Search results for: minimum data set

Authors: J. K Adedeji, O. H Olowomofe, C. O Alo, S.T Ijatuyi

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The issue of high blood sugar level, the effects of which might end up as diabetes mellitus, is now becoming a rampant cardiovascular disorder in our community. In recent times, a lack of awareness among most people makes this disease a silent killer. The situation calls for urgency, hence the need to design a device that serves as a monitoring tool such as a wrist watch to give an alert of the danger a head of time to those living with high blood glucose, as well as to introduce a mechanism for checks and balances. The neural network architecture assumed 8-15-10 configuration with eight neurons at the input stage including a bias, 15 neurons at the hidden layer at the processing stage, and 10 neurons at the output stage indicating likely symptoms cases. The inputs are formed using the exclusive OR (XOR), with the expectation of getting an XOR output as the threshold value for diabetic symptom cases. The neural algorithm is coded in Java language with 1000 epoch runs to bring the errors into the barest minimum. The internal circuitry of the device comprises the compatible hardware requirement that matches the nature of each of the input neurons. The light emitting diodes (LED) of red, green, and yellow colors are used as the output for the neural network to show pattern recognition for severe cases, pre-hypertensive cases and normal without the traces of diabetes mellitus. The research concluded that neural network is an efficient Accu-Chek design tool for the proper monitoring of high glucose levels than the conventional methods of carrying out blood test.

Keywords: Accu-Check, diabetes, neural network, pattern recognition

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Authors: Ethel E. Phiri, Lionel Hartzenberg, Percy Chimwamuromba, Emmanuel Nepolo, Jens Kossmann, James R. Lloyd

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Orphan crops are underresearched and underutilized food plant species that have not been categorized as major food crops, but have the potential to be economically and agronomically significant. They have been documented to have the ability to tolerate extreme environmental conditions. However, limited research has been conducted to uncover their potential as food crop species. The New Partnership for Africa’s Development (NEPAD) has classified Marama bean, Tylosema esculentum, as an orphan crop. The plant is one of the 101 African orphan crops that must have their genomes sequenced, assembled, and annotated in the foreseeable future. Marama bean is a perennial leguminous plant that primarily grows in poor, arid soils in southern Africa. The plants produce large tubers that can weigh as much as 200kg. While the foliage provides fodder, the tuber is carbohydrate rich and is a staple food source for rural communities in Namibia. Also, the edible seeds are protein- and oil-rich. Marama Bean plants respond rapidly to increased temperatures and severe water scarcity without extreme consequences. Advances in molecular biology and biotechnology have made it possible to effectively transfer technologies between model- and major crops to orphan crops. In this research, the aim was to assemble the first transcriptomic analysis of Marama Bean RNA-sequence data. Many model plant species have had their genomes sequenced and their transcriptomes assembled. Therefore the availability of transcriptome data for a non-model crop plant species will allow for gene identification and comparisons between various species. The data has been sequenced using the Ilumina Hiseq 2500 sequencing platform. Data analysis is underway. In essence, this research will eventually evaluate the potential use of Marama Bean as a crop species to improve its value in agronomy. data for a non-model crop plant species will allow for gene identification and comparisons between various species. The data has been sequenced using the Ilumina Hiseq 2500 sequencing platform. Data analysis is underway. In essence, this researc will eventually evaluate the potential use of Marama bean as a crop species to improve its value in agronomy.

Keywords: 101 African orphan crops, RNA-Seq, Tylosema esculentum, underutilised crop plants

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Authors: Kassahun Tilahun

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Most of the studies available on adult psychological well-being have been centered on Western countries. However, psychological well-being does not have the same meaning across the world. The Euro-American and African conceptions and experiences of psychological well-being differ systematically. As a result, questions like, how do people living in developing African countries, like Ethiopia, report their psychological well-being; what would the context-specific prominent determinants of their psychological well-being be, needs a definitive answer. This study was, therefore, aimed at developing a new theory that would address these socio-cultural issues of psychological well-being. Consequently, data were obtained through interview and open ended questionnaire. A total of 438 adults, working in governmental and non-governmental organizations situated in Addis Ababa, participated in the study. Appropriate qualitative method of data analysis, i.e. thematic content analysis, was employed for analyzing the data. The thematic analysis involves a type of abductive analysis, driven both by theoretical interest and the nature of the data. Reliability and credibility issues were addressed appropriately. The finding identified five major categories of themes, which are viewed as essential in determining the conceptions and experiences of psychological well-being of Ethiopian adults. These were; socio-cultural harmony, social cohesion, security, competence and accomplishment, and the self. Detailed discussion on the rational for including these themes was made and appropriate positive psychology interventions were proposed. Researchers are also encouraged to expand this qualitative research and in turn develop a suitable instrument taping the psychological well-being of adults with different sociocultural orientations.

