Regression Model In Order Research Articles

A study on machinability evaluation of Al-Gr-B<SUB align="right">4C MMC using response surface methodology-based desirability analysis and artificial neural network technique

In this work, machinability behaviour of aluminium-graphite-boron carbide metal matrix composite is performed during wire-cut electrical discharge machining (WEDM) process. Experiments were designed using central composite-face centred design of response surface methodology (RSM) and with the application of desirability function multiple quality characteristics viz., kerf width, surface roughness and material removal rate (MRR) were optimised simultaneously. Input parameters gap voltage, pulse ON-time, pulse OFF-time and % reinforcement of boron carbide particles in the aluminium matrix are considered. The optimised machining condition obtained is a gap voltage of 150 V, pulse ON-time of 124.56 ms, pulse OFF-time of 48.03 ms and 2.5% reinforcement of boron carbide. From the experimental values, it is observed that better output responses are achieved with lower reinforcement of boron carbide. Second order regression models are developed individually for the output responses. An artificial neural network model is developed to predict the output responses, results obtained show that a better prediction can be achieved through artificial intelligent technique.

Empirical Modeling of Roughness Parameters in Drilling Composites- A Response Surface Approach

Abstract Particle composite panels are alternative to traditional wood because of their superior advantages in furniture industry. The present study describes the development of mathematical models to predict the surface roughness in drilling particleboard composite using forstner carbide drill bits. Plain Particleboard panel of IS 3087 grade was selected as work material to conduct experiment. Experiments were planned as per Taguchi L12 orthogonal array. Experiments were conducted under different drilling input parameters of spindle speed, feed rate and drill diameter. A mathematical model on surface roughness has been developed in terms of input parameters. Residual plots were constructed to analyze the variation between the experimental values and predicted values. Analysis of Variance (ANOVA) was employed to find the effect of various drilling parameters on surface roughness. It showed a high coefficient of determination (R2) value, which ensures perfect fit of the second order regression model with experimental data.

Optimization of Process Parameters during the Laser Cutting of Inconel-718 Sheet using Regression based Genetic Algorithm

Abstract Inconel-718 is a nickel-based super-alloy which is extensively used in various modern manufacturing industries like chemical, aerospace, nuclear, marine, automobile and medical due to its excellent mechanical behavior. The conventional cutting method faces some problems for the cutting of these materials due to better mechanical properties. The unconventional cutting methods may be alternative methods for the cutting of these difficult-to-cut materials. Among the different unconventional methods, the laser cutting may be applied with satisfactorily quality by operating the machine at optimum settings. The laser beam cutting process is one of the most suitable unconventional cutting processes to achieve the geometrical accuracy with high precision at the optimum parameter level. The main aim of this experimental work is to optimize the different quality characteristics viz. top kerf deviation (TKD) and bottom kerf deviation (BKD), during the pulsed Nd: YAG laser cutting of Inconel-718 sheet by using the hybrid approach methodology regression based genetic algorithm. The design of experiment based L27 orthogonal array has been applied for performing the experiments. By using the experimental data, second order regression models for all quality characteristics have been developed. The hybrid optimization technique regression based genetic algorithm has been used for optimizing the quality characteristics. The optimum results found by proposed methodology have been compared experimentally. The comparison of optimized results with the experimental results shows that considerable improvements have been observed in all quality characteristics individually as well as simultaneously.

328: Prediction of success in external cephalic version for breech presentation at term

