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...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.展开更多
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 landslid...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 %.展开更多
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...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.展开更多
The precision of dynamic reserve calculations in gas reservoirs is crucial for the rational and efficient development of oil and gas fields and the formulation of gas well production plans.The Shaximiao gas reservoir ...The precision of dynamic reserve calculations in gas reservoirs is crucial for the rational and efficient development of oil and gas fields and the formulation of gas well production plans.The Shaximiao gas reservoir in the ZT block of northwestern Sichuan is densely packed and highly heterogeneous,featuring complex gas-water distribution,substantial variations in test production among gas wells,and a rapid decline rate.To precisely determine the dynamic reserves of these tight water-bearing gas wells,this study focuses on the water-tight gas reservoirs in the ZT block of northwestern Sichuan,conducting core X-ray diffraction,constant-rate mercury injection,and reservoir rock stress sensitivity experiments.Utilizing the experimental findings,the porosity and permeability of the rock samples under effective stress conditions are adjusted via binary linear regression.These adjusted parameters are then incorporated into the water-sealed gas material balance method,thereby establishing a novel approach for calculating dynamic reserves in water-tight gas reservoirs under stress sensitivity conditions.The results show that:(1)the rock porosity ranges from 6.08%to 10.22%,permeability ranges from 0.035 mD to 0.547 mD,clay mineral content ranges from 6.58%to 19.14%,pore radius distribution ranges from 90μm to 180μm,throat radius distribution ranges from 0.61μm to 3.41μm,with significant differences in throat distribution,indicating poor reservoir fluid flow capacity and strong tightness;(2)after aging experiments,rock samples exhibit plastic deformation,with porosity and permeability unable to fully recover after pressure relief.The stress sensitivity curve of rock samples shows a two-stage characteristic,with moderate to strong stress sensitivity;(3)porosity stress sensitivity is mainly influenced by pore radius and mineral composition-larger pore radius and higher clay content lead to stronger stress sensitivity,with porosity loss rates ranging from 8.26%to 23.69%.Permeability stress sensitivity is mainly influenced by throat radius and mineral composition-smaller throat radius and higher clay content result in stronger stress sensitivity,with permeability loss rates ranging from 47.91%to 62.03%;(4)a comparative analysis between the traditional dynamic reserve calculation method for gas wells and the new method considering stress sensitivity shows a relative error between 0.90%and 2.41%,with the new method demonstrating better accuracy.This study combines physical experimental results with an effective stress model of reservoir rocks to develop a new method for calculating dynamic reserves of water-bearing tight gas reservoirs under effective stress conditions,providing experimental data and example calculation results to support subsequent dynamic evaluation of gas reservoirs and the establishment of rational well allocation plans.展开更多
Polymetallic nodules,hereinafter referred to as PN,enriched with Co,Ni,Mn,and Cu,are likely to be commercially mined in the near future.These metals in PN are potential strategic alternatives for the world’s energy t...Polymetallic nodules,hereinafter referred to as PN,enriched with Co,Ni,Mn,and Cu,are likely to be commercially mined in the near future.These metals in PN are potential strategic alternatives for the world’s energy transition.Therefore,intensive studies are necessary on the spatial distribution patterns of PN in the deep sea.In this study,the distribution probabilities of PN in the Pacific,Indian and Atlantic oceans were estimated based on binary logistic regression of PN occurrence with ore-controlling factors including water depth(WD),marine sediment thickness(SedTh),Calcium carbonate(CaCO_(3))concentrations in surface sediments,primary productivity(PP),near bottom current velocities(BC).Furthermore,the distribution probability of PN was constrained by seafloor ages and PN sites,and subsequently,the prospects for nodules in the Pacific,Indian and Atlantic oceans were obtained.The results indicate that the low-latitude Pacific region(30°N-30°S),particularly the Clarion-Clipperton Zone and the Penrhyn Basin,is the most promising area for PN exploration.展开更多
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 ri...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.展开更多
This paper provides a tool to identify key aspects for an airport to achieve global hub status for a given airline and determines whether these factors are related to the facility’s infrastructure,its region,or both....This paper provides a tool to identify key aspects for an airport to achieve global hub status for a given airline and determines whether these factors are related to the facility’s infrastructure,its region,or both.Despite the frequent use of the term‘hub’,there is little academic consensus on its exact definition in air transport.Many define a hub based on passenger numbers rather than the concentration of flights and passengers from the main carrier.This study addresses this gap by analyzing the factors influencing the definition of a hub and the commonalities among global hubs.Data from 300 major airports,including internal variables(runways,terminals,gates and area)and external variables(economy,population,attractiveness),were collected.A Binary Logistic Regression(BLR)model identified key aspects influencing hub status,with the assistance of an Exploratory Factor Analysis(EFA)that grouped the variables into factors.The binary‘hub’variable was defined by the main carrier’s activity and the Global Airport Connectivity Index(GACI).The factor with the highest coefficient primarily involved internal variables and,to a lesser extent,global attractiveness and population.The factor with the lowest coefficient related to the region economy.The BLR correctly identified hub status in 93.3%of cases,with 68.3%accuracy for hub airports.Airports not correctly identified by the model mostly present a lack or underutilization of existing infrastructure.展开更多
