Search results for: international ovarian tumor analysis classification

Authors: Amil Suleimanov, Aigul Saduakassova, Denis Vinnikov

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Objective: To assess functional visceral fat (VAT) activity evaluated by ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) as a predictor of metastases in colorectal cancer (CRC). Materials and methods: We assessed 60 patients with histologically confirmed CRC who underwent 18F-FDG PET/CT after a surgical treatment and courses of chemotherapy. Age, histology, stage, and tumor grade were recorded. Functional VAT activity was measured by maximum standardized uptake value (SUVmax) using ¹⁸F-FDG PET/CT and tested as a predictor of later metastases in eight abdominal locations (RE – Epigastric Region, RLH – Left Hypochondriac Region, RRL – Right Lumbar Region, RU – Umbilical Region, RLL – Left Lumbar Region, RRI – Right Inguinal Region, RP – Hypogastric (Pubic) Region, RLI – Left Inguinal Region) and pelvic cavity (P) in the adjusted regression models. We also report the best areas under the curve (AUC) for SUVmax with the corresponding sensitivity (Se) and specificity (Sp). Results: In both adjusted for age regression models and ROC analysis, 18F-FDG accumulation in RLH (cutoff SUVmax 0.74; Se 75%; Sp 61%; AUC 0.668; p = 0.049), RU (cutoff SUVmax 0.78; Se 69%; Sp 61%; AUC 0.679; p = 0.035), RRL (cutoff SUVmax 1.05; Se 69%; Sp 77%; AUC 0.682; p = 0.032) and RRI (cutoff SUVmax 0.85; Se 63%; Sp 61%; AUC 0.672; p = 0.043) could predict later metastases in CRC patients, as opposed to age, sex, primary tumor location, tumor grade and histology. Conclusions: VAT SUVmax is significantly associated with later metastases in CRC patients and can be used as their predictor.

Keywords: ¹⁸F-FDG, PET/CT, colorectal cancer, predictive value

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Authors: Abdulaziz Alsadhan, Naveed Khan

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In recent years intrusions on computer network are the major security threat. Hence, it is important to impede such intrusions. The hindrance of such intrusions entirely relies on its detection, which is primary concern of any security tool like Intrusion Detection System (IDS). Therefore, it is imperative to accurately detect network attack. Numerous intrusion detection techniques are available but the main issue is their performance. The performance of IDS can be improved by increasing the accurate detection rate and reducing false positive. The existing intrusion detection techniques have the limitation of usage of raw data set for classification. The classifier may get jumble due to redundancy, which results incorrect classification. To minimize this problem, Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Local Binary Pattern (LBP) can be applied to transform raw features into principle features space and select the features based on their sensitivity. Eigen values can be used to determine the sensitivity. To further classify, the selected features greedy search, back elimination, and Particle Swarm Optimization (PSO) can be used to obtain a subset of features with optimal sensitivity and highest discriminatory power. These optimal feature subset used to perform classification. For classification purpose, Support Vector Machine (SVM) and Multilayer Perceptron (MLP) used due to its proven ability in classification. The Knowledge Discovery and Data mining (KDD’99) cup dataset was considered as a benchmark for evaluating security detection mechanisms. The proposed approach can provide an optimal intrusion detection mechanism that outperforms the existing approaches and has the capability to minimize the number of features and maximize the detection rates.

Keywords: Particle Swarm Optimization (PSO), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), Local Binary Pattern (LBP), Support Vector Machine (SVM), Multilayer Perceptron (MLP)

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Authors: Yung-Chung Chuang

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The cultural layers of archaeological sites are mainly affected by surface land use, land cover, and root system of surface vegetation. For this reason, continuous monitoring of land use and land cover change is important for archaeological sites protection and management. However, in actual operation, on-site investigation and orthogonal photograph interpretation require a lot of time and manpower. For this reason, it is necessary to perform a good alternative for surface vegetation survey in an automated or semi-automated manner. In this study, we applied semi-automatic digital aerial survey technology and canopy characters classification with very high-resolution aerial photographs for surface vegetation interpretation of archaeological sites. The main idea is based on different landscape or forest type can easily be distinguished with canopy characters (e.g., specific texture distribution, shadow effects and gap characters) extracted by semi-automatic image classification. A novel methodology to classify the shape of canopy characters using landscape indices and multivariate statistics was also proposed. Non-hierarchical cluster analysis was used to assess the optimal number of canopy character clusters and canonical discriminant analysis was used to generate the discriminant functions for canopy character classification (seven categories). Therefore, people could easily predict the forest type and vegetation land cover by corresponding to the specific canopy character category. The results showed that the semi-automatic classification could effectively extract the canopy characters of forest and vegetation land cover. As for forest type and vegetation type prediction, the average prediction accuracy reached 80.3%~91.7% with different sizes of test frame. It represented this technology is useful for archaeological site survey, and can improve the classification efficiency and data update rate.

