Related Variables Research Articles

Wildfire assessment using machine learning algorithms in different regions

BackgroundClimate change and human activities are two main forces that affect the intensity, duration, and frequency of wildfires, which can lead to risks and hazards to the ecosystems. This study uses machine learning (ML) as an effective tool for predicting wildfires using historical data and influential variables. The performance of two machine learning algorithms, including logistic regression (LR) and random forest (RF), to construct wildfire susceptibility maps is evaluated in regions with different physical features (Okanogan region in the US and Jamésie region in Canada). The models’ inputs are eleven physically related variables to output wildfire probabilities.ResultsResults indicate that the most important variables in both areas are land cover, temperature, wind, elevation, precipitation, and normalized vegetation difference index. In addition, results reveal that both models have temporal and spatial generalization capability to predict annual wildfire probability at different times and locations. Generally, the RF outperforms the LR model in almost all cases. The outputs of the models provide wildfire susceptibility maps with different levels of severity (from very high to very low). Results highlight the areas that are more vulnerable to fire. The developed models and analysis are valuable for emergency planners and decision-makers in identifying critical regions and implementing preventive action for ecological conservation.

Analisis Hubungan Antar Manusia dan Lingkungan Kerja Fisik Terhadap Etos Kerja

The purpose of this study is to describe human relations, physical work environment, and work ethic. Then to analyze the effect of human relations, physical work environment on employee work ethic at Perumda Air Minum Tirta Jaya Mandiri Sukabumi Regency. This study uses associative research methods with a quantitative approach. The population is all employees of Perumda Air Minum Tirta Jaya Mandiri totaling 58 people using saturated sampling. The type of data used is primary and secondary data, the measurement of this study using a semantic differential scale. In this research analysis technique, namely using validity test, reliability test, correlation coefficient analysis, coefficient of determination analysis, multiple linear regression analysis, research model testing (F test), and hypothesis testing (T test). Based on the results of the study, it is obtained that the relationship between the variables of Human Relations and the Physical Work Environment on Work Ethic is 0.874, including the criteria for a very strong relationship, the magnitude of the influence of Human Relations on Work Ethic is 0.667, and the magnitude of the influence of the Physical Work Environment on Work Ethic is 0.615 or 76.4% while the remaining 23.6% is influenced by other factors not included in this study.

Individual and Situational Characteristics of the Occurrence of Cyber Sickness in the Context of Virtually Supported Military Training

Abstract Cyber sickness (CS) is considered a major challenge in the use of virtual reality (VR). This impacts the planned implementation of VR in the training of operational forces. The present work aimed to investigate the prevalence and onset of CS during virtually supported military training using head-mounted displays (HMDs) and explore related predictor variables. For this purpose, a quantitative cross-sectional study was conducted in which German soldiers (N = 100) were exposed to an immersive fifteen-minute VR scenario. We measured CS severity, age, heart rate (HR), and skin conductance (SC). Using newly developed categories to classify CS severity, the results showed a small prevalence (4%) of CS in the studied sample. Susceptibility to CS was the only predictor of the occurrence of CS symptoms. Accordingly, the present work provides evidence that CS may play a minor role in affecting virtually supported operational training. At the same time, the easily detectable susceptibility to CS promises rapid detection of vulnerable users. Implications and further research are discussed to detect, control, and mitigate CS.

Spirituality in Patients With Cancer: A Synthesis of a Program of Research.

Spiritual care is a key domain of quality palliative care as defined by national palliative care guidelines. The aim of this project was to synthesize data and research experience by the authors over 30years related to spirituality in patients with cancer. The research objectives for this analysis were to: Describe spirituality in patients with cancer. Determine the outcomes of palliative care interventions on spirituality and related variables in patients with cancer in these studies conducted by the authors. Summarize methodological issues in conducting research related to spirituality in oncology patients. A synthesis of 15 prior studies conducted by the authors was completed and also compared to current literature regarding spirituality in cancer and other serious illness. Findings demonstrate the broad scope of spirituality, key spiritual concerns in cancer and methodological approaches to assessing spirituality. Spirituality is a major concern for patients, yet limited training has been provided for clinicians who deliver this care. Additional studies are needed to advance this important aspect of palliative care.

