Search results for: penalized spline regression method

Authors: Charlotte K. Lee, Henrique R. N. Aguiar, Ralph Smith, James Baldock, Sam Botchey

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Background: Femoral neck stress fractures (FNSF) are uncommon, making up 1 to 7.2% of stress fractures in healthy subjects. FNSFs are prevalent in young women, military recruits, endurance athletes, and individuals with energy deficiency syndrome or female athlete triad. Presentation is often non-specific and is often misdiagnosed following the initial examination. There is limited research addressing the return–to–activity time after FNSF. Previous studies have demonstrated prognostic time predictions based on various imaging techniques. Here, (1) OxSport clinic FNSF practice standards are retrospectively reviewed, (2) FNSF cohort demographics are examined, (3) Regression models were used to predict return–to–activity prognosis and consequently determine bone stress risk factors. Methods: Patients with a diagnosis of FNSF attending Oxsport clinic between 01/06/2020 and 01/01/2020 were selected from the Rheumatology Assessment Database Innovation in Oxford (RhADiOn) and OxSport Stress Fracture Database (n = 14). (1) Clinical practice was audited against five criteria based on local and National Institute for Health Care Excellence guidance, with a 100% standard. (2) Demographics of the FNSF cohort were examined with Student’s T-Test. (3) Lastly, linear regression and Random Forest regression models were used on this patient cohort to predict return–to–activity time. Consequently, an analysis of feature importance was conducted after fitting each model. Results: OxSport clinical practice met standard (100%) in 3/5 criteria. The criteria not met were patient waiting times and documentation of all bone stress risk factors. Importantly, analysis of patient demographics showed that of the population with complete bone stress risk factor assessments, 53% were positive for modifiable bone stress risk factors. Lastly, linear regression analysis was utilized to identify demographic factors that predicted return–to–activity time [R2 = 79.172%; average error 0.226]. This analysis identified four key variables that predicted return-to-activity time: vitamin D level, total hip DEXA T value, femoral neck DEXA T value, and history of an eating disorder/disordered eating. Furthermore, random forest regression models were employed for this task [R2 = 97.805%; average error 0.024]. Analysis of the importance of each feature again identified a set of 4 variables, 3 of which matched with the linear regression analysis (vitamin D level, total hip DEXA T value, and femoral neck DEXA T value) and the fourth: age. Conclusion: OxSport clinical practice could be improved by more comprehensively evaluating bone stress risk factors. The importance of this evaluation is demonstrated by the population found positive for these risk factors. Using this cohort, potential bone stress risk factors that significantly impacted return-to-activity prognosis were predicted using regression models.

Keywords: eating disorder, bone stress risk factor, femoral neck stress fracture, vitamin D

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Authors: Melkamu A. Zeru, Yamral M. Warkaw, Aweke A. Mitku, Muluwerk Ayele

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Introduction: Malaria is a severe health threat in the World, mainly in Africa. It is the major cause of health problems in which the risk of morbidity and mortality associated with malaria cases are characterized by spatial variations across the county. This study aimed to investigate the spatial patterns and predictors of malaria distribution in Ethiopia. Methods: A weighted sample of 15,239 individuals with rapid diagnosis tests was obtained from the Central Statistical Agency and Ethiopia malaria indicator survey of 2015. Global Moran's I and Moran scatter plots were used in determining the distribution of malaria cases, whereas the local Moran's I statistic was used in identifying exposed areas. In data manipulation, machine learning was used for variable reduction and statistical software R, Stata, and Python were used for data management and analysis. The auto logistics spatial binary regression model was used to investigate the predictors of malaria. Results: The final auto logistics regression model reported that male clients had a positive significant effect on malaria cases as compared to female clients [AOR=2.401, 95 % CI: (2.125 - 2.713)]. The distribution of malaria across the regions was different. The highest incidence of malaria was found in Gambela [AOR=52.55, 95%CI: (40.54-68.12)] followed by Beneshangul [AOR=34.95, 95%CI: (27.159 - 44.963)]. Similarly, individuals in Amhara [AOR=0.243, 95% CI:(0.1950.303],Oromiya[AOR=0.197,95%CI:(0.1580.244)],DireDawa[AOR=0.064,95%CI(0.049-0.082)],AddisAbaba[AOR=0.057,95%CI:(0.044-0.075)], Somali[AOR=0.077,95%CI:(0.059-0.097)], SNNPR[OR=0.329, 95%CI: (0.261- 0.413)] and Harari [AOR=0.256, 95%CI:(0.201 - 0.325)] were less likely to had low incidence of malaria as compared with Tigray. Furthermore, for a one-meter increase in altitude, the odds of a positive rapid diagnostic test (RDT) decrease by 1.6% [AOR = 0.984, 95% CI :( 0.984 - 0.984)]. The use of a shared toilet facility was found as a protective factor for malaria in Ethiopia [AOR=1.671, 95% CI: (1.504 - 1.854)]. The spatial autocorrelation variable changes the constant from AOR = 0.471 for logistic regression to AOR = 0.164 for auto logistics regression. Conclusions: This study found that the incidence of malaria in Ethiopia had a spatial pattern that is associated with socio-economic, demographic, and geographic risk factors. Spatial clustering of malaria cases had occurred in all regions, and the risk of clustering was different across the regions. The risk of malaria was found to be higher for those who live in soil floor-type houses as compared to those who live in cement or ceramics floor type. Similarly, households with thatched, metal and thin, and other roof-type houses have a higher risk of malaria than ceramic tiles roof houses. Moreover, using a protected anti-mosquito net reduced the risk of malaria incidence.

