Search results for: prediction capability

Authors: Rajai Al-Rousan

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This paper presents a proposed analytical model for predicting the shear strength of reinforced concrete beams strengthened with CFRP composites as external reinforcement. The proposed analytical model can predict the shear contribution of CFRP composites of RC beams with an acceptable coefficient of correlation with the tested results. Based on the comparison of the proposed model with the published well-known models (ACI model, Triantafillou model, and Colotti model), the ACI model had a wider range of 0.16 to 10.08 for the ratio between tested and predicted ultimate shears at failure. Also, an acceptable range of 0.27 to 2.78 for the ratio between tested and predicted ultimate shears by the Triantafillou model. Finally, the best prediction (the ratio between the tested and predicted ones) of the ultimate shear capacity is observed by using Colotti model with a range of 0.20 to 1.78. Thus, the contribution of the CFRP composites as external reinforcement can be predicted with high accuracy by using the proposed analytical model.

Keywords: predicting, shear capacity, reinforced concrete, beams, strengthened, externally, CFRP composites

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Authors: B. Manyonga

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Across the world, higher education (HE) is expanding rapidly and issues of curriculum change have become more contentious and political than ever before. Although research informing curricula review in social sciences and particularly sociology has been conducted, much analysis has been devoted to teaching and transmitting disciplinary knowledge, student identity and epistemology, with little focus on curriculum conceptualisation and capability formation. This paper builds on and contributes to accumulating knowledge in the field of sociology curriculum design in the South African HE context. Drawing from the principles of Capabilities Approach (CA) of Amartya Sen and Martha Nussbaum, the paper argues that sociology curriculum conceptualisation may be enriched by capabilities identification for students. Thus, the sociological canon ought to be the vehicle through which student capabilities could be developed. The CA throws a fresh light on how curriculum ought to be designed to offer students real opportunities, expanding choices for individuals to be what they want to be and do. The paper uses a case of two South African universities to present analysis of qualitative data collected from undergraduate sociology lecturers. The major findings of the paper indicate that there is no clear philosophy guiding the conceptualisation of curriculum. The conceptualisation is based on lecturer expertise, carrying out research, response to topical and societal issues. Sociology lecturers highlighted that they do not consult students on what they want to do and to be as a result of studying for a sociology degree. Although lecturers recognise some human development capabilities such as critical thinking, multiple perspectives and problem solving as important for sociology students, there is little evidence to illustrate how these are being cultivated in students. Taken together, the results suggest that sociological canon is being regarded as the starting point for curriculum planning and construction.

Keywords: capabilities approach, graduate attributes, higher education, sociology curriculum

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Authors: Ahsan Abdullah, Ahmed A. S. Bakshwain

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Livestock is one of the fastest-growing sectors in agriculture. If carefully managed, have potential opportunities for economic growth, food sovereignty and food security. In this study we mainly analyse and compare long-term i.e. for year 2030 climate variability impact on predicted productivity of meat i.e. beef, mutton and poultry for the Kingdom of Saudi Arabia w.r.t three factors i.e. i) climatic-change vulnerability ii) CO2 fertilization and iii) water scarcity and compare the results with two countries of the region i.e. Iraq and Yemen. We do the analysis using data from diverse sources, which was extracted, transformed and integrated before usage. The collective impact of the three factors had an overall negative effect on the production of meat for all the three countries, with adverse impact on Iraq. High similarity was found between CO2 fertilization (effecting animal fodder) and water scarcity i.e. higher than that between production of beef and mutton for the three countries considered. Overall, the three factors do not seem to be favorable for the three Middle-East countries considered. This points to possibility of a vegetarian year 2030 based on dependency on indigenous live-stock population.

Keywords: prediction, animal-source foods, pastures, CO2 fertilization, climatic-change vulnerability, water scarcity

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Authors: Adrian-Gabriel Chifu, Sebastien Fournier

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One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document, paragraph, sentence, or aspect. Aspect-based sentiment analysis is a well-studied topic with many available data sets and models. However, there is no clear definition of what makes a sentence difficult for aspect-based sentiment analysis. In this paper, we explore this question by conducting an experiment with three data sets: ”Laptops”, ”Restaurants”, and ”MTSC” (Multi-Target-dependent Sentiment Classification), and a merged version of these three datasets. We study the impact of domain diversity and syntactic diversity on difficulty. We use a combination of classifiers to identify the most difficult sentences and analyze their characteristics. We employ two ways of defining sentence difficulty. The first one is binary and labels a sentence as difficult if the classifiers fail to correctly predict the sentiment polarity. The second one is a six-level scale based on how many of the top five best-performing classifiers can correctly predict the sentiment polarity. We also define 9 linguistic features that, combined, aim at estimating the difficulty at sentence level.

