Context Of Models Research Articles

Non-calcified plaque-based coronary stenosis grading in contrast enhanced CT

BackgroundThe high mortality rate associated with coronary heart disease has led to state-of-the-art non-invasive methods for cardiac diagnosis including computed tomography and magnetic resonance imaging. However, stenosis computation and clinical assessment of non-calcified plaques has been very challenging due to their ambiguous intensity response in CT i.e. a significant overlap with surrounding muscle tissues and blood. Accordingly, this research presents an approach for computation of coronary stenosis by investigating cross-sectional lumen behaviour along the length of 3D coronary segments. MethodsNon-calcified plaques are characterized by comparatively lower-intensity values with respect to the surrounding. Accordingly, segment-wise orthogonal volume was reconstructed in 3D space using the segmented coronary tree. Subsequently, the cross sectional volumetric data was investigated using proposed CNN-based plaque quantification model and subsequent stenosis grading in clinical context was performed. In the last step, plaque-affected orthogonal volume was further investigated by comparing vessel-wall thickness and lumen area obstruction w.r.t. expert-based annotations to validate the stenosis grading performance of model. ResultsThe experimental data consists of clinical CT images obtained from the Rotterdam CT repository leading to 600 coronary segments and subsequent 15786 cross-sectional images. According to the results, the proposed method quantified coronary vessel stenosis i.e. severity of the non-calcified plaque with an overall accuracy of 83%. Moreover, for individual grading, the proposed model show promising results with accuracy equal to 86%, 90% and 79% respectively for severe, moderate and mild stenosis. The stenosis grading performance of the proposed model was further validated by performing lumen-area versus wall-thickness analysis as per annotations of manual experts. The statistical results for lumen area analysis precisely correlates with the quantification performance of the model with a mean deviation of 5% only. ConclusionThe overall results demonstrates capability of the proposed model to grade the vessel stenosis with reasonable accuracy and precision equivalent to human experts.

Studi Literatur Riview: Manajemen Karir Meningkatkan Kinerja dan Kepuasan Kerja Karyawan

Career management is a crucial process for both individuals and organizations. For individuals, it facilitates the achievement of career goals and enhances job satisfaction. For organizations, it aids in attracting, developing, and retaining qualified employees. This study delves into the factors influencing career management, encompassing individual factors (abilities, skills, interests, values, and personality), organizational factors (organizational culture, structure, career opportunities, and career policies), and environmental factors (economic, socio-political, and technological conditions). The findings demonstrate that effective career management can enhance employee career effectiveness, job satisfaction, and organizational performance. The study also provides recommendations for improving career management, including regular career assessments, career development program development, information and training, a supportive organizational culture, and encouraging employees to proactively manage their careers. Further research is warranted to develop more effective and contextual career management models and investigate the impact of career management on other factors such as organizational commitment, employee turnover, and gender equality in the workplace.

A data-driven approach to aligning academic offerings with industry needs for business and economy in Slovenia

ABSTRACT The success of graduates and their employability hinge on a crucial alignment between the skills and competencies sought by employers and those possessed by job seekers. This study delves into the extent to which the competencies cultivated in the curricula of selected Slovenian universities align with the ever-evolving requirements of the Slovenian labour market. Employing a combination of LLaMA-2-7B-chat, a generative language model, and the multilingual Sentence Transformer, a contextual language model, we identify skill gaps and pinpoint the most in-demand competencies that align with those generated by the faculty. This data-driven approach offers a roadmap for confirming the relevance and enhancing the university curricula, thus fostering a stronger connection between education and employment as well as improving the labour market efficiency.

