Abstract: Essential oils of Heracleum persicum (Apiaceae) have been widely used from many years ago, but the difference of its properties among different populations have not been identified up to now. Hydrodistilation Clevenger type was used to obtaining the fruit essential oils of four populations of H. persicum from different localities in Iran, then they were characterized by GC-FID and GC-MS analyses. Some ecological factors were also measured. The oils of four populations were compared to determine the similarities and differences and the relationships between these factors and ecological factors. Based on the result, 18-32 different components were identified in four populations, while the percentage of the main components was higher in population with lower number of components. According to the statistical analyses of chemical components and ecological factors, it can be concluded that some ecological factors such as altitude, less humidity, high difference between day and night temperature and salty soil would lead to lower number of components in essential oil, whereas they consist the higher percentage.
Abstract: This paper deals with the problem of two-dimensional (2-D) recursive doubly complementary (DC) digital filter design. We present a structure of 2-D recursive DC filters by using 2-D symmetric half-plane (SHP) recursive digital all-pass lattice filters (DALFs). The novelty of using 2-D SHP recursive DALFs to construct a 2-D recursive DC digital lattice filter is that the resulting 2-D SHP recursive DC digital lattice filter provides better performance than the existing 2-D SHP recursive DC digital filter. Moreover, the proposed structure possesses a favorable 2-D DC half-band (DC-HB) property that allows about half of the 2-D SHP recursive DALF’s coefficients to be zero. This leads to considerable savings in computational burden for implementation. To ensure the stability of a designed 2-D SHP recursive DC digital lattice filter, some necessary constraints on the phase of the 2-D SHP recursive DALF during the design process are presented. Design of a 2-D diamond-shape decimation/interpolation filter is presented for illustration and comparison.
Abstract: The sintering step in powder metallurgy (P/M) processes is very sensitive as it determines to a large extent the properties of the final component produced. Spark plasma sintering over the past decade has been extensively used in consolidating a wide range of materials including metallic alloy powders. This novel, non-conventional sintering method has proven to be advantageous offering full densification of materials, high heating rates, low sintering temperatures, and short sintering cycles over conventional sintering methods. Ti6Al4V has been adjudged the most widely used α+β alloy due to its impressive mechanical performance in service environments, especially in the aerospace and automobile industries being a light metal alloy with the capacity for fuel efficiency needed in these industries. The P/M route has been a promising method for the fabrication of parts made from Ti6Al4V alloy due to its cost and material loss reductions and the ability to produce near net and intricate shapes. However, the use of this alloy has been largely limited owing to its relatively poor hardness and wear properties. The effect of sintering temperature on the densification, hardness, and wear behaviors of spark plasma sintered Ti6Al4V powders was investigated in this present study. Sintering of the alloy powders was performed in the 650–850°C temperature range at a constant heating rate, applied pressure and holding time of 100°C/min, 50 MPa and 5 min, respectively. Density measurements were carried out according to Archimedes’ principle and microhardness tests were performed on sectioned as-polished surfaces at a load of 100gf and dwell time of 15 s. Dry sliding wear tests were performed at varied sliding loads of 5, 15, 25 and 35 N using the ball-on-disc tribometer configuration with WC as the counterface material. Microstructural characterization of the sintered samples and wear tracks were carried out using SEM and EDX techniques. The density and hardness characteristics of sintered samples increased with increasing sintering temperature. Near full densification (99.6% of the theoretical density) and Vickers’ micro-indentation hardness of 360 HV were attained at 850°C. The coefficient of friction (COF) and wear depth improved significantly with increased sintering temperature under all the loading conditions examined, except at 25 N indicating better mechanical properties at high sintering temperatures. Worn surface analyses showed the wear mechanism was a synergy of adhesive and abrasive wears, although the former was prevalent.
Abstract: A methodology based on wavelets is proposed for the automatic location and delimitation of defects in limestone plates. Natural defects include dark colored spots, crystal zones trapped in the stone, areas of abnormal contrast colors, cracks or fracture lines, and fossil patterns. Although some of these may or may not be considered as defects according to the intended use of the plate, the goal is to pair each stone with a map of defects that can be overlaid on a computer display. These layers of defects constitute a database that will allow the preliminary selection of matching tiles of a particular variety, with specific dimensions, for a requirement of N square meters, to be done on a desktop computer rather than by a two-hour search in the storage park, with human operators manipulating stone plates as large as 3 m x 2 m, weighing about one ton. Accident risks and work times are reduced, with a consequent increase in productivity. The base for the algorithm is wavelet decomposition executed in two instances of the original image, to detect both hypotheses – dark and clear defects. The existence and/or size of these defects are the gauge to classify the quality grade of the stone products. The tuning of parameters that are possible in the framework of the wavelets corresponds to different levels of accuracy in the drawing of the contours and selection of the defects size, which allows for the use of the map of defects to cut a selected stone into tiles with minimum waste, according the dimension of defects allowed.