Keywords: malaria, Ethiopia, auto logistics, spatial model, spatial clustering

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Authors: Helena Bolini, Erick Esmerino, Adriano Cruz, Juliana Paixao

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The currently concern for health has increased demand for low-calorie ingredients and functional foods as probiotics. Understand the reasons that infer on food choice, besides a challenging task, it is important step for development and/or reformulation of existing food products. The use of appropriate multivariate statistical techniques, such as External Preference Map (PrefMap), associated with regression by Partial Least Squares (PLS) can help in determining those factors. Thus, this study aimed to determine, through PLS regression analysis, the sensory attributes considered drivers of liking in probiotic petit suisse cheeses, strawberry flavor, sweetened with different sweeteners. Five samples in same equivalent sweetness: PROB1 (Sucralose 0.0243%), PROB2 (Stevia 0.1520%), PROB3 (Aspartame 0.0877%), PROB4 (Neotame 0.0025%) and PROB5 (Sucrose 15.2%) determined by just-about-right and magnitude estimation methods, and three commercial samples COM1, COM2 and COM3, were studied. Analysis was done over data coming from QDA, performed by 12 expert (highly trained assessors) on 20 descriptor terms, correlated with data from assessment of overall liking in acceptance test, carried out by 125 consumers, on all samples. Sequentially, results were submitted to PLS regression using XLSTAT software from Byossistemes. As shown in results, it was possible determine, that three sensory descriptor terms might be considered drivers of liking of probiotic petit suisse cheese samples added with sweeteners (p<0.05). The milk flavor was noticed as a sensory characteristic with positive impact on acceptance, while descriptors bitter taste and sweet aftertaste were perceived as descriptor terms with negative impact on acceptance of petit suisse probiotic cheeses. It was possible conclude that PLS regression analysis is a practical and useful tool in determining drivers of liking of probiotic petit suisse cheeses sweetened with artificial and natural sweeteners, allowing food industry to understand and improve their formulations maximizing the acceptability of their products.

Keywords: acceptance, consumer, quantitative descriptive analysis, sweetener

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Authors: Tae-Uk Yun, Pullip Chung, Kee-Young Kwahk, Hyunchul Ahn

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Fake news is defined as the news articles that are intentionally and verifiably false, and could mislead readers. Spread of fake news may provoke anxiety, chaos, fear, or irrational decisions of the public. Thus, detecting fake news and preventing its spread has become very important issue in our society. However, due to the huge amount of fake news produced every day, it is almost impossible to identify it by a human. Under this context, researchers have tried to develop automated fake news detection using machine learning techniques over the past years. But, there have been no prior studies proposed an automated fake news detection method for Korean news to our best knowledge. In this study, we aim to detect Korean fake news using text mining and machine learning techniques. Our proposed method consists of two steps. In the first step, the news contents to be analyzed is convert to quantified values using various text mining techniques (topic modeling, TF-IDF, and so on). After that, in step 2, classifiers are trained using the values produced in step 1. As the classifiers, machine learning techniques such as logistic regression, backpropagation network, support vector machine, and deep neural network can be applied. To validate the effectiveness of the proposed method, we collected about 200 short Korean news from Seoul National University’s FactCheck. which provides with detailed analysis reports from 20 media outlets and links to source documents for each case. Using this dataset, we will identify which text features are important as well as which classifiers are effective in detecting Korean fake news.

Keywords: fake news detection, Korean news, machine learning, text mining

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Authors: S. Ghorbani, N. I. Polushin

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In this study, an approach to identify factors affecting on surface roughness in a machining process is presented. This study is based on 81 data about surface roughness over a wide range of cutting tools (conventional, cutting tool with holes, cutting tool with composite material), workpiece materials (AISI 1045 Steel, AA2024 aluminum alloy, A48-class30 gray cast iron), spindle speed (630-1000 rpm), feed rate (0.05-0.075 mm/rev), depth of cut (0.05-0.15 mm) and tool overhang (41-65 mm). A single decision tree (SDT) analysis was done to identify factors for predicting a model of surface roughness, and the CART algorithm was employed for building and evaluating regression tree. Results show that a single decision tree is better than traditional regression models with higher rate and forecast accuracy and strong value.

