Search Results (190)

Supply chain plasticity (SCP) is a critical dynamic capacity through which firms respond to disruptions by reconfiguring their supply chains. Its latent, regime-dependent nature makes SCP difficult to identify. Grounded in a social–ecological systems view, this study aims to infer SCP endogenously from longitudinal supply chain networks by developing a regime-conditioned probabilistic framework (RCPF) that integrates graph-theoretic measures with latent-variable models. The framework enables the endogenous inference of regime-transition states, the tracing of firm-level SCP dynamics, and the identification of cross-firm SCP archetypes through three sequential probabilistic modules. Specifically, a hidden Markov model is specified to decode the latent regime-transition-state trajectory from the graph edit distance and spectral distance between consecutive network snapshots. Next, a regime-conditioned hidden Markov model is formulated to trace firms’ SCP dynamics from local relational adjustments and network positional variations, with latent-state transitions conditioned on the posterior distribution over the inferred regime-transition states. Finally, a finite mixture model is applied to identify interpretable SCP archetypes from phase-specific profiles constructed from firms’ posterior plasticity-state probabilities across regime-shift phases. Applied to China’s electric vehicle supply chain network, the framework identifies regime shifts aligned with disruptive developments and shows that regime-shift conditions increase the likelihood and persistence of firms’ structural reconfiguration. The inferred SCP archetypes reveal role-specific adaptation pathways and resilience outcomes, informing firms’ differentiated strategic responses to disruptions. Full article

To address the challenge of satisfying strict Service-Level Agreement (SLA) requirements for concurrent smart video surveillance tasks in heterogeneous edge computing environments, an SLA-aware adaptive scheduling algorithm for heterogeneous computing collaboration is proposed. First, a mixed-task flow model is constructed, and a finite-state Markov chain is utilized to dynamically model the time-varying wireless channel. Second, a Dueling Double Deep Q-Network (Dueling DDQN) scheduling algorithm based on SLA awareness and channel adaptation is proposed, with a designed SLA action-masking mechanism. This mechanism advances hard delay constraints to the decision-generation stage, dynamically prunes the action space based on real-time channel conditions and node loads, and filters out actions predicted to violate the SLA before execution. Experimental results show that the proposed algorithm coordinates heterogeneous computing resources between the cloud center and the edge and exhibits earlier empirical reward stabilization and lower task-violation rates than the compared learning-based baselines under the tested workload conditions. Full article

This study investigates the role of institutional policy support in enhancing climate adaptation strategies and maize productivity among smallholder farming households in Southwest Nigeria, a region critical for maize production yet vulnerable to climate variability. Employing a household-level data approach, data were collected from maize-based households across six states using questionnaires and interviews, supplemented by secondary climate and policy records. We employed a multivariate probit (MVP) regression with instrumental variable correction, addressing endogeneity in institutional support variables. Bayesian linear regression modeled maize yield as a function of institutional support, incorporating weakly informative priors and Markov Chain Monte Carlo (MCMC) sampling for posterior estimation. The Bayesian approach provides full posterior distributions and credible intervals, facilitates probabilistic interpretation of policy effects, improves estimation stability in the presence of multicollinearity, and enables rigorous sensitivity. Model robustness was evaluated via Bayesian fit metrics and sensitivity analysis with bootstrap resampling across prior types. Multivariate probit regression identifies institutional support, credit, irrigation, market access, and road infrastructure as key drivers of adaptation strategy adoption, modulated by socioeconomic (gender, experience) and farm-specific factors (farm size). Bayesian linear regression confirms significant yield impacts from institutional variables. Subgroup analysis indicates greater benefits for large farms over small farms, with gender-neutral impacts. While institutional support significantly boosts adaptation and productivity, gaps in irrigation access, climate information, and smallholder targeting limit equitable outcomes. The findings advocate for enhanced infrastructure, financial incentives, and tailored policies to strengthen climate resilience and food security, aligning with Nigeria’s climate goals and the Sustainable Development Goals. Full article