Application of Contextual Teaching and Learning Model to Improve Disposition and Creative Thinking Skills of High School Students

Facing the challenges of the 21st century, learning must be able to produce learners into creative individuals to solve problems. The ability to think creatively is closely related to a person's character or creative habits; good character can trigger good skills. This study aims to determine how applying the Contextual Teaching and Learning (CTL) model improves students' creative disposition and creative thinking skills. The method in the form of Quasi-Experimental Design uses Pre-test and post-test control Group Design on as many as 64 X students at one of Aikmel State High Schools, NTB. The 32-person experimental class used the CTL model of the guided inquiry method, and the 32-person control class of the discussion method. Data on students' creative dispositions were collected using creative disposition questionnaires given before and after good treatment in both groups. Data on students' creative thinking skills were collected with a creative thinking skills test developed referring to the Torrance Creative test. The data were obtained and analyzed using the t-test to determine the comparison of the two classes and the N-gain test to see the improvement in each class. The results showed significant differences in creative disposition and creative thinking skills between the experimental class and the control class. Increased creative disposition in the experimental class with an N-gain of 0.45 means an increase in the medium category and an N-gain of 0.08 in the control class, which means an increase in the low category. Meanwhile, the creative thinking skills of experimental class students amounted to an N-gain of 0.48, which means there was an increase in the medium category, and an N-gain of 0.05 in the control class, which means there was an increase in low theory. This study concludes that applying the CTL model can improve creative disposition and thinking skills.

DIN 18009-2 – a New German Standard on Evacuation Simulation

After more than eight years of intensive work, the second part of the German DIN 18009 standard on "Simulation of evacuation and personal safety" was published in August 2022. The DIN 18009 series offers a guideline for fire protection engineering for Germany. The first part "Principles and application rules" was published in 2016, with further parts describing standards for smoke simulation and safety concepts to follow. This marks a milestone in the application of simulation models in the safety context for Germany. This article discusses the advantages and disadvantages of prescriptive guidelines and performance-based engineering methods in the context of building evacuation. Furthermore, aspects of the application of the DIN 18009-2 are highlighted, and what challenges are still to be tackled and how. This includes in particular the choice and definition of scenarios and performance criteria.

Anonymizing medical case-based explanations through disentanglement

Case-based explanations are an intuitive method to gain insight into the decision-making process of deep learning models in clinical contexts. However, medical images cannot be shared as explanations due to privacy concerns. To address this problem, we propose a novel method for disentangling identity and medical characteristics of images and apply it to anonymize medical images. The disentanglement mechanism replaces some feature vectors in an image while ensuring that the remaining features are preserved, obtaining independent feature vectors that encode the images’ identity and medical characteristics. We also propose a model to manufacture synthetic privacy-preserving identities to replace the original image’s identity and achieve anonymization. The models are applied to medical and biometric datasets, demonstrating their capacity to generate realistic-looking anonymized images that preserve their original medical content. Additionally, the experiments show the network’s inherent capacity to generate counterfactual images through the replacement of medical features.

SEC: A core hub during cell fate alteration.

Pol II pause release is a rate-limiting step in gene transcription, influencing various cell fate alterations. Numerous proteins orchestrate Pol II pause release, thereby playing pivotal roles in the intricate process of cellular fate modulation. Super elongation complex (SEC), a large assembly comprising diverse protein components, has garnered attention due to its emerging significance in orchestrating physiological and pathological cellular identity changes by regulating the transcription of crucial genes. Consequently, SEC emerges as a noteworthy functional complex capable of modulating cell fate alterations. Therefore, a comprehensive review is warranted to systematically summarize the core roles of SEC in different types of cell fate alterations. This review focuses on elucidating the current understanding of the structural and functional basis of SEC. Additionally, we discuss the intricate regulatory mechanisms governing SEC in various models of cell fate alteration, encompassing both physiological and pathological contexts. Furthermore, leveraging the existing knowledge of SEC, we propose some insightful directions for future research, aiming to enhance our mechanistic and functional comprehension of SEC within the diverse landscape of cell fate alterations.

