Search results for: panel data regression

Authors: Ahmed G. Mahgoub, Dahlia H. Hafez, Mostafa A. Abu Kiefa

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The Cone Penetration Test (CPT) is a common in-situ test which generally investigates a much greater volume of soil more quickly than possible from sampling and laboratory tests. Therefore, it has the potential to realize both cost savings and assessment of soil properties rapidly and continuously. The principle objective of this paper is to demonstrate the feasibility and efficiency of using artificial neural networks (ANNs) to predict the soil angle of internal friction (Φ) and the soil modulus of elasticity (E) from CPT results considering the uncertainties and non-linearities of the soil. In addition, ANNs are used to study the influence of different parameters and recommend which parameters should be included as input parameters to improve the prediction. Neural networks discover relationships in the input data sets through the iterative presentation of the data and intrinsic mapping characteristics of neural topologies. General Regression Neural Network (GRNN) is one of the powerful neural network architectures which is utilized in this study. A large amount of field and experimental data including CPT results, plate load tests, direct shear box, grain size distribution and calculated data of overburden pressure was obtained from a large project in the United Arab Emirates. This data was used for the training and the validation of the neural network. A comparison was made between the obtained results from the ANN's approach, and some common traditional correlations that predict Φ and E from CPT results with respect to the actual results of the collected data. The results show that the ANN is a very powerful tool. Very good agreement was obtained between estimated results from ANN and actual measured results with comparison to other correlations available in the literature. The study recommends some easily available parameters that should be included in the estimation of the soil properties to improve the prediction models. It is shown that the use of friction ration in the estimation of Φ and the use of fines content in the estimation of E considerable improve the prediction models.

Keywords: angle of internal friction, cone penetrating test, general regression neural network, soil modulus of elasticity

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Authors: Yun-Xuan Tang, Pei-Yuan Liu, Kun-Mu Lu, Min-Tsung Tseng, Liang-Kuang Chen, Yuh-Feng Tsai, Ching-Wen Lee, Jay Wu

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Breast cancer is predominant of malignant tumors in females. The increase in the glandular density increases the risk of breast cancer. BI-RADS is a frequently used density indicator in mammography; however, it significantly overestimates the glandularity. Therefore, it is very important to accurately and quantitatively assess the glandularity by mammography. In this study, 20%, 30% and 50% glandularity phantoms were exposed using a mammography machine at 28, 30 and 31 kVp, and 30, 55, 80 and 105 mAs, respectively. The regions of interest (ROIs) were drawn to assess the gray level. The relationship between the glandularity and gray level under various compression thicknesses, kVp, and mAs was established by the multivariable linear regression. A phantom verification was performed with automatic exposure control (AEC). The regression equation was obtained with an R-square value of 0.928. The average gray levels of the verification phantom were 8708, 8660 and 8434 for 0.952, 0.963 and 0.985 g/cm3, respectively. The percent differences of glandularity to the regression equation were 3.24%, 2.75% and 13.7%. We concluded that the proposed method could be clinically applied in mammography to improve the glandularity estimation and further increase the importance of breast cancer screening.

Keywords: mammography, glandularity, gray value, BI-RADS

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Authors: Nguyen Ngoc Que Tran

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Purpose: The purpose of this paper is to investigate the relationship between internal market orientation, external market orientation, employee commitment and job satisfaction. Design/methodology/approach: This study collected data through a survey and utilized simple linear regression and multiple regression analysis to determine if there was any support for the research hypotheses as presented in the previous chapter. Findings: Using data from 256 employees of four leading joint stock banks in Vietnam, the empirical results indicates that employee commitment is positively related with external market orientation, job satisfaction is positively related to employee commitment, and employee commitment and job satisfaction are positively related to external market orientation. However, job satisfaction has no significant positive effect on external market orientation. Theoretical contribution: The primary contribution to marketing theory arising from this study is the integration of job satisfaction, employee commitment, and external market orientation in a single research model. Practical implications: The major contribution to practice is an external market oriented bank has to respond rapidly to the future needs and preferences of its customers. This could result in high levels of commitment to the service process and in doing so provide Vietnamese joint-stock commercial banks with a competitive advantage. The finding is important for the banking service sector in general and the Vietnamese banking industry in particular.

Keywords: employee commitment, job satisfaction and external market orientation, vietnam, bank

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Authors: Trevor C. Brown, David J. Miron

