Statistical Analysis Techniques Research Articles

What innovation and technology delivers to internationalization? The case of European and Central Asia regions

Purpose This study aims to analyze the impact of technology and innovation on businesses’ internationalization process as well as the relationship between innovation and technology and internationalization. Design/methodology/approach The study was based on quantitative analysis of secondary data from 30 economies in Europe and Central Asia, extracted from the Enterprise Surveys – The World Bank database, using the multivariate statistical techniques of multiple linear regression and cluster analysis. Findings The results suggest that innovation and technology (I&T) mutually and positively influence the internationalization of firms. In this sense, innovative firms are more successful in competing internationally and exposure to alternative business contexts culminates in innovation. The results suggest a positive relationship between the percentage of firms that introduced a process innovation, the percentage of firms with websites and the businesses’ internationalization. Originality/value The originality of this research is based on the methodological approach that allowed the identification of the most significant variables for stimulating the internationalization of firms based on innovation and technology. Given the predominance of economies with higher I&T and internationalization in Europe, it is proposed as future research projects to ascertain the reasons for such a phenomenon.

Thermal Footprint of the Urbanization Process: Analyzing the Heat Effects of the Urbanization Index (UI) on the Local Climate Zone (LCZ) and Land Surface Temperature (LST) over Two Decades in Seville

Urbanization is a multifaceted process characterized by changes in urban areas through various means, such as sprawl, ribbon development, or infill and compact growth. This phenomenon changes the pattern of the local climate zone (LCZ) and significantly affects the climate, vegetation dynamics, energy consumption, water resources, and public health. This study aims to discern the impacts of changes in urban growth on the LCZ and land surface temperature (LST) over a two-decade period. A comprehensive methodology that integrates statistical analysis, data visualization, machine learning, and advanced techniques, such as remote sensing technology and geospatial analysis systems, is employed. ENVI, GEE, and GIS tools are utilized to collect, process, and monitor satellite data and imagery of temporal and spatial variations in intensive or diffuse urbanization processes from 2003 to 2023 to analyze and simulate land use and land cover (LULC) changes, urbanization index (UI), LCZ patterns, and LST changes over the years and to make overlapping maps of changes to recognize the relation between LULC, LCZ, and LST. This study focuses on Seville’s urban area, which has experienced rapid urbanization and a significant increase in average temperature during the last few decades. The findings of this study will provide actionable recommendations into the interplay between urban growth and climate and highlight the pivotal role of urban growth in shaping resilience and vulnerable areas based on microclimate changes. Urban planners can leverage these insights to predict alternatives for the future development of urban areas and define practical climate mitigation strategies.

Continuous heart rate monitoring in atrial fibrillation with multispectral photoplethysmography

Heart attacks and strokes account for more than four out of every five Cardiovascular Disease (CVD) deaths, with one-third occurring before age 70 years. Atrial fibrillation (AF), a common cause of ischemic strokes, can significantly reduce mortality rates through continuous monitoring, timely diagnosis, accurate confirmation, and early prognosis. This can be achieved by employing Multi-Spectral Photoplethysmography (MSPPG) technology as an alternative diagnostic tool to Electrocardiography (ECG). This investigation is implemented by determining the Heart Rate (HR) of AF-affected patients using a developed MSPPG-based wrist-worn device, keeping ECG as a standard gold reference. The Multispectral-based wrist-worn wearable Photoplethysmography device employs an optical bio-sensing, integrated BIOFY sensor to acquire the MSPPG signals using two green (526 nm), one red (660 nm) and one infrared (950 nm) LED and two photodetectors (Broadband detector, IR-cut detector). The MSPPG signals acquired using a microcontroller unit are stored in an On-device storage system simultaneously in the IoT Platform. Statistical analysis techniques were employed to compare the HR detected using the MSPPG technique with the HR measured from the gold-standard ECG. The accuracy paves the way for implementing the proposed MSPPG technology in detecting AF conditions in terms of HR, further laying the foundation for earlier detection of AF. Wearable PPG-based devices face a significant issue of motion artifacts that can be overcome using the MSPPG technique. The research advancements can potentially increase the accessibility of HR monitoring in AF patients, conveniently integrating into daily routines without restricting day-to-day activities.

