USV Trajectory Tracking Control Based on Receding Horizon Reinforcement Learning.

We present a novel approach for achieving high-precision trajectory tracking control in an unmanned surface vehicle (USV) through utilization of receding horizon reinforcement learning (RHRL). The control architecture for the USV involves a composite of feedforward and feedback components. The feedforward control component is derived directly from the curvature of the reference path and the dynamic model. Feedback control is acquired through application of the RHRL algorithm, effectively addressing the problem of achieving optimal tracking control. The methodology introduced in this paper synergizes with the rolling time domain optimization mechanism, converting the perpetual time domain optimal control predicament into a succession of finite time domain control problems amenable to resolution. In contrast to Lyapunov model predictive control (LMPC) and sliding mode control (SMC), our proposed method employs the RHRL controller, which yields an explicit state feedback control law. This characteristic endows the controller with the dual capabilities of direct offline and online learning deployment. Within each prediction time domain, we employ a time-independent executive-evaluator network structure to glean insights into the optimal value function and control strategy. Furthermore, we substantiate the convergence of the RHRL algorithm in each prediction time domain through rigorous theoretical proof, with concurrent analysis to verify the stability of the closed-loop system. To conclude, USV trajectory control tests are carried out within a simulated environment.

Post-pandemic healthcare for COVID-19 vaccine: Tissue-aware diagnosis of cervical lymphadenopathy via multi-modal ultrasound semantic segmentation.

With the widespread deployment of COVID-19 vaccines all around the world, billions of people have benefited from the vaccination and thereby avoiding infection. However, huge amount of clinical cases revealed diverse side effects of COVID-19 vaccines, among which cervical lymphadenopathy is one of the most frequent local reactions. Therefore, rapid detection of cervical lymph node (LN) is essential in terms of vaccine recipients' healthcare and avoidance of misdiagnosis in the post-pandemic era. This paper focuses on a novel deep learning-based framework for the rapid diagnosis of cervical lymphadenopathy towards COVID-19 vaccine recipients. Existing deep learning-based computer-aided diagnosis (CAD) methods for cervical LN enlargement mostly only depend on single modal images, e.g., grayscale ultrasound (US), color Doppler ultrasound, and CT, while failing to effectively integrate information from the multi-source medical images. Meanwhile, both the surrounding tissue objects of the cervical LNs and different regions inside the cervical LNs may imply valuable diagnostic knowledge which is pending for mining. In this paper, we propose an Tissue-Aware Cervical Lymph Node Diagnosis method (TACLND) via multi-modal ultrasound semantic segmentation. The method effectively integrates grayscale and color Doppler US images and realizes a pixel-level localization of different tissue objects, i.e., lymph, muscle, and blood vessels. With inter-tissue and intra-tissue attention mechanisms applied, our proposed method can enhance the implicit tissue-level diagnostic knowledge in both spatial and channel dimension, and realize diagnosis of cervical LN with normal, benign or malignant state. Extensive experiments conducted on our collected cervical LN US dataset demonstrate the effectiveness of our methods on both tissue detection and cervical lymphadenopathy diagnosis. Therefore, our proposed framework can guarantee efficient diagnosis for the vaccine recipients' cervical LN, and assist doctors to discriminate between COVID-related reactive lymphadenopathy and metastatic lymphadenopathy.

Design, synthesis, and antitumor study of a series of novel 1-Oxa-4-azaspironenone derivatives.

A series of 1-oxa-4-azaspiro[4,5]deca-6,9-diene-3,8-dione derivatives containing structural fragments of conjugated dienone have been synthesized previously by our group, however the Michael addition reaction between conjugated dienone and nucleophilic groups in the body may generate harmful and adverse effects. To reduce harmful side effects, the authors started with p-aminophenol to make 1-oxo-4- azaspirodecanedione derivatives, then utilized the Michael addition and cyclopropanation to eliminate α, ß unsaturated olefinic bond and lower the Michael reactivity of the compounds in vivo for optimization. At the same time, heteroatoms are put into the molecules in order to improve the hydrophilicity of the molecules and the binding sites of the molecules and the target molecules, establishing the groundwork for improved antitumor activity. The majority of the compounds had moderate to potent activity against A549 human lung cancer cells, MDA-MB-231 breast cancer cells, and Hela human cervical cancer cells. Among them, the compound 6d showed the strongest effect on A549 cell line with IC50 of 0.26 µM; the compound 8d showed the strongest cytotoxicity on MDA-MB-231 cell line with IC50 of 0.10 µM; and the compound 6b showed the strongest activity on Hela cell line with IC50 of 0.18 µM.

One-Pot Synthesis of Seven-Membered Heterocyclic Derivatives of Diazepines Involving Copper-Catalyzed Rearrangement Cascade Allyl-Amination.

