Background: This study explored nursing personality traits (Big Five Inventory BFI), emotional intelligence (EI), and thinking styles (Rational, RS, and Experiential, ES) together with demographic data to see how they...Background: This study explored nursing personality traits (Big Five Inventory BFI), emotional intelligence (EI), and thinking styles (Rational, RS, and Experiential, ES) together with demographic data to see how they could relate and the implication of this on nurses and patient safety. Design: A cross-sectional study. Methods: Nursing sample (n = 435). Participants completed a self-report online survey, which included demographic information, followed by questionnaires to measure personality traits, thinking styles, and emotional intelligence. Results: Spearman’s rank correlation was computed to assess the relationship between EI and Extraversion;there was a moderate positive correlation between the two variables, r = 0.487, p r = 0.731, p r = 0.723, p r = -0.666, p r = 0.467, p Conclusion: Different studies consolidated each other, and all converge and channel into the concept of characterization of healthcare providers for better support to them and safer patient care. EI correlated with all BFI components, and both positively impacted all desirable behaviors. Therefore, it would be valuable if organizations invested in increasing EI in their providers as it might highlight areas for improvement and equip providers with appropriate and advantageous coping strategies.展开更多
The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)o...The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.展开更多
彩色设备的特征化是指设备相关颜色空间与标准色空间之间的转换,它是色彩管理技术的关键技术之一。以RGB颜色空间与CIE L*a*b*颜色空间转换为例,采用小脑模型神经网络(cer-ebellar model articulation controller,CMAC)进行设备颜色空...彩色设备的特征化是指设备相关颜色空间与标准色空间之间的转换,它是色彩管理技术的关键技术之一。以RGB颜色空间与CIE L*a*b*颜色空间转换为例,采用小脑模型神经网络(cer-ebellar model articulation controller,CMAC)进行设备颜色空间的特征化,以BP神经网络的方法和颜色空间转换模糊神经辨识方法对该模型进行比较研究,并利用Matlab软件进行了仿真。仿真结果显示,当训练样本总数为729个时,经过不到100次的迭代,网络既可以满足输出精度要求,并且检验样本的平均色差为1.35,表明该算法具有更快的模型转换速度和更高的转换精度。展开更多
Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area ...Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area is laying in many countries.Formulation of SPEs is based on indirect mechanism,therefore,assessment and correction of associated uncertainties is required.In the present study,disintegration of uncertainties associated with four prominent real time SPEs,IMERG,TMPA,CMORPH and PERSIANN has been conducted at grid level,regional scale,and summarized in terms of regions as well as whole study area basis.The bias has been disintegrated into hit,missed,false biases,and Root Mean Square Error(RMSE)into systematic and random errors.A comparison among gauge-and satellite-based precipitation estimates at annual scale,showed promising result,encouraging use of real time SPEs in the study area.On grid basis,at daily scale,from box plots,the median values of total bias(-0.5 to 0.5 mm)of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias(-0.15 to 0.05 mm/day).Relatively higher values of missed(0.3 to 0.5 mm/day)and false(0.5 to 0.7 mm/day)biases were observed.The detected average daily RMSE,systematic errors,and random errors were also comparatively higher.Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas,depicting the better performance of satellite-based products in these areas.However,in areas of high altitude(>4000 m),due to complex topography and climatic conditions(orographic precipitation and glaciated peaks)higher values of biases and errors were observed.Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas,where precipitation is more on ridges and less in valleys where gauge stations are usually located.Precipitation system’s size and intensity can also be a reason of higher biases,because Microwave Imager underestimate precipitation in small systems(<200 km^(2))and overestimate in large systems(>2000 km^(2)).At present,use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area.Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors,missed and false biases.展开更多
文摘Background: This study explored nursing personality traits (Big Five Inventory BFI), emotional intelligence (EI), and thinking styles (Rational, RS, and Experiential, ES) together with demographic data to see how they could relate and the implication of this on nurses and patient safety. Design: A cross-sectional study. Methods: Nursing sample (n = 435). Participants completed a self-report online survey, which included demographic information, followed by questionnaires to measure personality traits, thinking styles, and emotional intelligence. Results: Spearman’s rank correlation was computed to assess the relationship between EI and Extraversion;there was a moderate positive correlation between the two variables, r = 0.487, p r = 0.731, p r = 0.723, p r = -0.666, p r = 0.467, p Conclusion: Different studies consolidated each other, and all converge and channel into the concept of characterization of healthcare providers for better support to them and safer patient care. EI correlated with all BFI components, and both positively impacted all desirable behaviors. Therefore, it would be valuable if organizations invested in increasing EI in their providers as it might highlight areas for improvement and equip providers with appropriate and advantageous coping strategies.
