Interaction Data Research Articles

Transpositioning in English medium instruction classroom discourse: insights from a translanguaging perspective

The concept of ‘transpositioning’ is an emerging notion in the field of Applied Linguistics, influenced by positioning theory and liquid modernity. Transpositioning involves individuals breaking away from their predefined roles and adopting different perspectives through translanguaging. However, there is currently a lack of research exploring how multilingual classroom teachers can transcend their positions using translanguaging to achieve their specific communication goals in multilingual classrooms. This study is the first study that investigates how an English-Medium-Instruction teacher undergoes transpositioning by adopting different positionings to construct an imaginary scenario and engaging in co-learning with students. The study employs Multimodal Conversation Analysis to analyse the classroom interaction data, and the findings are triangulated with video-stimulated-recall-interview data, which is analysed using Interpretative Phenomenological Analysis. The paper argues that transpositioning is a fundamental aspect of the teacher’s classroom translanguaging competence. This competence involves not only the strategic utilization of multilingual and multimodal resources to construct new knowledge and achieve specific pedagogical goals, but also requires the teacher to selectively draw from his repertoire and modify his identity positions in response to the evolving dynamics of classroom interaction.

Primordialisme dalam problematika childfree (Studi Kasus Etnografi Pada Masyarakat Kelurahan Mlajah, Kabupaten Bangkalan)

The Madurese people can be said to be an ethnic group that upholds existing cultural values. Just like when Kyai's descendants are required to marry Kyai's descendants as well. This research focuses on the phenomenon where the condition of the Madura community mentioned has a rejection of new ideas such as Childfree. In the initial observation of the researcher at the research location, namely in Mlajah Village, Bangkalan Regency, there was a condition where the environment had been transformed like the life of the metropolitan community and it was found that there were people there who had an understanding of the concept of Childfree. The theory used in this study is Albert Bandura's Social Cognitive Theory. This type of research is qualitative with an ethnographic case study approach. The paradigm used in this study uses the post-positivism paradigm. The technique for determining informants in this study uses a purposive sampling technique, where the researcher determines the conditions and criteria for a person to become a research informant. The data collection techniques in this study include observation, interviews, documentation and literature studies. The data validity technique in this study uses Miles and Huberman's interactive model, which includes, data collection, data reduction, data presentation and conclusion drawn.

Social Media Use of National Libraries: A Content Analysis Specific to Instagram

Europe’s top five most developed countries and five developing countries were determined based on the Human Development Index. The national libraries of these countries and the Instagram accounts of the National Library of the Presidency of Türkiye were examined, and content analysis was performed. Within the scope of content analysis, general views, post types, interaction data, purpose of sharing, narrative format of the content, personal values in the content, visual content, and message association themes were discussed. This study aims to reveal how national libraries use Instagram posts to interact with their target audiences. Within the scope of the study, Instagram posts shared by national libraries were analyzed through content analysis using the ReCall program. The findings showed that national libraries focus more on rational content and emphasize posts that highlight rootedness and nationalism rather than the goal of communicating with the masses or mobilizing users. It was also found that the posts of national libraries differed according to their Human Development Index values. In countries with a high Human Development Index, information and arts-related messages were more prominent, while in other countries, promotional and event-related content was more dominant.

Investigation of the size and shape of nano-microcarriers for targeted drug delivery to atherosclerotic plaque in ischemic stroke prevention

Recognizing the significance of drug carriers in the treatment of atherosclerotic plaque is crucial in light of the worldwide repercussions of ischemic stroke. Conservative methodologies, specifically targeted drug delivery, present encouraging substitutes that mitigate the hazards linked to invasive procedures. With the intention of illuminating their considerable significance and prospective benefits, this study examines the impact of the geometry and dimensions of drug-loaded nano-microcarriers on atherosclerotic plaque. The research utilizes a finite element approach to simulate the motion and fluid dynamics of nano-microcarriers loaded with drugs within the carotid arteries. Carriers are available in a variety of shapes and sizes to accommodate patient-specific geometries, pulsatile fluid flow, and non-Newtonian blood properties. Optimization of drug delivery is achieved through the examination of carrier interaction with the inner wall. The results demonstrated that the interaction data between particles and the inner wall of atherosclerotic plaques exhibits micro- and nanoscale patterns that are distinct. Symmetric plaques demonstrate that nanoparticles with a 0.4 shape factor and diameters below 200 nm show the highest interaction rate. Conversely, larger particles (200 and 500 nm) with shape factors of 1 demonstrate comparatively elevated interaction rates. The optimal shape factor for drug-loaded microparticles has been determined to be one, and the number of interactions increases as the diameter of the nanoparticles increases, with a significant increase observed at a shape factor of one. Asymmetric plaques exhibit the maximum interaction rates among particles that have a shape factor of 0.4 and have diameters smaller than 500 µm. The findings establish a foundation for novel therapeutic strategies, establishing nano-microparticles as auspicious contenders for accurate and efficacious drug delivery systems that inhibit plaque proliferation.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

