Search results for: interval regression

Authors: Niyati Sharma, Om Anand, Sanjeev Kumar Prasad

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Assuming future sale value for any of the organizations is one of the major essential characteristics of tactical development. Walmart Sales Forecasting is the finest illustration to work with as a beginner; subsequently, it has the major retail data set. Walmart uses this sales estimate problem for hiring purposes also. We would like to analyzing how the internal and external effects of one of the largest companies in the US can walk out their Weekly Sales in the future. Demand forecasting is the planned prerequisite of products or services in the imminent on the basis of present and previous data and different stages of the market. Since all associations is facing the anonymous future and we do not distinguish in the future good demand. Hence, through exploring former statistics and recent market statistics, we envisage the forthcoming claim and building of individual goods, which are extra challenging in the near future. As a result of this, we are producing the required products in pursuance of the petition of the souk in advance. We will be using several machine learning models to test the exactness and then lastly, train the whole data by Using linear regression and fitting the training data into it. Accuracy is 8.88%. The extra trees regression model gives the best accuracy of 97.15%.

Keywords: random forest algorithm, linear regression algorithm, extra trees classifier, mean absolute error

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Authors: Xhaklina Cani, Ilirjan Malollari, Ismet Beqiraj, Lorina Lici

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Oil characterization is an essential step in the design, simulation, and optimization of refining facilities. To achieve optimal crude selection and processing decisions, a refiner must have exact information refer to crude oil quality. This includes crude oil TBP-curve as the main data for correct operation of refinery crude oil atmospheric distillation plants. Crude oil is typically characterized based on a distillation assay. This procedure is reasonably well-defined and is based on the representation of the mixture of actual components that boil within a boiling point interval by hypothetical components that boil at the average boiling temperature of the interval. The crude oil assay typically includes TBP distillation according to ASTM D-2892, which can characterize this part of oil that boils up to 400 C atmospheric equivalent boiling point. To model the yield curves obtained by physical distillation is necessary to compare the differences between the modelling and the experimental data. Most commercial use a different number of components and pseudo-components to represent crude oil. Laboratory tests include distillations, vapor pressures, flash points, pour points, cetane numbers, octane numbers, densities, and viscosities. The aim of the study is the drawing of true boiling curves for different crude oil resources in Albania and to compare the differences between the modeling and the experimental data for optimal characterization of crude oil.

Keywords: TBP distillation curves, crude oil, optimization, simulation

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Authors: S. Benkraouda, Z. Djelloul-Khedda, B. Yagoubi

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we present a method for early detection of forest fires from a thermal infrared satellite image, using the image matrix of the probability of belonging. The principle of the method is to compare a theoretical mathematical model to an experimental model. We considered that each line of the image matrix, as an embodiment of a non-stationary random process. Since the distribution of pixels in the satellite image is statistically dependent, we divided these lines into small stationary and ergodic intervals to characterize the image by an adequate mathematical model. A standard deviation was chosen to generate random variables, so each interval behaves naturally like white Gaussian noise. The latter has been selected as the mathematical model that represents a set of very majority pixels, which we can be considered as the image background. Before modeling the image, we made a few pretreatments, then the parameters of the theoretical Gaussian model were extracted from the modeled image, these settings will be used to calculate the probability of each interval of the modeled image to belong to the theoretical Gaussian model. The high intensities pixels are regarded as foreign elements to it, so they will have a low probability, and the pixels that belong to the background image will have a high probability. Finally, we did present the reverse of the matrix of probabilities of these intervals for a better fire detection.

Keywords: forest fire, forest fire detection, satellite image, normal distribution, theoretical gaussian model, thermal infrared matrix image

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Authors: Rafael Zhindon Almeida

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Surface water quality is an important concern for the evaluation and prediction of water quality conditions. The objective of this study is to develop a statistical model that can accurately predict the water quality of the El Macho estuary in the city of Machala, El Oro province. The methodology employed in this study is of a basic type that involves a thorough search for theoretical foundations to improve the understanding of statistical modeling for water quality analysis. The research design is correlational, using a multivariate statistical model involving multiple linear regression and principal component analysis. The results indicate that water quality parameters such as fecal coliforms, biochemical oxygen demand, chemical oxygen demand, iron and dissolved oxygen exceed the allowable limits. The water of the El Macho estuary is determined to be below the required water quality criteria. The multiple linear regression model, based on chemical oxygen demand and total dissolved solids, explains 99.9% of the variance of the dependent variable. In addition, principal component analysis shows that the model has an explanatory power of 86.242%. The study successfully developed a statistical model to evaluate the water quality of the El Macho estuary. The estuary did not meet the water quality criteria, with several parameters exceeding the allowable limits. The multiple linear regression model and principal component analysis provide valuable information on the relationship between the various water quality parameters. The findings of the study emphasize the need for immediate action to improve the water quality of the El Macho estuary to ensure the preservation and protection of this valuable natural resource.

