Multi-regression Model Research Articles

Value Relevance of Tobin’s Q and Corporate Governance for the Taiwanese Tourism Industry

For knowledge- and invention-based industries, scholars have introduced the firm value, which is composed of traditional financial capital and intangible intellectual capital, and Tobin’s Q, which is the commonly used approach for intellectual capital valuation. Scholars have thus evaluated firm valuation appropriately by considering corporate governance. This study applies the multi-regression model to present a discussion on the value relevance of intellectual capital and corporate governance concerning the tourism industry in Taiwan. The results show that intellectual capital is positively related to firm valuation, and that corporate governance influences the positive relationship between intellectual capital and firm valuation. The tourism industry must focus on intellectual capital and corporate governance.

Non-destructive evaluation of quality and ammonia content in whole and fresh-cut lettuce by computer vision system

The paper describes the developed hardware and software components of a computer vision system that extracts colour parameters from calibrated colour images and identifies non-destructively the different quality levels exhibited by lettuce (either whole or fresh-cut) during storage. Several colour parameters extracted by computer vision system have been evaluated to characterize the product quality levels. Among these, brown on total and brown on white proved to achieve a good identification of the different quality levels on whole and fresh-cut lettuce (P-value<0.0001). In particular, these two parameters were able to discriminate three levels: very good or good products (quality levels from 5 to 4), samples at the limit of marketability (quality level of 3) and waste items (quality levels from 2 to 1). Quality levels were also chemically and physically characterized. Among the parameters analysed, ammonia content proved to discriminate the marketable samples from the waste in both product's typologies (either fresh-cut or whole); even the two classes of waste were well discriminated by ammonia content (P-value<0.0001).A function that infers quality levels from the extracted colour parameters has been identified using a multi-regression model (R2=0.77). Multi-regression also identified a function that predicts the level of ammonia (an indicator of senescence) in the iceberg lettuce from a colour parameter provided by the computer vision system (R2=0.73), allowing a non-destructive evaluation of a chemical parameter that is particularly useful for the objective assessment of lettuce quality.The developed computer vision system offers flexible and simple non-destructive tool that can be employed in the food processing industry to monitor the quality and shelf life of whole and fresh-cut lettuce in a reliable, objective and quantitative way.

Impact of Corporate Governance on the Performance of Commercial Banks in Zimbabwe

The background of corporate governance dates back to the 19th Century when state corporation laws enhanced the rights of corporate boards without unanimous consent of shareholders. The concept propounds that corporation should have a good board structure in order to enhance performance. It is firmly rooted on the assumption that good corporate governance practices enhance corporate performance. However, there is no consensus on the impact of corporate governance on performance. The increasing role of the financial sector, on both economic development and poverty alleviation, has seen the concept being applied more on the financial sector than before; this has been further aggravated by world financial crisis, and its consequences. In this regard, Zimbabwe is no exception, during the period 2003-2009 the Zimbabwe witnessed unprecedented failure in the financial sector rooted from a number of issues; but chief among them has been cited as poor corporate governance practices. However, there have been mixed feelings on the extent to which bank performance can be attributed to corporate governance. The paper presents the findings of the study that was conducted to investigate the impact of corporate governance on the performance of commercial banks in Zimbabwe. Using data gathered from 2009-2012, for a sample of five commercial banks, it applies multi-regression model, to assess the causal relationship between corporate governance measures (board size, board composition, internal board committees and board diversity) and bank performance. The results indicate unidirectional causal relationship from corporate governance to bank performance. In addition, there a positive relationship between board composition, board diversity and commercial bank performance, although a negative relationship appears between board size, board committees and bank performance. Therefore, in order to improve performance in commercial banks good corporate governance practices must implemented, this includes improving board structures, disclosure, and fiduciary duties of directors. On the hand the Reserve Bank of Zimbabwe should ensure or put in place robust supervisory and regulatory policies; the development and implementation of a national corporate governance code is long overdue. DOI: 10.5901/mjss.2014.v5n15p93

