Iron(II) Phthalocyanine-Catalyzed Olefination of Aldehydes with Diazoacetonitrile: A Novel Approach to Construct Alkenyl Nitriles.

A novel synthetic approach to preparing alkenyl nitriles via the olefination of aldehydes with diazoacetonitrile catalyzed by iron(II) phthalocyanine in the presence of PPh3 has been developed. A broad variety of aldehydes are efficiently transformed into the corresponding products with the high yields of 75%-97%. And it is also suitable for its gram-scale preparation. The suggested mechanism involves the transformation of the phosphazine to ylide by iron(II) phthalocyanine.

Decentralized Implicit Inverse Control for Large-Scale Hysteretic Nonlinear Time-Delay Systems and Its Application on Triple-Axis Giant Magnetostrictive Actuators.

This article proposes fuzzy-logic systems (FLSs)-based decentralized adaptive implicit inverse control scheme for a class of large-scale nonlinear systems with time delays and multihysteretic loops. Our novel algorithms feature hysteretic implicit inverse compensators designed to effectively mitigate multihysteretic loops in large-scale systems. In this article, hysteretic implicit inverse compensators can replace the traditional hysteretic inverse models, which are exceedingly difficult to construct, and no longer necessary. The authors provide three contributions: 1) a searching mechanism to obtain the approximate value of the practical input signal from the so-called hysteretic temporary control law; 2) the arbitrarily small L∞ norm of the tracking error attained by utilizing the proposed initializing technique, which applies the combination of FLSs and a finite covering lemma to deal with time delays; and 3) the construction of a triple-axis giant magnetostrictive motion control platform, which validates the effectiveness of the proposed control scheme and algorithms.

A Compact RF Energy Harvesting Wireless Sensor Node with an Energy Intensity Adaptive Management Algorithm.

This paper presents a compact RF energy harvesting wireless sensor node with the antenna, rectifier, energy management circuits, and load integrated on a single printed circuit board and a total size of 53 mm × 59.77 mm × 4.5 mm. By etching rectangular slots in the radiation patch, the antenna area is reduced by 13.9%. The antenna is tested to have an S11 of -24.9 dB at 2.437 GHz and a maximum gain of 4.8 dBi. The rectifier has a maximum RF-to-DC conversion efficiency of 52.53% at 7 dBm input energy. The proposed WSN can achieve self-powered operation at a distance of 13.4 m from the transmitter source. To enhance the conversion efficiency under different input energy densities, this paper establishes an energy model for two operating modes and proposes an energy-intensity adaptive management algorithm. The experiments demonstrated that the proposed WSN can effectively distinguish between the two operating modes based on input energy intensity and realize efficient energy management.

A Compact Stacked RF Energy Harvester with Multi-Condition Adaptive Energy Management Circuits.

This paper presents a compact stacked RF energy harvester operating in the WiFi band with multi-condition adaptive energy management circuits (MCA-EMCs). The harvester is divided into antennas, impedance matching networks, rectifiers, and MCA-EMCs. The antenna is based on a polytetrafluoroethylene (PTFE) substrate using the microstrip antenna structure and a ring slot in the ground plane to reduce the antenna area by 13.7%. The rectifier, impedance matching network, and MCA-EMC are made on a single FR4 substrate. The rectifier has a maximum conversion efficiency of 33.8% at 5 dBm input. The MCA-EMC has two operating modes to adapt to multiple operating conditions, in which Mode 1 outputs 1.5 V and has a higher energy conversion efficiency of up to 93.56%, and Mode 2 supports a minimum starting input voltage of 0.33 V and multiple output voltages of 2.85-2.45 V and 1.5 V. The proposed RF energy harvester is integrated by multiple-layer stacking with a total size of 53 mm × 43.5 mm × 5.9 mm. The test results show that the proposed RF energy harvester can drive a wall clock (30 cm in diameter) at 10 cm distance and a hygrometer at 122 cm distance with a home router as the transmitting source.

Paper microfluidics with deep learning for portable intelligent nucleic acid amplification tests.

