Preparation and Application of Volatilized Wormwood Essence Derived Naturally into Green Insecticide.

Naturally occurring substances and their derivatives function as vital resources for pesticides that can be used in fields, such as insecticide production and fungicide development. As a botanical entity displaying multifaceted biological functions, wormwood has received thorough scrutiny across multiple sectors. The insect repellency potency combined with antibacterial and antifungal activities of wormwood position it as a potential candidate for prospective development into eco-friendly chemical pesticides. In this research, Wormwood essential oil was procured via ethanol water under ultrasonic scenarios and subsequently diluted with PEG 400 to formulate green chemical pesticides. The defensive efficacy of this green pesticide on plants was validated through 2 weeks of clustered plant growth experiments. Active constituents that exerted their effects were scrutinized by GC-MS. Furthermore, this green pesticide also displays efficacious effects on the prevention and management of aphids, exhibiting a dose-dependent relationship. 4-terpenol, eucalyptol, carvacrol, and L-borneol were identified by GC-MS as the predominant active constituents in this green chemical pesticide. Wormwood can be leveraged to develop green chemical pesticides, which can protect plants without contaminating the environment.

Mutation detection dual correlation filter with an object-awareness module for real-time target tracking.

Discriminative correlation filter (DCF) based methods have recently been widely used for visual tracking tasks. The adaptive spatiotemporal-regulation based tracker (AutoTrack) can only partially solve some limitations of the DCF framework including filter degradation and the boundary effect, but its application scenarios need to be broadened, and performance improvements are also required. To further surmount these difficulties, this paper provides an object-awareness-module based mutation detection dual correlation filter (MDDCF-OAM). The main innovation points of this work are: (1) an object-mask based context enhancer is proposed to formulate a more robust appearance model; (2) a dual filter training-learning structure is adopted to allow the dual filters to restrict each other and suppress the filter degradation effect; (3) a Gaussian label map is updated with the refined joint response map to detect and attenuate the response mutation effects. Exhaustive experiments have been conducted to test the efficiency of the suggested MDDCF-OAM on four benchmarks, namely, OTB2015, UAV123, TC128, and VOT2019. The results indicate that: (1) the introduced MDDCF-OAM surpasses nine state-of-the-art trackers; (2) the MDDCF-OAM has a real-time speed of 32 frames per second, which is sufficient for target tracking tasks in numerous scenarios, especially unmanned aerial vehicles and camera tracking.

Visual Inspection System for Uncoupling Status of Control-Rod Drive Rod in Nuclear Reactor.

Under some unexpected conditions, drive rods and control-rod assemblies may not be disconnected. If this situation is not detected, the control rod will be lifted out of the reactor core together with the upper reactor internals. This situation will seriously affect the follow-up work and reduce the economy and safety protection of the nuclear power plant. To ensure safety, the tripping status must be checked after tripping. Follow-up work can be carried out after checking and confirming that all drive rods are in the tripping status. There are many problems for traditional inspection methods, such as misjudgment, low accuracy, and labor consumption. This paper proposes a visual inspection system for the uncoupling state of the control-rod drive rod of the nuclear reactor. The proposed method is based on the fitting model of the ellipse parameter of the drive-rod head and the height of the drive rod. The ellipse of the drive-rod head is firstly accurately detected. Then, a mathematical model between the ellipse parameter and the height of the drive rod is established. The measurement error caused by the swing of the head of the drive rod is eliminated. The accurate measurement of the height difference before and after the tripping of the drive rod is computed. Finally, the status of the uncoupling of the drive rod is judged according to the difference. Many experiments are carried out with our developed system. The experimental results show that the proposed system realizes remote operation, ensures the quality of trip-status inspection, improves work efficiency, and reduces the workload of staff.

Review on the COVID-19 pandemic prevention and control system based on AI.

