Determination of The Mechanical Properties of Guinea Pig Tympanic Membrane Using Combined Fringe Projection and Simulations

Measurement of the mechanical properties of guinea pig tympanic membrane (TM) with fringe projection and simulation. Fringe projection technique was used to provide optic access for measurement of the volume displacement of TM under different pressure levels. FEM model with hyperelastic constitute law was built to simulate the experimental procedure. Experimental data and simulation result were fitted to obtain the hyperelastic parameters of guinea pig TM. Hardening effects was observed through pressure- volume displacement curve. The stress- strain curve plotted based on the estimated mechanical properties was also reflect such phenomenon. Comparison between the positive and negative pressure loading from middle ear cavity indicate that, such hardening effect is stronger as negative pressure applied. The observation of the section profile of 3-D surface reconstruction of TM with Fringe projection technique shows that, the displacement around umbo is asymmetric. It means that the malleus tend to rotate rather than translate at high pressure level. The experimental design was compared with other research group. Full-field mechanical properties measured with the designed experiment were validated by comparison to existed data in literature.Mechanical & Aerospace Engineerin

Experimental Measurement and Modeling of Thermal Conductivities of Carbon Fibers and Their Composites Modified with Carbon Nanofibers

Carbon fiber reinforced composites (CFRCs) show superior thermal performance along the fiber direction due to high thermal conductivity of carbon fiber in the fiber direction. However, these composites suffer from poor through-thickness thermal performance due to (i) low thermal conductivity of carbon fiber in its transverse direction (radial direction), (ii) low thermal conductivity of polymer matrix, and (iii) high thermal resistance at the fiber-matrix interface. One of the contemporary ways to enhance the through-thickness thermal conductivity of the composite is to incorporate carbon based nano materials such as carbon nanofibers (CNFs) through matrix and/or fiber. This work provides a systematic characterization of thermal conductivities of carbon fibers and their composites modified with CNFs using experimental measurement and theoretical modeling. We choose 3ω method as the measurement technique is very fast, accurate, non-sensitive to convection and radiation heat loss, and its potential to measure different types of materials including carbon fibers. Therefore this research leads to the development of a number of instrumentation and procedures for measuring thermal conductivities of carbon fibers and their composites using 3ω method: Wire-based 3ω method was implemented to measure thermal conductivity of carbon fiber reinforced epoxy composites, with its procedure and sample preparation improved. Longitudinal thermal conductivity of individual carbon fibers was measured using specially designed 3ω setup with vacuum environment. Lu’s one-dimensional heat conduction model was incorporated to determine the longitudinal thermal conductivities of individual carbon fibers. Radial or transverse thermal conductivity of individual carbon fiber filament was developed for 3ω method by submerging the fiber into deionized water. Accordingly a two-phase heat conduction model was developed for determining the transverse thermal conductivities of individual carbon fibers. With the development of the aforementioned experimental setups and techniques, thermal conductivities of hierarchical carbon fibers and hierarchical carbon fiber composites were then studied. Different amount of CNFs were grown in the range of 5% to 38% by weight directly on carbon fiber tows and fabrics using chemical vapor deposition (CVD) method to produce hierarchical fibers and hierarchical composites, respectively. A consistent increase of thermal conductivities was observed with the increase amount of CNFs on the carbon fibers and their composites. Both SEM and the mass increase confirm that the enhancement of thermal conductivities stems from the increase of CNFs growth. It is also found that enhancement of thermal conductivities for carbon fibers is significantly higher than that for their composites, which indicates that defects in the composites level compromise the enhancement of thermal conductivities from the hierarchical carbon fibers. Thermal conductivities of carbon fibers and their hierarchical composites at different temperatures from 20 °C to 60 °C (up to 100 °C for composites) were also measured. It was found that the thermal conductivities of carbon fibers moderately increase with the temperature, while the thermal conductivities of carbon fiber composites significantly increase as the temperature increase. Therefore, it is believed that the thermal conductivity of epoxy matrix and the thermal interfacial resistance between the carbon fibers and the matrix determine the temperature dependency of thermal conductivity of the composites. Finally, a theoretical model for hierarchical carbon fiber composites was developed using effective medium theory to predict the through-thickness thermal conductivities of carbon fiber laminated composites. Parametric study has been done addressing thickness and length of the growth as well as growth pattern. Two types of growth patterns such as surface growth and full growth were investigated. It was found that the surface growth is more effective in enhancing the through-thickness thermal conductivity of carbon fiber reinforced composites