Factors affecting grade 6 students' learning following the B-Learning model

This study investigates the factors affecting Grade 6 students' learning experiences within the B-Learning model in the Vietnamese educational context. Blended learning, combining traditional face-to-face instruction with online components, has become increasingly significant in Vietnam's evolving educational landscape. The research explores the impact of technological access, self-engagement behaviors, and support mechanisms on Grade 6 students' learning within this innovative framework. The prevalence and proficiency with digital tools, particularly Tablets or iPads, are examined to understand their influence on students' engagement with online components. Additionally, the study delves into self-engagement behaviors, including goal-setting and time management, aiming to uncover correlations with academic performance. Furthermore, familial, peer, and teacher support structures are explored to comprehend their role in students' adaptability to the B-Learning model. The findings aim to provide nuanced insights for Vietnamese educators, policymakers, and researchers, facilitating the refinement of blended learning practices to better cater to the specific needs and challenges of Grade 6 students in Vietnam's educational milieu.

Gaussian process regression for three-dimensional soil mapping over multiple spatial supports

This study investigates the complexity of spatial soil modelling, particularly focusing on the challenge of variable vertical support in traditional soil data collection. Traditional soil sampling, described in terms of horizons, often fails to accurately pinpoint the specific depths for specific soil properties. This gap is significant, as depth-specific data is crucial for a thorough understanding of soil formation processes and for assessing potential environmental impacts. In digital soil mapping (DSM), the prevalent reliance on standardised depth intervals and mass-preserving spline functions for data resampling results in a modelling approach that tends to disregard depth-related details, thereby introducing potential uncertainties.To address these limitations, this work explores how effectively Gaussian process regression (GPR) can model soil in 3D. This technique comprises two key components: a mean function and a semivariogram-like kernel. Unlike conventional methods that make a single prediction, GPR provides detailed probability distributions. This capability allows for the quantification of prediction uncertainty at various points, offering insights for decision-making and risk assessment purposes. Moreover, GPR has the capability to make volume or block estimates and assess associated uncertainties. Enabling volume-based predictions enriches the range of strategies available for land management.In this research, we employ GPR as a novel 3D soil modelling technique and compare its performance with traditional spline-based methods. The comparison is conducted through a case study on a farm in northern New South Wales, Australia, focusing on the 3D mapping of soil pH and electrical conductivity (EC). Our results demonstrate the ability of GPR to estimate soil properties across various volumes, utilising data from multiple vertical supports, thereby offering a more versatile approach for soil modelling in diverse spatial contexts.

(Why) Do We Trust AI?: A Case of AI-based Health Chatbots

Automated chatbots powered by artificial intelligence (AI) can act as a ubiquitous point of contact, improving access to healthcare and empowering users to make effective decisions. However, despite the potential benefits, emerging literature suggests that apprehensions linked to the distinctive features of AI technology and the specific context of use (healthcare) could undermine consumer trust and hinder widespread adoption. Although the role of trust is considered pivotal to the acceptance of healthcare technologies, a dearth of research exists that focuses on the contextual factors that drive trust in such AI-based Chatbots for Self-Diagnosis (AICSD). Accordingly, a contextual model based on the trust-in-technology framework was developed to understand the determinants of consumers’ trust in AICSD and its behavioral consequences. It was validated using a free simulation experiment study in India (N = 202). Perceived anthropomorphism, perceived information quality, perceived explainability, disposition to trust technology, and perceived service quality influence consumers’ trust in AICSD. In turn, trust, privacy risk, health risk, and gender determine the intention to use. The research contributes by developing and validating a context-specific model for explaining trust in AICSD that could aid developers and marketers in enhancing consumers’ trust in and adoption of AICSD.