Long-term precipitation forecast for drought relief using atmospheric circulation factors: a study on the Maharloo Basin in Iran

Abstract. Long-term precipitation forecasts can help to reduce drought risk through proper management of water resources. This study took the saline Maharloo Lake, which is located in the north of Persian Gulf, southern Iran, and is continuously suffering from drought disaster, as a case to investigate the relationships between climatic indices and precipitation. Cross-correlation in combination with stepwise regression technique was used to determine the best variables among 40 indices and identify the proper time lag between dependent and independent variables for each month. The monthly precipitation was predicted using an artificial neural network (ANN) and multi-regression stepwise methods, and results were compared with observed rainfall data. Initial findings indicated that climate indices such as NAO (North Atlantic Oscillation), PNA (Pacific North America) and El Niño are the main indices to forecast drought in the study area. According to R2, root mean square error (RMSE) and Nash–Sutcliffe efficiency, the ANN model performed better than the multi-regression model, which was also confirmed by classification results. Moreover, the model accuracy to forecast the rare rainfall events in dry months (June to October) was higher than the other months. From the findings it can be concluded that there is a relationship between monthly precipitation anomalies and climatic indices in the previous 10 months in Maharloo Basin. The highest and lowest accuracy of the ANN model were in September and March, respectively. However, these results are subject to some uncertainty due to a coarse data set and high system complexity. Therefore, more research is necessary to further elucidate the relationship between climatic indices and precipitation for drought relief. In this regard, consideration of other climatic and physiographic factors (e.g., wind and physiography) can be helpful.

Spatial analysis on China's regional air pollutants and CO2 emissions: emission pattern and regional disparity

China has suffered from serious air pollution and CO2 emission. Challenges of emission reduction policy not only come from technology advancement, but also generate from the fact that, China has pronounced disparity between regions, in geographical and socioeconomic. How to deal with regional disparity is important to achieve the reduction target effectively and efficiently. This research conducts a spatial analysis on the emission patterns of three air pollutants named SO2, NOx and PM2.5, and CO2, in China's 30 provinces, applied with spatial auto-correlation and multi regression modeling. We further analyze the regional disparity and inequity issues with the approach of Lorenz curve and Gini coefficient. Results highlight that: there is evident cluster effect for the regional air pollutants and CO2 emissions. While emission amount increases from western regions to eastern regions, the emission per GDP is in inverse trend. The Lorenz curve shows an even larger unequal distribution of GDP/emissions than GDP/capita in 30 regions. Certain middle and western regions suffers from a higher emission with lower GDP, which reveal the critical issue of emission leakage. Future policy making to address such regional disparity is critical so as to promote the emission control policy under the “equity and efficiency” principle.

A New Nonlinear Multiregression Model Based on the Lower and Upper Integrals

A new nonlinear multiregression model based on a pair of extreme nonlinear integrals, lower and upper integrals, is established in this paper. A complete data set of predictive attributes and the relevant objective attribute is required for estimating the regression coefficients. Due to the nonadditivity of the model, a genetic algorithm combined with the pseudo gradient search is adopted to search the optimized solution in the regression problem. Applying such a nonlinear multiregression model, an interval prediction for the value of the objective attribute can be made once a new observation of predictive attributes is available.

Integrated Sono-Fenton ultrafiltration process for 4-chlorophenol removal from aqueous effluents: process modeling and simulation Part 2

