Search results for: geotechnical random variables

Authors: Atinkut Atinafu Yilma, Eyob Messele Sefene

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Anomaly detection is one of the essential mechanisms to control and reduce production loss, especially in today's smart manufacturing. Quick anomaly detection aids in reducing the cost of production by minimizing the possibility of producing defective products. However, developing an anomaly detection model that can rapidly detect a production change is challenging. This paper proposes Gated Recurrent Unit (GRU) combined with Random Forest (RF) to detect anomalies in the production process in real-time quickly. The GRU is used as a feature detector, and RF as a classifier using the input features from GRU. The model was tested using various synthesis and real-world datasets against benchmark methods. The results show that the proposed GRU-RF outperforms the benchmark methods with the shortest time taken to detect anomalies in the production process. Based on the investigation from the study, this proposed model can eliminate or reduce unnecessary production costs and bring a competitive advantage to manufacturing industries.

Keywords: anomaly detection, multivariate time series data, smart manufacturing, gated recurrent unit network, random forest

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Authors: Nawang Chhunid, Gagnesh Kumar

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On-chip memories consume a significant portion of the overall die space and power in modern microprocessors. On-chip caches depend on Static Random-Access Memory (SRAM) cells and scaling of technology occurring as per Moore’s law. Unfortunately, the scaling is affecting stability, performance, and leakage power which will become major problems for future SRAMs in aggressive nanoscale technologies due to increasing device mismatch and variations. 3T1D Dynamic Random-Access Memory (DRAM) cell is a non-destructive read DRAM cell with three transistors and a gated diode. In 3T1D DRAM cell gated diode (D1) acts as a storage device and also as an amplifier, which leads to fast read access. Due to its high tolerance to process variation, high density, and low cost of memory as compared to 6T SRAM cell, it is universally used by the advanced microprocessor for on chip data and program memory. In the present paper, it has been shown that 3T1D DRAM cell can perform better in terms of fast read access as compared to 6T, 4T, 3T SRAM cells, respectively.

Keywords: DRAM Cell, Read Access Time, Retention Time, Average Power dissipation

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Authors: Emma Serwaa Obobisaa, Haibo Chen

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International trade and outward foreign direct investment are important factors which are generally recognized in the economic growth and development. Though several scholars have struggled to reveal the influence of trade and outward foreign direct investment (FDI) on economic growth, most studies utilized common econometric models such as vector autoregression and aggregated the variables, which for the most part prompts, however, contradictory and mixed results. Thus, there is an exigent need for the precise study of the trade and FDI effect of economic growth while applying strong econometric models and disaggregating the variables into its separate individual variables to explicate their respective effects on economic growth. This will guarantee the provision of policies and strategies that are geared towards individual variables to ensure sustainable development and growth. This study, therefore, seeks to examine the causal effect of China-Africa trade and Outward Foreign Direct Investment on the economic growth of Africa using a robust and recent econometric approach such as system dynamics model. Our study impanels and tests an ensemble of a group of vital variables predominant in recent studies on trade-FDI-economic growth causality: Foreign direct ınvestment, international trade and economic growth. Our results showed that the system dynamics method provides accurate statistical inference regarding the direction of the causality among the variables than the conventional method such as OLS and Granger Causality predominantly used in the literature as it is more robust and provides accurate, critical values.

Keywords: economic growth, outward foreign direct investment, system dynamics model, international trade

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Authors: Anwar Hasan Abdullah Othman, Ahamed Kameel, Hasanuddeen Abdul Aziz

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In this study, an attempt has been made to investigate the relationship specifically the causal relation between fund unit prices of Islamic equity unit trust fund which measure by fund NAV and the selected macro-economic variables of Malaysian economy by using VECM causality test and Granger causality test. Monthly data has been used from Jan, 2006 to Dec, 2012 for all the variables. The findings of the study showed that industrial production index, political election and financial crisis are the only variables having unidirectional causal relationship with fund unit price. However, the global oil prices is having bidirectional causality with fund NAV. Thus, it is concluded that the equity unit trust fund industry in Malaysia is an inefficient market with respect to the industrial production index, global oil prices, political election and financial crisis. However, the market is approaching towards informational efficiency at least with respect to four macroeconomic variables, treasury bill rate, money supply, foreign exchange rate and corruption index.