Keywords: cutting condition, surface roughness, decision tree, CART algorithm

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Authors: Fariza Yunus, Jasmee Jaafar, Zamalia Mahmud, Nurul Nisa’ Khairul Azmi, Nursalleh K. Chang, Nursalleh K. Chang

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Variation of air temperature from one place to another is cause by air temperature controls. In general, the most important control of air temperature is elevation. Another significant independent variable in estimating air temperature is the location of meteorological stations. Distances to coastline and land use type are also contributed to significant variations in the air temperature. On the other hand, in homogeneous terrain direct interpolation of discrete points of air temperature work well to estimate air temperature values in un-sampled area. In this process the estimation is solely based on discrete points of air temperature. However, this study presents that air temperature controls also play significant roles in estimating air temperature over homogenous terrain of Peninsular Malaysia. An Inverse Distance Weighting (IDW) interpolation technique was adopted to generate continuous data of air temperature. This study compared two different datasets, observed mean monthly data of T, and estimation error of T–T’, where T’ estimated value from a multiple regression model. The multiple regression model considered eight independent variables of elevation, latitude, longitude, coastline, and four land use types of water bodies, forest, agriculture and build up areas, to represent the role of air temperature controls. Cross validation analysis was conducted to review accuracy of the estimation values. Final results show, estimation values of T–T’ produced lower errors for mean monthly mean air temperature over homogeneous terrain in Peninsular Malaysia.

Keywords: air temperature control, interpolation analysis, peninsular Malaysia, regression model, air temperature

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Authors: Likewin Thomas, M. V. Manoj Kumar, B. Annappa

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The aim of this study was to develop an Artificial Neural Network0 s recommendation model for an online process using the complexity of load, performance, and average servicing time of the resources. Here, the proposed model investigates the resource performance using stochastic gradient decent method for learning ranking function. A probabilistic cost function is implemented to identify the optimal θ values (load) on each resource. Based on this result the recommendation of resource suitable for performing the currently executing task is made. The test result of CoSeLoG project is presented with an accuracy of 72.856%.

Keywords: ADALINE, neural network, gradient decent, process mining, resource behaviour, polynomial regression model

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Bhaveek Maini, Sanjay Dhanka, Surita Maini

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Electroencephalography (EEG) is used to classify several epileptic seizures worldwide. It is a very crucial task for the neurologist to identify the epileptic seizure with manual EEG analysis, as it takes lots of effort and time. Human error is always at high risk in EEG, as acquiring signals needs manual intervention. Disease diagnosis using machine learning (ML) has continuously been explored since its inception. Moreover, where a large number of datasets have to be analyzed, ML is acting as a boon for doctors. In this research paper, authors proposed two different ML models, i.e., logistic regression (LR) and Naïve Bayes (NB), to predict epileptic seizures based on general parameters. These two techniques are applied to the epileptic seizures recognition dataset, available on the UCI ML repository. The algorithms are implemented on an 80:20 train test ratio (80% for training and 20% for testing), and the performance of the model was validated by 10-fold cross-validation. The proposed study has claimed accuracy of 81.87% and 95.49% for LR and NB, respectively.

Keywords: epileptic seizure recognition, logistic regression, Naïve Bayes, machine learning

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Authors: Jong Yeop Kim, Jong Bum Choi, Hyun Jeong Kwak, Sook Young Lee

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Background: Each supraglottic airway requires different anesthetic depth because it has a specific structure and different compressive force in the oropharyngeal cavity. We designed the study to investigate remifentanil effect-site concentration (Ce) in 50% of patients (EC50) for successful insertion of i- gel, and to compare it with that for laryngeal mask airway (LMA) insertion during propofol target-controlled infusion (TCI). Methods: Forty-one female patients were randomized to the i-gel group (n=20) or the LMA group (n=21). Anesthesia induction was performed using propofol Ce of 5 μg/ml and the predetermined remifentanil Ce, and i-gel or LMA insertion was attempted 5 min later. The remifentanil Ce was estimated by modified Dixon's up-and-down method (initial concentration: 3.0 ng/ml, step size: 0.5 ng/ml). The patient’s response to device insertion was classified as either ‘success (no movement)’ or ‘failure (movement)’. Results: Using the Dixon’s up and down method, EC50 of remifentanil Ce for i-gel (1.58 ± 0.41 ng/ml) was significantly lower than that for LMA (2.25 ± 0.55 ng/ml) (p=0.038). Using isotonic regression, EC50 (83% CI) of remifentanil in the i-gel group [1.50 (1.37-1.80) ng/ml] was statistically lower than that in the LMA group [2.00 (1.82-2.34) ng/ml]. EC95 (95% CI) of remifentanil in the i-gel group [2.38 (1.48-2.50) ng/ml] was statistically lower than that in the LMA group [3.35 (2.58-3.48) ng/ml]. Conclusion: We found that EC50 of remifentanil Ce for i-gel insertion (1.58 ng/ml) was significantly lower than that for LMA insertion (2.25 ng/ml), in female patients during propofol TCI without neuromuscular blockade.

