Semi-Supervised Building Extraction with Optical Flow Correction Based on Satellite Video Data in a Tsunami-Induced Disaster Scene.

Data and reports indicate an increasing frequency and intensity of natural disasters worldwide. Buildings play a crucial role in disaster responses and damage assessments, aiding in planning rescue efforts and evaluating losses. Despite advances in applying deep learning to building extraction, challenges remain in handling complex natural disaster scenes and reducing reliance on labeled datasets. Recent advances in satellite video are opening a new avenue for efficient and accurate building extraction research. By thoroughly mining the characteristics of disaster video data, this work provides a new semantic segmentation model for accurate and efficient building extraction based on a limited number of training data, which consists of two parts: the prediction module and the automatic correction module. The prediction module, based on a base encoder-decoder structure, initially extracts buildings using a limited amount of training data that are obtained instantly. Then, the automatic correction module takes the output of the prediction module as input, constructs a criterion for identifying pixels with erroneous semantic information, and uses optical flow values to extract the accurate corresponding semantic information on the corrected frame. The experimental results demonstrate that the proposed method outperforms other methods in accuracy and computational complexity in complicated natural disaster scenes.

Advancements and Prospects of Flexoelectricity.

The flexoelectric effect, as a novel form of the electromechanical coupling phenomenon, has attracted significant attention in the fields of materials science and electronic devices. It refers to the interaction between strain gradients and electric dipole moments or electric field intensity gradients and strain. In contrast to the traditional piezoelectric effect, the flexoelectric effect is not limited by material symmetry or the Curie temperature and exhibits a stronger effect at the nanoscale. The flexoelectric effect finds widespread applications ranging from energy harvesting to electronic device design. Utilizing the flexoelectric effect, enhanced energy harvesters, sensitive sensors, and high-performance wearable electronic devices can be developed. Additionally, the flexoelectric effect can be utilized to modulate the optoelectronic properties and physical characteristics of materials, holding the potential for significant applications in areas such as optoelectronic devices, energy storage devices, and flexible electronics. This review provides a comprehensive overview of the historical development, measurement of flexoelectric coefficients, enhancement mechanisms, and current research progress of the flexoelectric effect. Additionally, it offers a perspective on future prospects of the flexoelectric effect.

Intelligent Recognition Using Ultralight Multifunctional Nano-Layered Carbon Aerogel Sensors with Human-Like Tactile Perception.

Humans can perceive our complex world through multi-sensory fusion. Under limited visual conditions, people can sense a variety of tactile signals to identify objects accurately and rapidly. However, replicating this unique capability in robots remains a significant challenge. Here, we present a new form of ultralight multifunctional tactile nano-layered carbon aerogel sensor that provides pressure, temperature, material recognition and 3D location capabilities, which is combined with multimodal supervised learning algorithms for object recognition. The sensor exhibits human-like pressure (0.04-100 kPa) and temperature (21.5-66.2 °C) detection, millisecond response times (11 ms), a pressure sensitivity of 92.22 kPa-1 and triboelectric durability of over 6000 cycles. The devised algorithm has universality and can accommodate a range of application scenarios. The tactile system can identify common foods in a kitchen scene with 94.63% accuracy and explore the topographic and geomorphic features of a Mars scene with 100% accuracy. This sensing approach empowers robots with versatile tactile perception to advance future society toward heightened sensing, recognition and intelligence.

Differences and Similarities of Photocatalysis and Electrocatalysis in Two-Dimensional Nanomaterials: Strategies, Traps, Applications and Challenges.

Photocatalysis and electrocatalysis have been essential parts of electrochemical processes for over half a century. Recent progress in the controllable synthesis of 2D nanomaterials has exhibited enhanced catalytic performance compared to bulk materials. This has led to significant interest in the exploitation of 2D nanomaterials for catalysis. There have been a variety of excellent reviews on 2D nanomaterials for catalysis, but related issues of differences and similarities between photocatalysis and electrocatalysis in 2D nanomaterials are still vacant. Here, we provide a comprehensive overview on the differences and similarities of photocatalysis and electrocatalysis in the latest 2D nanomaterials. Strategies and traps for performance enhancement of 2D nanocatalysts are highlighted, which point out the differences and similarities of series issues for photocatalysis and electrocatalysis. In addition, 2D nanocatalysts and their catalytic applications are discussed. Finally, opportunities, challenges and development directions for 2D nanocatalysts are described. The intention of this review is to inspire and direct interest in this research realm for the creation of future 2D nanomaterials for photocatalysis and electrocatalysis.

