Multi-regression Model Research Articles

Factors Associated with the Mental Health of Health Care Workers Exposed to Pandemic-COVID-19

Background: Pandemic, COVID is spreading like a wild fire and it has already become a global issue. People all over the world are going through mental trauma due to the current situation of the globe. The most vulnerable situation is of the front line volunteers like doctors, health care workers, social workers who are coming in direct contact with the COVID patients and working in highly risky work environment. Since its inception in December 2019, Novel Corona Virus Disease started spreading rapidly both locally and internationally and looking to the adversity of the disease, World Health Organization (WHO) declared it has pandemic. The aim of this paper is to explore the determinants associated with the Mental Health of Health Care Workers (HCW) during the pandemic Novel Coronavirus (COVID-19). Questionnaire was developed having both demographic questions and questions related to mental health. Data was collected from 433 HCWs who were the front-line workers, involved directly in handling these patients. Questionnaire was classified into two parts; one included the demographic questions and the second part included questions related mental health and occupational stress. These HCW were the front line works and were more vulnerable and were having the high risk of getting affected. Percentage analysis was used to analyse the demographic data. Exploratory factor analysis was used to explore the dimensions related to mental health and occupational strength. Multi regression model was used to check the impact of emerged factors like increased workload, the continuous contact with COVID-19 patients and emotional aspects to mental health and occupational stress.
 Results: Value of R2 obtained was 0.778, which means, the derived factors namely Work pressure, optimism ,Risk factor, Emotional Exhaustion , Self control, Discomfort were able to explain 77.8 % of the dependent variable ‘Mental Health’. All the above factors influence Respondent’s Mental Health, as the significant the factors namely work pressure; risk factor and emotional exhaustion are directly proportional to Mental health. Work pressure is the most influencing factor among it. Optimism, self-control and discomfort are inversely proportional to Mental Health of HCW.
 Conclusion: Most important emerged from this study was ‘Work Pressure’. Due to the fast spread of this deadly virus, a war like situation has emerged and Health Care Workers are the most vulnerable people as they are serving the patients directly. They are sacrificing their own physical and mental health and are serving the mankind. These people deserves lots of appreciation and salutations.

An Empirical Evidence of the Impact of Government Tax Revenue on Nigerian Public Debt

Objective The major objective of this paper is to examine the existence of a mutual consen-sus on the effect of tax revenue and non-tax revenue on public debt in Nigeria.Design/methodology – The study uses documentary research design. Data was collect-ed using secondary method of data collection from Debt Management office and Bureau of statistics and Central Bank of Nigeria statistical bulletin data bank. Ordinary Least Square Multi regression model was used in analyzing the dataResults – The research found out that there is a negative an insignificant relationship be-tween tax revenue, non-tax revenue and interest rate in relation to Nigerian public debt. Moreover, the paper found out that exchange rate and population rate had significant and positive relationship with Nigerian public debt.Limitation/Suggestion – The implication of the study is that, the contribution of tax revenue to the reduction of public debt is minimal as could be shown from the results. While, non-tax revenue contributed more than tax revenue in public debt reduction in Nigeria. In addition, increase in exchange rate, and population rate contributed more to increase in pub-lic debt, while, an increase in interest rate does not increase public debt but rather it discour-ages the government from collecting more debt and push the government to go for other rev-enue sources that where not assessed. The study recommended that; government should harness untapped taxes to increase tax revenue generation to pay interest on loan and princi-pal. Must of the information at the researcher disposal used for the study was for 15 years, from 2003 to 2018. Thus, studies need to be conducted involving more year like from 20 years upward to see whether it will change the result.Novelty/Originality – The originality of this research lies on the government inability to generate enough tax revenue and non-tax revenue to meet expenditure without collecting debt. This is because collection of debt always leads to frequent increase in debt burden in Nigeria. Fewer or no one has carried out a careful analysis of the relationship between tax and non-tax revenue against total debt in Nigeria.