Keywords: statistical modeling, water quality, multiple linear regression, principal components, statistical models

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Authors: George Eduful, Ebenezer A. Jackson, Kingsford A. Atanga

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This paper investigates the impacts of cable length and capacity of transformer on ferroresonant overvoltage in cable-fed transformers. The study was conducted by simulation using the EMTP RV. Results show that ferroresonance can cause dangerous overvoltages ranging from 2 to 5 per unit. These overvoltages impose stress on insulations of transformers and cables and subsequently result in system failures. Undertaking Basic Multiple Regression Analysis (BMR) on the results obtained, a statistical model was obtained in terms of cable length and transformer capacity. The model is useful for ferroresonant prediction and control in cable-fed transformers.

Keywords: ferroresonance, cable-fed transformers, EMTP RV, regression analysis

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Authors: Yung-Feng Yen, Yun-Ju Lai

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Background: Animal studies have shown that alcohol exposure may cause immunosuppression and increase the susceptibility to tuberculosis (TB) infection. However, the temporality of alcohol consumption with subsequent TB development remains unclear. This nationwide population-based cohort study aimed to investigate the impact of alcohol exposure on TB development in Taiwanese adults. Methods: We included 46 196 adult participants from three rounds (2001, 2005, 2009) of the Taiwan National Health Interview Survey. Alcohol consumption was classified into heavy, regular, social, or never alcohol use. Heavy alcohol consumption was defined as intoxication at least once/week. Alcohol consumption and other covariates were collected by in-person interviews at baseline. Incident cases of active TB were identified from the National Health Insurance database. Multivariate logistic regression was used to estimate the association between alcohol consumption and active TB, with adjustment for age, sex, smoking, socioeconomic status, and other covariates. Results: A total of 279 new cases of active TB occurred during the study follow-up period. Heavy (adjusted odds ratio [AOR], 5.21; 95% confident interval [CI], 2.41-11.26) and regular alcohol use (AOR, 1.73; 95% CI, 1.26-2.38) were associated with higher risks of incident TB after adjusting for the subject demographics and comorbidities. Moreover, a strong dose-response effect was observed between increasing alcohol consumption and incident TB (AOR, 2.26; 95% CI, 1.59-3.21; P <.001). Conclusion: Heavy and regular alcohol consumption were associated with higher risks of active TB. Future TB control programs should consider strategies to lower the overall level of alcohol consumption to reduce the TB disease burden.

Keywords: alcohol consumption, tuberculosis, risk factor, cohort study

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Authors: Bersufekad Wubie

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Introduction: Near visual impairment is presenting near visual acuity of the eye worse than N6 at a 40 cm distance. Teachers' regular duties, such as reading books, writing on the blackboard, and recognizing students' faces, need good near vision. If a teacher has near-visual impairment, the work output is unsatisfactory. Objective: The study was aimed to assess the prevalence and associated factors near vision impairment among school teachers at Gondar city Northwest Ethiopia, August 2022. Methods: To select 567 teachers in Gondar city schools, an institutional-based cross-sectional study design with a multistage sampling technique were used. The study was conducted in selected schools from May 1 to May 30, 2022. Trained data collectors used well-structured Amharic and English language questionnaires and ophthalmic instruments for examination. The collected data were checked for completeness and entered into Epi data version 4.6, then exported to SPSS version 26 for further analysis. A binary and multivariate logistic regression model was fitted. And associated factors of the outcome variable. Result: The prevalence of near visual impairment was 64.6%, with a confidence interval of 60.3%–68.4%. Near visual impairment was significantly associated with age >= 35 years (AOR: 4.90 at 95% CI: 3.15, 7.65), having prolonged years of teaching experience (AOR: 3.29 at 95% CI: 1.70, 4.62), having a history of ocular surgery (AOR: 1.96 at 95% CI: 1.10, 4.62), smokers (AOR: 2.21 at 95% CI: 1.22, 4.07), history of ocular trauma (AOR : 1.80 at 95%CI:1.11,3.18 and uncorrected refractive error (AOR:2.01 at 95%CI:1.13,4.03). Conclusion and recommendations: This study showed the prevalence of near vision impairment among school teachers was high, and it is not a problem of the presbyopia age group alone; it also happens at a young age. So teachers' ocular health should be well accommodated in the school's eye health.

Keywords: Gondar, near visual impairment, school, teachers

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Authors: Christian Neunzig, Simon Fahle, Jürgen Schulz, Matthias Möller, Bernd Kuhlenkötter

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Digitalisation in production technology is a driver for the application of machine learning methods. Through the application of predictive quality, the great potential for saving necessary quality control can be exploited through the data-based prediction of product quality and states. However, the serial use of machine learning applications is often prevented by various problems. Fluctuations occur in real production data sets, which are reflected in trends and systematic shifts over time. To counteract these problems, data preprocessing includes rule-based data cleaning, the application of dimensionality reduction techniques, and the identification of comparable data subsets to extract stable features. Successful process control of the target variables aims to centre the measured values around a mean and minimise variance. Competitive leaders claim to have mastered their processes. As a result, much of the real data has a relatively low variance. For the training of prediction models, the highest possible generalisability is required, which is at least made more difficult by this data availability. The implementation of a machine learning application can be interpreted as a production process. The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that describes the life cycle of data science. As in any process, the costs to eliminate errors increase significantly with each advancing process phase. For the quality prediction of hydraulic test steps of directional control valves, the question arises in the initial phase whether a regression or a classification is more suitable. In the context of this work, the initial phase of the CRISP-DM, the business understanding, is critically compared for the use case at Bosch Rexroth with regard to regression and classification. The use of cross-process production data along the value chain of hydraulic valves is a promising approach to predict the quality characteristics of workpieces. Suitable methods for leakage volume flow regression and classification for inspection decision are applied. Impressively, classification is clearly superior to regression and achieves promising accuracies.