Keywords: sociocultural, psychological, well-being Ethiopia, adults

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Authors: Jinan Fiaidhi, Sabah Mohammed, Petros Zezos

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Capsule endoscopy (CE) is an established noninvasive diagnostic modality in investigating small bowel disease. CE has a pivotal role in assessing patients with suspected bleeding or identifying evidence of active Crohn's disease in the small bowel. However, CE produces lengthy videos with at least eighty thousand frames, with a frequency rate of 2 frames per second. Gastroenterologists cannot dedicate 8 to 15 hours to reading the CE video frames to arrive at a diagnosis. This is why the issue of analyzing CE videos based on modern artificial intelligence techniques becomes a necessity. However, machine learning, including deep learning, has failed to report robust results because of the lack of large samples to train its neural nets. In this paper, we are describing a thick data approach that learns from a few anchor images. We are using sound datasets like KVASIR and CrohnIPI to filter candidate frames that include interesting anomalies in any CE video. We are identifying candidate frames based on feature extraction to provide representative measures of the anomaly, like the size of the anomaly and the color contrast compared to the image background, and later feed these features to a decision tree that can classify the candidate frames as having a condition like the Crohn's Disease. Our thick data approach reported accuracy of detecting Crohn's Disease based on the availability of ulcer areas at the candidate frames for KVASIR was 89.9% and for the CrohnIPI was 83.3%. We are continuing our research to fine-tune our approach by adding more thick data methods for enhancing diagnosis accuracy.

Keywords: thick data analytics, capsule endoscopy, Crohn’s disease, siamese neural network, decision tree

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Authors: Valdir Moura, Raniele dos Anjos de Souza, Fernando Gomes de Souza, Jose Vagner da Silva, Jerry Adriani Johann

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The estimate of the Area of Land to be planted with annual crops and its stratification by the municipality are important variables in crop forecast. Nowadays in Brazil, these information’s are obtained by the Brazilian Institute of Geography and Statistics (IBGE) and published under the report Assessment of the Agricultural Production. Due to the high cloud cover in the main crop growing season (October to March) it is difficult to acquire good orbital images. Thus, one alternative is to work with remote sensing data from dates before the crop growing season. This work presents the use of multitemporal Landsat data gathered on July and September (before the summer growing season) in order to estimate the area of land to be planted with summer crops in an area of São Paulo State, Brazil. Geographic Information Systems (GIS) and digital image processing techniques were applied for the treatment of the available data. Supervised and non-supervised classifications were used for data in digital number and reflectance formats and the multitemporal Normalized Difference Vegetation Index (NDVI) images. The objective was to discriminate the tracts with higher probability to become planted with summer crops. Classification accuracies were evaluated using a sampling system developed basically for this study region. The estimated areas were corrected using the error matrix derived from these evaluations. The classification techniques presented an excellent level according to the kappa index. The proportion of crops stratified by municipalities was derived by a field work during the crop growing season. These proportion coefficients were applied onto the area of land to be planted with summer crops (derived from Landsat data). Thus, it was possible to derive the area of each summer crop by the municipality. The discrepancies between official statistics and our results were attributed to the sampling and the stratification procedures. Nevertheless, this methodology can be improved in order to provide good crop area estimates using remote sensing data, despite the cloud cover during the growing season.

Keywords: area intended for summer culture, estimated area planted, agriculture, Landsat, planting schedule

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Authors: Kefaya Qaddoum

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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

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Authors: Anoff Anim, Lila A. M. Mahmoud, Maria Katsikogianni, Sanjit Nayak

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Sustained and controlled delivery of antimicrobial drugs have been largely studied recently using metal organic frameworks (MOFs)and different polymers. However, much attention has not been given to combining both MOFs and biodegradable polymers, which would be a good strategy in providing a sustained gradual release of the drugs. Herein, we report a comparative study of the sustained and controlled release of widely used antibacterial drugs, cephalexin and metronidazole, from zinc-based MOF-5 incorporated in biodegradable polycaprolactone (PCL) and poly-lactic glycolic acid (PLGA) membranes. Cephalexin and metronidazole were separately incorporated in MOF-5 post-synthetically, followed by their integration into biodegradable PLGA and PCL membranes. The pristine MOF-5 and the loaded MOFs were thoroughly characterized by FT-IR, SEM, TGA and PXRD. Drug release studies were carried out to assess the release rate of the drugs in PBS and distilled water for up to 48 hours using UV-Vis Spectroscopy. Four bacterial strains from both the Gram-positive and Gram-negative types, Staphylococus aureus, Staphylococuss epidermidis, Escherichia coli, Acinetobacter baumanii, were tested against the pristine MOF, pure drugs, loaded MOFs and the drug-loaded MOF-polymer composites. Metronidazole-loaded MOF-5 composite of PLGA (PLGA-Met@MOF-5) was found to show highest efficiency to inhibit the growth of S. epidermidis compared to the other bacteria strains while maintaining a sustained minimum inhibitory concentration (MIC). This study demonstrates that the combination of biodegradable MOF-polymer composites can provide an efficient platform for sustained and controlled release of antimicrobial drugs and can be a potential strategy to integrate them in biomedical devices.

Keywords: antimicrobial resistance, biodegradable polymers, cephalexin, drug release metronidazole, MOF-5, PCL, PLGA

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Authors: Sangseung Kang, Kyekyung Kim, Suyoung Chi

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A good posture is the most important factor in riding. In this paper, we present an effective posture correction system for a riding simulator environment to provide position error detection and customized training functions. The proposed system detects and analyzes the rider's posture using depth data and inertial sensing data. Our experiments show that including these functions will help users improve their seat for a riding.

