An Iteratively Extended Target Tracking by Using Decorrelated Unbiased Conversion of Nonlinear Measurements.

Extended target tracking (ETT) based on random matrices typically assumes that the measurement model is linear. However, nonlinear measurements (such as range and azimuth) depending on locations of a series of unknown scattering centers always exist in many practical tracking applications. To address this issue, this paper proposes an iteratively extended target tracking based on random matrices by using decorrelated unbiased conversion of nonlinear measurements (ETT-IDUCM). First, we utilize a decorrelated unbiased converted measurement (DUCM) method to convert nonlinear measurements depending on unknown scatters of target extent in polar coordinates into the ones in Cartesian coordinates with equivalent measurement noise covariances. Subsequently, a novel method, combining iteratively extended Kalman filter (IEKF) updates with variational Bayesian (VB) cycles is developed for precise estimation of the target's kinematic state and extension. This method leverages the synergy between external IEKF iterations, which use the estimated state as a new prediction and input for DUCM, and internal VB iterations, which realize a closed-form approximation of the joint posterior probability. This approach progressively enhances estimation accuracy. Simulation results demonstrate the ETT-IDUCM algorithm's superior precision in estimating the target's kinematic state and extension compared to existing methods.

Facile one-pot preparation of a novel imidazolium-based monolith by thiol-ene click chemistry for capillary liquid chromatography.

In this work, a novel imidazolium-based monolith was fabricated through a simple route. With 1-vinyl-3-octadecylimidazolium bromide and ethylene dimethacrylate as monomers, pentaerythritol tetra-(3-mercaptopropionate) as crosslinker, AIBN as thermal initiator, the monolith was facilely fabricated by one-pot thiol-ene click chemistry. The influences of both the content of monomer/crosslinker and porogenic systems on the morphology, and permeability of the monolith were studied. The optimal reaction conditions were used to prepare a homogeneous and permeable monolith. The optimal preparation of monolithic column was characterized by scanning electron microscopy, Fourier transform infrared spectrometry, X-ray photoelectron spectroscopy, and thermogravimetric analysis. High efficiency and quick separation of alkylbenzenes, styrene and ethylbenzene, polycyclic aromatic hydrocarbon, phenols, anilines, and aromatic acids were achieved. The minimum plate height of this monolith were determined as 11.42 µm for thiourea and 13.26 µm for benzene. All results indicated that thiol-ene click chemistry provides a quick way for the fabrication of imidazolium-based monolith.

Bioinspired Synthesis of Cu2+ -Modified Covalent Triazine Framework: A New Highly Efficient and Promising Peroxidase Mimic.

Artificial enzymes is an emerging field of research owing to the remarkable advantages of enzyme mimics over their natural counterpart, including tunable catalytic efficiencies, lower cost, ease of preparation, and excellent tolerance to variations of the reaction system. Herein, we report an efficient peroxidase mimic based on a copper-modified covalent triazine framework (CCTF). Owing to its unique specific surface area, atomically dispersed active Cu sites, efficient electron transfer, and enhanced photo-assisted enzyme-like activity, the CCTF showed enhanced peroxidase-like enzyme activity. Therefore, copper modification represents an effective route to tailor the peroxidase-like activity of the covalent triazine frameworks. Furthermore, the mechanism of the enhanced peroxidase-like activity and stability of the CCTF were investigated. As a proof of concept, the CCTF was used for the colorimetric detection of H2 O2 and decomposition of organic pollutants. This work provides a new strategy for the design of enzyme mimics with a broad range of potential applications.

Iteratively distributed instrumental variable-based pseudo-linear information filter for angle-only tracking.

This paper presents a distributed filtering problem for three-dimensional angle-only target tracking (AOTT) in sensor (i.e., observer) networks. An instrumental variable-based pseudo-linear information filter (IVIF) is firstly derived on the basis of the designed bias-compensated pseudo-linear information filtering, with the help of summation forms of information quantities and bias compensation in a centralized fusion manner. Then, the distributed IVIF (DIVIF) is put forward by using finite-time average consensus to obtain the arithmetic means of defined information quantities and compensated bias in observer networks, which ensures that the filtering result of every observer is consistent with the centralized one. Finally, the iteratively DIVIF is proposed via gradually approaching the true values of relative distance and the corresponding angles between the target and every observer to get the filtering parameters more and more accurately, in order to achieve higher filtering precision. In addition, the computational complexity of the proposed method is also analyzed. The advantages of filtering precision of the proposed method over the existing pseudo-linear Kalman filter and its variants are demonstrated by an AOTT example in observer networks in terms of iteration steps, different levels of process noises and observer's accuracy.

A real-time rural domestic garbage detection algorithm with an improved YOLOv5s network model.

