Abstract: This paper is focused on the mechanical characterization of foamed concrete specimens with protein-based foaming agent. Unlike classic foamed concrete, a peculiar property of the analyzed foamed concrete is the extrudability, which is achieved via a specific additive in the concrete mix that significantly improves the cohesion and viscosity of the fresh cementitious paste. A broad experimental campaign was conducted to evaluate the compressive strength and the indirect tensile strength of the specimens. The study has comprised three different cement types, two water/cement ratios, three curing conditions and three target dry densities. The variability of the strength values upon the above mentioned factors is discussed.
Abstract: This article discusses the possibility of using dilatometer tests (DMT) together with in situ seismic tests (MASW) in order to get the shape of G-g degradation curve in cohesive soils (clay, silty clay, silt, clayey silt and sandy silt). MASW test provides the small soil stiffness (Go from vs) at very small strains and DMT provides the stiffness of the soil at ‘work strains’ (MDMT). At different test locations, dilatometer shear stiffness of the soil has been determined by the theory of elasticity. Dilatometer shear stiffness has been compared with the theoretical G-g degradation curve in order to determine the typical range of shear deformation for different types of cohesive soil. The analysis also includes factors that influence the shape of the degradation curve (G-g) and dilatometer modulus (MDMT), such as the overconsolidation ratio (OCR), plasticity index (IP) and the vertical effective stress in the soil (svo'). Parametric study in this article defines the range of shear strain gDMT and GDMT/Go relation depending on the classification of a cohesive soil (clay, silty clay, clayey silt, silt and sandy silt), function of density (loose, medium dense and dense) and the stiffness of the soil (soft, medium hard and hard). The article illustrates the potential of using MASW and DMT to obtain G-g degradation curve in cohesive soils.
Abstract: The process of determining the degree of membership for an element to an uncertain concept has been found in many ways, using equivalence and symmetry relations in information systems. In the case of similarity, these methods did not take into account the degree of symmetry between elements. In this paper, we use a new definition for finding the membership based on the degree of symmetry. We provide an example to clarify the suggested methods and compare it with previous methods. This method opens the door to more accurate decisions in information systems.
Abstract: Data mining has, over recent years, seen big advances because of the spread of internet, which generates everyday a tremendous volume of data, and also the immense advances in technologies which facilitate the analysis of these data. In particular, classification techniques are a subdomain of Data Mining which determines in which group each data instance is related within a given dataset. It is used to classify data into different classes according to desired criteria. Generally, a classification technique is either statistical or machine learning. Each type of these techniques has its own limits. Nowadays, current data are becoming increasingly heterogeneous; consequently, current classification techniques are encountering many difficulties. This paper defines new measure functions to quantify the resemblance between instances and then combines them in a new approach which is different from actual algorithms by its reliability computations. Results of the proposed approach exceeded most common classification techniques with an f-measure exceeding 97% on the IRIS Dataset.
Abstract: This work concerns with the experimental study of the adsorption of the Ni(II) on bentonite. The effects of various parameters such as contact time, stirring rate, initial concentration of Ni(II), masse of clay, initial pH of aqueous solution and temperature on the adsorption yield, were carried out. The study of the effect of the ionic strength on the yield of adsorption was examined by the identification and the quantification of the present chemical species in the aqueous phase containing the metallic ion Ni(II). The adsorbed species were investigated by a calculation program using CHEAQS V. L20.1 in order to determine the relation between the percentages of the adsorbed species and the adsorption yield. The optimization process was carried out using 23 factorial designs. The individual and combined effects of three process parameters, i.e. initial Ni(II) concentration in aqueous solution (2.10−3 and 5.10−3 mol/L), initial pH of the solution (2 and 6.5), and mass of bentonite (0.03 and 0.3 g) on Ni(II) adsorption, were studied.