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Gas-solid physical adsorption methods are central to the characterization and optimization of the effective surface area, pore size and porosity for applications such as heterogeneous catalysis, and gas separation and storage. Properties such as adsorption uptake, capacity, equilibrium constants and Gibbs free energy are dependent on the composition and structure of both the gas and the adsorbent. However, challenges remain, in accurately calculating these properties from experimental data. Gas adsorption experiments involve measuring the amounts of gas adsorbed over a range of pressures under isothermal conditions. Various constant-parameter models, such as Langmuir and Brunauer-Emmett-Teller (BET) theories are used to provide information on adsorbate and adsorbent properties from the isotherm data. These models typically do not provide accurate interpretations across the full range of pressures and temperatures. The Langmuir adsorption isotherm is a simple approximation for modelling equilibrium adsorption data and has been effective in estimating surface areas and catalytic rate laws, particularly for high surface area solids. The Langmuir isotherm assumes the systematic filling of identical adsorption sites to a monolayer coverage. The BET model is based on the Langmuir isotherm and allows for the formation of multiple layers. These additional layers do not interact with the first layer and the energetics are equal to the adsorbate as a bulk liquid. This BET method is widely used to measure the specific surface area of materials. Both Langmuir and BET models assume that the affinity of the gas for all adsorption sites are identical and so the calculated adsorbent uptake at the monolayer and equilibrium constant are independent of coverage and pressure. Accurate representations of adsorption data have been achieved by extending the Langmuir and BET models to include pressure-varying uptake capacities and equilibrium constants. These parameters are determined using a novel regression technique called flexible least squares for time-varying linear regression. For isothermal adsorption the adsorption parameters are assumed to vary slowly and smoothly with increasing pressure. The flexible least squares for pressure-varying linear regression (FLS-PVLR) approach assumes two distinct types of discrepancy terms, dynamic and measurement for all parameters in the linear equation used to simulate the data. Dynamic terms account for pressure variation in successive parameter vectors, and measurement terms account for differences between observed and theoretically predicted outcomes via linear regression. The resultant pressure-varying parameters are optimized by minimizing both dynamic and measurement residual squared errors. Validation of this methodology has been achieved by simulating adsorption data for n-butane and isobutane on activated carbon at 298 K, 323 K and 348 K and for nitrogen on mesoporous alumina at 77 K with pressure-varying Langmuir and BET adsorption parameters (equilibrium constants and uptake capacities). This modeling provides information on the adsorbent (accessible surface area and micropore volume), adsorbate (molecular areas and volumes) and thermodynamic (Gibbs free energies) variations of the adsorption sites.

Keywords: Langmuir adsorption isotherm, BET adsorption isotherm, pressure-varying adsorption parameters, adsorbate and adsorbent properties and energetics

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Authors: Svetlana Zhikrivetskaya, Nataliya Shirokova, Roman Bikanov, Elizaveta Musatova, Yana Kovaleva, Nataliya Vetrova, Ekaterina Pomerantseva

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Nowadays there is a growing number of couples with conceiving problems due to male or female infertility. Genetic abnormalities are responsible for about 31% of all cases of male infertility. These abnormalities include both chromosomal aberrations or aneuploidies and mutations in certain genes. Chromosomal abnormalities can be easily identified, thus the development of screening panels able to reveal genetic reasons of male infertility on gene level is of current interest. There are approximately 2,000 genes involved in male fertility that is the reason why it is very important to determine the most clinically relevant in certain population and ethnic conditions. An infertility screening panel containing 48 mutations in genes AMHR2, CFTR, DNAI1, HFE, KAL1, TSSK2 and AZF locus which are the most clinically relevant for the European population according to databases NCBI and ClinVar was designed. The aim of this research was to confirm clinic relevance of these mutations in the Russian population. Genotyping was performed in 220 patients with different types of male infertility and in 57 healthy males with normozoospermia. Mutations were identified by end-point PCR with TaqMan probes in microfluidic plates. The frequency of 5 mutations in healthy males and 13 mutations in patients with infertility was revealed and estimated. The frequency of mutation c.187C>G in HFE gene was significantly lower for healthy males (8.8%) compared with patients (17.7%) and the values for the European population according to ExAc database (13.7%) and dbSNP (17.2%). Analysis of c.3454G>C, and c.1545_1546delTA mutations in the CFTR gene revealed increased frequency (0.9 and 0.2%, respectively) in patients with infertility compared with data for the European population (0.04%, respectively (ExAc, European (Non-Finnish) and for the Aggregated Populations (0.002% (ExAc), because there is no data for European population for c.1545_1546delTA mutation. The frequency of del508 mutation (CFTR) in patients (1.59%) were lower comparing with male infertility Europeans (3.34-6.25% depending on nationality) and at the same level with healthy Europeans (1.06%, ExAc, European (Non-Finnish). Analysis of c.845G>A (HFE) mutation resulted in decreased frequency in patients (1.8%) in contrast with the European population data (5.1%, respectively, ExAc, European (Non-Finnish). Moreover, obtained data revealed no statistically significant frequency difference for c.845G>A mutation (HFE) between healthy males in the Russian and the European populations. Allele frequencies of mutations c.350G>A (CFTR), c.193A>T (HFE), c.774C>T, and c.80A>G (gene TSSK2) showed no significantly difference among patients with infertility, healthy males and Europeans. Analysis of AZF locus revealed increased frequency for AZFc microdeletion in patients with male infertility. Thereby, the new data of the allele frequencies in infertility patients in the Russian population was obtained. As well as the frequency differences of mutations associated with male infertility among patients, healthy males in the Russian population and the European one were estimated. The revealed differences showed that for high effectiveness of screening panel detecting genetically caused male infertility it is very important to consider ethnic and population characteristics of patients which will be screened.

Keywords: allele frequency, azoospermia, male infertility, mutation, population

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Authors: Po-Yuan Tung, Jen-Chueh Kuo, Chia-Ray Chen, Chien-Hsing Li, Kuo-Liang Pan

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The main-body structure of satellite is mainly constructed by lightweight material, it should be able to withstand certain vibration load during launches. Since various kinds of change possibility in the space, it is an extremely important work to study the random vibration response of satellite structure. This paper based on the reciprocity relationship between sound and structure response and it will try to evaluate the dynamic response of satellite main body under random acoustic load excitation. This paper will study the technical process and verify the feasibility of sonic-borne vibration analysis. One simple plate exposed to the uniform acoustic field is utilized to take some important parameters and to validate the acoustics field model of the reverberation chamber. Then import both structure and acoustic field chamber models into the vibro-acoustic coupling analysis software to predict the structure response. During the modeling process, experiment verification is performed to make sure the quality of numerical models. Finally, the surface vibration level can be calculated through the modal participation factor, and the analysis results are presented in PSD spectrum.

