Data Sorting Research Articles

Empowerment Among Adolescent Girls in Nepal: A Concept Mapping Exploratory Study.

The concept of empowerment is challenging to operationalize and measure; it is multidimensional, the outcomes are not always directly observable, and meanings of empowerment are highly contextual and socially and culturally situated. This study aimed to explore perspectives of empowerment among adolescent girls in Nepal to identify statements for inclusion in a context-specific empowerment measure. We used a participatory and mixed method research method called concept mapping in 3 districts in Nepal. Three sequential concept mapping sessions were used to solicit, organize, and process how participants responded to the prompt: "The life of an adolescent girl improves when she has/can…" The Concept Systems Global software was used to manage and analyze the concept mapping brainstorming, sorting, and rating data using established tools, such as multidimensional scaling and cluster analysis. Concept mapping was conducted with 113 participants, including national experts, program staff, adolescents, and their mothers. They identified 105 items that fall into a 4-cluster solution: education and knowledge, decision-making, supports and skills, and physical infrastructure. Rating data uncovered there was some overlap between the top 10 most important items between the national-level experts and other stakeholder groups; however, several components associated with empowerment differed by stakeholder group in terms of importance. This research represents a critical step in exploring definitions of empowerment among adolescent girls in the Nepal context and with expert input. These results led to the development of a contextually specific definition of empowerment. Researchers and practitioners interested in developing context-specific understandings of complex topics that incorporate community voices and perspectives could use a similar concept mapping approach in other countries to explore various topics with diverse populations.

Experiences Using MEMS Accelerometers on Railway Bearers at Switches and Crossings to Obtain Displacement—Awkward Situations

A sleeper, or more generally a “bearer”, moves vertically under a passing train load. The extent of this motion depends on the static and dynamic load of the train, the train speed, and the support conditions at the bearer and its neighbours. Excessive motion, typically from voiding see-sawing, low support stiffness or possibly excessive stiffness, or even too little stiffness, are all of interest to maintainers. Typically, problems arise around transition zones, switches and crossings, but plain track with poor support can also be a problem. Within the last decade, low-cost micro-electro-mechanical system (MEMS) accelerometers have been used to capture the time history of vertical motion for use in condition monitoring. Existing condition monitoring systems often overlook or sometimes even ignore the possibility of problematic data, which seem to be common in monitored locations. It is essential to understand whether such “bad” data require further attention. Three problematic sites are presented, focussing on examples where the acceleration was higher than expected or the computed displacement was not as expected. Potential causes include wheel defects, hammering of the ballast by a hanging bearer, or high acceleration at some structural resonant frequency. The present paper aims to show the challenges of using MEMS accelerometers to collect data for condition monitoring and offers insights into the sort of problematic data that may be collected from real sites.

Design and implementation of novel 6G cellular communication architecture using 3D modelling in real environments

<p>Academics and businesses alike are presently fixated on the Sixth Generation (6G) network, which is seen as the telecom industry’s next major game-changer. The 6G architecture has not been finalized and is not yet being used in commercial settings. Research and development for 6G is still in its early stages. Several important features and technologies have surfaced as possible 6G system underpinnings, while development is still in its early phases. To facilitate the next generation of 3D modeling applications, this article suggests a new 6G cellular communication architecture. Aware of 6G’s revolutionary potential, we investigate its fundamental features to build a collaborative, real-time 3D modeling environment with unmatched capabilities. The goal of this project is to design the architecture for the next generation of communications systems. This will incorporate elements from two existing designs: the decoupled RAN design, which improves security and smooth data sorting, and the high-level design, which incorporates numerous protocols inside the antenna for stringent protection. Lastly, we delve into the possible sector-specific disruptions caused by this design and examine its wider implications for the future of 3D modeling. We hope that by introducing this new 6G architecture, we may inspire more study and development towards a day when 6G technology completely changes the game when it comes to 3D modeling.</p>

Differences and common ground in the frameworks of health-related quality of life in traditional Chinese medicine and modern medicine: a systematic review

