Statistical Analysis Approach Research Articles

Assessment of Nurses' Perception toward Application of Evidence Based Practice Approach

Background Evidence-based nursing practice (EBNP) has become one of the most effective methods for improving patient outcomes and the quality of care as well as delivering cost-efficient health care. Nurses' perspectives are critical to the successful adoption of evidence -based practice. While positive views regarding EBP serve as a foundation for developing appropriate nursing approaches to improve patient care, there is a noticeable gap between belief in evidence-based practice advantages and actual implementation. Objective : To assess nurses’ perception toward the application of an evidence-based practice approach. Methods : A non-probability using a convenience sampling. Data were collected through self administered questionnaire technique for data collection, which is constructed through the extensive review of the related literature that consisted of two assessment parts first one consist of demographic data, and the second part of the assessment tool is concerned with assessing the nurse's perception scale which include three parts, the first part contains (4) statement, the second part contains (3) statement and the last part contains (2) statement developed by Majid et al (2011). The reliability of the instrument was the Cronbach alpha of perception section was between 0.68 to 0.95, and the face validity of the study instruments was determined by a panel of (12) experts in the Nursing and Medicine fields, and science of psychology, All the experts agreed that the instrument is valid with some suggested modifications. Data were analyzed through the application of frequency, percentage and the inferential statistical analysis approach is used to test the study null hypotheses within a confidence interval equal to 0.95(Chi-square which is used to determine the association between nurses' perception and demographic data). Such analyses are computed by using the SPSS program version 24. Results : The findings indicate that the total assessment of perception about application of evidence-based practice approach about (39.54%) of the nurses have poor perception of evidence-based practice,(51.23 %) of them have moderate perception; while (9.32 %) of them have good levels according to the total assessment of perception about evidence-based practice. Conclusion: According to the study results and discussion half of nurses have good perception toward application of evidence based practice approach when assessed by the statistical analysis.

Top-down proteomics.

Proteoforms, which arise from post-translational modifications, genetic polymorphisms and RNA splice variants, play a pivotal role as drivers in biology. Understanding proteoforms is essential to unravel the intricacies of biological systems and bridge the gap between genotypes and phenotypes. By analysing whole proteins without digestion, top-down proteomics (TDP) provides a holistic view of the proteome and can decipher protein function, uncover disease mechanisms and advance precision medicine. This Primer explores TDP, including the underlying principles, recent advances and an outlook on the future. The experimental section discusses instrumentation, sample preparation, intact protein separation, tandem mass spectrometry techniques and data collection. The results section looks at how to decipher raw data, visualize intact protein spectra and unravel data analysis. Additionally, proteoform identification, characterization and quantification are summarized, alongside approaches for statistical analysis. Various applications are described, including the human proteoform project and biomedical, biopharmaceutical and clinical sciences. These are complemented by discussions on measurement reproducibility, limitations and a forward-looking perspective that outlines areas where the field can advance, including potential future applications.