Keywords: i-gel, laryngeal mask airway, propofol, remifentanil

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Authors: Nagini Sabbineni, Rajini T. V. Kanth, B. V. Kiranmayee

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India’s economy primarily depends on agriculture yield growth and their allied agro industry products. The agriculture yield prediction is the toughest task for agricultural departments across the globe. The agriculture yield depends on various factors. Particularly countries like India, majority of agriculture growth depends on rain water, which is highly unpredictable. Agriculture growth depends on different parameters, namely Water, Nitrogen, Weather, Soil characteristics, Crop rotation, Soil moisture, Surface temperature and Rain water etc. In our paper, lot of Explorative Data Analysis is done and various predictive models were designed. Further various regression models like Linear, Multiple Linear, Non-linear models are tested for the effective prediction or the forecast of the agriculture yield for various crops in Andhra Pradesh and Telangana states.

Keywords: agriculture yield growth, agriculture yield prediction, explorative data analysis, predictive models, regression models

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Authors: Firouz Amani, Adalat Hoseinian, Sajjad Hakimian

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Background: The mortality due to Myocardial Infarction (MI) is often occur during the first hours after onset of symptom. So, for taking the necessary treatment and decreasing the mortality rate, timely visited of the hospital could be effective in this regard. The aim of this study was to investigate the impact of effective factors in mortality of MI patients by using Linear Logistic Regression. Materials and Methods: In this case-control study, all patients with Acute MI who referred to the Ardabil city hospital were studied. All of died patients were considered as the case group (n=27) and we select 27 matched patients without Acute MI as a control group. Data collected for all patients in two groups by a same checklist and then analyzed by SPSS version 24 software using statistical methods. We used the linear logistic regression model to determine the effective factors on mortality of MI patients. Results: The mean age of patients in case group was significantly higher than control group (75.1±11.7 vs. 63.1±11.6, p=0.001).The history of non-cardinal diseases in case group with 44.4% significantly higher than control group with 7.4% (p=0.002).The number of performed PCIs in case group with 40.7% significantly lower than control group with 74.1% (P=0.013). The time distance between hospital admission and performed PCI in case group with 110.9 min was significantly upper than control group with 56 min (P=0.001). The mean of delay time from Onset of symptom to hospital admission (patient delay) and the mean of delay time from hospital admissions to receive treatment (system delay) was similar between two groups. By using logistic regression model we revealed that history of non-cardinal diseases (OR=283) and the number of performed PCIs (OR=24.5) had significant impact on mortality of MI patients in compare to other factors. Conclusion: Results of this study showed that of all studied factors, the number of performed PCIs, history of non-cardinal illness and the interval between onset of symptoms and performed PCI have significant relation with morality of MI patients and other factors were not meaningful. So, doing more studies with a large sample and investigated other involved factors such as smoking, weather and etc. is recommended in future.

Keywords: acute MI, mortality, heart failure, arrhythmia

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Authors: Zakaria S. G. Hegazy, Walid M. A. Ahmed

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This paper examines the dependence structure of Islamic stock markets on Bitcoin’s realized volatility components in bear, normal, and bull market periods. A quantile regression approach is employed, after adjusting raw returns with respect to a broad set of relevant global factors and accounting for structural breaks in the data. The results reveal that upside volatility tends to exert negative influences on Islamic developed-market returns more in bear than in bull market conditions, while downside volatility positively affects returns during bear and bull conditions. For emerging markets, we find that the upside (downside) component exerts lagged negative (positive) effects on returns in bear (all) market regimes. By and large, the dependence structures turn out to be asymmetric. Our evidence provides essential implications for investors.

Keywords: cryptocurrency markets, bitcoin, realized volatility measures, asymmetry, quantile regression

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Authors: Fumiya Sugino, Naohiro Nakamura, Yuji Miyazu