We aimed at designing a clinically based predictive model for the likelihood of successful external cephalic versions (ECVs) near term. This single-center retrospective study was conducted in a single center between 2016 and 2018 and included all candidates for ECV between 35 and 41 weeks' gestation. The versions were performed by a single experienced obstetrician. Variables that may potentially affect the success rate of external version were collected for all participants and included: BMI, AFI, gestational age, parity, location of placenta, fetal trunk posture, period of time in breech presentation prior to the procedure and the size of the amniotic fluid preceding the fetal presenting part (fore-bag). Patients' characteristics were compared between successful and unsuccessful versions based on multiple logistic regression models in order to generate a discriminative model depicted by a conditional inference classification tree (ctree). Overall 250 pregnant women were eligible and opted for a trial of ECV with a success rate of 64.8%. Variables such as location of the placenta, period of time at breech presentation and previous uterine scar had no statistically significant impact on the success rate of ECV while BMI, size of fore-bag and parity were independent determinants of the final presentation. Prediction model for the outcome of ECV based on the most discriminatory factors is suggested and shown as a classification tree. According to our model, patients with a forebag size of 0 had a predicted probability of 10% for successful version, patients with a forebag size >0 and a BMI >29 had a probability of 0%, patients with a forebag size >1 and BMI <=29 had a probability of 96.3% and patients with forebag size =1 and BMI <=29 and number of previous births>0 had a probability of 81.25%. The model accuracy was 91.9%±5.4% which is significantly better than the no information rate of 64.8% (P<0.001). Sensitivity for predicting successful versions was 0.975 and specificity was 0.818. A prediction model composed of three easily measurable variables enables accurate outcome prediction of ECV at term. Fore-bag was identified as the most important discriminator. Our model holds in internal validation and it can be used to support patient counseling and decision making for ECV.

The effect of explanatory variables on income: A tool that allows a closer look at the differences in income

Investigation of the effect of covariates on income typically relies on regression models with a transformed income. An underlying assumption is that the exact income is available. However, in surveys reported income is often available in income brackets only. For such grouped data one can use ordered regression models, which in their simplest form with a linear predictor work in a similar way as regression models for exact income. They yield an overall measure of the effect of covariates but fail to detect the specific structure of the effects of single covariates. A model is proposed that allows a closer look at the effect of single covariates, showing in more detail how the income is determined by explanatory variables. The model exploits the potential of sequential regression models, which are extended to allow for varying coefficients. The model is not harder to use than classical regression models but is much more informative. The method is illustrated by using data from the German Socio-Economic Panel Study and the United States Census Bureau.

Early fetal ultrasound screening for major congenital heart defects without Doppler

ObjectiveCongenital heart defects are the most common major structural fetal abnormalities. Color flow mapping has played a dominant role in the detection of abnormalities during the first trimester, regardless of the International Society of Ultrasound in Obstetrics and Gynecology warning on the use of Doppler during early pregnancy. The aim of our study was to investigate the use of transvaginal two-dimensional sonography without Doppler for assessing the four-chamber view and the outflow tract view of fetuses at 11–13 weeks of gestation for cardiac screening of major congenital heart defects. Study DesignThis was a prospective observational study conducted in the Fetal Medicine Unit of Cabueñes University Hospital, between May 2014 and August 2015. Only low risk-pregnancies were studied. All ultrasonographic examinations were performed by two experienced sonographers in maternal-fetal medicine. The combination of high-frequency transvaginal (nine MHz) and transabdominal (six MHz) ultrasonography transducers were used. An early cardiac screening was performed in 97% of cases. Statistical analysis was carried out using successive multivariate logistic regression models in order to investigate the effect of crown-rump length and body mass index on the probability of visualizing the four-chamber view and/or the outflow tract view. Results663 low-risk pregnant women were included. Regarding the transvaginal approach, neither the crown-rump length nor the body mass index had a statistically significant relationship on the probability of visualization of the four-chamber view and outflow tract view. For the transabdominal approach, the crown-rump length and the body mass index presented a statistically significant effect on the visualization of the four-chamber view and the outflow tract view. Using the transvaginal approach: the success rate of performing a four-chamber view was 89.4% and 82.4% for the outflow tract view. Using the transabdominal approach: the success rate of performing a four-chamber view was 77.8% and 61.5% for the outflow tract view. Four major congenital heart defects were diagnosed, and the prenatal ultrasonagraphic diagnosis was confirmed for all cases. ConclusionsRoutine first-trimester ultrasonagraphy without Doppler, when performed by experienced sonographers, can effectively identify major congenital heart defects. Additional multicenter well designed studies should clarify the feasibility of this approach.

