Search results for: regression models

Authors: Zhihan Li

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Nowadays, most universities use the course enrollment system considering students’ registration orders. However, the students’ preference level to certain courses is also one important factor to consider. In this research, the possibility of applying a preference-first system has been discussed and analyzed compared to the order-first system. A bipartite graph is applied to resemble the relationship between students and courses they tend to register. With the graph set up, we apply Ford-Fulkerson (F.F.) Algorithm to maximize parings between two sets of nodes, in our case, students and courses. Two models are proposed in this paper: the one considered students’ order first, and the one considered students’ preference first. By comparing and contrasting the two models, we highlight the usability of models which potentially leads to better designs for school course registration systems.

Keywords: bipartite graph, Ford-Fulkerson (F.F.) algorithm, graph theory, maximum matching

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Authors: Abrar R. Al-Hasan

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Despite the widespread use of social media by consumers and marketers, empirical research investigating their economic value in the healthcare industry still lags. This study explores the impact of the use of social media marketing efforts on a hospital's brand equity and, ultimately, consumer response. Using social media data from Twitter and Facebook, along with an online and offline survey methodology, data is analyzed using logistic regression models. A random sample of (728) residents of the Kuwaiti population is used. The results of this study found that social media marketing efforts (SMME) in terms of use and validation lead to higher hospital brand equity and in turn, patient loyalty and patient visit. The study highlights the impact of SMME on hospital brand equity and patient response. Healthcare organizations should guide their marketing efforts to better manage this new way of marketing and communicating with patients to enhance their consumer loyalty and financial performance.

Keywords: brand equity, healthcare marketing, patient visit, social media, SMME

Procedia PDF Downloads 159

Authors: Nikolett Deutsch, Éva Pintér, Péter Bagó, Miklós Hetényi

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Strategic technology management has emerged and evolved parallelly with strategic management paradigms. It focuses on the opportunity for organizations operating mainly in technology-intensive industries to explore and exploit technological capabilities upon which competitive advantage can be obtained. As strategic technology management involves multifunction within an organization, requires broad and diversified knowledge, and must be developed and implemented with business objectives to enable a firm’s profitability and growth, excellence in strategic technology management provides unique opportunities for organizations in terms of building a successful future. Accordingly, a framework supporting the evaluation of the technological readiness level of management can significantly contribute to developing organizational competitiveness through a better understanding of strategic-level capabilities and deficiencies in operations. In the last decade, several innovation maturity assessment models have appeared and become designated management tools that can serve as references for future practical approaches expected to be used by corporate leaders, strategists, and technology managers to understand and manage technological capabilities and capacities. The aim of this paper is to provide a comprehensive review of the state-of-the-art innovation maturity frameworks, to investigate the critical lessons learned from their application, to identify the similarities and differences among the models, and identify the main aspects and elements valid for the field and critical functions of technology management. To this end, a systematic literature review was carried out considering the relevant papers and articles published in highly ranked international journals around the 27 most widely known innovation maturity models from four relevant digital sources. Key findings suggest that despite the diversity of the given models, there is still room for improvement regarding the common understanding of innovation typologies, the full coverage of innovation capabilities, and the generalist approach to the validation and practical applicability of the structure and content of the models. Furthermore, the paper proposes an initial structure by considering the maturity assessment of the technological capacities and capabilities - i.e., technology identification, technology selection, technology acquisition, technology exploitation, and technology protection - covered by strategic technology management.

Keywords: innovation capabilities, innovation maturity models, technology audit, technology management, technology management maturity models

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Authors: Oluwatosin M. A. Jesuyon

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In order to propose a simpler tool that eliminates the age-long problems associated with the traditional index method for selection of multiple traits in broilers, the Barttlet factor regression equation is being proposed as an alternative selection tool. 100 day-old chicks each of Arbor Acres (AA) and Annak (AN) broiler strains were obtained from two rival hatcheries in Ibadan Nigeria. These were raised in deep litter system in a 56-day feeding trial at the University of Ibadan Teaching and Research Farm, located in South-west Tropical Nigeria. The body weight and body dimensions were measured and recorded during the trial period. Eight (8) zoometric measurements namely live weight (g), abdominal circumference, abdominal length, breast width, leg length, height, wing length and thigh circumference (all in cm) were recorded randomly from 20 birds within strain, at a fixed time on the first day of the new week respectively with a 5-kg capacity Camry scale. These records were analyzed and compared using completely randomized design (CRD) of SPSS analytical software, with the means procedure, Factor Scores (FS) in stepwise Multiple Linear Regression (MLR) procedure for initial live weight equations. Bartlett Factor Score (BFS) analysis extracted 2 factors for each strain, termed Body-length and Thigh-meatiness Factors for AA, and; Breast Size and Height Factors for AN. These derived orthogonal factors assisted in deducing and comparing trait-combinations that best describe body conformation and Meatiness in experimental broilers. BFS procedure yielded different body conformational traits for the two strains, thus indicating the different economic traits and advantages of strains. These factors could be useful as selection criteria for improving desired economic traits. The final Bartlett Factor Regression equations for prediction of body weight were highly significant with P < 0.0001, R2 of 0.92 and above, VIF of 1.00, and DW of 1.90 and 1.47 for Arbor Acres and Annak respectively. These FSR equations could be used as a simple and potent tool for selection during poultry flock improvement, it could also be used to estimate selection index of flocks in order to discriminate between strains, and evaluate consumer preference traits in broilers.

