Search results for: gender based violence

Authors: Pieter Conradie, M. Marina Moller

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Web 2.0 Internet-based technologies have intruded all aspects of human life. Presently, this phenomenon is especially evident in the educational context, with increased disruptive Web 2.0 technology infusions dramatically changing educational practice. The most prominent of these Web 2.0 intrusions can be identified as Massive Open Online Courses (Coursera, EdX), video and photo sharing sites (Youtube, Flickr, Instagram), and Web 2.0 online tools utilize to create Personal Learning Environments (PLEs) (Symbaloo (aggregator), Delicious (social bookmarking), PBWorks (collaboration), Google+ (social networks), Wordspress (blogs), Wikispaces (wiki)). These Web 2.0 technologies have supported the realignment from a teacher-based pedagogy (didactic presentation) to a learner-based pedagogy (problem-based learning, project-based learning, blended learning), allowing greater learner autonomy. No longer is the educator the source of knowledge. Instead the educator has become the facilitator and mediator of the learner, involved in developing learner competencies to support life-long learning (continuous learning) in the 21st century. In this study, the self-regulated learning skills of thirty first-year university learners were explored by utilizing the Online Self-regulated Learning Questionnaire. Implementing an action research method, an intervention was affected towards improving the self-regulation skill set of the participants. Statistical significant results were obtained with increased self-regulated learning proficiency, positively impacting learner performance. Goal setting, time management, environment structuring, help seeking, task (learning) strategies and self-evaluation skills were confirmed as determinants of improved learner success.

Keywords: andragogy, online self-regulated learning questionnaire, self-regulated learning, web 2.0

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Authors: Nikolay P. Brayanov, Anna V. Stoynova

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Model-based development approach is gaining more support and acceptance. Its higher abstraction level brings simplification of systems’ description that allows domain experts to do their best without particular knowledge in programming. The different levels of simulation support the rapid prototyping, verifying and validating the product even before it exists physically. Nowadays model-based approach is beneficial for modelling of complex embedded systems as well as a generation of code for many different hardware platforms. Moreover, it is possible to be applied in safety-relevant industries like automotive, which brings extra automation of the expensive device certification process and especially in the software qualification. Using it, some companies report about cost savings and quality improvements, but there are others claiming no major changes or even about cost increases. This publication demonstrates the level of maturity and autonomy of model-based approach for code generation. It is based on a real live automotive seat heater (ASH) module, developed using The Mathworks, Inc. tools. The model, created with Simulink, Stateflow and Matlab is used for automatic generation of C code with Embedded Coder. To prove the maturity of the process, Code generation advisor is used for automatic configuration. All additional configuration parameters are set to auto, when applicable, leaving the generation process to function autonomously. As a result of the investigation, the publication compares the quality of generated embedded code and a manually developed one. The measurements show that generally, the code generated by automatic approach is not worse than the manual one. A deeper analysis of the technical parameters enumerates the disadvantages, part of them identified as topics for our future work.

Keywords: embedded code generation, embedded C code quality, embedded systems, model-based development

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Authors: Adnan A. Y. Mustafa

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Image matching is a fundamental problem that arises frequently in many aspects of robot and computer vision. It can become a time-consuming process when matching images to a database consisting of hundreds of images, especially if the images are big. One approach to reducing the time complexity of the matching process is to reduce the search space in a pre-matching stage, by simply removing dissimilar images quickly. The Probabilistic Matching Model for Binary Images (PMMBI) showed that dissimilarity detection between binary images can be accomplished quickly by random pixel mapping and is size invariant. The model is based on the gamma binary similarity distance that recognizes an image and its inverse as containing the same scene and hence considers them to be the same image. However, in many applications, an image and its inverse are not treated as being the same but rather dissimilar. In this paper, we present a comparative analysis of dissimilarity detection between PMMBI based on the gamma binary similarity distance and a modified PMMBI model based on a similarity distance that does distinguish between an image and its inverse as being dissimilar.

Keywords: binary image, dissimilarity detection, probabilistic matching model for binary images, image mapping

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Authors: Jerry Szatko, Edan L. Jorgensen, Stacia Jorgensen

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A crucial component to the success of any financial advising relationship is for the financial professional to understand the perceptions, preferences and thought-processes carried by the financial clients they serve. Armed with this information, financial professionals are more quickly able to understand how they can tailor their approach to best match the individual preferences and needs of each personal investor. Our research explores the use of a quantitative assessment tool in the financial services industry to assist in the identification of the personal investor’s consumer behaviors, especially in terms of financial risk tolerance, as it relates to their financial decision making. Through this process, the Unitifi Consumer Insight Tool (UCIT) was created and refined to capture and categorize personal investor financial behavioral categories and the financial personality tendencies of individuals prior to the initiation of a financial advisement relationship. This paper discusses the use of this tool to place individuals in one of four behavior-based financial risk tolerance categories. Our discoveries and research were aided through administration of a web-based survey to a group of over 1,000 individuals. Our findings indicate that it is possible to use a quantitative assessment tool to assist in predicting the behavioral tendencies of personal consumers when faced with consumer financial risk and decisions.

