Volume 9 | Issue 2

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Otherness, Disabled, Self, The Glass Menagerie

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This research paper attempts to have an ecological analysis on the relationship between the learning of language and the understanding of culture among Chinese college EFL learners. As an analytical research paper, it employs mainly three research methods including method of theoretical analysis, ecological analysis and comparative analysis to acquire a full understanding of the relationship between language and culture among Chinese college EFL learners from an ecological view. Through understanding the process from language learning to culture understanding, the author thus tries to show an ecological view of how the learning of language matters with the understanding of culture as well as the ecological significance reflected by the interaction between language learning and culture understanding among Chinese college EFL learners. With the help of an ecological view, this research paper also makes efforts to show how language learning, which is deeply rooted by the ecology of a natural environment, a humanistic community, a country and even a nation, promotes the development of culture understanding. Thus it is fully analyzed and concluded that there is a very close relationship between language learning and culture understanding, which is also an inseparable relationship. Overall, this research paper also interprets that language is not just a tool or carrier of culture. In fact, language is not only a cultural phenomenon, but also a living fossil of history and culture of a country and a nation as well as a special and comprehensive cultural combination and aggregation. An ecological analysis from language learning to culture understanding among Chinese college EFL learners will definitely help to comprehend such a fact that, on the one hand, language plays an irreplaceable role in cultural construction, inheritance, and communication between different cultures; on the other hand, different cultural characteristics often lead to different language characteristics.

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Language Learning; Culture Understanding; Chinese College EFL Learners; An Ecological Analysis

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Arbitration, Domestic courts, legal Mechanisms, dispute resolution, Mediation

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MOS, Sio2, CMOS, SOI, CSA, GCA, FET.

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ABSTRACT Background: Today's Filipinas have made their legacy in each of their endeavors. (Convergys Corp., 2018). For years, Filipina women have been considered people who stayed at home, namely, homemakers or those who cannot do masculine jobs. Method: This study is a qualitative research design, in which the method used to gather the data is by interviewing the participants selected by the researchers. The type of qualitative research used is a phenomenological approach. It is related to the central question "How do Filipina leaders employed in a Qatar-based company adapt to their work environment?". In line with our title, "Women Empowerment: A Study of the lived experiences of Filipina Leaders in the State of Qatar," the researchers chose Filipina leaders who are currently working and are employed in Qatar as their target respondents for the study. The conducting of this research follows the IMRAD format and aims to be published and reproduced easily. The researchers deemed the target respondents sufficient and knowledgeable for this study. As students currently studying in Philippine School Doha, the researchers' research will be respectfully conducted in the host country, Qatar. Findings have revealed that Filipina leaders have encountered many challenges and have shown the advantages and disadvantages of being a Filipina leader as discussed through work parity, work conformity, work impartiality, and work proclivity. Conclusion: Women 's leadership is about continuing to implement changes that promote women 's advancement within their organizations and businesses, resolve implicit bias problems, and inspire women and men to create solutions.

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Leadership, Women Empowerment, Qatar-based Company, Workplace Discrimination, Lived Experiences, Phenomenological Approach

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Urban Politics refers to the diverse political structure that occurs in urban regions where there is diversity in caste, religion and socio-economic status of people. The present study includes the structure of politics of a geographic space and spatial variations in voter�s choice. Delhi is one of the fastest growing cities in the world, it offers vast economic opportunities, education and medical facilities etc, has thus created very strong attractive force pulling migrants not only from the immediate neighborhood but also from the far-off places in the country. The phenomenal growth rate of the city has creates many developmental issues. This study tells us how urban politics and urban development are interconnected. The political skyline in Delhi saw dramatic changes since 2013 assembly election when Aam Adami Party came out as a second largest party and after that in 2015 and recently in 2020 it won maximum 62 seats and all the old National Parties like INC and BJP failed to attract voters. The recent election of Delhi was the best example where in many constituencies people cast their votes without keeping in mind the history of political parties, caste and religion of candidates.

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Urban politics, Urbanization, Political parties, Voters, Constituencies.

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Melanoma is considered a fatal type of skin cancer. However, it is sometimes hard to distinguish it from Nevus due to their identical visual appearance and symptoms. The mortality rate because of this disease is higher than all other skin related consolidated malignancies. The number of cases is growing amongst young people but if it is diagnosed at its earlier stage then the survival rates become very high. The cost and time required for the doctors to diagnose all patients for Melanoma are very high. In this work, we propose an intelligent system to detect and distinguish Melanoma from Nevus by using state of the art image processing techniques. At first, Gaussian Filter is used for removing noise from the skin lesion of the acquired images followed by the use of fuzzy c-mean clustering to segment out the lesion. A distinctive hybrid super feature vector is formed by the extraction of textural and color features from the lesion. Support Vector Machine (SVM) is utilized for the classification of skin cancer into melanoma and nevus. Our aim is to test the effectiveness of the proposed segmentation technique, extract the most suitable features and compare the classification results with the kNN techniques present in the literature. The proposed methodology is tested on DERMIS dataset.

