scholarly journals Hierarchical Agglomerative Clustering of Bicycle Sharing Stations Based on Ultra-Light Edge Computing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3550 ◽  
Author(s):  
Juan José Vinagre Díaz ◽  
Rubén Fernández Pozo ◽  
Ana Belén Rodríguez González ◽  
Mark R. Wilby ◽  
Carmen Sánchez Ávila
Keyword(s):  
Real Data ◽  
Mobility Model ◽  
Edge Computing ◽  
Agglomerative Clustering ◽  
Residential Areas ◽  
The Public ◽  
Sensor Platform ◽  
Hierarchical Agglomerative Clustering ◽  
Spatio Temporal ◽  
Geographical Maps

Bicycle sharing systems (BSSs) have established a new shared-economy mobility model. After a rapid growth they are evolving into a fully-functional mobile sensor platform for cities. The viability of BSSs is floored by their operational costs, mainly due to rebalancing operations. Rebalancing implies transporting bicycles to and from docking stations in order to guarantee the service. Rebalancing performs clustering to group docking stations by behaviour and proximity. In this paper we propose a Hierarchical Agglomerative Clustering based on an Ultra-Light Edge Computing Algorithm (HAC-ULECA). We eliminate the proximity and let Hierarchical Agglomerative Clustering (HAC) focus on behaviour. Behaviour is represented by ULECA as an activity profile based on the net flow of arrivals and departures in a docking station. This drastically reduces the computing requirements which allows ULECA to run as an edge computing functionality embedded into the physical layer of the Internet of Shared Bikes (IoSB) architecture. We have applied HAC-ULECA to real data from BiciMAD, the public BSS in Madrid (Spain). Our results, presented as dendograms, graphs, geographical maps, and colour maps, show that HAC-ULECA is capable of separating behaviour profiles related to business and residential areas and extracting meaningful spatio-temporal information about the BSS and the city’s mobility.

scholarly journals Analysis of Electric Energy Consumption Profiles Using a Machine Learning Approach: A Paraguayan Case Study

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 267
Author(s):  
Félix Morales ◽  
Miguel García-Torres ◽  
Gustavo Velázquez ◽  
Federico Daumas-Ladouce ◽  
Pedro E. Gardel-Sotomayor ◽  
...  
Keyword(s):  
Clustering Algorithms ◽  
Electric Energy ◽  
Real Data ◽  
Original Data ◽  
Data Sets ◽  
Agglomerative Clustering ◽  
Daily Consumption ◽  
Load Curve ◽  
Electric Energy Consumption ◽  
Hierarchical Agglomerative Clustering

Correctly defining and grouping electrical feeders is of great importance for electrical system operators. In this paper, we compare two different clustering techniques, K-means and hierarchical agglomerative clustering, applied to real data from the east region of Paraguay. The raw data were pre-processed, resulting in four data sets, namely, (i) a weekly feeder demand, (ii) a monthly feeder demand, (iii) a statistical feature set extracted from the original data and (iv) a seasonal and daily consumption feature set obtained considering the characteristics of the Paraguayan load curve. Considering the four data sets, two clustering algorithms, two distance metrics and five linkage criteria a total of 36 models with the Silhouette, Davies–Bouldin and Calinski–Harabasz index scores was assessed. The K-means algorithms with the seasonal feature data sets showed the best performance considering the Silhouette, Calinski–Harabasz and Davies–Bouldin validation index scores with a configuration of six clusters.

scholarly journals What Are Good Situations for Running? A Machine Learning Study Using Mobile and Geographical Data

2021 ◽  
Vol 8 ◽  
Author(s):  
Shihan Wang ◽  
Simon Scheider ◽  
Karlijn Sporrel ◽  
Marije Deutekom ◽  
Joris Timmer ◽  
...  
Keyword(s):  
Large Scale ◽  
Weather Type ◽  
Agglomerative Clustering ◽  
Residential Areas ◽  
Environmental Determinants ◽  
Different Types ◽  
Hierarchical Agglomerative Clustering ◽  
Feature Values ◽  
The Individual

