Co-composting process can be acquired by combining organic fraction of municipal solid waste (OFMSW) with sewage sludge (SS) and mature compost (MC) as enhancement and bulking agent to overcome the problems of municipal solid waste and wastewater treatment plants besides the finally produced fertilizer usage for agriculture and horticulture. The effects of different mixture ratios of (OFMSW), (SS) and (MC) on the performance of composting process were investigated in this study. Piles of about 10 kg were prepared by mixing OFMSW, SS and MC in three different ratios (w/w) [OFMSW: SS: MC= 3:1:1, 3:2:1, and 3:3:1]. Results showed that the pile [3:1:1] was most beneficial to composting. The final compost products contained a

Abstract Introduction: Aphthous stomatitis is a recurring oral ulceration condition that affects a significant proportion of the population. While several factors have been proposed to contribute to its occurrence, the link between caffeine consumption, anxiety levels, and the development of aphthous stomatitis remains unknown. This survey-based study aimed to understand the association between coffee, anxiety, and the development of aphthous stomatitis in this specific demographic, which can provide useful insights for medical management as well as preventive strategies. Material and methods: A structured online questionnaire was distributed via various social media platforms targeting a sample of the Armenian population. The quest

    Nine fish specimens of Thryssa setirostris (Broussonet, 1782) were collected from the Arabian Gulf, during the period from July 2015 to April 2016. Because of the scarcity of this fish and overlapping and ambiguous of its taxonomic characters with other Thryssa spp., a detailed taxonomic study was conducted. The present study includes the most important meristic and morphometric characteristics.

    The mean of the total length of the specimens was 149.67 mm; dorsal fin consists of 12 rays, anal fin with 34-37 rays and pectoral fin with 12-13 rays; Gill rakers were 4 upper,1 medial an.10 lower. The most important character that isolates T. setiristis from

Developing and researching antenna designs are analogous to excavating in an undiscovered mine. This paper proposes a multi-band antenna with a new hexagonal ring shape, theoretically designed, developed, and analyzed using a CST before being manufactured. The antenna has undergone six changes to provide the best performance. The results of the surface current distribution and the electric field distribution on the surface of the hexagonal patch were theoretically analyzed and studied. The sequential approach taken to determine the most effective design is logical, and prevents deviation from the work direction. After comparing the six theoretical results, the fifth model proved to be the best for making a prototype. Measured results rep

During this research accomplish some chemical modifications on the Poly for new modified polymers with Kimaaaúah qualities, physical and unexpected new applications that the first of these chemical modifications that was completed is the introduction of a poly styrene butadiene swash and then this group

Background: despite the rise in the incidence of renal cell carcinoma attributed to availability of medical imaging, a considerable decline in mortality is an association. Morbidity-wise, the shift from radical nephrectomy to partial nephrectomy is the trend for now. Multiple scoring systems have been introduced over the past decades to help surgeons choose between radical and partial nephrectomy. One commonly used system is the RENAL nephrometry score that was first introduced by Kutikov and Uzzo in 2009.

Objective: to evaluate the role of RENAL nephrometry scoring system in predicting the surgical technique to use to resect renal masses and associated perioperative outcomes.

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class

This work describes the development of new spectrophotometric techniques for 3-aminophenol assessment. The first technique involves using benzidine in an alkaline solution to convert 3-aminophenol into a colored complex. The produced complex has a red color with an absorbance of 462 nm. Between the concentration range 5–14 μg mL−1, Beer's law is obeyed with a correlation coefficient (R2) of 0.99781, a limit of detection (LOD) of 0.0423 μg mL−1, and a limit of quantification (LOQ) of 0.1411 μg mL−1. The recovery was between 87.2–95.43%, the relative standard deviation (%RSD) was 2.40–3.31% and the molar absorptivity was 3.545 × 103 L mol−1 cm−1. Secondly, cloud point extraction (CPE) was used to determ

Gingival crevicular fluid (GCF) may reflect the events associated with orthodontic tooth movement. Attempts have been conducted to identify biomarkers reflecting optimum orthodontic force, unwanted sequallea (i.e. root resorption) and accelerated tooth movement. The aim of the present study is to find out a standardized GCF collection, storage and total protein extraction method from apparently healthy gingival sites with orthodontics that is compatible with further high-throughput proteomics. Eighteen patients who required extractions of both maxillary first premolars were recruited in this study. These teeth were randomly assigned to either heavy (225g) or light force (25g), and their site specific GCF was collected at baseline and aft

Image classification is the process of finding common features in images from various classes and applying them to categorize and label them. The main problem of the image classification process is the abundance of images, the high complexity of the data, and the shortage of labeled data, presenting the key obstacles in image classification. The cornerstone of image classification is evaluating the convolutional features retrieved from deep learning models and training them with machine learning classifiers. This study proposes a new approach of “hybrid learning” by combining deep learning with machine learning for image classification based on convolutional feature extraction using the VGG-16 deep learning model and seven class