Keywords: classification, CRISP-DM, machine learning, predictive quality, regression

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Authors: Ghada Abo-Zaid

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Introduction: Oil is one of the most income source in Bahrain. Low oil price influence on the economy growth and the investment rate in Bahrain. For example, the economic growth was 3.7% in 2012, and it reduced to 2.9% in 2015. Investment rate was 9.8% in 2012, and it is reduced to be 5.9% and -12.1% in 2014 and 2015, respectively. The inflation rate is increased to the peak point in 2013 with 3.3 %. Objectives: The objectives here are to build statistical models to examine the effect of the interest rate inflation rate on the growth economy in Bahrain from 2000 to 2018. Methods: This study based on 18 years, and the multiple regression model is used for the analysis. All of the missing data are omitted from the analysis. Results: Regression model is used to examine the association between the Growth national product (GNP), the inflation rate, and real interest rate. We found that (i) Increase the real interest rate decrease the GNP. (ii) Increase the inflation rate does not effect on the growth economy in Bahrain since the average of the inflation rate was almost 2%, and this is considered as a low percentage. Conclusion: There is a positive impact of the real interest rate on the GNP in Bahrain. While the inflation rate does not show any negative influence on the GNP as the inflation rate was not large enough to effect negatively on the economy growth rate in Bahrain.

Keywords: growth national product, egypt, regression model, interest rate

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Authors: Kang-Mo Jung

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Least squares support vector machine (LS-SVM) is a penalized regression which considers both fitting and generalization ability of a model. However, the squared loss function is very sensitive to even single outlier. We proposed a weighted absolute deviation loss function for the robustness of the estimates in least absolute deviation support vector machine. The proposed estimates can be obtained by a quadratic programming algorithm. Numerical experiments on simulated datasets show that the proposed algorithm is competitive in view of robustness to outliers.

Keywords: least absolute deviation, quadratic programming, robustness, support vector machine, weight

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Authors: Khashayar Kazemzadeh, Mohammad Hanif Dasoomi

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According to the increasing volume of traffic, lane changing plays a crucial role in traffic flow. Lane changing in traffic depends on several factors including road geometrical design, speed, drivers’ behavioral characteristics, etc. A great deal of research has been carried out regarding these fields. Despite of the other significant factors, the drivers’ behavioral characteristics of lane changing has been emphasized in this paper. This paper has predicted the effective equation based on personal characteristics of lane changing by regression models.

Keywords: effective equation, lane changing, drivers’ behavioral characteristics, regression models

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Authors: Lule Basha, Eralda Gjika

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This study is focused on analyzing climate change in Albania and its potential effects on cereal yields. Initially, monthly temperature and rainfalls in Albania were studied for the period 1960-2021. Climacteric variables are important variables when trying to model cereal yield behavior, especially when significant changes in weather conditions are observed. For this purpose, in the second part of the study, linear and nonlinear models explaining cereal yield are constructed for the same period, 1960-2021. The multiple linear regression analysis and lasso regression method are applied to the data between cereal yield and each independent variable: average temperature, average rainfall, fertilizer consumption, arable land, land under cereal production, and nitrous oxide emissions. In our regression model, heteroscedasticity is not observed, data follow a normal distribution, and there is a low correlation between factors, so we do not have the problem of multicollinearity. Machine-learning methods, such as random forest, are used to predict cereal yield responses to climacteric and other variables. Random Forest showed high accuracy compared to the other statistical models in the prediction of cereal yield. We found that changes in average temperature negatively affect cereal yield. The coefficients of fertilizer consumption, arable land, and land under cereal production are positively affecting production. Our results show that the Random Forest method is an effective and versatile machine-learning method for cereal yield prediction compared to the other two methods.

Keywords: cereal yield, climate change, machine learning, multiple regression model, random forest

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Authors: Antoine Vikkey Hinson, Menonli Adjobimey, Gemayel Ahmed Biokou, Rose Mikponhoue

