Efficient Estimation of a Varying-coefficient Partially Linear Binary Regression Model

This article considers a semiparametric varying-coefficient partially linear binary regression model. The semiparametric varying-coefficient partially linear regression binary model which is a generalization of binary regression model and varying-coefficient regression model that allows one to explore the possibly nonlinear effect of a certain covariate on the response variable. A Sieve maximum likelihood estimation method is proposed and the asymptotic properties of the proposed estimators are discussed. One of our main objects is to estimate nonparametric component and the unknowen parameters simultaneously. It is easier to compute, and the required computation burden is much less than that of the existing two-stage estimation method. Under some mild conditions, the estimators are shown to be strongly consistent. The convergence rate of the estimator for the unknown smooth function is obtained, and the estimator for the unknown parameter is shown to be asymptotically efficient and normally distributed. Simulation studies are carried out to investigate the performance of the proposed method.

GIS based Landslide Susceptibility Mapping of Tevankarai Ar Sub-watershed,Kodaikkanal,India using Binary Logistic Regression Analysis

Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of slope failure in an area.An attempt is made to map the landslide susceptibility in Tevankarai Ar subwatershed,Kodaikkanal,India using binary logistic regression analysis.Geographic Information System is used to prepare the database of the predictor variables and landslide inventory map,which is used to build the spatial model of landslide susceptibility.The model describes the relationship between the dependent variable(presence and absence of landslide) and the independent variables selected for study(predictor variables) by the best fitting function.A forward stepwise logistic regression model using maximum likelihood estimation is used in the regression analysis.An inventory of 84 landslides and cells within a buffer distance of 10m around the landslide is used as the dependent variable.Relief,slope,aspect,plan curvature,profile curvature,land use,soil,topographic wetness index,proximity to roads and proximity to lineaments are taken as independent variables.The constant and the coefficient of the predictor variable retained by the regression model are used to calculate the probability of slope failure and analyze the effect of each predictor variable on landslide occurrence in thestudy area.The model shows that the most significant parameter contributing to landslides is slope.The other significant parameters are profile curvature,soil,road,wetness index and relief.The predictive logistic regression model is validated using temporal validation data-set of known landslide locations and shows an accuracy of 85.29 %.

Residents'satisfaction of Beijing new regulations for domestic waste classification based on binary logistic regression:A case study of Daxing District

On the first anniversary of the implementation of the new regulations of Beijing Municipality on the management of domestic waste,to understand residents’views on the waste classification policy,the project conducted relevant investigation of the satisfaction of residents with the domestic waste classification policy in Daxing District of Beijing,China.Based on the analysis of the survey,this study uses the binary logistic regression model to explore the residents’satisfaction with the new domestic waste classification policy in Beijing and its influencing factors.The data from 398 valid questionnaires involve the demographic characteristics of residents,residents’cognition and views on Beijing municipal solid waste classification policy,and residents’satisfaction with Beijing domestic waste classification policy.The data show that the comprehensive satisfaction level of residents with the domestic waste classification policy in Beijing is quite high,up to 84.7%.Among them,the satisfaction level of residents with the details of the classification standards,the allocation of garbage cans,the publicity and supervision of the policy,incentive measures and the implementation process and effect of the policy is very high,exceeding 80%or even more than 90%.Through binary logistic regression analysis,we come to the conclusion that six factors significantly affect residents’satisfaction with Beijing municipal solid waste classification policy,such as residents’monthly income,household daily average domestic waste production,publicity of waste classification policy,supervisors’better understanding of waste classification standards,guidance of waste delivery by community classification supervisors,and convenience of waste classification process.

