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In this paper, a fully distributed control protocol is presented based on adaptive technology to solve the time‐varying formation problem of multi‐unmanned aerial vehicle (UAV) systems, where the coupling weights between neighbouring UAVs can be adjusted through adaptive method. Firstly, the proposed control protocol eliminates the need for global information on the interaction topology of multi‐UAV systems, the calculation of Laplacian matrix eigenvalues is avoided and reduces the computational complexity of the system. Secondly, an algorithm with two steps is proposed to determine the distributed adaptive control gain matrices, where the feasibility condition of time‐varying formation of multi‐UAV systems is given. Then, the closed‐loop stability of multi‐UAV systems is proved through Lyapunov theory. Finally, numerical simulation is conducted to verify the effectiveness of the theoretical results. [ABSTRACT FROM AUTHOR]

Traditional LiDAR odometry (LO) systems mainly leverage geometric information obtained from the traversed surroundings to register lazer scans and estimate LiDAR ego-motion, while they may be unreliable in dynamic or degraded environments. This paper proposes InTEn-LOAM, a low-drift and robust LiDAR odometry and mapping method that fully exploits implicit information of lazer sweeps (i.e., geometric, intensity and temporal characteristics). The specific content of this work includes method innovation and experimental verification. With respect to method innovation, we propose the cylindrical-image-based feature extraction scheme, which makes use of the characteristic of uniform spatial distribution of lazer points to boost the adaptive extraction of various types of features, i.e., ground, beam, facade and reflector. We propose a novel intensity-based point registration algorithm and incorporate it into the LiDAR odometry, enabling the LO system to jointly estimate the LiDAR ego-motion using both geometric and intensity feature points. To eliminate the interference of dynamic objects, we propose a temporal-based dynamic object removal approach to filter them out in the resulting points map. Moreover, the local map is organized and downsampled using a temporal-related voxel grid filter to maintain the similarity between the current scan and the static local map. With respect to experimental verification, extensive tests are conducted on both simulated and real-world datasets. The results show that the proposed method achieves similar or better accuracy with respect to the state-of-the-art in normal driving scenarios and outperforms geometric-based LO in unstructured environments. [ABSTRACT FROM AUTHOR]

Exploration is an important aspect of autonomous robotics, whether it is for target searching, rescue missions, or reconnaissance in an unknown environment. In this paper, we propose a solution to efficiently explore the unknown environment by unmanned aerial vehicles (UAV). Innovatively, a topological road map is incrementally built based on Rapidly-exploring Random Tree (RRT) and maintained along with the whole exploration process. The topological structure can provide a set of waypoints for searching an optimal informative path. To evaluate the path, we consider the information measurement based on prior map uncertainty and the distance cost of the path, and formulate a normalized utility to describe information-richness along the path. The informative path is determined in every period by a local planner, and the robot executes the planned path to collect measurements of the unknown environment and restructure a map. The proposed framework and its composed modules are verified in two 3-D environments, which exhibit better performance in improving the exploration efficiency than other methods. [ABSTRACT FROM AUTHOR]

In order to solve the problems of internal uncertainty and external disturbance of an unmanned aerial vehicle (UAV), this paper proposes the active disturbance rejection controller (ADRC) based on the adaptive radial basis function (RBF) neural network. Firstly, the dynamic model of a quadrotor with disturbance is analyzed and the controller is designed based on the ADRC method. Secondly, the RBF network is used to estimate the unknown parameter b of the system and the Lyapunov function is constructed to prove the stability of the closed-loop system. Finally, simulation results of a spiral ascent and a climbing maneuver flight illustrate that the controller proposed in this paper has high tracking accuracy and strong robustness under different flight scenarios; the average tracking error of the outdoor flight experiment is about 0.22 m, which further verifies the effectiveness of the proposed controller. [ABSTRACT FROM AUTHOR]

The recent advances in unmanned aerial vehicles (UAVs) enormously improve their utility and expand their application scope. The UAV and swarm implementation further prevail in Smart City practices with the aid of edge computing and urban Internet of Things. The lead–follow formation in UAV swarm is an important organization means and has been adopted in diverse exercises, for its efficiency and ease of control. However, the reliability of centralization makes the entire swarm system in risk of collapse and instability, if a fatal fault incident happens in the leader. The motivation is to build a mechanism helping the distributed swarm recover from possible failures. Existing ways include assigning definite backups, temporary clustering and traversing to select a new leader are traditional ways that lack flexibility and adaptability. In this article, we propose a voting-based leader election scheme inspired by the Raft method in distributed computation consensus to solve the problem. We further discuss the impact of communication conditions imposed on the decentralized voting process by implementing a network resource pool. To dynamically evaluate UAV individuals, we outline measurement design principles and provide a realizable calculation example. Lastly but not least, empirical simulation results manifest better performance than the Raft-based method. Our voting-based approach exhibits advantages and is a promising way for quick regrouping and fault recovery in lead–follow swarms. [ABSTRACT FROM AUTHOR]

