Search results for: nonparametric geographically weighted regression

Authors: Imtiaz Ali, Krishnan Jayaraman, Debes Bhattacharyya

Abstract:

Soybean concentrate is abundant material and renewable product that is recently been explored as an alternative to conventional formaldehyde based resins in wood based products. The main goal of this study is to evaluate the technical feasibility of manufacturing environment friendly MDF panels from renewable resources. The panels are made by using kenaf bast fibers (KB) as wood substitute and soy based adhesive as bonding material. Second order response surface regression models are used to understand the effects and interactions of resin content (RC) and pressing time (PT) on the mechanical and water soaking properties of kenaf panels. The mechanical and water soaking properties are significantly improved as the RC increased and reached at the highest level at maximum resin loading (12%). The effect of pressing time is significant in the first phase when the pressing time increased from 4 to 6 min; however the effect was not as significant when pressing time further increased to 8 min. The second order regression equations further confirm that the variation in process parameters has strong relationship with the physciomechanical properties. The MDF panels the minimum requirements of internal bond strength, modulus of rupture and modulus of elasticity as recommended by US wood MDF standard specifications for G110, G120, G130 and G140 grade MDF panels. However, the thickness swelling results are considerably poorer than the recommended values of general purpose standard requirements. This deficiency can be counterbalanced by the advantage of being formaldehyde free panels made from renewable sources and by making them suitable alternative for less humid environment applications.

Keywords: kenaf, Medium density fibreboard, soy adhesive, mechanical properties, water soaking properties

Procedia PDF Downloads 364

Authors: Kishore K. Pochampally

Abstract:

The success of a supply chain heavily relies on the efficiency of the suppliers involved. In this paper, we propose a neural network approach to evaluate the efficiency of a supplier, which is being considered for inclusion in a supply chain, using the available linguistic (fuzzy) data of suppliers that already exist in the supply chain. The approach is carried out in three phases, as follows: In phase one, we identify criteria for evaluation of the supplier of interest. Then, in phase two, we use performance measures of already existing suppliers to construct a neural network that gives weights (importance values) of criteria identified in phase one. Finally, in phase three, we calculate the overall rating of the supplier of interest. The following are the major findings of the research conducted for this paper: (i) linguistic (fuzzy) ratings of suppliers such as 'good', 'bad', etc., can be converted (defuzzified) to numerical ratings (1 – 10 scale) using fuzzy logic so that those ratings can be used for further quantitative analysis; (ii) it is possible to construct and train a multi-level neural network in order to determine the weights of the criteria that are used to evaluate a supplier; and (iii) Borda’s rule can be used to group the weighted ratings and calculate the overall efficiency of the supplier.

Keywords: fuzzy data, neural network, supplier, supply chain

Procedia PDF Downloads 103

Authors: Larry Turkish

Abstract:

Introduction: The NBA is an example of mediocrity and this will be evident in the following paper. The study examines and evaluates the characteristics of the NBA champions. As divisions and playoff teams increase, there is an increase in the probability that the champion originates from the mediocre category. Since it’s inception in 1947, the league has been mediocre and continues to this day. Why does a professional league allow any team with a less than 50% winning percentage into the playoffs? As long as the finances flow into the league, owners will not change the current algorithm. The objective of this paper is to determine if the regular season has meaning in finding an NBA champion. Statistical Analysis: The data originates from the NBA website. The following variables are part of the statistical analysis: Rank, the rank of a team relative to other teams in the league based on the regular season win-loss record; Winning Percentage of a team based on the regular season; Divisions, the number of divisions within the league and Playoff Teams, the number of playoff teams relative to a particular season. The following statistical applications are applied to the data: Pearson Product-Moment Correlation, Analysis of Variance, Factor and Regression analysis. Conclusion: The results indicate that the divisional structure and number of playoff teams results in a negative effect on the winning percentage of playoff teams. It also prevents teams with higher winning percentages from accessing the playoffs. Recommendations: 1. Teams that have a winning percentage greater than 1 standard deviation from the mean from the regular season will have access to playoffs. (Eliminates mediocre teams.) 2. Eliminate Divisions (Eliminates weaker teams from access to playoffs.) 3. Eliminate Conferences (Eliminates weaker teams from access to the playoffs.) 4. Have a balanced regular season schedule, (Reduces the number of regular season games, creates equilibrium, reduces bias) that will reduce the need for load management.

Keywords: alignment, mediocrity, regression, z-score

Procedia PDF Downloads 121

Authors: Pablo Martinez, Emad Mohamed, Osama Mohsen, Yasser Mohamed

Abstract:

Highway and bridge agencies, such as the Ministry of Transportation in Ontario, use the Bridge Condition Index (BCI) which is defined as the weighted condition of all bridge elements to determine the rehabilitation priorities for its bridges. Therefore, accurate forecasting of BCI is essential for bridge rehabilitation budgeting planning. The large amount of data available in regard to bridge conditions for several years dictate utilizing traditional mathematical models as infeasible analysis methods. This research study focuses on investigating different classification models that are developed to predict the bridge condition index in the province of Ontario, Canada based on the publicly available data for 2800 bridges over a period of more than 10 years. The data preparation is a key factor to develop acceptable classification models even with the simplest one, the k-NN model. All the models were tested, compared and statistically validated via cross validation and t-test. A simple k-NN model showed reasonable results (within 0.5% relative error) when predicting the bridge condition in an incoming year.

