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Aerial object detection is crucial in various computer vision tasks, including video monitoring, early warning systems, and visual tracking. While current methods can accurately detect normal-sized objects, they face challenges distinguishing small objects from cluttered backgrounds. Developing methods that can be deployed on edge devices to achieve fast, accurate, and energy-efficient performance is also an urgent challenge. This paper proposes a network for aerial object detection by incorporating an attention mechanism to enhance feature extraction and elevate the accuracy of aerial moving object detection. Additionally, we optimize the channel dimensions of the feature extraction framework, resulting in a reduction in model parameters, acceleration of inference speed, and alleviation of the computational burden. Ulteriorly, we optimize the Spatial Pyramid Pooling (SPP) module to enhance detection accuracy and processing speed. Inspired by the ResNet and RepVGG structures, we design a feature fusion module to combine early-extracted features, improving speed and accuracy. Based on the design mentioned above principles, we develop a neural network method with an impressively small model size of only 4.5M. The proposed approach achieves state-of-the-art performance on five benchmark datasets. Besides its superior performance, our method demonstrates excellent throughput on edge computing devices. Experimental results show that even when running on low-performance computing devices, the CPU and GPU temperatures remain below 50∘C and achieve a detection speed of 14.8 frames per second (fps) and power consumption of only 2.9W. These findings suggest that a high-accuracy, low-power, low-latency, and low-memory footprint aerial object detection solution is achievable.

3-D reconstruction is essential to defect localization. This article proposes LCM-MVSNet, a novel multi-view stereo (MVS) network with learnable cost metric (LCM) for more accurate and complete dense point cloud reconstruction. To adapt to the scene variation and improve the reconstruction quality in non-Lambertian low-textured scenes, we propose LCM to adaptively aggregate multi-view matching similarity into the 3-D cost volume by leveraging sparse point hints. The proposed LCM benefits the MVS approaches in four folds, including depth estimation enhancement, reconstruction quality improvement, memory footprint reduction, and computational burden alleviation, allowing the depth inference for high-resolution images to achieve more accurate and complete reconstruction. In addition, we improve the depth estimation by enhancing the shallow feature propagation via a bottom–up pathway and strengthen the end-to-end supervision by adapting the focal loss to reduce ambiguity caused by sample imbalance. Extensive experiments on three benchmark datasets show that our method achieves state-of-the-art performance on the DTU and BlendedMVS dataset, and exhibits strong generalization ability with a competitive performance on the Tanks and Temples benchmark. Furthermore, we deploy our LCM-MVSNet into our UAV-based infrastructure defect inspection framework for infrastructure reconstruction and defect localization, demonstrating the effectiveness and efficiency of our method. More experiment results can be found in the Appendix at https://github.com/CUHK-USR-Group/TIE_Appendices/blob/main/TIE_Appendix.pdf.

A set of laboratory experiments are carried out to investigate the effect of following/opposing currents on wave attenuation. Rigid vegetation canopies with aligned and staggered configurations were tested under the condition of various regular wave heights and current velocities, with the constant water depth being 0.60 m to create the desired submerged scenarios. Results show that the vegetation-induced wave dissipation is enhanced with the increasing incident wave height. A larger velocity magnititude leads to a greater wave height attenuation for both following and opposing current conditions. Moreover, there is a strong positive linear correlation between the damping coefficient βand the relative wave height H0/h, especially for pure wave conditions. For the velocity profile, the distributions of Uminand Umaxshow different patterns under combined wave and current. The time-averaged turbulent kinetic energy (TKE) vary little under pure wave and Uc= ±0.05 m/s conditions. With the increase of flow velocity amplitude, the time-averaged TKE shows a particularly pronounced increase trend at the top of the canopy. The vegetation drag coefficients are obtained by a calibration approach. The empirical relations of drag coefficient with Reynolds and Keulegane-Carpenter numbers are proposed to further understand the wave-current-vegetation interaction mechanism.