During global outbreaks such as COVID-19, regular nucleic acid amplification tests (NAATs) have posed unprecedented burden on hospital resources. Data of traditional NAATs are manually analyzed post assay. Integration of artificial intelligence (AI) with on-chip assays give rise to novel analytical platforms via data-driven models. Here, we combined paper microfluidics, portable optoelectronic system with deep learning for SARS-CoV-2 detection. The system was quite streamlined with low power dissipation. Pixel by pixel signals reflecting amplification of synthesized SARS-CoV-2 templates (containing ORF1ab, N and E genes) can be real-time processed. Then, the data were synchronously fed to the neural networks for early prediction analysis. Instead of the quantification cycle (Cq) based analytics, reaction dynamics hidden at the early stage of amplification curve were utilized by neural networks for predicting subsequent data. Qualitative and quantitative analysis of the 40-cycle NAATs can be achieved at the end of 22nd cycle, reducing time cost by 45%. In particular, the attention mechanism based deep learning model trained by microfluidics-generated data can be seamlessly adapted to multiple clinical datasets including readouts of SARS-CoV-2 detection. Accuracy, sensitivity and specificity of the prediction can reach up to 98.1%, 97.6% and 98.6%, respectively. The approach can be compatible with the most advanced sensing technologies and AI algorithms to inspire ample innovations in fields of fundamental research and clinical settings.

Streptomonospora mangrovi sp. nov., isolated from mangrove soil showing similar metabolic capabilities, but distinct secondary metabolites profiles.

A novel actinomycete, designated strain S1-112 T, was isolated from a mangrove soil sample from Hainan, China, and characterized using a polyphasic approach. Strain S1-112 T showed the highest similarity of the 16S rRNA gene to Streptomonospora nanhaiensis 12A09T (99.24%). Their close relationship was further supported by phylogenetic analyses, which placed these two strains within a stable clade. The highest values of digital DNA-DNA hybridization (dDDH, 41.4%) and average nucleotide identity (ANI, 90.55%) were detected between strain S1-112 T and Streptomonospora halotolerans NEAU-Jh2-17 T. Genotypic and phenotypic characteristics demonstrated that strain S1-112 T could be distinguished from its closely related relatives. We also profiled the pan-genome and metabolic features of genomic assemblies of strains belonging to the genus Streptomonospora, indicating similar functional capacities and metabolic activities. However, all of these strains showed promising potential for producing diverse types of secondary metabolites. In conclusion, strain S1-112 T represents a novel species of the genus Streptomonospora, for which the name Streptomonospora mangrovi sp. nov. was proposed. The type strain is S1-112 T (= JCM 34292 T).

Lamina Variation and Its Relationship with Sedimentary Facies in Alkaline Lacustrine, Permian Fengcheng Formation, Junggar Basin, Northwest China.

The Permian Fengcheng Formation in the Mahu Sag was deposited in a volcanic-alkaline lacustrine evaporative environment and contains a unique variety of fine-grained sediments. This study examines, at a millimeter-scale, the influence of sedimentary microfacies on variability of lamina quality in fine-grained sediments in the second member of the Fengcheng Formation (P1f2). The methods used include thin-section identification, X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy (SEM), nitrogen adsorption, and nuclear magnetic resonance (NMR). Six types of lamina were identified in two different lithofacies: fan-delta front facies (FDFF) and semideep/deep lacustrine facies (SDDLF). The laminae in FDFF are predominantly feldspar-quartz laminae (FQL), reedmergnerite laminae (RL), shortite laminae (SL), alkaline mineral laminae (AML), and chert laminae (CL). The laminae in SDDLF are predominantly FQL, RL, SL, CL, and dolomite laminae (DOL). Variations in reservoir quality, oil-bearing properties, and the fracability of laminae in different sedimentary facies are determined by the combined effects of lamina density, mineral composition, rock structure, organic matter abundance, and microfractures. Analysis of these factors indicates superior reservoir qualities in FDFF. In SDDLF, the pore structure is limited by high lamina density, chert content, and fine grain size with the NMR porosities of FQL, RL, SL, and CL being 1.32, 0.18, 0.84, and 0.39%, respectively. However, in FDFF, the combination of high organic matter content, feldspar, pyrite, and clay minerals has a superior effect on the organic matter and minerals deposited resulting in better pore structure and more storage space for shale oil. The NMR porosities of FQL, RL, SL, and CL are 2.81, 2.53, 1.80, and 1.12%, respectively. Overall, analysis of lamina variations and their relationships with sedimentary facies indicates that the reservoir in FDFF may offer more favorable targets for "sweet spot" evaluation.

