Antimicrobial Action and Scaring of 10%Green Banana Shell in Chronic Wounds

The percentage of diabetic patients with contaminated lesions increases from 3% to 10%. Treatment with herbal products shows benefits in their use, as well as antioxidant and antifungal activities. Objective: to evaluate the antimicrobial action of 10% banana peel gel and the contraction of diabetic and venous wounds. Methods: individual, analytical, interventional, longitudinal, prospective, randomized study from February to December 2015. Five patients were included in the study;3 with venous ulcer and 2 with diabetic wound. Application of 10% green banana peel gel and weekly samples were performed. After six days, the second collection was performed. The samples were seeded in the Mannitol salt agar culture medium, MacConkey agar and Saboraund agar with chloramphenicol for isolation of cocci and Gram-positive and Gram-negative bacilli;and fungi. The total counts of bacteria were determined by PCA (Plate Count Agar) and measurement of the lesion margin. There was a reduction of microorganisms with the use of the gel in 53.57% of the patients, and reduction of wound areas in 48.1%.

On 2-(v,k,λ)Designs with Flag-Transitive Automorphism Groups of Product Action Type

In this paper we present two new families of 2-(v,k,λ)designs with a flag-transitive and point-primitive automorphism group of product action type.More sur-prisingly,one of them is still a family of 2-(v,k,λ)designs with a fag-transitive and point-imprimitive automorphism group.

TAD-Net:An approach for real-time action detection based on temporal convolution network and graph convolution network in digital twin shop-floor[version 1;peer review:2 approved]

Background:Intelligent monitoring of human action in production is an important step to help standardize production processes and construct a digital twin shop-floor rapidly.Human action has a significant impact on the production safety and efficiency of a shop-floor,however,because of the high individual initiative of humans,it is difficult to realize real-time action detection in a digital twin shop-floor.Methods:We proposed a real-time detection approach for shop-floor production action.This approach used the sequence data of continuous human skeleton joints sequences as the input.We then reconstructed the Joint Classification-Regression Recurrent Neural Networks(JCR-RNN)based on Temporal Convolution Network(TCN)and Graph Convolution Network(GCN).We called this approach the Temporal Action Detection Net(TAD-Net),which realized real-time shop-floor production action detection.Results:The results of the verification experiment showed that our approach has achieved a high temporal positioning score,recognition speed,and accuracy when applied to the existing Online Action Detection(OAD)dataset and the Nanjing University of Science and Technology 3 Dimensions(NJUST3D)dataset.TAD-Net can meet the actual needs of the digital twin shop-floor.Conclusions:Our method has higher recognition accuracy,temporal positioning accuracy,and faster running speed than other mainstream network models,it can better meet actual application requirements,and has important research value and practical significance for standardizing shop-floor production processes,reducing production security risks,and contributing to the understanding of real-time production action.