Statistical Analysis Techniques Research Articles

Method level static source code analysis on behavioral change impact analysis in software regression testing

Though a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons of software modifications. In assessing the impact of the changes made in software, static source code analysis plays a key role. However, static source code analysis can be a little complex depending on the reason for the expedition. Despite the work done so far, little focus has been made on the potential of changed methods analysis during static source code analysis in assessing the impact of the changes made in a software system. We propose and investigate a static source code analysis technique that would generate information on the modified methods in the source code. This study analyzes four aThough a myriad of changes take place in a software system during maintenance, behavioral changes carry the bulk of the reasons for software modifications. In assessing the impact of the changes made in the software, static source code analysis can be a little complex depending on the reason for the expedition. Despite the works done so far, little focus has been directed on the potential of changed methods during static source code analysis, in assessing the impact of the changes made in software. This study investigates a method-level static source code analysis technique that would generate information on the methods affected by changes made in the software. The work analyzed three Java projects. The results indicate an improvement in leveraging on the knowledge of edited methods in change impact assessment during regression testing. The approach enhances code review efforts in light of assessing operational behavior impacted by the changes made.Java projects and shows that an analysis of the changed methods reveals the level of regression testing that ought to be conducted for the changes made.

Judgment prediction from legal documents using Texas wolf optimization based deep BiLSTM model

The complicated nature of legal texts, a lack of labeled data, concerns about fairness, and difficulties with interpretation represent some of the challenges that judicial judgment prediction models encounter. The approach we propose seeks to conquer these challenges by using advanced techniques for deep learning, such as deep Bidirectional Long Short-Term Memory (BiLSTM) networks to recognize complex linguistic patterns and transfer learning to make more efficient use of data. Employing a deep BiLSTM classifier (TWO-BiLSTM) model based on Texas wolf optimization, the research aims to predict legal judgments. To prepare it for evaluation, it initially collects and preprocesses judicial data. Feature extraction involves statistical and Principal component Analysis (PCA) techniques to generate an extensive feature set. The model undergoes training utilizing these features in addition to preprocessed data. A hybrid Texas wolf optimization tactic, based on the optimization of gray wolves and Harris hawks, is employed to boost performance. The ability of the model to accurately and effectively predict legal judgment has been demonstrated by testing it on different sets of judicial data. The model achieved reasonably well in TP 90, having an accuracy of 97.00%. It also achieved exceedingly well in f-score, precision, and recall, having scores of 97.29, 97.10, and 97.19, correspondingly. The model’s effectiveness was further demonstrated in the k-fold 10 assessment, which exhibited 96.00% accuracy and robustness. In addition, using f-score, precision, and recall metrics of 96.25, 96.89, and 95.96, respectively, the model showed outstanding performance. These outstanding results demonstrate the model’s effectiveness and dependability for providing accurate predictions.

Spatiotemporal analysis of urban expansion in Srinagar city, Kashmir

Urbanization is a multifaceted process and a global phenomenon that encompasses various environmental and land cover changes. The expansion of Srinagar city, Kashmir, has changed its Land Use and Land Cover (LULC) due to an increase in population, horizontal expansion of the city, migration, and an increase in the built-up area. This has resulted in a change in Land Surface Temperature (LST). This study evaluates the dynamics of LULC in Srinagar city and its effect on LST using remote sensing, geographic information systems (GIS), statistical analysis techniques, and Landsat 4–5 TM and Landsat-8 OLI datasets. We analysed the LULC in Srinagar city from 2000 to 2020. The study calculated the Normalized Difference Vegetation Index (NDVI), LST, Fractional Vegetation Cover (FVC), and Emissivity from satellite remote sensing data of Srinagar city and analysed the relationship between them. The study shows that in terms of percentage change in LULC in Srinagar city from the year 2000 to 2020, built-up area showed the maximum change (75.58), followed by dense forest (− 62.74), vegetation (− 40.2), water bodies (− 25.41%), crop (23.16%), swamp (− 8.22), sparse forest (− 7.89), and open spaces (− 6.02). The findings of this study hold significance for researchers, urban planners, and policymakers to understand urbanization trends and their implications in the city.