Analysis of factors related to the incidence of hypertension in the elderly

Introduction: Hypertension or high blood pressure is one of the main health problems in the elderly population in various parts of the world. The elderly are more susceptible to hypertension due to the ageing process that affects the elasticity of blood vessels, as well as increased peripheral resistance. In addition, various risk factors, such as an unhealthy lifestyle, poor diet, and certain medical conditions, can worsen the condition of hypertension in the elderly. Objective: This study aims to analyze factors related to the incidence of hypertension in the elderly. Methods: This research uses a cross-sectional design, an analytical study that studies the causes of incidents or incidents. Data collection uses a questionnaire. The sample in this study was 64 elderly hypertensive patients in the working—data analysis using the chi-square test. Result: The results of this study indicate that there is a relationship between physical activity (p=0.000), obesity (p=0.000), diet (p= 0.029), smoking habits (p=0.000) and work (p=0.020) with the incidence of hypertension in the elderly in working. Conclusion: The incidence of hypertension in the elderly with the variables studied shows that there is a relationship, and the most related variable is obesity. So, it is hoped that the elderly, especially those who are obese, should be given intervention to maintain their diet so that complications do not occur and can reduce the risk of hypertension.

Evaluation of 1064 nm Raman for meat and bone meal species discrimination: From raw form to chemical and physical components

The 1064 nm Raman spectroscopy technique was employed to characterize raw, bone fragments (BF), meat fragments (MF), lipid, and residue of meat and bone meal (MBM) samples. Employing three chemometric algorithms, species discrimination models were optimized for the aforementioned sample types, indicating that the presence of BF favored discriminant analysis for poultry samples, yielding a classification error of 0.000. The species discrimination model constructed using Raman spectra combined with PLS-DA algorithm for residue samples was found to be overall optimal, with classification error for four species samples all below 0.061. A detailed analysis of the BF related spectral variables contributing significantly to PLS-DA discriminant analysis was conducted. Finally, this study established the complementarity of Raman spectroscopy in species discrimination analysis from both a physical and chemical components perspective, laying the theoretical groundwork for the development of a high-precision 1064 nm Raman spectroscopic analysis method for MBM species discrimination.

Powder dosing within continuous manufacturing: A lean approach for gravimetric dosing configuration and equipment selection

This work presents a novel approach to select gravimetric dosing configurations for continuous unit operations in pharmaceutical processing. It optimizes material, time, and personnel use, and allows selections for configurations of materials not included in the model by robust material attribute-based interpolation. The approach does not apply Principal Component Analysis (PCA) and Partial Least Squares (PLS). Instead, a reduced set of material attributes that require measurement was determined from existing material attribute libraries found in literature. Only those identified 17 attributes were measured for 13 materials and employed in this study. The established model proved to be predictive for an even further reduced attribute set of 12 attributes. The model introduces a simplified method for selecting feeding equipment combining precision metrics (relative mean intra-bin variability) and model parameters (fmax, fmin, beta). The reduction of Material Attributes (MAs) in this short-cut method enables fast evaluation and enhances resource efficiency by incorporating only a few selected MAs into the model and therefore introduces a different line of thinking. It was also possible to compare feeding of different hopper geometries, showing that flat bottom-hoppers have higher dosing precision than round bottom-hoppers. In conclusion, this work introduces a smart and innovative model that simplifies the equipment selection process and brings objectivity through a comprehensive numerical description of the discharge characteristics, and provides new options for continuous dosing in pharma through combining precision and curve fitting parameters.