Strategy to Improve Employee Performance Through Strengthening Empowerment, Teamwork and Job Satisfaction

The purpose of this study was to determine the Strategy for Improving Employee Performance through Strengthening Empowerment, Teamwork and Job Satisfaction at the Bogor City Environmental Service. The study population was 280 people with a sample size of 165 people calculated using the Slovin formula. Data collection for each variable studied using a questionnaire with a rating scale. Data analysis techniques using descriptive statistical analysis techniques and path analysis techniques. The results showed (a) there is a positive direct effect of empowerment on job satisfaction, with a path coefficient value of βzx1 = 0.155, a t-value of 1.998> t table value of 1.654 (b) there is a positive direct effect of teamwork on job satisfaction, with a path coefficient value of βzx2 = 0.415, and a t-value of 5. 167> t table value of 1.654 (c) there is a positive direct effect of job satisfaction on performance, with a path coefficient value of βzy 415, and a calculated t-value of 5.167 > t table value of 1.654 (c) there is a positive direct effect of job satisfaction on performance, with a path coefficient value of βzy 209, and a calculated t-value of 2.722 > t table value of 1.654. (d) there is a positive direct effect of empowerment on performance, with a path coefficient value of βyx1 = 0.470, and a calculated t value of 4.149 > t table value of 1.654. (e) there is a positive direct effect of teamwork on performance, with a path coefficient value of βyx2 = 0.466, and a calculated t value of 5.599> t table value of 1.654. (f) there is a positive indirect effect of empowerment on performance through job satisfaction, with a regression coefficient value of 0.891 and 0.238, and from the calculation of the Sobel test, the z-count value is 10.241> 1.96. (g) there is a positive indirect effect of teamwork on performance through job satisfaction, with a coefficient value of 0.706 and 0.238 and from the calculation of the Sobel test, the z-count value is 8.115> z-table 1.96. Thus, it can be concluded that by strengthening empowerment, teamwork and job satisfaction, employee performance in the organization can be improved.

Use Artificial Neural Networks to Predict Seepage in the Hilla Canal Regulator

The variation in the seepage under hydraulic structures significantly impacts their stability and effective water management, especially considering recent water scarcity challenges. This paper aims to calculate seepage and investigate the hydraulic performance of the Al-Hilla canal regulator's foundation. The methodology involves constructing a model (SEEP/W) and comparing its results with one-site piezometer readings. Using a mixed-methods approach, this study integrates software modelling and statistical analysis techniques. This study integrates software modelling and statistical analysis techniques. The Geo-studio program facilities modelling of seepage flow, while JASP software is used for statistical analysis and predictive equation development. The methodology consists of data collection, Geo-Studio modelling, JASP analysis, and validation of equation accuracy. After verifying the models’ efficiency, the data for the seepage equation was established under various upstream and downstream conditions, incorporating artificial intelligence algorithms. This data was analyzed to drive a predictive equation for seepage with a high coefficient of determination (R2) OF 97%. Additionally, another equation was formulated to determine the total water pressure head, achieving an R2 value of 95 %. These equations are invaluable tools for predicting the total water pressure head and seepage and enhancing the management of hydraulic structure.

Host Based-Intrusion Detection System (Anomaly Detection System)

The objective of our statistical anomaly detection project is to develop an IDS that notices behavior which is based on observed action, detects abnormalities or possible intrusions. The system utilizes statistical analysis techniques, specifically features to discover anomalous patterns of activity, and receives input data in the form of stats.txt and event.txt files. The project's goal is to offer a dependable and efficient IDS that can assist in identifying and averting cyberattacks on networks and computer systems

Differences in Body Mass Index, Physical self-concept, and self-confidence in adolescent boys and girls

