Search results for: international ovarian tumor analysis classification

Authors: Mohammad Hossein Habibi, Atena Sadat Hosseini

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Colorectal cancer is the third leading cause of cancer-related death in the world. Colorectal cancer screening, early detection, and treatment programs could benefit from the most up-to-date information on the disease's burden, given the present worldwide trend of increasing colorectal cancer incidence. Tumor recurrence and resistance are exacerbated by the presence of chemotherapy-resistant cancer stem cells that can generate rapidly proliferating tumor cells. In addition, tumor cells can evolve chemoresistance through adaptation mechanisms. In this work, we used in silico analysis to select suitable GEO datasets. In this study, we compared slow-growing cancer stem cells with high-growth colorectal cancer-derived cancer stem cells. We then evaluated the signal pathways, transcription factors, and kinases associated with these two types of cancer stem cells. A total of 980 upregulated genes and 870 downregulated genes were clustered. MAPK signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Fc gamma R-mediated phagocytosis, and Steroid biosynthesis signaling pathways were observed in upregulated genes. Also, caffeine metabolism, amino sugar and nucleotide sugar metabolism, TNF signaling pathway, and cytosolic DNA-sensing pathway were involved in downregulated genes. In the next step, we evaluated the best transcription factors and kinases in two types of cancer stem cells. In this regard, NR2F2, ZEB2, HEY1, and HDGF as transcription factors and PRDM5, SMAD, CBP, and KDM2B as critical kinases in upregulated genes. On the other hand, IRF1, SPDEF, NCOA1, and STAT1 transcription factors and CTNNB1 and CDH7 kinases were regulated low expression genes. Using bioinformatics analysis in the present study, we conducted an in-depth study of colorectal cancer stem cells at low and high growth rates so that we could take further steps to detect and even target these cells. Naturally, more additional tests are needed in this direction.

Keywords: colorectal cancer, bioinformatics analysis, transcription factor, kinases, cancer stem cells

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Authors: Shiza Zafar

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The tannery effluents contaminated about 400 acres land area in Kasur, Pakistan, has been reclaimed by removing polluted water after the long term effluent logging from the nearby tanneries. In an effort to describe the status of reclaimed soil for agricultural practices, the results of physicochemical analysis of the soil are reported in this article. The concentrations of the parameters such as pH, Electrical Conductivity (EC), Organic Matter (OM), Organic Carbon (OC), Available Phosphorus (P), Potassium (K), and Sodium (Na) were determined by standard methods of analysis and results were computed and compared with various international standards for agriculture recommended by international organizations, groups of experts and or individual researchers. The results revealed that pH, EC, OM, OC, K, and Na are in accordance with the prescribed limits but P in soil exceeds the satisfactory range of P in agricultural soil. Thus, the reclaimed soil in Kasur can be inferred fit for the purpose of agricultural activities.

Keywords: soil toxicity, agriculture, reclaimed land, physico-chemical analysis

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Authors: Dechen Tshering Vogel, Johannes T. Heverhagen, Bernard Kiss, Spyridon Arampatzis

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In addition to computed tomography (CT), contrast enhanced ultrasound (CEUS), and magnetic resonance imaging (MRI) are being increasingly used for imaging of renal lesions. The aim of this prospective study was to compare the classification of complex cystic renal lesions using the Bosniak classification with CEUS and MRI to CT. Forty-eight patients with 65 cystic renal lesions were included in this study. All participants signed written informed consent. The agreement between the Bosniak classifications of complex renal lesions ( ≥ BII-F) on CEUS and MRI were compared to that of CT and were tested using Cohen’s Kappa. Sensitivity, specificity, positive and negative predictive values (PPV/NPV) and the accuracy of CEUS and MRI compared to CT in the detection of complex renal lesions were calculated. Twenty-nine (45%) out of 65 cystic renal lesions were classified as complex using CT. The agreement between CEUS and CT in the classification of complex cysts was fair (agreement 50.8%, Kappa 0.31), and was excellent between MRI and CT (agreement 93.9%, Kappa 0.88). Compared to CT, MRI had a sensitivity of 96.6%, specificity of 91.7%, a PPV of 54.7%, and an NPV of 54.7% with an accuracy of 63.1%. The corresponding values for CEUS were sensitivity 100.0%, specificity 33.3%, PPV 90.3%, and NPV 97.1% with an accuracy 93.8%. The classification of complex renal cysts based on MRI and CT scans correlated well, and MRI can be used instead of CT for this purpose. CEUS can exclude complex lesions, but due to higher sensitivity, cystic lesions tend to be upgraded. However, it is useful for initial imaging, for follow up of lesions and in those patients with contraindications to CT and MRI.

