Integrated Model Research Articles

An integrated model for flexible simulation of biomass combustion in a travelling grate-fired boiler

Grate-fired boiler is of considerable interest for burning biomass owing to its reduced sensitivity to variations in fuel composition and size. However, grate-fired technology exhibits unsatisfactory performance in terms of combustion efficiency, contaminant emissions, and flame stability. CFD modelling can provide reasonable optimizations to overcome these drawbacks and ultimately achieve clean and efficient energy production. Conventional approach simulates the fuel-bed and freeboard combustion separately using an in- and over-bed coupling procedure, where data of the gases leaving from the packed-bed top and the radiative heat flux emitted by the high-temperature flame onto the bed cannot be interacted in real-time. This may cause distinct deviations in the calculations. In this paper, a three-dimensional (3D) full-scale integrated model is developed for the simulation of grate-fired boiler, in which the intensive combustion of both the freeboard and fuel-bed is solved in one scheme using the Eulerian-Lagrangian method. The gas phase is considered as a continuous medium and calculated by the Eulerian model, while the solid particles are considered as a discrete phase and tracked via the Lagrangian method. The reliability of the model is well verified through various field measurements and observations. Results shows that the key parameters are non-uniformly distributed along the grate width, i.e., the aerodynamic and combustion environment is poor at the vicinity of water-cooled walls compared with that at the furnace center. This results in a significant lag in volatile gases release and char oxidation near the water-cooled wall on both sides, which finally causes obvious differences in the distribution of temperature and species. For instance, the peak of CO concentration at the center is 12.6 % at 3.3m from the feed entrance, while that near the water-cooled wall is 8.6 % around 3.5m. The char burnout ratio in the bottom ash can be accurately calculated by this model, with a negligible relative error between the simulation of 81.97 % and the measurement of 82.50 %. It also provides a novel method for the calculation of fly ash particles by using size-grouped and non-spherical particles. Small-size particles in the vicinity of the feed inlet, as well as a part of the particles burning at the end of the grate are entrained into the freeboard by the primary air supplied beneath the grate and the high-momentum secondary air on the bed top. This model can provide valuable theoretical guidance for optimizing the refined distribution of fuel and air in grate-fired power plants and mitigating irregular deposition (corrosion) on heat exchangers and water-cooled walls caused by fly ash.

COVID-19 Disruptions to Social Care Delivery: A Qualitative Study in Two Large, Safety-Net Primary Care Clinics.

Social care integration refers to the incorporation of activities into health systems that assist patients with health-related social needs (HRSNs) that negatively impact the health outcomes of their patients, such as food insecurity or homelessness. Social care integration initiatives are becoming more common. The COVID-19 pandemic strained health systems while simultaneously increasing levels of unmet social needs. To describe the effects of the COVID-19 pandemic on established social care delivery in a primary care setting. We used qualitative semi-structured interviews of stakeholders to assess barriers and facilitators to social care delivery in the primary care setting during the COVID-19 health emergency. Data was analyzed using a hybrid inductive/deductive thematic analysis approach with both the Consolidated Framework for Implementation Research (CFIR) and the Screen-Navigate-Connect-Address-Evaluate model of social care integration. Two safety-net, hospital-based primary care clinics with established screening for food insecurity, homelessness, and legal needs. Six physicians, six nurses, six members of the social work team (clinical social workers and medical case workers), six community health workers, and six patients (total N = 30) completed interviews. Four major themes were identified. (1) A strained workforce experienced challenges confronting increased levels of HRSNs. (2) Vulnerable populations experienced a disproportionate negative impact in coping with effects of the COVID-19 pandemic on HRSNs. (3) COVID-19 protections compounded social isolation but did not extinguish the sense of community. (4) Fluctuations in the social service landscape led to variable experiences. The COVID-19 pandemic disrupted established social care delivery in a primary care setting. Many of the lessons learned about challenges to social care delivery when health systems are strained are important considerations that can inform efforts to expand social care delivery.