Keywords: posture correction, posture training, riding posture, riding simulator

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Authors: Shaukat Ali Shahee, Usha Ananthakumar

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A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

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Authors: Amanat Ali, Muhammad S. Al-Siyabi, Mostafa I. Waly, Hashem Al-Kilani

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In a cross-sectional research design, we assessed the nutrient intake, nutritional status, nutritional knowledge and dietary habits of Sultan Qaboos University (SQU) student athletes. A total of 71 (49 male and 22 female) student athletes with a mean age of 21.0 ± 1.81 and 19.32 ± 0.72 years and body mass index (BMI) of 22.51 ± 1.98 and 20.34 ± 2.97 kg/m2 for male and female respectively, participated in this study. A study questionnaire consisting of 2 sections was distributed to the participants. Section I included 18 questions regarding the demographic information, whereas the Section II consisted of 20 questions regarding the nutrition knowledge. The dietary intake of participants was collected by using a 7-days food diary identifying the frequency as well as the variety of food consumption. Significant differences (P < 0.05) were observed in the main sources of nutrition information used by the male and female athletes. Male athletes mainly had most of the nutrition information from friends (17%) whereas female athletes relied mainly on the family (20%). More female athletes (20%) were using TV as a source of nutrition information as compared to male athletes (15%). Both male and female athletes had the minimum nutrition information from dietitians and physicians. Significant (P < 0.05) differences were also observed in the nutritional knowledge and dietary habits scores of male and female athletes, which were 57 % and 49 %, respectively. Male athletes were classified to have fair nutritional knowledge and dietary habits, whereas the female athletes had poor nutritional knowledge and dietary habits. The average daily energy intake of male athletes was 2595 ± 358 kcal/day. Carbohydrate, fat, and protein contributed 64%, 22%, and 14%, of the total energy intake for the male athletes, respectively. The energy and macronutrients intake of male athletes was within the recommended dietary intake. The results indicated some gaps in the nutritional knowledge of SQU student athletes and suggest that there is a need for developing strategies in counseling and teaching the athletes to improve their nutritional knowledge and dietary habits.

Keywords: nutrient assessment, nutritional knowledge, dietary habits, Omani University athletes

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Authors: Sadaf Kalhori

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Introduction: Doxorubicin (DOX)-induced cardiotoxicity is widely known as the most severe complication of anthracycline-based chemotherapy in patients with cancer. It is unknown whether Deferiprone (DFP), could reduce the severity of DOX-induced cardiotoxicity by inhibiting free radical reactions. Thus, this study was performed to assess the protective effect of Deferiprone on DOX-induced cardiotoxicity in a rat model. Methods: The rats were divided into five groups. Group one was a control group. Group 2 was DOX (2 mg/kg/day, every other day for 12 days), and Group three to five which receiving DOX as in group 2 and DFP 75,100 and 150 mg/kg/day, for 19 days, respectively. DFP was starting 5 days prior to the first DOX injection and two days after the last DOX injection throughout the study. Electrocardiographic and hemodynamic studies, along with histopathological examination, were conducted. In addition, serum sample was taken and total cholesterol, Malone dialdehyde, triglyceride, albumin, AST, ALT, total protein, lactate dehydrogenase, total anti-oxidant and creatine kinase were assessed. Result: Our results showed the normal structure of endocardial, myocardial and pericardial in the control group. Pathologic data such as edema, hyperemia, bleeding, endocarditis, myocarditis and pericarditis, hyaline degeneration, cardiomyocyte necrosis, myofilament degeneration and nuclear chromatin changes were assessed in all groups. In the DOX group, all pathologic data was seen with mean grade of 2±1.25. In the DFP group with a dose of 75 and 100 mg, the mean grade was 1.41± 0.31 and 1±.23, respectively. In DFP group with a dose of 150, the pathologic data showed a milder change in comparison with other groups with e mean grade of 0.45 ±0.19. Most pathologic data in DFP groups showed significant changes in comparison with the DOX group (p < 0.001). Discussion: The results also showed that DFP treatment significantly improved DOX-induced heart damage, structural changes in the myocardium, and ventricular function. Our data confirm that DFP is protective against cardiovascular-related disorders induced by DOX. Clinical studies are needed to be involved to examine these findings in humans.

Keywords: cardiomyopathy, deferiprone, doxorubicin, rat

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Authors: Mariam Tsitsagi, Ana Berdzenishvili