Body mass index predicts lower connectivity in associational fibers of the temporal lobe in older men

Increased body mass index (BMI) is related to changes in white matter (WM) connectivity1 . We investigated whether WM connectivity patterns as a function of BMI varies across sex differences.We enrolled 289 individuals (58 CDR=0 & 231 CDR=0.5) from the Knight Alzheimer Disease Research Center (ADRC) (Table 1). Participants were included if they had a physical evaluation within 12 months of a diffusion MRI scan and excluded if they had clinical dementia. Connectome analyses were performed using the DSI Studio software (http://dsi-studio.labsolver.org/). Diffusion data were reconstructed in the MNI space using q-space diffeomorphic reconstruction (QSDR)2 . The quantitative anisotropy was extracted as the local connectome fingerprint. A multi-regression model were used to derive the correlation and a false discovery rate threshold of 0.05 was adopted to select tracts using a deterministic fiber tracking algorithm.A positive association between connectivity in the left arcuate fasciculus (AF) and the left middle cerebellar peduncle in men did not survive regression for age (Figure 1-A). Men also showed a statistically significant negative association between connectivity in the bilateral inferior longitudinal fasciculi (ILF), right inferior fronto-occipital fasciculus (IFOF), the frontoparietal and parahippocampal parts of the right cingulum, the tapetum part of the corpus callosum (CC), right fornix and right corticospinal, corticostriatal, and corticopontine tracts. All of these relationships persisted when co-varying for age (Figure 1-B). Results in women revealed a significant positive correlation between connectivity of the right IFOF, right superior longitudinal fasciculus (SLF), right corticopontine, corticospinal tracts, bilateral reticulospinal tracts, and left arcuate fasciculus (AF) (Figure 2-A) even when controlling for age. Connectivity in the bilateral ILF, right IFOF and the tapetum part of the CC showed an inverse correlation with BMI in women (Figure 2-B).Increased BMI is related to lower structural connectivity in important associational WM fibers of the temporal lobe, particularly in older men.

Nonlinear Dissipative System Mathematical Equations in the Multi-regression Model of Information-based Teaching

Abstract The advancement of Chinese education informatisation construction has injected new vitality into the development of Chinese educational technology in the new era and brought new challenges to the development of Chinese educational technology. Nonlinear dissipative structure theory has been a necessary enlightenment for the development of education informatisation. Based on the theory of nonlinear dissipative structure, the paper explores the relationship between the theory and education and teaching. It constructs a diversified regression calculation model of the information-based teaching ecology. Finally, it points out the strategies and ways to apply the dissipative structure theory to improve information-based teaching.

Phytohormone dynamics impact fatty acid and oil accumulation during soybean seed maturation

AbstractFatty acid (FA) levels and profiles are vital for soybean oil quality, while cytokinins (CKs) and abscisic acid (ABA) are potent regulators of plant growth and development. Previous research suggested associations between FA biosynthesis and hormonal signalling networks; however, hormonal regulation of FA accumulation during soybean (Glycine max) seed maturation has never been measured. We analysed hormone and FA profiles obtained from HPLC-(ESI)-MS/MS and GC-FID screening during soybean seed maturation. A multilayered data processing approach, involving heat-maps, principal component analysis (PCA), correlation and multiregression models, suggested a strong relationship between hormone metabolism and FA/oil accumulation during seed maturation. Most strikingly, positive correlations were found between the levels of CK ribosides [transZeatin riboside (tZR), N6-isopentenyladenosine (iPR)] at the early stages of SM (R5-R6) and C18:0, C18:2 and oil content at the R8 stage. Moreover, multiple regression models revealed functional linkages between several CK derivatives and FA and oil content in mature seeds. To further test the significance of hormone regulation in FA metabolism, plants of two soybean accessions with contrasting hormone and FA profiles were sprayed with exogenous ABA and transZeatin (tZ) during the seed-filling period (R5-R6). Depending on the hormone type and concentration, these treatments distinctly modified biosynthesis of all tested FAs, except for C18:0. Most remarkably, tZ (50 nM) promoted production of C16:0, C18:1, C18:2, C18:3, and oil accumulation in maturing seeds. Overall, the results indicate impactful roles for ABA and CKs in FA accumulation during SM and represent a further step towards understanding FA biosynthesis, and potential improvements of soybean oil profiles.