Remote sensing-based estimation of precipitation data (2000-2015) in Ecuador's Loja province

The primary climatic parameter frequently scrutinized in water balance assessments for water utilization is precipitation. Given its considerable variability across locations and over time, it is imperative to rely on high-quality statistical information to facilitate accurate analyses. This study aims to refine the estimation of precipitation data by enhancing information obtained from freely accessible satellite sensors with data collected from established observation stations. Monthly precipitation data spanning from 2000 to 2015 were gathered from 24 stations. Three distinct methodologies were employed to adjust individual station data to address missing data. Consistency analysis and data refinement were conducted for stations requiring adjustments, utilizing graphical analysis and non-parametric statistical techniques. The satellite products under evaluation correspond to the IMERG v6 algorithm. Subsequently, statistical metrics were used to compare observed and estimated data. A correction coefficient was computed by aligning monthly means between observed and calculated data to mitigate random and systemic errors. The IMERG algorithm demonstrates proficiency in accounting for altitude and seasonal variations, with the adjustment significantly enhancing its performance under these conditions.

THE INFLUENCE OF PARENTING STYLES AND STUDY DISCIPLINE ON THE LEARNING OUTCOMES OF PANCASILA EDUCATION FOR FIFTH GRADE ELEMENTARY SCHOOL STUDENTS

This research aims to prove the influence of parenting on learning outcomes, study discipline on learning outcomes, and the influence of parenting styles and study discipline on learning outcomes. The research method used is a Survey. The population in this research are all fifth-grade students of Advent Tikala Manado Elementary School, totaling 42 students. The research data comes from primary data in the form 1of questionnaire entries and secondary data used is data on Pancasila education learning outcomes in the mid-semester exam. Data analysis techniques using descriptive statistical analysis techniques, data testing stages using normality tests, linearity tests, then multiple linear regression analysis tests, and hypothesis testing with partial tests or t-tests and simultaneous tests or F tests. The results showed (1) there is an influence of parenting styles on the learning outcomes of Pancasila Education for fifth grade elementary school students. (2) There is an influence of study discipline on the learning outcomes of Pancasila Education for fifth grade elementary school students. (3) there is an influence of parenting styles and study discipline on the learning outcomes of Pancasila Education for fifth grade students at Advent Tikala Manado Elementary School. Therefore, to improve learning outcomes, we must pay attention to and understand the factors of parenting styles and the application of student study discipline. KEYWORDS: Parenting Styles, Study Discipline, Learning Outcomes, Pancasila Education

Contribution of Attitude, Motivation and Learning Strategy to the Achievement of Grade II Junior High School in Districts Throughout Banten Province

This study aims to: 1) determine the contribution of attitude to student learning achievement, 2) determine the contribution of motivation to student learning achievement, 3) determine the contribution of learning strategies to student learning achievement, 4) determine the contribution of attitude, motivation and learning strategies to student learning achievement. The independent variables in this study are attitude, learning motivation, and learning strategies. While the dependent variable in this study is student learning achievement. The method used is quantitative correlation with inferential statistical analysis technique using product moment regression tests. Data collection techniques were obtained using questionnaires and document study methods. Data on attitude, learning motivation, and learning strategies were collected using a questionnaire. While data on students’ learning achievement was taken from the ledger of students’ average score when they went to grade II. The population of this study was all grade II students from three schools in Tangerang Regency, Serang Regency, and Pandeglang Regency, by taking a sample of 89 students from three schools using random sampling technique. Hypothesis testing in this study used simple regression analysis, multiple regression, and partial correlation. Based on the research that has been conducted, it can be concluded that: 1) there is a positive and significant relationship between learning attitudes towards learning achievement of grade II junior high school students in Banten Province with Fcount = 22.199 and a contribution of 20.3%. 2) there is a positive and significant relationship between learning motivation and learning achievement of grade II junior high school students in districts throughout Banten Province with Fcount = 12.619 and a contribution of 12.7%. 3) there is a positive and significant relationship between learning strategies and learning achievement of grade II junior high school students in districts of Banten Province with Fcount = 7.613 and a contribution of 8%. 4) there is a simultaneous positive and significant relationship between attitude, motivation and learning strategies on the learning achievement of grade II junior high school students in districts throughout Banten Province with Fcount = 8.445 and a contribution of 23%.

Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective

AI-powered chatbots hold great promise for enhancing learning experiences and outcomes in today's rapidly evolving education system. However, despite the increasing demand for such technologies, there remains a significant research gap regarding the factors influencing users' acceptance and adoption of AI-powered chatbots in educational contexts. This study aims to address this gap by investigating the factors that shape users' attitudes, intentions, and behaviors towards adopting ChatGPT for smart education systems. This research employed a quantitative research approach, data were collected from 458 of participants through a structured questionnaire designed to measure various constructs related to technology acceptance, including perceived ease of use, perceived usefulness, feedback quality, assessment quality, subject norms, attitude towards use, and behavioral intention to use ChatGPT. Structural model analysis (SEM) Statistical techniques were then utilized to examine the relationships between these constructs. The findings of the study revealed that Perceived ease of use and perceived usefulness emerged as significant predictors of users' attitudes towards ChatGPT for smart education. Additionally, feedback quality, assessment quality, and subject norms were found to positively influence users' behavioral intentions to use ChatGPT for smart educational purposes. Moreover, users' attitudes towards use and behavioral intentions were significantly proved for the actual adoption of ChatGPT. However, a few hypotheses, such as the relationship between trust in ChatGPT and perceived usefulness, were not supported by the data. This study contributes to the existing body information systems applications for the determining factor of technology acceptance in smart education context.

Synergizing Transfer Learning and Multi-Agent Systems for Thermal Parametrization in Induction Traction Motors

Maintaining optimal temperatures in the critical parts of an induction traction motor is crucial for railway propulsion systems. A reduced-order lumped-parameter thermal network (LPTN) model enables computably inexpensive, accurate temperature estimation; however, it requires empirically based parameter estimation exercises. The calibration process is typically performed in labs in a controlled experimental setting, which is associated with a lot of supervised human efforts. However, the exploration of machine learning (ML) techniques in varied domains has enabled the model parameterization in the drive system outside the laboratory settings. This paper presents an innovative use of a multi-agent reinforcement learning (MARL) approach for the parametrization of an LPTN model. First, a set of reinforcement learning agents are trained to estimate the optimized thermal parameters using the simulated data in several driving cycles (DCs). The selection of a reinforcement learning agent and the level of neurons in the RL model is made based on variability of the driving cycle data. Furthermore, transfer learning is performed on a new driving cycle data collected on the measurement setup. Statistical analysis and clustering techniques are proposed for the selection of an RL agent that has been pre-trained on the historical data. It is established that by synergizing within reinforcement learning techniques, it is possible to refine and adjust the RL learning models to effectively capture the complexities of thermal dynamics. The proposed MARL framework shows its capability to accurately reflect the motor’s thermal behavior under various driving conditions. The transfer learning usage in the proposed approach could yield significant improvement in the accuracy of temperature prediction in the new driving cycles data. This approach is proposed with the aim of developing more adaptive and efficient thermal management strategies for railway propulsion systems.

Navigating Challenges: How Divorce Affects the Quality of Children's Education

Divorce is a complex life event that often profoundly affects various aspects of family life, including children's education. This study aims to explore the impact of divorce on the quality of children's education, considering both academic and socio-emotional dimensions. A mixed-methods approach was employed, combining quantitative surveys and qualitative interviews. The participants consisted of parents and teachers from diverse socio-economic backgrounds, providing a comprehensive understanding of the phenomenon. Surveys were utilized to collect quantitative data on academic performance, attendance, and behavioral changes among children from divorced families. Additionally, in-depth interviews were conducted to gather qualitative insights into the emotional and social adjustments experienced by these children. Data were analyzed using statistical techniques for quantitative data and thematic analysis for qualitative data. The findings reveal multifaceted impacts of divorce on children's education, encompassing academic challenges, emotional distress, and changes in social dynamics. These results underscore the need for targeted interventions and support systems within educational settings to address the unique needs of children affected by divorce.