Keywords: digital aerial survey, canopy characters classification, archaeological sites, multivariate statistics

Procedia PDF Downloads 127

Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 154

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

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Authors: Hala Alshamlan, Ghada Badr, Yousef Alohali

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Cancer is one of the dreadful diseases, which causes considerable death rate in humans. DNA microarray-based gene expression profiling has been emerged as an efficient technique for cancer classification, as well as for diagnosis, prognosis, and treatment purposes. In recent years, a DNA microarray technique has gained more attraction in both scientific and in industrial fields. It is important to determine the informative genes that cause cancer to improve early cancer diagnosis and to give effective chemotherapy treatment. In order to gain deep insight into the cancer classification problem, it is necessary to take a closer look at the proposed gene selection methods. We believe that they should be an integral preprocessing step for cancer classification. Furthermore, finding an accurate gene selection method is a very significant issue in a cancer classification area because it reduces the dimensionality of microarray dataset and selects informative genes. In this paper, we classify and review the state-of-art gene selection methods. We proceed by evaluating the performance of each gene selection approach based on their classification accuracy and number of informative genes. In our evaluation, we will use four benchmark microarray datasets for the cancer diagnosis (leukemia, colon, lung, and prostate). In addition, we compare the performance of gene selection method to investigate the effective gene selection method that has the ability to identify a small set of marker genes, and ensure high cancer classification accuracy. To the best of our knowledge, this is the first attempt to compare gene selection approaches for cancer classification using microarray gene expression profile.

Keywords: gene selection, feature selection, cancer classification, microarray, gene expression profile

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Authors: V. R. Ahire, A. Kumar, B. N. Pandey, K. P. Mishra, G. R. Kulkarni

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Radiation therapy is an effective vital strategy used globally in the treatment of cervical cancer. However, radiation efficacy principally depends on the radiosensitivity of the tumor, and not all patient exhibit significant response to irradiation. A radiosensitive tumor is easier to cure than a radioresistant tumor which later advances to local recurrence and metastasis. Herbal polyphenols are gaining attention for exhibiting radiosensitization through various signaling. Current work focuses to study the radiosensitization effect of ellagic acid (EA), on HeLa cells. EA intermediated radiosensitization of HeLa cells was due to the induction γ-H2AX foci formation, G1 phase cell cycle arrest, and loss of reproductive potential, growth inhibition, drop in the mitochondrial membrane potential and protein expression studies that eventually induced apoptosis. Irradiation of HeLa in presence of EA (10 μM) to doses of 2 and 4 Gy γ-radiation produced marked tumor cytotoxicity. EA also demonstrated radio-protective effect on normal cell, NIH3T3 and aided recovery from the radiation damage. Our results advocate EA to be an effective adjuvant for improving cancer radiotherapy as it displays striking tumor cytotoxicity and reduced normal cell damage instigated by irradiation.

Keywords: apoptotic radiosensitivity, ellagic acid, mitochondrial potential, cell-cycle arrest

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Authors: Qiang Ren

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A growing number of states are improving domestic law to better protect and promote foreign investment by changing/upgrading the existing law. However, inconsistency occurs because the new law is different from the ‘old’ law. For example, China has issued an unprecedented Foreign Investment Law and several regulations allowing comprehensive market access for foreign investment in most energy sectors since 2020. However, some laws, rules, regulations, etc. enacted previously remain valid, and the provisions regulating foreign investment do not grant full market access to foreign investment as such. The inconsistency above makes it necessary to investigatehow the international investment treaty law and dispute settlement practice respond to the ‘inconsistency and conflict’ in municipal law andwhat remedy foreign investors can seek under international law if the investment is denied due to inconsistency. Ultimately, it aims to examine how international tribunals should balance the gradually developing legal system of host states and the protection of foreign investors and investments if the host states cannot provide consistency during such a transition period of law development. The research seeks to answer these questions by making a comparative analysis of domestic law on market access to foreign investment, international investment treaties, and dispute arbitral practice. The objective is to examine how international investment treaty law and international investment dispute settlement practice evaluate the conflicts in the municipal law of host states in the admission of foreign investment. It also explores the possibility of harmonisation among them.

