Exploring the meaning of care among lived experiences of patients with blindness:an interpretive phenomenological study

Objective:To explore the meaning of care experienced by people with blindness in hospitals.Methods:Interpretive phenomenology along with the 6-step method of van Manen was used to conduct the study.Using purposeful sampling,15 people with legal blindness were interviewed.Thematic analysis was used to isolate the meaning of care.Results:Five themes emerged:(a)nurses in the eyes of patients with blindness;(b)negligence in the caring moments;(c)being cared for in ambiguity;(d)Uncoordinated care;and(e)Psychological discomfor t.These sub-themes were condensed into an overarching theme titled as“marginalized patients inside the stereotypical healthcare system.”Conclusions:Lived experiences of patients with blindness revealed that hospitals provide stereotypic or inappropriate care for this minority group in society.Health professionals par ticularly nurses should be skilled to provide person-centered and coordinated care for patients with blindness.

Study of the Magnetocaloric Effect in La0.5Sm0.2Sr0.3Mn1-xFexO3 (x = 0 and 0.05) Manganites with the Mean-Field Theory

In this paper, the magnetocaloric in La0.5Sm0.2Sr0.3Mn1-xFexO3 compounds with x = 0 (LSSMO) and x = 0.05 (LSSMFO) were simulated using mean field model theory. A strong consistency was observed between the theoretical and experimental curves of magnetizations and magnetic entropy changes, −ΔSM(T). Based on the mean-field generated −ΔSM(T), the substantial Temperature-averaged Entropy Change (TEC) values reinforce the appropriateness of these materials for use in magnetic refrigeration technology within TEC (10) values of 1 and 0.57 J∙kg−1∙K−1under 1 T applied magnetic field.

Charge self-consistent dynamical mean field theory calculations incombination with linear combination of numerical atomic orbitalsframework based density functional theory

We present a formalism of charge self-consistent dynamical mean field theory(DMFT)in combination with densityfunctional theory(DFT)within the linear combination of numerical atomic orbitals(LCNAO)framework.We implementedthe charge self-consistent DFT+DMFT formalism by interfacing a full-potential all-electron DFT code with threehybridization expansion-based continuous-time quantum Monte Carlo impurity solvers.The benchmarks on several 3d,4fand 5f strongly correlated electron systems validated our formalism and implementation.Furthermore,within the LCANOframework,our formalism is general and the code architecture is extensible,so it can work as a bridge merging differentLCNAO DFT packages and impurity solvers to do charge self-consistent DFT+DMFT calculations.

A Cognitive Psychological Study on the Acquisition of Chinese Word’s Coloring Meaning--Chinese as a Second Language

Lexical meaning mainly includes rational meaning,grammatical meaning,and coloring meaning.Mastering the coloring meaning of vocabulary is of great significance for foreign students to use Chinese vocabulary correctly.This study mainly examines the psychological mechanism of Chinese second language learners mastering the coloring meaning of words,examines the psychological characteristics of students mastering the color meaning of words from the perspectives of second language learning theory and cognitive theory,establishes a cognitive schema for coloring meaning learning,and proposes corresponding learning models and teaching strategies.

Target acquisition performance in the presence of JPEG image compression

This paper presents an investigation on the effect of JPEG compression on the similarity between the target image and the background,where the similarity is further used to determine the degree of clutter in the image.Four new clutter metrics based on image quality assessment are introduced,among which the Haar wavelet-based perceptual similarity index,known as HaarPSI,provides the best target acquisition prediction results.It is shown that the similarity between the target and the background at the boundary between visually lossless and visually lossy compression does not change significantly compared to the case when an uncompressed image is used.In future work,through subjective tests,it is necessary to check whether this presence of compression at the threshold of just noticeable differences will affect the human target acquisition performance.Similarity values are compared with the results of subjective tests of the well-known target Search_2 database,where the degree of agreement between objective and subjective scores,measured through linear correlation,reached a value of 90%.