Background and Study Aim. Adolescence is characterized as a critical stage in the development of health-related habits, whose importance extends beyond this period, as they predict health-related behaviors in adulthood. The main objective of this study was to analyze differences in body mass index, physical self-concept, and self-confidence of adolescent boys and girls. Materials and Methods. This is a comparative research. The population in this study was adolescents aged 13-19 years with a random sampling technique of 94 people (male 53, female 41). Inclusion criteria for adolescent students in this study include the absence of any disease-related limitations. The instruments used were Body Mass Index, Physical Self-Perception Profile (PSPP), and a self-confidence questionnaire. The statistical analysis technique used the Statistical Package for Social Science (SPSS) version 21 software. The level of statistical significance was set at p-value < 0.05. Results. The results of the analysis showed that: (1) Body Mass Index of adolescent girls is higher than boys (p-value < 0.05). (2) Physical Self-Concept of adolescent girls is higher than that of boys (p-value < 0.05). (3) Self-Confidence of adolescent boys is better than that of girls (p-value < 0.05). Conclusions. The results of this study are expected to be a guideline for schools and health agencies to pay attention to the psychological health conditions of adolescents. Adolescents are expected to improve their body image with a confident attitude in order to avoid psychological problems.

The spatiotemporal and dependency analysis of selected meteorological parameters and normalized difference vegetation index with aerosol optical depth over east Africa

The unprecedented rise in atmospheric aerosols, coupled with their intricate interactions with the environment through a wide array of physical, chemical, and biological processes, has profoundly impacted global climate. Their presence in the atmosphere scatters and absorbs solar radiation, thus altering the amount of sunlight reaching the Earth's surface. These direct effects, along with the indirect effects of aerosols, have significantly altered atmospheric temperatures, land surface processes, global surface temperature, hydrological cycle, and ecosystems. Understanding the complex interplay between aerosols and climatic variables necessitates a multidisciplinary approach, such as dependency modeling. Addressing these challenges, the current study conducts a spatiotemporal correlational analysis of selected key meteorological parameters with aerosol optical depth over East Africa (EA) using multisensory data from Moderate-resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Application (MERRA-2) model, and Tropical Rainfall Measurement Mission (TRMM). Employing a weighted least squares regression (WLS) model, the study quantifies trends in the time series of climatic variables and Normalized Difference Vegetation Index (NDVI), further utilizing a statistical dependency modeling technique for correlational analysis. The trend analysis reveals a significant decreasing trend in surface wind speed (SWS) in most months, with sporadic positive trends attributed to anthropogenic activities, notably biomass burning, observed in January. Spatial trend analysis of Precipitation Rate (PR) displays heterogeneity, with significant negative trends in January and March, and positive trends in February, April, November, and December. Negative trends during May to August are attributed to increased anthropogenic activities, while enhanced positive trends in May correlate with low aerosol optical depth (AOD) during this period. Surface air temperature (SAT) exhibits diverse variations across the region, with dry months recording higher averages and trends than wet months. The study notes heterogeneous correlations in NDVI over the study area, with positive and negative correlations observed in different regions. Specifically, positive correlations are noted along the coastal and Lake Victoria regions, attributed to improved PR enhancing vegetation cover in these areas.

Analisis Tinggi Gelombang Laut di Perairan Buton Selatan

Wave height information is very much needed by the people of South Buton, especially those who always use sea transportation, therefore this study aims to analyze the sea wave height in the waters of South Buton, using a quantitative descriptive method. The data collection technique is in the form of documentation. The data analysis technique used is a descriptive statistical analysis technique. Wave height information is very much needed by the community, especially in the shipping sector and fishermen in the waters of South Buton. The results of this study show that the wave height in South Buton varies greatly from January to December from 2019, 2020, and 2021. The months with great potential for high waves are January, February, April, and June with an average wave height of 1-3 meters with a wind speed of 4-15 knots. The lowest and moderate waves occur in March, May, July, August, September, October, November, and December. In these months, the average wave height reaches 0-2.5 meters with a wind speed of 6-15 knots.