Keywords: fund unit price, unit trust industry, Malaysia, macroeconomic variables, causality

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Authors: Minseo Jo

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The purpose of this paper is to examine the factors in the hedonic price models, that has significance impact in determining the price of apartments. There are many variables employed in the hedonic price models and their effectiveness vary differently according to the researchers and the regions they are analysing. In order to consider various conditions, the meta-regression analysis has been selected for the study. In this paper, four meta-independent variables, from the 65 hedonic price models to analysis. The factors that influence the prices of apartments, as well as including factors that influence the prices of apartments, regions, which are divided into two of the research performed, years of research performed, the coefficients of the functions employed. The covariance between the four meta-variables and p-value of the coefficients and the four meta-variables and number of data used in the 65 hedonic price models have been analyzed in this study. The six factors that are most important in deciding the prices of apartments are positioning of apartments, the noise of the apartments, points of the compass and views from the apartments, proximity to the public transportations, companies that have constructed the apartments, social environments (such as schools etc.).

Keywords: hedonic price model, housing price, meta-regression analysis, characteristics

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Authors: Hye Rim Lee, Eui Jun Jeong, Ji Hye Yoo

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This study makes an integrated investigation on how life satisfaction is associated with the Korean game users' psychological variables (self-esteem, game and life self- efficacy), social variables (bonding and bridging social capital), and demographic variables (age, gender). The data used for the empirical analysis came from a representative sample survey conducted in South Korea. Results show that self-esteem and game efficacy were an important antecedent to the degree of users’ life satisfaction. Both bonding social capital and bridging social capital enhance the level of the users’ life satisfaction. The importance of perspectives as well as their implications for the game users and further associated research, are explored.

Keywords: life satisfaction, self-esteem, game efficacy, life-efficacy, social capital

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Authors: Carlos Tapia Cortez, Serkan Saydam, Jeff Coulton, Claude Sammut

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Strategic mineral commodities (SMC) both energetic and metals have long been fundamental for human beings. There is a strong and long-run relation between the mineral resources industry and society's evolution, with the provision of primary raw materials, becoming one of the most significant drivers of economic growth. Due to mineral resources’ relevance for the entire economy and society, an understanding of the SMC market behaviour to simulate price fluctuations has become crucial for governments and firms. For any human activity, SMC price fluctuations are affected by economic, geopolitical, environmental, technological and psychological issues, where cognition has a major role. Cognition is defined as the capacity to store information in memory, processing and decision making for problem-solving or human adaptation. Thus, it has a significant role in those systems that exhibit dynamic equilibrium through time, such as economic growth. Cognition allows not only understanding past behaviours and trends in SCM markets but also supports future expectations of demand/supply levels and prices, although speculations are unavoidable. Technological developments may also be defined as a cognitive system. Since the Industrial Revolution, technological developments have had a significant influence on SMC production costs and prices, likewise allowing co-integration between commodities and market locations. It suggests a close relation between structural breaks, technology and prices evolution. SCM prices forecasting have been commonly addressed by econometrics and Gaussian-probabilistic models. Econometrics models may incorporate the relationship between variables; however, they are statics that leads to an incomplete approach of prices evolution through time. Gaussian-probabilistic models may evolve through time; however, price fluctuations are addressed by the assumption of random behaviour and normal distribution which seems to be far from the real behaviour of both market and prices. Random fluctuation ignores the evolution of market events and the technical and temporal relation between variables, giving the illusion of controlled future events. Normal distribution underestimates price fluctuations by using restricted ranges, curtailing decisions making into a pre-established space. A proper understanding of SMC's price dynamics taking into account the historical-cognitive relation between economic, technological and psychological factors over time is fundamental in attempting to simulate prices. The aim of this paper is to discuss the SMC market cognition hypothesis and empirically demonstrate its dynamic-cognitive capacity. Three of the largest and traded SMC's: oil, copper and gold, will be assessed to examine the economic, technological and psychological cognition respectively.

