Search results for: mixed effect logistic regression model

Authors: Meng-Huan Wu, Wei-Er Wang, Tsu-Nai Wang, Wei-Jian Hsu, Vincent Chin-Hung Chen

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Objective: Studies have reported a strong link between asthma and bipolar disorder. We conducted a 17-year community-based large cohort study to examine the relationship between asthma, early smoking initiation, and bipolar disorder during adolescence and early adulthood. Methods: A total of 162,766 participants aged 11–16 years were categorized into asthma and non-asthma groups at baseline and compared within the observation period. Covariates during late childhood or adolescence included parental education, cigarette smoking by family members of participants, and participant’s gender, age, alcohol consumption, smoking, and exercise habits. Data for urbanicity, prednisone use, allergic comorbidity, and Charlson comorbidity index were acquired from the National Health Insurance Research Database. The Cox proportional-hazards model was used to evaluate the association between asthma and bipolar disorder. Results: Our findings revealed that asthma increased the risk of bipolar disorder after adjustment for key confounders in the Cox proportional hazard regression model (adjusted HR: 1.31, 95% CI: 1.12-1.53). Hospitalizations or visits to the emergency department for asthma exhibited a dose–response effect on bipolar disorder (adjusted HR: 1.59, 95% CI: 1.22-2.06). Patients with asthma with onset before 20 years of age who smoked during late childhood or adolescence had the greatest risk for bipolar disorder (adjusted HR: 3.10, 95% CI: 1.29-7.44). Conclusions: Patients newly diagnosed with asthma had a 1.3 times higher risk of developing bipolar disorder. Smoking during late childhood or adolescence increases the risk of developing bipolar disorder in patients with asthma.

Keywords: adolescence, asthma, smoking, bipolar disorder, early adulthood

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Authors: Benjamin Bobbia, Matthias Picard

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In recent years active learning methodologies based on the representativity of the data seems more promising to limit overfitting. The presented query methodology for regression using the Wasserstein distance measuring the representativity of our labelled dataset compared to the global distribution. In this work a crucial use of GroupSort Neural Networks is made therewith to draw a double advantage. The Wasserstein distance can be exactly expressed in terms of such neural networks. Moreover, one can provide explicit bounds for their size and depth together with rates of convergence. However, heterogeneity of the dataset is also considered by weighting the Wasserstein distance with the error of approximation at the previous step of active learning. Such an approach leads to a reduction of overfitting and high prediction performance after few steps of query. After having detailed the methodology and algorithm, an empirical study is presented in order to investigate the range of our hyperparameters. The performances of this method are compared, in terms of numbers of query needed, with other classical and recent query methods on several UCI datasets.

Keywords: active learning, Lipschitz regularization, neural networks, optimal transport, regression

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Authors: Aref Aasi, Sahar Ebrahimi Bajgani, Erfan Aasi

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Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.

Keywords: breast cancer, diagnosis, machine learning, biomarker classification, neural network

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Authors: Donghee Kang

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Background: Cancer pain is very difficult to control as the mechanism of pain is varied, and the patient has several co-morbidities. The use of Intravenous Patient-Controlled Analgesia (IV-PCA) can effectively control underlying pain and breakthrough pain. Ketamine is used in many pain patients due to its unique analgesic effect. In this study, it was checked whether there was a difference in the amount of analgesic usage, pain control degree, and side effects between patients who controlled pain with fentanyl-based IV-PCA and those who added Ketamine for pain control. Methods: Among the patients referred to this department for cancer pain, IV-PCA was applied to patients who were taking sufficient oral analgesics but could not control them or had blood clotting disorders that made the procedure difficult, and this patient group was targeted. In IV-PCA, 3000 mcg of Fentanyl, 160 mg of Nefopam, and 0.3 mg of Ramosetrone were mixed with normal saline to make a total volume of 100 ml. Group F used this IV-PCA as it is, and group K mixed 250 mg of Ketamine with normal saline to make a total volume of 100 ml. For IV-PCA, the basal rate was 0.5ml/h, the bolus was set to 1ml when pressed once, and the lockout time was set to 15 minutes. If pain was not controlled after IV-PCA application, 500 mcg of Fentanyl was added, and if excessive sedation or breathing difficulties occurred, the use was stopped for one hour. After that, the degree of daily pain control, analgesic usage, and side effects were investigated for seven days using this IV-PCA. Results: There was no difference between the two groups in the demographic data. Both groups had adequate pain control. Initial morphine milligram equivalents did not differ between the two groups, but the total amount of Fentanyl used for seven days was significantly different between the two groups [p=0.014], and group F used more Fentanyl through IV-PCA. In addition, the amount of sleeping pills used during the seven days was higher in Group F [p<0.01]. Overall, there was no difference in the frequency of side effects between the two groups, but the nausea was more frequent in Group F [p=0.031]. Discussion: When the two groups were compared, pain control was good in both groups. This seems to be because Fentanyl-based IV-PCA showed an adequate pain control effect. However, there was a significant difference in the total amount of opioid (Fentanyl) used, which is thought to be the opioid-sparing effect of Ketamine. Also, among the side effects, nausea was significantly less, which is thought to be possible because the amount of opioids used in the Ketamine group was small. The frequency of requesting sleeping pills was significantly less in the group using Ketamine, and it seems that Ketamine also helped improve sleep quality. In conclusion, using Ketamine with an opioid to control pain seems to have advantages. IV-PCA, which can be used effectively when other procedures are difficult, is more effective and safer when used together with Ketamine than opioids alone.

