Statistical Analysis Techniques Research Articles

Quantitative assessment of refrigerant impact on vehicle air-conditioning systems via Z-Freq 2D statistical analysis

In this study, the impact of refrigerant types on the performance and efficiency of vehicle air-conditioning (AC) systems was quantitatively assessed using a novel two-dimensional Z-Freq 2D statistical analysis method. Wireless vibration accelerometers, capturing both horizontal and vertical vibrations, were utilized to measure the dynamic response of the air-conditioning compressor. Before data collection, meticulous calibration of sensors was conducted to ensure the accuracy and reliability of measurements. The introduction of the Z-Freq 2D statistical analysis technique, developed specifically for this research, allowed for a comprehensive examination of the vibrational data, facilitating a deeper understanding of the effects of different refrigerants on AC system performance. To validate the effectiveness and reliability of the Z-Freq 2D analysis, machine learning techniques were employed. These techniques provided a robust framework for the analysis of the statistical data, with performance evaluation indicators demonstrating the efficacy of the newly developed method. The experimental setup was based on an actual vehicle air-conditioning test rig, designed to simulate real-world operating conditions accurately. The findings of this research offer significant insights into the selection of refrigerants for vehicle AC systems, highlighting the potential for enhanced system performance and efficiency through the application of advanced statistical analysis and machine learning validation techniques. This study not only contributes to the field of automotive thermal management but also paves the way for future research in the optimization of vehicle AC systems.

Early Detection of Hard-Coded Secrets in Software Development: A Multi-Method Approach Integrating Static Analysis, Entropy-Based Detection, and Machine Learning

The inadvertent inclusion of hard-coded secrets—such as API keys, passwords, and tokens—within source code poses significant security risks, potentially leading to unauthorized access and data breaches. Recent studies have highlighted the prevalence of this issue; for instance, GitGuardian's 2023 report revealed a 67% increase in detected hard-coded secrets compared to the previous year, with 10 million new secrets discovered in public GitHub commits in 2022. To address this escalating concern, our research proposes a comprehensive, multi-method approach that integrates static code analysis, entropy-based detection, and machine learning techniques to enhance the early identification of hard-coded secrets during the software development lifecycle. By combining these methodologies, the proposed approach improves detection accuracy, reduces false positives, and provides developers with actionable insights to mitigate security vulnerabilities proactively. This integrated strategy not only strengthens the security posture of software applications but also fosters a culture of secure coding practices among development teams. Keywords Shift-Left Security, Secrets Detection, Static Analysis, Entropy-Based Detection, Machine Learning, Hard-Coded Secrets, Secure Development Lifecycle

ANALYSIS AND COMPARISON OF MACHINE LEARNING METHODS FOR MALWARE DETECTION

Our study aims to analyze and evaluate modern machine learning methods for detecting malware, a critical challenge given the increasing complexity and volume of cyber threats. We explore various algorithms, including Support Vector Machines, Random Forest, Logistic Regression, Decision Trees, and K-Means Clustering, comparing their effectiveness in identifying and classifying malware. Our methodology combines static, dynamic, and memory-based analysis techniques, offering a comprehensive approach to understanding malware behavior. Key findings reveal that Decision Trees and Random Forests demonstrate impressive accuracy in both binary and multi-class classification tasks. We also highlight novel methods such as the Self-Organizing Incremental Neural Network (SOINN), which effectively handles evolving malware threats. The integration of static and dynamic analysis methods deepens insights into malware behavior. This research underscores the importance of advancing machine learning techniques to enhance cybersecurity measures against evolving global malware threats, offering valuable insights for future research directions.

Financial Innovation, Economic Growth and Sustainable Development in an Emerging Market: Nigeria

This paper presents recent studies on the effects of financial innovation on economic growth and sustainable development in Nigeria. The financial sectors worldwide are becoming increasingly important and competitive. Therefore, in order to compete successfully, it is necessary to possess flexibility and adaptability to change, as well as a willingness to embrace innovation. Financial markets are becoming more advanced and complex due to financial innovation, which helps direct capital towards productive sectors that are necessary for economic growth and development. The study employed time series data. Statistical data analysis techniques were employed to evaluate the secondary data on financial innovation. Research indicates that financial innovation has a significant impact on both economic growth and sustainable development in Nigeria. The study found that financial innovation variables have a positive effect. Therefore, financial institutions should prioritize easy and secure access to financial services when developing their financial innovation strategies. Regulatory bodies, on the other hand, should establish a regulatory environment that promotes innovative solutions capable of influencing economic growth and sustainable development.