Keywords: Bosniak classification, computed tomography, contrast enhanced ultrasound, cystic renal lesions, magnetic resonance imaging

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Authors: Lubomir Vecera, Tomas Gabrhelik, Benjamin Tolmaci, Josef Srovnal, Emil Berta, Petr Prasil, Petr Stourac

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Cancer is the second leading cause of death worldwide and colon cancer is the second most common type of cancer. Currently, there are only a few studies evaluating the effect of postoperative analgesia on the prognosis of patients undergoing radical colon cancer surgery. Postoperative analgesia in patients undergoing colon cancer surgery is usually managed in two ways, either with strong opioids (morphine, piritramide) or epidural analgesia. In our prospective study, we evaluated the effect of postoperative analgesia on the presence of circulating tumor cells or minimal residual disease after colon cancer surgery. A total of 60 patients who underwent radical colon cancer surgery were enrolled in this prospective, randomized, two-center study. Patients were randomized into three groups, namely piritramide, morphine and postoperative epidural analgesia. We evaluated the presence of carcinoembryonic antigen (CEA) and cytokeratin 20 (CK-20) mRNA positive circulating tumor cells in peripheral blood before surgery, immediately after surgery, on postoperative day two and one month after surgery. The presence of circulating tumor cells was assessed by quantitative real-time reverse transcriptase-polymerase chain reaction (qRT-PCR). In the priritramide postoperative analgesia group, the presence of CEA mRNA positive cells was significantly lower on a postoperative day two compared to the other groups (p=0.04). The value of CK-20 mRNA positive cells was the same in all groups on all days. In all groups, both types of circulating tumor cells returned to normal levels one month after surgery. Demographic and baseline clinical characteristics were similar in all groups. Compared with morphine and epidural analgesia, piritramide significantly reduces the amount of CEA mRNA positive circulating tumor cells after radical colon cancer surgery.

Keywords: cancer progression, colon cancer, minimal residual disease, perioperative analgesia.

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Authors: Zhang Shuqi, Liu Dan

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For the problems in intelligent network anomaly traffic detection models, such as low detection accuracy caused by the lack of training samples, poor effect with small sample attack detection, a classification model enhancement method, F-ACGAN(Flow Auxiliary Classifier Generative Adversarial Network) which introduces generative adversarial network and adversarial training, is proposed to solve these problems. Generating adversarial data with category labels could enhance the training effect and improve classification accuracy and model robustness. FACGAN consists of three steps: feature preprocess, which includes data type conversion, dimensionality reduction and normalization, etc.; A generative adversarial network model with feature learning ability is designed, and the sample generation effect of the model is improved through adversarial iterations between generator and discriminator. The adversarial disturbance factor of the gradient direction of the classification model is added to improve the diversity and antagonism of generated data and to promote the model to learn from adversarial classification features. The experiment of constructing a classification model with the UNSW-NB15 dataset shows that with the enhancement of FACGAN on the basic model, the classification accuracy has improved by 8.09%, and the score of F1 has improved by 6.94%.

Keywords: data imbalance, GAN, ACGAN, anomaly detection, adversarial training, data augmentation

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Authors: R. Ananthanarayanan, S. B. Nakula, D. R. Seenivasagam, J. Naua, B. Sharma

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Product packaging is a critical component of production, trade, and marketing, playing numerous vital roles that often go unnoticed by consumers. Packaging is essential for maintaining the shelf life, quality assurance, and safety of both manufactured and agricultural products. For example, harvested produce or processed foods can quickly lose quality and freshness, making secure packaging crucial for preservation and safety throughout the food supply chain. In Fiji, agricultural packaging has primarily been managed by local companies for international trade, with gradual advancements in these practices. To further enhance the industry’s performance, this study examines the challenges and constraints hindering the optimization of agricultural packaging practices in Fiji. The study utilizes Multi-Criteria Decision Making (MCDM) tools, specifically Interpretive Structural Modeling (ISM) and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC). ISM analyzes the hierarchical structure of barriers, categorizing them from the least to the most influential, while MICMAC classifies barriers based on their driving and dependence power. This approach helps identify the interrelationships between barriers, providing valuable insights for policymakers and decision-makers to propose innovative solutions for sustainable development in the agricultural packaging sector, ultimately shaping the future of packaging practices in Fiji.

Keywords: agricultural packaging, barriers, ISM, MICMAC

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Authors: Gabriele Ciasca, Tanya E. Sassun, Eleonora Minelli, Manila Antonelli, Massimiliano Papi, Antonio Santoro, Felice Giangaspero, Roberto Delfini, Marco De Spirito

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Glioblastoma multiforme (GBM) is an extremely aggressive brain tumor, characterized by a diffuse infiltration of neoplastic cells into the brain parenchyma. Although rarely considered, mechanical cues play a key role in the infiltration process that is extensively mediated by the tumor microenvironment stiffness and, more in general, by the occurrence of aberrant interactions between neoplastic cells and the extracellular matrix (ECM). Here we provide a nano-mechanical characterization of the viscoelastic response of human GBM tissues by indentation-type atomic force microscopy. High-resolution elasticity maps show a large difference between the biomechanics of GBM tissues and the healthy peritumoral regions, opening possibilities to optimize the tumor resection area. Moreover, we unveil the nanomechanical signature of necrotic regions and anomalous vasculature, that are two major hallmarks useful for glioma staging. Actually, the morphological grading of GBM relies mainly on histopathological findings that make extensive use of qualitative parameters. Our findings have the potential to positively impact on the development of novel quantitative methods to assess the tumor grade, which can be used in combination with conventional histopathological examinations. In order to provide a more in-depth description of the role of mechanical cues in tumor progression, we compared the nano-mechanical fingerprint of GBM tissues with that of grade-I (WHO) meningioma, a benign lesion characterized by a completely different growth pathway with the respect to GBM, that, in turn hints at a completely different role of the biomechanical interactions.

