Abstract: This paper presents powerful techniques for the
development of a new monitoring method based on multi-scale
entropy (MSE) in order to characterize the behaviour of the
concentrations of different gases present in the synthesis of Ammonia
and soft-sensor based on Principal Component Analysis (PCA).
Abstract: Geometric and mechanical properties all influence the
resistance of RC structures and may, in certain combination of
property values, increase the risk of a brittle failure of the whole
system.
This paper presents a statistical and probabilistic investigation on
the resistance of RC beams designed according to Eurocodes 2 and 8,
and subjected to multiple failure modes, under both the natural
variation of material properties and the uncertainty associated with
cross-section and transverse reinforcement geometry. A full
probabilistic model based on JCSS Probabilistic Model Code is
derived. Different beams are studied through material nonlinear
analysis via Monte Carlo simulations. The resistance model is
consistent with Eurocode 2. Both a multivariate statistical evaluation
and the data clustering analysis of outcomes are then performed.
Results show that the ultimate load behaviour of RC beams
subjected to flexural and shear failure modes seems to be mainly
influenced by the combination of the mechanical properties of both
longitudinal reinforcement and stirrups, and the tensile strength of
concrete, of which the latter appears to affect the overall response of
the system in a nonlinear way. The model uncertainty of the
resistance model used in the analysis plays undoubtedly an important
role in interpreting results.
Abstract: The purpose of this study is to forecast the influences
of information and communication technology (ICT) on the structural
changes of Japanese economies. In this study, input-output (IO) and
statistical approaches are used as analysis instruments. More
specifically, this study employs Leontief IO coefficients and
constrained multivariate regression (CMR) model in order to achieve
the purpose. The periods of initial and forecast in this study are 2005
and 2015, respectively. In this study, ICT is represented by ICT capital
stocks. This study conducts two levels of analysis, namely macro and
micro. The results of macro level analysis show that the dynamics of
Japanese economies on the forecast period, relative to the initial period,
are not so high. We focus on (1) commerce, (2) business services and
office supplies, and (3) personal services sectors when conducting the
analysis of the micro level. Further, we analyze its specific IO
coefficients when doing this analysis. The results of the analysis
explain that ICT gives a strong influence on the changes of these
coefficients from initial to forecast periods.
Abstract: Curcuma longa L. (Zingiberaceae), commonly known
as turmeric, has a long history of traditional uses for culinary
purposes as a spice and a food colorant. The present study aimed to
document the ethnobotanical knowledge about Curcuma longa, and
to assess the variation in the herbalists’ experience in Northeastern
Algeria. Data were collected using semi-structured questionnaires
and direct interviews with 30 herbalists. Ethnobotanical indices,
including the fidelity level (FL%), the relative frequency citation
(RFC), and use value (UV) were determined by quantitative methods.
Diversity in the level of knowledge was analyzed using univariate,
non-parametric, and multivariate statistical methods. Three main
categories of uses were recorded for C. longa: for food, for medicine,
and for cosmetic purposes. As a medicine, turmeric was used for the
treatment of gastrointestinal, dermatological, and hepatic diseases.
Medicinal and food uses were correlated with both forms of
preparation (rhizome and powder). The age group did not influence
the use. Multivariate analyses showed a significant variation in
traditional knowledge, associated with the use value, origin, quality,
and efficacy of the drug. The findings suggested that the geographical
origin of C. longa affected the use in Algeria.
Abstract: The purpose of this project is to carry out an analysis
and determine the profile of actual lean manufacturing processes in
the Metropolitan Area of Bucaramanga. Through the analysis of
qualitative and quantitative variables it was possible to establish how
these manufacturers develop production practices that ensure their
competitiveness and productivity in the market.
In this study, a random sample of metallurgic and wrought iron
companies was applied, following which a quantitative focus and
analysis was used to formulate a qualitative methodology for
measuring the level of lean manufacturing procedures in the industry.
A qualitative evaluation was also carried out through a multivariate
analysis using the Numerical Taxonomy System (NTSYS) program
which should allow for the determination of Lean Manufacturing
profiles.
