Integrated Model Research Articles

Pathways to persuasion: The impact of social media influencers’ self-disclosure and follower size on persuasion outcomes

A 2 (influencer type: nano with 5000 followers vs mega with 1.1 million followers) × 2 (influencer self-disclosure: low depth vs high depth) between-subjects online experiment tested the different pathways social media influencers take to achieve persuasion outcomes in one model. Participants viewed an Instagram influencer’s profile page with either 5000 or 1.1 million followers, and a post from the influencer with either low or high depth self-disclosure. Participants then answered questions regarding social attraction, parasocial relationship, perceived expertise, persuasion outcomes, covariates, and demographic information. Results showed three serial pathways facilitate persuasion: two pathways through self-disclosure depth by way of social attraction and parasocial relationship development and another pathway through follower size by way of perceived expertise. This research advances understanding of the influencing process by using theory and empirical precedent to derive and test an integrated model of the influencing process.

Navigating evolving employment realities: an integrative model of psychological contract in contemporary workplaces

Navigating evolving employment realities: an integrative model of psychological contract in contemporary workplaces

The Mediating Role of the Two Aspects of Job Embeddedness between, Perceived Social Support, the Quality of Work Life and Career Success, an Integrative Model of Organization, Community (Fit-Link-Sacrifice)

The Mediating Role of the Two Aspects of Job Embeddedness between, Perceived Social Support, the Quality of Work Life and Career Success, an Integrative Model of Organization, Community (Fit-Link-Sacrifice)

PENGEMBANGAN MODEL PEMBELAJARAN TEMATIK INTEGRATIF BERBASIS KEARIFAN LOKAL UNTUK MEMBENTUK KARAKTER ANAK (PENELITIAN PENGEMBANGAN DI RA KHALIFAH INSANI METRO

This research was motivated by problems discovered during initial observations in Group A RA. Khalifah Insani Metro City, namely some students do not understand their respective characters because their learning activities are still conventional so they rarely link local culture with learning themes. The aim of this research is to develop a prototype of an integrative thematic learning model based on local wisdom for early childhood and determine the effectiveness of an integrative thematic learning model based on local wisdom in forming character. The research approach used in this research is Research and Development (R&D) with J. Moonen's developmental instructional model. The sample used was 1 teacher and 20 group A students at RA Khalifah Insani Metro City. The results of the research show that the development of an integrative thematic learning model based on local wisdom to shape children's character regarding product use is able to facilitate and instill good manners in students and can create appropriate and effective learning.

The prognostic implications and oncogenic role of NSUN5 in clear cell renal cell carcinoma.

Clear cell renal cell carcinoma (ccRCC), a predominant form of urinary malignancy, requires the identification of reliable biomarkers to enhance both prognostic outcomes and therapeutic developments specific to ccRCC. NSUN5, a member of the NOL1/NOP2/SUN domain (NSUN) family, plays a critical role in RNA stabilization and exhibits widespread expression across various tumor types. However, the exact function of NSUN5 in ccRCC remains insufficiently understood. Data were collated from cohorts of ccRCC patients who underwent nephrectomy, including those from the Cancer Genome Atlas (TCGA) and the Sun Yat-sen University Cancer Center (SYSUCC), to evaluate the clinical relevance of NSUN5. Integrative models based on NSUN5 expression were subsequently developed to predict the prognosis of ccRCC within the TCGA and SYSUCC cohorts. Furthermore, the impact of NSUN5 on RCC cells and its association with cellular senescence were corroborated through in vitro experimental analyses. NSUN5 exhibited elevated expression in both ccRCC patients and renal cancer cell lines, whose upregulation significantly correlated with age, tumor size, TNM stage, WHO/International Society of Urological Pathology (ISUP) grade, presence of necrosis, and a poor prognosis. An accessible nomogram, incorporating NSUN5 along with various clinicopathological parameters, was adept at predicting outcomes for ccRCC patients. Additionally, in vitro findings indicated that reduced expression of NSUN5 enhanced tumor cell senescence and simultaneously inhibiting cell proliferation and migration. These observations suggest that elevated NSUN5 expression is linked to poorer overall survival (OS) and progression-free survival (PFS), positioning NSUN5 as a viable diagnostic and prognostic biomarker in ccRCC.

