Context Of Models Research Articles

A Socio-cognitive Discourse Analysis of Expats’ Construction of COVID-19 Pandemic

This study employed socio-cognitive discourse analysis to examine interviews with expatriates in China and explore how their cognitive discourse regarding collectivism amid the COVID-19 pandemic was formed. It revealed that the discursive strategies that the expats adopted to address disparities in values included reframing and de-ideologization. Additionally, the context model was introduced to investigate the process of how the interviewees’ social memories evolved into discourse, further unveiling the significance of intercultural communication elements in context, such as the politeness principle and intercultural ethics.

Application of the XGBoost Model with Hyperparameter Tuning for Industry Classification for Job Applicants

The development of technology and changes in job market dynamics have created new challenges in aligning education with industry needs. In this research, the XGBoost model with hyperparameter tuning was applied for industry classification on job applicant data taken from the Kaggle dataset LinkedIn Job Postings in 2023. This dataset consists of 23 attributes with a total of 33,085 job vacancy data points. The experimental results show that both the model without hyperparameter tuning and with GridSearchCV produce the same classification accuracy, which is 0.89 or 89%, with stable precision, recall, and F1-Score values. The best parameters found in this study are colsample_bytree = 1.0, learning_rate = 0.3, max_depth = 6, min_child_weight = 1, n_estimators = 100, and subsample = 1.0. However, cross-validation using k-fold shows a significant increase in accuracy to 0.90, or 90%. This finding confirms that the use of cross-validation can improve the performance estimation of the model more accurately and robustly by utilizing all available data for training and testing. Moreover, the implementation of cross-validation demonstrates the importance of leveraging all data points to enhance model reliability and robustness. Future research can explore alternative hyperparameter tuning methods and apply the model to larger datasets to further validate the generalizability and reliability of the XGBoost model in different application contexts. Thus, this study underscores the significance of rigorous model evaluation techniques in achieving high-performing machine learning models

Self and other-perceived deception detection abilities are highly correlated but unassociated with objective detection ability: Examining the detection consensus effect

Subjective lying rates are often strongly and positively correlated. Called the deception consensus effect, people who lie often tend to believe others lie often, too. The present paper evaluated how this cognitive bias also extends to deception detection. Two studies (Study 1: N = 180 students; Study 2: N = 250 people from the general public) had participants make 10 veracity judgments based on videotaped interviews, and also indicate subjective detection abilities (self and other). Subjective, perceived detection abilities were significantly linked, supporting a detection consensus effect, yet they were unassociated with objective detection accuracy. More overconfident detectors—those whose subjective detection accuracy was greater than their objective detection accuracy—reported telling more white and big lies, cheated more on a behavioral task, and were more ideologically conservative than less overconfident detectors. This evidence supports and extends contextual models of deception (e.g., the COLD model), highlighting possible (a)symmetries in subjective and objective veracity assessments.

Pengaruh Model Contextual Teaching and Learning terhadap Hasil Belajar Kognitif Siswa pada Tema 5 Subtema 3 Kelas III SDN Batang-Batang Daya II

This research was conducted with the aim of knowing the Effect of Contextual Teaching And Learning Model on Students' Cognitive Learning Outcomes on Theme 5 Subtheme 3 Class III SDN Batang-Batang Daya II. This research is a quantitative study that uses the experimental method, with a Pre-Experimental design using the One-Group Pre-Test Post-Test Design. Data collection was carried out using the sampling method from the population. The number of students sampled was 21 students. Data was collected through giving Pre-Test and Post-Test questions. The results showed that the application of the Contextual Teaching and learning (CTL) learning model had a significant impact on student learning outcomes. The significance level of the results of the Paired sample T-Test test shows a value of 0.000, this indicates that there is a significant difference found in the effect. Based on the testing criteria if the Sig. (2-tailed) <0.05 (0.000<0.05) then Ho is rejected and Ha is accepted. This means that there is a significant influence in the application of the Contextual Teaching And Learning Model on Students' Cognitive Learning Outcomes on Theme 5 Subtheme 3 Class III SDN Batang-Batang Daya II.

