Dynamic Framework Research Articles

Utilizing PRAME Expression and a Meta-Analytic Framework for iSALT to Explore Atypical Late-Onset Nevi of the Elderly and Their Relationship With Lentiginous and Nested Nevoid Melanomas.

In contrast to early-onset dysplastic nevi, late-onset atypical nevi of the elderly are more often precursors to distinctive nevoid melanomas. PReferentially expressed Antigen in MElanoma (PRAME) immunohistochemistry was applied to delineate the nevoid aspect of late-onset oncogenic nevoid pathway. Inducible Skin-Associated Lymphoid Tissue, regulatory T-cell mesenchymal hubs, has emerged as a translational tool and was used to define nevoid oncogenesis within a dynamic meta-analytic pathway. PRAME immunohistochemistry was applied after designating a histopathologic diagnosis. Late-onset atypical nested lentiginous nevus, lentiginous nested melanoma, and hypercellular nested nevoid melanoma were the diagnostic categories. A positive PRAME for melanoma was set at 75% percentage labeling.A wide-ranging published evidence-based database was incorporated to develop a meta-analytic framework for oncogenic nevogenesis. This combined inducible Skin-Associated Lymphoid Tissue incorporating the pleiotropic functions of regulatory T cells regulating immunity and gene regulatory epigenetics as principal modulators. Concordant-negative PRAME expression was present in 64 of 81 (79%) atypical nested lentiginous nevi, concordant-positive PRAME expression occurred in 54 of 75 (72%) nevoid lentiginous and nested melanomas, and 18 of 23 (78%) nevoid hypercellular nested melanomas. PRAME expression confirmed the existence of a late-onset oncogenic nevoid pathway that can be defined by histopathology. Subsequent meta-analysis data linked to the meta-analytic framework revealed that PRAME is an epigenetic surrogate antigen expressed because of repression of retinoic acid receptor signaling, preventing ligand-induced retinoic acid cellular differentiation, growth arrest, and apoptosis, and promoting melanoma growth and survival for melanomas. PRAME is only a single antigen within a highly complex dynamic framework that governs nevoid oncogenesis. Significantly, the retinoic acid/retinoic acid receptor complex has been shown to modulate the immunosuppressive arm of regulatory T cells underpinning immune tolerance and is pertinent to the broad framework but is not linked to PRAME expression in this arm.

Perspectives on Necessity-Driven Immigrant Entrepreneurship: Interactions with Entrepreneurial Ecosystems through the Lens of Dynamic Capabilities

This paper explores the interaction between immigrant small and medium-sized enterprises (SMEs) and the entrepreneurial ecosystems of their home and host countries. These SMEs, often established out of necessity due to economic hardship or unemployment, contrast with opportunity-driven ventures that seek to exploit new market prospects. Employing a dynamic capabilities framework, the study investigates how immigrant SMEs identify and mobilize critical resources in unstable environments, and how these resources are dynamically adapted and transformed to meet organizational objectives while responding to ecosystem changes, ultimately cultivating competitive advantages. Interviews were conducted with 25 immigrant entrepreneurs from eight countries, and the interviews took place within their business environments, with native speakers of the interviewees′ home countries present to ensure accuracy and authenticity. The findings highlight that immigrant entrepreneurs effectively leverage resources from both home and host country ecosystems, with human capital being the most essential asset. Additionally, the role of immigrants′ social networks is emphasized as a crucial mechanism for resource acquisition. This study further illustrates the positive contributions of immigrant enterprises to entrepreneurial ecosystems, revealing a co-evolutionary relationship whereby the businesses and ecosystems both mutually evolve. This co-evolution strengthens the resilience and sustainability of immigrant ventures, significantly enhancing their long-term success.

Comparative And Dynamic Analysis of Türkiye's Competitiveness in Unmanned Aerial Vehicles

The study aims to determine the competitiveness of Türkiye’s UAV exports in a dynamic framework. The analysis used data from the first 20 countries with the largest share in the production and trade of products designated as UAVs by the Harmonic System six-digit and by the European Commission for 2012-2021. Within the scope of the study, it is determined that some countries have high competitiveness in 4 product groups examined with five different foreign trade indexes. It is concluded that Türkiye has strategic and dynamic superiority in two of the four product groups.

