Although the concept of difference is as old as the foundational concept of similarity, the modern (and contemporary) understanding of difference as a working notion that not only differentiates, but also approximates conflicting elements in an all encompassing system owes a great deal to the German philosopher Georg Wilhelm Friedrich Hegel (1770-1831). An idealist to the backbone, Hegel bequeathed to modern philosophy the postulation that the identity of an individual rests not in itself but in the relationship that individual‟s identity entertains with other members of society. In his classic Phenomenology of Spirit, Hegel explains how humans come to consciousness (pivotal concept in Idealism) through a strenuous, albeit apparently intuitive, process which he calls “the dialectic” that he exemplifies in the famous Master-Slave dialectic.1
Hegel assumes that humans are not born with an independent, formative consciousness, but, on the contrary, they aspire to acquire self-consciousness when the self (which Hegel alternatively calls “being-for-self”) is acknowledged and recognized by other fellows—an arduous, but imperative, dynamic that Hegel terms “being-for-others.” Self-consciousness is attained only after the self
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undergoes painstaking “stages” involved in the system of human relationships, which is representative of the Hegelian dialectic. This all-encompassing, ever changing system holistically places the individual “self” in relation to other “selves” while itself remains in constant motion. Accordingly, meaning and truth are never determinately fixed because they are always in process since, says Hegel, “the action has a double significance not only because it is directed against itself as well as against the other, but also because it is indivisibly the action of one as well as of the other.”2
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
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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
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Polycaprolactone is one of the natural biodegradable polymers mainly used in bioplastics production for packaging, usually composed of non-toxic compounds and biodegradable. The aim was to examine the role of zinc oxide (ZnO) nanopowder on the,wettability , thermal and anti-bacterial effect nanocomposites. Pure PCL and PCL-based bio- nanocomposites doped with various ratios of ZnO nanoparticles from 0% to 5wt% were prepared through the arrangement of throwing procedure. The results show that wettability properties in relation to ideal PCL and that they were increasingly hydrophobic from 57º.8 to 69º.53 because add ZnO nanocomposites,the thermal stability between 300 and 400 ° C makes them perfect for the application
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Objective: Zerumbone (ZER) is a well-known natural compound that has been reported to have anti-cancer effect. Thus, this study investigated the ZER potential to inhibit Thymidine Phosphorylase (TP) and the ability to trigger Reactive oxygen species (ROS)-mediated cytotoxicity in non-small cell lung cancer, NCI-H460, cell line. Material and Method: The antiangiogenic activity for ZER was evaluated by using the thymidine phosphorylase inhibitory test. Reactive oxygen species (ROS) production was determined via DCFDA dye by using flow cytometry. Result and Discussion: ZER was found to be potent TP inhibitory with the IC50 value of 50.3± 0.31 μg/ml or 230±1.42 µM. NCI-H460 cells upon treatment with ZER produced sign
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Investigation of mesomorphic properties of new 1,3,4-thiadiazolines (which are synthesised via many steps in Scheme 1) was carried out in this study. These compounds are designed to have a heterocyclic unit, a carboxylate linkage group and a polar ether chain at the end of the molecule adjacent to the benzene ring, which enhance the dipolar interactions forces (varied from one to eight carbons) to investigate the association properties of their phases. The structure of the target compounds and the intermediates were confirmed by 1H NMR, 13C NMR, mass and FTIR spectral techniques. Polarised microscopic studies revealed that all the compounds in the series exhibited enantiotropic liquid crystalline properties. This was further confirmed using
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In this research two algorithms are applied, the first is Fuzzy C Means (FCM) algorithm and the second is hard K means (HKM) algorithm to know which of them is better than the others these two algorithms are applied on a set of data collected from the Ministry of Planning on the water turbidity of five areas in Baghdad to know which of these areas are less turbid in clear water to see which months during the year are less turbid in clear water in the specified area.
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