Search results for: international frontal sinus anatomy classification

Authors: Rajkumar Kolangarakandy

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Feature extraction and selection is the primary part of any mammogram classification algorithms. The choice of feature, attribute or measurements have an important influence in any classification system. Discrete Wavelet Transformation (DWT) coefficients are one of the prominent features for representing images in frequency domain. The features obtained after the decomposition of the mammogram images using wavelet transformations have higher dimension. Even though the features are higher in dimension, they were highly correlated and redundant in nature. The dimensionality reduction techniques play an important role in selecting the optimum number of features from the higher dimension data, which are highly correlated. PCA is a mathematical tool that reduces the dimensionality of the data while retaining most of the variation in the dataset. In this paper, a multilevel classification of mammogram images using reduced discrete wavelet transformation coefficients and lazy classifiers is proposed. The classification is accomplished in two different levels. In the first level, mammogram ROIs extracted from the dataset is classified as normal and abnormal types. In the second level, all the abnormal mammogram ROIs is classified into benign and malignant too. A further classification is also accomplished based on the variation in structure and intensity distribution of the images in the dataset. The Lazy classifiers called Kstar, IBL and LWL are used for classification. The classification results obtained with the reduced feature set is highly promising and the result is also compared with the performance obtained without dimension reduction.

Keywords: PCA, wavelet transformation, lazy classifiers, Kstar, IBL, LWL

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Authors: Sunday Nathaniel Obasi, Chiamak Chinasa Obasi

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Soil characterization and classification provide the basic information necessary to create a functional evaluation and soil classification schemes. Fertility capability classification (FCC) on the other hand is a technical system that groups the soils according to kinds of problems they present for management of soil physical and chemical properties. This research was carried out in Ebonyi state, southeastern Nigeria, which is an agrarian state and a leading rice producing part of southeastern Nigeria. In order to maximize the soil and enhance the productivity of rice in Ebonyi soils, soil classification, and fertility classification information need to be supplied. The state was grouped into three locations according to their agricultural zones namely; Ebonyi north, Ebonyi central and Ebonyi south representing Abakaliki, Ikwo and Ivo locations respectively. Major rice growing areas of the soils were located and two profile pits were sunk in each of the studied zones from which soils were characterized, classified and fertility capability classification (FCC) developed. Soil classification was done using United State Department of Agriculture (USDA) Soil Taxonomy and correlated with World Reference Base for soil resources. Results obtained classified Abakaliki 1 and Abakaliki 2 as Typic Fluvaquents (Ochric Fluvisols). Ikwo 1 was classified as Vertic Eutrudepts (Eutric Vertisols) while Ikwo 2 was classified as Typic Eutrudepts (Eutric Cambisols). Ivo 1 and Ivo 2 were both classified as Aquic Eutrudepts (Gleyic Leptosols). Fertility capability classification (FCC) revealed that all studied soils had mostly loamy topsoils and subsoils except Ikwo 1 with clayey topsoil. Limitations encountered in the studied soils include; dryness (d), low ECEC (e), low nutrient capital reserve (k) and water logging/ anaerobic condition (gley). Thus, FCC classifications were Ldek for Abakaliki 1 and 2, Ckv for Ikwo 1, LCk for Ikwo 2 while Ivo 1 and 2 were Legk and Lgk respectively.

Keywords: soil classification, soil fertility, limitations, modifiers, Southeastern Nigeria

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Authors: Thanh Noi Phan, Martin Kappas, Jan Degener

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The currently launched Sentinel 2 (S2) satellite (June, 2015) bring a great potential and opportunities for land use/cover map applications, due to its fine spatial resolution multispectral as well as high temporal resolutions. So far, there are handful studies using S2 real data for land cover classification. Especially in northern Vietnam, to our best knowledge, there exist no studies using S2 data for land cover map application. The aim of this study is to provide the preliminary result of land cover classification using Sentinel -2 data with a rising state – of – art classifier, Random Forest. A case study with heterogeneous land use/cover in the eastern of Hanoi Capital – Vietnam was chosen for this study. All 10 spectral bands of 10 and 20 m pixel size of S2 images were used, the 10 m bands were resampled to 20 m. Among several classified algorithms, supervised Random Forest classifier (RF) was applied because it was reported as one of the most accuracy methods of satellite image classification. The results showed that the red-edge and shortwave infrared (SWIR) bands play an important role in land cover classified results. A very high overall accuracy above 90% of classification results was achieved.