Machine Learning Applications in Stock Price Prediction

With economic fluctuations, investors focus on the stock market where scientific investment strategies become increasingly crucial. This article utilizes linear regression to predict stock price trends, achieving an R value close to 1, indicating high accuracy in the model's predictions. Additionally, logistic regression and random forest classification models were employed to predict stock price movements, revealing a superior performance with the random forest model achieving an AUC value of 0.64. By employing a combination of regression and classification algorithms, this study offers a diverse perspective on stock price prediction, highlighting the importance of scientific forecasting in financial markets. The findings demonstrate that integrating multiple predictive models enhances the accuracy and reliability of stock price predictions, providing investors with precise market information to facilitate rational investment decisions. Furthermore, this research contributes valuable insights for future studies in stock market prediction, promoting continuous innovation and development in the theoretical and methodological aspects of this field. The results underscore the potential of advanced machine learning techniques in financial forecasting, offering a robust framework for understanding market dynamics and improving investment strategies. This study serves as a reference point for both academic research and practical applications, driving forward the evolution of predictive modeling in finance.

Predictive Framework for Flow Reversals and Excursions in Turbulence.

We present a dynamical framework for intermittent reversals and excursions (R&Es) of large-scale circulations in turbulence. We show that R&Es can occur when turbulent trajectories in phase space shadow invariant manifolds of certain unstable periodic orbits (UPOs). Consequently, substantial flow reorganization and extreme fluctuations in flow metrics observed during R&Es can be reconstructed by splicing the unstable manifolds of such dynamically relevant UPOs. Using this geometrical framework, we predict imminent R&Es and preemptively avert these extreme events using closed-loop control.

Torsional Mechanics of Circular DNA.

Circular DNA found in the cell is actively regulated to an underwound state, with their superhelical density close to σ ∼ - 0.06. While this underwound state is essential to life, how it impacts the torsional mechanical properties of DNA is not fully understood. In this work, we performed simulations to understand the torsional mechanics of circular DNA and validated our results with single-molecule measurements and analytical theory. We found that the torque generated at σ ∼ - 0.06 is near but slightly below that required to melt DNA, significantly decreasing the energy barrier for proteins that interact with melted DNA. Furthermore, supercoiled circular DNA experiences force (tension) and torque that are equally distributed through the DNA contour. We have also extended a previous analytical framework to show how the plectonemic twist persistence length depends on the intrinsic bending persistence length and twist persistence length. Our work establishes a framework for understanding DNA supercoiling and torsional dynamics of circular DNA.

Emergence of the Molecular Geometric Phase from Exact Electron-Nuclear Dynamics.

Geometric phases play a crucial role in diverse fields. In molecules, they appear when a reaction path encircles an intersection between adiabatic potential energy surfaces and the molecular wave function experiences quantum-mechanical interference effects. This intriguing effect, closely resembling the magnetic Aharonov-Bohm effect, crucially relies on the adiabatic description of the dynamics, and it is an open issue whether and how it persists in an exact quantum dynamical framework. Recent works suggest that the molecular geometric phase is an artifact of the adiabatic approximation, thereby challenging the entire concept. Here, building upon a recent investigation (Martinazzo, R.; Burghardt, I. Phys. Rev. Lett. 2024, 132, 243002), we address this issue using the exact factorization of the total wave function. We introduce instantaneous gauge-invariant phases separately for the electrons and nuclei and use them to monitor the phase difference between the trailing edges of a wavepacket encircling a conical intersection between adiabatic surfaces. The transition from the time-dependent open-path phase differences to the closed-path limit is examined, revealing how the phase differences in the electronic and nuclear subspaces compensate for each other upon path closure. In this way, we unambiguously demonstrate the role of the geometric phase in the interference process and shed light on its persistence beyond the adiabatic approximation.

