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The mud film thickness on the outer surface of casing and its distribution laws are important factors affecting the cementation quality at casing–cement interfaces. So far, however, the distribution laws of mud film thickness on the outer surface of casing at different inclinations and its effect on the interfacial cementing strength have never been reported in literatures. In this paper, the mud film thickness at different parts on the outer surface of simulated casing (steel pipe) was measured at the inclination of 0°, 30°, 60° and 86°, respectively, using the independently designed experimental device. Then, the distribution law of mud film thickness on the outer surface of casing and its effect on the interfacial cementing strength were summarized. And the following research results were obtained. First, when the inclination increases from 0° to 86°, the mud film thickness changes from uniform distribution to thin-and-thick distribution, and the area proportions of thinner and thicker mud films both increase. And especially at the inclination of 86°, the mud film thickness on the upper surface is very large, and there are large exposed areas on the left and right surfaces of the steel pipe. Second, the cementing strength at the casing–cement interface of the exposed casing is much higher than that at the interface with mud film. And with the increase of inclination, the interfacial cementing strength with mud film tends to increase. Third, with the increase of inclination, the interfacial cementing strength increases successively, which is closely related to the area proportion of the exposed steel pipe to the solid mud film on the outer surface. In conclusion, the exposed area of the simulated casing on the outer surface is a key factor affecting its interfacial cementing strength. Keywords: Casing–cement interface, Inclination, Mud film thickness, Distribution law, Cementing strength, Exposed outer surface of casing

One-dimensional (1D) chain models are frequently employed to investigate heat conduction in various systems. Despite their widespread use, there has been limited exploration of 1D chain models with anisotropic interactions. In this study, we examine heat conduction in a 1D chain model with orientation–position coupled interaction, namely the compressible XY model, and compare it with isotropic Fermi-Pasta-Ulam-Tsingou β (FPUT- β ) systems. At low temperatures, the local temperatures in the translational and rotational degrees of freedom differ due to the difference in the contact thermal resistance in the two degrees of freedom. The system maintains orientational order, and the orientation–position-coupling effect lowers thermal conductivity in translational degrees of freedom. As the temperature rises above a transition point, the rotation of particles switches from oscillation to diffusion, leading to a solid-like to fluid-like transition in the rotational degree of freedom. The anisotropic interactions become negligible under time averaging, making the system’s properties similar to those of isotropic FPUT- β systems. Additionally, we investigate the system’s orientational structure to elucidate this transition. These findings will enhance our understanding of the dynamics of nanoscale anisotropic systems, such as the heat conduction of spin chains.

Electroactive anticorrosion coatings are specialized surface treatments that prevent or minimize corrosion. The study employs strategic thermodynamic equilibriumcalculations to pioneer a novel factor in corrosion protection. A first-time proposal, the total acidity (TA) potential of the hydrogen (pH) concept significantly shapes medical magnesium alloys. These coatings are meticulously designed for robust corrosion resistance, blending theoretical insights and practical applications to enhance our grasp of corrosion prevention mechanisms and establish a systematic approach to coating design. The groundbreaking significance of this study lies in its innovative integration of the TA/pH concept, which encompasses the TA/pH ratio of the chemical environment. This approach surpasses convention by acknowledging the intricate interplay between the acidity and pH levels within the coating formulation, thereby optimizing metal-phosphate-based conversion coatings and transforming corrosion mitigation strategies. To authenticate the TA/pH concept, the study comprehensively compares its findings with existing research, rigorously validating the theoretical framework and reinforcing the correlates among TA/pH values and observed corrosion resistance in the coatings. The influence of mutations that occur naturally in the detergent solution on persistent phosphorus changes is shown by empirical confirmation, which improves corrosion resistance. This realization advances the field of materials and the field's knowledge of coated generation, particularly anticorrosion converter layers. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning has strong perception and decision-making capabilities that can effectively solve the problem of continuous high-dimensional state-action space and has become the mainstream method in the field of traffic light timing. However, due to model structural defects or different strategic mechanisms of models, most deep reinforcement learning models have problems such as convergence and divergence or poor exploration capabilities. Therefore, this paper proposes a multi-agent Soft Actor–Critic (SAC) for traffic light timing. Multi-agent SAC adds an entropy item to measure the randomness of the strategy in the objective function of traditional reinforcement learning and maximizes the sum of expected reward and entropy item to improve the model’s exploration ability. The system model can learn multiple optimal timing schemes, avoid repeated selection of the same optimal timing scheme and fall into a local optimum or fail to converge. Meanwhile, it abandons low reward value strategies to reduce data storage and sampling complexity, accelerate training, and improve the stability of the system. Comparative experiments show that the method based on multi-agent SAC traffic light timing can solve the existing problems of deep reinforcement learning and improve the efficiency of vehicles passing through in different traffic scenarios. Practical Applications: This paper is devoted to research on the timing method of traffic lights at multiple intersections. The experimental results show that the method proposed in this paper can effectively improve the throughput of each intersection, the waiting time of vehicles, and the number of queued vehicles. In comparison with related algorithms, it is fully proven that the method proposed in this paper can effectively solve the problems pervasive in existing algorithms. In the actual application process, the traffic status information is obtained via interactions with real traffic environment, and the timing scheme of the traffic lights is dynamically adjusted according to the information, so as to achieve the effect of alleviating traffic congestion at the intersection. [ABSTRACT FROM AUTHOR]