Search Results (2,052)

To address the challenges of difficult value quantification, lack of market benchmarks, and scarcity of historical data for embedded software amidst the intelligent transformation of equipment systems, this study develops a scientific price estimation method based on functional capability contribution. A nonlinear pricing model is constructed to accurately characterize the two-stage evolution of software price: diminishing marginal utility during the mature technology accumulation stage and exponential growth during the technical bottleneck breakthrough stage. To ensure the consistency of pricing logic between hardware and software, a penalty function is innovatively designed to modify the standard likelihood function, effectively transforming practical business logic into a model regularization term. Parameter estimation is achieved by employing a Bayesian inference framework integrated with operational constraints, utilizing Markov Chain Monte Carlo (MCMC) sampling to realize robust posterior inference under small-sample constraints. Empirical analysis demonstrates that the proposed method achieves superior cross-domain data transfer performance compared to traditional baseline models, with a Leave-One-Out Cross-Validation (LOOCV) Mean Absolute Percentage Error (MAPE) of 21.2%. This research provides a practical value-oriented price estimation method for embedded equipment software pricing. Full article

An algorithm based on matrix analytic methods for the exact numerical performance evaluation of a two-echelon vendor-managed inventory system with lost sales is presented in this paper. Both supply and demand uncertainties are taken into consideration. Lead times are modeled using a phase-type (Coxian) distribution with two phases, while the stochastic nature of external demand is captured with a compound Poisson distribution comprised of a pure Poisson arrival process and a discrete empirical distribution for the demand of individual customers. A computer program based on the algorithm is developed and then it is used for an extensive numerical investigation with a view to obtain insights of possible managerial importance. Full article

Moldy pear core constitutes a severe internal defect that compromises fruit quality. This study proposes a nondestructive detection method for Korla pear moldy core using Vis/NIR spectral signals, aimed at supporting post-harvest quality control and automated industrial sorting. We collected spectral signals from pears and quantified the moldy pear core area to classify samples into healthy (S = 0%), slightly moldy (0 < S ≤ 10%), and severely moldy (S > 10%) categories. We constructed a three-tier comparative framework to evaluate the progression from conventional machine learning to advanced deep learning: traditional methods using univariate selection (US) and random forest (RF) for feature extraction followed by support vector machine (SVM) classification; 1D-ResNet for direct processing of spectral signals; and two-dimensional approaches transforming signals into improved gramian angular field (IGAF) or Laplacian pyramid Markov transition field (LPMTF) images processed through deep belief network (DBN), MobileNetv3, and Vision Transformer (ViT). The LPMTF-ViT combination delivered the best performance with 98.98% test accuracy and 94.44% external validation accuracy, significantly exceeding traditional approaches and 1D-ResNet. This innovative approach delivers effective technical support for early-stage, nondestructive detection of internal fruit defects. It also establishes a scalable foundation for automated industrial inspection systems, potentially reducing post-harvest losses while ensuring premium quality control in modern fruit supply chains. Full article

Resource-constrained Multi-Access Edge Computing (MEC) nodes cannot fully replace cloud infrastructure, yet existing service placement models treat edge hosting as an all-or-nothing decision. This paper proposes a queueing-theoretic framework for split-user offloading in hybrid MEC–cloud environments. The system is modeled as a Continuous-Time Markov Chain (CTMC) over a load-vector state space that admits a product-form stationary distribution. A delay-aware greedy orchestration policy determines, at every arrival and departure event, which service occupies the MEC node and how many of its users are offloaded from the cloud. Closed-form expressions are derived for average end-to-end (E2E) delay, MEC occupancy and saturation probabilities, per-service hosting probabilities, and delay-saving indicators. Numerical analysis of a five-service industrial scenario shows that the proposed split-user mechanism keeps the MEC node occupied for most of the observation time (around 97% at the baseline load), naturally prioritizes services with the largest aggregate latency benefit, and substantially reduces the average delay compared with a cloud-only configuration. The analytical results are validated by discrete-event simulation, which matches the CTMC values with relative discrepancy below 1% under the Poisson/exponential assumptions; additional simulations quantify the sensitivity to alternative arrival and service-time distributions. The framework provides analytically tractable, interpretable decision logic with negligible runtime overhead, making it a suitable analytical foundation for cloud service orchestration platforms that must meet strict QoS targets in next-generation edge networks. Full article