Keywords: asset management, bridge condition index, data mining, forecasting, infrastructure, knowledge discovery in databases, maintenance, predictive models

Procedia PDF Downloads 178

Authors: Farnoush Haghanipour

Abstract:

Recent research studies relation between coping strategies with stress and mental health situation in flying addicted family of self-introducer and private, Units of Guilan province. For this purpose 251 family (parent, spouse), that referred to private and self-introducer centers to break out of drug are selected in random sampling form. Research method was cross sectional-descriptive and purpose of research was fixing of between kinds of coping strategies with stress and mental health condition with attention to demographic variables. Therefore to collection of information, coping strategies questionnaire (CSQ) and mental health questionnaire (GHQ) was used and finally data analyzed by descriptive statistical methods (average, standard deviation) and inferential statistical correlation coefficient and regression. Study of correlation coefficient between mental healths with problem focused emotional focused and detachment strategies in level more than %99 is confirmed. Also mental health with avoidant focused hasn't correlation in other words relation is between mental health with problem focused strategies (r= 0/34) and emotional focused with mental health (r=0.52) and detachment with mental health (r= 0.18) in meaningful level 0.05. And also relation is between emotional focused strategies and mental health (r= 0.034) that is meaningless in Alpha 0.05. Also relation between problem processed coping strategies and mental health situation with attention to demographic variable is meaningful and relation level verified in confidence level more than 0.99. And result of anticipation equation regression statistical test has most a have in problem focused coping strategy, mental health, but relation of the avoidant emotional, detachment strategy with mental health was meaningless with attention to demographic variables.

Keywords: stress, coping strategy with stress, mental health, self introducer and private

Procedia PDF Downloads 303

Authors: Phanida Phukoetphim, Asaad Y. Shamseldin

Abstract:

In this study, the novel Stochastic Gradient Boosting (SGB) combination method is addressed for producing daily river flows from four different rain-runoff models of Ohinemuri catchment, New Zealand. The selected rainfall-runoff models are two empirical black-box models: linear perturbation model and linear varying gain factor model, two conceptual models: soil moisture accounting and routing model and Nedbør-Afrstrømnings model. In this study, the simple average combination method and the weighted average combination method were used as a benchmark for comparing the results of the novel SGB combination method. The models and combination results are evaluated using statistical and graphical criteria. Overall results of this study show that the use of combination technique can certainly improve the simulated river flows of four selected models for Ohinemuri catchment, New Zealand. The results also indicate that the novel SGB combination method is capable of accurate prediction when used in a combination method of the simulated river flows in New Zealand.

Keywords: multi-model combination, rainfall-runoff modeling, stochastic gradient boosting, bioinformatics

Procedia PDF Downloads 324

Authors: V. Saccà, A. Sarica, F. Novellino, S. Barone, T. Tallarico, E. Filippelli, A. Granata, P. Valentino, A. Quattrone

Abstract:

The work aim was to evaluate how well Random Forest (RF) and Support Vector Machine (SVM) algorithms could support the early diagnosis of Multiple Sclerosis (MS) from resting-state functional connectivity data. In particular, we wanted to explore the ability in distinguishing between controls and patients of mean signals extracted from ICA components corresponding to 15 well-known networks. Eighteen patients with early-MS (mean-age 37.42±8.11, 9 females) were recruited according to McDonald and Polman, and matched for demographic variables with 19 healthy controls (mean-age 37.55±14.76, 10 females). MRI was acquired by a 3T scanner with 8-channel head coil: (a)whole-brain T1-weighted; (b)conventional T2-weighted; (c)resting-state functional MRI (rsFMRI), 200 volumes. Estimated total lesion load (ml) and number of lesions were calculated using LST-toolbox from the corrected T1 and FLAIR. All rsFMRIs were pre-processed using tools from the FMRIB's Software Library as follows: (1) discarding of the first 5 volumes to remove T1 equilibrium effects, (2) skull-stripping of images, (3) motion and slice-time correction, (4) denoising with high-pass temporal filter (128s), (5) spatial smoothing with a Gaussian kernel of FWHM 8mm. No statistical significant differences (t-test, p < 0.05) were found between the two groups in the mean Euclidian distance and the mean Euler angle. WM and CSF signal together with 6 motion parameters were regressed out from the time series. We applied an independent component analysis (ICA) with the GIFT-toolbox using the Infomax approach with number of components=21. Fifteen mean components were visually identified by two experts. The resulting z-score maps were thresholded and binarized to extract the mean signal of the 15 networks for each subject. Statistical and machine learning analysis were then conducted on this dataset composed of 37 rows (subjects) and 15 features (mean signal in the network) with R language. The dataset was randomly splitted into training (75%) and test sets and two different classifiers were trained: RF and RBF-SVM. We used the intrinsic feature selection of RF, based on the Gini index, and recursive feature elimination (rfe) for the SVM, to obtain a rank of the most predictive variables. Thus, we built two new classifiers only on the most important features and we evaluated the accuracies (with and without feature selection) on test-set. The classifiers, trained on all the features, showed very poor accuracies on training (RF:58.62%, SVM:65.52%) and test sets (RF:62.5%, SVM:50%). Interestingly, when feature selection by RF and rfe-SVM were performed, the most important variable was the sensori-motor network I in both cases. Indeed, with only this network, RF and SVM classifiers reached an accuracy of 87.5% on test-set. More interestingly, the only misclassified patient resulted to have the lowest value of lesion volume. We showed that, with two different classification algorithms and feature selection approaches, the best discriminant network between controls and early MS, was the sensori-motor I. Similar importance values were obtained for the sensori-motor II, cerebellum and working memory networks. These findings, in according to the early manifestation of motor/sensorial deficits in MS, could represent an encouraging step toward the translation to the clinical diagnosis and prognosis.