Hydrogeochemical Processes in Basement Areas Using Principal Component in Burkina Faso (West African Sahel)

The basement aquifers in Burkina Faso are increasingly exposed to groundwater pollution, largely due to socio-economic activities and climatic fluctuations, particularly the reduction in rainfall. This pollution makes the management and understanding of these aquifers particularly complex. To elucidate the processes controlling this contamination, a methodological approach combining principal component analysis (PCA) and multivariate statistical techniques was adopted. The study analyzed sixteen physicochemical parameters from 58 water samples. The primary objective of this research is to assess groundwater quality and deepen the understanding of the key factors influencing the spatial variation of their chemical composition. The results obtained will contribute to better planning of preservation and sustainable management measures for water resources in Burkina Faso. The results show that three principal components explain 72% of the variance, identifying anthropogenic inputs, with two components affected by mineralization and one by pollution. The study reveals that the groundwater is aggressive and highly corrosive, with calcite saturation. Water-rock interactions appear to be the main mechanisms controlling the hydrochemistry of groundwater, with increasing concentrations of cations and anions as the water travels through percolation pathways. PCA also revealed that the residence time of the water and leaching due to human activities significantly influence water quality, primarily through mineralization processes. These results suggest that rock weathering, coupled with reduced rainfall, constitutes a major vulnerability for aquifer recharge.

Analysis of the Impact of Sino-US Poultry Trade on China's Chicken Prices

Abstract: The trade relationship between China and the United States, two of the world's largest economies, is of significant global importance, particularly in the agricultural sector. This sector plays a crucial role in both nations' economies, contributing substantially to their GDPs and employment rates.This paper delves into the intricate dynamics of poultry trade between China and the United States, focusing on its impact on chicken prices in China. Utilizing comprehensive datasets spanning from 2007 to 2022 sourced from the United States Department of Agriculture (USDA) and the National Bureau of Statistics of China, the study employs static economic analysis and regression techniques to explore the factors influencing poultry prices. The findings substantiated the hypothesis posited: a rise in poultry trade volume between the U.S. and China correlates with a decrease in chicken prices in China. Findings reveal a complex relationship between trade dynamics and price movements, providing insights into the broader economic and trade policies shaping agricultural trade between these two economic powerhouses.

Factors Influencing the Utilization of Health Services for JKN-KIS Participants (Study: Grilled Pematang III.B Health Center, Mesuji Makmur District, Ogan Komering Ilir Regency)

This study aims to determine whether there is an influence of the level of knowledge of JKN-KIS participants, the attitude of health workers and the accessibility of health facilities simultaneously on the utilization of health services for JKN-KIS participants at Community Health Centers and their networks.The method used in this study is a quantitative method. Minimum of 99 samples from JKN-KIS participants in the work area of ​​Pematang Panggang III.b Health Center, Mesuji Makmur District, Ogan Komering Ilir Regency. In this study, there were two hypothesis tests, namely the Partial Significance Test (T-Test) and the Simultaneous Test (F-Test). The data analysis technique used descriptive statistical analysis techniques; multiple linear regression analysis. The results showed that the level of knowledge of JKN-KIS participants did not have a significant effect on the utilization of health services at the Pematang Panggang III.b Health Center, Mesuji Makmur District and its network. The accessibility of health facilities had a positive and significant effect on the utilization of health services for JKN-KIS participants at the Pematang Panggang III.b Health Center, Mesuji Makmur District. The level of knowledge of JKN-KIS participants, the attitude of health workers and the accessibility of health facilities simultaneously had a positive and significant effect on the utilization of health services for JKN-KIS participants at the Pematang Panggang III.b Health Center, Mesuji Makmur District, Prosperous Mesuji.

Automated methodology for calculating key production indicators of oil field development for business planning model

Background: In order to secure the stable and efficient operations of oil-producing companies, it is essential to forecast the profitability and ROI of the enterprise, as well as to regulate development for the next 5 years. Accurate forecasts facilitate more informed planning and decision-making, directly influencing the economic sustainability and competitiveness of the enterprise. Aim: The purpose of this study is to develop an innovative approach to automate the methodology for calculating key development indicators in a business planning model. Materials and methods: The study utilizes methods for collecting and analyzing production and geological data, empirical forecasting models, and statistical analysis techniques to enchance the accuracy and reliability of forecasts. This approach employs modern algorithms and technologies to process large volumes of data, which allows for more accurate and reasonable forecasts of key production indicators of the field development. Results: This methodology can be applied to forecast a five-year business plan and evaluate its expected implementation. It is integrated into the ‘Production planning and monitoring’ of the ABAI information system, which allows direct data export from the database, automates the monthly monitoring of production indicators, and generate reports for further export. Conclusion: The proposed method for automated planning of key production indicators of the development enhances the accuracy and efficiency of forecasting, thereby improving the quality of planning and evaluating the implementation of the business plan. This contributes to more informed and strategically validated management of oil production processes. Automation of planning processes reduces the labor costs traditionally associated with manual analysis and calculations, freeing up resources for more strategic purposes. This enables rapid responses to changes in production conditions and prompt adjustments of plans. As a result, managers can allocate resources more efficiently, minimize risks, and increase the overall productivity of oil production operations.