Keywords: classify algorithm, classification, land cover, random forest, sentinel 2, Vietnam

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Authors: Aynur Charkasova

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The scientific and economic contributions of international students cannot be underestimated. International education continues to be a competitive global industry, and many countries are seeking to recruit the best and the brightest to reinforce scientific innovations, boost intercultural learning, and bring more funding to universities and colleges. Substantial changes in international educational policies and migration regulations have been made in the hopes of recruiting global talent. This paper explores and compares recruitment strategies, employment opportunities, and a legal path to permanent residency policies related to international students in the United States of America and Canada. This study will utilize the legal information available from the government websites of both countries and peer-reviewed scholarly articles and will highlight which approach promises a better path in recruiting and retention of international students. The findings from the study will be discussed and recommendations will be provided.

Keywords: International students, current immigration policies, STEM, employability, visa reforms for international students, Canadian recruitment policy

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Authors: Bryan Crompton, Daniel Giger, Tanay Mehta, Apurva Mody

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The task of classifying radio frequency (RF) signals has seen recent success in employing deep neural network models. In this work, we present a combined signal processing and machine learning approach to signal classification for cochannel anomalous signals. The power spectral density and cyclostationary signal processing features of a captured signal are computed and fed into a neural net to produce a classification decision. Our combined signal preprocessing and machine learning approach allows for simpler neural networks with fast training times and small computational resource requirements for inference with longer preprocessing time.

Keywords: signal processing, machine learning, cyclostationary signal processing, signal classification

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Authors: Rotimi Sunday Ajani, Omowumi Femi-Akinlosotu

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Background: The usually paired human kidneys are retroperitoneal urinary organs with some endocrine functions. Standard text books of anatomy ascribe single value to each of the dimension of length, width and thickness. Research questions: These values do not give consideration to racial and genetic variability in human morphology. They may thus be erroneous to students and clinicians working on Nigerians. Objectives: The study aimed at establishing reference values of the kidney length, width and thickness for Nigerians using the cadaveric model. Methodology: The length, width, thickness and weight of sixty kidneys harvested from cadavers of thirty adult Nigerians (Male: Female; 27: 3) were measured. Respective volume was calculated using the ellipsoid formula. Results: The mean length of the kidney was 9.84±0.89 cm (9.63±0.88 {right}; 10.06±0.86 {left}), width- 5.18±0.70 cm (5.21±0.72 {right}; 5.14±0.70 {left}), thickness-3.45±0.56 cm (3.36±0.58 {right}, 3.53±0.55 {left}), weight-125.06±22.34 g (122.36±21.70 {right}; 127.76 ±24.02 {left}) and volume of 95.45± 24.40 cm3 (91.73± 26.84 {right}; 99.17± 25.75 {left}). Discussion: Though the values of the parameters measured were higher for the left kidney (except for the width), they were not statistically significant. The various parameters obtained by this study differ from those of similar studies from other continents. Conclusion: Stating single value for each of the parameter of length, width and thickness of the kidney as currently obtained in textbooks of anatomy may be incomplete information and hence misleading. Thus, there is the need to emphasize racial differences when stating the normal values of kidney dimensions in textbooks of anatomy. Implication for Research and Innovation: The results of the study showed the dimensions of the kidney (length, width and thickness) have interracial vagaries as they were different from those of similar studies and values stated in standard textbooks of human anatomy. Future direction: This is a preliminary report and the study will continue so that more data will be obtained.

Keywords: kidney dimensions, cadaveric estimation, adult nigerians, racial differences

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Authors: Xuefan Li, Donghua Li, Juanjuan Li

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This study explores the changes in policies toward Chinese students studying abroad in different countries during the pre-pandemic, pandemic, and post-pandemic periods. Interviews and questionnaire surveys were conducted with participating institutions at the China International Education Exhibition. The results indicate that institutions were impacted by the pandemic differently, with a gradual recovery in the two years following the initial outbreak. Institutions encourage and support Chinese students to resume offline studies during the post-pandemic period. The impact of the pandemic on the recruitment of Chinese students by international institutions varied, with different measures being adopted by different institutions. Compared with universities, colleges were more affected in terms of student employment rates. Some institutions were able to respond quickly and effectively to the pandemic due to their online teaching platforms. Overall, this study is expected to provide insights into the changes in policies toward Chinese students studying abroad during the pandemic and highlights the diverse responses of international institutions.

Keywords: international education, Chinese international education, COVID-19 pandemic, international institutions

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Authors: J. K. Alhassan, S. A. Lawal

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This work is on decision tree-based classification for the disbursement of scholarship. Tree-based data mining classification technique is used in other to determine the generic rule to be used to disburse the scholarship. The system based on the defined rules from the tree is able to determine the class (status) to which an applicant shall belong whether Granted or Not Granted. The applicants that fall to the class of granted denote a successful acquirement of scholarship while those in not granted class are unsuccessful in the scheme. An algorithm that can be used to classify the applicants based on the rules from tree-based classification was also developed. The tree-based classification is adopted because of its efficiency, effectiveness, and easy to comprehend features. The system was tested with the data of National Information Technology Development Agency (NITDA) Abuja, a Parastatal of Federal Ministry of Communication Technology that is mandated to develop and regulate information technology in Nigeria. The system was found working according to the specification. It is therefore recommended for all scholarship disbursement organizations.