Abstract: Past earthquakes have shown that seismic events may incur large economic losses in buildings. FEMA P-58 provides engineers a practical tool for the performance seismic assessment of buildings. In this study, FEMA P-58 is applied to two typical Italian pre-1970 reinforced concrete frame buildings, characterized by plain rebars as steel reinforcement and masonry infills and partitions. Given that suitable tools for these buildings are missing in FEMA P- 58, specific fragility curves and loss functions are first developed. Next, building performance is evaluated following a time-based assessment approach. Finally, expected annual losses for the selected buildings are derived and compared with past applications to old RC frame buildings representative of the US building stock.
Abstract: With digitalization increasingly changing the rules of competition, firms face the need to adapt and assimilate digital technologies in order to remain competitive. Firms can choose from various possibilities to integrate digital technologies including the option to embed digital technologies aiming to innovate products or to develop digital products. However, the question of which specific factors influence a firm’s decision to pursue digital product innovation remains unanswered in research. By adopting the Technology-Organization-Environment (TOE)-framework we have designed a qualitative exploratory study including eleven German practitioners to investigate relevant contingency factors. Our results indicate that the most critical factors for a company’s decision to pursue digital product innovation can be found in the technological and environmental dimensions, namely customers, competitive pressure, technological change, as well as digitalization fit.
Abstract: Analysing unbalanced datasets is one of the challenges that practitioners in machine learning field face. However, many researches have been carried out to determine the effectiveness of the use of the synthetic minority over-sampling technique (SMOTE) to address this issue. The aim of this study was therefore to compare the effectiveness of the SMOTE over different models on unbalanced datasets. Three classification models (Logistic Regression, Support Vector Machine and Nearest Neighbour) were tested with multiple datasets, then the same datasets were oversampled by using SMOTE and applied again to the three models to compare the differences in the performances. Results of experiments show that the highest number of nearest neighbours gives lower values of error rates.
Abstract: Experimental and analytical studies were accomplished to examine the structural behavior of precast foamed concrete sandwich panel (PFCSP) under vertical in-plane shear load. PFCSP full-scale specimens with total number of six were developed with varying heights to study an important parameter slenderness ratio (H/t). The production technique of PFCSP and the procedure of test setup were described. The results obtained from the experimental tests were analysed in the context of in-plane shear strength capacity, load-deflection profile, load-strain relationship, slenderness ratio, shear cracking patterns and mode of failure. Analytical study of finite element analysis was implemented and the theoretical calculations of the ultimate in-plane shear strengths using the adopted ACI318 equation for reinforced concrete wall were determined aimed at predicting the in-plane shear strength of PFCSP. The decrease in slenderness ratio from 24 to 14 showed an increase of 26.51% and 21.91% on the ultimate in-plane shear strength capacity as obtained experimentally and in FEA models, respectively. The experimental test results, FEA models data and theoretical calculation values were compared and provided a significant agreement with high degree of accuracy. Therefore, on the basis of the results obtained, PFCSP wall has the potential use as an alternative to the conventional load-bearing wall system.
Abstract: Virtualization technologies are experiencing a renewed interest as a way to improve system reliability, and availability, reduce costs, and provide flexibility. This paper presents the development on leverage existing cloud infrastructure and virtualization tools. We adopted some virtualization technologies which improve portability, manageability and compatibility of applications by encapsulating them from the underlying operating system on which they are executed. Given the development of application virtualization, it allows shifting the user’s applications from the traditional PC environment to the virtualized environment, which is stored on a remote virtual machine rather than locally. This proposed effort has the potential to positively provide an efficient, resilience and elastic environment for online cloud service. Users no longer need to burden the platform maintenances and drastically reduces the overall cost of hardware and software licenses. Moreover, this flexible and web-based application virtualization service represents the next significant step to the mobile workplace, and it lets user executes their applications from virtually anywhere.
Abstract: In this paper, we present a fast and efficient mesh coarsening algorithm for 3D triangular meshes. Theis approach can be applied to very complex 3D meshes of arbitrary topology and with millions of vertices. The algorithm is based on the clustering of the input mesh elements, which divides the faces of an input mesh into a given number of clusters for clustering purpose by approximating the Centroidal Voronoi Tessellation of the input mesh. Once a clustering is achieved, it provides us an efficient way to construct uniform tessellations, and therefore leads to good coarsening of polygonal meshes. With proliferation of 3D scanners, this coarsening algorithm is particularly useful for reverse engineering applications of 3D models, which in many cases are dense, non-uniform, irregular and arbitrary topology. Examples demonstrating effectiveness of the new algorithm are also included in the paper.