Keywords: cancer pain, intravenous patient-controlled analgesia, Ketamine, opioid

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Authors: Adila Shafqat

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Climate change and urbanization has changed the face of many cities in developing countries. Urbanization is linked with land use and land cover change, that is further intensify by the effects of changing climates. Green infrastructures provide numerous ecosystem services which effect the physical set up of the cities in the long run. Land surface temperatures is considered as defining parameter in the studies of the thermal impact on the land cover. Current study is conducted in the semi-arid urban areas of the Bahawalpur region. Accordingly, Land Surface Temperatures and land cover maps are derived from Landsat image through remote sensing techniques. The cooling impact of green infrastructure is determined by calculating land surface temperature of buffered zones around green infrastructures. A regression model is applied for results. It is seen that land surface temperature around green infrastructures in 1 to 3 degrees lower than the built up surroundings. The result indicates that the urban green infrastructures should be planned according to the local needs and characteristics of landuse so that they can effectively tackle land surface temperatures of urban areas.

Keywords: climate change, surface temperatures, green spaces, urban planning

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Authors: Kadiri Kayode Ibrahim

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Small and Medium Enterprises (SMEs) play significant roles in the economic development of Nigeria. However, the performance of these SMEs is influenced by various factors, including cultural factors. Keffi Local Government Area (LGA) in Nasarawa State, Nigeria, has a large number of registered SMEs. Understanding the impact of cultural factors on the performance of these SMEs in Keffi LGA is essential for their growth and sustainability. Therefore, this study aims to investigate the effect of cultural factors on the performance of selected SMEs in Keffi LGA, Nasarawa State. A cross-sectional survey research design was used to collect data from 165 purposefully selected SME owners out of the 283 registered SMEs in Keffi LGA. The data was collected using a questionnaire divided into three sections, and analysed using descriptive and ordinary least square regression (OLS). The results indicate that socio-cultural factors and ethical values have a positive and significant effect on the performance of SMEs in Keffi LGA, while attitude has a negative and significant effect on the performance of SMEs in Keffi LGA. Therefore, the study recommends that SMEs in Keffi LGA should understand the socio-cultural elements of their operating environment, adopt socio-cultural factors as elements to guide their planning and strategizing and take into consideration the ethical values of the business environment when offering new products or services. Additionally, SME Managers should take cognisance of people’s attitudes and use them to gauge their activities and ensure they support the overall performance of the business.

Keywords: cultural, factors, performance, SMEs

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Authors: Amine Beloufa, Mohamed Amirat

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In the electric vehicle, high power leads to high current; automotive power connector should resist to this high current in order to avoid a serious damage caused by the increase of contact temperature. The purpose of this paper is to analyze experimentally and numerically the effect of the cable diameter variation on the decrease of contact temperature. For this reason, a finite element model was developed to calculate the numerical contact temperature for several cable diameters and several electrical high currents. Also, experimental tests were established in order to validate this numerical model. Results show that the influence of cable diameter on the contact temperature is never neglected.

Keywords: contact temperature, experimental test, finite element, power automotive connector

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Authors: Chanjuan Sun, Shijie Hong, Jialing Zhang, Yuchao Guo, Zhijun Zou, Chen Huang

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Background: Studies on associations between air temperature and childhood respiratory diseases are lack in China. Objectives: We aim to analyze the relationship between air temperature and childhood respiratory diseases. Methods: We conducted the on-site inspection into 454 residences and questionnaires survey. Indoor air temperature were from field inspection and outdoor air temperature were from website. Multiple logistic regression analyses were used to investigate the associations. Results: Indoor extreme hot air temperature was positively correlated with duration of a common cold (>=2 weeks), and outdoor extreme hot air temperature was also positively related with pneumonia among children. Indoor and outdoor extreme cold air temperature was a risk factor for rhinitis among children. The biggest indoor air temperature difference (indoor maximum air temperature minus indoor minimum air temperature) (Imax minus Imin) (the 4th quartile, >4 oC) and outdoor air temperature difference (outdoor maximum air temperature minus outdoor minimum air temperature) (Omax minus Omin) (the 4th quartile, >8oC) were positively related to pneumonia among children. Meanwhile, indoor air temperature difference (Imax minus Imin) (the 4th quartile, >4 oC) was positively correlated with diagnosed asthma among children. Air temperature difference between indoor and outdoor was negatively related with the most childhood respiratory diseases. This may be partly related to the avoidance behavior. Conclusions: Improper air temperature may affect the respiratory diseases among children.

