Analytical solution for dynamic response of segment lining subjected to explosive loads

The existence of various types of joints, one of the typical characteristics of prefabricated lining structures, makes the mechanical performance of shield tunnel linings quite different from that of monolithic linings. A simplified calculation method for the dynamic elastic-plastic analysis of segment lining subjected to explosive loads is proposed. The lining is composed of a number of rigid arch segments that are interconnected by elastic-plastic hinges. The dynamic interaction between the segments and the bolts, and the interaction between tunnel lining segment and soil-structure can be properly simulated with the method. As an example, the calculation of the shield section of Nanjing metro subjected to blast loading was discussed. The time-history curves of displacement and speed of some key points of section lining were obtained. Furthermore, the influences of rock grade and joint stiffness on dynamic response of tunnel lining were taken into account. The result indicates that the simplified method of blasting response analysis can reflect the response of structure subjected to blast loading accurately. The results will be a reference for antiknock analysis and design of tunnel lining

Explainable Intelligent Fault Diagnosis for Nonlinear Dynamic Systems: From Unsupervised to Supervised Learning

The increased complexity and intelligence of automation systems require the development of intelligent fault diagnosis (IFD) methodologies. By relying on the concept of a suspected space, this study develops explainable data-driven IFD approaches for nonlinear dynamic systems. More specifically, we parameterize nonlinear systems through a generalized kernel representation for system modeling and the associated fault diagnosis. An important result obtained is a unified form of kernel representations, applicable to both unsupervised and supervised learning. More importantly, through a rigorous theoretical analysis, we discover the existence of a bridge (i.e., a bijective mapping) between some supervised and unsupervised learning-based entities. Notably, the designed IFD approaches achieve the same performance with the use of this bridge. In order to have a better understanding of the results obtained, both unsupervised and supervised neural networks are chosen as the learning tools to identify the generalized kernel representations and design the IFD schemes; an invertible neural network is then employed to build the bridge between them. This article is a perspective article, whose contribution lies in proposing and formalizing the fundamental concepts for explainable intelligent learning methods, contributing to system modeling and data-driven IFD designs for nonlinear dynamic systems

Layer assignment and routing optimization for advanced technologies

As VLSI technology scales to deep sub-micron and beyond, it becomes increasingly challenging to achieve timing closure for VLSI design. Since a complete design flow consists of several phases, such as logic synthesis, placement, and routing, interconnect synthesis plays an important role which includes buffer insertion/sizing and timing-driven routing. Although progress has been achieved by many advanced routing techniques, the following aspects can be exploited sufficiently for further improvement: (1) incremental layer assignment for timing optimization; (2) signal routing with the requirement of regularity; (3) power-efficient optical-electrical interconnect paradigm. Thus, to perform the layer assignment and routing optimization for advanced technologies, an automated routing engine in a global view is essential to benefit the interconnect design while satisfying specific requirements. This dissertation proposes a set of algorithms and methodology on layer assignment and routing optimization for advanced technologies. The research includes two timing-driven incremental layer assignment approaches, synergistic topology generation and routing synthesis for signal groups, and optical-electrical routing design for power efficiency. For incremental layer assignment, most of the conventional approaches target via minimization but neglect the timing issues. Meanwhile, via delays are ignored but should be considered in emerging technology nodes. Then two timing-driven incremental layer assignment frameworks are proposed, where all the nets are solved simultaneously with the integration of via delays: (1) optimization of the total sum of net delays and reduction of slew violations; (2) minimization of critical path timing in selected nets. For on-chip signal routing, the bundled bits in one group may have different pin locations, but they have to be routed in a regular manner by sharing common topologies. Very few previous works target inter-bit regularity via multi-layer topology selection. Furthermore, the routability and wire-length of the signal bits should also be optimized. Then an advanced synergistic routing engine is promoted, which is able to not only control routability and wire-length but also guide each bit routing intelligently for design regularity. For optical-electrical co-design routing, optical interconnect shows its advantage due to the dominance of bandwidth-distance-power properties. The previous works lack a detailed exploration of optical-electrical co-design for on-chip interconnects. During the transmission, signal quality can be affected by various loss sources and Electrical to Optical (EO)/Optical to Electrical (OE) conversion overheads should also be considered. Then a power-efficient routing flow for on-chip signals is presented, where optical connections can collaborate with electrical wires seamlessly. The effectiveness of proposed algorithms and techniques is demonstrated in this dissertation. These approaches are able to achieve the improvements regarding specific metrics and eventually benefit the routing flow.Electrical and Computer Engineerin