A novel and efficient method has been proposed for the synthesis of 1,4-benzodiazepine-5-ones from o-nitrobenzoic N-allylamides by using molybdenyl acetylacetonate and copper(II) trifluoromethanesulfonate as catalysts in the presence of triphenylphosphine. This synthesis process involves nitrene formation, C-H bond insertion, C═C bond rearrangement, and C-N bond formation cascade reactions via copper- and molybdenum-catalyzed mediation. The method features a wide substrate scope and a moderate to high yield (up to 90%), exhibiting the possibility for practical applications.

Development of a Meander-Coil-Type Dual Magnetic Group Circumferential Magnetostrictive Guided Wave Transducer for Detecting Small Defects Hidden behind Support Structures.

In order to solve the problem that small defects hidden behind pipeline support parts are difficult to detect effectively in small spaces, such as offshore oil platforms, a meander-coil-type dual magnetic group circumferential magnetostrictive guided wave transducer is developed in this paper. The transducer, which consists of a coil, two sets of permanent magnets, and a magnetostrictive patch, can excite a high-frequency circumferential shear horizontal (CSH) guided wave. The energy conversion efficiency of the MPT is optimized through magnetic field simulation and experiment, and the amplitude of the defect signal is enhanced 1.9 times. The experimental results show that the MPT developed in this paper can effectively excite and receive CSH2 mode guided waves with a center frequency of 1.6 MHz. Compared with the traditional PPM EMAT transducer, the excitation energy of the transducer is significantly enhanced, and the defects of the 2 mm round hole at the back of the support can be effectively detected.

Molecular dynamics simulation of crack propagation in very small grain size nanocopper with different grain size gradients.

In this paper, we use molecular dynamics to simulate the crack propagation behavior of gradient nano-grained (GNG) copper models with different grain size gradients, compare the crack propagation rates of different models, and analyze the microstructural changes and the mechanism of crack propagation. The simulation results show that the increase of the grain size gradient of the GNG copper model can improve the fracture resistance of the material, and the crack propagation mode undergoes a transition from brittle propagation along the grain boundaries to the formation of pores at the grain boundaries, and then to ductile fracture along the inclined plastic shear zone. The number of dislocations increases with the grain size gradient, while the crack passivation is more serious, indicating that a larger grain size gradient is more effective in inhibiting crack propagation. The introduction of gradient grain size promotes crack propagation and weakens the plasticity of the material relative to the nano-grained (NG) copper model.

Automatic 3D reconstruction of vertebrae from orthogonal bi-planar radiographs.

When conducting spine-related diagnosis and surgery, the three-dimensional (3D) upright posture of the spine under natural weight bearing is of significant clinical value for physicians to analyze the force on the spine. However, existing medical imaging technologies cannot meet current requirements of medical service. On the one hand, the mainstream 3D volumetric imaging modalities (e.g. CT and MRI) require patients to lie down during the imaging process. On the other hand, the imaging modalities conducted in an upright posture (e.g. radiograph) can only realize 2D projections, which lose the valid information of spinal anatomy and curvature. Developments of deep learning-based 3D reconstruction methods bring potential to overcome the limitations of the existing medical imaging technologies. To deal with the limitations of current medical imaging technologies as is described above, in this paper, we propose a novel deep learning framework, ReVerteR, which can realize automatic 3D Reconstruction of Vertebrae from orthogonal bi-planar Radiographs. With the utilization of self-attention mechanism and specially designed loss function combining Dice, Hausdorff, Focal, and MSE, ReVerteR can alleviate the sample-imbalance problem during the reconstruction process and realize the fusion of the centroid annotation and the focused vertebra. Furthermore, aiming at automatic and customized 3D spinal reconstruction in real-world scenarios, we extend ReVerteR to a clinical deployment-oriented framework, and develop an interactive interface with all functions in the framework integrated so as to enhance human-computer interaction during clinical decision-making. Extensive experiments and visualization conducted on our constructed datasets based on two benchmark datasets of spinal CT, VerSe 2019 and VerSe 2020, demonstrate the effectiveness of our proposed ReVerteR. In this paper, we propose an automatic 3D reconstruction method of vertebrae based on orthogonal bi-planar radiographs. With the 3D upright posture of the spine under natural weight bearing effectively constructed, our proposed method is expected to better support doctors make clinical decision during spine-related diagnosis and surgery.

Design, synthesis and biological evaluation of novel DCLK1 inhibitor containing purine skeleton for the treatment of pancreatic cancer.