A Survey on Artificial-Intelligence-Based Internet of Vehicles Utilizing Unmanned Aerial Vehicles

As Autonomous Vehicles continue to advance and Intelligent Transportation Systems are implemented globally, vehicular ad hoc networks (VANETs) are increasingly becoming a part of the Internet, creating the Internet of Vehicles (IoV). In an IoV framework, vehicles communicate with each other, roadside units (RSUs), and the surrounding infrastructure, leveraging edge, fog, and cloud computing for diverse tasks. These networks must support dynamic vehicular mobility and meet strict Quality of Service (QoS) requirements, such as ultra-low latency and high throughput. Terrestrial wireless networks often fail to satisfy these needs, which has led to the integration of Unmanned Aerial Vehicles (UAVs) into IoV systems. UAV transceivers provide superior line-of-sight (LOS) connections with vehicles, offering better connectivity than ground-based RSUs and serving as mobile RSUs (mRSUs). UAVs improve IoV performance in several ways, but traditional optimization methods are inadequate for dynamic vehicular environments. As a result, recent studies have been incorporating Artificial Intelligence (AI) and Machine Learning (ML) algorithms into UAV-assisted IoV systems to enhance network performance, particularly in complex areas like resource allocation, routing, and mobility management. This survey paper reviews the latest AI/ML research in UAV-IoV networks, with a focus on resource and trajectory management and routing. It analyzes different AI techniques, their training features, and architectures from various studies; addresses the limitations of AI methods, including the demand for computational resources, availability of real-world data, and the complexity of AI models in UAV-IoV contexts; and considers future research directions in UAV-IoV.

Enterprise Architecture Implementation Scholastic Learning Zone Literacy Improvement St. Kristoforus 2 High-School

This research aims to explore the transformation of Enterprise Architecture in the implementation of a Scholastic Learning Zone for the improvement of learners' literacy at Santo Kristoforus 2 High School. The background of this research is based on the importance of literacy as the primary foundation in learning, as well as the need to integrate innovative educational technology. The main objective of the research is to understand how the implementation of Enterprise Architecture can support the implementation of the Scholastic Learning Zone effectively and efficiently. The research method used is a qualitative approach with a case study at Santo Kristoforus 2 High School, involving in-depth interviews with educators, direct observation, and analysis of related documents. The results showed that the implementation of Enterprise Architecture significantly contributed to improving the structure and process of education, thus supporting the improvement of learners' literacy. Key findings include improved accessibility of learning resources, school administration efficiency, and increased learner engagement in the learning process. Additionally, the implementation facilitated the creation of eBooks for learning materials, further enhancing literacy by providing students with readily accessible and interactive content. The conclusion of this study shows that the transformation of Enterprise Architecture in the implementation of the Scholastic Learning Zone not only improves literacy but also strengthens the education system. Further research is recommended to test this model in different educational contexts to extend the validity of the findings.

From personal convictions to collective action: Predicting employees’ pro-environmental behaviors in public and private spheres

Promoting changes in human behavior is crucial for mitigating and reducing environmental damage. While there is much research focused on private-sphere or generalized pro-environmental behavior, there is a relative dearth of research explicitly examining public-sphere pro-environmental behavior from a collective action perspective. In response to the mixed empirical results from previous applications of the value-belief-norm model in organizational contexts, this study explores the full value-belief-norm model, taking into account public-sphere and private-sphere pro-environmental behaviors. A sample of 504 hospitality industry employees in Kazakhstan and 515 hospitality industry employees in the United States participated in this study. The results indicate a hierarchical relationship between values, beliefs, and behaviors and confirm that values indirectly influence pro-environmental behaviors through belief variables. The findings suggest that these beliefs play vital roles in translating values into actionable behaviors. Theoretical and practical implications are provided.

