The binding of myosin to actin to form crossbridges is a critical step for force generation by sarcomeres. A recent cryo-electron microscopy structure has resolved the weakly-bound actomyosin complex (AM.ADP.Pi). However, the structural and dynamic factors that influence actin-myosin association are unclear. The disordered loop 2 of myosin is thought to mediate actomyosin interactions in a complex, chemomechanical state-dependent fashion. Loop 2 is usually unresolved in structural studies due to its intrinsic disorder. Here, we utilize a combination of molecular dynamics simulations and electrostatic calculations to investigate the dynamics of these actin binding regions of myosin. Our results show that loop 2 experiences disordered dynamics and that specific conformations sampled modulate the strength of the associative electrostatic force between actin and myosin. Variation in the actin-myosin associative force was associated with the presentation and orientation of positively charged residues in loop 2. We provide an in-depth analysis of the conformational state space occupied by loop 2 during molecular dynamics simulations of pre-powerstroke human β-myosin S1, with three 500 ns replicates each of wildtype and two different mutant (E525K and V606M) myosin structures. This data set allowed for exploration of how loop 2 conformational sampling is altered by these two mutations which have experimentally been shown to alter actin binding affinity and clinically associated with cardiomyopathy. The E525K and V606M mutations altered the conformational ensemble sampled by loop 2 and were associated with associative actin binding strength. These results highlight the importance of the positive charges on loop 2 for actomyosin interactions and demonstrate how disease-causing mutations outside of loop 2 can still affect it.

The integration of solar energy into smart grids requires high-efficiency power conversion to support grid stability. However, Partial Shading Conditions (PSCs) remain a primary obstacle by inducing multiple local maxima on P–V characteristic curves. This paper presents a hardware-aware and memory-enhanced Maximum Power Point Tracking (MPPT) approach based on a modified Dragonfly Algorithm (DA) for grid-connected microinverter-based photovoltaic (PV) systems. The proposed method utilizes a quasi-switched Boost-Switched Capacitor (qSB-SC) topology, where the DA is specifically tailored by combining Lévy-flight exploration with a dynamic damping factor to suppress steady-state oscillations within the qSB-SC ripple constraints. Coupling the MPPT stage to a seven-level Packed-U-Cell (PUC) microinverter ensures that each PV module operates at its independent Global Maximum Power Point (GMPP). A ZigBee-based Wireless Sensor Network (WSN) facilitates rapid data exchange and supports ‘swarm-memory’ initialization, matching current shading patterns with historical data to seed the population near the most probable GMPP region. This integration reduces the overall response time to 0.026 s. Hardware-in-the-loop experiments validated the approach, attaining a tracking accuracy of 99.32%. Compared to current state-of-the-art benchmarks, the proposed model demonstrated a significant improvement in tracking speed, outperforming the most recent 2025 GWO implementation (0.0603 s) by approximately 56% and conventional metaheuristic variants such as GWO-Beta (0.46 s) by over 94%.These results confirmed that the modified DA-based MPPT substantially enhanced the microinverter efficiency under PSC through cross-layer parameter adaptation.

ABSTRACT Measures of an organism's weight at a given length are often considered reliable indicators of energy reserves or ‘condition’, which can be related to fecundity and risk of mortality. Understanding the impact of environmental change on fish condition may therefore inform sustainable management of human activities in marine ecosystems. We investigated how changes in Canadian Pacific waters may be influencing the average condition of 35 commercially, culturally, and/or ecologically important demersal fish species. Because the condition of mature male and female, and immature individuals differs in its implications for population dynamics, ecological drivers, and measurement variability, we analysed these three maturity groups separately. We estimated density distributions, calculated Le Cren's relative body condition deviations, modelled spatiotemporal change in these deviations, and generated density‐weighted annual indices of body condition. We used Bayesian Dynamic Factor Analysis to identify common trends across species and tested for correlations with environmental variables. For most species, warmer sea surface temperature and lagged North Pacific Gyre Oscillation appeared neutrally or positively correlated with condition. Only the immature condition was also strongly correlated with primary production, but this effect was equally likely to be negative (e.g., Pacific Spiny Dogfish, Lingcod, and Sablefish) as positive (e.g., Quillback Rockfish, Southern Rock Sole, and Spotted Ratfish). Our approach propagates uncertainty from condition estimation through to environmental correlations to provide both ecosystem‐level and species‐specific inference. Robust estimates of relationships between condition and environmental variables can inform ecosystem approaches to fisheries management, including short‐term forecasts of weight‐at‐age or recruitment.