Keywords: behavior-based advising, financial relationship building, risk capacity based on behavior, risk tolerance, systematic way to assist in financial relationship building

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Authors: Mahmoud B. Rokaya

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The study of opinion mining and sentiment analysis includes analysis of opinions, sentiments, evaluations, attitudes, and emotions. The rapid growth of social media, social networks, reviews, forum discussions, microblogs, and Twitter, leads to a parallel growth in the field of sentiment analysis. The field of sentiment analysis tries to develop effective tools to make it possible to capture the trends of people. There are two approaches in the field, lexicon-based and corpus-based methods. A lexicon-based method uses a sentiment lexicon which includes sentiment words and phrases with assigned numeric scores. These scores reveal if sentiment phrases are positive or negative, their intensity, and/or their emotional orientations. Creation of manual lexicons is hard. This brings the need for adaptive automated methods for generating a lexicon. The proposed method generates dynamic lexicons based on the corpus and then classifies text using these lexicons. In the proposed method, different approaches are combined to generate lexicons from text. The proposed method classifies the tweets into 5 classes instead of +ve or –ve classes. The sentiment classification problem is written as an optimization problem, finding optimum sentiment lexicons are the goal of the optimization process. The solution was produced based on mathematical programming approaches to find the best lexicon to classify texts. A genetic algorithm was written to find the optimal lexicon. Then, extraction of a meta-level feature was done based on the optimal lexicon. The experiments were conducted on several datasets. Results, in terms of accuracy, recall and F measure, outperformed the state-of-the-art methods proposed in the literature in some of the datasets. A better understanding of the Arabic language and culture of Arab Twitter users and sentiment orientation of words in different contexts can be achieved based on the sentiment lexicons proposed by the algorithm.

Keywords: social media, Twitter sentiment, sentiment analysis, lexicon, genetic algorithm, evolutionary computation

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Authors: Ashenafi Sileshi Etefa, Bezawit Worku Yenealem

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Micro-insurance is a relatively new concept that is just being introduced in Ethiopia. For an agrarian economy dominated by small holder farming and vulnerable to natural disasters, mainly drought, the need for an Index-Based Micro Insurance (IBMI) is crucial. Since IBMI solves moral hazard, adverse selection, and access issues to poor clients, it is preferable over traditional insurance products. IBMI is being piloted in drought prone areas of Ethiopia with the aim of learning and expanding the service across the country. This article analyses the demand of IBMI and the barriers to demand and finds that the demand for IBMI has so far been constrained by lack of awareness, trust issues, costliness, and the level of basis risk; and recommends reducing the basis risk and increasing the role of government and farmer cooperatives.

Keywords: agriculture, index based micro-insurance (IBMI), drought, micro-finance institution (MFI)

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Authors: Fares Hedayatit, Arnauld Joly, Panagiotis Papadimitriou

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Many machine learning tasks such as text annotation usually require training over very big datasets, e.g., millions of web documents, that can be represented in a sparse input space. State-of the-art tree-based ensemble algorithms cannot scale to such datasets, since they include operations whose running time is a function of the input space size rather than a function of the non-zero input elements. In this paper, we propose an efficient splitting algorithm to leverage input sparsity within decision tree methods. Our algorithm improves training time over sparse datasets by more than two orders of magnitude and it has been incorporated in the current version of scikit-learn.org, the most popular open source Python machine learning library.

Keywords: big data, sparsely representable data, tree-based models, scalable learning

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Authors: Piotr Fiszeder, Marcin Fałdziński, Peter Molnár

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The dynamic conditional correlation model of Engle (2002) is one of the most popular multivariate volatility models. However, this model is based solely on closing prices. It has been documented in the literature that the high and low price of the day can be used in an efficient volatility estimation. We, therefore, suggest a model which incorporates high and low prices into the dynamic conditional correlation framework. Empirical evaluation of this model is conducted on three datasets: currencies, stocks, and commodity exchange-traded funds. The utilisation of realized variances and covariances as proxies for true variances and covariances allows us to reach a strong conclusion that our model outperforms not only the standard dynamic conditional correlation model but also a competing range-based dynamic conditional correlation model.