“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 Bu...“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.展开更多
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 seve...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.展开更多
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 ...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.展开更多
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...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.展开更多
Late-stage or later-successional ectomycorrhizal fungi,dominant ectomycorrhizal species in mature forest,are generally important symbiotic partners of dominant tree species in many forest ecosystems.Spatial patterns o...Late-stage or later-successional ectomycorrhizal fungi,dominant ectomycorrhizal species in mature forest,are generally important symbiotic partners of dominant tree species in many forest ecosystems.Spatial patterns of fungal sporocarps of three families,i.e.Amanitaceae,Boletaceae and Russulaceae,in a subtropical forest in Dujiangyan were examined using second-order analysis in the present paper.The woody plant compositions of the plots associated with ectomycorrhizal fungi of three families were also compared using binary logistic regression analysis.Results indicated that presences of non-ectomycorrhizal and some ectomycorrhizal plants might have negative effects on the occurrence of ectomyconrhizas(ECM)fungal sporocarps and the characteristics in clonal growth of fungal taxa would not be the only determinant in the spatial pattern of ECM fungi.We suggest that besides host plants,non-ectomycorrhizal woody plants and interaction of ECM fungi should also be considered in spatial studies of ECM fungal communities in natural forests.展开更多
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 ...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.展开更多
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 h...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.展开更多
The non-invasive method for estimating the pulmonary arterial pressure(PAP)plays an important role in the screening and diagnosis of pulmonary arterial hypertension(PAH).We aimed to establish a phase-contrast magnetic...The non-invasive method for estimating the pulmonary arterial pressure(PAP)plays an important role in the screening and diagnosis of pulmonary arterial hypertension(PAH).We aimed to establish a phase-contrast magnetic resonance imaging(PC-MRI)based method to estimate PAP.Through analyzing a patient's morphologic and hemodynamic features,this method could be used to identify PAH,and provide diagnostic and grading information for PAH.We selected 39 study participants,comprising 18 healthy volunteers and 21 patients with PAH.Morphologic and hemodynamic parameters of each participant's pulmonary arteries were obtained from 4D Flow images.Hemodynamic features were performed selected by principal component analysis(PCA).PAH identification model was built with binary logistic regression.Furthermore,A multiple linear regression(MLR)model was developed to estimate PAP,the accuracy of which was evaluated by comparing it with the value measured by right heart catheterization(RHC).PAH identification was achieved with high accuracy,using the features of pulmonary arterial morphology or blood flow velocity(BFV).Compared with RHC,MLR results showed that using pulmonary arterial morphology and BFV features in combination can greatly improve the accuracy of PAP estimation.Our results showed that the mean relative error of PAP estimation in PAH patients could reach<10%.A highly accurate PC-MRI based method for PAH identification and PAP estimation was successfully established.Using hemodynamic features of the pulmonary artery could improve PAP estimation accuracy,which highlights the importance of hemodynamic evaluation of pulmonary arteries in the screening and diagnosis of PAH.展开更多
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...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.展开更多
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 ...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.展开更多
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 interrup...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.展开更多
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 livelihoo...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.展开更多
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 cle...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.展开更多
基金Supported by National Natural Science Foundation of China (Grant Nos.10771017,10971015,10901020)Key Project of MOE,PRC (Grant No.309007)
文摘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.
文摘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 %.
基金supported by the National College Students Innovation and Entrepreneurship Training Programs(CN)(Grant Nos.2021J00054&2019J00127)
文摘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.