Keywords: municipal law, protect and promote foreign investment, international law, host states

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Authors: Agung Rizki Perdana, Asrofi Mursalin, Adniwan Shubhi Banuzaki, M. Indra Novian

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The understanding about sediment-derived plastiglomerate has a wide-range of merit in the academic realm. It can cover discussions about the Anthropocene Epoch in the scope of geoscience knowledge to even provide a solution for the environmental problem of plastic waste. Albeit its importance, very few research has been done regarding this issue. This research aims to create a classification as a pioneer for the study of sediment-derived plastiglomerate. This research was done in Bantul Regency, Daerah Istimewa Yogyakarta Province as an analogue of plastic debris sedimentation process. Observation is carried out in five observation points that shows three different depositional environments, which are terrestrial, fluvial, and transitional environment. The resulting classification uses three parameters and forms in a taxonomical manner. These parameters are composition, degree of lithification, and abundance of matrix respectively in advancing order. There is also a compositional ternary diagram which should be followed before entering the plastiglomerate nomenclature classification.

Keywords: plastiglomerate, classification, sedimentary mechanism, microplastic

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Authors: Ravi Vishwanath, Saumya Kumaar, S. N. Omkar

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Brain-computer interfacing (BCI) is a technology that is almost four decades old, and it was developed solely for the purpose of developing and enhancing the impact of neuroprosthetics. However, in the recent times, with the commercialization of non-invasive electroencephalogram (EEG) headsets, the technology has seen a wide variety of applications like home automation, wheelchair control, vehicle steering, etc. One of the latest developed applications is the mind-controlled quadrotor unmanned aerial vehicle. These applications, however, do not require a very high-speed response and give satisfactory results when standard classification methods like Support Vector Machine (SVM) and Multi-Layer Perceptron (MLPC). Issues are faced when there is a requirement for high-speed control in the case of fixed-wing unmanned aerial vehicles where such methods are rendered unreliable due to the low speed of classification. Such an application requires the system to classify data at high speeds in order to retain the controllability of the vehicle. This paper proposes a novel method of classification which uses a combination of Common Spatial Paradigm and Linear Discriminant Analysis that provides an improved classification accuracy in real time. A non-linear SVM based classification technique has also been discussed. Further, this paper discusses the implementation of the proposed method on a fixed-wing and VTOL unmanned aerial vehicles.

Keywords: brain-computer interface, classification, machine learning, unmanned aerial vehicles

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Authors: Abraham J. Rizkalla, Tristan D. Yan

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Atrial myxoma is the most common primary cardiac tumor, and can result in cardiac failure secondary to obstruction, or systemic embolism due to fragmentation. Traditionally, excision of atrial an myxoma has been performed through median sternotomy, however the robotic approach offers several advantages including less pain, improved cosmesis, and faster recovery. Here, we highlight the less well recognized advantages and technical aspects to robotic myxoma resection. This video-presentation demonstrates the resection of a papillary subtype left atrial myxoma using the DaVinci© Xi surgical robot. The 10x magnification and 3D vision allows for the interface between the tumor and the interatrial septum to be accurately dissected, without the need to patch the interatrial septum. Several techniques to avoid tumor fragmentation and embolization are demonstrated throughout the procedure. The tumor was completely excised with clear margins. There was no atrial septal defect or mitral valve injury on post operative transesophageal echocardiography. The patient was discharged home on the fourth post-operative day. This video-presentation highlights the advantages of the robotic approach in atrial myxoma resection compared with sternotomy, as well as emphasizing several technical considerations to avoid potential complications.

Keywords: cardiac surgery, left atrial myxoma, cardiac tumour, robotic resection

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Authors: Rana Nasiri, Hamid Vahidkia

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Recently, most courts in democratic nations were hesitant to consider foreign and international law. Their approach was to steer clear of using foreign sources of law that conflicted with their own government's stance. Numerous legal experts consider turning to foreign and international law unsuitable. However, those who advocate for using external sources of law also believe that relying on foreign and international law will always be in conflict with the importance of national sovereignty. Therefore, the academic discussion revolves around the commonly known broader debate on ‘the counter-majoritarian difficulty’. This article challenges the idea of tension. It suggests that in many democratic nations' legal systems, including those in the U.S., using foreign and international law can help strengthen domestic democratic processes by protecting them from outside economic, political, and legal influences. Citing international law supports domestic democratic processes and regains national sovereignty from various globalization forces. In other words, national courts must consider foreign and international law to uphold their national political institutions and protect their own status in relation to political branches.

