Search results for: short text classification

Authors: Sumaira Mukhtar

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The present research aims at the discussion of the subversion of the hegemony of the feminine self in the text Moth Smoke by a Pakistani novelist Mohsin Hamid. It presents the notion of the subversion of the grand narratives of the ‘positioning’ of feminine identity in Pakistani patriarchal society by presenting a de-stereotyped personality of Mumtaz, the protagonist in Moth Smoke. The dominant masculine traits in Mumtaz’s personality have been negotiated since she is an untraditional female character in the novel. In this regard, the researcher has taken a feministic stance in this study by presenting the proposition that subaltern can also speak. Mumtaz’s character reminds one of Hedda from Henrik Ibsen’s play Hedda Gabler. So, the masculine traits in Mumtaz’s personality have also been compared with Hedda’s. Besides, the research study will also bring into notice that how that in the postmodern scenario, marginalization of the women have been responded back by women and hereby Mumtaz by uplifting her social status and class. Her de-stereotyped feminine self has been reinforced by the dialogues and incidents in the text. This research is qualitative in design and is based on the textual analysis. An interpretive research method has also been utilized since the researcher has tried to decode the text in supporting the notion of de-stereotyping of feminine self. This research would add to the body of Pakistani literature and Feministic theory.

Keywords: de-stereotyped, feminine identity, marginalization, masculine traits

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Neneng Hayatul Milah

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The most important reading skill for students is comprehension. Understanding the reading text will have an impact on learning outcomes. However, reading comprehension instruction in Indonesian elementary schools is lacking. A more effective learning model is needed to enhance students' reading comprehension. This study aimed to evaluate the effectiveness of the CIRC (Cooperative Integrated Reading and Composition) model with Padlet integration in improving the reading comprehension skills of grade IV students in elementary schools in Cimahi City, Indonesia. This research methodology was quantitative with a pre-experiment research type and one group pretest-posttest research design. The sample of this study consisted of 30 students. The results of statistical analysis showed that there was a significant effect of using the CIRC learning model using Padlet on improving students' reading comprehension skills of narrative text. The mean score of students' pretest was 67.41, while the mean score of the posttest increased to 84.82. The paired sample t-test resulted in a t-count score of -13.706 with a significance score of <0.001, which is smaller than α = 0.05. This research is expected to provide useful insights for educational practitioners on how the use of the CIRC model using Padlet can improve the reading comprehension skills of elementary school students.

Keywords: reading comprehension skills, CIRC, Padlet, narrative text

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Authors: Amirali Mahouti, Hooshang Katebi

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Enlargement of Tabriz metropolis to the east and north-east caused urban construction to be built on Marl layers and because of increase in excavations depth, further information of this layer is inescapable. Looking at geotechnical investigation shows there is not enough information about Tabriz Marl and this soil has been classified only by color. Tabriz Marl is lacustrine carbonate sediment outcrops, surrounds eastern, northern and southern region of city in the East Azerbaijan Province of Iran and is known as bed rock of city under alluvium sediments. This investigation aims to characterize geotechnical parameters of this soil to identify and set it in classification system of carbonated soils. For this purpose, specimens obtained from 80 locations over the city and subjected to physical and mechanical tests, such as Atterberg limits, density, moisture content, unconfined compression, direct shear and consolidation. CaCO3 content, organic content, PH, XRD, XRF, TGA and geophysical downhole tests also have been done on some of them.

Keywords: carbonated soils, classification of soils, mineralogy, physical and mechanical tests for Marls, Tabriz Marl

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Authors: Albert Oluwole Uzodimma Authority

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Nigeria is a diverse nation with a rich cultural heritage that has produced numerous art musicians and a vast range of art songs. The compositional styles, tonal rhythm, text rhythm, word painting, and text-tone relationship vary extensively from one dialect to another, indicating the need for standardized tools for the structural and analytical deconstruction of Nigerian indigenous art music. The purpose of this research is to examine the structures of Nigerian indigenous art music and outline some crucibles for analyzing it, by investigating how dialectical inflection influences the choice of text tone, scale mode, tonal rhythm, and the general ambiance of Nigerian art music. The research used a structured questionnaire to collect data from 50 musicologists, out of which 41 responded. The study's focus was on the works of two prominent twentieth-century composers, Stephen Olusoji, and Nwamara Alvan-Ikoku, titled "Oyigiyigi" and "O Chineke, Inozikwa omee," respectively. The data collected was presented in percentages using pie charts and tables. The study shows that in Nigerian Indigenous music, several aspects are to be considered for proper analysis, such as linguistic sensitivity, dialectical inflection influences text-tone relationship, text rhythm and tonal rhythm, which help to convey the proper meanings of messages in songs. It also highlights the lack of standardized rubrics for analysis, which necessitated the proposal of robust criteria for analyzing African music, known as Neo-Eclectic-Crucibles. Hinging on eclectic approach, this research makes significant contributions to music scholarship by addressing the need for standardized tools and crucibles for the structural and analytical deconstruction of Nigerian indigenous art music. It provides a template for further studies leading to standardized rubrics for analyzing African music. This research collected data through a structured questionnaire and analyzed it using pie charts and tables to present the findings accurately. The analysis focused on the respondents' perspectives on the research objectives and structural analysis of two indigenous music compositions by Olusoji and Nwamara. This research answers the questions on the structures and analytical crucibles used in Nigerian indigenous art music, how dialectical inflection influences text-tone relationship, scale mode, tonal rhythm, and the general ambiance of Nigerian art music. This paper demonstrates the need for standardized tools and crucibles for the structural and analytical deconstruction of Nigerian indigenous art music. It highlights several aspects that are crucial to analyzing Nigerian indigenous music and proposes the Neo-Eclectic-Crucibles criteria for analyzing African music. The contribution of this research to music scholarship is significant, providing a template for further studies and research in the field.

