Data Analysis Process Research Articles

Barriers to strategic direction in community-based social organizations

The objective of this study was to analyze possibilities for strategic direction according to the ways in which management processes are arranged in community social organizations which are anchored in local territories. The qualitative processes of data collection and analysis were developed with a phenomenological design through in-depth interviews, considering that reality is understood from the interactions and meanings that social leaders give to the management of community organizations. The findings were constructed inductively from the texts of the interviews to configure three categories: a) The administrative processes of community social organizations; b) Identity with the corporate platform of these organizations and c) Internal and external analysis of their dynamics. It was found that, although these organizations formally have a legal constitution that defines their purpose and government and documentation is available on administrative processes such as planning, their management practices are empirical and based on traditional experience. It was found that deficiencies in the commitment to the corporate platform, situational analysis and business conception are factors that become barriers for social managers to advance with strategic direction, seeking resources and increasing the scope of programs and projects.

A valid and reliable self-peace scale with indicators of love, care and fearlessness for adolescent drug users

Self-peace is a crucial component for individuals/teenagers/students to live happily and recover from drug use. However, self-peace isn't wholly owned by individuals affected by drug use or recovering from drugs. Peace counsellors measure self-peace as a form of the addiction counsellor's role in the growth of self-peace due to drug use. This research aims to develop a valid and reliable self-peace scale. The research method uses a sequential exploratory mixed method from the qualitative data analysis process supported by quantitative data. The research targets were three expert validators and N=151 subjects among students/individuals/adolescents. Qualitative descriptive analysis compiles grids and statement items and provides expert input in developing the self-peace scale. Quantitative data analysis from the expert assessment with Aikan-V and instrument testing with confirmatory factor analysis will be used to determine the validity and reliability of the self-peace scale. The results of the data analysis compiled a blueprint of the peace scale (sub-aspects of caring, courage and love) and 12 statement items that were included in the very feasible category (Aikan-V ≥0.888). Validity of statement items with 12 valid statement items with factor loading ≥0.5, KMO=0.793 ≥0.50, reliability score 0.842 (high criteria). School counsellors can use student problem assessments to analyze student problems and needs. Counsellors, psychologists and researchers can use the self-peace scale to determine the development of an individual's self-peace to recover from drug use with four answer choices. The analysis results can be applied to further intervention in peace counselling. Keywords: self-peace, scale, validity, reliability

Machine Learning for Single-Molecule Localization Microscopy: From Data Analysis to Quantification.

Single-molecule localization microscopy (SMLM) is a versatile tool for realizing nanoscale imaging with visible light and providing unprecedented opportunities to observe bioprocesses. The integration of machine learning with SMLM enhances data analysis by improving efficiency and accuracy. This tutorial aims to provide a comprehensive overview of the data analysis process and theoretical aspects of SMLM, while also highlighting the typical applications of machine learning in this field. By leveraging advanced analytical techniques, SMLM is becoming a powerful quantitative analysis tool for biological research.

Exploration of Politeness Strategies Between Main Characters in the Green Book Movie

This study aims to analyze Brown and Levinson's language politeness strategies in the conversation between Dr. Shirley and Tony Lip in the film Green Book. The approach used in this research is descriptive qualitative, using document analysis as the method of analysis. The data analysis process was conducted through four stages: data collection, data reduction, data presentation, and conclusion drawing. The results of the data analysis revealed some significant findings. First, Dr. Shirley tends to use politeness strategies more frequently than Tony Lip, comparing 43 uses by Dr. Shirley and 25 by Tony Lip. Second, the positive face politeness strategy is the dominant strategy both main characters use. Third, several factors influence politeness strategies, including power, social distance, personality differences, and social culture.

