Goodness Of Model Research Articles

Testing coverage based NHPP software reliability growth modeling with testing effort and change-point

This paper investigates a software reliability growth model based on non-homogeneous Poisson process that includes coverage testing and change-points. The model integrate effort spent into a model to evaluate software reliability based on testing coverage. The amount of testing effort spent follows the Weibull distribution, while the amount of coverage is modeled by logistic, delayed S-shaped and exponential functions. Additionally, we look into the cost requirement-based software release time for exponential functions with a reliability constraint. We introduce the genetic algorithm, which is a powerful tool for dealing with search and optimization issues. For the purpose of testing the model's goodness of fit, two real failure datasets are used, and the model’s performance is analyzed based on goodness-of-fit metrics. By comparing the proposed model to perfect debugging models that have been described in the literature, it is demonstrated that the proposed model demonstrates notable advancements in this regard. The results of our analysis indicate that a software reliability estimation model should include change-points in a testing process if they exist.

Behavior of Decision Making of Purchasing the One District One Product (ODOP) Product from Champhon District, Savannakhet Province

This research aims to explore the buying behavior of One District One Product (ODOP) and analyze the factors that affect the buying behavior of One District One Product in Champhon District, Savannakhet Province, which includes 3 types of products: natural incense, saving stove and ancient salt. It is a question of opinions from the people of Kaisonpomvihan city, Savannakhet province, using a questionnaire to interview buyers and shopkeepers. Also, there is a mobilization of ideas to promote one city, one product to become a business from producer groups, group heads and village heads. The statistics used for analysis include descriptive statistics: frequency, percentage and inferential statistics used Binary logit regression analysis. The results of the study found that most of the people of KaisonPhomvihan City focus attention on Intellectual products at a moderate level for 48.9%, most of them prefer to buy the saving stove from Buang Thong Village for 72.2%, buy natural incense from Nook Kok Village for 27.8% and 61.1% were introduced by friends, followed by family and relatives for 26.7%. The decision to buy the product is wise because the features of the product are suitable for household needs for 75.6% who will buy immediately when they see the product, the place of purchase is mostly from the grocery store in the merchant's house directly because of the convenience of travel. They also buy at Sawannxay Market and Samakyxai Market in Kaisonphomvihan City, Savannakhet Province cover for 37.8%. The findings of an examination of the variables gender, age, and educational level that influence a one district one product's purchasing behavior in Champhon District, Savannakhet Province; Product, price, location, sales promotion, and customer understanding are factors that influence purchase behavior. The test of model suitability, which resulted in a statistical value of Chi-squared 78.45, Prob = 0.00, indicated that all of the initial variables had an effect on the opportunity to buy a single product in a single district. According to the model's goodness of fit test, 31.2% of the factors influencing (Odop) product's purchasing behavior can be predicted with 100% accuracy using all of the initial variables. The behavior simulation model's forecast, along with the variables influencing the purchasing decisions of a single district's worth of products, is 83.96% accurate. In Champhon District, factors including age, educational level, and customer awareness influence one district one product purchasing behavior. A bachelor's degree or higher will increase the likelihood of a customer purchasing an Odop product by 17.3%, and when a customer has a total of one point of opinion and understanding, the likelihood of a customer purchasing an Odop product in Champhon will increase by 1.3% at the level of statistical significance. These are the results of taking into account each variable.

Development and psychometric properties of the Japanese version of Child and Youth Resilience Measure-Revised (CYRM-R) among Japanese youth