This paper establishes a theoretical framework for treating communication quality as a navigable resource in long-endurance unmanned aerial vehicle (UAV) control under stochastic degradation. We prove that a hierarchical architecture integrating communication-aware model predictive control (MPC) achieves $\epsilon$-optimality with respect to the intractable stochastic dynamic programming formulation while maintaining exponential stability guarantees under switched system dynamics governed by continuous-time Markov chains. Three primary theoretical contributions were made: (1) A stochastic optimality theorem is given showing that sigmoid penalty function approximation yields bounded suboptimality of $\eta \le 0.12$ under mild ergodicity conditions; (2) a formal stability result for mode switching based on hysteresis was established using multiple Lyapunov functions, and it showed exponentially fast convergence with a decay rate of $\lambda \ge 0.23$; and (3) bifurcation analysis showed that there is a critical time threshold of 72 h at which thermal-induced gyro-drift in the GPS sensor causes a transition in navigation error dynamics from linear to catastrophic nonlinear growth. The validation through 2430 Monte Carlo missions over 54,686 flight hours resulted in an average increase in endurance by 243% (18.2 days versus 5.3 days), while keeping CEP at approximately 8.7 m and achieving 82% mission success under extreme communication degradation ($q_{comm}<0.3$). The statistical results confirm a very strong positive relationship between the Resilience Quotient (RQ) and the length of successful missions ($R^2=0.89$, $p<0.001$), supporting the theoretical model with empirical evidence. Full article

Coordinated development between new-type urbanization and rural revitalization is important for sustainable urban–rural transformation and balanced regional development in China. Using panel data for 30 provincial-level units from 2014 to 2023, this study examines the spatiotemporal evolution, dynamic transitions, and external drivers of the coupling coordination degree between the two systems. Spatial Markov chains and an interpretable machine-learning framework are used to identify neighborhood effects, nonlinear relationships, and interaction patterns. The results show four main findings. First, the coupling coordination degree increased over the study period, but clear spatial differences and clustering remained. This suggests that coordinated urban–rural development did not advance evenly across regions. Second, the evolution of coordination shows strong state dependence, and neighborhood context is closely related to transition probabilities. Provinces located in high-coordination neighborhoods were more likely to move to higher levels, while provinces in low-coordination neighborhoods were more likely to remain trapped at lower levels. Third, digital inclusive finance and fiscal self-sufficiency were the most important external factors. Both showed clear nonlinear patterns. Per capita electricity consumption and aging rate also showed heterogeneous relationships at different value ranges. Fourth, the interaction results suggest that higher coordination is more likely to emerge when digital finance, fiscal capacity, openness, human capital, and infrastructure improve together, rather than when only one factor expands on its own. The findings indicate that sustainable urban–rural transformation is shaped by spatial dependence, nonlinear changes, and context-specific factor combinations. Beyond their relevance for more targeted urban–rural coordination and place-based sustainability governance in China, these findings also provide a useful reference for other developing countries seeking to address similar urban–rural development challenges. Full article

We consider a Markov chain that can be termed a discrete version of Blackwell’s example from 1958. It is constructed with the aid of a sequence of independent Markov chains with two states. It turns out its stationary distribution $\pi$ and transition matrix P are in detailed balance. As a result, the transition operator associated with P is self-adjoint in ${\ell }^2\left(\pi \right)$, the Hilbert space of all square summable sequences with respect to $\pi$. All eigenvalues of P are therefore real, and we give explicit formulae for them. Their corresponding eigenvectors form an orthogonal family in ${\ell }^2\left(\pi \right)$. Consequently, P can be diagonalized, and we find manageable formulae for $P^n$, where $n\ge 2$. Full article

