Knowledge Of Processes Research Articles

Enhancing L2 Writing through Scaffolding Concept: A Study With Reference To Rural Students in a Government College of Thanjavur, Tamil Nadu

This paper attempts to highlight the importance of Scaffolding concept in L2 writing. As writing requires complex psycholinguistic skills it is necessary study writing to come out with new insights. A number of studies have highlighted the importance of learner scaffolding. This paper tries to answer the following research question. How does scaffolding concept in L2 writing, enhance the writing ability of the target group of learners?It touches upon a number factors that affect L2 writing. Besides, the paper makes use of scaffolding frame work with regard to four major components namely, content knowledge, context knowledge, process knowledge and linguistic knowledge and finally it reveals the indispensable role of scaffolding framework in L2 writing context.

Epistemology and its Application in the World of Education

The development of education in Indonesia continues to face various challenges, including the quality of teachers, curriculum, budget, and education regulations. This research aims to analyze the role of epistemology in improving the quality of education through the application of the philosophy of knowledge in the learning process. This study uses a qualitative approach with a literature review method to explore the concept of epistemology and its application in education. The main findings indicate that idealism, realism, pragmatism, and existentialism epistemologies can be integrated into learning to enhance educational effectiveness. Each philosophical school contributes to the development of critical thinking, creativity, and learner autonomy. This research concludes that the comprehensive application of epistemology can strengthen teaching methods and produce more independent and critical individuals, ultimately improving the quality of education in Indonesia.

Multimodal heterogeneous graph fusion for automated obstructive sleep apnea-hypopnea syndrome diagnosis

Polysomnography is the diagnostic gold standard for obstructive sleep apnea-hypopnea syndrome (OSAHS), requiring medical professionals to analyze apnea-hypopnea events from multidimensional data throughout the sleep cycle. This complex process is susceptible to variability based on the clinician’s experience, leading to potential inaccuracies. Existing automatic diagnosis methods often overlook multimodal physiological signals and medical prior knowledge, leading to limited diagnostic capabilities. This study presents a novel heterogeneous graph convolutional fusion network (HeteroGCFNet) leveraging multimodal physiological signals and domain knowledge for automated OSAHS diagnosis. This framework constructs two types of graph representations: physical space graphs, which map the spatial layout of sensors on the human body, and process knowledge graphs which detail the physiological relationships among breathing patterns, oxygen saturation, and vital signals. The framework leverages heterogeneous graph convolutional neural networks to extract both localized and global features from these graphs. Additionally, a multi-head fusion module combines these features into a unified representation for effective classification, enhancing focus on relevant signal characteristics and cross-modal interactions. This study evaluated the proposed framework on a large-scale OSAHS dataset, combined from publicly available sources and data provided by a collaborative university hospital. It demonstrated superior diagnostic performance compared to conventional machine learning models and existing deep learning approaches, effectively integrating domain knowledge with data-driven learning to produce explainable representations and robust generalization capabilities, which can potentially be utilized for clinical use. Code is available at https://github.com/AmbitYuki/HeteroGCFNet.

Different lenses on distributions and the integration of context knowledge in data-ing processes of primary school students

Different lenses on distributions and the integration of context knowledge in data-ing processes of primary school students

Effectiveness of an Inquiry-Based Science Program on Enhancing Science Process Skills and Knowledge Among Moroccan Preschool Children

<p style="text-align:justify">This study evaluates the effectiveness of a twelve-week Inquiry-Based Science (IBS) program on enhancing science process skills and scientific knowledge among preschool children in Morocco. Conducted in a quasi-experimental setting, it involved 105 children (M = 60.46 months, SD = 4.32), with 37 in the IBS group and 68 in the control group. The program utilized the 5Es instructional model and the Engineering Design Process (EDP) to engage children in active, hands-on learning experiences. Statistical analysis demonstrated that the IBS group achieved substantial improvements in both science process skills and scientific knowledge relative to the control group, with between-group effect sizes (Cohen’s d) ranging from 1.02 and 2.31. These findings highlight the significant impact of structured inquiry-based approaches in early childhood education. The study underscores the need for integrating such methods into the preschool curriculum to foster scientific understanding and skills from a young age, thereby better preparing Moroccan children for future academic and professional challenges. The results advocate for educational stakeholders to consider adopting inquiry-based learning frameworks to enhance the overall quality of early childhood education in Morocco.</p>

Improving computational models of deep brain stimulation through experimental calibration