Keywords: air temperature, extreme air temperature, air temperature difference, respiratory diseases, children

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Authors: Zahra Majd, Mohsen Kalantar

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Power management and voltage regulation is one of the most important issues in microgrid (MG) control and scheduling. This paper proposes a multiobjective scheduling formulation that consists of active power costs, voltage fluctuations summation, and technical constraints of MG. Furthermore, load flow and reserve constraints are considered to achieve proper voltage regulation. A modified Jacobian matrix is presented for calculating voltage variations and Mont Carlo simulation is used for generating and reducing scenarios. To convert the problem to a mixed integer linear program, a linearization procedure for nonlinear equations is presented. The proposed model is applied to a typical low-voltage MG and two different cases are investigated. The results show the effectiveness of the proposed model.

Keywords: microgrid, energy management system, voltage fluctuations, modified Jacobian matrix

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Authors: Nazli Uren, Ayse Okur

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Tactile comfort of a textile product is an essential property and a major concern when it comes to customer perceptions and preferences. The subjective nature of comfort and the difficulties regarding the simulation of human hand sensory feelings make it hard to establish a well-accepted link between tactile comfort and objective evaluations. On the other hand, shear behavior of a fabric is a mechanical parameter which can be measured by various objective test methods. The principal aim of this study is to determine the tactile comfort of commercially available denim fabrics by subjective measurements, create a tactile score database for denim fabrics and investigate the relations between tactile comfort and shear behavior. In-plane shear behaviors of 17 different commercially available denim fabrics with a variety of raw material and weave structure were measured by a custom design shear frame and conventional bias extension method in two corresponding diagonal directions. Tactile comfort of denim fabrics was determined via subjective customer evaluations as well. Aforesaid relations were statistically investigated and introduced as regression equations. The analyses regarding the relations between tactile comfort and shear behavior showed that there are considerably high correlation coefficients. The suggested regression equations were likewise found out to be statistically significant. Accordingly, it was concluded that the tactile comfort of denim fabrics can be estimated with a high precision, based on the results of in-plane shear behavior measurements.

Keywords: denim fabrics, in-plane shear behavior, linear regression estimation, tactile comfort

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Authors: Farshid Fathinia

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This paper examines numerically the laminar steady magneto-hydrodynamic mixed convection flow and heat transfer in a wavy lid-driven cavity filled with water-Al2O3 nanofluid using FDM method. The left and right sidewalls of the cavity have a wavy geometry and are maintained at a cold and hot temperature, respectively. The top and bottom walls are considered flat and insulated while, the bottom wall moves from left to right direction with a uniform lid-driven velocity. A magnetic field is applied vertically downward on the bottom wall of the cavity. Based on the numerical results, the effects of the dominant parameters such as Rayleigh number, Hartmann number, solid volume fraction, and wavy wall geometry parameters are examined. The numerical results are obtained for Hartmann number varying as 0 ≤ Ha ≤ 0.6, Rayleigh numbers varying as 103≤ Ra ≤105, and the solid volume fractions varying as 0 ≤ φ ≤ 0.0003. Comparisons with previously published numerical works on mixed convection in a nanofluid filled cavity are performed and good agreements between the results are observed. It is found that the flow circulation and mean Nusselt number decrease as the solid volume fraction and Hartmann number increase. Moreover, the convection enhances when the amplitude ratio of the wavy surface increases. The results also show that both the flow and thermal fields are significantly affected by the amplitude ratio (i.e., wave form) of the wavy wall.

Keywords: nanofluid, mixed convection, magnetic field, wavy cavity, lid-driven, SPH method

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Authors: Sofia Stoica

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In this paper, we use historical data to predict the stock price of a tech company. To this end, we use a dataset consisting of the stock prices in the past five years of ten major tech companies – Adobe, Amazon, Apple, Facebook, Google, Microsoft, Netflix, Oracle, Salesforce, and Tesla. We experimented with a variety of models– a linear regressor model, K nearest Neighbors (KNN), a sequential neural network – and algorithms - Multiplicative Weight Update, and AdaBoost. We found that the sequential neural network performed the best, with a testing error of 0.18%. Interestingly, the linear model performed the second best with a testing error of 0.73%. These results show that using historical data is enough to obtain high accuracies, and a simple algorithm like linear regression has a performance similar to more sophisticated models while taking less time and resources to implement.

Keywords: finance, machine learning, opening price, stock market

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Authors: K. Radha Krishnan, Mirajul Alom

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Dehydrated powder prepared from fresh radish (Raphanus sativus) leaves were investigated for the color stability by different drying methods (tray, sun and solar). The effect of blanching conditions, drying methods as well as drying temperatures (50 – 90°C) were considered for studying the color degradation kinetics of chlorophyll in the dehydrated powder. The hunter color parameters (L*, a*, b*) and total color difference (TCD) were determined in order to investigate the color degradation kinetics of chlorophyll. Blanching conditions, drying method and drying temperature influenced the changes in L*, a*, b* and TCD values. The changes in color values during processing were described by a first order kinetic model. The temperature dependence of chlorophyll degradation was adequately modeled by Arrhenius equation. To predict the losses in green color, a mathematical model was developed from the steady state kinetic parameters. The results from this study indicated the protective effect of blanching conditions on the color stability of dehydrated radish powder.