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In earthquake resistant design of buildings, evaluation of vibration characteristics is important. In recent years, due to the increment of super high-rise buildings, the evaluation of response is important for not only the first mode but also higher modes. The knowledge of vibration characteristics in buildings is mostly limited to the first mode and the knowledge of higher modes is still insufficient. In this paper, using earthquake observation records of a SRC building by applying frequency filter to ARX model, characteristics of first and second modes were studied. First, we studied the change of the eigen frequency and the damping ratio during the 3.11 earthquake. The eigen frequency gradually decreases from the time of earthquake occurrence, and it is almost stable after about 150 seconds have passed. At this time, the decreasing rates of the 1st and 2nd eigen frequencies are both about 0.7. Although the damping ratio has more large error than the eigen frequency, both the 1st and 2nd damping ratio are 3 to 5%. Also, there is a strong correlation between the 1st and 2nd eigen frequency, and the regression line is y=3.17x. In the damping ratio, the regression line is y=0.90x. Therefore 1st and 2nd damping ratios are approximately the same degree. Next, we study the eigen frequency and damping ratio from 1998 after 3.11 earthquakes, the final year is 2014. In all the considered earthquakes, they are connected in order of occurrence respectively. The eigen frequency slowly declined from immediately after completion, and tend to stabilize after several years. Although it has declined greatly after the 3.11 earthquake. Both the decresing rate of the 1st and 2nd eigen frequencies until about 7 years later are about 0.8. For the damping ratio, both the 1st and 2nd are about 1 to 6%. After the 3.11 earthquake, the 1st increases by about 1% and the 2nd increases by less than 1%. For the eigen frequency, there is a strong correlation between the 1st and 2nd, and the regression line is y=3.17x. For the damping ratio, the regression line is y=1.01x. Therefore, it can be said that the 1st and 2nd damping ratio is approximately the same degree. Based on the above results, changes in eigen frequency and damping ratio are summarized as follows. In the long-term study of the eigen frequency, both the 1st and 2nd gradually declined from immediately after completion, and tended to stabilize after a few years. Further it declined after the 3.11 earthquake. In addition, there is a strong correlation between the 1st and 2nd, and the declining time and the decreasing rate are the same degree. In the long-term study of the damping ratio, both the 1st and 2nd are about 1 to 6%. After the 3.11 earthquake, the 1st increases by about 1%, the 2nd increases by less than 1%. Also, the 1st and 2nd are approximately the same degree.

Keywords: eigenfrequency, damping ratio, ARX model, earthquake observation records

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Authors: Myoungsoon You, Heejung Son

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Background: In the current study, we investigated the effects of emotions induced by an infectious disease outbreak on the various protective behaviors taken during the crisis and on the perception after the crisis. The investigation was based on two psychological theories of appraisal tendency and action tendency. Methods: A total of 900 participants in South Korea who experienced the 2015 Middle East Respiratory Syndrome outbreak were sampled by a professional survey agency. To assess the influence of the emotions fear and anger, a regression approach was used. The effect of emotions on various protective behaviors and perceptions was observed using a hierarchical regression method. Results: Fear and anger induced by the infectious disease outbreak were both associated with increased protective behaviors during the crisis. However, the differences between the emotions were observed. While protective behaviors with avoidance tendency (adherence to recommendations, self-mitigation), were raised by both fear and anger, protective behaviors with approach tendency (information-seeking) were increased by anger, but not fear. Regarding the effect of emotion on the risk perception after the crisis, only fear was associated with a higher level of risk perception. Conclusions: This study confirmed the role of emotions in crisis protective behaviors and post-crisis perceptions regarding an infectious disease outbreak. These findings could enhance understanding of the public’s protective behaviors during infectious disease outbreaks and afterward risk perception corresponding to emotions. The results also suggested strategies for communicating with the public that takes into account emotions that are prominently induced by crises associated with disease outbreaks.

Keywords: crisis communication, emotion, infectious disease outbreak, protective behavior, risk perception

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Authors: M. J. Davidson, N. Selvaraj, L. Venugopal

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The elevated temperature forming studies on the expansion of thin walled tubes have been studied in the present work. The influence of process parameters namely the die angle, the die ratio and the operating temperatures on the expansion of tube ends at elevated temperatures is carried out. The range of operating parameters have been identified by perfoming extensive simulation studies. The hot forming parameters have been evaluated for AA2014 alloy for performing the simulation studies. Experimental matrix has been developed from the feasible range got from the simulation results. The design of experiments is used for the optimization of process parameters. Response Surface Method’s (RSM) and Box-Behenken design (BBD) is used for developing the mathematical model for expansion. Analysis of variance (ANOVA) is used to analyze the influence of process parameters on the expansion of tube ends. The effect of various process combinations of expansion are analyzed through graphical representations. The developed model is found to be appropriate as the coefficient of determination value is very high and is equal to 0.9726. The predicted values are found to coincide well with the experimental results, within acceptable error limits.

Keywords: expansion, optimization, Response Surface Method (RSM), ANOVA, bbd, residuals, regression, tube

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Authors: Inna R. Edara, Haw-Lin Wu