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Dermis dataset, Skin Cancer, kNN, SVM

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The objective of this research is to develop a real-time system for hand gesture recognition that recognizes hand gestures, features of hands such as peak calculation and angle calculation, and then convert gesture images into text. To implement this system we use a sign language hand gesture dataset..The proposed system has four main modules such as preprocessing, segmentation, feature extraction, and classification. In the preprocessing module, the input image is converted into a grayscale image, noise removal, normalization, and rescale the image. After preprocessing, the image is segmented using an automatic threshold algorithm. Then the feature extraction is calculated using SIFT and SURF descriptors. The collected features are trained using an excel file. Finally, the input is classified using the SVM algorithm.

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SIFT, SURF

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Ozone dataset, Datamining Algorithms

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Bio-synthesis of nanoparticles is gaining huge importance, these days. Gold nanoparticles (GNPs) are widely used for various applications such as drug delivery, bioprobe, Imaging and drug targeting. Various sources such as plant extracts, fungi and microbes are used for the synthesis of GNPs. Algae are living in a sessile aquatic environment for its survival and it has certain bioactive compounds such as carbohydrates, aminoacids, proteins, fatty acids, flavonoids, antioxidants, saponins, etc. However, there are only limited studies for the synthesis of GNPs using algae. Hence, in this study, clinically potent GNPs were synthesized using fresh water green algae Charavulgaris. In the present study, the colour of the prepared solution changes from yellowish green to red wine indicates the synthesis of gold nanoparticles. In addition, UV-Vis spectrophotometer, SEM, XRD and FT-IR analysis was done for the analyses of gold nanoparticles. Furthermore, antibacterial study was done and it showed maximum sensitivity to the given bacterial species.

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Bio synthesis, gold nanoparticles, fresh water algae, Charavulgaris, anti-microbial study

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Detecting and classifying fetal brain abnormalities from magnetic resonance imaging (MRI) is important, as approximately 3 in 1000 women are pregnant with a fetal of abnormal brain. Early detection of fetal brain abnormalities using machine learning techniques can improve the quality of diagnosis and treatment planning. The literature has shown that most of the work made to classify brain abnormalities in a very early age is for preterm infants and neonates not fetuses. However, research papers that studied fetal brain MRI images have mapped these images with the neonates MRI images to classify an abnormal behaviour in newborns not fetal. In this work, a pipeline process is proposed for fetal brain classification (FBC) which uses machine learning techniques. The main contribution of this work is the classification of fetal brain abnormalities in early stage, before the fetal is born. The proposed algorithm is capable of detecting and classifying a variety of abnormalities from MRI images with a wide range of fetal gestational age (GA) (from 16 to 39 weeks) using a flexible and simple method with low computational cost. The novel proposed method consists of four phases; pre-processing, segmentation, feature extraction and classification. In the pre-process, the input image is converted into gray scale and apply the wiener filter for image enhancement. After pre-processing, the adaptive segmentation is applied for segmenting the image. Then DWT and statistical features are extracted from segmented images. Finally, the input image is classified using the Decision Tree algorithm.

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Fetal Brain Abnormalities, machine learning algorithms

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SVD, GLCM, certificate authentication

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Diabetes occurs when the pancreas fails to secrete enough insulin, slowly affecting the retina of the human eye. As it progresses, the vision of a patient starts deteriorating, leading to diabetic retinopathy. In this regard, retinal images are collected from DRIVE dataset images which are analyzing the consequences, nature, and status of the effect of diabetes on the eye. The objectives of this work are to (i) detect blood vessel, (ii) identify hemorrhages and (iii) classify different stages of diabetic retinopathy into normal or abnormal. The basis of the classification of different stages of diabetic retinopathy is the detection and quantification of blood vessels and hemorrhages present in the retinal image. Retinal vascular is segmented utilizing the contrast between the blood vessels and surrounding background. Hemorrhage candidates were detected using density analysis and bounding box techniques. Finally, classification of the different stages of eye disease was done using Random Forests technique based on the GLCM and LBP features of the blood vessels and hemorrhages. The proposed methodology for detection and grading of Diabetic Retinopathy is divided into following stages such as preprocessing, Optic Disc Removal, Blood Vessel Segmentation and Removal, Features Extraction and classification.

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Diabetic retinopathy detection, LBP, GLCM

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Dengue fever is a major problem in many developing countries, including India. For dengue patient monitoring, platelet count is vital to ensure early treatment in order to prevent disease complications. In primary health care centers platelet counting is typically performed manually, which is labor intensive and requires an experienced laboratory technician. Another method used, is the Advia hematology analyzer, which is very expensive, not affordable for rural and remote areas. To address present day challenges, developed an automated approach for the detection of platelet, along with the symptoms helps in assisting the detection of dengue. The technology is based on microscopic images derived from blood smears obtained using a digitalized camera attached to a microscope. Image processing and segmentation techniques are applied to estimate the platelet count from these blood slides. To further improve the accuracy of the results, an analysis of symptoms present in the patient is used in conjunction with the platelet analysis. Proposed vector based method for screening the samples. The classifier performance is evaluated with the sensitivity and specificity values. The results of platelet counts obtained from other platelet counting machines and manually are compared. The reported tool helps in the modernization of pathology laboratory into digital, as well it speedup the mass screening.