Deep learning-based approach for COVID-19 spread prediction

AbstractSpread prediction models are vital tools to help health authorities and governments fight against infectious diseases such as COVID-19. The availability of historical daily COVID-19 cases, in conjunction with other datasets such as temperature and humidity (which are believed to play a key role in the spread of the disease), has opened a window for researchers to investigate the potential of different techniques to model and thereby expand our understanding of the factors (e.g., interaction or exposure resulting from mobility) that govern the underlying dynamics of the spread. Traditionally, infectious diseases are modeled using compartmental models such as the SIR model. However, this model shortcoming is that it does not account for mobility, and the resulting mixing or interactions, which we conjecture are a key factor in the dynamics of the spread. Statistical analysis and deep learning-based approaches such as autoregressive integrated moving average (ARIMA), gated recurrent units, variational autoencoder, long short-term memory (LSTM), convolution LSTM, stacked LSTM, and bidirectional LSTM have been tested with COVID-19 historical data to predict the disease spread mainly in medium- and high-income countries with good COVID-19 testing capabilities. However, few studies have focused on low-income countries with low access to COVID-19 testing and, hence, highly biased historical datasets. In addition to this, the arguable best model (BiLSTM) has not been tested with an arguably good set of features (people mobility data, temperature, and relative humidity). Therefore, in this study, the multi-layer BiLSTM model is tested with mobility trend data from Google, temperature, and relative humidity to predict daily COVID-19 cases in low-income countries. The performance of the proposed multi-layer BiLSTM is evaluated by comparing its RMSE with the one from multi-layer LSTM (with the same settings as BiLSTM) in four developing countries namely Mozambique, Rwanda, Nepal, and Myanmar. The proposed multi-layer BiLSTM outperformed the multi-layer LSTM in all four countries. The proposed multi-layer BiLSTM was also evaluated by comparing its root mean-squared error (RMSE) with multi-layer LSTM models, ARIMA- and stacked LSTM-based models in eight countries, namely Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Finally, the proposed multi-layer BiLSTM model was evaluated at the city level by comparing its average relative error with the other four models, namely the LSTM-based model considering multi-layer architecture, Google Cloud Forecasting, the LSTM-based model with mobility data only, and the LSTM-based model with mobility, temperature, and relative humidity data for 7 periods (of 28 days each) in six highly populated regions in Japan, namely Tokyo, Aichi, Osaka, Hyogo, Kyoto, and Fukuoka. The proposed multi-layer BiLSTM model outperformed the multi-layer LSTM model and other previous models by up to 1.6 and 0.6 times in terms of RMSE and ARE, respectively. Therefore, the proposed model enables more accurate forecasting of COVID-19 cases and can support governments and health authorities in their decisions, mainly in developing countries with limited resources.

Oral Health Impact Profile in Patients with and without Type 2 Diabetes in Al-Baha Region, Saudi Arabia

Introduction: The Oral Health Impact Profile (OHIP) serves as a crucial tool in understanding the intricate relationship between oral health and systemic conditions like type 2 Diabetes. This study delves into the comparative analysis of OHIP in patients with and without type 2 diabetes, shedding light on the nuanced impact of this metabolic disorder on oral health-related quality of life (OHrQoL). By scrutinising factors such as oral discomfort, functional limitations and psychological well-being, this research aims to unearth valuable insights into the unique challenges faced by diabetic individuals in maintaining optimal oral health. Such findings hold immense significance in guiding tailored interventions and enhancing holistic healthcare approaches. Materials and Methods: A cross-sectional study was carried out with 232 diabetic and non-diabetic individuals. Clinical examinations were conducted, and a validated Arabic version of the OHIP-14 instrument was used. Data were analysed using the Statistical Package for the Social Sciences version 20.0. Results: In total, 232 patients with and without diabetes were included in this study. A statistically significant effect was observed amongst diabetic compared to non-diabetic participants (P = 0.015). The overall OHrQoL across the seven domains of participants with diabetes was significantly lower than those without diabetes (P = 0.0001). Oral lesions were more common and showed statistically significant effects amongst diabetic compared to non-diabetic patients (P = 0.0001). Conclusion: Patients with type 2 diabetes have shown undesirable oral health which impacted their quality of life compared to those without diabetes. Future research focusing on developing preventive interventional strategies for diabetic patients, in addition to enhancing the awareness of oral diseases and dental care amongst those patients, is highly encouraged. Developing a standardised protocol with a unified OHrQoL instrument and methodological statistical analysis approaches that could be generalised and used worldwide periodically amongst diabetic patients may also be advocated.