Running is a popular form of physical activity. Personal, social, and environmental determinants influence the engagement of the individual. To get insight in the relation between running behavior and external situations for different types of users, we carried out an extensive data mining study on large-scale datasets. We combined 4 years of historical running data (collected by a mobile exercise application from over 10K participants) with weather, topographical and demographical datasets. We introduce weighted frequent item mining for the analysis of the data. In this way, we capture temporal and environmental situations that frequently associate with different running performances. The results show that specific temporal and environmental situations (hour in a day, day in a week, temperature, distance to residential areas, and population density) influence the running performance of users more than other situational features. Hierarchical agglomerative clustering on the running data is used to split runners in two clusters (with sustained and less sustained running behavior). We compared the two groups of runners and found that runners with less sustained behavior are more sensitive to the environmental situations (especially several weather and location related features, such as temperature, weather type, distance to the nearest park) than regular runners. Further analysis focused on the situational features for the less sustained runners. Results show that specific feature values correspond to a better or worse running distance. Not only the influence of individual features was examined but also the interplay between features. Our findings provide important empirical evidence that the role of external situations in the running behavior of individuals can be derived from analysis of the combined historical datasets. This opens up a large potential to take those situations specifically into consideration when supporting individuals which show less sustained behavior.

scholarly journals Clustering Techniques for Secondary Substations Siting

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 1028
Author(s):  
Silvia Corigliano ◽  
Federico Rosato ◽  
Carla Ortiz Dominguez ◽  
Marco Merlo
Keyword(s):  
Rural Areas ◽  
Urban Areas ◽  
Universal Access ◽  
Distribution Networks ◽  
Industrialized Countries ◽  
Agglomerative Clustering ◽  
Clustering Techniques ◽  
Hierarchical Agglomerative Clustering ◽  
Efficient Planning ◽  
Target Set

The scientific community is active in developing new models and methods to help reach the ambitious target set by UN SDGs7: universal access to electricity by 2030. Efficient planning of distribution networks is a complex and multivariate task, which is usually split into multiple subproblems to reduce the number of variables. The present work addresses the problem of optimal secondary substation siting, by means of different clustering techniques. In contrast with the majority of approaches found in the literature, which are devoted to the planning of MV grids in already electrified urban areas, this work focuses on greenfield planning in rural areas. K-means algorithm, hierarchical agglomerative clustering, and a method based on optimal weighted tree partitioning are adapted to the problem and run on two real case studies, with different population densities. The algorithms are compared in terms of different indicators useful to assess the feasibility of the solutions found. The algorithms have proven to be effective in addressing some of the crucial aspects of substations siting and to constitute relevant improvements to the classic K-means approach found in the literature. However, it is found that it is very challenging to conjugate an acceptable geographical span of the area served by a single substation with a substation power high enough to justify the installation when the load density is very low. In other words, well known standards adopted in industrialized countries do not fit with developing countries’ requirements.

scholarly journals Identifying organ dysfunction trajectory-based subphenotypes in critically ill patients with COVID-19

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chang Su ◽  
Zhenxing Xu ◽  
Katherine Hoffman ◽  
Parag Goyal ◽  
Monika M. Safford ◽  
...  
Keyword(s):  
New York ◽  
Respiratory Failure ◽  
Sofa Score ◽  
Severity Of Illness ◽  
Agglomerative Clustering ◽  
Baseline Severity ◽  
Organ Systems ◽  
Hierarchical Agglomerative Clustering ◽  
Dynamic Time ◽  
Post Intubation