성인여성의 학습공간 경험에 관한 질적 사례연구

Objectives This study attempts to understand the experiences of participants in learning spaces based on Lefebvre's perspective, which claims that space is produced, focusing on the problem of autonomous resistance against the homogenization of social space. The research questions are: Firstly, what is the meaning of the learning spaces experienced by the participants? Secondly, what social significance and impact do the participants' experiences in learning spaces have? Thirdly, what are the lifelong educational implications of participants' experiences in learning spaces? Methods The study participants consisted of three individuals living in the same area as the researcher and three participants from programs at the institution where the researcher works. The study was conducted from October 2022 to August 2023. To prevent any bias that might arise from the researcher's prior understanding of the participants, the researcher sought the review of a doctoral expert in lifelong education and a field specialist during the data analysis and interpretation process. Results The research findings are as follows: Firstly, participants were able to experience the joy of learning by having learning opportunities they had postponed in the learning space. Secondly, through their participation in the learning space, participants encouraged learning among local residents, contributing to the roots of social change. Thirdly, the learning space provided vitality through interaction with other participants and offered the potential for lifelong learning through continuous experiences. Conclusions It was revealed that the learning space created through the expansion of lifelong learning was reborn as another learning space through interaction with other people. Through this, learning takes place not only in specific spaces but also in everyday life, and learning spaces have the potential to have a positive impact on individuals and the community.

IN-SERVICE TRAINING PRACTICES AND TRAINERS€™ PERFORMANCE IN TECHNICAL AND VOCATIONAL EDUCATION AND TRAINING SCHOOLS IN RWANDA, A CASE OF KICUKIRO DISTRICT RWANDA

The primary aim of this study was to investigate the effect of in-service training practices on the performance of trainers in Technical and Vocational Education and Training (TVET) schools in Kicukiro District, Rwanda.The participants included personnel from the Rwanda Technical and Vocational Training Institute (RTTI), trainers, and school managers, totalling 398 respondents. This number comprises 12 school managers, 26 RTTI staff members, and 360 trainers from TVET schools in the Kicukiro district.The data analysis process utilized SPSS 22.0 software. The results indicated a strong relationship between in-service training practices and trainers performance in TVET schools in Kicukiro, Rwanda. This relationship was supported by a Pearson correlation coefficient (r) of 0.958, with a statistically significant p-value of 0.000 for a two-tailed test. This suggests that there was a statistically significant relationship between in-service training practices and trainers Performance in TVET Schools in Kicukiro District, Rwanda and confirmed by the R Square=.918which means that in-services trainings are highly appreciated to enhance trainers performance in TVET Schools in Rwanda. The study reveals the positive impact of industry partnerships on trainers performance. Industrial exposure allows trainers to gain firsthand experience of current industry practices and technological innovations. This study recommended that the government should allocate more financial resources to support in-service training programs in TVET schools.

Web-based Educational Game Application to Improve the Ability to Identifying the Main Idea in Indonesian Language Learning

The limited provision of innovative, interesting, and technology-based learning media can affect the running of teaching and learning activities in schools to be less than optimal, especially in Indonesian subjects. The purpose of this research is to create a web-based educational game media application to improve the ability to identify the main idea in Indonesian language subjects on learning entrepreneurship material for grade V elementary school students. This study uses a type of Research and Development research with the ADDIE research and development model. Techniques applied in data collection are test techniques through pretests and posttests, as well as non-test techniques through observation activities, interviews, data in the form of documents, and questionnaires or questionnaires. The questionnaire instrument in data collection is in the form of a rating scale that has passed testing from media experts, material experts, practitioners, and students. The sample for this research was 24 fifth-grade elementary school students. Quantitative descriptive analysis techniques and qualitative descriptive analysis are the two techniques used in this study. The data analysis process in this study uses the SPSS 29 application which includes several test processes including the normality test, homogeneity test, t-test, and N-Gain test. The results of the study obtained that the web-based educational game learning media developed is very feasible and effective to be applied to the Indonesian language learning process to improve the understanding and learning outcomes of fifth-grade elementary school.

Leveraging LangChain agents to automate data analysis for SaaS.