Overweight and obesity are rising globally, with Ghana experiencing significant increases among women over the past two decades, raising public health concerns. This study aimed to identify and quantify the key drivers of overweight and obesity among women of reproductive age in Ghana, analysing how these factors have contributed to prevalence changes over time. Data from the 2003, 2008, 2014, and 2022 Ghana Demographic and Health Surveys were analysed using binary logistic regression to assess associations with factors such as age, wealth, and education. Multivariate decomposition analysis quantified the contributions of these factors to the observed increases in overweight and obesity prevalence over time. Here we show overweight and obesity among Ghanaian women rise significantly, reaching 43% in 2022. Key drivers of change in overweight and obesity include wealth, education, urban residence, age, and region. Women in the wealthiest quintile have three times the odds of overweight (aOR: 3.07 [2.02-4.67]) and over six times the odds of obesity (aOR: 6.73 [3.80-11.91]) compared to the poorest quintile. Decomposition analysis shows that 22.5% of the increase in prevalence was due to changes in population characteristics, such as marital and educational status. Our findings reveal that socio-demographic changes in society, beyond individual behavioural factors, drive the rising overweight and obesity prevalence among Ghanaian women of childbearing age. These findings highlight the dynamic factors influencing weight outcomes and the need for tailored strategies addressing the diverse and evolving determinants of overweight and obesity in Ghanaian women.

When the demand for public health care increases, governments often prioritize citizens over foreign nationals. In South Africa, limited resources and socio-economic inequalities pose unique challenges to epidemic control. The overcrowding and increasing demand for public healthcare have led to protests by some community groups, which have led to the denial of healthcare to migrants. Denying treatment to some infected individuals has the propensity to lead to an increase in the size of an epidemic. We introduce a novel epidemiological model that incorporates health care denial as a dynamic factor influencing the transmission of COVID-19. It incorporates healthcare denial as a key parameter influencing the progression and recovery rates of infections. The study presents a novel framework for understanding the intersection of healthcare access denial and the transmission dynamics of COVID-19. While much of the existing literature has focused on the direct effects of healthcare interventions on pandemic control, this research uniquely emphasizes the role that restricted access to healthcare services, whether due to policy decisions, resource shortages, or system inefficiencies, can exacerbate the spread of infectious diseases. The treatment class of the model is partitioned to account for individuals denied treatment at public healthcare facilities. Analytical results establish conditions for the existence and stability of both disease-free and endemic equilibria, with the basic reproduction number R0 explicitly derived to quantify transmission potential under varying healthcare access scenarios. Sensitivity analysis reveals that increasing denial of care can significantly elevate R0, resulting in higher infection peaks, prolonged epidemic duration and greater cumulative mortality. Numerical simulations further illustrate the non-linear relationship between treatment accessibility and outbreak severity. The findings highlight that equitable healthcare provision is not only a public health necessity but also a critical determinant for reducing the COVID-19 burden. Policy implications stress the integration of inclusive healthcare strategies to ensure epidemic resilience and minimize transmission risks, especially in vulnerable populations. Strategies that will accommodate every infected person who goes to the hospital for treatment should be adopted to reduce the disease burden.

Dynamic Factor Mining Based on Multi-Objective Fitness and Its Empirical Study in Multi-Factor Strategies

Cable corrosion has become a critical concern for long-span bridge structures, particularly in cable-supported systems such as suspension, cable-stayed, and tied arch bridges. Many of these bridges, constructed in the 1980s and 1990s, face increasing risks due to progressive cable deterioration. The collapse of the Nanfang'ao tied arch bridge in Taiwan (2019), attributed to corroded cables, underscores the urgent need for a deeper understanding of failure mechanisms. This study develops a numerical bridge model inspired by the Taiwan bridge collapse to examine the dynamic chain reaction failure triggered by cable corrosion. Corrosion is simulated by reducing the yield stress and fracture strain of the cables, while the initial cable rupture is modeled as an instantaneous loss of tensile force. Unlike prior studies that assume ideal cable conditions, this study considers degraded cables to simulate realistic failure propagation. The analysis reveals that in corroded conditions, a single cable rupture can propagate through sequential failures, leading to progressive collapse. This corresponds to the initial dynamic phase of progressive collapse, which we refer to as a “chain reaction failure” in this study to highlight the rapid sequential rupture of adjacent cables immediately after the first failure. Results show that the Dynamic Amplification Factor (DAF) can exceed 5.6 in corroded cables, amplifying impact forces, while the Load Redistribution Ratio (LRR) is highly sensitive to the initial rupture location. These findings highlight how corrosion-induced strength degradation in cables significantly increases structural vulnerability and emphasize the importance of proactive inspection and maintenance strategies to mitigate failure risks in aging cable-stayed bridges.