Keywords: alternative selection tool, Bartlet factor regression model, consumer preference trait, linear and body measurements, live body weight

Procedia PDF Downloads 198

Authors: Fidelis Chigondo

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This study focuses on investigating the effectiveness of watermelon rind in phenol removal from aqueous solution. The effects of various parameters (pH, initial phenol concentration, biosorbent dosage and contact time) on phenol adsorption were investigated. The pH of 2, initial phenol concentration of 40 ppm, the biosorbent dosage of 0.6 g and contact time of 6 h also deduced to be the optimum conditions for the adsorption process. The maximum phenol removal under optimized conditions was 85%. The sorption data fitted to the Freundlich isotherm with a regression coefficient of 0.9824. The kinetics was best described by the intraparticle diffusion model and Elovich Equation with regression coefficients of 1 and 0.8461 respectively showing that the reaction is chemisorption on a heterogeneous surface and the intraparticle diffusion rate only is the rate determining step. The study revealed that watermelon rind has a potential of removing phenol from industrial wastewaters.

Keywords: biosorption, phenol, biosorbent, watermelon rind

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Authors: Xiang Zhang, David Rey, S. Travis Waller

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Stochastic User Equilibrium (SUE) model is a widely used traffic assignment model in transportation planning, which is regarded more advanced than Deterministic User Equilibrium (DUE) model. However, a problem exists that the performance of the SUE model depends on its error term parameter. The objective of this paper is to propose a systematic method of determining the appropriate error term parameter value for the SUE model. First, the significance of the parameter is explored through a numerical example. Second, the parameter calibration method is developed based on the Logit-based route choice model. The calibration process is realized through multiple nonlinear regression, using sequential quadratic programming combined with least square method. Finally, case analysis is conducted to demonstrate the application of the calibration process and validate the better performance of the SUE model calibrated by the proposed method compared to the SUE models under other parameter values and the DUE model.

Keywords: parameter calibration, sequential quadratic programming, stochastic user equilibrium, traffic assignment, transportation planning

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Authors: Mohamed S. Sasi, Adel M. Mlitan, Abdulfattah M. Alkherraz

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This research project aims to investigate difference in relative rates concerning phosphoryl transfer relevant to biological catalysis of DNA and RNA in the pH-independent reactions. Activated Models of DNA and RNA for alkyl-aryl phosphate diesters (with 4-nitrophenyl as a good leaving group) have successfully been prepared to gather kinetic parameters. Eyring plots for the pH–independent hydrolysis of 1 and 2 were established at different temperatures in the range 100–160 °C. These measurements have been used to provide a better estimate for the difference in relative rates between the reactivity of DNA and RNA cleavage. Eyring plot gave an extrapolated rate of kH2O = 1 × 10-10 s -1 for 1 (RNA model) and 2 (DNA model) at 25°C. Comparing the reactivity of RNA model and DNA model shows that the difference in relative rates in the pH-independent reactions is surprisingly very similar at 25°. This allows us to obtain chemical insights into how biological catalysts such as enzymes may have evolved to perform their current functions.

Keywords: DNA and RNA models, relative rates, reactivity, phosphoryl transfe

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Authors: Byung Ho Jung, Ji Eun Shin, Dong Hoon Lim

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Rhipe that integrates R and Hadoop environment made it possible to process and analyze massive amounts of data using a distributed processing environment. In this paper, we implemented multiple regression analysis using Rhipe with various data sizes of actual data. Experimental results for comparing the performance of our Rhipe with stats and biglm packages available on bigmemory, showed that our Rhipe was more fast than other packages owing to paralleling processing with increasing the number of map tasks as the size of data increases. We also compared the computing speeds of pseudo-distributed and fully-distributed modes for configuring Hadoop cluster. The results showed that fully-distributed mode was faster than pseudo-distributed mode, and computing speeds of fully-distributed mode were faster as the number of data nodes increases.

