Integrated Model Research Articles

An integrated mechanism and data model for adaptive wear state diagnosis via moving wear particles

An integrated mechanism and data model for adaptive wear state diagnosis via moving wear particles

Strengthening AYUSH Integration with Preventive Medicine

Integrating Ayurveda, Yoga, Unani, Siddha, and Homoeopathy (AYUSH) into preventive medicine enhances holistic health care by addressing lifestyle, mental well-being, and disease prevention. This integration is vital for addressing India’s quadruple burden of non-communicable diseases (NCDs), communicable diseases, poor maternal and child health (MCH) outcomes, and an aging population. The National Health Policy 2017 promotes this integration through “Pluralism,” aligning with the World Health Organization (WHO)’s Sustainable Development Goal (SDG) 3 and global traditional medicine priorities. Essential actions include adopting integrative care models, standardizing treatment protocols, training healthcare professionals, and advancing research. Despite its promise, challenges persist, such as limited awareness among modern physicians, minimal interaction between practitioners, lack of standardized protocols, and insufficient research on AYUSH’s synergistic effects. The Indian Association of Preventive and Social Medicine (IAPSM) can play a pivotal role in overcoming these barriers. By advocating for policy development, adopting integrative care frameworks, and fostering educational and research collaborations with AYUSH institutes, IAPSM can advance the integration of AYUSH into preventive medicine. Promoting research and increasing public awareness of AYUSH’s role in disease prevention and health promotion is essential for achieving a comprehensive healthcare system.

A three-level model for integration of smart homes, electric vehicle charging stations and hydrogen fuelling stations in reconfigurable microgrids considering vehicle-to-infrastructure (V2I) technology

A three-level model for integration of smart homes, electric vehicle charging stations and hydrogen fuelling stations in reconfigurable microgrids considering vehicle-to-infrastructure (V2I) technology

Knowledge management and technology for enhanced cybersecurity effectiveness

Cybersecurity continues to be a major concern and presents significant challenges for businesses, governments, nonprofits, and individuals. Organizations are working diligently to mitigate threats to these entities while the rate of cybercrime continues to increase in sophistication and reach. Current models in cybersecurity are becoming increasingly ineffective against the velocity, agility, and persistence of cyber adversaries. This challenge presents an opportunity to consider concepts and methods in knowledge management to strategically evaluate and prioritize adversarial cyber threat activities. This research aims to explore a conceptual framework designed to provide cybersecurity professionals with new models for knowledge management to increase the effectiveness of the detection, mitigation, and attribution of these threats. This conceptual approach draws from the literature on knowledge management and cybersecurity to integrate key concepts into a new theoretical framework. This paper presents a new hybrid model for cybersecurity that integrates core concepts of the classic Nonaka and Takeuchi Knowledge Spiral and the National Institute of Standards and Technology (NIST) Cybersecurity Framework augmented with emerging artificial intelligence and machine learning technologies. The development of a hybrid cybersecurity model combined with proven knowledge management strategies for continual knowledge creation and innovation represents an integrative model designed to address emerging cybersecurity challenges.

Emotional reactivity mediates and moderates the longitudinal associations between mothers' depressive symptoms and behavioral problems in youth.

Despite the well-established link between mothers' depressive symptoms and youth behavioral problems, it remains unclear whether the mechanism by which mothers' depressive symptoms promote behavioral problems may also be related to individual differences in this relation. Following the three-variable system integrating mediation and moderation, this study used an integrative model to simultaneously examine the mediating and moderating role of emotional reactivity in the longitudinal associations between maternal depressive symptoms and internalizing and externalizing problems in youth. Participants were 1060 youth and their mothers from the NICHD Study of Early Child Care and Youth Development (49.7% boys, 81.6% white). Mothers reported their own depressive symptoms at nine waves from infancy through the fifth grade. They also rated adolescents' emotional reactivity in the fifth grade and behavioral problems in the fifth and sixth grades. The results showed that emotional reactivity mediated the longitudinal relations between maternal depressive symptoms and internalizing and externalizing problems in youth. The results also supported the moderation of emotional reactivity. That is, mothers' depressive symptoms were associated with high levels of internalizing and externalizing problems, particularly among youth who were high in emotional reactivity. The findings highlight the dual role of emotional reactivity to better understand the associations between mothers' depressive symptoms and offspring behavioral problems, and emphasize the need to target emotional reactivity to alleviate the adverse impacts of maternal depressive symptoms on behavioral adjustment in early adolescence.