Performance Studies of an Axial Flow Waterjet Pump Using an Unsteady Reynolds-Averaged Navier-Stokes Model

In this study, an Unsteady Reynolds-Averaged Navier-Stokes (URANS) model is demonstrated its suitability for studying the flow and performance of open marine propellers and waterjet pumps. First, the accuracy of the URANS model is validated by studying turbulent flow past counter-rotating propellers (CRPs). Specifically, experimental data from Miller (1976) is employed for comparison against the URANS results. Subsequently, URANS is used to study the flow and performance of an Office of Naval Research (ONR) axial flow waterjet pump (AxWJ-2). Due to the large number of degrees of freedom for both simulations, parallel computations over 80 cores are performed. For the CRP study, torque and thrust coefficients are assessed against a range of advance ratios, ensuring a Reynolds number of less than 600,000. For the waterjet, torque and head coefficients are computed for a range of flow rates at a Reynolds number of 1.25 million. For both studies, two levels of mesh resolution are utilized. The finer meshes of both studies contained roughly four times the total number of cells employed in their respective coarser counterparts. These refinements lead to minor improvements, suggesting good grid resolutions with the coarser grids. Across all advance ratios for the CRP set, the URANS torque and thrust coefficients show good agreement with experimental results, remaining within 10% difference. The torque and head coefficients for the waterjet displayed even better agreement, with the greatest error across all flow conditions remaining under 3%. Moreover, URANS studies revealed that the stator is responsible for 20% of the waterjet’s power production

ANALYSIS OF LATERAL DISPLACEMENT AND EVALUATION OF TREATMENT MEASURES OF CURVED BEAM: A CASE STUDY

The curved beam bridge exhibits lateral displacement during construction and operation. Taking a curved beam bridge as an example, the status of lateral displacement of the bridge is investigated in detail in this paper. To understand the mechanism of the curved beam lateral displacement, further to determine the curved beam lateral displacement under temperature effect, using ANSYS software to establish solid element model of the curved beam, steady state thermal analysis method is applied to analyze temperature field. Based on the analysis, the lateral displacement under temperature effect is analyzed. Then in order to further explain the lateral displacement mechanism, to discuss the frictional force causing the residual deformation of the rubber bearing to make the lateral displacement of the curved beam, the mechanical mechanism of curved beam under temperature effect is approximately analyzed. On the basis of clarifying the mechanism of lateral displacement, the paper puts forward the reinforcement measures for the curved beam bridge. In order to verify the treatment effect, long-term displacement monitoring is performed on the bridge. Numerical studies and monitoring data show that temperature is the main factor that causes the lateral displacement. Monitoring data over the past year shows that the displacement of the bearing is less than the value of allowable displacement after the reinforcement measures are adopted, and the bridge is in a safe state

Towards Unifying Diffusion Models for Probabilistic Spatio-Temporal Graph Learning

Spatio-temporal graph learning is a fundamental problem in the Web of Things era, which enables a plethora of Web applications such as smart cities, human mobility and climate analysis. Existing approaches tackle different learning tasks independently, tailoring their models to unique task characteristics. These methods, however, fall short of modeling intrinsic uncertainties in the spatio-temporal data. Meanwhile, their specialized designs limit their universality as general spatio-temporal learning solutions. In this paper, we propose to model the learning tasks in a unified perspective, viewing them as predictions based on conditional information with shared spatio-temporal patterns. Based on this proposal, we introduce Unified Spatio-Temporal Diffusion Models (USTD) to address the tasks uniformly within the uncertainty-aware diffusion framework. USTD is holistically designed, comprising a shared spatio-temporal encoder and attention-based denoising networks that are task-specific. The shared encoder, optimized by a pre-training strategy, effectively captures conditional spatio-temporal patterns. The denoising networks, utilizing both cross- and self-attention, integrate conditional dependencies and generate predictions. Opting for forecasting and kriging as downstream tasks, we design Gated Attention (SGA) and Temporal Gated Attention (TGA) for each task, with different emphases on the spatial and temporal dimensions, respectively. By combining the advantages of deterministic encoders and probabilistic diffusion models, USTD achieves state-of-the-art performances compared to deterministic and probabilistic baselines in both tasks, while also providing valuable uncertainty estimates