The aim of this study is to analyze the performances of an integrated homogeneous Sono-Fenton-ultrafiltration process for the removal of 4-chlorophenol from wastewaters both from technical performance (expressed as phenolics concentration and chemical oxygen demand, COD, reduction) and energy consumption point of view, based on simulations using Aspen Plus® software. Based on experimental data (described in Cailean et al. 2014: “Integrated Sono-Fenton Ultrafiltration Process for 4-Chlorophenol Removal from Aqueous Effluents: Assessment of Operational Parameters (Part 1)”) and statistical multi-regression models developed in MATLAB, a simulation model was implemented in Aspen Plus® and developed to calculate mass and energy balances. This model was used to assess the system behavior under various initial settings and process configurations. The effluent type, the presence of additional membrane units (ultrafiltration) and the variation of concentrate recirculation fractions were investigated. The results show that the integrated process registers over 90 % removal efficiency in the permeate of ultrafiltration and reduces between 70 and 90 % of the pollution load in the concentrate, expressed as phenolics concentration, depending on the scenarios analyzed. Recommendations concerning the use of recirculation options and suitable recirculation fractions of the concentrate are indicated, considering the quality of the resulting permeate and concentrate as well as the energy consumption.

Variations in contact patterns and dispatch guideline adherence between Norwegian emergency medical communication centres - a cross-sectional study

BackgroundThe 19 Norwegian Emergency medical communication centres (EMCCs) use Norwegian Index for medical emergency assistance (Index) as dispatch guidelines. Little is known about the use of Index, nor its validity. We aimed to document the epidemiology of contacts made to the public emergency medical phone number and the operators’ self-reported use of Index as a first step towards a validation study.MethodsWe registered all medical emergency calls to the EMCCs during a 72 h period in a national cross sectional study. We subsequently sent a questionnaire to all EMCC operators in Norway, asking how they use Index. A combined outcome variable “use of Index” was computed through a Likert scale, range 1–5. Regression models were used to examine factors influencing use.Results2 298 contacts were included. National contact rate was 56/1 000 inhabitants per year, range between EMCCs 34 – 119. Acute contact (life-threatening situations) rate was 21/1 000 per year, range between EMCCs 5 – 31. Index criteria 6 – ’Unresolved problem’ accounts for 20% of the 113 contacts, range between EMCCs 10 – 42%. The mean use of Index was 3.95 (SD 0.39), corresponding to “more than 75% of emergency calls”. There were differences in use of Index on EMCC level, range 3.7 – 4.4, and a multi regression model explained 23.4% of the variation in use. Operators working rotation with ground ambulance services reported reduced use of Index compared to operators not working in rotation, while distinct EMCC focus on Index increased use of Index compared to EMCCs with no focus on Index. Use of electronic records and operators experience were the main reasons given for not using Index.ConclusionsThere is a large variation between the EMCCs with regard to both contact patterns and use of Index. There is a relatively high overall self-reported use of Index by the operators, with variations on both individual and EMCC level.

The Impact of Food Price Crisis on Government Debt in the Arab Region

This study explores the Impact of Food Prices Crisis on budget deficits in the Arab region. Panel data is used for the period of 2000 to 2012 in sixteen countries in the region. We rum multi regression models are used to estimate the Impact of food crisis on government debt in high, middle and low income countries before and after the food crisis period in 2006. The analysis reveals that the changes in food prices have a negative and significant impact only on the low and middle income countries’ government debt before the crisis. We also found that the slow growth in GDP after the global economic and food crisis period, political instability, lagged deficits, real interest rates, and unemployment were also important determinants of government debt in most of the models estimated.

Modelling Land-use and Land-cover Changes Using Markov-CA, and Multiple Decision Making in Kirkuk City

This paper tries to highlight on the analysis of the land-use and land-cover changes in Kirkuk city / Iraq that's due to repeated changes in structures of governments, wars and economic blockade over the past three decades that drove to chaos. The Markov-cellular automata model is an important and powerful tool in simulation. In this article a new Markov-CA model was developed based on built-in multi-regression model and multi-criteria evaluation approach to improve the representation of Markov-CA transition rules. Utilised data relate to the environmental and socio-economic are produce criteria layers and weights in order to output the suitability maps via a ranking method for periods 1984, 1990, 2000 and 2010, which have become conditional for the next step of Markov-CA generation. The suitability maps are compared in order to determine the best fit maps based on the values of the root equation (R 2 ). This comparison helpful to the stakeholders make better decisions and the best maps indicated best transition rules, which is represent the core of Markov-CA model. Thus, the resultant of best suitability maps derives a predefined transition rule for the end of Markov-CA model step. The approach used in the present study a mechanism for monitoring the land-use and land-cover changes in Kirkuk depend on Markov-CA is implemented and evaluated thru the different decision maker. The results assert that the model bears a high applicability and flexibility degree in land-use and land-cover changes.