A systematic review and meta-analysis of the variables related to the confidence in performing core nursing skills of nursing students

Purpose: This study aimed to identify the variables related to the confidence of Korean nursing students in performing core nursing skills and to examine the effect sizes of correlations between the related variables and that confidence.Methods: A meta-analysis was conducted upon 37 Korean master and doctorate degree dissertations and academic journal articles.Results: The related variables were categorized into four domains: task competencies, socio-emotional competencies, professionalism competencies, and personal thinking competencies. The effect size of the variables related to confidence in performing core nursing skills were, in order, task competencies (effect size, ESr=.47), socio-emotional competencies (ESr=.45), professionalism competencies (ESr=.40), and personal thinking competencies (ESr=.33).Conclusion: The results of this study are valuable when giving consideration to the variables related to nursing students’ confidence in performing core nursing skills for developing management and education programs to enhance those core nursing skills.

Key factors related to happiness and anxiety in Aotearoa New Zealand during the COVID-19 pandemic

aim: Mental wellbeing has been one of the most prominent health concerns in Aotearoa New Zealand and has only been exacerbated by the COVID-19 pandemic. Here we explored factors associated with anxiety and happiness in a mid-pandemic climate in Aotearoa. methods: Analyses were performed on the anxiety and happiness scores from the wellbeing survey in December 2020 (Statistics NZ Tatauranga Aotearoa; 30,000 responders contacted for the Household Labour Force Survey). Correlations and general linear models were used to identify significant predictor variables related to anxiety and happiness scores. results: A number of factors correlated with both anxiety and happiness, including loneliness, physical health, family wellbeing, financial wellbeing, age and gender. After controlling for many ethnically stratified social burdens, Māori and Pacific populations demonstrated higher levels of happiness. Discrimination was only associated with anxiety, while generalised trust, trust in the police and in the health system all related to happiness. conclusion: Anxiety and happiness in a mid-pandemic environment shared many related variables spanning physical, social and financial domains. Additionally, anxiety was associated with greater levels of discrimination, and happiness with trust in public services. Here we provide a window into the state of mental wellbeing in Aotearoa during a global health crisis.

Effects of Socio-Economic and Health Variables on Survival Time of HIV/AIDS Patients in Manipuir

Background: The survival time of patients with human immunodeficiency virus (HIV)/ acquired immune deficiency syndrome (AIDS) is associated with many socio-economic, education, employment and socio-cultural factors. High prevalence rates of HIV/AIDS are observed in some Indian States including Manipur. Objectives: It is to examine the effects of socio-economic and health related variables on the survival time of HIV and Hepatitis-C virus (HCV) patients. Materials and Methods: Under simple random sampling without replacement (SRSWOR), it analysed a sample of 200 HIV+ patients attending the ART Centre of Jawaharlal Institute of Medical Science (JNIMS), Imphal during March – July 2016. Using multiple regression models, the significant covariates of the survival time of patients are explored. Findings: The five factors – sex of patient (P<0.05), marital status (P<0.05), family income (P<0.05), patient type (P<0.01) and CD4 count (P<0.05) are found to be significantly influencing the dynamics of survival duration of the patients. Conclusion: In Manipur, the average survival time of the HIV/ AIDS patients is observed to be about six years varying with their sex, marital status, size of family, employment and CD4 count. Keywords: Manipur, regression model, sex, marital status, CD4

Private Expenditures of Teacher Candidates Studying at Mersin University, Türkiye

Private higher education expenditures constitute one of the factors determining higher education demand. In this study, it is aimed at determining the level and types of teacher candidates’ private expenditures, and the factors influencing the probability of making higher private expenditures. The sample of the study was drawn among the students enrolled in Mersin University Faculty of Education in 2013, 2016, 2019, and 2022. A model comprising five factors influencing the probability of making private expenditures higher than the average was developed. In this model educational background, family background, other cost related variables, higher education aspirations and future teaching labor market expectations were included as determining factors. Data were collected by a questionnaire developed by the researcher. Model was estimated by logistic regression analysis. Results show that variables representing all five factors have significant impact on the probability of making higher private expenditures. It is concluded that the model has a power on explaining the problem of the study.