Keywords: big data, Hadoop, Parallel regression analysis, R, Rhipe

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Authors: Diego Tancara, Raul Coto, Ariel Norambuena, Hoseein T. Dinani, Felipe Fanchini

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Quantum machine learning is a growing research field that aims to perform machine learning tasks assisted by a quantum computer. Kernel-based quantum machine learning models are paradigmatic examples where the kernel involves quantum states, and the Gram matrix is calculated from the overlapping between these states. With the kernel at hand, a regular machine learning model is used for the learning process. In this paper we investigate the quantum support vector machine and quantum kernel ridge models to predict the degree of non-Markovianity of a quantum system. We perform digital quantum simulation of amplitude damping and phase damping channels to create our quantum dataset. We elaborate on different kernel functions to map the data and kernel circuits to compute the overlapping between quantum states. We observe a good performance of the models.

Keywords: quantum, machine learning, kernel, non-markovianity

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Authors: Elfatih M. Abdel-Rahman, Tobias Landmann, Richard Kyalo, George Ong’amo, Bruno Le Ru

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Maize (Zea mays L.) is a major staple food crop in Africa, particularly in the eastern region of the continent. The maize growing area in Africa spans over 25 million ha and 84% of rural households in Africa cultivate maize mainly as a means to generate food and income. Average maize yields in Sub Saharan Africa are 1.4 t/ha as compared to global average of 2.5–3.9 t/ha due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In East Africa, yield losses due to stem borers are currently estimated between 12% to 40% of the total production. The objective of the present study was therefore to predict stem borer larvae density in maize fields using RapidEye reflectance data and generalized linear models (GLMs). RapidEye images were captured for a test site in Kenya (Machakos) in January and in February 2015. Stem borer larva numbers were modeled using GLMs assuming Poisson (Po) and negative binomial (NB) distributions with error with log arithmetic link. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were employed to assess the models performance using a leave one-out cross-validation approach. Results showed that NB models outperformed Po ones in all study sites. RMSE and RPD ranged between 0.95 and 2.70, and between 2.39 and 6.81, respectively. Overall, all models performed similar when used the January and the February image data. We conclude that reflectance data from RapidEye data can be used to estimate stem borer larvae density. The developed models could to improve decision making regarding controlling maize stem borers using various integrated pest management (IPM) protocols.

Keywords: maize, stem borers, density, RapidEye, GLM

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Authors: Shaomei Li, Yawen Wang, Chao Gao

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To improve tracking drift which often occurs in adaptive tracking, an algorithm based on the fusion of tracking and detection is proposed in this paper. Firstly, object tracking is posed as a binary classification problem and is modeled by partial least squares (PLS) analysis. Secondly, tracking object frame by frame via particle filtering. Thirdly, validating the tracking reliability based on both positive and negative models matching. Finally, relocating the object based on SIFT features matching and voting when drift occurs. Object appearance model is updated at the same time. The algorithm cannot only sense tracking drift but also relocate the object whenever needed. Experimental results demonstrate that this algorithm outperforms state-of-the-art algorithms on many challenging sequences.

Keywords: object tracking, tracking drift, partial least squares analysis, positive and negative models matching

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Authors: Jian-Heng Wu, Bor-Shen Lin

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The temperature-salinity relationship is one of the most important characteristics used for identifying water masses in marine research. Temperature-salinity characteristics, however, may change dynamically with respect to the geographic location and is quite sensitive to the depth at the same location. When depth is taken into consideration, however, it is not easy to compare the characteristics of different water masses efficiently for a wide range of areas of the ocean. In this paper, the Gaussian mixture model was proposed to analyze the temperature-salinity-depth characteristics of water masses, based on which comparison between water masses may be conducted. Gaussian mixture model could model the distribution of a random vector and is formulated as the weighting sum for a set of multivariate normal distributions. The temperature-salinity-depth data for different locations are first used to train a set of Gaussian mixture models individually. The distance between two Gaussian mixture models can then be defined as the weighting sum of pairwise Bhattacharyya distances among the Gaussian distributions. Consequently, the distance between two water masses may be measured fast, which allows the automatic and efficient comparison of the water masses for a wide range area. The proposed approach not only can approximate the distribution of temperature, salinity, and depth directly without the prior knowledge for assuming the regression family, but may restrict the complexity by controlling the number of mixtures when the amounts of samples are unevenly distributed. In addition, it is critical for knowledge discovery in marine research to represent, manage and share the temperature-salinity-depth characteristics flexibly and responsively. The proposed approach has been applied to a real-time visualization system of ocean data, which may facilitate the comparison of water masses by aggregating the data without degrading the discriminating capabilities. This system provides an interface for querying geographic locations with similar temperature-salinity-depth characteristics interactively and for tracking specific patterns of water masses, such as the Kuroshio near Taiwan or those in the South China Sea.