Innovative strategies for overcoming barriers to technology adoption in small and medium-sized enterprises

ABSTRACT This study introduces the SME [small and medium-sized enterprise] innovation acceleration framework, a novel approach to addressing technology adoption barriers in Malaysia’s manufacturing and related services SMEs. Integrating Lewin’s three-stage model, Kotter’s eight-step change management framework, and Parasuraman’s technology readiness index, the research presents a comprehensive, culturally sensitive roadmap designed for the unique challenges SMEs face. Employing a Delphi study methodology with industry experts, the article identifies critical barriers to technology adoption and offers actionable strategies for enhancing technological readiness and innovation. The framework distinguishes itself by providing a mechanism to improve global competitiveness and economic contributions of SMEs, offering a replicable model for similar contexts internationally. This contribution enriches the literature with a practical, culturally attuned model for advancing SME innovation and technology adoption, which is of significant interest to scholars, policy makers, and practitioners worldwide.

A Framework for School-Based Mental Health Programs in Conflict Zones.

The significant rise in the prevalence of mental health disorders among school children and adolescents in Lebanon and conflict zones necessitate immediate interventions. Despite this elevated prevalence, such communities currently lack effective programs which clearly identify the concepts of mental health promotion and prevention among school children. Addressing this gap, our paper aims to propose a contextual model and framework for educators, researchers and policy-makers. The proposed model includes 3-levels comprising set of actions and interventions. Interventions adopted at each level should be fine-tuned to produce a caring and supportive learning environment that effectively incorporates the three essential domains of student's mental health: promoting mental health protective factors, notably social and emotional learning (SEL) and resilience, as well as preventing mental health disorders (MHD). Interventions set at each level are elaborated in the framework and linked to the corresponding segments present in the school environment to include teachers, family, community, media, and policymakers. The framework also extends these interventions to encompass the core competencies of SEL, resilience, as well as the prevention of the most prevalent mental health disorders in conflict zones.

Nordic biogas model in international contexts: Early-stage decision support for adaptation.

Global waste management challenges demand innovative and multi-functional solutions. The Nordic Biogas Model (NBM) based on anaerobic digestion of organic waste and valorization of its outputs provides several benefits beyond waste treatment such as energy recovery, nutrient recycling and climate impact mitigation. Despite these benefits, its international adoption remains limited, revealing an implementation gap. One way to address this gap is to adapt technology and knowledge from the provider to each specific context. This involves the embedding of the technology into the local context and the development of conditions such as formal and informal institutions over time. Based on decade-long interactions with Nordic companies and municipal decision-makers, we highlight the importance of communication between the technology provider and potential adopter, to ensure that the diverse sustainability benefits of NBM are acknowledged. Furthermore, most provider companies can benefit from a systematic guideline that supports early-stage decision-making as an essential component of the adaptation and implementation of the NBM in diverse international contexts. In this article, we offer suggestions for both: (1) how to better communicate the sustainability benefits of the NBM, and (2) how to assess the risk and opportunities of entering new markets at the early stages of decision-making.

Expand or not? Trade-offs of the remanufacturer on recycling channels

The integration of online trading has emerged as a crucial driver of operational efficiency in the reverse supply chain. This study expands the recycling model to include three distinct scenarios: basic-offline recycling (BR), single-platform recycling (SR), and dual-platform recycling (DR). The research delves into the implications of varying platform numbers, profit-sharing contracts, consumer recycling preferences, and recycling price adjustment speeds on supply chain stability within a multi-period decision-making framework. Findings reveal that the SR model is not always yield higher profits for remanufacturers through lower revenue allocation proportions to platforms, in stark contrast to the DR model. Furthermore, rapid decision adjustments do not consistently enhance the stability of the recycling system across multiple decision cycles. Remanufacturers' expansion strategies are significantly impacted by the costs associated with acquiring e-waste from individual recyclers. Importantly, an excess of platforms does not always result in favorable outcomes. Conversely, the SR model proves superior to the DR model in practical contexts that may arise. These insights offer valuable guidance to remanufacturers seeking to develop viable expansion strategies that foster mutually beneficial relationships among individual recyclers, remanufacturers, and platforms.