Accounting for post-release mortality in data-limited, length-based assessments for four demersal fish species with varying life histories

Released (or discarded) fish from recreational and commercial line fisheries can experience significant levels of post-release mortality (PRM), affecting sustainability. Although PRM effects are often considered in data-rich (integrated model) assessments, this is uncommon in data-limited assessments. This study investigates impacts of PRM when applying data-limited, length-based assessment methods for four reef-dwelling demersal fish species in temperate Western Australia (Glaucosoma hebraicum, Chrysophrys auratus, Choerodon rubescens and Epinephelides armatus). These species differ considerably in their biology and susceptibility to PRM. For each species simulations were undertaken to (1) evaluate the reliability of a length-based catch curve (LBCC) method for estimating fishing mortality (F) and selectivity parameters, and (2) predict the impacts of different PRM levels on values of relative biomass, i.e. ratios of fished to unfished equilibrium spawning biomass (Brel), estimated using a length-based equilibrium analysis (LBEA). LBEA extends length-based per-recruit analysis to incorporate a stock-recruitment relationship as well as allow for PRM. On average, estimates of F and selectivity parameters from LBCC for each species were unbiased when applied to simulated data. Precision decreased, however, when LBCC was fitted to data simulated with recruitment variation. For a given F, increased PRM levels reduced Brel for all species, with the extent of impacts varying depending on species biology. LBCC was then applied to two actual datasets, firstly to lengths for all fish that had either been retained or released during historical recreational fishing trips (to estimate gear selectivity), and secondly to length data for retained fish from recent charter fishing (to estimate F and selectivity of landings). The resultant estimates were then used in LBEA to estimate Brel with and without accounting for PRM. Relative impacts of PRM varied among species depending on their biology and size limits. While PRM was predicted to have limited impact on Brel for C. rubescens (as fish are not fully selected by the gear until they attain a relatively large size), accounting for PRM resulted in estimates of Brel (at their current estimated F levels) being reduced by 31 % for G. hebraicum, 36 % for C. auratus and 23 % for E. armatus. It is recommended that, where evidence of PRM exists for a species, this be accounted for in data-limited assessments.

Farmers' credit risk evaluation with an explainable hybrid ensemble approach: A closer look in microfinance

Artificial intelligence stimulates the vitality of microcredit by reshaping credit risk evaluation models, especially targeting the group of farmers. Therefore, the paper aims to establish a new interpretable hybrid ensemble model for evaluating the credit risk of microfinance for farmers, which is called ADASYN (Adaptive Synthetic Sampling)-LCE (Local Cascade Ensemble)-Shapash. It integrates the advantages of three ensemble models: bagging, boosting, and local cascading, including reducing model variance, reducing model bias, and simplifying complex problems by learning different parts of the training data. And it alleviates the problem of low generalization performance of traditional ensemble models caused by imbalanced loan data of farmers. Through the empirical analysis of the data of farmers' loans of China poverty alleviation agency “CHONGHO BRIDGE”, it is found that its average rank is 2.1, which is better than other integrated models in the credit risk evaluation of farmers' microfinance. Finally, the global and local interpretation of our model is preliminarily explored.