Keywords: feature selection, machine learning, multiple sclerosis, random forest, support vector machine

Procedia PDF Downloads 228

Authors: Sepehr Moradi, Mehdi Asadi Rad

Abstract:

This study is to evaluate the live weight of adult pigeons to investigate about their sex, race, their mutual effects and some auxiliary variables in 4 races of Kabood, Tizpar, Parvazy, and Namebar. In this paper, 152 pieces of pigeons as 76 male and female pairs with equal age are studied randomly. Then the birds were weighted by a scale with one gram precision. Software was used for statistical analysis. Mean live weight of adult male and female pigeons in 4 races (Kabood, Tizpar, Parvazy and Namebar with (15, 20, 20, 21) and (20, 21, 18, 17) records were, (530±56, 388.75±32, 392±34, 552±48) and (446±34, 342±32, 341±46, 457±57) gr, respectively. Difference weight of adult live of male with female was significant in 1% level (P < 0.01). Difference live weight of male adult pigeon was significant in 5% level (P < 0.05). Different live weight of female adult pigeon between Kabood, Parvazy and Tizpar races were significant in 5% level (P < 0.05) but mean live weight Kabood race with Namebar race and Parvazy with Tizpar were not significant. The results showed that most and least mean live weights belonged to Namebar of the male pigeon race and Parvazy of the female pigeon race.

Keywords: Iranian Native Pigeons, adult weight, live weight, adult pigeons

Procedia PDF Downloads 193

Authors: A. Hayrapetyan, A. Simon, P. Marques, G. Renart

Abstract:

Business survival is a question of greatest interest for any economy. Firm characteristics that can explain or predict performance and, ultimately, business survival become of the greatest significance, as the sustainable longevity of any business can mean health for the future of the country. Family Firms (FFs) are one of the most ubiquitous forms of business worldwide, as more than half of European firms (60%) are considered as family firms. Therefore, the inherent characteristics of FFs are one of the possible explanatory variables for firm survival because FFs have strategic goals that differentiate them from other types of businesses. Although there is literature on the performance of FFs across generations, there are fewer studies on the factors that impact the survival of family and non-family FFs, as there is a lack of data on failed firms. To address this gap, this paper explores the differential survival of family firms versus non-family firms with a representative sample of companies of the region of Catalonia (Northeast of Spain) that were adhoc classified as family or nonfamily firms, as well as classified as failed or surviving, since no census data for family firms or for failed firms is available in Spain. By using the COX regression model on a representative sample of 629 family and non-family firms, this study investigates to what extent financial ratios, such as Liquidity, Solvency Rate can impact business survival, taking into consideration the socioemotional side of family firms, as well as revealing the differences between family and non-family firms. The findings show that the liquidity rate is significant for non-family firm survival, whereas not for family firms. On the other hand, FFs can benefit while having a higher solvency rate. Ultimately, this paper discovers that FFs increase their chances of survival when they are small, as the growth in size starts negatively impacting the socioemotional objectives of the firm. This study proves the existence of significant differences between family and non-family firms’ survival during economic crises, suggesting that the prioritization of emotional wealth creates distinct conditions for both types of firms.

Keywords: COX regression, economy crises, family firm, non-family firm, survival

Procedia PDF Downloads 57

Authors: Maiya Smith, Tyler Thorne

Abstract:

Background: The Pacific faces multiple healthcare crises, including high rates of noncommunicable diseases, infectious disease outbreaks, and susceptibility to natural disasters. These issues are expected to worsen in the coming decades, increasing the burden on an already understaffed healthcare system. Telehealth is not new to the Pacific, but improvements in technology and accessibility have increased its utility and have already proven to reduce costs and increase access to care in remote areas. Telehealth includes distance learning; a form of education that can help alleviate many healthcare issues by providing continuing education to healthcare professionals and upskilling staff, while decreasing costs. This study examined distance learning programs at the Ministry of Health in the Pacific nation of Palau and identified key elements to their successful distance learning programs. Methods: Staff at the Belau National Hospital in Koror, Palau as well as private practitioners were interviewed to assess distance learning programs utilized. This included physicians, IT personnel, public health members, and department managers of allied health. In total, 36 people were interviewed. Standardized questions and surveys were conducted in person throughout the month of July 2019. Results: Two examples of successful distance learning programs were identified. Looking at the factors that made these programs successful, as well as consulting with staff who undertook other distance learning programs, four factors for success were determined: having a cohort, having a facilitator, dedicated study time off from work, and motivation. Discussion: In countries as geographically isolated as the Pacific, with poor access to specialists and resources, telehealth has the potential to radically change how healthcare is delivered. Palau shares similar resources and issues as other countries in the Pacific and the lessons learned from their successful programs can be adapted to help other Pacific nations develop their own distance learning programs.

Keywords: distance learning, Pacific, Palau, telehealth

Procedia PDF Downloads 127

Authors: Sun-Young Jang, Sung-Ah Kim, Dongyoun Shin

Abstract:

Current real-estate value estimation, difficult for laymen, usually is performed by specialists. This paper presents an automated estimation process based on big data and machine-learning technology that calculates influences of building conditions on real-estate price measurement. The present study analyzed actual building sales sample data for Nonhyeon-dong, Gangnam-gu, Seoul, Korea, measuring the major influencing factors among the various building conditions. Further to that analysis, a prediction model was established and applied using RapidMiner Studio, a graphical user interface (GUI)-based tool for derivation of machine-learning prototypes. The prediction model is formulated by reference to previous examples. When new examples are applied, it analyses and predicts accordingly. The analysis process discerns the crucial factors effecting price increases by calculation of weighted values. The model was verified, and its accuracy determined, by comparing its predicted values with actual price increases.