In the cooperative searching scenario, most traditional methods are based on the top–down mechanisms. These mechanisms are usually offline and centralized. The characteristics limit the adaptability of unmanned aerial vehicle (UAV) swarm to the complex mission environments, such as those with inaccurate information of the targets and grids. In order to improve the searching ability of UAV swarm, a novel searching method named emergent search of UAV swarm guided by the target probability map (ESUSTPM) is proposed. ESUSTPM is based on local rules to organize and guide UAV agents to achieve the flocking state, search the mission area and detect the hidden targets concurrently. In ESUSTPM, local rules contain the flocking rules and the guiding rules. The flocking rules are the interactions between the agents, which are designed by a novel constructed function based on two exponential functions in this paper. The new constructed function can better maintain the relatively stable distances between the agents and realize the smooth transition of the positions at the given centers. The local guiding rules based on the target probability information of the nearby grids are firstly designed to realize the multi-function of the swarm, including full area coverage, target detection and reduction in environmental uncertainty (EU). Finally, the simulations verify that ESUSTPM can achieve the full coverage of the mission area while taking into account the target search. The statistical results also indicate that the searching efficiency of the proposed ESUSTPM is higher than the traditional searching algorithms based on the division and allocation of the area or the heuristic algorithms. [ABSTRACT FROM AUTHOR]

This study is mainly focusing on the problem of spacecraft close-range proximity with obstacle avoidance in the presence of complex shape. A novel Gaussian mixture model–based nonsingular terminal sliding mode control (GMM-NTSMC) is proposed. This is achieved by developing GMM-based potential function with a switching surface of NTSMC. It is theoretically proved that the closed-loop system is globally stable. The main contribution of this study is that the GMM-based avoiding strategies, which include the GMM-based terminal sliding mode control (GMM-TSMC) and GMM-NTSMC, can solve the collision avoidance problem considering complex shape while the artificial potential function–based terminal sliding model control (APF-TSMC) fails. Moreover, the GMM-NTSMC and the GMM-TSMC require less energy with respect to the APF-TSMC. Furthermore, the GMM-NTSMC retains the advantage of the NTSMC and can avoid singularity problem while GMM-TSMC cannot. Finally, numerical simulations are performed to verify the effectiveness and superiority of the proposed GMM-NTSMC. [ABSTRACT FROM AUTHOR]

Background modeling is a popular issue in computer vision society during the past few decades. We analyze the usual pixel-level approach and build an adaptive background mixture model with spatio-temporal samples called STS. These samples are described based on a joint domain-range formulation and the pixel is classified with a weight marginal probability density function. On-line update including proximal sample selection, parameter learning, and neighbor update are used to regulate the model to adapt to varying scenes. We perform a bootstrap modeling experiment, and results show that our method outperforms other widely adopted techniques which use only intensity or region description as pixel feature with higher F - measure on most datasets. And our method is a matched rival to combination methods which use both pixel intensity and region description. [ABSTRACT FROM AUTHOR]

The prevalence of cardiovascular disease (CVD) is higher in patients with schizophrenia than in the general population. We aimed to investigate whether atypical antipsychotics (AAP) increase the levels of lipoprotein‐associated phospholipase A2 (Lp‐PLA2), thereby increasing the risk of CVD. The data were from inpatients aged 18–60 years with a diagnosis of schizophrenia according to ICD‐10 at the Affiliated Brain Hospital of Nanjing Medical University who underwent physical examination between 1 October 2014 and 30 September 2016. A retrospective cohort study was used to analyse the correlation between AAP, Lp‐PLA2 levels and the CVD risk (it was determined that Lp‐PLA2 values >200 ng/mL were defined as high CVD risk) in patients treated with monotherapy, olanzapine, clozapine or quetiapine. Data were collected for 452 patients with eligible schizophrenia: 163 treated with clozapine, 186 treated with olanzapine, 47 treated with quetiapine and 56 receiving no medication. Compared with the no‐medication patients, AAP administration in patients with olanzapine, clozapine or quetiapine had higher serum Lp‐PLA2 levels when age, sex, BMI and fasting glucose level were matched. AAP were significantly associated with serum Lp‐PLA2 level by Spearman's correlation coefficients. The results of logistic regression analysis showed that AAP administration was an independent factor of CVD risk when adjusted by potential confounding factors. This study is the first to confirm that AAP administration, especially clozapine and olanzapine, could increase Lp‐PLA2 levels and CVD risk, independent of drug‐induced weight gain in schizophrenia. The extent and the factors of increasing Lp‐PLA2 level and CVD risk in olanzapine, clozapine and quetiapine are discrepant. The possible effects of AAP on Lp‐PLA2 in schizophrenia patients are involved in pro‐inflammatory cytokines and hormones. [ABSTRACT FROM AUTHOR]