Keywords: commodity price simulation, commodity price uncertainties, dynamic-cognition, dynamic systems

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Authors: Chin-Chia Jane

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In a transportation network, travel time refers to the transmission time from source node to destination node, whereas reliability refers to the probability of a successful connection from source node to destination node. With an increasing emphasis on quality of service (QoS), both performance indexes are significant in the design and analysis of transportation systems. In this work, we extend the well-known flow network model for transportation networks so that travel time and reliability are integrated into the QoS measurement simultaneously. In the extended model, in addition to the general arc capacities, each intermediate node has a time weight which is the travel time for per unit of commodity going through the node. Meanwhile, arcs and nodes are treated as binary random variables that switch between operation and failure with associated probabilities. For pre-specified travel time limitation and demand requirement, the QoS of a transportation network is the probability that source can successfully transport the demand requirement to destination while the total transmission time is under the travel time limitation. This work is pioneering, since existing literatures that evaluate travel time reliability via a single optimization path, the proposed QoS focuses the performance of the whole network system. To compute the QoS of transportation networks, we first transfer the extended network model into an equivalent min-cost max-flow network model. In the transferred network, each arc has a new travel time weight which takes value 0. Each intermediate node is replaced by two nodes u and v, and an arc directed from u to v. The newly generated nodes u and v are perfect nodes. The new direct arc has three weights: travel time, capacity, and operation probability. Then the universal set of state vectors is recursively decomposed into disjoint subsets of reliable, unreliable, and stochastic vectors until no stochastic vector is left. The decomposition is made possible by applying existing efficient min-cost max-flow algorithm. Because the reliable subsets are disjoint, QoS can be obtained directly by summing the probabilities of these reliable subsets. Computational experiments are conducted on a benchmark network which has 11 nodes and 21 arcs. Five travel time limitations and five demand requirements are set to compute the QoS value. To make a comparison, we test the exhaustive complete enumeration method. Computational results reveal the proposed algorithm is much more efficient than the complete enumeration method. In this work, a transportation network is analyzed by an extended flow network model where each arc has a fixed capacity, each intermediate node has a time weight, and both arcs and nodes are independent binary random variables. The quality of service of the transportation network is an integration of customer demands, travel time, and the probability of connection. We present a decomposition algorithm to compute the QoS efficiently. Computational experiments conducted on a prototype network show that the proposed algorithm is superior to existing complete enumeration methods.

Keywords: quality of service, reliability, transportation network, travel time

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Authors: Malay Kumar Pramanik

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Climate change induced sea level rise increases storm surge, erosion, and inundation, which are stirred by an intricate interplay of physical environmental components at the coastal region. The Mumbai coast is much vulnerable to accelerated regional sea level change due to its highly dense population, highly developed economy, and low topography. To determine the significant causes behind coastal vulnerability, this study analyzes four different iterations of CVI by incorporating the pixel-based differentially weighted rank values of the selected five geological (CVI5), three physical (CVI8 with including geological variables), and four socio-economic variables (CVI4). However, CVI5 and CVI8 results yielded broadly similar natures, but after including socio-economic variables (CVI4), the results CVI (CVI12) has been changed at Mumbai and Kurla coastal portion that indicates the study coastal areas are mostly sensible with socio-economic variables. Therefore, the results of CVI12 show that out of 274.1 km of coastline analyzed, 55.83 % of the coast is very low vulnerable, 60.91 % of the coast is moderately vulnerable while 50.75 % is very high vulnerable. Finding also admits that in the context of growing urban population and the increasing rate of economic activities, socio-economic variables are most important variable to use for validating and testing the CVI. Finally, some recommendations are presented for concerned decision makers and stakeholders to develop appropriate coastal management plans, nourishment projects and mitigation measures considering socio-economic variables.