CT-guided Transthoracic Core-Needle Biopsies of Mediastinal and Lung Lesions in 235 Consecutive Patients: Factors Affecting the Risks of Complications and Occurrence of a Final Diagnosis of Malignancy

ObjectiveTo assess the impact of patient-, lesion- and procedure-related factors on the risks of complications and final diagnosis of malignancy in PCNB of mediastinal and lung lesions. Material and MethodsWe studied a large single-center cohort of 235 consecutive patients (66.8% men; 58.5±18.0 years) with a range of thoracic benign and malignant lesions, who underwent PCNB performed along 24 months by a single experienced radiologist. Diagnostic accuracy analyses of PCNB for malignancy were performed, as well as estimations of relative risk and logistic regression models in order to assess possible associations between such factors and malignancy/complications. Results155 lesions (65.9%) were diagnosed as malignant. Overall accuracy was 91.1%, with sensitivity of 87.1%, specificity of 98.8%, positive predictive value of 99.3%, and negative predictive value of 79.8%. Pneumothorax (49/235; 20.8%) and hemorrhage (37/235; 15.7%) were the most common complications. Emphysema, smoking, older age, intrapulmonary location, deeper location, smaller size, presence of cavitations and irregular contours of the lesions, and smaller needle-pleural angles were the most consistent factors related to the occurrence of complications. Emphysema, older age, smoking, solid and deeper lesions were also significantly associated with a final diagnosis of malignancy after PCNB. ConclusionCT-guided PCNB of mediastinal and lung lesions is a safe procedure with high diagnostic accuracy for malignancy.

Estimation of number of ever born children using zero truncated count model: evidence from Bangladesh Demographic and Health Survey.

Fertility is an important demographic indicator for any country and there has always been a concern for number of ever born children to know the transition of fertility pattern intensively. Child ever born is the count response variable ranges from 1 to 15 and was originally collected by the Bangladesh Demographic and Health Survey (BDHS) considering the reproductive women who had given at least one birth. This study proposes zero truncated Poisson and zero truncated negative binomial regression models in order to find the best fitted model to estimate number of ever born children using BDHS 2014 dataset. Findings reveal that, the number of children increases with the increment of respondent's age but number of children declines if education status of respondents as well as their husbands' increases. Similarly, religion, wealth index and wanted last child have significantly influenced the number of child ever born. Surprisingly, the number of children ever born to a mother from rural area does not differ significantly from that of urban area in Bangladesh, though there exists a little fluctuation in the number of children ever born to a mother living in seven administrative divisions. Intension of contraceptive use has no influence on number of ever born children to a mother.

Race and income moderate the association between depressive symptoms and obesity

Complex interrelationships between race, sex, obesity and depression have been well-documented. Because of differences in associations between socioeconomic status (SES) and health by race, determining the role of SES may help to further explicate these relationships. The aim of this study was to determine how race and income interact with obesity on depression. Combining data from the 2007–2014 National Health and Nutrition Examination Survey, depressive symptoms was measured with the Patient Health Questionnaire-9 and obesity was assessed as body mass index ≥30 kg/m2. Three-way interactions between race, income and obesity on depressive symptoms were determined using ordered regression models. Significant interactions between race, middle income and obesity (OR = 0.66, 95% CI = 0.22–1.96) suggested that, among white women, obesity is positively associated with depressive symptoms across income levels, while obesity was not associated with depression for African American women at any income level. Obesity was only associated with depressive symptoms among middle-income white men (OR = 1.44, 95% CI = 1.02–2.03) and among high-income African American men (OR = 4.65, 95% CI = 1.48–14.59). The associations between obesity and depressive symptoms vary greatly by race and income. Findings from this study underscore the importance of addressing obesity and depression among higher income African American men.

