Event detection from geotagged tweets considering spatial autocorrelation and heterogeneity

Twitter, as the most popular social media platform, has made a great revolution in producing real-time user-generated data. This research aims to propose a method to extract the latent spatial pattern from geotagged tweets. We take both spatial autocorrelation and spatial heterogeneity into account while revealing the underlying pattern from geotagged tweets. Moreover, the textual similarity is considered to extract spatial-textual clusters. The method was implemented and tested during hurricane Dorian on the east coast of the U.S. The results proved the superiority of the proposed method against Moran’s Index and VDBSCAN algorithms in extracting clusters with various densities

A Varied Density-based Clustering Approach for Event Detection from Heterogeneous Twitter Data

Extracting the latent knowledge from Twitter by applying spatial clustering on geotagged tweets provides the ability to discover events and their locations. DBSCAN (density-based spatial clustering of applications with noise), which has been widely used to retrieve events from geotagged tweets, cannot efficiently detect clusters when there is significant spatial heterogeneity in the dataset, as it is the case for Twitter data where the distribution of users, as well as the intensity of publishing tweets, varies over the study areas. This study proposes VDCT (Varied Density-based spatial Clustering for Twitter data) algorithm that extracts clusters from geotagged tweets by considering spatial heterogeneity. The algorithm employs exponential spline interpolation to determine different search radiuses for cluster detection. Moreover, in addition to spatial proximity, textual similarities among tweets are also taken into account by the algorithm. In order to examine the efficiency of the algorithm, geotagged tweets collected during a hurricane in the United States were used for event detection. The output clusters of VDCT have been compared to those of DBSCAN. Visual and quantitative comparison of the results proved the feasibility of the proposed method. View Full-Tex

Potential of artificial intelligence and response surface methodology to predict CO2 capture by KOH-modified activated alumina

The present study focused on developing predictive neural networks and response surface methodology (RSM)-based model. In order to develop the predictive model, experimental data of CO2 capture by KOH-modified activated alumina was obtained through laboratory scale adsorption setup. Three independent input variables, including time (t: 0–1800 sec), initial temperature (Tin: 20–80 °C), and initial pressure (Pin:1.651–10.028 bar) of the reactor, were considered in the training process. Furthermore, CO2 adsorption capacity, and final pressure were considered as the output. The multilayer perceptron (MLP) and radial basis function (RBF) networks have been employed. The best corresponding optimized MLP network, out of all the 460 different structures, was chosen to be a structure trained with Levenberg Marquardt back propagation algorithm with four hidden layers, in which there were 25, 23, 7, and 20 neurons. The best corresponding transfer functions for the first, second, third, and fourth hidden layers plus the output layer were Purelin, Logsig, Tansing, Logsig, and Purelin, respectively. Finally, the performance of the RBF was explored on the experimental data. Out of the 385 built structures, the optimized corresponding RBF network was chosen to be the one with 2.5 as its spread value and 40 as its neuron numbers. Lastly, in the RSM design, the cubic model with square root transformation presented a comparably better performance. The coefficient of determination values for CO2 adsorption capacity in MLP, RBF, and RSM models were calculated as 0.999, 0.998, and 0.949, respectively

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

Evaluation of Health Worker Education to Patients Recovered From COVID-19

Background and Purpose: The COVID-19 pandemic imposes a significant burden on healthcare systems. Proper self-care practice in people can reduce the pressure on the medical staff and save time and expenses for the patients. We assessed the quality of self-care education of healthcare worker from the viewpoint of patients who recovered from COVID-19. Materials and Methods: This cross-sectional study was conducted by convenience sampling on 346 recovered patients from COVID-19 who referred to the clinics and hospitals of Babol University of Medical Sciences, Iran, in 2021. A valid and reliable researcher-made questionnaire evaluated the quality of self-care education provided by a healthcare worker to patients. Data were analyzed by SPSS software, version 21 applying t test, analysis of variance, and Pearson correlation at a significant level of less than 0.05. Results: The mean quality of the self-care education questionnaire was 98.28±12.12 out of 110 for 346 participants with a mean age of 46.17± 14.71 years. The mean score for communication skills, educational method, and content were 12.83±3.55 out of 15, 13.72±3.81 out of 20, and 71.71±7.6 out of 75, respectively. There was a relationship between marital status and educational content (P=0.005). Communication skills (P=0.002) and educational method (P=0.05) had a relationship with educational level. Age had a negative relationship with communication skills (P=0.005) and educational method (P=0.01). Conclusion: This study showed the high quality of self-care education of healthcare worker on recovered COVID-19 patients. The design, implementation, and evaluation of self-care training should be considered according to the factors related to it such as marital status, educational level, and age