Keywords: volatility, DCC model, high and low prices, range-based models, covariance forecasting

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Authors: Sasan Chalaki, Seyed Ali Mirgholange, Touba Nadri, Saman Chalaki

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In the current world, proper poultry nutrition has garnered special attention as one of the fundamental factors for enhancing their health and performance. Concerns related to the excessive use of antibiotics in the poultry industry and their role in antibiotic resistance have transformed this issue into a global challenge in public health and the environment. On the other hand, poultry farming plays a vital role as a primary source of meat and eggs in human nutrition, and improving their health and performance is crucial. One effective approach to enhance poultry nutrition is the utilization of the antibiotic properties of plant-based ingredients. The use of plant-based alternatives as natural antibiotics in poultry nutrition not only aids in improving poultry health and performance but also plays a significant role in reducing the consumption of synthetic antibiotics and preventing antibiotic resistance-related issues. Plants contain various antibacterial compounds, such as flavonoids, tannins, and essential oils. These compounds are recognized as active agents in combating bacteria. Plant-based antibiotics are compounds extracted from plants with antibacterial properties. They are acknowledged as effective substitutes for chemical antibiotics in poultry diets. The advantages of plant-based antibiotics include reducing the risk of resistance to chemical antibiotics, increasing poultry growth performance, and lowering the risk of disease transmission.

Keywords: poultry, antibiotics, essential oils, plant-based

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Authors: Lamiaa Shehata

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The current (COVID-19) pandemic is one of the international crises, and a lot of efforts have been directed toward the improvement of efficient vaccines, however vaccine hesitancy is one of the universal menaces that make the fulfillment of society immunity very hard. The World Health Organization acknowledges vaccine hesitancy as the society’s maximum risk to people's health protection, especially in little and moderate-revenue nations. Social media is strong in observing audience behaviors and evaluating the circulation, which would supply useful data for strategy makers. It has a significant function in spreading facts during the pandemic, it could assist to boost protective manners. The objective of this study is to determine the effects of social media exposure on vaccine hesitancy. Data were collected using a survey in a form of a structured questionnaire conducted during December 2021- January 2022 using convenient sampling techniques (680) in Egypt and the United Arab Emirates. The results revealed that there was a significant relationship between the high exposure to social media and the refusal of the Covid19 vaccine also, the percentage of the refusal of the vaccine is higher in Egypt, however, UAE forced people to take the vaccine. Furthermore, public attitudes toward COVID-19 vaccination vary from gender and region. In conclusion, policymakers must adjust their policies through the use of social media to immediate actions to vaccine-related news to support vaccination approval.

Keywords: COVID-19, hesitancy, social media, vaccine

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Authors: Adnan Sahin, Ufuk Uylas, Ercument Pasaoglu, Tarik Caga, Enver Ihtiyar, Serdar Erkasap, Ersin Ates, Fatih Yasar

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Background: In this study, it was evaluated the demographic features, etiological factors and the management of acute pancreatitis. Methods: 106 patient hospitalized due to acute pancreatitis were retrospectively examined from 1 January 2015 to 31 December 2015 in Department of General Surgery of ESOGUMF. The data of gender, signs and symptoms, etiological factors, WBC, AST, ALT, Amilase, USG and CT findings treatment options ERCP, and complications, mortality rate were analysed. Results: The mean age of patients were 58.8 (53 men and 53 women). The causes of acute pancreatitis were as follows: gallbladder stone was 89, hyperlipidemia was 5 and idiopathic were 16 patients. Severe pancreatitis was developed in 16 patients in the biliary pancreatitis group and ERCP was performed. Cholecystectomy was performed to all biliary pancreatitis group patients after acute pancreatitis subside. The mean hospital stay period was 9.33 (2-37) day. Discussion and conclusion: Severe acute pancreatitis is a mortal disease. The most common etiological cause of acute pancreatitis is biliary origin. The first line treatment modality of acute pancreatitis is medical. Cholecystectomy should be planned to the all-biliary caused acute pancreatitis patients after the attack subside. ERCP is a useful treatment modality in the case of clinical worsening and suspicion of acute cholangitis. ERCP procedure used 16 patients in our series and these patients have a good morbidity and mean hospital period is lower than the others. We suppose that ERCP procedure should be planned selectively and conservatively.

Keywords: acute pancreatitis, ERCP, morbidity, treatment

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Authors: Mohammad Bakhshandeh

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Structural Health Monitoring (SHM) is a crucial and necessary practice that plays a vital role in ensuring the safety and integrity of critical structures, and in particular, bridges. The continuous monitoring of bridges for signs of damage or degradation through Bridge Health Monitoring (BHM) enables early detection of potential problems, allowing for prompt corrective action to be taken before significant damage occurs. Although all monitoring techniques aim to provide accurate and decisive information regarding the remaining useful life, safety, integrity, and serviceability of bridges, understanding the development and propagation of damage is vital for maintaining uninterrupted bridge operation. Over the years, extensive research has been conducted on BHM methods, and experts in the field have increasingly adopted new methodologies. In this article, we provide a comprehensive exploration of the various BHM approaches, including sensor-based, non-destructive testing (NDT), model-based, and artificial intelligence (AI)-based methods. We also discuss the challenges associated with BHM, including sensor placement and data acquisition, data analysis and interpretation, cost and complexity, and environmental effects, through an extensive review of relevant literature and research studies. Additionally, we examine potential solutions to these challenges and propose future research ideas to address critical gaps in BHM.