Keywords: water mass, Gaussian mixture model, data visualization, system framework

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Authors: Hadush Kidane Meresa

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The hydrological extremes frequency analysis is the foundation for the hydraulic engineering design, flood protection, drought management and water resources management and planning to utilize the available water resource to meet the desired objectives of different organizations and sectors in a country. This spatial variation of the statistical characteristics of the extreme flood and drought events are key practice for regional flood and drought analysis and mitigation management. For different hydro-climate of the regions, where the data set is short, scarcity, poor quality and insufficient, the regionalization methods are applied to transfer at-site data to a region. This study aims in regional high and low flow frequency analysis for Poland River Basins. Due to high frequent occurring of hydrological extremes in the region and rapid water resources development in this basin have caused serious concerns over the flood and drought magnitude and frequencies of the river in Poland. The magnitude and frequency result of high and low flows in the basin is needed for flood and drought planning, management and protection at present and future. Hydrological homogeneous high and low flow regions are formed by the cluster analysis of site characteristics, using the hierarchical and C- mean clustering and PCA method. Statistical tests for regional homogeneity are utilized, by Discordancy and Heterogeneity measure tests. In compliance with results of the tests, the region river basin has been divided into ten homogeneous regions. In this study, frequency analysis of high and low flows using AM for high flow and 7-day minimum low flow series is conducted using six statistical distributions. The use of L-moment and LL-moment method showed a homogeneous region over entire province with Generalized logistic (GLOG), Generalized extreme value (GEV), Pearson type III (P-III), Generalized Pareto (GPAR), Weibull (WEI) and Power (PR) distributions as the regional drought and flood frequency distributions. The 95% percentile and Flow duration curves of 1, 7, 10, 30 days have been plotted for 10 stations. However, the cluster analysis performed two regions in west and east of the province where L-moment and LL-moment method demonstrated the homogeneity of the regions and GLOG and Pearson Type III (PIII) distributions as regional frequency distributions for each region, respectively. The spatial variation and regional frequency distribution of flood and drought characteristics for 10 best catchment from the whole region was selected and beside the main variable (streamflow: high and low) we used variables which are more related to physiographic and drainage characteristics for identify and delineate homogeneous pools and to derive best regression models for ungauged sites. Those are mean annual rainfall, seasonal flow, average slope, NDVI, aspect, flow length, flow direction, maximum soil moisture, elevation, and drainage order. The regional high-flow or low-flow relationship among one streamflow characteristics with (AM or 7-day mean annual low flows) some basin characteristics is developed using Generalized Linear Mixed Model (GLMM) and Generalized Least Square (GLS) regression model, providing a simple and effective method for estimation of flood and drought of desired return periods for ungauged catchments.

Keywords: flood , drought, frequency, magnitude, regionalization, stochastic, ungauged, Poland

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Authors: Yixin Zhu, Lishengsa Yue, Jian Sun, Lanyue Tang

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Modeling driving distraction in microscopic traffic simulation is crucial for enhancing simulation accuracy. Current driving distraction models are mainly derived from physical motion constraints under distracted states, in which distraction-related error terms are added to existing microscopic driver models. However, the model accuracy is not very satisfying, due to a lack of modeling the cognitive mechanism underlying the distraction. This study models driving distraction based on the Queueing Network Human Processor model (QN-MHP). This study utilizes the queuing structure of the model to perform task invocation and switching for distracted operation and control of the vehicle under driver distraction. Based on the assumption of the QN-MHP model about the cognitive sub-network, server F is a structural bottleneck. The latter information must wait for the previous information to leave server F before it can be processed in server F. Therefore, the waiting time for task switching needs to be calculated. Since the QN-MHP model has different information processing paths for auditory information and visual information, this study divides driving distraction into two types: auditory distraction and visual distraction. For visual distraction, both the visual distraction task and the driving task need to go through the visual perception sub-network, and the stimuli of the two are asynchronous, which is called stimulus on asynchrony (SOA), so when calculating the waiting time for switching tasks, it is necessary to consider it. In the case of auditory distraction, the auditory distraction task and the driving task do not need to compete for the server resources of the perceptual sub-network, and their stimuli can be synchronized without considering the time difference in receiving the stimuli. According to the Theory of Planned Behavior for drivers (TPB), this study uses risk entropy as the decision criterion for driver task switching. A logistic regression model is used with risk entropy as the independent variable to determine whether the driver performs a distraction task, to explain the relationship between perceived risk and distraction. Furthermore, to model a driver’s perception characteristics, a neurophysiological model of visual distraction tasks is incorporated into the QN-MHP, and executes the classical Intelligent Driver Model. The proposed driving distraction model integrates the psychological cognitive process of a driver with the physical motion characteristics, resulting in both high accuracy and interpretability. This paper uses 773 segments of distracted car-following in Shanghai Naturalistic Driving Study data (SH-NDS) to classify the patterns of distracted behavior on different road facilities and obtains three types of distraction patterns: numbness, delay, and aggressiveness. The model was calibrated and verified by simulation. The results indicate that the model can effectively simulate the distracted car-following behavior of different patterns on various roadway facilities, and its performance is better than the traditional IDM model with distraction-related error terms. The proposed model overcomes the limitations of physical-constraints-based models in replicating dangerous driving behaviors, and internal characteristics of an individual. Moreover, the model is demonstrated to effectively generate more dangerous distracted driving scenarios, which can be used to construct high-value automated driving test scenarios.