An increasing number of researchers are using deep learning technology to classify and process garbage in rural areas, and have achieved certain results. However, the existing garbage detection models still have problems such as high complexity, missed detection of small targets, low detection accuracy and poor real-time performance. To address these issues, we train a model and apply it to garbage classification and detection in rural areas. In general, we propose an attention combination mechanism based on the YOLOv5 algorithm to build a better backbone network structure, add a new small object detection layer in the head network to enhance the model's ability to detect small objects, adopt the CIoU loss function to optimize the output prediction bounding box, and choose the Adam optimization algorithm to train the model. Our proposed YOLOv5s-CSS model detects a single garbage image in 0.021 s with a detection accuracy of 96.4%. Compared with the YOLOv5 algorithm and the classic detection algorithm, the improved algorithm has better detection speed and detection accuracy. At the same time, the complexity of the network model is reduced to a certain extent, which can meet the requirements of real-time detection of rural domestic garbage.

Study on microwave pyrolysis and production characteristics of Chlorella vulgaris using different compound additives.

Pyrolysis characteristics and bio-oil of Chlorella vulgaris were investigated under SiC and ZnO (SZ) mixture (compound additive) with various mixing ratios (S/Z = 10:0, 7:3, 5:5, 3:7, 0:10) and addition amounts (5%, 10%, 15%) by thermogravimetric analysis and GC-MS. At three experimental groups of 10% compound additive, as ZnO in compound additive increased, maximum weight loss rate (Rp) increased, the time (tp) corresponding to Rp and the weight stabilization time (tf) first decreased and then increased, while average rate of weight loss (Ra) and total weight loss (M) first increased and then decreased; maximum temperature rising rate (Hx) and average rate of temperature rising (Hg) increased, while the time (tx) corresponding to Hx decreased. Compound additives reduced the bio-oil yield, increased the gas yield, and reduced the acid compounds in bio-oil. Besides, it might promote the production of alicyclic hydrocarbons and oxygen/nitrogen-containing long-chain compounds.

Adaptive Gaussian mixture filter for Markovian jump nonlinear systems with colored measurement noises.

This paper considers the state estimation of discrete-time Markovian jump nonlinear systems with colored measurement noises obeying a nonlinear autoregressive process of order n, which is motivated by tracking the maneuvering target under electronic countermeasures with high speed sampling or persistent perturbations. In order to remove the measurement noises correlation, the left zero divisor is explored to reconstruct a new measurement equation via difference approach, with the help of applying statistical linear regression to the colored measurement noise model. Then, a novel hypothesis set constituted of all possible values of multi-step Markov jumping parameters is defined and the posterior probability density of the state is derived recursively. By using Gaussian mixtures to approximate the posterior probability densities, an adaptive Gaussian mixture filter for the considered system is proposed, where the Gaussian components with small weights are pruned adaptively through measuring the Alpha (or Beta) divergence for the original and approximated Gaussian mixtures, to achieve a tradeoff between the estimation accuracy and running time. A maneuvering target tracking accompanied by range gate pull-off with different colored measurement noises cases is simulated to validate the proposed method.

One-pot synthesis of a metal-organic framework-based drug carrier for intelligent glucose-responsive insulin delivery.

We have developed a glucose-responsive metal-organic framework (MOF)-based insulin delivery nanosystem via a one-pot process. The system relies on the MOF response to glucose stimulation and this can promote insulin delivery. This nanosystem was successfully applied for glucose-responsive and self-regulated insulin release.

A label-free fluorescence assay for hydrogen peroxide and glucose based on the bifunctional MIL-53(Fe) nanozyme.

A label-free nanozyme MIL-53(Fe) with the dual-function of catalyzing and emitting fluorescence was utilized for turn-on fluorescence detection of hydrogen peroxide and glucose. The proposed strategy provides a cost-effective, safe and sensitive method for the design and development of multiple enzyme cascade assays for various biomolecules.

Preparation of Cationic MOFs with Mobile Anions by Anion Stripping to Remove 2,4-D from Water.

A cationic porous framework with mobile anions (MIL-101(Cr)-Cl) was easily and successfully synthesized by utilizing the stronger affinity of F- to Al3+ than Cr3+ in the charge-balanced framework of MIL-101(Cr). The structure, morphology and porosity of MIL-101(Cr)-Cl were characterized. The obtained new materials retain the high surface area, good thermostability, and structure topology of MIL-101(Cr). With the mobile Cl- anion, MIL-101(Cr)-Cl can be used as an ion-exchange material for anionic organic pollutions. In this work, 2,4-dichlorophenoxyacetic acid (2,4-D) was used as a model to test the absorption performance of this new material. This new material exhibited improved adsorbability compared to that of the original metal-organic frameworks (MOFs). At the same time, this material also shows high anti-interference performance with changing solution pH.