Background: Imported halal meat supply chains face substantial challenges caused by fragmented certification systems, information gaps, and uncertainties. These factors challenge procurement management and hinder the assurance of halal compliance. Conventional Enterprise Resource Planning (ERP) systems are mainly designed for transaction processing and offer limited support for flexible decision-making. Methods: An Adaptive Bayesian-Feedback Framework was developed that integrates Digital Halal Authentication, Evidence-Based Decision Support, Bayesian inference, adaptive feedback learning, and the Markov Decision Process (MDP) within the ERP Purchasing Module. The framework was evaluated using 30 imported meat shipment cases, expanded into 300 procurement transaction records. Results: Implementation of the framework reduced halal authentication processing time from 2509 s to 290 s, representing an 88.44% decrease and an approximately 8.65-fold improvement in processing speed. The framework also enabled continuous revisions to evidence on vendors and procurement risks and improved the consistency of decision-making amid uncertain supply chain circumstances. Conclusions: The framework develops a unified decision-support system comprising adaptive halal authentication, evidence-based decision support, and intelligent ERP. This integration improves procurement governance, traceability, transparency, and uncertainty management in imported halal meat supply chains. Full article

The quality and safety supervision of agricultural products at the grassroots level has long faced the triple superposition dilemma of small-sample sampling, multi-source evidence conflict, and risk chain evolution. Although existing data-driven models have considerable accuracy, they are difficult to leverage for intervention decisions, and the simple serial connection of traditional Bayesian networks and evidence theory cannot respond to dynamic scenarios. Aiming at this research gap, this paper constructs a dynamic risk assessment model, CIBE-DR, that deeply couples Bayesian networks with evidential reasoning. It contains three core innovations. First, the structure learning method of the causally identifiable Bayesian network embeds a graded do-calculus identifiability score covering both back-door and front-door criteria into the BDeu scoring function and combines this reward with an expert-prior divergence penalty that breaks Markov equivalence so as to realize the transition from relevance modeling to intervention decision modeling. Second, the conflict-aware adaptive evidence synthesis rule orthogonally decomposes multi-source conflict into an epistemic component and an ontological component, which are modeled respectively by Tsallis belief entropy and abductive inference over a discrete twenty-seven-point heterogeneity hypothesis space and are then fused under a reparameterized Dempster–Yager interpolation in which the two endpoints recover the two named rules under a single consistent interpretation. Third, the bidirectional closed-loop coupling mechanism between BN and ER realizes the mutual calibration between the conditional probability table and the evidence credibility prior under a Lyapunov monotone descent argument with the explicit Lipschitz bound $L_{\theta }$ ≤ 0.028 < 1, endowing the model with time-varying self-correction ability. Based on experiments on 156,847 sampling samples from counties and townships in East China, Central China, and Southwest China from 2021 to 2024, the proposed method achieved the best value in six of the seven evaluation indicators, with a minority recall of 0.864 ± 0.014, an intervention effect estimation error of 0.063 ± 0.005, and a dynamic response delay of 2.8 ± 0.3 days, significantly ahead of eleven mainstream baselines under the McNemar test on classification ($p$ < 0.001) and the Wilcoxon signed-rank test on intervention-effect estimation ($p$ < 0.001). The only indicator on which CIBE-DR does not lead is overall accuracy, which is 0.002 lower than that of Transformer; this difference does not reach statistical significance under the McNemar test ($p$ = 0.32) and does not weaken the value of grassroots supervision in the strong-imbalance scenario where the positive rate is only 1.04%. The robustness advantage of the model is particularly prominent in the scenarios of sparse data, adversarial perturbation, and prior-graph incompleteness, and the intervention-effect estimates were additionally validated against two post-2022 policy interventions with absolute deviations of 1.4 and 1.2 percentage points respectively. These results verify the product gain and grassroots deployability of the three mechanisms. Full article

The extensive deployment of the Power Internet of Things (PIoT) relies on dual-mode communication (HPLC + HRF) for robust data acquisition. However, under massive bursty traffic, conventional static MAC superframe scheduling struggles to reconcile high throughput with stringent reliability constraints. To mitigate this, we propose a dynamic adaptive scheduling scheme. Initially, a joint PHY-MAC layer dual-mode system architecture is proposed. At the MAC layer, a dual-link parallel multiplexing contention access mechanism is applied; at the physical layer, a capacity bottleneck determination model is established, incorporating log-normal–Bernoulli–Gaussian mixed noise and multipath fading. Subsequently, an extended two-dimensional Markov chain analytically derives key performance indicators, including equivalent collision probability, joint outage probability, access delay, and network throughput. Building upon this, a Q-learning-based algorithm is proposed. By constructing an asymmetric penalty–reward function, the central coordinator (CCO) autonomously optimizes the Contention Access Period (CAP) to Contention-Free Period (CFP) ratio under dynamic node scales. Simulations demonstrate this methodology effectively averts channel congestion during extreme concurrent traffic surges. Ultimately, it strictly preserves service reliability while substantially augmenting the concurrent carrying capacity and resource utilization of the dual-mode network. Full article