This paper investigates the implementation of LangChain, a language model-powered framework, in automating data analysis within the SaaS sector.The approach included setting up LangChain agents for exploratory, univariate, and bivariate analyses, as well as hypothesis testing, transforming extensive data into human language text answers. Experiments confirmed the effectiveness of the proposed method using GPT-3.5 LLM agents, tested on the Amazon AWS SaaS Sales Dataset. Identified deficiencies need to be addressed for complex queries and comprehensive reports. Future research prospects include improving the method for complex queries, providing more detailed information about companies and business models, creating report templates, and training the model to solve complex questions. To automate data analysis, the method of using LangChain agents was proposed. A software implementation was developed, and data analysis indicators were studied using SaaS sales data as a case study. The study demonstrated LangChain agents’ capability to automate data analysis processes in the SaaS industry. Future research will aim to expand its application across more complex data, larger number of data questions, and pre-trained LLMs

Peran Guru Mata Pelajaran Pendidikan Agama Islam dalam Pembelajaram Tata Cara Wudhu pada Siswa Sekolah Khusus Tunarungu Don Bosco Wonosobo

This thesis aims to: 1) Find out the concept of Islamic religious education teachers in learning ablution procedures for students at the special school for the deaf, Don Bosco Wonosobo, 2) Find out what the role of Islamic religious education teachers is in learning ablution procedures for students at the special school for the deaf, Don Bosco Wonosobo. , 3) knowing how to carry out learning ablution procedures for students at the Don Bosco Wonosobo special school for the deaf.This thesis uses a qualitative research approach where the type of research is field research presented with descriptive data. The subjects in this research were Islamic religious education teachers at the Don Boso special school, the principal of the Don Bosco special school and also students at the Don Bosco special school. Technique data collection uses observation methods, semi-structured interviews and also documentation. Data sources were obtained from field research and also qualitative research. The analysis technique used is a data analysis technique, namely the analysis of data that has been collected by researchers through methodological tools. Where data is obtained using a data analysis process in the form of data reduction, data presentation and also conclusions or verification. To test the validity of the data, extended observations, persistence of observations, and triangulation were carried out. The research results show that the concept of Islamic religious education teachers at the Don Bosco special school regarding learning ablution procedures is to emphasize the repetition method where students with hearing impairments have quite short memories which requires teachers to take steps by having students perform ablution practices one by one. The role of the Islamic religious education teacher at the Don Bosco special school is the main factor in students' success in carrying out daily worship, for example introducing ablution material from the pre-primary class. Then, with the implementation of learning about ablution procedures carried out by Don Bosco special school students using the repetition method as well as practice carried out in each learning activity with the aim of increasing students' memory regarding the steps for ablution and also the number of washings in each movement. By practicing ablution one by one, the teacher can find out where each student's mistakes are.

AI Revolution in Exit Polls: Enhancing Accuracy, Efficiency, and Inclusivity

Exit polls have long been integral to understanding electoral dynamics, offering insights into voter behavior and preferences. Traditional exit poll methodologies, involving manual data collection and in-person interviews, face significant challenges in accuracy, sample size, and inclusivity. This paper explores the transformative impact of Artificial Intelligence (AI) on the exit polling process. By leveraging neural networks for enhanced sampling, employing automated Voice-AI for data collection, and utilizing Natural Language Processing (NLP) for real-time data analysis, AI significantly improves the efficiency, accuracy, and inclusivity of exit polls. The paper discusses the technical aspects of implementing AI-driven exit polls, including the architecture of neural networks, real-time data processing systems, and machine learning models for prediction. Additionally, it addresses the advantages of AI, such as cost-effectiveness and reduced human biases, while acknowledging challenges like algorithmic bias, privacy concerns, and the complexities of accurately interpreting voter behavior. As AI continues to evolve, it promises to further enhance the reliability and comprehensiveness of exit polls, providing valuable tools for understanding public opinion in democratic processes