Keywords: international law, social science, US, democracy, politics

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Authors: Mohammad Saberian, Reza Porhoseini, Mohammad Ali Rahgozar

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Peat is a partially carbonized vegetable tissue which is formed in wet conditions by decomposition of various plants, mosses and animal remains. This restricted definition, including only materials which are entirely of vegetative origin, conflicts with several established soil classification systems. Peat soils are usually defined as soils having more than 75 percent organic matter. Due to this composition, the structure of peat soil is highly different from the mineral soils such as silt, clay and sand. Peat has high compressibility, high moisture content, low shear strength and low bearing capacity, so it is considered to be in the category of problematic. Since this kind of soil is generally found in many countries and various zones, except for desert and polar zones, recognizing this soil is inevitably significant. The objective of this paper is to review the classification of peats based on various properties of peat soils such as organic contents, water content, color, odor, and decomposition, scholars offer various classification systems which Von Post classification system is one of the most well-known and efficient system.

Keywords: peat soil, degree of decomposition, organic content, water content, Von Post classification

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Authors: Babak Masoomian, Masoud Khorrami Nejad, Hamid Riazi Esfahani

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Purpose: To report the result of strabismus surgery in eye-salvaged retinoblastoma (Rb) patients. Methods: A retrospective case series including 18 patients with Rb and strabismus who underwent strabismus surgery after completing tumor treatment by a single pediatric ophthalmologist. Results: A total of 18 patients (10 females and 8 males) were included with a mean age of 13.3 ± 3.0 (range, 2-39) months at the time tumor presentation and 6.0 ± 1.5 (range, 4-9) years at the time of strabismus surgery. Ten (56%) patients had unilateral, and 8(44%) had bilateral involvement, and the most common worse eye tumor’s group was D (n=11), C (n=4), B (n=2) and E (n=1). Macula was involved by the tumors in 12 (67%) patients. The tumors were managed by intravenous chemotherapy (n=8, 47%), intra-arterial chemotherapy (n=7, 41%) and both (n=3, 17%). After complete treatment, the average time to strabismus surgery was 29.9 ± 20.5 (range, 12-84) months. Except for one, visual acuity was equal or less than 1.0 logMAR (≤ 20/200) in the affected eye. Seven (39%) patients had exotropia, 11(61%) had esotropia (P=0.346) and vertical deviation was found in 8 (48%) cases. The angle of deviation was 42.0 ± 10.4 (range, 30-60) prism diopter (PD) for esotropic and 35.7± 7.9 (range, 25-50) PD for exotropic patients (P=0.32) that after surgery significantly decreased to 8.5 ± 5.3 PD in esotropic cases and 5.9±6.7 PD in exotropic cases (P<0.001). The mean follow-up after surgery was 15.2 ± 2.0 (range, 10-24) months, in which 3 (17%) patients needed a second surgery. Conclusion: Strabismus surgery in treated Rb is safe, and results of the surgeries are acceptable and close to the general population. There was not associated with tumor recurrence or metastasis.

Keywords: retinoblastoma, strabismus, chemotherapy, surgery

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: Aleksandra Zysk, Pawel Badura

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Recognizing and controlling vocal registers during singing is a difficult task for beginner vocalist. It requires among others identifying which part of natural resonators is being used when a sound propagates through the body. Thus, an application has been designed allowing for sound recording, automatic vocal register recognition (VRR), and a graphical user interface providing real-time visualization of the signal and recognition results. Six spectral features are determined for each time frame and passed to the support vector machine classifier yielding a binary decision on the head or chest register assignment of the segment. The classification training and testing data have been recorded by ten professional female singers (soprano, aged 19-29) performing sounds for both chest and head register. The classification accuracy exceeded 93% in each of various validation schemes. Apart from a hard two-class clustering, the support vector classifier returns also information on the distance between particular feature vector and the discrimination hyperplane in a feature space. Such an information reflects the level of certainty of the vocal register classification in a fuzzy way. Thus, the designed recognition and training application is able to assess and visualize the continuous trend in singing in a user-friendly graphical mode providing an easy way to control the vocal emission.