High-Dimensional Data Analysis Using Parameter Free Algorithm Data Point Positioning Analysis

Clustering is an effective statistical data analysis technique; it has several applications, including data mining, pattern recognition, image analysis, bioinformatics, and machine learning. Clustering helps to partition data into groups of objects with distinct characteristics. Most of the methods for clustering use manually selected parameters to find the clusters from the dataset. Consequently, it can be very challenging and time-consuming to extract the optimal parameters for clustering a dataset. Moreover, some clustering methods are inadequate for locating clusters in high-dimensional data. To address these concerns systematically, this paper introduces a novel selection-free clustering technique named data point positioning analysis (DPPA). The proposed method is straightforward since it calculates 1-NN and Max-NN by analyzing the data point placements without the requirement of an initial manual parameter assignment. This method is validated using two well-known publicly available datasets used in several clustering algorithms. To compare the performance of the proposed method, this study also investigated four popular clustering algorithms (DBSCAN, affinity propagation, Mean Shift, and K-means), where the proposed method provides higher performance in finding the cluster without using any manually selected parameters. The experimental finding demonstrated that the proposed DPPA algorithm is less time-consuming compared to the existing traditional methods and achieves higher performance without using any manually selected parameters.

Regional Differences and Spatial-Temporal Evolution Characteristics of Digital Economy Development in the Yangtze River Economic Belt

Digital economy has emerged as one of the primary driving forces for economic globalization. However, assessing digital economy development in a robust and scientific manner remains a great challenge. This paper proposes an evaluation system with measurement errors correction to accurately research the regional differences in and the spatial-temporal evolution characteristics of digital economy development in the Yangtze River Economic Belt (YEB), combining the entropy method, the Dagum–Gini coefficient, an σ convergence model and grey correlation analysis. The results present that the digital economy development index in the YEB rose from 2012 to 2021, with the greatest weight being social livelihood benefits. Meanwhile, there were noticeable regional differences in digital economy development in the YEB; in particular, the middle reaches showed obvious convergence. The grey correlation degree between the influence factors and the digital economy development ranged from 0.5286 to 0.9144, demonstrating a robust positive correlation. The theoretical framework of this paper integrates economic development models with advanced statistical analysis techniques, providing a robust analytical perspective for examining the complexities of digital economy evolution. The insights offer a blueprint for policymakers seeking to foster a robust and equitable digital economy, underscoring the potential of data-driven policy formulations in navigating the intricate landscape of economic globalization.

Motivation & work efficacy of MBA course in online teaching mode during and after pandemic

The Covid-19 pandemic has had a profound impact on various sectors worldwide, including education. This research investigates the effects of technology, communication, and administrative support on the motivation and work efficacy of business school teaching faculty engaged in online teaching during the pandemic. A structured questionnaire was distributed among 105 faculty members from business schools in Delhi, Haryana, and Uttar Pradesh. Statistical analysis techniques including KMO & Bartlett’s Test, Exploratory Factor Analysis (EFA), regression analysis, and correlation analysis were employed using IBM SPSS 26.0. The findings reveal that online platforms were highly effective for communication and teaching during the pandemic, contributing to increased motivation and work efficacy among teachers. This study provides valuable insights into the role of technology in crisis education management.