The misuse of drones can jeopardize public safety and privacy. The detection and catching of intruding drones are crucial and urgent issues to be investigated. This work proposes VDTNet, an accurate, lightweight, and fast network for visually detecting and tracking intruding drones. We first incorporate an SPP module into the first head of YOLOv4 to enhance detection accuracy. Model compression is utilized to shrink the model size and concurrently speed up inference. We then propose and insert an SPPS module and a ResNeck module into the neck, and introduce an effective attention module for the backbone to compensate for the accuracy drop brought on by compression. With the above strategies, we present the accurate and compact VDTNet with a model size of merely 3.9 MB, ensuring low computational cost and fast detection and tracking performance in real time. Extensive experiments on four challenging public datasets show that our proposed network outperforms state-of-the-art approaches. In real-world scenarios, the comparative ground-to-air detection testing proves the generalization ability of the VDTNet, and we further demonstrate the portability and practicability of the network by deploying it on drone onboard edge-computing devices for air-to-air real-time detection of the intruding drones.

With the order of better understanding the early hydration process and reaction mechanism of CaO-MgO combined expansion agent-involved cement-based systems (CMEA-CBS), CMEA dosage, CaO to MgO ratio (Ca/Mg), MgO activity, hydration temperature, and water-binder ratios (w/b) were chosen as the influencing factor. Accordingly, isothermal calorimetry was implemented to analyze the hydration heat release law, and the Krstulovic–Dabic model was applied to investigate the hydration kinetic. Moreover, the activation energy of CMEA-CBS was calculated. The findings of this study are as follows. The incorporation of CMEA can accelerate the hydration of cementitious systems. Hydration temperature increase markedly promote the hydration reaction of CMEA-CBS. The hydration reaction of CMEA-CBS is a complex associated reaction process dominated by multiple reaction mechanisms in sequence. At both 20 °C and 45 °C, the hydration process of CMEA-involved cement-based systems experienced three stages of crystallization nucleation and crystal growth (NG), phase boundary reaction (I), and diffusion (D), and the hydration mechanism was NG → I → D. However, the hydration of CMEA-CBS at 65 °C switched from the NG stage to the D stage directly. The hydration promotion effect of increasing temperature on I and D process of CMEA-CBS is less than I and D process of pure cement. Compared to the other groups, the temperature increase had the least effect on the early hydration process of 10%-C1M1-M-0.4 with the lowest apparent activation energy.

The construction of 3,4-dihydroquinolone derivatives has attracted a considerable amount of attention due to their extensive applications in medicinal chemistry. In this study, we present the Pd-catalyzed [4+2] cycloaddition of vinyl benzoxazinanones with α-alkylidene succinimides for the efficient synthesis of 3,4-dihydroquinolones. This approach presents numerous advantages, including the ready availability of starting materials, mild reaction conditions without the use of additional bases, and a wide range of substrates. In particular, all of the desired products can be easily afforded in high yields (≤99%) and excellent diastereoselectivities (>20:1). The practicality and reliability of this strategy were demonstrated by the successful scale-up synthesis and subsequent straightforward synthetic transformations.

In this paper, we present in this work a fairly complete process for developing an unmanned aerial–aquatic vehicle system, TJ-FlyingFish, which includes an innovative design methodology of the aerial–aquatic platform and the cross-medium localization, dynamics modeling, and flight control systems. The development faces the challenge how to manipulate locomotion effectively in both water and air which presents substantial differences in fluid properties. Additionally, there are difficulties in perception and navigation because of the discontinuity of mediums. To cope with these challenges, we designed an innovative unmanned aerial–aquatic vehicle with an optimized dual-speed and tilting propulsion configuration. The rotors/propellers operate in different ranges of rotating speed in the two different mediums, providing sufficient thrust and ensuring output efficiency. Besides, thrust vectoring is achieved by rotating each propulsion unit around its mounted arm, facilitating agile underwater cruising. Another key component of our approach is a sophisticated multi-sensor-based cross-medium localization system that combines SLAM, sensor synchronization, and data capture mechanisms, enabling seamless transitions between aerial and aquatic environments, and supporting autonomous operations. The results are fully validated through actual flight experiments.