Keywords: AFM, nano-mechanics, nanomedicine, brain tumors, glioblastoma

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Authors: Hung-Sheng Lin, Cheng-Hsuan Li

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Over the past few years, kernel-based algorithms have been widely used to extend some linear feature extraction methods such as principal component analysis (PCA), linear discriminate analysis (LDA), and nonparametric weighted feature extraction (NWFE) to their nonlinear versions, kernel principal component analysis (KPCA), generalized discriminate analysis (GDA), and kernel nonparametric weighted feature extraction (KNWFE), respectively. These nonlinear feature extraction methods can detect nonlinear directions with the largest nonlinear variance or the largest class separability based on the given kernel function. Moreover, they have been applied to improve the target detection or the image classification of hyperspectral images. The double nearest proportion feature extraction (DNP) can effectively reduce the overlap effect and have good performance in hyperspectral image classification. The DNP structure is an extension of the k-nearest neighbor technique. For each sample, there are two corresponding nearest proportions of samples, the self-class nearest proportion and the other-class nearest proportion. The term “nearest proportion” used here consider both the local information and other more global information. With these settings, the effect of the overlap between the sample distributions can be reduced. Usually, the maximum likelihood estimator and the related unbiased estimator are not ideal estimators in high dimensional inference problems, particularly in small data-size situation. Hence, an improved estimator by shrinkage estimation (regularization) is proposed. Based on the DNP structure, LDA is included as a special case. In this paper, the kernel method is applied to extend DNP to kernel-based DNP (KDNP). In addition to the advantages of DNP, KDNP surpasses DNP in the experimental results. According to the experiments on the real hyperspectral image data sets, the classification performance of KDNP is better than that of PCA, LDA, NWFE, and their kernel versions, KPCA, GDA, and KNWFE.

Keywords: feature extraction, kernel method, double nearest proportion feature extraction, kernel double nearest feature extraction

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Authors: Abdullah Abdu Qaseem Shamsan

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Background: Gliomas represent the most frequent, heterogeneous group of tumors arising from glial cells, characterized by difficult monitoring, poor prognosis, and fatality. Tissue biopsy is an established procedure for tumor cell sampling that aids diagnosis, tumor grading, and prediction of prognosis. We studied and compared the levels of liquid biopsy markers in patients with different grades of glioma. Also, it tried to establish the potential association between glioma and specific blood groups antigen. Result: 78 patients were identified, among whom maximum percentage with glioblastoma possessed blood group O+ (53.8%). The second highest frequency had blood group A+ (20.4%), followed by B+ (9.0%) and A- (5.1%), and least with O-. Liquid biopsy biomarkers comprised of ALT, LDH, lymphocytes, Urea, Alkaline phosphatase, AST Neutrophils, and CRP. The levels of all the components increased significantly with the severity of glioma, with maximum levels seen in glioblastoma (grade IV), followed by grade III and grade II respectively. Conclusion: Gliomas possess significant clinical challenges due to their progression with heterogeneous nature and aggressive behavior. Liquid biopsy is a non-invasive approach which aids to establish the status of the patient and determine the tumor grade, therefore may show diagnostic and prognostic utility. Additionally, our study provides evidence to demonstrate the role of ABO blood group antigens in the development of glioma. However, future clinical research on liquid biopsy will improve the sensitivity and specificity of these tests and validate their clinical usefulness to guide treatment approaches.

Keywords: GBM: glioblastoma multiforme, CT: computed tomography, MRI: magnetic resonance imaging, ctRNA: circulating tumor RNA

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Authors: Usama Shahid, Muhammad Zunnurain Hussain

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This research paper presents a comprehensive analysis of a sentiment analysis approach that employs valence and arousal as its foundational pillars, in comparison to traditional techniques. Sentiment analysis is an indispensable task in natural language processing that involves the extraction of opinions and emotions from textual data. The valence and arousal dimensions, representing the intensity and positivity/negativity of emotions, respectively, enable the creation of four quadrants, each representing a specific emotional state. The study seeks to determine the impact of utilizing these quadrants to identify distinct emotional states on the accuracy and efficiency of sentiment analysis, in comparison to traditional techniques. The results reveal that the valence and arousal-based approach outperforms other approaches, particularly in identifying nuanced emotions that may be missed by conventional methods. The study's findings are crucial for applications such as social media monitoring and market research, where the accurate classification of emotions and opinions is paramount. Overall, this research highlights the potential of using valence and arousal as a framework for sentiment analysis and offers invaluable insights into the benefits of incorporating specific types of emotions into the analysis. These findings have significant implications for researchers and practitioners in the field of natural language processing, as they provide a basis for the development of more accurate and effective sentiment analysis tools.