Keywords: apartment complex, big data, life-cycle building value analysis, machine learning

Procedia PDF Downloads 364

Authors: Imen Dhaou

Abstract:

This study examines conditional Value at Risk by applying the GJR-EVT-Copula model, and finds the optimal portfolio for eight Dow Jones Islamic-conventional pairs. Our methodology consists of modeling the data by a bivariate GJR-GARCH model in which we extract the filtered residuals and then apply the Peak over threshold model (POT) to fit the residual tails in order to model marginal distributions. After that, we use pair-copula to find the optimal portfolio risk dependence structure. Finally, with Monte Carlo simulations, we estimate the Value at Risk (VaR) and the conditional Value at Risk (CVaR). The empirical results show the VaR and CVaR values for an equally weighted portfolio of Dow Jones Islamic-conventional pairs. In sum, we found that the optimal investment focuses on Islamic-conventional US Market index pairs because of high investment proportion; however, all other index pairs have low investment proportion. These results deliver some real repercussions for portfolio managers and policymakers concerning to optimal asset allocations, portfolio risk management and the diversification advantages of these markets.

Keywords: CVaR, Dow Jones Islamic index, GJR-GARCH-EVT-pair copula, portfolio optimization

Procedia PDF Downloads 247

Authors: Gerald Gwamba, Bo Zhou, Yajun Song, Dong Changyin

Abstract:

The challenges that sand production presents to the oil and gas industry, particularly while working in poorly consolidated reservoirs, cannot be overstated. From restricting production to blocking production tubing, sand production increases the costs associated with production as it elevates the cost of servicing production equipment over time. Production in reservoirs that present with high viscosities, flow rate, cementation, clay content as well as fine sand contents is even more complex and challenging. As opposed to the one-factor at a-time testing, investigating the interactive effects arising from a combination of several factors offers increased reliability of results as well as representation of actual field conditions. It is thus paramount to investigate the conditions leading to the onset of sanding during production to ensure the future sustainability of hydrocarbon production operations under viscous conditions. We adopt the Design of Experiments (DOE) to analyse, using Taguchi factorial designs, the most significant interactive effects of sanding. We propose an optimized regression model to predict the drawdown time at sand production. The results obtained underscore that reservoirs characterized by varying (high and low) levels of viscosity, flow rate, cementation, clay, and fine sand content have a resulting impact on sand production. The only significant interactive effect recorded arises from the interaction between BD (fine sand content and flow rate), while the main effects included fluid viscosity and cementation, with percentage significances recorded as 31.3%, 37.76%, and 30.94%, respectively. The drawdown time model presented could be useful for predicting the time to reach the maximum drawdown pressure under viscous conditions during the onset of sand production.

Keywords: factorial designs, DOE optimization, sand production prediction, drawdown time, regression model

Procedia PDF Downloads 138

Authors: Nanine Fouche

Abstract:

The Cape Flats is a low-lying sand-covered expanse of approximately 460 square kilometres, situated to the southeast of the central business district of Cape Town in the Western Cape of South Africa. The aeolian sands masking this area are often loose and compressible in the upper 1m to 1.5m of the surface, and there is a general exceedance of the maximum allowable settlement in these sands. The settlement of shallow foundations on Cape Flats sands is commonly predicted using the results of in-situ tests such as the SPT or DPSH due to the difficulty of retrieving undisturbed samples for laboratory testing. Varying degrees of accuracy and reliability are associated with these methods. More recently, shear wave velocity (Vs) profiles obtained from seismic testing, such as continuous surface wave tests (CSW), are being used for settlement prediction. Such predictions have the advantage of considering non-linear stress-strain behaviour of soil and the degradation of stiffness with increasing strain. CSW tests are rarely executed in the Cape Flats, whereas SPT’s are commonly performed. For this reason, and to facilitate better settlement predictions in Cape Flats sand, equations representing shear wave velocity (Vs) as a function of SPT blow count (N60) and vertical effective stress (v’) were generated by statistical regression of site investigation data. To reveal the most appropriate method of overburden correction, analyses were performed with a separate overburden term (Pa/σ’v) as well as using stress corrected shear wave velocity and SPT blow counts (correcting Vs. and N60 to Vs1and (N1)60respectively). Shear wave velocity profiles and SPT blow count data from three sites masked by Cape Flats sands were utilised to generate 80 Vs-SPT N data pairs for analysis. Investigated terrains included sites in the suburbs of Athlone, Muizenburg, and Atlantis, all underlain by windblown deposits comprising fine and medium sand with varying fines contents. Elastic settlement analysis was also undertaken for the Cape Flats sands, using a non-linear stepwise method based on small-strain stiffness estimates, which was obtained from the best Vs-N60 model and compared to settlement estimates using the general elastic solution with stiffness profiles determined using Stroud’s (1989) and Webb’s (1969) SPT N60-E transformation models. Stroud’s method considers strain level indirectly whereasWebb’smethod does not take account of the variation in elastic modulus with strain. The expression of Vs. in terms of N60 and Pa/σv’ derived from the Atlantis data set revealed the best fit with R2 = 0.83 and a standard error of 83.5m/s. Less accurate Vs-SPT N relations associated with the combined data set is presumably the result of inversion routines used in the analysis of the CSW results showcasing significant variation in relative density and stiffness with depth. The regression analyses revealed that the inclusion of a separate overburden term in the regression of Vs and N60, produces improved fits, as opposed to the stress corrected equations in which the R2 of the regression is notably lower. It is the correction of Vs and N60 to Vs1 and (N1)60 with empirical constants ‘n’ and ‘m’ prior to regression, that introduces bias with respect to overburden pressure. When comparing settlement prediction methods, both Stroud’s method (considering strain level indirectly) and the small strain stiffness method predict higher stiffnesses for medium dense and dense profiles than Webb’s method, which takes no account of strain level in the determination of soil stiffness. Webb’s method appears to be suitable for loose sands only. The Versak software appears to underestimate differences in settlement between square and strip footings of similar width. In conclusion, settlement analysis using small-strain stiffness data from the proposed Vs-N60 model for Cape Flats sands provides a way to take account of the non-linear stress-strain behaviour of the sands when calculating settlement.