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

AbstractHydrogel flexible sensors are widely used in wearable devices, health care, intelligent robots and other fields due to their excellent flexibility, biocompatibility and high sensitivity. With the development of single sensor to multi‐channel and multi‐mode sensor network, the sensor data also presents the characteristics of multi‐dimension, complex and massive. Traditional data analysis methods can no longer meet the data analysis requirements of hydrogel flexible sensor networks. The introduction of machine learning (ML) technology optimizes the process of data analysis. With the continuous development of multi‐layer neural network technology and the improvement of computer performance, deep learning (DL) algorithm is increasingly used to achieve higher efficiency and accuracy, provides a powerful tool for data analysis of hydrogel flexible sensor, and accelerates the intelligent process of hydrogel flexible sensor equipment. This paper introduces the classification of hydrogel flexible sensors and the working mechanism and common algorithms of ML, and summarizes the application of ML technology to assist hydrogel flexible sensors in data analysis in the fields of health care and information recognition. This review will provide inspiration and reference for integrating ML technology into the field of hydrogel flexible sensors.

METHOD FOR CREATING SVM CLASSIFIER FOR DATA ANALYSIS ON FPGA

The paper explores the use of SVM classifier method for data analysis on FPGA, which, despite its effectiveness, may face challenges related to limited resources and data processing speed. In this context, there is a need to develop new methods for integrating SVM classifiers with high-performance computing hardware. The increasing demand for speed and energy efficiency requires new approaches to implementing machine learning methods. One of the key tools for data classification and analysis is the Support Vector Machine (SVM), widely used in business, science, medicine, and many other fields. Developing an efficient and optimized method for creating SVM classifiers for FPGA requires further research and development, as existing methods may be suboptimal in terms of speed and FPGA resource utilization. The article provides an overview of known hardware solutions to this problem, proposed in the current scientific literature. Additionally, the effectiveness of combining hardware and software components to achieve significant acceleration of the data analysis process is discussed. The article emphasizes the need for further research and improvement to fully realize the transformative potential of machine learning classification methods.

Research on the influencing factors of housing rental prices-take Shanghai housing rents as an example

In todays era, renting offers lower costs and greater flexibility compared to buying, making it a preferred choice for many to meet their accommodation needs. Therefore, identifying factors that affect rental prices is of significant importance both for individuals and governments. Through the establishment of multiple linear regression and stepwise regression models, this paper analyzed 400 samples selected from a dataset of 20,000 rental listings in Shanghai, revealing the relationships between rental prices and key factors in the city. During the process of model establishment and data analysis, variables that significantly impact rent were identified through significance testing. The model indicated a strong relationship between the size of the property and its renovation status with rental prices, while other variables such as proximity to subway stations and the floor level of the property had a weaker impact on rent. These findings can assist individuals in making informed decisions when choosing rental properties. Overall, this research contributes valuable insights into the factors influencing rental prices in Shanghai, enabling better decision-making for both renters and policymakers.

Re-thinking of arsiparis as records and archives professional in Indonesia

Background of the study: To be able to manage archival documents according to applicable standards, organizations need professional and competent arsiparis. Professionalism and competence are represented in the qualification and requirements in the recruitment process. Purpose: This paper aims to present the results of a mapping study of the qualification and special requirement for arsiparis, also analyzes the implications of these aspects for the reinforcement of arsiparis profiles Method: this paper uses an inductive qualitative approach, with the data analysis process by content analysis Findings: Formal educational qualification are still dominant in archival science major, although many opportunities are also for individuals with non-archival science backgrounds. Job experience in archives and records management is also recommended to acquire by individual who wants to be arsiparis. However, these have implications for the arsiparis’ profile which tends to be interpreted that archives and records management can be done by anyone. Conclusion: The authors give three recommends. First, confirming educational qualification limits; second, limiting the scope of arsiparis taskforce, and strengthening the independence of professional associations.