Obstetric and perinatal outcomes of singleton pregnancies conceived via assisted reproductive technology complicated by gestational diabetes mellitus: a prospective cohort study

BackgroundGrowing evidence indicates that the risk of obstetric and perinatal outcomes is higher in women with assisted reproductive technology (ART). However, there is little known about pregnancy related complications and co-morbidity in gestational diabetes mellitus (GDM) following singleton pregnancies achieved by ART in comparison with spontaneous conception (SC).MethodsTwo hundred sixty singleton pregnant women conceived by ART and 314 pregnant women conceived by spontaneous conception (SC) were participated in this prospective cohort study. All participants were enrolled after GDM screening through one-step oral glucose tolerance test (OGTT) and then grouped into GDM and non-GDM groups. Women were followed for pregnancy outcomes including pregnancy-induced hypertension (PIH), preeclampsia, antepartum hemorrhage (APH), cesarean section (CS), preterm birth (PTB), intrauterine growth restriction (IUGR), being small or large for gestational age (SGA or LGA), macrosomia, low birth weight (LBW), respiratory distress, neonatal hypoglycemia, NICU admission and perinatal mortality from antenatal visits to delivery. Confounding factors were adjusted in logistic regression model in order to estimate adjusted odds ratios (aORs).ResultsAmong 260 ART and 314 SC, 135 and 152 women were GDM women, respectively. Higher maternal age and pre-gravid BMI, shorter duration of gestation and lower gestational weight gain were observed in GDM groups (ART-GDM and SC-GDM) compared to those of the SC group. ART-GDM group had a higher risk (95% confidence interval) of obstetric complications including PIH [aOR:7.04 (2.24–22.15)], preeclampsia [aOR:7.78 (1.62–37.47)], APH [aOR:3.46 (1.28–9.33)], emergency CS [aOR:2.64 (1.43–4.88)], and perinatal outcomes such as PTB [aOR:3.89 (1.51–10.10)], LBW [aOR:3.11 (1.04–9.30)] and NICU admission [aOR:4.36 (1.82–10.45)], as well as neonatal hypoglycemia [aOR: 4.91 (1.50–16.07)], compared to SC group. SC-GDM group showed a higher risk of PIH [aOR: 4.12 (1.31–12.89)], emergency CS [aOR: 2.01 (1.09–3.73] and LGA [aOR: 5.20 (1.07–25.20)], compared to SC group. Additionally, ART group had a higher risk of PIH [aOR: 3.46(1.02–11.68), preeclampsia 5.29 (1.03–27.09), and NICU admission [aOR: 2.53 (1.05–6.09)] compared to SC. Insulin requirement (41.8% vs. 25.7%) was significantly higher in ART-GDM group compared to SC-GDM group.ConclusionThe findings of this study suggest that GDM occurring after ART conception increases the risk of adverse obstetric and perinatal outcomes.

Gencrm: A New Command for Generalized Continuation-Ratio Models

A continuation-ratio model represents a variant of an ordered regression model that is suited to modeling processes that unfold in stages, particularly those in which a return to a previous stage is not possible (for example, educational attainment, job promotion, or disease progression). The parameters for covariates in continuation-ratio models may be constrained to be equal, vary by a set of common factors (that is, proportionality constraints), or freely vary across stages. Currently, there are three community-contributed commands that fit continuation-ratio models. Each of these commands fits some subset of continuation-ratio models involving parameter constraints, but none of them offer complete coverage of the range of possibilities. The new gencrm command expands the options for continuation-ratio models to include the possibility for some of or all the covariates to be constrained to be equal, to freely vary, or to vary by a set of common factors across stages. gencrm relies on Stata's maximum likelihood routines for estimation and avoids reshaping the data. gencrm includes options for three link functions (logit, probit, and cloglog) and supports Stata's multiple-imputation suites of commands.

Healthcare financing in South-East Asia: Does fiscal capacity matter?