The present study builds upon the authors′ previous research, which highlighted the fuel consumption advantage of LNG-powered (liquefied natural gas) trucks over conventional diesel vehicles. Expanding on this topic, the aim of this research is to analyze the influence of static and dynamic driving factors on the consumption advantage of LNG vehicles. The study was conducted in a test-track environment, ensuring optimal reproducibility with minimal external influencing factors, allowing for various types of measurements. In this research, fuel consumption values were recorded indirectly through the fleet management system (FMS) using controller area network (CAN) messages. Data distribution analysis, the Shapiro-Wilk test, and ANOVA were employed to validate the research hypotheses. Our study is unique in the field of heavy-duty vehicles (HDVs) as the measurements were performed at the test-track level, providing precise data for emission differences. The results indicate that the static driving environment (represented by different test track modules) has a stronger influence on the consumption advantage of LNG vehicles. In contrast, driving mode has a lesser effect on the consumption difference between LNG and diesel trucks.

BackgroundAortic diameter (AoD) changes with age and can decrease in shock states. Accurate AoD assessment is crucial for managing hypovolemic shock and guiding interventions such as resuscitative endovascular balloon occlusion of the aorta. This study hypothesized that clinical factors (eg, initial hemodynamic parameters, trauma severity, and laboratory results) would have a greater impact on the AoD than would age- or anthropometric-related factors in traumatic massive hemorrhage patients. We aimed to identify significant predictors of the descending AoD across two age groups (18–60 years and 61–91 years).MethodsA retrospective analysis was conducted on 243 massive hemorrhage patients at a level I trauma center. The aorta was automatically segmented in CT images via a deep learning architecture based on a Shallow Attention Network to obtain diaphragm-level AoD values. 152 patients were assigned to the younger group and 91 to the senior group. A random forest model was used to incorporate various clinical factors.ResultsIn the younger group, age and body surface area were the most important features (root mean square error (RMSE): train, 1.03; test, 2.70). In the senior group, hemoglobin, arterial pH, and heart rate were the most significant indicators (RMSE: train, 1.19; test, 3.95). The importance of age diminished in the senior group, whereas vital signs and laboratory values gained prominence.ConclusionOur findings reveal age-specific differences in factors influencing the AoD during traumatic hemorrhage. The results highlight the limitations of traditional methods for AoD estimation, especially in senior patients in whom dynamic physiological factors may play a major role. These insights can improve the accuracy of AoD assessment and management in hemorrhage patients across different age groups. The findings may contribute to developing an artificial intelligence-derived score that estimates the AoD, incorporating static and dynamic physiological factors.Level of evidenceIV, retrospective study having more than one negative criterion.

We introduce consistent estimators for the number of shocks driving large-dimensional dynamic factor models. Our estimator can be applied to single frequencies and specific frequency bands, making it suitable for disentangling shocks affecting dynamic models with a factor model representation. Noticeably, our estimator requires the time-series and cross-section sizes to diverge simultaneously without any constraint and it is free of nuisance parameters, such as penalization terms. Our methodology appears ideal for macroeconomic analysis, as one can investigate how many shocks drive the business cycle or the long run, although the applicability of our methods is much wider, given the popularity of GDFMs in economics and finance. Its small-sample performance in simulations is excellent. We apply our estimator to the FRED-QD dataset, finding that the U.S. macroeconomy is driven by two shocks: an inflationary demand shock and a deflationary supply shock. Our methodology permits one to accurately estimate the number of shocks that drive medium-sized DSGE models despite their moderate cross-sectional size. Supplementary materials for this article are available online.

Abstract Portfolio optimization is essential for investors seeking to manage risk, diversify assets, and maximize returns. Although recent studies have focused primarily on enhancing technical aspects such as model architecture through the application of deep learning or reinforcement learning, knowledge of factor portfolios, grounded in modern portfolio theory, remains paramount. Therefore, to effectively utilize the knowledge of factor investment strategies, we propose a novel hybrid portfolio investment method that integrates reinforcement learning with dynamic factors, called the dynamic factor portfolio model. The dynamic factors encompass five fundamental factors: size, value, beta, investment, and quality. The proposed model comprises two modules: a dynamic factor module that calculates a score on the basis of factors reflecting the macro market and a price score module that calculates a score on the basis of prices expressing the relationship between assets and their future value. With dynamic factor-informed knowledge, the proposed model can make portfolio decisions adaptively on the basis of market conditions. Through comprehensive experiments, we validate the effectiveness of the dynamic factor module and demonstrate that the proposed model outperforms both traditional portfolio investment strategies and existing reinforcement learning-based strategies. Moreover, the proposed model offers interpretability by identifying critical factors across varying market scenarios, thereby enhancing portfolio management practices.