Keywords: vibration, acoustic, modal, honeycomb panel

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Authors: Wenbo Du, Xiaomei Ma

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With the empowerment of Cognitive Diagnostic Assessment (CDA), various domains of language testing and assessment have been investigated to dig out more diagnostic information. What is noticeable is that most of the extant empirical CDA-based research puts much emphasis on individual-level diagnostic purpose with very few concerned about learners’ group-level performance. Even though the personalized diagnostic feedback is the unique feature that differentiates CDA from other assessment tools, group-level diagnostic information cannot be overlooked in that it might be more practical in classroom setting. Additionally, the group-level diagnostic information obtained via current CDA always results in a “flat pattern”, that is, the mastery/non-mastery of all tested skills accounts for the two highest proportion. In that case, the outcome does not bring too much benefits than the original total score. To address these issues, the present study attempts to apply cluster analysis for group classification and quantile regression analysis to pinpoint learners’ performance at different proficiency levels (beginner, intermediate and advanced) thus to enhance the interpretation of the CDA results extracted from a group of EFL learners’ reading performance on a diagnostic reading test designed by PELDiaG research team from a key university in China. The results show that EM method in cluster analysis yield more appropriate classification results than that of CDA, and quantile regression analysis does picture more insightful characteristics of learners with different reading proficiencies. The findings are helpful and practical for instructors to refine EFL reading curriculum and instructional plan tailored based on the group classification results and quantile regression analysis. Meanwhile, these innovative statistical methods could also make up the deficiencies of CDA and push forward the development of language testing and assessment in the future.

Keywords: cognitive diagnostic assessment, diagnostic feedback, EFL reading, quantile regression

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Authors: I. L. Kim, J. Y. Lee, A. K. Tekile

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In consequence of the very high urbanization rate of Korea, the number of traffic noise damages in areas congested with population and facilities is steadily increasing. The current environmental noise levels data in major cities of the country show that the noise levels exceed the standards set for both day and night times. This research was about comparative analysis in search for optimal soundproof panel shape and design factor that can minimize sound reflection noise. In addition to the normal flat-type panel shape, the reflection noise reduction of swelling-type, combined swelling and curved-type, and screen-type were evaluated. The noise source model Nord 2000, which often provides abundant information compared to models for the similar purpose, was used in the study to determine the overall noise level. Based on vehicle categorization in Korea, the noise levels for varying frequency from different heights of the sound source (directivity heights of Harmonize model) have been calculated for simulation. Each simulation has been made using the ray-tracing method. The noise level has also been calculated using the noise prediction program called SoundPlan 7.2, for comparison. The noise level prediction was made at 15m (R1), 30 m (R2) and at middle of the road, 2m (R3) receiving the point. By designing the noise barriers by shape and running the prediction program by inserting the noise source on the 2nd lane to the noise barrier side, among the 6 lanes considered, the reflection noise slightly decreased or increased in all noise barriers. At R1, especially in the cases of the screen-type noise barriers, there was no reduction effect predicted in all conditions. However, the swelling-type showed a decrease of 0.7~1.2 dB at R1, performing the best reduction effect among the tested noise barriers. Compared to other forms of noise barriers, the swelling-type was thought to be the most suitable for reducing the reflection noise; however, since a slight increase was predicted at R2, further research based on a more sophisticated categorization of related design factors is necessary. Moreover, as swellings are difficult to produce and the size of the modules are smaller than other panels, it is challenging to install swelling-type noise barriers. If these problems are solved, its applicable region will not be limited to other types of noise barriers. Hence, when a swelling-type noise barrier is installed at a downtown region where the amount of traffic is increasing every day, it will both secure visibility through the transparent walls and diminish any noise pollution due to the reflection. Moreover, when decorated with shapes and design, noise barriers will achieve a visual attraction than a flat-type one and thus will alleviate any psychological hardships related to noise, other than the unique physical soundproofing functions of the soundproof panels.

Keywords: reflection noise, shaped noise barriers, sound proof panel, traffic noise

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Authors: Mehdi Mirzaei Alavijeh, Farzad Jalilian

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Background: Marijuana is the most widely used illicit drug worldwide, especially among adolescents and young adults, which can cause numerous complications. The aim of this study was to determine the pattern, motivation use, and factors related to marijuana use among Iranian youths based on the integrative model of behavioral prediction Methods: A cross-sectional study was conducted among 174 youths marijuana user in Kermanshah County and Isfahan County, during summer 2014 which was selected with the convenience sampling for participation in this study. A self-reporting questionnaire was applied for collecting data. Data were analyzed by SPSS version 21 using bivariate correlations and linear regression statistical tests. Results: The mean marijuana use of respondents was 4.60 times at during week [95% CI: 4.06, 5.15]. Linear regression statistical showed, the structures of integrative model of behavioral prediction accounted for 36% of the variation in the outcome measure of the marijuana use at during week (R2 = 36% & P < 0.001); and among them attitude, marijuana refuse, and subjective norms were a stronger predictors. Conclusion: Comprehensive health education and prevention programs need to emphasize on cognitive factors that predict youth’s health-related behaviors. Based on our findings it seems, designing educational and behavioral intervention for reducing positive belief about marijuana, marijuana self-efficacy refuse promotion and reduce subjective norms encourage marijuana use has an effective potential to protect youths marijuana use.