Through the results it was possible to observe how the companies
in the sector are doing with respect to Lean Manufacturing Practices,
as well as identify the level of management that these companies
practice with respect to this topic. In addition, it was possible to
ascertain that there is no one dominant profile in the sector when it
comes to Lean Manufacturing.
It was established that the companies in the metallurgic and
wrought iron industry show low levels of Lean Manufacturing
implementation. Each one carries out diverse actions that are
insufficient to consolidate a sectoral strategy for developing a
competitive advantage which enables them to tie together a
production strategy.
Abstract: In this paper, we present a new maintenance model
for a partially observable system subject to two failure modes,
namely a catastrophic failure and a failure due to the system
degradation. The system is subject to condition monitoring and the
degradation process is described by a hidden Markov model. A
cost-optimal Bayesian control policy is developed for maintaining
the system. The control problem is formulated in the semi-Markov
decision process framework. An effective computational algorithm is
developed, illustrated by a numerical example.
Abstract: This paper proposes a linear mixed model (LMM) with spatial effects to forecast rice and cassava yields in Thailand at the same time. A multivariate conditional autoregressive (MCAR) model is assumed to present the spatial effects. A Bayesian method is used for parameter estimation via Gibbs sampling Markov Chain Monte Carlo (MCMC). The model is applied to the rice and cassava yields monthly data which have been extracted from the Office of Agricultural Economics, Ministry of Agriculture and Cooperatives of Thailand. The results show that the proposed model has better performance in most provinces in both fitting part and validation part compared to the simple exponential smoothing and conditional auto regressive models (CAR) from our previous study.
Abstract: A study to estimate the size of the cabin and major
aircraft components as well as detect and avoid interference between
internally placed components and the external surface, during the
conceptual design synthesis and optimisation to explore the design
space of a BWB, was conducted. Sizing of components follows the
Bradley cabin sizing and rubber engine scaling procedures to size
the cabin and engine respectively. The interference detection and
avoidance algorithm relies on the ability of the Class Shape Transform
parameterisation technique to generate polynomial functions of the
surfaces of a BWB aircraft configuration from the sizes of the
cabin and internal objects using few variables. Interference detection
is essential in packaging of non-conventional configuration like
the BWB because of the non-uniform airfoil-shaped sections and
resultant varying internal space. The unique configuration increases
the need for a methodology to prevent objects from being placed in
locations that do not sufficiently enclose them within the geometry.
Abstract: Although human resources are recognized as the crucial companies’ resources and their positive influence on companies’ performances has been confirmed through different researches, scientists are still debating it. In order to contribute this debate, this paper firstly discusses the most important human resource management elements and practices and its influence on companies’ success. Afterwards it defines human resource “bundles” – interrelated and internally consistent human resource practices, complementary to each other, or the most important human resource practices and elements regarding Croatian companies and its human resource management activities. Finally, the paper provides empirical results; more precisely it reveals the relation of the level of development of human resource management function (“bundles”) and companies’ financial performances (using profitability ratios, liquidity ratios, solvency ratios and a group of additional ratios related to employees’ indicators).
Abstract: The present work has been carried out to evaluate the diversity of a collection of 78 quinoa accessions developed through recurrent selection from Andean germplasm introduced to Morocco in the winter of 2000. Twenty-three quantitative and qualitative characters were used for the evaluation of genetic diversity and the relationship between the accessions, and also for the establishment of a core collection in Morocco. Important variation was found among the accessions in terms of plant morphology and growth behavior. Data analysis showed positive correlation of the plant height, the plant fresh and the dry weight with the grain yield, while days to flowering was found to be negatively correlated with grain yield. The first four PCs contributed 74.76% of the variability; the first PC showed significant variation with 42.86% of the total variation, PC2 with 15.37%, PC3 with 9.05% and PC4 contributed 7.49% of the total variation. Plant size, days to grain filling and days to maturity are correlated to the PC1; and seed size, inflorescence density and mildew resistance are correlated to the PC2. Hierarchical cluster analysis rearranged the 78 quinoa accessions into four main groups and ten sub-clusters. Clustering was found in associations with days to maturity and also with plant size and seed-size traits.