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Introduction: Construction industry is a critical concern with regard to Health and Safety Service worldwide. World health Organization revealed that work-related disease and trauma were held responsible for the death of one million nine hundred thousand people in 2016. The aim of this study it was to determine the prevalence and factors associated with the occurrence of work accidents in a construction industry in Benin. Method: It was a descriptive cross-sectional and analytical study. Data analysis was performed with R software 4.1.1. In multivariate analysis, we performed a binary logistic regression. OR adjusted (ORa) association measures and their 95% confidence interval [CI95%] were presented for the explanatory variables used in the final model. The significance threshold for all tests selected was 5% (p < 0.05) Result: In this study, 472 workers were included, and, of these, 452 (95.7%) were men corresponding to a sex ratio of 22.6. The average age of the workers was 33 years ± 8.8 years. Workers were mostly laborers (84.7%), and had declared having inadequate personal protective equipment (50.6%, n=239). The prevalence of work accidents is 50.8%. Collision with a rolling stock (25.8%), cut (16.2%), and stumbling (16.2%) were the main types of work accidents on the construction site. Four factors were associated with contributing to work accidents. Fatigue or exhaustion (ORa : 1.53[1.03 ; 2.28]); The use of dangerous tools (ORa : 1.81 [1.22 ; 2.71]); The various laborers’ jobs (ORa : 4.78 [2.62 ; 9.21]); and seniority in the company ≥ 4 years (ORa : 2.00 [1.35 ; 2.96]). Conclusion: This study allowed us to identify the associated factors. It is imperative to implement a rigorous policy of occupational health and security mostly the continuing training for workers safe, the supply of appropriate work tools and protective

Keywords: prevalence, work accident, associated factors, construction, benin

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Authors: Masaki Ohno, Cervinia Manalo, Tetsuji Okuda, Satoshi Nakai, Wataru Nishijima

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Reverse osmosis (RO) membranes have been widely used for desalination to purify water for drinking and other purposes. Although at present most RO membranes have no resistance to chlorine, chlorine-resistant membranes are being developed. Therefore, direct chlorine treatment or chlorine washing will be an option in preventing biofouling on chlorine-resistant membranes. Furthermore, if particle accumulation control is possible by using chlorine washing, expensive pretreatment for particle removal can be removed or simplified. The objective of this study was to determine the effective hypochlorite washing condition required for controlling biofilm formation and inorganic particle accumulation on RO membrane in a continuous flow channel with RO membrane and spacer. In this study, direct chlorine washing was done by soaking fouled RO membranes in hypochlorite solution and fluorescence intensity was used to quantify biofilm on the membrane surface. After 48 h of soaking the membranes in high fouling potential waters, the fluorescence intensity decreased to 0 from 470 using the following washing conditions: 10 mg/L chlorine concentration, 2 times/d washing interval, and 30 min washing time. The chlorine concentration required to control biofilm formation decreased as the chlorine concentration (0.5–10 mg/L), the washing interval (1–4 times/d), or the washing time (1–30 min) increased. For the sample solutions used in the study, 10 mg/L chlorine concentration with 2 times/d interval, and 5 min washing time was required for biofilm control. The optimum chlorine washing conditions obtained from soaking experiments proved to be applicable also in controlling biofilm formation in continuous flow experiments. Moreover, chlorine washing employed in controlling biofilm with suspended particles resulted in lower amounts of organic (0.03 mg/cm²) and inorganic (0.14 mg/cm²) deposits on the membrane than that for sample water without chlorine washing (0.14 mg/cm² and 0.33 mg/cm², respectively). The amount of biofilm formed was 79% controlled by continuous washing with 10 mg/L of free chlorine concentration, and the inorganic accumulation amount decreased by 58% to levels similar to that of pure water with kaolin (0.17 mg/cm²) as feed water. These results confirmed the acceleration of particle accumulation due to biofilm formation, and that the inhibition of biofilm growth can almost completely reduce further particle accumulation. In addition, effective hypochlorite washing condition which can control both biofilm formation and particle accumulation could be achieved.

Keywords: reverse osmosis, washing condition optimization, hypochlorous acid, biofouling control

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Authors: Jingjue Bao, Ye Li, Yujie Qi

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The explosive growth of ride-hailing has reshaped residents' travel behavior and plays a crucial role in urban mobility within the built environment. Contributing to the research of the spatial variation of ride-hailing demand and its relationship to the built environment and socioeconomic factors, this study utilizes multi-source data from Haikou, China, to construct a Multi-scale Geographically Weighted Regression model (MGWR), considering spatial scale heterogeneity. The regression results showed that MGWR model was demonstrated superior interpretability and reliability with an improvement of 3.4% on R2 and from 4853 to 4787 on AIC, compared with Geographically Weighted Regression model (GWR). Furthermore, to precisely identify the surrounding environment of sampling point, DeepLabv3+ model is employed to segment street-level images. Features extracted from these images are incorporated as variables in the regression model, further enhancing its rationality and accuracy by 7.78% improvement on R2 compared with the MGWR model only considered region-level variables. By integrating multi-scale geospatial data and utilizing advanced computer vision techniques, this study provides a comprehensive understanding of the spatial dynamics between ride-hailing demand and the urban built environment. The insights gained from this research are expected to contribute significantly to urban transportation planning and policy making, as well as ride-hailing platforms, facilitating the development of more efficient and effective mobility solutions in modern cities.

Keywords: travel behavior, ride-hailing, spatial relationship, built environment, street-level image

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Authors: Iman Fakhrian, Ali Salehi Manzari

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Consistent Profitability is one of the most important priorities in manufacturing companies. One of the fundamental factors for increasing the companies profitability is cost management. Isfahan's mobarakeh steel company is one of the largest producers of the slab product grades in the middle east. Raw material cost constitutes about 70% of the company's expenditures. The costs of the ferro alloys have a remarkable contribution of the raw material costs. This research aims to determine the ferro alloys which have significant effect on the variability of the standard cost of the slab product grades. Used data in this study were collected from standard costing system of isfahan's mobarakeh steel company in 2022. The results of conducting the regression analysis model show that expense items: 03020, 03045, 03125, 03130 and 03150 have dominant role in variability of the standard cost of the slab product grades. In other words, the mentioned ferro alloys have noticeable and significant role in variability of the standard cost of the slab product grades.