Keywords: classification, data mining, decision tree, scholarship

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Authors: Reza Mohammadi, Mahmod R. Sahebi, Mehrnoosh Omati, Milad Vahidi

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Classification of high resolution polarimetric Synthetic Aperture Radar (PolSAR) images plays an important role in land cover and land use management. Recently, classification algorithms based on Bag of Visual Words (BOVW) model have attracted significant interest among scholars and researchers in and out of the field of remote sensing. In this paper, BOVW model with pixel based low-level features has been implemented to classify a subset of San Francisco bay PolSAR image, acquired by RADARSAR 2 in C-band. We have used segment-based decision-making strategy and compared the result with the result of traditional Support Vector Machine (SVM) classifier. 90.95% overall accuracy of the classification with the proposed algorithm has shown that the proposed algorithm is comparable with the state-of-the-art methods. In addition to increase in the classification accuracy, the proposed method has decreased undesirable speckle effect of SAR images.

Keywords: Bag of Visual Words (BOVW), classification, feature extraction, land cover management, Polarimetric Synthetic Aperture Radar (PolSAR)

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Authors: Wanhyun Cho, Soonja Kang, Sangkyoon Kim, Soonyoung Park

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In this paper, we propose a novel inference algorithm for the multi-class Gaussian process classification model that can be used in the field of human behavior recognition. This algorithm can drive simultaneously both a posterior distribution of a latent function and estimators of hyper-parameters in a Gaussian process classification model with multi-class. Our algorithm is based on the Laplace approximation (LA) technique and variational EM framework. This is performed in two steps: called expectation and maximization steps. First, in the expectation step, using the Bayesian formula and LA technique, we derive approximately the posterior distribution of the latent function indicating the possibility that each observation belongs to a certain class in the Gaussian process classification model. Second, in the maximization step, using a derived posterior distribution of latent function, we compute the maximum likelihood estimator for hyper-parameters of a covariance matrix necessary to define prior distribution for latent function. These two steps iteratively repeat until a convergence condition satisfies. Moreover, we apply the proposed algorithm with human action classification problem using a public database, namely, the KTH human action data set. Experimental results reveal that the proposed algorithm shows good performance on this data set.

Keywords: bayesian rule, gaussian process classification model with multiclass, gaussian process prior, human action classification, laplace approximation, variational EM algorithm

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Authors: Mary Panko, Rashika Sharma

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Sustainability often appears to be an unfamiliar concept to many international students that enrol in a New Zealand technological degree. Lecturers’ experiences with classroom interactions and evaluation of assessments indicate that studying the concept enlightens and enhances international students understanding of sustainability. However, in most cases, even after studying sustainability in their degree programme, students are not given an opportunity to practice and apply this concept into their professions in their home countries. Therefore, using a qualitative approach, the academics conducted research to determine the change in international students understanding of sustainability before and after their enrolment in an Applied Technology degree. The research also aimed to evaluate if international students viewed sustainability of relevance to their professions and whether the students felt that they will be provided with an opportunity to apply their knowledge about sustainability in the industry. The findings of the research are presented in this paper.

Keywords: education for sustainability, international students, vocational education

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Authors: Najoua El Hajjaji El Idrissi, Necip Gokhan Kasapoglu

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Polarimetric Synthetic Aperture Radar-based imaging is a powerful technique used for earth observation and classification of surfaces. Forest evolution has been one of the vital areas of attention for the remote sensing experts. The information about forest areas can be achieved by remote sensing, whether by using active radars or optical instruments. However, due to several weather constraints, such as cloud cover, limited information can be recovered using optical data and for that reason, Polarimetric Synthetic Aperture Radar (PolSAR) is used as a powerful tool for forestry inventory. In this [14paper, we applied support vector machine (SVM) and Mahalanobis distance to the fully polarimetric AIRSAR P, L, C-bands data from the Nezer forest areas, the classification is based in the separation of different tree ages. The classification results were evaluated and the results show that the SVM performs better than the Mahalanobis distance and SVM achieves approximately 75% accuracy. This result proves that SVM classification can be used as a useful method to evaluate fully polarimetric SAR data with sufficient value of accuracy.