Keywords: chlorophyll, color stability, degradation kinetics, drying

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Authors: Wondmnew Derebe Yohannis

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Farming irrigation plays a crucial role in rural development strategies, impacting both annual household income and livelihood. This research aims to evaluate the factors influencing irrigation participation and assess the impact of small-scale irrigation on rural households' annual income. The study collected data from 287 farmers in the Dahana district of northern Ethiopia. The research investigates the driving forces behind farmers' decisions to adopt small-scale irrigation and its effect on annual income gain. The findings reveal that several factors positively influence the probability of adoption, including access to credit, cultivated land size, livestock holding, extension contact, and the education level of the household head. Conversely, the distance to local markets and water schemes negatively affects the likelihood of adoption. To understand the differences in annual income between farm households that adopted irrigation and those that did not, a simultaneous equations model with endogenous switching regression is estimated. This accounts for the heterogeneity in the adoption decision and unobservable characteristics of farmers and their farms. The analysis compares the expected income gain under actual and counterfactual scenarios, considering whether the farm household adopted irrigation or not. The study reveals that the group of farm households that adopted irrigation has distinct characteristics compared to those that did not adopt it. Furthermore, the research demonstrates that the adoption of irrigation practices leads to an increase in annual income. Interestingly, the impact of small-scale irrigation on annual income is greater for the farm households that actually adopted irrigation compared to those in the counterfactual scenario where they did not adopt. Based on the findings, the researcher concludes that small-scale irrigation is a practical solution for meeting household financial needs in the study area. It is recommended that investments in small-scale irrigation continue to further improve the livelihoods of rural farming communities by enhancing annual income gains.

Keywords: small-scale irrigation, income, rural farm households, endogenous switching regression, user, non-user

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Authors: Afshin Keshvadi, Johari Endan, Haniff Harun, Desa Ahmad, Farah Saleena

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Experiments were conducted to develop a model for analyzing the ripening process of oil palm fresh fruits in relation to oil yield and oil quality of palm oil produced. This research was carried out on 8-year-old Tenera (Dura × Pisifera) palms planted in 2003 at the Malaysian Palm Oil Board Research Station. Fresh fruit bunches were harvested from designated palms during January till May of 2010. The bunches were divided into three regions (top, middle and bottom), and fruits from the outer and inner layers were randomly sampled for analysis at 8, 12, 16 and 20 weeks after anthesis to establish relationships between maturity and oil development in the mesocarp and kernel. Computations on data related to ripening time, oil content and oil quality were performed using several computer software programs (MSTAT-C, SAS and Microsoft Excel). Nine nonlinear mathematical models were utilized using MATLAB software to fit the data collected. The results showed mean mesocarp oil percent increased from 1.24 % at 8 weeks after anthesis to 29.6 % at 20 weeks after anthesis. Fruits from the top part of the bunch had the highest mesocarp oil content of 10.09 %. The lowest kernel oil percent of 0.03 % was recorded at 12 weeks after anthesis. Palmitic acid and oleic acid comprised of more than 73 % of total mesocarp fatty acids at 8 weeks after anthesis, and increased to more than 80 % at fruit maturity at 20 weeks. The Logistic model with the highest R2 and the lowest root mean square error was found to be the best fit model.

Keywords: oil palm, oil yield, ripening process, anthesis, fatty acids, modeling

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Authors: Ali Asghar Memon

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The simulation modeling of switched reluctance (SR) machine often relies and uses the three data tables identified as static torque characteristics that include flux linkage characteristics, co energy characteristics and static torque characteristics separately. It has been noticed from the literature that the data of static torque used in the simulation model is often calculated so far the literature is concerned. This paper presents the simulation model that include the data of measured and calculated static torque separately to see its effect on instantaneous torque profile of the machine. This is probably for the first time so far the literature review is concerned that static torque from co energy information, and measured static torque directly from experiments are separately used in the model. This research is helpful for accurate modeling of switched reluctance drive.

Keywords: static characteristics, current chopping, flux linkage characteristics, switched reluctance motor

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Authors: Ma. Gracia Corazon Cayanan, Kai Yuen Cheong, Li Sha

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Training a language model for a minority language has been a challenging task. The lack of available corpora to train and fine-tune state-of-the-art language models is still a challenge in the area of Natural Language Processing (NLP). Moreover, the need for high computational resources and bulk data limit the attainment of this task. In this paper, we presented the following contributions: (1) we introduce and used a translation pair set of Tagalog and English (TL-EN) in pre-training a language model to a minority language resource; (2) we fine-tuned and evaluated top-ranking and pre-trained semantic textual similarity binary task (STSB) models, to both TL-EN and STS dataset pairs. (3) then, we reduced the size of the model to offset the need for high computational resources. Based on our results, the models that were pre-trained to translation pairs and STS pairs can perform well for STSB task. Also, having it reduced to a smaller dimension has no negative effect on the performance but rather has a notable increase on the similarity scores. Moreover, models that were pre-trained to a similar dataset have a tremendous effect on the model’s performance scores.