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Positive psychology investigates human strengths and virtues and promotes well-being. Relying on this assumption, positive interventions have been continuously designed to build pleasure and happiness, joy and contentment, engagement and meaning, hope and optimism, satisfaction and gratitude, spirituality, and various other positive measures of well-being. In line with this model of positive psychology and interventions, this study investigated certain measures of well-being in a group of 45 students enrolled in an 18-week positive psychology course and simultaneously engaged in service-oriented interventions that they chose for themselves based on the course content and individual interests. Students’ well-being was measured at the beginning and end of the course. The well-being indicators included positive automatic thoughts, optimism and hope, satisfaction with life, and spirituality. A paired-samples t-test conducted to evaluate the impact of class content and service-oriented interventions on students’ scores of well-being indicators indicated statistically significant increase from pre-class to post-class scores. There were also significant gender differences in post-course well-being scores, with females having higher levels of well-being than males. A two-way between groups analysis of variance indicated a significant interaction effect of age by gender on the post-course well-being scores, with females in the age group of 56-65 having the highest scores of well-being in comparison to the males in the same age group. Regression analyses indicated that positive automatic thought significantly predicted hope and satisfaction with life in the pre-course analysis. In the post-course regression analysis, spiritual transcendence made a significant contribution to optimism, and positive automatic thought made a significant contribution to both hope and satisfaction with life. Finally, a significant test between pre-course and post-course regression coefficients indicated that the regression coefficients at pre-course were significantly different from post-course coefficients, suggesting that the positive psychology course and the interventions were helpful in raising the levels of well-being. The overall results suggest a substantial increase in the participants’ well-being scores after engaging in the positive-oriented interventions, implying a need for designing more positive interventions in education to promote well-being.  

Keywords: hope, optimism, positive automatic thoughts, satisfaction with life, spirituality, well-being

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Authors: Shehu A. Salau, Robiu O. Aliu, Nofiu B. Nofiu

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Nigeria is among countries of the world confronted with food insecurity problem. The agricultural production systems that produces food for the teaming population is not endurable. Attention is thus being given to alternative approaches of intensification such as the use of Sustainable Land Management (SLM) technologies. Thus, this study assessed the effect of SLM technologies on food security of farming households in Kwara State, Nigeria. A-three stage sampling technique was used to select a sample of 200 farming households for this study. Descriptive statistics, Shriar index, Likert scale, food security index and logistic regression were employed for the analysis. The result indicated that majority (41%) of the household heads were between the ages of 51 and 70 years with an average of 60.5 years. Food security index revealed that 35% and 65% of the households were food secure and food insecure respectively. The logistic regression showed that SLM technologies, estimated income, household size, gender and age of the household heads were the critical determinants of food security among farming households. The most effective coping strategies adopted by households geared towards lessening the effects of food insecurity are reduced quality of food consumed, employed off-farm jobs to raise household income and diversion of money budgeted for other uses to purchase foods. Governments should encourage the adoption and use of SLM technologies at all levels. Policies and strategies that reduce household size should be enthusiastically pursued to reduce food insecurity.

Keywords: agricultural practices, coping strategies, farming households, food security, SLM technologies, logistic regression

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Authors: Maghsoud Nader Pilehroud, Mohmmad Alizadeh, Soheila Golipour, Sedigeh Tajabadipour

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Aim: in terms of importance, sexual issues are of the highest priority in married life issues and sexual compatibility is of the most important reasons of success in married life and consequently marital satisfaction.the present research was conducted with the aim of The role of sexual satisfaction sexual satisfaction in marital satisfaction married men. Study Design: this research is descriptive and is of correlation type.Method: The statistical population includes all the married men of Ardebil city out of which, 60 men were chosen using random sampling as the research samples. The research instruments were ENRICH couple scale and Hudson sexual satisfaction scale. The findings were analyzed using descriptive statistics method (mean and standard deviation) and inferential statistics (Pearson's correlation and regression) and SPSS-16 software. Results: the results showed that sexual satisfaction has a positive and significant relationship with marital satisfaction and all of its components, and that sexual satisfaction can predict marital satisfaction. The results also showed that sexual and marital satisfaction, are not significantly related to any of the variables of education level, duration of marriage and number of children. conclusion: according to the results, it can be claimed that sexual skills training for couples can be influential at increasing their martial satisfaction, and that also, sexual satisfaction has an important role in marital satisfaction.

Keywords: sexual satisfaction, marital satisfaction, married men, Iran

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Authors: Ekapon Thienthaworn

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This research aims to study the reliability of the forecasting factor in social media by using survey research methods with questionnaires. The sampling is the group of undergraduate students in Bangkok. A multiple-step random number of 400 persons, data analysis are descriptive statistics with multivariate regression analysis. The research found the average of the overall trust at the intermediate level for reading the news in social media and the results of the multivariate regression analysis to find out the factors that forecast credibility of the media found the only content that has the power to forecast reliability of undergraduate students in Bangkok to reading the news on social media at the significance level.at 0.05.These can be factors with forecasts reliability of news in social media by a variable that has the highest influence factor of the media content and the speed is also important for reliability of the news.

