207 research outputs found
Model-based design of motorized spindles with different bearing configurations
This paper conducts a dynamic design of motorized spindles with different configurations using an integrated dynamic electro-thermo-mechanical model. The dynamic electro-thermo-mechanical model consists of a thermo-mechanical bearing model, a shaft dynamic model and a thermal model. These sub-models interact on each other based on the bearing configuration, and general cases of bearing configurations can be modeled with the use of the pertinent mapping between shaft stiffness and bearing stiffness matrices. Based on the integrated model a design flow chart is developed and four design variables (DVs) are identified. The proposed model is validated experimentally and a design sensitivity analysis of the four DVs is then presented with a 170MD15Y20 type motorized spindle. The good agreement between the theoretical results and the experimental data indicates that the integrated model is capable of accurately predicting the multi-physics coupled dynamic behaviors of motorized spindles, and the sensitivities of the four DVs to the nature frequencies of the spindle system are obtained with different configurations
Model-based dynamical properties analysis of a motorized spindle system with an adjustable preload mechanism
This paper presents a dynamical model for an especially designed motorized spindle with an adjustable preload mechanism and analyzes the effects of bearing preload on the spindle dynamical properties in both of the non-working and working states. In the model, the housing, rear bearing pedestal, shaft, drawbar and tool are taken into account using the finite element (FE) method. The effects of bearing preload are provided by this mathematical model as well as the experiments, in which the axial displacement of spindle tool, frequency response function (FRF), vibration displacement etc. are measured under all kinds of operating conditions. Various results such as bearing nonlinear stiffness, inherent modal shapes and frequencies of the system, spindle stiffness and chatter stability have been obtained under different preload. The good agreement between the calculated results and the tested data indicates that the model is capable of predicting the dynamical properties of the motorized spindle system accurately. And it is indicated that choosing an appropriate bearing preload can contribute to acquire good dynamical properties for the motorized spindle
Computationally Efficient Approximations Using Adaptive Weighting Coefficients for Solving Structural Optimization Problems
With rapid development of advanced manufacturing technologies and high demands for innovative lightweight constructions to mitigate the environmental and economic impacts, design optimization has attracted increasing attention in many engineering subjects, such as civil, structural, aerospace, automotive, and energy engineering. For nonconvex nonlinear constrained optimization problems with continuous variables, evaluations of the fitness and constraint functions by means of finite element simulations can be extremely expensive. To address this problem by algorithms with sufficient accuracy as well as less computational cost, an extended multipoint approximation method (EMAM) and an adaptive weighting-coefficient strategy are proposed to efficiently seek the optimum by the integration of metamodels with sequential quadratic programming (SQP). The developed EMAM stems from the principle of the polynomial approximation and assimilates the advantages of Taylorās expansion for improving the suboptimal continuous solution. Results demonstrate the superiority of the proposed EMAM over other evolutionary algorithms (e.g., particle swarm optimization technique, firefly algorithm, genetic algorithm, metaheuristic methods, and other metamodeling techniques) in terms of the computational efficiency and accuracy by four well-established engineering problems. The developed EMAM reduces the number of simulations during the design phase and provides wealth of information for designers to effectively tailor the parameters for optimal solutions with computational efficiency in the simulation-based engineering optimization problems
SurrealDriver: Designing Generative Driver Agent Simulation Framework in Urban Contexts based on Large Language Model
Simulation plays a critical role in the research and development of
autonomous driving and intelligent transportation systems. However, the current
simulation platforms exhibit limitations in the realism and diversity of agent
behaviors, which impede the transfer of simulation outcomes to the real world.
