Nowadays, Location-Based Social Networks (LBSN) collect a vast range of information which can help us to understand the regional dynamics (i.e. human mobility) across an entire city. LBSN provides unprecedented opportunities to tackle various social problems. In this work, we explore dynamic features derived from Foursquare check-in data in short-term crime event prediction with fine spatio-temporal granularity. While crime event prediction has been investigated widely due to its social importance, its success rate is far from satisfactory. The existing studies rely on relatively static features such as regional characteristics, demographic information and the topics obtained from tweets but very few studies focus on exploring human mobility through social media. In this study, we identify a number of dynamic features based on the research findings in Criminology, and report their correlations with different types of crime events. In particular, we observe that some types of crime events are more highly correlated to the dynamic features, e.g., Theft, Drug Offence, Fraud, Unlawful Entry and Assault than others e.g. Traffic Related Offence. A key challenge of the research is that the dynamic information is very sparse compared to the relatively static information. To address this issue, we develop a matrix factorization based approach to estimate the missing dynamic features across the city. Interestingly, the estimated dynamic features still maintain the correlation with crime occurrence across different types. We evaluate the proposed methods in different time intervals. The results verify that the crime prediction performance can be significantly improved with the inclusion of dynamic features across different types of crime events.
Povidone-iodine (PVP-I) is a time-tested antiseptic agent with excellent virucidal (99.99%) properties. Repurposing it against coronavirus disease-19 (COVID-19) is a relatively newer concept and has been sparsely tested in vivo. The most common route of entry of severe acute respiratory syndrome coronavirus 2 (SARS CoV-2) is the nasopharynx. Averting colonization of the virus could be one of the best options to reduce the incidence of infection. PVP-I gargle and mouthwash were found to be effective in vitro rapid inactivation against SARS-CoV-2 on a smaller scale (Hassandarvish et al. in BDJ 1-4, 2020, Pelletier et al. in ENTJ 1-5, 2020. However, efficacy in humans is lacking. To assess the virucidal effect of PVP-I against SARS-CoV-2 located in the nasopharynx was the objective of this parallel armed randomized clinical trial. We screened all RT-PCR-confirmed COVID-19 cases aged 18 years and above with symptoms. Written informed consent was obtained before randomization. Nasopharyngeal clearance of SARS-CoV-2 was tested after single time application of PVP-I nasal irrigation (NI) at diluted concentrations of .4%, .5% and .6% and PVP-I nasal spray (NS) at diluted concentrations of .5% and .6%. All groups were compared to the corresponding controls (distilled water). The primary outcome was viral clearance in a repeat RT-PCR (qualitative), and the secondary outcome was the number of adverse events. Final data analysis was performed using the statistical software SPSS (Version 20). A total of 189 confirmed COVID-19 cases were randomized into seven groups: 27 patients in each group. Of all, 159 (84.1%) were male, and 30 (15.9%) were female. We observed a statistically significant proportion of nasopharyngeal clearance with all strengths of PVP-I NI and PVP-I NS compared to the corresponding controls. Additionally, 0.5% NI was significantly better than 0.5% NS for viral clearance (p = 0.018) and had the highest nasopharyngeal clearance among all
Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.
The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.
Objective General: To assess the virucidal efficacy of povidone iodine (PVP-I) on COVID-19 virus located in the nasopharynx Specific: i. To evaluate the efficacy of povidone iodine (PVP-I) to removeCOVID-19 virus located in the nasopharynx ii. To assess the adverse events of PVP-I Trial design This is a single-center, open-label randomized clinical trial with a 7-arm parallel-group design. Participants The study will be conducted at Dhaka Medical College Hospital, Dhaka, Bangladesh. Inclusion criteria All RT-PCR-confirmed COVID-19 cases aged between 15-90 years with symptoms for the past 4 days will be screened. Those who give informed consent, are willing to participate, and accept being randomized to any assigned group will also be considered for final inclusion. Exclusion criteria Patients with known sensitivity to PVP-I aqueous antiseptic solution or any of its listed excipients or previously diagnosed thyroid disease or who had a history of chronic renal failure: stage ≥3 by estimated glomerular filtration rate (eGFR) Modification of Diet in Renal Disease (MDRD) or had acute renal failure (KDIGO ≥stage 2: creatinine ≥2 times from the baseline) or patients who required invasive or noninvasive ventilation or planned within the next 6 hours were considered for exclusion. Moreover, lactating or pregnant women will also be restricted to include here. Intervention and comparator This RCT consist of seven arms: Arm-1 (intervention group): will receive povidone iodine (PVP-I) nasal irrigation (NI) at a concentration of 0.4% Arm-2 (intervention group): will receive PVP-I nasal irrigation at a concentration of 0.5% Arm-3 (intervention group): will receive PVP-I nasal irrigation at a concentration of 0.6%. Arm-4 (intervention group): will receive PVP-I nasal spray (NS) at a concentration of 0.5%. Arm-5 (intervention group): will receive PVP-I nasal spray at a concentration of 0.6%. Arm-6 (placebo comparator group): will receive distilled water through NI Arm-7 (Placebo comparator group): will receive distilled water through NS The intervention arms will be compared to the placebo comparator arms. Other supportive and routine care will be the same in both groups. Main outcomes The primary outcome is the proportion of cases that remain COVID-19 positive following the intervention. It will be assessed from 1 minutes to 15 minutes after the intervention. Any occurrence of adverse effects following the intervention will be documented as a secondary outcome. Randomization The assignment to the study (intervention) or control (comparator) group will be allocated in equal numbers through randomization using random number generation in Microsoft Excel by a statistician who is not involved in the trial. The allocation scheme will be made by an independent statistician using a sealed envelope. The participants will be allocated immediately after the eligibility assessment and consenting procedures. Blinding (masking) This is an open-label clinical trial, and no blinding or masking will be performed. Numbers to be randomized (sample size) A total of 189 confirmed cases of COVID-19 will be randomized into seven groups. In each arm, a total of 27 participants will be recruited. Trial Status The current trial protocol is Version 1.5 from September 10, 2020. Recruitment began September 30, 2020 and is anticipated to be completed, including data analysis by February 28, 2021. Trial registration The trial protocol has been registered in the ClinicalTrials.gov on September 16, 2020. NCT Identifier number: NCT04549376. Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting the dissemination of this material, the familiar formatting has been eliminated; this letter serves as a summary of the key elements of the full protocol.
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