Workspace Analysis of a Reconfigurable Mechanism Generated from the Network of Bennett Linkages

In this paper, a workspace triangle is introduced to evaluate the workspace of a reconfigurable mechanism generated from the network of Bennett linkages. Three evaluation indexes of workspace including movement locus of the joint, surface swept by the link and helical tube enveloped by the workspace triangle have been discussed. The comparison between the workspace of the reconfigurable mechanism and the sum of five resultant 5 R /6 R linkages including generalized Goldberg 5 R linkage, generalized variant of the L -shape Goldberg 6 R linkage, Waldron’s hybrid 6 R linkage, isomerized generalized L -shape Goldberg 6 R linkage and generalized Wohlhart’s double-Goldberg 6 R linkage is accomplished by using the evaluation indexes and mapping the workspace to the joint space which is defined by a vector whose components are joint variables

A Bilevel Optimization Method for Inverse Mean-Field Games

In this paper, we introduce a bilevel optimization framework for addressing inverse mean-field games, alongside an exploration of numerical methods tailored for this bilevel problem. The primary benefit of our bilevel formulation lies in maintaining the convexity of the objective function and the linearity of constraints in the forward problem. Our paper focuses on inverse mean-field games characterized by unknown obstacles and metrics. We show numerical stability for these two types of inverse problems. More importantly, we, for the first time, establish the identifiability of the inverse mean-field game with unknown obstacles via the solution of the resultant bilevel problem. The bilevel approach enables us to employ an alternating gradient-based optimization algorithm with a provable convergence guarantee. To validate the effectiveness of our methods in solving the inverse problems, we have designed comprehensive numerical experiments, providing empirical evidence of its efficacy.Comment: 35 pages, 8 figures, 4 table

Post-thrombolysis hemorrhage in a patient with hypothyroidism and acute ischemic stroke: Case report

Thrombolytic treatment with intravenous recombinant tissue plasminogen activator (rtPA) is an effective treatment for acute ischemic stroke. However, its effectiveness and risks in patients with hypothyroidism have not been reported. Here, we report the case of hemorrhagic transformation after intravenous rtPA thrombolysis treatment in a patient with acute ischemic stroke and hypothyroidism. An apparent edema formed around the hematoma and progressively worsened. He also developed lung infection, electrolyte imbalance, and abnormal liver and kidney functions, and eventually died within 1 month of symptom onset. Thus, our observations suggest that caution should be exercised for the administration of intravenous rtPA thrombolysis to patients with hypothyroidism

Reconfigurable mechanism generated from the network of Bennett linkages

A network of four Bennett linkages is proposed in this paper. Totally five types of overconstrained 5R and 6R linkages, including the generalized Goldberg 5R linkage, generalized variant of the L-shape Goldberg 6R linkage, Waldron's hybrid 6R linkage, isomerized case of the generalized L-shape Goldberg 6R linkage, and generalized Wohlhart's double-Goldberg 6R linkage, can be constructed by modifying this Bennett network. The 8R linkage formed by Bennett network serves as the basic mechanism to realise the reconfiguration among five types of overconstrained linkages by rigidifying some of the eight joints. The work also reveals the in-depth relationship among the Bennett-based linkages, which provides a substantial advancement in the design of reconfigurable mechanisms using overconstrained linkages

Imaging Genetic Based Mediation Analysis for Human Cognition

The brain connectome maps the structural and functional connectivity that forms an important neurobiological basis for the analysis of human cognitive traits while the genetic predisposition and our cognition ability are frequently found in close association. The issue of how genetic architecture and brain connectome causally affect human behaviors remains unknown. To seek for the potential causal relationship, in this paper, we carried out the causal pathway analysis from single nucleotide polymorphism (SNP) data to four common human cognitive traits, mediated by the brain connectome. Specifically, we selected 942 SNPs that are significantly associated with the brain connectome, and then estimated the direct and indirect effect on the human traits for each SNP. We found out that a majority of the selected SNPs have significant direct effects on human traits and discussed the trait-related brain regions and their implications

Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generation

Large diffusion models have been successful in text-to-audio (T2A) synthesis tasks, but they often suffer from common issues such as semantic misalignment and poor temporal consistency due to limited natural language understanding and data scarcity. Additionally, 2D spatial structures widely used in T2A works lead to unsatisfactory audio quality when generating variable-length audio samples since they do not adequately prioritize temporal information. To address these challenges, we propose Make-an-Audio 2, a latent diffusion-based T2A method that builds on the success of Make-an-Audio. Our approach includes several techniques to improve semantic alignment and temporal consistency: Firstly, we use pre-trained large language models (LLMs) to parse the text into structured pairs for better temporal information capture. We also introduce another structured-text encoder to aid in learning semantic alignment during the diffusion denoising process. To improve the performance of variable length generation and enhance the temporal information extraction, we design a feed-forward Transformer-based diffusion denoiser. Finally, we use LLMs to augment and transform a large amount of audio-label data into audio-text datasets to alleviate the problem of scarcity of temporal data. Extensive experiments show that our method outperforms baseline models in both objective and subjective metrics, and achieves significant gains in temporal information understanding, semantic consistency, and sound quality

Turnout Fault Diagnosis through Dynamic Time Warping and Signal Normalization

Turnout is one key fundamental infrastructure in the railway signal system, which has great influence on the safety of railway systems. Currently, turnout fault diagnoses are conducted manually in China; engineers are obliged to observe the signals and make problem solving decisions. Thus, the accuracies of fault diagnoses totally depend on the engineers’ experience although massive data are produced in real time by the turnout microcomputer-based monitoring systems. This paper aims to develop an intelligent diagnosis method for railway turnout through Dynamic Time Warping (DTW). We firstly extract the features of normal turnout operation current curve and normalize the collected turnout current curves. Then, five typical fault reference curves are ascertained through the microcomputer-based monitoring system, and DTW is used to identify the turnout current curve fault through test data. The analysis results based on the similarity data indicate that the analyzed five turnout fault types can be diagnosed automatically with 100% accuracy. Finally, the benefits of the proposed method and future research directions were discussed