Keywords: classification, synthetic aperture radar, SAR polarimetry, support vector machine, mahalanobis distance

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Authors: Hamid Reza Zeraatpisheh, Shamsoddin Hashemi, Farshad Negintaji

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This study has examined the effects of the architectural anatomy of opera hall on improving the quality of place identity. By measuring the effects of place identity on the inner aspects of human which are influenced by the physical and social environments it has investigated the results of a balance between internal and external environment. To assess the anatomical effects of urban landscape, two components of subjective landscape including perception and diversity and the component of objective landscape including form and order have been measured. The current survey is descriptive and the statistical population has been Shiraz which is a city in Iran. To analyze the data the SPSS software has been used. The results have been investigated in two levels of descriptive and inferential statistics. In the inferential statistics, Pearson correlation coefficient has been used to evaluate the research hypotheses. The results of this study indicate that between the dimensions of landscape, the component of the subjective landscape has the highest impact on the place identity and in the second place, an objective landscape has the impact on the place identity. Anatomical effects have an important role on improving the quality of place identity of Shiraz citizens and in order to enhance the place identity in the urban landscape it is also required that they will be inspired and operated.

Keywords: architectural anatomy, identity, place identity, urban landscape, perception

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Authors: Ola Elfmark, Lars M. Bardal, Luca Oggiano, H˚avard Myklebust

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Team pursuit is a relatively new event in international long track speed skating. For a single speed skater the aerodynamic drag will account for up to 80% of the braking force, thus reducing the drag can greatly improve the performance. In a team pursuit the interactions between athletes in near proximity will also be essential, but is not well studied. In this study, systematic measurements of the aerodynamic drag, body posture and relative positioning of speed skaters have been performed in the low speed wind tunnel at the Norwegian University of Science and Technology, in order to investigate the aerodynamic interaction between two speed skaters. Drag measurements of static speed skaters drafting, leading, side-by-side, and dynamic drag measurements in a synchronized and unsynchronized movement at different distances, were performed. The projected frontal area was measured for all postures and movements and a blockage correction was performed, as the blockage ratio ranged from 5-15% in the different setups. The static drag measurements where performed on two test subjects in two different postures, a low posture and a high posture, and two different distances between the test subjects 1.5T and 3T where T being the length of the torso (T=0.63m). A drag reduction was observed for all distances and configurations, from 39% to 11.4%, for the drafting test subject. The drag of the leading test subject was only influenced at -1.5T, with the biggest drag reduction of 5.6%. An increase in drag was seen for all side-by-side measurements, the biggest increase was observed to be 25.7%, at the closest distance between the test subjects, and the lowest at 2.7% with ∼ 0.7 m between the test subjects. A clear aerodynamic interaction between the test subjects and their postures was observed for most measurements during static measurements, with results corresponding well to recent studies. For the dynamic measurements, the leading test subject had a drag reduction of 3% even at -3T. The drafting showed a drag reduction of 15% when being in a synchronized (sync) motion with the leading test subject at 4.5T. The maximal drag reduction for both the leading and the drafting test subject were observed when being as close as possible in sync, with a drag reduction of 8.5% and 25.7% respectively. This study emphasize the importance of keeping a synchronized movement by showing that the maximal gain for the leading and drafting dropped to 3.2% and 3.3% respectively when the skaters are in opposite phase. Individual differences in technique also appear to influence the drag of the other test subject.

Keywords: aerodynamic interaction, drag force, frontal area, speed skating

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Authors: Shaik Ayesha Fathima, Shaik Noor Jahan, Duvvada Rajeswara Rao

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Earth's environment and its evolution can be seen through satellite images in near real-time. Through satellite imagery, remote sensing data provide crucial information that can be used for a variety of applications, including image fusion, change detection, land cover classification, agriculture, mining, disaster mitigation, and monitoring climate change. The objective of this project is to propose a method for classifying satellite images according to multiple predefined land cover classes. The proposed approach involves collecting data in image format. The data is then pre-processed using data pre-processing techniques. The processed data is fed into the proposed algorithm and the obtained result is analyzed. Some of the algorithms used in satellite imagery classification are U-Net, Random Forest, Deep Labv3, CNN, ANN, Resnet etc. In this project, we are using the DeepLabv3 (Atrous convolution) algorithm for land cover classification. The dataset used is the deep globe land cover classification dataset. DeepLabv3 is a semantic segmentation system that uses atrous convolution to capture multi-scale context by adopting multiple atrous rates in cascade or in parallel to determine the scale of segments.

Keywords: area calculation, atrous convolution, deep globe land cover classification, deepLabv3, land cover classification, resnet 50

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Authors: Michelle Sánchez de León Brajkovich, Nuria Martí Audi

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The envelope is one of the most important elements when one analyzes the operation of the building in terms of sustainability. Taking this into consideration, this research focuses on setting a classification system of the envelopes opaque systems, crossing the knowledge and parameters of construction systems with requirements in terms of sustainability that they may have, to have a better understanding of how these systems work with respect to their sustainable contribution to the building. Therefore, this paper evaluates the importance of the envelope design on the building sustainability. It analyses the parameters that make the construction systems behave differently in terms of sustainability. At the same time it explains the classification process generated from this analysis that results in a classification where all opaque vertical envelope construction systems enter.