Keywords: sentiment analysis, difficulty, classification, machine learning

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Authors: Tasir Khan, Yejuan Wang

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The study of extreme rainfall events is very important for flood management in river basins and the design of water conservancy infrastructure. Evaluation of quantiles of annual maximum rainfall (AMRF) is required in different environmental fields, agriculture operations, renewable energy sources, climatology, and the design of different structures. Therefore, the annual maximum rainfall (AMRF) was performed at different stations in Pakistan. Multiple probability distributions, log normal (LN), generalized extreme value (GEV), Gumbel (max), and Pearson type3 (P3) were used to find out the most appropriate distributions in different stations. The L moments method was used to evaluate the distribution parameters. Anderson darling test, Kolmogorov- Smirnov test, and chi-square test showed that two distributions, namely GUM (max) and LN, were the best appropriate distributions. The quantile estimate of a multi-parameter PD offers extreme rainfall through a specific location and is therefore important for decision-makers and planners who design and construct different structures. This result provides an indication of these multi-parameter distribution consequences for the study of sites and peak flow prediction and the design of hydrological maps. Therefore, this discovery can support hydraulic structure and flood management.

Keywords: RAMSE, multiple frequency analysis, annual maximum rainfall, L-moments

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Authors: Nanxi Miao, Junjie Wang

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The discovery of 2D materials with distinct compositions and properties has been a research aim since the report of graphene. One of the latest members of the 2D material family is MXene, which is produced from the topochemical deintercalation of the A layer from a laminate MAX phase. Recently, analogous 2D MBenes (transitional metal borides) have been predicted by theoretical calculations as excellent alternatives in applications such as metal-ion batteries, magnetic devices, and catalysts. However, the practical applications of two-dimensional (2D) transition-metal borides (MBenes) have been severely hindered by the lack of accessible MBenes because of the difficulties in the selective etching of traditional ternary MAB phases with orthorhombic symmetry (ort-MAB). Here, we discover a family of ternary hexagonal MAB (h-MAB) phases and 2D hexagonal MBenes (h-MBenes) by ab initio predictions and experiments. Calculations suggest that the ternary h-MAB phases are more suitable precursors for MBenes than the ort-MAB phases. Based on the prediction, we report the experimental synthesis of h-MBene HfBO by selective removal of in from h-MAB Hf2InB2. The synthesized 2D HfBO delivered a specific capacity of 420 mAh g-1 as an anode material in lithium-ion batteries, demonstrating the potential for energy-storage applications. The discovery of this h-MBene HfBO added a new member to the growing family of 2D materials and provided opportunities for a wide range of novel applications.

Keywords: 2D materials, DFT calculations, high-throughput screening, lithium-ion batteries

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Authors: Rouaz Idriss, Bourahla Nour-Eddine, Kahlouche Farah, Rafa Sid Ali

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The stability of structures made of light-gauge steel depends highly on the contribution of Shear Wall Panel (SWP) systems under horizontal forces due to wind or earthquake loads. Steel plate sheathing is often used with these panels made of cold formed steel (CFS) to improve its shear strength. In order to predict the shear strength resistance, two methods are presented in this paper. In the first method, the steel plate sheathing is modeled with plats strip taking into account only the tension and compression force due to the horizontal load, where both track and stud are modeled according to the geometrical and mechanical characteristics of the specimen used in the experiments. The theoretical background and empirical formulations of this method are presented in this paper. However, the second method is based on a micro modeling of the cold formed steel Shear Wall Panel “CFS-SWP” using Abaqus software. A nonlinear analysis was carried out with an in-plan monotonic load. Finally, the comparison between these two methods shows that the micro modeling with Abaqus gives better prediction of shear resistance of SWP than strips method. However, the latter is easier and less time consuming than the micro modeling method.

Keywords: cold formed steel 'CFS', shear wall panel, strip method, finite elements

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Authors: Stefan Gruden, Charlott Brunmark, Bo Holmqvist, Erwin D. Brenndorfer, Martin Johansson, Jian Liu, Ying Zhao, Niklas Axen, Moustapha Hassan

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Aim: The study was designed to evaluate the ability of the calcium sulfate-based NanoZolid® drug delivery technology to locally release the epidermal growth factor (EGF) protein while maintaining its biological activity. Methods: NanoZolid-formulated EGF protein labelled with a near-infrared dye (EGF-NIR) depots or EGF-NIR dissolved in PBS were injected subcutaneously into mice bearing EGF receptor (EGFR) positive human A549 lung cancer tumors inoculated subcutaneously. The release and biodistribution of the EGF-NIR were investigated in vivo longitudinally up to 96 hours post-administration, utilizing whole-body fluorescence imaging. In order to confirm the in vivo findings, histological analysis of tumor cryosections was performed to investigate EGF-NIR fluorescent signal and EGFR expression level by immunofluorescence labelling. Results: The in vivo fluorescence imaging showed a controlled release profile of the EGF-NIR loaded in the NanoZolid depots compared to free EGF-NIR. Histological analysis of the tumors further demonstrated a prevailing distribution of EGF-NIR in regions with high levels of EGFR expression. Conclusion: Calcium sulfate based depots can be used to formulate EGF while maintaining its biological activity, e.g., receptor binding capability. This may have good clinical potential for local delivery of biomolecules to enhance treatment efficacy and minimize systemic adverse effects.