Keywords: sands, settlement prediction, continuous surface wave test, small-strain stiffness, shear wave velocity, penetration resistance

Procedia PDF Downloads 164

Authors: Sepehr Moradi, Mehdi Asadi Rad

Abstract:

The aim of this study is to evaluate and compare live weight of pure and mixed races Tizpar 30-day pigeons to investigate about their sex, race, and some auxiliary variables. In this paper, 70 pieces of pigeons as 35 male and female pairs with equal age are studied randomly. A natural incubation was done from each pair. All produced chickens were weighted at 30 days age before and after hunger by a scale with one gram precision. A covariance analysis was used since there were many auxiliary variables and unequal observations. SAS software was used for statistical analysis. Mean weight of live in pure race (Tizpar-Tizpar) with 12 records, 182.3±60.9 gr and mixed races of Tizpar-Kabood, Tizpar-Parvazy, Tizpar-Namebar, Kabood-Tizpar, Namebar-Tizpar, and Parvazy-Tizpar with 10, 10, 8, 6, 12, and 12 records were 114.3±71.6, 210.6±71.7, 353.2±86, 520.8±81.5, 288.3±65.6, and 382.6±70.4 gr, respectively. Effects of sex, race and some auxiliary variables were also significant in 1% level (P < 0.01). Difference live weight of 30-day of Tizpar-Tizpar race with Tizpar-Namebar and Parvazi-Tizpar mixed races was significant in 5% level (P < 0.05) and with Kabood-Tizpar mixed races was significant in 1% level (P < 0.01) but with Tizpar-Kabood, Nmaebar-Tizpar and Tizpar-Parvazy mixed races was not significant. The results showed that most and least weights of live belonged to Kabood-Tizpar and Tizpar-Kabood.

Keywords: squabs, Tizpar race, 30-day live weight, pigeons

Procedia PDF Downloads 169

Authors: Farid Khosravikia, Patricia Clayton

Abstract:

The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

Procedia PDF Downloads 93

Authors: Nuttapimon Bhirommuang, Kulapong Jayanama

Abstract:

Background: Prevalence of malnutrition in hospitalized patient is higher than general population. As a result of the unawareness of consequence and the more concerning in the other aspects of care, many patients with high risk of malnutrition are unrecognized. Even if malnutrition has been identified as affecting in many patient outcomes, the impact may differ in each population and group of patients. Objectives: The aims of this study were to examine the association between the nutritional status and the length of stay and hospital costs in hospitalized patients, to investigate the factors related these outcomes and to determine the frequency of malnutrition in hospitals. Method: This retrospective cohort study enrolled all patients aged 15 years old or older and admitted in SDMC, Ramathibodi Hospital between 1st January 2016 and 30th September 2016. The nutritional status assessment by Nutrition Alert Form (NAF) was performed by well-trained nurses in all patients at admission. Baseline characteristics were recorded. Length of stay and hospital costs were collected during their hospitalization. Univariate analysis, nonparametric rank test, Kruskal-Wallis test were used to compare means in the case of nonnormally and noncontinuously distributed data. Chi-square used to analyze categorical variables, the nutritional status and the length of stay and hospital costs and identify possible confounding factors (data were analyzed using SPSS version 18.0). Result: Of the 2,906 patients, 3.9% were severe malnutrition (NAF-C score > 10) and 11.4% were moderate malnutrition (NAF-B score 6 - 10). Both length of stay and hospital costs were found significantly higher in more severe malnutrition group (p < 0.001), NAF = A: 3.21 days, 95% CI 3.06-3.35 and 111,544.25 THB, 95% CI 106,994.41 – 116,094.1; NAF = B: 7.54 days, 95% CI 6.32 – 8.76 and 162,302.4 THB, 95% CI 129,557.88 – 195,046.92; NAF =C: 14.77 days, 95% CI 11.34 – 18.2 and 323,572.11 THB, 95% CI 226,958.1 – 420,096.13 (1 THB = 0.03019 USD). Age of each nutritional status group had also significant increase from NAF A to NAF C (p < 0.001): 55.07, 67.03 and 73.88 years old, respectively. Conclusion: The prevalence of malnutrition in Ramathibodi hospital is voluminous. Severe malnutrition screening by NAF is significantly correlated with worse clinical outcome, especially higher length of stay and hospital costs. Elderly is also a significant factor which correlates with malnutrition. The results of this study could change the awareness of health personnel and the practice protocol. Moreover, the further study concerning nutritional support in high-risk group of malnutrition is ongoing to confirm this hypothesis.