Implementation of the 2013 Curriculum in Integrated Thematic Learning in Elementary Schools: Strategies, Challenges, and its Impact on Learning

Education is the process of imparting knowledge, character development, and producing a more harmonious social order and way of life, including through the development of students' intellectual capital, skills, and intellectual development. One of the innovations brought by the introduction of the 2013 curriculum is the integrative theme learning model. To provide meaningful educational experiences to students, integrative thematic learning connects various disciplines and materials through themes. The 2013 curriculum states that in order to place students as the center of the learning process, teachers must play an active role as facilitators and motivators of learning. This article aims to explain how the 2013 curriculum is applied to integrative thematic learning, and its impact on the teaching and learning process in elementary schools. The implementation of the curriculum in integrative thematic learning shows several challenges, such as difficulties in determining assessment or evaluation criteria for students, teachers' lack of understanding in compiling thematic integrative lesson plans, limited teaching aids, and the use of traditional learning methods that are less varied. The proposed solution is training and education for teachers, principals, and supervisors to acquire skills and knowledge in implementing the curriculum according to students' needs. By implementing a variety of learning methods, it is hoped that it can increase student motivation and independence in the learning process.

Integrative Review-Based Conceptual Modeling: An Agent-Based Modeling Synthesis of Dynamic Energy Tariff Research and Models

Adoption of dynamic energy tariffs by households is crucial for the transition to carbon-neutral energy systems. Influencing the adoption patterns of these tariffs necessitates an examination of the drivers, decision components, and contextual factors influencing household decisions. Few computational models address this comprehensively, often omitting non-financial decision variables. Moreover, methodologically robust integrative reviews on this topic are scarce. To address this gap, this paper develops a concept-centered integrative review methodology aimed at deriving computer models for socio-techno-economic simulations of household adoption of sustainable technologies. The methodology encompasses five sequential phases: Setup, Literature Search, Analysis, Synthesis and Conceptual Model, and Discussion. To illustrate the methodology, it is applied to the case of household adoption of dynamic energy tariffs, resulting in an abstract conceptual model adaptable to local contexts. The review reveals a lack of consensus on modeled tariffs but highlights the significance of tariff complexity, relative advantage, household heterogeneity, and various agent properties. It also identifies potential improvements in model fundamentals, particularly spatial modeling. The developed process model focuses on the stages ‘knowledge’, ‘decision’, and ‘reevaluation’. The article contributes by presenting a comprehensive review scheme and delivering a concept-centered integrative review along with an explicit conceptual model derived from it.

Revealing the antioxidant properties of alkyl gallates: a novel approach through quantum chemical calculations and molecular docking.

This study investigates the antioxidant potential of alkyl gallates (C1-C10), focusing on the impact of alkyl chain length and solvent polarity on their antioxidant properties. Known for their biomedical relevance in mitigating oxidative stress, alkyl gallates' structure-activity relationships, particularly regarding chain length and environmental factors, still need to be explored. Key thermochemical parameters, including bond dissociation enthalpy (BDE), ionization potential (IP), proton affinity (PA), and electron transfer enthalpy (ETE), reveal that shorter alkyl chains (C1-C4) exhibit superior antioxidant activity. In contrast, longer chains (C5-C10) show reduced effectiveness due to steric hindrance and lower solubility in polar solvents. Molecular docking studies also demonstrated favorable binding interactions with vital biological targets, further reinforcing their antioxidant potential. Quantum chemical calculations were performed using Gaussian 16 with the B3LYP/6-311G(dp) basis set for geometry optimizations. Solvent effects were modeled using the integral equation formalism-polarized continuum model (IEF-PCM). Molecular docking studies were conducted using AutoDockTools 4.2, targeting Tyrosine Kinase Hck, Heme Oxygenase, and Human Serum Albumin to evaluate fundamental binding interactions. These computational methods provided insights into alkyl gallates' chemical reactivity and antioxidant efficiency, allowing for the rational design of more potent antioxidant compounds.

Community Palliative Care: What are the Best Models?

Palliative care seeks to address the physical, psychosocial and spiritual concerns of patients with a life limiting illness and their caregivers. Early referral to palliative care improves symptoms and is the standard of care. This paper evaluates the evidence for different models of community palliative care and looks at the effects of homecare, hospice programs and residential aged care facility (RACF) interventions on symptom management, home death rate and acute health service utilization. It also examines the impact of COVID-19, telehealth, integration and staffing models on the efficacy of community palliative care. Evidence suggests that community palliative care increases the rate of death at home and may improve satisfaction with care, but effect on symptoms and acute health care utilization are less certain. Enrolment in a hospice program may decrease hospitalizations and improve satisfaction. RACF staff training interventions to improve the quality of palliative care provided to residents show mixed results across all indicators. COVID-19 saw a relative increase in the demand for community palliative care, as people opted out of the hospital system. Models of community palliative care that facilitate integration, support primary health providers, and promote technological innovation are worthy of further research.

