Analysis Of Numerical Data Research Articles

DETERMINANTS OF HAND HYGIENE COMPLIANCE AND PRACTICE AMONG NURSES FROM WEST-COAST MALAYSIA

Introduction: The Centers for Disease Control and Prevention emphasizes hand hygiene as crucial for reducing infection transmission in healthcare, yet its importance is underrecognized, and compliance rates are low. Objective: The study was conducted to assess self-reported hand hygiene compliance and practice among nurses in Malaysia. Methods: A cross-sectional study was conducted on 388 nurses randomly selected in four Malaysian hospitals using a structured, self-administered questionnaire. Measures were taken for convergent validity, concept, reliability, and content. The study used numerical data analysis to assess nurses’ self-reported hand-hygiene compliance and practices. Histograms were used to examine data normality, and post-hoc tests were conducted using the Games-Howell test after confirming the assumptions. Simple and multiple linear regression was used to identify associated factors with self-reported hand-hygiene compliance and practice. Results: The study found male nurses outperformed female nurses in terms of hand-hygiene practice and compliance scores, with the former having a nursing degree or diploma qualification. The hand-hygiene compliance score of male Indian nurses with degrees was the highest; however, Chinese nurses with degrees had a 12.36 times higher likelihood of scoring on the hand-hygiene practice scale. Conclusion: Male nurses with degrees and diplomas outperformed females in self-reported hand-hygiene compliance and practice scores, with both male and female Indian and Chinese nurses scoring higher than Malay and ‘other race’ nurses. A quality improvement project is needed to identify the root causes of hand hygiene compliance issues and identify areas for improvement.

The Stability of Trend Management Strategies in Chaotic Market Conditions

This study investigates the stability of trend management strategies under stochastic chaos conditions, with a focus on speculative trading in the Forex market. The primary aim is to evaluate the feasibility and robustness of these strategies for asset management. The experimental setup involves sequential optimization and testing of trend strategies across three EURUSD observation intervals, where each subsequent interval alternates between training and testing roles. Methods include numerical data analysis, parametric optimization, and the use of both conventional and bidirectional exponential filters to isolate system components and improve trend detection. Observations reveal that while trend strategies optimized for specific intervals yield positive results, their effectiveness diminishes on unseen intervals due to inherent market instability. The results show significant limitations in using linear trend-based strategies in chaotic environments, with optimized strategies often leading to losses in subsequent periods. The discussion highlights the potential of integrating trend statistics into multi-expert decision systems, leveraging fuzzy solutions based on fundamental analysis to enhance decision-making reliability. In conclusion, while standalone trend strategies are unsuitable for stable asset management in chaotic markets, their integration into hybrid systems may provide a pathway for improved performance and resilience.

Energy thickness in turbulent boundary layer flows

In this study, we investigate the properties of energy thickness $\delta _3$ in turbulent boundary layer (TBL) flows, a parameter derived solely from the mean streamwise velocity ( $U$ ) profile. Through an analysis of the energy integral equation for zero pressure gradient TBLs, we establish a close relationship between turbulent kinetic energy (TKE) production and $\delta _3$ , offering a practical method to estimate TKE production, which is particularly useful in physical experiments where direct measurements are challenging. The significance of $\delta _3$ becomes even more pronounced in TBLs under pressure gradient. Through extensive analysis of numerical and experimental data, we show that the ratio between $\delta _3$ and the momentum thickness $\delta _2$ is a promising criterion for predicting flow separation. Moreover, we derive a new energy integral equation for TBLs under arbitrary pressure gradients, and provide approximations for TKE productions terms by $R_{uv}\,\partial U/\partial y$ and $R_{uu}\,\partial U/\partial x$ , and dissipation term by the mean shear. Here, $x, y$ represent the streamwise and wall-normal directions, respectively, and $R_{uu}$ and $R_{uv}$ are the Reynolds normal and shear stresses. The accuracy and robustness of the new energy integral equation and the approximation equations are validated using direct numerical simulations data. Our results show that the TKE production by $R_{uv}\,\partial U/\partial y$ and the overall productions consistently remain positive, reflecting a continuous conversion of mean kinetic energy into TKE across all TBLs. However, under strong favourable pressure gradients, TKE production by $R_{uu}\,\partial U/\partial x$ becomes negative, indicating a reverse energy transfer from TKE to mean kinetic energy.