An integrated system theoretic accident model and process (STAMP)-Bayesian network (BN) for safety analysis of water mist system on tanker ships

A water mist on tanker ships is a specialized fire suppression system intended to mitigate fire hazards on board. This innovative safety measure disperses microscopic water droplets, creating a mist that rapidly absorbs heat and suppresses fires by reducing oxygen levels and cooling the surrounding area autonomously. This paper attempts to perform a comprehensive safety analysis of the water mist system on a tanker ship under the system theoretic accident model and process (STAMP) and Bayesian network (BN) robust modeling, which is capable of presenting a dynamic structure comprising complex elements. In the paper, whilst the STAMP is utilized to determine failure scenarios by establishing a hierarchical control and feedback model structure, the BN quantifies the probabilities of potential failures based on the outcomes of STAMP. A water mist system in tanker ships is analyzed under STAMP-BN modeling since it is an essential part of the ship, including an advanced system comprising elements of human, software, and hardware. The research results indicate that the failure probability of the water mist system has a failure probability of 3.93E-02. Besides its robust theoretical background, the research findings will provide a helpful reference for maritime safety managers, safety inspectors, technical inspectors, HSEQ managers, and ship crew on improving fire-fighting safety in tanker ships.

The use of Lean management to optimize business processes in the enterprise

The article examines the problems of using Lean management, which is significant in fierce competition, economic challenges, and shocks. Industrial enterprises are forced to look for effective methods of optimizing production. Traditional approaches that rely on reducing staff or increasing the load on equipment demonstrate their inconsistency in the long term. Within the framework of the conceptual provisions of lean management, a fundamentally different view is proposed on improving the efficiency of business processes through systematic detection and elimination of losses. There are contradictions among scientists between the theoretical models of integration of the considered tools and the practical results of their application. There are serious disagreements about the universality of the methods and the possibility of their adaptation to specific business conditions. Based on this, the purpose of the study is to systematize the existing ideas, taking into account the existing differences in views and judgments. As a result, it was found that the successful implementation of Lean management requires consistency: not only technical aspects are fundamental, but also the transformation of corporate culture. Based on the characteristics of advantages and limitations, the determining success factors are identified: management involvement, gradual change in production culture, and continuous staff training. The materials of the article are of practical value for company executives, production optimization specialists, and consultants.

Strategic Integration of Policy and Logistics: A Case Study of Copenhagen’s Model for Sustainable Urban Supply Chains

This study explores integrating logistics policies and practices in creating efficient and sustainable urban supply chains. The study analyzes how green transport policies, the use of digital technologies, and collaboration between government and the private sector contribute to improving logistics efficiency and reducing emissions through a qualitative approach with thematic interviews with stakeholders and a case study of Copenhagen. Copenhagen has successfully implemented low-emission zones and an intelligent traffic management system that optimizes delivery routes, thereby reducing congestion and increasing sustainability. The findings provide important insights for other cities, including Jakarta, that face similar challenges in the logistics sector. The study suggests that implementing an effective policy integration model can help address environmental and operational issues, as well as improve the quality of life for urban residents. The results of this study contribute to the literature on urban logistics and sustainability policies and offer strategic recommendations for policymakers.

Navigating complex mergers: A blueprint for strategic integration in emerging markets

This study explores strategic integration in mergers and acquisitions (M&A), focusing on emerging markets. As M&A activity in these regions grows, companies face unique challenges due to diverse regulatory environments, cultural dynamics, and operational complexities. This research aims to provide a comprehensive framework to navigate these complexities, identifying core integration components, external influencing factors, technological advancements, and associated challenges. Methodologically, the study synthesizes existing literature on M&A integration, analyzing key strategic approaches across cultural alignment, risk management, and operational harmonization. Key findings indicate that successful strategic integration requires technological and human-centered approaches, including tools like ERP systems, data analytics, and cloud computing, which streamline operations, and emphasize cultural alignment, leadership synergy, and proactive risk management. Additionally, the study underscores the impact of regulatory and economic factors on integration strategies, highlighting the need for adaptable frameworks tailored to the complexities of emerging markets. The study concludes that structured integration strategies and flexible, technology-driven frameworks foster long-term M&A success and competitive advantage. Recommendations include adopting sustainable integration models, enhanced cross-functional collaboration, and robust regulatory compliance strategies. Future research opportunities are outlined, particularly regarding open innovation and sustainable practices in emerging market M&A.