The pathogenesis of ulcerative colitis (UC) is associated with the shedding of the gut mucus. Herein, inspired by the biological functions of mucus, growth factors-loaded in situ hydrogel (PHE-EK) was designed for UC treatment by integrating dihydrocaffeic acid-modified poloxamer as a thermosensitive material with hyaluronic acid (colitis-specific adhesive), epigallocatechin-3-gallate (antibacterial agent), and bioactive factors (KPV tripeptide and epidermal growth factor). PHE-EK presented good thermosensitive properties, as a flowable liquid at room temperature and gelled within 10 s when exposed to body temperature. PHE-EK hydrogel presented good mechanical strength with a strain of 77.8%. Moreover, PHE-EK hydrogel displayed antibacterial activity against Escherichia coli. Importantly, in vitro and in vivo adhesive tests showed that the PHE-EK hydrogel could specifically adhere to the inflamed colon via electrostatic interaction. When PHE-EK as a biomimetic mucus was rectally administrated to colitis rats, it effectively hindered the body weight loss, reduced the disease activity index and improved the colonic shorting. Moreover, the expression of pro-inflammatory cytokines (e.g., IL-1β, IL-6, and TNF-α) at the laminae propria or epitheliums of the colon for colitis rats was substantially inhibited by PHE-EK. Besides, the colonic epitheliums were well rearranged, and the tight junction proteins (Zonula-1 and Claudin-5) between them were greatly upregulated after PHE-EK treatment. Collectively, PHE-EK might be a promising therapy for UC.

In this paper, the authors study the cooperative target-fencing problem for n-dimensional systems and a target with a general trajectory. Without using the velocity of the vehicles, a position feedback control law is proposed to fence the general target into the convex hull formed by the vehicles. Specifically, the dynamics of each vehicle is described by a double-integrator system. Two potential functions are designed to guarantee connectivity preservation of the communication network and collision avoidance among the vehicles. The proposed approach can deal with a target whose trajectory is any twice continuously differentiable function of time. The effectiveness of the result is verified by a numerical example.

In this article, distributed optimal solutions are designed for networked multiagent pursuit-evasion (MPE) games for capture and formation control. In the games, the pursuers aim to minimize the distance from their target evaders while the evaders attempt to maximize it, and at the same time, all the players desire to maintain cohesion with their teammates. The goals of agents are obviously reflected in the obtained optimal control strategies, which consist of an attracting term and/or a repelling term. Nash equilibrium is obtained by means of optimal strategies using the solutions of the Hamilton–Jacobi–Isaacs equations. Furthermore, three scenarios are considered in the MPE game: one-pursuer one-evader, multiple-pursuer one-evader, and multiple-pursuer multiple-evader, where sufficient conditions are given for pursuers in achieving exponential capture or formation control with ultimate zero or bounded errors. It is shown that the conditions depend on the structure of the communication graph, the parameters in the controllers, and the expected formation configurations. Finally, both simulations and real flight experiments successfully demonstrate the effectiveness of the proposed strategies.

In this article, we propose a distributed algorithm for cooperatively pursuing an adversarial evader in an unbounded environment with cluttered obstacles. The algorithm relies on constructing the buffered evader-centered bounded Voronoi cell (B-ECBVC) in real time for each pursuer to safely chase the evader among obstacles. Based on the B-ECBVC, an encirclement control law and a capture strategy are proposed. Specifically, the control law drives each pursuer toward the centroid of its B-ECBVC to trap the evader, while the capture strategy guides it to reduce the distance between a team of pursuers and the evader by adaptively compressing the B-ECBVC. By integrating the control law and the capture strategy, the pursuers can rapidly approach the evader, while simultaneously maintaining the encirclement. To guarantee collision avoidance, a rapid and reliable approach for creating secure regions of pursuers by integrating separating hyperplanes and buffered terms into B-ECBVCs. In addition, the proposed pursuit method is further extended to a higher order dynamics system with avoiding moving obstacles. Our B-ECBVC approach is validated with various escape policies of the evader in dense obstacle environments. Moreover, real-time experiments with an autonomous evader and a human evader are implemented in a multiple mobile robot platform to validate the effectiveness of our approach.

Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for inspections and repair works in large-scale and unstructured 3D infrastructures, but current approaches take a long time to cover the entire area. Planning using UAVs for inspections and repair works puts forward a requirement of improving time efficiency in large-scale and cluster environments. This paper presents a hierarchical multi-UAV cooperative framework for infrastructure inspection and reconstruction to balance the workload and reduce the overall task completion time. The proposed framework consists of two stages, the exploration stage and the exploitation stage, resolving the task in a sequential manner. At the exploration stage, the density map is developed to update global and local information for dynamic load-balanced area partition based on reconstructability and relative positions of UAVs, and the Voronoi-based planner is used to enable the UAVs to reach their best region. After obtaining the global map, viewpoints are generated and divided while taking into account the battery capacity of each UAV. Finally, a shortest path planning method is used to minimize the total traveling cost of these viewpoints for obtaining a high-quality reconstruction. Several experiments are conducted in both a simulated and real environment to show the time efficiency, robustness, and effectiveness of the proposed method. Furthermore, the whole system is implemented in real applications.

Underwater image enhancement (UIE) is crucial for high-level vision in underwater robotics. While convolutional neural networks (CNNs) have made significant achievements in UIE, the locality of convolution poses a challenge in capturing the global context. In contrast, transformer-based networks, adept at handling long-range dependencies, have shown promise in various vision tasks. Nonetheless, directly applying a transformer to UIE faces critical challenges: 1) it tends to produce results with coarse details due to the negligence of local texture and 2) the varicolored degraded images require the network to be adaptable to different underwater environments. In this article, we propose a novel transformer-based network that can effectively leverage both the global contextual and local detailed information with some key designs (a global–local transformer [GL-Trans] block and a detail-enhanced skip connector [DESC]) while being computationally efficient. Moreover, by introducing a simple but effective learnable environment adaptor, the proposed network is flexible to deal with different underwater environments. Extensive experiments have been conducted and have demonstrated the superiority of our proposed network compared with other state-of-the-art (SOTA) methods both qualitatively and quantitatively.

Due to the critical role of pavement crack detection for road maintenance and eventually ensuring safety, remarkable efforts have been devoted to this research area, and such a trend is further intensified for the coming unmanned vehicle era. However, such crack detection task still remains unexpectedly challenging in practice since the appearance of both cracks and the background are diverse and complex in real scenarios. In this work, we propose an automatic pavement crack detection method via synergizing low rank representation (LRR) and deep learning techniques. First, leveraging LRR which facilitates anomaly detection without making any specific assumption, we can easily discriminate most of the frames with cracks from the long sequence with a consistent pavement base, followed by a straightforward algorithm to localize the cracks. In order to achieve the intelligence of detecting cracks with different pavement basis under unconstrained imaging conditions, we resort to deep learning techniques and propose a deep convolutional neural network for crack detection leveraging on multi-level features and atrous spatial pyramid pooling (ASPP). We train this network based on the training data obtained in the previous stage in an end-to-end manner. Extensive experiments on a wide range of pavements demonstrate the high performance in terms of both accuracy and automaticity. Moreover, the dataset generated by us is much more extensive and challenging than public ones. We put it online at https://gaozhinuswhu.com to benefit the community.

The defect diagnosis of modern infrastructures is crucial to public safety. In this work, we propose an unsupervised domain adaptive crack recognition framework. To fulfill the unsupervised domain adaptation (UDA) task of cracks recognition in infrastructural inspections, we propose a robust UDA learning strategy termed Crack-DA to increase the generalization capacity of the model in unseen test circumstances. More specifically, we first propose leveraging the self-supervised depth information to help the learning of semantics. And then using the edge information to suppress nonedge background objects and noises. We also use the data augmentation-based consistency. More importantly, we use the disparity in depth to evaluate the domain gap in semantics and explicitly consider the domain gap in network optimization. A database consisting of 11 298 crack images with detailed pixel-level labels for network training in domain adaptations is established. Extensive experiments on unmanned aerial vehicle (UAV)-captured highway cracks and real-site UAV inspections of building cracks demonstrate the robustness and effectiveness of the proposed domain adaptive crack recognition approach.