Integrative prediction model for radiation pneumonitis incorporating genetic and clinical-pathological factors using machine learning

PurposeWe aimed to develop a machine learning-based prediction model for severe radiation pneumonitis (RP) by integrating relevant clinicopathological and genetic factors, considering the associations of clinical, dosimetric parameters, and single nucleotide polymorphisms (SNPs) of genes in the TGF-β1 pathway with RP. MethodsWe prospectively enrolled 59 primary lung cancer patients undergoing radiotherapy and analyzed pretreatment blood samples, clinicopathological/dosimetric variables, and 11 functional SNPs in TGFβ pathway genes. Using the Synthetic Minority Over-sampling Technique (SMOTE) and nested cross-validation, we developed a machine learning-based prediction model for severe RP (grade ≥ 2). Feature selection was conducted using four methods (filtered-based, wrapper-based, embedded, and logistic regression), and performance was evaluated using three machine learning models. ResultsSevere RP occurred in 20.3 % of patients with a median follow-up of 39.7 months. In our final model, age (>66 years), smoking history, PTV volume (>300 cc), and AG/GG genotype in BMP2 rs1979855 were identified as the most significant predictors. Additionally, incorporating genomic variables for prediction alongside clinicopathological variables significantly improved the AUC compared to using clinicopathological variables alone (0.822 vs. 0.741, p = 0.029). The same feature set was selected using both the wrapper-based method and logistic model, demonstrating the best performance across all machine learning models (AUC: XGBoost 0.815, RF 0.805, SVM 0.712, respectively). ConclusionWe successfully developed a machine learning-based prediction model for RP, demonstrating age, smoking history, PTV volume, and BMP2 rs1979855 genotype as significant predictors. Notably, incorporating SNP data significantly enhanced predictive performance compared to clinicopathological factors alone.

A Caregiver Perspective for Partners of PTSD Survivors: Understanding the Experiences of Partners.

Research affirms that survivors of post-traumatic stress disorder (PTSD) experience psychological distress that affects their romantic partners, and that a bi-directional effect between PTSD symptoms and romantic relationship satisfaction exists, indicating that improvements in the romantic relationship may lead to the improved well-being of the survivor. Indeed, as romantic partners of PTSD survivors are both negatively impacted by the distress of the survivor, and romantic relationship satisfaction can affect the distress of the PTSD survivor, partners are a key stakeholder for mental health. Unfortunately, theoretical models have not adequately captured the experience of this population to properly illuminate their experience and provide appropriate treatment directives. This paper examines the informal caregiving integrative model to determine its applicability to the romantic partners of PTSD survivors with respect to the determinants, mediators, and outcomes. The current literature on romantic partners is used to evaluate the adequacy of fit, as well as to provide the components unique to partners. Future directions, clinical implications, and limitations of current research are explored based on the results of this review.

Integrative Learning Model in Pediatric and Sorches on Athletics Materials with Science Education Biology Materials for Junior High School Students

This research aims to produce an integrative learning model for junior high school students that is suitable for use. The integrative learning model is expected to be used by physical education teachers in teaching and learning activities. This development research was carried out by adapting the following research steps: data collection, analysis of the information collected, development of initial product draft, expert validation and revision, small-scale trials and revisions, large-scale field trials and revisions, and final product creation. A small-scale trial was carried out on 25 students of SMP Negeri 1 Samarinda. Large-scale trials were carried out on 31 students of SMP Negeri 2 Samarinda, SMP Negeri 3 Samarinda, and SMP Negeri 4 Samarinda. Data collection instruments are interview guides, field notes, validation value scales, teacher participation, student responses, performance tests and knowledge tests. Data analysis uses qualitative and quantitative descriptive analysis. The results of this research are in the form of a guidebook containing syntax (steps), assessment forms, 6 integrative games, and plans for implementing appropriate and effective learning.

Rheumatoid arthritis, cardiometabolic comorbidities, and related conditions: need to take action.

Rheumatoid Arthritis (RA) is associated with an increased risk of cardiovascular disease and mortality, however, traditional cardiovascular risk factors do not fully explain this relationship. This high risk of cardiovascular morbidity and mortality in RA has been increasingly acknowledged in past decades, with accumulating evidence that RA is an independent cardiovascular risk factor; RA is also associated with metabolic syndrome, which correlates with disease activity, contributing to the increased prevalence of coronary heart disease in RA patients. Moreover, multimorbidity, including the presence of long-term conditions, impacts adverse clinical outcomes in RA patients, emphasizing the need for holistic management that requires an understanding of shared pathophysiological mechanisms, such as systemic inflammation and immune dysregulation. For all these reasons, the management of RA patients with cardiometabolic comorbidities is a complex endeavor that requires a patient-centered, multidisciplinary approach. In this sense, there is a need to re-evaluate the approach toward a proactive model of care, moving away from a reactive medical paradigm to a multidimensional integrated management model, including aggressive screening, preventive strategies, and tailored therapeutic interventions. The aim of this review was to thoroughly review the literature on cardiometabolic comorbidities and related conditions linked to RA to enable us to identify the necessary actions required to effectively tackle the increasing burden of illness from a fully comprehensive perspective.