Fractional Brownian motion (fBm) provides a powerful stochastic framework for modeling long-range temporal dependence that cannot be represented by classical Brownian motion. This study presents a numerical and theoretical investigation of constrained fractional Brownian motion with applications to stochastic financial systems. An efficient simulation framework combining Fast Fourier Transform (FFT)-based circulant embedding and Markov Chain Monte Carlo (MCMC) sampling is developed to generate long correlated trajectories under absorbing boundary conditions. The proposed algorithm enables simulations with trajectory lengths up to L = 10⁷ while reducing the computational complexity from O (L³) for direct covariance decomposition to approximately O(L log L). Numerical results accurately reproduce the theoretical autocorrelation function of fBm and confirm the expected persistence behavior governed by the Hurst exponent. Super-diffusive regimes (H > 0.5) exhibit persistent long-range correlations and enhanced survival probabilities, whereas sub-diffusive regimes (H < 0.5) display anti-persistent dynamics and increased boundary absorption. The fractional stochastic volatility formulation captures important characteristics associated with long-memory financial systems, including persistent volatility dynamics and implied-volatility structures. The proposed biophysical-based FFT–MCMC methodology provides an accurate, scalable, and computationally efficient framework for studying constrained fractional stochastic processes and offers a foundation for future investigations of fractional volatility models and related financial applications. A conceptual Adaptive Hurst Momentum framework is briefly discussed as a possible direction for future research. Full article

This study investigates parameter estimation utilizing an adaptive progressive Type-II competing risks framework. In this work, we provide a comprehensive analysis of the statistical features and estimation methods for the Generalized Inverted Exponential (GIE) model. Assuming a population subject to two independent failure causes, both following a GIE distribution, a competing risks model is formulated. Point and interval estimates are derived using both maximum likelihood, with associated asymptotic confidence intervals, and Bayesian approaches via Markov Chain Monte Carlo simulation to obtain credible intervals. The effectiveness of these methods is subsequently demonstrated by applying them to real datasets and through a comprehensive Monte Carlo simulation study designed to assess estimator performance. Full article

The preconditioned Crank–Nicolson (pCN) Markov chain Monte Carlo method is a standard tool for high-dimensional Bayesian inverse problems because its proposals preserve the prior and its performance degrades gracefully under mesh refinement. Yet its efficiency depends heavily on the step-size parameter $\beta$, which practitioners must tune manually for each problem. To remove this burden, we propose an acceptance rate-adaptive pCN algorithm that calibrates $\beta$ automatically during a finite burn-in phase via a Robbins–Monro stochastic approximation driven by cumulative acceptance rate feedback. We also studied a dual-signal extension that supplements acceptance rate adaptation with an auxiliary effective sample size (ESS) feedback term, activated gradually through a smooth logistic switch. To contextualize the proposed methods, we conducted a systematic comparison with the adaptive Metropolis (AM) and the Metropolis-adjusted Langevin algorithm (MALA). In both adaptive pCN variants, adaptation is confined to the burn-in and the step size is then frozen, so that the post-burn-in samples are produced by a time-homogeneous Metropolis–Hastings kernel targeting the correct posterior. Numerical experiments on five linear-Gaussian benchmarks with a controlled spectral structure (dimensions: 10 to 100, condition numbers: 2 to 200, SNR from $-9$ dB to $+8$ dB) and a nonlinear PDE-constrained inverse problem governed by the cubic–quintic nonlinear Schrödinger equation showed that the acceptance rate-adaptive pCN yields consistent improvements over vanilla pCN, reaching approximately an $1.8\times$ higher ESS in ill-conditioned regimes with low inter-seed variance. The AM achieved a superior ESS on well-conditioned and low-dimensional problems, while the MALA excelled in low-SNR regimes, but degraded under sharp posteriors. A sensitivity analysis confirmed the robustness to hyperparameter choices, and multi-seed experiments validated the reproducibility of all findings. These results indicate that automatic step-size adaptation can substantially improve the practical efficiency of pCN for challenging moderate-dimensional Bayesian inverse problems without manual tuning. Full article

