many off-grid electrification projects neglect mid- and long-term operation and maintenance costs. When this is the case, electricity services are unlikely to be affordable to the communities that are the project’s primary target. Here we show that, compared with diesel-powered electricity generation systems, solar photovoltaic systems are more affordable to no less than 36% of the unelectrified populations in East Asia, South Asia, and sub-Saharan Africa. We do so by developing geo-referenced estimates of affordability at a high level of resolution (1 km 2 ). The analysis illustrates the differences in affordability that may be found at the subnational level, which underscores that electrification investments should be informed by subnational data.

Introducing sustainable ways to use energy and transport resources is of paramount importance for creating pathways to more liveable futures. Islands not interconnected to the main grid offer, because of their typically small size, short point-to-point travel distances that suit better than most landscapes the range limitations of today’s electromobile eco-systems. This makes them a unique test-bed that may assist researchers, businesses and policy-makers in developing a better understanding of the diverse opportunities and challenges that come with supporting electric-drive vehicle (EV) infrastructure investments that actively prioritise renewable energy sources (RES). This paper reports the findings of a Q method study that looks into the attitudes of 44 key stakeholders that have a thorough theoretical and empirical knowledge of the existing power and mobility portfolios in such islandic landscapes. Our analysis identifies and presents three distinct groups of stakeholders with different priorities and visions: the ‘Tech Enthusiasts’, the ‘Transform Transport First Supporters’ and the ‘Fiscal Focus Executives’. All our respondents agree on the need for radically transforming the current transport-energy nexus offering. They identify the importance of integrated and clean solutions and recognise that support of pilot applications is more critical than research and development (R&D). They also expect technological breakthroughs to increase market maturities and reduce renewable energy production costs and feel that end-users are still hesitant to buy EVs and need incentives to do so.

There are over 650 million people in Africa who have no access to electricity; this is in sharp contrast to the continent's vast untapped renewable energy potential and due largely to the historical lack of investments in energy infrastructure. New investments in decentralised power generation within Sub-Saharan Africa play a progressively important role in increasing energy access and addressing the continent's electricity supply shortages. Tracking the performance of Sub-Saharan African countries along various socio-political and economic axes can spur the mobilisation of private, public and international sectors in investing in decentralised energy technologies. An increasing amount of high-resolution global spatial data are available, and used for various assessments. However, key multidimensional indicators are mainly still provided only at the national level. To this end, we present a comprehensive and consistent analysis of the attractiveness for decentralised photovoltaic technologies at an unprecedented level of detail using both high-resolution spatial data and national reports. We develop and build a new composite indicator that considers the interplay between social, political, environmental and financial factors at a granular regional level for Sub-Saharan Africa and embeds within it the importance of the local production costs at high-spatial resolution.

In this data article, we present datasets from the construction of a composite indicator, the Photovoltaic Decentralised Energy Investment (PV-DEI) index, presented in detail in [1]. This article consists of a comprehensive energy-related data collected in practice from several sources, and from the outputs of the methodology described in [1]. The PV-DEI was designed and developed to measure the multidimensional factors that currently direct decentralised renewable energy investments. The PV-DEI index includes 52 indicators and was constructed because factors stimulating investment cannot be captured by a single indicator, e.g. competitiveness, affordability, or governance [1]. The PV-DEI index was built in alignment with a theoretical framework guided by an extensive review of the literature surrounding investment in decentralised Photovoltaic (PV), which led to the selection of its indicators. The structure of the PV-DEI was evaluated for its soundness using correlational assessments and principal component analyses (PCA). The raw data provided in this article can enable stakeholders to focus on specific country indicators, and how scores on these indicators contributed to a countries overall rank within the PV-DEI index. The data can be used to weight indicators depending on the specifications of several different stakeholders (such as NGOs, private sector or international institutions).

Abstract This paper describes PWLFIT+, an extension to the frequency domain of PWLFIT, a new paradigm in time‐domain macromodeling for linear multiport systems, based on a piecewise‐linear (PWL) behavioral representation of the S‐parameters step response. While the impulse response of each S‐parameter is approximated as sum of delayed rectangles (rect) functions, its spectrum is interpolated as sum of the corresponding delayed cardinal sine (sinc) functions. Exploiting this correspondence, the model building is performed by an iterative procedure where the PWL macromodels can be determined in order to meet defined accuracy goals on the spectrum. At runtime, waves at macromodels ports are calculated using the Segment Fast Convolution (SFC) algorithm within the Digital Wave Simulator (DWS) framework. The proposed method is characterized by its simplicity, stability, speed and scalability, all features that are emphasized when it is used in the DWS framework. After an analysis of the excellent numerical features of SFC in the Z ‐domain, clearly differentiated with respect conventional macromodeling methods based on poles and residues, two suitable application examples are presented to demonstrate the unique features of PWLFIT+.