Integrative model of servant leadership style and critical pedagogy: Answers to school leadership of neoliberalism

This study aimed to analyse servant leadership's attributes in the theory and practice of critical pedagogy and find commonalities that produce an integrative model that will answer to the school leadership of neoliberalism. The study found an integrative model between servant leadership style and critical pedagogy that includes similar attributes of servant leadership and critical pedagogy related to the dimensions of transcendental spirituality, emotional healing and putting students first, creating value for the community, empowering students and helping students develop and succeed, demonstrating conceptual skills and behaving ethically. This integrative model is a constructive answer to the implementation of school leadership of neoliberalism.

How to study psychological mechanisms of mania? Asystematic review on the methodology of experimental studies on manic mood dysregulation of leading theories on bipolar disorder.

Although there are several psychological theories on bipolar disorders (BD), the empirical evidence on these theories through experimental studies is still limited. The current study systematically reviews experimental methods used in studies on the main theories of BD: Reward Hypersensitivity Theory (RST) or Behavioral Activation System (BAS), Integrative Cognitive Model (ICM), Positive Emotion Persistence (PEP), Manic Defense theory (MD), and Mental Imagery (MI). The primary aim is to provide an overview of the used methods and to identify limitations and suggest areas of improvement. A systematic search of six databases until October 2023 was conducted. Study selection involved two independent reviewers extracting data on experimental study design and methodology. A total of 84 experimental studies were reviewed. BAS and RST were the most frequently studied theories. The majority of these experimental studies focus on mechanisms of reward sensitivity. Other important elements of the reviewed theories, such as goal setting and-attainment, situation selection (avoidance or approach), activation, affective/emotional reactivity, and regulatory strategies, are understudied. Self-report and neuropsychological tasks are most often used, while mood induction and physiological measures are rarely used. There is a need for more consensus on the operationalization of psychological theories of mania. Standardization of test batteries could improve comparability among studies and foster a more systematic approach to experimental research. Research on affective (activated) states is still underrepresented in comparison with studies on trait vulnerabilities.

Simulation-Based Machine Learning for Predicting Academic Performance Using Big Data

In this study, simulation and big data analytics are combined with machine learning techniques, specifically K-means clustering, Apriori algorithm, and a stacked integrated learning model, to predict academic performance of college students with a high accuracy of 95.5%. By analyzing behavioral data from over 1,000 undergraduates, we correlate various behaviors with academic success, focusing on the use of libraries, self-study habits, and internet usage. Our findings highlight the benefits of using big data and simulation in educational strategies, promoting effective resource allocation and teaching enhancements. The study acknowledges limitations due to its regional focus and proposes future research directions to enhance model generalization and technological integration for broader application.

Mind the gap: Development and validation of an evolutionary mismatched lifestyle scale and its impact on health and wellbeing

Identifying an integrative framework that could appropriately delineate underlying mechanisms and individual risk/protective factors for human health has remained elusive. Evolutionary mismatch theory provides a comprehensive, integrative model for understanding the underlying causes and mechanisms of a wide range of modern health and well-being problems, ranging from obesity to depression. Despite growing interest regarding its importance though, no psychometrically-sound measure of evolutionary mismatch yet exists to facilitate research and intervention. To construct such a scale, aimed at gauging individual differences in the extent to which people's modern lifestyles are mismatched with ancestral conditions, we conducted four studies (a pilot study, followed by 3 main studies, with a final sample of 1901 participants across the main studies). Results from exploratory and confirmatory factor analyses have produced a 36-item evolutionary mismatched lifestyle scale (EMLS) with 7 subdomains of mismatched behaviours (e.g., diet, physical activity, relationships, social media use) that is psychometrically sound. Further, the EMLS is associated with physical, mental and subjective health. We explore the potential of the EMLS as a tool for examining interpersonal and cultural variations in health and wellbeing, while also discussing the limitations of the scale and future directions in relation to further psychometric examinations.

