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IoT data from Xi'an Fruit Technology Promotion Center in Shaanxi Province, China, verify that the proposed granular-GBDT-BO is effective for cherry tree evapotranspiration estimation with reduced computational time, and acceptable and robust predictive accuracy. Consequently, the precise estimation of crop evapotranspiration could provide operational guidance for plant irrigation, plant conservations, and pest control in the agricultural greenhouse.Temporal community detection is helpful to discover and analyze significant groups or clusters hidden in dynamic networks in the real world. A variety of methods, such as modularity optimization, spectral method, and statistical network model, has been developed from diversified perspectives. Recently, network embedding-based technologies have made significant progress, and one can exploit deep learning superiority to network tasks. Although some methods for static networks have shown promising results in boosting community detection by integrating community embedding, they are not suitable for temporal networks and unable to capture their dynamics. Furthermore, the dynamic embedding methods only model network varying without considering community structures. Hence, in this article, we propose a novel unsupervised dynamic community detection model, which is based on network embedding and can effectively discover temporal communities and model dynamic networks. More specifically, we propose the community prior by introducing the Gaussian mixture model (GMM) in the variational autoencoder, which can obtain community information and better model the evolutionary characteristics of community structure and node embedding by utilizing the variant of gated recurrent unit (GRU). Extensive experiments conducted in real-world and artificial networks demonstrate that our proposed model has a better effect on improving the accuracy of dynamic community detection.In this article, an adaptive sliding-mode disturbance observer (ASMDO)-based finite-time control scheme with prescribed performance is proposed for an unmanned aerial manipulator (UAM) under uncertainties and external disturbances. First, to take into account the dynamic characteristics of the UAM, a dynamic model of the UAM with state-dependent uncertainties and external disturbances is introduced. Then, note that a priori bounded uncertainty may impose a priori constraint on the system state before obtaining closed-loop stability. To remove this assumption, an ASMDO with a nested adaptive structure is introduced to effectively estimate and compensate the external disturbances and state-dependent uncertainties in finite time without the information of the upper bound of the uncertainties and disturbances and their derivatives. Furthermore, based on the proposed ASMDO, the finite-time control scheme with the prescribed performance is presented to ensure finite-time convergence and implement the specified transient and steady-state performance. The Lyapunov tools are utilized to analyze the stability of the proposed controller. Finally, the correctness and performance of the proposed controller are illustrated through numerical simulation comparisons and outdoor experimental comparisons.Missing values are ubiquitous in industrial data sets because of multisampling rates, sensor faults, and transmission failures. The incomplete data obstruct the effective use of data and degrade the performance of data-driven models. Numerous imputation algorithms have been proposed to deal with missing values, primarily based on supervised learning, that is, imputing the missing values by constructing a prediction model with the remaining complete data. They have limited performance when the amount of incomplete data is overwhelming. Moreover, many methods have not considered the autocorrelation of time-series data. Thus, an adaptive-learned median-filled deep autoencoder (AM-DAE) is proposed in this study, aiming to impute missing values of industrial time-series data in an unsupervised manner. It continuously replaces the missing values by the median of the input data and its reconstruction, which allows the imputation information to be transmitted with the training process. In addition, an adaptive learning strategy is adopted to guide the AM-DAE paying more attention to the reconstruction learning of nonmissing values or missing values in different iteration periods. Finally, two industrial examples are used to verify the superior performance of the proposed method compared with other advanced techniques.This article studies the problem of finite-time, fixed-time, and prescribed-time stability analysis and stabilization. First, a linear time-varying (LTV) inequality-based approach is introduced for prescribed-time stability analysis. BTK inhibitor Then, it is shown that the existing nonlinear Lyapunov inequalities-based finite- and fixed-time stability criteria can be recast into the unified framework of the LTV inequality-based approach for prescribed-time stability. Finally, the unified LTV inequality-based approach is used to solve the global prescribed-time stabilization problem of the attitude control system of a rigid spacecraft with disturbance, and a bounded nonlinear time-varying controller is proposed via back stepping. Numerical simulations are presented to show the effectiveness of the proposed methods.Latent low-rank representation (LatLRR) is a critical self-representation technique that improves low-rank representation (LRR) by using observed and unobserved samples. It can simultaneously learn the low-dimensional structure embedded in the data space and capture the salient features. However, LatLRR ignores the local geometry structure and can be affected by the noise and redundancy in the original data space. To solve the above problems, we propose a latent LRR