Keywords: malnutrition, NAF, length of stay, hospital costs

Procedia PDF Downloads 265

Authors: Ali Ashraf, Ghazaleh Aghaee, Sedigheh Samimian, Mohaya Farzin

Abstract:

Objectives: Previous studies have indicated the positive effect of physical activity and sports on different aspects of health, such as muscle endurance and sleep cycle. However, in university students, particularly medical students, who have limited time and a stressful lifestyle, there have been limited studies exploring this matter with proven statistical results. In this regard, this study aims to find out how regular physical activity can influence the academic performance of medical students during their internship period. Methods: This was a descriptive-analytical study. Overall, 160 medical students (including 80 women and 88 men) voluntarily participated in the study. The Baecke Physical Activity Questionnaire was applied to determine the student’s physical activity levels. The student's academic performance was determined based on their total average academic scores. The data were analyzed in SPSS version 16 software using the independent t-test, Pearson correlation, and linear regression. Results: The average age of the students was 26.0±1.5 years. Eighty-eight students (52.4%) were male, and 142 (84.5%) were single. The student's mean total average academic score was 16.2±1.2, and their average physical activity score was 8.3±1.1. The student's average academic score was not associated with their gender (P=0.427), marital status (P=0.645), and age (P=0.320). However, married students had a significantly lower physical activity level compared to single students (P=0.020). The results indicated a significant positive correlation between student's physical activity levels and average academic scores (r=+0.410 and P<0.001). This correlation was independent of the student’s age, gender, and marital status based on the regression analysis. Conclusion: The results of the current study suggested that the physical activity level in medical students was low to moderate in most cases, and there was a significant direct relationship between student’s physical activity level and academic performance, independent of age, gender, and marital status.

Keywords: exercise, education, physical activity, academic performance

Procedia PDF Downloads 29

Authors: Wajeeha Shahid

Abstract:

The study was designed to assess the impact of transformational and transactional leadership styles on work motivation and organizational commitment among faculty members of universities of Punjab. 713 faculty members were selected as sample through convenient random sampling technique. Three self-constructed questionnaires namely Leadership Styles Questionnaire (LSQ), Work Motivation Questionnaire (WMQ) and Organizational Commitment Questionnaire (OCMQ) were used as research instruments. Major objectives of the study included assessing the effect and impact of transformational and transactional leadership styles on work motivation and organizational commitment. Theoretical frame work of the study included Idealized Influence, Inspirational Motivation, Intellectual Stimulation, Individualized Consideration, Contingent Rewards and Management by Exception as independent variables and Extrinsic motivation, Intrinsic motivation, Affective commitment, Continuance commitment and Normative commitment as dependent variables. SPSS Version 21 was used to analyze and tabulate data. Cronbach's Alpha reliability, Pearson Correlation and Multiple regression analysis were applied as statistical treatments for the analysis. Results revealed that Idealized Influence correlated significantly with intrinsic motivation and Affective commitment whereas Contingent rewards had a strong positive correlation with extrinsic motivation and affective commitment. Multiple regression models revealed a variance of 85% for transformational leadership style over work motivation and organizational commitment. Whereas transactional style as a predictor manifested a variance of 79% for work motivation and 76% for organizational commitment. It was suggested that changing organizational cultures are demanding more from their leadership. All organizations need to consider transformational leadership style as an important part of their equipment in leveraging both soft and hard organizational targets.

Keywords: leadership styles, work motivation, organizational commitment, faculty member

Procedia PDF Downloads 295

Authors: M. S. Kamarulzaman, N. A. Muhamad, N. A. M. Jamail, M. A. M. Piah, N. F. Kasri

Abstract:

Insulation for high voltage application that has been service for a very long time is subjected to several types of degradation. The degradation can lead to premature breakdown and definitely will spent highly cost to replace the cable. Thus, there are many research on nano composite material get serious attention attention due to their abilities to enhance electrical performance by addition of nano filler. In this paper, water absorption of Low Linear Density Polyethyelene (LLDPE) with different amount of nano filler added is studied. This study is necessary to be conducted since most of electrical apparatus such as cable insulation are dominant used especially in high voltage application. The cable insulation are continuously exposed in uncontrolled environment may suffer degradation process. Three type of nano fillers, was used in this study are: Silicon dioxide (SiO2), Titanium dioxide (TiO2) and Monmorillonite (MMT). The percentage absorption of water was measured by weighted using high precision scales for absorption process up to 92 days. Experimental result demonstrate that SiO2 absorb less water than other filler while, the MMT has hydrophilic properties which it absorbs more water compare to another sample.

Keywords: nano composite, nano filler, water absorption, hydrophilic properties

Procedia PDF Downloads 345

Authors: Mohamed Fawzy Elzarei, Yousef Mohammed Al-Dakheel, Ali Mohamed Alseaf

Abstract:

Modern molecular techniques, like single marker analysis for linked traits to these markers, can provide us with rapid and accurate genetic results. In the last two decades of the last century, the applications of molecular techniques were reached a faraway point in cattle, sheep, and pig. In goats, especially in our region, the application of molecular techniques is still far from other species. As reported by many researchers, microsatellites marker is one of the suitable markers for lie studies. The single marker linked to traits of interest is one technique allowed us to early select animals without the necessity for mapping the entire genome. Simplicity, applicability, and low cost of this technique gave this technique a wide range of applications in many areas of genetics and molecular biology. Also, this technique provides a useful approach for evaluating genetic differentiation, particularly in populations that are poorly known genetically. The expected breeding value (EBV) and yield deviation (YD) are considered as the most parameters used for studying the linkage between quantitative characteristics and molecular markers, since these values are raw data corrected for the non-genetic factors. A total of 17 microsatellites markers (from chromosomes 6, 14, 18, 20 and 23) were used in this study to search for areas that could be responsible for genetic variability for some milk traits and search of chromosomal regions that explain part of the phenotypic variance. Results of single-marker analyses were used to identify the linkage between microsatellite markers and variation in EBVs of these traits, Milk yield, Protein percentage, Fat percentage, Litter size and weight at birth, and litter size and weight at weaning. The estimates of the parameters from forward and backward solutions using stepwise regression procedure on milk yield trait, only two markers, OARCP9 and AGLA29, showed a highly significant effect (p≤0.01) in backward and forward solutions. The forward solution for different equations conducted that R2 of these equations were highly depending on only two partials regressions coefficient (βi,) for these markers. For the milk protein trait, four marker showed significant effect BMS2361, CSSM66 (p≤0.01), BMS2626, and OARCP9 (p≤0.05). By the other way, four markers (MCM147, BM1225, INRA006, andINRA133) showed highly significant effect (p≤0.01) in both backward and forward solutions in association with milk fat trait. For both litter size at birth and at weaning traits, only one marker (BM143(p≤0.01) and RJH1 (p≤0.05), respectively) showed a significant effect in backward and forward solutions. The estimates of the parameters from forward and backward solution using stepwise regression procedure on litter weight at birth (LWB) trait only one marker (MCM147) showed highly significant effect (p≤0.01) and two marker (ILSTS011, CSSM66) showed a significant effect (p≤0.05) in backward and forward solutions.