Keywords: marijuana, youth, integrative model of behavioral prediction, Iran

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Authors: J. Jayasuriya

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In this research paper, a procedure for installing and assessment of a solar PV plant to generate optimal solar energy SPAV hostel at Vijayawada city was analyzed. The hostel was experiencing power disruption and had a need for an unceasing energy source. The solar panel is one of the best solutions to obtain uninterrupted clean renewable energy for an institutional building as it neither makes din nor pollutes the atmosphere. The electricity usage per month was initially measured to discriminate the energy change. The solar array was installed with its financial and environmental assessment considering recent market prices. All the aspects related to a solar PV plant were considered for the feasibility and efficiency of PV plant near this site i.e., the orientation of the site, the size and shape of the terrace, the sun path were considered while installing panels. Various precautions were taken to intercept the factors which cause interference in energy generation, with respect to temperature, overshadowing, the wiring of panels, pollution etc. The solar panels were frequently installed, monitored and maintained properly to procure optimal energy output. Result obtained with the assessment of the proposed plant and deflation in the electric bill will show the maximal energy that can be generated in a month on that particular site.

Keywords: solar efficiency, building sustainability, PV panel, solar energy

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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Authors: Ghada Soltane

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The objective of this study was to identify factors impacting supply chain collaboration. a quantitative study was carried out on a sample of 84 Tunisian industrial companies. To verify the research hypotheses and test the direct effect of these factors on supply chain collaboration a multiple regression method was used using SPSS 26 software. The results show that there are four factors direct effects that affect supply chain collaboration in a meaningful and positive way, including: trust, engagement, information sharing and information quality

Keywords: supply chain collaboration, factors of collaboration, principal component analysis, multiple regression

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Authors: Mehravar Javid, Laura Reid Harris, Zahra Khodadadi, Rachel Walton

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The study aimed to explore commitment precedence through self-expansion among students at the Medical Science University of Shiraz, Iran. Method: The statistical population was comprised of students at Shiraz University of Medical Science during the academic years 2013 to 2014. Using random sampling, 133 married students (50 males and 83 females) were selected. The commitment condition of this studied group was assessed using Adam and Jones' (1999) Marital Commitment Dimensions Scale (DCI), and self-expansion was measured using Aron and Lewandowski's (2002) Self-Expansion Questionnaire. Simple regression analyses investigated commitment precedence via self-expansion. Results: The data revealed a positive correlation between total commitment (r=0.35, p < 0.01), the subscales of commitment to the spouse (r=0.43, p < 0.01), and commitment to marriage (r=0.31, p < 0.01). Regression analyses indicated that perceived self-expansion positively correlated with commitment to marital relationships in married students. The findings suggest that an increased possibility of self-expansion in a marital relationship corresponds with heightened commitment.

Keywords: commitment to marital relationship, married students, relationship dynamics, self-expansion

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Authors: Mohsen Sheikholeslami, Zahra Khalili, Ladan Momayez

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Impacts of environmental parameters and dust deposition on the efficiency of solar panel have been scrutinized in this article. To gain thermal output, trapezoidal cooling channel has been attached in the bottom of the panel incorporating ternary nanofluid. To produce working fluid, water has been mixed with Fe₃O₄-TiO₂-GO nanoparticles. Also, the arrangement of fins has been considered to grow the cooling rate of the silicon layer. The existence of a thermoelectric layer above the cooling channel leads to higher electrical output. Efficacy of ambient temperature (Ta), speed of wind (V𝓌ᵢₙ𝒹) and inlet temperature (Tᵢₙ) and velocity (Vin) of ternary nanofluid on performance of PVT has been assessed. As Tin increases, electrical efficiency declines about 3.63%. Increase of ambient temperature makes thermal performance enhance about 33.46%. The PVT efficiency decreases about 13.14% and 16.6% with augment of wind speed and dust deposition. CO₂ mitigation has been reduced about 15.49% in presence of dust while it increases about 17.38% with growth of ambient temperature.

Keywords: photovoltaic system, CO₂ mitigation, ternary nanofluid, thermoelectric generator, environmental parameters, trapezoidal cooling channel

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Authors: Mansour Mohammadpour

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Data transformation refers to the modification of any point in a data set by a mathematical function. When applying transformations, the measurement scale of the data is modified. Data transformations are commonly employed to turn data into the appropriate form, which can serve various functions in the quantitative analysis of the data. This study addresses the investigation of the use of data transformations in Data Envelopment Analysis (DEA). Although data transformations are important options for analysis, they do fundamentally alter the nature of the variable, making the interpretation of the results somewhat more complex.