Keywords: classification, singing, spectral analysis, vocal emission, vocal register

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Authors: Han-Jeong Hwang, Jae-Hyun Jo, Fatemeh Alimardani

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An accurate diagnosis of psychiatric diseases is a challenging issue, in particular when distinct symptoms for different diseases are overlapped, such as delusions appeared in bipolar mood disorder (BMD) and schizophrenia (SCH). In the present study, we propose a useful way to discriminate BMD and SCH using electroencephalography (EEG). A total of thirty BMD and SCH patients (15 vs. 15) took part in our experiment. EEG signals were measured with nineteen electrodes attached on the scalp using the international 10-20 system, while they were exposed to a visual stimulus flickering at 16 Hz for 95 s. The flickering visual stimulus induces a certain brain signal, known as steady-state visual evoked potential (SSVEP), which is differently observed in patients with BMD and SCH, respectively, in terms of SSVEP amplitude because they process the same visual information in own unique way. For classifying BDM and SCH patients, machine learning technique was employed in which leave-one-out-cross validation was performed. The SSVEPs induced at the fundamental (16 Hz) and second harmonic (32 Hz) stimulation frequencies were extracted using fast Fourier transformation (FFT), and they were used as features. The most discriminative feature was selected using the Fisher score, and support vector machine (SVM) was used as a classifier. From the analysis, we could obtain a classification accuracy of 83.33 %, showing the feasibility of discriminating patients with BMD and SCH using EEG. We expect that our approach can be utilized for psychiatrists to more accurately diagnose the psychiatric disorders, BMD and SCH.

Keywords: bipolar mood disorder, electroencephalography, schizophrenia, machine learning

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

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In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

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Authors: Kajal Zarei

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Supply chain management is becoming an increasingly important issue in many businesses today. In such circumstances, a number of reasons such as management deficiency in different segments of the supply chain, lack of streamlined processes, resistance to change the current systems and technologies, and lack of advanced information system have paved the ground to ask for innovative research studies. To this end, information technology (IT) is becoming a major driver to overcome the supply chain limitations and deficiencies. The emergence of IT has provided an excellent opportunity for redefining the supply chain to be more effective and competitive. This paper has investigated the IT impact on two-digit industry codes in the International Standard Industrial Classification (ISIC) that are operating in four groups of the supply chains. Firstly, the primary fields of the supply chain were investigated, and then paired comparisons of different industry parts were accomplished. Using experts' ideas and Analytical Hierarchy Process (AHP), the status of industrial activities in Kurdistan Province in Iran was determined. The results revealed that manufacturing and inventory fields have been more important compared to other fields of the supply chain. In addition, IT has had greater impact on food and beverage industry, chemical industry, wood industry, wood products, and production of basic metals. The results indicated the need to IT awareness in supply chain management; in other words, IT applications needed to be developed for the identified industries.

Keywords: supply chain, information technology, analytical hierarchy process, two-digit codes, international standard industrial classification

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Authors: Taleb Lotfi, Benarbia Maheidine, Allam Hamza, Boutira Fatima, Boukerche Abdelbaki

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Introduction and purpose of the study: This is a retrospective single-center study with an analytical aim to determine the prognostic factors for relapse in patients treated with radiotherapy after total laryngectomy with lymph node dissection for laryngeal cancer at the Emir Abdelkader cancer center in Oran (Algeria). Material and methods: During the study period from January 2014 to December 2018, eighty-nine patients (n=89) with squamous cell carcinoma of the larynx were treated with postoperative radiotherapy. Relapse-free survival was studied in the univariate analysis according to pre-treatment criteria using Kaplan-Meier survival curves. We performed a univariate analysis to identify relapse factors. Statistically significant factors have been studied in the multifactorial analysis according to the Cox model. Results and statistical analysis: The average age was 62.7 years (40-86 years). It was a squamous cell carcinoma in all cases. Postoperatively, the tumor was classified as pT3 and pT4 in 93.3% of patients. Histological lymph node involvement was found in 36 cases (40.4%), with capsule rupture in 39% of cases, while the limits of surgical excision were microscopically infiltrated in 11 patients (12.3%). Chemotherapy concomitant with radiotherapy was used in 67.4% of patients. With a median follow-up of 57 months (23 to 104 months), the probabilities of relapse-free survival and five-year overall survival are 71.2% and 72.4%, respectively. The factors correlated with a high risk of relapse were locally advanced tumor stage pT4 (p=0.001), tumor site in case of subglottic extension (p=0.0003), infiltrated surgical limits R1 (p=0.001), l lymph node involvement (p=0.002), particularly in the event of lymph node capsular rupture (p=0.0003) as well as the time between surgery and adjuvant radiotherapy (p=0.001). However, in the subgroup analysis, the major prognostic factors for disease-free survival were subglottic tumor extension (p=0.001) and time from surgery to adjuvant radiotherapy (p=0.005). Conclusion: Combined surgery and postoperative radiation therapy are effective treatment modalities in the management of laryngeal cancer. Close cooperation of the entire cervicofacial oncology team is essential, expressed during a multidisciplinary consultation meeting, with the need to respect the time between surgery and radiotherapy.