Lethal multiple pterygium syndrome in a newborn, a case report

Key Clinical Message Lethal multiple pterygium syndrome is a very rare genetic disorder. The manifestations of this condition include growth deficiency of the fetus, craniofacial anomalies, joint contracture, and skin webbing (pterygia). This disorder is fatal before birth or shortly after birth. We reported a case of lethal multiple pterygium syndrome with multiple anomalies including pterygia involving the axilla, bilateral antecubital fossa, and groin. Arthrogryposis involving multiple lower and upper extremities joints. Cleft palate, microstomia and limitation of mouth opening, webbed neck, asymmetric small and narrow chest, ambiguous genitalia, depressed and wide nasal bridge, antemongoloid slant, low‐set, malformed, and posteriorly rotated ears, pterygia, syndactyly and camptodactyly of hands and rocket bottom feet. LMPS is a congenital genetic disease with multiple anomalies that is fatal in the second and third trimesters of pregnancy or shortly after birth. With genetic testing and counseling, it can be prevented from recurring in subsequent pregnancies

Evaluation of Nursing stress and its effective factors in nurses of Shahidzadeh Hospital in Behbahan in 2019: Challenges in Nursing

Background and Aim: Nursing is one of the occupations that face a lot of stress in medical settings, especially hospitals. Job stress can negatively affect a nurse's performance and how she cares for her patient. Therefore, the present study was performed to evaluate the level of job stress in nurses of Shahidzadeh Hospital in Behbahan. Materials and Methods: The present study was a descriptive cross-sectional analytical study. The instruments used in the study included demographic information and an expanded nursing stress scale questionnaire. The study was performed on 220 nurses of Shahidzadeh Hospital in Behbahan by census method. Statistical data were performed using SPSS software version 22 using descriptive and inferential statistics at a significant level of p <0.05. Results: The mean age of participants was 31.02 ± 06.68. The mean scores of participants in occupational stress and its subscales showed that about half of the subjects (55.9%) in the study had poor job stress. There was no significant relationship between job stress and demographic variables of gender and marital status using independent t-test and there was no significant relationship between job stress and the above variables. Conclusion: Due to the existence of stress as a negative factor in patient care and the gap between discrimination between physicians and nurses as one of the factors aggravating stress, it is recommended that nursing managers take measures to prevent and support nurses

Explaining the role and relationship between spiritual intelligence and hidden anxiety and test anxiety in students: Application of path analysis

Objective: Spiritual intelligence is manifested when a person spends his life in complete spirituality. Anxiety and test anxiety are among the most common psychological and emotional problems during student life that have a negative impact on students' mental health and academic performance. Therefore, this study was conducted to explain the role and relationship between spiritual intelligence and hidden anxiety and test anxiety. Methods: This analytical study was performed on 503 nursing and midwifery students of Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran (2018) using the available sampling method. King spiritual intelligence scale, Sarason's test anxiety scale, and Spielberger Anxiety Questionnaire were used to collect data. To analyze the data, statistical methods of path analysis and correlation coefficients were used. Results: The overall effect of test anxiety variable on spiritual intelligence variable is 0.1632 and the overall effect of hidden anxiety variable on spiritual intelligence variable is -0.07. Spiritual intelligence has a significant relationship with test anxiety, so that with increasing mental intelligence, test anxiety is decreased. While in the case of significant relationship between spiritual intelligence and hidden anxiety, with increasing spiritual intelligence, hidden anxiety is also increased. There was no significant relationship between hidden anxiety and test anxiety. The two variables of hidden anxiety and test anxiety have a very weak effect on spiritual intelligence. The effect of hidden anxiety is not significant at all, and the effect of test anxiety is very weak, although being significant. Conclusion: High spiritual intelligence indicates lower test anxiety, while it is inversely related to hidden anxiety. As a result, high spiritual intelligence does not indicate less hidden anxiety in individuals. So it cannot be said for sure that spiritual intelligence plays an important role in reducing anxiety