Machine Learning Technique Reveals Prognostic Factors of Vibrant Soundbridge for Conductive or Mixed Hearing Loss Patients.

Vibrant Soundbridge (VSB) was developed for treatment of hearing loss, but clinical outcomes vary and prognostic factors predicting the success of the treatment remain unknown. We examined clinical outcomes of VSB for conductive or mixed hearing loss, prognostic factors by analyzing prediction models, and cut-off values to predict the outcomes. Retrospective chart review. Tertiary care hospital. Thirty patients who underwent VSB surgery from January 2017 to December 2019 at our hospital. Audiological tests were performed prior to and 3 months after surgery; patients completed questionnaires 3 months after surgery. We used a multiregression and the random forest algorithm for predictions. Mean absolute errors and coefficient of determinations were calculated to estimate prediction accuracies. Coefficient values in the multiregression model and the importance of features in the random forest model were calculated to clarify prognostic factors. Receiver operation characteristic curves were plotted. All audiological outcomes improved after surgery. The random forest model (mean absolute error: 0.06) recorded more accuracy than the multiregression model (mean absolute error: 0.12). Speech discrimination score in a silent context in patients with hearing aids was the most influential factor (coefficient value: 0.51, featured value: 0.71). The candidate cut-off value was 36% (sensitivity: 89%, specificity: 75%). VSB is an effective treatment for conductive or mixed hearing loss. Machine learning demonstrated more precise predictions, and speech discrimination scores in a silent context in patients with hearing aids were the most important factor in predicting clinical outcomes.

Prevalence and Predictors of Dysmenorrhea, its Effects and Coping Mechanism among Adolescent

Background and Aim: Dysmenorrhea is one of the most common gynecological disorders in the world. The current study aims to know the prevalence of dysmenorrhea and its management practices among adolescent girls. The study also aim to assess predictors of dysmenorrhea treatment options. Materials and Methods: This institutional-based cross-sectional study was carried out at Obstetrics & Gynaecology department of Mardan Women Hospital, Sheikh Maltoon Town Mardan and Jinnah International Hospital, Abbottabad from June 2020 to November 2020. Pretested self-administrated and a semi-structured questionnaire was used for data collection from volunteer adolescent girls to take part in this study. The outcome and exposure variables association was evaluated through a multi regression model and chi-square test. Dysmenorrhea risk was reported by taking the odds ratio at a confidence interval of 95%. SPSS version 20 was used for data analysis and statistical significance was considered at p<0.05. Results: Of the total 680 questionnaires, 601 (88.4%) were completed. The response rate and dysmenorrhea prevalence were 88.4% and 53.3% respectively. The mean age of the adolescents was 16.3 ± 1.92. While mean age for Menarche was 12.41 ± 1.62. A significant difference (t= 5.78, p< 0.001) between dysmenorrhea and non-dysmenorrhea age girls was (16.81 ±1.98) and (12.56 ± 1.65) respectively. The common symptom of waist pain among participants was (345, 57.4%). The prevalence of participants taking Over-the-counter (OTC) drugs for menstrual symptoms was 191 (31.8%). Dysmenorrhea’s severity and age were the two significant predictors for the prospect of taking a dysmenorrhea pharmacological agent. Conclusion: Our study concluded that a higher prevalence of dysmenorrhea was observed among adolescents compared to Menarche which adversely affects the routine major activities. A significant association between self-reported dysmenorrhea and the abnormal menstrual cycle was found. Adolescents must be educated early on about the safety and efficacy of various dysmenorrhea management options. Keywords: Dysmenorrhea, Adolescence, Menarche