Keywords: computational cognitive model, driving distraction, microscopic traffic simulation, psychological-physical constraints

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Authors: M. O. Oke, T. S. Workneh

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Drying behaviour of blanched sweet potato in a cabinet dryer using different five air temperatures (40-80oC) and ten sweet potato varieties sliced to 5 mm thickness were investigated. The drying data were fitted to eight models. The Modified Henderson and Pabis model gave the best fit to the experimental moisture ratio data obtained during the drying of all the varieties while Newton (Lewis) and Wang and Singh models gave the least fit. The values of Deff obtained for Bophelo variety (1.27 x 10-9 to 1.77 x 10-9 m2/s) was the least while that of S191 (1.93 x 10-9 to 2.47 x 10-9 m2/s) was the highest which indicates that moisture diffusivity in sweet potato is affected by the genetic factor. Activation energy values ranged from 0.27-6.54 kJ/mol. The lower activation energy indicates that drying of sweet potato slices requires less energy and is hence a cost and energy saving method. The drying behavior of blanched sweet potato was investigated in a cabinet dryer. Drying time decreased considerably with increase in hot air temperature. Out of the eight models fitted, the Modified Henderson and Pabis model gave the best fit to the experimental moisture ratio data on all the varieties while Newton, Wang and Singh models gave the least. The lower activation energy (0.27-6.54 kJ/mol) obtained indicates that drying of sweet potato slices requires less energy and is hence a cost and energy saving method.

Keywords: sweet potato slice, drying models, moisture ratio, moisture diffusivity, activation energy

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Authors: Ya-Hui Lee, Ching-Yi Lu, Hui-Chuan Wei

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The purpose of this research was to explore the relation between proactive coping and well-being of middle-aged adults. We conducted survey research that with t-test, one way ANOVA, Pearson correlation and stepwise multiple regression to analyze. This research drew on a sample of 395 participants from the senior learning centers of Taiwan. The results provided the following findings: 1.The participants from different residence areas associated significant difference with proactive coping, but not with well-being. 2. The participants’ perceived of financial level associated significant difference with both proactive coping and well-being. 3. There was significant difference between participants’ income and well-being. 4. The proactive coping was positively correlated with well-being. 5. From stepwise multiple regression analysis showed that two dimensions of proactive coping had positive predictability. Finally, these results of this study can be provided as references for designing older adult educational programs in Taiwan.

Keywords: middle-age and older adults, learners, proactive coping, well-being

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Authors: Saheed A. Rufai

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Courses in Pedagogy and Curriculum Development expectedly occupy a core place in the professional education components of teacher education at Lagos, Lagos State, and Sokoto State Universities. This is in keeping with the National Teacher Education Policy statement that stipulates that for student teachers to learn effectively teacher education institutions must be equipped to prepare them adequately. However, there is a growing concern over the unfaithfulness of some of the dominant Nigerian models of teacher education, to this policy statement on teacher educators’ knowledge and skills. The purpose of this paper is to comparatively evaluate both the curricular provisions and the manpower for the pedagogy and curriculum development sub-components of the Lagos, Lagos State, and Sokoto State models of teacher preparation. The paper employs a combination of quantitative and qualitative methods. Preliminary analysis revealed a new trend in teacher educators’ pedagogical knowledge and understanding, with regard to the two intertwined sub-components. The significance of such a study lies in its potential to determine the degree of conformity of each of the three models to the stipulated standards. The paper’s contribution to scholarship lies in its correlation of deficiencies in teacher educators’ professional knowledge and skills and articulation of the implications of such deficiencies for the professional knowledge and skills of the prospective teachers, with a view to providing a framework for reforms.

Keywords: curriculum development, pedagogy, teacher education, dominant Nigerian teacher preparation models

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Authors: Peyman Sheikholharam Mashhadi

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Difficulties in analyzing real world images in classical image processing and machine vision framework have motivated researchers towards considering the biology-based vision. It is a common belief that mammalian visual cortex has been adapted to the statistics of the real world images through the evolution process. There are two well-known successful models of mammalian visual cortical cells: Independent Component Analysis (ICA) and Independent Subspace Analysis (ISA). In this paper, we statistically analyze the dependencies which remain in the components after applying these models to the natural images. Also, we investigate the response of feature detectors to gratings with various parameters in order to find optimal parameters of the feature detectors. Finally, the selectiveness of feature detectors to phase, in both models is considered.