Keywords: art-music, crucibles, dialectical inflections, indigenous, text-tone, tonal rhythm, word-painting

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Authors: Ronak Khosravi, Mahmood Abbasi Layegh, Siamak Haghipour, Avin Esmaili

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In this paper, a study on classification of musical woodwind instruments using a small set of features selected from a broad range of extracted ones by the sequential forward selection method was carried out. Firstly, we extract 42 features for each record in the music database of 402 sound files belonging to five different groups of Flutes (end blown and internal duct), Single –reed, Double –reed (exposed and capped), Triple reed and Quadruple reed. Then, the sequential forward selection method is adopted to choose the best feature set in order to achieve very high classification accuracy. Two different classification techniques of support vector machines and relevance vector machines have been tested out and an accuracy of up to 96% can be achieved by using 21 time, frequency and coefficient features and relevance vector machine with the Gaussian kernel function.

Keywords: coefficient features, relevance vector machines, spectral features, support vector machines, temporal features

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Authors: Mohammed Mustapha Alhaji, Sadiku Salawu

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A clay soil which classified under A-7-6 soil according to AASHTO soil classification system and CH according to the unified soil classification system was stabilized using A-3 soil (AASHTO soil classification system). The clay soil was replaced with 0%, 10%, 20% to 100% A-3 soil, compacted at both the BSL and BSH compaction energy level and using unconfined compressive strength as evaluation criteria. The MDD of the compactions at both the BSL and BSH compaction energy levels showed increase in MDD from 0% A-3 soil replacement to 40% A-3 soil replacement after which the values reduced to 100% A-3 soil replacement. The trend of the OMC with varied A-3 soil replacement is similar to that of MDD but in a reversed order. The OMC reduced from 0% A-3 soil replacement to 40% A-3 soil replacement after which the values increased to 100% A-3 soil replacement. This trend was attributed to the observed reduction in the void ratio from 0% A-3 soil replacement to 40% A-3 soil replacement after which the void ratio increased to 100% A-3 soil replacement. The maximum UCS for clay at varied A-3 soil replacement increased from 272 and 770kN/m2 for BSL and BSH compaction energy level at 0% A-3 soil replacement to 295 and 795kN/m2 for BSL and BSH compaction energy level respectively at 10% A-3 soil replacement after which the values reduced to 22 and 60kN/m2 for BSL and BSH compaction energy level respectively at 70% A-3 soil replacement. Beyond 70% A-3 soil replacement, the mixture cannot be moulded for UCS test.

Keywords: A-3 soil, clay minerals, pozzolanic action, stabilization

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Authors: Charline David, Alexandre Blondin Massé, Arnaud Zinflou

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In 2016, Clements, Hurn, and Li proposed a multiple equation time series approach for the short-term load forecasting, reporting an average mean absolute percentage error (MAPE) of 1.36% on an 11-years dataset for the Queensland region in Australia. We present an adaptation of their model to the electrical power load consumption for the whole Quebec province in Canada. More precisely, we take into account two additional meteorological variables — cloudiness and wind speed — on top of temperature, as well as the use of multiple meteorological measurements taken at different locations on the territory. We also consider other minor improvements. Our final model shows an average MAPE score of 1:79% over an 8-years dataset.