Keywords: structural health monitoring (SHM), bridge health monitoring (BHM), sensor-based methods, machine-learning algorithms, and model-based techniques, sensor placement, data acquisition, data analysis

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Authors: Bruno Trstenjak, Dzenana Donko

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Data mining and classification of objects is the process of data analysis, using various machine learning techniques, which is used today in various fields of research. This paper presents a concept of hybrid classification model improved with the expert knowledge. The hybrid model in its algorithm has integrated several machine learning techniques (Information Gain, K-means, and Case-Based Reasoning) and the expert’s knowledge into one. The knowledge of experts is used to determine the importance of features. The paper presents the model algorithm and the results of the case study in which the emphasis was put on achieving the maximum classification accuracy without reducing the number of features.

Keywords: case based reasoning, classification, expert's knowledge, hybrid model

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Authors: Wahiba Mohguen, Raïs El’hadi Bekka

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This paper presents a denoising method called EMD-Custom that was based on Empirical Mode Decomposition (EMD) and the modified Customized Thresholding Function (Custom) algorithms. EMD was applied to decompose adaptively a noisy signal into intrinsic mode functions (IMFs). Then, all the noisy IMFs got threshold by applying the presented thresholding function to suppress noise and to improve the signal to noise ratio (SNR). The method was tested on simulated data and real ECG signal, and the results were compared to the EMD-Based signal denoising methods using the soft and hard thresholding. The results showed the superior performance of the proposed EMD-Custom denoising over the traditional approach. The performances were evaluated in terms of SNR in dB, and Mean Square Error (MSE).

Keywords: customized thresholding, ECG signal, EMD, hard thresholding, soft-thresholding

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Authors: Chongya Song, Kang Yen, Alexander Pons, Jin Liu

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In the field of machine learning, the ensemble has been employed as a common methodology to improve the performance upon multiple base classifiers. However, the true predictions are often canceled out by the false ones during consensus due to a phenomenon called “curse of correlation” which is represented as the strong interferences among the predictions produced by the base classifiers. In addition, the existing practices are still not able to effectively mitigate the problem of imbalanced classification. Based on the analysis on our experiment results, we conclude that the two problems are caused by some inherent deficiencies in the approach of consensus. Therefore, we create an enhanced ensemble algorithm which adopts a designed rank-based chain-mode consensus to overcome the two problems. In order to evaluate the proposed ensemble algorithm, we employ a well-known benchmark data set NSL-KDD (the improved version of dataset KDDCup99 produced by University of New Brunswick) to make comparisons between the proposed and 8 common ensemble algorithms. Particularly, each compared ensemble classifier uses the same 22 base classifiers, so that the differences in terms of the improvements toward the accuracy and reliability upon the base classifiers can be truly revealed. As a result, the proposed rank-based chain-mode consensus is proved to be a more effective ensemble solution than the traditional consensus approach, which outperforms the 8 ensemble algorithms by 20% on almost all compared metrices which include accuracy, precision, recall, F1-score and area under receiver operating characteristic curve.

Keywords: consensus, curse of correlation, imbalance classification, rank-based chain-mode ensemble

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Authors: Rozhgar A. Khailany, Mehri Igci, Emine Bayraktar, Sakip Erturhan, Metin Karakok, Ahmet Arslan

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Kidney cancer is the most lethal urological cancer accounting for 3% of adult malignancies. VHL, a tumor-suppressor gene, is best known to be associated with renal cell carcinoma (RCC). The VHL functions as negative regulator of hypoxia inducible factors. Recent sequencing efforts have identified several novel frequent mutations of histone modifying and chromatin remodeling genes in ccRCC (clear cell RCC) including PBRM1 and SETD2. The PBRM1 gene encodes the BAF180 protein, which involved in transcriptional activation and repression of selected genes. SETD2 encodes a histone methyltransferase, which may play a role in suppressing tumor development. In this study, RNAs of 30 paired tumor and normal samples that were grouped according to the types of kidney cancer and clinical characteristics of patients, including gender and average age were examined by RT-PCR, SSCP and sequencing techniques. VHL, PBRM1 and SETD2 expressions were relatively down-regulated. However, statistically no significance was found (Wilcoxon signed rank test, p > 0.05). Interestingly, no mutation was observed on the contrary of previous studies. Understanding the molecular mechanisms involved in the pathogenesis of RCC has aided the development of molecular-targeted drugs for kidney cancer. Further analysis is required to identify the responsible genes rather than VHL, PBRM1 and SETD2 in kidney cancer.

