Construction of slope-included energy consumption-representative driving cycle for heavy-duty commercial vehicles using multi-dimensional index parameter selection and adaptive crayfish-genetic algorithm

Bernstein-type inequalities for Markov chains and Markov processes: A simple and robust proof

Exceeding binge-level alcohol drinking is common among adolescents and is associated with both short- and long-term adverse outcomes. This study evaluated the effects of repeated ethanol intoxication during early-adolescence on behavioral outcomes in early-adulthood. Male and female C57BL/6 mice (5 weeks old) received four intraperitoneal (i.p.) injections, over two weeks, of either saline (control) or 3.2 g/kg ethanol (intoxicated). A subset of intoxicated mice had blood collected on days 1 and 4, confirming heavy intoxication (∼289 mg/dL). At 9 weeks of age, animals were tested in the Light-Dark Box (LDB) and Elevated Plus Maze (EPM) immediately after receiving either saline or 1.2 g/kg ethanol (i.p.). In the LDB, ethanol intoxication in early-adolescence did not affect anxiety-like behaviors but reduced risk assessment in males, indicating riskier decision-making.In the EPM, pretest ethanol produced an anxiolytic effect, accompanied by increased exploration and a reduction in risk-assessment behaviors, while intoxication during adolescence did not yield significant effects. To better characterize behavioral organization beyond discrete measures, we applied Markov chain models to quantify first-order transition probabilities to and from risk-assessment behaviors. This analysis revealed that pretest ethanol markedly reduced the complexity of behavioral structure, especially in the EPM. Finally, a separate cohort of control and intoxicated mice underwent a two-bottle choice Intermittent Overnight Drinking protocol in adulthood. Ethanol intoxication in early-adolescence increased voluntary ethanol intake in adult females but not in males. These findings highlight long-lasting and sex-specific consequences of early-adolescent intoxication on risk-related behaviors and alcohol consumption.

Disparate size and stock status between two Sardina pilchardus stocks in northwest African waters.

Optimization of maintenance planning in power distribution systems using a discrete-time Markov chain model asset analysis and resource allocation

This study presents an integrated framework for calibrating material parameters and their associated uncertainties in continuum finite element (FE) simulations of progressive damage in thin wood veneer laminates. Compact tension tests on two laminate layups, [ 0 / 90 ] 2 s and [ ± 45 ] 2 s , serve as the basis for calibrating FE input parameters both along and perpendicular to the grain direction. To enable efficient uncertainty quantification, a deep Long Short-Term Memory (LSTM) neural network is developed to rapidly predict full force vs displacement curves from these fracture tests. The FE input parameters are treated as random variables, and Bayesian inference with Markov Chain Monte Carlo sampling is applied to the LSTM surrogate models to estimate their distributions based on variability observed in experiments. Validation against experimental data demonstrates that the calibrated parameters accurately simulate damage progression, including uncertainty, in both compact tension and open-hole tension tests of quasi-isotropic [ 90 / 45 / 0 / − 45 ] s laminates, with mean prediction errors below 7%. • Development of integrated FEA–ML–Bayesian framework for progressive fracture simulation including uncertainties. • Curve-based uncertainty quantification using deep LSTM surrogate modelling for rapid model evaluation. • Experimentally validated prediction of variability across layups and mechanical tests.

Bayesian inference of nonlinear malaria dynamics in Ghana via an ensemble Markov chain Monte Carlo sampler

The convergence and uniqueness of a discrete-time nonlinear Markov chain

To investigate a dilemma of statistical and computational efficiency faced by long-run variance estimators, we propose a decomposition of kernel weights in a quadratic form and some online inference principles. These proposals allow us to characterize efficient online long-run variance estimators. Our asymptotic theory and simulations show that this principle-driven approach leads to online estimators with a uniformly lower mean squared error than all existing works. We also discuss practical enhancements such as mini-batch and automatic updates to handle fast streaming data and optimal parameters tuning. Beyond variance estimation, we consider the proposals in the context of online quantile regression, online change point detection, Markov chain Monte Carlo convergence diagnosis, and stochastic approximation. Substantial improvements in computational cost and finite-sample statistical properties are observed when we apply our principle-driven variance estimator to original and modified inference procedures.