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Rainfall intensity change is one of the main indicators of climate change. It has a great influence on agriculture as one of the main factors causing soil erosion. Splash and sheet erosion are one of the most prevalence and harmful for agriculture. It is invisible for an eye at first stage, but the process will gradually move to stream cutting erosion. Our study provides the assessment of rainfall erosivity potential with the use of modern research methods in Kakheti region. The region is the major provider of wheat and wine in the country. Kakheti is located in the eastern part of Georgia and characterized quite a variety of natural conditions. The climate is dry subtropical. For assessment of the exact rate of rainfall erosion potential several year data of rainfall with short intervals are needed. Unfortunately, from 250 active metro stations running during the Soviet period only 55 of them are active now and 5 stations in Kakheti region respectively. Since 1936 we had data on rainfall intensity in this region, and rainfall erosive potential is assessed, in some old papers, but since 1990 we have no data about this factor, which in turn is a necessary parameter for determining the rainfall erosivity potential. On the other hand, researchers and local communities suppose that rainfall intensity has been changing and the number of haily days has also been increasing. However, finding a method that will allow us to determine rainfall erosivity potential as accurate as possible in Kakheti region is very important. The study period was divided into three sections: 1936-1963; 1963-1990 and 1990-2015. Rainfall erosivity potential was determined by the scientific literature and old meteorological stations’ data for the first two periods. And it is known that in eastern Georgia, at the boundary between steppe and forest zones, rainfall erosivity in 1963-1990 was 20-75% higher than that in 1936-1963. As for the third period (1990-2015), for which we do not have data of rainfall intensity. There are a variety of studies, where alternative ways of calculating the rainfall erosivity potential based on lack of data are discussed e.g.based on daily rainfall data, average annual rainfall data and the elevation of the area, etc. It should be noted that these methods give us a totally different results in case of different climatic conditions and sometimes huge errors in some cases. Three of the most common methods were selected for our research. Each of them was tested for the first two sections of the study period. According to the outcomes more suitable method for regional climatic conditions was selected, and after that, we determined rainfall erosivity potential for the third section of our study period with use of the most successful method. Outcome data like attribute tables and graphs was specially linked to the database of Kakheti, and appropriate thematic maps were created. The results allowed us to analyze the rainfall erosivity potential changes from 1936 to the present and make the future prospect. We have successfully implemented a method which can also be use for some another region of Georgia.

Keywords: erosivity potential, Georgia, GIS, Kakheti, rainfall

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Authors: Qiong Li

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This study investigated in which condition computer-mediated communication (CMC) could promote pragmatic development. The focal feature included four Chinese sentence final particles (SFPs), a, ya, ba, and ne. They occur frequently in Chinese, and function as mitigators to soften the tone of speech. However, L2 acquisition of SFPs is difficult, suggesting the necessity of additional exposure to or explicit instruction on Chinese SFPs. This study follows this line and aims to explore two research questions: (1) Is CMC combined with data-driven instruction more effective than CMC alone in promoting L2 Chinese learners’ SFP use? (2) How does L2 Chinese learners’ SFP use change over time, as compared to the production of native Chinese speakers? The study involved 19 intermediate-level learners of Chinese enrolled at a private American university. They were randomly assigned to two groups: (1) the control group (N = 10), which was exposed to SFPs through CMC alone, (2) the treatment group (N = 9), which was exposed to SFPs via CMC and data-driven instruction. Learners interacted with native speakers on given topics through text-based CMC over Skype. Both groups went through six 30-minute CMC sessions on a weekly basis, with a one-week interval after the first two CMC sessions and a two-week interval after the second two CMC sessions (nine weeks in total). The treatment group additionally received a data-driven instruction after the first two sessions. Data analysis focused on three indices: token frequency, type frequency, and acceptability of SFP use. Token frequency was operationalized as the raw occurrence of SFPs per clause. Type frequency was the range of SFPs. Acceptability was rated by two native speakers using a rating rubric. The results showed that the treatment group made noticeable progress over time on the three indices. The production of SFPs approximated the native-like level. In contrast, the control group only slightly improved on token frequency. Only certain SFPs (a and ya) reached the native-like use. Potential explanations for the group differences were discussed in two aspects: the property of Chinese SFPs and the role of CMC and data-driven instruction. Though CMC provided the learners with opportunities to notice and observe SFP use, as a feature with low saliency, SFPs were not easily noticed in input. Data-driven instruction in the treatment group directed the learners’ attention to these particles, which facilitated the development.

Keywords: computer-mediated communication, data-driven instruction, pragmatic development, second language Chinese, sentence final particles

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Authors: Messis A., Adjebli A., Ayeche R., Talbi M., Tighilet K., Louardiane M.

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Cancers are the second cause of death worldwide. Prevalence and incidence of cancers is getting increased by aging and population growth. This study aims to predict and modeling the evolution of breast, Colorectal, Lung, Bladder and Prostate cancers over the period of 2014-2019. In this study, data were analyzed using time series analysis with double exponential smoothing method to forecast the future pattern. To describe and fit the appropriate models, Minitab statistical software version 17 was used. Between 2014 and 2019, the overall trend in the raw number of new cancer cases registered has been increasing over time; the change in observations over time has been increasing. Our forecast model is validated since we have good prediction for the period 2020 and data not available for 2021 and 2022. Time series analysis showed that the double exponential smoothing is an efficient tool to model the future data on the raw number of new cancer cases.

Keywords: cancer, time series, prediction, double exponential smoothing

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Authors: Hosam A. Shawky, Amr A. Abdel Fatah, Moustafa M. S. Abo ElFad, Abdel Hameed M. El-Aassar

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Water desalination projects based on reverse osmosis technology are being introduced in Egypt to combat drinking water shortage in remote areas. Reverse osmosis (RO) desalination is a pressure driven process. This paper focuses on the design of an integrated brackish water and seawater RO desalination and solar Photovoltaic (PV) technology. A small Mobile PV driven RO desalination plant prototype without batteries is designed and tested. Solar-driven reverse osmosis desalination can potentially break the dependence of conventional desalination on fossil fuels, reduce operational costs, and improve environmental sustainability. Moreover, the innovative features incorporated in the newly designed PV-RO plant prototype are focusing on improving the cost effectiveness of producing drinkable water in remote areas. This is achieved by maximizing energy yield through an integrated automatic single axis PV tracking system with programmed tilting angle adjustment. An autonomous cleaning system for PV modules is adopted for maximizing energy generation efficiency. RO plant components are selected so as to produce 4-5 m3/day of potable water. A basic criterion in the design of this PV-RO prototype is to produce a minimum amount of fresh water by running the plant during peak sun hours. Mobility of the system will provide potable water to isolated villages and population as well as ability to provide good drinking water to different number of people from any source that is not drinkable.