A dual model of normal vs isogenic Nrf2-depleted murine epithelial cells to explore oxidative stress involvement

Cancer-derived cell lines are useful tools for studying cellular metabolism and xenobiotic toxicity, but they are not suitable for modeling the biological effects of food contaminants or natural biomolecules on healthy colonic epithelial cells in a normal genetic context. The toxicological properties of such compounds may rely on their oxidative properties. Therefore, it appears to be necessary to develop a dual-cell model in a normal genetic context that allows to define the importance of oxidative stress in the observed toxicity. Given that the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) is considered to be the master regulator of antioxidant defenses, our aim was to develop a cellular model comparing normal and Nrf2-depleted isogenic cells to qualify oxidative stress–related toxicity. We generated these cells by using the CRISPR/Cas9 technique. Whole-genome sequencing enabled us to confirm that our cell lines were free of cancer-related mutations. We used 4-hydroxy-2-nonenal (HNE), a lipid peroxidation product closely related to oxidative stress, as a model molecule. Here we report significant differences between the two cell lines in glutathione levels, gene regulation, and cell viability after HNE treatment. The results support the ability of our dual-cell model to study the role of oxidative stress in xenobiotic toxicity.

Narrative Analysis: Demonstrating the Iterative Process for New Researchers

<p>This article demonstrates and describes an iterative process of narrative analysis for researchers who want to familiarise themselves with this methodology. The method draws on the six-step process of how to analyse a narrative, the four modes of reading a narrative and the three-sphere model of external context. The application of the method is demonstrated through describing the process of analysis of New Zealand school counsellors’ narratives of strengths-based counselling. Furthermore, this article posits that committing to a narrative analysis process of repeated and in-depth engagement with participants’ narrative data may facilitate a more robust and engaging research outcome than may otherwise have been achieved through more prescriptive methods of narrative analysis. Finally, this article highlights the use of story-map grids (tables) and models as visual aids to assist in the process of narrative analysis.</p>

ANALISIS MANAJEMEN PENGELOLAAN KELAS DALAM MENINGKATKAN KEMAMPUAN KOMUNIKASI MULTILINGUAL DENGAN MODEL CONTEXTUAL TEACHING AND LEARNING

“The contextual teaching and learning model is Among several student management developed by combining classical learning by teaching staff at MTs Putri Nurul Huda Situbondo. The type of research used is qualitative research with a case study approach. The informants in this research were school principals, teachers, and students. Data collection techniques employed observation, interviews, and documentation studies. In this research, researchers used several stages of data analysis, namely the data condensation stage, data presentation stage, making temporary conclusions, and verification activities. The research results concluded that there are four stages of student management in improving multilingual communication competence: classroom layout management skills, time management skills and material coverage, and student management skills through language games. The CTL learning model is carried out in the planning stage, namely by making a learning implementation plan that refers to the independent curriculum, then the implementation stage, which contains three activity stages, namely preliminary activities, core activities, which contain the components of constructivism, inquiry, asking, learning community, modeling, reflection, then Closing activities use authentic assessment.

Enhancing Time-Series Prediction with Temporal Context Modeling: A Bayesian and Deep Learning Synergy

In time-series classification, conventional deep learning methods often treat continuous signals as discrete windows, each analyzed independently without considering the contextual information from adjacent windows. This study introduces a novel, lightweight Bayesian meta-classification approach designed to enhance prediction accuracy by integrating contextual label information from neighboring windows. Alongside training a deep learning model, we construct a Conditional Probability Table (CPT) during training to capture label transitions. During inference, these CPTs are utilized to adjust the predicted class probabilities of each window, taking into account the predictions of preceding windows. Our experimental analysis, focused on Human Activity Recognition (HAR) time series datasets, demonstrates that this approach not only surpasses the baseline performance of standalone deep learning models but also outperforms contemporary state-of-the-art methods that integrate temporal context into time series prediction.

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study.

There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables. We aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data. We developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model. For the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F1-score of 91.71%; our participant-inclusive generalized model achieved an average accuracy of 66.95% and an F1-score of 42.50%; and our participant-exclusive generalized model achieved an average accuracy of 67.65% and an F1-score of 43.05%. Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.

