Dry sliding wear behavior of an extruded Mg–Dy–Zn alloy with long period stacking ordered phase

AbstractThe dry sliding wear behavior of extruded Mg-2Dy-0.5Zn alloy (at.%) was investigated using a pin-on-disk configuration. The friction coefficient and wear rate were measured within a load range 20–760 N at a sliding velocity of 0.785 m/s. Microstructure and wear surface of alloy were examined using scanning electron microscopy. The mechanical properties of alloy were tested at room and elevated temperatures. Five wear mechanisms, namely abrasion, oxidation, delamination, thermal softening and melting dominated the whole wear behavior with increasing applied load. The extruded Mg-2Dy-0.5Zn alloy exhibited the better wear resistance as compared with as-cast Mg97Zn1Y2 alloy under the given conditions through contact surface temperature analysis. The improved wear resistance was mainly related to fine grain size, good thermal stability of long period stacking order (LPSO) phase and excellent higher-temperature mechanical properties

Diabetes mellitus promotes susceptibility to periodontitis—novel insight into the molecular mechanisms

Diabetes mellitus is a main risk factor for periodontitis, but until now, the underlying molecular mechanisms remain unclear. Diabetes can increase the pathogenicity of the periodontal microbiota and the inflammatory/host immune response of the periodontium. Hyperglycemia induces reactive oxygen species (ROS) production and enhances oxidative stress (OS), exacerbating periodontal tissue destruction. Furthermore, the alveolar bone resorption damage and the epigenetic changes in periodontal tissue induced by diabetes may also contribute to periodontitis. We will review the latest clinical data on the evidence of diabetes promoting the susceptibility of periodontitis from epidemiological, molecular mechanistic, and potential therapeutic targets and discuss the possible molecular mechanistic targets, focusing in particular on novel data on inflammatory/host immune response and OS. Understanding the intertwined pathogenesis of diabetes mellitus and periodontitis can explain the cross-interference between endocrine metabolic and inflammatory diseases better, provide a theoretical basis for new systemic holistic treatment, and promote interprofessional collaboration between endocrine physicians and dentists

A validated finite element model for predicting dynamic responses of cylinder liners in an IC engine*

Vibration of cylinder liners affects not only engine combustion performances but also tribological behaviour and noise radiations. However, it is difficult to characterize it experimentally due to multiple sources, strong background noise, and nonlinear transfer paths. Therefore, a finite element model is established in this study to predict the dynamic responses of cylinder liners under respective sources. The model takes into account both the characteristics of structural modes and nonlinearities of assembly constraints when selecting adequate elements for efficient computation of the responses under both the highly nonlinear combustion pressure excitations and subsequent piston slap impacts. The predictions are then evaluated against experimental results under different engine operating conditions. In addition, continuous wavelet analysis is employed to process the complicated responses for key response events and their frequency ranges. The results show agreeable correspondences between the numerical predictions and measured vibration signals, paving the way for investigating its effect on combustion and lubrication processes

A Comparative Study of Image Restoration Networks for General Backbone Network Design

Despite the significant progress made by deep models in various image restoration tasks, existing image restoration networks still face challenges in terms of task generality. An intuitive manifestation is that networks which excel in certain tasks often fail to deliver satisfactory results in others. To illustrate this point, we select five representative image restoration networks and conduct a comparative study on five classic image restoration tasks. First, we provide a detailed explanation of the characteristics of different image restoration tasks and backbone networks. Following this, we present the benchmark results and analyze the reasons behind the performance disparity of different models across various tasks. Drawing from this comparative study, we propose that a general image restoration backbone network needs to meet the functional requirements of diverse tasks. Based on this principle, we design a new general image restoration backbone network, X-Restormer. Extensive experiments demonstrate that X-Restormer possesses good task generality and achieves state-of-the-art performance across a variety of tasks

H-TSP: Hierarchically Solving the Large-Scale Travelling Salesman Problem

We propose an end-to-end learning framework based on hierarchical reinforcement learning, called H-TSP, for addressing the large-scale Travelling Salesman Problem (TSP). The proposed H-TSP constructs a solution of a TSP instance starting from the scratch relying on two components: the upper-level policy chooses a small subset of nodes (up to 200 in our experiment) from all nodes that are to be traversed, while the lower-level policy takes the chosen nodes as input and outputs a tour connecting them to the existing partial route (initially only containing the depot). After jointly training the upper-level and lower-level policies, our approach can directly generate solutions for the given TSP instances without relying on any time-consuming search procedures. To demonstrate effectiveness of the proposed approach, we have conducted extensive experiments on randomly generated TSP instances with different numbers of nodes. We show that H-TSP can achieve comparable results (gap 3.42% vs. 7.32%) as SOTA search-based approaches, and more importantly, we reduce the time consumption up to two orders of magnitude (3.32s vs. 395.85s). To the best of our knowledge, H-TSP is the first end-to-end deep reinforcement learning approach that can scale to TSP instances of up to 10000 nodes. Although there are still gaps to SOTA results with respect to solution quality, we believe that H-TSP will be useful for practical applications, particularly those that are time-sensitive e.g., on-call routing and ride hailing service.Comment: Accepted by AAAI 2023, February 202

Learning Personalized Story Evaluation

While large language models (LLMs) have shown impressive results for more objective tasks such as QA and retrieval, it remains nontrivial to evaluate their performance on open-ended text generation for reasons including (1) data contamination; (2) multi-dimensional evaluation criteria; and (3) subjectiveness stemming from reviewers' personal preferences. To address such issues, we propose to model personalization in an uncontaminated open-ended generation assessment. We create two new datasets Per-MPST and Per-DOC for personalized story evaluation, by re-purposing existing datasets with proper anonymization and new personalized labels. We further develop a personalized story evaluation model PERSE to infer reviewer preferences and provide a personalized evaluation. Specifically, given a few exemplary reviews from a particular reviewer, PERSE predicts either a detailed review or fine-grained comparison in several aspects (such as interestingness and surprise) for that reviewer on a new text input. Experimental results show that PERSE outperforms GPT-4 by 15.8% on Kendall correlation of story ratings, and by 13.7% on pairwise preference prediction accuracy. Both datasets and code will be released.Comment: 19 page