Keywords: laryngeal cancer, laryngectomy, postoperative radiotherapy, survival

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Authors: Hakam Junus, Natassha Chrysanti

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The international relations theory currently is dominated by the western theoretical perspectives. Although the western theories are often used by many scholars as the universal perspective to explain the phenomena that occur in the world, sometimes the existing theories are failed to explain various issues that occur in the non-western world, for example, in the studies concerning on terrorism issues. Using inappropriate theories to explain the international issues such as terrorism will cause a failure in the decision-making process. The lack of understanding regarding Islamic perspective could be one of the factors that make international society unable to eradicate violent terrorism in the name of religion. Thus, this paper is argued that considering Islamic perspective as one of the major studies in international relations is significant to build a bridge between the Islamic world and the western world. It is believed that enhancing the study of Islamic perspective will create better understanding of the Islamic world and will enrich the study of international relations. This paper is conducted through a qualitative approach, in which data is obtained from the literature analysis. Considering Islamic perspective is important because Islam is listed as one of the major religions in the world. It is also due to the geopolitical spread of the Muslim in the world and the likelihood of the Islamic perspective to shape and influence Muslim’s behavior in the international level. The study of Islamic perspective in the international level is neither to contempt nor to oppose the existing western theories; rather it is needed in order to broaden the perspective in the international relations studies. The Islamic perspective is different compared to the non-western school of thought such as realism, and liberalism in some respects. The Islamic perspective cannot be explained through the lens of rationalist approaches. Compares to the post-positivism international relations perspectives, Islamic perspective is probably closer to the constructivist school of thought. However, the Islamic perspective offers some uniqueness that is not limited to the socially constructed ideas as in the constructivist arguments. This paper will be developed according to the discussion of three aspects that make Islamic perspective different with the existing international relations theories. The first aspect is the main actors in the international level. The second aspect is regarding on what appears to be the most important point for the actors in the international relations. The third aspect is regarding the pattern of relationship between the actors in the international level. In addition, this paper will briefly discuss the perspective of Islam in economics compare to the existing theories in the realm of international political economy.

Keywords: international relations, Islam, non-western theories, societies

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Authors: Kavita Bodke

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Structural health monitoring (SHM) is experiencing growth, necessitating the development of distinct methodologies to address its expanding scope effectively. In this study, we developed automatic structure damage identification, which incorporates three unique types of a building’s structural integrity. The first pertains to the presence of fractures within the structure, the second relates to the issue of dampness within the structure, and the third involves corrosion inside the structure. This study employs image classification techniques to discern between intact and impaired structures within structural data. The aim of this research is to find automatic damage detection with the probability of each damage class being present in one image. Based on this probability, we know which class has a higher probability or is more affected than the other classes. Utilizing photographs captured by a mobile camera serves as the input for an image classification system. Image classification was employed in our study to perform multi-class and multi-label classification. The objective was to categorize structural data based on the presence of cracks, moisture, and corrosion. In the context of multi-class image classification, our study employed three distinct methodologies: Random Forest, Multilayer Perceptron, and CNN. For the task of multi-label image classification, the models employed were Rasnet, Xceptionet, and Inception.

Keywords: SHM, CNN, deep learning, multi-class classification, multi-label classification

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Authors: Luciano Tarantino, Emanuele Balzano, Aurelio Nasto