In this paper, we propose a generative driver agent simulation framework based
on large language models (LLMs), capable of perceiving complex traffic
scenarios and providing realistic driving maneuvers. Notably, we conducted
interviews with 24 drivers and used their detailed descriptions of driving
behavior as chain-of-thought prompts to develop a `coach agent' module, which
can evaluate and assist driver agents in accumulating driving experience and
developing human-like driving styles. Through practical simulation experiments
and user experiments, we validate the feasibility of this framework in
generating reliable driver agents and analyze the roles of each module. The
results show that the framework with full architect decreased the collision
rate by 81.04% and increased the human-likeness by 50%. Our research proposes
the first urban context driver agent simulation framework based on LLMs and
provides valuable insights into the future of agent simulation for complex
tasks.Comment: 12 pages, 8 figure
Application of diffusion kurtosis imaging in neonatal brain development
BackgroundDeviations from the regular pattern of growth and development could lead to early childhood diseases, suggesting the importance of evaluating early brain development. Through this study, we aimed to explore the changing patterns of white matter and gray matter during neonatal brain development using diffusion kurtosis imaging (DKI).Materials and methodsIn total, 42 full-term neonates (within 28 days of birth) underwent conventional brain magnetic resonance imaging (MRI) and DKI. The DKI metrics (including kurtosis parameters and diffusion parameters) of white matter and deep gray matter were measured. DKI metrics from the different regions of interest (ROIs) were evaluated using the KruskalāWallis test and Bonferroni method. Spearman rank correlation analysis of the DKI metrics was conducted, and the age at the time of brain MRI acquisition was calculated. The subjects were divided into three groups according to their age at the time of brain MRI acquisition: the first group, neonates aged ā¤7 days; the second group, neonates aged 8ā14 days; and the third group, neonates aged 15ā28 days. The rate of change in DKI metrics relative to the first group was computed.ResultsThe mean kurtosis (MK), axial kurtosis (Ka), radial kurtosis (Kr), and fractional anisotropy (FA) values showed positive correlations, whereas mean diffusion (MD), axial diffusion (Da), and radial diffusion (Dr) values showed negative correlations with the age at the time of brain MRI acquisition. The absolute correlation coefficients between MK values of almost all ROIs (except genu of the corpus callosum and frontal white matter) and the age at the time of brain MRI acquisition were greater than other metrics. The kurtosis parameters and FA values of central white matter were significantly higher than that of peripheral white matter, whereas the MD and Dr values were significantly lower than that of peripheral white matter. The MK value of the posterior limb of the internal capsule was the highest among the white matter areas. The FA value of the splenium of the corpus callosum was significantly higher than that of the other white matter areas. The kurtosis parameters and FA values of globus pallidus and thalamus were significantly higher than those of the caudate nucleus and putamen, whereas the Da and Dr values of globus pallidus and thalamus were significantly lower than those of the caudate nucleus and putamen. The relative change rates of kurtosis parameters and FA values of all ROIs were greater than those of MD, Da, and Dr values. The amplitude of MK values of almost all ROIs (except for the genu of the corpus callosum and central white matter of the centrum semiovale level) was greater than that of other metrics. The relative change rates of the Kr values of most ROIs were greater than those of the Ka value, and the relative change rates of the Dr values of most ROIs were greater than those of the Da value.ConclusionDKI parameters showed potential advantages in detecting the changes in brain microstructure during neonatal brain development
A Consumer-tier based Visual-Brain Machine Interface for Augmented Reality Glasses Interactions
Objective.Visual-Brain Machine Interface(V-BMI) has provide a novel
interaction technique for Augmented Reality (AR) industries. Several
state-of-arts work has demonstates its high accuracy and real-time interaction
capbilities. However, most of the studies employ EEGs devices that are rigid
and difficult to apply in real-life AR glasseses application sceniraros. Here
we develop a consumer-tier Visual-Brain Machine Inteface(V-BMI) system
specialized for Augmented Reality(AR) glasses interactions. Approach. The
developed system consists of a wearable hardware which takes advantages of fast
set-up, reliable recording and comfortable wearable experience that
specificized for AR glasses applications. Complementing this hardware, we have
devised a software framework that facilitates real-time interactions within the
system while accommodating a modular configuration to enhance scalability. Main
results. The developed hardware is only 110g and 120x85x23 mm, which with 1
Tohm and peak to peak voltage is less than 1.5 uV, and a V-BMI based angry bird
game and an Internet of Thing (IoT) AR applications are deisgned, we
demonstrated such technology merits of intuitive experience and efficiency
interaction. The real-time interaction accuracy is between 85 and 96
percentages in a commercial AR glasses (DTI is 2.24s and ITR 65 bits-min ).