Keywords: design, reverse osmosis, photovoltaic, energy, desalination, Egypt

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Authors: Dipti Patra, Guguloth Uma, Smita Pradhan

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Registration is a fundamental task in image processing. It is used to transform different sets of data into one coordinate system, where data are acquired from different times, different viewing angles, and/or different sensors. The registration geometrically aligns two images (the reference and target images). Registration techniques are used in satellite images and it is important in order to be able to compare or integrate the data obtained from these different measurements. In this work, mutual information is considered as a similarity metric for registration of satellite images. The transformation is assumed to be a rigid transformation. An attempt has been made here to optimize the transformation function. The proposed image registration technique hybrid PSO-GA incorporates the notion of Particle Swarm Optimization and Genetic Algorithm and is used for finding the best optimum values of transformation parameters. The performance comparision obtained with the experiments on satellite images found that the proposed hybrid PSO-GA algorithm outperforms the other algorithms in terms of mutual information and registration accuracy.

Keywords: image registration, genetic algorithm, particle swarm optimization, hybrid PSO-GA algorithm and mutual information

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Authors: Reka Zsuzsanna Mathe

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The globalization and the Europeanization have significantly contributed to a change in the role of the nation-states. The global economic crisis, the climate changes, and the recent refugee crisis, are just a few among many challenges that cannot be effectively addressed by the traditional role of the nation-states. One of the main roles of the states is to solve collective action problems, however due to their changing roles; apparently this is getting more and more difficult. Depending on political culture, collective action problems are solved either through cooperation or conflict. The political culture of Central and Eastern European (CEE) countries is marked by low civic participation and by a weak civil society. In this type of culture collective action problems are likely to be induced through conflict, rather than the democratic process of dialogue and any type of social change is probably to be introduced by social movements. Several studies have been conducted on the social movements of the CEE countries, yet, it is still not clear if the most significant social movements of the region tend to choose rather the cooperative or the conflictual way as action strategy. This study differentiates between a national and a European action field, having different social orders. The actors of the two fields are the broadly understood civil society members, conceptualized as social movements. This research tries to answer the following questions: a) What are the norms that best characterize the CEE countries’ social order? b) What type of actors would prefer a change and in which areas? c) Is there a significant difference between the main actors active in the national versus the European field? The main hypotheses are that there are conflicting norms defining the national and the European action field, and there is a significant difference between the action strategies adopted by social movements acting in the two different fields. In mapping the social order, the study uses data provided by the European Social Survey. Big data of the Global Data on Events, Location and Tone (GDELT) database offers information regarding the main social movements and their preferred type of action. The unit of the analysis is the so called ‘Visegrad 4’ countries: Poland, Czech Republic, Slovakia and Hungary and the research uses data starting from 2005 (after the European accession of these four countries) until May, 2017. According to the data, the main hypotheses were confirmed.

Keywords: big data, Central and Eastern Europe, civil society, GDELT, social movements

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Authors: Latha V. Prabhu, B.V. Murlimanju, P.J. Jiji, Mangala M. Pai

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Background: The capitate is the largest among the carpal bones. There exists no literature about the vascular foramina of the capitate bone. The objective of the present study was to investigate the morphology and number of the nutrient foramina in the cadaveric dried capitate bones of the Indian population. Methods: The present study included 59 capitate bones (25 right sided and 34 left sided) which were obtained from the gross anatomy laboratory of our institution. The bones were macroscopically observed for the nutrient foramina and the data was collected with respect to their number. The tabulation of the data and analysis were done. Results: All of our specimens (100%) exhibited the nutrient foramina over the non-articular and articular surfaces. The foramina were observed at the medial, lateral, palmar and dorsal surfaces of the capitate bones. The foramina were ranged from 6 to 23 in each capitate bone. In the medial surface, the foramina ranged from 1 to 6, lateral surface from 0 to 7, the foramina ranged between 0 and 5 in the palmar surface. However most of the foramina were located at the dorsal surface which ranged from 3 to 11. Conclusion: We believe that the present study has provided additional data about the nutrient foramina of the capitate bones. The data is enlightening to the orthopedic surgeon and would help in the hand surgeries. The knowledge about the foramina is also important to the radiologists to prevent the misinterpretation of the findings in the x ray and computed tomogram scan films. The foramina may mimick like erosions and ossicles. The morphological knowledge of the vasculature, their foramina of entry and number is required to understand the concepts in the avascular necrosis of the capitate.