As a new technology, artificial intelligence (AI) has recently received increasing attention from researchers and has been successfully applied to many domains. Currently, the outbreak of the COVID-19 pandemic has not only put people's lives in jeopardy but has also interrupted social activities and stifled economic growth. Artificial intelligence, as the most cutting-edge science field, is critical in the fight against the pandemic. To respond scientifically to major emergencies like COVID-19, this article reviews the use of artificial intelligence in the combat against the pandemic from COVID-19 large data, intelligent devices and systems, and intelligent robots. This article's primary contributions are in two aspects: (1) we summarized the applications of AI in the pandemic, including virus spreading prediction, patient diagnosis, vaccine development, excluding potential virus carriers, telemedicine service, economic recovery, material distribution, disinfection, and health care. (2) We concluded the faced challenges during the AI-based pandemic prevention process, including multidimensional data, sub-intelligent algorithms, and unsystematic, and discussed corresponding solutions, such as 5G, cloud computing, and unsupervised learning algorithms. This article systematically surveyed the applications and challenges of AI technology during the pandemic, which is of great significance to promote the development of AI technology and can serve as a new reference for future emergencies.

Finite-Time Pinning Synchronization Control for T-S Fuzzy Discrete Complex Networks with Time-Varying Delays via Adaptive Event-Triggered Approach.

This paper is concerned with the adaptive event-triggered finite-time pinning synchronization control problem for T-S fuzzy discrete complex networks (TSFDCNs) with time-varying delays. In order to accurately describe discrete dynamical behaviors, we build a general model of discrete complex networks via T-S fuzzy rules, which extends a continuous-time model in existing results. Based on an adaptive threshold and measurement errors, a discrete adaptive event-triggered approach (AETA) is introduced to govern signal transmission. With the hope of improving the resource utilization and reducing the update frequency, an event-based fuzzy pinning feedback control strategy is designed to control a small fraction of network nodes. Furthermore, by new Lyapunov-Krasovskii functionals and the finite-time analysis method, sufficient criteria are provided to guarantee the finite-time bounded stability of the closed-loop error system. Under an optimization condition and linear matrix inequality (LMI) constraints, the desired controller parameters with respect to minimum finite time are derived. Finally, several numerical examples are conducted to show the effectiveness of obtained theoretical results. For the same system, the average triggering rate of AETA is significantly lower than existing event-triggered mechanisms and the convergence rate of synchronization errors is also superior to other control strategies.

Discovery of novel (6S/12aS)-heptachpyridone capable of inhibiting thrombosis in vivo.

The in vitro conversion of (1S,3S)-1-dimethoxylethyl-1,2,3,4-tetrahydro-ß-carboline-3-carboxylic acid, (1S,3S)-DCCA, in rat plasma is monitored by HPLC-FT-ICR-MS. We show that the in vitro conversion of (1S,3S)-DCCA in rat plasma for 1 h leads to forming (6S/12aS)-bisdimethoxyethylheptachpyridone, reflecting intermolecular condensation of (1S,3S)-DCCA, and the in vitro conversion of (6S/12aS)-bisdimethoxyethylheptachpyridone in rat plasma for 1 h leads to forming (6S/12aS)-heptachpyridone, reflecting hydrolysis of (6S/12aS)-bisdimethoxyethylheptachpyridone. At a dose of 1.0 µmol/kg (6S/12aS)-heptachpyridone orally inhibits venous thrombosis and arterial thrombosis in vivo. Bleeding time, clotting time and international normalized ratio show that at this dose (6S/12aS)-heptachpyridone has no bleeding risk, does not lengthen clotting time and does not change the exogenous coagulation pathway. We also show that the reactions promoted by rat plasma are easy to practice by chemical synthesis. Thus our findings build a bridge across the in vivo conversion and the application.

Electroosmotic flow of non-Newtonian fluids in a constriction microchannel.