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S.R. 53 years. January 2009: HCV-related cirrhosis, Child-Pugh A5 class, EGDS no aesophageal Varices. No important comorbidities. Treated with PEG-IFN+Ribavirin (march-november 2009) with subsequent sustained virologic response. HCVRNA absent overtime. October 2016 :CT detected small HCC nodule in the VIII segment (diam.=12 mm). Treated with US guided RF-ablation. November 2016 CT: complete necrosis. Unfortunately, the patient dropped out US and CT follow-up controls.September 2018: asthenia and weight loss. CT showed a large tumor infiltrating V-VII-VI segments and complete PVTT of right portal vein and its branches . Surgical Consultation excluded indication to Liver resection and OLT . 23 october 2018: ECT of a large peri-hilar area of the tumor including the PVTT. 1 and 3 months post-treatment CT showed complete necrosis and retraction of the thrombus and residual viable tumor in the peripheral portion of the right lobe . Therefor, the patient was reevaluated for OLT and considered eligible in waiting list . March 2019: CT showed no perihilar or portal vein recurrence and distant progression in the right lobe . March 2019 : Trans-arterial-Radio-therapy (TARE) of the right lobe. Post-treatment CT demonstrated no perihilar or portal vein recurrence and extensive necrosis of the residual tumor . December 2019: CT demonstrated several recurrences of HCC infiltrating the VI and VII segment . Howewer no recurrence was observed at hepatic hilum and in portal vessels . Therefore, on February 2020 the patient received OLT. At 44 months follow-up, no complication or recurrence or liver disfunction have been observed.

Keywords: hepatocellular carcinoma, portal vein tumor thrombosis, interventional ultrasound, liver tumor ablation, liver transplantation

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Authors: Nour Mohammad

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The concept of International Criminal Court is not a new idea.It goes back to the late 19th century and was first mooted in 1872 by Gustave Moynier of the International Commitee of the Red Cross(ICRC). This paper attempts to focus on jurisdictional problem of the international criminal court (ICC) over national of states of non parties to the Rome statute. Mor than 120 countries are state parties to the Rome Statute representing all regions, Afria, the Asia-pacofoc Eastern Europe, Latin America and the Caribben as well as Western Europe and North America.The Statute is the core document of internationa criminal law todaycontaining 128 Articles and divided in 13 parts.The Rome Statute provides that the court may sit elsewhere the judge consider it desirable.The International Criminal Court is not in a position to adjudicate all international crimes but its jurisdiction is limited to the four categories of crime viz. genocide, crimes against humanity, war crimes and crime of aggression as stipulated in Article 5 of the ICC Statute. It also mention here that the Court will be able to exercise its jurisdiction over the crime of aggression only when this crime is defined. Due to the highly political nature of this crime, it is unlikely that a consensus in this regard would be arrived at in the near future.The main point of this article is to discuss the mandate of international criminal court to prosecute and punish persons responsible for the henious crimes of concern to the international community.The author highlighted the principles which support the delegation of criminal jurisdiction by state to international tribunals and discuss the precedents of such delegation.It also argued that the exercise of ICC jurisdiction over acts done pursuant to the officially policy of non-party state would not be contrary to the principles requiring consent for the exercise of jurisdiction by international tribunals. The article explore the limit to jurisdiction of ICC over non-party nationals.

Keywords: jurisdiction, international, criminal, court, non-parties

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Authors: Yuan-Hsiang Chang, Pin-Chi Lin, Li-Der Jeng

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Advance in techniques of image and video processing has enabled the development of intelligent video surveillance systems. This study was aimed to automatically detect moving human objects and to analyze events of dual human interaction in a surveillance scene. Our system was developed in four major steps: image preprocessing, human object detection, human object tracking, and motion trajectory analysis. The adaptive background subtraction and image processing techniques were used to detect and track moving human objects. To solve the occlusion problem during the interaction, the Kalman filter was used to retain a complete trajectory for each human object. Finally, the motion trajectory analysis was developed to distinguish between the interaction and non-interaction events based on derivatives of trajectories related to the speed of the moving objects. Using a database of 60 video sequences, our system could achieve the classification accuracy of 80% in interaction events and 95% in non-interaction events, respectively. In summary, we have explored the idea to investigate a system for the automatic classification of events for interaction and non-interaction events using surveillance cameras. Ultimately, this system could be incorporated in an intelligent surveillance system for the detection and/or classification of abnormal or criminal events (e.g., theft, snatch, fighting, etc.).

Keywords: motion detection, motion tracking, trajectory analysis, video surveillance

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Authors: Md. Shahriar Sabuz

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Different social factors influence individuals to migrate from their native lands. This qualitative study was designed to analyze the main social factors that have a significant role in the movement of people across borders. In this study, two research questions, i.e., ‘Which social factors of a country significantly influence the persons' decision to migrate from their homeland?’ and ’2: do different social factors of a country influence the process of international migration?" were formulated and relevant data were analyzed to get the logical answer to these two questions. Data analysis revealed that people migrate in large numbers due to deplorable and unsafe social conditions in their home countries. Sometimes migration occurs due to a lack of basic facilities in native countries. It is quite significant to know that these social conditions create a sense of deprivation and insecurity in individuals, and they move to other lands to get a sense of achievement and greater security for themselves and their whole families. This study is significant and distinct from previous studies in that it provides comprehensive information about the major social factors responsible for international migrations and their role in influencing an individual's proclivity to migrate. Besides this, it greatly opens new horizons of research and analysis for other researchers working on the agenda of international migration.