Keywords: data transformation, data envelopment analysis, undesirable data, negative data

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Authors: Katarzyna Wyka, Dana Sylvan, JoAnn Difede

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Background and significance: The following disaster, population-based screening programs are routinely established to assess physical and psychological consequences of exposure. These data sets are highly skewed as only a small percentage of trauma-exposed individuals develop health issues. Commonly used statistical methodology in post-disaster mental health generally involves population-averaged models. Such models aim to capture the overall response to the disaster and its aftermath; however, they may not be sensitive enough to accommodate population heterogeneity in symptomatology, such as post-traumatic stress or depressive symptoms. Methods: We use an archival longitudinal data set from Weill-Cornell 9/11 Mental Health Screening Program established following the World Trade Center (WTC) terrorist attacks in New York in 2001. Participants are rescue and recovery workers who participated in the site cleanup and restoration (n=2960). The main outcome is the post-traumatic stress symptoms (PTSD) severity score assessed via clinician interviews (CAPS). For a detailed understanding of response to the disaster and its aftermath, we are adapting quantile regression methodology with particular focus on predictors of extreme distress and resilience to trauma. Results: The response variable was defined as the quantile of the CAPS score for each individual under two different scenarios specifying the unconditional quantiles based on: 1) clinically meaningful CAPS cutoff values and 2) CAPS distribution in the population. We present graphical summaries of the differential effects. For instance, we found that the effect of the WTC exposures, namely seeing bodies and feeling that life was in danger during rescue/recovery work was associated with very high PTSD symptoms. A similar effect was apparent in individuals with prior psychiatric history. Differential effects were also present for age and education level of the individuals. Conclusion: We evaluate the utility of quantile regression in disaster research in contrast to the commonly used population-averaged models. We focused on assessing the distribution of risk factors for post-traumatic stress symptoms across quantiles. This innovative approach provides a comprehensive understanding of the relationship between dependent and independent variables and could be used for developing tailored training programs and response plans for different vulnerability groups.

Keywords: disaster workers, post traumatic stress, PTSD, quantile regression

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Authors: Khandoker Akib Mohammad

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While analyzing time-to-event data, it is possible that a certain fraction of subjects will never experience the event of interest, and they are said to be cured. When this feature of survival models is taken into account, the models are commonly referred to as cure models. In the presence of covariates, the conditional survival function of the population can be modelled by using the cure model, which depends on the probability of being uncured (incidence) and the conditional survival function of the uncured subjects (latency), and a combination of logistic regression and Cox proportional hazards (PH) regression is used to model the incidence and latency respectively. In this paper, we have shown the asymptotic normality of the profile likelihood estimator via asymptotic expansion of the profile likelihood and obtain the explicit form of the variance estimator with an implicit function in the profile likelihood. We have also shown the efficient score function based on projection theory and the profile likelihood score function are equal. Our contribution in this paper is that we have expressed the efficient information matrix as the variance of the profile likelihood score function. A simulation study suggests that the estimated standard errors from bootstrap samples (SMCURE package) and the profile likelihood score function (our approach) are providing similar and comparable results. The numerical result of our proposed method is also shown by using the melanoma data from SMCURE R-package, and we compare the results with the output obtained from the SMCURE package.

Keywords: Cox PH model, cure model, efficient score function, EM algorithm, implicit function, profile likelihood

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Authors: Qiuling Xie, Shanliang Zhu, Zongdi Sun

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In this paper, we analyzed the correlation relationship among PM2.5 from other five Air Quality Indices (AQIs) based on the grey relational degree, and built a multivariate nonlinear regression equation model of PM2.5 and the five monitoring indexes. For the optimal control problem of PM2.5, we took the partial large Cauchy distribution of membership equation as satisfaction function. We established a nonlinear programming model with the goal of maximum performance to price ratio. And the optimal control scheme is given.

Keywords: grey relational degree, multiple linear regression, membership function, nonlinear programming

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Authors: Jiahao Tian, Michael D. Porter

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Law enforcement agencies are tasked with crime prevention and crime reduction under limited resources. Having an accurate temporal estimate of the crime rate would be valuable to achieve such a goal. However, estimation is usually complicated by the interval-censored nature of crime data. We cast the problem of intensity estimation as a Poisson regression using an EM algorithm to estimate the parameters. Two special penalties are added that provide smoothness over the time of day and day of the week. This approach presented here provides accurate intensity estimates and can also uncover day-of-week clusters that share the same intensity patterns. Anticipating where and when crimes might occur is a key element to successful policing strategies. However, this task is complicated by the presence of interval-censored data. The censored data refers to the type of data that the event time is only known to lie within an interval instead of being observed exactly. This type of data is prevailing in the field of criminology because of the absence of victims for certain types of crime. Despite its importance, the research in temporal analysis of crime has lagged behind the spatial component. Inspired by the success of solving crime-related problems with a statistical approach, we propose a statistical model for the temporal intensity estimation of crime with censored data. The model is built on Poisson regression and has special penalty terms added to the likelihood. An EM algorithm was derived to obtain maximum likelihood estimates, and the resulting model shows superior performance to the competing model. Our research is in line with the smart policing initiative (SPI) proposed by the Bureau Justice of Assistance (BJA) as an effort to support law enforcement agencies in building evidence-based, data-driven law enforcement tactics. The goal is to identify strategic approaches that are effective in crime prevention and reduction. In our case, we allow agencies to deploy their resources for a relatively short period of time to achieve the maximum level of crime reduction. By analyzing a particular area within cities where data are available, our proposed approach could not only provide an accurate estimate of intensities for the time unit considered but a time-variation crime incidence pattern. Both will be helpful in the allocation of limited resources by either improving the existing patrol plan with the understanding of the discovery of the day of week cluster or supporting extra resources available.