Keywords: semantic matching, semantic textual similarity binary task, low resource minority language, fine-tuning, dimension reduction, transformer models

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Authors: J. Van Engeland, C. Lavigne, S. De Jaeger

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In the light of the transition towards a more circular economy, recovery of products, parts or materials will gain in importance. Additionally, the EU proximity principle related to waste management and emissions generated by transporting large amounts of end-of-life products, shift attention to local recovery networks. The Flemish inter-communal cooperation for municipal solid waste management Meetjesland (IVM) is currently investigating the set-up of such a network. More specifically, the network encompasses the recycling of polyvinyl chloride (PVC), which is collected in separate containers. When these containers are full, a truck should transport them to the processor which can recycle the PVC into new products. This paper proposes a model to optimize the container collection. The containers are located at different Civic Amenity sites (CA sites) in a certain region. Since people can drop off their waste at these CA sites, the containers will gradually fill up during a planning horizon. If a certain container is full, it has to be collected and replaced by an empty container. The collected waste is then transported to a single processor. To perform this collection and transportation of containers, the responsible firm has a set of vehicles stationed at a single depot and different personnel crews. A vehicle can load exactly one container. If a trailer is attached to the vehicle, it can load an additional container. Each day of the planning horizon, the different crews and vehicles leave the depot to collect containers at the different sites. After loading one or two containers, the crew has to drive to the processor for unloading the waste and to pick up empty containers. Afterwards, the crew can again visit sites or it can return to the depot to end its collection work for that day. All along the collection process, the crew has to respect the opening hours of the sites. In order to allow for some flexibility, a crew is allowed to wait a certain amount of time at the gate of a site until it opens. The problem described can be modelled as a variant to the PVRP-TW (Periodic Vehicle Routing Problem with Time Windows). However, a vehicle can at maximum load two containers, hence only two subsequent site visits are possible. For that reason, we will refer to the model as a model for building tactical waste collection schemes. The goal is to a find a schedule describing which crew should visit which CA site on which day to minimize the number of trucks and the routing costs. The model was coded in IBM CPLEX Optimization studio and applied to a number of test instances. Good results were obtained, and specific suggestions concerning route and truck costs could be made. For a large range of input parameters, collection schemes using two trucks are obtained.

Keywords: container collection, crew scheduling, mixed integer linear programming, waste management

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Authors: Guneet Saini, Shahrukh, Sunil Sharma

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Traffic congestion is the most critical issue faced by those in the transportation profession today. Over the past few years, roundabouts have been recognized as a measure to promote efficiency at intersections globally. In developing countries like India, this type of intersection still faces a lot of issues, such as bottleneck situations, long queues and increased waiting times, due to increasing traffic which in turn affect the performance of the entire urban network. This research is a case study of a non-conventional roundabout, in terms of geometric design, in a small town in India. These types of roundabouts should be analyzed for their functionality in mixed traffic conditions, prevalent in many developing countries. Microscopic traffic simulation is an effective tool to analyze traffic conditions and estimate various measures of operational performance of intersections such as capacity, vehicle delay, queue length and Level of Service (LOS) of urban roadway network. This study involves analyzation of an unsymmetrical non-circular 6-legged roundabout known as “Kala Aam Chauraha” in a small town Bulandshahr in Uttar Pradesh, India using VISSIM simulation package which is the most widely used software for microscopic traffic simulation. For coding in VISSIM, data are collected from the site during morning and evening peak hours of a weekday and then analyzed for base model building. The model is calibrated on driving behavior and vehicle parameters and an optimal set of calibrated parameters is obtained followed by validation of the model to obtain the base model which can replicate the real field conditions. This calibrated and validated model is then used to analyze the prevailing operational traffic performance of the roundabout which is then compared with a proposed alternative to improve efficiency of roundabout network and to accommodate pedestrians in the geometry. The study results show that the alternative proposed is an advantage over the present roundabout as it considerably reduces congestion, vehicle delay and queue length and hence, successfully improves roundabout performance without compromising on pedestrian safety. The study proposes similar designs for modification of existing non-conventional roundabouts experiencing excessive delays and queues in order to improve their efficiency especially in the case of developing countries. From this study, it can be concluded that there is a need to improve the current geometry of such roundabouts to ensure better traffic performance and safety of drivers and pedestrians negotiating the intersection and hence this proposal may be considered as a best fit.

Keywords: operational performance, roundabout, simulation, VISSIM

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Authors: Mayuko Matsuoka

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This presentation reports the relationships among EFL(English as a Foreign Language) students’ story comprehension in reading a story written in English and Japanese and empathic reactions. The main focus is put on their self-reflection on past study experiences, one of the empathic reactions after reading a story. One hundred fifty-five first-year university students in Japan read three SRL-themed stories written in English (their foreign language) and those written in Japanese (their mother tongue). The levels of the stories are equivalent, at CEFR(Common European Framework of Reference for Languages) B2 level. The result of categorical correlation analysis shows significant moderate correlations among three empathic reactions in a group reading English versions: having similar emotions as a protagonist, reflecting on their past study experiences, and getting lessons from a story. In addition, the result of logistic regression analysis for the data in a group reading English versions shows the chance of getting lessons from a story significantly approximately doubles if participants’ scores of a comprehension test increases by one, while it approximately triples if participants’ self-reflection occurs. These results do not appear in a group reading Japanese versions. The findings imply that self-reflection may support their comprehension of the English texts and leads to the participants’ getting lessons about SRL.