Keywords: consistent profitability, ferro alloys, slab product grades, regression analysis

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Authors: Seyedtaghi Mirmohammadi, Jamshid Yazdani, Syavash Etemadi Nejad

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One of the main indoor air parameters in classrooms is dust pollution and it depends on the particle size and exposure duration. However, there is a lake of data about the exposure level to PM2.5 concentrations in rural area classrooms. The objective of the current study was exposure assessment for PM2.5 for students in the classrooms. One year monitoring was carried out for fifteen schools by time-series sampling to evaluate the indoor air PM2.5 in the rural district of Sari city, Iran. A hygrometer and thermometer were used to measure some psychrometric parameters (temperature, relative humidity, and wind speed) and Real-Time Dust Monitor, (MicroDust Pro, Casella, UK) was used to monitor particulate matters (PM2.5) concentration. The results show the mean indoor PM2.5 concentration in the studied classrooms was 135µg/m3. The regression model indicated that a positive correlation between indoor PM2.5 concentration and relative humidity, also with distance from city center and classroom size. Meanwhile, the regression model revealed that the indoor PM2.5 concentration, the relative humidity, and dry bulb temperature was significant at 0.05, 0.035, and 0.05 levels, respectively. A statistical predictive model was obtained from multiple regressions modeling for indoor PM2.5 concentration and indoor psychrometric parameters conditions.

Keywords: classrooms, concentration, humidity, particulate matters, regression

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Authors: M. A. Alrabib, Y. Sherif, A. K. Mohamed, E. A. Elfandi, E. I. Fandi

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This paper introduces a paleo-environmental and hydrocarbon show in this well was identified in the interval of Dembaba formation to the Hassaona Formation was poor to very poor oil show. And from palaeo-environmental analysis there is neither particularly good reservoir nor source rock have been developed in the area. Recent palaeo-environment work undertakes that the sedimentary succession in this area comprises the Upper Paleozoic rock of the Carboniferous and Permian and the Mesozoic (Triassic) sedimentary sequences. No early Paleozoic rocks have been found in this area, these rocks were eroding during the Late Carboniferous and Early Permian time. During Latest Permian and earliest Triassic time evidence for major marine transgression has occurred. From depths 5930-5940 feet, to 10800-10810 feet, the TAI of the Al Guidr, the Bir Al Jaja Al Uotia, Hebilia and the top varies between 3+ to 4-(mature-dry gas). This interval corporate the rest part of the Dembaba Formation. From depth 10800- 10810 feet, until total sediment depth (11944 feet Log) which corporate the rest of the Dembaba and underlying equivalents of the Assedjefar and M Rar Formations and the underlying Indeterminate unit (Hassouna Formation) the TAI varies between 4 and 5 (dry gas-black and deformed).

Keywords: paleoenvironmental, thermal alteration index, north western Libya, hydrocarbon

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Authors: Wei-Po Wu, Wen-Yu Liu, Wei−Ting Lin, Hen-Yu Lien

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Introduction: Improving balance function for individuals after stroke is a crucial target in physiotherapy. Aquatic therapy which challenges individual’s postural control in an unstable fluid environment may be beneficial in enhancing balance functions. The purposes of the systematic review with meta-analyses were to validate the effects of aquatic therapy in improving balance functions for individuals with strokes in contrast to conventional physiotherapy. Method: Available studies were explored from three electronic databases: PubMed, Scopus, and Web of Science. During literature search, the published date of studies was not limited. The study design of the included studies should be randomized controlled trials (RCTs) and the studies should contain at least one outcome measurement of balance function. The PEDro scale was adopted to assess the quality of included studies, while the 'Oxford Centre for Evidence-Based Medicine 2011 Levels of Evidence' was used to evaluate the level of evidence. After the data extraction, studies with same outcome measures were pooled together for meta-analysis. Result: Ten studies with 282 participants were included in analyses. The research qualities of the studies were ranged from fair to good (4 to 8 points). Levels of evidence of the included studies were graded as level 2 and 3. Finally, scores of Berg Balance Scale (BBS), Eye closed force plate center of pressure velocity (anterior-posterior, medial-lateral axis) and Timed up and Go test were pooled and analyzed separately. The pooled results shown improvement in balance function (BBS mean difference (MD): 1.39 points; 95% confidence interval (CI): 0.05-2.29; p=0.002) (Eye closed force plate center of pressure velocity (anterior-posterior axis) MD: 1.39 mm/s; 95% confidence interval (CI): 0.93-1.86; p<0.001) (Eye closed force plate center of pressure velocity (medial-lateral) MD: 1.48 mm/s; 95% confidence interval (CI): 0.15-2.82; p=0.03) and mobility (MD: 0.9 seconds; 95% CI: 0.07-1.73; p=0.03) of stroke individuals after aquatic therapy compared to conventional therapy. Although there were significant differences between two treatment groups, the differences in improvement were relatively small. Conclusion: The aquatic therapy improved general balance function and mobility in the individuals with stroke better than conventional physiotherapy.