Keywords: cluster detection, EM algorithm, interval censoring, intensity estimation

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Authors: Muhammad Tahir

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Purpose The purpose of the study is to find anchoring bias behavior in mutual fund return with its parent firm performance in Pakistan. Research Methodology The paper used monthly returns of equity funds whose parent firm exist from 2011 to 2021, along with parent firm return. Proximity to 52-week highest return calculated by dividing fund return by parent firm 52-week highest return. Control variables are also taken and used pannel regression model to estimate our results. For robust results, we also used feasible generalize least square (FGLS) model. Findings The results showed that there exist anchoring biased in mutual fund return with its parent firm performance. The FGLS results reaffirms the same results as obtained from panner regression results. Proximity to 52-week highest Xc is significant in both models. Research Implication Since most of mutual funds has a parent firm, anchoring behavior biased found in mutual fund with its parent firm performance. Practical Implication Mutual fund investors in Pakistan invest in equity funds in which behavioral bias exist, although there might be better opportunity in market. Originality/Value Addition Our research is a pioneer study to investigate anchoring bias in mutual fund return with its parent firm performance. Research limitations Our sample is limited to only 23 equity funds, which has a parent firm and data was available from 2011 to 2021.

Keywords: mutual fund, anchoring bias, 52-week high return, proximity to 52-week high, parent firm performance, pannel regression, FGLS

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Authors: Wissam Aljundi, Loay Daas, Yaser Abu Dail, Barbara Käsmann-Kellner, Berthold Seitz, Alaa Din Abdin

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Purpose: To investigate the effectiveness of nonsteroidal anti-inflammatory eye drops (NSAIDs) combined with oral acetazolamide for postoperative macular edema (PME) after uncomplicated phacoemulsification (PE) and to identify predictors of non-response. Methods: We analyzed data of uncomplicated PE and identified eyes with PME. First-line therapy included topical NSAIDs combined with oral acetazolamide. In case of non-response, triamcinolone was administered subtenonally. Outcome measures included best-corrected visual acuity (BCVA) and central macular thickness (CMT). Results: 94 eyes out of 9750 uncomplicated PE developed PME, of which 60 eyes were included. Follow-ups occurred 6.4±1.8, 12.5±3.7, and 18.6±6.0 weeks after diagnosis. BCVA and CMT improved significantly in all follow-ups. 40 eyes showed response to first-line therapy at first follow-up (G1). The remaining 20 eyes showed no response and required subtenon triamcinolone (G2), of which 11 eyes showed complete regression at the second follow-up and 4 eyes at the third follow-up. 5 eyes showed no response and required intravitreal injection. Multivariate linear regression model showed that diabetes mellitus (DM) and increased cumulative dissipated energy (CDE) are predictors of non-response. Conclusion: Topical NSAIDs with acetazolamide resulted in complete regression of PME in 67% of all cases. DM and increased CDE might be considered as predictors of nonresponse to this treatment.

Keywords: postoperative macular edema, intravitreal injection, cumulative energy, irvine gass syndrome, pseudophakie

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Authors: Elnaz Bandeh

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This study aimed to predict marital burnout based on irrational beliefs and sexual dysfunction of couples. The research method was descriptive-correlational, and the statistical population included all couples who consulted to counseling clinics in the fall of 2016. The sample consisted of 200 people who were selected by convenience sampling and answered the Ahwaz Irrational Beliefs Questionnaire, Pines Couple Burnout, and Hudson Marital Satisfaction Questionnaire. The data were analyzed using regression coefficient. The results of regression analysis showed that there was a linear relationship between irrational beliefs and couple burnout and dimensions of helplessness toward change, expectation of approval from others, and emotional irresponsibility were positive and significant predictors of couple burnout. However, after avoiding the problem of power, it was not a significant predictor of marital dissatisfaction. There was also a linear relationship between sexual dysfunction and couple burnout, and sexual dysfunction was a positive and significant predictor of couple burnout. Based on the findings, it can be concluded that irrational beliefs and sexual dysfunction play a role in couple dysfunction.

Keywords: couple burnout, irrational beliefs, sexual dysfunction, marital relationship

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Authors: Sergio Haram Sarmiento, Arcady Ponosov

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Based on appropriate multivariate statistical methodology, we suggest a generic framework for efficient parameter estimation for ordinary differential equations and the corresponding nonlinear models. In this framework classical linear regression strategies is refined into a nonlinear regression by a locally linear modelling technique (known as metamodelling). The approach identifies those latent variables of the given model that accumulate most information about it among all approximations of the same dimension. The method is applied to several benchmark problems, in particular, to the so-called ”power-law systems”, being non-linear differential equations typically used in Biochemical System Theory.