Predicting intercellular communication based on metabolite-related ligand-receptor interactions with MRCLinkdb

BackgroundMetabolite-associated cell communications play critical roles in maintaining human biological function. However, most existing tools and resources focus only on ligand-receptor interaction pairs where both partners are proteinaceous, neglecting other non-protein molecules. To address this gap, we introduce the MRCLinkdb database and algorithm, which aggregates and organizes data related to non-protein L-R interactions in cell-cell communication, providing a valuable resource for predicting intercellular communication based on metabolite-related ligand-receptor interactions.ResultsHere, we manually curated the metabolite-ligand-receptor (ML-R) interactions from the literature and known databases, ultimately collecting over 790 human and 670 mouse ML-R interactions. Additionally, we compiled information on over 1900 enzymes and 260 transporter entries associated with these metabolites. We developed Metabolite-Receptor based Cell Link Database (MRCLinkdb) to store these ML-R interactions data. Meanwhile, the platform also offers extensive information for presenting ML-R interactions, including fundamental metabolite information and the overall expression landscape of metabolite-associated gene sets (such as receptor, enzymes, and transporter proteins) based on single-cell transcriptomics sequencing (covering 35 human and 26 mouse tissues, 52 human and 44 mouse cell types) and bulk RNA-seq/microarray data (encompassing 62 human and 39 mouse tissues). Furthermore, MRCLinkdb introduces a web server dedicated to the analysis of intercellular communication based on ML-R interactions. MRCLinkdb is freely available at https://www.cellknowledge.com.cn/mrclinkdb/.ConclusionsIn addition to supplementing ligand-receptor databases, MRCLinkdb may provide new perspectives for decoding the intercellular communication and advancing related prediction tools based on ML-R interactions.

CPIExtract: A software package to collect and harmonize small molecule and protein interactions.

The binding interactions between small molecules and proteins are the basis of cellular functions. Yet, experimental data available regarding compound-protein interaction is not harmonized into a single entity but rather scattered across multiple institutions, each maintaining databases with different formats. Extracting information from these multiple sources remains challenging due to data heterogeneity. Here, we present CPIExtract (Compound-Protein Interaction Extract), a tool to interactively extract experimental binding interaction data from multiple databases, perform filtering, and harmonize the resulting information, thus providing a gain of compound-protein interaction data. When compared to a single source, DrugBank, we show that it can collect more than 10 times the amount of annotations. The end-user can apply custom filtering to the aggregated output data and save it in any generic tabular file suitable for further downstream tasks such as network medicine analyses for drug repurposing and cross-validation of deep learning models.

Downscaling spatial interaction with socioeconomic attributes

A variety of complex socioeconomic phenomena, for example, migration, commuting, and trade can be abstracted by spatial interaction networks, where nodes represent geographic locations and weighted edges convey the interaction and its strength. However, obtaining fine-grained spatial interaction data is very challenging in practice due to limitations in collection methods and costs, so spatial interaction data such as transportation data and trade data are often only available at a coarse scale. Here, we propose a gravity downscaling (GD) method based on readily accessible socioeconomic data and the gravity law to infer fine-grained interactions from coarse-grained data. GD assumes that interactions of different spatial scales are governed by the similar gravity law and thus can transfer the parameters estimated from coarse-grained regions to fine-grained regions. Results show that GD has an average improvement of 24.6% in Mean Absolute Percentage Error over alternative downscaling methods (i.e., the areal-weighted method and machine learning models) across datasets with different spatial scales and in various regions. Using simple assumptions, GD enables accurate downscaling of spatial interactions, making it applicable to a wide range of fields, including human mobility, transportation, and trade.

Node-Level Community Detection within Edge Exchangeable Models for Interaction Processes

Scientists are increasingly interested in discovering community structure from modern relational data arising on large-scale social networks. While many methods have been proposed for learning community structure, few account for the fact that these modern networks arise from processes of interactions in the population. We introduce block edge exchangeable models (BEEM) for the study of interaction networks with latent node-level community structure. The block vertex components model (B-VCM) is derived as a canonical example. Several theoretical and practical advantages over traditional vertex-centric approaches are highlighted. In particular, BEEMs allow for sparse degree structure and power-law degree distributions within communities. Our theoretical analysis bounds the misspecification rate of block assignments while supporting simulations show the properties of the network can be recovered. A computationally tractable Gibbs algorithm is derived. We demonstrate the proposed model using post-comment interaction data from Talklife, a large-scale online peer-to-peer support network, and contrast the learned communities from those using standard algorithms including degree-corrected stochastic block models, popularity-adjusted block models, and weighted stochastic block models. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Selection and Mechanism Study of Q-Markers for Xanthocerais lignum Anti-Rheumatoid Arthritis Based on Serum Spectrum-Effect Correlation Analysis.