Advanced Multimodal Sentiment Analysis with Enhanced Contextual Fusion and Robustness (AMSA-ECFR): Symmetry in Feature Integration and Data Alignment

Multimodal sentiment analysis, a significant challenge in artificial intelligence, necessitates the integration of various data modalities for accurate human emotion interpretation. This study introduces the Advanced Multimodal Sentiment Analysis with Enhanced Contextual Fusion and Robustness (AMSA-ECFR) framework, addressing the critical challenge of data sparsity in multimodal sentiment analysis. The main components of the proposed approach include a Transformer-based model employing BERT for deep semantic analysis of textual data, coupled with a Long Short-Term Memory (LSTM) network for encoding temporal acoustic features. Innovations in AMSA-ECFR encompass advanced feature encoding for temporal dynamics and an adaptive attention-based model for efficient cross-modal integration, achieving symmetry in the fusion and alignment of asynchronous multimodal data streams. Additionally, the framework employs generative models for intelligent approximation of missing features. It ensures robust alignment of high-level features with multimodal data context, effectively tackling issues of incomplete or noisy inputs. In simulation studies, the AMSA-ECFR model demonstrated superior performance against existing approaches. The symmetrical approach to feature integration and data alignment contributed significantly to the model’s robustness and precision. In simulations, the AMSA-ECFR model demonstrated a 10% higher accuracy and a 15% lower mean absolute error than the current best multimodal sentiment analysis frameworks.

Predicting fatigue life with the IMWCW under complex loading

The Integration Modified Wöhler Curve Method (IMWCW), a novel approach for predicting fatigue life under complex strain-based non-proportional loading paths is introduced in this research. This method integrates concepts from the Modified Wöhler Curve Method (MWCW) with the principle of damage incremental integration to provide a more nuanced prediction of material fatigue. Besides two established E-N curves (strain-number of cycles) for uniaxial tension and torsion, a new fundamental E-N curve that characterizes the strain-life relationship under complete non-proportional loading paths is introduced. A novel strain path decomposition technique is presented, which dissects an infinite number of instantaneous micro-elements along any loading path into components parallel and perpendicular to the proportional loading direction. It is assumed that each instant will cause fatigue damage to the material. A concurrent model for the incremental integration of damage is formulated, allowing for the calculation of cumulative damage. The model is further expanded to provide analytical expressions for proportional loading and non-proportional loading, linking them to three fundamental E-N curve, so as to calibrate the parameters required for the integration model. The empirical validity of the model is confirmed through experimental testing on 7 different metallic materials under 20 loading conditions, with 391 data points analyzed. The results demonstrate that 91% of the model's predictions fall within a scatter band of 2, confirming its robustness and reliability in forecasting fatigue life in complex, real-world scenarios.

Analysis of the Current Situation and Influencing Factors of the Satisfaction of the Elderly Living in Social Welfare Institutions in Lhasa City

Elderly is an important livelihood issue, and the aging of China is becoming more and more serious, accompanied by the miniaturization of family structure and empty nest. In order to cope with these problems, the development of institutionalized elderly care has been urgent. At present, China's nursing homes introduced public-private partnership, medical care, embedded and other pension models, but by geography, natural environment and historical development and other factors, the level of nursing homes around the world to start a different level of nursing service satisfaction is one of the important indicators to measure its level. In this paper, we conducted a research on Lhasa Social Welfare Institutions that practiced the public-private partnership and medical-nursing integration model, and the results showed that their satisfaction score was 81.9%, which was high. Multiple linear regression analysis found that gender, age, and education level significantly affect satisfaction. This paper reveals the operation of nursing homes in border ethnic areas, adds information to related studies, and provides suggestions for the development of nursing homes and the supply-side reform of nursing services.