Abstract Surface form error (SFE) prediction in high-speed micromilling of thin-walled structures is challenging due to dynamic factors such as tool deflection, workpiece flexibility, and machining vibrations. The existing mechanistic model lacks the adaptability needed for diverse machining environments, as they are material and cutting conditions dependent, which limits its application. This study presents a novel validated framework that combines adaptive signal processing with stacking-type ensemble machine learning models to address these challenges. Pre-machining and post-machining laser scans capture surface deformation, and any temporal discrepancies between them are resolved using the dynamic time warping (DTW) technique to generate the machined surface envelope. This machined surface envelope has been used to obtain the SFE. Furthermore, machined surface analysis has been performed to compare the generated SFE envelop with experimentally obtained machined surface SFE, revealing a maximum prediction difference of less than 12%. A stacking-based ensemble approach has been proposed and validated to predict SFE by training the proposed models on the estimated SFE from different machining conditions. Experimental validation has also been carried out by performing low-immersion radial micromilling on thin-walled TC4 specimens, and the proposed framework achieved an R2 of 0.973 with less than 10% prediction error. The ensemble model with the highest R2 value has further been applied to develop a GUI that enables real-time SFE prediction for machining optimization.

ABSTRACT The best car-following model (Intelligent Driver Model) incorporates desired speed parameter, whereas the literature suggested to include such parameter in driving behavior of lane changing model. Previous researches, however, have overlooked few things that desired speed values of many vehicles are to be collected from big data, and these values may have a significant effect on discretionary lane changing action. This research proposes the desired speed values for lane changing drivers and target lane vehicle drivers from calibrated IDM using big data for on-ramp and off-ramp areas, and simulates this IDM using the proposed data for validation test. The calibration method uses a genetic algorithm against the real dataset. Further, finding results suggest overcoming conflicts in this dataset by controlling the used dynamic factors. High performance-based traffic simulation software in the future can use the further developed model to decrease traffic crashes, bottlenecks, and long signals in the intersection.

Human impacts outweigh environmental factors in shaping site use dynamics by terrestrial mammals and birds in restricted protected areas of the Atlantic forest

Integrated statistical and feature based time series analysis of natural radionuclides and physicochemical parameters in surface water and groundwater of a hydrogeological system.

Unravelling depression heterogeneity: Exploring the role of external risk factors in symptom network dynamics.

Generalizing across non-stationary series via learning dynamic causal factors

Single-phase grounding fault line detection in low-current grounding distribution networks based on dynamic contribution factors from dynamic mode decomposition

As an important part of public service, public sports service governance is an important dimension of the modernization of national governance, and the improvement of its efficiency is related to the health of the whole people and social equity. The existing studies are weak in the application of indicators, lack the analysis of the complex interaction process of different antecedents, and lack empirical support for policy optimization. Method: This study adopts mixed Firstly, data envelopment analysis (DEA) is used to evaluate the dynamic total factor productivity of public sports services. Secondly, fuzzy set qualitative comparative analysis (fsQCA) is used to identify the differentiated driving paths of high/low efficiency caused by multi-factor allocation, such as economy, policy, culture, and social security. Most importantly, based on the R software, the dynamic change of inter-group configuration was analyzed, and the rule of inter-group configuration change was revealed. Result: The 7-year tfpch, effch, and tech index of 31 provinces were 1.125, 1.018, and 1.105, respectively, indicating the improvement of management efficiency. FSQCA results show that there are seven main configurations that can explain the differences in efficiency. Among them, there are 4, 2, and 1 configurations driven by "guarantee factor", "guarantee + information factor", "guarantee + civilization degree", and their coverage is 0.546, 0.348, and 0.092, respectively. The security factors represented by income and environment occupy the most important position in the configuration combination, while the informatization factors and the degree of civilization are second. From the point of view of specific provinces, the higher level of public sports service in the more developed cities in the east is mainly explained by the configuration of "security + civilization degree". The high level of public sports service in Western cities is mainly due to the national guarantee. Based on the above results, this paper draws the following conclusions: First, the guaranteed service provided by the state is the fundamental guarantee of efficient public sports service. Second, the reason for the higher development level of public sports services in more developed provinces may be that knowledge groups with a higher education level and better information use are gathered in cities. Third, for less developed provinces, efficient public sports services are mainly supported by the state, so it is necessary to further explore development in accordance with local conditions.

This study employs a multiscale approach to investigate the interaction dynamics between the West Nile virus (WNV) envelope (E) protein and gold nanoclusters functionalized with DNA aptamers. By integrating aptamer secondary and tertiary structure prediction, structural docking and atomistic molecular dynamics simulations, we reveal the structural, dynamic and electrostatic factors governing the aptamer-target WNV E protein recognition at the nanoscale. Two gold nanoclusters, Au144(SR)60 and Au314(SR)96 (SR = thiolate ligand), were examined to assess how nanoparticle size and surface functionalization influence aptamer anchoring and binding stability under physiological salt conditions. This approach enhances aptamer functionalization strategies for detecting West Nile virus and creates a flexible framework for aptamer-based diagnostics for other emerging pathogens, with implications for diagnostics across a range of viral and protein biomarkers.