Pancreatic cancer is a highly lethal form of malignancy that continues to pose a significant and unresolved health challenge. Doublecortin-like kinase 1 (DCLK1), a serine/threonine kinase, is found to be overexpressed in pancreatic cancer and holds promise as a potential therapeutic target for this disease. However, few potent inhibitors have been reported currently. Herein, a series of novel purine, pyrrolo [2,3-d]pyrimidine, and pyrazolo [3,4-d] pyrimidine derivatives were designed, synthesized, and evaluated their biological activities in vitro. Among them, compound I-5 stood out as the most potent compound with strong inhibitory activity against DCLK1 (IC50 = 171.3 nM) and remarkable antiproliferative effects on SW1990 cell lines (IC50 = 0.6 µM). Notably, I-5 exhibited higher in vivo antitumor potency (Tumor growth inhibition value (TGI): 68.6 %) than DCLK1-IN-1 (TGI: 24.82 %) in the SW1990 xenograft model. The preliminary mechanism study demonstrated that I-5 not only inhibited SW1990 cell invasion and migration, but also decreased the expression of prominin-1 (CD133) and cluster of differentiation 44 (CD44), which are considered as differentiation markers for SW1990 stem cells. All the results indicated that I-5, a novel DCLK1 inhibitor, shows promise for further investigation in the treatment of pancreatic cancer.

Effects of arbuscular mycorrhizae and extraradical mycelium of subtropical tree species on soil nitrogen mineralization and enzyme activities.

Through symbiosis with plants, arbuscular mycorrhizal (AM) fungi effectively improve the availability of soil nitrogen (N). However, the mechanism through which AM and associated extraradical mycelium affect soil N mineralization remains unknow. We carried out an in situ soil culture experiment by using in-growth cores in plantations of three subtropical tree species, Cunninghamia lanceolata, Schima superba, and Liquidambar formosana. We measured soil physical and chemical properties, net N mineralization rate, and the activities of four kinds of hydrolase (leucine aminopeptidase (LAP), ß-1,4-N-acetylglucosaminidase (NAG), ß-1,4-glucosidase (ßG), cellobiohydrolase (CB)) and two kinds of oxidases (polyphenol oxidase (POX) and peroxidase (PER)) involved in soil organic matter (SOM) mineralization in treatments of mycorrhiza (with absorbing roots and hyphae), hyphae (hyphae only), and control (mycorrhiza-free). The results showed that mycorrhizal treatments significantly affected soil total carbon and pH but did not affect N mineralization rates and all enzymatic activities. Tree species significantly affected net ammonification rate, net N mineralization rate and activities of NAG, ßG, CB, POX and PER. The net N mineralization rate and enzyme activities in the C. lanceolata stand were significantly higher than that in monoculture broad-leaved stands of either S. superba or L. formosana. There was no interactive effect of mycorrhizal treatment and tree species on any of soil properties, nor on enzymatic activities or net N mineralization rates. Soil pH was negatively and significantly correlated with five kinds of enzymatic activities except for LAP, while net N mineralization rate significantly correlated with ammonium nitrogen content, available phosphorus content, and the activity level of ßG, CB, POX, and PER. In conclusion, there was no difference in enzymatic activities and N mineralization rates between rhizosphere and hyphosphere soils of three subtropical tree species in the whole growing season. The activity of particular carbon cycle-related enzymes was closely related to soil N mineralization rate. It is suggested that differences in litter quality and root functional traits among different tree species affect soil enzyme activities and N mineralization rates through organic matter inputs and shaping soil condition.

A Formation Control Method for AUV Group Under Communication Delay.

This article presents a consistency control algorithm for the autonomous underwater vehicle (AUV) group combined with the leader-follower approach under communication delay. First, the six-degree-of-freedom (DoF) model of AUV is represented, and the graph theory is used to describe the communication topology of the AUV group. Especially, a hybrid communication topology is introduced to adapt to large formation control. Second, the distributed control law is constructed by combining the consensus theory with the leader-follower method. The consistency control algorithms for homogeneous and heterogeneous AUV groups based on the leader-follower approach under communication delay are proposed. Stability criteria are established to guarantee the consensus based on the Gershgorin disk theorem and Nyquist law, respectively. Finally, numerous simulation experiments are carried out to show the effectiveness and superiority of the proposed algorithms.

A Static Area Coverage Algorithm for Heterogeneous AUV Group Based on Biological Competition Mechanism.

Static area coverage of the heterogeneous autonomous underwater vehicle (AUV) group is widely used in many fields. With the use of the centroidal Voronoi tessellation (CVT) algorithm, the coverage problem can be resolved. However, the CVT method, which is optimized with the location evaluation function, cannot consider the heterogeneity of AUVs when the group performs the static area coverage task and will cause a waste of resources. In this paper, considering different AUVs' task requirements and detection capabilities comprehensively, we propose a coverage control optimization algorithm based on a biological competition mechanism (BCM). By using BCM, the task load of each AUV can be distributed consistently. In addition, we provide strict proof of the consistency of the algorithm based on the Lyapunov method. Simulation results demonstrate that with the proposed algorithm, the location distribution of the heterogeneous AUV group for area coverage is close to the balanced value, and the performance is better than the CVT algorithm for static area coverage.