Abstract: This study is aimed to investigate the influence of
postharvest delays of 1-Methylcyclopropene (1-MCP) treatment on
prolonging the storage potential of melon. Melons were treated with
625-650 ppb 1-MCP at 10 °C for 24 hours on the 1st, 3rd and 5th day
after harvest. Decreased ethylene production and retarded softening
of melon fruits after 7 days of storage at 10 °C plus 3 days of shelflife
were obtained by 1-MCP applications. 1-MCP strongly affected
the chlorophyll fluorescence characteristics and hue angle values of
melon. After shelf-life, the peel color of treated melon was slow in
turning to yellow compared to the control. Additionally, firmness of
melons treated on the first day after harvest was 38% higher than that
of the control fruit. Results showed that fruits treated on the 1st and
the 3rd day after harvest could maintain the quality of melon.
Abstract: The purpose of this article is to find a method
of comparing designs for ordinal regression models using
quantile dispersion graphs in the presence of linear predictor
misspecification. The true relationship between response variable
and the corresponding control variables are usually unknown.
Experimenter assumes certain form of the linear predictor of the
ordinal regression models. The assumed form of the linear predictor
may not be correct always. Thus, the maximum likelihood estimates
(MLE) of the unknown parameters of the model may be biased due to
misspecification of the linear predictor. In this article, the uncertainty
in the linear predictor is represented by an unknown function. An
algorithm is provided to estimate the unknown function at the
design points where observations are available. The unknown function
is estimated at all points in the design region using multivariate
parametric kriging. The comparison of the designs are based on
a scalar valued function of the mean squared error of prediction
(MSEP) matrix, which incorporates both variance and bias of the
prediction caused by the misspecification in the linear predictor. The
designs are compared using quantile dispersion graphs approach.
The graphs also visually depict the robustness of the designs on the
changes in the parameter values. Numerical examples are presented
to illustrate the proposed methodology.
Abstract: The objective of this study is to analyze the evolution of some social and economic indicators of Mercosur´s economies from 1980 to 2012, based on the statistics of the Latin American Integration Association (LAIA). The objective is to observe if after the accession of these economies to Mercosur (the first accessions occurred in 1994) these indicators showed better performance, in order to demonstrate if economic integration contributed to improved trade, macroeconomic performance, and level of social and economic development of member countries. To this end, the methodologies used will be a literature review and descriptive statistics. The theoretical framework that guides the work are the theories of Integration: Classical Liberal, Marxist and structural-proactive. The results reveal that most social and economic indicators showed better performance in those economies that joined Mercosur after 1994. This work is the result of an investigation already completed.
Abstract: A recently developed one-equation turbulence model
has been successfully applied to simulate turbulent flows with
various complexities. The model, which is based on the
transformation of the k-ε closure, is wall-distance free and equipped
with lagging destruction/dissipation terms. Test cases included shockboundary-
layer interaction flows over the NACA 0012 airfoil, an
axisymmetric bump, and the ONERA M6 wing. The capability of the
model to operate in a Scale Resolved Simulation (SRS) mode is
demonstrated through the simulation of a massive flow separation
over a circular cylinder at Re= 1.2 x106. An assessment of the results
against available experiments Menter (k-ε)1Eq and the Spalart-
Allmaras model that belongs to the single equation closure family is
made.
Abstract: Since Google launched Google Glass in 2012, numbers of commercial wearable devices were released, such as smart belt, smart band, smart shoes, smart clothes ... etc. However, most of these devices perform as sensors to show the readings of measurements and few of them provide the interactive feedback to the user. Furthermore, these devices are single task devices which are not able to communicate with each other. In this paper a new health promotion system, Ubiquitous Life People Informatics Engine (U-Life PIE), will be presented. This engine consists of People Informatics Engine (PIE) and the interactive user interface. PIE collects all the data from the compatible devices, analyzes this data comprehensively and communicates between devices via various application programming interfaces. All the data and informations are stored on the PIE unit, therefore, the user is able to view the instant and historical data on their mobile devices any time. It also provides the real-time hands-free feedback and instructions through the user interface visually, acoustically and tactilely. These feedback and instructions suggest the user to adjust their posture or habits in order to avoid the physical injuries and prevent illness.