Keywords: avascular necrosis, capitate, morphology, nutrient foramen

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Authors: Gregory Michael McMahon

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Risk management for the purposes of minimizing the damages from the operations of dams has met with opposition emerging from organisations and authorities, and their practitioners. It appears that the cause may be a misunderstanding of risk management arising from exchanges that mix deterministic thinking with risk-centric thinking and that do not separate uncertainty from reliability and accuracy from probability. This paper sets out those misunderstandings that arose from dam operations at Wivenhoe in 2011, using a comparison of outcomes that have been based on the methodology and its rules and those that have been operated by applying misunderstandings of the rules. The paper addresses the performance of one risk-centric Flood Manual for Wivenhoe Dam in achieving a risk management outcome. A mixture of engineering, administrative, and legal factors appear to have combined to reduce the outcomes from the risk approach. These are described. The findings are that a risk-centric Manual may need to assist administrations in the conduct of scenario training regimes, in responding to healthy audit reporting, and in the development of decision-support systems. The principal assistance needed from the Manual, however, is to assist engineering and the law to a good understanding of how risks are managed – do not assume that risk management is understood. The wider findings are that the critical profession for decision-making downstream of the meteorologist is not dam engineering or hydrology, or hydraulics; it is risk management. Risk management will provide the minimum flood damage outcome where actual rainfalls match or exceed forecasts of rainfalls, that therefore risk management will provide the best approach for the likely history of flooding in the life of a dam, and provisions made for worst cases may be state of the art in risk management. The principal conclusion is the need for training in both risk management as a discipline and also in the application of risk management rules to particular dam operational scenarios.

Keywords: risk management, flood control, dam operations, deterministic thinking

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Authors: Phenias Nsabimana, Jérôme W. Some, Hilda Vasanthakaalam, Stefaan De Henauw, Souheila Abbeddou

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Tools for the dietary assessment in adults are limited in low- and middle-income settings. The objective of this study was to develop and validate a semi-quantitative food frequency questionnaire (FFQ) against the multiple pass-24 h recall tool for use in urban and rural Rwanda. A total of 212 adults (154 females and 58 males), 18-49 aged, including 105 urban and 107 rural residents, from the four regions of Rwanda, were recruited in the present study. A multiple-pass 24- H recall technique was used to collect dietary data in both urban and rural areas in four different rounds, on different days (one weekday and one weekend day), separated by a period of three months, from November 2020 to October 2021. The details of all the foods and beverages consumed over the 24h period of the day prior to the interview day were collected during face-to-face interviews. A list of foods, beverages, and commonly consumed recipes was developed by the study researchers and ten research assistants from the different regions of Rwanda. Non-standard recipes were collected when the information was available. A single semi-quantitative FFQ was also developed in the same group discussion prior to the beginning of the data collection. The FFQ was collected at the beginning and the end of the data collection period. Data were collected digitally. The amount of energy and macro-nutrients contributed by each food, recipe, and beverage will be computed based on nutrient composition reported in food composition tables and weight consumed. Median energy and nutrient contents of different food intakes from FFQ and 24-hour recalls and median differences (24-hour recall –FFQ) will be calculated. Kappa, Spearman, Wilcoxon, and Bland-Altman plot statistics will be conducted to evaluate the correlation between estimated nutrient and energy intake found by the two methods. Differences will be tested for their significance and all analyses will be done with STATA 11. Data collection was completed in November 2021. Data cleaning is ongoing and the data analysis is expected to be completed by July 2022. A developed and validated semi-quantitative FFQ will be available for use in dietary assessment. The developed FFQ will help researchers to collect reliable data that will support policy makers to plan for proper dietary change intervention in Rwanda.

Keywords: food frequency questionnaire, reproducibility, 24-H recall questionnaire, validation

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Authors: Ruangdech Sirikit

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The purposes of this research were to study the variables affecting the quality assessment of mathematics subject in Thailand by using value-added analysis on TIMSS 2011. The data used in this research is the secondary data from the 2011 Trends in International Mathematics and Science Study (TIMSS), collected from 6,124 students in 172 schools from Thailand, studying only mathematics subjects. The data were based on 14 assessment tests of knowledge in mathematics. There were 3 steps of data analysis: 1) To analyze descriptive statistics 2) To estimate competency of students from the assessment of their mathematics proficiency by using MULTILOG program; 3) analyze value added in the model of quality assessment using Value-Added Model with Hierarchical Linear Modeling (HLM) and 2 levels of analysis. The research results were as follows: 1. Student level variables that had significant effects on the competency of students at .01 levels were Parental care, Resources at home, Enjoyment of learning mathematics and Extrinsic motivation in learning mathematics. Variable that had significant effects on the competency of students at .05 levels were Education of parents and self-confident in learning mathematics. 2. School level variable that had significant effects on competency of students at .01 levels was Extra large school. Variable that had significant effects on competency of students at .05 levels was medium school.

Keywords: quality assessment, value-added model, TIMSS, mathematics, Thailand

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Authors: Devin Simmons

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At the USDOT’s Bureau of Transportation Statistics, a biannual census of ferry operators in the U.S. is conducted, with results such as route mileage used to determine federal funding levels for operators. AIS data allows for the possibility of using GIS software and geographical methods to confirm operator-reported mileage for individual ferry routes. As part of the USDOT’s work on the ferry census, an algorithm was developed that uses AIS data for ferry vessels in conjunction with known ferry terminal locations to model the average route travelled for use as both a cartographic product and confirmation of operator-reported mileage. AIS data from each vessel is first analyzed to determine individual journeys based on the vessel’s velocity, and changes in velocity over time. These trips are then converted to geographic linestring objects. Using the terminal locations, the algorithm then determines whether the trip represented a known ferry route. Given a large enough dataset, routes will be represented by multiple trip linestrings, which are then filtered by DBSCAN spatial clustering to remove outliers. Finally, these remaining trips are ready to be averaged into one route. The algorithm interpolates the point on each trip linestring that represents the start point. From these start points, a centroid is calculated, and the first point of the average route is determined. Each trip is interpolated again to find the point that represents one percent of the journey’s completion, and the centroid of those points is used as the next point in the average route, and so on until 100 points have been calculated. Routes created using this algorithm have shown demonstrable improvement over previous methods, which included the implementation of a LOESS model. Additionally, the algorithm greatly reduces the amount of manual digitizing needed to visualize ferry activity.