Keywords: bioresorbable, calcium sulfate, controlled release, NanoZolid

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Authors: Mohd Radzi Hilmi, Mohd Zulfaezal Che Azemin, Khairidzan Mohd Kamal, Azrin Esmady Ariffin, Mohd Izzuddin Mohd Tamrin, Norfazrina Abdul Gaffur, Tengku Mohd Tengku Sembok

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Purpose: The purpose of this study is to predict optical quality changes after pterygium surgery using tissue redness grading. Methods: Sixty-eight primary pterygium participants were selected from patients who visited an ophthalmology clinic. We developed a semi-automated computer program to measure the pterygium fibrovascular redness from digital pterygium images. The outcome of this software is a continuous scale grading of 1 (minimum redness) to 3 (maximum redness). The region of interest (ROI) was selected manually using the software. Reliability was determined by repeat grading of all 68 images and its association with contrast sensitivity function (CSF) and visual acuity (VA) was examined. Results: The mean and standard deviation of redness of the pterygium fibrovascular images was 1.88 ± 0.55. Intra- and inter-grader reliability estimates were high with intraclass correlation ranging from 0.97 to 0.98. The new grading was positively associated with CSF (p<0.01) and VA (p<0.01). The redness grading was able to predict 25% and 23% of the variance in the CSF and the VA respectively. Conclusions: The new grading of pterygium fibrovascular redness can be reliably measured from digital images and show a good correlation with CSF and VA. The redness grading can be used in addition to the existing pterygium grading.

Keywords: contrast sensitivity, pterygium, redness, visual acuity

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Authors: Rafia Rafique, Mutmina Zainab

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The present study investigated the relationship between fear of negative evaluation (FNE), social support and well-being in people with Vitiligo. It was hypothesized that low level of FNE and greater social support is likely to predict well-being. It was also hypothesized that social support is likely to moderate the relationship between FNE and well-being. Correlational research design was used for the present study. Non-probability purposive sampling technique was used to collect a sample (N=122) of people with Vitiligo. Hierarchical Moderated Regression analysis was used to test prediction and moderation. Brief Fear of Negative Evaluation Scale, Multidimensional Scale of Perceived Social Support (MSPSS) and Mental Health Continuum-Short form (MHC-SF) were used to evaluate the study variables. Fear of negative evaluation negatively predicted well-being (emotional and psychological). Social support from significant others and friends predicted social well-being. Social Support from family predicted emotional and psychological well-being. It was found that social support from significant others moderated the relationship between FNE and emotional well-being and social support from family moderated the relationship between FNE and social well-being. Dermatologists treating people with Vitiligo need to educate them and their families about the buffering role of social support (family and significant others). Future studies need to focus on other important mediating factors that can possibly explain the relationship between fear of negative evaluation and wellbeing.

Keywords: fear of negative evaluation, hierarchical moderated regression, vitiligo, well-being

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Authors: Urvee B. Trivedi, U. D. Dalal

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As wireless communication services grow quickly; the seriousness of spectrum utilization has been on the rise gradually. An emerging technology, cognitive radio has come out to solve today’s spectrum scarcity problem. To support the spectrum reuse functionality, secondary users are required to sense the radio frequency environment, and once the primary users are found to be active, the secondary users are required to vacate the channel within a certain amount of time. Therefore, spectrum sensing is of significant importance. Once sensing is done, different prediction rules apply to classify the traffic pattern of primary user. Primary user follows two types of traffic patterns: periodic and stochastic ON-OFF patterns. A cognitive radio can learn the patterns in different channels over time. Two types of classification methods are discussed in this paper, by considering edge detection and by using autocorrelation function. Edge detection method has a high accuracy but it cannot tolerate sensing errors. Autocorrelation-based classification is applicable in the real environment as it can tolerate some amount of sensing errors.