Overview on Spectral Analysis Techniques for Gamma Ray Spectrometry

Gamma-ray spectrometry (GRS) is an exploration technology that distinguishes itself from other non-contact sensing technologies because it provides information from 30 to 50 cm below the ground. This technology has evolved through three significant turning points in mapping output. The first turning point, in the 1960s-1970s, was the transition from U concentration maps to weathered zoning maps utilizing K or eTh. The second turning point, occurring from the 1980s to 1990s, was marked by the application of radionuclide mapping to assess radioactive contamination. A third turning point, in the early 2000s, was the development of soil maps for precision agriculture, supported by the free statistics software R. This paper reviews advances in gamma-ray spectrometry spectral analysis since 2000. Traditionally, the gamma-ray spectrum is depicted as a two-dimensional graph with energy on the horizontal axis and counts on the vertical axis. The NASVD and MNF methods, developed around 2000, necessitate a reevaluation of this concept. By conducting principal component analysis of the gamma-ray spectrum in hyperspace, these techniques have unveiled new spectra, such as ground and sky spectra, and have facilitated the removal of noise components from the gamma-ray spectrum. Naturally occurring gamma-ray spectra typically exhibit energies ranging from 0.04 to 3 MeV. Observations from fusion reactors measure energies up to 20 MeV for diagnostics of nuclear plasma. These spectra may yield valuable insights when applied to innovative statistical analysis techniques. A comprehensive spectral analysis method developed in the early 2000s has demonstrated the potential to extract a variety of information beyond window nuclides, previously unexplored. The regression coefficient plots from the PLSR regression model have revealed novel spectral images. This model is set to influence future research on GRS by expanding the number of objectives and covariates. The innovative calibration method for full-spectrum analysis, which assesses different concentration areas, has proven that calibration is achievable even in the absence of a calibration pad. It is expected to become a formidable approach for spectrum analysis in the upcoming period.

Exploring Digital Health Tool Adoption and the Impact of HR Analytics on Decision-making in Jalandhar’s Healthcare Organizations

This study assesses the virtual tool adoption panorama within healthcare organizations. The healthcare enterprise is undergoing a length of huge transformation, pushed by way of the ever-developing need for advanced performance, patient care, and value control. Digital fitness equipment, encompassing a wide variety of technologies from affected person portals and telemedicine platforms to wearable health trackers and digital fitness records (EHRs), are hastily reworking how healthcare is introduced. This study delves into now not most effective the extent of virtual tool adoption however additionally the influence of HR analytics on choice-making within these businesses. HR analytics, the software of statistics analysis techniques to human resource management practices, offers precious insights into personnel traits, recruitment strategies, and employee performance. By examining how healthcare organizations leverage HR statistics to shape their group of workers strategies, the have a look at explores the ability blended effect of those developments on decision-making inside the healthcare panorama. This research seeks to offer insights into the changing landscape of healthcare pushed via technology and its considerable impact on HR decision making. Understanding the elements that power virtual tool adoption within healthcare corporations is critical for optimizing using these technologies. This observe will explore the perceived benefits and demanding situations related to digital health equipment, in conjunction with the position of leadership and organizational tradition in facilitating a success implementation. Additionally, the studies will study how HR analytics are being utilized to tell staffing decisions, training programs, and overall performance control inside healthcare groups.

Αναπαραστάσεις εκπαιδευτικών για τη γλωσσική εκπαίδευση μαθητών/τριών με προσφυγικό υπόβαθρο

This research aims to contribute to the debate on the need for an intercultural-inclusive school by focusing on the perceptions of teachers working in public schools regarding the language education of children with refugee background. Data was collected using a questionnaire distributed online to 54 teachers who taught in classes with refugee students during the school year 2021-2022. Descriptive statistical analysis techniques and inferential statistical analysis techniques (x²) were chosen for data analysis. The findings summarize the teachers' positive attitude towards refugee students, while highlighting their perceptions about the obstacles and suggestions for more effective learning of Greek. Finally, an attempt is made to record the variations in the teachers' responses in relation to the variables such as service experience and educational level.

Code search engines for the next generation

Given the abundance of software in open source repositories, code search engines are increasingly turning to “big data” technologies such as natural language processing and machine learning, to deliver more useful search results. However, like the syntax-based approaches traditionally used to analyze and compare code in the first generation of code search engines, big data technologies are essentially static analysis processes. When dynamic properties of software, such as run-time behavior (i.e., semantics) and performance, are among the search criteria, the exclusive use of static algorithms has a significant negative impact on the precision and recall of the search results as well as other key usability factors such as ranking quality. Therefore, to address these weaknesses and provide a more reliable and usable service, the next generation of code search engines needs to complement static code analysis techniques with equally large-scale, dynamic analysis techniques based on its execution and observation. In this paper we describe a new software platform specifically developed to achieve this by simplifying and largely automating the dynamic analysis (i.e., observation) of code at a large scale. We show how this platform can combine dynamically observed properties of code modules with static properties to improve the quality and usability of code search results.