Keywords: ferry vessels, transportation, modeling, AIS data

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Authors: Kitti Leangkrua

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This paper presents a risk-based maintenance strategy of a power transformer in order to optimize operating and maintenance costs. The methodology involves the study and preparation of a database for the collection the technical data and test data of a power transformer. An evaluation of the overall condition of each transformer is performed by a program developed as a result of the measured results; in addition, the calculation of the main equipment separation to the overall condition of the transformer (% HI) and the criteria for evaluating the importance (% ImI) of each location where the transformer is installed. The condition assessment is performed by analysis test data such as electrical test, insulating oil test and visual inspection. The condition of the power transformer will be classified from very poor to very good condition. The importance is evaluated from load criticality, importance of load and failure consequence. The risk matrix is developed for evaluating the risk of each power transformer. The high risk power transformer will be focused firstly. The computerized program is developed for practical use, and the maintenance strategy of a power transformer can be effectively managed.

Keywords: asset management, risk-based maintenance, power transformer, health index

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Authors: Nora Neuteboom

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This study investigates household financial behaviors during unemployment spells in the Netherlands using high-frequency transaction data through a event study specification integrating propensity score matching. In our specification, we contrasted treated individuals, who underwent job loss, with non-treated individuals possessing comparable financial characteristics. The initial onset of unemployment triggers a substantial surge in income, primarily attributed to transition payments, but swiftly drops post-unemployment, with unemployment benefits covering slightly over half of former salary earnings. Despite a re-employment rate of around half within six months, the treatment group experiences a persistent average monthly earnings reduction of approximately 600 EUR by month. Spending patterns fluctuate significantly, surging before unemployment due to transition payments and declining below non-treated individuals post-unemployment, indicating challenges to fully smooth consumption after job loss. Furthermore, our study disentangles the effects of transition payments and liquidity holdings on spending, revealing that transition payments exert a more pronounced and prolonged impact on consumption smoothing than liquidity holdings. Transition payments significantly stimulate spending, particularly in pin and iDEAL categories, contrasting a much smaller relative spending impact of liquidity holdings.

Keywords: household consumption, transaction data, big data, propensity score matching

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Authors: Michelangelo Sofo, Giuseppe Labianca

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In recent years, the scientific community's interest in the exploratory analysis of biomedical data has increased exponentially. Considering the field of research of nutritional biologists, the curative process, based on the analysis of clinical data, is a very delicate operation due to the fact that there are multiple solutions for the management of pathologies in the food sector (for example can recall intolerances and allergies, management of cholesterol metabolism, diabetic pathologies, arterial hypertension, up to obesity and breathing and sleep problems). In this regard, in this research work a system was created capable of evaluating various dietary regimes for specific patient pathologies. The system is founded on a mathematical-numerical model and has been created tailored for the real working needs of an expert in human nutrition using the human-centered design (ISO 9241-210), therefore it is in step with continuous scientific progress in the field and evolves through the experience of managed clinical cases (machine learning process). DietAdhoc® is a decision support system nutrition specialists for patients of both sexes (from 18 years of age) developed with an agile methodology. Its task consists in drawing up the biomedical and clinical profile of the specific patient by applying two algorithmic optimization approaches on nutritional data and a symbolic solution, obtained by transforming the relational database underlying the system into a deductive database. For all three solution approaches, particular emphasis has been given to the explainability of the suggested clinical decisions through flexible and customizable user interfaces. Furthermore, the system has multiple software modules based on time series and visual analytics techniques that allow to evaluate the complete picture of the situation and the evolution of the diet assigned for specific pathologies.

Keywords: medical decision support, physiological data extraction, data driven diagnosis, human centered AI, symbiotic AI paradigm

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Authors: Patrick Cummings, Laura Cassani, Deirdre Kelliher

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In this paper we present findings from a research effort to apply a hybrid collaborative-context approach for a system focused on Marine Corps civil affairs data collection, aggregation, and analysis called the Marine Civil Information Management System (MARCIMS). The goal of this effort is to provide operators with information to make sense of the interconnectedness of entities and relationships in their area of operation and discover existing data to support civil military operations. Our approach to build a recommendation engine was designed to overcome several technical challenges, including 1) ensuring models were robust to the relatively small amount of data collected by the Marine Corps civil affairs community; 2) finding methods to recommend novel data for which there are no interactions captured; and 3) overcoming confirmation bias by ensuring content was recommended that was relevant for the mission despite being obscure or less well known. We solve this by implementing a combination of collective matrix factorization (CMF) and graph-based random walks to provide recommendations to civil military operations users. We also present a method to resolve the challenge of computation complexity inherent from highly connected nodes through a precomputed process.