This study develops a stochastic queueing framework to model the dynamical interaction between proliferating tumor cells and the adaptive immune system, incorporating the critical phenomenon of immune checkpoint inhibition. The immune response is conceptualized as a multi-server service system where CD8+ T cells and natural killer cells act as parallel servers eliminating cancerous cells, while tumor cell division follows a controlled birth process with logistic growth constraints. The novelty lies in embedding a state-dependent immune evasion mechanism through a dimensionless checkpoint parameter $\kappa \in [0,1]$ that modulates the effective killing rate as a function of tumor burden via the saturation function $\varphi \left(n\right)=n/(K+n)$. The resulting process $\left\{N\right(t),t\ge 0\}$ constitutes a non-homogeneous birth–death Markov chain with the following transition rates: $\begin{array}{ccc}\hfill {\lambda }_n& =\lambda \left(1-{\displaystyle \frac{n}{K}}\right){\mathbb{1}}_{\{n<K\}},{\mu }_n\hfill & \hfill =\left\{\begin{array}{cc}n\mu (1-\kappa \varphi (n\left)\right),\hfill & 1\le n<c,\hfill \\ c\mu (1-\kappa \varphi (n\left)\right),\hfill & n\ge c,\hfill \end{array}\right.\end{array}$ We derive the exact stationary distribution in closed form, establishing the fundamental stability condition ${\rho }_{\mathrm{eff}}=\lambda /\left[c\mu (1-\kappa )\right]<1$ for tumor elimination. The expected tumor burden L and mean elimination time W are computed explicitly. A critical threshold ${\kappa }_c=1-\lambda /\left(c\mu \right)$ emerges, above which the immune system loses control regardless of other parameters. Heavy-traffic analysis reveals quadratic divergence $L\sim K/{(1-{\rho }_{\mathrm{eff}})}^2$ as ${\rho }_{\mathrm{eff}}\to 1^{-}$, explaining catastrophic tumor escape. Extensive Monte Carlo simulations ($n={10}^4$ replications) validate theoretical predictions with relative errors <5% and p-values $>0.05$ from two-sample t-tests. The model provides quantitative tools for optimizing checkpoint inhibitor dosages and predicting patient-specific responses in immuno-oncology. Full article

How can an LLM-based agent recognize the limits of its own self-knowledge and improve that self-knowledge over time? This paper proposes the metacognitive evolutionary system (MES), a functional framework for studying metacognitive evolution at the prompt level in large language model (LLM)-based agents. MES does not modify model weights; it evolves the prompt program around a fixed base model, keeping the system readable, auditable, and easier for humans to inspect. The framework introduces a recursive metacognitive tower, $M^n\left(P\right)=\mathrm{LLM}\left(⟨M^{n-1}\left(P\right)⟩\right)$, to model layered self-evaluation. The fixed-point behavior of this tower is interpreted as a Strange Loop, with a formal analogy to Gödelian incompleteness used to describe its epistemic limits. The system is built using five primitive functions: inference, grounding, awareness, adaptive self-replication, and population-level selection. A grounded fitness function guides prompt evolution across generations, evaluating uncertainty calibration, error detection, strategy adaptation, and epistemic boundedness. Through Quine-style prompt rewriting, MES studies how agents can revise their own prompt-level structure while remaining constrained by grounded evaluation. The paper presents the formal architecture, analyzes tower dynamics using Markov chains, discusses convergence results, identifies six application domains, and proposes QuineBench as an evaluation design. Numerical case studies serve as illustrative analytical examples rather than empirical experiments, and QuineBench is presented as a structured protocol for future validation, providing a theoretical foundation and a clear path toward empirical evaluation. Full article

The increasing frequency of social and emergency situations in modern cities has exposed the limitations of traditional cellular networks, which are often designed based on average traffic demands. These networks struggle to handle sudden demand peaks, leading to service blockages and degraded quality of service. To address this issue, the use of Unmanned Aerial Vehicles (UAV) as mobile base stations has been proposed as a temporary solution to expand network capacity during high-demand periods. However, existing traffic models, such as Erlang-B, fail to capture the dynamic entry, exit, and variability of dwelling times associated with UAVs, limiting their accuracy in real-world scenarios. To overcome these challenges, this work proposes the Erlang-U model, which extends classical traffic analysis by incorporating Markov chains and combining Erlang and Hyperexponential distributions to accurately model the heterogeneous and dynamic nature of UAV sojourn times. This novel approach enables both analytical and computational modeling of UAV mobility and dynamic availability, providing a more realistic estimation of blocking probabilities in cellular networks. Simulation results demonstrate that the adaptive deployment of UAVs, guided by the proposed model, can reduce blocking probability by over 25% compared to conventional solutions. These findings highlight the importance of selecting appropriate sojourn time models to optimize network resilience and efficiency in dynamic and high-demand environments. Full article