Dynamic feature selection with distributional Markov chain error correction for financial time series forecasting

Zero-shot deep learning with multi-objective optimization improves thermostability of zearalenone hydrolase and xylanase.

Very long baseline interferometry (VLBI) measurements of the sizes of compact extragalactic radio sources, jetted active galactic nuclei, provide data for probing the angular size--redshift relation, offering a classical cosmological test complementary to other distance--redshift methods. Aiming to update and extend previous studies conducted in the 1990s, we analyse a significantly expanded and improved dataset to reassess the angular size--redshift relation and its potential for constraining cosmological model parameters, focusing on the matter density parameter, Ω_ m , in a flat Λ cold dark matter Universe. This is the first major update of the compact-source angular size test in the past quarter of a century, using a dataset an order of magnitude larger than in previous studies. We performed a Markov chain Monte Carlo analysis on real data and on multiple mock catalogues with varying Gaussian noise levels (10%,,20%,, 50%) to evaluate parameter constraints in the presence of observational scatter. In addition, we conducted a test with 100 randomized catalogues created by shuffling redshifts while preserving other observables to explore the statistical significance of the angular size--redshift dependence. We also explored how astrophysical parameters depend on fixed cosmological models with different m values. The randomization test showed that the posterior distributions from randomized data do not overlap with those from real observations, with significant deviations, confirming that the measured angular size--redshift relation is physically meaningful and not a chance alignment. The astrophysical model parameter that describes the redshift dependence of the source angular size exhibits strong sensitivity and degeneracy with Ω_ m . Simulated mock catalogues indicate that the method is able to constrain Ω_ m if the data scatter is below sim20%, but current real data noise levels are too high for reaching competitive cosmological constraints. Scaling estimates suggest that high-quality data of samples of several thousands to ∼ 100,000 sources, a standardization calibration approach, and/or refining sample selection criteria are needed to fully exploit the potential of the angular size--redshift test with this type of object.

Identifying cancer driver genes (CDGs) remains a central challenge in cancer genomics, as frequency-based mutation approaches often miss rare but functionally important regulators. We present PICDGI, a computational framework that predicts driver-like regulatory genes by integrating dynamic gene-gene interaction modeling with single-cell RNA sequencing (scRNA-seq) data. Rather than relying on DNA mutation calls, PICDGI infers functional driver activity from time-resolved expression patterns and latent regulatory influence among genes during tumor progression. Methodologically, PICDGI employs a time-varying state-space model with variational Bayesian inference and Markov Chain Monte Carlo (MCMC) sampling to estimate evolving gene interaction effects. The posterior distributions capture both the magnitude and uncertainty of each gene’s inferred regulatory influence. From these, PICDGI derives a driver coefficient that quantifies the strength and reliability of each gene’s contribution to progression-associated expression dynamics, enabling the prioritization of impactful regulators over neutral passengers. Applied to lung adenocarcinoma (LUAD) scRNA-seq data, PICDGI recovered known oncogenes and tumor suppressors and nominated novel candidate drivers, including JPH1 and CHEK1 , which are implicated in calcium signaling, mitochondrial regulation, and DNA repair. These genes exhibit trajectory-aligned activity consistent with tumor evolution and immune-modulatory processes. Comparative analysis using Moran’s I statistics in Monocle 3 showed that PICDGI-prioritized genes display stronger progression-associated dynamics than genes selected by spatial autocorrelation alone. We further validated PICDGI on an independent pediatric acute myeloid leukemia (AML) scRNA-seq cohort, where it consistently recovered known drivers and relapse-associated regulatory programs under fixed model parameters. By integrating interaction-informed dynamic modeling with single-cell resolution data, PICDGI provides a generalizable and biologically grounded framework for identifying rare and context-specific regulatory drivers of cancer progression, with broad applicability across tumor types.