AbstractCOVID-19-associated respiratory failure offers the unprecedented opportunity to evaluate the differential host response to a uniform pathogenic insult. Understanding whether there are distinct subphenotypes of severe COVID-19 may offer insight into its pathophysiology. Sequential Organ Failure Assessment (SOFA) score is an objective and comprehensive measurement that measures dysfunction severity of six organ systems, i.e., cardiovascular, central nervous system, coagulation, liver, renal, and respiration. Our aim was to identify and characterize distinct subphenotypes of COVID-19 critical illness defined by the post-intubation trajectory of SOFA score. Intubated COVID-19 patients at two hospitals in New York city were leveraged as development and validation cohorts. Patients were grouped into mild, intermediate, and severe strata by their baseline post-intubation SOFA. Hierarchical agglomerative clustering was performed within each stratum to detect subphenotypes based on similarities amongst SOFA score trajectories evaluated by Dynamic Time Warping. Distinct worsening and recovering subphenotypes were identified within each stratum, which had distinct 7-day post-intubation SOFA progression trends. Patients in the worsening suphenotypes had a higher mortality than those in the recovering subphenotypes within each stratum (mild stratum, 29.7% vs. 10.3%, p = 0.033; intermediate stratum, 29.3% vs. 8.0%, p = 0.002; severe stratum, 53.7% vs. 22.2%, p < 0.001). Pathophysiologic biomarkers associated with progression were distinct at each stratum, including findings suggestive of inflammation in low baseline severity of illness versus hemophagocytic lymphohistiocytosis in higher baseline severity of illness. The findings suggest that there are clear worsening and recovering subphenotypes of COVID-19 respiratory failure after intubation, which are more predictive of outcomes than baseline severity of illness. Distinct progression biomarkers at differential baseline severity of illness suggests a heterogeneous pathobiology in the progression of COVID-19 respiratory failure.

scholarly journals CORREDORES URBANOS COMO CONECTORES DE VIDA PÚBLICA. DIAGNÓSTICO DE LOS CORREDORES URBANOS CENTRALES EN LA AVENIDA 24 DE MAYO, AZOGUES

2021 ◽  
pp. 78-107
Author(s):  
Lizeth Benavides ◽  
Natasha Cabrera_Jara ◽  
Belén Campoverde_Bermeo
Keyword(s):  
Public Space ◽  
Mobility Model ◽  
Original Research ◽  
The Public ◽  
Palabras Clave ◽  
Relationship Of ◽  
Urban Corridor ◽  
Siglo Xx ◽  
The Relationship

El cambio de modelo urbano asumido durante el siglo XX, trajo un sinnúmero de problemas como la priorización del vehículo, por lo que en la última década han surgido esfuerzos para dotar de importancia al ciudadano de a pie, en el espacio público. Esta investigación estudió las condiciones físico-espaciales de un corredor urbano donde el modelo centrado en el vehículo se acentúa, con la fnalidad de generar posibles estrategias que reviertan esta situación. Se tomó como caso de estudio a la Av. 24 de Mayo, en Azogues, y se lo analizó mediante una metodología mixta, que evaluó, detalladamente, tres zonas de estudio, determinando que la falta de accesibilidad y conectividad y el modelo de movilidad defendido por la ciudadanía, en general, infuyen directamente en las condiciones del espacio público peatonal y por ende en la habitabilidad urbana, perjudicando los desplazamientos a pie. Palabras clave: Espacio público; habitabilidad urbana; conectividad; accesibilidad; percepción. AbstractThe change of urban model assumed during the 20th century, brought countless problems such as the prioritization of vehicles, so in the last decade eforts have emerged to give importance to the citizen on foot in the public space. This original research studied the relationship of urban habitability with the physical-spatial conditions of an urban corridor, where the vehicle-centered model is accentuated, to generate possible strategies to reverse this situation. The Av. 24 de Mayo in Azogues was taken as a case study and analyzed using a mixed methodology that evaluated in detail three study areas, determining that the lack of accessibility and connectivity and the mobility model defended by citizens in general have a direct infuence on the conditions of the pedestrianpublic space and, therefore, on urban habitability, which afects walking Keywords: Public space; urban habitability; connectivity; accessibility; perception.

scholarly journals Coopetitive Platform: Common Benefits in Electricity and Gas Distribution

Energies ◽  
2021 ◽  
Vol 14 (21) ◽  
pp. 7113
Author(s):  
Wojciech Czakon ◽  
Karolina Mucha-Kuś ◽  
Maciej Sołtysik
Keyword(s):  
Local Government ◽  
Real Data ◽  
Energy Costs ◽  
Electricity Distribution ◽  
Gas Distribution ◽  
Non Profit ◽  
The Public ◽  
Public Arena ◽  
Non Profit Organizations