Abstract: Millions of Syrian families have been displaced since the beginning of the Syrian war, and the negative effects of post-war trauma have shown detrimental effects on the mental health of refugee children. While educational strategies have focused on vocational training and academic achievement, little has been done to include music in the school curriculum to help these children improve their mental health. The literature of music education and psychology, on the other hand, shows the positive effects of music on traumatized children, especially when it comes to dealing with stress. This paper presents a brief literature review of trauma, music therapy, and music in the classroom, after having introduced the Syrian war and refugee situation. Furthermore, the paper highlights the benefits of using music with traumatized children from the literature and offers strategies for teachers (such as singing, playing an instrument, songwriting, and others) to include music in their classrooms to help Syrian refugee children deal with post-war trauma.
Abstract: Society has a lot of diversities according to sex, age, religion, abilities or disabilities, education, etc. According to differences, everybody needs to be tolerated and equally included in society. In order to provide quality inclusion, society needs to tolerate differences. This study relates to the differences in disability. To examine tolerance towards disability and inclusion, this study was conducted with students attending regular elementary and high school. The main goal was to examine their attitudes towards their classmates and elderly people with disabilities. The study begins with the hypothesis that the environment has a highly developed tolerance towards people with disabilities, regardless of age. The sample was divided according to tasks and methodology analysis. Students attending regular elementary school were asked to make drawings of their classmates with disabilities. The drawings were analyzed using quantitative methodology according to the colors children used and the position of character on the paper. Students attending high school and members of general population were asked to complete a questionnaire designed for this study during a workshop held on the International Day for Tolerance. Responses were analyzed using qualitative methodology. The hypothesis was confirmed.
Abstract: Bioassay is the measurement of the potency of a chemical substance by its effect on a living animal or plant tissue. Bioassay data and chemical structures from pharmacokinetic and drug metabolism screening are mined from and housed in multiple databases. Bioassay prediction is calculated accordingly to determine further advancement. This paper proposes a four-step preprocessing of datasets for improving the bioassay predictions. The first step is instance selection in which dataset is categorized into training, testing, and validation sets. The second step is discretization that partitions the data in consideration of accuracy vs. precision. The third step is normalization where data are normalized between 0 and 1 for subsequent machine learning processing. The fourth step is feature selection where key chemical properties and attributes are generated. The streamlined results are then analyzed for the prediction of effectiveness by various machine learning algorithms including Pipeline Pilot, R, Weka, and Excel. Experiments and evaluations reveal the effectiveness of various combination of preprocessing steps and machine learning algorithms in more consistent and accurate prediction.
Abstract: Many supervised machine learning tasks require
decision making across numerous different classes. Multi-class
classification has several applications, such as face recognition, text
recognition and medical diagnostics. The objective of this article is
to analyze an adapted method of Stacking in multi-class problems,
which combines ensembles within the ensemble itself. For this
purpose, a training similar to Stacking was used, but with three
levels, where the final decision-maker (level 2) performs its training
by combining outputs from the tree-based pair of meta-classifiers
(level 1) from Bayesian families. These are in turn trained by pairs
of base classifiers (level 0) of the same family. This strategy seeks to
promote diversity among the ensembles forming the meta-classifier
level 2. Three performance measures were used: (1) accuracy, (2)
area under the ROC curve, and (3) time for three factors: (a)
datasets, (b) experiments and (c) levels. To compare the factors,
ANOVA three-way test was executed for each performance measure,
considering 5 datasets by 25 experiments by 3 levels. A triple
interaction between factors was observed only in time. The accuracy
and area under the ROC curve presented similar results, showing
a double interaction between level and experiment, as well as for
the dataset factor. It was concluded that level 2 had an average
performance above the other levels and that the proposed method
is especially efficient for multi-class problems when compared to
binary problems.