Comprehensive Assessment of Large-Scale Regional Fluvial Flood Exposure Using Public Datasets: A Case Study from China

China’s vulnerability to fluvial floods necessitates extensive exposure studies. Previous large-scale regional analyses often relied on a limited set of assessment indicators due to challenges in data acquisition, compounded by the scarcity of corresponding large-scale flood distribution data. The integration of public datasets offers a potential solution to these challenges. In this study, we obtained four key exposure indicators—population, built-up area (BA), road length (RL), and average gross domestic product (GDP)—and conducted an innovative analysis of their correlations both overall and locally. Utilising these indicators, we developed a comprehensive exposure index employing entropy-weighting and k-means clustering methods and assessed fluvial flood exposure across multiple return periods using fluvial flood maps. The datasets used for these indicators, as well as the flood maps, are primarily derived from remote sensing products. Our findings indicate a weak correlation between the various indicators at both global and local scales, underscoring the limitations of using singular indicators for a thorough exposure assessment. Notably, we observed a significant concentration of exposure and river flooding east of the Hu Line, particularly within the eastern coastal region. As flood return periods extended from 10 to 500 years, the extent of areas with flood depths exceeding 1 m expanded markedly, encompassing 2.24% of China’s territory. This expansion heightened flood risks across 15 administrative regions with varying exposure levels, particularly in Jiangsu (JS) and Shanghai (SH). This research provides a robust framework for understanding flood risk dynamics, advocating for resource allocation towards prevention and control in high-exposure, high-flood areas. Our findings establish a solid scientific foundation for effectively mitigating river flood risks in China and promoting sustainable development.

The impact of social ties on balanced vs combined innovation: the role of dynamic capabilities and innovation climate in knowledge-intensive business services firms

Purpose This study aims to investigate the impact of business ties, political ties and dynamic capabilities on balanced and combined ambidextrous innovation within the knowledge-intensive business services sector using the dynamic capability view framework. It explores the mediation role of dynamic capabilities in the relationship between business and political tie with these forms of innovation. In addition, it examines how innovation climate moderates the relationship between dynamic capabilities and ambidextrous innovation. Design/methodology/approach Multilevel analysis was conducted on 520 individuals from 61 KIBSs firms in the UAE using AMOS 26. Findings Results showed that BT indirectly affect both balanced and combined AI, with a greater impact on combined innovation. However, DCs did not significantly mediate the relationship between PT and AI. In addition, the IC was found to moderate the effects of DCs on both types of AI, with a stronger effect on combined innovation. Originality/value This study contributes to the literature by highlighting the importance of DCs and climate in the context of innovation and ambidexterity and by advancing the understanding of how these capabilities and organizational factors promote superior innovation.

A Stochastic Dynamic Operator framework that improves the precision of analysis and prediction relative to the classical spike-triggered average method, extending the toolkit.

Here we test the Stochastic Dynamic Operator (SDO) as a new framework for describing physiological signal dynamics relative to spiking or stimulus events. The SDO is a natural extension of existing spike-triggered average (STA) or stimulus-triggered average techniques currently used in neural analysis. It extends the classic STA to cover state-dependent and probabilistic responses where STA may fail. In simulated data, SDO methods were more sensitive and specific than the STA for identifying state-dependent relationships. We have tested SDO analysis for interactions between electrophysiological recordings of spinal interneurons, single motor units, and aggregate muscle electromyograms (EMG) of major muscles in the spinal frog hindlimb. When predicting target signal behavior relative to spiking events, the SDO framework outperformed or matched classical spike-triggered averaging methods. SDO analysis permits more complicated spike-signal relationships to be captured, analyzed, and interpreted visually and intuitively. SDO methods can be applied at different scales of interest where spike-triggered averaging methods are currently employed, and beyond, from single neurons to gross motor behaviors. SDOs may be readily generated and analyzed using the provided SDO Analysis Toolkit We anticipate this method will be broadly useful for describing dynamical signal behavior and uncovering state-dependent relationships of stochastic signals relative to discrete event times.Significance Statement Here the authors introduce new tools and demonstrate data analysis using a new probabilistic and state-dependent technique, which is an expansion and extension of the classical spike-triggered average, the Stochastic Dynamic Operator. Stochastic Dynamic Operator methods extend application into domains where classical spike-triggered averages fail, capture more information on spike correlations, and match or outperform the spike-triggered average when generating predictions of signal amplitude based on spiking events. A data and code package toolkit for utilizing and interpreting Stochastic Dynamic Operator methods is provided together with example simulated and physiological data analyses. Both the method and the associated toolkit are expected to be broadly useful in research domains where the spike triggered average is currently used for analysis, and beyond.