Keywords: sentiment analysis, valence and arousal, emotional states, natural language processing, machine learning, text analysis, sentiment classification, opinion mining

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Authors: Liane Marcia Freitas Silva, Fernando Augusto Silva Marins, Maria Silene Alexandre Leite

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The adoption of a network structure, like in the supply chains, favors the increase of dependence between companies and, by consequence, their vulnerability. Environment disasters, sociopolitical and economical events, and the dynamics of supply chains elevate the uncertainty of their operation, favoring the occurrence of events that can generate break up in the operations and other undesired consequences. Thus, supply chains are exposed to various risks that can influence the profitability of companies involved, and there are several previous studies that have proposed risk classification models in order to categorize the risks and to manage them. The objective of this paper is to analyze and discuss thirty of these risk classification models by means a theoretical survey. The research method adopted for analyzing and discussion includes three phases: The identification of the types of risks proposed in each one of the thirty models, the grouping of them considering equivalent concepts associated to their definitions, and, the analysis of these risks groups, evaluating their similarities and differences. After these analyses, it was possible to conclude that, in fact, there is more than thirty risks types identified in the literature of Supply Chains, but some of them are identical despite of be used distinct terms to characterize them, because different criteria for risk classification are adopted by researchers. In short, it is observed that some types of risks are identified as risk source for supply chains, such as, demand risk, environmental risk and safety risk. On the other hand, other types of risks are identified by the consequences that they can generate for the supply chains, such as, the reputation risk, the asset depreciation risk and the competitive risk. These results are consequence of the disagreements between researchers on risk classification, mainly about what is risk event and about what is the consequence of risk occurrence. An additional study is in developing in order to clarify how the risks can be generated, and which are the characteristics of the components in a Supply Chain that leads to occurrence of risk.

Keywords: sisks classification, survey, supply chain management, theoretical discussion

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Authors: Tianyi Wang, Hieyong Jeong, An Guo, Yuko Ohno

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Healthcare robot for assisting sit-to-stand (STS) performance had aroused numerous research interests. To author’s best knowledge, knowledge about how evaluating healthcare robot is still unknown. Robot should be labeled as fault if users feel demanding during STS when they are assisted by robot. In this research, we aim to propose a method to evaluate sit-to-stand assist robot through center of pressure (CoP), then classify different STS performance. Experiments were executed five times with ten healthy subjects under four conditions: two self-performed STSs with chair heights of 62 cm and 43 cm, and two robot-assisted STSs with chair heights of 43 cm and robot end-effect speed of 2 s and 5 s. CoP was measured using a Wii Balance Board (WBB). Bayesian classification was utilized to classify STS performance. The results showed that faults occurred when decreased the chair height and slowed robot assist speed. Proposed method for fault classification showed high probability of classifying fault classes form others. It was concluded that faults for STS assist robot could be detected by inspecting center of pressure and be classified through proposed classification algorithm.

Keywords: center of pressure, fault classification, healthcare robot, sit-to-stand movement

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Authors: Burcu Oralhan, Zeki Oralhan, Nilsun Sariyer, Kumru Uyar

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Data mining has been becoming more and more important and a wide range of applications in recent years. Data mining is the process of find hidden and unknown patterns in big data. One of the applied fields of data mining is Customer Relationship Management. Understanding the relationships between products and customers is crucial for every business. Customer Relationship Management is an approach to focus on customer relationship development, retention and increase on customer satisfaction. In this study, we made an application of a data mining methods in telecommunication customer relationship management side. This study aims to determine the customers profile who likely to leave the system, develop marketing strategies, and customized campaigns for customers. Data are clustered by applying classification techniques for used to determine the churners. As a result of this study, we will obtain knowledge from international telecommunication industry. We will contribute to the understanding and development of this subject in Customer Relationship Management.

Keywords: customer churn analysis, customer relationship management, data mining, telecommunication industry

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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Every institute of learning is usually interested in the performance of enrolled students. The level of these performances determines the approach an institute of study may adopt in rendering academic services. The focus of this paper is to evaluate students' academic performance in given courses of study using machine learning methods. This study evaluated various supervised machine learning classification algorithms such as Logistic Regression (LR), Support Vector Machine, Random Forest, Decision Tree, K-Nearest Neighbors, Linear Discriminant Analysis, and Quadratic Discriminant Analysis, using selected features to predict study performance. The accuracy, precision, recall, and F1 score obtained from a 5-Fold Cross-Validation were used to determine the best classification algorithm to predict students’ performances. SVM (using a linear kernel), LDA, and LR were identified as the best-performing machine learning methods. Also, using the LR model, this study identified students' educational habits such as reading and paying attention in class as strong determinants for a student to have an above-average performance. Other important features include the academic history of the student and work. Demographic factors such as age, gender, high school graduation, etc., had no significant effect on a student's performance.