Artificial Intelligence in Imaging for Personalized Management of Coronary Artery Disease.

The precision of imaging and the number of other risk-assessing and diagnostic methods are constantly growing, allowing for the uptake of additional strategies for individualized therapies. Personalized medicine has the potential to deliver more adequate treatment, resulting in better clinical outcomes, based on each patient's vulnerability or genetic makeup. In addition to increased efficiency, costs related to this type of procedure can be significantly lower. Useful assistance in designing individual therapies may be assured by the adoption of artificial intelligence (AI). Recent years have brought essential developments in deep and machine learning techniques. Advances in technologies such as convolutional neural networks (CNNs) have enabled automatic analyses of images, numerical data, and video data, providing high efficiency in the creation of prediction models. The number of AI applications in medicine is constantly growing, and the effectiveness of these techniques has been demonstrated in coronary computed tomography angiography (CCTA), optical coherence tomography (OCT), and many others. Moreover, AI models may be useful in direct therapy optimization for patients with coronary artery disease (CAD), who are burdened with high risk. The combination of well-trained AI with the design of individual treatment pathways can lead to improvements in health care. However, existing limitations, such as non-adapted guidelines or the lack of randomized clinical trials to evaluate AI's true accuracy, may contribute to delays in introducing automatic methods into practical use. This review critically appraises the developed tools that are potentially useful for clinicians in guiding personalized patient management, as well as current trials in this field.

ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging.

Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.

Methods for measuring electron concentration in shock waves

The current state of research on measuring the electron concentration in low-temperature plasma in the vicinity of a strong shock wave, which simulates the conditions of the descend spacecraft entry into the Earth’s atmosphere is considered. Various physicochemical processes leading to the formation of low-temperature plasma both ahead of the shock wave front and in the shock-heated gas are analyzed. A critical review of various plasma diagnostic methods is made, their advantages and disadvantages are noted. An analysis of numerous experimental data on measuring the electron concentration in various shock-heated gases under various conditions was carried out.

Analysis Of Elementary School Students' Difficulties In Solve Mathematical Questions On Object Weight Measurement Material

The ability to measure the weight of objects is a skill needed in various fields, such as trade, industry and health. However, various studies mention that many students have difficulty in understanding and solving math problems related to measuring the weight of objects effectively. This study aims to analyze the difficulties faced by grade 6 students of UPT SDN 025 JANJI, Doloksanggul in solving mathematics problems measuring the weight of objects. Researchers made questions in the form of measuring the weight of objects and found various student errors in answering each question. The method used in this research is quantitative research, which is a research method that focuses on the collection, analysis, and interpretation of numerical data. Based on the results of research and analysis conducted by researchers on 13 sixth grade students at UPT SDN 025 JANJI above, students often face various errors in solving weight measurement problems, namely (1) errors in reading the problem, (2) understanding information, (3) choosing the right mathematical operation, and (4) performing calculations. Students often face various errors in solving weight measurement problems, including errors in reading the problem, understanding the information, choosing the right mathematical operation, and performing calculations. These errors can be categorized into several types, such as reading errors, comprehension errors, transformation errors, process skill errors, and other errors.