A Study of EM Algorithm as an Imputation Method: A Model-Based Simulation Study with Application to a Synthetic Compositional Data

Compositional data, such as relative information, is a crucial aspect of machine learning and other related fields. It is typically recorded as closed data or sums to a constant, like 100%. The statistical linear model is the most used technique for identifying hidden relationships between underlying random variables of interest. However, data quality is a significant challenge in machine learning, especially when missing data is present. The linear regression model is a commonly used statistical modeling technique used in various applications to find relationships between variables of interest. When estimating linear regression parameters which are useful for things like future prediction and partial effects analysis of independent variables, maximum likelihood estimation (MLE) is the method of choice. However, many datasets contain missing observations, which can lead to costly and time-consuming data recovery. To address this issue, the expectation-maximization (EM) algorithm has been suggested as a solution for situations including missing data. The EM algorithm repeatedly finds the best estimates of parameters in statistical models that depend on variables or data that have not been observed. This is called maximum likelihood or maximum a posteriori (MAP). Using the present estimate as input, the expectation (E) step constructs a log-likelihood function. Finding the parameters that maximize the anticipated log-likelihood, as determined in the E step, is the job of the maximization (M) phase. This study looked at how well the EM algorithm worked on a made-up compositional dataset with missing observations. It used both the robust least square version and ordinary least square regression techniques. The efficacy of the EM algorithm was compared with two alternative imputation techniques, k-Nearest Neighbor (k-NN) and mean imputation (), in terms of Aitchison distances and covariance.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

A Novel Fractional Dengue Transmission Model in the Presence of Wolbachia Using Stochastic Based Artificial Neural Network

The purpose of this research work is to investigate the numerical solutions of the fractional dengue transmission model(FDTM)in the presence of Wolbachia using the stochastic-based Levenberg-Marquardt neural network(LM-NN)technique.The fractional dengue transmission model(FDTM)consists of 12 compartments.The human population is divided into four compartments;susceptible humans(S_(h)),exposed humans(E_(h)),infectious humans(I_(h)),and recovered humans(R_(h)).Wolbachia-infected and Wolbachia-uninfected mosquito population is also divided into four compartments:aquatic(eggs,larvae,pupae),susceptible,exposed,and infectious.We investigated three different cases of vertical transmission probability(η),namely when Wolbachia-free mosquitoes persist only(η=0.6),when both types of mosquitoes persist(η=0.8),and when Wolbachia-carrying mosquitoes persist only(η=1).The objective of this study is to investigate the effectiveness of Wolbachia in reducing dengue and presenting the numerical results by using the stochastic structure LM-NN approach with 10 hidden layers of neurons for three different cases of the fractional order derivatives(α=0.4,0.6,0.8).LM-NN approach includes a training,validation,and testing procedure to minimize the mean square error(MSE)values using the reference dataset(obtained by solving the model using the Adams-Bashforth-Moulton method(ABM).The distribution of data is 80% data for training,10% for validation,and,10% for testing purpose)results.A comprehensive investigation is accessible to observe the competence,precision,capacity,and efficiency of the suggested LM-NN approach by executing the MSE,state transitions findings,and regression analysis.The effectiveness of the LM-NN approach for solving the FDTM is demonstrated by the overlap of the findings with trustworthy measures,which achieves a precision of up to 10^(-4).

Properties of collective flow and pion production in intermediate-energy heavy-ion collisions with a relativistic quantum molecular dynamics model

The relativistic mean-field approach was implemented in the Lanzhou quantum molecular dynamics transport model(LQMD.RMF). Using the LQMD.RMF, the properties of collective flow and pion production were investigated systematically for nuclear reactions with various isospin asymmetries. The directed and elliptic flows of the LQMD.RMF are able to describe the experimental data of STAR Collaboration. The directed flow difference between free neutrons and protons was associated with the stiffness of the symmetry energy, that is, a softer symmetry energy led to a larger flow difference. For various collision energies, the ratio between the π^(-) and π^(+) yields increased with a decrease in the slope parameter of the symmetry energy. When the collision energy was 270 MeV/nucleon, the single ratio of the pion transverse momentum spectra also increased with decreasing slope parameter of the symmetry energy in both nearly symmetric and neutron-rich systems.However, it is difficult to constrain the stiffness of the symmetry energy with the double ratio because of the lack of threshold energy correction on the pion production.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

A new method of calculating crown projection area and its comparative accuracy with conventional calculations for asymmetric tree crowns