Insulator-based dielectrophoresis has to date been almost entirely restricted to Newtonian fluids despite the fact that many of the chemical and biological fluids exhibit non-Newtonian characteristics. We present herein an experimental study of the fluid rheological effects on the electroosmotic flow of four types of polymer solutions, i.e., 2000 ppm xanthan gum (XG), 5% polyvinylpyrrolidone (PVP), 3000 ppm polyethylene oxide (PEO), and 200 ppm polyacrylamide (PAA) solutions, through a constriction microchannel under DC electric fields of up to 400 V/cm. We find using particle streakline imaging that the fluid elasticity does not change significantly the electroosmotic flow pattern of weakly shear-thinning PVP and PEO solutions from that of a Newtonian solution. In contrast, the fluid shear-thinning causes multiple pairs of flow circulations in the weakly elastic XG solution, leading to a central jet with a significantly enhanced speed from before to after the channel constriction. These flow vortices are, however, suppressed in the strongly viscoelastic and shear-thinning PAA solution.

The SseL protein inhibits the intracellular NF-κB pathway to enhance the virulence of Salmonella Pullorum in a chicken model.

To persist in the host, Salmonella is known to facultatively parasitize cells to escape the immune response. Intracellular Salmonella enterica can replicate using effector proteins translocated across the Salmonella-containing vacuolar membrane via a type III secretion system (T3SS) encoded by Salmonella pathogenicity island-2 (SPI-2). One of these factors, Salmonella secreted factor L (SseL), is a deubiquitinase that contributes to the virulence of Salmonella Typhimurium in mice by inhibiting the cellular NF-κB inflammatory pathway. However, the nature of its effect on the NF-κB pathway is controversial, and little research has been performed in other animal models. In this study, the SseL of Salmonella Pullorum was studied, and chickens were used as an infection model. An sseL gene deletion strain, a complementation strain and a eukaryotic expression plasmid were used to clarify the means by which SseL regulates Salmonella virulence and the cellular inflammatory response. SseL significantly enhanced the virulence of Salmonella Pullorum in chickens and suppressed activation of the cellular NF-κB pathway, thus inhibiting cellular inflammatory cytokine expression.

Identification by PCR signature-tagged mutagenesis of attenuated Salmonella Pullorum mutants and corresponding genes in a chicken embryo model.

A key feature of the fowl-specific pathogen Salmonella Pullorum is its vertical transmission to progeny via the egg. In this study, PCR signature-tagged mutagenesis identified nine genes of a strain of S. Pullorum that contributed to survival in the chicken embryo during incubation. The genes were involved in invasion, cell division, metabolism and bacterial defence. The competition index in vivo and in vitro together with a virulence evaluation for chicken embryos of all nine mutant strains confirmed their attenuation.

Docking based design of diastereoisomeric MTCA as GPIIb/IIIa receptor inhibitor.

In GPIIb/IIIa mediated arterial thrombosis platelet activation plays a central role. To discover platelet activation inhibitor the pharmacophores of GPIIb/IIIa receptor inhibitors and anti-thrombotic agents were analyzed. This led to the design of (1R,3S)- and (1S,3S)-1-methyl-1,2,3,4-tetrahydro-ß-carboline-3-carboxylic acids as GPIIb/IIIa inhibitors. Comparing to (1S,3S)-isomer (1R,3S)-isomer had lower cdocker interaction energy. AFM image showed that the minimal effective concentration of (1S,3S)-isomer and (1R,3S)-isomer inhibiting platelet activation were 10-5 M and 10-6 M, respectively. In vivo 1 µmol/kg of oral (1S,3S)-isomer effectively inhibited the rats to form arterial thrombus and down regulated GPIIb/IIIa expression, but the activities were significantly lower than those of 1 µmol/kg of oral (1R,3S)-isomer. Both (1S,3S)-isomer and (1R,3S)-isomer can be safely used for structural modifications, but (1R,3S)-isomer should be superior to (1S,3S)-isomer.

Automatic Camera Calibration Using Active Displays of a Virtual Pattern.