Keywords: credibility of news, behaviors and attitudes, social media, web board

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Authors: Lubena Achmad, Herman Kristanto, Julian Dewantiningrum

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Background: The Bishop score is a standard method used to predict the success of induction. This examination tends to be subjective with high inter and intraobserver variability, so it was presumed to have a low predictive value in terms of the outcome of labor induction. Cervical length measurement using transvaginal ultrasound is considered to be more objective to assess the cervical length. Meanwhile, this examination is not a complicated procedure and less invasive than vaginal touché. Objective: To compare transvaginal ultrasound and Bishop score in predicting successful induction. Methods: This study was a prospective cohort study. One hundred and twenty women with singleton pregnancies undergoing induction of labor at 37 – 42 weeks and met inclusion and exclusion criteria were enrolled in this study. Cervical assessment by both transvaginal ultrasound and Bishop score were conducted prior induction. The success of labor induction was defined as an ability to achieve active phase ≤ 12 hours after induction. To figure out the best cut-off point of cervical length and Bishop score, receiver operating characteristic (ROC) curves were plotted. Logistic regression analysis was used to determine which factors best-predicted induction success. Results: This study showed significant differences in terms of age, premature rupture of the membrane, the Bishop score, cervical length and funneling as significant predictors of successful induction. Using ROC curves found that the best cut-off point for prediction of successful induction was 25.45 mm for cervical length and 3 for Bishop score. Logistic regression was performed and showed only premature rupture of membranes and cervical length ≤ 25.45 that significantly predicted the success of labor induction. By excluding premature rupture of the membrane as the indication of induction, cervical length less than 25.3 mm was a better predictor of successful induction. Conclusion: Compared to Bishop score, cervical length using transvaginal ultrasound was a better predictor of successful induction.

Keywords: Bishop Score, cervical length, induction, successful induction, transvaginal sonography

Procedia PDF Downloads 325

Authors: Theresia Dwi Hastuti, Kristiana Haryanti, Agustine Eva Maria Soekesi

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Today's family business continues to grow in big cities and in rural areas throughout Indonesia in line with the development of the business world and global competition. This study aims to analyze the effect of company value, leadership, and ownership succession on the financial performance of the family business. The research method was carried out quantitatively with multiple regression. The respondent amounted to 63 entrepreneurs. This study found that company value, leadership succession, relationships, and communication affect the financial performance of the family business.

Keywords: company value, family business, financial performance, leadership succession, ownership succession

Procedia PDF Downloads 131

Authors: Farnoush Haghanipour

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

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

Procedia PDF Downloads 310

Authors: S. Shahrooi, A. Talavari

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Numeral study on the crack and discontinuity using element-free methods has been widely spread in recent years. In this study, for stress intensity factor calculation of the cracked axis under torsional loading has been used from a new element-free method as MLPG method. Region range is discretized by some dispersed nodal points. From method of moving least square (MLS) utilized to create the functions using these nodal points. Then, results of meshless method and finite element method (FEM) were compared. The results is shown which the element-free method was of good accuracy.

Keywords: stress intensity factor, crack, torsional loading, meshless method

Procedia PDF Downloads 565

Authors: Aditya Patil, Arun Patil, Vaishali Patil, Sudhir Chitnis, Anjum Patel

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Rapid growth has given e-commerce platforms a lot of client behavior and spending data. To maximize their strategy, businesses must understand how customers utilize online shopping platforms and what influences their purchases. Our research focuses on e-commerce user behavior and purchasing trends. This extensive study examines spending and user behavior. Regression and grouping disclose relevant data from the dataset. We can understand user spending trends via multilevel regression. We can analyze how pricing, user demographics, and product categories affect customer purchase decisions with this technique. Clustering groups consumers by spending. Important information was found. Purchase habits vary by user group. Our analysis illuminates the complex world of e-commerce consumer behavior and purchase trends. Understanding user behavior helps create effective e-commerce marketing strategies. This market can benefit from K-means clustering. This study focuses on tailoring strategies to user groups and improving product and price effectiveness. Customer buying behaviors across categories were shown via K-means clusters. Average spending is highest in Cluster 4 and lowest in Cluster 3. Clothing is less popular than gadgets and appliances around the holidays. Cluster spending distribution is examined using average variables. Our research enhances e-commerce analytics. Companies can improve customer service and decision-making with this data.

Keywords: e-commerce, regression, clustering, k-means

Procedia PDF Downloads 18

Authors: Hang Lo Lee, Ki Il Song, Hee Hwan Ryu

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An accurate prediction of TBM(Tunnel Boring Machine) performance is very difficult for reliable estimation of the construction period and cost in preconstruction stage. For this purpose, the aim of this study is to analyze the evaluation process of various prediction models published since 2000 for TBM performance, and to select the optimal input parameters for the prediction model. A classification system of TBM performance prediction model and applied methodology are proposed in this research. Input and output parameters applied for prediction models are also represented. Based on these results, a statistical analysis is performed using the collected data from shield TBM tunnel in South Korea. By performing a simple regression and residual analysis utilizinFg statistical program, R, the optimal input parameters are selected. These results are expected to be used for development of prediction model of TBM performance.