To elucidate the chemical profile of Xanthocerais lignum's extracts of different polarities and their impact on rheumatoid arthritis (RA), we identified anti-RA markers and predicted their action mechanisms. A collagen-induced arthritis rat model was established, and UPLC-Q-Exactive Orbitrap MS technology was employed to analyze and identify the chemical constituents within the alcohol extract of Xanthocerais lignum and its various extraction fractions, as well as their translocation into the bloodstream. Serum spectrum-effect correlation analysis was utilized to elucidate the pharmacodynamic material basis of Xanthocerais lignum against RA and to screen for Q-Markers. Finally, the potential anti-RA mechanisms of the Q-Markers were predicted through compound-target interaction data and validated using molecular docking techniques. We identified 71 compounds, with flavan-3-ols and flavanones as key components. Of these, 36 were detected in the bloodstream, including 17 original and 19 metabolized forms. Proanthocyanidin A2, dihydroquercetin, catechin, and epicatechin (plus glucuronides) showed potential anti-RA activity. These compounds, acting as Q-Markers, may modulate ERK, NF-κB, HIF-1α, and VEGF in the HIF-1 pathway. This research clarifies Xanthocerais lignum's pharmacodynamic material basis against RA, identifies 4 Q-Markers, and offers insights into their mechanisms, aiding quality assessment and lead compound development for RA treatment.

EzSingleCell: an integrated one-stop single-cell and spatial omics analysis platform for bench scientists

ezSingleCell is an interactive and easy-to-use application for analysing various single-cell and spatial omics data types without requiring prior programing knowledge. It combines the best-performing publicly available methods for in-depth data analysis, integration, and interactive data visualization. ezSingleCell consists of five modules, each designed to be a comprehensive workflow for one data type or task. In addition, ezSingleCell allows crosstalk between different modules within a unified interface. Acceptable input data can be in a variety of formats while the output consists of publication ready figures and tables. In-depth manuals and video tutorials are available to guide users on the analysis workflows and parameter adjustments to suit their study aims. ezSingleCell’s streamlined interface can analyse a standard scRNA-seq dataset of 3000 cells in less than five minutes. ezSingleCell is available in two forms: an installation-free web application (https://immunesinglecell.org/ezsc/) or a software package with a shinyApp interface (https://github.com/JinmiaoChenLab/ezSingleCell2) for offline analysis.

Implementasi Inovasi Program Multiple Intelligences Research (MIR) di Sekolah Dasar Inklusi Ponorogo

The educational tendency in Indonesia to equalize intelligence standards between students through formal tests can kill students' potential intelligence, interests and talents. Minimal awareness of the concept of multiple intelligences (multiple intelligences) educational institutions in Indonesia need more innovative solutions so that efforts to improve the quality of education can be more optimized. The MIR program innovation initiated by Munif Chatib is the right solution so that the concept of different student intelligence can be understood and appreciated as a basis for determining methods and styles in learning activities. The research method used in this research is descriptive qualitative research with a case study type of research (case study). The data collection technique used is an in-depth interview (indepth interview), participatory observation and documentation processed using Miles, Huberman and Saldana's interactive model data analysis technique which consists of four stages in the form of data collection, data condensation, data presentation and data verification. The location of this research was carried out at SD Immersion Ponorogo as a school with branding inclusion. This research aims to analyze the process of spreading MIR program innovation so that it can become a new breakthrough in the world of education to better appreciate and develop students' various intelligences. The research results show that the process of spreading innovation is carried out in line with Everett M Roger's theory of innovation diffusion which consists of innovation, communication channels, time period and social systems as an effective and efficient dissemination strategy. The MIR program is used as a reference for teachers in determining students' learning styles and models at Immersion Elementary School according to their multiple intelligences more effectively and efficiently. The learning styles used include: storry telling, brainstorming, tape recording, journal writing, fieldtrip, and project based learning.