Camera calibration plays a critical role in 3D computer vision tasks. The most commonly used calibration method utilizes a planar checkerboard and can be done nearly fully automatically. However, it requires the user to move either the camera or the checkerboard during the capture step. This manual operation is time consuming and makes the calibration results unstable. In order to solve the above problems caused by manual operation, this paper presents a full-automatic camera calibration method using a virtual pattern instead of a physical one. The virtual pattern is actively transformed and displayed on a screen so that the control points of the pattern can be uniformly observed in the camera view. The proposed method estimates the camera parameters from point correspondences between 2D image points and the virtual pattern. The camera and the screen are fixed during the whole process; therefore, the proposed method does not require any manual operations. Performance of the proposed method is evaluated through experiments on both synthetic and real data. Experimental results show that the proposed method can achieve stable results and its accuracy is comparable to the standard method by Zhang.

A novel lead of P-selectin inhibitor: Discovery, synthesis, bioassays and action mechanism.

By docking 126 derivatives of ß-carboline-3-carboxylic acid, tetrahydro-ß-carboline-3-carboxylic acid and indoloquinolizine into the active pocket of P-selectin (2-(3-(hydroxymethyl)-9H-pyrido[3,4-b]indol-1-yl)ethyl)-l-phenylalanine (HMCEF) was assigned a novel inhibitor. ELISA and flow cytometry experiments showed that HMCEF effectively down-regulated P-selectin expression and supported the rationality of the computer assistant screening, while UV spectrum experiments demonstrated that HMCEF directly bound to P-selectin. In vivo HMCEF dose dependently inhibited the rats and mice to form thrombus and had a minimal effective dose of 20nmol/kg, dose dependently inhibited inflammatory response of mice and had a minimal effective dose of 20nmol/kg. The decrease of serum TNFα and IL-8 of the treated mice was proposed to be the action mechanism of HMCEF inhibiting thrombosis and inflammation. All data imply that HMCEF is a novel lead of P-selectin inhibitor.

Metabolic fate analysis of Huang-Lian-Jie-Du Decoction in rat urine and feces by LC-IT-MS combining with LC-FT-ICR-MS: a feasible strategy for the metabolism study of Chinese medical formula.

1. Huang-Lian-Jie-Du Decoction (HLJDD) is widely used for the treatment of hypertension, diabetes, inflammation and neural system diseases in clinic. In the present study, the comprehensive metabolic profile of HLJDD was demonstrated reliably and rapidly followed by the metabolic pathway analysis of six typical pure compounds (four alkaloids, one flavonoid and one iridoid) in HLJDD using LC-IT-MS combined with high resolution LC-FT-ICR-MS. 2. Totally, 85 compounds, including 32 prototype components and 53 biotransformed metabolites were detected and characterized in the urine and feces after oral administration of HLJDD and six pure compounds to rats, respectively. Among them, 17 prototypes were identified definitely with standard references. 3. Hydroxylation, demethylation and glucuronidation reactions of alkaloids, as well as glucuronidation and sulfonation reactions of iridoids and flavonoids, were observed as the major metabolic pathways of HLJDD. Flavonoids, iridoids and their metabolites were mainly excreted from urine. However, amount of alkaloids were detected in feces. 4. In general, the distinctive metabolic process of three kinds of representative components in HLJDD was clarified. The in vivo metabolic network of HLJDD was demonstrated. Meanwhile, the investigation of representative pure compounds in metabolic study provided a valuable strategy to elucidate the full-scale metabolic fate of HLJDD. This might be helpful to understand the in vivo mechanism of Traditional Chinese medicine (TCM).

Design and Analysis of a Sensor System for Cutting Force Measurement in Machining Processes.