Keywords: principal component analysis, generalized law of mass action, parameter estimation, metamodels

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Authors: Syed Afrozuddin Ahmed, Junaid Saghir Siddiqi, Sabah Quaiser

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Various studies have reported that global warming causes unstable climate and many serious impact to physical environment and public health. The increasing incidence of dengue incidence is now a priority health issue and become a health burden of Pakistan. In this study it has been investigated that spatial pattern of environment causes the emergence or increasing rate of dengue fever incidence that effects the population and its health. The climatic or environmental structure data and the Dengue Fever (DF) data was processed by coding, editing, tabulating, recoding, restructuring in terms of re-tabulating was carried out, and finally applying different statistical methods, techniques, and procedures for the evaluation. Five climatic variables which we have studied are precipitation (P), Maximum temperature (Mx), Minimum temperature (Mn), Humidity (H) and Wind speed (W) collected from 1980-2012. The dengue cases in Karachi from 2010 to 2012 are reported on weekly basis. Principal component analysis is applied to explore the climatic variables and/or the climatic (structure) which may influence in the increase or decrease in the number of dengue fever cases in Karachi. PC1 for all the period is General atmospheric condition. PC2 for dengue period is contrast between precipitation and wind speed. PC3 is the weighted difference between maximum temperature and wind speed. PC4 for dengue period contrast between maximum and wind speed. Negative binomial and Poisson regression model are used to correlate the dengue fever incidence to climatic variable and principal component score. Relative humidity is estimated to positively influence on the chances of dengue occurrence by 1.71% times. Maximum temperature positively influence on the chances dengue occurrence by 19.48% times. Minimum temperature affects positively on the chances of dengue occurrence by 11.51% times. Wind speed is effecting negatively on the weekly occurrence of dengue fever by 7.41% times.

Keywords: principal component analysis, dengue fever, negative binomial regression model, poisson regression model

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Authors: Engy Eissa, Dina M. Yousri

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COVID-19 has hit massively the world economy, financial markets and even societies’ livelihood. The infectious disease caused by the most recently discovered coronavirus was claimed responsible for a shrink in the global economy by 4.4% in 2020. Shortly after the first case in Wuhan was identified, a quick surge in the number of confirmed cases in China was evident and a vast spread worldwide is recorded with cases surpassing the 500,000 cases. Irrespective of the disease’s trajectory in each country, a call for immediate action and prompt government intervention was needed. Given that there is no one-size-fits-all approach across the world, a number of containment and adoption policies were embraced. It was starting by enforcing complete lockdown like China to even stricter policies targeted containing the spread of the virus, augmenting the efficiency of health systems, and controlling the economic outcomes arising from this crisis. Hence, this paper has three folds; first, it examines the impact of containment policies taken by governments on controlling the number of cases and deaths in the given countries. Second, to assess the ramifications of COVID-19 on financial markets measured by stock returns. Third, to study the impact of containment policies measured by the government response index, the stringency index, the containment health index, and the economic support index on financial markets performance. Using a sample of daily data covering the period 31st of January 2020 to 15th of April 2021 for the 10 most hit countries in wave one by COVID-19 namely; Brazil, India, Turkey, Russia, UK, USA, France, Germany, Spain, and Italy. The aforementioned relationships were tested using Panel VAR Regression. The preliminary results showed that the number of daily deaths had an impact on the stock returns; moreover, the health containment policies and the economic support provided by the governments had a significant effect on lowering the impact of COVID-19 on stock returns.

Keywords: COVID-19, government policies, stock returns, VAR

Procedia PDF Downloads 181

Authors: Abdulrahman Sumayli, Saad M. AlShahrani

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For the hydrogenation process, knowing the solubility of hydrogen (H2) in hydrocarbons is critical to improve the efficiency of the process. We investigated the H2 solubility computation in four heavy crude oil feedstocks using machine learning techniques. Temperature, pressure, and feedstock type were considered as the inputs to the models, while the hydrogen solubility was the sole response. Specifically, we employed three different models: Support Vector Regression (SVR), Gaussian process regression (GPR), and Bayesian ridge regression (BRR). To achieve the best performance, the hyper-parameters of these models are optimized using the whale optimization algorithm (WOA). We evaluated the models using a dataset of solubility measurements in various feedstocks, and we compared their performance based on several metrics. Our results show that the WOA-SVR model tuned with WOA achieves the best performance overall, with an RMSE of 1.38 × 10− 2 and an R-squared of 0.991. These findings suggest that machine learning techniques can provide accurate predictions of hydrogen solubility in different feedstocks, which could be useful in the development of hydrogen-related technologies. Besides, the solubility of hydrogen in the four heavy oil fractions is estimated in different ranges of temperatures and pressures of 150 ◦C–350 ◦C and 1.2 MPa–10.8 MPa, respectively

Keywords: temperature, pressure variations, machine learning, oil treatment

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Authors: Triveni Gogoi, Rima Chatterjee

