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In this work, dielectric ultracapacitors were designed and fabricated using a combination of phase boundary and nanograin strategies. These ultracapacitors are based on submicron-thick Ba(Zr0.2Ti0.8)O3 ferroelectric films sputter-deposited on Si at 500 °C. With a composition near a polymorphic phase boundary (PPB), a compressive strain, and a high nucleation rate due to the lowered deposition temperature, these films exhibit a columnar nanograined microstructure with gradient phases along the growth direction. Such a microstructure presents three-dimensional polarization inhomogeneities on the nanoscale, thereby significantly delaying the saturation of the overall electric polarization. Consequently, a pseudolinear, ultraslim polarization (P)–electric field (E) hysteresis loop was obtained, featuring a high maximum applicable electric field (~5.7 MV/cm), low remnant polarization (~5.2 μC/cm2) and high maximum polarization (~92.1 μC/cm2). This P–E loop corresponds to a high recyclable energy density (Wrec ~208 J/cm3) and charge‒discharge efficiency (~88%). An in-depth electron microscopy study revealed that the gradient ferroelectric phases consisted of tetragonal (T) and rhombohedral (R) polymorphs along the growth direction of the film. The T-rich phase is abundant near the bottom of the film and gradually transforms into the R-rich phase near the surface. These films also exhibited a high Curie temperature of ~460 °C and stable capacitive energy storage up to 200 °C. These results suggest a feasible pathway for the design and fabrication of high-performance dielectric film capacitors.

Abstract In today's world, remote-controlled robots are widely used across various industries due to their ability to enhance working efficiency in various applications. Learning about robot operation and human–computer interaction has emerged as a popular topic in recent times. Indeed, learning robotics can be challenging for many students as it requires knowledge of programming, control systems, electronics, etc. Collaborative learning in a physical robotics setting is common in higher education and has received significant attention for its potential to enhance individual learning outcomes. However, the effectiveness of learning robotics in a remote setting is still a matter of debate. In this study, we establish a remote laboratory environment to teach undergraduate students in the engineering discipline. Students are required to utilize a robotic arm to grasp designated objects collaboratively among students through synchronous interactions online. To compare students’ performance under different pedagogical teaching approaches, students are divided into two groups. They each perform the task individually and collaboratively, albeit in a different order. Our study adopts a quantitative method to measure students' learning outcomes based on the assessment of performing the laboratory tasks and completion time. The results indicate a noteworthy improvement in the individual performance of the group of students who engage in collaborative work prior to the individual tasks. These findings have implications for other remote laboratory setups and highlight the effectiveness of collaborative learning in higher education.

Cardiac motion tracking from echocardiography can be used to estimate and quantify myocardial motion within a cardiac cycle. It is a cost-efficient and effective approach for assessing myocardial function. However, ultrasound imaging has the inherent characteristics of spatially low resolution and temporally random noise, which leads to difficulties in obtaining reliable annotation. Thus it is difficult to perform supervised learning for motion tracking. In addition, there is no end-to-end unsupervised method currently in the literature. This paper presents a motion tracking method where unsupervised optical flow networks are designed with spatial reconstruction loss and temporal-consistency loss. Our proposed loss functions make use of the pair-wise and temporal correlation to estimate cardiac motion from noisy background. Experiments using a synthetic 4D echocardiography dataset has shown the effectiveness of our approach, and its superiority over existing methods on both accuracy and running speed.

This paper introduces a new device concept and outlines the fabrication process of a bipolar junction transistor based on an IGZO/NiO/PbSe nanoband array heterostructure. We performed comprehensive electrical property testing and characterization analysis on the device to thoroughly assess the device's performance. The heterojunction structure efficiently amplifies the opto-electric responsivity and enhances the transmission efficiency of photogenerated carriers. Significantly, the phototransistor demonstrates a high photoresponsivity of 4000 A/W under an incident light power of 1 μW/cm2 and a wavelength of 850 nm. Hence, a solution detection system integrated with a bipolar phototransistor is designed. Through the analysis of the output signals, the system accurately determines the identity of the solute present in the solution.