Multi-component force sensors have infiltrated a wide variety of automation products since the 1970s. However, one seldom finds full-component sensor systems available in the market for cutting force measurement in machine processes. In this paper, a new six-component sensor system with a compact monolithic elastic element (EE) is designed and developed to detect the tangential cutting forces Fx, Fy and Fz (i.e., forces along x-, y-, and z-axis) as well as the cutting moments Mx, My and Mz (i.e., moments about x-, y-, and z-axis) simultaneously. Optimal structural parameters of the EE are carefully designed via simulation-driven optimization. Moreover, a prototype sensor system is fabricated, which is applied to a 5-axis parallel kinematic machining center. Calibration experimental results demonstrate that the system is capable of measuring cutting forces and moments with good linearity while minimizing coupling error. Both the Finite Element Analysis (FEA) and calibration experimental studies validate the high performance of the proposed sensor system that is expected to be adopted into machining processes.

Multichannel lens-free CMOS sensors for real-time monitoring of cell growth.

A low-cost platform is proposed for the growth and real-time monitoring of biological cells. The main components of the platform include a PMMA cell culture microchip and a multichannel lens-free CMOS (complementary metal-oxide-semiconductor) / LED imaging system. The PMMA microchip comprises a three-layer structure and is fabricated using a low-cost CO2 laser ablation technique. The CMOS / LED monitoring system is controlled using a self-written LabVIEW program. The platform has overall dimensions of just 130 × 104 × 115 mm(3) and can therefore be placed within a commercial incubator. The feasibility of the proposed system is demonstrated using HepG2 cancer cell samples with concentrations of 5000, 10 000, 20 000, and 40 000 cells/mL. In addition, cell cytotoxicity tests are performed using 8, 16, and 32 mM cyclophosphamide. For all of the experiments, the cell growth is observed over a period of 48 h. The cell growth rate is found to vary in the range of 44∼52% under normal conditions and from 17.4∼34.5% under cyclophosphamide-treated conditions. In general, the results confirm the long-term cell growth and real-time monitoring ability of the proposed system. Moreover, the magnification provided by the lens-free CMOS / LED observation system is around 40× that provided by a traditional microscope. Consequently, the proposed system has significant potential for long-term cell proliferation and cytotoxicity evaluation investigations.

Particles small angle forward-scattered light measurement based on photovoltaic cell microflow cytometer.

A method is proposed for detecting microparticles in a microflow cytometer by means of small angle forward-scattered light measurements. The proposed cytometer comprises a commercial photovoltaic cell, an adjustable power laser module, and a PDMS microfluidic chip. The detection performance of the proposed device is evaluated using particles with dimensions of 5, 8, 10, and 15 µm, respectively, given forward-light scattering angles of 5 and 8° and laser powers ranging from 15-25 mW. It is shown that for a constant laser power and particle size, the S/N of the detected light signal increases with a reducing forward-scattering angle. Moreover, for a constant forward-scattering angle and particle size, the S/N increases with an increasing laser power. The intensity of the forward-scattered light signal is found to vary linearly with the particle size and has a correlation coefficient of R(2) = 0.967, 0.967, and 0.963 given laser powers of 15, 20, and 25 mW, respectively, and a forward-scattering angle of 5°. Moreover, the CV of the forward-scattered light intensity is found to lie within the range of 20-30% for both forward-scattering angles. Overall, the present results suggest that the proposed device has significant potential for detection applications in the medical, environmental monitoring, and biological science fields.

A novel diagnosis method for a Hall plates-based rotary encoder with a magnetic concentrator.

In the last few years, rotary encoders based on two-dimensional complementary metal oxide semiconductors (CMOS) Hall plates with a magnetic concentrator have been developed to measure contactless absolute angle. There are various error factors influencing the measuring accuracy, which are difficult to locate after the assembly of encoder. In this paper, a model-based rapid diagnosis method is presented. Based on an analysis of the error mechanism, an error model is built to compare minimum residual angle error and to quantify the error factors. Additionally, a modified particle swarm optimization (PSO) algorithm is used to reduce the calculated amount. The simulation and experimental results show that this diagnosis method is feasible to quantify the causes of the error and to reduce iteration significantly.