Monte–Carlo ray-tracing studies of multiplexed prismatic graphite analyzers for the cold-neutron triple-axis spectrometer at the High Flux Isotope Reactor

A modern cold triple-axis spectrometer to study quantum condensed matter systems is planned for the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. Here, we describe the conceptual principles and design of a secondary spectrometer using a multiplexed, prismatic analyzer system relying on graphite crystals and inspired by the successful implementation of the Continuous Angle Multiple Energy Analysis (CAMEA) spectrometers at the Paul Scherrer Institute. This project is currently known as MANTA for Multi-Analyzer Neutron Triple-Axis. We report Monte-Carlo ray-tracing simulations on a simple but realistic sample scattering kernel to further illustrate the prismatic analyzer concept’s workings, calibration, and performance. Then, we introduce a new statistical analysis approach based on the prismatic analyzer concept to improve the number of final energies measured on the spectrometer. We also study possible evolutions in the CAMEA design relevant for MANTA.

Recognition and analysis system of steel stamping character based on machine vision

During the packaging process, it is essential to detect the steel stamping characters inside the box to identify any missing or repeated characters. Currently, manual detection suffers from low efficiency and a high false detection rate. To address these challenges, a steel stamping character recognition and analysis system based on machine vision has been developed. The enhanced YOLOv7 detection method was employed for character identification, complemented by a statistical analysis approach to achieve automated judgment and detection. To address the issue of size disparity between large and small characters, a small size anchor box and a larger detection head were integrated. Furthermore, modifications were made to the output structure of the YOLOv7 prediction network to enhance multi-scale detection capabilities. The inclusion of the location attention convolution module bolstered global feature extraction, thereby enhancing the detection accuracy of similar characters. Moreover, the utilization of a hash table was used to improve the efficiency of mapping steel stamping character recognition sequences. The experimental results demonstrate that the enhanced model achieves an accuracy of 99.83%, with a processing efficiency of 10.5 ms per single frame. These findings align with the performance criteria for automatic recognition and analysis of steel stamping characters.

Analisis Hubungan antara Kinerja Keuangan, Ukuran Perusahaan, dan Kebijakan Dividen dalam Mengoptimalkan Harga Saham

In the dynamic world of capital markets, companies do not only focus on their financial performance. Company size and dividend policy are also critical elements in shaping investor perceptions and driving share prices. This research has the main objective of investigating and analyzing how financial performance, company size, and dividend policy can jointly contribute to the optimization of a company's share price. This research uses a statistical analysis approach by collecting and analyzing financial data and company shares. The method used is a quantitative descriptive method. The total research population was 88 companies, while the total research sample was 20 companies. This approach allows research to identify and quantify the statistical significance of each observed relationship. The research results show that strong financial performance, optimal company size, and appropriate dividend policy have a positive impact on increasing company share prices. This means that companies that succeed in managing these factors well tend to have better stock performance in the capital market.

Assessment of cytotoxic T-lymphocyte antigen-4 (CD152) Levels Associated with HBV in Patients with Diabetes Mellitus

Background: Diabetes mellitus is a chronic disease characterized by an imbalance in glucose homeostasis. The hepatitis B virus is a liver-attacking virus that can cause viral hepatitis, cirrhosis, and liver cancer. A cytotoxic T-lymphocyte antigen-4 called CTLA-4 is an immune checkpoint protein that is necessary for T cells to control immune responses and stop the HBV infection from spreading. It does this by acting as an inhibitory receptor, limiting liver damage during an acute infection, and making it easier for the infection to stay in the body during a chronic case. Objective: The study aims to assess CTLA-4 levels in relation to HBV infection in diabetes mellitus patients. Subjects and methods: A cross-sectional study was conducted from July to October 2023. The serum was obtained from 200 diabetic patients who were Iraqis. All of the patients were tested using an ELISA technique for CTLA-4 and HBc IgG. They were tested using a five-panel kit for HBsAb, HBsAg, HBcAb, HBeAg, and HBeAb and measured blood sugar levels. The statistical analysis approach was conducted using SPSS version 26. Results: Serum CTLA-4 levels were correlated with serum HBc IgG positivity (P = 0.000), total HBc Ab positivity (P = 0.000), and HBs Ab positivity (P = 0.000), and CTLA-4 level was correlated with diabetes mellitus (P = 0.034). Conclusions: The study concluded that elevated serum CTLA-4 levels in diabetic patients with HBV infection and type I diabetes compared to type II diabetes. This suggests that diabetes affects the immune system's response to HBV.