Prioritizing Energy-Intensive Machining Operations and Gauging the Influence of Electric Parameters: An Industrial Case Study

Increasing the energy efficiency of machining operations can contribute to more sustainable manufacturing. Therefore, there is a necessity to investigate, evaluate, and optimize the energy consumed during machining operations. The research highlights a method employed to prioritize the most energy-intensive machining operation and highlights the significance of electric parameters as predictors in power estimation of machining operations. Multi regression modeling with standardized regression weights was used to identify significant power quality predictors for active power evaluation for machining operations. The absolute error and the relative error both decreased when the active power was measured by the power analyzer for each of the identified machining operations, compared to the standard power equation and that obtained from the modeled regression equations. Furthermore, to determine energy-intensive machining operation, a hybrid decision-making technique based on TOPSIS (a technique for order preference by similarity to ideal solution) and DoM (degree of membership) was utilized. Allocation of weights to energy responses was carried out using three methods, i.e., equal importance, entropy weights, and the AHP (analytical hierarchy process). Results revealed that a drilling process carried out on material ST 52.3 is energy-intensive. This accentuates the significance of electric parameters in the assessment of active power during machining operations.

Optimizing Large-Scale Solar Field Efficiency: Latvia Case Study

Solar energy transformation technologies are increasingly being used worldwide in the district heating sector. In the Baltic states, only one district heating company has implemented a large-scale solar collector field into its thermal energy production system, which is analyzed within this research. In this study, we analyzed the first year operation of the solar field, solar collector efficiency, and several influencing factors, i.e., ambient air temperature, heat carrier flow, and the temperature difference between the supply and return heat carrier temperatures. The study includes collecting and compilation of the data, analyzing influencing factors, and data analysis using the statistical analysis method. In addition, the research presents a simplified multi-regression model based on the actual performance of a large-scale solar field, which allows for forecasting the efficiency of solar collectors by taking into account the main operational parameters of the DH system. The results show that solar energy covers around 90% of the summer heat load of a particular district heating system. However, they also show room for improvements in producing all the necessary heat in the summer using solar energy. The regression analyses show that the most significant correlation between all parameters examined was obtained in May, reaching R2 = 0.9346 in solar field efficiency evaluation. This is due to several suitable conditions for solar energy production, i.e., placing solar collectors at an angle for them to be the most productive, having enough space in the storage tank, and the demand for thermal energy being still higher than in the summer months.

Investigation of distinctive physico-chemical soil correlations for Kirkuk city using spatial analysis technique incorporated with statistical modeling

In the sense of challenging economic situation, it is difficult to perform laboratory tests in a whole intended area to define the soil characteristics for whatever task or situation within an entire city. Hence, this study has used the spatial analysis through applying the inverse distance weighted (IDW) technique to integrate with the available data for 56 different soil samples collected from various depths of Kirkuk city. The studied characteristics were mainly classified to physical soil properties included gravel, sand, silt, and clay contents with chemical soil properties included organic and gypsum contents, pH content, sulfur trioxide, and total dissolved solids (TDS) content. Moreover, statistical investigations such as physico-chemical correlation, linear single and linear multi regression models have been applied. The results of this study have focused on constructing thematic digital maps for visualizing different physical and chemical characteristics of Kirkuk soil. In statistical analysis, negative linear correlations are obtained from the relation between the values of the pH, gypsum, organic, and SO3 contents with the increase of the gravel content, whereas a positive linear correlation is attained for the values of TDS content with the increase of the gravel content. Furthermore, the proposed linear multi regression model predicts the pH values reasonably where most of the values were close to the equality line. The obtained digital maps accompanied with the statistical models will provide comprehensive spatial, agricultural, chemical and mechanical visualizations of the nature and morphology of the soils in Kirkuk city.

