Statistical Analysis Techniques Research Articles

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.

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.

Unravelling the complexity of environmental exposures and health: A novel exposome-centered framework for occupational and environmental epidemiology

The exposome-centered approach marks a significant shift in occupational and environmental epidemiology, prioritizing a holistic evaluation of environmental exposures throughout an individual's life to elucidate their effects on health. Unlike traditional methods that single out environmental factors for their direct health impacts, this approach seeks an integrated analysis of all non-genetic influences, encompassing specific and general external environments and internal responses. While acknowledging environmental factors' pivotal role in chronic diseases and global mortality, existing methods struggle with precisely measuring and analyzing the broad spectrum of exposures and their combined effects. The exposome framework addresses these gaps through advanced omics technologies, refined exposure assessment models, and comprehensive data analytics. Yet, its application faces hurdles like the requirement for substantial infrastructure, cross-disciplinary training, and new statistical techniques for high-dimensional data analysis. Recent endeavors in applying the exposome concept show its promise in improving our understanding of environmental and occupational health, underlining its importance in advancing epidemiological research and public health. This abstract condenses the rationale, challenges, and ongoing efforts to incorporate the exposome into epidemiol-ogical studies, emphasizing its potential in enhancing the field.

DETERMINANTS OF THE DIMENSIONS OF FINANCING FOR INVESTMENT PROJECTS TO CREATE JOBS DURING THE PERIOD 2002-2013: A QUANTITATIVE STUDY OF THE NATIONAL AGENCY FOR INVESTMENT DEVELOPMENT IN ALGERIA

The study aims to fix the determinants of the financing dimensions of investment projects completed during the period 2002-2013. The purpose of undertaking these investment projects is to reduce unemployment and create new jobs. The study is purely quantitative after a complete database was obtained for each of the 48 states of Algeria, according to the administrative division in that period, from the National Agency for Investment Development (NAID) in Algeria. The determinants of the dimensions of financing for investment projects were obtained after we used the statistical technique of multiple linear regression analysis. From the perspective of the objective database, the methodology used to analyze the data, and complete transparency in dealing with the research process, we can confirm that the research results are honest and reliable, and the results can be generalized and a strategy for promoting investment in Algeria can be drawn up on a correct basis.

Static Program Analysis for IoT Risk Mitigation in Space-Air-Ground Integrated Networks

The space-air-ground integrated networks(SAGINs) are pivotal for modern communication and surveillance, with a growing number of connected devices. The proliferation of IoT devices within these networks introduces new risks due to potential erroneous synergistic interactions that could compromise system integrity and security. This paper addresses the challenges in coordination, synchronization, and security within SAGINs by introducing a novel static program analysis (SPA) technique using zero-knowledge (ZK) proofs. This approach ensures the detection of risky interactions without compromising sensitive source code, thus safeguarding intellectual property and privacy. The proposed method overcomes the incompatibility between SPA and ZK systems by developing an imperative programming language for SAGINs and a specialized abstract domain for interaction threats. The system translates network control algorithms into arithmetic circuits suitable for ZK analysis, maintaining high accuracy in detecting risks. Evaluations on real-world scenarios demonstrate the system's efficacy in identifying risky interactions with minimal computational overhead. This research presents the first ZK-based SPA scheme for SAGINs, enhancing security and confidentiality in network analysis while adhering to privacy regulations.

Preliminary study on the optical diagnosis of orbital rhabdomyosarcoma by Raman spectroscopy

To investigate the Raman spectral features of orbital rhabdomyosarcoma (ORMS) tissue and normal orbital tissue in vitro, and to explore the feasibility of Raman spectroscopy for the optical diagnosis of ORMS. 23 specimens of ORMS and 27 specimens of normal orbital tissue were obtained from resection surgery and measured in vitro using Raman spectroscopy coupled to a fiber optic probe. The important spectral differences between the tissue categories were exploited for tissue classification with the multivariate statistical techniques of principal component analysis (PCA) and linear discriminant analysis (LDA). Compared to normal tissue, the Raman peak intensities located at 1450 and 1655 cm−1 were significantly lower for ORMS (p < 0.05), while the peak intensities located at 721, 758, 1002, 1088, 1156, 1206, 1340, 1526 cm−1 were significantly higher (p < 0.05). Raman spectra differences between normal tissue and ORMS could be attributed to the changes in the relative amounts of biochemical components, such as nucleic acids, tryptophan, phenylalanine, carotenoid and lipids. The Raman spectroscopy technique together with PCA-LDA modeling provides a diagnostic accuracy of 90.0%, sensitivity of 91.3%, and specificity of 88.9% for ORMS identification. Significant differences in Raman peak intensities exist between normal orbital tissue and ORMS. This work demonstrated for the first time that the Raman spectroscopy associated with PCA-LDA diagnostic algorithms has promising potential for accurate, rapid and noninvasive optical diagnosis of ORMS at the molecular level.