Obstacle classification and 3D measurement in unstructured environments based on ToF cameras.

Inspired by the human 3D visual perception system, we present an obstacle detection and classification method based on the use of Time-of-Flight (ToF) cameras for robotic navigation in unstructured environments. The ToF camera provides 3D sensing by capturing an image along with per-pixel 3D space information. Based on this valuable feature and human knowledge of navigation, the proposed method first removes irrelevant regions which do not affect robot's movement from the scene. In the second step, regions of interest are detected and clustered as possible obstacles using both 3D information and intensity image obtained by the ToF camera. Consequently, a multiple relevance vector machine (RVM) classifier is designed to classify obstacles into four possible classes based on the terrain traversability and geometrical features of the obstacles. Finally, experimental results in various unstructured environments are presented to verify the robustness and performance of the proposed approach. We have found that, compared with the existing obstacle recognition methods, the new approach is more accurate and efficient.

LSKANet: Long Strip Kernel Attention Network for Robotic Surgical Scene Segmentation.

Surgical scene segmentation is a critical task in Robotic-assisted surgery. However, the complexity of the surgical scene, which mainly includes local feature similarity (e.g., between different anatomical tissues), intraoperative complex artifacts, and indistinguishable boundaries, poses significant challenges to accurate segmentation. To tackle these problems, we propose the Long Strip Kernel Attention network (LSKANet), including two well-designed modules named Dual-block Large Kernel Attention module (DLKA) and Multiscale Affinity Feature Fusion module (MAFF), which can implement precise segmentation of surgical images. Specifically, by introducing strip convolutions with different topologies (cascaded and parallel) in two blocks and a large kernel design, DLKA can make full use of region- and strip-like surgical features and extract both visual and structural information to reduce the false segmentation caused by local feature similarity. In MAFF, affinity matrices calculated from multiscale feature maps are applied as feature fusion weights, which helps to address the interference of artifacts by suppressing the activations of irrelevant regions. Besides, the hybrid loss with Boundary Guided Head (BGH) is proposed to help the network segment indistinguishable boundaries effectively. We evaluate the proposed LSKANet on three datasets with different surgical scenes. The experimental results show that our method achieves new state-of-the-art results on all three datasets with improvements of 2.6%, 1.4%, and 3.4% mIoU, respectively. Furthermore, our method is compatible with different backbones and can significantly increase their segmentation accuracy. Code is available at https://github.com/YubinHan73/LSKANet.

Flex-DLD: Deep Low-Rank Decomposition Model With Flexible Priors for Hyperspectral Image Denoising and Restoration.

Hyperspectral images (HSIs) are composed of hundreds of contiguous waveband images, offering a wealth of spatial and spectral information. However, the practical use of HSIs is often hindered by the presence of complicated noise caused by various factors such as non-uniform sensor response and dark current. Traditional methods for denoising HSIs rely on constrained optimization approaches, where selecting appropriate prior knowledge is critical for achieving satisfactory results. Nevertheless, these traditional algorithms are limited by hand-crafted priors, leaving room for improvement in their denoising performance. Recently, the supervised deep learning technique has emerged as a promising approach for HSI denoising. However, their requirement for paired training data and poor generalization ability on untrained noise distributions pose challenges in practical applications. In this paper, we design a novel algorithm by the synergism of optimization-based methods and deep learning techniques. Specifically, we introduce a plug-and-play Deep Low-rank Decomposition (DLD) model into the optimization framework. Furthermore, we propose an effective mechanism to incorporate traditional prior knowledge into the DLD model. Finally, we provide a detailed analysis of the optimization process and convergence of the proposed method. Empirical evaluations on various tasks, including hyperspectral image denoising and spectral compressive imaging, demonstrate the superiority of our approach over state-of-the-art methods.