Significance. Our study indicates the developed system can provide an essential
hardware-software framework for consumer based V-BMI AR glasses. Also, we
derive several pivotal design factors for a consumer-grade V-BMI-based AR
system: 1) Dynamic adaptation of stimulation patterns-classification methods
via computer vision algorithms is necessary for AR glasses applications; and 2)
Algorithmic localization to foster system stability and latency reduction.Comment: 15 pages,10 figure
In situ Chromatin Interaction Analysis Using Paired-End Tag Sequencing.
Chromatin Interaction Analysis Using Paired-End Tag Sequencing (ChIA-PET) is an established method to map protein-mediated chromatin interactions. A limitation, however, is that it requires a hundred million cells per experiment, which hampers its broad application in biomedical research, particularly in studies in which it is impractical to obtain a large number of cells from rare samples. To reduce the required input cell number while retaining high data quality, we developed an in situ ChIA-PET protocol, which requires as few as 1 million cells. Here, we describe detailed step-by-step procedures for performing in situ ChIA-PET from cultured cells, including both an experimental protocol for sample preparation and data generation and a computational protocol for data processing and visualization using the ChIA-PIPE pipeline. As the protocol significantly simplifies the experimental procedure, reduces ligation noise, and decreases the required input of cells compared to previous versions of ChIA-PET protocols, it can be applied to generate high-resolution chromatin contact maps mediated by various protein factors for a wide range of human and mouse primary cells. Ā© 2021 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Sample preparation and data generation Support Protocol: Bridge linker preparation Basic Protocol 2: Data processing and visualization
Crosstalk of RNA methylation writers defines tumor microenvironment and alisertib resistance in breast cancer
BackgroundThe five major RNA methylation modifications (m6A, m1A, m6Am, m5C, and m7G) exert biological roles in tumorigenicity and immune response, mediated mainly by āwriterā enzymes. Here, the prognostic values of the āwriterā enzymes and the TCP1 role in drug resistance in breast cancer (BC) were explored for further therapeutic strategies.MethodsWe comprehensively characterized clinical, molecular, and genetic features of subtypes by consensus clustering. RNA methylation modification āWritersā and related genes_risk (RMW_risk) model for BC was constructed via a machine learning approach. Moreover, we performed a systematical analysis for characteristics of the tumor microenvironment (TME), alisertib sensitivity, and immunotherapy response. A series of experiments in vitro were carried out to assess the association of TCP1 with drug resistance.ResultsOne āwriterā (RBM15B) and two related genes (TCP1 and ANKRD36) were identified for prognostic model construction, validated by GSE1456, GSE7390, and GSE20685 cohorts and our follow-up data. Based on the patterns of the genes related to prognosis, patients were classified into RMW_risk-high and RMW_risk-low subtypes. Lower RMW_Score was associated with better overall survival and the infiltration of immune cells such as memory B cells. Further analysis revealed that RMW_Score presented potential values in predicting drug sensitivity and response for chemo- and immunotherapy. In addition, TCP1 was confirmed to promote BC alisertib-resistant cell proliferation and migration in vitro.ConclusionRMW_Score could function as a robust biomarker for predicting BC patient survival and therapeutic benefits. This research revealed a potential TCP1 role regarding alisertib resistance in BC, providing new sights into more effective therapeutic plans
Chromatin topology reorganization and transcription repression by PML-RARĪ± in acute promyeloid leukemia.
BACKGROUND: Acute promyeloid leukemia (APL) is characterized by the oncogenic fusion protein PML-RARĪ±, a major etiological agent in APL. However, the molecular mechanisms underlying the role of PML-RARĪ± in leukemogenesis remain largely unknown.
RESULTS: Using an inducible system, we comprehensively analyze the 3D genome organization in myeloid cells and its reorganization after PML-RARĪ± induction and perform additional analyses in patient-derived APL cells with native PML-RARĪ±. We discover that PML-RARĪ± mediates extensive chromatin interactions genome-wide. Globally, it redefines the chromatin topology of the myeloid genome toward a more condensed configuration in APL cells; locally, it intrudes RNAPII-associated interaction domains, interrupts myeloid-specific transcription factors binding at enhancers and super-enhancers, and leads to transcriptional repression of genes critical for myeloid differentiation and maturation.
CONCLUSIONS: Our results not only provide novel topological insights for the roles of PML-RARĪ± in transforming myeloid cells into leukemia cells, but further uncover a topological framework of a molecular mechanism for oncogenic fusion proteins in cancers
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