Prevalence and factors in diabetic retinopathy in obese females presenting at lady reading hospitals

background: The aim of this study is to examine the prevalence of diabetic retinopathy (DR) and its related variables in patients with diabetes treated at Lady Reading Hospital in Peshawar between July 1, 2023, and June 30, 2024. Method: Patients in this descriptive cross-sectional study, who were diabetes patients between the ages of 30 and 60, were treated at Lady Reading Hospital in Peshawar. Patients with a diagnosis of either Type 1 or Type 2 diabetes mellitus, complete medical records, and the ability to give informed permission were the requirements for inclusion. Patients with insufficient medical data, individuals who were pregnant, patients with additional retinal illnesses, and patients who refused to give consent were all excluded. With an emphasis on demographics, comorbidities, lifestyle factors, and clinical data, data was gathered through structured interviews, physical examinations, and inspections of medical records. Examinations of the eyes verified the existence of DR. Using chi-square tests, logistic regression analysis, and descriptive statistics, SPSS software version 24.0 was used to analyze the data with sample size 121 patients. Result: Patients with DR had a lower prevalence of cardiovascular illnesses (6%) compared to those without (51%), with a p-value of 0.000. Significant correlations were found between DR and high blood pressure (36.7% vs. 5.7%, p=0.000) and inflammation (42.4% vs. 7.9%, p=0.000). A strong correlation was seen between DR and higher parity (>2 children) (50% vs. 16.9%, p=0.000). Significant variables included being older (46–60 years, 52.9% vs. 30-45 years, 17.2%, p=0.000), married (55.5% vs. single, 0%, p=0.000), and a housewife (31.1% vs. employed, 5.6%, p=0.025). There was no significant correlation found between smoking status and p=0.283. Conclusion: Inflammation, higher parity, age, married status, and homemaker status were all found to be significant risk factors for diabetic retinopathy in the study. In order to reduce the risk of DR, these findings emphasize the necessity of focused screening and management programs for at-risk populations. Reducing the prevalence of diabetes-related disorders (DR) in patients may need careful blood pressure control as well as managing inflammation.

Synthetic Ecosystems: From the Test Tube to the Biosphere.

The study of ecosystems, both natural and artificial, has historically been mediated by population dynamics theories. In this framework, quantifying population numbers and related variables (associated with metabolism or biological-environmental interactions) plays a central role in measuring and predicting system-level properties. As we move toward advanced technological engineering of cells and organisms, the possibility of bioengineering ecosystems (from the gut microbiome to wildlands) opens several questions that will require quantitative models to find answers. Here, we present a comprehensive survey of quantitative modeling approaches for managing three kinds of synthetic ecosystems, sharing the presence of engineered strains. These include test tube examples of ecosystems hosting a relatively low number of interacting species, mesoscale closed ecosystems (or ecospheres), and macro-scale, engineered ecosystems. The potential outcomes of synthetic ecosystem designs and their limits will be relevant to different disciplines, including biomedical engineering, astrobiology, space exploration and fighting climate change impacts on endangered ecosystems. We propose a space of possible ecosystems that captures this broad range of scenarios and a tentative roadmap for open problems and further exploration.

Time-lagged relation graph neural network for multivariate time series forecasting

Recently, Graph Neural Network-based approaches (GNNs) have been widely studied in Multivariate Time Series (MTS) prediction, which could extract information from the closely related variables for prediction. The variables contained in MTS data are lagged correlated, and the future trends of the lagging variables are guided by the leading variables. However, as the existing approaches only focus on delay-free relations, they cannot utilize the guidance information in leading variables to achieve accurate prediction. To address this issue, we propose a novel frame called the Time-Lagged Relation Graph Neural Network (TLGNN) including two key components: the time-lagged relation graph and the time-lagged relation graph learning. The time-lagged relation graph could explicitly model the time-delay relations among MTS variables by connecting variable nodes at lag intervals. The graph learning module could adaptively extract the time-delay relations among MTS variables. Based on the novel designed graph structure, the TLGNN could extract the guidance information from previous values of leading variables to generate more efficient feature representations for prediction. In experiments, the prediction accuracy is significantly improved due to the full exploration of the time-delay relations. Compared with existing methods, the TLGNN achieves the best results in both the single-step prediction and the multi-step prediction tasks.