Simultaneous collaboration and competition, or coopetition, has so far been studied among firms or non-profit organizations, leaving the public arena largely unexplored. This study focuses on identifying and measuring common benefit generation by local government actors in coopetition. We purposefully choose a metropolis composed of 41 municipalities setting to identify and measure those effects of coopetition that cannot be achieved by individual actors alone. We use real data and tariffs to run simulations that identify common and individual benefits generated in gas and electricity distribution. We find that savings can be achieved by mutualizing energy procurement, even though the distribution of savings is not equal among municipalities. We substantiate that energy communities can act as coopetitive platforms to optimize energy costs of municipalities.

scholarly journals A stochastic metapopulation state-space approach to modeling and estimating COVID-19 spread

2021 ◽  
Vol 18 (6) ◽  
pp. 7685-7710
Author(s):  
Yukun Tan ◽  
◽  
Durward Cator III ◽  
Martial Ndeffo-Mbah ◽  
Ulisses Braga-Neto ◽  
...  
Keyword(s):  
State Space ◽  
State Space Model ◽  
Synthetic Data ◽  
Public Health Intervention ◽  
Disease Outbreaks ◽  
Real Data ◽  
Epidemiological Data ◽  
Transmission Parameters ◽  
Intervention Measures ◽  
Spatio Temporal

<abstract><p>Mathematical models are widely recognized as an important tool for analyzing and understanding the dynamics of infectious disease outbreaks, predict their future trends, and evaluate public health intervention measures for disease control and elimination. We propose a novel stochastic metapopulation state-space model for COVID-19 transmission, which is based on a discrete-time spatio-temporal susceptible, exposed, infected, recovered, and deceased (SEIRD) model. The proposed framework allows the hidden SEIRD states and unknown transmission parameters to be estimated from noisy, incomplete time series of reported epidemiological data, by application of unscented Kalman filtering (UKF), maximum-likelihood adaptive filtering, and metaheuristic optimization. Experiments using both synthetic data and real data from the Fall 2020 COVID-19 wave in the state of Texas demonstrate the effectiveness of the proposed model.</p></abstract>

scholarly journals A Framework of Observer-Based Biases in Citizen Science Biodiversity Monitoring: Semi-Structuring Unstructured Biodiversity Monitoring Protocols

2021 ◽  
Vol 9 ◽  
Author(s):  
Ofer Arazy ◽  
Dan Malkinson
Keyword(s):  
Decision Making ◽  
Citizen Science ◽  
Temporal Distribution ◽  
Biodiversity Monitoring ◽  
Biodiversity Data ◽  
Science Data ◽  
The Public ◽  
Spatio Temporal ◽  
Monitoring Protocols ◽  
And Behavior

Citizen science, whereby ordinary citizens participate in scientific endeavors, is widely used for biodiversity monitoring, most commonly by relying on unstructured monitoring approaches. Notwithstanding the potential of unstructured citizen science to engage the public and collect large amounts of biodiversity data, observers’ considerations regarding what, where and when to monitor result in biases in the aggregate database, thus impeding the ability to draw conclusions about trends in species’ spatio-temporal distribution. Hence, the goal of this study is to enhance our understanding of observer-based biases in citizen science for biodiversity monitoring. Toward this goals we: (a) develop a conceptual framework of observers’ decision-making process along the steps of monitor – &gt; record and share, identifying the considerations that take place at each step, specifically highlighting the factors that influence the decisions of whether to record an observation (b) propose an approach for operationalizing the framework using a targeted and focused questionnaire, which gauges observers’ preferences and behavior throughout the decision-making steps, and (c) illustrate the questionnaire’s ability to capture the factors driving observer-based biases by employing data from a local project on the iNaturalist platform. Our discussion highlights the paper’s theoretical contributions and proposes ways in which our approach for semi-structuring unstructured citizen science data could be used to mitigate observer-based biases, potentially making the collected biodiversity data usable for scientific and regulatory purposes.

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