Abstract A well‐liked maneuvering target tracking algorithm is a variable structure multi‐model (VSMM). One of the crucial elements determining the tracking effect is the successful model set adaptation (MSA). The ability to further enhance tracking accuracy for the conventional VSMM method is constrained by the absence of a mechanism to thoroughly utilise observation and tracking data to optimise MSA. We incorporate the Reinforcement learning (RL) approach into the MSA procedure to address this issue and provide a VSMM algorithm based on Monte Carlo (MC) learning. To formulate the challenge of optimising the number of effective models as a RL problem, we first used the prediction error, the number of effective model sets, and tracking accuracy to build the models of the appropriate state space, decision space, and reward. The number of efficient models was optimised using MC learning, and the entire VSMM algorithm was then created. The proposed approach was compared to the simulated experiment's five maneuvering target tracking algorithms. The outcomes demonstrate that the suggested algorithm has a lower computation scale and accurate tracking accuracy.

Offshore wind energy, as a form of renewable power, has seen rapid development in recent years. While fixed-bottom wind turbines are typically used in water depths less than 50 ​m, the utilization of floating offshore wind turbines (FOWTs) becomes essential for deeper waters. Secure and effective mooring systems play a crucial role in making FOWTs commercially viable. The concept of a shared mooring system offers an innovative solution for deploying floating wind farms in clusters or arrays, which can reduce overall construction costs for large-scale floating wind farms. It is imperative to optimize the shared mooring arrangement for maximum cost-effectiveness and wind farm stability. However, implementing a shared mooring system introduces complexity to the dynamics of FOWTs, requiring the development of advanced simulation tools to meet modelling requirements. Under the shared mooring arrangement, mooring lines and anchors face more significant challenges, such as chain-seabed interactions, soil cyclic weakening, and anchor out-of-plane loading, which underscore the need for innovative, reliable, and efficient shared anchor designs. This article offers an overview of the current research status on shared mooring systems for floating wind farms, which might serve as a valuable reference for the construction of large-scale floating wind farms worldwide.

Abstract Assist-as-needed control with a soft robotic hand glove for active rehabilitation is studied in this work. There are two resources of the grasping force, the robotic glove and the subject. Compared with traditional passive rehabilitation where the grasping force is merely provided by a robotic hand rehabilitation device (such as hand exoskeleton, robotic glove), assist-as-needed control accounts for the user contribute to performing grasping tasks collaboratively. In this control method, the human muscle strength for grasping is estimated through the myoelectrical signals of the human forearm collected by the MYO armband. A neural network is used for the recognition of human-object contact estimation. The assist-as-needed control is finally implemented to assist humans in grasping tasks. Experiment results on a soft robotic glove show the effectiveness of the proposed assistive control method.

This paper introduces the Long Short-Term Memory with Dual-Stage Attention (LSTM-MSA) model, an approach for analyzing electromyography (EMG) signals. EMG signals are crucial in applications like prosthetic control, rehabilitation, and human-computer interaction, but they come with inherent challenges such as non-stationarity and noise. The LSTM-MSA model addresses these challenges by combining LSTM layers with attention mechanisms to effectively capture relevant signal features and accurately predict intended actions. Notable features of this model include dual-stage attention, end-to-end feature extraction and classification integration, and personalized training. Extensive evaluations across diverse datasets consistently demonstrate the LSTM-MSA’s superiority in terms of F1 score, accuracy, recall, and precision. This research provides a model for real-world EMG signal applications, offering improved accuracy, robustness, and adaptability.