PurposeThis systematic review aims to explore the conceptualization of health-related quality of life (HRQoL) in China. With HRQoL influenced by both modern medicine (MM) and traditional Chinese medicine (TCM), the study seeks to identify differences and common ground between the frameworks of MM and TCM as defined in the literature.MethodA systematic literature search was conducted across three Chinese databases and four English databases. The data was extracted including title, author(s), publication year, region, aim, method, category, and result. When sorting data, we broke down the HRQoL frameworks into concepts, domains and facets, with a focus on overlapped facets between the frameworks of MM and TCM.ResultsA total of 31 studies were included. In the perspective of TCM, HRQoL is centered around three key 'concepts': (1) 'xingshentongyi' (unity of body and spirit), (2) 'tianrenheyi' (harmony between man and nature), and (3) 'qiqing' (seven emotional forms). In contrast, the MM framework comprises 'physical,' 'mental,' 'social,' and 'environment' domains. Out of the 59 unique facets identified, 28 are common to both TCM and MM, 9 specific to TCM, and 22 specific to MM. 'Appetite,' 'sleep,' and 'energy' are the most frequently mentioned facets in both frameworks.ConclusionThe concept of HRQoL in China encompasses frameworks rooted in both TCM and MM. While TCM and MM have distinct healthcare approaches, they share overlapping domains when measuring HRQoL through questionnaires. Furthermore, TCM and MM demonstrate considerable convergence in terms of HRQoL facets, showing the potential for utilizing HRQoL instruments across different cultural settings.

Navigating Knowledge

In this workshop, we will demonstrate four research tools: 1) card sorting, 2) upward laddering, 3) think-aloud, and 4) a search visualizer. Card sorting can be useful as an exploratory knowledge modelling method to gain insight into people’s understanding of their surrounding world. Derived from Kelly’s Personal Construct Theory (PCT) (Kelly, 1955, Herd, 2001), card sorting can help to explore peoples’ constructs and their perceptions of how the constructs relate to each other; in other words, card sorting can help to elucidate mental frameworks. Although there are many kinds of card sorting, we will guide participants through a hands-on exercise using single-criterion card sorting (using both digital and in person techniques). Participants will learn about text-based, image-based, and object-based options, when to use card sorting, how to collect data, and how to analyse card sorting data through visual-numeric ‘heat maps’ (co-occurrence matrices). The card sorting exercise will lead into upward laddering, a technique that is often used to complement card sorting. While card sorting provides evidence of how constructs are related, upward laddering allows exploration into goals and values (Rugg & Gerrard, 2023). Workshop participants will have an opportunity to access a simple, robust, newly created online tool for upward laddering. Also used alongside card sorting is the think-aloud method which involves both observation to see how people perform a task and think-aloud to hear what people are thinking and noticing while they perform the task. This inexpensive, easy-to-use method allows researchers to tap into reasons and tacit knowledge. The fourth tool we will demonstrate is a newly launched search visualiser (SV). Using keywords, this search tool allows researchers to comb through specific databases and/or to access Google results. Rather than simply return a list of links to articles, the SV returns a visual depiction of the keywords within each text. Each key word is represented as a coloured square. A user can hover their mouse pointer over a given square to see the phrase within which the word appears. Using this tool, researchers can get a better, visual sense of whether the article is likely to offer useful content. There is now a version with audio for visually impaired users as well as a version that can explore synonyms to support textual-literary analysis.

Developing a prediction model in a lightweight packaging waste sorting plant using sensor-based sorting data combined with data of external near-infrared and LiDAR sensors.

Sensor-based material flow monitoring allows for continuously high output qualities, through quality management and process control. The implementation in the waste management sector, however, is inhibited by the heterogeneity of waste and throughput fluctuations. In this study, challenges and possibilities of using different types of sensors in a lightweight packaging waste sorting plant are investigated. Three external sensors have been mounted on different positions in an Austrian sorting plant: one Light Detection and Ranging (LiDAR) sensor for monitoring the volume flow and two near-infrared (NIR) sensors for measuring the pixel-based material composition and occupation density. Additionally, the data of an existing sensor-based sorter (SBS) were evaluated. To predict the newly introduced parameter material-specific occupation density (MSOD) of multi-coloured polyethylene terephthalate (PET) preconcentrate, different machine learning models were evaluated. The results indicate that using SBS data for both monitoring of throughput fluctuations caused by different bag opener settings as well as monitoring the material composition is feasible, if the pre-set teach-in is suitable. The ridge regression model based on SBS was comparable (RMSE = 3.59 px%, R² = 0.57) to the one based on NIR and LiDAR (RMSE = 3.1 px%, R² = 0.68). The demonstrated feasibility of the implementation at plant scale highlights the opportunities of sensor-based material flow monitoring for the waste management sector and paves the way towards a more circular plastics economy.

