Assessment of COVID-19 among healthcare workers in Non-COVID pediatrics departments, Tehran, Iran: A cross-sectional study

Objective: The severe acute respiratory syndrome coronavirus 2(SARS COV 2) is an important health problem, which is widespread around the world. This study describes the characteristics of COVID-19 infections in healthcare workers (HCWs), related factors and deaths in Non- COVID pediatrics departments from the early phases of COVID-19 pandemic; February 20th, 2020toJanuary19th, 2021 in Tehran-Iran. Materials and Methods: It is a multi-center cross-sectional descriptive study. The standardized questionnaire was designed according Demographics information, Coronavirus disease (COVID-19) history in HCW and Using and access to Personal Protective Equipment (PPE). All data analysis was performed by SPSS software version 21. Results: Of the 82 HCW, 67 (81.7%) was female. The median age was 37.6 ±10.3 years old (rang 24 to 65). 44 (53.6%) were nurses, 14 (17 %) pediatrics residents,13 (15.9%) pediatrics faculty members,8(9.8%) environmental services staff and 3(3.7%) secretaries. Twelve out of our cases (14.6%) have underlying medical diseases. Thirty-six (42.7%) confirmed COVID19. In COVID-19 positive group 28(80%) were female. Among whom 51.4% were identified nurses, 17% faculty member and14.3% pediatrics residents. Secretaries and environmental services staff are more vulnerable job category in this study. Eighty-six percent of them follow protective health protocol and use PPE. COVID-19 cases were infected more on July 2020(25.7%), November 2020 (17.1%) and August 2020(14.2%).   Conclusion: Approximately one half of HCWs in non-Covid-19 pediatrics department infected with corona virus 2 (SARS_COV_2) and most of them thought they were infected in workplac

Blockchain, an enabling technology for transparent and accountable decentralized public participatory GIS

Web-based public participatory GIS (PPGIS) has been used by governmental organizations to facilitate people's contribution to decision-making processes. However, these applications do not provide an open and transparent environment for public participation. This study suggests that PPGISs should be developed as decentralized applications (DApp) based on Ethereum blockchain technology to have a fully open, transparent, and accountable environment for public participation. In a blockchain-based PPGIS, the collected data are securely saved on the blockchain. The validity of the data, replicated on the nodes of the peer-to-peer blockchain network, is ensured through a consensus process without any central control. The data is tamper-free and immutable. Additionally, the data is openly accessible to institutions and citizens. A prototype PPGIS was developed as a DApp through which users can participate in the site selection of urban facilities. Using the application, they compare and rank different criteria. The system solves an analytic hierarchy process to calculate the weights of the criteria. A suitability map is generated afterward and published to be used by both citizens and decision-makers. The feasibility of the application, along with the issues that need to be considered while using blockchain technology for urban planning and development, are thoroughly discussed

Disaster planning using automated composition of semantic OGC web services: A case study in sheltering

Spatial data are crucial in disaster planning. However, because of the dynamic, urgent and uncertain nature of disasters, certain data and functionalities may be inaccessible to decision makers when they are required. Web service composition offers a possible solution whereby disaster planners can integrate spatial web services to generate new spatial data and functionalities, quickly, from existing ones. This paper proposes an automatic solution for composing OWSs (Open Geospatial Consortium Web Services) for disaster planning. A semantic annotation approach based on the Resource Description Framework (RDF) and SPARQL languages is used to describe OWSs semantically. A conceptual model for AI (Artificial Intelligence) planning is also proposed that works based on RDF and SPARQL. An AI planning algorithm was implemented based on the proposed conceptual model to compose semantic OWSs. The applicability of the proposed solution is investigated through a case study in evacuation sheltering. The case study demonstrates that the proposed automatic composition approach can enhance the efficiency of OWS integration and thereby improve the disaster management process. (c) 2013 Elsevier Ltd. All rights reserved

Multi-Agent Planning for Automatic Geospatial Web Service Composition in Geoportals

Automatic composition of geospatial web services increases the possibility of taking full advantage of spatial data and processing capabilities that have been published over the internet. In this paper, a multi-agent artificial intelligence (AI) planning solution was proposed, which works within the geoportal architecture and enables the geoportal to compose semantically annotated Open Geospatial Consortium (OGC) Web Services based on users’ requirements. In this solution, the registered Catalogue Service for Web (CSW) services in the geoportal along with a composition coordinator component interact together to synthesize Open Geospatial Consortium Web Services (OWSs) and generate the composition workflow. A prototype geoportal was developed, a case study of evacuation sheltering was implemented to illustrate the functionality of the algorithm, and a simulation environment, including one hundred simulated OWSs and five CSW services, was used to test the performance of the solution in a more complex circumstance. The prototype geoportal was able to generate the composite web service, based on the requested goals of the user. Additionally, in the simulation environment, while the execution time of the composition with two CSW service nodes was 20 s, the addition of new CSW nodes reduced the composition time exponentially, so that with five CSW nodes the execution time reduced to 0.3 s. Results showed that due to the utilization of the computational power of CSW services, the solution was fast, horizontally scalable, and less vulnerable to the exponential growth in the search space of the AI planning problem

Dynamic Spatio-Temporal Tweet Mining for Event Detection : A Case Study of Hurricane Florence