Background:Deep brain stimulation has become a well-established clinical tool to treat movement disorders. Nevertheless, the knowledge of processes initiated by the stimulation remains limited. To address this knowledge gap, computational models are developed to gain deeper insight. However, their predictive power remains constrained by model uncertainties and a lack of validation and calibration. New method:Exemplified with rodent microelectrodes, we present a workflow for validating electrode model geometry using microscopy and impedance spectroscopy in vitro before implantation. We address uncertainties in the tissue distribution and dielectric properties and outline a concept for calibrating the computational model based on in vivo impedance spectroscopy measurements. Results:The standard deviation of the volume of tissue activated across the 18 characterized electrodes was approximately 32.93%, underscoring the importance of electrode characterization. Thus, the workflow significantly enhances the model predictions’ credibility of neural activation exemplified in a rodent model. Comparison with existing methods:Computational models are frequently employed without validation or calibration, relying instead on manufacturers’ specifications. Our approach provides an accessible method to obtain a validated and calibrated electrode geometry, which significantly enhances the reliability of the computational model that relies on this electrode. Conclusion:By reducing the uncertainties of the model, the accuracy in predicting neural activation is increased. The entire workflow is realized in open-source software, making it adaptable for other use cases, such as deep brain stimulation in humans. Additionally, the framework allows for the integration of further experiments, enabling live updates and refinements to computational models.

Factors affecting the implementation and sustainability of an Australian community-based doula service: A qualitative study

BackgroundCommunity-based doula support for women and gender-diverse birthing people has a range of benefits in improving maternal outcomes and experiences. However, there is limited research on how doula services operate in Australia. Birth for Humankind is an Australian community-based volunteer doula service, which provides free doula support for women and gender diverse birthing people experiencing social discrimination and financial disadvantage. The aim of this study was to understand facilitators and barriers to implementation and sustainability of a community-based doula service delivered to clients concurrently receiving care at a major maternity hospital in Melbourne, Australia. MethodsWe conducted a qualitative study consisting of 30 in-depth interviews with doulas and managers of the community-based doula service Birth for Humankind, and midwives, doctors and social workers from a maternity hospital. Data were inductively thematically analysed, and themes were then deductively categorised using the Capability, Opportunity, and Motivation (COM-B) model and Theoretical Domains Framework (TDF) to understand behavioural influences and facilitators and barriers to implementing and sustaining the doula service within a hospital setting. ResultsFacilitators to implementation of the doula service included: collaborative relationships; having sound knowledge of referral processes; and being a valued professional doula service with dedicated volunteers. Barriers to implementation included: limited knowledge of the doula service from hospital staff; difficulty in retaining volunteer doulas; being a small non-profit doula service; and limited capacity of hospitals to financially support the doula service. Advocacy for renumerating doulas may be one means to strengthen and sustain existing doula-provider service relationships. ConclusionConsideration of establishing strategic partnerships between the doula service and hospitals may create financially-sustainable pathways to enable provision of high-quality, community-based doula support for women and gender diverse birthing people to continue.

Effects of sequential and non-sequential presentation conditions of multiple-stem facts on memory integration and cognitive resource allocation.

What limits the self-generation of new knowledge in the memory integration process? One striking contender is the amount of necessary pieces of information that are dispersed. Specifically, when essential information is scattered across multiple sources/places, it becomes challenging to effectively integrate and generate new knowledge. Most of the studies on memory integration have focused on the study of paired stem facts, but have neglected the exploration of multiple-stem facts. The present study examined college students' performance on memory integration under different conditions of three stem facts. In Experiment 1, participants were exposed to a series of novel, authentic stem facts in which every three relevant ones could be integrated to generate new knowledge. The results of Experiment 1 found that college students could spontaneously generate a new piece of information by integrating two or three separate but related facts. The integration can occur in at least two distinct types due to the different presentation orders of the learning materials: sequential recursive integration and non-recursive integration. College students performed better in sequential recursive integration than in non-sequential recursive integration, and this difference in integration performance is not caused by differences in memory for the stem facts. Based on Experiment 1, Experiment 2 used eye-tracking technology to explore the allocation of internal cognitive resources across different conditions of three stem facts. We found that in non-sequential recursive integration, college students had the longest visual duration and the highest number of fixations on the second stem fact. In sequential recursive integration, there were no other significant differences in the number and duration of visual fixations for the three stem facts. College students paid longer fixations to the second stem fact and the third stem fact in the non-sequential recursive condition than in the sequential recursive condition. Our study suggests that when information is related but cannot be integrated, longer fixation indicates stumbling when dealing with an unresolvable difficulty. When knowledge is presented in a stepwise manner (such as in the sequential recursive integration condition), it results in better semantic memory extension.