Keywords: student performance, supervised machine learning, classification, cross-validation, prediction

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Authors: Regina M. Chiechio, Rémi Leguevél, Helene Solhi, Marie Madeleine Gueguen, Stephanie Dutertre, Xavier, Jean-Pierre Bazureau, Olivier Mignen, Pascale Even-Hernandez, Paolo Musumeci, Maria Jose Lo Faro, Valerie Marchi

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Thanks to their ultra-small size, high electron density, and low toxicity, gold nanoclusters (Au NCs) have unique photoelectrochemical and luminescence properties that make them very interesting for diagnosis bio-imaging and theranostics. These applications require control of their delivery and interaction with cells; for this reason, the surface chemistry of Au NCs is essential to determine their interaction with the targeted biological objects. Here we demonstrate their ability as markers of pancreatic tumor cells. By functionalizing the surface of the NCs with a recognition peptite (U11), the nanostructures are able to preferentially bind to pancreatic cancer cells via a receptor (uPAR) overexpressed by these cells. Furthermore, the NCs can mark even the nucleus without the need of fixing the cells. These nanostructures can therefore be used as a non-toxic, multivalent luminescent platform, capable of selectively recognizing tumor cells for bioimaging, drug delivery, and radiosensitization.

Keywords: gold nanoclusters, luminescence, biomarkers, pancreatic cancer, biomedical applications, bioimaging, fluorescent probes, drug delivery

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Authors: Aster Misganaw

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This study investigates the reach, influence, and credibility of international broadcasters—specifically Voice of America (VOA), Deutsche Welle (DW), and British Broadcasting Corporation (BBC)—among Ethiopian audiences, comparing these perceptions to local media sources. Utilizing a mixed-methods approach that included quantitative surveys and qualitative interviews, the research reveals that the majority of respondents engage regularly with international broadcasters, with younger audiences showing a marked preference. Findings indicate that most of the participants perceive these international sources as more credible than local media, largely due to concerns over government influence on local reporting. Furthermore, the study finds that the majority of respondents believe international broadcasters significantly shape their understanding of both domestic and international issues, highlighting their critical role in public discourse. To enhance their relevance, it is recommended that international broadcasters incorporate more localized content while local media must work to improve their credibility and independence to better serve the Ethiopian public. This research contributes to the understanding of media consumption dynamics in Ethiopia, emphasizing the interplay between local and international narratives in shaping public opinion.

Keywords: international media, BBC, Deutsche Welle, Ethiopian media, Voice of America, audience

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Authors: Ali Asheibi

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The rapid increase in the size of databases required to store power quality monitoring data has demanded new techniques for analysing and understanding the data. One suggested technique to assist in analysis is data mining. Preparing raw data to be ready for data mining exploration take up most of the effort and time spent in the whole data mining process. Clustering is an important technique in data mining and machine learning in which underlying and meaningful groups of data are discovered. Large amounts of harmonic data have been collected from an actual harmonic monitoring system in a distribution system in Australia for three years. This amount of acquired data makes it difficult to identify operational events that significantly impact the harmonics generated on the system. In this paper, harmonic data preparation processes to better understanding of the data have been presented. Underlying classes in this data has then been identified using clustering technique based on the Minimum Message Length (MML) method. The underlying operational information contained within the clusters can be rapidly visualised by the engineers. The C5.0 algorithm was used for classification and interpretation of the generated clusters.

Keywords: data mining, harmonic data, clustering, classification

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Authors: Ali J. Ghandour, Houssam A. Krayem, Abedelkarim A. Jezzini

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High-resolution satellite images and remote sensing can provide global information in a fast way compared to traditional methods of data collection. Under such high resolution, a road is not a thin line anymore. Objects such as cars and trees are easily identifiable. Automatic vehicles enumeration can be considered one of the most important applications in traffic management. In this paper, autonomous vehicle detection and classification approach in highway environment is proposed. This approach consists mainly of three stages: (i) first, a set of preprocessing operations are applied including soil, vegetation, water suppression. (ii) Then, road networks detection and delineation is implemented using built-up area index, followed by several morphological operations. This step plays an important role in increasing the overall detection accuracy since vehicles candidates are objects contained within the road networks only. (iii) Multi-level Otsu segmentation is implemented in the last stage, resulting in vehicle detection and classification, where detected vehicles are classified into cars and trucks. Accuracy assessment analysis is conducted over different study areas to show the great efficiency of the proposed method, especially in highway environment.

Keywords: remote sensing, object identification, vehicle and road extraction, vehicle and road features-based classification