Keywords: aquatic therapy, balance function, meta-analysis, stroke, systematic review

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Authors: Min Wei

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The traditional prediction methods of the forecast for tourism market are paid more attention to the accuracy of the forecasts, ignoring the results of the feasibility of forecasting and predicting operability, which had made it difficult to predict the results of scientific testing. With the application of Linear Regression Model, this paper attempts to construct a scientific evaluation system for predictive value, both to ensure the accuracy, stability of the predicted value, and to ensure the feasibility of forecasting and predicting the results of operation. The findings show is that a scientific evaluation system can implement the scientific concept of development, the harmonious development of man and nature co-ordinate.

Keywords: linear regression model, tourism market, forecast, tourism economics

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Authors: Charles-Olivier Amédée-Manesme, Benoit Faye, Eric Le Fur

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In this paper, the heterogeneity of the Bordeaux Legends 50 wine market price segment is addressed. For this purpose, quantile regression is applied – with market segmentation based on wine bottle price quantile – and the hedonic price of wine attributes is computed for various price segments of the market. The approach is applied to a major privately held data set which consists of approximately 30,000 transactions over the 2003–2014 period. The findings suggest that the relative hedonic prices of several wine attributes differ significantly among deciles. In particular, the elasticity coefficient of the expert ratings shows strong variation among prices. If - as suggested in the literature - expert ratings have a positive influence on wine price on average, they have a clearly decreasing impact over the quantiles. Finally, the lower the wine price, the higher the potential for price appreciation over time. Other variables such as chateaux or vintage are also shown to vary across the distribution of wine prices. While enhancing our understanding of the complex market dynamics that underlie Bordeaux wines’ price, this research provides empirical evidence that the QR approach adequately captures heterogeneity among wine price ranges, which simultaneously applies to wine stock, vintage and auctions’ house.

Keywords: hedonics, market segmentation, quantile regression, heterogeneity, wine economics

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Authors: M. Gulam Kibria, Shourav Ahmed, Kais Zaman

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In system analysis, the information on the uncertain input variables cause uncertainty in the system responses. Different probabilistic approaches for uncertainty representation and propagation in such cases exist in the literature. Different uncertainty representation approaches result in different outputs. Some of the approaches might result in a better estimation of system response than the other approaches. The NASA Langley Multidisciplinary Uncertainty Quantification Challenge (MUQC) has posed challenges about uncertainty quantification. Subproblem A, the uncertainty characterization subproblem, of the challenge posed is addressed in this study. In this subproblem, the challenge is to gather knowledge about unknown model inputs which have inherent aleatory and epistemic uncertainties in them with responses (output) of the given computational model. We use two different methodologies to approach the problem. In the first methodology we use sampling-based uncertainty propagation with first order error analysis. In the other approach we place emphasis on the use of Percentile-Based Optimization (PBO). The NASA Langley MUQC’s subproblem A is developed in such a way that both aleatory and epistemic uncertainties need to be managed. The challenge problem classifies each uncertain parameter as belonging to one the following three types: (i) An aleatory uncertainty modeled as a random variable. It has a fixed functional form and known coefficients. This uncertainty cannot be reduced. (ii) An epistemic uncertainty modeled as a fixed but poorly known physical quantity that lies within a given interval. This uncertainty is reducible. (iii) A parameter might be aleatory but sufficient data might not be available to adequately model it as a single random variable. For example, the parameters of a normal variable, e.g., the mean and standard deviation, might not be precisely known but could be assumed to lie within some intervals. It results in a distributional p-box having the physical parameter with an aleatory uncertainty, but the parameters prescribing its mathematical model are subjected to epistemic uncertainties. Each of the parameters of the random variable is an unknown element of a known interval. This uncertainty is reducible. From the study, it is observed that due to practical limitations or computational expense, the sampling is not exhaustive in sampling-based methodology. That is why the sampling-based methodology has high probability of underestimating the output bounds. Therefore, an optimization-based strategy to convert uncertainty described by interval data into a probabilistic framework is necessary. This is achieved in this study by using PBO.

Keywords: aleatory uncertainty, epistemic uncertainty, first order error analysis, uncertainty quantification, percentile-based optimization

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Authors: Phan Thi Song Thuong

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This paper uses data from a survey on the green consumption behavior of Hanoi residents in October 2022. Data was gathered from a survey conducted in ten districts in the center of Hanoi, with 393 respondents. The hypothesis focuses on understanding the factors that may affect green consumption behavior, such as demographic characteristics, concerns about the environment and health, people living around, self-efficiency, and mass media. A number of methods, such as the T-test, exploratory factor analysis, and a linear regression model, are used to prove the hypotheses. Accordingly, the results show that gender, age, and education level have separate effects on the green consumption behavior of respondents.