Keywords: short-term load forecasting, special days, time series, multiple equations, parallelization, clustering

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Authors: Ahmed A. A. Fadol, Guangji Tong, Wlaa Mohamed

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This paper provides a comprehensive analysis of food insecurity in Sudan, focusing on consumption patterns and their implications, employing the Integrated Food Security Phase Classification (IPC) assessment framework. Years of conflict and economic instability have driven large segments of the population in Sudan into crisis levels of acute food insecurity according to the (IPC). A substantial number of people are estimated to currently face emergency conditions, with an additional sizeable portion categorized under less severe but still extreme hunger levels. In this study, we explore the multifaceted nature of food insecurity in Sudan, considering its historical, political, economic, and social dimensions. An analysis of consumption patterns and trends was conducted, taking into account cultural influences, dietary shifts, and demographic changes. Furthermore, we employ logistic regression and random forest analysis to identify significant independent variables influencing food security status in Sudan. Random forest clearly outperforms logistic regression in terms of area under curve (AUC), accuracy, precision and recall. Forward projections of the IPC for Sudan estimate that 15 million individuals are anticipated to face Crisis level (IPC Phase 3) or worse acute food insecurity conditions between October 2023 and February 2024. Of this, 60% are concentrated in Greater Darfur, Greater Kordofan, and Khartoum State, with Greater Darfur alone representing 29% of this total. These findings emphasize the urgent need for both short-term humanitarian aid and long-term strategies to address Sudan's deepening food insecurity crisis.

Keywords: food insecurity, consumption patterns, logistic regression, random forest analysis

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Authors: Sarah Werner, Michael J. Pritchard

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From pilots to engineers, the skills development within the aerospace industry is exceptionally broad. Employers often struggle with finding the right mixture of qualified skills to fill their organizational demands. This effort to find qualified talent is further complicated by the industrial delineation between two key areas: aviation and aerospace. In a broad sense, the aerospace industry overlaps with the aviation industry. In turn, the aviation industry is a smaller sector segment within the context of the broader definition of the aerospace industry. Furthermore, it could be conceptually argued that -in practice- there is little distinction between these two sectors (i.e., aviation and aerospace). However, through our unstructured text analysis of over 6,000 job listings captured, our team found a clear delineation between aviation-related jobs and aerospace-related jobs. Using techniques in natural language processing, our research identifies an integrated workforce skill pattern that clearly breaks between these two sectors. While the aviation sector has largely maintained its need for pilots, mechanics, and associated support personnel, the staffing needs of the aerospace industry are being progressively driven by integrative engineering needs. Increasingly, this is leading many aerospace-based organizations towards the acquisition of 'system level' staffing requirements. This research helps to better align higher educational institutions with the current industrial staffing complexities within the broader aerospace sector.

Keywords: aerospace industry, job demand, text mining, workforce development

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Authors: Chimey Lhamo, Ngawang Tsering

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Traditional Tibetan medicine, known as Sowa Rigpa (Science of healing), originated more than 2500 years ago with an insightful background, and it has been growing significant attention in many Asian countries like China, India, Bhutan, and Nepal. Particularly, the Indian government has targeted Traditional Tibetan medicine as its major Indian medical system, including Ayurveda. Although Traditional Tibetan medicine has been growing interest and has a long history, it is not easily recognized worldwide because it exists only in the Tibetan language and it is neither accessible nor understood by patent examiners at the international patent office, data about Traditional Tibetan medicine is not yet broadly exist in the Internet. There has also been the exploitation of traditional Tibetan medicine increasing. The Traditional Knowledge Digital Library is a database aiming to prevent the patenting and misappropriation of India’s traditional medicine knowledge by using India’s Traditional knowledge Digital Library on Sowa Rigpa in order to prevent its exploitation at international patent with the help of information technology tools and an innovative classification systems-traditional knowledge resource classification (TKRC). As of date, more than 3000 Sowa Rigpa formulations have been transcribed into a Traditional Knowledge Digital Library database. In this paper, we are presenting India's Traditional Knowledge Digital Library for Traditional Tibetan medicine, and this database system helps to preserve and prevent the exploitation of Sowa Rigpa. Gradually it will be approved and accepted globally.

Keywords: traditional Tibetan medicine, India's traditional knowledge digital library, traditional knowledge resources classification, international patent classification

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Authors: Muhammad Hamza

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This research is based on the effect of self-questioning strategy on reading comprehension of second language learners at medium level. This research is conducted to find out the effects of self-questioning strategy and how self-questioning strategy helps English learners to improve their reading comprehension. In this research study the researcher has analyzed that how much self-questioning is effective in the field of learning second language and how much it helps second language learners to improve their reading comprehension. For this purpose, the researcher has studied different reading strategies, analyzed, collected data from certificate level class at NUML, Peshawar campus and then found out the effects of self-questioning strategy on reading comprehension of ESL learners. The researcher has randomly selected the participants from certificate class. The data was analyzed through pre-test and post-test and then in the final stage the results of both tests were compared. After the pre-test and post-test, the result of both pre-test and post-test indicated that if the learners start to use self-questioning strategy before reading a text, while reading a text and after reading a particular text there’ll be improvement in comprehension level of ESL learners. The present research has addressed the benefits of self-questioning strategy by taking two tests (pre and post-test).After the result of post-test it is revealed that the use of the self-questioning strategy has a significant effect on the readers’ comprehension thus, they can improve their reading comprehension by using self-questioning strategy.