Keywords: statistics, independent component analysis, independent subspace analysis, phase, natural images

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Authors: Ma Lei-Lei, Zhou You

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Aiming at the problems of low detection accuracy of deep learning algorithm in traffic sign detection, this paper proposes improved EfficientDet based traffic sign detection algorithm. Multi-head self-attention is introduced in the minimum resolution layer of the backbone of EfficientDet to achieve effective aggregation of local and global depth information, and this study proposes an improved feature fusion pyramid with increased vertical cross-layer connections, which improves the performance of the model while introducing a small amount of complexity, the Balanced L1 Loss is introduced to replace the original regression loss function Smooth L1 Loss, which solves the problem of balance in the loss function. Experimental results show, the algorithm proposed in this study is suitable for the task of traffic sign detection. Compared with other models, the improved EfficientDet has the best detection accuracy. Although the test speed is not completely dominant, it still meets the real-time requirement.

Keywords: convolutional neural network, transformer, feature pyramid networks, loss function

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Authors: Jiun Jeon, Byung-Ju Yi

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This paper investigates modeling and shape prediction of elastic kinematic chains such as colonoscopy. 2D and 3D models of elastic kinematic chains are suggested and their behaviors are demonstrated through simulation. To corroborate the effectiveness of those models, experimental work is performed using a magnetic sensor system.

Keywords: elastic kinematic chain, shape prediction, colonoscopy, modeling

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Authors: Agus Masrukhin

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This research aims to find and analyze the model of character building in the Police Headquarters in Bali with a case study of Muslim members in improving the quality of the morality of its members. The formation of patterns of thinking, behavior, mentality, and police officers noble character, later can be used as a solution to reduce the hedonistic nature of the challenges in the era of globalization. The benefit of this study is expected to be a positive recommendation to find a constructive character building models of police officers in the Republic of Indonesia, especially Bali Police. For the long term, the discovery of the character building models can be developed for the entire police force in Indonesia. The type of research that would apply in this study researchers mix the qualitative research methods based on the narrative between the subject and the concrete experience of field research and quantitative research methods with 92 respondents from the police regional police Bali. This research used a descriptive analysis and SWOT analysis then it is presented in the FGD (focus group discussion). The results of this research indicate that the variable modeling the leadership of the police and variable police offices culture have significant influence on the implementation of spiritual development.

Keywords: positive constructive, hedonistic, character models, morality

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Authors: Zhuoneng Li, Zeeshan A. Rana, Karl W. Jenkins

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In industry, to validate a case, often a multitude of simulation are required and in order to demonstrate confidence in the process where users tend to use a coarser mesh. Therefore, it is imperative to establish the coarsest mesh that could be used while keeping reasonable simulation accuracy. To date, the two most reliable, affordable and broadly used advanced simulations are the hybrid RANS (Reynolds Averaged Navier Stokes)/LES (Large Eddy Simulation) and wall modelled LES. The potentials in these two simulations will still be developed in the next decades mainly because the unaffordable computational cost of a DNS (Direct Numerical Simulation). In the wall modelled LES, the turbulence model is applied as a sub-grid scale model in the most inner layer near the wall. The RANS turbulence models cover the entire boundary layer region in a hybrid RANS/LES (Detached Eddy Simulation) and its variants, therefore, the RANS still has a very important role in the state of art simulations. This research focuses on the turbulence model mesh sensitivity analysis where various turbulence models such as the S-A (Spalart-Allmaras), SSG (Speziale-Sarkar-Gatski), K-Omega transitional SST (Shear Stress Transport), K-kl-Omega, γ-Reθ transitional model, v2f are evaluated within the OpenFOAM. The simulations are conducted on a fully developed turbulent flow over a flat plate where the skin friction coefficient as well as velocity profiles are obtained to compare against experimental values and DNS results. A concrete conclusion is made to clarify the mesh sensitivity for different turbulence models.