Sacubitril/valsartan ameliorates cardiac function and ventricular remodeling in CHF rats via the inhibition of the tryptophan/kynurenine metabolism and inflammation

Sacubitril/valsartan has been highly recognized as a treatment for Chronic heart failure (CHF). Its potential cardioprotective benefits and mechanisms, however, remain to be explored. Metabolomics can be used to identify the metabolic characteristics and related markers, as well as the influence of drugs, thereby opening up the new mechanism for sacubitril/valsartan therapy in CHF disease. In this study, the ligation of left anterior descending and exhaustive swimming were used to induce a rat model of CHF after myocardial infarction. The efficacy was appraised with echocardiography, serum NT-proBNP, and histopathologica. UPLC-Q/TOF–MS combined with multivariate statistical analysis approach were used to analyze the effect of sacubitril/valsartan on CHF rats. RT-qPCR and western blot were performed to investigate the tryptophan/kynurenine metabolism pathway. Accordingly, the basal cardiac function were increased, while the serum NT-proBNP and collagen volume fraction decreased in CHF rats with sacubitril/valsartan. Sacubitril/valsartan regulated the expression of kynurenine et.al 8 metabolomic biomarkers in CHF rats serum, and it contributed to the cardioprotective effects through tryptophan metabolism pathway. In addition, the mRNA and protein expression of the indoleamine 2,3-dioxygenase (IDO) in the myocardial tissue of CHF rats, were down-regulated by sacubitril/valsartan, which was the same with the IL-1β, IFN-γ, TNF-α, COX-2, and IL-6 mRNA expression, and IL-1β, IFN-γ, and TNF-α expression in serum. In conclusion, sacubitril/valsartan can ameliorate cardiac function and ventricular remodeling in CHF rats, at least in part through inhibition of tryptophan/kynurenine metabolism.

PENSION STRATEGIES OF UNWILLING JOINING PENSION PLANS: AN ISSUE OF A RATIONAL CHOICE

This article presents the analysis of Russian «silencers» participation in mandatory pension accumulation. The paper examines the peculiarities of pension strategies of the Russians in forming a funded pension with automatic enrolment through an interdisciplinary approach and methods of statistical data analysis. Based on the achievements of economic science of neoclassical, institutional and behavioral directions, the author argues that the use of such methodological prerequisites as rationality and irrationality of individuals leads to an oversimplification of the relationship between behavioral strategies and their results. The purpose of this article is to analyze the features of “silent” pension strategies through the prism of irrationality and rationality and to assess the consequences of passive participation strategy in the Russian pension process in the 2000s. The article examines the works of foreign and Russian scientists on pension programs with automatic enrolment, while the factual base is presented by materials of the Federal state statistics service and Russian Pension Fund. The article provides an overview of prior works on аutomatic enrolment to retirement plans in the United States and other foreign countries: it reveals critical remarks on the practice of automatic enrolment and reveals the problems of using automatic enrolment in the pension process. The author argues that the economic behavior of the Russian «silent» is primarily due to the specifics of institutional environment as well as the level of trust in the state and financial institutions. It has been established that irrationality and financial illiteracy are not always the primary causes of “silent accession” strategy. The conclusions and proposals of the work can be used for further research in pension strategies and the development of pension policy in Russia.