ABSTRACT Financing healthcare and achieve sustainable development goals by 2030 is the prime focus of many low-income and middle income countries. Improved public healthcare and reduce the burden of out-of-pocket health expenditure is the overarching public health policy objective across the Asia-Pacific region. This study examines the impact of fiscal capacity on health expenditure by controlling socio-economic factors in South-East Asia for the period of 1995–2013. We have employed a panel fixed effects regression model in order to capture country-level unobserved heterogeneity and macroeconomic policy changes in the health sector. Overall result shows that annual change in per capita government health expenditure (pooled financing) is influenced positively by the per capita income, fiscal capacity, ageing, and the prevalence of tuberculosis and rate of urbanization. The income elasticity of government health expenditure is less than one, which implies that the health expenditure is treated as a necessity for the public health providers. Overall empirical analysis concludes that pooled financing mechanism positively influenced by the size of fiscal capacity thereby the share of out-of-pocket health expenditure to total health expenditure declines over the period. The result suggests that the faster movement towards health financing transition would possible through the generation of fiscal capacity by improving public finance policies.

Estimating bid-auction models of residential location using census data with imputed household income

Modeling residential location as a key component of the land-use system is essential to understand the relationship between land use and transport. The increasing availability of censuses such as the German Zensus 2011 has enabled residential location to be modeled with a large number of observations, presenting both opportunities and challenges. Censuses are statistically highly representative; however, they often lack variables such as income or mobility-related attributes as in the case of Zensus 2011. This is particularly problematic if missing variables define utility or willingness-to-pay functions that characterize choice options in a location model. One example for this is household income, which is an indispensable variable in land use models because it influences household location preferences and defines affordable location options. For estimating bid-auction location models for different income groups, we impute household income in census data applying an ordered regression model. We find that location models considering this imputation perform sufficiently well as they reveal reasonable and expected aspects of the location patterns. In general, imputing choice variables should thus be considered in the estimation of residential location models but is also promising for other decision problems. Comparing results for two imputation methods, we also show that while applying the deterministic first preference imputation may yield misleading results the probabilistic Monte Carlo simulation is the correct imputation approach.

Use of Geographically Weighted Poisson Regression to examine the effect of distance on Tuberculosis incidence: A case study in Nam Dinh, Vietnam.

ObjectivesThis study aimed to examine the potential of combining routine tuberculosis (TB) surveillance and demographic and socioeconomic variables into the Geographic Information System (GIS) to describe the geographical distribution of TB notified incidence in relation to distances to health services as well as local demographic and socioeconomic factors, including population density, urban/rural status, and household poverty rates in Nam Dinh, Vietnam. It also aimed to compare the conventional Generalized Linear Models (GLM) Poisson regression model and Geographically Weighted Poisson Regression (GWPR) models in order to determine the best fitting model that can be used to investigate the relationship between TB notified incidence and distances and the social risk factors.MethodsThe data of new and relapse patients with all forms of TB aged ≥15 years residing in Nam Dinh (Vietnam) from 2012 to 2015 were collected from the Administration of Medical Services’ (Ministry of Health of Vietnam) TB surveillance database. Data on the population and household poverty rates from 2012 to 2015 were gathered from the Nam Dinh Statistical Office. Distances between communes and the nearest TB diagnostic facilities in districts were computed. The TB notified incidence per 100,000 population was denoted by indirect age and sex standardized incidence ratio. GLM Poisson regression and GWPR were performed to assess the relationship between distance and TB incidence.ResultsThe average notified TB incidence level measured from 2012 to 2015 is 82 per 100,000 population (range: 79-84/100,000). The distance to the nearest TB diagnosis presents a negative effect on TB notified incidence. By capturing spatial heterogeneity, the GWPR may be better at fitting data (corrected Aikake information criterion [AICc] = 245.71, residual deviance = 221.12) than the traditional GLM (AICc = 251.53, residual deviance = 241.21)ConclusionsGIS technologies benefit TB surveillance system. Distances should be considered when planning methods of improving access for those who live far from TB diagnostic services, thereby improving TB detection. Additional studies must confirm the association between geographic distance and TB case detection and must explore other factors that may affect TB notified incidence.

