Directorate-General for Energy

Medical emergencies must be promptly treated to avoid death and serious complications. This study assessed the extent and factors contributing to the delayed care in public hospitals. This cross-sectional study was conducted from 07 to 23 October, 2024, involving 162 patients admitted to the medical emergency department of the regional hospital of Kaya. The Emergency Severity Index (ESI) scale was used to identify delays in care delivery. Mann-Whitney, Kruskal-Wallis, and Chi-square tests were used, at the 5% threshold, to estimate the statistical significance of relationships between variables. The results showed that the median time to care provision was 60 min (5 -213 min). In addition, 79% of patients were managed with delays. No significant link was found between the delay in care and patients' sociodemographic characteristics. On the other hand, the extent of delay differed significantly according to disease severity (p=0.002), with 100% of the most severe patients (grades 1 and 2) being managed late compared with 8% of the least severe cases (grade 5). The absence of emergency drugs available within the service, the absence of predefined emergency kits at the Essential and Generic Drugs deposit, and the payment system are factors contributing to the delay in care. Making emergency medicine kits available in the emergency department and introducing prepayment-free treatment would appear to be levers for reducing delays in treating medical emergencies in public hospitals.

Battery electric vehicles (BEVs) have increasingly positioned themselves as a critical technology in the power system, impacting the world’s energy consumption. Understanding the BEV energy dynamics can contribute to vehicle, infrastructure, and grid optimization. Currently, BEV manufacturers provide limited access to the vehicle’s high energy consuming components, such as the battery and the charger. Therefore, existing public datasets consist mostly of aggregated data collected from charging points outside the vehicle, resulting in lower data resolution and not capturing the actual energy dynamics. This paper fills this dataset gap by developing a data generation method to collect datasets, including the actual energy values for the charger, the battery, and the auxiliary devices, using measurement with a second resolution. The collected dataset illustrates energy dynamics under different modes (charging, driving, parking) and environmental conditions. This dataset provides detailed technical insights that can be used to optimize smart charging, reduce operational costs, understand usage, improve the operation of high energy consuming components, build AI models, and analyze grid impact.

Efficient CO2 electrolysis hinges on high CO2 availability and controlled water transport toward and away from the catalyst. Traditional methods, such as direct CO2 supply in the gas form or using CO2 sorbent liquid electrolytes face significant limitations. In this study, a pioneering approach is presented using a rationally designed CO2/H2O sorbent composite electrolyte, enabling low-temperature aqueous CO2 electroreduction without liquid catholyte nor anolyte. The development of a multi-functional electrolyte in the form of a solid sorbent ionogel is introduced in this work. This novel material is designed to perform several tasks at once, namely 1) ensuring ionic conductivity (1.5-3mScm-1) in a solid material, thereby removing salt precipitation issues linked to liquid aqueous electrolytes, 2) serving as local CO2 concentrator (0.03-0.13mmol CO2/g for PCO2 0.1-1 bar at ) to pave the way for flue gas based feed streams at lower CO2 partial pressures and 3) regulating water supply and removal (0.3-2.3mmol H2O/g range at 85% relative humidity depending on degree of methyl functionalization), to prevent preferential hydrogen evolution. This work offers a proof-of-concept for full-vapor phase CO2 reduction, where methyl-functionalization of an ionic liquid-silica nanocomposite enable to steer the selectivity from H2 to CO production.

<div class="section abstract"><div class="htmlview paragraph">The objective of this paper is two-fold. Firstly, provide guidance to best implement end to end traceability from program requirements to physical implementation, and Secondly provide techniques to review and understand large scale complex systems. Even with a Digital Engineering Environment (DEE) being an enabler towards applying Systems Engineering practices to develop large scale complex systems, many organizations are unclear on the methodology for modeling their architectures and enabling stakeholders to easily review, understand and assess those architectures. An architecture can be a conceptual, logical or physical architecture, depending on the system’s lifecycle state. For the context of this paper, the modeling environment is any System’s Modeling Language (SysML) based tool along with modeling tools for electrical, mechanical and software development and product life cycle management tool. The intended audience is any engineering organization defining end-to-end architecture within a DEE, and all stakeholders tasked with reviewing conceptual, logical and physical architecture. The outcome of this paper is to provide engineering organizations with guidance on underlying principles for modeling and understanding or assessing architecture that describe large complex systems.</div></div>