Evaluating Customer Satisfaction Through Customer Value: The Role Of Food Quality, Service Quality, And Physical Environment

This study investigates the relationship between customer satisfaction and customer value, focusing on the significance of food quality, service, and physical environment in Mie Gacoan Indonesia Restaurant located in South Tangerang City. By employing a case study approach, data was collected from customers to assess their perceptions and satisfaction levels regarding the mentioned factors. Statistical analysis techniques were applied to analyze the data and determine the impact of food quality, service, and physical environment on customer satisfaction. The findings reveal important insights into the dynamics of customer satisfaction in the restaurant industry and emphasize the critical role of customer value in enhancing overall satisfaction. This study contributes to the existing literature by providing empirical evidence and practical implications for restaurant management aiming to improve customer satisfaction and loyalty.

MeMalDet: A memory analysis-based malware detection framework using deep autoencoders and stacked ensemble under temporal evaluations

Malware attacks continue to evolve, making detection challenging for traditional static and dynamic analysis techniques. On the other hand, memory analysis provides valuable behavioral insights, but prior research lacks temporal evaluations which are critical for robust detection of new malware variants over time. This paper presents MeMalDet, a novel memory analysis-based malware detection technique using deep autoencoders and stacked ensemble learning. We introduce an improved dataset with temporal attributes enabling more realistic evaluations of memory-based malware detection techniques under concept drift (temporal data split). MeMalDet extracts optimal features from memory dumps using deep autoencoders in an unsupervised manner, avoiding manual feature engineering. A stacked ensemble of supervised classifiers then performs highly accurate malware detection. Extensive experiments on our improved large-scale public dataset demonstrate MeMalDet’s ability to maintain high performance when detecting obfuscated malware under temporal splits. We achieve up to 98.82% accuracy and 98.72% F1-score in detecting previously unseen advanced obfuscated malware, significantly improving upon state-of-the-art memory analysis-based malware detection techniques. The improved dataset enables temporally robust evaluations, which is a novel contribution. MeMalDet combines the benefits of representation learning and supervised machine learning ensemble classification for effective malware detection over time using memory analysis. This research provides a new capability for identifying evasive modern malware and combating evolving real-world threats.

An Assessment of Waste Management in Slum Communities in Freetown. It’s Implications on the Health and Safety of Inhabitants

Purpose: The study objective is to assess the state of Solid Waste Management (SWM) in slum communities in Freetown, its factors and effects on the safety and wellbeing of slum dwellers together with the challenges faced by authorities in combatting inadequate SWM system in slum and to provide recommendations with regards to interventions to promote adequate waste management in slums.&#x0D; Materials and Methods: The study took place in three slum areas around Freetown in Sierra Leone. A total sample size of 400 respondents comprising of household heads were selected. A self-administered structured questionnaire was given to a total of 400 respondents of household heads above eighteen years old. Only 392 of the 400 respondents that were initially targeted for the study completed their questionnaires at the end of the survey. The data collected was presented in tables and statistical analytical techniques or methods such as percentages and graphs were used to analyse the data and to find out the factors, effects and challenges associated with inappropriate solid waste management services in slum areas in Freetown.&#x0D; Findings: According to the study, it was found that the major categories of waste generated in households were biodegradable and food (39%) while plastic remains (31%). Most households stored their wastes in polythene bags (59.1%) and sacks (20.2%) before disposal and 10.3% of the households did not have waste storage containers and kept their wastes outside the house in the open. Poor knowledge in waste segregation practices with very minimal recycling, reuse of wastes and a resultant poor waste management system were observed in slum communities. The study helps provide a much-needed data on solid waste management (SWM) in Freetown for authorities and other stakeholders as there are limited sources of data on waste management in Sierra Leone. It also provides further literature on waste management issues for further research, policy development by the authorities and recommendations for best practice by communities and stakeholders.&#x0D; Implications to Practice and Policy: It was recommended that waste management should be prioritized as a social service, with adequate budget lines. Engage several stakeholders in the management of waste to generate a sense of responsibility and interest from all stakeholders.

Development of Electronic Student Worksheets (E-LKPD) Pendap Bengkulu Based on Discovery Learning as A Learning Media for Additive Material

This research aims to develop the electronic student worksheets (E-LKPD) Pendap Bengkulu based on Discovery Learning as a Learning Media for Additive Material in Science lesson content for class VIII SMP. This study used the 4D development model (Define, Design, Develop, and Disseminate), which was limited to the development stage with limited trials conducted at SMPN 03 Bengkulu City, with a sample of 33 students from class VIII.1. To collect data, several questionnaires were distributed to media experts, material experts, participants, and students. Data analysis was carried out using descriptive statistical analysis techniques using percentage analysis. The results of the research showed that the feasibility test of E-LKPD involving 3 material experts, 2 media experts, and 2 participants resulted in an 92.3% percentage of validation test results, and it can be said that E-LKPD Pendap Bengkulu, which was developed as a learning medium for additive material for junior high school students, was very feasible to use. Analysis of student readability of E-LKPD Pendap Bengkulu as a learning medium for additive material for junior high school students resulted in a percentage of 89.8% readability response, indicating that E-LKPD is very well applied to junior high school students in grade VIII.