In this paper, the evaluation method of fluidelastic instability (FEI) of newly designed steam generator tubing in pressurized water reactor (PWR) nuclear power plants is discussed. To obtain the parameters for prediction of the critical velocity of FEI for steam generator tubes, experimental research is carried out, and the design parameters are determined. Using CFD numerical simulation, the tube array scale of the model experiment is determined, and the experimental device is designed. In this paper, 7 groups of experiments with void fractions of 0% (water), 10%, 20%, 50%, 75%, 85% and 95% were carried out. The critical damping ration, fundamental frequency and critical velocity of FEI of tubes in flowing water were measured. Through calculation, the total mass and instability constant of the immersed tube are obtained. The critical damping ration measured in the experiment mainly included two-phase damping and viscous damping, which changed with the change in void fraction from 1.56% to 4.34%. This value can be used in the steam generator design described in this paper and is conservative. By introducing the multiplier of frequency and square root of total mass per unit length, it is found that the difference between the experimental results and the calculated results is less than 1%, which proves the rationality and feasibility of the calculation method of frequency and total mass per unit length in engineering design. Through calculation, the instability constant is greater than 4 when the void fraction is less than 75%, less than 4 when the void fraction exceeds 75% and only 3.04 when the void fraction is 95%.

To ensure the safety of mine trucks on unpaved roads and in dusty environments, it's necessary to improve the efficiency and robustness of targets detection and tracking. This paper proposes a multi-target detection and tracking algorithm based on the fusion of light detection and ranging (LiDAR) and millimeter-wave radar. The algorithm includes point cloud feature extraction module, target tracking module and heterogeneous sensor fusion module. Among them, feature extraction module uses point cloud gradient and distance feature extraction methods to solve the point cloud segmentation problem of small and irregular-shaped obstacles in changing scenes; target tracking module constructs track information for multi-target tracking, which improves the stability of target tracking; an asynchronous fusion strategy of multi-source sensors is designed for heterogeneous sensor fusion module, which overcomes the problem of heterogeneous sensor fusion, and improves the detection and tracking capabilities of small and various targets in unpaved road and dusty environment of open-pit mines. Finally, verification experiment is carried out on the mine trucks based on ROS(robot operating system) framework and the results show that it can accurately detect mine trucks in the range of 200 m and objects of 30 cm in the range of 50 m, which demonstrate the stability, accuracy and reliability of the method.

Traditional positioning methods, such as GNSS (global navigation satellite system) and location base service, have large positioning error when applied in complex traffic scenarios such as like tunnels buildings and viaducts. Odometer has accumulated errors, so it is difficult to achieve accurate positioning over long distances. For the problem of large positioning error in complex driving environments, this paper proposes a long time and high precision positioning system based on LiDAR - global map matching to achieve robust and accurate state estimation. The system mainly consists of two parts: global map construction and map matching location. Firstly, absolute localization constraint of global navigation satellite system is introduced in map construction to eliminate cumulative errors of LiDAR odometer and construct a globally consistent high-precision map. Secondly, in the part of map matching location, the original global map matching and real-time dynamic map matching are combined to effectively avoid positioning degradation caused by environmental changes, which can improve the positioning robustness. Test results of the autonomous-rail rapid tram on open urban roads show that this method can provide vehicle pose information stably and reliably. The angle estimation error of LiDAR is within 0.35° and the position error is within 0.15 m during the whole operation process, which verifies the effectiveness and robustness of the proposed algorithm.

The rare earth elements (REEs) provide significant geological information and serve as a reliable indicator for predicting the paleoclimate, paleoenvironment, and paleotectonic evolution of sedimentary rocks. The REEs, major elements, and trace elements of 75 marine shale samples collected from the Late Permian Dalong Formation at the Fantiansi (FTS) and Putaoling (PTL) sections in the Lower Yangtze region were analyzed. The results revealed that the major elements Al2O3, K2O, and TiO2 were primarily influenced by clay minerals associated with terrigenous detrital, whereas SiO2 and Na2O were not affected by clay minerals. The ΣREE values obtained from the Late Permian Dalong Formation at the Fantiansi (FTS) and Putaoling (PTL) sections in the Lower Yangtze region were found to be lower than those of the Post-Archean Average Shale (PAAS) (184.8 μg/g). The study of REE indicators related to the source, redox conditions, and tectonic settings led to the following conclusions: (a) ΣREE showed strong positive correlations with TiO2, Al2O3, K2O, and Na2O, but exhibited relatively weak correlations with Fe2O3 and P2O5, suggesting that REEs were mainly associated with clay minerals, but were also influenced by pyrite, phosphates, and other minerals; (b) The high values of Ce/Ce*, MoEF, UEF, and Corg/P were mainly concentrated in Stages I, II, III and V of the Dalong Formation at the FTS and PTL sections, and the corresponding strong anoxic environment was more conducive to the preservation of organic matter; (c) The diagram between La/Yb and ΣREE, Al2O3-TiO2, TiO2-Zr, and La/Th-Hf bivariate diagrams indicated that the provenance of the rocks from the Dalong formations was primarily felsic igneous rocks; (d) Discriminant-function diagrams and La-Th-Sc, Th-Sc-Zr/10 and Th-Co-Zr/10 triangular diagrams show that the clastic sediments of the Dalong formations were derived most likely from continental island arcs. This study corresponds to the background of the transition from continental margin to continental collision structure in South China during the Late Permian.