Keywords: coastal vulnerability index, sea level change, Mumbai coast, geospatial approach, coastal management, climate change

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Authors: Farooq A. Al-Sheikh, Ali Elkamel, William A. Anderson

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A fluidized catalytic cracking unit (FCCU) is one of the effective units in many refineries. Modeling and optimization of FCCU were done by many researchers in past decades, but in this research, comparison between post- and oxy-combustion was studied in the regenerator-FCCU. Therefore, a simplified mathematical model was derived by doing mass/heat balances around both reactor and regenerator. A state space analysis was employed to show effects of the flow rates variables such as air, feed, spent catalyst, regenerated catalyst and flue gas on the output variables. The main aim of studying dynamic responses is to figure out the most influencing variables that affect both reactor/regenerator temperatures; also, finding the upper/lower limits of the influencing variables to ensure that temperatures of the reactors and regenerator work within normal operating conditions. Therefore, those values will be used as side constraints in the optimization technique to find appropriate operating regimes. The objective functions were modeled to be maximizing the energy in the reactor while minimizing the energy consumption in the regenerator. In conclusion, an oxy-combustion process can be used instead of a post-combustion one.

Keywords: FCCU modeling, optimization, oxy-combustion, post-combustion

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Authors: Jonathan Olusegun Famoroti, Mathew Ekundayo Rotimi, Mishelle Doorasamy

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This study offers an empirical investigation into some selected macroeconomic drivers of the monetary policy rate in member countries of the West African Monetary Zone (WAMZ), considering both internal and external variables. We employed Autoregressive Distributed Lag (ARDL) to carry out the investigation between monetary policy and some macroeconomic variables in both the long-run and short-run relationship. The results suggest that the drivers of the policy rate in this zone, in the long run, include, among others, global oil price, exchange rate, inflation rate, and gross domestic product, while in the short run, federal fund rate, trade openness, exchange rate, inflation rate, and gross domestic product are core determinants of the policy rate. Therefore, in order to ensure long-run stability in the policy rate among the members’ states, these drivers should be given closer consideration so that the trajectory for effective structure can be designed and fused into the economic structure and policy frameworks accordingly.

Keywords: monetary policy rate, macroeconomic variables, WAMZ, ARDL

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Authors: Ja-Keoung Koo, Kensuke Nakamura, Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Byung-Woo Hong

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The machine learning techniques based on a convolutional neural network (CNN) have been actively developed and successfully applied to a variety of image analysis tasks including reconstruction, noise reduction, resolution enhancement, segmentation, motion estimation, object recognition. The classical visual information processing that ranges from low level tasks to high level ones has been widely developed in the deep learning framework. It is generally considered as a challenging problem to derive visual interpretation from high dimensional imagery data. A CNN is a class of feed-forward artificial neural network that usually consists of deep layers the connections of which are established by a series of non-linear operations. The CNN architecture is known to be shift invariant due to its shared weights and translation invariance characteristics. However, it is often computationally intractable to optimize the network in particular with a large number of convolution layers due to a large number of unknowns to be optimized with respect to the training set that is generally required to be large enough to effectively generalize the model under consideration. It is also necessary to limit the size of convolution kernels due to the computational expense despite of the recent development of effective parallel processing machinery, which leads to the use of the constantly small size of the convolution kernels throughout the deep CNN architecture. However, it is often desired to consider different scales in the analysis of visual features at different layers in the network. Thus, we propose a CNN model where different sizes of the convolution kernels are applied at each layer based on the random projection. We apply random filters with varying sizes and associate the filter responses with scalar weights that correspond to the standard deviation of the random filters. We are allowed to use large number of random filters with the cost of one scalar unknown for each filter. The computational cost in the back-propagation procedure does not increase with the larger size of the filters even though the additional computational cost is required in the computation of convolution in the feed-forward procedure. The use of random kernels with varying sizes allows to effectively analyze image features at multiple scales leading to a better generalization. The robustness and effectiveness of the proposed CNN based on random kernels are demonstrated by numerical experiments where the quantitative comparison of the well-known CNN architectures and our models that simply replace the convolution kernels with the random filters is performed. The experimental results indicate that our model achieves better performance with less number of unknown weights. The proposed algorithm has a high potential in the application of a variety of visual tasks based on the CNN framework. Acknowledgement—This work was supported by the MISP (Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by IITP, and NRF-2014R1A2A1A11051941, NRF2017R1A2B4006023.

