1,720,974 research outputs found
Humans in the city: Representing outdoor thermal comfort in urban canopy models
No full textThe negative effects of urban heat islands (UHIs) on citizens' well-being and life quality are widely acknowledged, but they still represent critical challenges, particularly since urban population is predicted to rise to 60% of the world population by 2030. Computational models have become useful tools for addressing these challenges and investigating urban microclimate repercussions on citizens' comfort and urban liveability. Despite that, humans typically remain absent from such models. This work bridges this gap, moving beyond purely thermodynamic Urban Canopy Models (UCMs) to highlight the importance of integrating even simplified pedestrians' biophysics for comfort assessment. Human physiology parameterization is therefore introduced into the Princeton Urban Canopy Model (PUCM), which had been designed to investigate the effect of greenery and novel materials on the UHI. Human thermal comfort is assessed in terms of the skin temperature and then evaluated against the apparent temperature, a widely-used thermal comfort indicator. Different configurations of the same urban canyon are therefore tested to assess the effectiveness of cool materials and trees for human thermal comfort enhancement. Results show that cool skins in the canyon's built environment lead to an air temperature reduction up to 1.92 K, but slightly worsen human comfort in terms of a warmer computed skin temperature by 0.27 K. The indirect effect of trees, that exclude shading, are negligible for human thermal comfort. The new integrated human-centric model can help policymakers and urban planners to easily assess the potential benefits or threats to citizens' well-being associated with specific urban configurations
Thermal discomfort in the workplace: measurement through the combined use of wearable sensors and machine learning algorithms
No full textThis study aims at evaluating the use of wearable sensors in the Industry 4.0 context to measure and assess the worker's thermal comfort, which impacts on the general wellbeing status and, consequently, on productivity and attention level conditions. An experimental protocol based on controlled environment was developed and tested on 14 volunteers using wearable sensors for the acquisition of multimodal physiological signals under different thermal conditions. Results show that the combined use of wearable sensors and Machine Learning (ML) algorithms allow to reach satisfying performance (prediction accuracy up to approximate to 76%) in classification between comfort/discomfort conditions, thus enabling to promptly intervene to optimize the subject's working conditions without interfering with working activities
Enhancing personal comfort: A machine learning approach using physiological and environmental signals measurements
Full text linkThe assessment of the occupants' thermal sensation (TS) in a living environment is fundamental to enhance well-being and optimize building energy consumption. Machine Learning (ML)-based approaches can be adopted for TS prediction exploiting physiological and environmental parameters, but identifying an optimal features subset is fundamental. This work aims at assessing the correlation between physiological parameters and TS, hence selecting the optimal feature subset for ML-based TS prediction. A dedicated experimental campaign was designed to gather signals through wearable sensors; the actual TS was collected via a specific questionnaire. The results prove the weight of physiological features on the TS determination; ML classifiers achieved an accuracy of up to approximate to 90% by using physiological and environmental parameters. The strategic potential of personalized comfort systems enables the optimization of both comfort and energy efficiency of a building according to a human-centric approach
Exploring the key factors affecting indoor thermal comfort through virtual reality: Enhancing labor productivity and achieving energy efficiency
Full text linkAre years-long field studies about window operation efficient? a data- driven approach based on information theory and deep learning
No full textScientific literature about building occupants' behaviour and the related energy performance analyses document about several strategies to monitor window operation, including different sensors and data series lengths. In this framework, the primary goal of this study is to propose effective guidelines for minimum experiment durations and their reliability. A six-year-long database from a living laboratory was used as a benchmark; and a recursive strategy enabled to split it into more than 2,500 subsets, supporting two main steps. First, information theory concepts were used to calculate uncertainty and subsets' divergence were compared to the full database. Second, the subsets were used to train deep neural networks and evaluate the influence of monitoring lengths combined with different kinds of environmental data (i.e. indoor or outdoor). From the information-theoretic metrics, the results support that indoor-related variables can reduce most of the uncertainty related to window operation. Besides, subsets influenced by autumn and winter diverge the most compared to the full database. Considering the modelling approach, the results demonstrated that by including indoor-related variables, higher shares of reliably-performing models were achieved, and smaller subsets were needed. Seasonality has also played a major role along these lines. As a consequence, the conclusions supported the feasibility of nine -monthlong field studies, starting in summer or spring, when indoor and outdoor variables are monitored.(c) 2022 Elsevier B.V. All rights reserved
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
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
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