Keywords: strategy, self-questioning, comprehension, intermediate level ESL learner

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Authors: Rema Vazhappilly, Tapas Das

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Oleic acid is a cis unsaturated fatty acid and is known to be a partially essential fatty acid due to its limited endogenous synthesis during pregnancy and lactation. Previous studies have demonstrated the role of oleic acid in neuronal differentiation and brain phospholipid synthesis. These evidences indicate a major role for oleic acid in learning and memory. Interestingly, oleic acid has been shown to enhance hippocampal long term potentiation (LTP), the physiological correlate of long term synaptic plasticity. However the effect of oleic acid on short term synaptic plasticity has not been investigated. Short term potentiation (STP) is the physiological correlate of short term synaptic plasticity which is the key underlying molecular mechanism of short term memory and neuronal information processing. STP in the hippocampal CA1 region has been known to require the activation of N-methyl-D-aspartate receptors (NMDARs). The NMDAR dependent hippocampal STP as a potential mechanism for short term memory has been a subject of intense interest for the past few years. Therefore in the present study the effect of oleic acid on NMDAR dependent hippocampal STP was determined in mouse hippocampal slices (in vitro) using Multi-electrode array system. STP was induced by weak tetanic Stimulation (one train of 100 Hz stimulations for 0.1s) of the Schaffer collaterals of CA1 region of the hippocampus in slices treated with different concentrations of oleic acid in presence or absence of NMDAR antagonist D-AP5 (30 µM) . Oleic acid at 20 (mean increase in fEPSP amplitude = ~135 % Vs. Control = 100%; P<0.001) and 30 µM (mean increase in fEPSP amplitude = ~ 280% Vs. Control = 100%); P<0.001) significantly enhanced the STP following weak tetanic stimulation. Lower oleic acid concentrations at 10 µM did not modify the hippocampal STP induced by weak tetanic stimulation. The hippocampal STP induced by weak tetanic stimulation was completely blocked by the NMDA receptor antagonist D-AP5 (30µM) in both oleic acid and control treated hippocampal slices. This lead to the conclusion that the hippocampal STP elicited by weak tetanic stimulation and enhanced by oleic acid was NMDAR dependent. Together these findings suggest that oleic acid may enhance the short term memory and neuronal information processing through the modulation of NMDAR dependent hippocampal short-term synaptic plasticity. In conclusion this study suggests the possible role of oleic acid to prevent the short term memory loss and impaired neuronal function throughout development.

Keywords: oleic acid, short-term potentiation, memory, field excitatory post synaptic potentials, NMDA receptor

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Authors: Maruska Vizek, Josip Glaurdic, Marina Tkalec, Goran Vuksic

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We empirically explore whether and how taxation affects bilateral real exchange rates in the euro area – relative unit labor costs and relative consumer price indices. We find that employers’ social security contributions and the value added tax changes have the expected effects put forward in the fiscal devaluation literature and simulations. Increases in employers’ contributions appreciate the relative unit labor costs in the short- and the long-run, while value added tax hike appreciates the relative consumer prices. Somewhat surprisingly, for personal income tax increases, we find a short-run depreciating impact on the relative unit labor costs, while increases in employees’ contributions depreciate both measures of real exchange rates in the short-run.

Keywords: sovereign bonds, European Union, developing countries, political determinants

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Authors: Zahow Muoftah

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Traditional manual visual grading of fruits has been one of the agricultural industry’s major challenges due to its laborious nature as well as inconsistency in the inspection and classification process. The main requirements for computer vision and visual processing are some effective techniques for identifying defects and estimating defect areas. Automated defect detection using computer vision and machine learning has emerged as a promising area of research with a high and direct impact on the visual inspection domain. Grading, sorting, and disease detection are important factors in determining the quality of fruits after harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have used computer vision to evaluate the quality level of fruits during post-harvest. Many studies have been conducted to identify diseases and pests that affect the fruits of agricultural crops. However, most previous studies concentrated solely on the diagnosis of a lesion or disease. This study focused on a comprehensive study to identify pests and diseases of apple and banana fruits using detection and classification defects on Banana and Apples by Computer Vision. As a result, the current article includes research from these domains as well. Finally, various pattern recognition techniques for detecting apple and banana defects are discussed.