In recent years, regional floods and typhoons have occurred in the Yangtze Estuary. Changing dynamic conditions and dramatic reduction of sediment discharge in the basin are affecting the dynamic equilibrium pattern of the Yangtze Estuary. Based on the field measurement data and theoretical derivation, this paper analyzed the changing process of runoff-sediment discharge into the sea after the operation of the Three Gorges Project (TGP), and the tidal dynamics and sediment variation characteristics of the Yangtze Estuary. The erosion of South Branch mainly occurs in the channel below −10 m contour, and the riverbed volume below contours 0 m and −10 m has a good correlation with the sediment discharge of Datong Station in the previous year. On this basis, the ratio of the horizontal distance from the starting point to the section centroid below the average water level (Bc) and the water depth at the section centroid (Hc) was proposed to describe the change of the section shape. The relationships between the water-diverting ratio, the sediment-diverting ratio and the water-diverting angle, the conditions of runoff and sediment discharge from the upper reach and the characteristics of the riverway section were established, and the theoretical calculation equations of the water-diverting ratio, the sediment-diverting ratio and the diverting angle of each bifurcation were also established.

Micro-Unmanned Air Vehicles (Micro-UAVs), especially quadrotor UAVs, have been widely used in recent years because of their simple structure and convenient operation. However, due to under-actuated and strongly-coupled characteristics, their flight performance is not perfect when suffering external uncertainty such as wind disturbance. Therefore, in this paper, the literature is reviewed related to wind acting on UAVs and makes some conclusions. First, we review the wind field model of quadrotor operation and decompose it into wind turbulent model, wind shear model, and other typical wind field models. Then we focus on the mathematic model of wind disturbance on UAVs and divide it into effect on rotors and effect on the fuselage. Finally, various control laws are used to study the wind resistance of quadrotors. We review the promising works which have been done before and determine the research emphasis to be focused on in the future.

This work presents FG-Net, a general deep learning framework for large-scale point cloud understanding without voxelizations, which achieves accurate and real-time performance with a single NVIDIA GTX 1080 8G GPU and an i7 CPU. First, a novel noise and outlier filtering method is designed to facilitate the subsequent high-level understanding tasks. For effective understanding purpose, we propose a novel plug-and-play module consisting of correlated feature mining and deformable convolution-based geometric-aware modeling, in which the local feature relationships and point cloud geometric structures can be fully extracted and exploited. For the efficiency issue, we put forward a new composite inverse density sampling (IDS)-based and learning-based operation and a feature pyramid-based residual learning strategy to save the computational cost and memory consumption, respectively. Compared with current methods which are only validated on limited datasets, we have done extensive experiments on eight real-world challenging benchmarks, which demonstrates that our approaches outperform state-of-the-art (SOTA) approaches in terms of accuracy, speed, and memory efficiency. Moreover, weakly supervised transfer learning is also conducted to demonstrate the generalization capacity of our method.

In this paper, we study the cooperative target-fencing problem for an n-dimensional target with a general trajectory. The goal is to fence the given target into the convex hull formed by a group of n-dimensional vehicles which are modeled by double-integrator systems. We propose a control law to accomplish this task while avoiding collisions among the vehicles and preserving the connectivity of the communication network. In particular, our approach can handle a target whose position trajectory is any twice continuously differentiable function. Moreover, our result does not rely on the velocity measurements of the vehicles. Thus, it is more practical compared with the existing approaches in the literature. The effectiveness of the proposed method is illustrated by a numerical example.

Ultrafine oxide dispersion strengthening (ODS)-Mo and ODS-W alloy powders containing different types of oxide nanoparticles were successfully synthesized by spraying method (solid−liquid mixing method) combined with the reductions with carbon black and hydrogen in sequence. It is concluded that the solution concentration and type of rare earth oxide have no effect on the grain size of ODS-Mo alloy powder, but have obvious effect on that of ODS-W alloy powder. The higher the concentration of rare earth solution is, the smaller the average grain size of ODS-W alloy powder is. Furthermore, compared with doping with CeO2, the grain sizes of reduction products of La2O3and Y2O3doped WO3are relatively larger. Compared with the undoped case, there is almost no change for grain size of ODS-Mo alloy powder, while the grain size of ODS-W alloy powder becomes much larger. This is probably due to the appearance of the composite oxide (such as La2WO6) formed by the reaction between tungsten oxide and rare earth oxides, which promotes the heterogeneous nucleation and growth of tungsten grains during the reduction process of ODS-W, while there is no complex oxide composed of molybdenum and rare earth oxides in the reduction process of ODS-Mo.

