Institution
Polytechnic University of Milan
Education•Milan, Italy•
About: Polytechnic University of Milan is a education organization based out in Milan, Italy. It is known for research contribution in the topics: Finite element method & Population. The organization has 18231 authors who have published 58416 publications receiving 1229711 citations. The organization is also known as: PoliMi & L-NESS.
Topics: Finite element method, Population, Laser, Nonlinear system, Detector
Papers published on a yearly basis
Papers
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TL;DR: In this article, the authors used established concepts in organizational change research to look into a rich empirical basis that documents the adoption of Open Innovation by four Italian firms operating in mature, asset-intensive industries.
Abstract: Open Innovation has been one of the most-debated topics in management research in the last decade. Although our understanding of this management paradigm has significantly improved over the last few years, a number of important questions are still unanswered. In particular, an issue that deserves further attention is the anatomy of the organizational change process through which a firm evolves from being a Closed to an Open Innovator. The paper represents a first step in overcoming this limitation. In particular, adopting a longitudinal, firm-level perspective, it addresses the following question: which changes in a firm's organizational structures and management systems does the shift from Closed to Open Innovation entail? In answering this question, the paper uses established concepts in organizational change research to look into a rich empirical basis that documents the adoption of Open Innovation by four Italian firms operating in mature, asset-intensive industries. The results show that the journey from Closed to Open Innovation involves four main dimensions of the firm's organization, i.e. inter-organizational networks, organizational structures, evaluation processes and knowledge management systems, along which change could be managed and stimulated.
360 citations
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TL;DR: The findings indicate a breakthrough in using evolutionary algorithms in solving highly constrained envelope, HVAC and renewable optimization problems and some future directions anticipated or needed for improvement of current tools are presented.
360 citations
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TL;DR: In this paper, the authors focus on the different alternatives of digestate valorisation, apart from land applications, such as the use of the digestate liquor for replacing freshwater and nutrients in algae cultivation, and the conversion of solid digestate into added-value products (char or activated carbons) through a pyrolysis process.
Abstract: In the agricultural sector of many European countries, biogas production through anaerobic digestion (AD) is becoming a very fast-growing market. AD is a simple and robust process that biologically converts an organic matrix into biogas and digestate, the latter corresponding to the anaerobically non-degraded fraction. So far, digestate has been mostly used at farm-scales for improving soils. However, its ever-increasing production induces problems related to transport costs, greenhouse-gas emissions during storage and high nitrogen content that constrains its use to land application only. Consequently, research on alternative valorisation routes to reduce its environmental impact and to improve the economical profitability of AD plants should draw increasing interest in the future. This review therefore focuses on the different alternatives of digestate valorisation, apart from land applications: (I) the use of the digestate liquor for replacing freshwater and nutrients in algae cultivation; (II) the use of solid digestate for energy production through biological (i.e. AD, bioethanol) or thermal processes (i.e. combustion, hydrothermal carbonization and pyrolysis); (III) the conversion of solid digestate into added-value products (char or activated carbons) through a pyrolysis process.
359 citations
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TL;DR: In this paper, a comprehensive literature review on ejector refrigeration systems and working fluids is presented, which deeply analyzes ejector technology and behavior, refrigerant properties and their influence over ejector performance.
Abstract: The increasing need for thermal comfort has led to a rapid increase in the use of cooling systems and, consequently, electricity demand for air-conditioning systems in buildings. Heat-driven ejector refrigeration systems appear to be a promising alternative to the traditional compressor-based refrigeration technologies for energy consumption reduction. This paper presents a comprehensive literature review on ejector refrigeration systems and working fluids. It deeply analyzes ejector technology and behavior, refrigerant properties and their influence over ejector performance and all of the ejector refrigeration technologies, with a focus on past, present and future trends. The review is structured in four parts. In the first part, ejector technology is described. In the second part, a detailed description of the refrigerant properties and their influence over ejector performance is presented. In the third part, a review focused on the main jet refrigeration cycles is proposed, and the ejector refrigeration systems are reported and categorized. Finally, an overview over all ejector technologies, the relationship among the working fluids and the ejector performance, with a focus on past, present and future trends, is presented.
359 citations
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TL;DR: It is shown that XCS's generalization mechanism is effective, but that the conditions under which it works must be clearly understood, and the compactness of the representation evolved by XCS is limited by the number of instances of each generalization actually present in the environment.
Abstract: The XCS classifier system represents a major advance in learning classifier systems research because (1) it has a sound and accurate generalization mechanism, and (2) its learning mechanism is based on Q-learning, a recognized learning technique. In taking XCS beyond its very first environments and parameter settings, we show that, in certain difficult sequential (“animat”) environments, performance is poor. We suggest that this occurs because in the chosen environments, some conditions for proper functioning of the generalization mechanism do not hold, resulting in overly general classifiers that cause reduced performance. We hypothesize that one such condition is a lack of sufficiently wide exploration of the environment during learning. We show that if XCS is forced to explore its environment more completely, performance improves dramatically. We propose a technique, based on Sutton's Dyna concept, through which wider exploration would occur naturally. Separately, we demonstrate that the compactness of the representation evolved by XCS is limited by the number of instances of each generalization actually present in the environment. The paper shows that XCS's generalization mechanism is effective, but that the conditions under which it works must be clearly understood.
358 citations
Authors
Showing all 18743 results
Name | H-index | Papers | Citations |
---|---|---|---|
Alex J. Barker | 132 | 1273 | 84746 |
Pierluigi Zotto | 128 | 1197 | 78259 |
Andrea C. Ferrari | 126 | 636 | 124533 |
Marco Dorigo | 105 | 657 | 91418 |
Marcello Giroletti | 103 | 558 | 41565 |
Luciano Gattinoni | 103 | 610 | 48055 |
Luca Benini | 101 | 1453 | 47862 |
Alberto Sangiovanni-Vincentelli | 99 | 934 | 45201 |
Surendra P. Shah | 99 | 710 | 32832 |
X. Sunney Xie | 98 | 225 | 44104 |
Peter Nijkamp | 97 | 2407 | 50826 |
Nicola Neri | 92 | 1122 | 41986 |
Ursula Keller | 92 | 934 | 33229 |
A. Rizzi | 91 | 653 | 40038 |
Martin J. Blunt | 89 | 485 | 29225 |