Mapping global value chain, production network, and commodity chain literature using scientometrics: an integrative resilient value chains model based on a review of the literature

Purpose Global value chains (GVCs) are facing unprecedented pressures arising from structural changes in the global economy and exogenous shocks including military conflicts and the aftermath of COVID-19. Considering the importance of value chain analysis in the current environment, the purpose of the study is to provide an up-to-date overarching global value chain literature review study that offers suggestions for research and practice to ensure resilient value and supply chains. Design/methodology/approach The authors provide a comprehensive review of literature of the value chain, commodity chain and production network research based on a systems overview of 5,628 publications to identify the extent of research on vulnerabilities and resilience of value chains globally and gaps therein. To provide the systems overview, the authors use scientometric content co-occurrence analysis methods to analyze and identify gaps within the existing literature. Findings Based on this overarching review of the literature, the authors identify gaps in the literature primarily related to the issue of unpreparedness of value chains to exogenous shocks. The authors suggest future research directions and propose an integrative model along with recommendations for restructuring value chains for resilience amidst exogenous shocks. Originality/value This study carries out an overarching study of interdisciplinary GVC literature in the age of geopolitical and societal challenges and is thus able to offer holistic insights and propositions for future research.

Third-party perceptions of mistreatment: A meta-analysis and integrative model of reactions to perpetrators and victims.

Third parties have increasingly become the focus of research on mistreatment in organizations. Much of that work is grounded in deonance theory, which argues that third parties should react to the perpetrators of mistreatment with anger. Deonance theory is less explicit as to how third parties should react to the victims of mistreatment, though empirical work has pointed to empathy as one potential reaction. Deonance theory is less capable of explaining recent findings suggesting that third parties may react to mistreatment events with schadenfreude. The purpose of our study was to conduct a meta-analytic test of an integrative model specifying the relationships between third-party perceptions of mistreatment and reactions to perpetrators and victims. That model predicted that third-party perceptions of mistreatment would be associated with emotional reactions (anger toward the perpetrator, empathy toward the victim, schadenfreude from the event), cognitive reactions (evaluations of the perpetrator and victim), and behavioral reactions (antisocial and prosocial behaviors toward the perpetrator and victim). Our model testing provides the first quantitative synthesis of the third-party mistreatment literature while surfacing counterintuitive findings that would not be anticipated from deonance theory's arguments. We discuss the theoretical and practical implications of our work while providing guidance for future research. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

What causes social class disparities in education? The role of the mismatches between academic contexts and working-class socialization contexts and how the effects of these mismatches are explained.

Within psychology, the underachievement of students from working-class backgrounds has often been explained as a product of individual characteristics such as a lack of intelligence or motivation. Here, we propose an integrated model illustrating how educational contexts contribute to social class disparities in education over and beyond individual characteristics. According to this new Social Class-Academic Contexts Mismatch model, social class disparities in education are due to several mismatches between the experiences that students from working-class backgrounds bring with them to the classroom and those valued in academic contexts-specifically, mismatches between (a) academic contexts' culture of independence and the working-class orientation to interdependence, (b) academic contexts' culture of competition and the working-class orientation toward cooperation, (c) the knowledge valued in academic contexts and the knowledge developed through working-class socialization, and (d) the social identities valued in academic contexts and the negatively stereotyped social identities of students from working-class backgrounds. Because of these mismatches, students from working-class backgrounds are likely to experience discomfort and difficulty in the classroom. We further propose that, when attempting to make sense of these first-order effects, students and teachers rely on inherent characteristics (e.g., ability, motivation) more often than warranted; conversely, they overlook extrinsic, contextual factors. In turn, this explanatory bias toward inherent features leads (a) students from working-class backgrounds to experience self-threat and (b) their teachers to treat them unfairly. These second-order effects magnify social class disparities in education. This integrated model has the potential to reshape research and discourse on social class and education. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Artificial Intelligence: Need of Hour in Ayurveda

Institutions are working on developing various artificial intelligence tools to improve the objectivity of ayurvedic medicine and to create a disciplinary model for better integration with other medical systems because a molecular understanding of ayurvedic medicine is crucial for the promotion of evidence-based ayurveda. Artificial intelligence has a significant impact on the health care business in areas such as diagnosis, personalized treatment, patient outcomes, research, administrative needs, patient data, etc.