Abstract The Backward Stochastic Differential Equation (BSDE) is an important tool for pricing and hedging. Highly accurate pricing for low computation time becomes interesting for minimizing monetary loss. Therefore, we explore the opportunity of parallelizing high-order multistep schemes in option pricing. In the multistep scheme the computations at each space grid point are independent and this fact motivates us to select massively parallel GPU computing using CUDA. In our investigations we identify performance bottlenecks and apply appropriate optimization techniques to reduce the computation time in a uniform space domain. Runtime experiments manifest optimistic speedups for the parallel implementation on a single GPU, NVIDIA GeForce 1070 Ti.

The Ningdu basin, located in southern Jiangxi province of southwest China, is one of the Mesozoic basin groups which has exploration prospects for geothermal energy. A study on the detailed velocity structure of the Ningdu basin can provide important information for geothermal resource exploration. In this study, we deployed a dense seismic array in the Ningdu basin to investigate the 3D velocity structure and discuss implications for geothermal exploration and geological evolution. Based on the dense seismic array including 35 short-period (5 s-100 ​Hz) seismometers with an average interstation distance of ∼5 ​km, Rayleigh surface wave dispersion curves were extracted from the continuous ambient noise data for surface wave tomographic inversion. Group velocity tomography was conducted and the 3D S-wave velocity structure was inverted by the neighborhood algorithm. The results revealed obvious low-velocity anomalies in the center of the basin, consistent with the low-velocity Cretaceous sedimentary rocks. The basement and basin-controlling fault can also be depicted by the S-wave velocity anomalies. The obvious seismic interface is about 2 ​km depth in the basin center and decreases to 700 ​m depth near the basin boundary, suggesting spatial thickness variations of the Cretaceous sediment. The fault features of the S-wave velocity profile coincide with the geological cognition of the western boundary basin-controlling fault, which may provide possible upwelling channels for geothermal fluid. This study suggests that seismic tomography with a dense array is an effective method and can play an important role in the detailed investigations of sedimentary basins.

Background: The growth and aging process of the human population has accelerated the increase in surgical procedures. Yet, the demand for increasing operations can be hardly met since the training of anesthesiologists is usually a long-term process. Closed-loop artificial intelligence (AI) model provides the possibility to solve intelligent decision-making for anesthesia auxiliary control and, as such, has allowed breakthroughs in closed-loop control of clinical practices in intensive care units (ICUs). However, applying an open-loop artificial intelligence algorithm to build up personalized medication for anesthesia still needs to be further explored. Currently, anesthesiologists have selected doses of intravenously pumped anesthetic drugs mainly based on the blood pressure and bispectral index (BIS), which can express the depth of anesthesia. Unfortunately, BIS cannot be monitored at some medical centers or operational procedures and only be regulated by blood pressure. As a result, here we aim to inaugurally explore the feasibility of a basic intelligent control system applied to drug delivery in the maintenance phase of general anesthesia, based on a convolutional neural network model with open-loop design, according to AI learning of existing anesthesia protocols. Methods: A convolutional neural network, combined with both sliding window sampling method and residual learning module, was utilized to establish an ''AI anesthesiologist'' model for intraoperative dosing of personalized anesthetic drugs (propofol and remifentanil). The fitting degree and difference in pumping dose decision, between the AI anesthesiologist and the clinical anesthesiologist, for these personalized anesthetic drugs were examined during the maintenance phase of anesthesia. Results: The medication level established by the “AI anesthesiologist” was comparable to that obtained by the clinical anesthesiologist during the maintenance phase of anesthesia. Conclusion: The application of an open-loop decision-making plan by convolutional neural network showed that intelligent anesthesia control is consistent with the actual anesthesia control, thus providing possibility for further evolution and optimization of auxiliary intelligent control of depth of anesthesia.