Markov CA, Multi Regression, and Multiple Decision Making for Modeling Historical Changes in Kirkuk City, Iraq

The cellular automata (CA) model is an important tool in land use change studies. Swift increases in population and long-term expectations of rapid urbanization have led to extensive land use change, and normal living conditions have affected the natural resources of the land. This paper highlights and analyzes the historical urban changes in Kirkuk City, Iraq, considering repeated changes undergone by the state such change as government infrastructures, wars, and economic blockade. In this paper, an integrated model, built-in multi regression model, and multi-criteria evaluation were considered to improve the representation of CA transition rules. Environmental and socioeconomic factors were used to produce Suitable Maps (SMs). These SMs were practicalities to create factor layers and weight usage, rating method process for variance expert decision-making groups, and geographic information systems for the periods 1984, 1990, 2000, and 2010. The roots of the equation (R2) values are compared and these values are chosen to produce a good model of suitable maps. The approach used in this study provides a mechanism for monitoring suitability maps in Kirkuk. Furthermore, the model Markov CA is implemented and evaluated. The results indicate that the model, its related concepts performs sufficiency

Changing rural development inequality in Jilin Province, Northeast China

Rural development inequality is an important practical issue during the course of full establishment of a ‘moderately well-off society’ in modern China, and the objective understanding and evaluation of the status and regional inequality of rural development can provide scientific basis for ‘building a new countryside’ and coordination development of rural-urban regions. Based on the county-level data of 2000, 2005 and 2009, this paper examines the rural development inequality of Jilin Province in Northeast China by establishing a rural development index. The spatio-temporal dynamic patterns and domain factors are discussed by using the method of exploratory spatial data analysis and multi-regression model. The results are shown as follows. Firstly, most of the counties were in lower development level, which accounted for 58.3%, 62.5% and 66.7% of the total counties in 2000, 2005 and 2009, respectively. The characteristics of spatial inequality were very obvious at county level. For example, rural development level of Changchun Proper and the proper of seven prefecture-level cities were much higher than that of the surrounding regions. The counties in the eastern and northern Jilin Province were the lowest regions of rural development level, while the middle counties were the rapid growth areas in rural economy. Secondly, Moran’s I of rural development index (RDI) was 0.01, −0.16 and −0.06 in 2000, 2005 and 2009, respectively, which indicated that spatial agglomeration of RDI was not obvious in Jilin Province, and took on the characteristic of random distribution. The counties of both the units and its adjacent units have higher development level (HH) were transferred from the western areas to the eastern areas, while the countries of both the units and its adjacent units have lower development level (LL) were diffused from the eastern to middle and western Jilin Province. Finally, the result of multi-regression analysis showed that the improvement of agricultural production condition, development of agricultural economics and the adjustment of industrial structure were the domain factors affecting rural development inequality of Jilin Province in the later ten years.

Modified quantum-behaved particle swarm optimization for parameters estimation of generalized nonlinear multi-regressions model based on Choquet integral with outliers

In this paper, a generalized nonlinear multi-regression model based on Choquet integral (NMRCI) is proposed and applied for estimation of non-additive systems that include outliers under inherent i...