Keywords: computer vision, banana, apple, detection, classification

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Authors: Fateme Montazeri

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Literary approaches towards the text of the Quran predate the modern period. Suyuti (d.1505)’s encyclopedia of Quranic sciences, Al-Itqan, provides a notable example. In the modern era, the study of the Quranic rhetorics received particular attention in the second half of the twentieth century by Egyptian scholars. Amin Al-Khouli (d. 1966), who might be considered the first to argue for the necessity of applying a literary-rhetorical lens toward the tafseer, Islamic exegesis, and his students championed the literary analysis as the most effective approach to the comprehension of the holy text. Western scholars continued the literary criticism of the Islamic scripture by applying to the Quran similar methodologies used in biblical studies. In the history of the literary examination of the Quran, the scope of the critical methods applied to the Quranic text has been limited. For, the rhetorical approaches to the Quran, in the premodern as well as the modern period, concerned almost exclusively with the lexical layer of the text, leaving the narratological dimensions insufficiently examined. Recent contributions, by Leyla Ozgur Alhassen, for instance, attempt to fill this lacunae. This paper aims at advancing the studies of the Quranic narratives by investigating the application of a literary device whose role in the Quranic stories remains unstudied, that is, “foreshadowing.” This paper shall focus on Chapter 12, “Surah al-Yusuf,” as its case study. Chapter 12, the single chapter that includes the story of Joseph in one piece, contains several instances in which the events of the story are foreshadowed. As shall be discussed, foreshadowing occurs either through a monolog or dialogue whereby one or more of the characters allude to the future happenings or through the manner in which the setting is described. Through a close reading of the text, it will be demonstrated that the usage of the rhetorical tool of foreshadowing meets a dual purpose: on the one hand, foreshadowing prepares the reader/audience for the upcoming events in the plot, and on the other hand, it highlights the psychological dimensions of the characters, their thoughts, intentions, and disposition. In analyzing the story, this study shall draw on psychoanalytical criticism to explore the layers of meanings embedded in the Quranic narrative that are unfolded through foreshadowing.

Keywords: foreshadowing, quranic narrative, literary criticism, surah yusuf

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Authors: Shahrzad Zolfagharnassab, Abdul Rashid Mohamed Shariff, Reza Ehsani, Hawa Ze Jaffar, Ishak Aris

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Ripeness estimation of oil palm fresh fruit is important processes that affect the profitableness and salability of oil palm fruits. The adulthood or ripeness of the oil palm fruits influences the quality of oil palm. Conventional procedure includes physical grading of Fresh Fruit Bunches (FFB) maturity by calculating the number of loose fruits per bunch. This physical classification of oil palm FFB is costly, time consuming and the results may have human error. Hence, many researchers try to develop the methods for ascertaining the maturity of oil palm fruits and thereby, deviously the oil content of distinct palm fruits without the need for exhausting oil extraction and analysis. This research investigates the potential of infrared images (Thermal Images) as a predictor to classify the oil palm FFB ripeness. A total of 270 oil palm fresh fruit bunches from most common cultivar of oil palm bunches Nigresens according to three maturity categories: under ripe, ripe and over ripe were collected. Each sample was scanned by the thermal imaging cameras FLIR E60 and FLIR T440. The average temperature of each bunches were calculated by using image processing in FLIR Tools and FLIR ThermaCAM researcher pro 2.10 environment software. The results show that temperature content decreased from immature to over mature oil palm FFBs. An overall analysis-of-variance (ANOVA) test was proved that this predictor gave significant difference between underripe, ripe and overripe maturity categories. This shows that the temperature as predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using the temperature of the FFB as predictors through Linear Discriminant Analysis (LDA), Mahalanobis Discriminant Analysis (MDA), Artificial Neural Network (ANN) and K- Nearest Neighbor (KNN) methods. The highest overall classification accuracy was 88.2% by using Artificial Neural Network. This research proves that thermal imaging and neural network method can be used as predictors of oil palm maturity classification.

Keywords: artificial neural network, maturity classification, oil palm FFB, thermal imaging

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Authors: Lynnette Lyzwinski, Liam Caffery, Matthew Bambling, Sisira Edirippulige

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Introduction: A novel method of delivering mindfulness interventions for populations at risk of weight gain and stress-related eating, in particular, college students, is through mHealth. While there have been qualitative studies on mHealth for weight loss, there has not been a study on mHealth for weight loss using mindfulness that has explored student perspectives on a student centred mindfulness app and mindfulness-based text messages for eating and stress. Student perspective data will provide valuable information for creating a specific purpose weight management app and mindfulness-based text messages (for the Mindfulness App study). Methods: A qualitative focus group study was undertaken at St Lucia campus at the University of Queensland in March 2017. Students over the age of 18 were eligible to participate. Interviews were audiotaped and transcribed. One week following the focus group, students were sent sample mindfulness-based text messages based on their responses. Students provided written feedback via email. Data were analysed using N Vivo software. Results: The key themes in a future mindfulness-based app are a simple design interface, a focus on education/practical tips, and real-life practical exercises. Social media should be avoided. Key themes surrounding barriers include the perceived difficulty of mindfulness and a lack of proper guidance or knowledge. The mindfulness-based text messages were received positively. Key themes were creating messages with practical tips about how to be mindful and how to integrate mindful reflection of both one’s body and environment while on campus. Other themes including creating positive, inspirational messages. There was lack of agreement on the ideal timing for messages. Discussion: This is the first study that explored student perspectives on a mindfulness-app and mindfulness-based text messages for stress and weight management as a pre-trial study for the Mindfulness App trial for stress, lifestyle, and weight in students. It is important to consider maximizing the potential facilitators of use and minimize potential identified barriers when developing and designing a future mHealth mindfulness-based intervention tailored to the student consumer. Conclusion: Future mHealth studies may consider integrating mindfulness-based text messages in their interventions for weight and stress as this is a novel feature that appears to be acceptable for participants. The results of this focus group provide the basis to develop content for a specific purpose student app for weight management.