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Dengue fever prediction, machine learning algorithms

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Lung cancer is considered to be the one among the most dreaded disease which will be the main reason for the death of individuals and having greater deterioration of death if it is not identified at primitive stage. Because of the fact that Lung cancer could be identified only after spreading to the parts of lungs to a greater extent and it is very tough to predict the presence of lung cancer at the earlier stage. Moreover, it involves greater error in the diagnosing the presence of Lung cancer by Radiologists and Expert Doctors. Therefor it is compulsory to design an intelligent and automated system for accurately predicting the cancer and stage at which the stage of cancer or enhancing the accuracy of prediction for detecting the cancer at earlier which will be much helpful in deciding the treatment type and depth of the treatment based on the extent of disease. Here this work establishes the idea of preprocessing which used correlation ranking algorithm and Adaboost Algorithm in the classification of lung cancer and its stages and the predicting the possibility of recurrence.

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Adaboost algorithm, Lung Cancer prediction

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Heart block diseases are one of the most deadly diseases with a high mortality rate. The shape and size of the block are random during the growth process. Heart Block segmentation is a heart block assisted diagnosis technology that separates different block structures such as edema and active and block necrosis tissues from normal heart tissue. Magnetic resonance imaging (MRI) technology has the advantages of no radiation impact on the human body, good imaging effect on structural tissues, and an ability to realize tomographic imaging of any orientation. Therefore, doctors often use MRI heart block images to analyze and process heart blocks. In these images, the block structure is only characterized by gray scale changes, and the developed images obtained by different equipment and different conditions may also be different. This makes it difficult for traditional image segmentation methods to deal well with the segmentation of heart block images. Considering that the traditional single-mode MRI heart block images contain incomplete heart block information, it is difficult to segment the single-mode heart block images to meet clinical needs. In this work, an automatic thresolding algorithm is introduced to process the diagnosis of MRI heart block images. In the absence of added noise, the proposed algorithm has better advantages than traditional methods. Experimental results show that the proposed algorithm has better noise immunity than a comparable algorithm.

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Image processing, feature extractions

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According to the World Health Organization (WHO), the corona virus (COVID-19) pandemic is putting even the best healthcare systems across the world under tremendous pressure. The early detection of this type of virus will help in relieving the pressure of the healthcare systems. Chest X-rays has been playing a crucial role in the diagnosis of diseases like Pneumonia. As COVID-19 is a type of influenza, it is possible to diagnose using this imaging technique. With rapid development in the area of Machine Learning (ML), there had been intelligent systems to classify between Pneumonia and Normal patients. This work proposes the machine learning-based logistic regression classification algorithm. Bioinformatics and genomic signal processing use computational techniques to solve various biological problems. They aim to study the information allied with genetic materials such as the Deoxyribonucleic Acid (DNA), the Ribonucleic acid (RNA), and the proteins. Fast and precise identification of the protein coding regions in DNA sequence is one of the most important tasks in analysis. Existing Digital Signal Processing (DSP) methods provide less accurate and computationally complex solution with greater background noise. Hence, improvements in accuracy, computational complexity, and reduction in background noise are essential in identification of the protein-coding regions in the DNA sequences. In this work, a new DSP based method is introduced to detect the protein coding regions in DNA sequences. Here, the DNA sequences are converted into numeric sequences using electron ion interaction potential (EIIP) representation. In this work, we use DNA sequence of the coronavirus. The test is conducted using the databases available in the National Centre for Biotechnology Information (NCBI) site.

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Corona DNA sequences

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Introduction, Knowledge Society, organization of Knowledge, objectives, Basic Principles, knowledge centers

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Abstract: The fossil fuel engines which were good enough for us before 30-40 years but now they are one of the sources of contributor of global warming and pollution with fossil fuel crises. The Air Powered Vehicle is an eco-friendly vehicle which works on compressed air. An Air Powered vehicle uses air as a fuel. An Air Powered Vehicle uses the expansion of compressed air to drive the pistons of an engine. An Air Driven Engine is a pneumatic actuator that creates useful work by expanding compressed air. There is no mixing of fuel with air as there is no combustion. In this project, we are using a 5/2 solenoid valve connected to a flasher which is connected to a double acting pneumatic cylinder which is in turn connected to a modified piston head which rotates the flywheel and moves the engine forward thus creating motion.

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Air Powered Vehicle, 5/2 Solenoid Valve, Double Acting Pneumatic Cylinder.

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