Keywords: comprehension, lesson, self-reflection, SRL

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Authors: Anudeep Appe, Bhanu Poluparthi, Lakshmi Kasivajjula, Udai Mv, Sobha Bagadi, Punya Modi, Aditya Singh, Hemanth Gunupudi, Spenser Troiano, Jeff Paul, Justin Stovall, Justin Yamamoto

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The necessity of data-driven decisions in healthcare strategy formulation is rapidly increasing. A reliable framework which helps identify factors impacting a healthcare provider facility or a hospital (from here on termed as facility) market share is of key importance. This pilot study aims at developing a data-driven machine learning-regression framework which aids strategists in formulating key decisions to improve the facility’s market share which in turn impacts in improving the quality of healthcare services. The US (United States) healthcare business is chosen for the study, and the data spanning 60 key facilities in Washington State and about 3 years of historical data is considered. In the current analysis, market share is termed as the ratio of the facility’s encounters to the total encounters among the group of potential competitor facilities. The current study proposes a two-pronged approach of competitor identification and regression approach to evaluate and predict market share, respectively. Leveraged model agnostic technique, SHAP, to quantify the relative importance of features impacting the market share. Typical techniques in literature to quantify the degree of competitiveness among facilities use an empirical method to calculate a competitive factor to interpret the severity of competition. The proposed method identifies a pool of competitors, develops Directed Acyclic Graphs (DAGs) and feature level word vectors, and evaluates the key connected components at the facility level. This technique is robust since its data-driven, which minimizes the bias from empirical techniques. The DAGs factor in partial correlations at various segregations and key demographics of facilities along with a placeholder to factor in various business rules (for ex. quantifying the patient exchanges, provider references, and sister facilities). Identified are the multiple groups of competitors among facilities. Leveraging the competitors' identified developed and fine-tuned Random Forest Regression model to predict the market share. To identify key drivers of market share at an overall level, permutation feature importance of the attributes was calculated. For relative quantification of features at a facility level, incorporated SHAP (SHapley Additive exPlanations), a model agnostic explainer. This helped to identify and rank the attributes at each facility which impacts the market share. This approach proposes an amalgamation of the two popular and efficient modeling practices, viz., machine learning with graphs and tree-based regression techniques to reduce the bias. With these, we helped to drive strategic business decisions.

Keywords: competition, DAGs, facility, healthcare, machine learning, market share, random forest, SHAP

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Authors: Syahira Ibrahim, Herlina Abdul Rahim

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The amylose content is an essential element in determining the texture and taste of rice grains. This paper evaluates the use of VIS-SWNIRS in estimating the amylose content for seven varieties of rice grains available in the Malaysian market. Each type consists of 30 samples and all the samples are scanned using the spectroscopy to obtain a range of values between 680-1000nm. The Savitzky-Golay (SG) smoothing filter is applied to each sample’s data before the Principal Component Regression (PCR) technique is used to examine the data and produce a single value for each sample. This value is then compared with reference values obtained from the standard iodine colorimetric test in terms of its coefficient of determination, R2. Results show that this technique produced low R2 values of less than 0.50. In order to improve the result, the range should include a wavelength range of 1100-2500nm and the number of samples processed should also be increased.

Keywords: amylose content, diffuse reflectance, Malaysia rice grain, principal component regression (PCR), Visible and Shortwave near-infrared spectroscopy (VIS-SWNIRS)

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Authors: Débora V. Busman, Venerando E. Amaro, Mattheus da C. Prudêncio

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Prognostic studies of the shoreline are of utmost importance for Ponta Negra Beach, located in Natal, Northeastern Brazil, where the infrastructure recently built along the shoreline is severely affected by flooding and erosion. This study compares shoreline predictions using three linear regression methods (LMS, LRR and WLR) and tries to discern the best method for different shoreline position scenarios. The methods have shown erosion on the beach in each of the scenarios tested, even in less intense dynamic conditions. The WLA_A with confidence interval of 95% was the well-adjusted model and calculated a retreat of -1.25 m/yr to -2.0 m/yr in hot spot areas. The change of the shoreline on Ponta Negra Beach can be measured as a negative exponential curve. Analysis of these methods has shown a correlation with the morphodynamic stage of the beach.

Keywords: coastal erosion, prognostic model, DSAS, environmental safety

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Authors: Ahmet Oguz Akturk, Kemal Izci, Gurbuz Caliskan, Ismail Sahin

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Rapid developments in technology are to necessitate societies to closely follow technological developments and change themselves to adopt those developments. It is obvious that one of the areas that are impacted from technological developments is education. Analyzing preservice teachers’ attitudes toward technology is crucial for both educational and professional purposes since teacher candidates are essential for educating future individual living in technological age. In this study, it is aimed to analyze preservice teachers’ attitudes toward technology and some variables (e.g., gender, daily internet usage and possessed technological devices) that predicting those attitudes. In this study, relational survey model used as research method and 329 preservice teachers who are studying in a large university located at the middle part of Turkey are voluntarily participated. Results of the study showed that mostly preservice teachers displayed positive attitudes toward technology while male preservice teachers’ attitudes toward technology was more positive than female preservice teachers. In order to analyze predicting factors for preservice teachers’ attitudes toward technology, stepwise multiple regressions were utilized. The results of stepwise multiple regression showed that daily internet use was the most strong predicting factor for predicting preservice teachers’ attitudes toward technology.