Abstract The performance of the distributed coherent aperture radar (DCAR) is heavily influenced by the antenna positions. Therefore, an antenna position optimization method is proposed based on the adaptive genetic algorithm with a self‐supervised differential operator. In the proposed method, the antenna positions are firstly coded as the chromosomes of the population with multiple constraints, and the reciprocal of the peak side lobe level (PSLL) of the beam pattern is calculated as the fitness function for optimization. Then, the adaptive probabilities are calculated for the crossover and mutation of chromosomes and a self‐supervised differential operator is utilized in the mutation. Finally, the optimal antenna positions for DCAR can be obtained with the lowest PSLL compared with the existing methods. The effectiveness of the proposed method is verified by linear and planar DCARs, respectively.

Interactive translation in echocardiography training system refers to the pixel-wise translation between ultrasound cardiac and theoretical sketch images in the course of hand-on operation. It is capable of efficiently gaining more insights into clinical ultrasound anatomy. However, major studies on the synthesis of ultrasound cardiac image primarily discuss the physical model simulation, while studies on cardiac image segmentation place an emphasis on image processing. Thus, they cannot be easily integrated into one pipeline for interactive translation. This paper presents an enhanced Cycle-GAN for interactive translation. Perceptual loss is introduced to enhance the quality of synthetic ultrasound texture, while Cycle-GAN translates between two modalities. The proposed method is trained on 300 pair images and tested on 68 pair images. As revealed from the experiment results, the proposed method is feasible in interactive translation, and it is superior over Cycle-GAN for ultrasound image synthesis.

Spaceborne Synthetic Aperture Radar (SAR) is a type of microwave imaging radar with 2D high resolution. This technological device achieves range high resolution by transmitting wideband signals and azimuth high resolution through the synthetic aperture approach. With the increasing demand for high-resolution imaging, the resolution of spaceborne SAR has moved toward the decimeter level. On the one hand, limited by the present hardware technology, achieving wideband signal transmission through stepped-frequency technology is necessary. In this case, we need to study high-precision bandwidth synthesis technology. The influence of slant range error and amplitude and phase error between sub-bands should be considered. On the other hand, due to limited beamwidth, the system needs to work in sliding spot mode to achieve a long synthetic aperture. In this case, we need to study the problem of imaging parameter variance caused by curved orbit, “Stop–go” error, and the influence of ionospheric and tropospheric transmission errors on imaging. To solve these problems, this paper introduces the principle of stepped-frequency signal design and bandwidth synthesis technology in detail. A time-domain algorithm and non-ideal factor compensation method are proposed for spaceborne high-resolution stepped-frequency SAR imaging. Finally, simulation verification and performance analysis of the imaging algorithm are conducted.

In hyperspectral image classification, the foremost task is that: how can we apply limited labeled samples to achieve good classification results? Spatial-spectral classification methods, which assign a label to each pixel regarding both spatial and spectral information, are effective to improve classification performance. Moreover, semisupervised learning (SSL) focuses on the scenario that the number of labeled data is rather small while a large number of unlabeled data are available. To complement spatial-spectral classification methods and semisupervised learning for each other, we propose a novel learning landscape features semisupervised framework (LLFSF) based on M-training algorithm and weighted spatial-spectral double layer SVM classifiers module (WSS-DSVM). In this novel framework, we first propose a SLIC (simple linear iterative clustering) based non-local superpixel segmentation algorithm to initially learn landscape feature and spatial composition. Then, we apply WSS-DSVM module to obtain initial classification maps. To better characterize complex scenes of hyperspectral images, we quantizes both the landscape diversity and separability from the initial classification map, which increase availability of spatial details and structural information of objects. Finally, we put some patches with lower accuracy into Multiple-training algorithm for further classification. In order to achieve an unbiased evaluation, we have evaluated the performance of LLFSF on three different scene hyperspectral data sets and compare it with that of three state-of-the-art hyperspectral image classification methods. The experimental results confirm the efficacy of the proposed framework.