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Authors: M. A. H. Barry

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The concept of Good faith (husn al-niyyah) and fair-dealing (Nadl) are the fundamental guiding elements in all contracts and other agreements under Islamic law. The preaching of Al-Quran and Prophet Muhammad’s (Peace Be upon Him) firmly command people to act in good faith in all dealings. There are several Quran verses and the Prophet’s saying which stressed the significance of dealing honestly and fairly in all transactions. Under the English law, the good faith is not considered a fundamental requirement for the formation of a legal contract. However, the concept of Good Faith in private contracts is recognized by the civil law system and in Article 7(1) of the Convention on International Sale of Goods (CISG-Vienna Convention-1980). It took several centuries for the international trading community to recognize the significance of the concept of good faith for the international sale of goods transactions. Nevertheless, the recognition of good faith in Civil law is only confined for the commercial contracts. Subsequently to the CISG, this concept has made inroads into the private international law. There are submissions in favour of applying the good faith concept to public international law based on tacit recognition by the international conventions and International Tribunals. However, under public international law the concept of good faith is not recognized as a source of rights or obligations. This weakens the spirit of the good faith concept, particularly when determining the international disputes. This also creates a fundamental flaw because the absence of good faith application means the breaches tainted by bad faith are tolerated. The objective of this research is to evaluate, examine and analyze the application of the concept of good faith in the modern laws and identify its limitation, in comparison with Islamic concept of good faith. This paper also identifies the problems and issues connected with the non-application of this concept to public international law. This research consists of three key components (1) the preliminary inquiry (2) subject analysis and discovery of research results, and (3) examining the challenging problems, and concluding with proposals. The preliminary inquiry is based on both the primary and secondary sources. The same sources are used for the subject analysis. This research also has both inductive and deductive features. The Islamic concept of good faith covers all situations and circumstances where the bad faith causes unfairness to the affected parties, especially the weak parties. Under the Islamic law, the concept of good faith is a source of rights and obligations as Islam prohibits any person committing wrongful or delinquent acts in any dealing whether in a private or public life. This rule is applicable not only for individuals but also for institutions, states, and international organizations. This paper explains how the unfairness is caused by non-recognition of the good faith concept as a source of rights or obligations under public international law and provides legal and non-legal reasons to show why the Islamic formulation is important.

Keywords: good faith, the civil law system, the Islamic concept, public international law

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Authors: Jerzy Banski

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The basic aim of this study is to discuss and assess different classifications and research approaches to small towns that take their social and economic functions into account, as well as relations with surrounding areas. The subject literature typically includes three types of approaches to the classification of small towns: 1) the structural, 2) the location-related, and 3) the mixed. The structural approach allows for the grouping of towns from the point of view of the social, cultural and economic functions they discharge. The location-related approach draws on the idea of there being a continuum between the center and the periphery. A mixed classification making simultaneous use of the different approaches to research brings the most information to bear in regard to categories of the urban locality. Bearing in mind the approaches to classification, it is possible to propose a synthetic method for classifying small towns that takes account of economic structure, location and the relationship between the towns and their surroundings. In the case of economic structure, the small centers may be divided into two basic groups – those featuring a multi-branch structure and those that are specialized economically. A second element of the classification reflects the locations of urban centers. Two basic types can be identified – the small town within the range of impact of a large agglomeration, or else the town outside such areas, which is to say located peripherally. The third component of the classification arises out of small towns’ relations with their surroundings. In consequence, it is possible to indicate 8 types of small-town: from local centers enjoying good accessibility and a multi-branch economic structure to peripheral supra-local centers characterised by a specialized economic structure.

Keywords: small towns, classification, functional structure, localization

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Authors: Indu Poornima

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This paper aims at understanding the Socio-historical, political and economic dimensions of Female Genital Mutilation in Africa. After throwing light on the definition of FGM and scrutinizing the misconceptions associated with it, the paper progresses to analyze the following questions. a) How do communities performing FGM rationalize their act? b) Are the victims (women) themselves the strongest proponents of FGM ? and c) Are legislations against FGM by international organizations counter-productive?

Keywords: female genital mutilation, Africa, rationalizing the act, international legislations

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Authors: N. C. van der Merwe, J. Oosthuizen, M. F. Makhetha, J. Adams, B. K. Dajee, S-R. Schneider

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Women representing high-risk breast cancer families, who tested negative for pathogenic mutations in BRCA1 and BRCA2, are four times more likely to develop breast cancer compared to women in the general population. Sequencing of genes involved in genomic stability and DNA repair led to the identification of novel contributors to familial breast cancer risk. These include BLM and PALB2. Bloom's syndrome is a rare homozygous autosomal recessive chromosomal instability disorder with a high incidence of various types of neoplasia and is associated with breast cancer when in a heterozygous state. PALB2, on the other hand, binds to BRCA2 and together, they partake actively in DNA damage repair. Archived DNA samples of 66 BRCA1/2 negative high-risk breast cancer patients were retrospectively selected based on the presence of an extensive family history of the disease ( > 3 affecteds per family). All coding regions and splice-site boundaries of both genes were screened using High-Resolution Melting Analysis. Samples exhibiting variation were bi-directionally automated Sanger sequenced. The clinical significance of each variant was assessed using various in silico and splice site prediction algorithms. Comprehensive screening identified a total of 11 BLM and 26 PALB2 variants. The variants detected ranged from global to rare and included three novel mutations. Three BLM and two PALB2 likely pathogenic mutations were identified that could account for the disease in these extensive breast cancer families in the absence of BRCA mutations (BLM c.11T > A, p.V4D; BLM c.2603C > T, p.P868L; BLM c.3961G > A, p.V1321I; PALB2 c.421C > T, p.Gln141Ter; PALB2 c.508A > T, p.Arg170Ter). Conclusion: The study confirmed the contribution of pathogenic mutations in BLM and PALB2 to the familial breast cancer burden in South Africa. It explained the presence of the disease in 7.5% of the BRCA1/2 negative families with an extensive family history of breast cancer. Segregation analysis will be performed to confirm the clinical impact of these mutations for each of these families. These results justify the inclusion of both these genes in a comprehensive breast and ovarian next generation sequencing cancer panel and should be screened simultaneously with BRCA1 and BRCA2 as it might explain a significant percentage of familial breast and ovarian cancer in South Africa.