Enhancing full-waveform inversion of field GPR data: A source-independent approach with dynamic reference selection via SE-Wave-U-Net

Ground-penetrating radar (GPR) is a widely used nondestructive detection tool. Full-waveform inversion (FWI) offers a reliable algorithm for accurately imaging GPR data. Despite its potential, FWI’s effectiveness is often compromised by unknown excitation sources, which can lead to computational failures, diminished imaging accuracy, or both. Addressing this challenge, this paper introduces a convolution-type objective function designed to mitigate the detrimental effects of these unknown excitation sources on FWI imaging. Critical to the success of this function is the precise selection of reference traces. Further refining the capability for signal separation, we have integrated squeeze-and-excitation (SE) modules into the traditional Wave-U-Net architecture, culminating in the development of the SE-Wave-U-Net framework. This framework is adept at dynamically extracting accurate reference traces from field GPR data, which are essential for accurate FWI imaging. The efficacy, accuracy, and practical applicability of our methods are validated through extensive analysis of numerical, laboratory experiments, and field GPR data. Our algorithm not only establishes a high-precision computational framework for FWI but also enhances the reliability of GPR imaging in complex environments.

Numerical analysis of multivariate data for fraud detection

Fraud detection in financial transactions is crucial for minimizing financial losses and maintaining security in digital environments. This paper presents a methodology for fraud detection using numerical analysis of multivariate data, combining dimensionality reduction, anomaly detection, and clustering techniques. Initially, transaction data undergoes preprocessing to remove inconsistencies, followed by Principal Component Analysis (PCA) to reduce dimensionality, preserving essential patterns while simplifying computation. An Isolation Forest algorithm assigns anomaly scores to each transaction, helping to identify unusual behavior indicative of fraud. KMeans clustering further organizes transactions into groups, making it easier to spot clusters of potentially fraudulent activities based on shared attributes. Finally, a feature correlation matrix enhances model refinement by revealing interdependencies between features, optimizing detection accuracy. Through this multivariate analysis approach, the model efficiently flags suspicious transactions, achieving a balance between identifying fraud and minimizing false positives. This method shows promise for real-time applications in financial sectors where rapid, accurate fraud detection is essential.

Evaluation of postoperative pain on Transversus Abdominis Plane block and Erector Spinae Plane block in patients undergoing gynecological surgery: a prospective, comparative study

INTRODUCTION: Moderate to severe pain after gynecological surgery has an impact on postoperative recovery and mobilization. Transversus Abdominis Plane (TAP) block and Erector Spinae Plane (ESP) block have been used as multimodal analgesia for abdominal surgery. OBJECTIVES: This study aims to compare postoperative pain in patients undergoing either ESP blocks or TAP blocks, and the need for postoperative morphine in patients undergoing gynecological surgery.MATERIALS AND METHODS: This was a single-center, prospective, double-arm, comparative study. Forty subjects were enrolled in this study, and classified into two groups. One group of 20 subjects underwent ESP block, and the remaining 20 subjects underwent TAP block following a gynecologic surgical procedure. Patients were then assessed using with the Numeric Rating Scale (NRS) pain score which will be observed at intervals of 0–1 hour, 1–6 hours, 6–12 hours, and 12–24 hours. Numerical data analysis was done using an unpaired T-test on normally distributed data and the Mann-Whitney test on non-normally distributed data. Categorical data analysis was done using Fisher's Exact Test. RESULTS: NRS score at rest was higher in the TAP block group at 1–6 hours and 6–12 hours (pain scale 3) than in the ESP block group (pain scale 1). Movement-evoked pain NRS score was higher in the TAP block groups significantly. CONCLUSION: ESP block results in a lower postoperative pain score NRS value and reduces the need for morphine in post-gynecological surgery patients with a median incision compared to the TAP block.