Keywords: sustainable, exterior walls, envelope, facades, construction systems, energy efficiency

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Authors: Bandhan Dey, Muhsina Bintoon Yiasha, Gulam Sulaman Choudhury

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Chest disease is one of the most problematic ailments in our regular life. There are many known chest diseases out there. Diagnosing them correctly plays a vital role in the process of treatment. There are many methods available explicitly developed for different chest diseases. But the most common approach for diagnosing these diseases is through X-ray. In this paper, we proposed a multi-classification deep learning model for diagnosing COVID-19, lung cancer, pneumonia, tuberculosis, and atelectasis from chest X-rays. In the present work, we used the transfer learning method for better accuracy and fast training phase. The performance of three architectures is considered: InceptionV3, VGG-16, and VGG-19. We evaluated these deep learning architectures using public digital chest x-ray datasets with six classes (i.e., COVID-19, lung cancer, pneumonia, tuberculosis, atelectasis, and normal). The experiments are conducted on six-classification, and we found that VGG16 outperforms other proposed models with an accuracy of 95%.

Keywords: deep learning, image classification, X-ray images, Tensorflow, Keras, chest diseases, convolutional neural networks, multi-classification

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Authors: Djehich Mohamed Yousri

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Nowadays, migration has become a phenomenon that attracts the attention of researchers, countries, agencies, and national and international bodies. Wars and climate change, demographics, poverty, natural disasters, and epidemics are all threats that are contributing daily to forcing more people to migrate. There are those who resort to emigration because of the deteriorating political conditions in their country, others resort to emigration to improve their financial situation, and others emigrate from their country for fear of some penalties and judgments issued against them. In the field of migration, becoming a member of the United Nations as a "relevant organization" gives the United Nations a clear mandate on migration. Its primary goal is to facilitate the management of international migration in an orderly and humane manner. In order to achieve this goal, the organization adopts an international policy to meet the challenges posed in the field of migration. This paper attempts to study the structure of this international organization and its strategy in dealing with the phenomenon of international migration.

Keywords: international organization for migration, immigrants, immigrant rights, resettlement, migration organization strategy

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Authors: Zakiullah Kalwar, Shabeer Abbassi

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This research encompasses the study of structural styles and evaluation of the hydrocarbon potential of Kotrum and Drigri anticlines located in Rajanpur Area, Midddle Indus Basin of Pakistan with the approach of geophysical data integration. The study area is situated between the Sulaiman Foldbelt on the west and Indus River in the east. It is an anticlinal fold, located to the southeast of Sakhi Sarwar anticline and separated from a prominent syncline. The structure has a narrow elongated crest, with the axis running in SSW-NNE direction. In the east, the structure is bounded by a gentle syncline. Structural Styles are trending East-West and perpendicular to tectonic transport and stress direction and the base of the structures gradually dipping Eastward beneath the deformation frontal part in Eastern Sulaiman Fold Belt. Middle Indus Basin can be divided into Foreland, Sulaiman fold belt and a broad foredeep. Sulaiman represents a blind thrust front, which suggests that all frontal folds of the fold belt are cored by blind thrust. The deformation of frontal part of Sulaiman Lobe represents the passive roof duplex stacked beneath the frontal passive roof thrust. The passive roof thrust, which has a back thrust sense of motion and extends into the interior of Fold belt. Left lateral Kingri Fault separates Eastern and Central Sulaiman fold belt. In Central Sulaiman fold belt the deformation front moved further towards fore deep as compared to Eastern Sulaiman. Two wells (Kotrum-01, Drigri-01) have been drilled in the study area with the objective to determine the potential of oil and gas in Habib Rahi Limestone of Eocene age, Dunghan Limestone of Paleocene age and Pab Sandstone of cretaceous age and role of structural styles in hydrocarbon potential of study area. Kotrum-01 well was drilled to its T.D of 4798m. Besides fishing and side tracking, tight whole conditions, high pressure, and losses of circulation were also encountered. During production, testing Pab sandstone were tested but abandoned found. Drigri-01 well was drilled to its T.D 3250 m. RFT was carried out at different points, but all points showed no pressure / seal failure and the well was plugged and declared abandoned.

Keywords: hydrocarbon potential, structural style, reserve calculation, enhance production

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Authors: Bharatendra Rai

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The sequence of words in text data has long-term dependencies and is known to suffer from vanishing gradient problems when developing deep learning models. Although recurrent networks such as long short-term memory networks help to overcome this problem, achieving high text classification performance is a challenging problem. Convolutional recurrent networks that combine the advantages of long short-term memory networks and convolutional neural networks can be useful for text classification performance improvements. However, arriving at suitable hyperparameter values for convolutional recurrent networks is still a challenging task where fitting a model requires significant computing resources. This paper illustrates the advantages of using convolutional recurrent networks for text classification with the help of statistically planned computer experiments for hyperparameter tuning.