Keywords: attitudes toward technology, preservice teachers, gender, stepwise multiple regression analysis

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Authors: Harinder Singh

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The paper sheds light on the perceptions of the uninsured workers employed in the urban informal sector of India, towards the health insurance. In addition to this, it also explores the association of the identified perceptions with household decisions to enroll for health insurance schemes in India. Firstly the data taken from the primary survey of the uninsured workers employed in the urban informal sector was analyzed using exploratory factor analysis to evaluate the perceptions. Thereafter, logistic regression was employed to determine the association of the identified perceptions regarding the enrollment. Our study identifies twelve perceptions related to the health insurance enrollment of the uninsured workers employed in the urban informal sector of India. The study demonstrates that perceptions have the strongest association with the voluntary enrollment. These specifically relate to the lack of awareness about the need to buy health insurance; comprehensive coverage; income constraint; future contingencies and social obligations; lack of information; availability of subsidized government health care; linkage with government hospitals and preference for government schemes. Conclusions: Along with the food security, health security has become a crying need of the workers employed in the informal sector and the time has come to scale up the health insurance schemes for them in the country. Policy makers or marketers of health insurance policies should recognize the household perceptions as a potential barrier and try to develop a health insurance package as per the actual needs of the informal sector (low income) in India.

Keywords: association, enrollment, health insurance, informal sector, perceptions, uninsured

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Authors: Moshfeghi Sogand

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Free radicals can be produced during the respiratory oxidation of different cells. These free radicals can damage to various macromolecules as protein ,fat, nucleic acids and … are harmful for body. The natural defence system that can prevent the damage of free radicals and nuteralized them , have tittled under the name total antioxidant capacity (TAC ). Se is one main antioxidant part in TAC , because it is one main part in structure of some body antioxidant enzymes such as GPX(glutathione peroxidase). Blood SE ,GPX and TAC probably can change by feeding of different selenium supplement diet in late pregnancy and also may transport from maternal blood to its fetus or by clostrum after calving. In this respect we have determined 100 pregnant dairy cattle (in the same condition of age , race and number of parturient) then devided them to 4 groups feed them in 3 last pregnancy months by different selenium diets. Group1:controle no se supplementation , group2: recived 0/3 ppm of the daily diet Saccharomyces Cervisiae . group3 :recived selenium _ rich yeast(containing200ppm selenium)was mixed with total daily ration fed. Group4: recived se _rich yeast(containing300 ppm selenium)was mixed with total daily ration fed. Then measured blood SE,GPX and TAC levels in them and in 3 days newborn calves after calving. The results were analysed by Tukey Anova test and the highest level of blood SE ,GPX and TAC was shown in cattle that feed fermented SE_yeast diet and in their 3 days newborn calves.

Keywords: SE, TAC, SE DIETS, FRAP

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Authors: Adam Chlapecka, Anna Kagstrom, Pavla Cermakova

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Background: Increasing educational attainment (EA) could decrease the occurrence of depression. We investigated the relationship between EA and depressive symptoms in older individuals across four European regions. Methods: We studied 108 315 Europeans (54 % women, median age 63 years old) from the Survey on Health, Ageing and Retirement in Europe assessing EA (7 educational levels based on ISCED classification); and depressive symptoms (≥ 4 points on EURO-D scale). Logistic regression estimated the association between EA and depressive symptoms, adjusting for sociodemographic and health-related factors; testing for sex/age/region and education interactions. Results: Higher EA was associated with lower odds of depressive symptoms, independent of sociodemographic and health-related factors. A threshold of the lowest odds of depressive symptoms was detected at the first stage of tertiary education (OR 0.60; 95% CI 0.55-0.65; p<0.001; relative to no education). Central and Eastern Europe showed the strongest association (OR for high vs. low education 0.37; 95% CI 0.33-0.40; p<0.001) and Scandinavia the weakest (OR for high vs. low education 0.69; 95% CI 0.60-0.80; p<0.001). The association was strongest amongst younger individuals. There was a sex and education interaction only within Central and Eastern Europe. Conclusion: The level of EA is reflected in later-life depressive symptoms, suggesting that supporting individuals in achieving EA, and considering those with lower EA at increased risk for depression, could lead to the decreased burden of depression across the life course. Further educational support in Central and Eastern Europe may decrease the higher burden of depressive symptoms in women.