CHALLENGES IMPEDING THE ADOPTION OF E-PROCUREMENT BY PUBLIC SECTOR ORGANISATIONS (MINISTRIES, DEPARTMENTS AND AGENCIES) IN SIERRA LEONE – A SURVEY

The introduction and adoption of public procurement and e-procurement processes as recommended by donor organisations for governments of developing and/or under-developed countries has not achieved the intended objective of value for money. The human and material resources poured into building the capacities of countries like Sierra Leone could be considered as one of the steps of achieving the milestone i.e. value for money. The objective of this study is to examine the factors affecting e-procurement in organizations taking into consideration three factors including the value of e-procurement, e-procurement capability and e-procurement models. Data was collected through the use of a questionnaire targeted at procurement practitioners and Specialists and Heads of government ministries, departments and agencies using convenience sampling which is one of the methods of non-probability sampling. Descriptive statistical analysis techniques were employed for data analysis. Findings from the study revealed that a vast majority of the respondents believe that inadequate or limited capacity, credit facility or credibility and rigid laid-down rules of public procurement procedures and processes are the impediments responsible for the adoption and implementation of e-procurement in Sierra Leone. The paper recommends that the Government of Sierra Leone should put the necessary ICT infrastructure in place, review the current procurement act and adopt the implementation of e-procurement in order to achieve value for money.&lt;p&gt; &lt;/p&gt;&lt;p&gt;&lt;strong&gt; Article visualizations:&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;img src="/-counters-/soc/0787/a.php" alt="Hit counter" /&gt;&lt;/p&gt;

A color image encryption scheme based on 2D coupled chaotic system and diagonal scrambling algorithm

Abstract In this research, a novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices (FDCM) with discrete cosine transform (DCT) operation. After that, a 2D coupled chaotic system (2DCS) is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually, under the function of the Encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.

The virtual drum circle: polyrhythmic music interactions in mixed reality

Emerging technologies in the domain of mixed reality offer rich, new possibilities for the study and practice of joint music performance. Apart from the technological challenges, bringing music players together in mixed reality raises important questions on their performance and embodied coordination. In this study, we designed a mixed reality platform to assess a remote, bidirectional polyrhythmic interaction between two players, mediated in real time by their three-dimensional embodied avatars and a shared, virtual ‘drum circle’. We leveraged a multi-layered analysis framework to assess their performance quality, embodied coregulation and first-person interaction experience, using statistical techniques for timeseries analysis and mixed-effect regression and focusing on contrasts of visual coupling (not seeing / seeing as avatars / seeing as real) and auditory context (metronome / music). Results reveal that an auditory context with music improved the performance output as measured by a prediction error, increased movement energy and levels of experienced agency. Visual coupling impacted experiential qualities and induced prosocial effects with increased levels of partner realism resulting in increased levels of shared agency and self-other merging. Embodied coregulation between players was impacted by auditory context and visual coupling, suggesting prediction-based compensatory mechanisms to deal with the novelty, difficulty, and expressivity in the musical interaction. This study contributes to the understanding of music performance in mixed reality by using a methodological approach to demonstrate how coregulation between players is impacted by visual coupling and auditory context and provides a basis and future directions for further action-oriented research.

ANALYSIS OF FACTORS THAT INFLUENCE CONSUMER DECISIONS IN PURCHASING PRODUCTS ONLINE AT SHOPEE (CASE STUDY OF FEB STUDENTS OF BANDAR LAMPUNG UNIVERSITY)

The e-commerce industry is now growing rapidly in Indonesia. This is reinforced by the number of e-commerce that now dominates the Indonesian market. The large number of e-commerce in Indonesia means that consumer shopping habits have changed in this digital era and globalization. The purpose of this study is to determine the influence of cultural factors and social factors on consumer decisions in purchasing products online at Shopee. The research method used is quantitative research that uses quantitative and statistical data analysis techniques, with the main aim of testing hypotheses that have been determined by using multiple Linear Regression Test, Determination Coefficient Test, T Test, and F Test. The population and sample are students of the Faculty of Economics and Business, Bandar Lampung University, totaling 100 people. Based on the results of the study, it shows that cultural factors and social factors have a positive and significant influence on product purchase decisions online at Shopee. This shows that cultural factors and social factors simultaneously influence purchasing decisions.

Exploring the Effect of Parental Support and School Environment on Student Academic Achievement: A Survey Study

Student academic achievement is a critical issue for educators and policymakers worldwide. Parents and schools play important roles in promoting student academic achievement. This study aims to investigate the impact of parental support and school environment on student academic achievement. To do so, we employed a quantitative approach using a survey research design. Data related to parental support and school were obtained by distributing two survey questionnaires to a total of 167 elementary school students, purposively drawn from diverse groups of Catholic elementary schools in Merauke District, South Papua Province, Indonesia. Meanwhile, data related to academic achievement were obtained by getting students’ academic transcripts. Obtained data were subjected to an inferential statistics analysis technique using the software SPSS version 21. The results of data analysis showed that parental support and school environment have a direct impact on student academic achievement, both partially and simultaneously. These findings underscore the importance of parental support and the need to create a supportive and positive school environment to improve students’ academic achievement.