Keywords: green consumption, urban residents, environment, sustainable, linear regression

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Authors: Niyada Naksuk, Thoetchai Peeraphatdit, Vitaly Herasevich, Peter A. Brady, Suraj Kapa, Samuel J. Asirvatham

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Introduction: Little is known about the safety of antipsychotic therapy for delirium in the coronary care unit (CCU). Our aim was to examine the effect of delirium and antipsychotic therapy among CCU patients. Methods: Pre-study Confusion Assessment Method-Intensive Care Unit (CAM–ICU) criteria were implemented in screening consecutive patients admitted to Mayo Clinic, Rochester, the USA from 2004 through 2013. Death status was prospectively ascertained. Results: Of 11,079 study patients, the incidence of delirium was 8.3% (n=925). Delirium was associated with an increased risk of in-hospital mortality (adjusted OR 1.49; 95% CI, 1.08-2.08; P=.02) and one-year mortality among patients who survived from CCU admission (adjusted HR 1.46; 95% CI, 1.12-1.87; P=.005). A total of 792 doses of haloperidol (5 IQR [3-10] mg/day) or quetiapine (25 IQR [13-50] mg/day) were given to 244 patients with delirium. The clinical characteristics of patients with delirium who did and did not receive antipsychotic therapy were not different (baseline corrected QT [QTc] interval 460±61 ms vs. 457±58 ms, respectively; P = 0.57). In comparison to baseline, mean QTc intervals after the first and third doses of the antipsychotics were not significantly prolonged in haloperidol (448±56, 458±57, and 450±50 ms, respectively) or quetiapine groups (459±54, 467±68, and 462±46 ms, respectively) (P > 0.05 for all). Additionally, in-hospital mortality (adjusted OR 0.67; 95% CI, 0.42-1.04; P=.07), ventricular arrhythmia (adjusted OR 0.87; 95% CI, 0.17-3.62; P=.85) and one-year mortality among the hospital survivors (adjusted HR 0.86; 95% CI 0.62-1.17; P = 0.34) were not different in patients with delirium irrespective of whether or not they received antipsychotics. Conclusions: In patients admitted to the CCU, delirium was associated with an increase in both in-hospital and one-year mortality. Low doses of haloperidol and quetiapine appeared to be safe, without an increase in risk of sudden cardiac death, in-hospital mortality, or one-year mortality in carefully monitored patients.

Keywords: arrhythmias, haloperidol, mortality, qtc interval, quetiapine

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Authors: Hassnen M. Jafer, Khalid S. Hashim, W. Atherton, Ali W. Alattabi

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This study investigates the effect of two important parameters (length of curing period and percentage of the added binder) on the strength of soil treated with OPC. An intermediate plasticity silty clayey soil with medium organic content was used in this study. This soft soil was treated with different percentages of a commercially available cement type 32.5-N. laboratory experiments were carried out on the soil treated with 0, 1.5, 3, 6, 9, and 12% OPC by the dry weight to determine the effect of OPC on the compaction parameters, consistency limits, and the compressive strength. Unconfined compressive strength (UCS) test was carried out on cement-treated specimens after exposing them to different curing periods (1, 3, 7, 14, 28, and 90 days). The results of UCS test were used to develop a non-linear multi-regression model to find the relationship between the predicted and the measured maximum compressive strength of the treated soil (qu). The results indicated that there was a significant improvement in the index of plasticity (IP) by treating with OPC; IP was decreased from 20.2 to 14.1 by using 12% of OPC; this percentage was enough to increase the UCS of the treated soil up to 1362 kPa after 90 days of curing. With respect to the statistical model of the predicted qu, the results showed that the regression coefficients (R2) was equal to 0.8534 which indicates a good reproducibility for the constructed model.

Keywords: cement admixtures, soft soil stabilisation, geotechnical parameters, multi-regression model

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Authors: Chisomo Patrick Kumbuyo, Katsuyuki Shimizu, Hiroshi Yasuda, Yoshinobu Kitamura

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This study deals with a way of predicting Malawi rainfall from global sea surface temperature (SST) using a simple multiple regression model. Monthly rainfall data from nine stations in Malawi grouped into two zones on the basis of inter-station rainfall correlations were used in the study. Zone 1 consisted of Karonga and Nkhatabay stations, located in northern Malawi; and Zone 2 consisted of Bolero, located in northern Malawi; Kasungu, Dedza, Salima, located in central Malawi; Mangochi, Makoka and Ngabu stations located in southern Malawi. Links between Malawi rainfall and SST based on statistical correlations were evaluated and significant results selected as predictors for the regression models. The predictors for Zone 1 model were identified from the Atlantic, Indian and Pacific oceans while those for Zone 2 were identified from the Pacific Ocean. The correlation between the fit of predicted and observed rainfall values of the models were satisfactory with r=0.81 and 0.54 for Zone 1 and 2 respectively (significant at less than 99.99%). The results of the models are in agreement with other findings that suggest that SST anomalies in the Atlantic, Indian and Pacific oceans have an influence on the rainfall patterns of Southern Africa.