Exploring the correlation between DNA methylation and biological age using an interpretable machine learning framework

DNA methylation plays a significant role in regulating transcription and exhibits a systematic change with age. These changes can be used to predict an individual’s age. First, to identify methylation sites associated with biological age; second, to construct a biological age prediction model and preliminarily explore the biological significance of methylation-associated genes using machine learning. A biological age prediction model was constructed using human methylation data through data preprocessing, feature selection procedures, statistical analysis, and machine learning techniques. Subsequently, 15 methylation data sets were subjected to in-depth analysis using SHAP, GO enrichment, and KEGG analysis. XGBoost, LightGBM, and CatBoost identified 15 groups of methylation sites associated with biological age. The cg23995914 locus was identified as the most significant contributor to predicting biological age by calculating SHAP values. Furthermore, GO enrichment and KEGG analyses were employed to initially explore the methylated loci’s biological significance.

Determination of Factors Affecting Farmers' Preference for Corn Seed by Conjoint Analysis

Modeling consumer preferences among multi-attribute alternatives has been one of the main activities in consumer research. Conjoint analysis is a multivariate statistical analysis technique that reveals the acceptance of criteria such as yield, price and adaptability that are important in consumers' usage habits and preferences. In this study, it was aimed to determine the degree of importance given by grain corn growers to seed traits in corn seed purchase. For this purpose, conjoint analysis was applied and preferences in seed purchase were determined according to the data obtained from the survey conducted among farmers. The survey questions used in the research were applied to 123 farmers. Kendall's Tau value, which is the representativeness of the model, was 0.935 (p

Automated Verification of Parametric Channel-Based Process Communication

A challenge of writing concurrent message passing programs is ensuring the absence of partial deadlocks, which can cause severe memory leaks in long running systems. Several static analysis techniques have been proposed for automatically detecting partial deadlocks in Go programs. For a large enterprise code base, we found these tools too imprecise to reason about process communication that is parametric, i.e., where the number of channel communication operations or the channel capacities are determined at runtime. We present a novel approach to automatically verify the absence of partial deadlocks in Go program fragments with such parametric process communication. The key idea is to translate Go fragments to a core language that is sufficiently expressive to represent real-world parametric communication patterns and can be encoded into Dafny programs annotated with postconditions enforcing partial deadlock freedom. In situations where a fragment is partial deadlock free only when the concurrency parameters satisfy certain conditions, a suitable precondition can often be inferred. Experimental results on a real-world code base containing 583 program fragments that are beyond the reach of existing techniques have shown that the approach can verify the absence of partial deadlocks in 145 cases. For an additional 228 cases, a nontrivial precondition is inferred that the surrounding code must satisfy to ensure partial deadlock freedom.

Optimized classification of diabetes using dynamic waterwheel plant optimization algorithm.

The classification of chronic diseases has been a prominent research focus in public health, extensively leveraging machine learning algorithms. One of these chronic diseases that has significant rates of occurrence all around the world is diabetes, which is a disease by itself. Many academics are working to construct robust machine-learning algorithms for accurate categorization, given the prevalence of this chronic disease. A revolutionary methodology that can accurately categorize diabetic disease is the focus of this study, which aims to provide new methods. The proposed technique in this work is based on developing a novel feature selection method, DWWPA, which stands for dynamic waterwheel plant algorithm. The DWWPA algorithm is utilized in the process of optimizing the K-nearest neighbors (KNN) model in order to improve the accuracy of its classification. In the feature selection process, a binary representation of this method is called binary DWWPA (bDWWPA). Several different machine learning models and optimization techniques are compared to the strategy that has been presented. When categorizing diabetes cases in the dataset, the findings demonstrate the superiority and success of the proposed method. Furthermore, several different statistical analysis techniques, such as Analyses of variance (ANOVA) and Wilcoxon signed-rank test, are carried out to investigate the statistical difference and importance of the suggested strategy in contrast to the other ways at the same level of competition. The conclusions of these tests were consistent with what was anticipated they would be. Based on the suggested feature selection and the optimization of the KNN model, the proposed method has an accuracy of 98.9% when taken as an entire. The suggested method was useful in accurately classifying diabetic disease, as evidenced by the fact that it achieved a higher level of accuracy than the contemporary approaches.

