Dynamic Integration Research Articles

Purpose This study aims to examine the causality relationship between environmental, social and governance (ESG) indices and traditional asset class indices across six countries with diverse economic conditions, utilizing the Continuous Wavelet Transform-Based Granger Causality (CWTC) method. The study covers the period from late 2014 to late 2024, depending on the availability of ESG index data for each country, and includes developing countries (China and Taiwan) and developed countries (UK, USA, Australia and Japan). The research focuses on periods marked by global crises, such as the COVID-19 pandemic and military conflicts. Design/methodology/approach The study employs the CWTC method to analyze causality between ESG indices and traditional asset class indices, covering various periods, including those of heightened volatility due to global events. The analysis spans six countries, including China, the USA and other significant economies, to identify patterns in the relationship between ESG and traditional financial markets. Findings The results demonstrate a consistent causality from ESG indices to traditional asset class indices in the USA and China across all analyzed periods. In contrast, other countries exhibit stronger causality in the short and medium term. This suggests the dynamic integration of ESG and traditional markets, with ESG information influencing market behavior, particularly in times of crises. Originality/value This study contributes to sustainable investing research by analyzing the causality between ESG indices and traditional asset class indices across six countries. With growing concerns about climate change and investor preference for sustainability, the findings underscore the integration of ESG factors into financial markets. By using the CWTC method, the study captures dynamic causal relationships, offering practical insights for investors and policymakers in optimizing sustainable investment strategies.

This study explores the implementation of pesantren-based curriculum in da'wah education at Madrasah Aliyah Mamba’ul Ulum, Jambi City, Indonesia. Positioned at the intersection of formal Islamic schooling and classical pesantren traditions, the curriculum reflects a dynamic integration of national educational standards with Kitab Kuning-based instruction. Employing a qualitative case study approach, the research investigates key aspects of curriculum execution, identifies structural and pedagogical challenges, and analyzes strategic responses adopted by the institution. Findings reveal that da'wah education is embedded within formal instructional hours and reinforced through experiential practices such as student sermons, daily worship routines, and muhadhoroh sessions. Despite facing constraints—particularly among non-residential students lacking pesantren backgrounds, limited Arabic proficiency, and resource shortages—the madrasah implements a tiered strategy comprising monthly, semester-based, and annual programs to cultivate da'wah competencies progressively. The integration of textual and contextual learning, supported by Arabic language development, enhances students’ rhetorical, spiritual, and social capacities. The study concludes that the pesantren curriculum serves not only as a vehicle for religious knowledge transmission but also as a transformative framework for shaping communicative and ethically grounded da’i and da’iyyah. These findings underscore the importance of adaptive, participatory, and experience-based approaches in sustaining relevant and impactful da'wah education within contemporary Islamic schooling. This study offers a model for integrating pesantren traditions with modern educational demands in da'wah education.

Advances and future needs for modelling sustainable and just food systems transformations.

AI-driven corporate reputation measurement in digital ecosystems: A systematic literature review.

Temporally adaptive hierarchical Choquet integrals: A measure-theoretic framework for dynamic non-additive integration in approximate reasoning

Climate change, along with the pathogens adaptive potential, challenges the robustness of criteria, forecasting models, and decision support systems for late blight (Phytophthora infestans) control, the most destructive disease affecting potato crops worldwide. Under PRISMA criteria, this meta-analysis examined the criteria and forecasting models in potato late blight over the last 106 years in 25 countries. The evaluation groups a total of 271 trials in which 59 different models were used. The criteria and the forecasting models were categorized by three generation types (G1 to G3) based on their statistical methodology, and by three mechanism types based on their internal structure (Semi-Mechanistic, SM; Non-Mechanistic, NM; Mechanistic, M). For each one of these groups, the accuracy, fungicide reduction capacity, and temporal consistency were evaluated. The results indicated that Mechanistic models (integrate pathogen biological variables) outperform Non-Mechanistic models (only consider environmental variables). Therefore, the integration of pathogen life cycle dynamics in the context of climate variability is crucial to developing robust forecasting models. This study highlights the limitations of Non-Mechanistic models and underscores the need for forecasting models to be developed under criteria of ecological realism of plant-pathogen interaction and pathogens adaptive potential under climate change scenarios.