Keywords: Bloom Syndrome, familial breast cancer, PALB2, South Africa

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Authors: Marissa Dallara, Amalia Ardeljan, Lexi Frankel, Nadia Obaed, Naureen Rashid, Omar Rashid

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Human Cytomegalovirus (HCMV) is a ubiquitous virus that remains latent in approximately 60% of individuals in developed countries. Viral load is kept at a minimum due to a robust immune response that is produced in most individuals who remain asymptomatic. HCMV has been recently implicated in cancer research because it may impose oncomodulatory effects on tumor cells of which it infects, which could have an impact on the progression of cancer. HCMV has been implicated in increased pathogenicity of certain cancers such as gliomas, but in contrast, it can also exhibit anti-tumor activity. HCMV seropositivity has been recorded in tumor cells, but this may also have implications in decreased pathogenesis of certain forms of cancer such as leukemia as well as increased pathogenesis in others. This study aimed to investigate the correlation between cytomegalovirus and the incidence of breast cancer. Methods The data used in this project was extracted from a Health Insurance Portability and Accountability Act (HIPAA) compliant national database to analyze the patients infected versus patients not infection with cytomegalovirus using ICD-10, ICD-9 codes. Permission to utilize the database was given by Holy Cross Health, Fort Lauderdale, for the purpose of academic research. Data analysis was conducted using standard statistical methods. Results The query was analyzed for dates ranging from January 2010 to December 2019, which resulted in 14,309 patients in both the infected and control groups, respectively. The two groups were matched by age range and CCI score. The incidence of breast cancer was 1.642% and 235 patients in the cytomegalovirus group compared to 4.752% and 680 patients in the control group. The difference was statistically significant by a p-value of less than 2.2x 10^-16 with an odds ratio of 0.43 (0.4 to 0.48) with a 95% confidence interval. Investigation into the effects of HCMV treatment modalities, including Valganciclovir, Cidofovir, and Foscarnet, on breast cancer in both groups was conducted, but the numbers were insufficient to yield any statistically significant correlations. Conclusion This study demonstrates a statistically significant correlation between cytomegalovirus and a reduced incidence of breast cancer. If HCMV can exert anti-tumor effects on breast cancer and inhibit growth, it could potentially be used to formulate immunotherapy that targets various types of breast cancer. Further evaluation is warranted to assess the implications of cytomegalovirus in reducing the incidence of breast cancer.

Keywords: human cytomegalovirus, breast cancer, immunotherapy, anti-tumor

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Authors: H. Nakamura

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This study analyzes international strategic alliances in the information industry. The purpose of this study is to clarify the strategic intention of an international alliance. Secondly, it investigates the influence of differences in the target markets of partner companies on alliances. Using an international strategy theory approach to analyze the global strategies of global companies, the study compares a database business and an electronic publishing business. In particular, these cases emphasized factors attributable to "people" and "learning", reliability and communication between organizations and the evolution of the IT infrastructure. The theory evolved in this study validates the effectiveness of these strategies.

Keywords: database business, electronic library, international strategic alliances, partner selection

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Authors: Nguyen The Hoang Anh, Tran Huy Hoang, Vu Tat Thang, T. T. Quyen Bui

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Mental state classification is an important step for realizing a control system based on electroencephalography (EEG) signals which could benefit a lot of paralyzed people including the locked-in or Amyotrophic Lateral Sclerosis. Considering that EEG signals are nonstationary and often contaminated by various types of artifacts, classifying thoughts into correct mental states is not a trivial problem. In this work, our contribution is that we present and realize a novel model which integrates different techniques: Independent component analysis (ICA), relative wavelet energy, and support vector machine (SVM) for the same task. We applied our model to classify thoughts in two types of experiment whether with two or three mental states. The experimental results show that the presented model outperforms other models using Artificial Neural Network, K-Nearest Neighbors, etc.