Understanding the Relationship Between Trait Mindfulness and Perceived Stress: The Serial Multiple Mediating Roles of Basic Psychological Needs Satisfaction and Psychological Flexibility.

Although research suggests that trait mindfulness has been negatively associated with perceived stress, an integrative examination of the underlying mechanisms is lacking. Consequently, the purpose of this study was to establish an integrative model within Monitor and Acceptance Theory (MAT). This model examined the relationship between trait mindfulness and perceived stress by assessing two important psychological resources: basic psychological needs satisfaction (BPNS) and psychological flexibility. A total of 679 young adults (Mage = 19.27 years, SD = 1.06) participated in this research. They completed a set of standardized instruments that assessed trait mindfulness, BPNS, psychological flexibility, and perceived stress. Serial multiple mediation analyses showed that trait mindfulness was associated with higher levels of BPNS and psychological flexibility, which in turn were associated with less perceived stress. Furthermore, the path coefficient of the serial multiple mediation was higher than other pathways. These findings demonstrate that BPNS and psychological flexibility are important mechanisms that underlie the relationship between trait mindfulness and perceived stress, particularly when they are synergistic.

Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.

Pathological and neuroimaging alterations in the cerebellum of Alzheimer's disease (AD) patients have been documented. However, the role of cerebellum-derived radiomic and structural connectome modeling in the prediction of AD progression remains unclear. Radiomic features were extracted from magnetic resonance imaging (MRI) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n=1319) and an in-house dataset (n=308). Integrated machine learning models were developed to predict the conversion risk of normal cognition (NC) to mild cognitive impairment (MCI) over a 6-year follow-up. The cerebellar models outperformed hippocampal models in distinguishing MCI from NC and in predicting transitions from NC to MCI across both cohorts. Key predictors included textural features in the right III and left I and II lobules, and network properties in Vermis I and II, which were associated with cognitive decline in AD. Cerebellum-derived radiomic-network modeling shows promise as a tool for early identification and prediction of disease progression during the preclinical stage of AD. Altered cerebellar radiomic features and topological networks were identified in the subjects with mild cognitive impairment (MCI). The cerebellar radiomic-network integrated models outperformed hippocampal models in distinguishing MCI from normal cognition. The cerebellar radiomic model effectively predicts MCI risk and can stratify individuals into distinct risk categories. Specific cerebellar radiomic features are associated with cognitive impairment across various stages of amyloid beta and tau pathology.

Penetration of a spherical vortex into turbulence

The penetration of a spherical vortex into turbulence is studied theoretically and experimentally. The characteristics of the vortex are first analysed from an integral perspective that reconciles the far-field dipolar flow with the near-field source flow. The influence of entrainment on the vortex drag force is elucidated, extending the Maxworthy (J. Fluid Mech., vol. 81, 1977, pp. 465–495) model to account for turbulent entrainment into the vortex movement and vortex penetration into an evolving turbulent field. The physics are explored numerically using a spherical vortex (initial radius $R_0$ , speed $U_{v0}$ ), characterised by a Reynolds number $Re_0(=2R_0U_{v0}/\nu$ , where $\nu$ is the kinematic viscosity) of 2000, moving into decaying homogeneous turbulence (root-mean-square $u_0$ , integral scale $L$ ) with turbulent intensity $I_t=u_0/U_{v0}$ . When the turbulence is absent ( $I_t=0$ ), a wake volume flux leads to a reduction of vortex impulse that causes the vortex to slow down. In the presence of turbulence ( $I_t> 0$ ), the loss of vortical material is enhanced and the vortex speed decreases until it is comparable to the local turbulent intensity and quickly fragments, penetrating a distance that scales as $I_t^{-1}$ . In the experimental study, a vortex ( $Re_0\sim 1490\unicode{x2013}5660$ ) propagating into a statistically steady, spatially varying turbulent field ( $I_{ve}=0.02$ to 0.98). The penetration distance is observed to scale with the inverse of the turbulent intensity. Incorporating the spatially and temporally varying turbulent fields into the integral model gives a good agreement with the predicted trend of the vortex penetration distance with turbulent intensity and insight into its dependence on the structure of the turbulence.