An Empirical Study on Indian Economy Over COVID-19 Pandemic

The infectious disease coronavirus disease 2019 (COVID-19) emerged as a human pathogen and contributed to a pandemic around the world. According to the reports released as of February 2021, around 103 million confirmed cases and 2,247,917 deaths are registered. The pandemic of COVID-19, or coronavirus, has exposed several global system vulnerabilities. Even after having all the abilities to handle hazards, the sudden outbreak of COVID-19 isolated every one of us from others. COVID-19 has caused the Indian economy to experience considerable disruption. The current pandemic of the coronavirus may lead to a permanent loss of 4 per cent of real Indian gross domestic product (GDP). Some key sectors such as tourism and aviation, telecommunication, automobiles and transport are negatively impacted. The other sectors such as e-learning, e-retail, logistics, pharmaceuticals and so on benefit from COVID-19. The present research is intended to study the positive and negative effects of COVID-19 in different industries, taking into account data of a secondary type. The survey also concludes with the initiatives, schemes and market effects for five important sectors using various relevant statistical instruments and techniques for analysis and conclusion.

The Influence of the Principal's Leadership Style on BOS Fund Fraud with Accountability as an Intervening

Therefore, this research aims to determine the influence of the Principal's leadership style on fraud in School Operational Costs (BOS) funds in Indonesia with accountability as an intervening variable. Methodology/approach – The research uses descriptive statistical analysis techniques and inferential statistical analysis to analyze sample data and the results are applied to the population, with the Structural Equation Model (SEM) with a Partial Least Square (PLS) approach based on the results of theoretical studies and hypotheses to be tested as well as this approach quantitative. Findings - The results obtained from this research are that there is a significant influence of the principal's leadership style on BOS fund fraud, there is a significant influence of the principal's leadership style on accountability, there is a significant influence of accountability on BOS fund fraud, and there is a significant influence of the principal's leadership style on fraud BOS funds with accountability as an intervening variable. So far previous research has been carried out to analyze the transparency and accountability of the use of BOS funds, but no one has specifically examined the influence of the school principal's leadership style on BOS fund fraud. Therefore, in this study, we want to examine the influence of the school principal's leadership style on BOS fund fraud by including accountability, which has been the subject of previous research, as an intervening variable.

DETERMINANTS OF ZAKAT PAYMENT: THE VIEWPOINT OF STAKEHOLDERS IN SEREMBAN

Islamic countries widely recognize zakat as a significant means to promote equity in sharing wealth among Muslims. Despite the obligation of Muslims to pay zakat, the total amount collected is considerably lower than the amount acquired through taxes. Thus, this study aims to identify the determinants influencing zakat payment among Muslims in Seremban, Negeri Sembilan. It will provide a better understanding of the factors that shape zakat payment behavior in this district. The data collection method involved a quantitative survey using structured questionnaires administered to a sample of 400 Muslim residents in Seremban. The study utilized a range of statistical analysis techniques, including correlation and regression to examine the relationships between the determinants and zakat payment practice. The study found that convenience is the most significant determinant motivating individuals to pay zakat through zakat agencies. This finding provides valuable insights for zakat agencies, policymakers, and religious authorities in Malaysia to develop targeted strategies to increase zakat compliance behavior among Muslims. These may include improving religious education programs, implementing transparent reporting mechanisms, developing tailored outreach programs, leveraging technology to simplify and streamline the zakat payment process, and collaborating with community leaders to reinforce positive social norms in paying zakat.