ABSTRACT African entrepreneurial ecosystems are characterized by rapid growth and profound instability, yet existing theoretical frameworks struggle to explain the dynamic pathways ventures navigate between states of growth, stagnation, and failure. This study addresses this gap by developing a dynamic transition theory of African entrepreneurship. Leveraging a unique longitudinal dataset of 2314 startups across 15 countries, we employ a Markov chain framework not as an end in itself, but as a lens to quantify ecosystem resilience, identify pervasive traps such as the 48% probability of early-stage stagnation, and uncover counter-intuitive patterns like inverted resilience, where failure becomes a catalyst for renewal, evidenced by a 37% rebound probability from decline. Beyond its theoretical contribution, our model is translated into a policy optimization tool, offering evidence-based, phase-sensitive intervention protocols such as technology-linked microcredit that guide policymakers in strategically allocating resources between R&D, financing and incubation, to maximize entrepreneurial value under uncertainty. This research provides a new, dynamic language for understanding entrepreneurial ecosystems and delivers a practical framework for strengthening resilience and fostering inclusive growth.

The study of the mechanical behavior of human muscle tissues has received increasing attention due to its relevance in biomechanical and biomedical applications, and is often described by hyperelastic models because of its nonlinear response under mechanical loading. In this context, constitutive modeling requires the definition of an appropriate strain energy density function; however, there is still no universal model capable of satisfactorily representing all classes of soft hyperelastic materials, which makes the use of systematic methodologies for parameter identification and model selection essential. Thus, the present work aims to apply a Bayesian inverse problem approach, through the Transitional Markov Chain Monte Carlo (TMCMC) method, to determine, based on experimental data from uniaxial compression tests carried out on a pure hyperelastic material and on a hyperelastic composite reinforced with unidirectional fibers, which strain energy density models best represent the observed response. The TMCMC method stands out for providing a robust and efficient exploration of posterior distributions, allowing not only the estimation of constitutive parameters, but also the quantification of the uncertainties associated with these parameters and the probabilistic comparison among competing models through Bayesian evidence. The results highlight the importance of using methods that explicitly incorporate experimental and inferential uncertainties, since different models may present similar fits under an exclusively deterministic analysis.

Abstract Writing places substantial self-regulatory, motivational, and cognitive demands on adolescents, yet less is known about how self-regulation-based writing strategies (SRWS) are associated with academic well-being (AWB) and through which proximal resources this association may operate. This cross-sectional study tested a gender-moderated serial mediation model in 658 Grade 7–8 students (334 girls) from socio-economically diverse schools. Bayesian structural equation modelling with Markov chain Monte Carlo estimation was used, and parameters were interpreted using 95% credibility intervals. SRWS was positively associated with writing motivation (SRWS → WM, β = 0.57) and cognitive flexibility (SRWS → CF, β = 0.49), and writing motivation was positively associated with cognitive flexibility (WM → CF, β = 0.21). Academic well-being was positively associated with cognitive flexibility (CF → AWB, β = 0.34) and SRWS (SRWS → AWB, β = 0.34), whereas the unique WM → AWB path was not credibly different from zero. The indirect effects SRWS → CF → AWB and SRWS → WM → CF → AWB were credible in both gender groups. A small negative SRWS × gender interaction in the AWB equation indicated that the SRWS–AWB association was weaker for boys than for girls. The model explained 36% of the variance in writing motivation, 37% in cognitive flexibility, and 31% in academic well-being. Overall, the findings are consistent with the view that SRWS is linked to academic well-being primarily through cognitive flexibility, with writing motivation contributing indirectly through its positive association with cognitive flexibility.

With increasingly stringent greenhouse gas emission regulations in the shipping industry, there is an urgent need for an efficient energy management strategy for new energy ship power systems. However, existing dispatch models often overlook the dynamic energy-saving potential of active drag reduction technologies and lack effective optimization algorithms capable of handling high-dimensional, multi-constrained problems. To address these problems, this paper proposes a novel integrated dispatch framework for hybrid energy ship power systems that incorporates air lubrication systems. First, a unified multi-energy dispatch model is established, coupling the dynamic operation of air lubrication systems with electrical, thermal, and propulsion energy flows. Second, an Improved Traffic Jam Optimizer algorithm is proposed, which enhances global exploration and local exploitation through a nonlinear parameter adaptation mechanism, differential mutation strategy, and dynamic hybrid search architecture. Convergence analysis based on Markov chain theory is provided to guarantee algorithmic reliability. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of convergence speed, solution accuracy, and stability. Furthermore, integrating air lubrication systems into the ship power system reduces total operating costs and greenhouse gas emissions by up to 20.569% and 6.310%, respectively.

