Jiangxi University of Technology

The maritime hospitality sector, a critical component of the Merchant Navy, is currently navigating a digital transformation driven by the need for operational efficiency and enhanced crew welfare. This paper explores the AI-Integrated Flow of Functional Management, examining how Artificial Intelligence restructures traditional administrative and service frameworks at sea. By synthesizing data-driven automation with core functional areas—such as supply chain logistics, victualling management, human resources, and waste optimization—the study illustrates a shift from reactive to predictive management. The key areas include Predictive Inventory Control: Utilizing machine learning to forecast consumption patterns and reduce spoilage in global supply chains. Automated Crew Services: Implementing AI for personalized meal planning and digital concierge services to improve life-at-sea standards. Operational Sustainability: Leveraging smart systems to monitor and reduce the environmental footprint of hospitality operations in compliance with international maritime regulations. The findings suggest that integrating AI into the functional flow does not merely replace manual tasks but acts as a force multiplier, allowing hospitality managers to focus on high-value leadership and safety protocols. Ultimately, this integration establishes a new benchmark for operational resilience and cost-effectiveness in the modern maritime navy

To capture the small signal of smart sensor system, the high-precision sigma delta analog to digital converter (ΣΔ ADC) is the essential components. Based on the reinforcement learning (RL) algorithm, a high-efficiency design method for 16-bit ΣΔ ADC is proposed in this research. The key circuit modules in the ΣΔ ADC are extracted to be separately optimized to decrease the design complexity. The prior knowledge of circuits can be introduced into the search process of RL algorithm to accelerate the search speed. In addition, the deep neural network (DNN) models are trained to replace the circuit simulator to reduce the cost of simulation resource. Two key circuits in the ΣΔ ADC are optimized. Compared with the advanced method, the proposed optimization method has the fastest convergency and best performance, which demonstrates the effectiveness of the proposed method. In addition, the simulation time can be decreased by 98.3 %, which can greatly speed up the optimization process. The optimized ΣΔ ADC is taped out. The test results show that the effective resolution is 16.16 bit, and the voltage noise is 8.7e-05 V. Compared with the original chip, the effective resolution can be improved by 6.3%, and the noise voltage can be decreased by 47%. Therefore, the proposed optimization design method for ΣΔ ADC can greatly decrease the design cycle and improve the performance of ΣΔ ADC, which presents the enormous potential application in the internet of things.

For earth pressure balance (EPB) shield tunneling, the stability of tunnel face is controlled by the excavation and discharge rates of soil within tunnel pressure chamber. To ensure continuous discharge of soil from pressure chamber, the soil is required to have favorable plastic flow. However, the plastic flow of conditioned soil primarily relies on experience that lacks relevant theoretical guidance. Additionally, when EPB shields are used for tunneling in deeply buried sandy strata, common soil conditioners may struggle to make the conditioned soil have ideal plastic flow. By assuming the conditioned soil as an ideal Bingham fluid, a simplified soil slump model and a calculated model for passive soil discharge are developed in this study to assess the plastic flow of conditioned soil using yield stress and dynamic viscosity coefficient. By incorporating macromolecular polyacrylamide (PAM) along with fine particles, the conditioned soil could have favorable plastic flow at any slump. Based on the measured soil discharge rate from Hengli - Panyu Square Station of Guangzhou Metro Line 18, the ideal plastic flow and slump of conditioned soil under various tunnel burial depths are derived. The results indicate that as the tunnel burial depth increases, the yield stress of the conditioned soil must be systematically increased, and consequently, the slump must be reduced to ensure the integrity of the earth plug and anti-spewing safety. This study provides a systematic inversion methodology for determining optimal soil conditioning parameters based on burial depth, offering a theoretical framework and safety thresholds for the adaptive management of soil conditioning in deep-buried EPB shield tunneling.

ABSTRACT To address key challenges in photocatalytic CO 2 reduction for syngas production—including low catalyst activity, difficult product ratio control, and poor photogenerated charge separation efficiency, a one‐step molten salt strategy was utilized to synthesize Cu + ‐doped In 2 S 3 , which achieves photocatalytic CO 2 reduction to syngas with yields of CO:H 2 ≈ 1:1. The introduced Cu + ions create sulfur vacancies, synergistically boosting CO 2 adsorption, charge separation, and light‐harvesting. Importantly, density functional theory (DFT) calculations confirm that Cu + doping effectively reduces the formation energy barrier of the key * CO intermediate, providing a thermodynamic driving force for the selective reduction of CO 2 to CO. This effectively promotes CO 2 adsorption and activation while thermodynamically lowering the energy barrier for the CO 2 reduction reaction. This study elucidates the synergistic enhancement mechanism between Cu + and sulfur vacancies and provides a feasible strategy for developing solar‐driven photocatalysts for CO 2 reduction.

This paper investigates the coordination between logistics and policy decisions for electric vehicle (EV) exports under the Belt and Road Initiative. Focusing on the two modes—maritime shipping and the China Railway Express (CR Express)—along with government production subsidies, import tariffs, and service premium, a Stackelberg game model for a cross-border supply chain comprising a domestic manufacturer and an overseas retailer is constructed. The equilibrium outcomes under four scenarios formed by combining subsidy policies and transportation modes (Models NM, NR, GM and GR) are compared theoretically and numerically, with further evaluation of capacity constraints and power structures, as well as the robustness verification of the core findings. Results show that the CR Express mode exhibits a service-driven nonlinear cost pattern, where its service premium amplifies positive market responses. Its appeal to the manufacturer, however, is tightly constrained by fixed cost. Furthermore, government subsidies can overcome this barrier by synergizing with the service premium, turning the CR Express into a relatively advantageous strategy. Moreover, subsidy efficacy is conditional, depending heavily on the service premium level and logistics cost coefficient, leading to a proposed differentiated subsidy framework. This study offers a theoretical basis for corporate logistics strategy and targeted policy design.