Keywords: microsatellites marker, estimated breeding value, stepwise regression, milk traits

Procedia PDF Downloads 79

Authors: Eric Mensah, Carlos Mensah

Abstract:

The road maintenance backlogs created as a result of deferred maintenance especially in developing countries has caused considerable deterioration of many road assets. This is usually due to difficulties encountered in selecting and prioritising maintainable roads based on objective criteria rather than some political or other less important criteria. In order to ensure judicious use of limited resources for road maintenance, five factors were identified as the most important criteria for road management within the study area. This was based on the judgements of 40 experts. The results were further used to develop weightings using the Multi-Criteria Decision Process (MCDP) to analyse and select road alternatives according to maintenance goal. Using Geographical Information Systems (GIS), maintainable roads were grouped using the Jenk’s natural breaks to allow for further prioritised in order of importance for display on a dashboard of maps, charts, and tables. This reduces the problems of subjective maintenance and road selections, thereby reducing wastage of resources and easing the maintenance process through an object organised spatial decision support system.

Keywords: decision support, geographical information systems, multi-criteria decision process, weighted sum

Procedia PDF Downloads 361

Authors: Belayneh Yohannes

Abstract:

In Ethiopia, onion production is increasing from time to time mainly due to its high profitability per unit area. Onion has a significant contribution to generating cash income for farmers in the Raya Azebo district. Therefore, enhancing onion producers’ access to the market and improving market linkage is an essential issue. Hence, this study aimed to analyze structure-conduct-performance of onion market and identifying factors affecting the market supply of onion producers. Data were collected from both primary and secondary sources. Primary data were collected from 150 farm households and 20 traders. Four onion marketing channels were identified in the study area. The highest total gross margin is 27.6 in channel IV. The highest gross marketing margin of producers of the onion market is 88% in channel II. The result from the analysis of market concentration indicated that the onion market is characterized by a strong oligopolistic market structure, with the buyers’ concentration ratio of 88.7 in Maichew town and 82.7 in Mekelle town. Lack of capital, licensing problems, and seasonal supply was identified as the major entry barrier to onion marketing. Market conduct shows that the price of onion is set by traders while producers are price takers. Multiple linear regression model results indicated that family size in adult equivalent, irrigated land size, access to information, frequency of extension contact, and ownership of transport significantly determined the quantity of onion supplied to the market. It is recommended that strengthening and diversifying extension services in information, marketing, post-harvest handling, irrigation application, and water harvest technology is highly important.

Keywords: oligopoly, onion, market chain, multiple linear regression

Procedia PDF Downloads 121

Authors: Arragaw Alemayehu, Woldeamlak Bewket

Abstract:

The aim of this study was to understand the influence of current climate variability on crop production in the central highlands of Ethiopia. We used monthly rainfall and temperature data from 132 points each representing a pixel of 10×10 km. The data are reconstructions based on station records and meteorological satellite observations. Production data of the five major crops in the area were collected from the Central Statistical Agency for the period 2004-2013 and for the main cropping season, locally known as Meher. The production data are at the Enumeration Area (EA ) level and hence the best available dataset on crop production. The results show statistically significant decreasing trends in March–May (Belg) rainfall in the area. However, June – September (Kiremt) rainfall showed increasing trends in Efratana Gidim and Menz Gera Meder which the latter is statistically significant. Annual rainfall also showed positive trends in the area except Basona Werana where significant negative trends were observed. On the other hand, maximum and minimum temperatures showed warming trends in the study area. Correlation results have shown that crop production and area of cultivation have positive correlation with rainfall, and negative with temperature. When the trends in crop production are investigated, most crops showed negative trends and below average production was observed. Regression results have shown that rainfall was the most important determinant of crop production in the area. It is concluded that current climate variability has a significant influence on crop production in the area and any unfavorable change in the local climate in the future will have serious implications for household level food security. Efforts to adapt to the ongoing climate change should begin from tackling the current climate variability and take a climate risk management approach.

Keywords: central highlands, climate variability, crop production, Ethiopia, regression, trend

Procedia PDF Downloads 424

Authors: Kyung-Sook Bang

Abstract:

Background: The health status of school-age children has a great influence on their growth and life-long health. The purposes of this study were to identify physical and mental health status of late school-age children in Seoul, South Korea and to investigate the related factors for their health. Methods: After gaining the approval from Institutional Review Board (IRB), a cross-sectional study was conducted with elementary students in grade 4 or 5. Questionnaires were distributed to eight elementary schools located different regions of Seoul in November, 2016, and 302 participants were finally included. From all participants, informed consents from the parents, and assents from children were received. Children's socioeconomic status, family functioning, peer relations, physical health symptoms, and depression were measured with self-reported questionnaires. Data were analyzed with descriptive statistics, t-test, Pearson’s correlations, and multiple regression. Results: Children's physical health symptoms and depression were not significantly different, and only their peer relations were significantly different according to their socioeconomic status (t=-3.93, p<.001). Depression showed significant positive correlation with physical health symptoms (r=.720, p<.001) and negative correlations with family functioning (r=-.428, p<.001) and peer relations (r=-.775, p<.001). The multiple regression model, which explained 73.5% of variance, showed peer relations (r2 =.604), physical health symptoms (r2 change=.125), and family functioning (r2 change=.005) as significant predictors for depression. Only the peer relations was significant predictor for their physical health symptoms and explained 50.6% of it. Conclusions: The peer relations was the most important factor in their physical and mental health at this age, and it can be affected by their socioeconomic status. Nursing interventions for promoting social relations and family functioning are required to improve children’s physical and mental health, especially for vulnerable population.