基金supported by the National Research Foundation of Korea(NRF)Grant funded by the Korean government(MSIT)(NRF-2023R1A2C1005950).
文摘The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting.The majority of currently available methods use either a generative adversarial network(GAN)or a recurrent neural network(RNN)to generate new handwriting styles.This is why these techniques frequently fall short of producing diverse and realistic text pictures,particularly for terms that are not commonly used.To resolve that,this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles.This network excels in generating conditional text by extracting style vectors from a series of style images.The model performs admirably on a range of handwriting synthesis tasks,including the production of text that is out-of-vocabulary.It works more effectively than previous approaches by displaying lower values on key Generative Adversarial Network evaluation metrics,such Geometric Score(GS)(3.21×10^(-5))and Fréchet Inception Distance(FID)(8.75),as well as text recognition metrics,like Character Error Rate(CER)and Word Error Rate(WER).A thorough component analysis revealed the steady improvement in image production quality,highlighting the importance of specific handwriting styles.Applicable fields include digital forensics,creative writing,and document security.
文摘彩色设备的特征化是指设备相关颜色空间与标准色空间之间的转换,它是色彩管理技术的关键技术之一。以RGB颜色空间与CIE L*a*b*颜色空间转换为例,采用小脑模型神经网络(cer-ebellar model articulation controller,CMAC)进行设备颜色空间的特征化,以BP神经网络的方法和颜色空间转换模糊神经辨识方法对该模型进行比较研究,并利用Matlab软件进行了仿真。仿真结果显示,当训练样本总数为729个时,经过不到100次的迭代,网络既可以满足输出精度要求,并且检验样本的平均色差为1.35,表明该算法具有更快的模型转换速度和更高的转换精度。
文摘Satellite-based Precipitation Estimates(SPEs)have gained importance due to enhanced spatial and temporal resolution,particularly in Indus basin,where raingauge network has fewer observation stations and drainage area is laying in many countries.Formulation of SPEs is based on indirect mechanism,therefore,assessment and correction of associated uncertainties is required.In the present study,disintegration of uncertainties associated with four prominent real time SPEs,IMERG,TMPA,CMORPH and PERSIANN has been conducted at grid level,regional scale,and summarized in terms of regions as well as whole study area basis.The bias has been disintegrated into hit,missed,false biases,and Root Mean Square Error(RMSE)into systematic and random errors.A comparison among gauge-and satellite-based precipitation estimates at annual scale,showed promising result,encouraging use of real time SPEs in the study area.On grid basis,at daily scale,from box plots,the median values of total bias(-0.5 to 0.5 mm)of the used SPEs were also encouraging although some under/over estimations were noted in terms of hit bias(-0.15 to 0.05 mm/day).Relatively higher values of missed(0.3 to 0.5 mm/day)and false(0.5 to 0.7 mm/day)biases were observed.The detected average daily RMSE,systematic errors,and random errors were also comparatively higher.Regional-scale spatial distribution of uncertainties revealed lower values of uncertainties in plain areas,depicting the better performance of satellite-based products in these areas.However,in areas of high altitude(>4000 m),due to complex topography and climatic conditions(orographic precipitation and glaciated peaks)higher values of biases and errors were observed.Topographic barriers and point scale gauge data could also be a cause of poor performance of SPEs in these areas,where precipitation is more on ridges and less in valleys where gauge stations are usually located.Precipitation system’s size and intensity can also be a reason of higher biases,because Microwave Imager underestimate precipitation in small systems(<200 km^(2))and overestimate in large systems(>2000 km^(2)).At present,use of bias correction techniques at daily time scale is compulsory to utilize real time SPEs in estimation of floods in the study area.Inter comparison of satellite products indicated that IMERG gave better results than the others with the lowest values of systematic errors,missed and false biases.