with weighted distance penalty (LLRRWD) for clustering in this article. First, a weighted distance is proposed to enhance the original Euclidean distance by enlarging the distance among the unconnected samples, which can enhance the discriminitation of the distance among the samples. By leveraging on the weighted distance, a weighted distance penalty is introduced to the LatLRR model to enable the method to preserve both the local geometric information and global information, improving discrimination of the learned affinity matrix. Moreover, a weight matrix is imposed on the sparse error norm to reduce the effect of noise and redundancy. Experimental results based on several benchmark databases show the effectiveness of our method in clustering.Reflection ultrasound computed tomography (RUCT) attains optimal image quality from objects that can be fully accessed from multiple directions, such as the human breast or small animals. Owing to the full-view tomography approach based on the compounding of images taken from multiple angles, RUCT effectively mitigates several deficiencies afflicting conventional pulse-echo ultrasound (US) systems, such as speckle patterns and interuser variability. On the other hand, the small interelement pitch required to fulfill the spatial sampling criterion in the circular transducer configuration used in RUCT typically implies the use of an excessive number of independent array elements. This increases the system's complexity and costs, and limits the achievable imaging speed. Here, we explore acquisition schemes that enable RUCT imaging with the reduced number of transmit/receive elements. We investigated the influence of the element size in transmission and reception in a ring array geometry. The performance of a sparse acquisition approach based on partial acquisition from a subset of the elements has been further assessed. A larger element size is shown to preserve contrast and resolution at the center of the field of view (FOV), while a reduced number of elements is shown to cause uniform loss of contrast and resolution across the entire FOV. The tradeoffs of achievable FOV, contrast-to-noise ratio, and temporal and spatial resolutions are assessed in phantoms and in vivo mouse experiments. The experimental analysis is expected to aid the development of optimized hardware and image acquisition strategies for RUCT and, thus, result in more affordable imaging systems facilitating wider adoption.The objective of this work was to develop an automated region of the interest selection method to use for adaptive imaging. The as low as reasonably achievable (ALARA) principle is the recommended framework for setting the output level of diagnostic ultrasound devices, but studies suggest that it is not broadly observed. One way to address this would be to adjust output settings automatically based on image quality feedback, but a missing link is determining how and where to interrogate the image quality. This work provides a method of region of interest selection based on standard, envelope-detected image data that are readily available on ultrasound scanners. Image brightness, the standard deviation of the brightness values, the speckle signal-to-noise ratio, and frame-to-frame correlation were considered as image characteristics to serve as the basis for this selection method. Region selection with these filters was compared to results from image quality assessment at multiple acoustic output levels. After selecting the filter values based on data from 25 subjects, testing on ten reserved subjects' data produced a positive predictive value of 94% using image brightness, the speckle signal-to-noise ratio, and frame-to-frame correlation. The best case filter values for using only image brightness and speckle signal-to-noise ratio had a positive predictive value of 97%. These results suggest that these simple methods of filtering could select reliable regions of interest during live scanning to facilitate adaptive ALARA imaging.The design of a high-performance Dielectrically Modulated Field Effect Transistor (DMFET) with smaller device dimension (channel length ≤ 100nm) has recently drawn significant research attention for point-of-care (POC) diagenesis applications. Driven by this paradigm, a Hetero-Gate Metal Dielectrically Modulated Junction-Less Nanotube Field Effect Transistor (DM-JLNFET) architecture is introduced and systematically investigated for label-free electrochemical biosensing application with the help of extensive numerical device simulations. The DM-JLNFET is carefully designed to exploit the advantages of superior gate control over channel electrostatics and electron injection component as well as strong immunity towards the short channel effects that lead to a notably high sensing performance compared to its conventional counterparts. In this context, the underlying physics of the transduction mechanism is analyzed in detail based on the device electrostatics and the carrier transport mechanism. The sensing performance of the proposed biosensor is quantified in terms of the drain current and threshold voltage sensitivities, which represents the relative modulations in these parameters with biomolecule conjugation. Typically, the DM-JLNFET exhibits a drain current and threshold voltage sensitivities as high as 1×1012 and 0.70, respectively, for biomolecule dielectric constant above 2. Furthermore, the sensing performance demonstrates strong immunities towards non-uniform cavity occupancy. Finally, extensive comparative performance analysis with Dielectrically Modulated Nanowire Field Effect Transistor (DM-NWFET) is performed. The results exhibit that the proposed DM-JLNFET can offer more than 100% and eight orders of magnitude improvements in the threshold voltage and drain current sensitivities, respectively, for a range of small biomolecule dielectric constants.