The demand for the fabric warehouse presents obvious characteristics of hockey stick effect. This leads to problems such as peak congestion and labor shortages during its operation. In order to alleviate this phenomenon, we propose a combination strategy that uses a SARIMA–Markov hybrid model for demand forecasting, and then applies Actor-Critic reinforcement learning for dynamic pricing. This model integrates SARIMA with Markov chains for residual correction, capturing linear trends and seasonal patterns while correcting residuals, yielding more accurate predictions for highly volatile demand in textile logistics. Experimental results indicate that our approach achieves better performance than SARIMA, Temporal Fusion Transformer (TFT), and Ensemble, especially in identifying and reproducing sharp demand peaks. By combining forecasting results with price elasticity, the proposed dynamic pricing scheme cuts peak-hour demand by 12.54%, which in turn eases pressure on labor scheduling and boosts the efficiency of workforce allocation. This work offers a data-driven approach to flattening demand fluctuations via intelligent pricing, improves operational efficiency without requiring extra hardware investment, and provides a practical response to a long-standing bottleneck in the textile logistics sector. Full article

Theoretical perspective: The function of rural human settlements serves as a crucial perspective for interpreting rural community restructuring, and hybridity is a powerful tool for decoding the function of rural human settlements. Objectives and methodology: Based on rural communities’ sample survey data in typical counties and cities of the Jianghan Plain in 2012 and 2022, an evaluation index system for rural community restructuring was constructed across three dimensions: entity hybridity, network hybridity, and meaning hybridity. This paper aimed to analyze the characteristics of rural community restructuring and human settlement functions evolving in the traditional agricultural plain using the weighted TOPSIS method, the Markov chain, and the synergy model. Results and conclusions: From 2012 to 2022, the level of rural community restructuring in the Jianghan Plain has significantly improved, and the comprehensive index of rural community restructuring has gradually narrowed across different rural communities. Meaning hybridity plays a major role in rural community restructuring in the Jianghan Plain. Social culture is the dominant element of rural community restructuring. Public security situation contributes 21.35% to rural community restructuring as the key indicator. Both macro humanistic environment and micro spatial attributes have significant influences on rural community restructuring. These findings can provide a scientific basis for the optimization of the rural community system in the Jianghan Plain. Full article

The Clavicipitaceae family, including saprobes and insect and myco-pathogens, are widely distributed in nature across various trophic regions, and play important roles in insect population control, plant interactions, and symbiotic evolution. Members of the genus Moelleriella within this family primarily specialize in infecting scale insects and whiteflies. Using five genomic loci (SSU, LSU, tef1-α, rpb1, and rpb2), we report on the inferred divergence times among members of Clavicipitaceae using molecular dating analyses. Molecular clock estimates revealed that the ancestor of Moelleriella likely emerged in the Late Cretaceous (91.60 Mya; 95% highest posterior density of 79.29–100.13 Mya). Historical biogeographic reconstruction of Moelleriella, performed using the Bayesian Binary Markov chain Monte Carlo (BBM) method, indicates that it most likely originated in Asia. Moreover, based on taxonomic and phylogenetic analyses, we describe three species within the genus Moelleriella, including one new species (Moelleriella microstroma) and two new records for China (Moelleriella chiangmaiensis and Moelleriella phukhiaoensis). Full article

The growing need to enhance observability in distribution networks has driven the development of load pseudomeasurement generation methods, particularly under partial smart meter (SM) penetration. This paper proposes a load pseudomeasurement framework that builds representative daily load profiles (load curves) from hourly SM time series using clustering techniques, with and without weather information. Markov chain models are then used to capture day-to-day dynamics by predicting the most likely next-day profile to be assigned to customers without SM. To enable this transfer, a hierarchical grouping scheme based on monthly energy consumption is introduced to map behaviors from SM-equipped customers to customers without SM measurement. The methodology is validated with real residential data from the Low-Carbon London project under multiple observability scenarios including different SM availability levels, where SM measurements are withheld from the inputs to emulate customers without SM measurement, and the resulting pseudomeasurements are benchmarked against the original measurements. The results show that the Euclidean representative curve method achieved the most robust overall performance, with a minimum MAE of 1.65 in the Reduced × 75% SM configuration. The best-performing configuration depended on the observability level: Reduced was the most robust option under medium-to-high observability, whereas Temp_reduced with a 21-day window performed best under the lowest-observability condition. In addition, the Euclidean method showed low practical deviation in the Reduced × 25% SM case, with a bias of 0.63 and Cohen’s d = 0.27. Overall, the proposed approach accurately reproduces the hourly load shape and captures inter-day variability under partial observability conditions. Full article