A knowledge empowered graph learning feature selection method based on variation propagation effect representation and analysis for human-centric manufacturing systems

As the construction goals of manufacturing systems shift from Industry 4.0 to Industry 5.0, the development goals of manufacturing systems have also shifted from technology driven to human-centric. Feature Selection (FS) of manufacturing systems plays an important role in helping human control and upgrade manufacturing system quality. However, due to the propagation of variations in manufacturing systems, traditional FS methods cannot accurately select the key features that have an important disturbance to quality indicator. In this paper, a process knowledge-enabled FS method is proposed to obtain key features of quality indictor (QI) that would cause bias of prediction model accuracy. Firstly, a process representation learning algorithm embedded in the process knowledge graph is proposed, which represents the manufacturing variation propagation process during the manufacturing process. Secondly, a feature importance evaluation algorithm based on disturbance intensity judgment rules is proposed to evaluate the importance of process features. Finally, the top k important features are selected as key features based on feature importance ranking. Experimental results demonstrate the proposed method outperforms state-of-art and classic methods in four evaluated indicators. Moreover, the four indicators of MSE, MAE, RMSE, and R 2 have respectively increased by 18%, 16%, 18%, and 33% with 7 features compared to the cutting-edge method.

StructuGraphRAG: Structured Document-Informed Knowledge Graphs for Retrieval-Augmented Generation

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by incorporating external data sources beyond their training sets and querying predefined knowledge bases to generate accurate, context-rich responses. Most RAG implementations use vector similarity searches, but the effectiveness of this approach and the representation of knowledge bases remain underexplored. Emerging research suggests knowledge graphs as a promising solution. Therefore, this paper presents StructuGraphRAG, which leverages document structures to inform the extraction process and constructs knowledge graphs to enhance RAG for social science research, specifically using NSDUH datasets. Our method parses document structures to extract entities and relationships, constructing comprehensive and relevant knowledge graphs. Experimental results show that StructuGraphRAG outperforms traditional RAG methods in accuracy, comprehensiveness, and contextual relevance. This approach provides a robust tool for social science researchers, facilitating precise analysis of social determinants of health and justice, and underscores the potential of structured document-informed knowledge graph construction in AI and social science research.

Judicial Discretion and Judicial Precedent: A Legal Linguistic Context

Introduction: in modern science, there is an interest in legal linguistics, which is aimed at solving various problems. Starting from linguistic expertise and ending with methodological recommendations for the development of texts of draft laws. The issue of recognizing judicial precedent as a source of Russian law is relevant. At the heart of the legal linguistic context, judicial discretion and judicial precedent are concepts that require analysis. The paper discusses the concepts of judicial discretion and judicial precedent. Their specifics and characteristic features are defined, and the place of judicial precedent in the system of Russian law is highlighted. The role of the decisions of the Constitutional Court of the Russian Federation and the differences between the plenum of the Supreme Court of the Russian Federation and the judicial precedent are analyzed. The study highlighted the distinctive features that distinguished judicial discretion from other types of law enforcement discretion. Methods: the methodological framework is based on the methods of historicism, systematicity, and comparative law. Results: the author’s position justified in the work is based on knowledge in the criminal process. Conclusions: as a result of the study, it is revealed that the study of the discretion of the court includes an analysis of the relationship with judicial precedent. It is one of the sources of law, so its analysis and mutual influence on judicial discretion seem to be important aspects. It is established that the judicial precedent is a decision of the higher courts. The discretion of the court is able to ensure the legality of the law-application and law enforcement discretion of other bodies, organizations, citizens, and officials.

'Virtual Mentorship is a No-Brainer': The Application of a Virtual Mentorship Programme for Prospective Plastic Surgery Trainees.

Aims This study aimed to evaluate the effectiveness of a virtual mentorship programme in plastic surgery designed for medical students and foundation doctors in the United Kingdom. The programme sought to enhance understanding of common and emergency conditions, provide guidance on the application process for speciality training, and facilitate networking opportunities. Materials and methods The programme consisted of six sessions delivered via Microsoft Teams (Microsoft® Corporation, Redmond, WA) over a four-month period from May to August 2024. Participants completed online pre- and post-mentoring questionnaires. Wilcoxon signed-rank test was used to compare paired data responses. Results Ten participants completed both questionnaires; 90% were medical students, and 10% were foundation-year doctors. There was a significant increase in the understanding of common plastic surgery conditions and emergencies (p < 0.05), as well as improved knowledge of the application processes for core surgical training (p < 0.05) and higher speciality training (p < 0.05). Interest in the speciality significantly increased (p < 0.05), and participants were more likely to seek in-person mentorship (p < 0.05). The programme was well-received, with 100% rating it as 'excellent' or 'very good'. Conclusions The virtual mentorship programme effectively enhanced foundational knowledge, career preparation, and mentor-mentee relationships. Its implementation is recommended both alone and in combination with traditional face-to-face mentorship.