Association between people’s attitudes towards human-elephant conflict and their locational,demographic,and socio-economic characteristics in Buxa Tiger Reserve and its adjoining area,India

“Human-elephant conflict(HEC)”,the alarming issue,in present day context has attracted the attention of environmentalists and policy makers.The rising conflict between human beings and wild elephants is common in Buxa Tiger Reserve(BTR)and its adjoining area in West Bengal State,India,making the area volatile.People’s attitudes towards elephant conservation activity are very crucial to get rid of HEC,because people’s proximity with wild elephants’habitat can trigger the occurrence of HEC.The aim of this study is to conduct an in-depth investigation about the association of people’s attitudes towards HEC with their locational,demographic,and socio-economic characteristics in BTR and its adjoining area by using Pearson’s bivariate chi-square test and binary logistic regression analysis.BTR is one of the constituent parts of Eastern Doors Elephant Reserve(EDER).We interviewed 500 respondents to understand their perceptions to HEC and investigated their locational,demographic,and socio-economic characteristics including location of village,gender,age,ethnicity,religion,caste,poverty level,education level,primary occupation,secondary occupation,household type,and source of firewood.The results indicate that respondents who are living in enclave forest villages(EFVs),peripheral forest villages(PFVs),corridor village(CVs),or forest and corridor villages(FCVs),mainly males,at the age of 18–48 years old,engaged with agriculture occupation,and living in kancha and mixed houses,have more likelihood to witness HEC.Besides,respondents who are illiterate or at primary education level are more likely to regard elephant as a main problematic animal around their villages and refuse to participate in elephant conservation activity.For the sake of a sustainable environment for both human beings and wildlife,people’s attitudes towards elephants must be friendly in a more prudent way,so that the two communities can live in harmony.

Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships

Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p 〈0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to cheek the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height （X1 ） and potential energy （X2 ） significantly impact （p 〈 0. 0001 ） the amplitude-based refleeted rate; the P-values for the deviance and Pearson are all 〉 0.05 （0.2839, 0.3438, respectively）. That is, the goodness-of-fit between ridge height （ X1 ） and potential energy （X2） can further predict parameters under the scenario of the best parsimonious model. Investigation of 6 predictive powers （ R2, Max-rescaled R^2, Sorners＇ D, Gamma, Tau-a, and c, respectively） indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.

Selection for high quality pepper seeds by machine vision and classifiers

This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features： three color features （R, a＊, brightness）, width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features,＇ germination percentage increased from 59.3 to 71.8% when a＊〉3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight 〉0.0064 g. The most effective model was based on a multilayer perceptron （MLP） neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.

A Time-dependent Stochastic Grassland Fire Ignition Probability Model for Hulun Buir Grassland of China

Grassland fire is one of the most important disturbance factors in the natural ecosystems.This paper focuses on the spatial distribution of long-term grassland fire patterns in the Hulun Buir Grassland located in the northeast of Inner Mongolia Autonomous Region in China.The density or ratio of ignition can reflect the relationship between grassland fire and different ignition factors.Based on the relationship between the density or ratio of ignition in different range of each ignition factor and grassland fire events,an ignition probability model was developed by using binary logistic regression function and its overall accuracy averaged up to 81.7%.Meanwhile it was found that daily relative humidity,daily temperature,elevation,vegetation type,distance to county-level road,distance to town are more important determinants of spatial distribution of fire ignitions.Using Monte Carlo method,we developed a time-dependent stochastic ignition probability model based on the distribution of inter-annual daily relative humidity and daily temperature.Through this model,it is possible to estimate the spatial patterns of ignition probability for grassland fire,which will be helpful to the quantitative evaluation of grassland fire risk and its management in the future.