Researchers often develop ordinal hazard distributions, whether increasing or decreasing, into multi-parameter distributions to derive various forms of the hazard function. This process necessitates the formulation of a multi-parameter hazard function, which involves a more complex mathematical expression. In contrast, this study introduces a new one-parameter lifetime model, termed the Inverted Z–Lindley (IZL) distribution, which is capable of capturing an upside-down bathtub-shaped failure rate without sacrificing analytical simplicity. Fundamental distributional properties of the IZL model are rigorously established, including closed-form expressions for the probability density, cumulative distribution, reliability, and hazard rate functions. Theoretical analysis shows that the density is strictly positive, unimodal, positively skewed, and heavy-tailed, while the hazard rate is unimodal with vanishing limits at both extremes. Fractional moments are obtained, and the non-existence of classical moments is formally justified, motivating the use of quantile-based and inactivity-time reliability measures. Besides the quantile function, several key reliability measures, including the mean inactivity time and strong mean inactivity time functions, and order statistics, are also developed. Inferential procedures are constructed under Type-II censoring using both likelihood-based and Bayesian frameworks. The existence and uniqueness of the frequentist estimator are established, while Bayesian estimation is implemented via Markov chain Monte Carlo methods under informative gamma priors. Several interval estimation techniques—including asymptotic, bootstrap, Bayesian credible, and highest posterior density intervals—are developed and compared through extensive Monte Carlo simulations. The practical relevance of the proposed model is demonstrated using real datasets from environmental health and communication engineering, where the IZL distribution consistently outperforms fifteen well-established inverted lifetime models according to likelihood-based criteria, information measures, and goodness-of-fit diagnostics. Overall, the IZL model offers a powerful, interpretable, and computationally efficient alternative for modeling heavy-tailed lifetime data with non-monotone failure behavior, contributing meaningfully to modern distribution theory and applied reliability analysis. Full article

Peruvian fresh blueberry exports have expanded rapidly since 2012, yet strong seasonality and price–volume fluctuations continue to complicate trade planning and export decision-making, thereby threatening the long-term economic sustainability of the sector. Using monthly series for 2012–2025, this study compares three forecasting approaches to export value (FOB), export volume and unit price: (i) a seasonal Markov chain with Monte Carlo simulation (Markov–Monte Carlo), (ii) a log-linear growth model, and (iii) a seasonal ARIMA (SARIMA) model estimated using logarithmic data. The models are evaluated under a common train–test design, with the last 12 months (September 2024–August 2025) reserved for out-of-sample assessment. Model performance was evaluated through standard metrics, specifically Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), while model adequacy was examined through residual diagnostics, including Ljung–Box tests. For the Markov–Monte Carlo approach, simulated distributions were also used to characterize forecast uncertainty. Findings indicate that the log-linear growth model provides the most accurate short-term point forecasts for FOB values, and the SARIMA model performs better for export volume; the Markov–Monte Carlo approach, however, yields the best performance for export prices and provides additional insights into seasonal regimes. Overall, these results suggest that no single model dominates across all dimensions of the export chain. Instead, the combined use of forecast approaches offers a more comprehensive basis for sustainable trade management, export planning, and risk management in dynamic agricultural export sectors. Full article

An adaptive progressive first-failure censoring scheme is used to enhance the efficiency of statistical analyses and minimize test time in life-testing experiments. This paper focuses on statistical inferences for the unknown parameters, survival, and hazard rate functions of the Burr XII distribution under this censoring scheme. Since the maximum likelihood estimates for the model parameters and reliability characteristics cannot be obtained explicitly, the Newton–Raphson method is employed for numerical derivation. The delta method is used to determine the variances of reliability characteristics and is applied to construct confidence intervals. Bayesian estimates of the unknown parameters and reliability characteristics are derived under the squared error and linear exponential loss functions. As these estimates are not explicitly obtainable, the Lindley and Markov chain Monte Carlo methods are used as approximation techniques. Additionally, asymptotic confidence intervals and highest posterior density credible intervals are developed for the parameters and reliability characteristics. A Monte Carlo simulation is performed to evaluate the proposed estimators, and the methodology is validated through a real dataset analysis on arthritic patients. Full article