Keywords: kidney cancer, molecular biomarker, expression analysis, mutation screening

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Authors: P. Suresh, K. Gunasekaran, R. Thanigaivelan

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Radio frequency identification (RFID) technology has been attracting considerable attention with the expectation of improved supply chain visibility for consumer goods, apparel, and pharmaceutical manufacturers, as well as retailers and government procurement agencies. It is also expected to improve the consumer shopping experience by making it more likely that the products they want to purchase are available. Recent announcements from some key retailers have brought interest in RFID to the forefront. A modified K- Means Cluster based Heuristic approach, Hybrid Genetic Algorithm (GA) - Simulated Annealing (SA) approach, Hybrid K-Means Cluster based Heuristic-GA and Hybrid K-Means Cluster based Heuristic-GA-SA for Open Loop Supply Chain Network problem are proposed. The study incorporated uniform crossover operator and combined crossover operator in GAs for solving open loop supply chain distribution network problem. The algorithms are tested on 50 randomly generated data set and compared with each other. The results of the numerical experiments show that the Hybrid K-means cluster based heuristic-GA-SA, when tested on 50 randomly generated data set, shows superior performance to the other methods for solving the open loop supply chain distribution network problem.

Keywords: RFID, supply chain distribution network, open loop supply chain, genetic algorithm, simulated annealing

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Authors: Tamer Mesallam, Medhat Yousef, Ayna Almasaad

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BACKGROUND: Cochlear implantation (CI) in children with additional disabilities can be a fundamental and supportive intervention. Although, there may be some positive impacts of CI on children with multiple disabilities such as better outcomes of communication skills, development, and quality of life, the families of those children complain from the post-implant habilitation efforts that considered as a burden. OBJECTIVE: To investigate the outcomes of CI children with different co-disabilities through using the Meaningful Auditory Integration Scale (MAIS) and the Meaningful Use of Speech Scale (MUSS) as outcome measurement tools. METHODS: The study sample comprised 25 hearing-impaired children with co-disability who received cochlear implantation. Age and gender-matched control group of 25 cochlear-implanted children without any other disability has been also included. The participants' auditory skills and speech outcomes were assessed using MAIS and MUSS tests. RESULTS: There was a statistically significant difference in the different outcomes measure between the two groups. However, the outcomes of some multiple disabilities subgroups were comparable to the control group. Around 40% of the participants with co-disabilities experienced advancement in their methods of communication from behavior to oral mode. CONCLUSION: Cochlear-implanted children with multiple disabilities showed variable degrees of auditory and speech outcomes. The degree of benefits depends on the type of the co-disability. Long-term follow-up is recommended for those children.

Keywords: children with disabilities, Cochlear implants, hearing impairment, language development

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Authors: Frank Kuebler, Rolf Steinhilper

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Artificial neural networks (ANN) as well as Design of Experiments (DOE) based regression analysis (RA) are mainly used for modeling of complex systems. Both methodologies are commonly applied in process and quality control of manufacturing processes. Due to the fact that resource efficiency has become a critical concern for manufacturing companies, these models needs to be extended to predict resource-consumption of manufacturing processes. This paper describes an approach to use neural networks as well as DOE based regression analysis for predicting resource consumption of manufacturing processes and gives a comparison of the achievable results based on an industrial case study of a turning process.

Keywords: artificial neural network, design of experiments, regression analysis, resource efficiency, manufacturing process

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Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

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Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

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Authors: Kulsoom S. Bughio, Naeem K. Janjua, Gordana Dermody, Leslie F. Sikos, Shamsul Islam

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The advancement in information and communication technologies (ICT) and wireless sensor networks have played a pivotal role in the design and development of real-time healthcare solutions, mainly targeting the elderly living in health-assistive smart homes. Such smart homes are equipped with sensor technologies to detect and record activities of daily living (ADL). This survey reviews and evaluates existing approaches and techniques based on real-time sensor-based modeling and reasoning in single-user and multi-user environments. It classifies the approaches into three main categories: learning-based, knowledge-based, and hybrid, and evaluates how they handle temporal relations, granularity, and uncertainty. The survey also highlights open challenges across various disciplines (including computer and information sciences and health sciences) to encourage interdisciplinary research for the detection and recognition of ADLs and discusses future directions.