PSATF-6mA: an integrated learning fusion feature-encoded DNA-6 mA methylcytosine modification site recognition model based on attentional mechanisms

DNA methylation is of crucial importance for biological genetic expression, such as biological cell differentiation and cellular tumours. The identification of DNA-6mA sites using traditional biological experimental methods requires more cumbersome steps and a large amount of time. The advent of neural network technology has facilitated the identification of 6 mA sites on cross-species DNA with enhanced efficacy. Nevertheless, the majority of contemporary neural network models for identifying 6 mA sites prioritize the design of the identification model, with comparatively limited research conducted on the statistically significant DNA sequence itself. Consequently, this paper will focus on the statistical strategy of DNA double-stranded features, utilising the multi-head self-attention mechanism in neural networks applied to DNA position probabilistic relationships. Furthermore, a new recognition model, PSATF-6 mA, will be constructed by continually adjusting the attentional tendency of feature fusion through an integrated learning framework. The experimental results, obtained through cross-validation with cross-species data, demonstrate that the PSATF-6 mA model outperforms the baseline model. The in-Matthews correlation coefficient (MCC) for the cross-species dataset of rice and m. musus genomes can reach a score of 0.982. The present model is expected to assist biologists in more accurately identifying 6 mA locus and in formulating new testable biological hypotheses.

Modeling of Wildfire Digital Twin: Research Progress in Detection, Simulation, and Prediction Techniques

Wildfires occur frequently in various regions of the world, causing serious damage to natural and human resources. Traditional wildfire prevention and management methods are often hampered by monitoring challenges and low efficiency. Digital twin technology, as a highly integrated virtual simulation model, shows great potential in wildfire management and prevention. At the same time, the virtual–reality combination of digital twin technology can provide new solutions for wildfire management. This paper summarizes the key technologies required to establish a wildfire digital twin system, focusing on the technical requirements and research progress in fire detection, simulation, and prediction. This paper also proposes the wildfire digital twin (WFDT) model, which integrates real-time data and computational simulations to replicate and predict wildfire behavior. The synthesis of these techniques within the framework of a digital twin offers a comprehensive approach to wildfire management, providing critical insights for decision-makers to mitigate risks and improve emergency response strategies.

An Integrated Fuzzy Delphi and Fuzzy AHP Model for Evaluating Factors Affecting Human Errors in Manual Assembly Processes

Human errors (HEs) are prevalent issues in manual assembly, leading to product defects and increased costs. Understanding and knowing the factors influencing human errors in manual assembly processes is essential for improving product quality and efficiency. This study aims to determine and rank factors influencing HEs in manual assembly processes based on expert judgments. To achieve this objective, an integrated model was developed using two multi-criteria decision-making (MCDM) techniques—specifically, the fuzzy Delphi Method (FDM) and the fuzzy Analytic Hierarchy Process (FAHP). Firstly, two rounds of the FDM were conducted to identify and categorize the primary factors contributing to HEs in manual assembly. Expert consensus with at least 75% agreement determined that 27 factors with influence scores of 0.7 or higher significantly impact HEs in these processes. After that, the priorities of the 27 influencing factors in assembly HEs were determined using a third round of the FAHP method. Data analysis was performed using SPSS 22.0 to evaluate the reliability and normality of the survey responses. This study has divided the affecting factors on assembly HEs into two levels: level 1, called main factors, and level 2, called sub-factors. Based on the final measured weights for level 1, the proposed model estimation results revealed that the most influential factors on HEs in a manual assembly are the individual factor, followed by the tool factor and the task factor. For level 2, the model results showed a lack of experience, poor instructions and procedures, and misunderstanding as the most critical factors influencing manual assembly errors. Sensitivity analysis was performed to determine how changes in model inputs or parameters affect final decisions to ensure reliable and practical results. The findings of this study provide valuable insights to help organizations develop effective strategies for reducing worker errors in manual assembly. Identifying the key and root factors contributing to assembly errors, this research offers a solid foundation for enhancing the overall quality of final products.

Web-Series Ads as a New Marketing Media: Toward a Commercial-Independent Digital Integration Model

This study presents a new type of advertisement—web-series ads. This undiscussed form of advertising is unique, as it presents an independent digital cultural field. By examining the main structural and narrative features of web-series ads and consumers’ responses, this research presents an integration model which explains how advertisements, as a commercial system, integrate into an independent new media system. Using thematic, narrative–structural, and textual analysis methods, this research shows how marketers convey messaging by introducing a free-from-constraints new online advertising media type. The use of polysystem theory offers a new theoretical perspective to media research and explains the findings.

Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts.

Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers.

