Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.

CiteSpace-Based Metrical and Visualization Analysis of Tai Chi Chuan Analgesia

Objective:The objective of the study was to explore the research status and hot topics that are most studied about in Tai Chi Chuan(TCC)analgesia through a metrical and visualization analysis of the literature and provide some references for the experimental research on the analgesic effect of TCC and its clinical applications.Methods:The literature on TCC analgesia was collected from the Web of Science database,and the metrical and visualization analysis was performed using the Cite Space.5.6.R4 software in terms of publication outputs,countries,institutions,keywords,highly cited articles,and highly cited journals.Results:The number of annual publications gradually increased over time.The five research groups presented stable cooperative relationships and more publications.The authors ranked as top 1 were from America rather than China,which has more publications.The most common keywords were Tai Chi,randomized controlled trial,older adults,exercise,pain,low back pain,quality of life,management,etc.The literature on knee osteoarthritis and fibromyalgia had the highest citation frequency.The journals with high citation frequency included Cochrane Database System Review,Pain,and Plos One.Conclusions:Increasing attention has been paid to TCC analgesia.Randomized controlled trials,older adults,low back pain,and quality of life were found to be most studied in this field.Investigating clinical efficacy and conducting meta-analyses could be a promising direction in the future.The international cooperation and literature quality of TCC analgesia should be further strengthened.

Application of Basin Morphometry Laws in catchments of the south-western quadrangle of south-eastern Nigeria

The relationship between process and form has been at the core of research in fluvial geomorphology. Form-process relationships of a natural river basin are strongly influenced by its hydrologic and sedimentologic processes as basin morphometric properties of length, shape, and relief, change in response to various hydrologic stimuli from the environment, but usually in line with well established laws. In the four fiver basins （Orashi, Otamiri, Sombreiro, New Calabar） examined in this study, however, empirical evidence does not conform neatly with theoretical postulates. Remarkable variations are noted in the molphometric properties of the catchments, when compared with established morphometric laws. The most varied in conformity are the Orashi and New Calabar basins, although the Sombreiro and Otamiri catchments also show some level of variation. Prime explanation for the morphometric and topographic non-conformity is caused by the nature of surficial material and the profoundly shallow relief of much of the study area, especially the alluvial flood and deltaic plains to the south and south-west of the study area.