Useful, but Not Necessarily Idiots

Using evidence from roll-call voting in the European Parliament and data from the Euromanifesto project, this study employs statistical analysis (linear and logistic regression models) in order to identify the ideological linkages among Putin-sympathizer parties in Europe. The analysis demonstrates that one of the linkages among them is provided by their rejection of European integration, and that Putin-sympathizer parties tend to display ideological radicalism on both ends of the left–right ideological continuum, thus falling into the category of anti-system parties. At the same time, one of the important shared characteristics of Putin-sympathizer parties is populism.

Determinants of Commercial Banks' Profitability. Evidence from Hungary

This paper aims to find out whether bank‑specific (internal) factors impact on the profitability of commercial banks in Hungary for 16 a year period ranging from 2000–2015. The study employs a sample of twenty‑six commercial banks with four hundred sixteen observations. The study employs return on average assets (ROAA) as a proxy for bank profitability, and it also considers bank‑specific (internal) factors as independent variables. These include asset quality (non‑performing loans), overhead costs, bank size, net interest margin, and liquidity risk plus capital adequacy ratio. The study uses panel regressions, descriptive statistics and correlation analysis for the investigations. The panel regression models are to estimate the impact of bank‑specific (internal) factors on bank profitability. The Hausman specification test was conducted on the panel regression models in order to identify the best and appropriate model for the study. The empirical findings reveal that non‑performing loans, overhead costs and liquidity had a significant negative impact on bank profitability as bank size had a significant positive impact on profitability. However, net interest margin and capital adequacy ratio had no impact on bank profitability. The study concludes that bank size and asset quality are bank‑specific factors that have the biggest impact on commercial banks’ profitability in Hungary for the period under investigation. The study recommends that commercial banks should endeavor to manage and reduce overhead costs to be able to earn more profits since overhead costs adversely affect bank profitability. More so, commercial banks’ managers should regularly monitor credit and liquidity risk indicators as well as pursuing diversification policies of income sources while upholding optimisation of operational costs.

White matter connectivity reductions in the pre‐clinical continuum of psychosis: A connectome study

Widespread white matter connectivity disruptions have commonly been reported in schizophrenia. However, it is questionable whether structural connectivity decline is specifically associated with schizophrenia or whether it extends along a continuum of psychosis into the healthy population. Elucidating brain structure changes associated with psychotic-like experiences in healthy individuals is insofar important as it is a necessary first step towards our understanding of brain pathology preceding florid psychosis. High resolution, multishell diffusion-weighted magnetic resonance images (MRI) were acquired from 89 healthy individuals. Whole-brain white matter fibre tracking was performed to quantify the strength of white matter connections. Network-based statistics were applied to white matter connections in a regression model in order to test for a linear relationship between streamline count and psychotic-like experiences. A significant subnetwork was identified whereby streamline count declined with increasing psychotic-like experiences. This network of structural connectivity reductions affected all cortical lobes, subcortical structures and the cerebellum and spanned along prominent association and commissural white matter pathways. A widespread network of linearly declining connectivity strength with increasing number of psychotic-like experiences was identified in healthy individuals. This finding is in line with white matter connectivity reductions reported from early to chronic stages of schizophrenia and might therefore aid the development of tools to identify individuals at risk of transitioning to psychosis.

