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Driven by the demand for efficient plant protection in orchards, the autonomous navigation system for orchards is hereby designed and developed in this study. According to the three modules of unmanned system “perception-decision-control,” the environment perception and map construction strategy based on 3D lidar is constructed for the complex environment in orchards. At the same time, millimeter-wave radar is further selected for multi-source information fusion for the perception of obstacles. The extraction of orchard navigation lines is achieved by formulating a four-step extraction strategy according to the obtained lidar data. Finally, aiming at the control problem of plant protection machine, the ADRC control strategy is adopted to enhance the noise immunity of the system. Different working conditions are designed in the experimental section for testing the obstacle avoidance performance and navigation accuracy of the autonomous navigation sprayer. The experimental results show that the unmanned vehicle can identify the obstacle quickly and make an emergency stop and find a rather narrow feasible area when a moving person or a different thin column is used as an obstacle. Many experiments have shown a safe distance for obstacle avoidance about 0.5 m, which meets the obstacle avoidance requirements. In the navigation accuracy experiment, the average navigation error in both experiments is within 15 cm, satisfying the requirements for orchard spray operation. A set of spray test experiments are designed in the final experimental part to further verify the feasibility of the system developed by the institute, and the coverage rate of the leaves of the canopy is about 50%.

Solar thermochemical energy storage based on calcium looping (CaL) process is a promising technology for next-generation concentrated solar power (CSP) systems. However, conventional calcium carbonate (CaCO3) pellets suffer from slow reaction kinetics, poor stability, and low solar absorptance. Here, we successfully realized high power density and highly stable solar thermochemical energy storage/release by synergistically accelerating energy storage/release via binary sulfate and promoting cycle stability, mechanical strength, and solar absorptance via Al–Mn–Fe oxides. The energy storage density of proposed CaCO3 pellets is still as high as 1455 kJ kg−1 with only a slight decay rate of 4.91% over 100 cycles, which is higher than that of state-of-the-art pellets in the literature, in stark contrast to 69.9% of pure CaCO3 pellets over 35 cycles. Compared with pure CaCO3, the energy storage power density or decomposition rate is improved by 120% due to lower activation energy and promotion of Ca2+ diffusion by binary sulfate. The energy release or carbonation rate rises by 10% because of high O2− transport ability of molten binary sulfate. Benefiting from fast energy storage/release rate and high solar absorptance, thermochemical energy storage efficiency is enhanced by more than 50% under direct solar irradiation. This work paves the way for application of direct solar thermochemical energy storage techniques via achieving fast energy storage/release rate, high energy density, good cyclic stability, and high solar absorptance simultaneously. [Display omitted] •High power density and highly stable calcium-based solar thermochemical energy storage is achieved simultaneously.•The energy storage density is as high as 1455 kJ/kg with only a slight decay rate of 4.91% over 100 cycles.•The energy storage rate is enhanced by 120% due to enhanced Ca2+ diffusion and lower activation energy.•The energy release rate is improved by 10% due to excellent O2− transport ability of binary sulfate.

The discovery of extensive layered witherite (BaCO3) deposits in the Early Cambrian strata in South China offers valuable insights into the unique paleo-marine environment of this region. Based on stratigraphy, petrography, geochemistry, fluid inclusions, and pervious published multi-isotope geochemical analysis, we aim to explore the distinct genesis mechanism of the witherite deposits in the Chengkou area of South China and unveil the specific paleo-marine environment during their formation. This study concludes that the wide 87Sr/86Sr ratios (0.708266 to 0.708504) of witherite and barytocalcite (BaCa(CO3)2) support the seawater-derived barium. Negative δ13C values (−15.6 to −12.5‰) indicate the involvement of organic matter during the formation of witherite. The complex vapor-phase (including CH4, H2S, etc.) and HS−-containing liquid-phase compositions in the primary liquid–vapor inclusions of the witherite and barytocalcite imply that the two minerals are formed in sulfur-rich euxinic seawater. The broad homogenization temperatures are generated by thermal re-equilibration of the inclusions, rather than the actual temperatures of the trapped fluids. The salinity range of fluid inclusions in the Bashan witherite deposit (0.2 to 16.2 wt.%) records mixing between moderate-salinity basinal-derived fluids and low-salinity seawater-sourced fluids. We propose that the formation of Chengkou witherite deposits is linked to a sulfate-limited euxinic seawater environment, highlighting the spatiotemporal heterogeneity in Early Cambrian paleo-oceanic sulfate concentrations.