Modified quantum-behaved particle swarm optimization for parameters estimation of generalized nonlinear multi-regressions model based on Choquet integral with outliers

In this paper, a generalized nonlinear multi-regression model based on Choquet integral (NMRCI) is proposed and applied for estimation of non-additive systems that include outliers under inherent interaction among inputs. The parameters estimation for the proposed model is also performed via a modified algorithm based on particle swarm optimization with quantum-behavior (QPSO), named MQPSO. From the proposed model, the high breakdown regression estimator, a least trimmed squares (LTS) is applied to eliminate the influence caused by these observations that contain outliers. Besides, elitist crossover of genetic algorithm (GA) and adaptive decay of simulated annealing (SA) are used for conquering premature and controlling search policy, respectively. Hence, the proposed MQPSO algorithm which combines the mechanisms of GA, SA and LTS within the QPSO algorithm can deal with the proposed model with outliers. From simulation results, the proposed MQPSO readily corrects the deviation caused by outliers without losing precisions and swiftly achieves convergences on estimating the parameters of the proposed generalized NMRCI model for the non-additive systems with outliers.

Phylogenetic relationships among host plants explain differences in fungal species richness and community composition in ectomycorrhizal symbiosis

Geographic and taxonomic host ranges determine the distribution of biotrophic organisms. Host phylogenetic distance strongly affects the community composition of pathogens and parasites, but little is known about the host phylogeny effect on communities of mutualists, such as plant-pollinator and plant-mycorrhizal fungi systems. By incorporating phylogenetic eigenvectors into univariate and multivariate models, we aimed to determine the relative contribution of host phylogeny and environmental variables to mycorrhizal traits and community composition of ectomycorrhizal (EcM) fungi in Salicaceae at the local scale. Host phylogeny explained 75% of the variation in fungal species richness and 20% of the variation in community composition. We also re-analyzed a system involving eight hosts from Japan, in which host phylogeny explained 26% and 9% of the variation in fungal richness and community composition, respectively. [Correction added after online publication 21 May 2013: in the preceding sentence the values 9% and 26% have been transposed.] Phylogenetic eigenvectors that differentially account for clades and terminal taxa across the phylogeny revealed stronger host effects than did the treatment of host species as categorical or dummy variables in multiregression models, and in comparison with methods such as Mantel test and its analogs. Our results indicate the usefulness of the eigenvector method for the quantification of the host phylogeny effect, which represents an integrated complex function of taxonomic sampling effect and phylogenetic distance per se.

Study of supernatant of SBR in dead-end microfiltration

The activated sludge supernatant of sequencing batch reactor was filtrated in dead-end mode by a 0.1μm microfiltration hydrophilic polyvinylidene fluoride (PVDF) hollow-fiber membrane module, and a multiregression model was used to study the influence of operating conditions on flux of the membrane module and the effluent quality (TOC and NH3-N) quantificationally. The impact of the following seven parameters—(1) transmembrane pressure (TMP), (2) the sludge concentration of the mixed liquor suspended solids (MLSS), (3) dissolved oxygen concentration (DO), (4) hydraulic retention time (HRT), (5) sludge retention time (SRT), (6) temperature (T), and (7) pH value (pH) on the membrane permeate flux (J) and water quality (TOC, NH3-N)—were investigated. The results show that: (i) The effluent quality from this microfiltration membrane module could meet the quality standard for reused wastewater issued by the Chinese Ministry of Construction (CJ25.1–89); (ii) TMP, T and pH are the influence factors of the membrane permeate flux and the relative influence degree are 50.71, 45.08 and 4.21%, respectively. The mathematic expression obtained by the multivariate linear regression model between the membrane permeate flux and operating conditions was: J=3.35×10-5-1.3×10-7T-3.4×10-6pH+1.1×10-4TMP; (iii) TMP has no effect on TOC and NH3-N, the relative influence degree of other operational conditions on TOC as following: MLSS (49.36%) > pH (32.52%) > HRT (9.87%) > SRT (4.60%) > DO (2.21%) > T (1.44%), and the relative influence degree of them on NH3-N as following: MLSS (61.39%) > pH (21.38%) > HRT (8.82%) > SRT (3.51%) > T (4.11%) > DO (0.79%): The relationship between TOC and operational conditions was the following: TOC=-394.553-48.38MISS+2.81T-13.79DO+106.52pH-0.60SRT-11.12HRT. The relationship between NH3-N and operational condition was: NH3-N=390.648+63.84MISS-5.61T+9.73DO-102.20pH+0.62SRT+12.44HRT.

