The effect of asymmetry of the coil block on self-assembly in ABC coil-rod-coil triblock copolymers

Using the self-consistent field approach, the effect of asymmetry of the coil block on the microphase separation is focused in ABC coil-rod-coil triblock copolymers. For different fractions of the rod block $f_{\text B}$, some stable structures are observed, i.e., lamellae, cylinders, gyroid, and core-shell hexagonal lattice, and the phase diagrams are constructed. The calculated results show that the effect of the coil block fraction $f_{\text A}$ is dependent on $f_{\text B}$. When $f_{\text B}=0.2$, the effect of asymmetry of the coil block is similar to that of the ABC flexible triblock copolymers; When $f_{\text B}=0.4$, the self-assembly of ABC coil-rod-coil triblock copolymers behaves like rod-coil diblock copolymers under some condition. When $f_{\text B}$ continues to increase, the effect of asymmetry of the coil block reduces. For $f_{\text B}=0.4$, under the symmetrical and rather asymmetrical conditions, an increase in the interaction parameter between different components leads to different transitions between cylinders and lamellae. The results indicate some remarkable effect of the chain architecture on self-assembly, and can provide the guidance for the design and synthesis of copolymer materials.Comment: 9 pages, 3 figure

NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explicitly limit the probability of collision to an acceptable bound in uncertain environments. However, convex obstacles or Gaussian uncertainties are usually assumed to make the problem tractable in the existing method. These assumptions limit the generalization and application of path planners in real-world implementations. In this article, we propose to apply deep learning methods to the sampling-based planner, developing a novel risk bounded near-optimal path planning algorithm named neural risk-aware RRT (NR-RRT). Specifically, a deterministic risk contours map is maintained by perceiving the probabilistic nonconvex obstacles, and a neural network sampler is proposed to predict the next most-promising safe state. Furthermore, the recursive divide-and-conquer planning and bidirectional search strategies are used to accelerate the convergence to a near-optimal solution with guaranteed bounded risk. Worst-case theoretical guarantees can also be proven owing to a standby safety guaranteed planner utilizing a uniform sampling distribution. Simulation experiments demonstrate that the proposed algorithm outperforms the state-of-the-art remarkably for finding risk bounded low-cost paths in seen and unseen environments with uncertainty and nonconvex constraints

Localization of Fusobacterium nucleatum in oral squamous cell carcinoma and its possible directly interacting protein molecules: A case series

Introduction. While 15 to 20% of cancers are associated with microbial infection, the relationship between oral microorganisms and oral squamous cell carcinoma (OSCC) remains unclear. The location of bacteria in a tumor is closely related to its carcinogenic mechanism. The aim of this study was to analyse bacterial diversity in clinical OSCC tissue samples and tumor distant normal tissues, locate target bacteria, and search for proteins that may interact with target bacteria. Materials and Methods. The 16S rDNA method was used to analyse bacterial diversity in clinical OSCC tissue samples and tumor distant normal tissues. Correlations between Fusobacterium abundance and clinicopathological characteristics were analysed using the χ2 test. The position of target bacteria was analysed by fluorescence in situ hybridization (FISH), and the expression of CK, CD31, CD45, CD68, cyclin D1, βcatenin, E-cadherin, NF-κB, and HIF-1α was analysed by immunohistochemistry (IHC) in OSCC tumor tissues and tumor distant normal tissues. Results. The 16S rDNA results showed that the detected amount of Fusobacterium in OSCC tumor tissues was significantly larger than that in tumor distant normal tissues. High expression of Fusobacterium was significantly correlated with the lifestyle-related oral risk habits, including smoking (p=0.036) and alcohol consumption (p=0.022), but did not correlate with patient sex, age, tumor laterality, tumor size, grade or TNM stage. Fusobacterium nucleatum was enriched in tumor stroma, where CD31+ blood vessels and inflammatory cells (including CD45+ leukocytes and CD68+ macrophages) were densely distributed. Cyclin D1 was mainly expressed in the nucleus of tumor cells. β-catenin was expressed in the tumor cell membrane and was positively expressed in tumor interstitial vascular endothelial cells. E-cadherin was mainly expressed in tumor cell membranes. NF-κB was positively expressed in the cytoplasm of tumor cells, tumor interstitial cells and myo-fibrocytes. HIF-1α was mainly expressed in the cytoplasm of tumor interstitial cells. HIF-1α was highly expressed where Fusobacterium nucleatum was densely distributed. Conclusion. According to our study, the detected amount of Fusobacterium in OSCC tumor tissues was significantly larger than that in tumor distant normal tissues, and Fusobacterium nucleatum might aggravate inflammation and hypoxia by interacting with NF-κB and HIF-1α in OSCC