This paper introduces a new method of calculating crown projection area(CPA),the area of level ground covered by a vertical projection of a tree crown from measured crown radii through numerical interpolation and integration.This novel method and other four existing methods of calculating CPA were compared using detailed crown radius measurements from 30 tall trees of Eucalyptus pilularis variable in crown size,shape,and asymmetry.The four existing methods included the polygonal approach and three ways of calculating CPA as the area of a circle using the arithmetic,geometric and quadratic mean radius.Comparisons were made across a sequence of eight non-consecutive numbers(from 2 to 16)of measured crown radii for each tree over the range of crown asymmetry of the 30 trees through generalized linear models and multiple comparisons of means.The sequence covered the range of the number of crown radii measured for calculating the CPA of a tree in the literature.A crown asymmetry index within the unit interval was calculated for each tree to serve as a normative measure.With a slight overestimation of 2.2%on average and an overall mean error size of 7.9%across the numbers of crown radii that were compared,our new method was the least biased and most accurate.Calculating CPA as a circle using the quadratic mean crown radius was the second best,which had an average overestimation of 4.5%and overall mean error size of 8.8%.These two methods remained by and large unbiased as crown asymmetry increased,while the other three methods showed larger bias of underestimation.For the conventional method of using the arithmetic mean crown radius to calculate CPA as a circle,bias correction factors were developed as a function of crown asymmetry index to delineate the increasing magnitude of bias associated with greater degrees of crown asymmetry.This study reveals and demonstrates such relationships between the accuracy of CPA calculations and crown asymmetry and will help increase awareness among researchers and practitioners on the existence of bias in their CPA calculations and for the need to use an unbiased method in the future.Our new method is recommended for calculating CPA where at least four crown radius measurements per tree are available because that is the minimum number required for its use.

Analytical and NumericalMethods to Study the MFPT and SR of a Stochastic Tumor-Immune Model

The Mean First-Passage Time (MFPT) and Stochastic Resonance (SR) of a stochastic tumor-immune model withnoise perturbation are discussed in this paper. Firstly, considering environmental perturbation, Gaussian whitenoise and Gaussian colored noise are introduced into a tumor growth model under immune surveillance. Asfollows, the long-time evolution of the tumor characterized by the Stationary Probability Density (SPD) and MFPTis obtained in theory on the basis of the Approximated Fokker-Planck Equation (AFPE). Herein the recurrenceof the tumor from the extinction state to the tumor-present state is more concerned in this paper. A moreefficient algorithmof Back-Propagation Neural Network (BPNN) is utilized in order to testify the correction of thetheoretical SPDandMFPT.With the existence of aweak signal, the functional relationship between Signal-to-NoiseRatio (SNR), noise intensities and correlation time is also studied. Numerical results show that both multiplicativeGaussian colored noise and additive Gaussian white noise can promote the extinction of the tumors, and themultiplicative Gaussian colored noise can lead to the resonance-like peak on MFPT curves, while the increasingintensity of the additiveGaussian white noise results in theminimum of MFPT. In addition, the correlation timesare negatively correlated with MFPT. As for the SNR, we find the intensities of both the Gaussian white noise andthe Gaussian colored noise, as well as their correlation intensity can induce SR. Especially, SNR is monotonouslyincreased in the case ofGaussian white noisewith the change of the correlation time.At last, the optimal parametersin BPNN structure are analyzed for MFPT from three aspects: the penalty factors, the number of neural networklayers and the number of nodes in each layer.

On the Equality of Weighted BajratarevićMeans to Quasi-Arithmetic Means

In this paper, we considered the equality problem of weighted Bajraktarević means with weighted quasi-arithmetic means. Using the method of substituting for functions, we first transform the equality problem into solving an equivalent functional equation. We obtain the necessary and sufficient conditions for the equality equation.

Digital Literacy in the Rural Environment of Rivers State, Nigeria

While literacy can generally be defined as the basic ability to read,write,and also count,digital literacy can be described as the ability to perform all these tasks using electronic means.These means would include modern electronic gadgets,such as mobile phones,tablets,computers,kindle books,and the like.To be digitally literate would therefore require the existence of modern technologies such as internet facilities that would make it possible to access online reading and writing.The rural environment,particularly in developing areas,is usually characterised by a seeming lack of modern amenities and even worse,digital internet networks.Yet,those who live in the rural areas of Rivers State belong to the modern digital era and deserve to be digitally literate.This paper examines the prerequisites for digital literacy and explores how these can be achieved for citizens who inhabit the rural areas of an industrially-nascent state like Rivers State.

Gradient Estimate of Solutions to a Class of Mean Curvature Equations with Prescribed Contact Angle Boundary Problem

This paper studies the prescribed contact angle boundary value problem of a certain type of mean curvature equation.Applying the maximum principle and the moving frame method and based on the location of the maximum point,the boundary gradient estimation of the solutions to the equation is obtained.