Education as a Tool of Engagement in Sport and Physical Activity

The paper deals with the issue of education and its relationship with sports and other physical activities among the Czech adult population. The main goal of the paper is to find out if the level of reached education is related with attitudes towards sports and other physical activities and with their consequent realization as well. The research was realized by the questionnaire SFSPA (Social Role of the Sport and Physical Activity) in the years 2019-2020. The research sample consisted of 1000 respondents (486 men and 514 women) from all over the Czech Republic. All respondents were questioned personally, their participation was voluntary, and all data were anonymized. For determination the research sample, we used a stratified quota selection with regards to sex, age and residence. The data were processed by using first and second level data sorting, specifically pivot tables. Furthermore, correlation analysis and statistical significance (χ2 ) were used. The results clearly testified that the grade of reached education has a positive connection with a relationship towards sports and other physical activities.

Research on Chinese Financial Industry Application Scenarios based on ChatGPT

The emergence of the artificial intelligence ChatGPT has brought a huge information impact to the traditional industry, promoting the deep integration of ChatGPT and financial institutions, stimulating its innovation vitality, and comprehensively empowering financial services. This technology is the key to promote the digital intelligence transformation of the financial industry. Based on the systematic study of the integration scenario of ChatGPT and innovative application in the financial industry and related limitations, this paper constructively puts forward suggestions and expectations for the future development of the financial industry under the background of artificial intelligence, to make some innovative exploration for the application of ChatGPT in the financial industry. According to the research, ChatGPT-like artificial intelligence's powerful natural language processing, data sorting, and analysis capabilities have great application value for financial institutions covering customer service, operation, marketing, risk control, investment and budget, etc., but there are also problems such as high cost, low credibility and security, and technical limitations. Finally, this paper puts forward effective empowerment suggestions based on three aspects: government policy, institutions and technology development.

Analysis of Social Business Strategy (Case Study in Rumah Harapan Kita Foundation)

This research aims to create an overview of business strategies, identify business strategies using the social enterprise model canvas and SWOT models, and formulate appropriate business social business strategies. The object of this research is the Rumah Harapan Kita Foundation, which is one of the social businesses in Tasikmalaya City. The method used is qualitative with a case study approach. The types of data used are primary and secondary with data collection techniques using observation techniques and unstructured interviews. Primary data was obtained from an in-depth interview process with the Rumah Harapan Kita Foundation, while secondary data was obtained from documents, websites, and foundation archives. The informant for this research is the CEO and Director of Entrepreneur of Rumah Harapan Kita Foundation. Data analysis uses the model analysis method from Miles and Hubermen (1992), namely: reducing data as a form of data sorting; processing data as data presentation; and verifying the conclusions of the data that has been analyzed. The research results show that the Rumah Harapan Kita Foundation's business strategy has not been structured systematically. Then, based on the results of the analysis using SWOT and SEMC, 8 SEMC elements need to be developed. So the formulation of Rumah Harapan Kita Foundation's business strategy is focused on the elements of customer segments, value propositions, channels, customer relationships, revenue streams, key activities, key partners, and impact & measurement.

Impact of a Networked Improvement Community’s Interventions on STEM Student Persistence. Review of Results and a Call to Action.

It is well known that high impact practices such as student learning communities and peer-mentoring, early engagement in research and faculty-student relationship building can have a positive influence on undergraduate student engagement and retention in their chosen field of study. The First2 Network is a Networked Improvement Community comprised of students, faculty, staff and administrators from eight higher education institutions in West Virginia and one in Kentucky, that has, over the past six years, engaged in creating interventions to implement and test these high impact practices with the aim of doubling the graduation rate of first-generation and other underrepresented STEM students. Relying on data collected, mostly from WVU, we see very promising outcomes on the persistence of STEM majors. These results will be shared. However, our data story is incomplete as there is no mechanism to sort data on first-generation status across all universities and colleges in the state. The value of this information and its inclusion in a future state funding formula will be explored.