Although numerous chiral pyrazolones with a six-membered spirocyclic center at the C4 position have been developed, the asymmetric construction of six-membered oxa-spiropyrazolones is still a challenging task in organic synthesis. Herein, we describe the [4 + 2] annulation of cyclobutanones and pyrazoline-4,5-diones for the efficient synthesis of δ-lactone-fused spiropyrazolone derivatives with generally high yields and good enantioselectivities under mild conditions. The successful scale-up synthesis and further transformation of the final product highlight the practicality and reliability of this reaction.

In this study, a g-C3N4-Cu2O-TiO2photocatalyst with a novel three-dimensional ordered macroporous (3DOM) structure was successfully prepared using a sacrificial template strategy and a photodeposition method. The influence of the special porous structure with cross pore channels on the photocatalytic properties of the as-prepared sample was studied in detail. Compared with the original photocatalyst (TiO2with 3 wt% Pt), g-C3N4-Cu2O-TiO2exhibited a higher specific surface area and more active sites, thus accelerating the separation efficiency of the photogenerated electron-hole pair. Consequently, the as-prepared photocatalyst showed good photocatalytic performance, reaching a maximum hydrogen production rate of 12,108 µmol g−1h−1and approximately five times higher than that of the pristine comparison sample. The enhanced photoactivity of the g-C3N4-Cu2O-TiO2heterojunction can be ascribed to its double p-n heterojunction and robust porous structure, where the photodeposited Cu2O plays a synergistic catalytic role in the photocatalytic process and the outer clad g-C3N4layer prevents Cu2O oxidation. Additionally, the possible photocatalytic mechanism was briefly discussed based on the experimental results. This work identifies viable pathways for developing low-cost heterojunction photocatalysts with highly efficient photocatalytic activity toward improved solar energy conversion.

Though numerous cyanation reactions have been developed for the synthesis of benzonitriles, the construction of valuable fully substituted benzonitriles is still a challenging task. Herein, we reported a tertiary amine-catalyzed [3 + 3]-benzannulation for the green synthesis of CF3-functionalized fully substituted benzonitriles. This strategy features exclusive chemoselectivity, high atom-economy, and good step-economy with environment-friendly reagents and mild conditions. Unique triphenyl-substituted dicyanobenzoate products could be rapidly constructed using this method. The practicality and reliability of this reaction were proved by the successful scale-up synthesis. A mechanistic study indicates that the [3 + 3]-benzannulation was initiated by an intermolecular Rauhut–Currier reaction.

A refined frontal muscle flap suspension does not introduce any exogenous materials into the upper eyelid, focusing on the tension balance of the eyelid, and is a safe and reliable surgery in treating severe ptosis.Supplemental Digital Content is available in the text.

The pendular spectra of linear (HCCCN)_n(n=1−3) molecules at several rotational temperatures under a certain range of electric field strength E are calculated using the finite element matrix diagonalization method. In addition, the normalized “Q-branch” intensity (I_Q) and the gradient (K_E) of I_Q under corresponding conditions are investigated. Based on the results, the range and sensitivity of measuring the electric field strength with linear (HCCCN)_n(n=1−3) molecules are analyzed. A scheme to measure electric field strength based on (HCCCN)_n(n=1−3) with high sensitivity is proposed and the spatial resolution is also discussed. The feasibility of measuring the electric field strength of the electrostatic Stark decelerator based on a pendular spectrum is studied.

As the development of mobile robots matures, there is an increasing amount of interest in expanding the functionality of such robots through developing multimodal locomotion. As compared to land–water or land–air hybrids, the design of air–water vehicles is much less straightforward due to the fact that both mediums are three-dimensional fluid spaces and there is inherent disparity in fluid properties between them. As such, the development of these vehicles has received limited attention until very recently. Nevertheless, the potential applications of such vehicles range widely from military surveillance, oceanic data collection to heterogeneous robot team operation, which has led to an increasing number of projects working on aerial–aquatic hybrid mobility. In this paper, we discuss the fundamental challenges associated with aerial–aquatic hybrid locomotion as well as the necessary trade-offs in design decisions. We also summarize and review the existing work and prototypes of aerial–aquatic vehicles that have been designed thus far, analyzing the range of solutions that have been adopted to solve the aforementioned challenges. Lastly, the limitations of these solutions are analyzed to offer a perspective on how future developments in the area can enable greater functionality for the concept.