Keywords: Recommendation engine, collaborative filtering, context based recommendation, graph analysis, coverage, civil affairs operations, Marine Corps

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Authors: Hazaila Binti Hassan, Abu Yazid Bin Abu Bakar, Salleh Amat

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This study is to get the validity and reliability of the questionnaire for behaviour change on low-performing officers. This study aimed to develop and evaluate the behaviour of low performing officers. There are 75 items in this questionnaire which involves 5 subscales, which are the 5 dimensions intended to be studied: 1st emotional stability, 2nd psycho-spiritual enhancement, 3rd social skills development, 4th cognitive and rationality improvement and 5th behavioural alignment and adjustment. There are 2 processes in this research whereby to check the validity and reliability. Both use quantitative methods. Validity content testing has been conducted to validate the behavioural change questionnaire of the low performing officers. For the face validity, 4 people are involved, two are psychologists who carried out the program and the other two are officers of the same rank, i.e. supporting officers. They are involved in correction of sentences, languages, and grammar as well as the sentence structures so that it tallies with the purpose of studies. The questionnaire underwent content validity by the experts. Five experts are appointed to attend this session, 3 are directly involved in the construction of this questionnaire and 2 others are experts from the university with a background in questionnaire development. The result shows that the content validity obtained a high coefficient of 0.745 with a minimum and maximum value of more than 0.60 which satisfies the characteristic of Content Value Ratio. The Cronbach’s alpha result is 0.867. The highest scores are the behavioural alignment and adjustment sub-scale recorded the highest value, followed by social skills development sub-scale, cognitive and rational improvements sub-scale, psycho-spiritual enhancement sub-scale, and lastly emotional stability. Therefore, both of validity and reliability result were accepted that this questionnaire is valid and reliable can be used in the study of behaviour changes of low performing officers in the civil service.

Keywords: content validity, reliability, five dimension, low-performing officers, questionnaire

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Authors: Ibrahima Diallo

Abstract:

The literature on teacher education foregrounds reflection as an important aspect of professional practice. Reflection for a teacher consists in critically analysing and evaluating retrospectively a lesson to see what worked, what did not work, and how to improve it for the future. Now, many teacher education programmes worldwide consider the ability to reflect as one of the hallmarks of an effective educator. However, in some context like Senegal, reflection has not been given due consideration in teacher education programmes. In contexts where it has been in the education landscape for some time now, reflection is mostly depicted as an individual written activity and many teacher trainees have become disenchanted by the repeated enactments of this task that is solely intended to satisfy course requirements. This has resulted in whitewashing weaknesses or even ‘faking’ reflection. Besides, the “one-size-fits-all” approach of reflection could not flourish because how reflection impacts on practice is still unproven. Therefore, reflective practice needs to be contextualised and made more thought-provoking through dialogue and by using classroom data. There is also a need to highlight change brought in teachers’ practice through reflection. So, this study introduces reflection in a new context and aims to show evidenced change in novice EFL teachers’ practice through dialogic data-led reflection. The purpose of this study is also to contribute to the scarce literature on reflection in sub-Saharan Africa by bringing new perspectives on contextualised teacher-led reflection. Eight novice EFL teachers participated in this qualitative longitudinal study, and data have been gathered online through post-lesson reflection recordings and lesson videos for a period of four months. Then, the data have been thematically analysed using NVivo to systematically organize and manage the large amount of data. The analysis followed the six steps approach to thematic analysis. Major themes related to teachers’ classroom practice and their conception of reflection emerged from the analysis of the data. The results showed that post-lesson reflection with a peer can help novice EFL teachers gained more awareness on their classroom practice. Dialogic reflection also helped them evaluate their lessons and seek for improvement. The analysis of the data also gave insight on teachers’ conception of reflection in an EFL context. It was found that teachers were more engaged in reflection when using their lesson video recordings. Change in teaching behaviour as a result of reflection was evidenced by the analysis of the lesson video recordings. This study has shown that video-stimulated reflection is practical form of professional development that can be embedded in teachers’ professional life.

Keywords: novice EFL teachers, practice, professional development, video-stimulated reflection

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Authors: Souâad Boudebza, Omar Nouali, Faiçal Azouaou

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Social networks have recently gained a growing interest on the web. Traditional formalisms for representing social networks are static and suffer from the lack of semantics. In this paper, we will show how semantic web technologies can be used to model social data. The SemTemp ontology aligns and extends existing ontologies such as FOAF, SIOC, SKOS and OWL-Time to provide a temporal and semantically rich description of social data. We also present a modeling scenario to illustrate how our ontology can be used to model social networks.

Keywords: ontology, semantic web, social network, temporal modeling

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Authors: Hao-Hsiang Ku, Ching-Ho Chi

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Big data applications have become an imperative for many fields. Many researchers have been devoted into increasing correct rates and reducing time complexities. Hence, the study designs and proposes an Ontology-based backpropagation neural network classification and reasoning strategy for NoSQL big data applications, which is called ON4NoSQL. ON4NoSQL is responsible for enhancing the performances of classifications in NoSQL and SQL databases to build up mass behavior models. Mass behavior models are made by MapReduce techniques and Hadoop distributed file system based on Hadoop service platform. The reference engine of ON4NoSQL is the ontology-based backpropagation neural network classification and reasoning strategy. Simulation results indicate that ON4NoSQL can efficiently achieve to construct a high performance environment for data storing, searching, and retrieving.

Keywords: Hadoop, NoSQL, ontology, back propagation neural network, high distributed file system

Procedia PDF Downloads 262