Ethnic villages are a multidimensional interactive space between cultural inheritance and modernization; analyzing their spatial reconstruction is fundamental for promoting agricultural and rural modernization and sustainable ethnic development. This study examined ethnic villages in Yongcong Township, Liping Country, from 2016 to 2022, focusing on changes in function and suitability under relocation through a function and suitability evaluation index. Case comparisons were made between administrative villages with high functional and suitability levels and those with resettlement sites. In 2016, ethnic villages followed a growth pattern of Yongcong–Dundong–Guantuan, with low patch density, dispersed distribution, and simple shapes. By 2022, functionality and suitability significantly improved, with an increase in village patches and larger patch areas shifting toward spatial aggregation. Horizontally, land use within reconstruction boundaries diversified by function, whereas vertically, housing structures were reorganized: non-settlement villages retained traditional and modern types while settlement villages combined both, leading to a shift from functional singularity to multifunctionality. Relocation-induced reconstruction may lag local knowledge systems and reduce well-being. Initially, government-led suitability enhancement dominates; gradually, villages increasingly internalize regional identity and competitiveness. By analyzing post-relocation village reconstruction, this study supports the integration of ethnic and regional dynamics, achieving high-quality sustainable development in minority regions.

ABSTRACT This study explored counselors’ self-awareness development using constructivist grounded theory. Interviews with 22 trainees and educators yielded the Dynamic Self-Awareness Integration Process (DSAIP) model, featuring fluid integration, three central processes, and five key processes. The model illustrates how counselors cultivate self-awareness across cognitive, emotional, physical, and relational dimensions. Findings highlight the role of self-awareness in preparing counselors for leadership and advocacy. Implications include strategies to foster embodied, relational, and contextual awareness across counselors’ professional development.

We obtain a formal expression for the quantum mechanical potential of mean force (PMF) in terms of the reduced density matrix associated with a reaction coordinate. We derive two path integral estimators for the derivative of the quantum mechanical PMF, which may be integrated to yield the PMF. For the first estimator, we perform the differentiation on the exact path integral, and for the second, the differentiation is performed after discretisation. These estimators are successfully validated for harmonic oscillator and Lennard-Jones dimer systems using constrained path integral Monte Carlo (PIMC) simulations at a series of temperatures. We propose two path integral molecular dynamics (PIMD) integrators, c-OBABO and c-BAOAB, to further test the estimators.Those are based on the path integral Langevin equation (PILE) with the addition of holonomic constraints. When the reaction coordinate is the distance between two centres of mass, we find that several exact expressions are accessible: the Fixman correction, the position constraint Lagrange multiplier, and various reaction coordinate derivatives. It is observed that c-BAOAB has a smaller time step error than c-OBABO. We show that both the PMF of a water dimer and its derivative obtained using c-BAOAB are in agreement with earlier path integral umbrella sampling results.

This systematic literature review analyzes machine learning approaches for mobile phone price prediction based on device specifications through a comprehensive examination of 25 research studies from 2018 to 2024.The review reveals that ensemble methods, particularly Random Forest (achieving up to 97% accuracy) and Gradient Boosting (R² = 0.9829), consistently outperform individual algorithms across various datasets. Support Vector Machine models demonstrate superior classification performance with 96-97% accuracy, while neural networks show perfect best-performer ratios but remain underutilized (4.88% of implementations). The following keywords were used in this systematic review's extensive search strategy across IEEE Xplore, ACM Digital Library, ScienceDirect, and Google Scholar: ("mobile phone price prediction" OR "smartphone price prediction") AND ("machine learning" OR "artificial intelligence") AND ("specifications" OR "features") AND ("classification" OR "regression"). Strict inclusion/exclusion criteria were used to select 25 studies from an initial pool of 45 studies, with an emphasis on empirical research with quantitative performance metrics published between 2018 and 2024. The study reveals RAM, internal memory, battery capacity, and processor specifications as the key determining features for mobile phone pricing. According to the study, the primary factors influencing mobile phone pricing are processor specifications, RAM, internal memory, and battery capacity. This review identifies critical research gaps, including insufficient neural network exploration, poor dataset reporting practices (52% of studies omit dataset sizes), and lack of real-time market dynamics integration. The findings provide evidence-based guidance for researchers, manufacturers, and consumers in selecting optimal prediction algorithms and understanding key price-determining features in the evolving smartphone market. Study limitations include geographic bias toward specific markets represented in available datasets, limited access to proprietary datasets, and a primary focus on specification-based features that exclude market sentiment analysis

