Shear banding in monodisperse polymer melt

We performed a series of molecular dynamics simulations on monodisperse polymer melts to investigate the formation of shear banding. Under high shear rates, shear banding occurs, which is accompanied with the entanglement heterogeneity intimately. Interestingly, the same linear relationship between the end-to-end distance $R_{ee}$ and entanglement density $Z$ is observed at homogeneous flow before the onset of shear banding and at shear banding state, where $R_{ee} \sim [ln(W_i^{0.87})- \xi_0]Z$ is proposed as the criterion to describe the dynamic force balance of molecular chain in flow with a high rate. We establish a scaling relation between the disentanglement rate $V_d$ and Weissenberg number $W_i$ as $V_d \sim W_i^{0.87}$ for stable flow in homogeneous shear and shear banding states. Deviating from this relation leads to force imbalance and results in the emergence of shear banding. The formation of shear banding prevents chain from further stretching and disentanglement. The transition from homogeneous shear to shear banding partially dissipates the increased free energy from shear and reduces the free energy of the system

Numerical calculation of free-energy barriers for entangled polymer nucleation.

The crystallization of entangled polymers from their melt is investigated using computer simulation with a coarse-grained model. Using hybrid Monte Carlo simulations enables us to probe the behavior of long polymer chains. We identify solid-like beads with a centrosymmetry local order parameter and compute the nucleation free-energy barrier at relatively high supercooling with adaptive-bias windowed umbrella sampling. Our results demonstrate that the critical nucleus sizes and the heights of free-energy barriers do not significantly depend on the molecular weight of the polymer; however, the nucleation rate decreases with the increase in molecular weight. Moreover, an analysis of the composition of the critical nucleus suggests that intra-molecular growth of the nucleated cluster does not contribute significantly to crystallization for this system.National Key R&D Program of China (2016YFB0302500); National Natural Science Foundation of China (51633009); Royal Society Newton Mobility Grant (MBAG/240 RG82754

Dynamic changes in habitat quality and the driving mechanism in the Luoxiao Mountains area from 1995 to 2020

The strengthening of regional habitat quality is crucial to protect biodiversity and fully utilize ecosystem services such as those provided by forestry and aquatic ecosystems. However, the long-term patterns of change in the habitat quality of the Luoxiao Mountains area, which is both an important ecological barrier area and a concentrated poverty-stricken area, and the driving mechanism remain unclear. In this study, the InVEST model was used to assess the habitat quality of the Luoxiao Mountains area in 1995 to 2020, and the spatial autocorrelation model was used to explore the spatial and temporal variation and distribution characteristics of habitat quality. Further, ordinary least squares (OLS) model, geographically weighted regression (GWR) model, and random forest (RF) algorithm were combined with multidimensional datasets to explore the underlying mechanisms driving changes in habitat quality. According to the results, the habitat quality indices of the Luoxiao Mountains area in 1995, 2005, 2015, and 2020 were 0.822, 0.818, 0.817, and 0.813, respectively, with an overall decreasing trend. The RF model was the best fit for habitat quality, better than the GWR and OLS models. Physical geographic factors such as slope and precipitation, as well as socioeconomic factors such as gross domestic product, were key drivers of habitat quality in the Luoxiao Mountains. Precise implementation of ecological protection and restoration measures, improvements in the efficiency of spatial utilization, and exploration of the value of ecological products are key factors in maintaining a balance between habitat quality and economic growth into the future.publishedVersio

Multiscale Latent-Guided Entropy Model for LiDAR Point Cloud Compression

The non-uniform distribution and extremely sparse nature of the LiDAR point cloud (LPC) bring significant challenges to its high-efficient compression. This paper proposes a novel end-to-end, fully-factorized deep framework that encodes the original LPC into an octree structure and hierarchically decomposes the octree entropy model in layers. The proposed framework utilizes a hierarchical latent variable as side information to encapsulate the sibling and ancestor dependence, which provides sufficient context information for the modelling of point cloud distribution while enabling the parallel encoding and decoding of octree nodes in the same layer. Besides, we propose a residual coding framework for the compression of the latent variable, which explores the spatial correlation of each layer by progressive downsampling, and model the corresponding residual with a fully-factorized entropy model. Furthermore, we propose soft addition and subtraction for residual coding to improve network flexibility. The comprehensive experiment results on the LiDAR benchmark SemanticKITTI and MPEG-specified dataset Ford demonstrates that our proposed framework achieves state-of-the-art performance among all the previous LPC frameworks. Besides, our end-to-end, fully-factorized framework is proved by experiment to be high-parallelized and time-efficient and saves more than 99.8% of decoding time compared to previous state-of-the-art methods on LPC compression

Effectiveness of decision support tools on reducing antibiotic use for respiratory tract infections: a systematic review and meta-analysis

Background: Clinical decision support tools (CDSs) have been demonstrated to enhance the accuracy of antibiotic prescribing among physicians. However, their effectiveness in reducing inappropriate antibiotic use for respiratory tract infections (RTI) is controversial.Methods: A literature search in 3 international databases (Medline, Web of science and Embase) was conducted before 31 May 2023. Relative risk (RR) and corresponding 95% confidence intervals (CI) were pooled to evaluate the effectiveness of intervention. Summary effect sizes were calculated using a random-effects model due to the expected heterogeneity (I2 over 50%).Results: A total of 11 cluster randomized clinical trials (RCTs) and 5 before-after studies were included in this meta-analysis, involving 900,804 patients met full inclusion criteria. Among these studies, 11 reported positive effects, 1 reported negative results, and 4 reported non-significant findings. Overall, the pooled effect size revealed that CDSs significantly reduced antibiotic use for RTIs (RR = 0.90, 95% CI = 0.85 to 0.95, I2 = 96.10%). Subgroup analysis indicated that the intervention duration may serve as a potential source of heterogeneity. Studies with interventions duration more than 2 years were found to have non-significant effects (RR = 1.00, 95% CI = 0.96 to 1.04, I2 = 0.00%). Egger’s test results indicated no evidence of potential publication bias (p = 0.287).Conclusion: This study suggests that CDSs effectively reduce inappropriate antibiotic use for RTIs among physicians. However, subgroup analysis revealed that interventions lasting more than 2 years did not yield significant effects. These findings highlight the importance of considering intervention duration when implementing CDSs.Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42023432584, Identifier: PROSPERO (CRD42023432584)

Dynamic separation minima prediction with collision risk modelling (CRM)

In this paper, we modelled the geometry between 2 proximate aircraft as an oblate-spheroid and obtained a collision risk model based on collision probability. The methodology entails translating the communication, navigation and surveillance error characteristics, and wind uncertainty into the spatial domain of spheroid. Furthermore, we used the collision probability to design a dynamic separation minima based on the parameters of the oblate-spheroid geometry. The results showed that by varying the parameters of the spheroid, allows for a dynamic setting of the separation minima. The collision probability was compared to Monte Carlo simulations as a baseline model. Therefore we proposed a dynamic configuration of the separation minima between aircraft as a function of the collaborative geometry to increase the airspace capacity, especially with great demand from unmanned operations