Keywords: deep learning, convolutional neural network, random kernel, random projection, dimensionality reduction, object recognition

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Authors: Alam Ali, Ashok Kumar Pathak

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Path analysis is a statistical technique used to evaluate the direct and indirect effects of variables in path models. One or more structural regression equations are used to estimate a series of parameters in path models to find the better fit of data. However, sometimes the assumptions of classical regression models, such as ordinary least squares (OLS), are violated by the nature of the data, resulting in insignificant direct and indirect effects of exogenous variables. This article aims to explore the effectiveness of a copula-based regression approach as an alternative to classical regression, specifically when variables are linked through an elliptical copula.

Keywords: path analysis, copula-based regression models, direct and indirect effects, k-fold cross validation technique

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Authors: Chin-Chih Chang

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Many manufacturing systems suffer failures due to complex degradation processes and various environment conditions such as random shocks. Consider an operating system is subject to random shocks and works at random times for successive jobs. When successive jobs often result in production losses and performance deterioration, it would be better to do maintenance or replacement at a planned time. A preventive replacement (PR) policy is presented to replace the system before a failure occurs at a continuous time T. In such a policy, the failure characteristics of the system are designed as follows. Each job would cause a random amount of additive damage to the system, and the system fails when the cumulative damage has exceeded a failure threshold. Suppose that the deteriorating system suffers one of the two types of shocks with age-dependent probabilities: type-I (minor) shock is rectified by a minimal repair, or type-II (catastrophic) shock causes the system to fail. A corrective replacement (CR) is performed immediately when the system fails. In summary, a generalized maintenance model to scheduling replacement plan for an operating system is presented below. PR is carried out at time T, whereas CR is carried out when any type-II shock occurs and the total damage exceeded a failure level. The main objective is to determine the optimal continuous schedule time of preventive replacement through minimizing the mean cost rate function. The existence and uniqueness of optimal replacement policy are derived analytically. It can be seen that the present model is a generalization of the previous models, and the policy with preventive replacement outperforms the one without preventive replacement.

Keywords: preventive replacement, working time, cumulative damage model, minimal repair, imperfect maintenance, optimization

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Authors: Sahar Jami, Iman Valizadeh

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The purpose of the study: The aim of this study was to evaluate the influence of culture on the choice of capital structure management companies are listed in the Tehran Stock Exchange. Methods: This study was a cross-document using data after the event (Retrospective) in 1394 was performed. To select a sample of elimination sampling (screening) is used to determine the sample size was 123 companies. Results: The results showed that the variables of culture, return on equity, a significant positive impact on the capital structure (ROA, QTobins) and financial leverage and firm size variables and a significant negative impact on the capital structure (ROA, QTobins).

Keywords: culture management, capital structure, ROA, QTobins, variables of culture

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Authors: Chonlada Choovanichchanon

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This research paper was aimed to investigate a relationship between the customer service marketing mix and the level of customers’ satisfaction from purchasing goods and service from small business around campus, Suan Sunandha Rajabhat University, Bangkok, Thailand. Based on the survey of 200 customers who frequently purchased goods and service around campus, the level of satisfaction for each factor of marketing mix was reached. An accidental random sampling was applied by using questionnaire in collecting the data. The findings revealed that the means values can help to rank these variables from high to low mean as follows: 1) forms and system of service, 2) physical environment of service center, 3) service from staff and employee, 4) product quality and service, 5) market channel and distribution, 6) market price, and 7) market promotion and distribution.

Keywords: service marketing mix, satisfaction, small business, survey

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Authors: Roger L. Goodwin

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This paper presents a rank correlation curve. The traditional correlation coefficient is valid for both continuous variables and for integer variables using rank statistics. Since the correlation coefficient has already been established in rank statistics by Spearman, such a calculation can be extended to the correlation curve. This paper presents two survey questions. The survey collected non-continuous variables. We will show weak to moderate correlation. Obviously, one question has a negative effect on the other. A review of the qualitative literature can answer which question and why. The rank correlation curve shows which collection of responses has a positive slope and which collection of responses has a negative slope. Such information is unavailable from the flat, "first-glance" correlation statistics.