Introduction: This study addresses the challenge of concept drift in multivariate time series (MTS) forecasting, where data distributions evolve, degrading model performance. The objective is to propose an adaptive hybrid model that combines Long Short-Term Memory (LSTM) networks with the ADaptive WINdowing (ADWIN) algorithm for effective drift detection and adaptive forecasting in real-time environments. Methods: The proposed ADWIN-LSTM framework integrates Bi-LSTM layers for temporal sequence modelling and ADWIN for monitoring prediction residuals to detect concept drift. A dynamic sliding window mechanism adjusts the training data scope based on drift type. Data preprocessing includes normalization, STL-based detrending, and PCA for dimensionality reduction. Hyperparameters are optimized using grid search. Results: Experiments on real-world and synthetic MTS datasets demonstrate that the proposed model outperforms baseline models (LSTM, GRU, CNN-LSTM, and ADWIN-RF) across RMSE, MAE, MAPE, and R² metrics. The model detects both abrupt and gradual drifts with minimal false positives and low detection delay (≤ 5 steps). Post-drift adaptation significantly improves forecasting accuracy. Discussion: The adaptive retraining strategy triggered by drift detection ensures computational efficiency and robustness in volatile environments. The dynamic integration of forecasting and drift detection enhances model adaptability to evolving data distributions. Conclusion: The ADWIN-LSTM framework effectively combines predictive learning and realtime drift adaptation, making it suitable for dynamic, high-stakes environments such as energy, traffic, and environmental systems. Future work includes online optimization and deployment on streaming platforms.

Acute Respiratory Distress Syndrome (ARDS) remains a significant contributor to morbidity and mortality in critical care, yet its diagnosis and classification have long been constrained by resource availability and technological dependence. In 2024, a new global definition of ARDS was proposed to address these limitations, marking the most substantial revision since the Berlin definition of 2012. This review outlines the key updates in the new criteria, including the formal inclusion of lung ultrasound, SpO₂/FiO₂ ratios, and the recognition of non-intubated ARDS and resource-limited settings. These changes aim to enhance diagnostic inclusivity, early recognition, and research applicability. We critically evaluate the strengths and limitations of the new framework, with attention to the variability of oxygenation indices, FiO₂ estimation challenges, and imaging interpretation. The review also highlights emerging areas for improvement, such as the need for standardized ventilator settings, integration of carbon dioxide dynamics, and the role of phenotypic stratification in advancing precision medicine. This redefinition represents a paradigm shift in ARDS diagnosis and paves the way for broader, more equitable critical care practices and research inclusion.

The supply chain holds a vital position in today’s economic landscape and is essential to the functioning of modern markets, and a well-managed supply chain can significantly enhance the market competitiveness of enterprises. However, supply chain disruptions caused by uncertainties, such as natural disasters, can result in significant economic losses and may even drive firms out of the market. The supply chain serves as a vital link between suppliers and consumers in the context of globalization, allowing firms to secure competitive advantages and expand into new markets. To preserve the integrity of supply chain resilience, enterprises must stabilize operations by implementing core strategies, leveraging digital systems for monitoring and optimization, and thereby enhancing agility and adaptability. Accordingly, this study investigates the role of supply chains within the consumer electronics industry. Findings indicate that resilient supply chains rely on a dynamic integration of strategic supplier management, advanced digital technologies, and regional diversification. With growing global market uncertainty, enterprises need to combine flexibility, innovation, and localized resources to sustain continuity, mitigate risks, and safeguard long-term competitiveness.

The increasing availability of User-Generated Content during large-scale events is transforming spectators into active co-creators of live narratives while simultaneously introducing challenges in managing heterogeneous sources, ensuring content quality, and orchestrating distributed infrastructures. A trial was conducted to evaluate automated orchestration, media enrichment, and real-time quality assessment in a live sporting scenario. A key innovation of this work is the use of a cloud-native architecture based on Kubernetes, enabling dynamic and scalable integration of smartphone streams and remote production tools into a unified workflow. The system also included advanced cognitive services, such as a Video Quality Probe for estimating perceived visual quality and an AI Engine based on YOLO models for detection and recognition of runners and bib numbers. Together, these components enable a fully automated workflow for live production, combining real-time analysis and quality monitoring, capabilities that previously required manual or offline processing. The results demonstrated consistently high Mean Opinion Score (MOS) values above 3 72.92% of the time, confirming acceptable perceived quality under real network conditions, while the AI Engine achieved strong performance with a Precision of 93.6% and Recall of 80.4%.

The dynamic integration of the lateralized and specialized capacities of the two cerebral hemispheres constitutes a hallmark feature of human brain function. This interhemispheric exchange of information critically depends upon the corpus callosum. Classical anatomical descriptions of callosal organization outline a topographic gradient from front to back, such that specific transcallosal fibers support distinct aspects of integrated brain function. Here, we present a challenge to this conventional model. Using neuroimaging data obtained from a new cohort of adult corpus callosotomy patients, we leverage modern network neuroscience techniques to show that full interhemispheric integration can be achieved via a small proportion of posterior callosal fibers. Partial callosotomy patients with spared callosal fibers retained widespread patterns of interhemispheric functional connectivity and showed no signs of behavioral disconnection, even with only 1 cm of the splenium intact. Conversely, only complete callosotomy patients demonstrated sweeping disruptions of interhemispheric network architectures, aligning with disconnection syndromes long-thought to reflect diminished information propagation and communication across the brain. These findings motivate an evolving mechanistic understanding of synchronized interhemispheric neural activity for large-scale human brain function and behavior.

