Three‐dimensional (3D) printing, also known as additive manufacturing, is a fabrication method that has recently received worldwide attention. It provides a convenient and economical way to prepare ...3D structures in designable ways. As the technology has developed and the operational costs have decreased, the applications of 3D printing have greatly expanded. Catalyst fabrication is a promising area for 3D printing. Printing processes result in better control of catalyst structures and catalyst distribution. In this perspective, a general overview of the commonly available 3D printing methods that are feasible for the preparation of heterogeneous catalysts is given. Additionally, recent works on printing strategies and new materials for catalysts are discussed. Future development is also addressed.
Three‐dimensional (3D) printing is a fabrication method that has received worldwide attention. Recently, 3D printing has been applied to catalyst fabrication. In this perspective, a general overview of the commonly available 3D printing methods that are feasible for the preparation of heterogeneous catalysts is given. Recent works on printing strategies and new materials for catalysts are discussed. Future development is also addressed.
A look at how biochar is formed in the biomass pyrolysis process is offered. Research points toward a biochear-based sustainable platform carbon material.
This paper concentrates upon the problem of finite-time fault-tolerant control for a class of switched nonlinear systems in lower-triangular form under arbitrary switching signals. Both loss of ...effectiveness and bias fault in actuator are taken into account. The method developed extends the traditional finite-time convergence from nonswitched lower-triangular nonlinear systems to switched version by designing appropriate controller and adaptive laws. In contrast to the previous results, it is the first time to handle the fault tolerant problem for switched system while the finite-time stability is also necessary. Meanwhile, there exist unknown internal dynamics in the switched system, which are identified by the radial basis function neural networks. It is proved that under the presented control strategy, the system output tracks the reference signal in the sense of finite-time stability. Finally, an illustrative simulation on a resistor-capacitor-inductor circuit is proposed to further demonstrate the effectiveness of the theoretical result.
Plant diseases and pests are important factors determining the yield and quality of plants. Plant diseases and pests identification can be carried out by means of digital image processing. In recent ...years, deep learning has made breakthroughs in the field of digital image processing, far superior to traditional methods. How to use deep learning technology to study plant diseases and pests identification has become a research issue of great concern to researchers. This review provides a definition of plant diseases and pests detection problem, puts forward a comparison with traditional plant diseases and pests detection methods. According to the difference of network structure, this study outlines the research on plant diseases and pests detection based on deep learning in recent years from three aspects of classification network, detection network and segmentation network, and the advantages and disadvantages of each method are summarized. Common datasets are introduced, and the performance of existing studies is compared. On this basis, this study discusses possible challenges in practical applications of plant diseases and pests detection based on deep learning. In addition, possible solutions and research ideas are proposed for the challenges, and several suggestions are given. Finally, this study gives the analysis and prospect of the future trend of plant diseases and pests detection based on deep learning.
Developing environmentally friendly perovskites has become important in solving the toxicity issue of lead‐based perovskite solar cells. Here, the first double perovskite (Cs2AgBiBr6) solar cells ...using the planar structure are demonstrated. The prepared Cs2AgBiBr6 films are composed of high‐crystal‐quality grains with diameters equal to the film thickness, thus minimizing the grain boundary length and the carrier recombination. These high‐quality double perovskite films show long electron–hole diffusion lengths greater than 100 nm, enabling the fabrication of planar structure double perovskite solar cells. The resulting solar cells based on planar TiO2 exhibit an average power conversion efficiency over 1%. This work represents an important step forward toward the realization of environmentally friendly solar cells and also has important implications for the applications of double perovskites in other optoelectronic devices.
Cs2AgBiBr6 films composed of high‐crystal‐quality grains with diameters equal to the film thickness are fabricated. These high‐quality double‐perovskite films show electron–hole diffusion lengths greater than 100 nm, enabling the fabrication of planar‐structure double‐perovskite solar cells with a maximum value of 1.22%.
Telomeres with G-rich repetitive DNA and particular proteins as special heterochromatin structures at the termini of eukaryotic chromosomes are tightly maintained to safeguard genetic integrity and ...functionality. Telomerase as a specialized reverse transcriptase uses its intrinsic RNA template to lengthen telomeric G-rich strand in yeast and human cells. Cells sense telomere length shortening and respond with cell cycle arrest at a certain size of telomeres referring to the "Hayflick limit." In addition to regulating the cell replicative senescence, telomere biology plays a fundamental role in regulating the chronological post-mitotic cell ageing. In this review, we summarize the current understandings of telomere regulation of cell replicative and chronological ageing in the pioneer model system
and provide an overview on telomere regulation of animal lifespans. We focus on the mechanisms of survivals by telomere elongation, DNA damage response and environmental factors in the absence of telomerase maintenance of telomeres in the yeast and mammals.
In this paper, an adaptive fuzzy optimal control design is addressed for a class of unknown nonlinear discrete-time systems. The controlled systems are in a strict-feedback frame and contain unknown ...functions and nonsymmetric dead-zone. For this class of systems, the control objective is to design a controller, which not only guarantees the stability of the systems, but achieves the optimal control performance as well. This immediately brings about the difficulties in the controller design. To this end, the fuzzy logic systems are employed to approximate the unknown functions in the systems. Based on the utility functions and the critic designs, and by applying the backsteppping design technique, a reinforcement learning algorithm is used to develop an optimal control signal. The adaptation auxiliary signal for unknown dead-zone parameters is established to compensate for the effect of nonsymmetric dead-zone on the control performance, and the updating laws are obtained based on the gradient descent rule. The stability of the control systems can be proved based on the difference Lyapunov function method. The feasibility of the proposed control approach is further demonstrated via two simulation examples.
Tomato is affected by various diseases and pests during its growth process. If the control is not timely, it will lead to yield reduction or even crop failure. How to control the diseases and pests ...effectively and help the vegetable farmers to improve the yield of tomato is very important, and the most important thing is to accurately identify the diseases and insect pests. Compared with the traditional pattern recognition method, the diseases and pests recognition method based on deep learning can directly input the original image. Instead of the tedious steps such as image preprocessing, feature extraction and feature classification in the traditional method, the end-to-end structure is adopted to simplify the recognition process and solve the problem that the feature extractor designed manually is difficult to obtain the feature expression closest to the natural attribute of the object. Based on the application of deep learning object detection, not only can save time and effort, but also can achieve real-time judgment, greatly reduce the huge loss caused by diseases and pests, which has important research value and significance. Based on the latest research results of detection theory based on deep learning object detection and the characteristics of tomato diseases and pests images, this study will build the dataset of tomato diseases and pests under the real natural environment, optimize the feature layer of Yolo V3 model by using image pyramid to achieve multi-scale feature detection, improve the detection accuracy and speed of Yolo V3 model, and detect the location and category of diseases and pests of tomato accurately and quickly. Through the above research, the key technology of tomato pest image recognition in natural environment is broken through, which provides reference for intelligent recognition and engineering application of plant diseases and pests detection.
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