Harriet Jacobs was a writer and a reformer. As a female writer in the nineteenth century, Jacobs wrote her narrative as a means of resisting the system of slavery. She wrote her book Incidents in the Life of a Slave Girl: Written by Herself, (1842) to reflect upon the exploitation of the black people and the need to change the hierarchal attitude that governs white/black relations. She was engaged in many abolitionist events and her anti-slavery approach appeared clearly in her writings. She shares Du Bios ideas about freedom and emancipation and the need for a political and cultural change. Thus, Du Bois’s theory provides a framework for her autobiographical novel where she portrays Linda Brent, the main character, a strong wille

Harriet Jacobs was a writer and a reformer. As a female writer in the nineteenth century, Jacobs wrote her narrative as a means of resisting the system of slavery. She wrote her book Incidents in the Life of a Slave Girl: Written by Herself, (1842) to reflect upon the exploitation of the black people and the need to change the hierarchal attitude that governs white/black relations. She was engaged in many abolitionist events and her anti-slavery approach appeared clearly in her writings. She shares Du Bios ideas about freedom and emancipation and the need for a political and cultural change. Thus, Du Bois’s theory provides a framework for her autobiographical novel where she portrays Linda Brent, the main character, a strong w

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

The Cu(II) was found using a quick and uncomplicated procedure that involved reacting it with a freshly synthesized ligand to create an orange complex that had an absorbance peak of 481.5 nm in an acidic solution. The best conditions for the formation of the complex were studied from the concentration of the ligand, medium, the eff ect of the addition sequence, the eff ect of temperature, and the time of complex formation. The results obtained are scatter plot extending from 0.1–9 ppm and a linear range from 0.1–7 ppm. Relative standard deviation (RSD%) for n = 8 is less than 0.5, recovery % (R%) within acceptable values, correlation coeffi cient (r) equal 0.9986, coeffi cient of determination (r2) equal to 0.9973, and percentage capita

Recently, numerous the generalizations of Hurwitz-Lerch zeta functions are investigated and introduced. In this paper, by using the extended generalized Hurwitz-Lerch zeta function, a new Salagean’s differential operator is studied. Based on this new operator, a new geometric class and yielded coefficient bounds, growth and distortion result, radii of convexity, star-likeness, close-to-convexity, as well as extreme points are discussed.

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

As one type of heating furnaces, the electric heating furnace (EHF) typically suffers from time delay, non-linearity, time-varying parameters, system uncertainties, and harsh en-vironment of the furnace, which significantly deteriorate the temperature control process of the EHF system. In order to achieve accurate and robust temperature tracking performance, an integration of robust state feedback control (RSFC) and a novel sliding mode-based disturbance observer (SMDO) is proposed in this paper, where modeling errors and external disturbances are lumped as a lumped disturbance. To describe the characteristics of the EHF, by using convection laws, an integrated dynamic model is established and identified as an uncertain nonlinear second ord

The introduction of concrete damage plasticity material models has significantly improved the accuracy with which the concrete structural elements can be predicted in terms of their structural response. Research into this method's accuracy in analyzing complex concrete forms has been limited. A damage model combined with a plasticity model, based on continuum damage mechanics, is recommended for effectively predicting and simulating concrete behaviour. The damage parameters, such as compressive and tensile damages, can be defined to simulate concrete behavior in a damaged-plasticity model accurately. This research aims to propose an analytical model for assessing concrete compressive damage based on stiffness deterioration. The prop

In this work ester derivatives were synthesized by the reaction of imidazole derivatives (C1) with ethylchloroacetate in ethanol and NaOH to give the corresponding (C2) .While compound (C3) acetohydrazide was synthesized by the reaction of ester derivatives (C2) with hydrazine hydrat in ethanol. Compound (C3) from the reaction with different aromatic aldehydes in absolute ethanol gave the Schiff′s bases (C4,C5). The product compounds were characterized by FT-IR, U.V and 1HNMR spectra and the biological activities were studied as antibacterial.

The oil and gas industry relies heavily on IT innovations to manage business processes, but the exponential generation of data has led to concerns about processing big data, generating valuable insights, and making timely decisions. Many companies have adopted Big Data Analytics (BDA) solutions to address these challenges. However, determining the adoption of BDA solutions requires a thorough understanding of the contextual factors influencing these decisions. This research explores these factors using a new Technology-Organisation-Environment (TOE) framework, presenting technological, organisational, and environmental factors. The study used a Delphi research method and seven heterogeneous panelists from an Oman oil and gas company