Abstract: The performance of multiple tuned mass dampers to
mitigate the seismic vibration of structures considering real time
history data is investigated in this paper. Three different real
earthquake time history data like Kobe, Imperial Valley and
Mammoth Lake are taken in the present study. The multiple tuned
mass dampers (MTMD) are distributed at each storey. For
comparative study, single tuned mass damper (STMD) is installed at
top of the similar structure. This study is conducted for a fixed mass
ratio (5%) and fixed damping ratio (5%) of structures. Numerical
study is performed to evaluate the effectiveness of MTMDs and
overall system performance. The displacement, acceleration, base
shear and storey drift are obtained for both combined system
(structure with MTMD and structure with STMD) for all earthquakes.
The same responses are also obtained for structure without damper
system. From obtained results, it is investigated that the MTMD
configuration is more effective for controlling the seismic response of
the primary system with compare to STMD configuration.
Abstract: Slope stability analyses are largely carried out by deterministic methods and evaluated through a single security factor. Although it is known that the geotechnical parameters can present great dispersal, such analyses are considered fixed and known. The probabilistic methods, in turn, incorporate the variability of input key parameters (random variables), resulting in a range of values of safety factors, thus enabling the determination of the probability of failure, which is an essential parameter in the calculation of the risk (probability multiplied by the consequence of the event). Among the probabilistic methods, there are three frequently used methods in geotechnical society: FOSM (First-Order, Second-Moment), Rosenblueth (Point Estimates) and Monte Carlo. This paper presents a comparison between the results from deterministic and probabilistic analyses (FOSM method, Monte Carlo and Rosenblueth) applied to a hypothetical slope. The end was held to evaluate the behavior of the slope and consequent risk analysis, which is used to calculate the risk and analyze their mitigation and control solutions. It can be observed that the results obtained by the three probabilistic methods were quite close. It should be noticed that the calculation of the risk makes it possible to list the priority to the implementation of mitigation measures. Therefore, it is recommended to do a good assessment of the geological-geotechnical model incorporating the uncertainty in viability, design, construction, operation and closure by means of risk management.
Abstract: Recently, fluidized bed gasification becomes an attractive technology for power generation due to its higher efficiency. The main objective pursued in this work is to investigate the producer gas production potential from sized biomass (sawdust and pigeon pea) by applying the air gasification technique. The size of the biomass selected for the study was in the range of 0.40-0.84 mm. An experimental study was conducted using a fluidized bed gasifier with 210 mm diameter and 1600 mm height. During the experiments, the fuel properties and the effects of operating parameters such as gasification temperatures 700 to 900 °C, equivalence ratio 0.16 to 0.46 were studied. It was concluded that substantial amounts of producer gas (up to 1110 kcal/m3) could be produced utilizing biomass such as sawdust and pigeon pea by applying this fluidization technique. For both samples, the rise of temperature till 900 °C and equivalence ratio of 0.4 favored further gasification reactions and resulted into producer gas with calorific value 1110 kcal/m3.
Abstract: Energy disaggregation has been focused by many energy companies since energy efficiency can be achieved when the breakdown of energy consumption is known. Companies have been investing in technologies to come up with software and/or hardware solutions that can provide this type of information to the consumer. On the other hand, not all people can afford to have these technologies. Therefore, in this paper, we present a methodology for breaking down the aggregate consumption and identifying the highdemanding end-uses profiles. These energy profiles will be used to build the forecast model for optimal control purpose. A facility with high cooling load is used as an illustrative case study to demonstrate the results of proposed methodology. We apply a high level energy disaggregation through a pattern recognition approach in order to extract the consumption profile of its rooftop packaged units (RTUs) and present a forecast model for the energy consumption.
Abstract: One of the major developments in machine learning in the past decade is the ensemble method, which finds highly accurate classifier by combining many moderately accurate component classifiers. In this research work, new ensemble classification methods are proposed with homogeneous ensemble classifier using bagging and heterogeneous ensemble classifier using arcing and their performances are analyzed in terms of accuracy. A Classifier ensemble is designed using Radial Basis Function (RBF) and Support Vector Machine (SVM) as base classifiers. The feasibility and the benefits of the proposed approaches are demonstrated by the means of standard datasets of intrusion detection. The main originality of the proposed approach is based on three main parts: preprocessing phase, classification phase, and combining phase. A wide range of comparative experiments is conducted for standard datasets of intrusion detection. The performance of the proposed homogeneous and heterogeneous ensemble classifiers are compared to the performance of other standard homogeneous and heterogeneous ensemble methods. The standard homogeneous ensemble methods include Error correcting output codes, Dagging and heterogeneous ensemble methods include majority voting, stacking. The proposed ensemble methods provide significant improvement of accuracy compared to individual classifiers and the proposed bagged RBF and SVM performs significantly better than ECOC and Dagging and the proposed hybrid RBF-SVM performs significantly better than voting and stacking. Also heterogeneous models exhibit better results than homogeneous models for standard datasets of intrusion detection.