Objectives Resilience is the ability to navigate adversity and recover from challenging situations. Developing resilience at a young age promotes mental health and provides benefits throughout one's lifespan. Validated measures for assessing resilience at a young age are required in both clinical practice and epidemiological studies. The Child and Youth Resilience Measure-Revised (CYRM-R) is a tool used worldwide to assess resilience in children and young people. The objective of this study was to develop a Japanese version of the CYRM-R and investigate its psychometric properties.Methods Children in grades 5-9 were recruited via a two-stage cluster random sampling taken from the Japan Adolescent and Youth (JAY) Longitudinal Cohort Study. The participants completed the Japanese version of the CYRM-R . The CYRM-R consists of two subscales, personal resilience and caregiver resilience, with a total of 17 items. The linguistic validity of the Japanese version of the CYRM-R was ensured through translation and back-translation.Cronbach's alpha coefficients were examined for the total score and the two subscales to assess the internal consistency of the Japanese version of the CYRM-R scale. In addition, correlation coefficients of the subscales were calculated. For factorial validity, a confirmatory factor analysis was conducted on the same two-factor structure as in the original version to assess the model's goodness of fit. Results A total of 2,266 children (50.0% male) were included in the study. The overall Cronbach's alpha of the Japanese version of the CYRM-R obtained from this sample was 0.956; For the personal resilience subscale, Cronbach's alpha was 0.932; for the caregiver resilience subscale, it was 0.919. Significant positive correlations were also found between subscales (r = 0.827, p < 0.001). A confirmatory factor analysis was conducted using a two-factor structure for validity. The model fit was good (RMSEA = 0.085, SRMR = 0.041, CFI = 0.934).Conclusion The Japanese version of the CYRM-R maintained the same two-factor structure as the original version. The study findings showed that the Japanese version of the CYRM-R had adequate internal consistency and factorial validity for assessing resilience in children and youth. Therefore, this scale is a valuable tool for identifying individuals or groups at risk in terms of children's resilience and for evaluating the effectiveness of support and interventions.

Ownership structure and female board representation in selected Nigeria-listed firms

Purpose This paper examines the influence of various ownership structures on female participation on the boards of listed firms in Nigeria between 2012 and 2021. It focuses on how ownership concentration, institutional ownership, and foreign ownership impact gender diversity at the board level. Methodology The study uses ownership concentration, institutional ownership, and foreign ownership as proxies for ownership structure, while firm size is included as a control variable to improve the model's goodness of fit. Pooled binary logistic regression was employed to test the hypotheses. Findings The results reveal that ownership concentration significantly decreases the presence of females on the boards of quoted companies in Nigeria. In contrast, both institutional ownership and foreign ownership significantly increase female board representation during the study period. Practical Implications The study recommends diversifying ownership structures by reducing concentration while promoting institutional and foreign ownership to enhance female representation on corporate boards in Nigeria. These changes could foster gender diversity in corporate governance. Originality This research contributes to the literature on board gender diversity by identifying specific ownership structures that facilitate female participation on boards. It provides valuable insights for policymakers seeking to encourage greater institutional and foreign ownership to support gender diversity in Nigerian firms.

Energy poverty and health in Turkey: Evidence from Longitudinal data

This study critically examines the effect of energy poverty on health in Turkey between 2018 and 2021 using the Income and Living Conditions Survey Longitudinal Micro Data Set. It considers the multidimensional structure of household energy poverty and its individual effects on health, resulting in several significant findings. Firstly, the multidimensional energy poverty index at the household level is obtained as a continuous variable using principal component analysis, considering subjective and objective indicators of energy poverty. Secondly, the impact of energy poverty on health at the individual level and the effect of several socioeconomic variables, including unobserved heterogeneity, are estimated with the random effects ordered logit model. Finally, commonly used measures of energy poverty in the literature and their health impacts are compared. The robustness analysis results show that the model's goodness of fit is highest when the multidimensional energy poverty index is constructed using principal component analysis. In addition, the analysis results show that unobserved heterogeneity across individuals significantly impacts health. These results indicate that decentralized policies should be implemented to increase policy effectiveness in combating energy poverty.

Greener pasture in the government sector? A factor structure on public service motivation among government employees in the Philippines

Working in the government is both a dream of securing a greener pasture and the opportunity to serve society with a noble intention and values. The main purpose of this study is to establish the factor structure of PSM in the Philippine context. A total of 456 randomly selected government employees from different agencies representing the three big islands of the country, namely Luzon, Visayas, and Mindanao, willingly participated in the survey. Data were analyzed using Exploratory Factor Analysis and Confirmatory Factor Analysis. The results of the Exploratory Factor Analysis validated the original 40-item and four-indicator-PSM but with a shorter version with only three underlying factors comprising 11 items, namely, Self-Sacrifice with six items, Commitment to Public Interest with two items, and Attraction to Policy Making with three items. Furthermore, Confirmatory Factor Analysis validated the model's goodness of fit and established a significant relationship among the latent variables, signifying the ability to measure public service motivation in the Philippine context. Hence, aside from the lure of a greener pasture, the results highlight the higher public service values as motivating factors. The findings can help the Philippine government tailor the agencies’ respective human resource development programs while maximizing the identified motivation factors to propel individual productivity and effectiveness in public service.