ANALYSIS OF THE ISLAMIC BUSINESS ETHICS APPLICATION AND ITS URGENCY IN BUSINESS UNITS OF COMMUNITY ORGANIZATION

This research aims to analyze the application of Islamic business ethics in community organization business units and their urgency for the continuity of these business units, based on the perceptions of the actors and consumers, by taking case studies of business units within the DPP Wahdah Islamiyah in Makassar City. This research is field research using descriptive statistical analysis techniques. The research results show that Islamic business ethics, in the form of monotheism, justice, trust, and benevolence, have generally been applied to business units within the Wahdah Islamiyah DPP in Makassar City. Apart from that, the Islamic business ethics that have been implemented have great urgency in the management and service of business units, including as a factor that can bring blessings, create a work environment that is fair, inclusive, dignified, and in accordance with Islamic law, increase customer trust, and other parties so as to provide benefits to the company, improve, and provide excellent service to all parties because these ethics can support business management in accordance with company service guidelines, SOPs, and agreed rules. Keywords: Ethics, Business, Islam, Wahdah Islamiyah

Forest and Land Fire Management Strategy in South Sumatra Province through the Penta Helix Model

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 quantitative. A 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; and 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.

Design Expert-Supported Method Development and Validation of Cilostazol in Pharmaceutical Formulation Using High-Performance Liquid Chromatography

Background: Developing a simple HPLC method requires an expansive array of literary evidence and experimental routines to perceive the nature of a drug and eventually determine the specific mobile phase and column to be used for attaining better results. Objective: The study aimed to develop and optimize a new, simplified, robust, and sensitive method for the determination of cilostazol in tablets by high-performance liquid chromatography using a Box Behnken design. Methods: The chromatographic separation was carried out on an ODS C18 (4.6 X 250mm and 5μm) column with acetonitrile and methanol (25:75 % v/v) at an effluent flow rate of 1 mL/min and detected at 257 nm. Result: The method was found to be linear in the concentration range of 10-50 μg/mL, and the correlation coefficient was found to be 0.988, and the recovery of cilostazol was 98.16%. The optimized method validated as per ICH Q2A guidelines was found to be accurate, precise, robust, and stable. Conclusion: This research thus throws light on the implementation of statistical multivariate analysis techniques used for drug analysis.

Mining Fine-Grained Code Change Patterns Using Multiple Feature Analysis

Maintaining high code quality is a crucial concern in software development. Existing studies demonstrated that developers frequently face recurrent bugs and adopt similar fix measures, known as code change patterns. As an essential static analysis technique, code pattern mining supports various tasks, including code refactoring, automated program repair, and defect prediction, thus significantly improving software development processes. A prevalent approach to identifying code patterns involves translating code changes to edit actions into a Bag-of-Words (BoW) model. However, when applied to open-source projects, this method exhibits several limitations. For instance, it overlooks function call information and disregards feature word order. This study introduces MIFA, a novel technique for mining code change patterns using multiple feature analysis. MIFA extends existing BoW methods by incorporating analysis of function calls and overall changes in the Abstract Syntax Tree (AST) structure. We selected 20 popular Python projects and evaluated MIFA in both intra-project and cross-project scenarios. The experimental results indicate that: (1) MIFA achieved higher silhouette coefficients and F1 scores compared to other state-of-the-art methods, demonstrating a superior accuracy; (2) MIFA can assist developers in detecting unique change patterns more earlier, with an efficiency improvement of over 40% compared to random sampling. Additionally, we discussed critical parameters for measuring the similarity of code changes, guiding users to apply our method effectively.

