Improvement of Single Seeded Region Growing Algorithm on Image Segmentation

To form a hybrid approach for image segmentation, several researches have been done to combine some techniques for better improvements. This article is concerned with image segmentation using combined methods. To separate foreground from background in image the pixel intensities have been considered. For image segmentation region growing with seed pixel is one of the most important segmentation methods. In single seeded region growing, it is very difficult to find out the proper position of the pixel during the selection. By considering the limitation of single seeded region growing an improved algorithm for region growing has proposed. The position of the seed pixel can be chosen before growing the region for segmentation using the proposed technique. Then combine this method with existing single seeded region growing algorithm. After the comparison using segmentation evaluation parameters it can be seen that, this combined method works better than others existing methods

Gold-coated photonic crystal fiber biosensor based on surface plasmon resonance: Design and analysis

The particularly sensitive circular lattice Photonic Crystal Fiber (PCF) based Surface Plasmon Resonance (SPR) sensor is proposed to gain high sensitivity for the detection of unknown analytes. In this model, two-layer PCF based on the SPR has been designed. A plasmonic chemically inactive material gold (Au) with thickness 35 nm is used to the outside of the PCF structure which exhibits negative real permittivity. A circular perfectly match layer (PML) outside the structure is applied to evaluate the performance of the sensor. The raised design has consisted of symmetric air-hole. Three small air-holes are used in second layer and center which help us to produce more evanescent field. Using the wavelength interrogation method the proposed model shows the maximum wavelength sensitivity of 9000 nm/RIU (Refractive Index Unit) and using the amplitude interrogation method it shows the maximum amplitude sensitivity of 318 RIU−1 with maximum sensor resolution 1.11 × 10−5 in the sensing range among analyte 1.34–1.37. Here the proposed model is investigated how phase matching points are varied with changing parameters as like diameter, PML, thickness of gold (Au), sensing layer and pitch. The obtained result reveals that the proposed model may be used in biochemical and biological analyte detection to find out the important application

Development of Flexible Composite Sheet with Chrome Shavings Using Polyvinyl Alcohol as a Cross-Linker

Leather processing generates a huge amount of chromium (Cr) containing wastes, and one of them is chrome shavings (CS), which frequently end up in landfills. It may be harmful to the environment and human health due to the oxidation of Cr(III) to poisonous Cr(VI). Herein, CS and polyvinyl alcohol (PVA) are used for the preparation of flexible CS-PVA composite sheets, using CS as a skeletal and PVA as a cross-linker by a simple and facile technique. CS-PVA composite sheets are characterized by FT-IR, SEM, STA, and UTM. FT-IR analysis of CS-PVA composite sheets indicated the existence of dominating peaks corresponding to collagen amide bands as well as PVA characteristic bands, and it demonstrates the uniformity of the developed composite sheets. When the amount of PVA is increased, the tensile strength of CS-PVA composite sheets increases from 0.21 to 4.17 N/mm2. With increasing of the amount of PVA, the softness decreases from 6.47 to 3.7 mm, and SEM shows decreasing of pores in the composite sheet. The addition of more PVA makes CS-PVA composite sheets more thermally stable. This facile method of preparing CS-PVA composite sheet is low-cost and eco-friendly, having potential applications in various fields, including clothing, leather goods, decoration, packaging, and footwear products, as well as presenting promising platforms for effective utilization of industrial waste materials

Predicting the risk of hypertension using machine learning algorithms: A cross sectional study in Ethiopia.

Background and objectivesHypertension (HTN), a major global health concern, is a leading cause of cardiovascular disease, premature death and disability, worldwide. It is important to develop an automated system to diagnose HTN at an early stage. Therefore, this study devised a machine learning (ML) system for predicting patients with the risk of developing HTN in Ethiopia.Materials and methodsThe HTN data was taken from Ethiopia, which included 612 respondents with 27 factors. We employed Boruta-based feature selection method to identify the important risk factors of HTN. The four well-known models [logistics regression, artificial neural network, random forest, and extreme gradient boosting (XGB)] were developed to predict HTN patients on the training set using the selected risk factors. The performances of the models were evaluated by accuracy, precision, recall, F1-score, and area under the curve (AUC) on the testing set. Additionally, the SHapley Additive exPlanations (SHAP) method is one of the explainable artificial intelligences (XAI) methods, was used to investigate the associated predictive risk factors of HTN.ResultsThe overall prevalence of HTN patients is 21.2%. This study showed that XGB-based model was the most appropriate model for predicting patients with the risk of HTN and achieved the accuracy of 88.81%, precision of 89.62%, recall of 97.04%, F1-score of 93.18%, and AUC of 0. 894. The XBG with SHAP analysis reveal that age, weight, fat, income, body mass index, diabetes mulitas, salt, history of HTN, drinking, and smoking were the associated risk factors of developing HTN.ConclusionsThe proposed framework provides an effective tool for accurately predicting individuals in Ethiopia who are at risk for developing HTN at an early stage and may help with early prevention and individualized treatment

Aluminum-silicon carbide composites for enhanced physio- mechanical properties

Stir casting method is used in the production of SiC reinforced-aluminium metal matrix composite (AMMC) to enhance the properties of base metal. Different weight fraction of Silicon carbide, SiC (5 wt%, 10 wt% and 15 wt %) particulate-reinforced AMMCs are fabricated and characterizations of physical and mechanical properties of the materials are performed based on the experimental. The microstructure of the fabricated composite material are studied and analyzed. The results indicate that the mechanical properties of the composite, including yield strength, tensile strength and hardness are enhanced by the increment of the weight fraction of reinforcing phase. Nevertheless, the elongation and fracture toughness of the composite decreased as the reinforcing phase increased. This is mainly due to the brittleness of the SiC particles which act as micro void initiator

Exploring the effects of different parameters on the incorporation of K+ ions in eggshell derived CaO reveals highly variable catalytic efficiency for biodiesel conversion

This study was designed to observe the effects of temperature, impregnation time, and stirring rate on the structure of potassium ion-impregnated calcium oxide prepared from potassium hydroxide and waste eggshell-derived calcium oxide. A total of 18 reactions were carried out in different environments to impregnate K+. The catalyst products were analyzed using Infrared spectroscopy (IR), X-ray diffraction (XRD), Field emission scanning electron microscopy (FE-SEM), and Energy dispersive X-ray spectroscopy (EDX). The XRD and EDX results indicated that at higher temperatures calcium hydroxide tends to form as well a greater incorporation of K+ ions is achieved. Moreover, stirring speed was observed to affect the growth of specific phases in both calcium oxide and calcium hydroxide crystals. However, the impregnation time variation did not show any significant effect. The prepared catalysts were applied to the transesterification reaction using waste cooking oil as the fatty acid methyl ester (FAME) source, achieving a maximum of 98.28 % conversion to FAME calculated from 1H-NMR spectra. This indicates that tailoring of the catalyst may be carried out by changing these parameters in order to achieve a desired catalyst or sorbent structure which in turn may significantly improve catalytic efficiency

Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)

In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field