Keywords: long short-term memory networks, convolutional recurrent networks, text classification, hyperparameter tuning, Tukey honest significant differences

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Authors: Raveendranath Veeramani, Parkash Chand, H. Y. Suma, A. Umamageswari

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Background and Aims: The aim of this study was to evaluate students’ perception of Integrated Learning Program[ILP]. Settings and Design: A questionnaire was used to survey and evaluate the perceptions of 1styear MBBS students at the Department of Anatomy at our medical college in India. Materials and Methods: The first MBBS Students of Anatomy were involved in the ILP on the Liver and extra hepatic biliary apparatus integrating the Departments of Anatomy, Biochemistry and Hepato-biliary Surgery. The evaluation of the ILP was done by two sets of short questionnaire that had ten items using the Likert five-point grading scale. The data involved both the students’ responses and their grading. Results: A majority of students felt that the ILP was better in as compared to the traditional lecture method of teaching.The integrated teaching method was better at fulfilling learning objectives (128 students, 83%), enabled better understanding (students, 94%), were more interesting (140 students, 90%), ensured that they could score better in exams (115 students, 77%) and involved greater interaction (100 students, 66%), as compared to traditional teaching methods. Most of the students (142 students, 95%) opined that more such sessions should be organized in the future. Conclusions: Responses from students show that the integrated learning session should be incorporated even at first phase of MBBS for selected topics so as to create interest in the medical sciences at the entry level and to make them understand the importance of basic science.

Keywords: integrated learning, students response, vertical integration, horizontal integration

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Authors: K. Pankaj, R. K. Chaudhary, B. P. Mishra, S. Kochar

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Introduction: Consultation-Liaison psychiatry is a branch of psychiatry that includes clinical service, teaching and research. A consultation-liaison psychiatrist plays a role in having an expert opinion and linking the patients to other medical professionals and the patient’s bio-psycho-social aspects that may be leading to his/her symptoms. Consultation-Liaison psychiatry has been recognised as 'The guardian of the holistic approach to the patient', underlining its pre-eminent role in the management of patients who are admitted in a tertiary care hospital. Aims/ Objectives: The aim of the study was to analyse the utilization of psychiatric services and reasons for referrals in a tertiary care hospital. Materials and Methods: The study was done in a tertiary care hospital. The study included all the cases referred from different Inpatient wards to the psychiatry department for consultation. The study was conducted on 300 patients over a 3 month period. International classification of diseases 10 was used to diagnose the referred cases. Results: The majority of the referral was from the Medical Intensive care unit (22%) followed by general medical wards (18.66%). Majority of the referral was taken for altered sensorium (24.66%), followed by low mood or unexplained medical symptoms (21%). Majority of the referrals had a diagnosis of alcohol withdrawal syndrome (21%) as per International classification of diseases criteria, followed by unipolar Depression and Anxiety disorder (~ 14%), followed by Schizophrenia (5%) and Polysubstance abuse (2.6%). Conclusions: Our study concludes the importance of utilization of consultation-liaison psychiatric services. Also, the study signifies the need for sensitization of our colleagues regarding psychiatric sign and symptoms from time to time and seek psychiatric consult timely to decrease morbidity.

Keywords: consultation-liaison, psychiatry, referral, tertiary care hospital

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Authors: Taikchan Lildar, Ayesha Samad, Suraj Sookhu

