In modern information warfare, the assessment of ammunition lethality has evolved from single-dimensional evaluations of hit accuracy to multidimensional, multiphase analyses of damage effectiveness. However, exorbitant-tech munition testing is hindered by exorbitant costs, limited sample sizes, and significant uncertainty, rendering traditional binomial or multinomial probability models inadequate. These conventional models either oversimplify damage states (compromising accuracy) or introduce prohibitive computational complexity (impeding practical application). To address these limitations, this paper proposes a Bayesian multi-stage binomial modeling approach for multi-level damage assessment under small-sample conditions. The multinomial representation of discrete damage categories is decomposed into a series of conditional binomial distributions aligned with progressive thresholds (“mild or above”，“moderate or above”, “severe or above”, and “complete destruction”), thereby enables low-dimensional modeling without sacrificing damage granularity, significantly enhancing computational tractability. To construct robust prior distributions, physical simulation results and expert domain knowledge are fused using Dempster–Shafer (D-S) evidence theory. The reliability of this fused information is further validated via a consistency test that integrates the Riemannian manifold of Fisher information and quantum entanglement entropy—mitigating subjectivity biases inherent in expert judgments Leveraging conjugate prior properties and Gibbs sampling within the Markov Chain Monte Carlo (MCMC) framework, the posterior distribution of each damage level is obtained with exorbitant precision despite limited data availability. Comparative experiments demonstrate that the proposed method achieves superior convergence stability, estimation accuracy, and computational efficiency over conventional binomial and multinomial approaches, provides a more comprehensive and precise tool for evaluating ammunition damage effectiveness, with direct implications for operational decision-making in information warfare.

Under the guidance of global carbon neutrality goals, the accuracy of corporate carbon emission accounting has become an increasingly key issue. Currently, the carbon emission reduction contribution of renewable energy power is uniformly included in the calculation of the national power grid’s average emission factor. This makes it difficult for companies to achieve effective carbon emission reductions through the purchase of green power or green power certificates. At the same time, a dynamic correction mechanism for electricity carbon emission factors has not yet been established in the accounting of indirect emissions caused by corporate electricity consumption, resulting in the risk of double accounting for the environmental value of green electricity. In view of the above problems, this study proposes a deduction mechanism based on green power consumption and a method for reducing green certificates. It constructs an enterprise carbon emission accounting index system that integrates green power and green certificate consumption, further establishing a comprehensive calculation model of enterprise carbon emissions. Through the case analysis of typical manufacturing enterprises, objects with annual electricity consumption of 10 million kilowatt hours and green electricity consumption accounting for 30% were selected for verification. After applying this model for calculation, the company’s carbon emissions decreased by about 20% compared with the traditional method, proving that the model can scientifically reflect the actual impact of green electricity and green certificates on the company’s carbon emissions. The novelty of this study is primarily demonstrated through three key contributions: First, it develops a practical approach for green electricity deduction and green certificate offset, addressing limitations in existing accounting frameworks; Second, an indicator system for carbon emission accounting that incorporates both green electricity and green certificate usage has been established, enhancing the precision and relevance of the accounting process; Third, the model’s reliability and practical utility have been confirmed through real-world enterprise data, offering a solid empirical foundation for corporate carbon emission accounting.

The growing demand for customization in manufacturing industries such as automotive and home appliances has brought significant production challenges, making Mixed-Model Assembly Lines (MMALs) widely adopted in mass customization due to their flexibility advantages. The integrated optimization of order allocation and lot-sizing sequencing for MMALs under the Assembly-To-Order (ATO) mode is crucial, which needs to balance the minimization of assembly completion time, production line load balancing, and material consumption equalization. This paper addresses this integrated optimization problem by constructing a multi-objective mathematical model for joint decision-making. Furthermore, an improved multi-objective evolutionary algorithm (INSGA-II) is proposed. Specific encoding-decoding methods and neighborhood operators are designed to achieve effective search. Variable Neighborhood Descent (VND) is embedded to enhance local search capability. An elite archive with information feedback combined with the population diversity detection strategy is adopted to improve algorithm diversity. The purpose of this study is to enhance the efficiency of the production system and ensure the flexible production of multi-variety products and on-time delivery of orders through the proposed optimization scheme. By constructing multiple instances and conducting comparative experiments with other competitive algorithms, the results demonstrate that the performance of the improved algorithm is superior to that of other algorithms.