Adaptive Fuzzy Neural Network Command Filtered Impedance Control of Constrained Robotic Manipulators With Disturbance Observer.

This article proposes an adaptive fuzzy neural network (NN) command filtered impedance control for constrained robotic manipulators with disturbance observers. First, barrier Lyapunov functions are introduced to handle the full-state constraints. Second, the adaptive fuzzy NN is introduced to handle the unknown system dynamics and a disturbance observer is designed to eliminate the effect of unknown bound disturbance. Then, a modified auxiliary system is designed to suppress the input saturation effect. In addition, the command filtered technique and error compensation mechanism are used to directly obtain the derivative of the virtual control law and improve the control accuracy. The barrier Lyapunov theory is used to prove that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded. Finally, simulation studies are performed to illustrate the effectiveness of the proposed control method.

Neural-Network-Based Adaptive Finite-Time Output Feedback Control for Spacecraft Attitude Tracking.

This brief is concerned with neural network (NN)-based adaptive finite-time output feedback attitude tracking control for rigid spacecraft in the presence of actuator saturation, inertial uncertainty, and external disturbance. First, a neural state observer is designed to estimate the unknown state. Then, based on the estimated state, the adaptive neural finite-time command filtered backstepping (CFB) is applied to construct virtual control signal and controller with updating law. The finite-time command filter is given to avoid the computation complexity problem in traditional backstepping, and the compensation signals based on fractional power are constructed to remove filtering errors. Using Lyapunov stability theory, we show that the attitude tracking error (TE) can converge into the desired neighborhood of the origin in finite time and all the signals in the closed-loop system are bounded in finite time although input saturation exists. The numerical simulations are used to show the effectiveness of the given algorithm.

Adaptive Neural Digital Control of Hysteretic Systems With Implicit Inverse Compensator and Its Application on Magnetostrictive Actuator.

Hysteresis is a complex nonlinear effect in smart materials-based actuators, which degrades the positioning performance of the actuator, especially when the hysteresis shows asymmetric characteristics. In order to mitigate the asymmetric hysteresis effect, an adaptive neural digital dynamic surface control (DSC) scheme with the implicit inverse compensator is developed in this article. The implicit inverse compensator for the purpose of compensating for the hysteresis effect is applied to find the compensation signal by searching the optimal control laws from the hysteresis output, which avoids the construction of the inverse hysteresis model. The adaptive neural digital controller is achieved by using a discrete-time neural network controller to realize the discretization of time and quantizing the control signal to realize the discretization of the amplitude. The adaptive neural digital controller ensures the semiglobally uniformly ultimately bounded (SUUB) of all signals in the closed-loop control system. The effectiveness of the proposed approach is validated via the magnetostrictive-actuated system.

Steroidobacter gossypii sp. nov., isolated from cotton field soil.

A Gram-negative bacterium, designated S1-65T, was isolated from soil samples collected from a cotton field located in the Xinjiang region of PR China. Results of 16S rRNA gene sequence analysis revealed that strain S1-65T was affiliated to the genus Steroidobacter with its closest phylogenetic relatives being 'Steroidobacter cummioxidans' 35Y (98.4 %), 'Steroidobacter agaridevorans' SA29-B (98.3 %) and Steroidobacter agariperforans KA5-BT (98.3 %). 16S rRNA-directed phylogenetic analysis showed that strain S1-65T formed a unique phylogenetic subclade next to 'S. agaridevorans' SA29-B and S. agariperforans KA5-BT, suggesting that strain S1-65T should be identified as a member of the genus Steroidobacter. Further, substantial differences between the genotypic properties of strain S1-65T and the members of the genus Steroidobacter, including average nucleotide identity and digital DNA-DNA hybridization, resolved the taxonomic position of strain S1-65T and suggested its positioning as representing a novel species of the genus Steroidobacter. The DNA G+C content of strain S1-65T was 62.5 mol%, based on its draft genome sequence. The predominant respiratory quinone was ubiquinone-8. The main fatty acids were identified as summed feature 3 (C16:1ω6c/C16:1ω7c), C16 : 0 and iso-C15 : 0. In addition, its polar lipid profile was composed of aminophospholipid, diphosphatidylglycerol, phosphatidylethanolamine and phosphatidylglycerol. Here, we propose a novel species of the genus Steroidobacter: Steroidobacter gossypii sp. nov. with the type strain S1-65T (=JCM 34287T=CGMCC 1.18736T).