Keywords: daily living activities, smart homes, single-user environment, multi-user environment

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Authors: Riza Yilmaz, Samet Kiyak, Sezin Nur Yilmaz, Yasemin Yilmaz

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Child delinquency has been increasing in our country as well as in many countries of the world. Child intelligence, abilities, family's social environment and life conditions are the factors which affect the child delinquency. The reports of 73 cases ages of 12-15 which were sent to the University of Bulent Ecevit, School of Medicine, Forensic Medicine Department between January 2011-September 2015, in order to evaluate medically, children pushed to crime by the judicial authorities are examined in terms of age, gender, educational background, place of residence, reasons for being sent, whether it’s a repeating crime or not, type of intelligence test, results revealed by forensic medicine and department of mental and neurological disorders. When children pushed to crime examined in terms of their crimes, the most common type of crime was identified as theft (n = 24). The crimes with 19 physical attacks and 12 sexual abuse were seen. Following that other 12 crimes were determined as damage to property, hemp crop, insult, incitement to crime, forgery of private documents, illegal excavation, threatening, involuntary manslaughter. The alleged crimes in 6 cases were more than one. The children pushed to crime are one of the major social problems of many countries. In this sense, it is not only the responsibility of government agencies to protect children pushed to crime, also, the civil society organizations should take place in this struggle.

Keywords: delinquent behaviour, forensic medicine, crime, punishment

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Authors: Mustafa Tevfik Hebebci, Ismail Sahin, Sirin Kucuk, Ismail Celik, Ahmet Oguz Akturk

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ADDIE is an instructional design model which has the five core elements: analyze, design, develop, implement, and evaluate. The ADDIE approach provides a systematic process for the analysis of instructional needs, the design and development of instructional programs and materials, implementation of a program, and the evaluation of the effectiveness of an instruction. The case-based study is an instructional design model that is a variant of project-oriented learning. Collecting and analyzing stories can be used in two primary ways -perform task analysis and as a learning support during instruction- by instructional designers. Besides, teachers use technology to develop students’ thinking, enriching the learning environment and providing permanent learning. The purpose of this paper is to introduce an interactive online case-study library website developed in a national project. The design goal of the website is to provide interactive, enhanced, case-based and online educational resource for educators through the purpose and within the scope of a national project. The ADDIE instructional design model was used in the development of the website for the interactive case-based library. This web-based library contains the navigation menus as the follows: “Homepage”, "Registration", "Branches", "Aim of The Research", "About TPACK", "National Project", "Contact Us", etc. This library is developed on a web-based platform, which is important in terms of manageability, accessibility, and updateability of data. Users are able to sort the displayed case-studies by their titles, dates, ratings, view counts, etc. In addition, they encouraged to rate and comment on the case-studies. The usability test is used and the expert opinion is taken for the evaluation of the website. This website is a tool to integrate technology in education. It is believed that this website will be beneficial for pre-service and in-service teachers in terms of their professional developments.

Keywords: design, ADDIE, case based library, technology integration

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Authors: Anna Mirczak

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Introduction: In connection with the aging of the population and the increase in the number of people with chronic illnesses, the priority objective for public health has become not only lengthening life, but also improving quality of life in older persons, as well as maintenance of their relative independence and active participation in social life. The most important determinant of a person’s quality of life is health. According to the literature, older people with chronic illness who live in rural settings are at greater risk for poor outcomes than their urban counterparts. Furthermore research characterizes the rural elderly as having a higher incidence of sickness, dysfunction, disability, restricted mobility, and acute and chronic conditions than their urban citizens. It is dictated by the overlapping certain specific socio-economic factors typical for rural areas which include: social and geography exclusion, limited access to health care centers, and low socioeconomic status. Aim of the study: The objective of this study was to recognize health status and needs of older people living in selected rural areas in Poland and evaluate the impacts of working in the farm on their health status. Material and methods: The study was performed personally, using interviews based on the structural questionnaires, during the period from March 2011 to October 2012. The group of respondents consisted 203 people aged 65 years and over living in selected rural areas in Poland. The analysis of collected research material was performed using the statistical package SPSS 19 for Windows. The level of significance for the tested the hypotheses assumed value of 0.05. Results: The mean age of participants was 75,5 years (SD=5,7) range from 65 to 94 years. Most of the interviewees had children (89.2%) and grandchildren (83.7) and lived mainly with family members (75.9%) mostly in double (46.8%) and triple (20.8%) household. The majority of respondents (71,9%) were physical working on the farm. At the time of interview, each of the respondents reported that they had been diagnosed with at least one chronic diseases by their GP. The most common were: hypertension (67,5%), osteoarthritis (44,8%), atherosclerosis (43,3%), cataract (40,4%), arrhythmia (28,6%), diabetes mellitus (19,7%) and stomach or duodenum ulcer diseases (17,2%).The number of diseases occurring of the sample was dependent on gender and age. Significant associations were observed between working on the farm and frequency of occurrence cardiovascular diseases, the gastrointestinal tract dysfunction and sensory disorders. Conclusions: The most common causes of disability among older citizens were: chronic diseases, malnutrition and complaints about access to health services (especially to cardiologist and an ophthalmologist). Health care access and health status are a particular concern in rural areas where the population is older, has lower education and income levels, and is more likely to be living in medically underserved areas than is the case in urban areas.

