3D bioprinting technology innovation in female reproductive system.

Several diseases affect the female reproductive system, and both disease factors and treatments impact its integrity and function. Consequently, understanding the mechanisms of disease occurrence and exploring treatment methods are key research focuses in obstetrics and gynecology. However, constructing accurate disease models requires a microenvironment closely resembling the human body, and current animal models and 2D in vitro cell models fall short in this regard.

Carbon footprint of piezoelectrics from multi-layer PZT stacks to piezoelectric energy harvesting systems in roads.

Piezoelectric energy harvesting devices (PEHDs) for road installation are being researched as potentially low-emission electrical generators, but their life cycle greenhouse gas (GHG) emissions must be assessed to guide their design and implementation toward minimizing their carbon footprint. Parametric life cycle assessments (LCAs) of a PEHD, its generator, and the piezoelectric material were performed using data from a pilot-scale PEHD installation. The GHG emissions of the lead zirconate titanate (PZT) piezoelectric stack were determined to be lower than previously reported in the literature at 10.

Advancing Hyperspectral and Multispectral Image Fusion: An Information-Aware Transformer-Based Unfolding Network.

In hyperspectral image (HSI) processing, the fusion of the high-resolution multispectral image (HR-MSI) and the low-resolution HSI (LR-HSI) on the same scene, known as MSI-HSI fusion, is a crucial step in obtaining the desired high-resolution HSI (HR-HSI). With the powerful representation ability, convolutional neural network (CNN)-based deep unfolding methods have demonstrated promising performances. However, limited receptive fields of CNN often lead to inaccurate long-range spatial features, and inherent input and output images for each stage in unfolding networks restrict the feature transmission, thus limiting the overall performance.

Predictive Neural Network Modeling for Almond Harvest Dust Control.

This study introduces a neural network-based approach to predict dust emissions, specifically PM2.5 particles, during almond harvesting in California. Using a feedforward neural network (FNN), this research predicted PM2.

Design of an Integrated Controller for a Sweeping Mechanism of a Low-Dust Almond Pickup Machine.

California is the world's biggest producer and exporter of almonds. Currently, the sweeping of almonds during the harvest creates a significant amount of dust, causing air pollution in the neighboring urban areas. A low-dust sweeping system was designed to reduce the dust during the sweeping of almonds in the orchard.

A reinforcement learning-based near-optimal hierarchical approach for motion control: Design and experiment.

As a data-driven design method, model-free optimal control based on reinforcement learning provides an effective way to find optimal control strategies. The design of model-free optimal control is sensitive to system data because it relies on data rather than detailed dynamic models. A prerequisite for generating applicable data is that the system must be open-loop stable (with a stable equilibrium point), which restricts the data-based control design methods in actual control problems and leads to rare experimental studies or verification in the literature.

Optimal full-state feedback observer integrated backstepping control of chemical processes with unknown internal dynamics.

This paper studies the estimation and control problems of chemical processes with unknown internal dynamics. An observer with optimal full-state feedback characteristics for estimating the state variables and unknown dynamics is presented. Unlike other observers that need to know the frequency characteristics of the system, the pole of the proposed observer is determined automatically in a LQR formulation and the observer stability is also inherently ensured.

Preparation and evaluation of osteogenic nano-MgO/PMMA bone cement for bone healing in a rat critical size calvarial defect.

The clinical outcomes of polymethylmethacrylate (PMMA) bone cement used to fill gaps or marrow cavities of bones and bone defects are limited due to poor handling properties, mismatched mechanical properties with natural bone and lack of osteogenesis for bone healing. In this study, a series of PMMA bone cements containing active nano-MgO particles (nano-MgO/PMMA) were prepared. The handling and mechanical properties were systemically evaluated according to an International Standardization Organization standard (ISO 5833:2002).

New periodic-chaotic attractors in slow-fast Duffing system with periodic parametric excitation.

A new type of responses called as periodic-chaotic motion is found by numerical simulations in a Duffing oscillator with a slowly periodically parametric excitation. The periodic-chaotic motion is an attractor, and simultaneously possesses the feature of periodic and chaotic oscillations, which is a new addition to the rich nonlinear motions of the Duffing system including equlibria, periodic responses, quasi-periodic oscillations and chaos. In the current slow-fast Duffing system, we find three new attractors in the form of periodic-chaotic motions.

A neural network-evolutionary computational framework for remaining useful life estimation of mechanical systems.

This paper presents a framework for estimating the remaining useful life (RUL) of mechanical systems. The framework consists of a multi-layer perceptron and an evolutionary algorithm for optimizing the data-related parameters. The framework makes use of a strided time window along with a piecewise linear model to estimate the RUL for each mechanical component.

Dynamics of delay-coupled FitzHugh-Nagumo neural rings.

This paper studies the dynamical behaviors of a pair of FitzHugh-Nagumo neural networks with bidirectional delayed couplings. It presents a detailed analysis of delay-independent and delay-dependent stabilities and the existence of bifurcated oscillations. Illustrative examples are performed to validate the analytical results and to discover interesting phenomena.