Screening influencing factors of blood stasis constitution in traditional Chinese medicine

Objective To study the influencing factors of blood stasis constitution and provide a basis for treating blood stasis-related diseases by traditional Chinese medicine(TCM) constitution identification.Methods Data were collected using the self-developed TCM constitution identification platform based on B/S model by the project team. The obtained data were divided into blood stasis constitution and normal constitution groups. The differences of the categorical type influencing factors(gender, birth mode, feeding mode within four months of birth, family history, marital status, eating habits, sleeping habits, exercise habits, emotional state, stress situation, and living environment) and the quantitative type influencing factors(sleep time, age,and mother’s age at birth) on the constitution of the two groups were analyzed. In the singlefactor analysis, the Pearson’s chi-square test was selected for the categorical variable, and the independent sample t test and Mann-Whitney U nonparametric test were selected for the quantitative variables according to whether they conformed to the positive-terrestrial distribution;the binary logistic stepwise regression method was selected for the multi-factor analysis.Results The data of 318 cases were collected from the TCM composition identification platform, and 159 cases of blood stasis constitution were used as the experimental group and 159 cases of normal constitution were used as the control group. The Pearson’s chi-square test yielded significant differences(P < 0.05) in the effects of gender, pressure situation, family history, living environment, emotional state, exercise habits, and dietary habits on blood stasis constitution. The independent samples t test yielded differences in sleep duration between the blood stasis constitution and normal constitution populations(P < 0.05), which meant sleep duration of the blood stasis constitution population was less than that of the normal constitution population. The Mann-Whitney U nonparametric test results accepted the original hypothesis that there was no difference in the distribution of age and mother’s age at birth across constitution types(P > 0.05). Binary logistic regression analysis showed that gender, family history, marital status, living environment, exercise habits, and emotional state were risk factors for blood stasis constitution(P < 0.05).Conclusion Gender, family history, living environment, emotional state, and exercise habits were significant influencing factors of blood stasis constitution. Blood stasis constitution populations can pay more attention to these influencing factors in their daily life for the prevention and reconciliation of blood stasis constitution.

A Study on the Determinants of Location Choice in Inward Foreign Direct Investment （FDI）： Focusing on the Incheon Free Economic Zone

Inward foreign direct investment （FDI） is expected to grow further by virtue of economic globalization. A thorough understanding of the locational determinants of inward FDI will be conducive to enhanced efficiency in attracting direct and SOC-related investments from foreign entities. This study analyzes 51 cases of inward direct foreign investment made in the Incheon free economic zone （IFEZ） from 2002 to 2009 to determine the factors influencing FDI volume, the relevance of locations and the correlation between investment size and location. First, the relationship between the loeational determinants of FDI and the total investment size （total expected project cost） is analyzed. Second, the relationship between the locational determinants of FDI and the FDI is analyzed. Third, the relationship between the locational determinants of FDI and the location choice is analyzed. The results indicate the determinants that influence locations and investment size of FDI entities; whether these factors exercise influence in the zone; and the factors that have relatively significant effects. Ultimately, based on the analytical findings, a few implications for policy and practice are derived.

Households’ Willingness-to-Pay to Reduce the Use of Plastic Bags: Case Study in Viet Nam

This study used the Binary Logistic regression model to estimate the willingness to pay (WTP) to reduce the use of plastic bags in the daily life of people in the Linh Nam ward. This study notes that households with higher incomes and higher levels of education tend to be more willing to pay. In addition, those who do not have access to information about the harmful effects of plastic bags and receive a higher proposed price often refuse to pay.

Virtual Determination of Sex:Estimating Cut off Value of Digital Metric Traits of Foramen Magnum on Three-dimensional Computed Tomography with Receiver Operating Characteristic and Logistic Regression Analysis