This study introduces a pioneering approach to automate the creation of search schemes for lossless approximate pattern matching. Search schemes are combinatorial structures that define a series of searches over a partitioned pattern. Each search specifies the processing order of these parts and the cumulative lower and upper bounds on the number of errors in each part of the pattern. Together, these searches ensure the identification of all approximate occurrences of a search pattern within a predefined limit of k errors. While existing literature offers designed schemes for up to k = 4 errors, designing search schemes for larger k values incurs escalating computational costs. Our method integrates a greedy algorithm and a novel Integer Linear Programming (ILP) formulation to design efficient search schemes for up to k = 7 errors. Comparative analyses demonstrate the superiority of our ILP-optimal schemes over alternative strategies in both theoretical and practical contexts. Additionally, we propose a dynamic scheme selection technique tailored to specific search patterns, further enhancing efficiency. Combined, this yields runtime reductions of up to 53% for higher k values. To facilitate search scheme generation, we present Hato, an open-source software tool (AGPL-3.0 license) employing the greedy algorithm and utilizing CPLEX for ILP solving. Furthermore, we introduce Columba 1.2, an open-source lossless read-mapper (AGPL-3.0 license) implemented in C++. Columba surpasses existing state-of-the-art tools by identifying all approximate occurrences of 100,000 Illumina reads (150 bp) in the human reference genome within 24 seconds (maximum edit distance of 4) and 75 seconds (maximum edit distance of 6) using a single CPU core. Notably, our study showcases Columba’s capability to align 100,000 reads of length 50, with high error rates and up to an edit distance of 7, in a mere 2 hours and 15 minutes. This achievement is unmatched by other lossless aligners, which require over 3 hours for edit distance 5 alignments. Moreover, Columba exhibits a mapping rate four times higher than that of a lossy tool for this dataset.

Abstract Nowadays, many complex multi‐vendor production environments, such as telecom infrastructures in smart cities or on‐board passenger information systems in trains, are based on micro‐services and deployed in the cloud. From a service integrator viewpoint, building new solutions for these environments, which can host a large number of externally designed and developed micro‐services, is often complex and error‐prone. This is in part due to undocumented behaviour or undocumented architectural specifications of such systems. Advanced service monitoring can offer a solution to quickly detect anomalies or unexpected service interaction behaviour during on‐site integration. However, the monitoring service should not have an impact on the production environment itself. Therefore, this article proposes an agent‐based unobtrusive monitoring platform, capable of monitoring both internally developed and externally developed services through the use of sidecar containers. It monitors state, metrics and network traffic at micro‐service level and the research was conducted as part of the DynAMo research project, a collaboration with various industry partners. Prototype evaluation proves that our solution has a negligible impact (below 0.02% CPU usage on average) on an existing micro‐service environment just as other monitoring systems like Prometheus while offering additional functionality focused on multi‐vendor service integration. This makes it suitable to be deployed in complex production domains to further aid on‐site integration and quickly find potential new anomalies.

eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.

eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.

eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.

Gravity dataset collected in the Ivrea-Verbano Zone (IVZ, Western Alps, Italy). The data was collected in the frame of a gravity-based investigation and modelling of the Ivrea Geophysical Body. For citation and further details on the work see Scarponi et al. (2020, GJI): https://doi.org/10.1093/gji/ggaa263 The file contains the gravity data collected in the IVZ region, including free-air anomaly and Bouguer gravity anomaly (in mGal). Longitude, Latitude coordinates are in degrees, elevation in meters. Uncertainty on the final gravity data products and gravity data is 1 mGal. --- Data collection, as well as the associated research, were supported by the Swiss National Science Foundation (SNF) (grant numbers PP00P2_157627 and PP00P2_187199).

Gravity dataset collected in the Ivrea-Verbano Zone (IVZ, Western Alps, Italy). The data was collected in the frame of a gravity-based investigation and modelling of the Ivrea Geophysical Body. For citation and further details on the work see Scarponi et al. (2020, GJI): https://doi.org/10.1093/gji/ggaa263 The file contains the gravity data collected in the IVZ region, including free-air anomaly and Bouguer gravity anomaly (in mGal). Longitude, Latitude coordinates are in degrees, elevation in meters. Uncertainty on the final gravity data products and gravity data is 1 mGal. --- Data collection, as well as the associated research, were supported by the Swiss National Science Foundation (SNF) (grant numbers PP00P2_157627 and PP00P2_187199).