320 A Model for Examining Local Geographic Variations in Out-of-Hospital Cardiac Arrest Outcomes

Out-of-hospital cardiac arrest (OHCA) is a leading cause of death in the United States. Although global and national trends have been examined, geographic disparities of OHCA outcomes at local and community levels is less understood. In this study, we developed and tested a replicable, community assessment strategy aimed to identify spatial variations in OHCA outcomes using socioeconomic, out-of-hospital, and inhospital factors. Emergency medical services records of OHCA within Alachua County between 01/2012 and 01/2017 were retrospectively reviewed. Adult non-traumatic OHCA that had recorded dispatch locations and dispatch time stamps were included. These records were individually matched to medical records at the University of Florida (UF). Out-of-hospital-inhospital data and other patient-level variables were extracted from electronic medical records. Socioeconomic demographics for census tracts were extracted from 2010 Census data. Location of cardiac arrest was geocoded using ArcGIS. Primary outcomes were survival to emergency department (ED), survival to admission, survival to discharge, and discharge to home. Multiple imputation was implemented to account for incomplete records. Individual cases were nested into census tracts to develop multilevel logistic regression models in order to assess the geographic variance and probabilities of survival. Two sets of adjusted models were created for each outcome by adding patient-level and then census-level variables to test associations with survival. Chi square deviance test was applied to assess model fit of after the addition of census-level variables. Of the 1562 OHCA cases extracted from out-of-hospital records, 1335 (85.5%) cases were included and geocoded. Predicted probability of survival to ED was 0.57 (95% CI: 0.51 - 0.63). Three-hundred seventy-two patients transferred to UF and 318 (85.5%) were successfully matched to inhospital records. Predicted probabilities of survival to admission, survival to discharge, and survival to home discharge of these patients were 0.42 (95% CI: 0.36 - 0.48), 0.16 (95% CI: 0.11 - 0.23), and 0.07 (95% CI: 0.04 - 0.13), respectively. Census tracts accounted for significant variability in survival to ED, discharge, and discharge home outcomes. There was no significant geographic variation in survival to admission outcomes. Addition of census-level variables in adjusted models significantly improved model fit for survival to ED, discharge, and discharge home outcomes. Multiple modifiable patient and census-level variables of interest were identified in unadjusted and spatially adjusted models, including rural-urban differences. We identified important geographic differences that exist in outcomes of OHCA at the community level, especially in rates of survival to ED, discharge, and discharge with good neurological outcome. The methods used in this research not only identifies specific locations within a county that vary in survival rates but also explains this variation through potentially modifiable socioeconomic and out-of-hospital factors. This research represents a replicable schematic for assessing geographic variations in survival outcomes and factors that influence these outcomes in local communities.

Generation of food waste in the hospitality and food retail and wholesale sectors: lessons from developed economies

Food wastage is a major challenge in the global food value chain. In an attempt to explain and better understand the determinants of food waste across the supply chain, this paper closely examines the Hospitality, Restaurants and Canteens/Cafeterias (HORECA) as well as food retail and wholesale sectors, as being interrelated with the final consumer at the post-harvest and post-processing levels. Data from 33 developed countries in North America, Europe, Asia and Oceania were compiled from secondary sources and analyzed by means of a regression model in order to identify the macroeconomic factors contributing to generation of food waste in the HORECA industry at the national level. The results of our analysis suggest that a well-defined and inclusive legislative framework, awareness campaigns as well as fiscal incentives significantly reduce generation of food waste by an average of 17.6, 21.3 and 14.3%; respectively. Accordingly, these measures can be used in conjunction as effective schemes for controlling food wastage in the HORECA industry. On the other hand, non-legally binding initiatives by government authorities among other actors did not seem to have any significant impact on the food waste generated and quantities reported.

Perspectives of Ecological Footprint in European Context under the Impact of Information Society and Sustainable Development

The perspective of ecological footprint, which is a tool for measuring and monitoring the sustainability of the new information society and the higher degree of openness in the European economy powered by the globalization process, was approached from the viewpoint of accelerated technical, scientific, and innovative progress. This research aims at identifying and forecasting patterns of environmental footprint behavior in European countries, depending on factors reflecting the innovation activity, the degree of economic freedom, and EU membership status. In the article, three factors are identified that explain to a large extent the variation of the ecological footprint values: employment in foreign controlled enterprises, eco-innovation index, and region. The statistical and econometric methods used in the analysis are aimed at applying a Proportional-Odds Cumulative Logistic regression model in order to verify the existence of the association between the statistical variables and to forecast the likelihood of changing the ecological footprint from a lower to a higher score under the impact of selected factors by quantifying their influence. The results have led to the conclusion that, by applying the model, both the share of the employed population in the foreign-controlled enterprises and the eco-innovation index will have a significant direct influence on the variability in the ecological footprint (through the odds-ratio). Referring to the EU membership, the model shows that non-EU member countries or newer EU member countries are predominantly assigned low ecological footprint scores.