Background:Radiological imaging plays a pivotal role in forensic anthropology.As have the imaging techniques advances,so have the digital skeletal measurements inched towards precision.Secular trends of the population keep on changing in modem times.Hence,finding the precise technique of bone measurement,with greater reproducibility,in modem population is always needed in making population specific biological profile.Aim and Objective:The aim of this study was to estimate the accuracy of the foramen magnum measurement,obtained by three dimensional multi-detector computed tomography using volume rendering technique with the cut off value of each variable,in sex determination of an individual.Materials and Methods:Two metric traits,an antero-posterior diameter(APD)and transverse diameter(TD),were measured digitally in an analysis of 130 radiological images having equal proportion of male and female samples.Foramen magnum index and area of foramen magnum(Area by Radinsky's[AR],Area by Teixeira5s[AT])were derived from APD and TD.Results:Descriptive statistical analysis,using unpaired t-test,showed significant higher value in males in all the variables.Using Pearson correlation analysis,maximum correlation was observed between area(AT and AR r=0.999)and between area and TD(AR r=0.955 and AT r=0.945 respectively).When used individually,TD had the highest predictive value(67.7%)for sex detennination among all the parameters followed by AT(65.4%)and AR(64.6%).Cutoff value of variables TD,AR and AT were 29.9 mm,841.80 mm2 and 849.70 mm2 respectively.Receiver operating characteristic curve predicted male and female sex with 96.2%and 89.2%accuracy respectively.The overall accuracy of the model was 92.7%.Conclusion:Measurements from 3D CT using volume rendering technique were precise,and the application of logistic regression analysis predicted the sex with more accuracy.

Local people’s knowledge on the adverse impacts and their attitudes towards growing Eucalyptus woodlot in Gudo Beret Kebele, Basona Worena district, Ethiopia

Introduction:Growing Eucalyptus at a farm level in the form of woodlot has become popular among rural households in Ethiopia.For example,rural households mainly establish Eucalyptus woodlot as a component of livelihood improvement and diversification to meet household wood demand and generating cash income.However,there is lack of information on the growth parmeters of Eucalyptus woodlot and the factors influencing the household decision on their establishment at the individual farmland level.The objective of this study was to examine local people’s knowledge on the adverse impacts and their attitudes towards growing Eucalyptus woodlot in Gudo Beret Kebele.We hypothesized that local people’s knowledge on the adverse impacts and their attitudes towards growing Eucalyptus woodlot in Gudo Beret Kebele is affected by socioeconomic and cognitive variables.Methods:A structured questionnaire comprising closed-and open-ended questions was developed and administered to a total of n=94 households to collect information on local people’s knowledge on the adverse impacts and their attitudes towards growing Eucalyptus woodlot.The households were randomly selected through a lottery system based on their house identification numbers.Descriptive statistics,binary logit,and multiple linear regression were used to analyze and interpret the data.Results:The results revealed that about 92%of the respondents noted that growing Eucalyptus woodlot had positive impacts on the socioeconomic situation of the community considering that it contributes to economic benefits through the sale of wood products,such as poles,construction materials,and fuelwood.However,only 8%of the respondents noted that the negative impacts of Eucalyptus woodlot were attributed to the decline in crop and forage production due to its allelopatic effect,and the reduction in ground water availability.Majority of the respondents(about 68%)preferred to grow Eucalyptus woodlot in Gudo Beret Kebele.Thus,most of the respondents(about 69%)had strongly agreed to have a positive attitude towards growing Eucalyptus woodlot.On the other hand,the binary logit regression model explained about 70.6%of the variance of local people’s knowledge on the adverse impacts of Eucalyptus woodlot.Overall,the multiple linear regression model revealed that socioeconomic and cognitive variables had significant effect on local people’s attitudes towards growing Eucalyptus woodlot(39.5%variance explained).Conclusions:We recommended that foresters,natural resource experts and managers,environmentalists,land use planners,and policy-makers should take the right and careful decision by assessing the overall socioeconomic and ecological aspects of Eucalyptus woodlot based on the interests of various stakeholders including local communities.