Implementing menstrual cycle tracking: A pilot concept mapping study investigating considerations of coaches, support staff, and female athletes

ObjectivesTo identify what coaches, support staff, and athletes believe should be considered when implementing menstrual cycle tracking in sport. DesignConcept mapping (mixed-methods participatory approach). MethodsParticipants brainstormed statements in response to the focus prompt “What should be considered when tracking menstrual cycles in sport?” Participants then sorted the statements into groups according to meaning and rated each statement on its importance and feasibility to address. ResultsTwenty-six participants (12 athletes and 14 coaches/support staff) generated 53 ideas that the research team synthesised to 57 unique statements. Sixteen participants sorted the statements into an average of 8 (±4) groups. Six clusters were identified as the most appropriate representation of the sorting data following multidimensional scaling and hierarchical cluster analysis: 1) Ethics, 2) Tracking Tools and Methods, 3) Performance and Health, 4) Education, 5) Interpretation and Framing and 6) Communication. Participants rated each statement based on their importance (n = 12) and feasibility (n = 10), respectively. The most important (mean = 3.88 out of 5) and feasible (mean = 3.85 out of 5) cluster was Ethics, whilst the least important (mean = 3.17) and feasible (mean = 3.04) was Communication. ConclusionsEthical considerations pertaining to privacy, consent, and scope of practice should be prioritised when tracking menstrual cycles in sport. A low burden method of tracking, which allows for some individualisation based on athletes' preferences or needs, and menstrual health education should also be undertaken to improve the uptake and impact of menstrual cycle tracking.

Need Assessment of Education Management According to Sub-District Non-Formal and Informal Education Centre Standards for Excellence

This study aims to study the Current Conditions, Desirable Conditions, and Necessary Needs of Educational Management according to standards for Excellence of Sub – District Non – Formal and Informal Education Centre. And to study educational management guidelines according to the Sub – District Non – Formal and Informal Education Centre Standards for Excellence. There were two steps: Step 1: Study the Current Conditions, Desirable Conditions, and Needs of educational management standards of Sub – District Non – Formal and Informal Education Centre Standards for Excellence. The sample consisted of 40 school administrators and 328 of the heads of Sub – District Non – Formal and Informal Education Centre, a total of 368 attended in the academic year 2022 using Multi-stage Random Sampling. Step 2: Study the standard educational management guidelines. The informants are qualified by studying best practices from five Models of Sub – District Non – Formal and Informal Education Centres, two persons, totaling ten people, by specific sampling. The research instruments: 1) The Current Conditions and Desirable Conditions Questionnaire following educational management according to standards for Excellence of Sub–District Non–Formal and Informal Education Centre, a 5-level rating scale. 2) Semi-structured interview 3) The Educational Management Approach Assessment Form is a 5-level estimation scale with statistics used to analyze the data, including mean, standard deviation, priority sorting of PNImodified data, and content analysis. The results showed that 1) the current condition as a whole and in terms of study is at a high level; Overall desirable conditions are at the highest level. One aspect is at the highest level: six are at the highest level. Considering the necessary needs, it found that the needs of educational management according to the standards. There is a need to develop all seven areas as follows Organizational Leadership, Strategic Planning, Learner and Stakeholder Focus, Knowledge Analysis and Management Measurement, Processing Management, and Personnel Focus. 2) the suggestions were seven aspects and 135 approaches as follows Workshops, Learning from other people, Brainstorming, Operating Integration, Mentoring, and Learning from successful organizations.

Constructing Trading Strategies by Applying and Adapting Q-factor Model in the Australian Market

The Q-factor model is a quantitative pricing model similar to the Fama-French multi-factor model, which is an investment approach using investment (IA) and profitability (ROE) as the significant factors. This paper collects historical data (1987~2022) from Australian stocks and constructs long-short portfolios to investigate the effectiveness of applying this model in the Australian market. Net returns will be validated by Carhart regression. Finally, the optimal factor portfolios and trading strategies will be tested for factor substitution based on cumulative return trends and risk-return metrics. Overall, both the Q-factor model and the augmented Q-factor effectively analyzed investments in the Australian market. The difference between theoretical and practical-based long-short is mainly due to the abnormal ranking performance of individual factors, especially during the COVID-19 pandemic. In most cases, strategies based on actual data sorting generate better risk-return outcomes than theory-based approaches, suggesting that the models must be continually adapted to suit Australia's unique economic and market conditions.

