1,720,974 research outputs found
Swarm intelligence for hole detection and healing in wireless sensor networks
No full textThe increasing demand for wireless sensor networks to monitor specific regions has prompted extensive research on sustaining coverage over time. The main threat to this goal arises from coverage holes caused by random node deployment or failures. This study proposes a swarm intelligence-based algorithm to detect and heal coverage holes. The swarm of agents relies on local and relative information, activating in response to detected holes and navigating a potential field toward the closest hole. The agents quantize their perceptions to disperse efficiently, approaching holes from different directions to accelerate healing. Based on geometric criteria, the swarm deploys at locally optimal positions along hole borders while preventing redundant deployments. Agents deployment update the potential field, guiding the rest of the swarm toward unhealed areas and ensuring dynamic detection and tracking of new holes, even near the region frontier. Experimental studies demonstrate superior coverage restoration compared to state-of-the-art solutions, showing good scalability and flexibility to different hole sizes, shapes, and multiplicity. Moreover, it exhibits high robustness to the corruption of agents’ perceptions and to their failure, while efficiently managing the battery level
Swarms of Artificial Platelets for Emergent Hole Detection and Healing in Wireless Sensor Networks
No full textMost of the applications of wireless sensor networks require the continuous coverage of a region of interest. The irregular deployment of the nodes, or their failure, could result in holes in the coverage, thus jeopardizing such requirement. Methods to recover the sensing capabilities usually demand the availability of redundant full-fledged nodes, whose relocation should heal the holes. These solutions, however, do not consider the high cost of obtaining redundant, typically complex, devices, nor that they could in turn fail. In this work, we propose a bio-inspired and emergent approach toward hole detection and healing using a swarm of resource-constrained agents with reduced sensing capabilities, whose behavior draws inspiration from the concepts underlying blood coagulation. The swarm follows three rules: Activation, adhesion, and cohesion, adapted from the behavior exhibited by platelets during the human healing process. Relying only on local and relative information, the mobile agents can detect the holes border and place themselves in locally optimal positions to temporarily restore the service. To validate the algorithm, we have developed a distributed, multi-process simulator. Experimental results show that the proposed method efficiently detects and heals the holes, outperforming two state-of-The-Art solutions. It also demonstrates good robustness and flexibility to agent failure
Machine learning and engineering feature approaches to detect events perturbing the indoor microclimate in Ringebu and Heddal stave churches (Norway)
No full textPurpose Climate-induced damage is a pressing problem for the preservation of cultural properties. Their physical deterioration is often the cumulative effect of different environmental hazards of variable intensity. Among these, fluctuations of temperature and relative humidity may cause nonrecoverable physical changes in building envelopes and artifacts made of hygroscopic materials, such as wood. Microclimatic fluctuations may be caused by several factors, including the presence of many visitors within the historical building. Within this framework, the current work is focused on detecting events taking place in two Norwegian stave churches, by identifying the fluctuations in temperature and relative humidity caused by the presence of people attending the public events. Design/methodology/approach The identification of such fluctuations and, so, of the presence of people within the churches has been carried out through three different methods. The first is an unsupervised clustering algorithm here termed "density peak," the second is a supervised deep learning model based on a standard convolutional neural network (CNN) and the third is a novel ad hoc engineering feature approach "unexpected mixing ratio (UMR) peak." Findings While the first two methods may have some instabilities (in terms of precision, recall and normal mutual information [NMI]), the last one shows a promising performance in the detection of microclimatic fluctuations induced by the presence of visitors. Originality/value The novelty of this work stands in using both well-established and in-house ad hoc machine learning algorithms in the field of heritage science, proving that these smart approaches could be of extreme usefulness and could lead to quick data analyses, if used properly
The advent and development of organophotoredox catalysis
No full textIn the last decade, photoredox catalysis has unlocked unprecedented reactivities in synthetic organic chemistry. Seminal advancements in the field have involved the use of well-studied metal complexes as photoredox catalysts (PCs). More recently, the synthetic community, looking for more sustainable approaches, has been moving towards the use of purely organic molecules. Organic PCs are generally cheaper and less toxic, while allowing their rational modification to an increased generality. Furthermore, organic PCs have allowed reactivities that are inaccessible by using common metal complexes. Likewise, in synthetic catalysis, the field of photocatalysis is now experiencing a green evolution moving from metal catalysis to organocatalysis. In this feature article, we discuss and critically comment on the scientific reasons for this ongoing evolution in the field of photoredox catalysis, showing how and when organic PCs can efficiently replace their metal counterparts. This journal i
Machine learning and engineering feature approaches to detect events perturbing the indoor microclimate in Ringebu and Heddal stave churches (Norway)
Full text linkPurpose Climate-induced damage is a pressing problem for the preservation of cultural properties. Their physical deterioration is often the cumulative effect of different environmental hazards of variable intensity. Among these, fluctuations of temperature and relative humidity may cause nonrecoverable physical changes in building envelopes and artifacts made of hygroscopic materials, such as wood. Microclimatic fluctuations may be caused by several factors, including the presence of many visitors within the historical building. Within this framework, the current work is focused on detecting events taking place in two Norwegian stave churches, by identifying the fluctuations in temperature and relative humidity caused by the presence of people attending the public events. Design/methodology/approach The identification of such fluctuations and, so, of the presence of people within the churches has been carried out through three different methods. The first is an unsupervised clustering algorithm here termed "density peak," the second is a supervised deep learning model based on a standard convolutional neural network (CNN) and the third is a novel ad hoc engineering feature approach "unexpected mixing ratio (UMR) peak." Findings While the first two methods may have some instabilities (in terms of precision, recall and normal mutual information [NMI]), the last one shows a promising performance in the detection of microclimatic fluctuations induced by the presence of visitors. Originality/value The novelty of this work stands in using both well-established and in-house ad hoc machine learning algorithms in the field of heritage science, proving that these smart approaches could be of extreme usefulness and could lead to quick data analyses, if used properly
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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