When several foreign fighters with the same type enter detection range, the electronic warfare (EW) receivers will intercept many the same type radar emitter signals. If the intercepted pulse is processed by the traditional sorting methods, the number of emitters cannot be identified. The main reason is that the same type of radar has similar parameters. It will cause a devastating influence on subsequent strategic decisions. A novel sorting method based on the trajectory features is proposed to solve the aforementioned problems. First, the trajectory features of the intercepted pulse signal are extracted. Then, the segmentation method is utilized to preprocess the signals, which enhances the computing efficiency and improves the sorting accuracy. Meanwhile, a prediction framework based on long short-term memory (LSTM) recurrent neural network is established to forecast pulses. Finally, the radar stagger pulses are sorted by forecast pulses. The simulation results show that the proposed method can recognize the number of emitters and achieve high sorting accuracy. It provides a new idea for the radar signals sorting of the same type.

Recently emerging, high maneuvering near space targets have many characteristics that differ from conventional targets, like ultra-high speed, high-maneuverability, ultra-far range, low Radar Cross Section (RCS), plasma sheath, ionosphere layer pollution, and cosmic ray interference. Based on general signal modeling for near space targets of ground-based, airborne, and spaceborne radars, this paper proposes novel focus-before-detection methods with respect to a distributed radar network, multi-dimensions, multiple targets, micro motion, varied model, and non-parametric processing. The proposed FBD based methods can effectively suppress the strong ionosphere layer pollution and active jamming, as well as problems like the scaled effect of echoes, arbitrary motion, aperture fill time, sparse sub-band frequency synthesis, across range cell, across Doppler cell, and across beam width. The proposed Focus-Before-Detection (FBD) based methods can remarkably improve the signal processing performance on target detection, parameter estimation, maneuver tracking, high-resolution imaging, feature extraction, and target recognition. Additionally, they are suitable for both high maneuvering near space targets and conventional targets, and can be applied for both new-generation radars and conventional targets. Therefore, the proposed FBD based methods for high maneuvering near space target detection have both important academic research value and impact a wide variety of applications.

Objective To examine the effects of arnebin-1 on nonalcoholic fatty liver disease (NAFLD) induced by a high-fat diet (HFD). Methods Male Sprague–Dawley rats were fed an HFD for 10 weeks and then treated with arnebin-1 at a dose of 5, 10 or 20 mg/kg/day by gavage for a further 12 weeks of a 22-week HFD. Peripheral blood and liver tissues were collected for biochemical and histopathological examination. The mechanisms of arnebin-1 on liver fibrosis and insulin resistance (IR) were determined by Western blotting and real-time quantitative polymerase chain reaction. Results Arnebin-1 treatment attenuated the increase of total cholesterol, triglycerides, low-density lipoprotein cholesterol, aspartate aminotransferase and alanine aminotransferase in serum and lipid accumulation in the livers of HFD-fed rats. Furthermore, arnebin-1 abrogated HFD-induced liver fibrosis and the increase of fibrotic biomarkers. The HFD-induced decrease of hepatic proliferator-activated receptor γ and pro-matrix-metalloproteinase (MMP)-9 levels and the increase of tissue inhibitor of metalloproteinase-1 (TIMP-1) levels were reversed after arnebin-1. Arnebin-1 attenuated IR through activating the insulin receptor substrate-1/Akt/mTOR signalling pathway. Conclusion This study demonstrated that arnebin-1 ameliorates NAFLD, in part, by attenuating hepatic fibrosis and IR, suggesting that arnebin-1 may be a therapeutic agent for NAFLD treatment.