Effect of Corporate Governance Mechanisms on Value Relevance of EPS and BV: Evidence from the Indian Tourism Industry

This paper examines corporate governance's effect on the valuation of Earnings per Share (EPS) and Book Value (BV).Differently from empirical previous studies in the area of corporate governance and value relevance of EPS and BV, this study investigates this impact within a unique setting of publicly listed tourism firms Using panel data from a selection of some Bombay Stock Exchange (BSE) listed companies from 2013 to 2015. The paper explored three aspects of the mechanisms of corporate governance: the board of directors (size, composition and diligence), the audit committee (size, composition and diligence) and foreign ownership .The study uses descriptive statistics, correlation and multi-regression model to analyse the influence of corporate governance on the value relevance of EPS and BV for the Indian tourism industry. The results show that the interaction between corporate governance mechanisms and value relevance of BV has more impact on the share prices than EPS. It is recommended that the Indian tourism industry should pay more focus to corporate governance mechanisms in order to improve its value relevance of EPS, BV and share prices.

ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE

Abstract. This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.

Silicon availability in relation to soil properties in Inceptisols on uncultivated lands and paddy fields in Taiwan

Silicon (Si), which mainly exists in soils as orthosilicic acid (H4SiO4), is beneficial for rice growth. In total, 17 Inceptisols soil profiles, 4 from uncultivated lands and 13 from paddy fields derived from felsic to ultramafic parent materials were collected for the extraction of available Si from horizon soil samples by using an acetate buffer solution. The aim of this work was to illustrate the relationship between extractable Si and general soil properties for predicting the availability of Si for rice growth. The means of available Si in the studied Inceptisols on the uncultivated lands and paddy fields were 244 mg kg−1 and 114 mg kg−1, respectively. The amount of available Si increased from the surface to subsurface soils in the soils on the uncultivated lands, whereas the available Si was irregular along with depth in the paddy soils. The amount of available Si was linearly and significantly (p < 0.001) correlated with clay and dithionite-citrate-bicarbonate extractable iron oxides (Fed) in all soils. The amount of available Si increased with an increase in pH in the soils from the uncultivated lands. However, a poor correlation was observed between pH and available Si in the paddy soils because labile Si was affected by human activities, such as liming and flooding, especially for surface soils. Nevertheless, the available Si was successfully estimated using crystalline iron oxides in the soils on the uncultivated lands; however, in the paddy soils, clay and organic carbon were used in the multi-regression model to estimate the amount of available Si.

Experimental studies and drying shrinkage prediction model for concrete containing waste foundry sand

Concrete being most extensively used construction material all over the globe has resulted in the over-exploitation of natural resources such as river-sand and gravels. Meanwhile, advancing industries and increasing population have also lead to an increased generation of waste materials. Many of these waste materials have the potential to be used in concrete. This study has investigated the effect of one such waste material referred to as Waste Foundry Sand (WFS) on the properties of concrete. Many researchers have studied the effect of WFS on the mechanical properties of concrete. However, no consensus has been reached yet and very contradictory results have been reported. Moreover, shrinkage of concrete containing WFS has not received much of the researchers’ attention and very limited literature is available related to this property. This study explores the workability, compressive strength, split tensile strength and drying shrinkage of such concretes. WFS was used as partial replacement of fine aggregate and the replacement levels were varied from 0 to 50% in equal increments of 10%. It was observed that the compressive strength and split tensile strength of concrete decreased with the addition of WFS. However, the mix WFS3 (30% WFS) showed the strength very similar to that of control concrete. A significant increase of 16.7%, 23.44%, 29.05%, 36.35% and 45.18% with respect to control concrete at the age of 28 days was observed in the magnitude of drying shrinkage when the amount of WFS in concrete was varied from 10 to 50%. Furthermore, it was seen that the available shrinkage prediction models could not be applied to the concrete containing waste foundry sand. Hence, a multivariable regression model inspired by ACI-209 model was developed for prediction of drying shrinkage in concrete. The proposed model included the parameters related to drying age and percentage of WFS in concrete and gave a high value of the coefficient of regression (R 2 ) indicating the effectiveness of the proposed model. • Incorporation of waste foundry sand (WFS) in concrete reduced the compressive strength and split tensile strength. • Least reduction in strength was observed in the mix containing 30% WFS as a replacement of river sand. • Drying shrinkage of concrete increased when WFS was added. • A multi-regression model with high regression coefficient was developed to predict the shrinkage in concrete containing WFS.