Understanding Business Models in the Brazilian Context of Electric Mobility: A Proposed Framework

In the transition to electric mobility (EM), business model innovation plays a crucial role in expanding the use of electric vehicles and increasing acceptance of this technology. The transition pathways differ between countries in the largest economies and those in Latin America. Brazil presents a unique scenario, benefiting from an early start with biofuels, the production of flex-fuel vehicles, predominantly renewable electric energy generation (>80%), and the absence of a structured national policy aligned with local governments. This study introduces a framework that surveys and categorizes businesses in EM, relating them to sustainable development aspects and regulatory maturity. It builds a solid conceptual foundation, incorporating data from technical and commercial events, as well as interviews with Brazilian specialists for validation. The proposed framework aids in understanding the Brazilian context, identifying regulatory gaps, and developing a common language to advance studies on business model innovation, contributing to electromobility studies in Latin America. Additionally, it can guide the construction of regional and local public policies and help identify more sustainable projects.

Contextual AI models for single-cell protein biology

Understanding protein function and developing molecular therapies require deciphering the cell types in which proteins act as well as the interactions between proteins. However, modeling protein interactions across biological contexts remains challenging for existing algorithms. Here we introduce PINNACLE, a geometric deep learning approach that generates context-aware protein representations. Leveraging a multiorgan single-cell atlas, PINNACLE learns on contextualized protein interaction networks to produce 394,760 protein representations from 156 cell type contexts across 24 tissues. PINNACLE’s embedding space reflects cellular and tissue organization, enabling zero-shot retrieval of the tissue hierarchy. Pretrained protein representations can be adapted for downstream tasks: enhancing 3D structure-based representations for resolving immuno-oncological protein interactions, and investigating drugs’ effects across cell types. PINNACLE outperforms state-of-the-art models in nominating therapeutic targets for rheumatoid arthritis and inflammatory bowel diseases and pinpoints cell type contexts with higher predictive capability than context-free models. PINNACLE’s ability to adjust its outputs on the basis of the context in which it operates paves the way for large-scale context-specific predictions in biology.

Contextual AI models for context-specific prediction in biology.

Contextual AI models for context-specific prediction in biology.

SPREADING THE UNDERSTANDING OF WOMEN'S IDENTITY WITHIN THE METO CULTURE THROUGH THE CHRISTIAN EDUCATION SPACE USING THE CONTEXTUAL TEACHING AND LEARNING MODEL

Women in Meto culture have a uniqueness that can only be understood if they understand the construction of Meto culture. In general, many people always associate women's issues with patriarchal culture whose assumptions are oppressive and unfair to women. Women are often seen as marginalized. This paper aims to build a new understanding of women's identity in Meto culture that is more positive in patriarchal culture. There are discussions of women's identity in the Meto culture and its approach through Christian education. This positive understanding is then formed in dialogue with the appropriate biblical texts. Furthermore, the process of seeding this understanding can be carried out in the Christian education space with the Contextual Teaching and Learning learning model. This research was carried out using a literature study that is relevant to the issue of women’s identity in the Meto culture and the results found as content in the Christian education space are that the identity of women in the Meto culture is unique, relational, collective, and cosmic.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.

A guide to developing population files for physiologically-based pharmacokinetic modeling in the Simcyp Simulator.

The Simcyp Simulator is a software platform widely used in the pharmaceutical industry to conduct stochastic physiologically-based pharmacokinetic (PBPK) modeling. This approach has the advantage of combining routinely generated invitro data on drugs and drug products with knowledge of biology and physiology parameters to predict a priori potential pharmacokinetic changes in absorption, distribution, metabolism, and excretion for populations of interest. Combining such information with pharmacodynamic knowledge of drugs enables planning for potential dosage adjustment when clinical studies are feasible. Although the conduct of dedicated clinical studies in some patient groups (e.g., with hepatic or renal diseases) is part of the regulatory path for drug development, clinical studies for all permutations of covariates potentially affecting pharmacokinetics are impossible to perform. The role of PBPK in filling the latter gap is becoming more appreciated. This tutorial describes the different input parameters required for the creation of a virtual population giving robust predictions of likely changes in pharmacokinetics. It also highlights the considerations needed to qualify the models for such contexts of use. Two case studies showing the step-by-step development and application of population files for obese or morbidly obese patients and individuals with Crohn's disease are provided as the backbone of our tutorial to give some hands-on and real-world examples.