Synthesis of dynamic modelling framework and optimal control strategy for virtually coupled train sets with guaranteed safety

Virtual coupling represents a novel railway operational mechanism, wherein several trains share state information and adjust their behaviours based on control objectives, which include safety (collision avoidance) assurance and stability (uniform velocity and constant spacing between trains) maintenance. However, the conflict between safety and stability requirements becomes more apparent, as the closer spacing between trains. Moreover, the hybrid nature of the train platoon operation, which involves both discrete events and continuous dynamics, also poses a significant barrier to the control strategy synthesis. This paper presents the Virtually Coupled Train Sets (VCTS) dynamic modelling framework and strategy synthesis approach to address these problems. A Hybrid Markov Decision Process (HMDP) is adopted for relative longitudinal dynamics modelling of trains, considering the influence of the uncertainty dynamic of trains ahead. A Stackelberg game-based strategy synthesis algorithm is applied for the safe guarantee by overcoming the uncertainty, and a Q-learning method is used for stability strategy selection from the safe strategy. The results showed a 17.95% expansion in the safe state space when compared to the general relative braking scheme (which assumes maximal acceleration during the delay horizon). In a four-train tracking operation scenario, compared with Q-learning without safe constraints, our approach gives similar stability performance; compared with Q-learning with general relative braking safe constraints, our approach not only assurance safety but has a better stability performance.

Oligo-Adenine Derived Secondary Nucleic Acid Frameworks: From Structural Characteristics to Applications.

Oligo-adenine (polyA) is primarily known for its critical role in mRNA stability, translational status, and gene regulation. Beyond its biological functions, extensive research has unveiled the diverse applications of polyA. In response to environmental stimuli, single polyA strands undergo distinctive structural transitions into diverse secondary configurations, which are reversible upon the introduction of appropriate counter-triggers. In this review, we systematically summarize recent advances of noncanonical structures derived from polyA, including A-motif duplex, A-cyanuric acid triplex, A-coralyne-A duplex, and T ⋅ A-T triplex. The structural characteristics and mechanisms underlying these conformations under specific external stimuli are addressed, followed by examples of their applications in stimuli-responsive DNA hydrogels, supramolecular fibre assembly, molecular electronics and switches, biosensing and bioengineering, payloads encapsulation and release, and others. A detailed comparison of these polyA-derived noncanonical structures is provided, highlighting their distinctive features. Furthermore, by integrating their stimuli-responsiveness and conformational characteristics, advanced material development, such as pH-cascaded DNA hydrogels and supramolecular fibres exhibiting dynamic structural transitions adapting environmental cues, are introduced. An outlook for future developments is also discussed. These polyA derived, stimuli-responsive, noncanonical structures enrich the arsenal of DNA "toolbox", offering dynamic DNA frameworks for diverse future applications.

Evaluating accession decisions in customs unions: a dynamic machine learning approach

Previous work in the literature on regional economic integration has proposed the use of machine learning algorithms to evaluate the composition of customs unions, specifically, to estimate the degree to which customs unions match “natural markets” arising from trade flow data or appear to be driven by other factors such as political considerations. This paper expands upon the static approaches used in previous studies to develop a dynamic framework that allows to evaluate not only the composition of customs unions at a given point in time, but also changes in the composition over time resulting from accessions of new member states. We then apply the dynamic algorithm to evaluate the evolution of the global landscape of customs unions using data on bilateral trade flows of 200 countries from 1958 to 2018. A key finding is that there is considerable variation across different accession rounds of the European Union as to the extent to which these are aligned with the structure of “natural markets,” with some accession rounds following more strongly a commercial logic than others. Similar results are also found for other customs unions in the world, complementing the insights obtained from static analyses.