Deadlock Prevention Policy with Behavioral Optimality or Suboptimality Achieved by the Redundancy Identification of Constraints and the Rearrangement of Monitors

This work develops an iterative deadlock prevention method for a special class of Petri nets that can well model a variety of flexible manufacturing systems. A deadlock detection technique, called mixed integer programming (MIP), is used to find a strict minimal siphon (SMS) in a plant model without a complete enumeration of siphons. The policy consists of two phases. At the first phase, SMSs are obtained by MIP technique iteratively and monitors are added to the complementary sets of the SMSs. For the possible existence of new siphons generated after the first phase, we add monitors with their output arcs first pointed to source transitions at the second phase to avoid new siphons generating and then rearrange the output arcs step by step on condition that liveness is preserved. In addition, an algorithm is proposed to remove the redundant constraints of the MIP problem in this paper. The policy improves the behavioral permissiveness of the resulting net and greatly enhances the structural simplicity of the supervisor. Theoretical analysis and experimental results verify the effectiveness of the proposed method

Graph Neural Processes for Spatio-Temporal Extrapolation

We study the task of spatio-temporal extrapolation that generates data at target locations from surrounding contexts in a graph. This task is crucial as sensors that collect data are sparsely deployed, resulting in a lack of fine-grained information due to high deployment and maintenance costs. Existing methods either use learning-based models like Neural Networks or statistical approaches like Gaussian Processes for this task. However, the former lacks uncertainty estimates and the latter fails to capture complex spatial and temporal correlations effectively. To address these issues, we propose Spatio-Temporal Graph Neural Processes (STGNP), a neural latent variable model which commands these capabilities simultaneously. Specifically, we first learn deterministic spatio-temporal representations by stacking layers of causal convolutions and cross-set graph neural networks. Then, we learn latent variables for target locations through vertical latent state transitions along layers and obtain extrapolations. Importantly during the transitions, we propose Graph Bayesian Aggregation (GBA), a Bayesian graph aggregator that aggregates contexts considering uncertainties in context data and graph structure. Extensive experiments show that STGNP has desirable properties such as uncertainty estimates and strong learning capabilities, and achieves state-of-the-art results by a clear margin.Comment: SIGKDD 202

On calculating the probability of a set of orthologous sequences

Probabilistic DNA sequence models have been intensively applied to genome research. Within the evolutionary biology framework, this article investigates the feasibility for rigorously estimating the probability of a set of orthologous DNA sequences which evolve from a common progenitor. We propose Monte Carlo integration algorithms to sample the unknown ancestral and/or root sequences a posteriori conditional on a reference sequence and apply pairwise Needleman–Wunsch alignment between the sampled and nonreference species sequences to estimate the probability. We test our algorithms on both simulated and real sequences and compare calculated probabilities from Monte Carlo integration to those induced by single multiple alignment

UniTime: A Language-Empowered Unified Model for Cross-Domain Time Series Forecasting

Multivariate time series forecasting plays a pivotal role in contemporary web technologies. In contrast to conventional methods that involve creating dedicated models for specific time series application domains, this research advocates for a unified model paradigm that transcends domain boundaries. However, learning an effective cross-domain model presents the following challenges. First, various domains exhibit disparities in data characteristics, e.g., the number of variables, posing hurdles for existing models that impose inflexible constraints on these factors. Second, the model may encounter difficulties in distinguishing data from various domains, leading to suboptimal performance in our assessments. Third, the diverse convergence rates of time series domains can also result in compromised empirical performance. To address these issues, we propose UniTime for effective cross-domain time series learning. Concretely, UniTime can flexibly adapt to data with varying characteristics. It also uses domain instructions and a Language-TS Transformer to offer identification information and align two modalities. In addition, UniTime employs masking to alleviate domain convergence speed imbalance issues. Our extensive experiments demonstrate the effectiveness of UniTime in advancing state-of-the-art forecasting performance and zero-shot transferability