Deep Learning Solution for Quality Control in a Die Casting Process

Industry 4.0 aims for a digital transformation of manufacturing and production systems, producing what is known as smart factories, where information coming from Cyber-Physical Systems (core elements in Industry 4.0) will be used in all the manufacturing stages to improve productivity. Cyber-physical systems through their control and sensor systems, provide a global view of the process, and generate large amounts of data that can be used for instance to produce datadriven models of the processes. However, having data is not enough, we must be able to store, visualize and analyze them, and to integrate induced knowledge in the whole production process. In this work, we present a solution to automate the quality control process of manufactured parts through image analysis. In particular, we present a Deep Learning solution to detect defects in manufactured parts from thermographic images of a die casting machine at an aluminum foundry.

Development and Validation of the Career Counseling Outcome Questionnaire in Two Clinical Settings

This study reports the development and initial validation of the Career Counseling Outcome Questionnaire (CCOQ) in two individual career counseling settings using a college sample ( n = 1,140) and a community sample ( n = 161). Exploratory and confirmatory factor analyses revealed five correlated factors in both samples: (a) knowledge of the career decision-making process (4 items), (b) knowledge of the self (3 items), (c) knowledge of career information (3 items), (d) anxiety towards career decision-making (3 items), and (e) career undecidedness (2 items). The CCOQ scale scores changed in the expected theoretical direction during the career counseling interventions and did not change when clients were not receiving counseling. Except for anxiety, all CCOQ subscales predicted satisfaction with the career decision twelve months after counseling. The CCOQ total score predicted satisfaction with the decision twelve months after counseling, over and beyond a widely used instrument assessing sources of career decision difficulties (i.e. the Career Decision Difficulties Questionnaire; CDDQ). Career counselors could use the CCOQ to monitor the effectiveness of their interventions in complement to diagnostic measures such as the CDDQ.

Spectral Analysis of the Morphology of Fresh Lunar Craters I: Rim Crest, Floor, and Rim Flank Outlines

AbstractThe morphology of fresh lunar craters contains information about the physical properties of both the impactors and the lunar surface, and is therefore crucial to our knowledge of the impact cratering process. Spectral analysis is a powerful tool to study crater morphology, as it can reveal the topographic variation on different scales. In this study, we calculate the power spectral densities of the radial distance and elevation of the rim crest, floor, and rim flank outlines of fresh lunar craters. The resulting power spectral density can be decomposed into an average component and a natural variability component. For the average component, we derive the classic morphometric parameter‐crater diameter relations that are consistent with previous studies. For the natural variability component, we find that in general the spectral power increases with wavelength, which can be fitted by a piecewise function with four breakpoints. Among the four breakpoints, the power of the third breakpoint (i.e., the degree‐2 power) is of particular interest, as it determines the ellipticity of the outline. The power of the third breakpoint is found to have a diameter dependence with a peak at 20 km, which indicates that transitional craters are more elliptical than simple and complex craters. The diameter dependence of the power spectral density enables us to generate the synthetic outlines of a crater of a particular size, which can be used to develop a preliminary 3‐dimensional shape model for fresh lunar craters that is useful for improving Monte Carlo modeling of cratered surfaces on the Moon.

The Impact of Knowledge Management on Employee Performance in Knowledge Process Outsourcing Companies in Sri Lanka

Abstract In the modern business world, organizations strive for competitive advantage. This study examines the impact of knowledge acquisition, knowledge creation, knowledge capture and storage, knowledge sharing, and knowledge application on employee performance in Knowledge Process Outsourcing (KPO) companies in Sri Lanka. The study adopted a quantitative approach, employing a structured online questionnaire distributed amongst the KPO industry employees. The data is analyzed using statistical methods such as correlation and multiple regression. According to the findings, knowledge management has a significant impact on employee performance in KPO companies in Sri Lanka. This result indicates a significant relationship between the selected knowledge management components - acquisition, creation, capture and storage, sharing, application and employee performance. However, challenges such as a lack of comprehensive knowledge management strategies, limited employee engagement, and cultural barriers are identified. The findings prove the need for KPO organizations in Sri Lanka to prioritize knowledge management strategies. The study contributes to the literature on knowledge management in the KPO industry.