Maternal Complications and The Association with Baseline Variables in Pregnant Women with Sickle Cell Disease

Sickle cell disease is an inherited hemoglobinopathy with multi system complications. It has been associated with multiple maternal complications. A retrospective review of 68 consecutive pregnant women with sickle cell disease, followed in a tertiary center, was conducted over 5 years, to estimate the incidence of different maternal complications and the impact of baseline characteristics.Sixty-eight patients were analyzed (mean age 30 years). Sixty-two patients had a Hb SS genotype. The initial mean hemoglobin (Hb) level was 9.5 g/dL. Twelve patients delivered by Cesarean section. Sixty-five patients required admission for sickle cell disease/pregnancy-related complications [96.0%; 95% confidence interval (95% CI) 91-100]. Infection was seen in 17 patients (25.0%, 95% CI 14-36). Blood transfusions were given to 61 patients (90.0%, 95% CI 82-97). Eight patients had gestational hypertension (18.0%, 95% CI 4-20), while five patients (7.0%, 95% CI 1-14) had pre term labor. One patient developed eclampsia and one had a uterine rupture. One patient died due to post partum hemorrhage. The multi variable logistic regression model on the impact on the major maternal complications revealed none of the baseline factors to be statistically significant. Sickle cell disease patients have low mortality and pregnancy-related morbidity but high sickle cell disease-related morbidity. Prospective studies are needed to confirm these results.

Haemoglobin

Haemoglobin

Introducing Anisotropic Minkowski Functionals and Quantitative Anisotropy Measures for Local Structure Analysis in Biomedical Imaging.

The ability of Minkowski Functionals to characterize local structure in different biological tissue types has been demonstrated in a variety of medical image processing tasks. We introduce anisotropic Minkowski Functionals (AMFs) as a novel variant that captures the inherent anisotropy of the underlying gray-level structures. To quantify the anisotropy characterized by our approach, we further introduce a method to compute a quantitative measure motivated by a technique utilized in MR diffusion tensor imaging, namely fractional anisotropy. We showcase the applicability of our method in the research context of characterizing the local structure properties of trabecular bone micro-architecture in the proximal femur as visualized on multi-detector CT. To this end, AMFs were computed locally for each pixel of ROIs extracted from the head, neck and trochanter regions. Fractional anisotropy was then used to quantify the local anisotropy of the trabecular structures found in these ROIs and to compare its distribution in different anatomical regions. Our results suggest a significantly greater concentration of anisotropic trabecular structures in the head and neck regions when compared to the trochanter region (p < 10-4 ). We also evaluated the ability of such AMFs to predict bone strength in the femoral head of proximal femur specimens obtained from 50 donors. Our results suggest that such AMFs, when used in conjunction with multi-regression models, can outperform more conventional features such as BMD in predicting failure load. We conclude that such anisotropic Minkowski Functionals can capture valuable information regarding directional attributes of local structure, which may be useful in a wide scope of biomedical imaging applications.

Application of multi regressive linear model and neural network for wear prediction of grinding mill liners

The liner of an ore grinding mill is a critical component in the grinding process, necessary for both high metal recovery and shell protection. From an economic point of view, it is important to keep mill liners in operation as long as possible, minimising the downtime for maintenance or repair. Therefore, predicting their wear is crucial. This paper tests different methods of predicting wear in the context of remaining height and remaining life of the liners. The key concern is to make decisions on replacement and maintenance without stopping the mill for extra inspection as this leads to financial savings. The paper applies linear multiple regression and artificial neural networks (ANN) techniques to determine the most suitable methodology for predicting wear. The advantages of the ANN model over the traditional approach of multiple regression analysis include its high accuracy.