Keywords: International migration, social factors, income inequality, social discrimination

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Authors: Valeria Panzetta, Ida Musella, Sabato Fusco, Paolo Antonio Netti

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The study of the biophysics of living cells drew attention to the pivotal role of the cytoskeleton in many cell functions, such as mechanics, adhesion, proliferation, migration, differentiation and neoplastic transformation. In particular, during the complex process of malignant transformation and invasion cell cytoskeleton devolves from a rigid and organized structure to a more compliant state, which confers to the cancer cells a great ability to migrate and adapt to the extracellular environment. In order to better understand the malignant transformation process from a mechanical point of view, it is necessary to evaluate the direct crosstalk between the cells and their surrounding extracellular matrix (ECM) in a context which is close to in vivo conditions. In this study, human biopsy tissues of lung adenocarcinoma were analyzed in order to define their mechanical phenotype at cell and ECM level, by using particle tracking microrheology (PTM) technique. Polystyrene beads (500 nm) were introduced into the sample slice. The motion of beads was obtained by tracking their displacements across cell cytoskeleton and ECM structures and mean squared displacements (MSDs) were calculated from bead trajectories. It has been already demonstrated that the amplitude of MSD is inversely related to the mechanical properties of intracellular and extracellular microenvironment. For this reason, MSDs of particles introduced in cytoplasm and ECM of healthy and tumor tissues were compared. PTM analyses showed that cancerous transformation compromises mechanical integrity of cells and extracellular matrix. In particular, the MSD amplitudes in cells of adenocarcinoma were greater as compared to cells of normal tissues. The increased motion is probably associated to a less structured cytoskeleton and consequently to an increase of deformability of cells. Further, cancer transformation is also accompanied by extracellular matrix stiffening, as confirmed by the decrease of MSDs of matrix in tumor tissue, a process that promotes tumor proliferation and invasiveness, by activating typical oncogenic signaling pathways. In addition, a clear correlation between MSDs of cells and tumor grade was found. MSDs increase when tumor grade passes from 2 to 3, indicating that cells undergo to a trans-differentiation process during tumor progression. ECM stiffening is not dependent on tumor grade, but the tumor stage resulted to be strictly correlated with both cells and ECM mechanical properties. In fact, a greater stage is assigned to tumor spread to regional lymph nodes and characterized by an up-regulation of different ECM proteins, such as collagen I fibers. These results indicate that PTM can be used to get nanomechanical characterization at different scale levels in an interpretative and diagnostic context.

Keywords: cytoskeleton, extracellular matrix, mechanical properties, particle tracking microrheology, tumor

Procedia PDF Downloads 267

Authors: Heba F. Hozyen, Hodallah H. Ahmed, S. I. A. Shalaby, G. E. S. Essawy

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Biochemical and hormonal changes that occur in both follicular fluid and blood are involved in the control of ovarian physiology. The present study was conducted on follicular fluid and serum samples obtained from 708 buffaloes. Samples were examined for estradiol, progesterone, and cholesterol concentrations in relation to seasonal changes, ovarian follicular size, and stage of estrous cycle. The obtained results revealed that follicular fluid and serum levels of estradiol, progesterone, and cholesterol were significantly lower during summer and autumn when compared to winter and spring seasons. With the increase in follicular size, the follicular fluid levels of progesterone and cholesterol were significantly decreased, while estradiol levels were significantly increased. Estradiol and progesterone levels were significantly higher in follicular fluid than blood, while cholesterol was significantly lower in follicular fluid than serum. In conclusion, the current study threw a light on the hormonal changes in the follicular fluid and blood under the effect of heat stress which could be related to the low fertility of buffalo in the summer.

Keywords: buffalo, follicular fluid, folliculogenesis, seasonal changes, steroids

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Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari

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Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life of the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or Covidi-19 induced pneumonia. The early prediction and classification of such lung diseases help to reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans have pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publically available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scan, Covid-19, deep learning, image processing, lung disease classification

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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 the 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 by the government websites of both countries, 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, visa reforms for international students

Procedia PDF Downloads 51