Process-Informed Neural Networks: A Hybrid Modelling Approach to Improve Predictive Performance and Inference of Neural Networks in Ecology and Beyond.

Despite deep learning being state of the art for data-driven model predictions, its application in ecology is currently subject to two important constraints: (i) deep-learning methods are powerful in data-rich regimes, but in ecology data are typically sparse; and (ii) deep-learning models are black-box methods and inferring the processes they represent are non-trivial to elicit. Process-based (= mechanistic) models are not constrained by data sparsity or unclear processes and are thus important for building up our ecological knowledge and transfer to applications. In this work, we combine process-based models and neural networks into process-informed neural networks (PINNs), which incorporate the process knowledge directly into the neural network structure. In a systematic evaluation of spatial and temporal prediction tasks for C-fluxes in temperate forests, we show the ability of five different types of PINNs (i) to outperform process-based models and neural networks, especially in data-sparse regimes with high-transfer task and (ii) to inform on mis- or undetected processes.

AUTOMATIC PATH PROGRAMMING FOR THE MANUFACTURING OF SHEET METAL COMPONENTS USING INCREMENTAL FORMING TECHNOLOGY

Asymmetric Incremental Sheet Forming (AISF) is an unconventional forming technology that involves localized and progressive plastic deformation of a sheet using a tool, typically a punch, following a continuous and numerically controlled path. Due to its specific characteristics and advantages, such as flexibility and relatively low implementation cost, it is considered a technology with great potential for competitively producing small series of sheet metal components. However, there are still some challenges, such as the lack of specific CAM tools for this technology, which has hindered its industrialization. Although the process may appear simple, with a 3D path sufficient to transform the original flat sheet into the final part, it has been observed that the manufacturing of real designs requires extensive knowledge of the technology to choose the appropriate path based on the target geometry. In this regard, this study presents a new solution developed by Tecnalia that incorporates expert process knowledge and enables automatic programming of Incremental Forming paths. Specifically, the solution allows programming paths aimed at manufacturing different geometric features commonly found in sheet metal components. This development has been validated through the manufacturing of several parts in a robotic cell implemented at Southeast University in Nanjing.

Teachers’ Challenges in Research Engagement: Basis for a Proposed Technical Assistance Plan

This study aimed to investigate the factors teachers perceived as the challenges in their research engagement. A total of sixty (60) teacher-respondents were included in this study. Forty-five (45) teachers participated in the survey, while the other fifteen (15) participated in the in-depth interview. The research also utilized an exploratory sequential mixed-method design to sustain triangulation. Based on the findings, three (3) superordinate themes emerged from the qualitative data, which include: (1) Limitations in Teachers’ Time and Workloads; (2) Unsupportive School Culture and Teachers’ Attitude toward Research; and (3) Teachers’ Limited Research Knowledge and Skills. The survey reveals that teachers face challenges in research engagement due to heavy workloads, lack of support, limited knowledge of the research process, lack of confidence, fear of rejection, and insufficient skills in crafting titles, research questions, reading research works, and preparing references using various citation styles. The most important finding of this study is that it demonstrates how education is altering its perceptions about research. Educators concur that education research is crucial, particularly in enhancing the provision of basic educational services. Teachers were deterred from conducting research despite their willingness due to several factors. For successful engagement of teachers in research, all identified factors preventing them from research engagement must be addressed properly

Analysis of Students' Questioning Skills During Discovery Learning: A Descriptive Study Based on The Revised Bloom's Taxonomy Framework

The problem in this study is the lack of questioning skills based on cognitive process dimensions and knowledge dimensions. This research aims to analyze questioning skills through students' written work using the revised Bloom's Taxonomy framework, specifically the cognitive process and knowledge dimensions. The participants were 32 students (15 males and 17 females) from class XI MIPA 3 at a high school in Bandung. This descriptive study employed documentation analysis using Google Forms for individual questions and worksheets for group questions. The results depict students' cognitive processes at the levels of remembering, understanding, applying, analyzing, evaluating, and creating as 18.25%, 47.44%, 9.49%, 18.25%, 2.92%, and 3.65%, respectively. In terms of knowledge dimensions, the percentages for factual, conceptual, procedural, and metacognitive knowledge were 24.82%, 72.99%, 2.19%, and 0%, respectively. It can be concluded that questioning based on cognitive processes is distributed across all levels from remembering to creating, while questions based on knowledge dimensions are predominantly conceptual.