Corporate Social Responsibility and Media Visibility —Brazilian Companies’ Donations during the COVID-19 Pandemic

Recent behavioral finance studies have focused on understanding how online investors react to corporate giving announcements. This research analyzed Google Trends search indexes during the first two years of the COVID-19 pandemic for companies that made donations and also examined the variation in their financial returns. The study aimed to determine whether donor companies experienced greater growth in media visibility during the pandemic compared to non-donor companies. Using a sample of 289 organizations listed on the Brazilian Stock Exchange and employing the “differences in differences” method, the study found no statistical significance when the pandemic effect was applied, leaving the hypothesis on visibility inconclusive. However, favorable results were observed without the pandemic effect, consistent with studies demonstrating a link between social actions and positive consumer responses. As a result, Google Trends proved to be a valid predictor of Brazilian financial data, with the behavior of marketing variables in relation to search trends reinforcing ongoing research on the tool’s potential to gauge public sentiment and market fluctuations.

Current smoking is related to severe damage in systemic lupus erythematosus patients

Objective To assess the relationship between smoking exposure and organ damage accrual measured by Systemic Lupus International Collaborating Clinics/American College of Rheumatology Damage Index for Systemic Lupus Erythematosus score (SLICC-SDI) in consecutive patients with systemic lupus erythematosus (SLE) from Argentina. Methods 623 consecutive SLE patients (fulfilling ≥4, 1997 ACR criteria) were included in this cross-sectional study. Sociodemographic and disease related variables including SLICC-SDI score and smoking status were collected. Patients currently smoking were considered “smokers”, and “non-smokers” those who never smoked and former smokers. SLICC-SDI was divided into two categories: <3 and ≥3 was defined as severe damage. Results Six hundred and 23 patients were included in the analysis, 89% women. Eighty-four per cent were non-smokers and 16 % were current smokers 83 percent of patients had SLICC-SDI <3 and 17 % had SLICC-SDI ≥3. Twenty one percent of patients with SLICC-SDI ≥3 and 15% with <3 SLICC-SDI were current smokers ( p 0.081). In the multiple regression analysis, current smoking (OR 1.82, CI 95% 1.01-3.31, p 0.046), older age (OR 1.04, CI 95% 1.00-1.05, p 0.034), disease duration (OR 1.03, CI 95% 1.00-1.07, p 0.021) and cyclophosphamide exposure (OR 2.97, CI 95% 1.49-5.88, p 0.002) were related to SLICC-SDI ≥3. Conclusion In our sample of patients, current smoking, older age, disease duration and cyclophosphamide were related to severe damage (SLICC-SDI ≥3).

Vapor Pressure Deficit Controls the Extent of Burned Area Over the Himalayas

AbstractGlobally, increasing occurrences of forest fires are major threats to the ecosystem. The rich forests in the Himalayas also suffer from high incidents of forest fires in the pre‐monsoon summer months, March to June. Research studies are limited in identifying the meteorological factors governing the spatiotemporal distribution of forest fires. Using three satellite‐based data sets of monthly burned area (BA) for March to June, we found higher BA in the Eastern Himalayas (EH) compared to the Central (CH) and Western Himalayas (WH). Using statistical methods, we found Vapor Pressure Deficit (VPD) to be the most dominating variable controlling BA in the Himalayas. Precipitation, soil moisture and temperature, with their relative variability, control VPD that governs the interannual and intraseasonal variations of BA. Our results imply that a good forecast of VPD will facilitate alert generation for the Himalayan forest fires.