Analysis of incorporating modified Weibull model fault detection rate function into software reliability modeling

When software systems are introduced, they are typically deployed in field environments similar to those used during development and testing. However, these systems may also be used in various other locations with different environmental conditions, making it challenging to improve software reliability. Factors such as the specific operating environment and the location of bugs in the code contribute to this difficulty. In this paper, we propose a new software reliability model that accounts for the uncertainty of operating environments. We present the explicit closed-form mean value function solution for the proposed model. The model's goodness of fit is demonstrated by comparing it to the nonhomogeneous Poisson process (NHPP) model based on Weibull model, using four sets of failure data sets from software applications. The proposed model performs well under various estimation techniques, making it a versatile tool for practitioners and researchers alike. The proposed model outperforms other existing NHPP Weibull based in terms of fitting accuracy under two different methods of estimation and provides a more detailed and precise evaluation of software reliability. Additionally, sensitivity analysis shows that the parameters of the suggested distribution significantly impact the mean value function.

Construction of a Risk Prediction Model for Intraoperative Hypothermia in Patients Undergoing Lower Extremity Joint Replacement

PurposeTo investigate the influencing factors of intraoperative hypothermia (IOH) in patients undergoing total joint arthroplasty (TJA) of the lower extremities, establish a risk prediction model, and test the effect of application. DesignA prospective, observational study was conducted. MethodsPatients who underwent total knee arthroplasty and total hip arthroplasty from June 2020 to December 2021 were prospectively analyzed. According to the occurrence of IOH, patients were divided into the IOH group (temperature less than 36 °C) and non-IOH group (temperature ≥36 °C). We collected demographic, anesthesia, and surgical data for both groups to identify risk factors for IOH and develop a predictive model. The model's goodness of fit was assessed using the Hosmer-Lemeshow test, and its predictive efficacy was evaluated using the receiver operating characteristic curve. FindingsA total of 258 patients were included in this study, with 79 patients in the IOH group and 179 patients in the non-IOH group. Logistic regression analysis showed that American Society of Anesthesiologists' grade, blood loss, and duration of surgery were independent risk factors for IOH in lower extremity TJA patients. Hosmer-Lemeshow test P = .803, area under receiver operating characteristic curve was 0.846, Youden index was 0.490, sensitivity was 65.4%, specificity was 83.6%. In the external validation cohort, the application accuracy of the model was 83.3%. ConclusionsThe prediction model established in this study is suitable for the risk assessment of IOH in TJA patients with good prediction effect, which can provide a tool for clinical medical staff to identify high-risk populations preoperatively.

Cosmic evolution in f(Q,T) gravity with observational constraints: A comparative analysis with ΛCDM

In this investigation, we explore the flat Friedmann-Lemaitre-Robertson-Walker (FLRW) cosmological model within the framework of the f(Q,T) modified gravitation theory. This theory was recently introduced to account for the late cosmic acceleration of our Universe. To address the modified field equations derived from the f(Q,T)=αQ+βQ2+kT form of the f(Q,T) function, we assume a redshift-dependent deceleration parameter. Our goal is to impose observational constraints on the model parameters, ensuring its viability and alignment with the observable Universe's characteristics. The observational constraints are derived from a diverse dataset, encompassing Cosmic Chronometers (CC), type Ia supernovae (SNIa), Baryon Acoustic Oscillation (BAO), Gamma Ray Burst (GRB), and Quasar (Q) measurements. Employing the Markov Chain Monte Carlo (MCMC) technique with the CC + BAO + SNIa + GRB + Q dataset, we conduct simulations to obtain constraints on the model parameters. In the subsequent segments of the study, we delve into the evolution of the constrained model from the past to the present. We utilize metrics such as deceleration and jerk parameters, statefinder pairs, the Om diagnostic, and various physical parameters of the model. This detailed analysis allows us to scrutinize the transition of the model from a decelerating phase in the past to the late accelerating phase, comparing its evolution with established dark energy models. To assess the model's goodness of fit, we employ statistical criteria, including the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), P-value, and L-statistic. Remarkably, these criteria consistently indicate that the ΛCDM model is slightly favored by the observational data compared to the proposed cosmological model.

Exploring the role of spending on malaria incidence in Uganda using the auto-regressive distributed lag approach.