In this letter, we present a novel upsampling method to enhance the spatial resolution of depth maps. Given a low resolution depth map from a depth sensor and a registered high resolution intensity image from a camera, we formulate it as a convex optimization problem and solve it using the first-order primal-dual algorithm. The formulation combines an L^2 data term and an anisotropic total variation (TV) regularization term, thus it is more robust to noise and better preserves the fine details. Experimental results on Middlebury and real world datasets demonstrate that our method achieves favorable performance compared with state-of-the-art upsampling methods. [ABSTRACT FROM AUTHOR]

Ruscogenin (RUS), a natural steroidal sapogenin, exerts various biological activities. However, its effectiveness for preventing myocardial ischemia (MI) and its molecular mechanisms need further clarification. The model of MI mice and oxygen-glucose deprivation-induced cardiomyocytes injury was performed. RUS significantly alleviated MI, as evidenced by decreased infarct size, ameliorated biochemical indicators and cardiac pathological features, and markedly inhibited ferroptosis by means of the up-regulation of GPX4 and down-regulation of ACSL4 and FLC. Simultaneously, RUS notably mitigated cell injury and oxidative stress, and ameliorated ferroptosis in vitro. Subsequently, HPLC-Q-TOF/MS-based metabolomics identified BCAT1/BCAT2 as possible regulatory enzymes responsible for the cardioprotection of RUS. Importantly, RUS treatment significantly increased the expression of BCAT1 and BCAT2 in MI. Furthermore, we found that BCAT1 or BCAT2 siRNA significantly decreased cell viability, promoted ferroptosis, and increased Keap1 expression, and induced Nrf2 and HO-1 degradation in cardiomyocytes. Conversely, cardiac overexpression of BCAT1 or BCAT2 in MI mice activated the Keap1/Nrf2/HO-1 pathway. Moreover, RUS significantly activated the Keap1/Nrf2/HO-1 pathway in MI, whereas BCAT1 or BCAT2 siRNA partially weakened the protective effects of RUS, suggesting that RUS might suppress myocardial injury through BCAT1 and BCAT2. Overall, this study demonstrated that BCAT1/BCAT2 could alleviate MI-induced ferroptosis through the activation of the Keap1/Nrf2/HO-1 pathway and RUS exerted cardioprotective effects via BCAT1/BCAT2. [ABSTRACT FROM AUTHOR]

The application of the Internet of Things has produced large amounts of data in different scenarios, which are accompanied with problems, such as consistency and integrity violations. Existing research on dealing with data availability violations is insufficient. In this work, the detection and repair of data availability violations (DRAV) framework is proposed to detect and repair data violations in Internet of Things with a distributed parallel computing environment. DRAV uses algorithms in the MapReduce programming framework, and these include detection and repair algorithms based on enhanced conditional function dependency for data consistency violation, MapJoin, and ReduceJoin algorithms based on master data for k -nearest neighbor–based integrity violation detection, and repair algorithms. Experiments are conducted to determine the effect of the algorithms. Results show that DRAV improves data availability in Internet of Things compared with existing methods by detecting and repairing violations. [ABSTRACT FROM AUTHOR]

Looking for early diagnostic markers and therapeutic targets is the key to ensuring prompt treatment of myocardial ischemia (MI). Here, a novel biomarker xanthurenic acid (XA) was identified based on metabolomics and exhibited high sensitivity and specificity in the diagnosis of MI patients. Additionally, the elevation of XA was proved to induce myocardial injury in vivo by promoting myocardial apoptosis and ferroptosis. Combining metabolomics and transcriptional data further revealed that kynurenine 3-monooxygenase (KMO) profoundly increased in MI mice, and was closely associated with the elevation of XA. More importantly, pharmacological or heart-specific inhibition of KMO obviously suppressed the elevation of XA and profoundly ameliorated the OGD-induced cardiomyocytes injury and the ligation-induced MI injury. Mechanistically, KMO inhibition effectively restrained myocardial apoptosis and ferroptosis by modulating mitochondrial fission and fusion. In addition, virtual screening and experimental validation were adopted to identify ginsenoside Rb3 as a novel inhibitor of KMO and exhibited great cardioprotective effects by regulating mitochondrial dynamical balance. Taken together, targeting KMO may provide a new approach for the clinical treatment of MI through maintaining mitochondrial fusion and fission balance, and ginsenoside Rb3 showed great potential to be developed as a novel therapeutic drug targeting KMO., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

The relationship between food and health is complex. Everyone needs food to live, but too little food, too much food, or the wrong type of food has negative consequences for health. To increase understanding of this relationship, we describe trends and patterns in food-related diseases among both adults and children. Using an ecological framework, we then describe why food intake is complex with a discussion of three broad levels--macro (including policy and social-cultural norms), local community, and individual environments--and their relationship to food consumption. Given the strong relationship between an individual's food choice and his or her surrounding environment, we end with examples of policy responses that aim to help people overcome environmental disincentives toward healthy eating. Finding ways to make eating healthfully easier and affordable for all populations is essential to shifting the average American diet toward one that promotes health., (Project HOPE—The People-to-People Health Foundation, Inc.)