Extracting information about emerging events in large study areas through spatiotemporal and textual analysis of geotagged tweets provides the possibility of monitoring the current state of a disaster. This study proposes dynamic spatio-temporal tweet mining as a method for dynamic event extraction from geotagged tweets in large study areas. It introduces the use of a modified version of ordering points to identify the clustering structure to address the intrinsic heterogeneity of Twitter data. To precisely calculate the textual similarity, three state-of-the-art text embedding methods of Word2vec, GloVe, and FastText were used to capture both syntactic and semantic similarities. The impact of selected embedding algorithms on the quality of the outputs was studied. Different combinations of spatial and temporal distances with the textual similarity measure were investigated to improve the event detection outcomes. The proposed method was applied to a case study related to 2018 Hurricane Florence. The method was able to precisely identify events of varied sizes and densities before, during, and after the hurricane. The feasibility of the proposed method was qualitatively evaluated using the Silhouette coefficient and qualitatively discussed. The proposed method was also compared to an implementation based on the standard density-based spatial clustering of applications with noise algorithm, where it showed more promising results

A Personalized location-based and serendipity-oriented point of interest recommender assistant based on behavioral patterns

The technological evolutions have promoted mobile devices from rudimentary communication facilities to advanced personal assistants. According to the huge amount of accessible data, developing a time-saving and cost-effective method for location-based recommendations in mobile devices has been considered a challenging issue. This paper contributes a state-of-the-art solution for a personalized recommender assistant which suggests both accurate and unexpected point of interests (POIs) to users in each part of the day of the week based on their previously monitored, daily behavioral patterns. The presented approach consists of two steps of extracting the behavioral patterns from users’ trajectories and location-based recommendation based on the discovered patterns and user’s ratings. The behavioral pattern of the user includes their activity types in different parts of the day of the week, which is monitored via a combination of a stay point detection algorithm and an association rule mining (ARM) method. Having the behavioral patterns, the system exploits two recommendation procedures based on conventional collaborative filtering and K-furthest neighborhood model to recommend typical and serendipitous POIs to the users. The suggested POI list contains not only relevant and precise POIs but also unpredictable and surprising items to the users. To evaluate the system, the values of RMSE of each procedure were computed and compared. Conducted experiments proved the feasibility of the proposed solution

Proposing and investigating PCAMARS as a novel model for NO2 interpolation

Effective measurement of exposure to air pollution, not least NO2, for epidemiological studies along with the need to better management and control of air pollution in urban areas ask for precise interpolation and determination of the concentration of pollutants in nonmonitored spots. A variety of approaches have been developed and used. This paper aims to propose, develop, and test a spatial predictive model based on multivariate adaptive regression splines (MARS) and principle component analysis (PCA) to determine the concentration of NO2 in Tehran, as a case study. To increase the accuracy of the model, spatial data (population, road network and point of interests such as petroleum stations and green spaces) and meteorological data (including temperature, pressure, wind speed and relative humidity) have also been used as independent variables, alongside air quality measurement data gathered by the monitoring stations. The outputs of the proposed model are evaluated against reference interpolation techniques including inverse distance weighting, thin plate splines, kriging, cokriging, and MARS3. Interpolation for 12 months showed better accuracies of the proposed model in comparison with the reference methods

Spatial analysis of HIV-TB co-clustering in Uganda

BACKGROUND: Tuberculosis (TB) is the leading cause of death for individuals infected with Human immunodeficiency virus (HIV). Conversely, HIV is the most important risk factor in the progression of TB from the latent to the active status. In order to manage this double epidemic situation, an integrated approach that includes HIV management in TB patients was proposed by the World Health Organization and was implemented in Uganda (one of the countries endemic with both diseases). To enable targeted intervention using the integrated approach, areas with high disease prevalence rates for TB and HIV need to be identified first. However, there is no such study in Uganda, addressing the joint spatial patterns of these two diseases.METHODS: This study uses global Moran's index, spatial scan statistics and bivariate global and local Moran's indices to investigate the geographical clustering patterns of both diseases, as individuals and as combined. The data used are TB and HIV case data for 2015, 2016 and 2017 obtained from the District Health Information Software 2 system, housed and maintained by the Ministry of Health, Uganda.RESULTS: Results from this analysis show that while TB and HIV diseases are highly correlated (55-76%), they exhibit relatively different spatial clustering patterns across Uganda. The joint TB/HIV prevalence shows consistent hotspot clusters around districts surrounding Lake Victoria as well as northern Uganda. These two clusters could be linked to the presence of high HIV prevalence among the fishing communities of Lake Victoria and the presence of refugees and internally displaced people camps, respectively. The consistent cold spot observed in eastern Uganda and around Kasese could be explained by low HIV prevalence in communities with circumcision tradition.CONCLUSIONS: This study makes a significant contribution to TB/HIV public health bodies around Uganda by identifying areas with high joint disease burden, in the light of TB/HIV co-infection. It, thus, provides a valuable starting point for an informed and targeted intervention, as a positive step towards a TB and HIV-AIDS free community

FLCSS: A fuzzy-based longest common subsequence method for uncertainty management in trajectory similarity measures

The large quantity of movement data collected from various sources can be inherently uncertain and heterogeneous. In the movement data analysis and mining spectrum, computing the similarity of trajectories while considering the uncertainty and heterogeneity has been less addressed. Generally, two factors of sampling and positioning error cause uncertainty in trajectory databases. Therefore, in this research, a method based on the longest common subsequence (LCSS), named FLCSS, is proposed that uses fuzzy theory and the bead model to consider the uncertainty of trajectories originated from positioning and sampling errors. The performance of FLCSS is evaluated by implementations on real and synthetic datasets, and compared with six important and commonly used similarity measurement methods, namely, LCSS, edit distance on real sequence (EDR), dynamic time warping (DTW), edit distance with real penalty (ERP), Hausdorff distance (HD), and Fréchet distance (FD). The results show that FLCSS has a better performance compared to other methods, in terms of sensitivity to point displacement, noise, and different sampling rates. Furthermore, the high correlation between FLCSS and LCSS (ρ = 0.91) confirms the robustness of the proposed method in considering uncertainty in the trajectory databases