In this work, we extend the Heston stochastic volatility model by including a time-dependent correlation that is driven by isospectral flows instead of a constant correlation, being motivated by the fact that the correlation between, e.g., financial products and financial institutions is hardly a fixed constant. We apply different numerical methods, including the method for backward stochastic differential equations (BSDEs) for a fast computation of the extended Heston model. An example of calibration to market data illustrates that our extended Heston model can provide a better volatility smile than the Heston model with other considered extensions.

For the high-speed, high-maneuverability and stealthy target detection via modern radar in complicated electromagnetic environment, a novel radar signal processing approach called Space-Time-Frequency Focus-Before-Detection (STF-FBD) via multi-dimensional coherent integration is proposed. Based on space-timefrequency signal modeling for modern radar systems, the proposed method can effectively suppress the strong interference, such as clutter and active jamming, and overcome the problems of scaled effect of high-speed targets, aperture fill time, sparse frequency sub-band synthesis, across range units, across Doppler units and across beam units. The proposed methods improves radar signal processing performance on the steps like energy integration, target detection, parameter estimation, maneuver tracking, feature extraction and target recognition. It also outperforms the existing Track-Before-Detection (TBD) methods and establish a unified STF-FBD and STF-FBD-TBD radar signal processing frame work. The proposed method is suitable for high-speed, high-maneuverability and stealthy target, as well as for conventional targets. It is applicable for new-generation modern radar, as well as for conventional radars, and may find application to different field.

Plant root exudates affect root-knot nematodes egg hatch. Chemicals in root exudates can attract nematodes to the roots or result in repellence, motility inhibition or even death. However, until recently little was known about the relationship between tomato root exudates chemicals and root-knot nematodes. In this study, root exudates were extracted from three tomato rootstocks with varying levels of nematode resistance: Baliya (highly resistant, HR), RS2 (moderately resistant, MR) and L-402 (highly susceptible, T). The effects of the root exudates on Meloidogyne incognita (M. incognita) egg hatch, survival and chemotaxis of second-stage juveniles (J2) were explored. The composition of the root exudates was analysed by gas chromatography/mass spectrometry (GC/MS) prior to and following M. incognita inoculation. Four compounds in root exudates were selected for further analysis and their allopathic effect on M. incognita were investigated. Root exudates from each tomato rootstocks (HR, MR and T strains) suppressed M. incognita egg hatch and increased J2 mortality, with the highest rate being observed in the exudates from the HR plants. Exudate from HR variety also repelled M. incognita J2 while that of the susceptible plant, T, was demonstrated to be attractive. The relative amount of esters and phenol compounds in root exudates from HR and MR tomato rootstocks increased notably after inoculation. Four compounds, 2,6-Di-tert-butyl-p-cresol, L-ascorbyl 2,6-dipalmitate, dibutyl phthalate and dimethyl phthalate increased significantly after inoculation. The egg hatch of M. incognita was suppressed by each of the compound. L-ascorbyl 2,6-dipalmitate showed the most notable effect in a concentration-dependent manner. All four compounds were associated with increased J2 mortality. The greatest effect was observed with dimethyl phthalate at 2 mmol·L-1. Dibutyl phthalate was the only compound observed to repel M. incognita J2 with no effect being detected in the other compounds. Each of the four compounds were correlated with a reduction in disease index in the susceptible cultivar, T, and tomato seedlings irrigated with L-ascorbyl 2,6-dipalmitate at 2 mmol·L-1 showed the best resistance to M. incognita. Taken together, this study provided a valuable contribution to understanding the underlying mechanism of nematode resistance in tomato cultivars.

Stochastic correlation models have become increasingly important in financial markets. In order to be able to price vanilla options in stochastic volatility and correlation models, in this work, we study the extension of the Heston model by imposing stochastic correlations driven by a stochastic differential equation. We discuss the efficient algorithms for the extended Heston model by incorporating stochastic correlations. Our numerical experiments show that the proposed algorithms can efficiently provide highly accurate results for the extended Heston by including stochastic correlations. By investigating the effect of stochastic correlations on the implied volatility, we find that the performance of the Heston model can be proved by including stochastic correlations.