Abstract: Non-negative matrix factorization (NMF) is a useful computational method to find basis information of multivariate nonnegative data. A popular approach to solve the NMF problem is the multiplicative update (MU) algorithm. But, it has some defects. So the columnwisely alternating gradient (cAG) algorithm was proposed. In this paper, we analyze convergence of the cAG algorithm and show advantages over the MU algorithm. The stability of the equilibrium point is used to prove the convergence of the cAG algorithm. A classic model is used to obtain the equilibrium point and the invariant sets are constructed to guarantee the integrity of the stability. Finally, the convergence conditions of the cAG algorithm are obtained, which help reducing the evaluation time and is confirmed in the experiments. By using the same method, the MU algorithm has zero divisor and is convergent at zero has been verified. In addition, the convergence conditions of the MU algorithm at zero are similar to that of the cAG algorithm at non-zero. However, it is meaningless to discuss the convergence at zero, which is not always the result that we want for NMF. Thus, we theoretically illustrate the advantages of the cAG algorithm.
Abstract: Urban road dust comprises of a range of potentially
toxic metal elements and plays a critical role in degrading urban
receiving water quality. Hence, assessing the metal composition and
concentration in urban road dust is a high priority. This study
investigated the variability of metal composition and concentrations
in road dust in 4 different urban land uses in Gold Coast, Australia.
Samples from 16 road sites were collected and tested for selected 12
metal species. The data set was analyzed using both univariate and
multivariate techniques. Outcomes of the data analysis revealed that
the metal concentrations inroad dust differs considerably within and
between different land uses. Iron, aluminum, magnesium and zinc are
the most abundant in urban land uses. It was also noted that metal
species such as titanium, nickel, copper and zinc have the highest
concentrations in industrial land use. The study outcomes revealed
that soil and traffic related sources as key sources of metals deposited
on road surfaces.
Abstract: Family identification has the potential to play a very decisive role in psychopathology. In this study we aimed to investigate the impact of family identifications on female psychopathology. A community sample of 101 women (mean age 20.81 years, SD = 0.91 ranged 20-25) participated to the present study. The girls completed a) the Symptom Check-List Revised (SCL-90) and b) questionnaire concerning socio-demographic information and questions for family identifications. The majority of women reported that they matched to the father in terms of identifications (47.1%). Age and birth order were not contributed on family identifications (F(5) =2.188, p=.062 and F(3)=1.244, p=.299 respectively). Multivariate analysis by using MANCOVA found statistical significant associations between family identifications and domains of psychopathology as provided by SCL-90 (P
Abstract: This study is conducted to investigate the disparity of between learning styles and cognitive abilities specifically in Vocational Education. Felder and Silverman Learning Styles Model (FSLSM) was applied to measure the students’ learning styles while the content in Building Construction Subject consists; knowledge, skills and problem solving were taken into account in constructing the elements of cognitive abilities. Building Construction is one of the vocational courses offered in Vocational Education structure. There are four dimension of learning styles proposed by Felder and Silverman intended to capture student learning preferences with regards to processing either active or reflective, perception based on sensing or intuitive, input of information used visual or verbal and understanding information represent with sequential or global learner. Felder-Solomon Learning Styles Index was developed based on FSLSM and the questions were used to identify what type of student learning preferences. The index consists 44 item-questions characterize for learning styles dimension in FSLSM. The achievement test was developed to determine the students’ cognitive abilities. The quantitative data was analyzed in descriptive and inferential statistic involving Multivariate Analysis of Variance (MANOVA). The study discovered students are tending to be visual learners and each type of learner having significant difference whereas cognitive abilities there are different finding for each type of learners in knowledge, skills and problem solving. This study concludes the gap between type of learner and the cognitive abilities in few illustrations and it explained how the connecting made. The finding may help teachers to facilitate students more effectively and to boost the student’s cognitive abilities.
Abstract: In this paper, we present a simplified higher-order Markov chain model for multiple categorical data sequences also called as simplified higher-order multivariate Markov chain model.
