Carbon fibre reinforced polymers are widely used to strengthen steel structural elements. These structural elements are normally subjected to static, dynamic and fatigue loadings during their life-time. A number of studies have focused on the characteristics of CFRP sheets bonded to steel members under static, dynamic and fatigue loadings. However, there is a gap in understanding the bonding behaviour between CFRP laminates and steel members under impact loading. This paper shows the effect of different load rates from quasi-static to 300 × 103 mm/min on this bond. Two types of CFRP laminate, CFK 150/2000 and CFK 200/2000, were used to strengthen steel joints using Araldite 420 epoxy. The results show a significant bond strength enhancemen

This work implements an Electroencephalogram (EEG) signal classifier. The implemented method uses Orthogonal Polynomials (OP) to convert the EEG signal samples to moments. A Sparse Filter (SF) reduces the number of converted moments to increase the classification accuracy. A Support Vector Machine (SVM) is used to classify the reduced moments between two classes. The proposed method’s performance is tested and compared with two methods by using two datasets. The datasets are divided into 80% for training and 20% for testing, with 5 -fold used for cross-validation. The results show that this method overcomes the accuracy of other methods. The proposed method’s best accuracy is 95.6% and 99.5%, respectively. Finally, from the results, it

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

The aim of this paper is to introduce the concept of N  and Nβ -closed sets in terms of neutrosophic topological spaces. Some of its properties are also discussed.

The goal of this article is to construct fibrewise w-compact (resp. locally w-compact) spaces. Some related results and properties of these concepts will be investigated. Furthermore, we investigate various relationships between these concepts and three classes of fibrewise w-separation axioms.

Empirical equations for estimating thickening time and compressive strength of bentonitic - class "G" cement slurries were derived as a function of water to cement ratio and apparent viscosity (for any ratios). How the presence of such an equations easily extract the thickening time and compressive strength values of the oil field saves time without reference to the untreated control laboratory tests such as pressurized consistometer for thickening time test and Hydraulic Cement Mortars including water bath ( 24 hours ) for compressive strength test those may have more than one day.

Background. Diabetes mellitus (DM) is a prevalent disease that, if not appropriately managed, can lead to a variety of problems, including diabetic foot. Glycated hemoglobin A1c (HbA1c), FBS, amylase, and lipase are important diabetic management indicators now employed as diagnostic tests. Objective. This study aimed to evaluate the value of amylase and lipase as predictive markers in patients with diabetic foot. Patients and methods. This study included 50 patients who reported to Baghdad Hospital with diabetic feet between November 2023 and February 2025. All patients had their HbA1c, amylase, lipase, and FBS levels tested. Means, independent t-tests, and the F-test were used in the statistical analysis. Results. The study evaluat