Microseismic Electronic Fencing for Monitoring of Transboundary Mining in Mines

Mine transboundary mining has been occurring frequently in recent years, and this illegal behavior has brought great potential danger to mine safety while also causing greater losses of state-owned assets. However, the current method of monitoring transboundary mining is still mainly based on underground verification by supervisors, which is far from meeting the demand for supervision. Microseismic monitoring technology is effective for monitoring transboundary mining due to its ability to locate vibration signals. For mine transboundary mining monitoring, this paper proposes a microseismic electronic fence method focusing on mine boundary locating, which differs from the routine microseismic monitoring used in mining operations. This method focuses its key monitoring area on the mine boundary. The deployment mode, number of sensors, and localization theory are analyzed, and numerical simulation and field measurement data analysis results show that the microseismic electronic fence method can achieve a localization accuracy of 15–20 m for underground microseismic events in the vicinity of mine boundaries, which can be effectively applied to the monitoring of transboundary mining activities.

E-Platform for Purification of Islamic Thoughts: A Preliminary Study on The Needs Among UiTM Students

This study examines the needs of University Teknology MARA (UiTM) students for an e-platform to purify their thinking. Recognizing the increasing influence of negative thinking on the formation and personal development of students, this study aims to understand how such platforms can meet the spiritual and intellectual needs of Muslim students in the modern world. This study is a quantitative study. To achieve the objectives of the study, data collection and numerical data analysis are carried out. This study involved a total of 371 students at University UiTM Shah Alam from various faculties of study. The questionnaire was distributed using the Google Forms platform through a WhatsApp group with the lecturer. In this study, the questionnaire consisted of three (4) parts that were designed as research instruments, namely part A: respondents' backgrounds, part B containing questions related to Islamic understanding and practice by UiTM students, part C containing questions related to UiTM students' thoughts, and part D containing questions related to the need for an e-platform to purify Islamic thought. The choice of answer is to use a Likert scale of 1–5. The findings of the study found that students have a high level of understanding and practice of Islam, but their Islamic thinking is still at a moderate level. Therefore, the need to purify Islamic thought is very appropriate to overcome uncertainty and ambiguity as well as students' confusion on issues related to Islamic thought.

The influence of pathological conditions on vocal cord’s displacement: A numerical method and data analysis

In this study, one-mass model of vocal cord is solved by using cubic trigonometric B-spline (CTBS) in order to generate displacements of healthy and pathological vocal cord. By utilizing CTBS, this work aims to investigate the relationship between five vocal cord conditions and its displacements generation throughout phonation process. The approximate displacements derived from CTBS are then compared to displacement generated by ode45 built-in solver through MATLAB software. The errors from both methods are then calculated which represented the effectiveness of the CTBS method in generating vocal cord displacements. Any discrepancies between the results generated by using CTBS and ode45 can be analysed to prove the reliability of CTBS method. Besides, an error analysis is demonstrated in order to quantitatively evaluate the accuracy of the approximate solutions. The generated approximate displacements of CTBS have shown to be approximately close to ode45 results. Thus, it can be concluded that CTBS is a reliable numerical method. The relationship between vocal cord conditions and its generated displacements has represented the distinction between healthy and pathological vocal cord outcomes. Healthy vocal cord has generated a steady decline displacements value while vocal cords with pathological condition have generated fluctuated displacement values throughout the phases of phonation process. The fluctuation trend of displacements value has revealed the irregular displacements value generation which is one of pathological vocal cord feature caused by low stiffness.

Numerical Data Processing by The Implementation of Trees and Graphs

Trees and Graphs play a vital role in transport and logistics. In tree, decision tree is one of the important, not only implemented for data processing, but also considered for Numerical data analysis. The decision tree is a flow chart-like structure, in which each internal node represents a ‘test’ on an attribute, which has a node known as root being at the top, which further divides the given data into branches depending upon the conditions. Every branch consists of a rule, and each leaf node is its outcome. A support tool with a tree-like structure that models probable outcomes, cost of resources, utilities, and possible consequences. Decision trees are also used in operations research along with planning logistics. They can help in determining appropriate plans that will help a company achieve its target. In Graph, Dijkstra's algorithm is an admired algorithm used to find the shortest paths between nodes in a graph, which may represent road networks for example. Dijkstra's algorithm finds the shortest path from a given source node to every other node. It can also be used to find the shortest path to a specific destination node, by concluding the algorithm once the shortest path to the destination node is found. It is also commonly used on graphs where the edge weights are in real numbers. A common application of shortest path algorithms is network routing protocols and also support in route optimization for delivery services, ensuring timely and cost-effective deliveries by finding the best paths for transportation.