Genomic molecular signatures determined characterization of Mycolicibacterium gossypii sp. nov., a fast-growing mycobacterial species isolated from cotton field soil.

A Gram-positive, acid-fast and rapidly growing rod, designated S2-37 T, that could form yellowish colonies was isolated from one soil sample collected from cotton cropping field located in the Xinjiang region of China. Genomic analyses indicated that strain S2-37 T harbored T7SS secretion system and was very likely able to produce mycolic acid, which were typical features of pathogenetic mycobacterial species. 16S rRNA-directed phylogenetic analysis referred that strain S2-37 T was closely related to bacterial species belonging to the genus Mycolicibacterium, which was further confirmed by pan-genome phylogenetic analysis. Digital DNA-DNA hybridization and the average nucleotide identity presented that strain S2-37 T displayed the highest values of 39.1% (35.7-42.6%) and 81.28% with M. litorale CGMCC 4.5724 T, respectively. And characterization of conserved molecular signatures further supported the taxonomic position of strain S2-37 T belonging to the genus Mycolicibacterium. The main fatty acids were identified as C16:0, C18:0, C20:3ω3 and C22:6ω3. In addition, polar lipids profile was mainly composed of diphosphatidylglycerol, phosphatidylethanolamine and phosphatidylinositol. Phylogenetic analyses, distinct fatty aids and antimicrobial resistance profiles indicated that strain S2-37 T represented genetically and phenotypically distinct from its closest phylogenetic neighbour, M. litorale CGMCC 4.5724 T. Here, we propose a novel species of the genus Mycolicibacterium: Mycolicibacterium gossypii sp. nov. with the type strain S2-37 T (= JCM 34327 T = CGMCC 1.18817 T).

Compound Adaptive Fuzzy Quantized Control for Quadrotor and Its Experimental Verification.

This article aims to realize a precise position and attitude tracking control for the quadrotor using a proposed fuzzy approximator-based compound adaptive fuzzy quantized control scheme. In the control scheme, a quantized output-feedback control for position tracking and a state-feedback quantized control for attitude trajectory tracking are combined to deal with the underactuated and strong coupling problems of the quadrotor. The main contributions are: 1) the adaptive fuzzy quantized control is realized, then the strong nonlinearities caused by the quantizer are effectively mitigated, which implies that the control precision can be improved when a low communication rate is required in the real-time control system of quadrotor; 2) by applying the adaptive fuzzy dynamic surface control (DSC) technique to the underactuated quadrotor control system, the "explosion of complexity" problem in the backstepping method is overcome and the L∞ tracking performance is achieved with the proposed initializing technique inspired by Zhang et al. This guarantees that the attitude signals promptly converge to the desired trajectories, then the underactuated problem of the quadrotor is overcome by solving the designed adaptive fuzzy-quantized control equations; and 3) the experiments on the platform of the Quanser Qball-X4 quadrotor are conducted and the effectiveness of the proposed control scheme is validated.

High-frequency mutation and recombination are responsible for the emergence of novel porcine reproductive and respiratory syndrome virus in northwest China.

Porcine reproductive and respiratory syndrome (PRRS) is one of the most highly infectious diseases in the pig industry, resulting in enormous economic losses worldwide. In this study, a PRRS virus (PRRSV) strain was isolated from primary porcine alveolar macrophage cells in Xinjiang in northwest China. This new strain was sequenced and designated as XJzx1-2015, and its sequence was then compared to those of other representative PRRSV strains from around the world. Complete genomic characterisation showed that the full-length nucleotide sequence of XJzx1-2015 exhibited low-level similarity to NB/04 (91.6%), JXA1 (90.5%), CH-1a (90.2%), VR-2332 (86.9%), QYYZ (85.7%), and JL580 (82.2%), with the highest similarity to HK13 (91.7%) sequence identity. Nonstructural protein 2 (NSP2) and glycosylated protein (GP) 2 of XJzx1-2015 had deletions of five and two amino acids, respectively, corresponding to strain VR-2332 positions 475-479 and 173-174. Phylogenetic analysis based on complete genome sequences showed that XJzx1-2015 and four other strains from China formed a new subgenotype closely related to other sublineage 8.7 (JXA1-like) strains belonging to the North American genotype. However, phylogenetic analysis based on NSP2 and GP5 showed that XJzx1-2015 clustered with sublineage 8.7 (JXA1-like, CH-1a-like) and lineage 3 (QYYZ-like) strains, respectively. Recombination analysis indicated that XJzx1-2015 is an intersubgenotype recombinant of CH-1a-like and QYYZ-like strains. Overall, our findings demonstrate that XJzx1-2015 is a novel PRRSV strain with a significantly high frequency of mutation and a recombinant between lineage 3 and sublineage 8.7 identified in northwest China. These results provide important insights into PRRSV evolution.