Keywords: child, depression, health, peer relation

Procedia PDF Downloads 213

Authors: C. L. Wu, W. W. Tseng

Abstract:

From the National Health Insurance (NHI) system was introduced in Taiwan in 2000, there have been some problems in transformed small-scale hospitals, such as mobility of patients, shortage of nursing staff, medical pipelines breaking fire compartments and insufficient fire protection systems. Due to shrinking of the funding scale and the aging society, fire safety in small-scale hospitals has recently given cause for concern. The aim of this study is to determine fire risk index for small-scale hospital through a systematic approach The selection of fire safety mitigation methods can be regarded as a multi-attribute decision making process which must be guaranteed by expert groups. First of all, identify and select safety related factors and identify evaluation criteria through literature reviews and experts group. Secondly, application of the Fuzzy Analytic Hierarchy Process method is used to ascertain a weighted value which enables rating of the importance each of the selected factors. Overall, Sprinkler type and Compartmentation are the most crucial indices in mitigating fire, that is to say, structural approach play an important role to decrease losses in fire events.

Keywords: Fuzzy Delphi Method, fuzzy analytic hierarchy, process risk assessment, fire events

Procedia PDF Downloads 436

Authors: Shivani Saini

Abstract:

The effect of vertical throughflow on the onset of convection in Rivlin-Ericksen Elastico-Viscous nanofluid with convective boundary condition is investigated. The flow is stimulated with modified Darcy model under the assumption that the nanoparticle volume fraction is not actively managed on the boundaries. The heat conservation equation is formulated by introducing the convective term of nanoparticle flux. A linear stability analysis based upon normal mode is performed, and an approximate solution of eigenvalue problems is obtained using the Galerkin weighted residual method. Investigation of the dependence of the Rayleigh number on various viscous and nanofluid parameter is performed. It is found that through flow and nanofluid parameters hasten the convection while capacity ratio, kinematics viscoelasticity, and Vadasz number do not govern the stationary convection. Using the convective component of nanoparticle flux, critical wave number is the function of nanofluid parameters as well as the throughflow parameter. The obtained solution provides important physical insight into the behavior of this model.

Keywords: Darcy model, nanofluid, porous layer, throughflow

Procedia PDF Downloads 124

Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

Procedia PDF Downloads 66

Authors: Siyu Wang, Anthony Ward

Abstract:

Emotional intelligence (EI) is a well-established personal characteristic. It has been viewed as a critical factor which can influence an individual's academic achievement, ability to work and potential to succeed. When working in a group, EI is fundamentally connected to the group members' interaction and ability to work as a team. The ability of a group member to intelligently perceive and understand own emotions (Intrapersonal EI), to intelligently perceive and understand other members' emotions (Interpersonal EI), and to intelligently perceive and understand emotions between different groups (Cross-boundary EI) can be considered as Group emotional intelligence (Group EI). In this research, a more representative Group EI measurement approach, which incorporates the information of the composition of a group and an individual’s role in that group, is proposed. To demonstrate the claim of being more representative Group EI measurement approach, this study adopts a multi-method research design, involving a combination of both qualitative and quantitative techniques to establish a metric of Group EI. From the results, it can be concluded that by introducing the weight coefficient of each group member on group work into the measurement of Group EI, Group EI will be more representative and more capable of understanding what happens during teamwork than previous approaches.

Keywords: case study, emotional intelligence, group EI, multi-method research

Procedia PDF Downloads 115

Authors: Ben Kwame Agyei-Mensah

Abstract:

This study investigated the influence of firm-specific characteristics which include proportion of Non-Executive Directors, ownership concentration, firm size, profitability, debt equity ratio, liquidity and leverage on the extent and quality of financial ratios disclosed by firms listed on the Ghana Stock Exchange. The research was conducted through detailed analysis of the 2012 financial statements of the listed firms. Descriptive analysis was performed to provide the background statistics of the variables examined. This was followed by regression analysis which forms the main data analysis. The results of the extent of financial ratio disclosure level, mean of 62.78%, indicate that most of the firms listed on the Ghana Stock Exchange did not overwhelmingly disclose such ratios in their annual reports. The results of the low quality of financial ratio disclosure mean of 6.64% indicate that the disclosures failed woefully to meet the International Accounting Standards Board's qualitative characteristics of relevance, reliability, comparability and understandability. The results of the multiple regression analysis show that leverage (gearing ratio) and return on investment (dividend per share) are associated on a statistically significant level as far as the extent of financial ratio disclosure is concerned. Board ownership concentration and proportion of (independent) non-executive directors, on the other hand were found to be statistically associated with the quality of financial ratio disclosed. There is a significant negative relationship between ownership concentration and the quality of financial ratio disclosure. This means that under a higher level of ownership concentration less quality financial ratios are disclosed. The findings also show that there is a significant positive relationship between board composition (proportion of non-executive directors) and the quality of financial ratio disclosure.

Keywords: voluntary disclosure, firm-specific characteristics, financial reporting, financial ratio disclosure, Ghana stock exchange

Procedia PDF Downloads 577