V. S. Kozlov and his Tsifrar-diagrammometer

In the late 19th century a military statistician and a pioneers of the Russian cooperative movement, Viktor Semyonovich Kozlov (1842–1920), designed and built a unique mechanical analog computing device he named “tsifrar-diagrammometer”. It was intended for operational visual presentation of numerical results of observations, calculations, experiments, etc. (tsifrar) and for calculating their statistical characteristics (diagrammometer). At the 1889 Paris Exposition this device that represented Russia was awarded the bronze medal. Drawing on the archival records and the materials from the Russian and European periodicals, the history of the inventor’s work on this device is reconstructed for the first time and some facts from his biography are described. The authors also attempt to describe the design of the device and analyze the mechanism of its functioning. Since no sources contain a detailed description of the design with the diagrams, charts, etc. or the mechanism of its functioning while available information is often contradictory, the authors present their own version of the device’s description and operation. The tsifrar-diagrammometer aroused much interest among the most prominent Russian and French scientists of the time, including P. L. Chebyshev, D. I. Mendeleev, E. Lucas, and many others. They viewed Kozlov’s device as a forerunner of a future universal instrument for numerical data analysis and for discovering new laws of nature. The appraisals of the device by the contemporaries are also given and analyzed in the article.

Visualization Methods of Vortex Structures Acting on the Wing in High-Speed Flows

This paper considers the use of vortex structures scientific identification and visualization methods in analyzing the effects of these structures on the wing at an angle of attack in supersonic flows. The results of applying the methods of scientific identification and visualization of vortex flows for the analysis and processing of numerical data of supersonic flow are presented. In the considered problem, the generator wing and the main wing are located at an angle of attack of 20° degrees to the incoming flow. Two configurations with different length of the vortex generator are considered. The simulation was carried out using URANS equations. Numerical simulations were performed on the hybrid supercomputer system K-60 at the Keldysh Institute of Applied Mathematics of the Russian Academy of Sciences. The scientific visualization of the simulation results was carried out primarily using the Liutex vortex structures identification criterion, which belongs to the third generation of such methods. And this method was effectively complemented and enhanced by the use of other methods.

Pengaruh Kepemimpinan, Kepuasan Kerja dan Motivasi Kerja Terhadap Loyalitas Karyawan

Employee loyalty is reflected in employee obedience, responsibility and discipline. Initial observations show that the number of employees is decreasing from year to year, and some employees violate regulations, such as arriving late and not following company regulations. Therefore, the aim of this research is to test whether leadership style, motivation and rewards influence employee retention. The research method used is a quantitative approach, namely analysis of numerical data processed using statistical methods. This research was conducted to determine the influence of leadership, job satisfaction, and motivation on employee loyalty. The sampling technique used saturated samples with a sample size of 13 respondents. The data collection methods used were questionnaires, interviews, documentation and observation. The data analysis technique uses multiple regression analysis using the IBM SPSS 25 program. The conclusion from these results is that the influence of leadership, job satisfaction and motivation has a significant influence on employee loyalty. Suggestions for future researchers are to collect a larger sample in the company and fill out the questionnaire in language that is easier for employees to understand.