Keywords: Malawi rainfall, forecast model, predictors, SST

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Authors: Martin Popescu D. Dana Maria

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The aims of this research are to broaden the study on the relationship between emotional intelligence and counterproductive work behavior (CWB). The study sample consisted in 441 Romanian employees from companies all over the country. Data has been collected through web surveys and processed with SPSS. The results indicated an average correlation between the two constructs and their sub variables, employees with a high level of emotional intelligence tend to be less aggressive. In addition, labeling was considered an individual difference which has the power to influence the level of employee aggression. A regression model was used to underline the importance of emotional intelligence together with labeling as predictors of CWB. Results have shown that this regression model enforces the assumption that labeling and emotional intelligence, taken together, predict CWB. Employees, who label themselves as victims and have a low degree of emotional intelligence, have a higher level of CWB.

Keywords: aggression, CWB, emotional intelligence, labeling

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Authors: Hafiz Mehtab Gull Nasir

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Industrial development and urbanization have significant impacts on air emissions, and their relationship diverges at different stages of economic progress. The revolution further propelled these activities as principal paths to economic and social transformation; nevertheless, the paths also promoted environmental degradation. Resultantly, both developed and developing countries undergone through fast-paced development; in which developed countries implemented legislation towards environmental pollution control however developing countries took the advantage of technology without caring about the environment. In this study, meta-analysis is performed on production of particulate matter (i.e., PM10 and PM2.5) from urbanized cities of first, second and third world countries to assess the air quality. The cities were selected based on ranked set principles. In case of PM10, third world countries showed highest PM level (~95% confidence interval of 0.74-1.86) followed by second world countries but with managed situation. Besides, first, world countries indicated the lowest pollution (~95% confidence interval of 0.12-0.2). Similarly, highest level of PM2.5 was produced by third world countries followed by the second and first world countries. Hereby, level of PM2.5 was not significantly different for both second and third world countries; however, first world countries showed minimum PM load. Finally, the study revealed different that levels of pollution status exist among different countries; whereas developed countries also devised better strategies towards pollution control while developing countries are least caring about their environmental resources. It is suggested that although industrialization and urbanization are directly involved with interference in natural elements, however, production of nature appears to be more societal rather hermetical.

Keywords: meta-analysis, particulate matter, developing countries, urbanization

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Authors: Ishapathik Das

Abstract:

The purpose of this article is to find a method of comparing designs for ordinal regression models using quantile dispersion graphs in the presence of linear predictor misspecification. The true relationship between response variable and the corresponding control variables are usually unknown. Experimenter assumes certain form of the linear predictor of the ordinal regression models. The assumed form of the linear predictor may not be correct always. Thus, the maximum likelihood estimates (MLE) of the unknown parameters of the model may be biased due to misspecification of the linear predictor. In this article, the uncertainty in the linear predictor is represented by an unknown function. An algorithm is provided to estimate the unknown function at the design points where observations are available. The unknown function is estimated at all points in the design region using multivariate parametric kriging. The comparison of the designs are based on a scalar valued function of the mean squared error of prediction (MSEP) matrix, which incorporates both variance and bias of the prediction caused by the misspecification in the linear predictor. The designs are compared using quantile dispersion graphs approach. The graphs also visually depict the robustness of the designs on the changes in the parameter values. Numerical examples are presented to illustrate the proposed methodology.

Keywords: model misspecification, multivariate kriging, multivariate logistic link, ordinal response models, quantile dispersion graphs

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Authors: Yegnanew A. Shiferaw

Abstract:

Background: commodity price volatility originating from excessive commodity price fluctuation has been a global problem especially after the recent financial crises. Volatility is a measure of risk or uncertainty in financial analysis. It plays a vital role in risk management, portfolio management, and pricing equity. Objectives: the core objective of this paper is to examine the relationship between the prices of agricultural commodities with oil price, gas price, coal price and exchange rate (USD/Rand). In addition, the paper tries to fit an appropriate model that best describes the log return price volatility and estimate Value-at-Risk and expected shortfall. Data and methods: the data used in this study are the daily returns of agricultural commodity prices from 02 January 2007 to 31st October 2016. The data sets consists of the daily returns of agricultural commodity prices namely: white maize, yellow maize, wheat, sunflower, soya, corn, and sorghum. The paper applies the three-state Markov-switching (MS) regression, the standard single-regime GARCH and the two regime Markov-switching GARCH (MS-GARCH) models. Results: to choose the best fit model, the log-likelihood function, Akaike information criterion (AIC), Bayesian information criterion (BIC) and deviance information criterion (DIC) are employed under three distributions for innovations. The results indicate that: (i) the price of agricultural commodities was found to be significantly associated with the price of coal, price of natural gas, price of oil and exchange rate, (ii) for all agricultural commodities except sunflower, k=3 had higher log-likelihood values and lower AIC and BIC values. Thus, the three-state MS regression model outperformed the two-state MS regression model (iii) MS-GARCH(1,1) with generalized error distribution (ged) innovation performs best for white maize and yellow maize; MS-GARCH(1,1) with student-t distribution (std) innovation performs better for sorghum; MS-gjrGARCH(1,1) with ged innovation performs better for wheat, sunflower and soya and MS-GARCH(1,1) with std innovation performs better for corn. In conclusion, this paper provided a practical guide for modelling agricultural commodity prices by MS regression and MS-GARCH processes. This paper can be good as a reference when facing modelling agricultural commodity price problems.

Keywords: commodity prices, MS-GARCH model, MS regression model, South Africa, volatility

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