Simulation-Based Evaluation of Advanced Threat Detection and Response in Financial Industry Networks Using Zero Trust and Blockchain Technology

The financial sector is increasingly facing advanced cyber threats, necessitating a shift from traditional security measures to more dynamic frameworks. This study presents a novel integration of Zero Trust architecture with hybrid access control system and blockchain technology to enhance security in financial institutions. Zero Trust enforces continuous authentication and dynamic access controls, while blockchain secures digital identities and transaction logs through its immutable ledger, ensuring data integrity and non-repudiation. The proposed framework, evaluated using OMNeT++ simulations enhanced by Ethereum-Ganache, shows improved detection accuracy, reduced false positives, and increased resistance to insider threats and other attacks. It also strengthens compliance with regulatory requirements through robust audit trails, providing enhanced protection for sensitive financial data.

A dynamic resilience management framework for deep-tier supply networks

The unprecedented supply chain disruptions caused by COVID-19 has had severe operational and financial consequences to firms across industries and continents. While tactical reactionary strategies can help, firms are in need of proactive management approaches to design more resilient supply chain networks in the first place. Firms are looking for an effective framework to design and monitor supply networks, mitigate disruption consequences, and manage resilience under different scenarios. We propose a framework to manage the resilience of deep-tier automotive supply networks by integrating a simulation-based resilience assessment scheme for effectiveness with an efficient optimization-based framework to find optimal strategies for handling regular disruption events. The framework promotes network analysis techniques combined with discrete-event simulation informed by secondary data sources and global supply risk databases for improving resilience management. We validate the effectiveness of the proposed framework using a real-world global automotive original equipment manufacturer case study. Our results demonstrate that the proposed dynamic framework relying on deep-tier visibility can optimize resilience strategies through all key performance indicators. The results show an average of 35% and 40% reductions in back-ordered cost and shipment delays, respectively, with a marginal growth in holding cost when the proposed framework is implemented with deep-tier visibility.

OpenSBLI v3.0: High-Fidelity Multi-Block Transonic Aerofoil CFD Simulations using Domain Specific Languages on GPUs

OpenSBLI is an automatic code-generation framework for compressible Computational Fluid Dynamics (CFD) simulations on heterogeneous computing architectures (previous release: Lusher et al, CPC (2021)). OpenSBLI is coupled to the Oxford Parallel Structured (OPS) Domain Specific Language (DSL), which uses source-to-source translation to enable parallel execution of the code on large-scale supercomputers, including multi-GPU clusters. To date, OpenSBLI has largely been applied to compressible turbulence and shock-wave/boundary-layer interactions on very simple geometries comprised of single mesh blocks with essentially orthogonal grid lines. OpenSBLI has been extended in this new release to target strongly curvilinear cases, including transonic aerofoils using multi-block grids. In addition to multi-block mesh support, more efficient numerical shock-capturing methods and filters have been added to the codebase. Improvements to post-processing, reduced-dimension data output, and coupling to a modal decomposition library are also included. A set of validation cases are presented to showcase the new code features. Furthermore, state-of-the-art wide-span transonic aerofoil simulations on up to N=2.5×109 grid points demonstrate that wider aspect ratios can alter buffet predictions and increase the regularity of the low-frequency shock oscillations by accommodating fully-developed trailing edge flow separation. Spectral Proper Orthogonal Decomposition (SPOD) analysis showed that overly-narrow aerofoil simulations contain additional domain-dependent energy content at a Strouhal number of St≈3 associated with wake modes.