Exploring the Application of Force Intelligent Wearable Devices in Quantitative Management of Daily Physical Activity

This study looks into how force-intelligent wearable devices can be used to quantitatively manage daily physical activity levels. The study intends to investigate the effectiveness of these devices in measuring various physical activity metrics such as step count, distance walked, calories burned, and exercise intensity. A thorough methodology is used, which includes device selection, participant recruiting, data collecting, and analysis methods. Participants wear the selected wearable devices continuously for a set amount of time, enabling longitudinal tracking of daily physical activity patterns. The acquired data is evaluated using statistical analysis approaches such as descriptive statistics, correlation analysis, and cross-demographic group comparisons. The findings provide insights into participants' activity levels, device accuracy, user engagement, and relationships between various physical activity measures. The study's findings help to better understand the usefulness of force-intelligent wearable devices in promoting physical activity and improving health outcomes. Further research in this area could help to inform programs aimed at increasing physical activity and general well-being.

An mass ratio of permanent loads to variable loads of reinforced concrete buildings

ABSTRACT Environmental and resource crises necessitate sustainable development, yet construction lags in material conservation and carbon mitigation. Extensive use of reinforced concrete results in excessive permanent loads and larger building self-weight, elevating material use and energy consumption. A concept of mass ratio of permanent loads and variable loads ( R m ) is established for reinforced concrete buildings, and its reduction measures are outlined. Through statistical and regression analysis of 60 residential and 45 public buildings, R m have been validated as an indicator of structural value and design rationality, and its importance for material conservation and cost-effectiveness is underscored. Results shows that most reinforced concrete residential buildings have R m above 10, public buildings exceeding 7, indicating excessive structural self-weight. It requires lowering R m in structural and material innovations and optimized engineering design, to enhance material conservation. The innovation of this paper lies in three aspects: firstly, the concept modeling of R m addresses the quantitative assessment criteria in areas such as architectural materials and engineering design; secondly, the calculation of R m directly utilizes raw data from engineering design, providing a scientific and feasible approach for statistical analysis; thirdly, the inherent deficiencies in reinforced concrete buildings are intuitively indicated from the perspective of R m .

Comprehensive investigation on non-volatile and volatile compounds in Auricularia auricula from different regions by UPLC-MS/MS-based metabolomics and GC-IMS

Auricularia auriculata from two regions of China were studied for their metabolite fingerprints, metabolic pathways, and volatile organic compounds (VOCs) using UPLC-MS/MS and GC-IMS techniques in conjunction with multivariate statistical analysis approaches. A total of 881 metabolites were identified by UPLC-MS/MS, of which 39 compounds displayed significant differences. Acetyl-l-carnitine, N-acetyl-l-alanine, PC (18:3(9Z,12Z,15Z)/0:0), 4-fumaryl-acetoacetate, S-2-hydroxybutanedioic acid, and 4-guanidinobutyric acid belonged to the characteristic non-volatile metabolites. The umami-tasted amino acid, essential amino acid was abundant in Heilongjiang and Shaanxi province, respectively. KEGG pathway analysis indicated that differential metabolites were mainly enriched in lipid and amino acid metabolism. The GC-IMS study revealed 64 different types of VOCs, including acids, alcohols, aldehydes, esters, ketones, and other compounds. A. auricula from Shaanxi province was abundant in 2,3-pentanedione, 1-octen-3-one (dimer), 2-acetylfuran, beta-pinene (monomers, dimers) and alpha-pinene. While the primary distinctive VOCs of A. auricula from Heilongjiang province were butyl acetate, methyl acetate and propyl acetate. PCA and OPLS-DA allowed for clear differentiation between the A. auricula samples from the two regions. This research established foundations for investigating potential biomarkers for A. auricula and has important applications values for the creation and implementation of traceability systems within the edible mushroom industry.

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively.