Malaria has remained a persistent global health problem. Despite multiple government and donor initiatives to eradicate malaria and its detrimental effects on Uganda's health outcomes, the incidence of malaria is worrying as it appears higher than the average of 219 cases per 1000 for sub-Saharan Africa for the period 2017-2018. This study investigated the effect of public and private healthcare spending on the incidence of malaria in Uganda. Employing time series data spanning over 20years from the first quarter of 2000 to the last quarter of 2019, the study builds a model based on the Grossman framework for analysing demand for health. The estimation technique used was the ARDL approach that takes into account reverse causality and incidental relationships. Prior to the adoption of the technique, a bounds test was performed to determine whether the variables contained in the model have a long-term relationship. Several diagnostic tests for serial correlation, functional normality, and heteroskedastic specification error were carried out to verify the ARDL model's goodness of fit. Additionally, the cumulative sum of recursive (CUSUM) and cumulative sum of squares of recursive residuals (CUSUMSQ) were used to test model stability. The results indicate that in the long run, an increase in public spending of one percent significantly reduces malaria incidence by 0.196 at the 10 percent level of significance. On the other hand, there is no significant evidence of private health expenditure's effect on malaria incidence. However, in the short run, public spending reduces malaria incidence by a smaller magnitude of 0.158 percent relative to the long-run. Still, private expenditure is found to exhibit no significant effect. Additional findings point to the importance of GDP per capita and urban population growth in reducing malaria incidence, whereas female unemployment, income inequality, as well as female-headed household. In the short run, however, the female-headed households and urban population growth are found to significantly reduce malaria incidence while an improvement in regulatory quality decreases malaria incidence by 0.129 percent. There is need for further government interventions to reduce malaria incidence in the country via budget allocation, as well as the strengthening of programmes to raise household income to support private health spending, in addition to the development of strategies to promote well-planned and organized urban centres.

Assessment of Impact of Country Business Ranking on Economi c Development: An Empirical Analysis

Purpose: The World Bank's “Ease of Doing Business” index, among other business rankings, can help a country's economic progress by attracting foreign investments, encouraging entrepreneurship, and improving the business climate. Superior rankings indicate an optimised environment for business activities, which could boost investments, job opportunities, and overall economic growth. The research is conducted to assess the impact of a country's business ranking on economic development. Design/methodology/approach: The Structural Equation Modelling (SEM) method was used to assess the impact of a country's business ranking on its economic trajectory. The official World Bank report was used to obtain data from 2015 to 2021. Panel data of Global Innovation Ranking, Ease of Doing Business Ranking, and Business Freedom Ranking from 2016 to 2021 is the basis of this study. The researcher has used a conceptual framework to establish and understand the dependence among the dependent and independent variables. For measuring statistical association, the researcher has used structural equation modelling and path diagram along with correlation and regression analysis. The correlation has been used to assess their association, while the researcher has used regression to determine the impact of country business ranking. Findings: The investigator investigated the impact of India's standings in the Global Innovation Ranking, Ease of Doing Business Ranking, and Business Freedom Ranking on its Economic Development, specifically GDP, in this study. The study's findings show that India's business rankings have a significant impact on its economic development. Such global business rankings provide critical data points for policymakers, businesses, and investors to make informed decisions, thereby promoting economic growth. Furthermore, the study examined the effects of GDP growth on the combustion of fossil fuels, as measured in million tonnes, emphasising the environmental consequences of economic expansion. Research limitations/implications: It's imperative to note that this is just an estimate, and the actual relationship between the variables in the population may differ from this estimate. The estimate is based on the sample data used to fit the model and may not accurately reflect the true relationship in the population. Further analysis and interpretation of the model's goodness of fit and consideration of other factors, such as outliers or omitted variables, are essential to fully apprehend the relationship between “GIR” and the dependent variable. The results of global business rankings can help policymakers identify areas for improvement in their country's business environment. By focusing on the areas where their country is lagging behind, they can develop policies and initiatives to attract more investment, create a more favourable business environment, and support economic growth. Businesses can use the results of global rankings to evaluate potential locations for expansion or investment. It can provide insights into the ease of starting and running a business, access to credit, property rights protection, and other important factors to businesses. Investors can also use the results of global rankings to make informed decisions about where increased investment and economic growth. Global business rankings generally provide valuable information that policymakers, businesses, and investors can use to make informed decisions and support economic development. Originality/value: I hereby attest that the research paper I have submitted is the result of my own independent and unique labor. All of the sources from which the thoughts and passages were derived have been properly credited. The work has not been submitted for publication anywhere and is devoid of any instances of plagiarism.