Keywords: Bayesian estimation, regression model, rank statistics, correlation, correlation curve

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Authors: Valeria Bondarenko

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We proposed algorithms for: estimation of parameters fBm (volatility and Hurst exponent) and for the approximation of random time series by functional of fBm. We proved the consistency of the estimators, which constitute the above algorithms, and proved the optimal forecast of approximated time series. The adequacy of estimation algorithms, approximation, and forecasting is proved by numerical experiment. During the process of creating software, the system has been created, which is displayed by the hierarchical structure. The comparative analysis of proposed algorithms with the other methods gives evidence of the advantage of approximation method. The results can be used to develop methods for the analysis and modeling of time series describing the economic, physical, biological and other processes.

Keywords: mathematical model, random process, Wiener process, fractional Brownian motion

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Authors: Zhidong Zhang, Amanda Resendez

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This study is to examine how teachers’ perspective on education technology use in their class influence their students’ achievement. The authors hypothesized that teachers’ perspective can directly or indirectly influence students’ learning, performance, and achievements. In this study, a questionnaire entitled, Teacher’s Perspective on Educational Technology, was delivered to 63 teachers and 1268 students’ mathematics and reading achievement records were collected. The questionnaire consists of four parts: a) demographic variables, b) attitudes on technology integration, c) outside factor affecting technology integration, and d) technology use in the classroom. Kruskal-Wallis and hierarchical regression analysis techniques were used to examine: 1) the relationship between the demographic variables and teachers’ perspectives on educational technology, and 2) how the demographic variables were causally related to students’ mathematics and reading achievements. The study found that teacher demographics were significantly related to the teachers’ perspective on educational technology with p < 0.05 and p < 0.01 separately. These teacher demographical variables included the school district, age, gender, the grade currently teach, teaching experience, and proficiency using new technology. Further, these variables significantly predicted students’ mathematics and reading achievements with p < 0.05 and p < 0.01 separately. The variations of R² are between 0.176 and 0.467. That means 46.7% of the variance of a given analysis can be explained by the model.

Keywords: teacher's perception of technology use, mathematics achievement, reading achievement, Kruskal-Wallis test, hierarchical regression analysis

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Authors: Samir Bentata, Fatima Bendahma

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We have numerically investigated the effect of Aluminium concentration on the the laser wavelength of random trimer barrier AlxGa1-xAs superlattices (RTBSL). Such systems consist of two different structures randomly distributed along the growth direction, with the additional constraint that the barriers of one kind appear in triply. An explicit formula is given for evaluating the transmission coefficient of superlattices (SL's) with intentional correlated disorder. The method is based on Airy function formalism and the transfer-matrix technique. We discuss the impact of the Aluminium concentration associate to the structure profile on the laser wavelengths.

Keywords: superlattices, correlated disorder, transmission coefficient, laser wavelength

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Authors: Sarah Beaver, Renee Bryce

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Audiograms detect hearing impairment, but missing values pose problems. This work explores imputations in an attempt to improve accuracy. This work implements Linear Regression, Lasso, Linear Support Vector Regression, Bayesian Ridge, K Nearest Neighbors (KNN), and Random Forest machine learning techniques to impute audiogram frequencies ranging from 125Hz to 8000Hz. The data contains patients who had or were candidates for cochlear implants. Accuracy is compared across two different Nested Cross-Validation k values. Over 4000 audiograms were used from 800 unique patients. Additionally, training on data combines and compares left and right ear audiograms versus single ear side audiograms. The accuracy achieved using Root Mean Square Error (RMSE) values for the best models for Random Forest ranges from 4.74 to 6.37. The R\textsuperscript{2} values for the best models for Random Forest ranges from .91 to .96. The accuracy achieved using RMSE values for the best models for KNN ranges from 5.00 to 7.72. The R\textsuperscript{2} values for the best models for KNN ranges from .89 to .95. The best imputation models received R\textsuperscript{2} between .89 to .96 and RMSE values less than 8dB. We also show that the accuracy of classification predictive models performed better with our best imputation models versus constant imputations by a two percent increase.

Keywords: machine learning, audiograms, data imputations, single imputations

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Authors: Fawaz Abdulmalek

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The two-stage flowshop assembly scheduling problem is considered in this paper. There are more than one parallel machines at stage one and an assembly machine at stage two. The jobs will be processed into the flowshop based on Johnson rule and two extensions of Johnson rule. A simulation model of the two-stage flowshop is constructed where both machines at stage one are subject to random failures. Three simulation experiments will be conducted to test the effect of the three job ranking rules on the makespan. Johnson Largest heuristic outperformed both Johnson rule and Johnson Smallest heuristic for two performed experiments for all scenarios where each experiments having five scenarios.