Evaluation of artificial intelligence-powered screening for sexually transmitted infections-related skin lesions using clinical images and metadata

BackgroundSexually transmitted infections (STIs) pose a significant global public health challenge. Early diagnosis and treatment reduce STI transmission, but rely on recognising symptoms and care-seeking behaviour of the individual. Digital health software that distinguishes STI skin conditions could improve health-seeking behaviour. We developed and evaluated a deep learning model to differentiate STIs from non-STIs based on clinical images and symptoms.MethodsWe used 4913 clinical images of genital lesions and metadata from the Melbourne Sexual Health Centre collected during 2010–2023. We developed two binary classification models to distinguish STIs from non-STIs: (1) a convolutional neural network (CNN) using images only and (2) an integrated model combining both CNN and fully connected neural network (FCN) using images and metadata. We evaluated the model performance by the area under the ROC curve (AUC) and assessed metadata contributions to the Image-only model.ResultsOur study included 1583 STI and 3330 non-STI images. Common STI diagnoses were syphilis (34.6%), genital warts (24.5%) and herpes (19.4%), while most non-STIs (80.3%) were conditions such as dermatitis, lichen sclerosis and balanitis. In both STI and non-STI groups, the most frequently observed groups were 25–34 years (48.6% and 38.2%, respectively) and heterosexual males (60.3% and 45.9%, respectively). The Image-only model showed a reasonable performance with an AUC of 0.859 (SD 0.013). The Image + Metadata model achieved a significantly higher AUC of 0.893 (SD 0.018) compared to the Image-only model (p < 0.01). Out of 21 metadata, the integration of demographic and dermatological metadata led to the most significant improvement in model performance, increasing AUC by 6.7% compared to the baseline Image-only model.ConclusionsThe Image + Metadata model outperformed the Image-only model in distinguishing STIs from other skin conditions. Using it as a screening tool in a clinical setting may require further development and evaluation with larger datasets.

Religiosity, ethical leadership and local wisdom in moderating the effect of fraud risk management on asset misappropriation: evidence from local government in Indonesia

Purpose This study aims to examine the effects of religiosity, ethical leadership and local wisdom on the relationship between fraud risk management and asset misappropriation in Indonesia. Design/methodology/approach Data were collected using a set of questionnaire surveys administered to the head office, local government internal auditors (inspectorate) and local government employees in Indonesia. Sample selection used purposive techniques and obtained 151 respondents who became research data. The dependent variable was asset misappropriation. The independent variable was fraud risk management. The moderating variables for this study were religiosity, leader ethics and local wisdom. The analysis technique applied the structural equation model-partial least square (SEM-PLS). Findings Fraud risk management has a significant negative effect on asset misappropriation. In addition, this study finds evidence that religiosity, ethical leadership and local wisdom increase fraud risk management against asset misappropriation. Research limitations/implications This study proposes an integrative model that enables local governments to understand fraud risk management. By integrating religiosity, ethical leadership and local wisdom, managers can design strategies to prevent asset misappropriation. Originality/value This research has the advantage of proposing an integrative model for mitigating asset misappropriation. Research on asset misappropriation is limited. Therefore, this study provides insights into fraud risk management, particularly in Indonesia’s local governments. In addition, this study adds ethical aspects such as religiosity, leadership and local wisdom to complement the weaknesses of fraud risk management and reduce the potential for asset misappropriation.

Revolutionizing anemia detection: integrative machine learning models and advanced attention mechanisms

This study addresses the critical issue of anemia detection using machine learning (ML) techniques. Although a widespread blood disorder with significant health implications, anemia often remains undetected. This necessitates timely and efficient diagnostic methods, as traditional approaches that rely on manual assessment are time-consuming and subjective. The present study explored the application of ML – particularly classification models, such as logistic regression, decision trees, random forest, support vector machines, Naïve Bayes, and k-nearest neighbors – in conjunction with innovative models incorporating attention modules and spatial attention to detect anemia. The proposed models demonstrated promising results, achieving high accuracy, precision, recall, and F1 scores for both textual and image datasets. In addition, an integrated approach that combines textual and image data was found to outperform the individual modalities. Specifically, the proposed AlexNet Multiple Spatial Attention model achieved an exceptional accuracy of 99.58%, emphasizing its potential to revolutionize automated anemia detection. The results of ablation studies confirm the significance of key components – including the blue-green-red, multiple, and spatial attentions – in enhancing model performance. Overall, this study presents a comprehensive and innovative framework for noninvasive anemia detection, contributing valuable insights to the field.