Exploration of family caregivers’ experiences on coping in dementia care in Ghana: a phenomenological study

BackgroundDementia is an important public health and geriatric concern for sub-Saharan African countries, including Ghana. Evidence shows that persons living with dementia are often supported and cared for by family caregivers in the community. In the execution of these services to the persons living with dementia, family caregivers are overwhelmed and experience heightened stress that results in serious repercussions. Therefore, the aim of this study was to explore family caregivers’ experiences on coping in dementia care in Ghana.MethodsAdopting a descriptive phenomenological design, individual face-to-face interviews were conducted among thirty unpaid family caregivers of persons living with dementia in Ghana. Semi-structured interview guides were used. The data analysis process followed Clarke and Braun’s framework analysis.ResultsSix themes were generated from the textual data. These themes were captioned as: (1) empathy and perspective-taking; (2) family support and cohesion; (3) coaxing and pampering of persons living with dementia; (4) humour and positive communication; (5) spiritual support; and (6) ethical/moral consideration in dementia caregiving.ConclusionWe conclude that unpaid family caregivers of persons living with dementia in Ghana adopt varied strategies to cope with the strains of caregiving. Healthcare facilities that provide services to persons living with dementia could incorporate caregiver preparatory training or education for family caregivers. This training should focus on briefing family caregivers about the potential strains that they are likely to encounter. Also, the training could focus on equipping family caregivers with the knowledge and skills to effectively communicate and care for the persons living with dementia using person-centered approaches. Key stakeholders such as the Ghana Health Service and Alzheimer’s Ghana must raise awareness about the dangers of caregivers’ violation of the autonomy and freedom of persons living with dementia as they navigate through the challenges of caregiving. Lastly, faith-based institutions need to be considered as key stakeholders in dementia interventions since they could play a critical role.

A GA based SVM-Bayesian technique and its application in process monitoring for mixed distributed data

Traditional data-driven fault diagnosis methods, such as principal component analysis (PCA) and independent component analysis (ICA), have significantly improved condition monitoring and fault diagnosis of industrial process by analyzing process data. However, their accuracy is limited due to the assumption that the data follows Gaussian or non-Gaussian distribution, respectively. In reality, process data is often a mixture of both Gaussian and non-Gaussian distributions, leading to reduced accuracy of the training model and fault diagnosis results. To address this issue, a genetic algorithm-based SVM-Bayesian (GSB) scheme is proposed in this paper for feature combination and model integration. The scheme first employs ICA and PCA to extract the independent/principal components of the original data, and then constructs the corresponding fault features for SVM training to obtain basic models. The extraction of fault features are optimized using genetic algorithms based on defined accuracy and diversity functions. During online monitoring, the final diagnosis result is obtained by combining the results of the basic models through Bayesian inference. The effectiveness of the proposed scheme is evaluated using a numerical example and a case study of the Tennessee Eastman process. The results show that the accuracy of fault classification is improved by about 5% compared with traditional methods.

Structural violence and necropolitics among Indigenous Peoples living with HIV who use substances in the Prairie provinces during COVID-19

BackgroundWithin Manitoba and Saskatchewan, pre-existing health inequities amongst Indigenous groups were intensified during the COVID-19 pandemic. Service disruptions in the health and social service sector—combined with the effects of intersectional stigma—disproportionately impacted Indigenous peoples living with HIV (IPLH). IPLH experience structural violence and necropolitical exclusion through systemic forms of stigma situated within Canada's expansive colonial history. Utilizing the theoretical foundations of structural violence and necropolitics, this qualitative study examines how the COVID-19 pandemic amplified preceding states of inequity for IPLH. MethodsSemi-structured interviews were conducted with 60 participants. The sample comprised of those with lived experience (n = 45) as well as those who provided services for IPLH (n = 15). Indigenous Storywork guided the data collection and analysis process. Topics explored within each interview included access to health and social services, harm reduction, substance use, and experiences in providing services during COVID-19 pandemic. Thematic analysis was used to identify common themes throughout each story. ResultsOur results indicate that the COVID-19 pandemic exposed and amplified pre-existing forms of structural violence and necropolitical logics for IPLH within Manitoba and Saskatchewan. Specifically, we describe how structural violence and necropolitics are manifested via three main avenues— (i) restrictions and removal of care, (ii) bureaucracy and institutional care politics, and (iii) discrimination and systemic racism within the Canadian healthcare system. ConclusionThe COVID-19 pandemic within Manitoba and Saskatchewan sparked massive changes in service provision within settler-colonial and neoliberal institutions of care. For those services that remained open to IPLH, masking requirements, questionnaire requirements, scheduling requirements, and a lack of in-person services acted as only some of the barriers described by community members as detrimental to care access. Increased experiences of discrimination in health care on the basis of substance use or HIV status further limited access to needed services.