IMPACT OF MACROECONOMIC FACTORS ON THE INDIAN STOCK MARKET

The research paper investigates the impact of selected macroeconomic factors on the Indian stock market performance. Using a comprehensive dataset spanning key macroeconomic indicators and stock market performance metrics, the statistical analysis techniques to examine the relationship between macroeconomic variables and stock market movements is employed. The study focuses on understanding how changes in Foreign Portfolio Net Investments, Index of Industrial Production, Consumer Price Index and Exchange Rate can influence investor sentiment and trading behaviour in the Indian stock market. The findings of this research contribute to a deeper understanding of the dynamics between macroeconomic factors and stock market performance in India, providing valuable insights for investors, policymakers, and financial institutions. Keywords: Foreign Portfolio Net Investments (FPI), Index of Industrial Production (IIP), Consumer Price Index (CPI), Exchange Rate(ER), NIFTY, Indian Stock Market, Inflation.

Investigation on Interfacial Bond Strength Characteristics of Concrete Filled in Galvanized Steel Tubes Utilizing Statistical Analysis and Advanced Prediction Techniques

Investigation on Interfacial Bond Strength Characteristics of Concrete Filled in Galvanized Steel Tubes Utilizing Statistical Analysis and Advanced Prediction Techniques

Analysis and Zonation of Freeze-Thaw Action in the Chinese Plateau Region Considering Spatiotemporal Climate Characteristics

Concerns about the durability of transportation infrastructure due to freeze-thaw (F-T) cycles are particularly significant in the Chinese plateau region, where concrete aging and performance deterioration pose substantial challenges. The current national standards for the frost resistance design of concrete structures are based predominantly on the coldest monthly average temperature and do not adequately address the comprehensive effects of the spatiotemporal variance, amplitude, and frequency of F-T cycles. To address this issue, this study introduced a spatiotemporal distribution model to analyze the long-term impact of F-T action on concrete structures by employing statistical analysis and spatial interpolation techniques. Cluster analysis was applied to create a nationwide zonation of F-T action level from data on the freezing temperature, temperature difference, and the number of F-T cycles. Furthermore, this study explored the similarity between natural environmental conditions and laboratory-accelerated tests using hydraulic pressure and cumulative damage theories. A visualization platform that incorporates tools for meteorological data queries, environmental characteristic analyses, and F–T action similarity calculations was designed. This research lays theoretical groundwork and provides technical guidance for assessing service life and enhancing the quantitative durability design of concrete structures in the Chinese plateau region.

VAE-Based Interpretable Latent Variable Model for Process Monitoring.

Latent variable-based process monitoring (PM) models have been generously developed by shallow learning approaches, such as multivariate statistical analysis and kernel techniques. Owing to their explicit projection objectives, the extracted latent variables are usually meaningful and easily interpretable in mathematical terms. Recently, deep learning (DL) has been introduced to PM and has exhibited excellent performance because of its powerful presentation capability. However, its complex nonlinearity prevents it from being interpreted as human-friendly. It is a mystery how to design a proper network structure to achieve satisfactory PM performance for DL-based latent variable models (LVMs). In this article, a variational autoencoder-based interpretable LVM (VAE-ILVM) is developed for PM. Based on Taylor expansions, two propositions are proposed to guide the design of appropriate activation functions for VAE-ILVM, allowing nondisappearing fault impact terms contained in the generated monitoring metrics (MMs). During threshold learning, the sequence of counting that test statistics exceed the threshold is considered a martingale, a representative of weakly dependent stochastic processes. A de la Peña inequality is then adopted to learn a suitable threshold. Finally, two chemical examples verify the effectiveness of the proposed method. The use of de la Peña inequality significantly reduces the minimum required sample size for modeling.