A study on the clinical prediction model of the yin deficiency type of perimenopausal syndrome

BackgroundThe yin deficiency type of perimenopausal syndrome (PMS) as a common category of PMS based on the theory of traditional Chinese medicine (TCM) has a high prevalence with severe symptoms and long course of disease. Therefore, it is necessary to construct a prediction model to assist in diagnosis. ObjectiveThis study aimed to investigate the independent predictors of the yin deficiency type of PMS and to develop a clinical prediction model of this disease. MethodsPMS patients who attended the Third Affiliated Hospital of Zhejiang Chinese Medical University between February 2020 and August 2023 were selected and divided chronologically into training and validation groups. Logistic regression analysis was applied in the training group to clarify the independent predictors of the yin deficiency type of PMS, and a nomogram was plotted. Internal and external validations were performed in the training and validation groups to evaluate the model's accuracy, goodness of fit, and clinical adaptability. ResultsHot flashes and sweating (≥10 episodes/day), palpitations, emotional fluctuations, and abnormal sexual activity were independent predictors of the yin deficiency type of PMS (P > 0.05). Based on the clinical prediction model constructed, the area under the receiver operating characteristic curve (AUR OC) in the training group was 0.989 (95%CI 0.980–0.998), and the AUR OC in the validation group was 0.971 (95%CI 0.940–0.999). This demonstrates that the model has superior prediction performance. The Hosmer-Lemeshow test was used to evaluate the model's goodness of fit with P = 0.596 for the training group and P = 0.883 for the validation group, indicating a good fit. The decision curve analysis (DCA) curve and clinical impact curve (CIC) indicated good clinical adaptability. ConclusionThe model can accurately predict the occurrence of the yin deficiency type of PMS, which may help clinicians identify such patients at an early stage.

Face anthropometry for filtering facepiece respirators: analysis of the association between facial dimensions and respirator fit.

Ensuring proper respirator fit for individuals remains a persistent challenge in occupational environments, yet there is limited knowledge about how respirators interact with the face to "'fit." Previous studies have attempted to understand the association between face dimensions and respirator fit using traditional head/face anthropometry not specifically tailored for respirators. The purpose of this study was to assess and compare the ability of filtering facepiece respirator (FFR)-specific face anthropometry with traditional head/face anthropometry in exploring the relationship between facial dimensions and the fit of FFR. The study utilized 3D face scans and quantitative fit factor scores from 56 participants to investigate the relationship between face anthropometry and FFR fit. Both FFR-specific and traditional anthropometric measurements were obtained through 3D anthropometric software. Intra-correlation of anthropometry was analyzed to evaluate the efficiency and effectiveness of FFR-specific and traditional anthropometry respectively. Principal component analysis (PCA) was conducted to test the usefulness of the PCA method for investigating various facial features. Logistic regression was used to develop fit association models by estimating the relationship between each face measurement set and the binary outcome of the fit test result. The prediction accuracy of the developed regression models was tested. FFR-specific face anthropometry consists of a set of measurements that can inform the detailed facial shape associated with the FFRs more effectively than traditional head/face anthropometry. While PCA may have been effective in reducing the variable dimensions for the relatively large parts of the human body such as upper and lower bodies in previous literature, PCA results of FFR-specific and traditional anthropometry were inconsistent and insufficient to describe face dimensions with complex anatomy in a small-detailed area, suggesting that facial shape should be understood through a variety of approaches including statistical methods. Logistic regression analysis results confirmed that the association models of FFR-specific face anthropometry were significant with higher prediction accuracy and had a better model's goodness of fit than those of traditional head/face anthropometry in 3 conditions inputting all measurements, all PC scores, or top 5 measurements from PCA. The findings showed that the FFR fit association model enables an understanding of the detailed association between face and respirator fit and allows for the development of a system to predict respirator fit success or failure based on facial dimensions. Future research would include testing the validity of the model and FFR-specific measurement set on different respirator types, expanding the population set, and developing an integrated approach using automated and machine learning technologies to inform FFR selection for occupation workers and the general population.