Fecal microbiota is associated with extraintestinal manifestations in inflammatory bowel disease

Introduction A large proportion of patients with inflammatory bowel disease (IBD) experience IBD-related inflammatory conditions outside of the gastrointestinal tract, termed extraintestinal manifestations (EIMs) which further decreases quality of life and, in extreme cases, can be life threatening. The pathogenesis of EIMs remains unknown, and although gut microbiota alterations are a well-known characteristic of patients with IBD, its relationship with EIMs remains sparsely investigated. This study aimed to compare the gut microbiota of patients with IBD with and without EIMs. Methods A total of 131 Danish patients with IBD were included in the study, of whom 86 had a history of EIMs (IBD-EIM) and 45 did not (IBD-C). Stool samples underwent 16S rRNA sequencing. Amplicon sequence variants (ASVs) were mapped to the Silva database. Diversity indices and distance matrices were compared between IBD-EIM and IBD-C. Differentially abundant ASVs were identified using a custom multiple model statistical analysis approach, and modules of co-associated bacteria were identified using sparse correlations for compositional data (SparCC) and related to patient EIM status. Results Patients with IBD and EIMs exhibited increased disease activity, body mass index, increased fecal calprotectin levels and circulating monocytes and neutrophils. Microbiologically, IBD-EIM exhibited lower fecal microbial diversity than IBD-C (Mann–Whitney’s test, p = .01) and distinct fecal microbiota composition (permutational multivariate analysis of variance; weighted UniFrac, R 2 = 0.018, p = .01). A total of 26 ASVs exhibited differential relative abundances between IBD-EIM and IBD-C, including decreased Agathobacter and Blautia and increased Eggerthella lenta in the IBD-EIM group. SparCC analysis identified 27 bacterial co-association modules, three of which were negatively related to EIM (logistic regression, p < .05) and included important health-associated bacteria, such as Agathobacter and Faecalibacterium. Conclusions The fecal microbiota in IBD patients with EIMs is distinct from that in IBD patients without EIM and could be important for EIM pathogenesis.

DIMENSÕES ESTATÍSTICAS DA ECONOMIA CIRCULAR

Purpose: The aim of the research is to study the various aspects of the circular economy in Bulgaria and the countries of the European Union. Method: The article evaluates the circular economy by applying different statistical analysis approaches: multivariate statistical methods, convergence analysis and cross-correlation analysis. Results and conclusion: First, as a result of the conducted dynamic classification of EU countries, it was found that the transition to a circular economy is most advanced in Germany, the Netherlands, Belgium, Spain, Italy and Luxembourg. Second, according to each of the indicators included in the analysis, the absence of a process of general convergence and the presence of club convergence between the EU-27 countries during the period 2012-2019 was established. Convergence among countries in four of the clubs formed is relative, while in the others there is a process of transitional divergence and phase reversal. Third, based on the cross-correlation analysis, we find no relationship between generated and recycled packaging waste in Bulgaria from 2005 to 2019. Between these indicators in the European Union for the same period, there is reason to argue that there is a relationship that occurs within the current year. Fourth, the values of the indicators of waste utilization and rendering waste harmless clearly show that in Bulgaria the waste from economic activities is mainly landfilled, and this is associated with additional costs and leads to environmental pollution. Fifth, the taxonomic analysis shows the existence of minimal differences between the Bulgarian districts in terms of municipal solid waste management.

Uncovering SMEs’ knowledge management capability in innovation adoption and business performance: The moderating role of environmental turbulence

BACKGROUND: Knowledge serves as the foundation for SMEs to enhance capabilities in navigating turbulent environments. This study highlights that SMEs capability is a determinant in adopting innovations and business performance. OBJECTIVE: This study aims to uncover the capabilities of SMEs in relation to innovation adoption and business performance, with environmental turbulence acting as a moderating variable between innovation adoption and business performance. METHODS: This study employed an explanatory approach that established quantitative foundations. PLS-SEM was utilized as the statistical analysis approach to test the developed hypotheses. An offline survey was conducted using data collected from 159 SMEs in the creative economy sectors. RESULTS: The results reveal that knowledge management capability and dynamic capability significantly influence on innovation adoption. However, innovation capability has an insignificant effect on innovation adoption. Additionally, innovation adoption has a significant effect on business performance. Environmental turbulence, as a moderating variable between innovation adoption and business performance, demonstrates a weakening effect. Furthermore, this study also examines the mediating role of innovation adoption. CONCLUSIONS: The interplay of knowledge management capability, innovation adoption, and business performance enhances the optimal capabilities of SMEs to achieve competitive advantage. On the basis of the KBV, knowledge fortifies the potential capacities, thereby fostering unique capabilities.