DFC-Igloo: A Dynamic Functional Connectome Learning Framework for Identifying Neurodevelopmental Biomarkers in Very Preterm Infants

Background and ObjectiveVery preterm infants are susceptible to neurodevelopmental impairments, necessitating early detection of prognostic biomarkers for timely intervention. The study aims to explore possible functional biomarkers for very preterm infants at born that relate to their future cognitive and motor development using resting-state fMRI. Prior studies are limited by the sample size and suffer from efficient functional connectome (FC) construction algorithms that can handle the noisy data contained in neonatal time series, leading to equivocal findings. Therefore, we first propose an enhanced functional connectome construction algorithm as a prerequisite step. We then apply the new FC construction algorithm to our large prospective very preterm cohort to explore multi-level neurodevelopmental biomarkers. MethodsThere exists an intrinsic relationship between the structural connectome (SC) and FC, with a notable coupling between the two. This observation implies a putative property of graph signal smoothness on the SC as well. Yet, this property has not been fully exploited for constructing intrinsic dFC. In this study, we proposed an advanced dynamic FC (dFC) learning model, dFC-Igloo, which leveraged SC information to iteratively refine dFC estimations by applying graph signal smoothness to both FC and SC. The model was evaluated on artificial small-world graphs and simulated graph signals. ResultsThe proposed model achieved the best and most robust recovery of the ground truth graph across different noise levels and simulated SC pairs from the simulation. The model was further applied to a cohort of very preterm infants from five Neonatal Intensive Care Units, where an enhanced dFC was obtained for each infant. Based on the improved dFC, we identified neurodevelopmental biomarkers for neonates across connectome-wide, regional, and subnetwork scales. ConclusionThe identified markers correlate with cognitive and motor developmental outcomes, offering insights into early brain development and potential neurodevelopmental challenges.

Two-stage dynamic real-time optimization framework using parameter-dependent differential dynamic programming

The purpose of chemical process control includes proactive adjustment of the operation to make the most profit out of it. Within this context, real-time optimization (RTO) is proposed and extended to dynamic RTO (DRTO) in the hierarchical control structure, usually having model predictive control (MPC) below. However, online tractability confined the model complexity of RTO and MPC, which results in model inconsistency and, even, incompatible solutions. Here we use parameter-dependent differential dynamic programming (PDDP) to incorporate the closed-loop behavior of the controller in an RTO layer to reduce problem complexity and online computation time. The adaptive control performance of PDDP and the efficacy of closed-loop DRTO formulation with PDDP is demonstrated with the reaction-storage-separation network system control. Consequently, PDDP provides a useful parameterization method to express closed-loop system dynamics, which enables fast feedback control and integrated plant optimization.

A Dynamic Programming Framework for Optimal Planning of Redundant Robots Along Prescribed Paths With Kineto-Dynamic Constraints

A Dynamic Programming Framework for Optimal Planning of Redundant Robots Along Prescribed Paths With Kineto-Dynamic Constraints

Multiscale investigations on RDI-SPI teleconnections of Çoruh and Aras Basins, Türkiye using Time Dependent Intrinsic Correlation

This paper proposes a multiscale dynamic correlation framework based on time-dependent intrinsic correlation (TDIC) to investigate the teleconnections between reconnaissance drought index (RDI) and standardized precipitation index (SPI). The SPI and RDI indices were calculated at 3-, 6-, and 12-month time scales using data from six stations in the Çoruh and Aras (CA) basins in Turkey for the 1969 - 2020 period. The spatial variability of the evaluation of the drought class demonstrated that, with the exception of the northwestern region (Bayburt station), the correlation between RDI and SPI exceeded 97% and the difference in occurrence between drought classes is found to be marginal. In the multiscale analysis of the two indices, firstly, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (I-CEEMDAN) was employed to decompose the series. The modes of RDI and SPI at different process scales exhibited a strong positive linear correlation, however, the association in their long-term trends may not be of the same nature. The TDIC analysis captured the long-range correlations between the modes of RDI and SPI at diverse process scales, and the newly proposed average significant correlation (ASC) measure could quantify their strength. Within the basin, there exists strong interconnections between SPI and RDI, with the exception of the observed alterations in their nature and strength at short time spells in some inter-annual scales. The proposed TDIC based framework is a generic and robust alternative for multiscale investigation studies while ASC can quantify the strength of non-linear associations at distinct process scales.