Keywords: ageing, health status, older people, rural

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Authors: Rajae Tahri, Omar Chokairi, Asmae Saadi, Souad Chaouir

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Background: The factors motivating students to choose a medical career is a long-standing topic of publication and discussion. To our knowledge, no national study on the motivation for choosing medical studies has been published to date. Population and methods: This is an observational, descriptive, and cross-sectional study of first-year medical students at the Faculty of Medicine and Pharmacy of Rabat. An anonymous questionnaire comprising 16 questions was developed and distributed to students during Embryology tutorials. The students were free to fill it in or not. The number of students who consented to participate in the survey was 266. The variables studied are the socio-demographic variables of the students and the reasons for choosing medical studies. Results: The most strongly and frequently chosen reasons for choice by our students were saving lives (64.9%), helping others (62.1%), love of medicine (57%), and reducing suffering (56.5%). The comparison of the results according to gender showed a significant difference between the degree of self-motivation of girls compared to that of boys (p <0.001). The reason that stood out the most for them was teamwork. The presence of a health professional in the family was associated with strong extrinsic motivation (p = 0.005). Conclusion: Understanding medical student career choices would improve our knowledge of the factors that influence medical student learning and performance. This knowledge will make it possible to adapt the educational strategies to maintain the motivation of the students throughout their course as well as during their exercise.

Keywords: motivation, motivational profiles, medical studies, Morocco

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Authors: Harsh Agarwal, Pratikkumar Patel, Maharshi Pathak

Abstract:

Recently the low prices of Crude oil and increase in strict environmental regulations limit limits the use of diesel based muds as these muds are relatively costlier and toxic, as a result disposal of cuttings into the eco-system is a major issue faced by the drilling industries. To overcome these issues faced by the Oil Industry, an attempt has been made to develop oil-in-water emulsion mud system using nettlespurge oil. Nettlespurge oil could be easily available and its cost is around ₹30/litre which is about half the price of diesel in India. Oil-based mud (OBM) was formulated with Nettlespurge oil extracted from Nettlespurge seeds using the Soxhlet extraction method. The formulated nettlespurge oil mud properties were analysed with diesel oil mud properties. The compared properties were rheological properties, yield point and gel strength, and mud density and filtration loss properties, fluid loss and filter cake. The mud density measurement showed that nettlespurge OBM was slightly higher than diesel OBM with mud density values of 9.175 lb/gal and 8.5 lb/gal, respectively, at barite content of 70 g. Thus it has a higher lubricating property. Additionally, the filtration loss test results showed that nettlespurge mud fluid loss volumes, oil was 11 ml, compared to diesel oil mud volume of 15 ml. The filtration loss test indicated that the nettlespurge oil mud with filter cake thickness of 2.2 mm had a cake characteristic of thin and squashy while the diesel oil mud resulted in filter cake thickness of 2.7 mm with cake characteristic of tenacious, rubbery and resilient. The filtration loss test results showed that nettlespurge oil mud fluid loss volumes was much less than the diesel based oil mud. The filtration loss test indicated that the nettlespurge oil mud filter cake thickness less than the diesel oil mud filter cake thickness. So Low formation damage and the emulsion stability effect was analysed with this experiment. The nettlespurge oil-in-water mud system had lower coefficient of friction than the diesel oil based mud system. All the rheological properties have shown better results relative to the diesel based oil mud. Therefore, with all the above mentioned factors and with the data of the conducted experiment we could conclude that the Nettlespurge oil based mud is economically and well as eco-logically much more feasible than the worn out and shabby diesel-based oil mud in the Drilling Industry.

Keywords: economical feasible, ecological feasible, emulsion stability, nettle spurge oil, rheological properties, soxhlet extraction method

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Authors: Joo-Heon Lee, Seo-Yeon Park, Chanyang Sur, Ho-Won Jang

Abstract:

Satellite-based remote sensing technique has been widely used in the area of drought and environmental monitoring to overcome the weakness of in-situ based monitoring. There are many advantages of remote sensing for drought watch in terms of data accessibility, monitoring resolution and types of available hydro-meteorological data including environmental areas. This study was focused on the applicability of drought monitoring based on satellite imageries by applying to the historical drought events, which had a huge impact on meteorological, agricultural, and hydrological drought. Satellite-based drought indices, the Standardized Precipitation Index (SPI) using Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Mission (GPM); Vegetation Health Index (VHI) using MODIS based Land Surface Temperature (LST), and Normalized Difference Vegetation Index (NDVI); and Scaled Drought Condition Index (SDCI) were evaluated to assess its capability to analyze the complex topography of the Korean peninsula. While the VHI was accurate when capturing moderate drought conditions in agricultural drought-damaged areas, the SDCI was relatively well monitored in hydrological drought-damaged areas. In addition, this study found correlations among various drought indices and applicability using Receiver Operating Characteristic (ROC) method, which will expand our understanding of the relationships between hydro-meteorological variables and drought events at global scale. The results of this research are expected to assist decision makers in taking timely and appropriate action in order to save millions of lives in drought-damaged areas.