WORKFORCE DIVERSITY AND EMPLOYEE PERFORMANCE: A CASE OF COMMERCIAL BANKS IN PAKISTAN

Purpose of the Study: The fundamental objective of this study was to examine the association between workforce diversity and employee performance in commercial banks of Pakistan. Methodology: Gender, age, education, and ethnic diversity contribute to the workforce's diversity. A standardized questionnaire was distributed amongst the 400 employees of commercial banks of Pakistan to collect data. However, 281 employees, constituting 70.25%, responded. The research hypotheses were tested by using a multi-regression model through Stata16 for data analysis. Main Findings: The outcomes indicated a positive and significant association between gender diversity and employee performance. Gender diversity and age diversity are strong predictors of employee performance in Pakistan. However, the findings revealed that employee performance is not affected by education or ethnic diversity. Applications of the study: This study has significant implications from both a theoretical and a realistic perspective. This study contributes by adding sound theoretical literature to the body of knowledge on workforce diversity and employee performance. From a practical perspective, this study sheds more light on how workplace diversity affects employee performance in the banking sector of an emerging economy like Pakistan. The results of this study can also be used to build strategies to improve the implementation of employee diversity in an existing market. Novelty/Originality of this Study: Studies have been conducted to analyze the impact of diversity on employee performance, but this is the first study that has investigated the association between workforce diversity and employees performance with rich facets and particularly in the context of an emerging economy like Pakistan and specifically in commercial banks.

Delineation of potential hot spots of aeolian dust using normalized difference water index

Due to the drop in water levels during the monsoon season, the soil particles of the exposed riverbed of Zhuoshui River are easily suspended in the air, causing serious river dust problems and endangering the quality of life and health of local residents. Exposed riverbed is the main source of dust, and it affects PM10 concentration in air quality monitoring stations. In addition to meteorological conditions, the moisture content of the source dust is also an important factor. This study used satellite images from the lower river section (Bridge Ziqiang to Bridge Xibin) to extract the exposed riverbed, used the cluster analysis based on normalized difference water index (NDWI) to further classify the bare land into three categories, and then combined with meteorological factors to construct multi-regression models of PM10 (PM10 = 45.89W + 18.44T − 373.85; R2 = 0.64, p < 0.001 and PM10 = 21.80T + 39.70W + 37.2M2 − 646.25; R2 = 0.74, p < 0.001; W, wind speed; T, air temperature; M2, bare land with medium NDWI). The results showed that the meteorological factor interpretation rate was about 64%, and if the index of bare land with medium NDWI was included, the model interpretation rate could be increased to 74%, hence the M2 factor had a high influence on the PM10 concentration.

Prediction of performance and exhaust emissions of a CI engine fueled with multi-wall carbon nanotube doped biodiesel-diesel blends using response surface method

This study was designed to analyses the performance and exhaust emissions of a direct-injection diesel engine fueled with multi-walled carbon nanotubes (MWCNTs) included in biodiesel-blended diesel fuel using response surface method (RSM). The influence of input parameters —engine load and MWCNTs concentration — on the response parameters (i.e., BSFC, BTE, CO, NOX, and UHC) were investigated and predicted. MWCNTs were added into B20 fuel (20% biodisesel+80% diesel) in various concentrations (25, 50, 75, and 100 ppm). The tests performed under varying engine load conditions (5, 10, 15, and 20 Nm) at a constant engine speed of 1800 rpm. Multi-regression models for BTE, BSFC, and CO, UHC, and NOX emissions were derived using RSM and were found to be statistically significant. Exhaust UHC and CO concentrations for the studied fuel blend decreases with the addition of MWCNTs into B20 fuel, while NOx emissions drastically increased. The optimal engine working conditions were found to be an engine load of 10 Nm and MWCNTs concentration of 98 ppm. Based on the optimized values, the most optimal results for BTE and BSFC along with CO, UHC, and NOX emissions were found to be 28.57 (%), 269.84 (g/kWh), 0.03 (%Vol.), 44.16 (ppm), and 458.81 (ppm).