Keywords: mesh sensitivity, turbulence models, OpenFOAM, RANS

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Authors: Sachin Jain, Daniel Perez-Astudillo, Dunia A. Bachour, Antonio P. Sanfilippo

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The future of solar energy in Qatar is evolving steadily. Hence, high-quality spatial solar radiation data is of the uttermost requirement for any planning and commissioning of solar technology. Generally, two types of solar radiation data are available: satellite data and ground observations. Satellite solar radiation data is developed by the physical and statistical model. Ground data is collected by solar radiation measurement stations. The ground data is of high quality. However, they are limited to distributed point locations with the high cost of installation and maintenance for the ground stations. On the other hand, satellite solar radiation data is continuous and available throughout geographical locations, but they are relatively less accurate than ground data. To utilize the advantage of both data, a product has been developed here which provides spatial continuity and higher accuracy than any of the data alone. The popular satellite databases: National Solar radiation Data Base, NSRDB (PSM V3 model, spatial resolution: 4 km) is chosen here for merging with ground-measured solar radiation measurement in Qatar. The spatial distribution of ground solar radiation measurement stations is comprehensive in Qatar, with a network of 13 ground stations. The monthly average of the daily total Global Horizontal Irradiation (GHI) component from ground and satellite data is used for error analysis. The normalized root means square error (NRMSE) values of 3.31%, 6.53%, and 6.63% for October, November, and December 2019 were observed respectively when comparing in-situ and NSRDB data. The method is based on the Empirical Bayesian Kriging Regression Prediction model available in ArcGIS, ESRI. The workflow of the algorithm is based on the combination of regression and kriging methods. A regression model (OLS, ordinary least square) is fitted between the ground and NSBRD data points. A semi-variogram is fitted into the experimental semi-variogram obtained from the residuals. The kriging residuals obtained after fitting the semi-variogram model were added to NSRBD data predicted values obtained from the regression model to obtain the final predicted values. The NRMSE values obtained after merging are respectively 1.84%, 1.28%, and 1.81% for October, November, and December 2019. One more explanatory variable, that is the ground elevation, has been incorporated in the regression and kriging methods to reduce the error and to provide higher spatial resolution (30 m). The final GHI maps have been created after merging, and NRMSE values of 1.24%, 1.28%, and 1.28% have been observed for October, November, and December 2019, respectively. The proposed merging method has proven as a highly accurate method. An additional method is also proposed here to generate calibrated maps by using regression and kriging model and further to use the calibrated model to generate solar radiation maps from the explanatory variable only when not enough historical ground data is available for long-term analysis. The NRMSE values obtained after the comparison of the calibrated maps with ground data are 5.60% and 5.31% for November and December 2019 month respectively.

Keywords: global horizontal irradiation, GIS, empirical bayesian kriging regression prediction, NSRDB

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Authors: Khadija Mushtaq, Mian Saqib Mehmood

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This paper attempts to determine the impact of leadership style and learning orientation on organizational performance in Pakistan. A sample of 158 middle managers selected from sports and surgical factories from Sialkot. The empirical estimation is based on a multiple linear regression analysis of the relationship between leadership style, learning orientation and organizational performance. Leadership style is measure through transformational leadership and transactional leadership. The transformational leadership has insignificant impact on organizational performance. The transactional leadership has positive and significant relation with organizational performance. Learning orientation also has positive and significant relation with organizational performance. Linear regression used to estimate the relation between dependent and independent variables. This study suggests top manger should prefer continuous process for improvement for any change in system rather radical change.

Keywords: transformational leadership, transactional leadership, learning orientation, organizational performance, Pakistan

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Authors: Caroline Dupre, David Hupin, Christ Goumou, Francois Belan, Frederic Roche, Thomas Celarier, Bienvenu Bongue

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Background: Previous cohorts have been notably criticized for not studying the different type of physical activity and not investigating household activities. The objective of this work was to analyse the relationship between physical activity and cognitive decline in older people living in the community. Impact of type of physical activity on the results has been realised. Methods: The study used data from the longitudinal and observational study , FrèLE (FRagility: Longitudinal Study of Expressions). The collected data included: socio-demographic variables, lifestyle, and health status (frailty, comorbidities, cognitive status, depression). Cognitive decline was assessed by using: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA). Physical activity was assessed by the Physical Activity Scale for the Elderly (PASE). This tool is structured in three sections: the leisure activity, domestic activity, and professional activity. Logistic regressions and proportional hazards regression models (Cox) were used to estimate the risk of cognitive disorders. Results: At baseline, the prevalence of cognitive disorders was 6.9% according to MMSE. In total, 1167 participants without cognitive disorders were included in the analysis. The mean age was 77.4 years, and 52.1% of the participants were women. After a 2 years long follow-up, we found cognitive disorders on 53 participants (4.5%). Physical activity at baseline is lower in older adults for whom cognitive decline was observed after two years of follow-up. Subclass analyses showed that leisure and domestic activities were associated with cognitive decline, but not professional activities. Conclusions: Analysis showed a relationship between cognitive disorders and type of physical activity. The current study will be completed by the MoCA for mild cognitive impairment. These findings compared to other ongoing studies, will contribute to the debate on the beneficial effects of physical activity on cognition.

