1,720,992 research outputs found
Energy saving starts in the kitchen
Full text linkThe field of sustainable construction and interior design demands innovative solutions to optimize energy efficiency and minimize environmental impact. This need has been further amplified by the European Community's Directive on Energy Efficiency 2012/27/EU and its subsequent revisions. The kitchen, as a central hub of domestic energy use, presents a significant opportunity for improvement. This paper introduces a novel application of heat pump technology for energy reuse in household appliances. The proposed SMACK (SMArt energy-Conserving Kitchen) system embodies a holistic approach, merging eco-sustainability with advanced design principles. SMACK's modularity enables customization and scalability to meet diverse requirements. A comprehensive assessment indicates potential average energy savings approaching 50%, contingent upon appliance configuration. Importantly, this study incorporates preliminary real-world measurements on a minimal appliance, offering empirical insights into the SMACK system's performance. The SMACK project signifies a major step towards environmentally conscious design, setting a new standard for sustainable technology integration within the home
A Logic Programming Approach to Reaction Systems
Full text linkReaction systems (RS) are a computational framework inspired by the functioning of living cells, suitable to model the main mechanisms of biochemical reactions. RS have shown to be useful also for computer science applications, e.g. to model circuits or transition systems. Since their introduction about 10 years ago, RS matured into a fruitful and dynamically evolving research area. They have become a popular novel model of interactive computation. RS can be seen as a rewriting system interacting with the environment represented by the context. RS pose some problems of implementation, as it is a relatively recent computation model, and several extensions of the basic model have been designed. In this paper we present some preliminary work on how to implement this formalism in a logic programming language (Prolog). To the best of our knowledge this is the first approach to RS in logic programming. Our prototypical implementation does not aim to be highly performing, but has the advantage of being high level and easily modifiable. So it is suitable as a rapid prototyping tool for implementing several extensions of reaction systems in the literature as well as new ones. We also make a preliminary implementation of a kind of memoization mechanism for stopping potentially infinite and repetitive computations. Then we show how to implement in our interpreter an extension of RS for modeling a nondeterministic context and interaction between components of a (biological) system. We then present an extension of the interpreter for implementing the recently introduced networks of RS
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Federated Learning and Neural Circuit Policies: A Novel Framework for Anomaly Detection in Energy-Intensive Machinery
Full text linkIn the realm of predictive maintenance for energy-intensive machinery, effective anomaly detection is crucial for minimizing downtime and optimizing operational efficiency. This paper introduces a novel approach that integrates federated learning (FL) with Neural Circuit Policies (NCPs) to enhance anomaly detection in compressors utilized in leather tanning operations. Unlike traditional Long Short-Term Memory (LSTM) networks, which rely heavily on historical data patterns and often struggle with generalization, NCPs incorporate physical constraints and system dynamics, resulting in superior performance. Our comparative analysis reveals that NCPs significantly outperform LSTMs in accuracy and interpretability within a federated learning framework. This innovative combination not only addresses pressing data privacy concerns but also facilitates collaborative learning across decentralized data sources. By showcasing the effectiveness of FL and NCPs, this research paves the way for advanced predictive maintenance strategies that prioritize both performance and data integrity in energy-intensive industries
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
A Reinforcement Learning approach to the management of Renewable Energy Communities
No full textOptimal management of renewable energy is an important pillar of environmental sustainability, as it maximizes the use of clean and renewable resources. This article considers the optimal management of a renewable energy community that receives incentives for virtual self-consumption. This incentive scheme has been adopted in the Italian energy framework since 2020. The optimization problem maximizes the social welfare of the community, which includes the incentive together with the exploitation of renewable energy sources. A key role in such a problem is played by the battery energy storage system (BESS), which is crucial in balancing supply and demand. We propose a novel Reinforcement Learning-based BESS controller, aiming at maximizing the community social welfare by acting in real time and relying only on data available at the current time-step. Through different simulations in several scenarios, we demonstrate the effectiveness of our approach and its ability to outperform a state-of-the-art rule-based controller. Moreover, we assess the proposed approach by comparing its performance with that of the actual, though ideal, optimal control policy based on an oracle providing perfect knowledge of future data
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