Keywords: mindfulness, college students, mHealth, weight loss

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Authors: Emilee M. Cruz

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Teachers take part in short-term teaching fellowships abroad, and their preparation before, during, and after the experience is critical to affecting teachers’ feelings of success in the international classroom. A program evaluation of the teacher preparation within TALMA: The Israel Program for Excellence in English (TALMA) full-year teaching fellowship was conducted. A questionnaire was developed that examined professional development, deliberate reflection, and cultural and language immersion offered before, during, and after the short-term experience. The evaluation also surveyed teachers’ feelings of preparedness for the Israeli classroom and any recommendations they had for future teacher preparation within the fellowship program. The review suggests the TALMA program includes integrated professional learning communities between fellows and Israeli co-teachers, more opportunities for immersive Hebrew language learning, a broader professional network with Israelis, and opportunities for guided discussion with the TALMA community continued participation in TALMA events and learning following the full-year fellowship. Similar short-term international programs should consider the findings in the design of their participation preparation programs. The review also offers direction for future program evaluation of short-term participant preparation, including the need for frequent response item updates to match current offerings and evaluation of participant feelings of preparedness before, during, and after the full-year fellowship.

Keywords: educational program evaluation, international teaching, short-term teaching, teacher beliefs, teaching fellowship, teacher preparation

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Authors: Syed Romat Ali Shah, Bilal Shoaib, Saleem Akhtar, Munib Ahmad, Shahan Sadiqui

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Classification is a data mining technique, which is recognizedon Machine Learning (ML) algorithm. It is used to classifythe individual articlein a knownofinformation into a set of predefinemodules or group. Web mining is also a portion of that sympathetic of data mining methods. The main purpose of this paper to analysis and compare the performance of Naïve Bayse Algorithm, Decision Tree, K-Nearest Neighbor (KNN), Artificial Neural Network (ANN)and Support Vector Machine (SVM). This paper consists of different ML algorithm and their advantages and disadvantages and also define research issues.

Keywords: Data Mining, Web Mining, classification, ML Algorithms

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Authors: Mona Masood, Norshazlina Shaik Othman

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The purpose of this study was to investigate graduate students’ visual attention and perceptions of a Prezi presentation. Ten post-graduate master students were presented with a Prezi presentation at the Centre for Instructional Technology and Multimedia, Universiti Sains Malaysia (USM). The eye movement indicators such as dwell time, average fixation on the areas of interests, heat maps and focus maps were abstracted to indicate the students’ visual attention. Descriptive statistics was employed to analyze the students’ perception of the Prezi presentation in terms of text, slide design, images, layout and overall presentation. The result revealed that the students paid more attention to the text followed by the images and sub heading presented through the Prezi presentation.

Keywords: eye tracking, Prezi, visual attention, visual perception

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Authors: Andrey V. Timofeev, Dmitry V. Egorov

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This paper introduces an original method of parametric optimization of the structure for multimodal decision-level fusion scheme which combines the results of the partial solution of the classification task obtained from assembly of the mono-modal classifiers. As a result, a multimodal fusion classifier which has the minimum value of the total error rate has been obtained.

Keywords: classification accuracy, fusion solution, total error rate, multimodal fusion classifier

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Authors: Matthew Heidman, Susan Dallabrida, Analice Costa