Keywords: cognitive radio (CR), probability of detection (PD), probability of false alarm (PF), primary user (PU), secondary user (SU), fast Fourier transform (FFT), signal to noise ratio (SNR)

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Authors: Churamani Pokhrel, C. B. Jha, S. R. Niraula, P. R. Pokharel

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Human identification is one of the most challenging subjects that man has confronted. The determination of adult sex and stature are two of the four key factors (sex, stature, age, and race) in identification of an individual. Craniofacial and odontometric parameters are important tools for forensic anthropologists when it is not possible to apply advanced techniques for identification purposes. The present study provides anthropometric correlation of the parameters with stature and gender and also devises regression formulae for reconstruction of stature. A total of 312 Nepalese students with equal distribution of sex i.e., 156 male and 156 female students of age 18-35 years were taken for the study. Total of 10 parameters were measured (age, sex, stature, head circumference, head length, head breadth, facial height, bi-zygomatic width, mesio-distal canine width and inter-canine distance of both maxilla and mandible). Co-relation and regression analysis was done to find the association between the parameters. All parameters were found to be greater in males than females and each was found to be statistically significant. Out of total 312 samples, the best regressor for the determination of stature was head circumference and mandibular inter-canine width and that for gender was head circumference and right mandibular teeth. The accuracy of prediction was 83%. Regression equations and analysis generated from craniofacial and odontometric parameters can be a supplementary approach for the estimation of stature and gender when extremities are not available.

Keywords: craniofacial, gender, odontometric, stature

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Authors: Mahtab Davari, Charles Edward Okon, Somayeh Aghanavesi

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

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

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Authors: Leila Momenzadeh, Alexander V. Evteev, Elena V. Levchenko, Tanvir Ahmed, Irina Belova, Graeme Murch

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In this work, the phonon thermal conductivity of f.c.c. Al is investigated in detail in the temperature range 100 – 900 K within the framework of equilibrium molecular dynamics simulations making use of the Green-Kubo formalism and one of the most reliable embedded-atom method potentials. It is found that the heat current auto-correlation function of the f.c.c. Al model demonstrates a two-stage temporal decay similar to the previously observed for f.c.c Cu model. After the first stage of decay, the heat current auto-correlation function of the f.c.c. Al model demonstrates a peak in the temperature range 100-800 K. The intensity of the peak decreases as the temperature increases. At 900 K, it transforms to a shoulder. To describe the observed two-stage decay of the heat current auto-correlation function of the f.c.c. Al model, we employ decomposition model recently developed for phonon-mediated thermal transport in a monoatomic lattice. We found that the electronic contribution to the total thermal conductivity of f.c.c. Al dominates over the whole studied temperature range. However, the phonon contribution to the total thermal conductivity of f.c.c. Al increases as temperature decreases. It is about 1.05% at 900 K and about 12.5% at 100 K.

Keywords: aluminum, gGreen-Kubo formalism, molecular dynamics, phonon thermal conductivity

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Authors: R. T. Aggangan

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Woodfuels continue to be a major component of the energy supply mix of the Philippines due to increasing demand for energy that are not adequately met by decreasing supply and increasing prices of fuel oil such as liquefied petroleum gas (LPG) and kerosene. The Development Academy of the Philippines projects the demand of woodfuels in 2016 as 28.3 million metric tons in the household sector and about 105.4 million metric tons combined supply potentials of both forest and non-forest lands. However, the Revised Master Plan for Forestry Development projects a demand of about 50 million cu meters of fuelwood in 2016 but the capability to supply from local sources is only about 28 million cu meters indicating a 44 % deficiency. Household demand constitutes 82% while industries demand is 18%. Domestic household demand for energy is for cooking needs while the industrial demand is for steam power generation, curing barns of tobacco: brick, ceramics and pot making; bakery; lime production; and small scale food processing. Factors that favour increased use of wood-based energy include the relatively low prices (increasing oil-based fuel prices), availability of efficient wood-based energy utilization technology, increasing supply, and increasing population that cannot afford conventional fuels. Moreover, innovations in combustion technology and cogeneration of heat and power from biomass for modern applications favour biomass energy development. This paper recommends policies and strategic directions for the development of the woodfuel industry with the twin goals of sustainably supplying the energy requirements of households and industry.

Keywords: biomass energy development, fuelwood, households and industry, innovations in combustion technology, supply and demand

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Authors: O. S. Ebrahim, K. O. Shawky, M. O. S. Ebrahim, P. K. Jain

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Induction Motor (IM) driving pump is the main consumer of electricity in a typical fluid transportation system (FTS). It was illustrated that changing the connection of the stator windings from delta to star at no load could achieve noticeable active and reactive energy savings. This paper proposes a supervisory hysteresis liquid-level control with three-state energy saving mode (ESM) for IM in FTS including storage tank. The IM pump drive comprises modified star/delta switch and hydromantic coupler. Three-state ESM is defined, along with the normal running, and named analog to computer ESMs as follows: Sleeping mode in which the motor runs at no load with delta stator connection, hibernate mode in which the motor runs at no load with a star connection, and motor shutdown is the third energy saver mode. A logic flow-chart is synthesized to select the motor state at no-load for best energetic cost reduction, considering the motor thermal capacity used. An artificial neural network (ANN) state estimator, based on the recurrent architecture, is constructed and learned in order to provide fault-tolerant capability for the supervisory controller. Sequential test of Wald is used for sensor fault detection. Theoretical analysis, preliminary experimental testing and, computer simulations are performed to show the effectiveness of the proposed control in terms of reliability, power quality and energy/coenergy cost reduction with the suggestion of power factor correction.

