This deliverable presents the methodology for developing Policy Options to improve the effectiveness of EPC and SRI across participating Member States in alignment with the European Commission’s Better Regulation Guidelines. It includes a list of policy measures organised by thematic pillars, analysis of baselines and Policy Options shaped by Energy Agencies, and Theory of Change.
This deliverable presents the Improved Good Practice Guidance developed within the tunES project. The collection consolidates and updates good practices related to the design, deployment, and implementation of Energy Performance Certificates and the Smart Readiness Indicator across the European Union, supporting Member States in the implementation of the revised EPBD.
Demographic change represents the most significant challenge of the 21st century. The aging population around the world will foster a consequent change in labor markets, wealth distribution, request of goods and services, as well as social and healthcare needs. The number of survivors to once deadly conditions, as well as the age associated increase in degenerative chronic conditions will represent a severe stress to our systems, that will not be sustainable any longer. At the same time, longevity might become an opportunity for the re-organization of services, leading to a new era of opportunities for economic and social prosperity. These challenges require a strong interdisciplinary approach in the identification of unmet needs, and new social, health and technological solutions, that are demand-driven and user-oriented. ICT is going to be the key to many of these revolutionary services. The European Community has proposed a new grant instrument to foster the collaboration between public administration and ICT industry for purchasing Research and Development services in order to develop a novel Information and Communication Technology solution, called Pre Commercial Procurement. ProEmpower is such a project aimed to enable patients to early diagnosis, daily management, and clinical data collection for people with type 2 diabetes. The project goes through a call for tenders issued by a consortium of four public procurers, that jointly defined a set of requirements and use cases, in collaboration with end-users, for the development of innovative solutions This manuscript describes the process that has led to the selection of two solutions that will be tested by end users in the four regions.
This data set was generated in accordance with the semiconductor industry and contains sensor recordings from high-precision and high-tech production equipment. Basically, the semiconductor production consists of hundreds of process steps performing physical and chemical operations on so-called wafers, i.e. slices based on semiconductor material. Typically, bunches of wafers are aggregated into so-called lots of size 25, which always pass through the same operations in the production chain. In the production chain, each process equipment is equipped with several sensors recording physical parameters like gas flow, temperature, voltage, etc., resulting in so-called sensor data recorded during each process step. To keep the entire production as stable as possible, the sensor data is used in order to intervene in case of deviations. After the production, each device on the wafer is tested in the most careful way resulting in so-called wafer test data. In some cases, suspicious patterns occur in the wafer test data potentially leading to failure. In this case the root cause must be found in the production chain. For this purpose, the given sensor data is provided. The aim is to find correlations between the wafer test data and the sensor data in order to identify the root cause. The given data is divided into three data sets: "equipment1.csv", "equipment2.csv" and "response.csv". "equipment1.csv" and "equipment2.csv" represent the sensor data for two process equipment. The "response.csv" data set contains the corresponding wafer test data. For the unique identification, the first two columns in each data set are the lot number and the wafer number respectively. It must be mentioned that the number of wafers contained can vary within but also between the equipment. The exact column structure is given as follows: for "equipment1.csv" and "equipment2.csv": lot: the lot number wafer: the wafer number timestamp: the timestamp of the respective sensor recordings (176 timestamps per wafer - represented as approximately every second one recording for the sensors) sensor_1: the recordings of the first sensor sensor_2: the recordings of the second sensor ... sensor_56: the recordings of the last sensor "sensor_1"-"sensor_24" belongs to "equipment1" and "sensor_25"-"sensor_56" belongs to "equipment2". for "response.csv": lot: the lot number wafer: the wafer number response: the numerical test values class: the "good"/"bad" classification depending on the response value (threshold: 0,75)