Keywords: aging, cognitive function, physical activity, mixed models

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Authors: C. A. Bradley, D. Patsios, D. Berridge

Abstract:

This paper presents evidence to better understand the risk and protective factors related to sibling conflict and the patterns of association between sibling conflict and negative adjustment outcomes by incorporating additional familial and societal factors within statistical models of risk and adjustment. It was conducted through the secondary analysis of a large representative cross-sectional dataset of children in the UK. The original study includes proxy interviews for young children and self-report interviews for adolescents. The study applies an ecological systems framework for the analyses. Hierarchical regression models assess risk and protective factors and adjustment outcomes associated with sibling conflict. Interactions reveal differential effect between contextual risk factors and the social context of influence. The general pattern of findings suggested that, although factors affecting likelihood of experiencing sibling conflict were often determined by child age, some remained consistent across childhood. These factors were often conditional on each other, reinforcing the importance of an ecological framework. Across both age-groups, sibling conflict was associated with siblings closer in age; male sibling groups; most advantaged socio-economic group; and exposure to community violence, such as witnessing violent assault or robbery. The study develops the evidence base on the influence of ethnicity and socio-economic group on sibling conflict by exploring interactions between social context. It also identifies key new areas of influence – such as family structure, disability, and community violence in exacerbating or reducing risk of conflict. The study found negative associations between sibling conflict and young children’s mental well-being and adolescents' mental well-being and anti-social behaviour, but also more context specific associations – such as sibling conflict moderating the negative impact of adversity and high risk experiences for young children such as parental violence toward the child.

Keywords: adjustment, conflict, ecological systems, family systems, risk and protective factors, sibling

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Authors: Mohieddine Benghersallah, Mohamed Zakaria Zahaf, Ali Medjber, Idriss Tibakh

Abstract:

The objective of this study is to analyse the effects of cutting parameters on surface roughness in dry face milling using statistical techniques. We studied the effect of the microstructure of AISI 52100 steel on machinability before and after hardening. The machining tests were carried out on a high rigidity vertical milling machine with a 25 mm diameter face milling cutter equipped with micro-grain bicarbide inserts with PVD (Ti, AlN) coating in GC1030 grade. A Taguchi L9 experiment plan is adopted. Analysis of variance (ANOVA) was used to determine the effects of cutting parameters (Vc, fz, ap) on the roughness (Ra) of the machined surface. Regression analysis to assess the machinability of steel presented mathematical models of roughness and the combination of parameters to minimize it. The recorded results show that feed per tooth has the most significant effect on the surface condition for both steel treatment conditions. The best roughnesses were obtained for the hardened AISI 52100 steel.

Keywords: machinability, heat treatment, microstructure, surface roughness, Taguchi method

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Authors: Carl van Walraven, Meltem Tuna

Abstract:

Background: Network meta-analysis (NMA) quantifies the relative efficacy of 3 or more interventions from studies containing a subgroup of interventions. This study applied the analytical approach of NMA to quantify the relative accuracy of prediction models with distinct inclusion criteria that are evaluated on a common population (‘concurrent external validation’). Methods: We simulated binary events in 5000 patients using a known risk function. We biased the risk function and modified its precision by pre-specified amounts to create 15 prediction models with varying accuracy and distinct patient applicability. Prediction model accuracy was measured using the Scaled Brier Score (SBS). Overall prediction model accuracy was measured using fixed-effects methods that accounted for model applicability patterns. Prediction model accuracy was summarized as the Network Relative Model Accuracy (NeRMA) Score which ranges from -∞ through 0 (accuracy of random guessing) to 1 (accuracy of most accurate model in concurrent external validation). Results: The unbiased prediction model had the highest SBS. The NeRMA score correctly ranked all simulated prediction models by the extent of bias from the known risk function. A SAS macro and R-function was created to implement the NeRMA Score. Conclusions: The NeRMA Score makes it possible to quantify the accuracy of binomial prediction models having distinct inclusion criteria in a concurrent external validation.

Keywords: prediction model accuracy, scaled brier score, fixed effects methods, concurrent external validation

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Authors: Praveen S. Muthukumarana, Achala C. Aponso

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A large percentage of global crop harvest is lost due to crop diseases. Timely identification and treatment of crop diseases is difficult in many developing nations due to insufficient trained professionals in the field of agriculture. Many crop diseases can be accurately diagnosed by visual symptoms. In the past decade, deep learning has been successfully utilized in domains such as healthcare but adoption in agriculture for plant disease detection is rare. The literature shows that models trained with popular datasets such as PlantVillage does not generalize well on real world images. This paper attempts to find out how to make plant disease identification models that generalize well with real world images.

Keywords: agriculture, convolutional neural network, deep learning, plant disease classification, plant disease detection, plant disease diagnosis

Procedia PDF Downloads 135

Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

Abstract:

This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

Procedia PDF Downloads 141