Keywords: ANN, ESM, IM, star/delta switch, supervisory control, FT, reliability, power quality

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Authors: Jyotismita Talukdar

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In this paper, a study has been made on the possibility and accuracy of early prediction of several Heart Disease using Artificial Neural Network. (ANN). The study has been made in both noise free environment and noisy environment. The data collected for this analysis are from five Hospitals. Around 1500 heart patient’s data has been collected and studied. The data is analysed and the results have been compared with the Doctor’s diagnosis. It is found that, in noise free environment, the accuracy varies from 74% to 92%and in noisy environment (2dB), the results of accuracy varies from 62% to 82%. In the present study, four basic attributes considered are Blood Pressure (BP), Fasting Blood Sugar (FBS), Thalach (THAL) and Cholesterol (CHOL.). It has been found that highest accuracy(93%), has been achieved in case of PPI( Post-Permanent-Pacemaker Implementation ), around 79% in case of CAD(Coronary Artery disease), 87% in DCM (Dilated Cardiomyopathy), 89% in case of RHD&MS(Rheumatic heart disease with Mitral Stenosis), 75 % in case of RBBB +LAFB (Right Bundle Branch Block + Left Anterior Fascicular Block), 72% for CHB(Complete Heart Block) etc. The lowest accuracy has been obtained in case of ICMP (Ischemic Cardiomyopathy), about 38% and AF( Atrial Fibrillation), about 60 to 62%.

Keywords: coronary heart disease, chronic stable angina, sick sinus syndrome, cardiovascular disease, cholesterol, Thalach

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Authors: Manasvi Pinnaka, Brianna Chrisman

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Cystic fibrosis patients often develop lung infections that range anywhere in severity from mild to life-threatening due to the presence of thick and sticky mucus that fills their airways. Since many of these infections are chronic, they not only affect a patient’s ability to breathe but also increase the chances of mortality by respiratory failure. With a publicly available dataset of DNA sequences from bacterial species in the lung microbiome of cystic fibrosis patients, the correlations between different microbial species in the lung and the extent of deterioration of lung function were investigated. 16S sequencing technologies were used to determine the microbiome composition of the samples in the dataset. For the statistical analyses, referencing helped distinguish between taxonomies, and the proportions of certain taxa relative to another were determined. It was found that the Fusobacterium, Actinomyces, and Leptotrichia microbial types all had a positive correlation with the FEV1 score, indicating the potential displacement of these species by pathogens as the disease progresses. However, the dominant pathogens themselves, including Pseudomonas aeruginosa and Staphylococcus aureus, did not have statistically significant negative correlations with the FEV1 score as described by past literature. Examining the lung microbiology of cystic fibrosis patients can help with the prediction of the current condition of lung function, with the potential to guide doctors when designing personalized treatment plans for patients.

Keywords: bacterial infections, cystic fibrosis, lung microbiome, 16S sequencing

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Authors: Hatem A. El-Mezayen, Hossam Darwesh

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Background/Aim: Hepatocellular carcinoma (HCC) is often diagnosed at advanced stage where effective therapies are lacking. Identification of new scoring system is needed to discriminate HCC patients from those with chronic liver disease. Based on the link between vascular endothelial growth factor (VEGF) and HCC progression, we aimed to develop a novel score based on combination of VEGF and routine laboratory tests for early prediction of HCC. Methods: VEGF was assayed for HCC group (123), liver cirrhosis group (210) and control group (50) by Enzyme Linked Immunosorbent Assay (ELISA). Data from all groups were retrospectively analyzed including α feto protein (AFP), international normalized ratio (INR), albumin and platelet count, transaminases, and age. Areas under ROC curve were used to develop the score. Results: A novel index named hepatocellular carcinoma-vascular endothelial growth factor score (HCC-VEGF score)=1.26 (numerical constant) + 0.05 ×AFP (U L-1)+0.038 × VEGF(ng ml-1)+0.004× INR –1.02 × Albumin (g l-1)–0.002 × Platelet count × 109 l-1 was developed. HCC-VEGF score produce area under ROC curve of 0.98 for discriminating HCC patients from liver cirrhosis with sensitivity of 91% and specificity of 82% at cut-off 4.4 (ie less than 4.4 considered cirrhosis and greater than 4.4 considered HCC). Conclusion: Hepatocellular carcinoma-VEGF score could replace AFP in HCC screening and follow up of cirrhotic patients.