Data Sorting Influence on Short Text Manual Labeling Quality for Hierarchical Classification

The precise categorization of brief texts holds significant importance in various applications within the ever-changing realm of artificial intelligence (AI) and natural language processing (NLP). Short texts are everywhere in the digital world, from social media updates to customer reviews and feedback. Nevertheless, short texts’ limited length and context pose unique challenges for accurate classification. This research article delves into the influence of data sorting methods on the quality of manual labeling in hierarchical classification, with a particular focus on short texts. The study is set against the backdrop of the increasing reliance on manual labeling in AI and NLP, highlighting its significance in the accuracy of hierarchical text classification. Methodologically, the study integrates AI, notably zero-shot learning, with human annotation processes to examine the efficacy of various data-sorting strategies. The results demonstrate how different sorting approaches impact the accuracy and consistency of manual labeling, a critical aspect of creating high-quality datasets for NLP applications. The study’s findings reveal a significant time efficiency improvement in terms of labeling, where ordered manual labeling required 760 min per 1000 samples, compared to 800 min for traditional manual labeling, illustrating the practical benefits of optimized data sorting strategies. Comparatively, ordered manual labeling achieved the highest mean accuracy rates across all hierarchical levels, with figures reaching up to 99% for segments, 95% for families, 92% for classes, and 90% for bricks, underscoring the efficiency of structured data sorting. It offers valuable insights and practical guidelines for improving labeling quality in hierarchical classification tasks, thereby advancing the precision of text analysis in AI-driven research. This abstract encapsulates the article’s background, methods, results, and conclusions, providing a comprehensive yet succinct study overview.

Enabling unsupervised discovery in astronomical images through self-supervised representations

ABSTRACT Unsupervised learning, a branch of machine learning that can operate on unlabelled data, has proven to be a powerful tool for data exploration and discovery in astronomy. As large surveys and new telescopes drive a rapid increase in data size and richness, these techniques offer the promise of discovering new classes of objects and of efficient sorting of data into similar types. However, unsupervised learning techniques generally require feature extraction to derive simple but informative representations of images. In this paper, we explore the use of self-supervised deep learning as a method of automated representation learning. We apply the algorithm Bootstrap Your Own Latent to Galaxy Zoo DECaLS images to obtain a lower dimensional representation of each galaxy, known as features. We briefly validate these features using a small supervised classification problem. We then move on to apply an automated clustering algorithm, demonstrating that this fully unsupervised approach is able to successfully group together galaxies with similar morphology. The same features prove useful for anomaly detection, where we use the framework astronomaly to search for merger candidates. While the focus of this work is on optical images, we also explore the versatility of this technique by applying the exact same approach to a small radio galaxy data set. This work aims to demonstrate that applying deep representation learning is key to unlocking the potential of unsupervised discovery in future data sets from telescopes such as the Vera C. Rubin Observatory and the Square Kilometre Array.

Development of Islamic Religious Education Teacher Performance for Students in Facing the World of Work

An Islamic religious education teacher is required to develop his performance so that students are always ready to face the world of work. This qualitative descriptive research using literature study will examine the development of the performance of Islamic religious education teachers for students in facing the world of work. The data analysis technique uses content analysis by sorting data according to the problem and research objectives. This article finds several things as follows. First, in developing their performance, an Islamic religious education teacher must have the following five criteria, including: commitment to students in the learning process, must master the material, carry out evaluations, be able to think systematically, and be part of society in their professional environment. Second, in developing his performance, an Islamic religious education teacher must be able to be a corrector, inspirer, informer, motivator, initiator and facilitator. Also, an Islamic religious education teacher must master several teaching methods, including: lecture methods, exercises, question and answer, discussions, experiments, assignments, field trips, socio-drama, and the like. Third, to prepare students for the world of work, an Islamic religious education teacher must formulate a curriculum that suits the needs of society, that is in line with developments in science and technology, and that suits individual needs. Fourth, to balance the curriculum with the field of work, an Islamic religious education teacher must pay attention to skills education which is usually directed at employment preparation, directed at several types of work and in accordance with his field of expertise.