Abstract: Investigation was carried out at Opic Estate in Isheri-Osun River Basin environment using Electrical Resistivity method to study saltwater intrusion into a fresh water aquifer system from the proximal estuarine water body. The investigation is aimed at aquifer characterisation using electrical resistivity method in order to provide the depth to which fresh water fit for both domestic and industrial consumption. The 2D Electrical Resistivity and Vertical Electrical Resistivity techniques alongside Laboratory analysis of water samples obtained from the boreholes were adopted. Three traverses were investigated using Wenner and Pole-Dipole array with multi-electrode system consisting of 84 electrodes and a spread of 581 m, 664 m and 830 m were attained on the traverses. The main lithologies represented in the study area are Sand, Clay and Clayey Sand of which Sand constitutes the aquifer in the study area. Vertical Electrical Sounding data obtained at different lateral distance on the traverses have indicated that the water in the aquifer in the subsurface is brackish. Brackish water is represented by lowelectrical resistivity value signature while fresh water is characterized by relatively high electrical resistivity and in some regionfresh water is existent at depth greater than 200 m. Results of laboratory analysis of samples showed that the pH, Salinity, Total Dissolved Solid and Conductivity indicated existence of water with poor quality, indicating that salinity, TDS and Conductivity is higher in the Northern part of the study area. The 2D electrical resistivity and Vertical Electrical Sounding methods indicate that fresh water region is at ≥200m depth. Aquifers not fit for domestic use in the study area occur downwards to about 200 m in depth. In conclusion, it is recommended that wells should be sunkbeyond 220 m for the possible procurement of portable fresh water.
Abstract: The aim of this paper is the comparison of three different methods, in order to produce fuzzy tolerance relations for rainfall data classification. More specifically, the three methods are correlation coefficient, cosine amplitude and max-min method. The data were obtained from seven rainfall stations in the region of central Greece and refers to 20-year time series of monthly rainfall height average. Three methods were used to express these data as a fuzzy relation. This specific fuzzy tolerance relation is reformed into an equivalence relation with max-min composition for all three methods. From the equivalence relation, the rainfall stations were categorized and classified according to the degree of confidence. The classification shows the similarities among the rainfall stations. Stations with high similarity can be utilized in water resource management scenarios interchangeably or to augment data from one to another. Due to the complexity of calculations, it is important to find out which of the methods is computationally simpler and needs fewer compositions in order to give reliable results.
Abstract: This study aims to present a proposal for an economic integration model for the development of the manufacturing industry in Cabinda, Angola. It seeks to analyze the degree of economic integration of Cabinda and the dynamics of the manufacturing industry. Therefore, in the same way, to gather information to support the decision-making for public financing programs that will aim at the disengagement of the manufacturing industry in Angola and Cabinda in particular. The Cabinda Province is the 18th of Angola, the enclave is located in a privileged area of the African and arable land.
Abstract: In Japan, meat-type chickens are mainly classified into three categories: (1) Broilers, (2) Branded chickens, and (3) Jidori (Free-range local traditional pedigree chickens). The Jidori chickens are certified by the Japanese Ministry of Agriculture, whilst, for the Branded chickens, there is no regulation with respect to their breed (genotype) or methods for rearing them. It is, therefore, relatively easy for poultry farmers to introduce Branded than Jidori chickens. The Branded chickens are normally fed a low-calorie diet with ingredients such as herbs, which lengthens their breeding period (compared with that of the Broilers) and increases their market value. In the field of inventory management, fast-growing animals such as broilers are categorised as ameliorating items. To the best of our knowledge, there are no previous studies that have explicitly considered smaller sized poultry farmers with limited breeding areas. This study develops an inventory model for a small sized poultry farmer that produces both the Broilers (Product 1) and the Branded chickens (Product 2) with different amelioration rates. The poultry farmer’s total profit per unit of time is formulated as a function of selling prices by using a price-dependent demand function. The existence of a unique optimal selling price for each product, which maximises the total profit, established. It has also been confirmed through numerical examples that, when the breeding area is fixed, the total profit could increase if the poultry farmer reduced the product quantity of Product 1 to introduce Product 2.
Abstract: Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.
Abstract: In this paper, a theoretical study on the forced vibration of one degree of freedom system equipped with inerter, working under load-type or displacement-type excitation, is presented. Differential equations of movement are solved under cosinusoidal excitation, and explicit relations for the magnitude, resonant magnitude, phase angle, resonant frequency, and critical frequency are obtained. Influence of the inertance and damping on these dynamic characteristics is clarified. From the obtained results, one concludes that the inerter increases the magnitude of vibration and the phase angle of the damped mechanical system. Moreover, the magnitude ratio and difference of phase angles are not depending on the actual type of excitation. Consequently, such kind of similitude allows for the comparison of various theoretical and experimental results, which can be broadly found in the literature.