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Background:Accuracy of caregiver reported outcomes is essential for infant growth and tolerability study success. Crying/fussiness, stool consistencies, and other gastrointestinal characteristics are important parameters regarding tolerability, and inter-caregiver reporting can see a significant amount of subjectivity and vary greatly within a study, compromising data. This study sought to elucidate how caregiver reported questions related to stool frequency are answered before and after a short amount of training and how training impacts caregivers’ understanding, and how they would answer the question. Methods:A digital survey was issued for 90 daysin the US (n=121) and 30 days in Mexico (n=88), targeting respondents with children ≤4 years of age. Respondents were asked a question in two formats, first without a line of training text and second with a line of training text. The question set was as follows, “If your baby had stool in his/her diaper and you changed the diaper and 10 min later there was more stool in the diaper, how many stools would you report this as?” followed by the same question beginning with “If you were given the instruction that IF there are at least 5 minutes in between stools, then it counts as two (2) stools…”.Four response items were provided for both questions, 1) 2 stools, 2) 1stool, 3) it depends on how much stool was in the first versus the second diaper, 4) There is not enough information to be able to answer the question. Response frequencies between questions were compared. Results: Responses to the question without training saw some variability in the US, with 69% selecting “2 stools”,11% selecting “1 stool”, 14% selecting “it depends on how much stool was in the first versus the second diaper”, and 7% selecting “There is not enough information to be able to answer the question” and in Mexico respondents selected 9%, 78%, 13%, and 0% respectively. However, responses to the question after training saw more consolidation in the US, with 85% of respondents selecting“2 stools,” representing an increase in those selecting the correct answer. Additionally in Mexico, with 84% of respondents selecting “1 episode” representing an increase in the those selecting the correct response. Conclusions: Caregiver reported outcomes are critical for infant growth and tolerability studies, however, they can be highly subjective and see a high variability of responses without guidance. Training is critical to standardize all caregivers’ perspective regarding how to answer questions accurately in order to provide an accurate dataset.

Keywords: infant nutrition, clinical trial optimization, stool reporting, decentralized clinical trials

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Authors: Hammad T. Janjuhah, Josep Sanjuan, Mohamed K. Salah

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Biological and chemical activities in carbonates are responsible for the complexity of the pore system. Primary porosity is generally of natural origin while secondary porosity is subject to chemical reactivity through diagenetic processes. To understand the integrated part of hydrocarbon exploration, it is necessary to understand the carbonate pore system. However, the current porosity classification scheme is limited to adequately predict the petrophysical properties of different reservoirs having various origins and depositional environments. Rock classification provides a descriptive method for explaining the lithofacies but makes no significant contribution to the application of porosity and permeability (poro-perm) correlation. The Central Luconia carbonate system (Malaysia) represents a good example of pore complexity (in terms of nature and origin) mainly related to diagenetic processes which have altered the original reservoir. For quantitative analysis, 32 high-resolution images of each thin section were taken using transmitted light microscopy. The quantification of grains, matrix, cement, and macroporosity (pore types) was achieved using a petrographic analysis of thin sections and FESEM images. The point counting technique was used to estimate the amount of macroporosity from thin section, which was then subtracted from the total porosity to derive the microporosity. The quantitative observation of thin sections revealed that the mouldic porosity (macroporosity) is the dominant porosity type present, whereas the microporosity seems to correspond to a sum of 40 to 50% of the total porosity. It has been proven that these Miocene carbonates contain a significant amount of microporosity, which significantly complicates the estimation and production of hydrocarbons. Neglecting its impact can increase uncertainty about estimating hydrocarbon reserves. Due to the diversity of geological parameters, the application of existing porosity classifications does not allow a better understanding of the poro-perm relationship. However, the classification can be improved by including the pore types and pore structures where they can be divided into macro- and microporosity. Such studies of microporosity identification/classification represent now a major concern in limestone reservoirs around the world.

Keywords: overview of porosity classification, reservoir characterization, microporosity, carbonate reservoir

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Authors: Adesuyi Ayodeji Steve, Zahn Munch

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This study investigates the use of MODIS NDVI to identify agricultural land cover change areas on an annual time step (2007 - 2012) and characterize the trend in the study area. An ISODATA classification was performed on the MODIS imagery to select only the agricultural class producing 3 class groups namely: agriculture, agriculture/semi-natural, and semi-natural. NDVI signatures were created for the time series to identify areas dominated by cereals and vineyards with the aid of ancillary, pictometry and field sample data. The NDVI signature curve and training samples aided in creating a decision tree model in WEKA 3.6.9. From the training samples two classification models were built in WEKA using decision tree classifier (J48) algorithm; Model 1 included ISODATA classification and Model 2 without, both having accuracies of 90.7% and 88.3% respectively. The two models were used to classify the whole study area, thus producing two land cover maps with Model 1 and 2 having classification accuracies of 77% and 80% respectively. Model 2 was used to create change detection maps for all the other years. Subtle changes and areas of consistency (unchanged) were observed in the agricultural classes and crop practices over the years as predicted by the land cover classification. 41% of the catchment comprises of cereals with 35% possibly following a crop rotation system. Vineyard largely remained constant over the years, with some conversion to vineyard (1%) from other land cover classes. Some of the changes might be as a result of misclassification and crop rotation system.