Keywords: EEG, ICA, SVM, wavelet

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Abdesselem Dakhli, Wajdi Bellil, Chokri Ben Amar

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DNA Barcode, a short mitochondrial DNA fragment, made up of three subunits; a phosphate group, sugar and nucleic bases (A, T, C, and G). They provide good sources of information needed to classify living species. Such intuition has been confirmed by many experimental results. Species classification with DNA Barcode sequences has been studied by several researchers. The classification problem assigns unknown species to known ones by analyzing their Barcode. This task has to be supported with reliable methods and algorithms. To analyze species regions or entire genomes, it becomes necessary to use similarity sequence methods. A large set of sequences can be simultaneously compared using Multiple Sequence Alignment which is known to be NP-complete. To make this type of analysis feasible, heuristics, like progressive alignment, have been developed. Another tool for similarity search against a database of sequences is BLAST, which outputs shorter regions of high similarity between a query sequence and matched sequences in the database. However, all these methods are still computationally very expensive and require significant computational infrastructure. Our goal is to build predictive models that are highly accurate and interpretable. This method permits to avoid the complex problem of form and structure in different classes of organisms. On empirical data and their classification performances are compared with other methods. Our system consists of three phases. The first is called transformation, which is composed of three steps; Electron-Ion Interaction Pseudopotential (EIIP) for the codification of DNA Barcodes, Fourier Transform and Power Spectrum Signal Processing. The second is called approximation, which is empowered by the use of Multi Llibrary Wavelet Neural Networks (MLWNN).The third is called the classification of DNA Barcodes, which is realized by applying the algorithm of hierarchical classification.

Keywords: DNA barcode, electron-ion interaction pseudopotential, Multi Library Wavelet Neural Networks (MLWNN)

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Authors: Aisha Iddrsiu

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Public Relations (PR) has rapidly evolved around the world, just as companies have expanded to reach other parts of the world. With most multinational corporations conducting businesses in more than one country, only a few of these Multinational Corporations (MNC’s) are actual public relations firms, many have public relations departments or divisions that conduct public relations practices internationally. Hence international public relations is seen as a fast-growing specialty in the field of Public Relations. Multinational companies have devised strategies to effectively communicate and execute their roles within and between foreign publics and other cultures in which they operate through various means including the internet which is among the major inventions that have enabled corporations to establish their presents while targeting anonymous and diverse publics from varied cultures. International public relations practitioners rely on strategies coupled with internet use to communicate among and with foreign publics. Corporate websites and various social media handles have served as an important channel for public relations activities targeting both internal and international publics. In an incessant expansion of corporations and interactions with the publics from different cultures, it has become eminent to understand the public relation strategies used by MNCs in their international communication. This study therefore seeks to establish the international public relation strategies or models employed by Multinational Corporations specifically Arcelik Global in the management of its subsidiaries and communicating with international public. This study analyses both Arçelik global’s (one of the largest multinational companies in Turkey) website and social media accounts to understand the management strategy used with it subsidiary as well as strategies used to communicate with its global and local publics. Other underlying objective of this study are, 1. To examine the dominant international public relations models used by Multinational Corporations (Arcelik global). 2. To understand how Multinational Corporations manage (Arcelik global) its subsidiaries. 3. To understand how Multinational Corporations (Arcelik global) communicate with international or global publics. Research Questions 1. The main global PR strategies employed by multinational corporations (Arcelik global) 2. How subsidiaries of multinational corporations like Arcelik Global are managed. 3. How multinational corporations, like Arcelik worldwide, interact with international publics.

Keywords: multinational corporation, ethnocentric model, polycentric model, international public relations

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Authors: Vishwa Joshi

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The study of mood recognition in the field of music has gained a lot of momentum in the recent years with machine learning and data mining techniques and many audio features contributing considerably to analyze and identify the relation of mood plus music. In this paper we consider the same idea forward and come up with making an effort to build a system for automatic recognition of mood underlying the audio song’s clips by mining their audio features and have evaluated several data classification algorithms in order to learn, train and test the model describing the moods of these audio songs and developed an open source framework. Before classification, Preprocessing and Feature Extraction phase is necessary for removing noise and gathering features respectively.

Keywords: music, mood, features, classification

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Authors: Ravi Kiran Pothamsetty, Radha Rani Ghosh, Baby Paul Thaliath

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Radiation therapy has undergone many advancements and evloved from 2D to 3D. Recently, with rapid pace of drug discoveries, cutting edge technology, and clinical trials has made innovative advancements in computer technology and treatment planning and upgraded to intensity modulated radiotherapy (IMRT) which delivers in homogenous dose to tumor and normal tissues. The present study was a hospital-based experience comparing two different conformal radiotherapy techniques for brain tumors. This analytical study design has been conducted at Regional Cancer Centre, India from January 2014 to January 2015. Ten patients have been selected after inclusion and exclusion criteria. All the patients were treated on Artiste Siemens Linac Accelerator. The tolerance level for maximum dose was 6.0 Gyfor lenses and 54.0 Gy for brain stem, optic chiasm and optical nerves as per RTOG criteria. Mean and standard deviation values of PTV98%, PTV 95% and PTV 2% in IMRT were 93.16±2.9, 95.01±3.4 and 103.1±1.1 respectively; for 3DCRT were 91.4±4.7, 94.17±2.6 and 102.7±0.39 respectively. PTV max dose (%) in IMRT and 3D-CRT were 104.7±0.96 and 103.9±1.0 respectively. Maximum dose to the tumor can be delivered with IMRT with acceptable toxicity limits. Variables such as expertise, location of tumor, patient condition, and TPS influence the outcome of the treatment.

Keywords: brain tumors, intensity modulated radiotherapy (IMRT), three dimensional conformal radiotherapy (3D-CRT), radiation therapy oncology group (RTOG)

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