Keywords: depression, education, epidemiology, Europe

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Authors: I. M. L. Nadeesha Jayaweera, Adao Alex Trindade

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In choosing a candidate model in likelihood-based modeling via an information criterion, the practitioner is often faced with the difficult task of deciding just how far up the ranked list to look. Motivated by this pragmatic necessity, we construct an uncertainty band for a generalized (model selection) information criterion (GIC), defined as a criterion for which the limit in probability is identical to that of the normalized log-likelihood. This includes common special cases such as AIC & BIC. The method starts from the asymptotic normality of the GIC for the joint distribution of the candidate models in an independent and identically distributed (IID) data framework and proceeds by deriving the (asymptotically) exact distribution of the minimum. The calculation of an upper quantile for its distribution then involves the computation of multivariate Gaussian integrals, which is amenable to efficient implementation via the R package "mvtnorm". The performance of the methodology is tested on simulated data by checking the coverage probability of nominal upper quantiles and compared to the bootstrap. Both methods give coverages close to nominal for large samples, but the bootstrap is two orders of magnitude slower. The methodology is subsequently extended to two other commonly used model structures: regression and time series. In the regression case, we derive the corresponding asymptotically exact distribution of the minimum GIC invoking Lindeberg-Feller type conditions for triangular arrays and are thus able to similarly calculate upper quantiles for its distribution via multivariate Gaussian integration. The bootstrap once again provides a default competing procedure, and we find that similar comparison performance metrics hold as for the IID case. The time series case is complicated by far more intricate asymptotic regime for the joint distribution of the model GIC statistics. Under a Gaussian likelihood, the default in most packages, one needs to derive the limiting distribution of a normalized quadratic form for a realization from a stationary series. Under conditions on the process satisfied by ARMA models, a multivariate normal limit is once again achieved. The bootstrap can, however, be employed for its computation, whence we are once again in the multivariate Gaussian integration paradigm for upper quantile evaluation. Comparisons of this bootstrap-aided semi-exact method with the full-blown bootstrap once again reveal a similar performance but faster computation speeds. One of the most difficult problems in contemporary statistical methodological research is to be able to account for the extra variability introduced by model selection uncertainty, the so-called post-model selection inference (PMSI). We explore ways in which the GIC uncertainty band can be inverted to make inferences on the parameters. This is being attempted in the IID case by pivoting the CDF of the asymptotically exact distribution of the minimum GIC. For inference one parameter at a time and a small number of candidate models, this works well, whence the attained PMSI confidence intervals are wider than the MLE-based Wald, as expected.

Keywords: model selection inference, generalized information criteria, post model selection, Asymptotic Theory

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Authors: Partha P. Gopmandal, S. Bhattacharyya

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We investigate the thermal end effect on the pseudo-steady state behavior of the isotachophoretic transport of ionic species in a 2-D microchannel. Both ends of the channel are kept at a constant temperature which may lead to significant changes in electrophoretic migration speed. A mathematical model based on Nernst-Planck equations for transport of ions coupled with the equation for temperature field is considered. In addition, the charge conservation equations govern the potential field due to the external electric field. We have computed the equations for ion transport, potential and temperature in a coupled manner through the finite volume method. The diffusive terms are discretized via central difference scheme, while QUICK (Quadratic Upwind Interpolation Convection Kinematics) scheme is used to discretize the convective terms. We find that the thermal end effect has significant effect on the isotachophoretic (ITP) migration speed of the analyte. Our result shows that the ITP velocity for temperature dependent case no longer varies linearly with the applied electric field. A detailed analysis has been made to provide a range of the key parameters to minimize the Joule heating effect on ITP transport of analytes.

Keywords: finite volume method, isotachophoresis, QUICK scheme, thermal effect

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Authors: Mohammad Noor Hisham Osman, Ahmed Razman Abdul Latiff, Zaidi Mat Daud, Zulkarnain Muhamad Sori

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The problem that Malaysian auditors do not issue going concern opinion (GC opinion) to seriously financially distressed companies is still a pressing issue. Policy makers, particularly the Financial Statement Review Committee (FSRC) of Malaysian Institute of Accountant, have raised this issue as early as in 2009. Similar problem happened in the US, UK, and many developing countries. It is important for auditors to issue GC opinion properly because such opinion is one signal about the viability of a company much needed by stakeholders. There are at least two unanswered questions or research gaps in the literature on determinants of GC opinion. Firstly, is client’s probability of failure associated with GC opinion issuance? Secondly, to what extent influential owners (management, family, and institution) moderate the association between client probability of failure and GC opinion issuance. The objective of this study is, therefore, twofold; (1) To examine the extent of the relationship between the probability of client failure and the issuance of GC opinion and (2) To examine the level of management, family, and institutional ownerships moderate the association between client probability of failure and the issuance of GC opinion. This study is quantitative in nature, and the sources of data are secondary (mainly company’s annual reports). A total of four hypotheses have been developed and tested on data accumulated from annual reports of seriously financially distressed Malaysian public listed companies. Data from 2006 to 2012 on a sample of 644 observations have been analyzed using panel logistic regression. It is found that certainty (rather than probability) of client failure affects the issuance of GC opinion. In addition, it is found that only the level of family ownership does positively moderate the relationship between client probability of failure and GC opinion issuance. This study is a contribution to auditing literature as its findings can enhance our understanding about audit quality; particularly on the variables that are associated with the issuance of GC opinion. The findings of this study shed light on the roles family owners in GC opinion issuance process, and this would open ways for the researcher to suggest measures that can be used to tackle the problem of auditors do not want to issue GC opinion to financially distressed clients. The measures to be suggested can be useful to policy makers in formulating future promulgations.

Keywords: audit quality, auditing, auditor characteristics, going concern opinion, Malaysia

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