Effect of Freeze-Thaw Cycle on Shear Strength of Lime-Solidified Dispersion Soils

The freeze-thaw cycle of saline soil in the seasonal frozen area will produce diseases such as frost heave and thaw settlement, road frost boiling, collapse and uneven settlement. In order to reduce the occurrence of these undesirable phenomena, it is often necessary to improve the saline soil in engineering. In this paper, the typical carbonate saline soil in the west of Jilin Province, China is taken as the research object. By adding different content of lime (0%, 3%, 6%, 9%, 12%, 15%), the change of mechanical strength of lime solidified saline soil under different freeze-thaw cycles (0, 1, 3, 6, 10, 30, 60 times) is studied. The mechanical analysis is carried out by combining particle size analysis test and SEM image. The test results show that although repeated freeze-thaw cycles make the soil structure loose and the mechanical strength greatly reduced, the soil particles agglomerate obviously after adding lime, its dispersion is restrained by the flocculation of clay colloid, and the shear strength of soil is improved by the increase of the cohesive force between clay particles, and the optimal lime mixing ratio of the saline soil in this area is 9%

Deep Reinforcement Learning-driven Cross-Community Energy Interaction Optimal Scheduling

In order to coordinate energy interactions among various communities and energy conversions among multi-energy subsystems within the multi-community integrated energy system under uncertain conditions, and achieve overall optimization and scheduling of the comprehensive energy system, this paper proposes a comprehensive scheduling model that utilizes a multi-agent deep reinforcement learning algorithm to learn load characteristics of different communities and make decisions based on this knowledge. In this model, the scheduling problem of the integrated energy system is transformed into a Markov decision process and solved using a data-driven deep reinforcement learning algorithm, which avoids the need for modeling complex energy coupling relationships between multi-communities and multi-energy subsystems. The simulation results show that the proposed method effectively captures the load characteristics of different communities and utilizes their complementary features to coordinate reasonable energy interactions among them. This leads to a reduction in wind curtailment rate from 16.3% to 0% and lowers the overall operating cost by 5445.6 Yuan, demonstrating significant economic and environmental benefits.Comment: in Chinese language, Accepted by Electric Power Constructio

Enhance Connectivity of Promising Regions for Sampling-based Path Planning

Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many scenarios, such as the environment with a few dead ends. Our previous work proposes to use the promising region to guide the sampling process to address the issue. However, the predicted promising regions are often disconnected, which means they cannot connect the start and goal state, resulting in a lack of probabilistic completeness. This work focuses on enhancing the connectivity of predicted promising regions. Our proposed method regresses the connectivity probability of the edges in the x and y directions. In addition, it calculates the weight of the promising edges in loss to guide the neural network to pay more attention to the connectivity of the promising regions. We conduct a series of simulation experiments, and the results show that the connectivity of promising regions improves significantly. Furthermore, we analyze the effect of connectivity on sampling-based path planning algorithms and conclude that connectivity plays an essential role in maintaining algorithm performance.Comment: Accepted in Transactions on Automation Science and Engineering, 202