This study develops a leakage-free hybrid Markov–Random Forest framework for cryptocurrency price forecasting and evaluates it on Bitcoin and Ethereum. Daily OHLCV features are lagged by one trading day to prevent look-ahead bias, while regime labels are assigned from observed price changes using a two-state Markov chain with increasing and decreasing states. Regime-specific Random Forest models are then tuned independently via time-series cross-validation, allowing the predictive structure to adapt to regime-specific market conditions. The empirical results exhibit clear asset dependence. For Ethereum, the hybrid model outperforms the standalone Random Forest on magnitude-based metrics, attaining lower MAE and RMSE while also delivering a modest improvement in directional accuracy. Regime-specific tuning further identifies distinct optimal hyperparameter configurations across the increasing and decreasing states, suggesting that Ethereum’s upward and downward dynamics are structurally heterogeneous and can be better captured through regime-aware learning. By contrast, for Bitcoin, the standalone Random Forest delivers superior magnitude forecasting performance, while the regime-specific models differ only in tree depth and share the remaining tuning parameters, indicating that regime conditioning adds limited incremental value in a more persistent market. Statistical tests reinforce these findings. For Ethereum, Diebold–Mariano tests show that the hybrid significantly outperforms the standalone Random Forest under squared loss, while the absolute-loss comparison is only marginal. Across both assets, directional accuracy remains close to random chance, confirming the limited predictability of next-day price direction from lagged OHLCV features. Overall, the hybrid framework is most valuable when regime-specific dynamics are sufficiently distinct, offering improved forecasting performance and greater interpretability than a single global model. Full article

Scheduling in multi-layer industrial networks must remain stable even when the feedback mechanism of the environment changes inside a single production episode. The system can switch between a step-continuous regime with dense process feedback and a task-driven regime with sparse milestone feedback, so that the same state requires different behaviour before and after the switch. A regime-oblivious policy may therefore optimise the wrong action preference after a switch. We formulate this setting as a mode-switched multi-industrial-chain Markov decision process (MS-MIC-MDP) and prove that a single fixed action preference is necessarily suboptimal in at least one regime. We then propose BHERA, a belief-guided homeostatic estimation framework for regime adaptation. BHERA builds cross-layer representations, performs structured variational inference of slow and fast latent beliefs, estimates the posterior probability of the task-driven regime, and uses that posterior to regulate sample weights, entropy strength, return-prediction emphasis, and latent information capacity. A homeostatic feedback rule on the Kullback–Leibler (KL) divergence keeps the latent representation informative without allowing uncontrolled information growth, and we analyse it as a two-timescale stochastic approximation with an associated convergence argument and a per-iteration complexity bound. Experiments in a multi-layer industrial scheduling simulator show that BHERA achieves higher return, lower cost, and higher utility than CReSCENT, HiTAC-MuSE, Informed Switching, and WToE across all tested perturbations, with paired statistical tests confirming significance. Expanded ablations and parameter-sensitivity studies confirm the importance of regime belief, regime-balanced weighting, bootstrap prediction, homeostatic capacity control, and the dual-timescale latent split. Full article

This paper investigates classical and Bayesian inferences for the parameters and reliability metrics of the Hjorth distribution using a new censoring mechanism called the adaptive progressive first-failure censoring scheme. This new strategy combines guaranteed observation of a fixed number of failures with adaptive control of test duration, providing a flexible and practically efficient framework for modern reliability experiments. The Hjorth distribution is considered due to its capability to model various hazard-rate shapes within a simple two-parameter structure. Maximum likelihood estimation is developed, and approximate confidence intervals are constructed using normal approximation and logarithmic transformation methods based on the observed Fisher information matrix and the delta method. A Bayesian framework is also established using independent gamma prior distributions, with posterior inference carried out through maximum a posteriori estimation and Markov chain Monte Carlo simulation. Bayes estimates and both equal-tail and highest-posterior-density credible intervals are obtained. The performance of the proposed methods is evaluated through simulation studies and illustrated using real lifetime data from an engineering domain consisting of the tensile strength of polyester fibers, demonstrating their effectiveness under adaptive censoring settings. Full article