Technological Factor and Technological Adoption in Agricultural Enterprises of the San Quintín Valley: Validity And Reliability of the Instrument

Objective: to present the process of validation and reliability of the design of a measurement instrument to measure the variables technological factor and technological adoption in agricultural enterprises. Theoretical framework: The research addresses the technological factor that emerges from the Technology-Organization-Environment (TOE) model as a determinant of technological adoption, developing the concepts and focusing on their behavior in agricultural companies. Method: The methodology used is based on a non-experimental cross-sectional study with an explanatory correlational scope, applying statistical analysis techniques to assess the validity and reliability of the instrument. Results: The values of KMO = 0.853 and Cronbach's Alpha = 0.926 allow us to infer that the instrument has an adequate validity and reliability to measure the constructs. Discussion and conclusions: Based on the results obtained, it was concluded that the instrument is suitable to measure the influence of the technological factor on the adoption of technology in the case of agricultural companies in the San Quentin Valley. Implications of research: The research contributes to the understanding of the influence of the technological factor in the technological adoption in agricultural companies, allowing to formulate strategies for their management and with it to increase in this type of companies the level of systematization according to the current digital economy. Originality/value: The research provides a measurement tool that allows data to be collected to understand the context of agricultural companies regarding technological adoption.

An assessment of the impacts of community development initiatives in Awgbu Town in Anambra State, Nigeria

This research evaluates how the Awgbu community has contributed to the general development of Anambra State, how it has benefited from state government efforts, and how it has responded to demands and initiatives from the government between 2015 and 2023. Community Development (CD), Social Capital, and the Diffusion of Innovations theories are the underpinning theoretical framework of analysis used for the study. The research design used in this paper was a descriptive survey, with data collected from primary and secondary sources. Since the study was intended for the entire population of Awgbu town, Slovin’s sample determination formula and the purposive random sampling technique were employed. Specifically, a sample size of 400 respondents was targeted, and 286 returned questionnaires were used for data analysis. The gathered data were displayed on the Logical Data Framework (LDF) and subjected to a quantitative statistical data analysis technique that involved weighted mean and frequency tables. The results show that the Awgbu community has significantly influenced Anambra State’s growth. Through its participation in the Choose Your Project Initiative (CYPI), the community has actively reacted to requests from the government. Nonetheless, the research discovers that, among other things, inadequate money, oversight, and leadership crises impede the community’s advancement.

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.

Breaking the digitalization barrier for SMEs: a fuzzy logic approach to overcoming challenges in business transformation

The purpose of this study was to investigate the barriers and drivers of digitalization in small- and medium-sized enterprises (SMEs). Specifically, the study aimed to identify the key factors influencing the adoption and implementation of digital technologies in SMEs, understand the challenges faced by these organizations in their digital transformation journey. In this study, a fuzzy logic methodology was employed to assess the digitalization process of 4531 SMEs. Additionally, an analytical hierarchy process (AHP) was applied to determine the weights of the dimensions in the fuzzy analysis. This allowed for a more accurate and precise assessment of the digitalization levels in SMEs. Furthermore, statistical analysis techniques were utilized to analyze the data and draw meaningful conclusions. The study revealed that insufficient financial resources, high investment costs, resistance from workers, a shortage of well-qualified staff, lack of knowledge about technology providers, high IT security requirements, the absence of a digital transformation-oriented business culture, and the application of fuzzy logic as a methodology were the key findings in the digitalization process of SMEs. These findings highlight the complex challenges faced by SMEs and emphasize the need to address these barriers to facilitate successful digital transformation.

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.

Optimizing Hotel CRM System Decision Support with Big Data Analysis

This study delves into the application of big data analysis in hotel customer relationship management (CRM) systems for decision support, examining its impact on enhancing the speed and quality of hotel business decision-making. By integrating data mining, statistical analysis, and advanced analytical techniques, hotels can more accurately predict market demand, optimize customer service, and improve customer satisfaction and loyalty. The research findings indicate that the integration of big data analysis significantly strengthens hotel decision support systems, providing a competitive edge for the hotel industry. The paper also discusses challenges in the implementation process and proposes system optimization strategies based on feedback to ensure hotels can continuously adapt to market changes and technological innovations.

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.