Deep Learning Integration of Multi-Model Forecast Precipitation Considering Long Lead Times

Reliable forecast precipitation can support disaster prevention and mitigation and sustainable socio-economic development. Improving forecast precipitation accuracy remains a challenge. Therefore, a novel method for multi-model forecast precipitation integration considering long lead times was proposed based on deep learning. First, the accuracy of numerical forecast precipitation was evaluated under different lead times. Secondly, an integrated model was built by coupling the attention mechanism and a long short-term memory neural network (LSTM). Finally, integrated forecast precipitation was obtained by taking high-precision numerical forecast precipitation as an input and examining its accuracy and applicability. Considering the example of the Yalong River, the results showed the following: (1) numerical forecast precipitation fails to forecast precipitation of a ≥10 mm/d intensity well, and is less applicable in streamflow forecast; (2) traditional machine learning methods for integrating multi-model forecast precipitation fail to forecast precipitation of a ≥25 mm/d intensity; (3) the LSTM-A integration model formed by attention weighting after the LSTM output can combine the advantages of numerical forecast precipitation under different intensities and improve the forecast precipitation accuracy for 7-day lead times; and (4) the LSTM-A integrated forecast precipitation has the best applicability in streamflow forecast, with an NSE above 0.82 and an MRE below 30% with 7-day lead times. These findings contribute to improving precipitation forecast accuracy at different intensities and enhancing defense against extreme weather events.

Spending Review Integration Model in Budget Decision Making at the Ministry of Health of the Republic of Indonesia

The aim of this study is to identify an effective model for integrating spending reviews into the budget decision-making process. Using a qualitative approach, the study collects data through interviews and documentation. The findings suggest that the budget decision-making process would benefit from implementing an integration model known as the Comprehensive Monitoring and Evaluation Model of Budget Management. This model facilitates a more thorough review and oversight of budget allocations, ensuring better-informed decision making. The study recommends further research to assess the impact of this model on the quality of budget decision making, particularly focusing on how it enhances the decision-making process and improves budget management outcomes.

Schizophrenia Detection and Classification: A Systematic Review of the Last Decade.

Artificial Intelligence (AI) in healthcare employs advanced algorithms to analyze complex and large-scale datasets, mimicking aspects of human cognition. By automating decision-making processes based on predefined thresholds, AI enhances the accuracy and reliability of healthcare data analysis, reducing the need for human intervention. Schizophrenia (SZ), a chronic mental health disorder affecting millions globally, is characterized by symptoms such as auditory hallucinations, paranoia, and disruptions in thought, behavior, and perception. The SZ symptoms can significantly impair daily functioning, underscoring the need for advanced diagnostic tools. This systematic review has been conducted following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 guidelines and examines peer-reviewed studies from the last decade (2015-2024) on AI applications in SZ detection as well as classification. The review protocol has been registered in the International Prospective Register of Systematic Reviews (PROSPERO) under registration number: CRD42024612364. Research has been sourced from multiple databases and screened using predefined inclusion criteria. The review evaluates the use of both Machine Learning (ML) and Deep Learning (DL) methods across multiple modalities, including Electroencephalography (EEG), Structural Magnetic Resonance Imaging (sMRI), and Functional Magnetic Resonance Imaging (fMRI). The key aspects reviewed include datasets, preprocessing techniques, and AI models. The review identifies significant advancements in AI methods for SZ diagnosis, particularly in the efficacy of ML and DL models for feature extraction, classification, and multi-modal data integration. It highlights state-of-the-art AI techniques and synthesizes insights into their potential to improve diagnostic outcomes. Additionally, the analysis underscores common challenges, including dataset limitations, variability in preprocessing approaches, and the need for more interpretable models. This study provides a comprehensive evaluation of AI-based methods in SZ prognosis, emphasizing the strengths and limitations of current approaches. By identifying unresolved gaps, it offers valuable directions for future research in the application of AI for SZ detection and diagnosis.