基金supported by CNPC Southwest Oil and Gas Field Branch's 2023 Scientific Research Program Project(20230303-14).
文摘The precision of dynamic reserve calculations in gas reservoirs is crucial for the rational and efficient development of oil and gas fields and the formulation of gas well production plans.The Shaximiao gas reservoir in the ZT block of northwestern Sichuan is densely packed and highly heterogeneous,featuring complex gas-water distribution,substantial variations in test production among gas wells,and a rapid decline rate.To precisely determine the dynamic reserves of these tight water-bearing gas wells,this study focuses on the water-tight gas reservoirs in the ZT block of northwestern Sichuan,conducting core X-ray diffraction,constant-rate mercury injection,and reservoir rock stress sensitivity experiments.Utilizing the experimental findings,the porosity and permeability of the rock samples under effective stress conditions are adjusted via binary linear regression.These adjusted parameters are then incorporated into the water-sealed gas material balance method,thereby establishing a novel approach for calculating dynamic reserves in water-tight gas reservoirs under stress sensitivity conditions.The results show that:(1)the rock porosity ranges from 6.08%to 10.22%,permeability ranges from 0.035 mD to 0.547 mD,clay mineral content ranges from 6.58%to 19.14%,pore radius distribution ranges from 90μm to 180μm,throat radius distribution ranges from 0.61μm to 3.41μm,with significant differences in throat distribution,indicating poor reservoir fluid flow capacity and strong tightness;(2)after aging experiments,rock samples exhibit plastic deformation,with porosity and permeability unable to fully recover after pressure relief.The stress sensitivity curve of rock samples shows a two-stage characteristic,with moderate to strong stress sensitivity;(3)porosity stress sensitivity is mainly influenced by pore radius and mineral composition-larger pore radius and higher clay content lead to stronger stress sensitivity,with porosity loss rates ranging from 8.26%to 23.69%.Permeability stress sensitivity is mainly influenced by throat radius and mineral composition-smaller throat radius and higher clay content result in stronger stress sensitivity,with permeability loss rates ranging from 47.91%to 62.03%;(4)a comparative analysis between the traditional dynamic reserve calculation method for gas wells and the new method considering stress sensitivity shows a relative error between 0.90%and 2.41%,with the new method demonstrating better accuracy.This study combines physical experimental results with an effective stress model of reservoir rocks to develop a new method for calculating dynamic reserves of water-bearing tight gas reservoirs under effective stress conditions,providing experimental data and example calculation results to support subsequent dynamic evaluation of gas reservoirs and the establishment of rational well allocation plans.
基金The Marine Science and Technology Fund of Shandong Province for Laoshan Laboratory under contract No.LSKJ202203600-2the China Ocean Mineral Resources Research and Development Association(COMRA)project under contract No.DY135-N2-1-04.
文摘Polymetallic nodules,hereinafter referred to as PN,enriched with Co,Ni,Mn,and Cu,are likely to be commercially mined in the near future.These metals in PN are potential strategic alternatives for the world’s energy transition.Therefore,intensive studies are necessary on the spatial distribution patterns of PN in the deep sea.In this study,the distribution probabilities of PN in the Pacific,Indian and Atlantic oceans were estimated based on binary logistic regression of PN occurrence with ore-controlling factors including water depth(WD),marine sediment thickness(SedTh),Calcium carbonate(CaCO_(3))concentrations in surface sediments,primary productivity(PP),near bottom current velocities(BC).Furthermore,the distribution probability of PN was constrained by seafloor ages and PN sites,and subsequently,the prospects for nodules in the Pacific,Indian and Atlantic oceans were obtained.The results indicate that the low-latitude Pacific region(30°N-30°S),particularly the Clarion-Clipperton Zone and the Penrhyn Basin,is the most promising area for PN exploration.
基金This paper was financially supported by NSC96-2628-E-366-004-MY2 and NSC96-2628-E-132-001-MY2
文摘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.
基金funded through the programmatic funding-UIDP/04427/2020the research grant UI/BD/153356/2022 awarded by the Fundação para a Ciência e a Tecnologia(FCT)of Portugal to the Research Centre for Territory,Transports and Environment(CITTA).