Analytical solution for dynamic response of segment lining subjected to explosive loads

The existence of various types of joints, one of the typical characteristics of prefabricated lining structures, makes the mechanical performance of shield tunnel linings quite different from that of monolithic linings. A simplified calculation method for the dynamic elastic-plastic analysis of segment lining subjected to explosive loads is proposed. The lining is composed of a number of rigid arch segments that are interconnected by elastic-plastic hinges. The dynamic interaction between the segments and the bolts, and the interaction between tunnel lining segment and soil-structure can be properly simulated with the method. As an example, the calculation of the shield section of Nanjing metro subjected to blast loading was discussed. The time-history curves of displacement and speed of some key points of section lining were obtained. Furthermore, the influences of rock grade and joint stiffness on dynamic response of tunnel lining were taken into account. The result indicates that the simplified method of blasting response analysis can reflect the response of structure subjected to blast loading accurately. The results will be a reference for antiknock analysis and design of tunnel lining

MOELoRA: An MOE-based Parameter Efficient Fine-Tuning Method for Multi-task Medical Applications

The recent surge in the field of Large Language Models (LLMs) has gained significant attention in numerous domains. In order to tailor an LLM to a specific domain such as a web-based healthcare system, fine-tuning with domain knowledge is necessary. However, two issues arise during fine-tuning LLMs for medical applications. The first is the problem of task variety, where there are numerous distinct tasks in real-world medical scenarios. This diversity often results in suboptimal fine-tuning due to data imbalance and seesawing problems. Additionally, the high cost of fine-tuning can be prohibitive, impeding the application of LLMs. The large number of parameters in LLMs results in enormous time and computational consumption during fine-tuning, which is difficult to justify. To address these two issues simultaneously, we propose a novel parameter-efficient fine-tuning framework for multi-task medical applications called MOELoRA. The framework aims to capitalize on the benefits of both MOE for multi-task learning and LoRA for parameter-efficient fine-tuning. To unify MOE and LoRA, we devise multiple experts as the trainable parameters, where each expert consists of a pair of low-rank matrices to maintain a small number of trainable parameters. Additionally, we propose a task-motivated gate function for all MOELoRA layers that can regulate the contributions of each expert and generate distinct parameters for various tasks. To validate the effectiveness and practicality of the proposed method, we conducted comprehensive experiments on a public multi-task Chinese medical dataset. The experimental results demonstrate that MOELoRA outperforms existing parameter-efficient fine-tuning methods. The implementation is available online for convenient reproduction of our experiments

Plant-Mediated RNAi for Controlling Apolygus lucorum

The polyphagous mirid bug Apolygus lucorum (Heteroptera: Miridae) is a serious pest of agricultural crops in China, with more than 200 species of host plants including two very important crops, maize and soybean. Currently, prevention and control of A. lucorum rely mainly on chemical pesticides that cause environmental as well as health related problems. Plant-mediated RNAi has proven to offer great potential for pest control in the past decade. In this study, we screened and obtained seven candidate genes (Alucβ-actin, AlucV-ATPase-A/D/E, AlucEif5A, AlucEcR-A, AlucIAP) by injection-based RNAi which produced A. lucorum mortality rates of 46.01–82.32% at day 7 after injection. Among them, the plant-mediated RNAi of AlucV-ATPase-E was successfully introduced into transgenic maize and soybean, and the populations of A. lucorum were significantly decreased following feeding on the transgenic maize and soybean. These results are intended to provide helpful insight into the generation of bug-resistant plants through a plant-mediated RNAi strategy