Food and land system transformations under different societal perspectives on sustainable development

Abstract The future of food and land systems is crucial for achieving multiple UN Sustainable Development Goals, given their essential role in providing adequate nutrition and their significant impact on Earth system processes. Despite widespread consensus on the need for transformation, discussed strategies vary widely, from technology-driven to sufficiency-focused approaches, emphasizing different agents of change and policy mixes. This study assesses the implications of a new generation of target-seeking scenarios incorporating such diverse sustainability perspectives. We apply two integrated assessment models to explore food and land futures under three whole-economy Sustainable Development Pathways (SDPs): Economy-driven Innovation, Resilient Communities, and Managing the Global Commons. Our assessment shows that all SDPs align sufficient food supply with progress towards planetary integrity, halting biodiversity loss, mitigating adverse impacts from irrigation, and significantly reducing nitrogen pollution. While all pathways comply with the Paris climate target, they diverge in the timing of climate mitigation efforts and focus on different greenhouse gases and emission sources. The Economy-driven Innovation pathway rapidly achieves net-negative CO2 emissions from the land system, whereas Resilient Communities and Managing the Global Commons significantly decrease agricultural non-CO2 emissions. Moreover, sustainability interventions attenuate trade-offs associated with narrowly focused mitigation pathways and reduce reliance on carbon dioxide removal strategies like re/afforestation and bioenergy with carbon capture and storage.

Resilience agility in tourism and hospitality: Empirical research using 3D modelling

The paper presents an empirical application of the integrative model of resilience knowledge in tourism, the ReKo-Model. This study employs a quantitative method to analyse seven dimensions of resilience, with a specific focus on three areas of measurement: awareness, current implementation, and strategic cooperation. The research included obtaining virtual expert evaluations from 62 managers in the hotel industry in Germany. The interpretation of empirical findings is facilitated through the use of visualization techniques. 3D models allow for cluster and gap-analysis, providing insight into the implementation of ReKo-factors and dimensions of resilience agility. By examining how hotel-managers adapt their behaviour, the research reveals that applying resilience theory in tourism and hospitality can have transformative effects. This helps bridge strategic and expertise gaps for a more comprehensive approach to resilience agility. The research supports using a deductive approach and consolidation strategy to build resilience agility in the tourism and hospitality industry.

A multimodal expert system for the intelligent monitoring and maintenance of transformers enhanced by multimodal language large model fine‐tuning and digital twins

AbstractThe development of multimodal large models and digital twin technology is set to revolutionise the methods of intelligent monitoring and maintenance for transformers. To address the issues of low intelligence level, single application mode, and poor human–machine collaboration in traditional transformer monitoring and maintenance methods, an intelligent monitoring and maintenance digital twin multimodal expert reasoning system, fine‐tuned on visual language‐based large models, is proposed. The paper explores the modes and methods for implementing intelligent monitoring and maintenance of transformers based on multimodal data, large models, and digital twin technology. A multimodal language large model (MLLM) framework for intelligent transformer maintenance, grounded on the Large Language and Vision Assistant model, has been designed. To enable large models to understand and reason about image annotation areas, an adaptive grid‐based positional information processor has been designed. To facilitate the compatibility and learning of large models with transformer Dissolved Gas Analysis data, a heterogeneous modality converter based on the Gram–Schmidt angular field has been developed. For the unified modelling and management of multimodal reasoning and comprehensive resource integration in human–machine dialogue, a central linker based on an identity resolution asset management shell has been designed. Subsequently, a visual‐language multimodal dataset for transformer monitoring and maintenance was constructed. Finally, by fine‐tuning parameters, a multimodal expert reasoning system for intelligent transformer monitoring and maintenance was developed. This system not only achieves real‐time monitoring of the transformer's operational status but also generates maintenance strategies intelligently based on operational conditions. The expert system possesses robust human–machine dialogue capabilities and reasoning generation abilities. This research provides a reference for the deep integration of MLLM and digital twin in industrial scenarios, particularly in the application modes of intelligent operation and maintenance for transformers.