Predominant types of regional cold waves in North China and their historical changes

寒潮事件对东亚地区的社会经济,生态系统和人体健康影响巨大.根据1980-2019年间10月至次年3月的每日寒潮记录和K-means聚类,本文识别出了中国两种不同类型的区域性寒潮(T1和T2). T1区域性寒潮主要影响东北地区,T2区域性寒潮则主要影响华北和东部地区.与T1区域性寒潮相比, T2区域性寒潮强度更强,持续时间更长,影响范围更广. 1980–2019年期间, T1区域性寒潮的频率显著增加,而T2区域性寒潮的频率则没有趋势变化. T1和T2区域性寒潮事件均与西伯利亚高压增强有关,然而与它们相关的对流层中层波列明显不同.在T1区域性寒潮事件发生期间,西伯利亚-蒙古上空出现负-正模态的500-hPa位势高度异常波列,削弱了东亚大槽,导致西伯利亚冷空气东移.T1区域寒潮频次增加趋势可能与全球变暖引起的500-hPa位势高度的线性趋势变化有关.与T2区域寒潮事件相关的波列则在乌拉尔山脉,蒙古和华北地区形成了脊-槽-脊环流异常,导致冷空气向东南方向入侵.本文得出结论,由于两类区域寒潮影响不同,西伯利亚高压增强和对流层中层波列模态在我国区域性寒潮事件的预报中应综合考虑.

Psychological and Physiological Health Benefits of a Structured Forest Therapy Program for Children and Adolescents with Mental Health Disorders

Mental health conditions in children and adolescents can be improved by slow mindful nature connection known as forest therapyor bathing.Forest therapy has recently received growing attention as an enabler of relaxation and preventive health care withdemonstrated clinical efficacy.However,it is not well-known that forest therapy also decreases mental health issues amongindividuals with mental health disorders.This study explored the psychological and physiological health benefits of structuredforest therapy programs for children and adolescents with mental health disorders.A one-group pre-test-posttest design wasemployed for our study participants.Twelve participants(aged 9–14 years)engaged in two one-hour guided standard sequenceforest therapy experiences.A Mindful Attention Awareness Scale(MAAS),Connectedness to Nature Scale(CNS),Profile ofMood States(POMS),place meanings(e.g.,functional,emotional,and cognitive attachment to the forest)questionnaire,andphysiological health assessment were administered to the participants.Our results showed that negative mood states weresignificantly reduced and that a positive mood state was significantly improved after the structured forest therapy programs.Also,mindfulness,nature connection,place meanings,and physiological health were significantly boosted after theinterventions.The results demonstrate substantial psychological and physiological health and well-being outcomes ofstructured forest therapy for similar individuals.

Analysis of the Symbolic Meaning of James Joyce’s Araby

Araby is a short story by the famous Irish stream-of-consciousness writer James Joyce.Through a series of images,the novel expresses the theme of the story:the“mental paralysis”of Dubliners and the“spiritual Epiphany”of the little boy,which reflects the spiritual barren of Dubliners at that time.Through the analysis of the symbolic meaning of many images in the work,this paper reveals the social background and religious significance hidden behind the images.

The Comparative Study of Chuangtse’s English Translations Under the Theory of Spirit Transmission and Meaning Conveyance

Cultural-loaded word is one of the most popular topics in translation studies.The theory of spirit transmission and meaning conveyance provides a new way for the translation of cultural-loaded word.Based on the A Happy Excursion,this paper makes a comparative analysis of the translation strategies of seven English versions.This study will reveal the similarities and differences between Chinese and foreign translators’translation strategies.

An Exploratory Study of the Interpersonal Meanings of President Xi’s New Year Speech Based on Appraisal Theory

The research examines President Xi’s 2021 New Year speech with research questions centering around its abundant interpersonal meanings.Through qualitative content analysis,the research finds that it is typical for Chinese president to frequently use judgment and appreciation resources in reviewing the past year.Even in the face of the pandemic and natural disasters,the overall emotions of the speech remain positive,which corresponds to the forward-looking feature of New Year speech.Significance of the study abounds and future research can investigate how COVID-19 impacts the ideologies conveyed through political leaders’speeches through a comparative lens and how to produce more understandings that can help dismantle stereotypes and discrimination hidden in reports about COVID-19 by using Appraisal Theory critically,systematically,and comprehensively.