Multi-Fidelity Aerodynamic Data Fusion with a Deep Neural Network Modeling Method.

To generate more high-quality aerodynamic data using the information provided by different fidelity data, where low-fidelity aerodynamic data provides the trend information and high-fidelity aerodynamic data provides value information, we applied a deep neural network (DNN) algorithm to fuse the information of multi-fidelity aerodynamic data. We discuss the relationships between the low-fidelity and high-fidelity data, and then we describe the proposed architecture for an aerodynamic data fusion model. The architecture consists of three fully-connected neural networks that are employed to approximate low-fidelity data, and the linear part and nonlinear part of correlation for the low- and high-fidelity data, respectively. To test the proposed multi-fidelity aerodynamic data fusion method, we calculated Euler and Navier-Stokes simulations for a typical airfoil at various Mach numbers and angles of attack to obtain the aerodynamic coefficients as low- and high-fidelity data. A fusion model of the longitudinal coefficients of lift CL and drag CD was constructed with the proposed method. For comparisons, variable complexity modeling and cokriging models were also built. The accuracy spread between the predicted value and true value was discussed for both the training and test data of the three different methods. We calculated the root mean square error and average relative deviation to demonstrate the performance of the three different methods. The fusion result of the proposed method was satisfactory on the test case, and showed a better performance compared with the other two traditional methods presented. The results provide evidence that the method proposed in this paper can be useful in dealing with the multi-fidelity aerodynamic data fusion problem.

Piezoelectric Materials for Controlling Electro-Chemical Processes.

Piezoelectric materials have been analyzed for over 100 years, due to their ability to convert mechanical vibrations into electric charge or electric fields into a mechanical strain for sensor, energy harvesting, and actuator applications. A more recent development is the coupling of piezoelectricity and electro-chemistry, termed piezo-electro-chemistry, whereby the piezoelectrically induced electric charge or voltage under a mechanical stress can influence electro-chemical reactions. There is growing interest in such coupled systems, with a corresponding growth in the number of associated publications and patents. This review focuses on recent development of the piezo-electro-chemical coupling multiple systems based on various piezoelectric materials. It provides an overview of the basic characteristics of piezoelectric materials and comparison of operating conditions and their overall electro-chemical performance. The reported piezo-electro-chemical mechanisms are examined in detail. Comparisons are made between the ranges of material morphologies employed, and typical operating conditions are discussed. In addition, potential future directions and applications for the development of piezo-electro-chemical hybrid systems are described. This review provides a comprehensive overview of recent studies on how piezoelectric materials and devices have been applied to control electro-chemical processes, with an aim to inspire and direct future efforts in this emerging research field.

Piezoelectric Material-Polymer Composite Porous Foam for Efficient Dye Degradation via the Piezo-Catalytic Effect.

Piezoelectric nanomaterials have been utilized to realize effective charge separation for degrading organic pollutants in water under the action of mechanical vibrations. However, in particulate form, the nanostructured piezoelectric catalysts can flow into the aqueous pollutant and limit its recyclability and reuse. Here, we report a new method of using a barium titanate (BaTiO3, BTO)-polydimethylsiloxane composite porous foam catalyst to address the challenge of secondary pollution and reusable limits. Piezo-catalytic dye degradation activity of the porous foam can degrade a Rhodamine B (RhB) dye solution by ∼94%, and the composite material exhibits excellent stability after repeated decomposition of 12 cycles. It is suggested that under ultrasonic vibrations, the piezoelectric BTO materials create separated electron-hole pairs that react with hydroxyl ions and oxygen molecules to generate superoxide (•O2-) and hydroxyl (•OH) radicals for organic dye degradation. The degradation efficiency of RhB is associated with the piezoelectric constant, the specific surface area, and the shape of the material.