Urban traffic congestion and carbon emissions pose significant challenges to the sustainable development of megacities. Traditional single-policy interventions often fail to simultaneously mitigate congestion and reduce emissions effectively. This study employs a system dynamics approach to construct a multidimensional dynamic model that analyzes the feedback mechanisms and dynamic interactions of policy variables within the urban traffic system. Furthermore, a TOPSIS multi-criteria decision-making framework is integrated to quantitatively evaluate the overall effectiveness of multiple policy combinations, exploring optimization pathways for achieving synergistic governance. Using Shanghai’s traffic system as a case study, simulation analyses under six policy scenarios reveal significant discrepancies in short- and long-term policy performance. Results demonstrate that traffic congestion, carbon emissions, and environmental pollution are tightly coupled, forming a non-coordinated feedback loop that challenges single-policy solutions. For example, the “two-license-plate restriction” policy reduces traffic congestion by 2.72%, carbon emissions by 10.37%, and pollution by 2.47% compared to the baseline scenario, achieving the highest TOPSIS score of 0.68. The “new energy vehicle promotion” policy significantly contributes to long-term emission reduction; however, its overall effectiveness score is relatively low at 0.5. These findings underscore the need for a systematic approach to urban traffic governance, providing actionable insights for balancing short-term effectiveness and long-term sustainability through dynamic policy integration.

Recent advances in strong light-matter interactions have revealed a wealth of new physical phenomena in molecules embedded in optical cavities, including modified chemical reactivity, altered excitation spectra, and novel quantum correlations. To describe these effects from first-principles, the field of ab initio quantum electrodynamics (QED) has emerged as a compelling extension of quantum chemistry that treats electronic and photonic degrees of freedom on equal footing. In this Perspective, we review the growing landscape of many-body QED methods, including Hartree-Fock, density functional theory (QEDFT), time-dependent DFT (QED-TDDFT), configuration interaction (QED-CI), complete active space (QED-CASSCF), coupled cluster (QED-CC), quantum Monte Carlo (QED-QMC), and density matrix renormalization group (QED-DMRG), highlighting recent developments and implementations. We further explore real-time methods, gradient and Hessian formalisms, and the integration of nonadiabatic nuclear dynamics. Applications range from benchmark simulations of polaritonic chemistry to quantum simulations on emerging quantum hardware. We conclude by outlining future directions for theory development and interdisciplinary efforts at the interface of quantum chemistry, condensed matter, and quantum optics.

ABSTRACTSystem of Systems (SoS) environments are inherently complex, involving numerous operationally and managerially independent component systems with hidden interdependencies and frequent interactions based on unstructured data. In this paper, we propose using graphical Retrieval‐Augmented Generation (GraphRAG), a tool that combines large language models with knowledge graph (KG) techniques to address these challenges. Using metrics from information entropy and graph theory, we demonstrate how KG construction and clustering nodes can reduce complexity in SoS. An example application in the Urban Air Mobility setting illustrates that GraphRAG can solve concrete data integration challenges while outperforming traditional Retrieval‐Augmented Generation (RAG) methods. The upshot is improved execution of model‐based systems engineering in an SoS context: mitigating risks from incomplete information, enhancing system integration, and improving decision‐making.

In India, most collaborative robots (cobots) are fixed in one place and designed for specific tasks, requiring multiple cobots for different manufacturing processes. This increases costs and causes production delays during malfunctions. This study presents a simulated concept of using a cobot with a changeable end effector mounted on an automated guided vehicle (AGV). The AGV-based cobot can move between different workstations and perform various tasks, such as material handling, assembly, and inspection. A diesel engine manufacturing process is simulated in CoppeliaSim to demonstrate the potential benefits of this system. Simulation results show improved productivity, reduced defect rates, and enhanced flexibility, highlighting the potential of AGV-based cobot collaboration in streamlining complex manufacturing processes. Although this research focuses on simulation, future work aims at real-world implementation through industry partnerships. This simulation highlights a promising idea for enhancing flexibility and productivity in Indian manufacturing systems, promoting Industry 4.0 adoption, and paving the way for sustainable, intelligent manufacturing systems.

GI as a dynamic integrator: Synchronizing photoperiod and temperature signals to control flowering time in Arabidopsis.