Novel behavior-enhanced long- and short-term interest model for sequential recommendation

In the realm of modern recommender systems, user-item interaction data often exhibit sequential patterns in relation to various behaviors, such as clicks and purchases on e-commerce platforms. The objective of heterogeneous sequential recommendation (HSR) is to predict each user's next item of interest under a specific behavior based on these interactions. However, existing approaches to HSR struggle to fully capture item transition relationships, as they consider these relationships from a single dimension and at a coarse-grained level. To bridge this gap, we propose the novel Behavior-enhanced Long- and Short-term Interest (BLSI) model, which explores fine-grained item transition relationships in both local and global dimensionalities. At its core, BLSI incorporates a behavior-enhanced self-attention network (BSAN) to capture short-term user preferences. BSAN distinguishes the effects of different behaviors and considers cross-type behavior influences during the linear projection and attention score calculation stages. Additionally, BLSI employs a heterogeneous graph neural network (HGNN) to model long-term user interests by discriminatively aggregating the information of neighboring nodes according to their behavior transition relationships. Furthermore, a gating mechanism is implemented to adaptively fuse short- and long-term preferences for personalized recommendations. Extensive experimental results on three datasets demonstrate that BLSI significantly outperforms state-of-the-art recommendation methods, highlighting the advantages of leveraging sequentiality and behavioral heterogeneity.

Modular pharmacology: research progress and development of analysis platform

Based on the systematic deconstruction of multi-dimensional and multi-target biological networks, modular pharmacology explains the complex mechanism of diseases and the interactions of multi-target drugs. It has made progress in the fields of pathogenesis of disease, biological basis of disease and traditional Chinese medicine(TCM) syndrome, pharmacological mechanism of multi-target herbs, compatibility of formulas, and discovery of new drug of TCM compound. However, the complexity of multi-omics data and biological networks brings challenges to the modular deconstruction and analysis of the drug networks. Here, we constructed the "Computing Platform for Modular Pharmacology" online analysis system, which can implement the function of network construction, module identification, module discriminant analysis, hub-module analysis, intra-module and inter-module relationship analysis, and topological visualization of network based on quantitative expression profiles and protein-protein interaction(PPI) data. This tool provides a powerful tool for the research on complex diseases and multi-target drug mechanisms by means of modular pharmacology. The platform may have broad range of application in disease modular identification and correlation mechanism, interpretation of scientific principles of TCM, analysis of complex mechanisms of TCM and formulas, and discovery of multi-target drugs.

Vocational School Revitalization: Opportunities and Challenges (Case Study of Implementation of West Nusa Tenggara Governor Policy Number 75 of 2020)

The aim of this research is to understand the opportunities and challenges of implementing the vocational school revitalization policy at SMKN 01 Jerowaru, East Lombok. This research is a qualitative descriptive study with the research location at SMKN 01 Jerowaru, East Lombok. Collection techniques through interviews. Data analysis in this research uses the Interactive Model data analysis from Miles, Huberman and Saldana (2014). The results of this research show that there are opportunities and challenges for SMKN 01 Jerowaru in implementing the Vocational School Revitalization policy. Where opportunities and challenges come internally and externally. where the research results in this year, the opportunity for policy implementation obtained by SMK 01 Jerowaru is the collaboration between the school and industry in the form of an agreement in creating a curriculum, as a follow-up to this in the form of activities to increase competency and develop skills for teachers through the OJT program. Meanwhile, the challenge is the provision of practical equipment such as bratending and barista practice rooms which still do not exist. For the culinary arts department, even though they already have an RPS equipped with IDUKA standard equipment, the production of products in schools is still not productive and unable to produce a surplus.

Integrating Farmers’ Perspectives into Earth System Model Development: Interviews with End Users in the Willamette Valley, Oregon, to Guide Actionable Science

Abstract This paper analyzes findings from semistructured interviews and focus groups with 31 farmers in the Willamette Valley in which farmers were asked about their needs for climate data and about the usability of a range of outputs from the Community Earth System Model, version 2 (CESM2), for their soil management practices. Findings indicate that climate and soils data generated from CESM and other Earth system models (ESMs), despite their coarse spatial scale resolutions, can inform farmers’ long-term decisions, but that the data would be more usable if the outputs were provided in a format that allowed farmers to choose the variables and thresholds relevant to their particular needs and if ESMs incorporated farmer practices including residue removal, cover cropping, and tillage levels into the model operations so that farmers could better understand the impacts of their decisions. Findings also suggest that although there is a significant gap in the spatial resolution at which these global ESMs generate data and the spatial resolution needed by farmers to make most decisions, farmers are adept at making scalar adjustments to apply coarse-resolution data to the specifics of their own farm’s microclimate. Thus, our findings suggest that, to support agricultural decision-making, development priorities for ESMs should include developing better representations of agricultural management practices within the models and creating interactive data dashboards or platforms.