Keywords: Hepatocellular carcinoma, cirrhosis, HCV, diagnosis, tumor markers

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Authors: Gashaw Abebaw

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Concrete usage in Ethiopia is expanding at a faster rate than before. Cement is the most important and costly ingredient in this respect. The construction industry is currently challenged by cement scarcity and stock market inflation. Scholars' trays, on the other hand, will use natural pozzolan material to substitute cement. Apart from that, Metakaolin has pozzolanic characteristics. According to the industrial mineral occurrence map, Ethiopia kaolin may be found in abundance. Some of them include Debretabor, so it is good to utilize Metakaolin as cement replacement material. In this study, the capability of Ethiopian Metakaolin as a partial substitute for cement in C-25 concrete production with 0%, 5%, 10%, 15%, and 20% replacement of PPC by MA with 0.49 percent water to cement ratio is investigated. The study examines; the chemical properties of MA, Physical properties of cement paste, workability, compressive strength, water absorption, density and sulfate attack of concrete was investigated. The chemical composition of Metakaolin was examined and the summation of SiO₂, AlO₃, and FeO₃ is 86.25% and the ash was classified class N pozzolan. The normal consistency percent of water increases as the MA replacement amount increase and both initial and final setting time rang increase as the MA replacement amount increase. On the 28th day, the compressive strength of concrete with MA replacement of 5%, 10%, and 15% exceeds the goal mean strength (33.5Mpa) with compressive strength enhancements of 2.23 %, 4.05 %, and 2.23 %, respectively. Similarly, on the 56th day, 5 %, 10%, and 15% replacement enhance concrete strength by 2.06 %, 3.06 %, and 1.2 %, respectively. The MA mixed concrete has improved significantly in terms of water absorption and sulphate attack, with a 15% replacement level. MA content Metakaolin could possibly replace cement up to 15%, according to the studies. The study's findings will help to offset cement price increases while also boosting house affordability without significantly degrading.

Keywords: metakaolin, compressive strength, sulphate attack, water absorption, N pozzolan

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Authors: Manit Pollar

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Identifying the dengue epidemic periods early would be helpful to take necessary actions to prevent the dengue outbreaks. Providing an accurate prediction on dengue epidemic seasons will allow sufficient time to take the necessary decisions and actions to safeguard the situation for local authorities. This study aimed to develop a forecasting model on number of dengue incidences in Nakhon Si Thammarat Province, Southern Thailand using time series analysis. We develop Seasonal Autoregressive Moving Average (SARIMA) models on the monthly data collected between 2003-2011 and validated the models using data collected between January-September 2012. The result of this study revealed that the SARIMA(1,1,0)(1,2,1)12 model closely described the trends and seasons of dengue incidence and confirmed the existence of dengue fever cases in Nakhon Si Thammarat for the years between 2003-2011. The study showed that the one-step approach for predicting dengue incidences provided significantly more accurate predictions than the twelve-step approach. The model, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for disease prevention.

Keywords: SARIMA, time series model, dengue cases, Thailand

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Authors: P. Larpsomboonchai

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Agro-industry is one of major industries that has strong impacts on national economic incomes, growth, stability, and sustainable development. Moreover, this industry also has strong influences on social, cultural and political issues. Furthermore, this industry, as producing primary and secondary products, is facing challenges from such diverse factors such as demand inconsistency, intense international competition, technological advancements and new competitors. In order to maintain and to improve industry’s competitiveness in both domestics and international markets, science and technology are key factors. Besides hard sciences and technologies, modern industrial engineering concepts such as Just in Time (JIT) Total Quality Management (TQM), Quick Response (QR), Supply Chain Management (SCM) and Lean can be very effective to supportant to increase efficiency and effectiveness of these agricultural products on world stage. Onion is one of Thailand’s major export products which brings back national incomes. But, it also facing challenges in many ways. This paper focused its interests in onion packing process and its related activities such as storage and shipment from one of major packing plant and storage in Mae Wang District, Chiang Mai, Thailand, by applying Toyota Production System (TPS) or Lean concepts, to improve process capability throughout the entire packing and distribution process which will be profitable for the whole onion supply chain. And it will be beneficial to other related agricultural products in Thailand and other ASEAN countries.