An exploratory study on just-in-time multi-programming-language bug prediction

Context:An increasing number of software systems are written in multiple programming languages (PLs), which are called multi-programming-language (MPL) systems. MPL bugs (MPLBs) refers to the bugs whose resolution involves multiple PLs. Despite high complexity of MPLB resolution, there lacks MPLB prediction methods. Objective:This work aims to construct just-in-time (JIT) MPLB prediction models with selected prediction metrics, analyze the significance of the metrics, and then evaluate the performance of cross-project JIT MPLB prediction. Methods:We develop JIT MPLB prediction models with the selected metrics using machine learning algorithms and evaluate the models in within-project and cross-project contexts with our constructed dataset based on 18 Apache MPL projects. Results:Random Forest is appropriate for JIT MPLB prediction. Changed LOC of all files, added LOC of all files, and the total number of lines of all files of the project currently are the most crucial metrics in JIT MPLB prediction. The prediction models can be simplified using a few top-ranked metrics. Training on the dataset from multiple projects can yield significantly higherAUC than training on the dataset from a single project for cross-project JIT MPLB prediction. Conclusions:JIT MPLB prediction models can be constructed with the selected set of metrics, which can be reduced to build simplified JIT MPLB prediction models, and cross-project JIT MPLB prediction is feasible.

Pemahaman Hadis Al-Ghazali Tentang Menasihati Pemimpin: Studi Kasus Pejabat Negara di Indonesia

This research examines Al-Ghazali's understanding of advising leaders and its relevance to the context of leadership in Indonesia. Employing a qualitative approach with a case study method, data was gathered through documentation of Al-Ghazali's works, case studies of corruption and ethical violations among Indonesian state officials, and in-depth interviews with experts in Islamic leadership, academics, and legal practitioners. The findings indicate that Al-Ghazali's teachings on character, integrity, and morality in leadership are highly relevant to addressing leadership challenges in Indonesia, such as corruption and abuse of power. Ulama (Islamic scholars) play a crucial role in advising leaders to enhance leadership quality and governance. This research also contributes theoretically to the study of Islamic leadership ethics, demonstrating that ethical principles derived from hadiths can develop an ethical and effective governance model in diverse cultural contexts. By implementing Al-Ghazali's leadership principles, it is hoped that more just, transparent, and people-oriented leadership can be established in Indonesia.

How does corporate social irresponsibility affect organisational reputation? The mediating role of moral anger and distrust

Purpose Once established, a strong reputation is not indestructible. To protect this intangible asset, the process of reputation damage needs to be explained. Drawing on the irresponsibility and crisis communication literature, this study seeks to better understand this process. Specifically, this study aims to show how moral anger and distrust mediate the relationship between the awareness of an irresponsible incident and organisational reputation. Design/methodology/approach This study examined the proposed conceptual model in the empirical context of retailers bending the law. A large survey (n = 991) on consumer responses to retailers’ misbehaviour was used to collect data. The hypotheses were tested using Hayes’ PROCESS macro. Findings The study revealed that the mere awareness of corporate misbehaviour is not enough to directly harm accumulated positive information about a firm. Discrete emotions of moral anger and distrust fully mediate the effects of knowledge about wrongdoing. The irresponsibility appraisal is a moderator of this process that substantially changes the impacts of misbehaviour on organisational reputation. Originality/value By demonstrating how moral anger and distrust mediate the effects of misbehaviour awareness and identifying a boundary condition, this study advances our understanding of how corporate wrongdoing affects organisational reputation.