Increasing scientific evidence demonstrates that the gut microbiota influences normal physiological homeostasis and contributes to pathogenesis, ranging from obesity to neurodegenerative diseases, such as Alzheimer’s disease (AD). Gut microbiota can interact with the central nervous system (CNS) through the microbiota-gut-brain axis. The interaction is mediated by microbial secretions, metabolic interventions, and neural stimulation. Here, we review and summarize the regulatory pathways (immune, neural, neuroendocrine, or metabolic systems) in the microbiota-gut-brain axis in AD pathogenesis. Besides, we highlight the significant roles of the intestinal epithelial barrier and blood–brain barrier (BBB) in the microbiota-gut-brain axis. During the progression of AD, there is a gradual shift in the gut microbiota and host co-metabolic relationship, leading to gut dysbiosis, and the imbalance of microbial secretions and metabolites, such as lipopolysaccharides (LPS) and short-chain fatty acids (SCFAs). These products may affect the CNS metabolic state and immune balance through the microbiota-gut-brain axis. Further, we summarize the potential microbiota-gut-brain axis-targeted therapy including carbohydrates, probiotics, dietary measures, and propose new strategies toward the development of anti-AD drugs. Taken together, the data in this review suggest that remodeling the gut microbiota may present a tractable strategy in the management and development of new therapeutics against AD and other neurodegenerative diseases.

Unmanned aerial systems provide many applications with the ability to perform flying tasks autonomously, and hence have received significant research and commercial attention in the past decade. One of the most popular unmanned aerial platforms for such tasks is the small-scale rotorcraft with multiple rotors, commonly known as multicopters. In order for these platforms to perform fully autonomous missions and tasks, they require a sophisticated low-level flight control system that is integrated with advanced task and motion planning modules, which combine together to form the complete unmanned aerial system (UAS). In this paper, the planning module of unmanned multicopter systems is discussed in detail, and a comprehensive survey on techniques for both motion and task planning reported in literature and by the Unmanned Systems Research Group at the National University of Singapore is presented.

The aim of this study was to examine the antitumor activity of hinokitiol for its clinical application in the treatment of human cervical carcinoma.

In this paper, we study the formation control of multiple Euler-Lagrange systems with nonautonomous leaders, which means the leaders have bounded unknown inputs. Firstly, adaptive distributed observers to the leaders’ input bounds and states are designed for every follower. In addition, a discontinuous function in the adaptive distributed observer is applied to make up for the influence of the leaders’ unknown inputs. Secondly, a distributed control law is constructed using the distributed observer to accomplish the formation control. Our control law achieves not only affine maneuver control but also containment control performance. All agents as a whole can rotate, shear and scale, and maneuver to destination safely.

The present study reported the preparation of a novel mutual prodrug containing meso-tetra (m-hydroxy phenyl) chlorin (m-THPC), PEG and folate. The folate receptor (FR) targeting compound is a water-soluble chemical incorporating a hydrophilic PEG spacer unit and a reducible selfimmolative disulfide-based linker system between the FR-targeting ligand and the chlorin. The m- THPC-s-s-PEG-Folate was proved to release the parent drug and exhibited significant targeting effects on HeLa cells. In addition, the redox-responsive folate targeting photosensitizer m-THPC-s-s-PEG-Folate showed slightly higher phototoxicity than m-THPC-PEG-Folate for HeLa cells.

Integrator based model is used to describe a wide range of systems in robotics. In this paper, we present an axis-coupled trajectory generation algorithm for chains of integrators with an arbitrary order. Special notice has been given to problems with pre-existing nominal plans, which are common in robotic applications. It also handles various type of constraints that can be satisfied on an entire time interval, including non-convex ones which can be transformed into a series of convex constraints through time segmentation. The proposed approach results in a linearly constrained quadratic programming problem, which can be solved effectively with off-the-shelf solvers. A closed-form solution is achievable with only the boundary constraints considered. Finally, the proposed method is tested in real experiments using quadrotors which represent high-order integrator systems.