Keywords: TBM performance prediction model, classification system, simple regression analysis, residual analysis, optimal input parameters

Procedia PDF Downloads 309

Authors: Hussaini Bala

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There are different opinions in the literature on the relationship between Audit Committee characteristics and earnings management. The mix of opinions makes the direction of their relationship ambiguous. This study investigated the relationship between Audit Committee characteristics and earnings management of listed food and beverages Firms in Nigeria. The study covered the period of six years from 2007 to 2012. Data for the study were extracted from the Firms’ annual reports and accounts. After running the OLS regression, a robustness test was conducted for the validity of statistical inferences. The dependent variable was generated using two steps regression in order to determine the discretionary accrual of the sample Firms. Multiple regression was employed to run the data of the study using Random Model. The results from the analysis revealed a significant association between audit committee characteristics and earnings management of the Firms. While audit committee size and committees’ financial expertise showed an inverse relationship with earnings management, committee’s independence, and frequency of meetings are positively and significantly related to earnings management. In line with the findings, the study recommended among others that listed food and beverages Firms in Nigeria should strictly comply with the provision of Companies and Allied Matters Act (CAMA) and SEC Code of Corporate Governance on the issues regarding Audit Committees. Regulators such as SEC should increase the minimum number of Audit Committee members with financial expertise and also have a statutory position on the maximum number of Audit Committees meetings, which should not be greater than four meetings in a year as SEC code of corporate governance is silent on this.

Keywords: audit committee, earnings management, listed Food and beverages size, leverage, Nigeria

Procedia PDF Downloads 271

Authors: Sai AKhila Budaraju

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Heritage sites are an integral part of cities and carry a sense of identity to the cities/ towns, but the process of urbanization is a carrying potential threat for the loss of these heritage sites/monuments. Both Central and State Governments listed the historic Golconda fort as National Important Monument and the Heritage precinct with eight heritage-listed buildings and two historical sites respectively, for conservation and preservation, due to the presence of IT Corridor 6kms away accommodating more people in the precinct is under constant pressure. The heritage precinct possesses high property values, being a prime location connecting the IT corridor and CBD (central business district )areas. The primary objective of the study was to assess and identify the factors that are affecting the heritage precinct through Mapping and documentation, Identifying and assessing the factors through empirical analysis, Ordinal regression analysis and Hedonic Pricing Model. Ordinal regression analysis was used to identify the factors that contribute to the changes in the precinct due to urbanization. Hedonic Pricing Model was used to understand and establish a relation whether the presence of historical monuments is also a contributing factor to the property value and to what extent this influence can contribute. The above methods and field visit indicates the Physical, socio-economic factors and the neighborhood characteristics of the precinct contributing to the property values. The outturns and the potential elements derived from the analysis of the Development Control Rules were derived as recommendations to Integrate both Old and newly built environments.

Keywords: heritage planning, heritage conservation, hedonic pricing model, ordinal regression analysis

Procedia PDF Downloads 193

Authors: Dharshini S.

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India is currently experiencing a shift in wage discrimination from gender, caste and religion to different age groups in both formal and informal sectors. In this milieu, this study examines wage discrimination in the informal labour market between young people (15-29 years) and middle-aged people (30-59 years) among regular and casual employees in the Indian informal sector. The data was collected using periodic labour force (PLFS), and the original data was extracted from the National Sample Survey Office (NSSO) under the Ministry of Statistics and Programme Implementation (MOSPI), Government of India. The OLS regression model explores the determinants of wages for both regular and casual employees. Moreover, the Blinder Oaxaca decomposition method is used to explore the explained and unexplained components of this wage discrimination. The younger people (regular and casual employees) get lower wages as compared to middle-aged employees in the informal sector. The regression result follows the human capital theory, where education, job experience and higher occupation help to raise the wage rate of middle-aged people more than young-aged people in regular work. Furthermore, we found the rising trend of wage discrimination between the above groups over the years from 2017-18 to 2022-23. Unexplained factors (discrimination effects) contribute more to the wage differentiation between the young and middle age groups. It indicates that wage discrimination persists among regular and casual employees in the informal labour market, which is not a good sign for the economy. For the betterment of workers who face discrimination for age, the policies and programs should be implemented like other countries such as the U.S.A to stop age discrimination due to stereotypes in India.

Keywords: wage discrimination, young workers, middle workers, Informal sector, blinder oaxaca decomposition, PLFS.

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Authors: Abu Hashan Md Mashud, M. Sharif Uddin, Aminur Rahman Khan

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In this paper, we formulate a new problem as a linear programming and Integer Programming problem and maximize profit within the limited budget and limited resources based on the construction of a tea garden problem. It describes a new idea about how to optimize profit and focuses on the practical aspects of modeling and the challenges of providing a solution to a complex real life problem. Finally, a comparative study is carried out among Graphical method, Simplex method and Branch and bound method.

Keywords: integer programming, tea garden, graphical method, simplex method, branch and bound method

Procedia PDF Downloads 623