Abstract: Real-time or in-line process monitoring frameworks are designed to give early warnings for a fault along with meaningful identification of its assignable causes. In artificial intelligence and machine learning fields of pattern recognition various promising approaches have been proposed such as kernel-based nonlinear machine learning techniques. This work presents a kernel-based empirical monitoring scheme for batch type production processes with small sample size problem of partially unbalanced data. Measurement data of normal operations are easy to collect whilst special events or faults data are difficult to collect. In such situations, noise filtering techniques can be helpful in enhancing process monitoring performance. Furthermore, preprocessing of raw process data is used to get rid of unwanted variation of data. The performance of the monitoring scheme was demonstrated using three-dimensional batch data. The results showed that the monitoring performance was improved significantly in terms of detection success rate of process fault.
Abstract: Human society, there are many uncertainties, such as economic growth rate forecast of the financial crisis, many scholars have, since the the Song Chissom two scholars in 1993 the concept of the so-called fuzzy time series (Fuzzy Time Series)different mode to deal with these problems, a previous study, however, usually does not consider the relevant variables selected and fuzzy process based solely on subjective opinions the fuzzy semantic discrete, so can not objectively reflect the characteristics of the data set, in addition to carrying outforecasts are often fuzzy rules as equally important, failed to consider the importance of each fuzzy rule. For these reasons, the variable selection (Factor Selection) through self-organizing map (Self-Organizing Map, SOM) and proposed high-end weighted multivariate fuzzy time series model based on fuzzy neural network (Fuzzy-BPN), and using the the sequential weighted average operator (Ordered Weighted Averaging operator, OWA) weighted prediction. Therefore, in order to verify the proposed method, the Taiwan stock exchange (Taiwan Stock Exchange Corporation) Taiwan Weighted Stock Index (Taiwan Stock Exchange Capitalization Weighted Stock Index, TAIEX) as experimental forecast target, in order to filter the appropriate variables in the experiment Finally, included in other studies in recent years mode in conjunction with this study, the results showed that the predictive ability of this study further improve.
Abstract: The national economy development affects the vehicle
ownership which ultimately increases fuel consumption. The rise of
the vehicle ownership is dominated by the increasing number of
motorcycles. This research aims to analyze and identify the
characteristics of fuel consumption, the city transportation system,
and to analyze the relationship and the effect of the city
transportation system on the fuel consumption. A multivariable
analysis is used in this study. The data analysis techniques include: a
Multivariate Multivariable Analysis by using the R software. More
than 84% of fuel on Java is consumed in metropolitan and large
cities. The city transportation system variables that strongly effect the
fuel consumption are population, public vehicles, private vehicles and
private bus. This method can be developed to control the fuel
consumption by considering the urban transport system and city
tipology. The effect can reducing subsidy on the fuel consumption,
increasing state economic.
Abstract: Microscopic emission and fuel consumption models
have been widely recognized as an effective method to quantify real
traffic emission and energy consumption when they are applied with
microscopic traffic simulation models. This paper presents a
framework for developing the Microscopic Emission (HC, CO, NOx,
and CO2) and Fuel consumption (MEF) models for light-duty
vehicles. The variable of composite acceleration is introduced into
the MEF model with the purpose of capturing the effects of historical
accelerations interacting with current speed on emission and fuel
consumption. The MEF model is calibrated by multivariate
least-squares method for two types of light-duty vehicle using
on-board data collected in Beijing, China by a Portable Emission
Measurement System (PEMS). The instantaneous validation results
shows the MEF model performs better with lower Mean Absolute
Percentage Error (MAPE) compared to other two models. Moreover,
the aggregate validation results tells the MEF model produces
reasonable estimations compared to actual measurements with
prediction errors within 12%, 10%, 19%, and 9% for HC, CO, NOx
emissions and fuel consumption, respectively.
Abstract: In this paper, we have presented a new multivariate fuzzy time series forecasting method. This method assumes mfactors with one main factor of interest. History of past three years is used for making new forecasts. This new method is applied in forecasting total number of car accidents in Belgium using four secondary factors. We also make comparison of our proposed method with existing methods of fuzzy time series forecasting. Experimentally, it is shown that our proposed method perform better than existing fuzzy time series forecasting methods. Practically, actuaries are interested in analysis of the patterns of causalities in road accidents. Thus using fuzzy time series, actuaries can define fuzzy premium and fuzzy underwriting of car insurance and life insurance for car insurance. National Institute of Statistics, Belgium provides region of risk classification for each road. Thus using this risk classification, we can predict premium rate and underwriting of insurance policy holders.