Adaptive Estimated Inverse Output-Feedback Quantized Control for Piezoelectric Positioning Stage.

Focusing on the piezoelectric positioning stage, this paper proposes an adaptive estimated inverse output-feedback quantized control scheme. First, the quantized issue due to the use of computer is addressed by introducing a linear time-varying quantizer model where the quantizer parameters can be estimated on-line. Second, by using the fuzzy approximator, the developed controller can avoid the identification of the parameters in the piezoelectric positioning stage. Third, by constructing the estimated inverse compensator of the hysteresis, the hysteresis nonlinearities in the piezoelectric actuator are mitigated; Fourth, the states observer is designed to avoid the measurements of the velocity and acceleration signals. The analysis of stability shows all the signals in the piezoelectric positioning stage are uniformly ultimately bounded and the prespecified tracking performance of the quantized control system is achieved by employing the error transformed function. Finally, a computer controlled experiments for the piezoelectric positioning stage is conducted to show the effectiveness of the proposed quantized controller.

Nonlinear Decoupling Control With ANFIS-Based Unmodeled Dynamics Compensation for a Class of Complex Industrial Processes.

Complex industrial processes are multivariable and generally exhibit strong coupling among their control loops with heavy nonlinear nature. These make it very difficult to obtain an accurate model. As a result, the conventional and data-driven control methods are difficult to apply. Using a twin-tank level control system as an example, a novel multivariable decoupling control algorithm with adaptive neural-fuzzy inference system (ANFIS)-based unmodeled dynamics (UD) compensation is proposed in this paper for a class of complex industrial processes. At first, a nonlinear multivariable decoupling controller with UD compensation is introduced. Different from the existing methods, the decomposition estimation algorithm using ANFIS is employed to estimate the UD, and the desired estimating and decoupling control effects are achieved. Second, the proposed method does not require the complicated switching mechanism which has been commonly used in the literature. This significantly simplifies the obtained decoupling algorithm and its realization. Third, based on some new lemmas and theorems, the conditions on the stability and convergence of the closed-loop system are analyzed to show the uniform boundedness of all the variables. This is then followed by the summary on experimental tests on a heavily coupled nonlinear twin-tank system that demonstrates the effectiveness and the practicability of the proposed method.

Tracking control of piezoelectric actuator using adaptive model.

Piezoelectric actuators (PEAs) have been widely used in micro- and nanopositioning applications due to their fine resolution, rapid responses, and large actuating forces. However, a major deficiency of PEAs is that their accuracy is seriously limited by hysteresis. This paper presents adaptive model predictive control technique for reducing hysteresis in PEAs based on autoregressive exogenous model. Experimental results show the effectiveness of the proposed method.

A novel estimation algorithm based on data and low-order models for virtual unmodeled dynamics.

In this paper, the challenging issue of estimating virtual unmodeled dynamics is addressed. A novel estimation algorithm based on historical data and the output of low-order approximation models for virtual un-modeled dynamics is presented. In particular, the virtual un-modeled dynamics are decomposed into known and unknown parts, where only the unknown part is to be estimated. The method effectively avoids the need to use the unknown control input directly, and enables the estimation of the un-modeled dynamics with a relatively simple algorithm. Moreover, it is shown that the proposed algorithm overcomes the difficulty in obtaining the control solutions caused by the fact that the controller input is embedded in un-modeled dynamics. Finally, simulation studies are presented to demonstrate the effectiveness of the proposed method.