Linking social media addiction and student retention through the lenses of student engagement

PurposeThis research is designed to explore the relationship between social media addiction, student engagement and student retention. Social media addiction is dealt with as an independent variable student engagement acts as a mediating variable and student retention as a dependent variable.Design/methodology/approachThis is a cross-sectional and quantitative research. Primary data are collected from 600 respondents (university students) with the help of a structured questionnaire. Multistage sampling techniques, i.e. simple random sampling and judgment sampling, are used for the selection of respondents.FindingsResults indicate that for direct relationships, social media addiction has a significant positive impact on student engagement and student retention, respectively, while student engagement is partially mediating the relationship between social media addiction with student retention.Research limitations/implicationsIn the future, these kinds of research may also be conducted on students of different universities in Pakistan, which are located in other cities of Pakistan besides Lahore. This research provides a practical framework for the higher authorities of the universities of Pakistan and explains how the use of media positively fosters the levels of student retention directly and indirectly through the path of student engagement. It is commonly believed that media addiction is bad but the result of this research indicates that anything is not dangerous but depends upon its use, media addiction itself is not bad but if someone uses this for a good purpose in limitation then it has better outcomes. The result indicates that the media addiction of students has a positive impact on student retention. This means that if someone uses media for a positive purpose then he/she will use it as a supporting tool for success. Longitudinal research on these variables will also help to check the status after a specific interval of time.Practical implicationsThe current study will help the practitioners or policymakers (Managers) of higher education institutions by providing practical insights into the positive use of media by students for increasing their knowledge and grades. This research can also help practitioners or policymakers to focus their students on the positive use of social media for fostering the levels of student retention.Originality/valueTo the best of the researcher’s knowledge, no previous study has been done to incorporate social media addiction and student engagement in a single model in the Pakistani cultural context. Similarly, the relationship of variables social media addiction with student engagement is rarely checked empirically because the research of Wang et al. (2011) proposed that social media addiction has a relationship with student engagement so that is why this is the rationale of the research is to check this empirically. Moreover, this study is an initial effort to check the mediating effect of student engagement in the relationship between social media addiction and student retention. This research is also proposing the framework of social media addiction, student engagement and student retention based on the social exchange theory (SET).

Towards real-time practical image compression with lightweight attention

The rate–distortion (RD) performance of learning-based image compression (LIC) has already surpassed that of traditional Versatile Video Coding (VVC) intra-coding. However, the gain of the compression efficiency is at the cost of high computational complexity, which is prohibitively expensive and becomes a bottleneck for practical applications. To this end, we proposed an efficient LIC method with lightweight designs for real-time practical applications, which achieves a better trade-off between compression performance and computational complexity. Specifically, residual connected lightweight attention units (RLAUs) are stacked for feature extraction, which achieves effective global spatial information embedding while keeping relatively low complexity. Meanwhile, a trainable channel-gained adaptive module (CGAM) is introduced into the nonlinear transform network and multi-stage context model. This module re-distributes the importance of different channels, further improving compression efficiency. The experimental results demonstrate that the proposed method achieves better compression efficiency than VVC intra-coding. Furthermore, compared to other state-of-the-art LIC approaches, the proposed method significantly reduces the coding time while maintaining comparable coding efficiency. The source codes are available at https://github.com/llsurreal919/LightweightLIC.

An Adaptive Contextual Relation Model for Improving Response Generation

An Adaptive Contextual Relation Model for Improving Response Generation

Does dynamical wormhole evolve from emergent scenario?

In the present work we analyze a dynamical wormhole solution with two fluids system (one isotropic and homogeneous and the other being inhomogeneous and anisotropic in nature) as the matter at the throat. We choose two different forms of Equation of State(EoS) and investigate two solutions of the wormhole geometry. The properties to ensure existence and traversability has been analyzed. Also, the model of the dynamic wormhole has been examined for a possibility of the Emergent Universe(EU) model in cosmological context. Finally, for the dynamical wormholes so obtained, Null Energy Condition(NEC) has been examined near the throat.

Revolutionizing decision support: a systematic literature review of contextual implementation models for electronic health records systems

PurposeThis study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.Design/methodology/approachA systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.FindingsThe findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.Originality/valueThis study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.