Abstract
This study intends to characterise and analyse the role that university spin-offs play in the employability of recent graduates and their function in opposing youth unemployment. For that purpose, this study uses empirical data from Portugal to analyse the evolution of the population with higher education levels, the evolution of the unemployment rate and the geographical distribution of university spin-offs. Additionally, a survey was adopted to collect data from 117 Portuguese university spin-offs to identify the main advantages and difficulties in hiring young graduates. The results indicate that the number of young graduates in these companies is small, representing only 11.8% of their total employees. The main advantages of hiring young graduates include their learning ability and new knowledge brought from abroad. By contrast, the main difficulties are their low capacity in dealing with pressure, low autonomy, and difficulties in taking responsibilities.

Abstract
Recent studies found that the adoption of distributed energy resources (DER) tends to cluster spatially and temporally which has significant implications for distribution network planning. Currently, residential DER adoption is mostly driven by public support schemes, also called incentive designs. Therefore, changes in those incentive designs will result in alternative spatiotemporal DER adoption patterns that affect distribution networks differently. Consequently, distribution network operators urgently need to understand the effects of energy policy changes on the spatial distribution of DER to guide network expansion based on realistic scenarios. The presented work and tool allow network operators to plan network expansion with robustness under future incentive design changes.

Abstract
This paper presents MARTINE (Multi-Agent based Real-Time INfrastruture for Energy), a simulation, emulation and energy management platform for the study of problems related to buildings and smart grids. Relevant advances related to buildings and smart grid management and operation have been proposed, focusing either on software models for decision support or on physical infrastructure and control approaches. These two perspectives are, however, complementary, and no practical assessment can be achieved without a suitable interaction and analysis of the impact that decision-making models have on physical resources, and vice-versa. MARTINE overcomes this limitation by integrating, in a single platform: real buildings with the associated devices and resources; emulated components that complement the ones present in the buildings; simulated resources, players and buildings using multi-agent systems, real-time simulation with hardware in the loop capabilities, which enables integrating virtual and physical components; and a knowledge layer that incorporates all the required decision support and energy management models. MARTINE thus provides a comprehensive platform for the study and management of energy resources. The advantages of this platform are demonstrated in this paper through three use cases, related to agriculture irrigation, practical implementation of demand response and load modeling using various network configurations.

Abstract
The increasing level of awareness gained, by citizens in general and companies in particular, around the sustainability issues and of the climate change are producing changes in how organizations are dealing and projecting their future vision. Therefore, new managerial approaches are being embraced towards adopting a set of a strategies fully aligned with the reduction of the greenhouse gas emissions. Due to this increase evidence of sensitivity, organizations are embracing their role as stakeholders that need to contribute, throughout its corporate social agenda, to a responsible and smart policy promoting the implementation of strategies that could endeavor the cultural shift of their workers, clients, suppliers, among others, towards effectively contributing to sustainability and social responsibility. The case study of a medium size company reported is related to a structural change in how the organization foresees its mobility behavior and how it intends to follow the concepts of Corporate Mobility as a Service (CMaaS). This case study discloses the strategies that have been implemented and the Information and Communication Technologies (ICT) platform that has been developed towards having a broader view about the impacts of the mobility requested by all the organization. In addition, it is presented a group of Key Performance Indicator (KPI) that point the benefits attained with this effort as well as projecting the following steps that will support the CMaaS roadmap implementation in the future. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

Abstract
Precise machine learning models for the early identification of anomalies based on biosignal data retrieved from bedside monitors could improve intensive care, by helping clinicians make decisions in advance and produce on-time responses. However, traditional models show limitations when dealing with the high complexity of this task. Layered Learning (LL) emerges as a solution, as it consists of the hierarchical decomposition of the problem into simpler tasks. This paper explores the uncovered potential of LL in the early detection of Acute Hypotensive Episodes (AHEs). We leverage information from the MIMIC-III Database to test different subdivisions of the main task and study how to combine the outcomes from distinct layers. In addition to this, we also test a novel approach to reduce false positives in AHE predictions.

Abstract
Entity-oriented search tasks heavily rely on exploiting unstructured and structured collections. Moreover, it is frequent for text corpora and knowledge bases to provide complementary views on a common topic. While, traditionally, the retrieval unit was the document, modern search engines have evolved to also retrieve entities and to provide direct answers to the information needs of the users. Cross-referencing information from heterogeneous sources has become fundamental, however a mismatch still exists between text-based and knowledge-based retrieval approaches. The former does not account for complex relations, while the latter does not properly support keyword-based queries and ranked retrieval. Graphs are a good solution to this problem, since they can be used to represent text, entities and their relations. In this survey, we examine text-based approaches and how they evolved to leverage entities and their relations in the retrieval process. We also cover multiple aspects of graph-based models for entity-oriented search, providing an overview on link analysis and exploring graph-based text representation and retrieval, leveraging knowledge graphs for document or entity retrieval, building entity graphs from text, using graph matching for querying with subgraphs, exploiting hypergraph-based representations, and ranking based on random walks on graphs. We close with a discussion on the topic and a view of the future to motivate the research of graph-based models for entity-oriented search, particularly as joint representation models for the generalization of retrieval tasks.