Low Impedance Fault Identification and Classification Based on Boltzmann Machine Learning for HVDC Transmission Systems

Identification and classification of DC faults are considered as fundamentals of DC grid protection.A sudden rise of DC fault current must be identified and classified to immediately operate the corresponding interrupting mechanism.In this paper,the Boltzmann machine learning(BML)approach is proposed for identification and classification of DC faults using travelling waves generated at fault point in voltage source converter based high-voltage direct current(VSC-HVDC)transmission system.An unsupervised way of feature extraction is performed on the frequency spectrum of the travelling waves.Binomial class logistic regression(BCLR)classifies the HVDC transmission system into faulty and healthy states.The proposed technique reduces the time for fault identification and classification because of reduced tagged data with few characteristics.Therefore,the faults near or at converter stations are readily identified and classified.The performance of the proposed technique is assessed via simulations developed in MATLAB/Simulink and tested for pre-fault and post-fault data both at VSC1 and VSC2,respectively.Moreover,the proposed technique is supported by analyzing the root mean square error to show practicality and realization with reduced computations.

Effect of time-of-day and day-of-the-week on congestion duration and breakdown:A case study at a bottleneck in Salem,NH

This work uses regression models to analyze two characteristics of recurrent congestion： breakdown, the transition from freely flowing conditions to a congested state, and duration, the time between the onset and clearance of recurrent congestion. First, we apply a binary logistic regression model where a continuous measurement for traffic flow and a dichoto- mous categorical variable for time-of-day （AM- or PM-rush hours） is used to predict the probability of breakdown. Second, we apply an ordinary least squares regression model where categorical variables for time-of-day （AM- or PM-rush hours） and day-of-the-week （Monday-Thursday or Friday） are used to predict recurrent congestion duration. Models are fitted to data collected from a bottleneck on 1-93 in Salem, NH, over a period of 9 months. Results from the breakdown model, predict probabilities of recurrent congestion, are consistent with observed traffic and illustrate an upshift in breakdown probabilities between the AM- and PM-rush periods. Results from the regression model for congestion duration reveal the presences of significant interaction between time-of-day and day-of-the-week. Thus, the effect of time-of-day on congestion duration depends on the day-of-the-week. This work provides a simplification of recurrent congestion and recovery, very noisy processes. Simplification, conveying complex relationships with simple statistical summaries-facts, is a practical and powerful tool for traffic administrators to use in the decision-making process.

PARAMETRIC AND NON-PARAMETRIC COMBINATION MODEL TO ENHANCE OVERALL PERFORMANCE ON DEFAULT PREDICTION

The probability of default(PD) is the key element in the New Basel Capital Accord and the most essential factor to financial institutions' risk management.To obtain good PD estimation,practitioners and academics have put forward numerous default prediction models.However,how to use multiple models to enhance overall performance on default prediction remains untouched.In this paper,a parametric and non-parametric combination model is proposed.Firstly,binary logistic regression model(BLRM),support vector machine(SVM),and decision tree(DT) are used respectively to establish models with relatively stable and high performance.Secondly,in order to make further improvement to the overall performance,a combination model using the method of multiple discriminant analysis(MDA) is constructed.In this way,the coverage rate of the combination model is greatly improved,and the risk of miscarriage is effectively reduced.Lastly,the results of the combination model are analyzed by using the K-means clustering,and the clustering distribution is consistent with a normal distribution.The results show that the combination model based on parametric and non-parametric can effectively enhance the overall performance on default prediction.

Factors associated with crash severities in built-up areas along rural highways of Nevada:A case study of 11 towns

In 2014, 32,675 deaths were recorded in vehicle crashes within the United States. Out of these, 51% of the fatalities occurred in rural highways compared to 49% in urban highways. No specific crash data are available for the built-up areas along rural highways. Due to high fatalities in rural highways, it is important to identify the factors that cause the vehicle crashes. The main objective of this study is to determine the factors associated with se- verities of crashes that occurred in built-up areas along the rural highways of Nevada. Those factors could aid in making informed decisions while setting up speed zones in these built-up areas. Using descriptive statistics and binary logistic regression model, 337 crashes that occurred in 11 towns along the rural highways from 2002 to 2010 were analyzed. The results showed that more crashes occurred during favorable driving conditions, e.g., 87% crashes on dry roads and 70% crashes in clear weather. The binary logistic regression model showed that crashes occurred from midnight until 4 a.m. were 58.3% likely to be injury crashes rather than property damage only crashes, when other factors were kept at their mean values. Crashes on weekdays were three times more likely to be injury crashes than that occurred on weekends. When other factors were kept at their mean value, crashes involving motorcycles had an 80.2% probability of being injury crashes. Speeding was found to be 17 times more responsible for injury crashes than mechanical defects of the vehicle. As a result of this study, the Nevada Department of Transportation now can take various steps to improve public safety, including steps to reduce speeding and encourage the use of helmets for motorcycle riders.