Abstract: In a nuclear reactor, an array of fuel rods containing stacked uranium dioxide pellets clad with zircalloy is the heat source for a thermodynamic cycle of energy conversion from heat to electricity. After fuel is used in a nuclear reactor, the assemblies are stored underwater in a spent nuclear fuel pool at the nuclear power plant while heat generation and radioactive decay rates decrease before it is placed in packages for dry storage or transportation. A computational model of a Boiling Water Reactor spent fuel assembly is modeled using FLUENT, the computational fluid dynamics package. Heat transfer simulations were performed on the two-dimensional 9x9 spent fuel assembly to predict the maximum cladding temperature for different input to the FLUENT model. Uncertainty quantification is used to predict the heat transfer and the maximum temperature profile inside the assembly.
Abstract: A study of the tidal current behavior and bathymetric changes was undertaken in order to establish an information base for future coastal management. The average velocity for tidal current was 0.46 m/s and the maximum velocity was 1.08 m/s during ebb tide. During spring tides, maximum velocities range from 0.90 m/s to 1.08 m/s, whereas maximum velocities vary from 0.40 m/s to 0.60 m/s during neap tides. Despite greater current velocities during flood tide, the bathymetric features enhance the dominance of the ebb tide. This can be related to the abundance of fine sediments from the ebb current approaching the study area, and the relatively coarser sediment from the approaching flood current. Significant bathymetric changes for the period from 1985 to 1998 were found with dominance of erosion process. Approximately 96.5% of depth changes occurred within the depth change classes of -5 m to 5 m. The high erosion processes within the study area will subsequently result in high accretion processes, particularly in the north, the location of the proposed Boubyan Port and its navigation channel.
Abstract: This paper presents an automatic normal and abnormal heart sound classification model developed based on deep learning algorithm. MITHSDB heart sounds datasets obtained from the 2016 PhysioNet/Computing in Cardiology Challenge database were used in this research with the assumption that the electrocardiograms (ECG) were recorded simultaneously with the heart sounds (phonocardiogram, PCG). The PCG time series are segmented per heart beat, and each sub-segment is converted to form a square intensity matrix, and classified using convolutional neural network (CNN) models. This approach removes the need to provide classification features for the supervised machine learning algorithm. Instead, the features are determined automatically through training, from the time series provided. The result proves that the prediction model is able to provide reasonable and comparable classification accuracy despite simple implementation. This approach can be used for real-time classification of heart sounds in Internet of Medical Things (IoMT), e.g. remote monitoring applications of PCG signal.
Abstract: Most movie recommendation systems have been developed for customers to find items of interest. This work introduces a predictive model usable by small and medium-sized enterprises (SMEs) who are in need of a data-based and analytical approach to stock proper movies for local audiences and retain more customers. We used classification models to extract features from thousands of customers’ demographic, behavioral and social information to predict their movie genre preference. In the implementation, a Gaussian kernel support vector machine (SVM) classification model and a logistic regression model were established to extract features from sample data and their test error-in-sample were compared. Comparison of error-out-sample was also made under different Vapnik–Chervonenkis (VC) dimensions in the machine learning algorithm to find and prevent overfitting. Gaussian kernel SVM prediction model can correctly predict movie genre preferences in 85% of positive cases. The accuracy of the algorithm increased to 93% with a smaller VC dimension and less overfitting. These findings advance our understanding of how to use machine learning approach to predict customers’ preferences with a small data set and design prediction tools for these enterprises.
Abstract: This paper presents an improved Direct Power Control (DPC) scheme applied to the multilevel inverter that forms a Distributed Generation Unit (DGU). This paper demonstrates the performance of active and reactive power injected by the DGU to the smart grid. The DPC is traditionally operated by the hysteresis controller with the Space Vector Modulation (SVM) which is applied on the 2-level inverters or 3-level inverters. In this paper, the DPC is operated by the PI controller with the Phase-Disposition Pulse Width Modulation (PD-PWM) applied to the 5-level diode clamped inverter. The new combination of the DPC, PI controller, PD-PWM and multilevel inverter proves that its performance is much better than the conventional hysteresis-SVM based DPC. Simulations results have been presented to validate the performance of the suggested control scheme in the grid-connected mode.