Constitutive modeling the elasto-viscoplastic behavior of Regina clay soil under the drained stress path condition

The stress–strain behavior of soft clay soil is dependent on the applied strain rate and such time dependency behavior should be considered in geotechnical engineering projects such as the bearing capacity analysis for a foundation. Most previous studies focus on elastic-viscoplastic behavior of clay soil under an undrained stress path condition. The developed constitutive models cannot simulate the strong time-dependent stress-strain relation of soft clay under the drained stress path condition. In the long-term bearing capacity analysis for a shallow foundation, the neglect of the time-dependent stress-strain relation of soft clay may result in inaccurate result. In this study, the strain-rate-dependent soil behavior is modeled using the Drucker-Prager/Cap model with the consideration of soil creep behavior. Both consolidation creep and shear creep mechanisms are considered in the modeling. A simplified approach is proposed to derive creep parameters from stress relaxation test results. The constitutive modeling is validated against experimental measurements on strain-rate-dependent behavior of Regina clay soil under the drained stress path condition. Finite element modeling on the long-term bearing capacity of a shallow foundation on soft clay is also performed. The result shows that the considering of viscoplastic behavior of clay soils does not affect the ultimate bearing capacity. However, it yields the lower bound of developed shear stress of a shallow foundation at a given vertical displacement, which is to be conservative from the engineering point of view

Risk-aware Safe Control for Decentralized Multi-agent Systems via Dynamic Responsibility Allocation

Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agents to determine how to efficiently interact with others. In this paper, we present a risk-aware decentralized control framework that provides guidance on how much relative responsibility share (a percentage) an individual agent should take to avoid collisions with others while moving efficiently without direct communications. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We use this measurement to allocate responsibility shares among agents dynamically and develop risk-aware decentralized safe controllers. In this way, we are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety. We demonstrate the validity and efficiency of our proposed approach through two examples: ramp merging in autonomous driving and a multi-agent position-swapping game

Using Jackknife to Assess the Quality of Gene Order Phylogenies

Background In recent years, gene order data has attracted increasing attention from both biologists and computer scientists as a new type of data for phylogenetic analysis. If gene orders are viewed as one character with a large number of states, traditional bootstrap procedures cannot be applied. Researchers began to use a jackknife resampling method to assess the quality of gene order phylogenies. Results In this paper, we design and conduct a set of experiments to validate the performance of this jackknife procedure and provide discussions on how to conduct it properly. Our results show that jackknife is very useful to determine the confidence level of a phylogeny obtained from gene orders and a jackknife rate of 40% should be used. However, although a branch with support value of 85% can be trusted, low support branches require careful investigation before being discarded. Conclusions Our experiments show that jackknife is indeed necessary and useful for gene order data, yet some caution should be taken when the results are interpreted

Strategies for overcoming bottlenecks in allogeneic CAR-T cell therapy

Patient-derived autologous chimeric antigen receptor (CAR)-T cell therapy is a revolutionary breakthrough in immunotherapy and has made impressive progress in both preclinical and clinical studies. However, autologous CAR-T cells still have notable drawbacks in clinical manufacture, such as long production time, variable cell potency and possible manufacturing failures. Allogeneic CAR-T cell therapy is significantly superior to autologous CAR-T cell therapy in these aspects. The use of allogeneic CAR-T cell therapy may provide simplified manufacturing process and allow the creation of ‘off-the-shelf’ products, facilitating the treatments of various types of tumors at less delivery time. Nevertheless, severe graft-versus-host disease (GvHD) or host-mediated allorejection may occur in the allogeneic setting, implying that addressing these two critical issues is urgent for the clinical application of allogeneic CAR-T cell therapy. In this review, we summarize the current approaches to overcome GvHD and host rejection, which empower allogeneic CAR-T cell therapy with a broader future

Thermoacoustic heat pump utilizing medium/low-grade heat sources for domestic building heating

Thermoacoustic heat pumps are a promising heating technology that utilizes medium/low-grade heat to reduce reliance on electricity. This study proposes a single direct-coupled configuration for a thermoacoustic heat pump, aimed at minimizing system complexity and making it suitable for domestic applications. Numerical investigations were conducted under typical household heating conditions, including performance analysis, exergy loss evaluation, and axial distribution of key parameters. Results show that the proposed thermoacoustic heat pump achieves a heating capacity of 5.7 kW and a coefficient of performance of 1.4, with a heating temperature of 300 °C and a heat-sink temperature of 55 °C. A comparison with existing absorption heat pumps reveals favorable adaptability for large temperature lift applications. A case study conducted in Finland over an annual cycle analyzes the economic and environmental performance of the system, identifying two distinct modes based on the driving heat source: medium temperature (≥250 °C) and low temperature (<250 °C), both of which exhibit favorable heating performance. When the thermoacoustic heat pump is driven by waste heat, energy savings of 20.1 MWh/year, emission reductions of 4143 kgCO$_2$/year, and total environmental cost savings of 1629 €/year are obtained. These results demonstrate the potential of the proposed thermoacoustic heat pump as a cost-effective and environmentally friendly option for domestic building heating using medium/low-grade heat sources

3D-STMN: Dependency-Driven Superpoint-Text Matching Network for End-to-End 3D Referring Expression Segmentation

In 3D Referring Expression Segmentation (3D-RES), the earlier approach adopts a two-stage paradigm, extracting segmentation proposals and then matching them with referring expressions. However, this conventional paradigm encounters significant challenges, most notably in terms of the generation of lackluster initial proposals and a pronounced deceleration in inference speed. Recognizing these limitations, we introduce an innovative end-to-end Superpoint-Text Matching Network (3D-STMN) that is enriched by dependency-driven insights. One of the keystones of our model is the Superpoint-Text Matching (STM) mechanism. Unlike traditional methods that navigate through instance proposals, STM directly correlates linguistic indications with their respective superpoints, clusters of semantically related points. This architectural decision empowers our model to efficiently harness cross-modal semantic relationships, primarily leveraging densely annotated superpoint-text pairs, as opposed to the more sparse instance-text pairs. In pursuit of enhancing the role of text in guiding the segmentation process, we further incorporate the Dependency-Driven Interaction (DDI) module to deepen the network's semantic comprehension of referring expressions. Using the dependency trees as a beacon, this module discerns the intricate relationships between primary terms and their associated descriptors in expressions, thereby elevating both the localization and segmentation capacities of our model. Comprehensive experiments on the ScanRefer benchmark reveal that our model not only set new performance standards, registering an mIoU gain of 11.7 points but also achieve a staggering enhancement in inference speed, surpassing traditional methods by 95.7 times. The code and models are available at https://github.com/sosppxo/3D-STMN