Factors affecting farmers’participation in watershed management programs in the Northeastern highlands of Ethiopia:a case study in the Teleyayen sub-watershed

Introduction:This study investigated factors affecting farmers’participation in watershed management programs in the Northeastern highlands of Ethiopia by taking the Teleyayen sub-watershed as a case study.Data were collected from 215 farm households which were selected from the four villages using a multistage sampling procedure,involving a combination of purposive and random sampling.Data were gathered using a structured survey questionnaire,focus group discussion,and key informant interviews.Descriptive analysis,Pearson correlation analysis,and regression analysis were employed to analyze the data.Results:Findings of this study showed that farmer’s perception has a strong positive correlation(r=0.612,P=0.000)with the farmer’s decision to participate in the watershed management programs followed by government support(r=0.163,P=0.017),while the slope of the farmland and the gender of the household head have shown significant and negative associations.The binary logistic regression analysis also revealed that six independent variables were significant in explaining the factors affecting the farmers’decision to participate in watershed management programs.These variables were land redistribution,gender,agricultural labor force,extension service,farm size,and slope.Of these,land redistribution,gender,agricultural labor force,extension service,and slope of the farmland indicated a negative influence,while farm size of a household exerted a positive impact.The study also examined the role of discrete variables in explaining variations of variables in affecting the farmers’decision to participate in the programs.Thus,two variables found to be significant.These variables are the gender of the household head and land tenure security.Accordingly,the chi-square result of the variable(χ^(2)=9.052)of gender was found to be statistically significant at the 95%level of significance.Similarly,the chi-square result(X^(2)=8.792)of land tenure security was found to be statistically significant at the 95%level of significance.Conclusions:The result of the study suggests to work on raising the awareness of farmers’about the long-term benefits of the watershed programs and to design a strategy to diversify their livelihoods.

Factors influencing farmer willingness to plant forage triticale in winter fallow fields in Northern China:An example from central Shanxi Province

Background:Using winter fallow fields for plant forage is important to ensure food security.Forage triticale(×Triticosecale)has higher yields than other available forage crops and can be planted widely in winter fallow fields.Recently,the planted area of forage triticale in Shanxi Province,China,has exceeded 3500 ha;however,problems such as low farmer willingness to plant(WTP)winter forage still remain.Methods:A total of 219 farmers were surveyed in Taiyuan,Lvliang,and Jinzhong.We analyzed the factors influencing farmer WTP forage triticale,focusing on personal,family,land,and cognition characteristics.We used a binary logistic regression model to quantify the influence of various factors on farmer behavior and conducted a robustness check and heterogeneity analysis.Results:“Age”was negatively correlated with farmer WTP—farmers 50 years of age and older showed less WTP.“Land lease situation”was also negatively correlated with WTP.Factors that positively correlated with WTP were“land areas,”“raising of livestock,”“size of labor force,”and“development prospect.”Conclusions:Many farmers are over 50 years of age,land lessors,and have low WTP winter forage.Farmers who raise livestock and have large labor forces,huge land areas,and good cultivation prospects have a high WTP.This study identifies the factors influencing farmers'WTP to assist in the development of the forage triticale industry in the study region,improving land resource utilization and efficiency.The findings are likely to have wider relevance and application.