Novel Fractional Order Differential and Integral Models for Wind Turbine Power–Velocity Characteristics

This work presents an improved modelling approach for wind turbine power curves (WTPCs) using fractional differential equations (FDE). Nine novel FDE-based models are presented for mathematically modelling commercial wind turbine modules’ power–velocity (P-V) characteristics. These models utilize Weibull and Gamma probability density functions to estimate the capacity factor (CF), where accuracy is measured using relative error (RE). Comparative analysis is performed for the WTPC mathematical models with a varying order of differentiation (α) from 0.5 to 1.5, utilizing the manufacturer data for 36 wind turbines with capacities ranging from 150 to 3400 kW. The shortcomings of conventional mathematical models in various meteorological scenarios can be overcome by applying the Riemann–Liouville fractional integral instead of the classical integer-order integrals. By altering the sequence of differentiation and comparing accuracy, the suggested model uses fractional derivatives to increase flexibility. By contrasting the model output with actual data obtained from the wind turbine datasheet and the historical data of a specific location, the models are validated. Their accuracy is assessed using the correlation coefficient (R) and the Mean Absolute Percentage Error (MAPE). The results demonstrate that the exponential model at α=0.9 gives the best accuracy of WTPCs, while the original linear model was the least accurate.

Balancing heterogeneous assembly line with multi-skilled human-robot collaboration via Adaptive cooperative co-evolutionary algorithm

In human-centred manufacturing, deploying collaborative robots (cobots) is recognized as a promising strategy to enhance the inclusiveness and resilience of production systems. Despite notable progress, current production scheduling methods for human-robot collaboration (HRC) still fail to adequately accommodate workforce heterogeneity, significantly reducing their adoption and implementation. To address this gap, we introduce a novel model for the Assembly Line Worker Integration and Balancing Problem considering Multi-skilled Human-Robot Collaboration (ALWIBP-mHRC). This model aims to optimize task scheduling between semi-skilled workers and cobots, aiming to maximize productivity and minimize costs. It features a multi-skilled human-robot collaboration (mHRC) task assignment scheme that selects the optimal assembly/collaboration mode from seven scenarios, based on specific task requirements and resource-skill availability, thus maximizing resource-skill complementarity. To tackle the complexities of this problem, we propose an adaptive multi-objective cooperative co-evolutionary algorithm (a-MOCC) that incorporates a sub-problem decomposition and decoding framework tailored for ALWIBP-mHRC, enhanced by an adaptive evolutionary strategy based on Q-learning (Q-Coevolution). Experimental tests demonstrate the superior performance of the proposed method compared to other established metaheuristic algorithms across various instance sizes, underscoring its effectiveness in enhancing the productivity of production systems for semi-skilled workers. The findings are significant for investment decision-making and resource planning, as they highlight the strategic value of integrating cobots in large-scale heterogeneous workforce production. This work underscores the potential of cobots to mitigate skill gaps in assembly systems, laying the groundwork for future research and industrial strategies focused on enhancing productivity, inclusivity, and adaptability in a dynamically changing labour market.

A preregistered meta-meta-analysis on the global distribution of Hepatotropic Viruses.

Hepatotropic viruses (HAV, HBV, HCV, HDV, and HEV) significantly impact global health, with varying prevalence across regions. This study aims to systematically consolidate data from diverse meta-analyses to provide a contemporary reference on virus distribution and prevalence. Adhering to PRISMA guidelines, the study utilized a mixed effects model for data integration. Quality evaluation was carried out with QUOROM and AMSTAR tools, with heterogeneity assessed via the Higgins I2 statistic, Q-statistic and Tau squared (τ2) values. The study analyzed 86 meta-analyses from 56 studies (2017-2022) with minimal overlap. Prevalence rates by region were as follows: MENA - 29.2%, Afghanistan - 9.14%, Africa - 8.10%. Prevalence rates by virus type: HAV - 82.5%, HBV - 8.6%, HCV - 15.1%, HDV - 8.9%, HEV - 13.9%, dual HBV-HCV coinfection - 2.2%. Prevalence rates by risk groups: general population - 8.3%, healthcare workers - 4.0%. Continent-specific HBV-HCV prevalence rates: Africa - 9.2%, China - 6.9%, others. HCVprevalence rates among at-risk groups: healthcare workers - 5.58%, hemodialysis patients - 34.8%. Regional HCV rates: Africa - 7.42%, Middle East - 25.30%. Diverse global hepatotropic virus prevalence patterns are influenced by multifaceted factors. MENA faces higher rates due to healthcare challenges, while Africa struggles with limited resources. Tailored public health strategies, including vaccination and awareness campaigns, are essential to alleviate burdens and enhance global health. This consolidated data serves as a valuable resource for informed decision-making.