Keywords: flowshop scheduling, random failures, johnson rule, simulation

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Authors: Najib Ahmad Marzuki, Che Su Mustaffa, Johana Johari, Nur Haffiza Rahaman

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The purpose of this paper is to examine the effects and relationship of stress and social support towards the quality of life among flood victims in Malaysia. A total of 764 respondents took part in the survey via random sampling. The depression, anxiety, and stress scales were utilized to measure stress while The Multidimensional Scale of Perceived Social Support was used to measure the quality of life. The findings of this study indicate that there were significant correlations between variables in the study. The findings show a significant negative relation between stress and quality of life, and significant positive correlations between support from family as well as support from friends with the quality of life. Stress and support from family were found to be significant predictors and influences the quality of life among flood victims.

Keywords: stress, social support, quality of life, flood victims

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Authors: Ahmad Yame, Ahad Ali, Badih Jawad, Daw Al-Werfalli Mohamed Nasser, Sabah Abro

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Industries in general have a lot of waste. Wool textile company, Baniwalid, Libya has many complex problems that led to enormous waste generated due to the lack of lean strategies, expertise, technical support and commitment. To successfully address waste at wool textile company, this study will attempt to develop a methodical approach that integrates lean manufacturing tools to optimize performance characteristics such as lead time and delivery. This methodology will utilize Value Stream Mapping (VSM) techniques to identify the process variables that affect production. Once these variables are identified, Design of Experiments (DOE) Methodology will be used to determine the significantly influential process variables, these variables are then controlled and set at their optimal to achieve optimal levels of productivity, quality, agility, efficiency and delivery to analyze the outputs of the simulation model for different lean configurations. The goal of this research is to investigate how the tools of lean manufacturing can be adapted from the discrete to the continuous manufacturing environment and to evaluate their benefits at a specific industrial.

Keywords: lean manufacturing, DOE, value stream mapping, textiles

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Authors: Lynndee Kemmet

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The U.S. military plays an increasing role in supporting political stability efforts, and this includes efforts to prevent the food insecurity that can trigger political and social instability. This paper presents a model that assists military commanders in identifying variables that impact food production and distribution in their areas of operation (AO), in identifying connections between variables and in assessing the impacts of those variables on food production and distribution. Through use of the model, military units can better target their data collection efforts and can categorize and analyze data within the data categorization framework most widely-used by military forces—PMESII-PT (Political, Military, Economic, Infrastructure, Information, Physical Environment and Time). The model provides flexibility of analysis in that commanders can target analysis to be highly focused on a specific PMESII-PT domain or variable or conduct analysis across multiple PMESII-PT domains. The model is also designed to assist commanders in mapping food systems in their AOs and then identifying components of those systems that must be strengthened or protected.

Keywords: food security, food system model, political stability, US Military

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Authors: Ralpian Randolian

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Eighty-five Romanian teachers and principals participated on this study to examine their perspectives of different leadership styles. Demographic variables such as the source of degree (Romania, Europe institutes, USA institutes, etc.), gender, region, level taught, years of experience, and specialty were identified. The researcher developed a questionnaire that consisted of 4 leadership styles. The data were analyzed using structural equation modeling (SEM) to identify which of the variables best predict the leadership styles. Results indicated that the democracy style was the most preferred leadership style by Jordanian parents, while the authoritarian styles ranked second. The results also found statistically significant differences were found related to the study variables. This study ends by putting forward a number of suggestions and recommendation.