The comprehensive utilization of water conservancy projects adopts the PPP development model, which not only introduces social capital to alleviate government debt pressure, but also utilizes the management technology of social capital to improve the level of engineering construction and operation management and the quality of social services. This article explores the profit losses during the implementation of public welfare emergency dispatch, reduced irrigation water demand in flood years, and adjustments to operation dispatch rules caused by major public activities under the "BOOT+BTO" operation mode of water conservancy PPP projects. Based on the modified total cost method, the function model and constraints of franchise profit losses are analyzed. The Copula function theory is used to analyze the joint profit losses of electricity generation and water supply profits, and a joint profit loss calculation model for the "BOOT+BTO" mode of water conservancy projects is constructed, providing a theoretical basis for governments and PPP project companies to scientifically implement emergency dispatch and reduce joint revenue losses.

Precision quantification is a core metric in industrial engineering (e.g., production quality control, sensor data calibration, automated assembly accuracy), where the traditional assumption of isotropic (homoscedastic) error variances often fails to capture real-world heteroscedastic characteristics (e.g., uneven measurement errors in assembly lines, divergent process variations in mass production). To address this critical discrepancy, this study develops a rigorous probabilistic framework for precision quantification in heteroscedastic normal populations, leveraging advanced distribution theory and numerical optimization. For the first time, the closed-form probability density function (pdf) and cumulative distribution function (cdf) of the planar precision index (PPI, defined as the modulus of a 2D heteroscedastic normal vector for industrial measurement data) are derived by integrating polar coordinate transformation with modified Bessel function theory. This resolves the long-standing absence of a strict analytical representation for this fundamental distribution, establishing a "first-principle" mathematical basis for industrial precision assessment. Building on this distributional foundation, a dual-tier computational framework is proposed: (1) A benchmark numerical solver that combines the bisection method (for convergence guarantee) and Brent’s algorithm (for superlinear efficiency) to yield exact precision index values, suitable for offline industrial system calibration; (2) A theoretically grounded linear approximation derived via moment matching and small-parameter perturbation, optimized for real-time production quality monitoring. This framework advances precision quantification from "ideal assumption-dependent models" to "data-driven, physics-consistent computation," and extends seamlessly to complex error structures in industrial scenarios (e.g., correlated sensor data, multimodal process variations). Theoretical analyses demonstrate that within the engineering-relevant variance ratio range (0.3–3.0), the average relative error of the approximation is constrained to <5%, with maximum error below 10%—well within industrial acPPItance thresholds. Validation via Monte Carlo simulations (100,000 trials) and field tests of automated welding processes confirms the method’s accuracy (mean absolute error <0.5%) and robustness. Compared to traditional homoscedastic methods, this approach reduces systematic bias in product qualification rate prediction by up to 23%, providing a reliable tool for industrial quality control and system certification.

Trade-in services, coupled with technological innovation for product update, are widely adopted by businesses. However, the practical implications of this strategy for enterprise operations and consumer purchasing behavior remain unclear, necessitating further exploration of how firms should respond. This study investigates a manufacturer offering trade-in services by comparing two scenarios: one where the manufacturer implements technological innovation and another where it refrains from doing so. Through the development of decision-making models and a comparative analysis of game-theoretic results, we examine the effects on enterprise operations and consumer responses to technological innovation. Additionally, we conduct a factor analysis to assess the determinants of technological innovation’s impact. Our findings reveal that, under trade-in services, technological innovation enhances the manufacturer’s profitability but may also lead to supplier hitchhiking. Both new and existing consumers exhibit homogeneous responses to innovation; however, under certain conditions, technological innovation may trigger consumer resistance. Furthermore, trade-in services can generate a synergistic effect with technological innovation, amplifying both its positive and negative consequences. Based on these insights, we propose operational adjustments to mitigate the identified adverse effects. This research provides managerial guidance for optimizing decision-making and addressing consumer reactions when implementing technological innovation in trade-in supply chains.

With the increasing integration of new energy sources into the power system, their inherent volatility and intermittency have exacerbated the challenges of energy consumption. This study examines source-grid-load-storage systems that incorporate adjustable loads and decentralized energy storage, including distributed new energy, power grids, air conditioners, and electric vehicles. A multi-time-scale collaborative optimization strategy is proposed to enhance the capacity for new energy consumption. The article investigates the response characteristics and consumption potential of flexible resources across different time scales, namely, monthly, day-ahead, and intraday, and develops a multi-objective optimization model aimed at maximizing new energy consumption while minimizing system operating costs. Corresponding collaborative consumption strategies are formulated for each time scale. Specifically, a multi-time-scale source-grid-load-storage collaborative framework that accounts for the flexibility of demand-side management is initially established. Subsequently, a rolling adjustment method based on multi-objective optimization is proposed for monthly, day-ahead, and intraday operations. Finally, the detailed modeling and collaborative utilization of adjustable loads and decentralized energy storage are achieved. Simulation results demonstrate that the proposed strategy reduces the system’s wind and solar curtailment rate to below 3.5%, decreases operating costs by 12.7%, and significantly improves the system’s economic performance and new energy utilization efficiency.