Analisis Data Time Series Menggunakan Model Kernel: Pemodelan Data Harga Saham MDKA

&lt;p&gt;Classic time series data analysis techniques, such as autoregressive, model stationary data in which the values of prior observations influence the current observations through a process known as linear regression. There are several requirements for error assumptions in autoregressive, including independence, normal distribution with a zero mean and constant variance. It is frequently discovered that these assumptions are challenging to verify when modelling real data. Kernel time series regression is an alternative model that does not require error assumptions. Non-stationary time series data can be effectively modelled using the kernel time series method. Time series data that isn't yet stationary is made stationary first, then the data is modified by forming the current stationary time series data as the response variable and the previous period data as the predictor variable. Next, regression kernel modelling is carried out while applying kernel weight function and determining the optimal bandwidth. For development of science, the optimal bandwidth can be achieved by minimizing the MSE, CV, GCV, or UBR values. It is possible to use R&lt;sup&gt;2&lt;/sup&gt; or MAPE as the kernel time series regression model's goodness metric. A strong model is generated while modelling MDKA stock price data using kernel regression utilizing the Gaussian kernel function and optimal bandwidth selection using GCV since R&lt;sup&gt;2&lt;/sup&gt; is 0.9828372 more than 0.67 and MAPE is 1.985681% under 10%.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords&lt;/strong&gt;&lt;strong&gt;: &lt;/strong&gt;3 time series; kernel regression; GCV; MDKA stock price.&lt;/p&gt;

Fall risk prediction ability in rehabilitation professionals: structural equation modeling using time pressure test data for Kiken-Yochi Training.

Falls occur frequently during rehabilitation for people with disabilities. Fall risk prediction ability (FRPA) is necessary to prevent falls and provide safe, high-quality programs. In Japan, Kiken Yochi Training (KYT) has been introduced to provide training to improve this ability. Time Pressure-KYT (TP-KYT) is an FRPA measurement specific to fall risks faced by rehabilitation professionals. However, it is unclear which FRPA factors are measured by the TP-KYT; as this score reflects clinical experience, a model can be hypothesized where differences between rehabilitation professionals (licensed) and students (not licensed) can be measured by this tool. To identify the FRPA factors included in the TP-KYT and verify the FRPA factor model based the participants' license status. A total of 402 participants, with 184 rehabilitation professionals (physical and occupational therapists) working in 12 medical facilities and three nursing homes, and 218 rehabilitation students (physical and occupational therapy students) from two schools participated in this study. Participant characteristics (age, gender, job role, and years of experience and education) and TP-KYT scores were collected. The 24 TP-KYT items were qualitatively analyzed using an inductive approach based on content, and FRPA factors were extracted. Next, the correction score (acquisition score/full score: 0-1) was calculated for each extracted factor, and an observation variable for the job role (rehabilitation professional = 1, rehabilitation student = 0) was set. To verify the FRPA factors associated with having or not having a rehabilitation professional license, FRPA as a latent variable and the correction score of factors as an observed variable were set, and structural equation modeling was performed by drawing a path from the job role to FRPA. The results of the qualitative analysis aggregated patient ability (PA), physical environment (PE), and human environment (HE) as factors. The standardized coefficients of the model for participants with or without a rehabilitation professional license and FRPA were 0.85 (p<0.001) for FRPA from job role, 0.58 for PA, 0.64 for PE, and 0.46 for HE from FRPA to each factor (p<0.001). The model showed a good fit, with root mean square error of approximation < 0.001, goodness of fit index (GFI) = 0.998, and adjusted GFI = 0.990. Of the three factors, PA and PE were common components of clinical practice guidelines for fall risk assessment, while HE was a distinctive component. The model's goodness of fit, which comprised three FRPA factors based on whether participants did or did not have rehabilitation professional licenses, was good. The system suggested that rehabilitation professionals had a higher FRPA than students, comprising three factors. To provide safe and high-quality rehabilitation for patients, professional training to increase FRPA should incorporate the three factors into program content.

