To accommodate utilities in buildings, different sizes of openings are provided in the web of reinforced concrete deep beams, which cause reductions in the beam strength and stiffness. This paper aims to investigate experimentally and numerically the effectiveness of using carbon fiber reinforced polymer (CFRP) strips, as a strengthening technique, to externally strengthen reinforced concrete continuous deep beams (RCCDBs) with large openings. The experimental work included testing three RCCDBs under five-point bending. A reference specimen was prepared without openings to explore the reductions in strength and stiffness after providing large openings. Openings were created symmetrically at the center of spans of the other specimens

The issue of image captioning, which comprises automatic text generation to understand an image’s visual information, has become feasible with the developments in object recognition and image classification. Deep learning has received much interest from the scientific community and can be very useful in real-world applications. The proposed image captioning approach involves the use of Convolution Neural Network (CNN) pre-trained models combined with Long Short Term Memory (LSTM) to generate image captions. The process includes two stages. The first stage entails training the CNN-LSTM models using baseline hyper-parameters and the second stage encompasses training CNN-LSTM models by optimizing and adjusting the hyper-parameters of

Variable selection is an essential and necessary task in the statistical modeling field. Several studies have triedto develop and standardize the process of variable selection, but it isdifficultto do so. The first question a researcher needs to ask himself/herself what are the most significant variables that should be used to describe a given dataset’s response. In thispaper, a new method for variable selection using Gibbs sampler techniqueshas beendeveloped.First, the model is defined, and the posterior distributions for all the parameters are derived.The new variable selection methodis tested usingfour simulation datasets. The new approachiscompared with some existingtechniques: Ordinary Least Squared (OLS), Least Absolute Shrinkage

A reversed-phase HPLC method with fluorescence detection for the determination of the aflatoxins B1, B2, G1 and G2 in 42 animal feeds, comprising corn (16), soya bean meal (8), mixed meal (13), sunflower, wheat, canola, palm kernel, copra meals (1 each) was carried out. The samples were first extracted using acetonitrile:water (9:1), and was further cleaned-up using a multifunctional column. Optimum conditions for the extraction and chromatographic separation were investigated. By adopting an isocratic chromatographic system using a mobile phase comprising acetonitrile:methanol:water (8:27:65, v/v/v), the separation of the four aflatoxins was possible within 30 min. Recoveries for aflatoxins B1, B2, G1 and G2 were 98 ± 0.7%, 95 ± 1.0%, 94

In this research, the effect of reinforcing epoxy resin composites with a filler derived from chopped agriculture waste from oil palm (OP). Epoxy/OP composites were formed by dispersing (1, 3, 5, and 10 wt%) OP filler using a high-speed mechanical stirrer utilizing a hand lay-up method. The effect of adding zinc oxide (ZnO) nanoparticles, with an average size of 10-30 nm, with different wt% (1,2,3, and 5wt%) to the epoxy/oil palm composite,  on the behavior of an epoxy/oil palm composite was studied with different ratios (1,2,3, and 5wt%) and an average size of  10-30 nm.  Fourier Transform Infrared (FTIR) spectrometry and mechanical properties (tensile, impact, hardness, and wear rate) were used to examine the composites. The FTIR

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

Abstract

A sensitive, precise and reliable indirect spectrophotometric method for the determination of chlordiazepoxide (CDE) in pure and pharmaceutical dosage forms is described. The method is based on oxidative coupling reaction between amino group resulting from acidic decomposition of CDE with phenothiazine in the presence of sodium periodate to produce an intense green soluble dye that is stable and shows a maximum absorption at 602 nm. The calibration plot indicates that Beer’s law is obeyed over the concentration range of 0.1?50 µg/mL, with a molar absorptivity of 1×10⁴ L/mol cm and correlation coefficient of 0.9994.All the conditions that affecting on the stability and sensitivity of the fo