Let f be a monic quadratic polynomial over a finite field of odd characteristic. In 2012, Boston and Jones constructed a Markov process based on the post-critical orbit of f and conjectured that its limiting distribution explains the factorization of large iterates of f . Later, Xia, Boston, and the author performed extensive Magma computations and found some exceptional families of quadratics that do not seem to follow the original Markov model conjectured by Boston and Jones. They discovered this by empirically observing that certain factorization patterns predicted by the Boston–Jones model never seem to occur for these polynomials, and they suggested a multi-step Markov model that accounts for these missing factorization patterns. In this note, we provide proofs for all these missing factorization patterns. These are the first results that explain why the original conjecture of Boston and Jones does not hold for all monic quadratic polynomials.

Entropy rate (ER) and sample entropy (SE) are two metrics that have been used to quantify the syntactic complexity of electroencephalography (EEG) microstate sequences. We here present a theoretical and numerical comparison of these two metrics and apply them to a resting-state EEG dataset from individuals with Alzheimer's disease (AD) and a control group. We first derive theoretical ER and SE estimates for first-order discrete Markov processes, providing a null hypothesis for statistical testing of higher-order syntax properties. Under the first-order syntax null hypothesis, we find a close mathematical relationship between both metrics that can be expressed by the microstate transition probability matrix. An inequality is derived that shows ER to be an upper bound to SE under the Markov approximation. We quantify accuracy and precision of the theoretical ER and SE estimates on EEG microstate sequences from the healthy control group. We then show that ER and SE identify significant higher-order syntax properties in microstate sequences from the control and AD groups. We investigate continuous and jump microstate sequences. In the former, each time point is labelled with the best matching microstate label, and in the latter, duplicate labels are removed, exclusively retaining transitions between non-identical microstates. Group comparison demonstrates that continuous microstate sequences from the AD group have lower entropy values (ER, SE), whereas jump sequences from the AD group have higher entropy values compared to control. Finally, we introduce a new syntax metric that normalizes ER and SE values with respect to their first-order syntax levels, to assess differences that only depend on syntax order. This metric revealed no differences between control and AD groups for either continuous or jump microstate sequences. This study provides further insights into higher-order microstate syntax and how it can be quantified with respect to the underlying first-order syntax. Similarities and differences between ER and SE as syntax metrics are highlighted and exemplified on experimental data. Our results show that (i) EEG microstate sequences from control and AD subjects show higher-order syntax properties across the tested syntax levels, (ii) continuous and jump sequences from control and AD groups are syntactically different, and (iii) differences between the control and AD groups disappear when higher-order syntax properties are normalized to the group-specific Markov level.

Abstract This paper extends the traditional group self-annuitisation framework by explicitly incorporating mortality heterogeneity among participants. Heterogeneity stems from multiple factors that lead individuals to age at different paces, despite being born in the same year. Ageing is modelled as a finite-state continuous-time Markov process where each state represents a distinct phase of physiological deterioration, and transitions capture the stochastic progression towards death. Benefits are differentiated by ageing state and, after issue, they are dynamically adjusted in response to the realised evolution of both ageing and mortality. Our design is novel in its use of the Markov ageing framework within a risk-sharing scheme and in how benefits are updated. Indeed, both benefits and their respective adjustment coefficients are state-specific. Through the explicit modelling of cross-subsidies across states, the design ensures that actuarial equivalence between benefits and available resources is preserved both at the pool level and within each ageing state. However, we find that benefit adjustments based on actuarial equivalence may display undesirable patterns in some ageing classes, when their size shrinks substantially; this happens, in particular, in the younger ageing states, which are likely to empty out. To contrast such effects, we introduce a design preserving a target level of differentiation across states that mitigates the unfavourable impact of a declining size for younger ages. In our analysis, we point out that such a design (which is desirable in many respects) implies solidarity effects across states. Such effects can be identified by comparing benefit amounts under the two assumptions (i.e., benefits adjusted according to actuarial equivalence or so to preserve a predefined level of differentiation). The proposed framework is tested using Australian mortality data.