As the global energy structure undergoes transformation, large-scale access to renewable energy presents the power system with unprecedented dynamic balance challenges. Traditional centralized power supply architecture is challenging to adapt to complex scenarios where high proportions of new energy, high-density power electronic devices, and diversified load demands are intertwined. There is an urgent need to build a new balancing mechanism for collaborative interaction between source, grid, load and storage. Aiming at the scientific problem of deep integration of cluster security constraints and multi-level interaction, this study proposes an integrated optimization strategy. By quantitatively characterizing the CIA (Confidentiality, Integrity, Availability) triple security criterion, it establishes three types of constraint models, including extreme weather equipment current carrying capacity correction coefficient, node health index and adjustment instruction convergence time threshold. Experimental verification demonstrates that this strategy effectively controls the system frequency deviation within 0.010 Hz and stabilizes the voltage deviation to below 1.50% during the 16-period scheduling cycle. At the same time, it improves energy utilization efficiency to 92%, with clean energy accounting for 61%. Carbon emissions were reduced to 10,200 tons, and pollutant emissions were reduced to 5,100 tons. The direct trust of the high-precision recommendation module in the system is positively correlated with its precision trust value, and the source-grid-load-storage (SGLS) samples exhibit significant differences in aggregation characteristics under different feature representation methods. In addition, the short-circuit current capacity of the system is stable at 31 kA, and the transient stability index reaches 0.94, which verifies the robustness of the strategy under extreme working conditions. By analyzing the dynamic influence of the ψ parameter on the detection results of each cluster, the effectiveness of the security constraint embedding method is further confirmed.

Global warming, driven largely by carbon emissions, makes emission reduction a critical issue for both policymakers and firms. Voluntary carbon reduction mechanism provides an important pathway for non-controlled emission enterprises to participate in the carbon market. This study investigates the low-carbon transition and behavioral decision-making of non-controlled emission enterprises within the carbon market framework. A dual-sourcing newsvendor model is developed to analyze optimal ordering quantities and carbon reduction strategies under both sufficient and constrained financial conditions, and further to examine the effects of bank credit and equity financing on corporate decisions. The results show that the voluntary carbon market encourages enterprises to increase offshore orders and carbon reduction efforts, especially among those with abundant funds. Enterprises with limited financial resources also respond to carbon reduction incentives, but their improvement in emission reduction and ordering scale remains modest due to capital constraints. At moderate interest rates, bank financing effectively alleviates liquidity pressure and enhances the marginal return on carbon reduction investment, whereas high interest rates suppress such effects. Equity financing alleviates liquidity constraints to a greater degree under certain conditions, enabling enterprises to reach the optimal levels of carbon reduction and ordering decisions observed under sufficient funding.

To effectively balance emission reduction and preservation in the agricultural product sales system, this study models the carbon emission allocation of the agricultural product supply chain based on differential game theory. Starting from decentralized and centralized strategies without subsidies, research and modeling are conducted on the influencing factors of emission reduction, publicity, and preservation. Secondly, considering the scenario of government subsidies, the research analyzes the dynamic impact of different emission reduction, publicity, and preservation measures on the operation of the system. In the unsubsidized analysis model, the trajectory of goodwill shows variability, while the trajectory of freshness and emission reduction is mainly monotonic. In addition, freshness preference can enhance emission reduction decisions, and centralized strategies are superior to decentralized strategies in emission reduction. The increase in goodwill preference enhances the preservation, emission reduction, and sales status variables of agricultural products, but the centralized strategy is better than the decentralized strategy. Under the bilateral coordination mechanism, the maximum profit of the centralized strategy and bidirectional cost coordination mechanism system is 4350. In addition, under the two-way cost coordination mechanism, both retailers and suppliers have the highest profits, which are 1950 and 2352 respectively. In subsidy analysis, when the profit ratio is 0.5, the benefits of retailers and sellers reach equilibrium. In addition, bilateral coordination mechanisms have better system economic benefits, environmental benefits, and social welfare. This study is beneficial for improving carbon emissions from agricultural products and enhancing the effectiveness of agricultural product market development. This study provides technical support for agricultural emissions reduction and optimization of agricultural product supply chains.