Research on the application value of Multimedia-Based virtual reality technology in drama education activities

The research and moral use of academic technology focuses on developing, implementing, and overseeing the use of suitable technical resources and procedures to enhance learning and achievement. Multimedia has found its position in some form as an educational technology platform in the contemporary environment of academic universities. The use of virtual reality software as an intellectual tool and learning provider allows students to perform cognitive rehabilitation of preexisting information frameworks. People are paying more and more attention to how preschoolers' holistic skills develop as education reform progresses. Drama education is incorporated into the school curriculum to enhance young children's artistic, intellectual, and linguistic skills. Therefore, this study aims to examine the potential of multimedia-based virtual reality technology (MVRT) in drama education. The participants in this study were students from different universities in China. Students were exposed to multimedia-based virtual reality technology, and its efficacy was assessed using a statistical analytic approach called Analysis of variance (ANOVA). Drama understanding rate, educational improvement ratio, teaching quality rate, student achievement ratio, computation time, and parental support rate are among the performance metrics used to assess performance. Multimedia-based virtual reality technology (MVRT) for drama education showed outstanding success, with a 98% improvement ratio in educational outcomes and higher teaching quality. Students exhibited improved performance, supported by solid parental approval, demonstrating the effectiveness of MVRT in enhancing educational experiences.

Financial Behavior in Personal Investment: Influence of Psychological Factors on Investment Decision

In the ever-evolving realm of financial markets, understanding the underlying psychological dynamics shaping investor behavior is paramount. Financial behavior refers to the choices and actions people make when managing their own cash across a range of investment vehicles, including stocks, bonds, mutual funds, and real estate. It includes components that are vital in determining the results of investments, including risk tolerance, investment objectives, decision biases, emotions, and financial literacy. Achieving long-term investment success and financial security requires an understanding of and ability to control one's own financial behavior. An extensive research model has been painstakingly built to clarify the complex connections between these psychological variables and the process of making investing decisions. This study examines the impact of three psychological aspects on investment decisions made by 220 investors who trade on the National Stock Exchange (NSE) and the Bombay Stock Exchange (BSE): information asymmetry, problem framing, and risk propensity. Effective investing strategies necessitate an awareness of the interplay between psychological biases and cognitive processes, as these aspects influence personal investment decisions. By using a quantitative research methodology, information was gathered by sending surveys to investors who were actively trading stocks on the BSE and NSE platforms. Regression analysis and correlation studies are two statistical analytic approaches that were used to investigate the connections between investing decisions and psychological aspects. The study's conclusions add to the corpus of knowledge by illuminating the complex dynamics of financial decision-making and the part played by psychological variables. Furthermore, the knowledge gained from this study has important ramifications for financial advisors, investors, and legislators that aim to improve investor welfare and encourage well-informed stock market decision-making.&#x0D;

Data Mining in Different Fields: A Study

A revolutionary technique for managing massive amounts of data from databases is data mining. These days, information is more commonplace in all types of businesses and industries. For a range of business objectives, fields choose data mining approaches, and data mining is what makes it possible for fields to establish the ideal customer baseline and cultivate long-lasting relationships. Statistical analysis, machine learning, predictive modelling, and database approaches are all combined in knowledge discovery. This review research focuses on and quickly discusses the various domains in which data mining is applied. Here, we discuss several sectors where data mining is relevant in order to comprehend its significance. We also discuss the numerous technologies used in related fields, which depend on user desire. To learn about it, many algorithms are described.