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Atrial fibrillation with rapid ventricular response results in the loss of atrial kick and shortened ventricular filling time, which often leads to decompensated heart failure. Pharmacologic rhythm control is the treatment of choice, and patients frequently benefit from the restoration of sinus rhythm. When pharmacologic treatment is unsuccessful or a patient declines hemodynamically, direct cardioversion is the treatment of choice. Torsades de pointes or “twisting of the points'' in French, is a rare but under-appreciated risk of cardioversion therapy and accounts for a significant number of sudden cardiac death each year. A 61-year-old female with no significant past medical history presented to the Emergency Department with worsening dyspnea. An electrocardiogram showed atrial fibrillation with rapid ventricular response, and a chest X-ray was significant for bilateral pulmonary vascular congestion. Full-dose anticoagulation and diuresis were initiated with moderate improvement in symptoms. A transthoracic echocardiogram revealed biventricular systolic dysfunction with a left ventricular ejection fraction of 30%. After consultation with an electrophysiologist, the consensus was to proceed with the restoration of sinus rhythm, which would likely improve the patient’s heart failure symptoms and possibly the ejection fraction. A transesophageal echocardiogram was negative for left atrial appendage thrombus; the patient was treated with a loading dose of amiodarone and underwent successful direct current cardioversion with 200 Joules. The patient was placed on telemetry monitoring for 24 hours and was noted to have frequent premature ventricular contractions with subsequent degeneration to torsades de pointes. The patient was found unresponsive and pulseless; cardiopulmonary resuscitation was initiated with cardioversion, and return of spontaneous circulation was achieved after four minutes to normal sinus rhythm. Post-cardiac arrest electrocardiogram showed sinus bradycardia with heart-rate corrected QT interval of 592 milliseconds. The patient continued to have frequent premature ventricular contractions and required two additional cardioversions to achieve a return of spontaneous circulation with intravenous magnesium and lidocaine. An automatic implantable cardioverter-defibrillator was subsequently implanted for secondary prevention of sudden cardiac death. The backup pacing rate of the automatic implantable cardioverter-defibrillator was set higher than usual in an attempt to prevent premature ventricular contractions-induced torsades de pointes. The patient did not have any further ventricular arrhythmias after implantation of the automatic implantable cardioverter-defibrillator. Overdrive pacing is a method utilized to treat premature ventricular contractions-induced torsades de pointes by preventing a patient’s susceptibility to R on T-wave-induced ventricular arrhythmias. Pacing at a rate of 90 beats per minute succeeded in controlling the arrhythmia without the need for traumatic cardiac defibrillation. In our patient, conversion of atrial fibrillation with rapid ventricular response to normal sinus rhythm resulted in a slower heart rate and an increased probability of premature ventricular contraction occurring on the T-wave and ensuing ventricular arrhythmia. This case highlights direct current cardioversion for atrial fibrillation with rapid ventricular response resulting in persistent ventricular arrhythmia requiring an automatic implantable cardioverter-defibrillator placement with overdrive pacing to prevent a recurrence.

Keywords: refractory atrial fibrillation, atrial fibrillation, overdrive pacing, torsades de pointes

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Authors: K. E. Ch. Vidyasagar, M. Moghavvemi, T. S. S. T. Prabhat

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Epilepsy is a global problem, and with seizures eluding even the smartest of diagnoses a requirement for automatic detection of the same using electroencephalogram (EEG) would have a huge impact in diagnosis of the disorder. Among a multitude of methods for automatic epilepsy detection, one should find the best method out, based on accuracy, for classification. This paper reasons out, and rationalizes, the best methods for classification. Accuracy is based on the classifier, and thus this paper discusses classifiers like quadratic discriminant analysis (QDA), classification and regression tree (CART), support vector machine (SVM), naive Bayes classifier (NBC), linear discriminant analysis (LDA), K-nearest neighbor (KNN) and artificial neural networks (ANN). Results show that ANN is the most accurate of all the above stated classifiers with 97.7% accuracy, 97.25% specificity and 98.28% sensitivity in its merit. This is followed closely by SVM with 1% variation in result. These results would certainly help researchers choose the best classifier for detection of epilepsy.

Keywords: classification, seizure, KNN, SVM, LDA, ANN, epilepsy

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Authors: Xiaoli Zhang, Xiongfei Li, Yuncong Feng

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ROC curves, as a widely used evaluating tool in machine learning field, are the tradeoff of true positive rate and negative rate. However, they are blamed for ignoring some vital information in the evaluation process, such as the amount of information about the target that each instance carries, predicted score given by each classification model to each instance. Hence, in this paper, a new classification performance method is proposed by extending the Receiver Operator Characteristic (ROC) curves to 3D space, which is denoted as 3D ROC Histogram. In the histogram, the

Keywords: classification, performance evaluation, receiver operating characteristic histogram, hardness prediction

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Authors: Boukari Nassim

Abstract:

This paper describes the use of odd pair autoregressive coefficients (Yule _Walker and Burg) for the feature extraction of electroencephalogram (EEG) signals. In the classification: the radial basis function neural network neural network (RBFNN) is employed. The RBFNN is described by his architecture and his characteristics: as the RBF is defined by the spread which is modified for improving the results of the classification. Five types of EEG signals are defined for this work: Set A, Set B for normal signals, Set C, Set D for interictal signals, set E for ictal signal (we can found that in Bonn university). In outputs, two classes are given (AC, AD, AE, BC, BD, BE, CE, DE), the best accuracy is calculated at 99% for the combined odd pair autoregressive coefficients. Our method is very effective for the diagnosis of epileptic EEG signals.