External Factors Sustaining Indonesian EFL Teachers’ Professional Identity in the 21st Century Era

Becoming more professional teachers is an indispensable thing that matters most in this 21st-century era as more fruitful learning outcomes attainment will be fully obtained by learners. This present small-scale qualitative investigation employed a library study approach to generate more reliable research results that can help Indonesian EFL teachers to cultivate a robust professional identity construction in the 21st-century era. For the data analysis processes, the researcher employed a library analysis method by reviewing the major research findings addressed by 20 previously-published professional identity studies published in Indonesian EFL teaching-learning settings. During the data analysis stage, the researcher concurrently utilized a thematic analysis approach to yield more understandable research results for the targeted research site. All these findings suggested educational institutions prioritize the continual development of Indonesian EFL teachers’ professional identity in order to bring about more qualified educational outcomes to all learners.

Characterize direct protein interactions with enrichable, cleavable and latent bioreactive unnatural amino acids

Latent bioreactive unnatural amino acids (Uaas) have been widely used in the development of covalent drugs and identification of protein interactors, such as proteins, DNA, RNA and carbohydrates. However, it is challenging to perform high-throughput identification of Uaa cross-linking products due to the complexities of protein samples and the data analysis processes. Enrichable Uaas can effectively reduce the complexities of protein samples and simplify data analysis, but few cross-linked peptides were identified from mammalian cell samples with these Uaas. Here we develop an enrichable and multiple amino acids reactive Uaa, eFSY, and demonstrate that eFSY is MS cleavable when eFSY-Lys and eFSY-His are the cross-linking products. An identification software, AixUaa is developed to decipher eFSY mass cleavable data. We systematically identify direct interactomes of Thioredoxin 1 (Trx1) and Selenoprotein M (SELM) with eFSY and AixUaa.

Pushing Boundaries: AI and Computer Science in the Era of Technological Revolution

The advent of Artificial Intelligence (AI) has heralded a transformative era in Computer Science, revolutionizing various facets of technology. This paper explores the profound impact of AI on the field of Computer Science, delving into its advancements, innovations, and the potential future trajectory of technology. The primary objective of this paper is to elucidate the significant role of AI in shaping the landscape of Computer Science. Through comprehensive research and analysis, it aims to provide insights into the evolution of AI, its applications, and the implications for future technological developments. This study employs a library research approach, gathering information from academic journals, conference proceedings, books, and reputable online sources. By synthesizing existing literature and scholarly works, it seeks to construct a comprehensive overview of the advancements and innovations in AI within the realm of Computer Science. The findings reveal a dynamic and rapidly evolving field driven by AI technologies. From machine learning algorithms to neural networks and deep learning models, AI has revolutionized data analysis, pattern recognition, and decision-making processes. Moreover, AI-driven applications such as natural language processing, computer vision, and robotics have reshaped various industries, including healthcare, finance, transportation, and manufacturing. However, alongside these advancements come ethical considerations, privacy concerns, and challenges related to algorithmic biases and societal implications. Looking ahead, the future of technology promises further integration of AI into various domains, leading to unprecedented opportunities and challenges in the realm of Computer Science