Keywords: change detection, land cover, modis, NDVI

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Authors: Wei Yao, Constantinos Alexiou

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In the existing literature, many studies have focused on the implementation and effectiveness of quantitative easing (QE) since 2008, but only a few have evaluated QE’s effect on commodity prices. In this context, by following Frankel’s (1986) commodity price overshooting model, we study the dynamic covariation between the expected real interest rates and six agricultural commodities’ real returns over the period from 2000:1 to 2018 for the US economy. We use wavelet analysis to investigate the causal relationship and co-movement of time series data by calculating the coefficient of determination in different frequencies. We find that a) US unconventional monetary policy may cause more positive and significant covariation between the expected real interest rates and agricultural commodities’ real returns over the short horizons; b) a lead-lag relationship that runs from agricultural commodities’ real returns to the expected real short-term interest rates over the long horizons; and c) a lead-lag relationship from agricultural commodities’ real returns to the expected real long-term interest rates over short horizons. In the realm of monetary policy, we argue that QE may shift the negative relationship between most commodities’ real returns and the expected real interest rates to a positive one over a short horizon.

Keywords: QE, commodity price, interest rate, wavelet coherence

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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 259

Authors: Xueying Li

Abstract:

Different from English, it is common to observe Chinese clauses logically related in an implicit way without any conjunctions. This typological difference has posed a great challenge for Chinese-English translators, as 1) translators may interpret logical meaning in different ways when there are no conjunctions in Chinese Source Text (ST); 2) translators may have questions whether to make Chinese implicit logical meaning explicit or to remain implicit in Target Text (TT), and whether other dimensions of logical meaning (e.g., type of logical meaning) should be shifted or not. Against this background, this study examines a comprehensive arrange of Chinese-English translation choices of logical meaning to deal with this challenge in a systematic way. It compiles several ST-TT passages from a set of translation textbooks in a corpus, namely Ying Yu Bi Yi Shi Wu (Er Ji)) [Translation Practice between Chinese and English: Intermediate Level] and its supportive training book, analyzes how logical meaning in ST are translated in TT in texts across different text types with Systemic Functional Linguistics (SFL) as the theoretical framework, and finally draws a system network of translation choices of logical meaning from Chinese into English. Since translators may probably think about semantic meaning rather than lexico-grammatical resources in translation, this study goes away from traditional lexico-grammatical choices, but rather describing translation choices from the semantic level. The findings in this study can provide some help and support for translation practitioners so that they can understand that besides explicitation, there are a variety of possible linguistic choices available for making informed decisions when translating Chinese logical meaning into English.

Keywords: Chinese-English translation, logical meaning, systemic functional linguistics, translation choices

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Authors: Ismail Elkhrachy

Abstract:

Determination of land use changing is an important component of regional planning for applications ranging from urban fringe change detection to monitoring change detection of land use. This data are very useful for natural resources management.On the other hand, the technologies and methods of change detection also have evolved dramatically during past 20 years. So it has been well recognized that the change detection had become the best methods for researching dynamic change of land use by multi-temporal remotely-sensed data. The objective of this paper is to assess, evaluate and monitor land use change surrounding the area of Najran city, Kingdom of Saudi Arabia (KSA) using Landsat images (June 23, 2009) and ETM+ image(June. 21, 2014). The post-classification change detection technique was applied. At last,two-time subset images of Najran city are compared on a pixel-by-pixel basis using the post-classification comparison method and the from-to change matrix is produced, the land use change information obtained.Three classes were obtained, urban, bare land and agricultural land from unsupervised classification method by using Erdas Imagine and ArcGIS software. Accuracy assessment of classification has been performed before calculating change detection for study area. The obtained accuracy is between 61% to 87% percent for all the classes. Change detection analysis shows that rapid growth in urban area has been increased by 73.2%, the agricultural area has been decreased by 10.5 % and barren area reduced by 7% between 2009 and 2014. The quantitative study indicated that the area of urban class has unchanged by 58.2 km〗^2, gained 70.3 〖km〗^2 and lost 16 〖km〗^2. For bare land class 586.4〖km〗^2 has unchanged, 53.2〖km〗^2 has gained and 101.5〖km〗^2 has lost. While agriculture area class, 20.2〖km〗^2 has unchanged, 31.2〖km〗^2 has gained and 37.2〖km〗^2 has lost.

Keywords: land use, remote sensing, change detection, satellite images, image classification

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Authors: Atefeh Saljooghi Khoshkar, Jafar Hassanpour

Abstract:

One of the main problems of the design stage relating to many tunneling projects is the lack of an appropriate standard for the provision of engineering geological data in a predefined format. In particular, this is more reflected in highway and railroad tunnel projects in which there is a number of tunnels and different professional teams involved. In this regard, comprehensive software needs to be designed using the accepted methods in order to help engineering geologists to prepare standard reports, which contain sufficient input data for the design stage. Regarding this necessity, applied software has been designed using macro capabilities and Visual Basic programming language (VBA) through Microsoft Excel. In this software, all of the engineering geological input data, which are required for designing different parts of tunnels, such as discontinuities properties, rock mass strength parameters, rock mass classification systems, boreability classification, the penetration rate, and so forth, can be calculated and reported in a standard format.

Keywords: engineering geology, rock mass classification, rock mechanic, tunnel

Procedia PDF Downloads 70