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Shear wave velocity (Vs) estimation is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. There are varying methods for prediction of S-wave velocity, if recorded S-wave log is not available. But all the available methods for Vs prediction are empirical mathematical models. Shear wave velocity can be estimated using P-wave velocity by applying Castagna’s equation, which is the most common approach. The constants used in Castagna’s equation vary for different lithologies and geological set-ups. In this study, multiple regression analysis has been used for estimation of S-wave velocity. The EMERGE module from Hampson-Russel software has been used here for generation of S-wave log. Both single attribute and multi attributes analysis have been carried out for generation of synthetic S-wave log in Upper Assam basin. Upper Assam basin situated in North Eastern India is one of the most important petroleum provinces of India. The present study was carried out using four wells of the study area. Out of these wells, S-wave velocity was available for three wells. The main objective of the present study is a prediction of shear wave velocities for wells where S-wave velocity information is not available. The three wells having S-wave velocity were first used to test the reliability of the method and the generated S-wave log was compared with actual S-wave log. Single attribute analysis has been carried out for these three wells within the depth range 1700-2100m, which corresponds to Barail group of Oligocene age. The Barail Group is the main target zone in this study, which is the primary producing reservoir of the basin. A system generated list of attributes with varying degrees of correlation appeared and the attribute with the highest correlation was concerned for the single attribute analysis. Crossplot between the attributes shows the variation of points from line of best fit. The final result of the analysis was compared with the available S-wave log, which shows a good visual fit with a correlation of 72%. Next multi-attribute analysis has been carried out for the same data using all the wells within the same analysis window. A high correlation of 85% has been observed between the output log from the analysis and the recorded S-wave. The almost perfect fit between the synthetic S-wave and the recorded S-wave log validates the reliability of the method. For further authentication, the generated S-wave data from the wells have been tied to the seismic and correlated them. Synthetic share wave log has been generated for the well M2 where S-wave is not available and it shows a good correlation with the seismic. Neutron porosity, density, AI and P-wave velocity are proved to be the most significant variables in this statistical method for S-wave generation. Multilinear regression method thus can be considered as a reliable technique for generation of shear wave velocity log in this study.

Keywords: Castagna's equation, multi linear regression, multi attribute analysis, shear wave logs

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Authors: Teja Joshi

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Laterality has shown to play a role in performance as well as injuries especially in unilateral sports disciplines. Uniquely, Artistic Gymnastics involves combination of unilateral, bilateral and complex multi-planer elements as well as gymnastics specific rotational preference. Therefore, this study was conducted to explore if any such preferences are associated with number of injuries in artistic gymnasts. To explore the association between lateral preferences, rotational preferences and injuries incidence in artistic gymnastics. Artistic gymnasts above 16 years of age, were invited to participate in an online survey. The survey included consent, lateral preference inventory, injury data collection according to anatomical locations and rotational preference for selected gymnastics elements performed on the floor exercise. SPSS version 24 was used to analyse Non-parametric data using Kruskal-Wallis (K- independent test) test. Multiple regression was performed to identify the predictor for injuries and their side in gymnasts. Total number of injuries per gymnast was associated with handedness (p value-0.049) and no significant association was noted for footdness (p value-0.207), eyedness (p value-0.491) and eardness (p value-0.798). Additionally, rotational preferences did not influence number of injuries (p value-0.521). In multiple regression, eyedness was identified as a predicting factor to determine the number of injuries. Rotational preferences were neither determined as a national strategy nor a product of lateral preference. Dominant hand had higher number of injuries in artistic gymnasts. Rotational preference is independent of laterality, number of injuries and nationality.

Keywords: sports injury, rotational preference, gymnastics, handedness

Procedia PDF Downloads 118

Authors: Terefe Alemu

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This study is intended to examine Ethiopia’s trade flow determinants and trade potential with G20 countries whether it was overtraded or there is/are trade potential by using trade gravity model. The sources of panel data used were IMF, WDI, United Nations population division, The Heritage Foundation, Washington's No. 1 think tank online website database, online distance calculator, and others for the duration of 2010 to 2019 for 10 consecutive years. The empirical data analyzing tool used was Random effect model (REM), which is effective in estimation of time-invariant data. The empirical data analyzed using STATA software result indicates that Ethiopia has a trade potential with seven countries of G20, whereas Ethiopia overtrade with 12 countries and EU region. The Ethiopia’s and G20 countries/region bilateral trade flow statistically significant/ p<0.05/determinants were the population of G20 countries, growth domestic products of G20 countries, growth domestic products of Ethiopia, geographical distance between Ethiopia and G20 countries. The top five G20 countries exported to Ethiopia were china, United State of America, European Union, India, and South Africa, whereas the top five G20 countries imported from Ethiopia were EU, China, United State of America, Saudi Arabia, and Germany, respectively. Finally, the policy implication were Ethiopia has to Keep the consistence of trade flow with overtraded countries and improve with under traded countries through trade policy revision, and secondly, focusing on the trade determinants to improve trade flow is recommended.

Keywords: trade gravity model, trade determinants, G20, international trade, trade potential

Procedia PDF Downloads 213

Authors: Yi-Ling Lin, Lu-Ming Tseng

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Customer sexual harassment is recognized as a serious problem in the personal selling industry. At a personal level, customer sexual harassment could have very negative impacts on the salespeople's physical and mental health. At the organizational level, customer sexual harassment is destructive in terms of organizational reputation. Therefore, this research takes Taiwan's life insurance salesperson as the research sample and explores the impacts of customer power and perceived behavioral control on the life insurance salespeople's whistleblowing intentions to report quid pro quo and hostile work environment types of customer sexual harassment. This study then investigates how personal factors (such as gender difference) may relate to the intentions. Questionnaires are often used as a data collection instrument in studies on workplace sexual harassment. This study collects data through questionnaire surveys, and the research sample of this research is the full-time life insurance salespeople in Taiwan. The hypotheses are examined by using PLS regression approach. The main results show that the types of customer sexual harassment, customer power, and gender are related to the whistleblowing intentions. To our best knowledge, this is the first empirical study to test the relationships among customer reward power, customer coercive power, perceived behavioral control, and the salespeople's whistleblowing intentions toward customer sexual harassment. The findings may provide some implications for the researchers and official authorities.

Keywords: customer sexual harassment, life insurance salespeople, perceived behavioral control, PLS regression

Procedia PDF Downloads 128