Optimalisasi SIPD dan SPIP dalam Meningkatkan Kualitas Laporan Keuangan: Pentingnya Kompetensi SDM

<p><em><span>This study aims to describe the implementation of Local Government Information Systems (LGIS), Government Internal Control Systems (GICS), and Financial Report Quality (FRQ); evaluate the effect of LGIS and GICS implementation on FRQ; and analyze the role of Human Resource Competence in moderating the relationship between LGIS and GICS on FRQ. The research employs a quantitative approach, focusing on theory testing through numerical variables and data analysis using statistical procedures. The study utilizes the Partial Least Square (PLS) method in Structural Equation Modeling (SEM) with the WarpPLS 8.0 software, involving 128 respondents from various Local Government Agencies. The results indicate that the implementation of LGIS and GICS positively and significantly affects FRQ. Furthermore, Human Resource Competence enhances the relationship between LGIS and GICS with FRQ, suggesting that improving HR competence can increase the effectiveness of LGIS and GICS implementation in enhancing financial report quality. This study is expected to provide input and contribute to the District Government as an evaluation tool for the implementation of LGIS and GICS. This study also combines the variables of LGIS and GICS on FRQ using the Agency Theory approach.</span></em></p>

Integration of Metrology in Grinding and Polishing Processes for Rotationally Symmetrical Aspherical Surfaces with Optimized Material Removal Functions

Aspherical surfaces, with their varying curvature, minimize aberrations and enhance clarity, making them essential in optics, aerospace, medical devices, and telecommunications. However, manufacturing these surfaces is challenging because of systematic errors in CNC equipment, tool wear, measurement inaccuracies, and environmental disturbances. These issues necessitate precise error compensation to achieve the desired surface shape. Traditional methods for spherical optics are inadequate for aspherical components, making accurate surface shape error detection and compensation crucial. This study integrates advanced metrology with optimized material removal functions in the grinding and polishing processes. By combining numerical control technology, computer technology, and data analysis, we developed CAM software (version 1) tailored for aspherical surfaces. This software uses a compensation correction algorithm to process error data and generate NC programs for machining. Our approach automates and digitizes the grinding and polishing process, improving efficiency and surface accuracy. This advancement enables high-precision mass production of rotationally symmetrical aspherical optical components, addressing existing manufacturing challenges and enhancing optical system performance.

THE RELATIONSHIP BETWEEN RELIGIOSITY AND SELF EFFICACY WITH ANXIETY IN FACING THESIS EXAMS IN FINAL SEMESTER STUDENTS OF ISLAMIC GUIDANCE AND COUNSELING STUDY PROGRAM, KH MUKHTAR SYAFAAT UNIVERSITY

This researcher examined the relationship between religiosity and self- efficacy and anxiety about facing thesis exams in final semester students of the Islamic Guidance and Counseling Study Program at KH Mukhtar Syafaat University. This research was motivated by students' anxiety in facing the thesis exam. One of the factors that influence student anxiety is religiosity and self-efficacy. The aim of this research is to find out how religiosity is related to anxiety about facing the thesis exam. To be able to find out how religiosity and self-efficacy are related to anxiety facing the thesis exam. This research uses quantitative research methods. Quantitative research emphasizes analysis of numerical data processed using statistical methods. The method used in this research is the correlational method. Data was collected through questionnaire techniques, with different data collection strategies for two groups of respondents: using Google Forms for respondents who were not in the boarding school and offline questionnaires for respondents who were in the boarding school. The results obtained in this research are the correlation coefficient value of 0.478 contained in the table, it can be concluded that there is a positive relationship between religiosity and anxiety. This means that if a person's level of religiosity increases, it is likely that their level of anxiety will also increase, and vice versa. The positive correlation coefficient value of 0.679 concludes that there is a strong positive relationship between self-efficacy and anxiety. This means that if a person's level of self-efficacy increases, it is likely that their level of anxiety will also increase, and vice versa. The correlation coefficient value between X1 (religiosity) and X2 (self efficacy) with Y (anxiety) is 0.695. This value is positive, which indicates that there is a positive relationship between religiosity and self-efficacy on anxiety levels. This means that if the religiosity value increases, anxiety also tends to increase, as does self-efficacy. Overall, religiosity, self-efficacy, and anxiety have a unidirectional relationship. The correlation coefficient value of 0.695 is included in the strong category according to the r value interpretation table, which is between 0.600 to 0.799.