대학 메타버스 활용 교육 연구동향

Objectives This study analyzed research trends in university metaverse utilization education and attempted to provide basic data for future university metaverse utilization education research. Methods This study was conducted from February 2024 to April 2024. Related prior studies were analyzed for two weeks, a research plan was established for two weeks, and papers to be analyzed were collected for four weeks from March 1. 42 domestic academic journals were selected, and the research period, research type, research content, major field, related variables, class type, class period, and platform trends were analyzed and described for four weeks from April 1. Results First, the most educational research on university metaverse was conducted in 2023. Second, the trend by research type showed a high proportion of mixed research, followed by quantitative research, development research, qualitative research, and literature research. Third, trends by major area showed high utilization in education. Fourth, in terms of trends by research content, the most research was conducted on program development and effectiveness for education using the metaverse. Fifth, the trend for each related variable showed that metaverse utilization classes were used as an independent variable, immersion was used as a mediating variable, and learning satisfaction and learning presence were used as dependent variables. Sixth, trends by class type showed a high proportion of subject areas. Seventh, the trend by class period was highest at 13 weeks. Eighth, the trend by platform showed high utilization of Gathertown. Conclusions This study is significant in that it analyzed trends in educational research using university metaverse and proposed directions for future research.

Development of Land Use Regression (LUR) models and high-resolution spatial mapping of criteria air pollutants: Leveraging Delhi's continuous air monitoring network and remote sensing data

High-resolution air pollution maps can help researchers and governments better characterize and mitigate pollution levels in a given region. Land Use Regression (LUR) modeling is a statistical approach capable of predicting pollution levels at a high spatial resolution. In this study, we used pollution data (for the calendar year 2019) from a dense (compared to other Indian regions) regulatory monitoring network in Delhi, India, to develop simple linear and interpretable LUR models for various criteria pollutants. The observed annual mean PM2.5 and PM10 over Delhi were found to be ∼110 μg m−3 and 220 μg m−3, respectively. The PM concentration levels were 2.5–4 times higher than the prescribed national ambient standards, while the gaseous criteria pollutants were found to be within the standards (over most of the study area). The performance of the developed LUR models ranged from poor to moderate levels (adjusted-R2 values of the models were between 0.14 and 0.63). Land use and road-network related variables were found to be the most common predictors of the observed pollution levels. Moderately performing models (11 out of the developed 20) were then used to predict pollution levels at 50 m spatial intervals and to identify the most polluted districts. The advantages and limitations of using the existing regulatory network data for LUR development, and the other probable potential reasons responsible for the underperformance of the developed models are extensively discussed. To our knowledge, this is one of the few studies carried over Indian region to develop LUR models utilizing regulatory monitoring network data.

The Influence of Pre-Competitive Anxiety and Self-Confidence on Dancesport Performance.

Competitive dancesport (DS) performance is a multifactorial phenomenon influenced by physical and mental factors. The emotions experienced by athletes in competition are strongly linked to their sports performance. However, to our knowledge, no studies have investigated the direct relationship between performance and emotional states in DS. Consequently, the aims were four: (I) to investigate the influence of anxiety and self-confidence on DS performance; (II) to examine the influence of years of experience, prior victories, and perceived preparedness on performance outcomes; (III) to identify the optimal emotional state levels for peak performance; (IV) to investigate differences between different athletes' levels and class. The participants were 71 Italian DS athletes divided into three groups (22 B-class, 25 C-class, 24 D-class). Before competition, they supplied demographic information about their gender, years of experience, perceived preparedness, previous winnings in the current class, followed by the completion of the Italian version of Revised Competitive State Anxiety Inventory-2 (CSAI-2R). To assess the athletes' performance, the final classification of the competition was taken into consideration. The results showed that both overall and relative variables from the CSAI-2R significantly predicted performance outcomes (p < 0.05), although somatic anxiety did so to a lesser extent. Significant differences emerged between athletes of different classes in terms of years of experience (p = 0.000), perceived preparedness (p = 0.000), cognitive anxiety (p = 0.000) and self-confidence (p = 0.000). The optimal levels for good performance were cognitive anxiety (11.61 ± 2.27), somatic anxiety (15.77 ± 1.72) and self-confidence (15.12 ± 2.56). The findings of this study provide valuable insights into the multifactorial nature of competitive DS performance, particularly highlighting the significant role of emotional states such as anxiety and self-confidence, as well as other variables such as class, level, years of experience and perceived preparedness.