Keywords: packing process, Toyota Production System (TPS), lean concepts, waste reduction, lean in agro-industries activities

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Authors: Aml M. El-Sharkawy, Hossam M. Darwesh

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Abstract— Background/Aim: Hepatocellular carcinoma (HCC) is often diagnosed at advanced stage where effective therapies are lacking. Identification of new scoring system is needed to discriminate HCC patients from those with chronic liver disease. Based on the link between tumor associated trypsin inhibitor (TATI) and HCC progression, we aimed to develop a novel score based on combination of TATI and routine laboratory tests for early prediction of HCC. Methods: TATI was assayed for HCC group (123), liver cirrhosis group (210) and control group (50) by Enzyme Linked Immunosorbent Assay (ELISA). Data from all groups were retrospectively analyzed including α feto protein (AFP), international normalized ratio (INR), albumin and platelet count, transaminases, and age. Areas under ROC curve were used to develop the score. Results: A novel index named hepatocellular carcinoma-vascular endothelial growth factor score (HCC-TATI score) = 3.1 (numerical constant) + 0.09 ×AFP (U L-1) + 0.067 × TATI (ng ml-1) + 0.16 × INR – 1.17 × Albumin (g l-1) – 0.032 × Platelet count × 109 l-1 was developed. HCC-TATI score produce area under ROC curve of 0.98 for discriminating HCC patients from liver cirrhosis with sensitivity of 91% and specificity of 82% at cut-off 6.5 (ie less than 6.5 considered cirrhosis and greater than 4.4 considered HCC). Conclusion: Hepatocellular carcinoma-TATI score could replace AFP in HCC screening and follow up of cirrhotic patients.

Keywords: Hepatocellular carcinoma, cirrhosis, HCV, diagnosis, TATI

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Authors: Konrad Rejdak

Abstract:

Introduction: Neurodegenerative diseases are frequently accompanied by loss and unwanted change in functional independence, social relationships, and economic circumstances. Currently, the achievements of social robots to date is being projected to improve multidimensional quality of life among people with cognitive impairment and others. Objectives: Identification of particular human needs in the context of the changes occurring in course of neurodegenerative diseases. Methods: Based on the 110 surveys performed in the Medical University of Lublin from medical staff, patients, and caregivers we made prioritization of the users' needs as high, medium, and low. The issues included in the surveys concerned four aspects: user acceptance, functional requirements, the design of the robotic assistant and preferred types of human-robot interaction. Results: We received completed questionnaires; 50 from medical staff, 30 from caregivers and 30 from potential users. Above 90% of the respondents from each of the three groups, accepted a robotic assistant as a potential caregiver. High priority functional capability of assistive technology was to handle emergencies in a private home-like recognizing life-threatening situations and reminding about medication intake. With reference to the design of the robotic assistant, the majority of the respondent would like to have an anthropomorphic appearance with a positive emotionally expressive face. The most important type of human-robot interaction was a voice-operated system and by touchscreen. Conclusion: The results from our study might contribute to a better understanding of the system and users’ requirements for the development of a service robot intended to support patients with dementia.

Keywords: assistant robot, dementia, long term care, patients

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Authors: Ruchika Malhotra, Megha Khanna

Abstract:

The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

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Authors: Sayed M. Soleimani, Nader H. Ghareeb, Nourhan H. Shaker, Muhammad B. Siddiqui

Abstract:

Honeycomb sandwich panels have been widely used as protective structural elements against blast loading. The main advantages of these panels include their light weight due to the presence of voids, as well as their energy absorption capability. Terrorist activities have imposed new challenges to structural engineers to design protective measures for vital structures. Since blast loading is not usually considered in the load combinations during the design process of a structure, researchers around the world have been motivated to study the behavior of potential elements capable of resisting sudden loads imposed by the detonation of explosive materials. One of the best candidates for this objective is the honeycomb sandwich panel. Studying the effects of explosive materials on the panels requires costly and time-consuming experiments. Moreover, these type of experiments need permission from defense organizations which can become a hurdle. As a result, modeling and simulation using an appropriate tool can be considered as a good alternative. In this research work, the finite element package ABAQUS® is used to study the behavior of hexagonal and squared honeycomb steel sandwich panels under the explosive effects of different amounts of trinitrotoluene (TNT). The results of finite element modeling of a specific honeycomb configuration are initially validated by comparing them with the experimental results from literature. Afterwards, several configurations including different geometrical properties of the honeycomb wall are investigated and the results are compared with the original model. Finally, the effectiveness of the core shape and wall thickness are discussed, and conclusions are made.

Keywords: Abaqus, blast loading, finite element modeling, steel honeycomb sandwich panel

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Authors: Stefano M. Spagocci

Abstract:

The efficiency of photovoltaic panels can be calculated with such software packages as RETScreen that allow design engineers to take financial as well as technical considerations into account. RETScreen is interfaced with meteorological databases, so that efficiency calculations can be realistically carried out. The author has recently contributed to the development of solar modules with accumulation capability and an embedded water purifier, aimed at off-grid users such as users in developing countries. The software packages examined do not allow to take ancillary equipment into account, hence the decision to implement a technical and financial model of the system. The author realized that, rather than re-implementing the quite sophisticated model of RETScreen - a mathematical description of which is anyway not publicly available - it was possible to drastically simplify it, including the meteorological factors which, in RETScreen, are presented in a numerical form. The day-by-day efficiency of a photovoltaic solar panel was parametrized by the product of factors expressing, respectively, daytime duration, solar right ascension motion, solar declination motion, cloudiness, temperature. For the sun-motion-dependent factors, positional astronomy formulae, simplified by the author, were employed. Meteorology-dependent factors were fitted by simple trigonometric functions, employing numerical data supplied by RETScreen. The accuracy of our model was tested by comparing it to the predictions of RETScreen; the accuracy obtained was 11%. In conclusion, our study resulted in a model that can be easily implemented in a spreadsheet - thus being easily manageable by non-specialist personnel - or in more sophisticated software packages. The model was used in a number of design exercises, concerning photovoltaic solar panels and ancillary equipment like the above-mentioned water purifier.

Keywords: clean energy, energy engineering, mathematical modelling, photovoltaic panels, solar energy

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Authors: Mustafa Bayram Gücen, Coşkun Yakar

Abstract:

In this study, we have investigated the strict stability of fuzzy differential systems and we compare the classical notion of strict stability criteria of ordinary differential equations and the notion of strict stability of fuzzy differential systems. In addition that, we present definitions of stability and strict stability of fuzzy differential equations and also we have some theorems and comparison results. Strict Stability is a different stability definition and this stability type can give us an information about the rate of decay of the solutions. Lyapunov’s second method is a standard technique used in the study of the qualitative behavior of fuzzy differential systems along with a comparison result that allows the prediction of behavior of a fuzzy differential system when the behavior of the null solution of a fuzzy comparison system is known. This method is a usefull for investigating strict stability of fuzzy systems. First of all, we present definitions and necessary background material. Secondly, we discuss and compare the differences between the classical notion of stability and the recent notion of strict stability. And then, we have a comparison result in which the stability properties of the null solution of the comparison system imply the corresponding stability properties of the fuzzy differential system. Consequently, we give the strict stability results and a comparison theorem. We have used Lyapunov second method and we have proved a comparison result with scalar differential equations.

Keywords: fuzzy systems, fuzzy differential equations, fuzzy stability, strict stability

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Authors: Venkatesh Krishna Murthy

Abstract:

The era before government-regulated automotive designs gave us some astonishing vehicles that are well worth to keep on the road. The fact that restoring an automobile in 2015 does not mean it will perform like one designed in 2021. This is one of the reasons that manufacturers continue to turn to vintage hardware for future enhancements in their vehicles. Now we need to understand that a modern chassis could possibly allow manufacturers to give vintage performance cars a level of braking capability, compatibility with tires, chassis rigidity, suspension sophistication, and steering response, an experience only racers got until now. However, half a century of advancements in engineering can have a great impact on design in any field, and the automotive realm which holds no exception. In the current situation, a growing number of companies offer chassis and braking components to onboard manufacturers to retrofit contemporary technology for their vintage vehicles to modernize them at the foundation level. The recent question arises on performance on lithium batteries, as opposed to simply bolting upgraded components, for ex. lithium batteries with graphene as superconductive material to enhance performance, an area deeply investigated. Serving as the “bones” of the vehicle, the chassis and frame play a central role in dictating how that automobile will perform. While the desire to maintain originality is alluring for many, the benefits of a modern chassis are vast. In some situations, it also allows builders to put cars back on the road that might otherwise be too far gone. “There’s a couple of different factors at play here – one of them being that these older cars from the ’40s, ’50s, and ’60s have seen a lot of weather and a lot of road miles over the years, more often than not,” says Craig Morrison of Art Morrison Enterprises.

Keywords: hybrid electric vehicles, internet of things, lithium graphene batteries, classic car chassis

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Authors: P. Charvin, R. Bourrou, F. Lemont, C. Lafon, A. Russello

Abstract:

For nuclear waste treatment and confinement, a specific IN-CAN melting module based on low-frequency induction heating have been designed. The frequency of 50Hz has been chosen to improve penetration length through metal. In this design, the liquid metal, strongly stirred by electromagnetic effects, presents shape of a dome caused by strong Laplace forces developing in the bulk of bath. Because of a lower density, the glass phase is located above the metal phase and is heated and stirred by metal through interface. Electric parameters (Intensity, frequency) give precious information about metal load and composition (resistivity of alloy) through impedance modification. Then, power supply can be adapted to energy transfer efficiency for suitable process supervision. Modeling of this system allows prediction of metal dome shape (in agreement with experimental measurement with a specific device), glass and metal velocity, heat and motion transfer through interface. MHD modeling is achieved with COMSOL and Fluent. First, a simplified model is used to obtain the shape of the metal dome. Then the shape is fixed to calculate the fluid flow and the thermal part.

Keywords: electromagnetic stirring, induction heating, interface modeling, metal load

Procedia PDF Downloads 267