Anti-parallel β -sheet crystallite as the main component of silk fibroin has attracted much attention due to its superior mechanical properties. In this study, we examine the processes of pulling a peptide chain from β -sheet crystallite using steered molecular dynamics simulations to investigate the rupture behavior of the crystallite. We show that the failure of β -sheet crystallite was accompanied by a propagation of instability of hydrogen-bonds (H-bonds) in the crystallite. In addition, we find that there is an optimum size of the crystallite at which the H-bonds can work cooperatively to achieve the highest shear strength. In addition, we find that the stiffness of loading device and the loading rates have significant effects on the rupture behavior of β -sheet crystallite. The stiff loading device facilitates the rebinding of the H-bond network in the stick-slip motion between the chains, while the soft one suppresses it. Moreover, the rupture force of β -sheet crystallites decreases with loading rate. Particularly, when the loading rate decreases to a critical value, the rupture force of the β -sheet crystallite becomes independent of the loading rates. This study provides atomistic details of rupture behaviors of β -sheet crystallite, and, therefore, sheds valuable light on the underlying mechanism of the superior mechanical properties of silk fibroin.

Visual analytics for machine learning has recently evolved as one of the most exciting areas in the field of visualization. To better identify which research topics are promising and to learn how to apply relevant techniques in visual analytics, we systematically review 259 papers published in the last ten years together with representative works before 2010. We build a taxonomy, which includes three first-level categories: techniques before model building, techniques during modeling building, and techniques after model building. Each category is further characterized by representative analysis tasks, and each task is exemplified by a set of recent influential works. We also discuss and highlight research challenges and promising potential future research opportunities useful for visual analytics researchers.

Stress sensing is the basis of human-machine interface, biomedical engineering, and mechanical structure detection systems. Stress sensing based on mechanoluminescence (ML) shows significant advantages of distributed detection and remote response to mechanical stimuli and is thus expected to be a key technology of next-generation tactile sensors and stress recorders. However, the instantaneous photon emission in ML materials generally requires real-time recording with a photodetector, thus limiting their application fields to real-time stress sensing. In this paper, we report a force-induced charge carrier storage (FICS) effect in deep-trap ML materials, which enables storage of the applied mechanical energy in deep traps and then release of the stored energy as photon emission under thermal stimulation. The FICS effect was confirmed in five ML materials with piezoelectric structures, efficient emission centres and deep trap distributions, and its mechanism was investigated through detailed spectroscopic characterizations. Furthermore, we demonstrated three applications of the FICS effect in electronic signature recording, falling point monitoring and vehicle collision recording, which exhibited outstanding advantages of distributed recording, long-term storage, and no need for a continuous power supply. The FICS effect reported in this paper provides not only a breakthrough for ML materials in the field of stress recording but also a new idea for developing mechanical energy storage and conversion systems.Lung disease: Mechanoluminescence: stress-sensitive phosphors hold deep memoriesA material that records mechanical impacts and provides optical readouts at later dates shows promise for anti-counterfeiting devices and structural damage analysis. Phosphors, such as rare earth-doped silicates, can emit light in response to physical stress because they store charge carriers in easy-to-access energy states. Rong-Jun Xie from China’s Xiamen University and colleagues now report development of phosphors that release charges to less accessible ‘deep’ energy states after being stimulated mechanically. These carriers are retained in the deep states and then released on-demand as photon emissions following thermal treatments. The team demonstrated several applications for the new phosphors including sensors that can record signature traces and composite films that attach to vehicles to monitor for potential collisions.

Background The functional and structural abnormalities of cortico-striato-thalamo-cortex have been reported to be associated with the neurobiological basis of obsessive–compulsive disorder (OCD). OCD can also occur in the striatum damaging after brain infarction, which is called OCD caused by physical diseases. The relationship and mechanism between temporal lobe abnormalities and OCD are still unclear. Case presentation A young male with temporal lobe porencephaly presented with obsessive–compulsive symptoms. The cognition of this OCD participant was not significantly impaired, and his social function was well maintained. After treatment with sertraline, aripiprazole, and fluvoxamine, he was improved but relapsed several times after drug withdrawal. However, the therapeutic effect was sustained upon reinitiation of the medication. Conclusion This is the first reported case of OCD in an individual with left temporal lobe porencephaly. The possible relationship between obsessive–compulsive symptoms and temporal lobe structural abnormalities needs to be further studied. The maintenance treatment and prognosis of organic obsessive–compulsive disorder also deserve further study.