<sec> <title>BACKGROUND</title> Data quality is fundamental to maintain the trust and reliability of health data for both primary and secondary purposes. However, before secondary use of health data, it is essential to assess the quality at the source and to develop systematic methods for the assessment of important data quality dimensions. </sec> <sec> <title>OBJECTIVE</title> This case study aims to offer a dual aim: to assess the data quality of height and weight measurements across seven Belgian hospitals and to outline the obstacles these hospitals face in sharing and improving data quality standards </sec> <sec> <title>METHODS</title> Focusing on data quality dimensions completeness and consistency, this study examined height and weight data collected from 2021 to 2022 within three distinct departments – surgical, geriatrics, and paediatrics – in each of the seven hospitals. </sec> <sec> <title>RESULTS</title> Variability was observed in the completeness scores for height across hospitals and departments, especially within surgical and geriatric wards. In contrast, weight data uniformly achieved high completeness scores. Notably, the consistency of height and weight data recording was uniformly high across all departments. </sec> <sec> <title>CONCLUSIONS</title> A collective collaboration among Belgian hospitals, transcending network affiliations, was formed to conduct this data quality assessment. This study demonstrates the potential for improving data quality across healthcare organizations by sharing knowledge and good practices, establishing a foundation for future, similar research. </sec>

Significant experiences in the natural environment, especially during early childhood, are potential for learning and developing bonds and values ​​in relation to nature, resulting in caring actions. This article reports on a pedagogical activity carried out with a kindergarten class in a garden laboratory, involving symbolic aspects of playing to discuss and generate reflection in the face of food-individual-nature relationships. The approach of children to a living environment, naturally rich in stimuli, furthered engagement, promoted curiosity and enabled discoveries and discussions on transdisciplinary themes, stimulating an integrative vision about nature, as well as the understanding of the individual as an inseparable part of it.

eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.

BACKGROUND: The performance status (PS) is an indicator of a cancer patient's ability to perform everyday activities and plays a key role in oncology. Research suggests that the documentation of PS scores in electronic health records (EHR) is deficient. METHODS: We analysed PS score documentation (Karnofsky or ECOG/Zubrod/WHO) in the hospital, consultation, and multidisciplinary team meeting (MDT) records of patients newly referred for a cancer at a large, public, multisite hospital system, between 1 January 2019 and 1 June 2021. We developed a regular expression (RegEx) to automatically identify PS in documents and assessed what patient and hospital characteristics were associated with PS documentation. RESULTS: Our RegEx achieved accuracy, and weighted- and macro-average F1 score, > 0.95 for all document types. We included 68,479 patients. 35% had a documented PS between -90 and +365 days of their first ICD-10 cancer code. 18% of MDT reports contained a PS score. In multivariate analysis, without accounting for metastatic status at diagnosis, odds ratios (ORs) for PS documentation in patient files varied by cancer type, from 0.47 (95% confidence interval: [0.42; 0.52]) for genitourinary to 3.30 [3.00; 3.61] for lung cancer, and hospital, from 0.27 [0.23; 0.33] to 3.38 [3.14; 3.63]. Male patients were more likely to have a documented PS (OR = 1.08 [1.04; 1.13]), as well as older patients. The number of each type of document was positively correlated with the presence of a score. When adding metastatic status at diagnosis, the OR for metastatic status was large (3.29 [3.13; 3.46]), but associations with other covariates were not noticeably affected. Documented PS close to diagnosis was associated with poorer 1-year survival (25% of patients with PS died within 1 year, vs 12% without PS). CONCLUSION: PS score documentation was variable and generally low. Improved documentation is required if EHRs are to be used as a source of real-world data.

eSource – particularly EHR-to-EDC – is an emerging paradigm in clinical research that enables automated transfer of electronic health record (EHR) data into electronic data capture (EDC) systems, with the potential to reduce site burden, improve data quality and accelerate oncology clinical trial workflows. However, widespread implementation remains limited due to technical, regulatory and operational barriers. To address these challenges, the European Institute for Innovation through Health Data (i~HD) launched the eSource Scale-Up Task Force in 2024. This multi-stakeholder initiative brings together leading oncology centres and pharmaceutical sponsors to establish a consensus-driven roadmap for eSource adoption. Central to this effort are three foundational resources: readiness criteria for early adopters, a performance indicator framework for monitoring success and an operational playbook to guide implementation. This article provides a structured overview of the Task Force’s objectives, collaborative model and outputs, with specific attention to its focus on interoperability, regulatory alignment and real-world validation. While initially developed for oncology, the Task Force’s framework is applicable across therapeutic areas characterized by data-intensive workflows.