Keywords: epilepsy, EEG signals classification, combined odd pair autoregressive coefficients, radial basis function neural network

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Authors: Sundip Charmode, Simmi Mehra, Sudhir Kushwaha, Shalom Philip, Pratik Amrutiya, Ranjna Jangal

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Introduction: Quadrangular space syndrome involves compression of the axillary nerve and posterior circumflex humeral artery and its management in few cases, requires surgical decompression. The current study reviews the surgical approaches used in the decompression of neurovascular structures and presents our reflections and recommendations. Methods: Four human cadavers, in the Department of Anatomy were used for dissection of the Axillae and the Scapular region by the senior residents of the Department of Anatomy and Department of Orthopedics, who dissected quadrangular space in the eight upper limbs, using anterior and posterior surgical approaches. Observations: Posterior approach to identify the quadrangular space and secure its contents was recognized as the easier and much quicker method by both the Anatomy and Orthopedic residents, but it may result in increased postoperative morbidity. Whereas the anterior (Delto-pectoral) approach involves more skill but reduces postoperative morbidity. Conclusions: Anterior (Delto-pectoral) approach with suggested modifications can prove as an effective method in surgical decompression of quadrangular space syndrome. The authors suggest more cadaveric studies to facilitate anatomists and surgeons with the opportunities to practice and evaluate older and newer surgical approaches.

Keywords: surgical approach, anatomical approach, decompression, axillary nerve, quadrangular space

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Authors: Vahid Mehrabi, Iman Sharifi, H. A. Talebi

Abstract:

The automation of rehabilitation procedure by the implementation of robotic devices can overcome the limitation in conventional physiotherapy methods by increasing training sessions and duration of process. In this paper, the design of a simple rehabilitation robot for forearm-wrist therapy in stroke and spinal cord injuries is presented. Wrist’s biological joint motion is modeled by a gimbal-like mechanism which resembles the human arm anatomy. Presented device is an exoskeleton robot with rotation axes corresponding to human skeleton anatomy. The mechanical structure, actuator and sensor selection, system kinematics and comparison between our device range of motion and required active daily life values is illustrated.

Keywords: rehabilitation, robotic devices, physiotherapy, forearm-wrist

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Authors: Raluca Popescu, Diana Costinel, Elisabeta-Irina Geana, Oana-Romina Botoran, Roxana-Elena Ionete, Yazan Falah Jadee 'Alabedallat, Mihai Botu

Abstract:

Walnut production is high in Romania, different varieties being cultivated dependent on high yield, disease resistance or quality of produce. Walnuts have a highly nutritional composition, the kernels containing essential fatty acids, where the unsaturated fraction is higher than in other types of nuts, quinones, tannins, minerals. Walnut consumption can lower the cholesterol, improve the arterial function and reduce inflammation. The purpose of this study is to determine and compare the composition of walnuts of indigenous and international varieties all grown in Romania, in order to identify high-quality indigenous varieties. Oil has been extracted from the nuts of 34 varieties, the fatty acids composition and IV (iodine value) being afterwards measured by NMR. Furthermore, δ13C of the extracted oil had been measured by IRMS to find specific isotopic fingerprints that can be used in authenticating the varieties. Chemometrics had been applied to the data in order to identify similarities and differences between the varieties. The total saturated fatty acids content (SFA) varied between n.d. and 23% molar, oleic acid between 17 and 35%, linoleic acid between 38 and 59%, linolenic acid between 8 and 14%, corresponding to iodine values (IV - total amount of unsaturation) ranging from 100 to 135. The varieties separated in four groups according to the fatty acids composition, each group containing an international variety, making possible the classification of the indigenous ones. At both ends of the unsaturation spectrum, international varieties had been found.

Keywords: δ13C-IRMS, fatty acids composition, 1H-NMR, walnut varieties

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Authors: Da Eun Sung

Abstract:

In today’s world, cyber security has become an important international agenda. As the information age has arrived, the need for cyber defense against cyber attacks is mounting, and the significance of cyber cooperation in the international community is drawing attention. Through the course, international society has agreed that the institutionalization of international norms dealing with cyber space and cyber security is crucial ever. Nevertheless, the West, led by the United States of America, and 'the East', composed of Russia and China, have shown conflicting views on forming international norms and principles which would regulate and ward off the possible threats in cyber space. Thus, the international community hasn’t yet to reach an agreement on cyber security. In other words, the difference between both sides on the approach and understanding of principles, objects, and the definition has rendered such. Firstly, this dissertation will cover the Russia’s perception, strategy, and definition on cyber security through analyzing primary source. Then, it will delve into the two contrasting cyber security strategy between Russia and the US by comparing them. And in the conclusion, it will seek the possible solution for the cooperation in the field of cyber security. It is quite worthwhile to look into Russia’s views, which is the main counterpart to the US in this field, especially when the efforts to institutionalize cyber security by the US-led international community have met with their boundaries, and when the legitimacy of them have been challenged.

Keywords: cyber security, cyber security strategic, international relation in cyberspace, Russia

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