The Best and the Worst: TEFL Experts’ Opinions of the Most and the Least Important Predictors of Situational Willingness to Communicate

The current study aimed to identify the most and the least important situational properties of second language willingness to communicate (L2WTC) in an EFL context. After reviewing the related literature, 24 influencing factors were identified and then they were prioritized. 180 TEFL students participated in the study to answer the research questionnaire. A quantitative research approach applying paired comparison questionnaire was employed. Data analysis was done using Excel spreadsheet for sorting data and calculating the mean and WinQSB software for solving linear programming model. The results showed that, among the selected variables, “the size of the group,”“familiarity with topics under discussion,” and “interlocutors and familiarity with them” were determined to be the first most important situational variables which highly influence L2WTC. The findings also showed that “attitudes toward the learning situation,”“course evaluation criteria,” and “alignment with the classroom norms” were the least important factors influencing L2WTC. The significance of the study lies in its theoretical contributions and pedagogical implications it has for the field of second language teaching and learning.

ANALISIS PENGGUNAAN SISTEM INFORMASI AKUNTANSI PONDOK PESANTREN MODERN AL-HASYIMIYAH DARUL ULUM

The purpose of this study was to determine the method of recording financial statements at Pondok Pesantren Modern Alhasyimiyah Darul Ulum. Qualitative research is used as a research method. The sources of information used are important information obtained through direct meetings and optional information obtained from related reports. The area of this examination is North Padang Lawas. The phenomena that occur in the school of Modern Islamic Boarding School Alhasyimiyah Darul Ulum have not been by PSAK 45 regulations. The data sources conducted by researchers are interviews, documentation, and observation at Modern Islamic Boarding School Alhasyimiyah Darul Ulum and the techniques used by researchers to collect data are data sorting, data presentation, and data verification. The results of the research at the Modern Islamic Boarding School Alhasyimiyah Darul Ulum are currently not following the bookkeeping data framework plan at the Islamic Living School which is not by PSAK 45, considering that there are still a few financial statement data framework reports that are not made by the pesantren, especially the Money Position Report, Explanation of Income, Notes on Fiscal Summary.

Faktor-Faktor Pertumbuhan Laba pada Perusahaan Manufaktur Yang Terdaftar Pada Bursa Efek Indonesia

The purpose of this study is to identify the variables that affect profit growth. Secondary data is the sort of data employed in this associative research method. Purposive sampling was utilized to choose the sample from the population of 67 manufacturing businesses that were listed on the Indonesia Stock Exchange for the years 2017 through 2021. Multiple linear regression on panel data and descriptive statistical analysis are the methods utilized for data analysis. The research's findings indicate that the current ratio and inventory turnover have a negative and insignificant impact on profit growth, while the total asset turnover has positive and significant effects on profit growth, and debt to equity ratio has negative and significant effects on profit growth. As both excellent and bad profit growth will directly affect a company's financial condition, it is envisaged that determining profit growth will be a key concern for any business. The consequences of inaccurately estimating profit growth will be extensive. Given that these factors have been shown to have an impact on the company's profit growth, management should pay close attention to the factors that affect profit growth, particularly determining total asset turnover,and debt to equity ratio, so that the company can determine the best possible profit growth.

Prediction of Economic Growth by Using Machine-Learning Algorithms through Sentiment Index Analysis in Economy of Pakistan

The neural network method's ability to mimic nonlinear communications without prior information is a major benefit for accurate function approximation. It differs from ARIMA and linear regression, which need linearity and may not work for all data sorts. Compared to (seasonal) ARIMA models, neural networks yield similar results. Neural networks also model and predict multivariate time series using causal and co-integration research. In contrast to the traditional economic forecasting approach, machine-learning techniques have prioritized precise forecasting without predetermined assumptions or conclusions. Owing to their enhanced predictive abilities and technological advancements, they provide more adaptability and have gained widespread acceptance in several fields. In terms of estimating US real estate values, machine-learning technology fared better than traditional econometric models, as shown. Total there were 131601 economic and financial news, which may include the phrases along with the names specified by the bank, and it may also include local media and mainstream. Machine learning uses the strength of data-driven algorithms and state-of-the-art computational techniques to improve decision-making in a range of industries. The findings of this research contribute to better forecasting practices and show how machine learning may provide more accurate forecasts in real-world scenarios.