文摘This paper provides a tool to identify key aspects for an airport to achieve global hub status for a given airline and determines whether these factors are related to the facility’s infrastructure,its region,or both.Despite the frequent use of the term‘hub’,there is little academic consensus on its exact definition in air transport.Many define a hub based on passenger numbers rather than the concentration of flights and passengers from the main carrier.This study addresses this gap by analyzing the factors influencing the definition of a hub and the commonalities among global hubs.Data from 300 major airports,including internal variables(runways,terminals,gates and area)and external variables(economy,population,attractiveness),were collected.A Binary Logistic Regression(BLR)model identified key aspects influencing hub status,with the assistance of an Exploratory Factor Analysis(EFA)that grouped the variables into factors.The binary‘hub’variable was defined by the main carrier’s activity and the Global Airport Connectivity Index(GACI).The factor with the highest coefficient primarily involved internal variables and,to a lesser extent,global attractiveness and population.The factor with the lowest coefficient related to the region economy.The BLR correctly identified hub status in 93.3%of cases,with 68.3%accuracy for hub airports.Airports not correctly identified by the model mostly present a lack or underutilization of existing infrastructure.
文摘“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.
基金supported by the Beijing Municipal Science and Technology Project,China (Z151100001015004)
文摘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.
基金Under the auspices of National Science & Technology Support Program of China(No.2006BAD20B00)
文摘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.
基金The Youth Fund of National Natural Science Foundation of China (81904324)Xinglin Talent Plan of Chengdu University of Traditional Chinese Medicine(QNXZ2020015)。
文摘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.
文摘Late-stage or later-successional ectomycorrhizal fungi,dominant ectomycorrhizal species in mature forest,are generally important symbiotic partners of dominant tree species in many forest ecosystems.Spatial patterns of fungal sporocarps of three families,i.e.Amanitaceae,Boletaceae and Russulaceae,in a subtropical forest in Dujiangyan were examined using second-order analysis in the present paper.The woody plant compositions of the plots associated with ectomycorrhizal fungi of three families were also compared using binary logistic regression analysis.Results indicated that presences of non-ectomycorrhizal and some ectomycorrhizal plants might have negative effects on the occurrence of ectomyconrhizas(ECM)fungal sporocarps and the characteristics in clonal growth of fungal taxa would not be the only determinant in the spatial pattern of ECM fungi.We suggest that besides host plants,non-ectomycorrhizal woody plants and interaction of ECM fungi should also be considered in spatial studies of ECM fungal communities in natural forests.
文摘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.
文摘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.
基金supported by Nature Science Foundation of Hubei Province(2024AFD183)State Key Laboratory of Virtual Reality Technology and Systems.
文摘The non-invasive method for estimating the pulmonary arterial pressure(PAP)plays an important role in the screening and diagnosis of pulmonary arterial hypertension(PAH).We aimed to establish a phase-contrast magnetic resonance imaging(PC-MRI)based method to estimate PAP.Through analyzing a patient's morphologic and hemodynamic features,this method could be used to identify PAH,and provide diagnostic and grading information for PAH.We selected 39 study participants,comprising 18 healthy volunteers and 21 patients with PAH.Morphologic and hemodynamic parameters of each participant's pulmonary arteries were obtained from 4D Flow images.Hemodynamic features were performed selected by principal component analysis(PCA).PAH identification model was built with binary logistic regression.Furthermore,A multiple linear regression(MLR)model was developed to estimate PAP,the accuracy of which was evaluated by comparing it with the value measured by right heart catheterization(RHC).PAH identification was achieved with high accuracy,using the features of pulmonary arterial morphology or blood flow velocity(BFV).Compared with RHC,MLR results showed that using pulmonary arterial morphology and BFV features in combination can greatly improve the accuracy of PAP estimation.Our results showed that the mean relative error of PAP estimation in PAH patients could reach<10%.A highly accurate PC-MRI based method for PAH identification and PAP estimation was successfully established.Using hemodynamic features of the pulmonary artery could improve PAP estimation accuracy,which highlights the importance of hemodynamic evaluation of pulmonary arteries in the screening and diagnosis of PAH.
文摘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.
基金Scientific Research Project for Recruited Talents of Shanxi Agricultural University,Grant/Award Number:2021BQ63National Natural Science Foundation of China,Grant/Award Number:72374130+1 种基金Scientific Research Reward Projects for Doctoral Graduates and Postdoctoral Researchers Working in Shanxi Province,Grant/Award Number:SXBYKY2022005Shanxi Forage and Grass-industry Technology Research System Fund Abstract。
文摘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.
文摘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.
文摘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.
文摘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.