Application of entertainment and fitness robots based on game interaction in sports training data analysis

This study aims to explore the application of sports data analysis of recreational fitness robots in sports training. By introducing interactive elements of games, recreational fitness robots can provide more interesting and challenging fitness experience, thus attracting more people to participate in sports training. Research and design and develop a game interactive entertainment fitness robot, with action recognition and user interaction functions, can perceive the user’s actions and make corresponding responses. When the user interacts with the robot, the sensor continuously collects and records data, establishes a data acquisition and storage system, analyzes and processes the collected interactive data, identifies the user’s movement pattern, assesses the user’s physical fitness level, and identifies potential improvement points. According to the user’s physical condition and training objectives, the appropriate exercise plan and game interaction mode are designed to provide better training results and entertainment experience. It is found that the game-based interactive entertainment fitness robot can effectively improve the user’s exercise motivation and participation in sports training. Through the analysis of sports data, we can develop personalized training plans according to the specific needs and goals of users, so as to achieve better training results.

Analysis of Personal Data Preservation Policy in Utilizing AI-Based Chatbot Applications in Indonesia

The increasing adoption of AI-based chatbot applications in Indonesia raises concerns about personal data preservation. This qualitative case study investigated the issue through in-depth interviews with chatbot users, developers, and government officials/regulators. This study's findings revealed a gap in user awareness of privacy policies and concerns about data misuse. Developers face challenges balancing personalization with privacy, while regulators acknowledge the need to continuously adapt the legal framework. The study recommends enhancing transparency, user empowerment, and regulatory oversight to ensure the responsible and ethical use of personal data in chatbot interactions.

Developing Farmers’ Community Resilience in the Volcanic Area of Mount Merapi, Indonesia

Mount Merapi is one of the active stratovolcanoes in Indonesia, and its slopes are not only home to the powerful forces of nature but also to thriving communities of farmers. Many farmers' communities are found around the slopes of this mountain, relying on the fertile volcanic soil for their agricultural activities. This research was undertaken near Mount Merapi, approximately 4 km from the volcano peak, to investigate the resilience of farmers' communities in the face of Mount Merapi's potential disasters. It affects the farmer’s behavior, with the majority of residents being farmers. The primary objective of this study was to understand why and how these farmers’ communities demonstrate resilience in the wake of Mount Merapi's potential disasters. The qualitative approach was used in this research and the key informants were determined by using the purposive sampling technique. Data obtained were analyzed using an interactive data analysis model. The study identified three primary themes that significantly influenced the resilience of farmers' communities. The first theme encompasses social aspects, highlighting the importance of social capital, including strong social relations, trust, and community networks. The second theme revolves around economic factors, specifically business capital and the various forms of income within the farmers' communities. Lastly, the third theme focuses on environmental considerations, emphasizing the role of natural capital, particularly effective land use, as the most impactful in maximizing community resilience. Additionally, it was also found that the strong engagement among farmers within their communities made their resilience ability toward disaster highly formidable

Application Tracking System

Abstract— Application Tracking System:- This research paper examines the user experience (UX) aspects of Applicant Tracking System (ATS) dashboards, focusing on enhancing their usability and effectiveness. Through a combination of qualitative interviews with HR professionals and quantitative analysis of user interaction data, the study identifies common challenges and opportunities for improvement within ATS dashboards. The findings suggest design recommendations to streamline navigation, optimize functionality, and enhance overall satisfaction for both HR practitioners and job applicants. By addressing these key issues, the research aims to contribute to the advancement of ATS dashboard design, ultimately improving recruitment processes and user engagement in the HR technology domain . Objective and Scope of the Project : The primary objective of the Application Tracking System (ATS) project is to streamline and enhance the recruitment process by automating the tracking, management and organization of job applications. The system aims to reduce the time and effort involved in recruitment, improve the candidate experience and provide a centralized platform to manage all recruitment-related activities. It is designed to ensure efficient communication between recruiters and applicants, track application status in real-time and maintain a comprehensive database of candidate profiles and interactions. The scope of the ATS project includes the development and deployment of a web-based platform accessible to HR departments and applicants. Key functionalities include job posting creation, applicant tracking, resume parsing, interview scheduling and automated notifications. The system will support integration with job boards, social media platforms and email services. In addition, it will facilitate reporting and analytics tools to evaluate recruitment metrics. The project will cover user training, data migration from existing systems and ongoing maintenance and support. Keywords – Applicant Tracking System (ATS), dashboard design, user experience (UX), usability, HR technology, qualitative research, quantitative analysis, HR professionals, user interaction, recruitment processes, design recommendations, usability optimization.