Keywords: drought monitoring, moderate resolution imaging spectroradiometer (MODIS), remote sensing, receiver operating characteristic (ROC)

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Authors: N. J. Nawarathna, N. R. Kmarasinghe, D. Chandrasekara, B. M. R. S. Balasooriya, R. A. A. Shaminda, R. J. K. Senevirathne

Abstract:

Introduction: Thyroid malignancy associated with hyperthyroidism is considered rare. Retrospective studies have shown the incidence of thyroid malignancy in hyperthyroid patients to be low (0.7-8.5%). To assess the clinical relevance of this association, thyroid status in a cohort of patients with thyroid malignancy were analyzed. Method: Thyroid malignancies diagnosed histologically in 56 patients, over a 18 month period beginning from April 2013, in a single surgical unit at Teaching Hospital Kandy were included. Preoperative patient details and progression of thyroid status were asessed with Thyroid Stimulating Hormone, free Thyroxin and free Triiodothyronine levels. Results: Amongst 56 patients Papillary carcinoma was diagnosed in 44(78.6%), follicular carcinomas in 7(12.5%) and 5(8.9%) with medullary and anaplastic carcinomas. 12(21.4%) were males and 44(78.6%) were females. 20(35.7%) were less than 40years, 29(51.8%) were between 40 to 59years and 7(12.5%) were above 59years. Cross tabulation of Type of carcinoma with Gender revealed likelihood ratio of 6.908, Significance p = 0.032. Biochemically 12(21.4%) were hyperthyroid. Out of them 5(41.7%) had primary hyperthyroidism and 7(58.3%) had secondary hyperthyroidism. Mean age of euthyroid patients was 43.77years (SD 10.574) and hyperthyroid patients was 53.25years(SD 16.057). Independent Samples Test t is -2.446, two tailed significance p =0.018. When cross tabulate thyroid status with Age group Likelihood Ratio was 9.640, Significance p = 0.008. Conclusion: Papillary carcinoma is seen more among females. Among the patients with thyroid carcinomas, those with biochemically proven hyperthyroidism were more among the older age group than those who were euthyroid. Hence careful evaluation of elderly hyperthyroid patients to select the most suitable therapeutic approach is justified.

Keywords: age, hyperthyroidism, thyroid malignancy, thyroid status

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Authors: Davood Shishebori, Mohammad Saber Fallah Nezhad, Sina Seifi

Abstract:

In this paper, a variable multiple dependent state (MDS) sampling plan is developed based on the process capability index using Bayesian approach. The optimal parameters of the developed sampling plan with respect to constraints related to the risk of consumer and producer are presented. Two comparison studies have been done. First, the methods of double sampling model, sampling plan for resubmitted lots and repetitive group sampling (RGS) plan are elaborated and average sample numbers of the developed MDS plan and other classical methods are compared. A comparison study between the developed MDS plan based on Bayesian approach and the exact probability distribution is carried out.

Keywords: MDS sampling plan, RGS plan, sampling plan for resubmitted lots, process capability index (PCI), average sample number (ASN), Bayesian approach

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Authors: Zahra Ramezanpanah, Joachim Carvallo, Aurelien Rodriguez

Abstract:

This paper aims to detect anomalies in streaming data using machine learning algorithms. In this regard, we designed two separate pipelines and evaluated the effectiveness of each separately. The first pipeline, based on supervised machine learning methods, consists of two phases. In the first phase, we trained several supervised models using the UNSW-NB15 data-set. We measured the efficiency of each using different performance metrics and selected the best model for the second phase. At the beginning of the second phase, we first, using Argus Server, sniffed a local area network. Several types of attacks were simulated and then sent the sniffed data to a running algorithm at short intervals. This algorithm can display the results of each packet of received data in real-time using the trained model. The second pipeline presented in this paper is based on unsupervised algorithms, in which a Temporal Graph Network (TGN) is used to monitor a local network. The TGN is trained to predict the probability of future states of the network based on its past behavior. Our contribution in this section is introducing an indicator to identify anomalies from these predicted probabilities.

Keywords: temporal graph network, anomaly detection, cyber security, IDS

Procedia PDF Downloads 92