A study on the first hydrogenation behavior of TiFe0.9Cr0.1 hydrogen storage alloy with the laves phase

This study investigated the first hydrogenation behavior of TiFe-based hydrogen storage alloy with the laves phase. A TiFe0.9Cr0.1 alloy was prepared through vacuum arc melting, and it had a dual-phase microstructure with B2 and laves phases. The first hydrogenation kinetics was measured by applying a hydrogen pressure of 40 bar at 30 °C, where the alloy absorbed hydrogen without thermal activation. The first hydrogenation kinetics was divided into three sections to understand the first hydrogenation behavior, and each section was analyzed using a solid–gas reaction model. The model's goodness of fit was evaluated by fitting each section. In the early hydrogen absorption stage, a surface-controlled mechanism (Chemisorption model) was analyzed to be the most dominant. In the second stage, where hydrogen was quickly absorbed, an interface-controlled mechanism (CV3D model) was most suitable. In the last hydrogen absorption stage, where hydrogen absorption was saturated, a diffusion-controlled mechanism (GB3D model) was most suitable.

Validity and reliability of the Chinese version of digital health readiness questionnaire among hypertension patients in rural areas of China.

Digital health has the potential to support health care in rural areas by overcoming the problems of distance and poor infrastructure, however, rural areas have extremely low use of digital health because of the lack of interaction with technology. There is no existing tool to measure digital health literacy in rural China. This study aims to test and validate the digital health readiness questionnaire for assessing digital readiness among patients in rural China. Due to the different Internet environments in China compared to Belgium, a cultural adaptation is needed to optimize the use of Digital Health Readiness Questionnaire in China. Then, a prospective single-center survey study was conducted in rural China among patients with hypertension. Confirmatory factor analysis was computed to test the measurement models. A total of 330 full questionnaires were selected and included in the analysis. The model-fit measures were used to assess the model's overall goodness of fit (Chi-square/degrees of freedom = 5.060, comparative fit index = 0.889, Tucker-Lewis index (TLI) = 0.869, root mean square error of approximation (RMSEA) = 0.111, standardized root mean square residual (SRMR) = 0.0880). TLI is a little bit lower than the borderline (more than 0.9) and RMSEA is higher than it (less than 0.08 means good model fit). We deleted two items 2 and 4 and the result shows a better goodness of fit (Chi-square/degrees of freedom = 4.897, comparative fit index = 0.914, TLI = 0.895, RMSEA = 0.109, SRMR = 0.0765). To increase applicability and generalizability in rural areas, it should be considered to use the calculation of only the parts Digital skills, Digital literacy and Digital health literacy which are equally applicable in a Belgian population as in a rural Chinese population.

Outsourcing Strategies and Human Resource Performance in Tea Manufacturing Companies in Rwanda: A case of Ekaterratearwanda Limited

The objective of this research was to study the human resource outsourcing practices among tea manufacturing companies in Rwanda. With specific objective being, To determine the influence of outsourcing strategies and recruitment process on human resource performance at Ekaterra Tea Rwanda Limited, to assess how outsourcing strategies on employee benefits management influence human resource performance at Ekaterra Tea Rwanda Limited and To establish how outsourcing strategies influences employee relations and human resource performance at Ekaterra Tea Rwanda. All the tea manufacturing companies were targeted population of 100 respondent from the factories. The research design to be used was description survey and data was collected from one of the tea manufacturing company operating in Rwanda called Ekaterra Tea Rwanda Ltd. The respondents were comprised mostly of Management team and supervisors with experience in various departments. Questionnaires using semi structured questions were used to collect data. Primary data were collected from the respondents and were supplemented by secondary data from journals, magazines and supplements and analyzed using descriptive statistics such as frequencies, mean scores, standard deviations and percentages. The findings were presented in form of tables. The sample population of 80 determined using Slovin‘s formula were used. Simple random sampling were used to identify the people to answer questions. At Ekaterra tea manufacturing company, study used purposive sampling method to interview top managers of the project in order to get detailed information of the subject matter. Data were collected by using questionnaires, interviews and documentary sources. Using the statistical package for social sciences (SPSS), the collected data was presented using tables and figure while interpretation and analysis were done based on percentages, mean and frequencies of respondents views. The data was also analyzed using correlation techniques and regression technique. In summary, the information provides key statistics to assess the performance of a regression model. The R² value indicates that the model explains approximately 28% of the variance in the dependent variable, and the adjusted R² adjusts for the number of predictors. The standard error of the estimate provides information about the typical prediction error in the model. These statistics help in understanding the model's goodness of fit and its ability to explain the variation in the dependent variable. Keywords: Outsourcing, Human resource management, Outsourcing Strategies, Recruitment process, Employee’s benefits, Employee’s relations.