Integrated testing and modelling of substructures using full-field imaging and data fusion

A new integrated testing and modelling paradigm based on full-field imaging and finite element (FE) analysis that utilises full-field data fusion is proposed for structural evaluation and model validation at substructure level. The approach is developed for the assessment of a composite wind turbine blade (WTB) substructure subjected to multiaxial loading, mimicking in-service conditions, using a new reconfigurable loading rig. A steel mock-up equivalent to the WTB substructure was used to demonstrate the new experimental, numerical, full-field imaging, and data fusion approaches. Digital Image Correlation (DIC) and Thermoelastic Stress Analysis (TSA) were used to obtain the complex load response of the substructure. Strains and displacements derived from DIC were fused with numerical predictions obtained using a FE-based stereo-DIC simulator, which provided unparalleled like-for-like data comparisons. A numerical FEA solution for TSA was also obtained that accounts for heat transfer and allowed an independent means of structural evaluation. The challenges of deploying full-field imaging on the substructure scale are highlighted alongside procedures for mitigating multiple deleterious effects that are concatenated in large structures testing. It is demonstrated that high quality and fidelity experimental data can be obtained and fused with numerical models to provide a comprehensive and quantitative structural assessment at the substructure scale. It is shown that the proposed full-field data fusion efficiently reveals uncertainties in both the models and experiments. The work provides important steps to support virtual testing at higher length scales and their integration into the design, development, and certification programs of next generation, high-performance structures.

Integration in Mixed Methods Research With an Exemplar Explanatory Sequential Study.

Integration-a hallmark of mixed methods research (MMR)-is crucial throughout the entire research process. Despite its importance, integration remains an elusive concept often inadequately addressed by researchers, with many publications focusing solely on the final stages of data analysis and interpretation. The authors aim to synthesize methodologic discussions of the mixed method research integration process and provide an illustration of integration across all phases of an exemplar explanatory sequential study. Integration in MMR includes four distinct dimensions encompassing philosophical assumptions and paradigms, methodological methods, and dissemination of integrated study findings. This paper presents our integrated study findings, illustrated by a research study conducted to investigate sleep health among women with breast cancer. Integration of quantitative and qualitative data analyses is demonstrated through consistently matching the guiding paradigm with design choice, meta-inferences, and visual joint displays. The connecting displays linking quantitative and qualitative data, compare sleep characteristics scores, and the relationship between anxiety and vasomotor symptoms. The results highlight the significance of visual tools in enhancing the analytic processes and providing a comprehensive understanding of complex clinical phenomena. Integrating across all stages of mixed method research studies presents many challenges. The proposed four-dimension model of integration contributes to understanding the unique value of fully integrated mixed methods results. The exemplary study findings confirm the value of integration in achieving a nuanced and comprehensive understanding of a significant clinical problem.

Applications of marine microbial community models in the nature-based economy

Marine microorganisms are central to global ecological and biogeochemical systems, with their intricate interactions shaping community dynamics. While meta-omics data sets have revolutionized marine microbial ecology, they often provide fragmented insights, underscoring the need for advanced integrative modeling frameworks. In this review, we highlight the potential that community genome-scale metabolic models (cGEMs), in combination with meta-omics and environmental data sets, offer in advancing marine microbial ecology. We explore 3 key applications: quantifying marine ecosystem services, guiding bioremediation strategies for environmental challenges, and enhancing climate and biogeochemical models. Furthermore, we propose novel indices derived from cGEMs to assess microbial contributions to ecosystem functions, potentially informing economic valuation strategies for marine conservation. This interdisciplinary approach paves the way for innovative strategies in biotechnology, environmental restoration, and the development of nature-aligned economic systems, ultimately contributing to the preservation and sustainable use of marine ecosystems.