Estimating parking generation rate for Karbala holy city using multi-variables approach

Car parking planning, design, and management processes are very important to all cities and places to ensure efficient traffic system. Estimating the demand of car parking represents the significant start point for the success of these processes. Generally, there are many local and international estimating criteria, but such criteria need continuing update due to many reasons related to socioeconomic factors, lifestyle changes, development in technology, etc. Moreover, the majority of these criteria depend on single parameter for the estimation of parking demand; such as bed or employee for hospital, gross floor area or employee for office, and so on. The main aim of this research is to estimate the park generation rate for specific land uses depend on multivariable to increase the accuracy and limiting the effect of variation in parameters. Statistical analysis was conducted to create predicting models for each land use. The collected data was nominated for Karbala holy city, where different parameters are scaled for different city sectors. Groups of statistical models (i.e., simple, multi linear and nonlinear statistical models, and Weighted Linear Regression (WLR)) were used to create best representative relationship between the number of demands for car parking and multivariable parameters or factors affecting these lands used demands. Resulted statistical models were tested for best fit using statistical indices for model verification. Results disclose the significant of multivariable model compare with simple models. Also, WLR model shows it validity compare with multi-regression model for almost land use models. Consequently, for more accurate estimation the multi variable models are initiated with continuous need for updating.

Application of Statistic Model and Backpropagation Neural Network to Analyzing and Forecasting Hydropower Dam Displacement

Horizontal displacement of Hoa Binh dam in operation phase is analyzed and then forecasted by using three methods: the multi-regression model (MTR), the Seasonal Integrated Auto-regressive Moving Average (SARIMA) model and the Back-propagation Neural Network (BPNN) model. The monitoring data of the Hoa Binh Dam in 137 monitoring periods, including horizontal displacement, time, reservoir water level and air temperature, are used for the experiments. The results indicate that all of these three methods can describe the real trend of dam deformation and achieve the required accuracy in short-term forecast up to 9 months. In addition, forecast results of BPNN have the highest stability and accuracy.&#x0D; &#x0D; &#x0D;

Extreme fine particulate matter events in Taiwan Island related to synoptic weather patterns

Extreme fine particulate matter (PM2.5) events heavily impact residents, incurring high social and medical costs. As such, it is important to understand the characteristics of extreme PM2.5 events. This study used hourly PM2.5 and meteorological data to elucidate the effects, and predict the occurrence of these extreme weather events in Taiwan. The results show that synoptic conditions are unique for extreme PM2.5 events. During the maximum mean PM2.5 concentrations, weather conditions in Taiwan were dominated by synoptic weather patterns and the north-easterly monsoon. The maximum mean surface air pressure indicator had also occurred at this time. The azimuth of the resultant surface air pressure was 36.8° + 7.6°, while 96.2% of winds were in the north-north-easterly and north-easterly direction. The back trajectories suggest that the cold continental high air pressure system introduced dry and cold air masses with PM2.5. The SImax (μg/m3/h), relative humidity (%), global solar radiation (MJ/m2), visibility (km), weather type I, and weather type II predictor variables of the multi-regression model accounted for 80.6% of the variance in the magnitude of maximum hourly PM2.5 events. Extreme PM2.5 events were related to synoptic weather characteristics including type, strength, and position. The new quantitative variables aid the development of an efficient alarm system for extreme PM2.5 events that will help protect public health.