Keywords: teachers’ perspectives, leadership styles, gender, structural equation modeling

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Authors: Arti Bagada, Kantilal Vadalia, Mihir Raval

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Cilnidipine is practically insoluble in water which results in its insufficient oral bioavailability. The purpose of the present investigation was to formulate cilnidipine nanoparticles by nanoprecipitation method to increase the aqueous solubility and dissolution rate and hence bioavailability by utilizing various experimental statistical design modules. Experimental design were used to investigate specific effects of independent variables during preparation cilnidipine nanoparticles and corresponding responses in optimizing the formulation. Plackett Burman design for independent variables was successfully employed for optimization of nanoparticles of cilnidipine. The influence of independent variables studied were drug concentration, solvent to antisolvent ratio, polymer concentration, stabilizer concentration and stirring speed. The dependent variables namely average particle size, polydispersity index, zeta potential value and saturation solubility of the formulated nanoparticles of cilnidipine. The experiments were carried out according to 13 runs involving 5 independent variables (higher and lower levels) employing Plackett-Burman design. The cilnidipine nanoparticles were characterized by average particle size, polydispersity index value, zeta potential value and saturation solubility and it results were 149 nm, 0.314, 43.24 and 0.0379 mg/ml, respectively. The experimental results were good correlated with predicted data analysed by Plackett-Burman statistical method.

Keywords: dissolution enhancement, nanoparticles, Plackett-Burman design, nanoprecipitation

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Authors: Su Yi Ming, Hou Ying, Zou Guang Ping

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Metal-net rubber is a new dry friction damping material, compared with the traditional metal rubber, which has high mechanization degree, and the mechanical performance of metal-net rubber is more stable. Through the sine sweep experiment and random vibration experiment of metal-net rubber vibration isolator, the influence of several important factors such as the lines slope, relative density and wire diameter on the transfer rate, natural frequency and root-mean-square response acceleration of metal-net rubber vibration isolation system, were studied through the method of control variables. Also, several relevant change curves under different vibration levels were derived, and the effects of vibration level on the natural frequency and root-mean-square response acceleration were analyzed through the curves.

Keywords: metal-net rubber vibration isolator, relative density, vibration level, wire diameter

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Authors: Laid Fekih

Abstract:

Background: Depression affects people in all communities across the world and in all aspects of their lives. Its spread varies from one country to another, can happen at any age and get rid of it is not easy. There is no clear policy in Algeria's higher education institutions to detect and treat these disorders or pay particular attention to those at risk. Identifying the prevalence of depression among Algerian students, its correlation with different variables, and studying gender differences in the light of a range of variables is necessary to develop an appropriate plan to raise the level of hope and love of life among students. Method: Random samples of 1500 University of Tlemcen students (967 girls and 533 boys), aged 19 to 24 years completed a self-administered questionnaire that included Beck's Depression Inventory ®-II (BDI®-II), (School Health Promotion: The Mood part), Other questions included in this survey focused on demographic characteristics including gender, age and year of study, academic performance (Annual Average Score (0-20) AAS), were examined. Results: The rate of depression (moderate, severe and extreme) varied from 03% to 13% among university students in Tlemcen University. There was no difference in the rates of depression in male and female students, which means that male and female students do have similar rates of depression. The rate of depression in the first-year of the study shows a higher score relative to students of other years. Depression has a negative relationship with academic performance, which means that depressed students have many difficulties in academic tasks at university. Conclusion: Depression among university students is an important center of interest in the world, not only because of the ease with which they can be followed, or the difficulties encountered during their studies and their technical courses but for the link between the level of depression and the quality of care of mental health services, especially if many students with mood and emotional problems don't meet the criteria for psychotherapy.

Keywords: depression, epidemiology, university students, academic performance

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Authors: A. Medjnoun, R. Bahar

Abstract:

The disorders caused by the shrinking-swelling phenomenon are prevalent in arid and semi-arid in the presence of swelling clay. This soil has the characteristic of changing state under the effect of water solicitation (wetting and drying). A set of geotechnical parameters is necessary for the characterization of this soil type, such as state parameters, physical and chemical parameters and mechanical parameters. Some of these tests are very long and some are very expensive, hence the use or methods of predictions. The complexity of this phenomenon and the difficulty of its characterization have prompted researchers to use several identification parameters in the prediction of swelling potential. This document is an analytical and statistical study of geotechnical parameters affecting the potential of swelling clays. This work is performing on a database obtained from investigations swelling Algerian soil. The obtained observations have helped us to understand the soil swelling structure and its behavior.

Keywords: analysis, estimated model, parameter identification, swelling of clay

Procedia PDF Downloads 393