Neuroimaging findings suggest that social anxiety disorder (SAD) may be correlated with changes in regional- or network-level brain function. However, few studies have explored alterations in intrinsic resting cerebral function in patients with SAD at both the regional and network levels, particularly focusing on the theory of mind (ToM)-related regions. This study was performed to investigate changes in neural activity and functional connectivity (FC) in ToM-related regions during the resting state in SAD patients and to determine how these alterations are correlated with the clinical symptoms of SAD. Forty-three SAD patients and 43 matched healthy controls underwent resting-state functional magnetic resonance imaging (rsfMRI) scans. First, the amplitude of low-frequency fluctuation (ALFF) approach was used to explore regional activity. Then, the ToM-related region, i.e., the left precuneus, which showed altered ALFF values, was adopted as a seed for further FC analyses to assess network-level alterations in SAD. Between-group differences were compared using voxel-based two-sample t-tests (P<0.05, with Gaussian random field correction). Pearson's correlation analyses were performed to examine relationships between alterations in ALFF and FC and clinical symptoms. Compared with the healthy controls, SAD patients showed decreased ALFF in the bilateral putamen (PUT) and left supplementary motor area (SMA) and increased ALFF in the right inferior parietal lobule (IPL), left precuneus and right cerebellar posterior lobe. Moreover, SAD patients exhibited lower connectivity between the left precuneus and the cerebellar posterior lobe, right inferior temporal gyrus (ITG), right parahippocampal gyrus (PHG) and left medial prefrontal cortex (mPFC). The altered ALFF values in the left precuneus and the hypoconnectivity between the left precuneus and left cerebellar posterior lobe were correlated with the patients' clinical symptoms (P<0.05). The precuneus, a ToM-related region, was altered at both the regional and network level in patients with SAD. Pathological fear and avoidance in SAD were correlated with abnormal regional function in the precuneus, whereas depression and anxiety were primarily correlated with functional deficits in the precuneus-related network. The altered FC within the precuneus-cerebellar region may reflect an imbalance in the neuromodulation of anxiety and depressive symptoms in SAD. These findings may facilitate a greater understanding of potential SAD neural substrates and could be used to identify potential targets for further treatment. •Theory of mind (ToM) related region had both regional- and network-level alterations in patients with SAD.•Pathological fear and avoidance in SAD were correlated to abnormal regional functions in the precuneus.•Depression and anxiety were primarily correlated to functional deficits in the precuneus-related network.•The cerebellar dysfunctions may play an important role in the neuromodulation of clinical symptoms in SAD patients.

Ultrafine particles have been increasingly linked to adverse health effects in restaurant workers. This study aimed to clarify the exposure characteristics and risks of ultrafine particles during the cooking process, and to provide a reasonable standard for protecting the workers in the Chinese restaurant. The temporal variations in particle concentrations (number concentration (NC), mass concentration (MC), surface area concentration (SAC), and personal NC), and size distributions by number were measured by real-time system. The hazard, exposure, and risk levels of ultrafine particles were analyzed using the control banding tools. The NC, MC, and SAC increased during the cooking period and decreased gradually to background levels post-operation. The concentration ratios of MC, total NC, SAC, and personal NC ranged from 3.82 to 9.35. The ultrafine particles were mainly gathered at 10.4 and 100 nm during cooking. The exposure, hazard and risk levels of the ultrafine particles were high. These findings indicated that the workers during cooking were at high risk due to exposure to high levels of ultrafine particles associated with working activity and with a bimodal size distribution. The existing control strategies, including engineering control, management control, and personal protection equipment need to be improved to reduce the risk.

In a complex underwater environment, finding a viable, collision-free path for an autonomous underwater vehicle (AUV) is a challenging task. The purpose of this paper is to establish a safe, real-time, and robust method of collision avoidance that improves the autonomy of AUVs. We propose a method based on active sonar, which utilizes a deep reinforcement learning algorithm to learn the processed sonar information to navigate the AUV in an uncertain environment. We compare the performance of double deep Q-network algorithms with that of a genetic algorithm and deep learning. We propose a line-of-sight guidance method to mitigate abrupt changes in the yaw direction and smooth the heading changes when the AUV switches trajectory. The different experimental results show that the double deep Q-network algorithms ensure excellent collision avoidance performance. The effectiveness of the algorithm proposed in this paper was verified in three environments: random static, mixed static, and complex dynamic. The results show that the proposed algorithm has significant advantages over other algorithms in terms of success rate, collision avoidance performance, and generalization ability. The double deep Q-network algorithm proposed in this paper is superior to the genetic algorithm and deep learning in terms of the running time, total path, performance in avoiding collisions with moving obstacles, and planning time for each step. After the algorithm is trained in a simulated environment, it can still perform online learning according to the information of the environment after deployment and adjust the weight of the network in real-time. These results demonstrate that the proposed approach has significant potential for practical applications.