Establishment and validation of a risk predictive model of preoperative drug-induced limitation of pupil dilation in type 2 diabetes mellitus patients with concomitant cataract

Objective: To establish and validate a risk predictive model of preoperative drug-induced limitation of pupil dilation (PD) in type 2 diabetes mellitus (T2DM) patients with concomitant cataract. Methods: A cross-sectional study was performed, in which 376 T2DM patients with concomitant cataract who received cataract operation in the Second Affiliated Hospital of Zhejiang University School of Medicine from October 2022 to March 2023 were randomly selected as the study subjects. Of the 376 patients, 268 who were admitted to the hospital from October to December 2022 served as the modeling group, and were divided into PD limited group (n=187) and PD unlimited group (n=81) based on whether they had drug-induced limitation of PD. Logistic regression was used to establish a risk predictive model, R software was used to draw the nomogram, Hosmer-Lemeshow test was utilized to judge the model's goodness of fit, and receiver operating characteristic (ROC) curve was adopted to validate the predicting efficacy of the model. Another 108 T2DM patients who received cataract operation in the same hospital from January to March 2023 served as the validation group, and Hosmer-Lemeshow test and ROC curve were used for the external validation of the model. Results: In the modeling group (n=268), there were 124 males and 144 females, with the mean age of (66.6±6.8) years, while in the validation group (n=108), there were 51 males and 57 females, with the mean age of (64.9±9.1) years. The incidence of preoperative drug-induced limitation of PD was 69.8% (187/268) in T2DM patients with concomitant cataract. T2DM disease course (OR=1.134, 95%CI: 1.074-1.198, P<0.001), body mass index (BMI) (OR=0.863, 95%CI: 0.767-0.972, P=0.015), glycohemoglobin (HbA1c) level (OR=1.397, 95%CI: 1.055-1.849, P=0.019) and baseline pupil dimeter (OR=0.089, 95%CI: 0.045-0.179, P<0.001) were the risk factors of drug-induced limitation of PD. Hosmer-Lemeshow test showed χ2=6.231 and P=0.621, the area under curve (AUC) of ROC curve was 0.897 (95%CI: 0.857-0.937, P<0.001), and when the Youden index was the maximum (0.655), the model's sensitivity and specificity was 0.877 and 0.778, respectively. The external validation results demonstrated that the AUC of ROC curve was 0.928 (95%CI: 0.875-0.981, P<0.001), the maximum Youden index was 0.761, the sensitivity was 0.932, the specificity was 0.829, and the overall accuracy was 89.8%. Conclusion: The risk predictive model established in the current study can provide reference for the clinical assessment of the risk of preoperative drug-induced limitation of PD in T2DM patients with concomitant cataract.

Construction of a risk prediction model for detecting postintensive care syndrome-mental disorders.

Postintensive care syndrome (PICS) has adverse multidimensional effects on nearly half of the patients discharged from ICU. Mental disorders such as anxiety, depression and post-traumatic stress disorder (PTSD) are the most common psychological problems for patients with PICS with harmful complications. However, developing prediction models for mental disorders in post-ICU patients is an understudied problem. To explore the risk factors of PICS mental disorders, establish the prediction model and verify its prediction efficiency. In this cohort study, data were collected from 393 patients hospitalized in the ICU of a tertiary hospital from April to September 2022. Participants were randomly assigned to modelling and validation groups using a 7:3 ratio. Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis was performed to select the predictors, multiple logistic regression analysis was used to establish the risk prediction model, and a dynamic nomogram was developed. The Hosmer-Lemeshow (HL) test was performed to determine the model's goodness of fit. The area under the receiver operating characteristic (ROC) curve was used to evaluate the model's prediction efficiency. The risk factors of mental disorders were Sepsis-related organ failure assessment (SOFA) score, Charlson comorbidity index (CCI), delirium duration, ICU depression score and ICU sleep score. The HL test revealed that p = .249, the area under the ROC curve = 0.860, and the corresponding sensitivity and specificity were 84.8% and 71.0%, respectively. The area under the ROC curve of the verification group was 0.848. A mental disorders dynamic nomogram for post-ICU patients was developed based on the regression model. The prediction model provides a reference for clinically screening patients at high risk of developing post-ICU mental disorders, to enable the implementation of timely preventive management measures. The dynamic nomogram can be used to systematically monitor various factors associated with mental disorders. Furthermore, nurses need to develop and apply accurate nursing interventions that consider all relevant variables.