Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)emerged.Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks.Therefore,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis.The results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic.The high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of death.The results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the COVID-19.It can be suggested that social media posts may affect human psychology and behavior.These results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.

Does Surgery Benefit Postmenopausal Overweight Women with Pelvic Floor Dysfunction?

Introduction: Pelvic floor muscle function of 30 overweight postmenopausal women prior to and after colporrhahpy was monitored in this study. Material and Methods: Patients diagnosed with cystokele or combined cystorectokele was involved. 1 mg oral estriol and local estriol cream were administered for 30 days preoperatively. Pelvic floor muscle function was monitored by surface electromyography 1 month before (1st) 1 day prior to surgery (2nd), and six weeks after the surgery (3rd measurement). Body composition parameters (intra- and extracellular water and body fat) were also measured. Results: The ability to relax significantly improved (p = 0.03) in the preoperative period (between 1st and 2nd occasions). Six weeks after surgery a non-significant (p = 0.054) decrease in average muscle activity was detected when compared with values obtained before the surgery. Muscle-activity declined significantly from the first to the last measurements (p = 0.005). Conclusion: Our results confirm that postmenopausal obese women who undergo anterior or posterior colporrhaphy need a follow-up concerning pelvic floor muscle function and suggest that physiotherapy started the earliest possible may aid in preserving postoperative functionality on the long run.

Computed Tomography Image Enhancement Using Cuckoo Search: A Log Transform Based Approach

Medical image enhancement is an essential process for superior disease diagnosis as well as for detection of pathological lesion accurately. Computed Tomography (CT) is considered a vital medical imaging modality to evaluate numerous diseases such as tumors and vascular lesions. However, speckle noise corrupts the CT images and makes the clinical data analysis ambiguous. Therefore, for accurate diagnosis, medical image enhancement is a must for noise removal and sharp/clear images. In this work, a medical image enhancement algorithm has been proposed using log transform in an optimization framework. In order to achieve optimization, a well-known meta-heuristic algorithm, namely: Cuckoo search (CS) algorithm is used to determine the optimal parameter settings for log transform. The performance of the proposed technique is studied on a low contrast CT image dataset. Besides this, the results clearly show that the CS based approach has superior convergence and fitness values compared to PSO as the CS converge faster that proves the efficacy of the CS based technique. Finally, Image Quality Analysis (IQA) justifies the robustness of the proposed enhancement technique.

Locomotion-based Hybrid Salp Swarm Algorithm for Parameter Estimation of Fuzzy Representation-based Photovoltaic Modules

Identifying the parameters of photovoltaic(PV)modules is significant for their design and simulation.Because of the instabilities in the weather action and land surface of the earth,which cause errors in measuring,a novel fuzzy representation-based PV module is formulated and developed.In this paper,a novel locomotion-based hybrid salp swarm algorithm(LHSSA)is presented to identify the parameters of PV modules accurately and reliably.In the LHSSA,better leader salps based on particle swarm optimization(PSO)are incorporated to the traditional salp swarm algorithm(SSA)in a serialized scheme with the aim of providing more valuable information for the leader salps of the SSA.By this integration,the proposed LHSSA can escape the local optima as well as guide the seeking process to attain the promising region.The proposed LHSSA is investigated on different PV models,i.e.,single-diode(SD),double-diode(DD),and PV module in crisp and fuzzy aspects.By comparing with different algorithms,the comprehensive results affirm that the LHSSA can achieve a highly competitive performance,especially on quality and reliability.