Estimating forest structural attributes in planted forests is crucial for sustainably management of forests and helps to understand the contributions of forests to global carbon storage. The Unmanned ...Aerial Vehicle-Light Detecting and Ranging (UAV-LiDAR) has become a promising technology and attempts to be used for forest management, due to its capacity to provide highly accurate estimations of three-dimensional (3D) forest structural information with a lower cost, higher flexibility and finer resolution than airborne LiDAR. In this study, the effectiveness of plot-level metrics (i.e., distributional, canopy volume and Weibull-fitted metrics) and individual-tree-summarized metrics (i.e., maximum, minimum and mean height of trees and the number of trees from the individual tree detection (ITD) results) derived from UAV-LiDAR point clouds were assessed, then these metrics were used to fit estimation models of six forest structural attributes by parametric (i.e., partial least squares (PLS)) and non-parametric (i.e., k-Nearest Neighbors (k-NN) and Random Forest (RF)) approaches, within a Ginkgo plantation in east China. In addition, we assessed the effects of UAV-LiDAR point cloud density on the derived metrics and individual tree segmentation results, and evaluated the correlations of these metrics with aboveground biomass (AGB) by a sensitivity analysis. The results showed that, in general, models based on both plot-level and individual-tree-summarized metrics (CV-R2 = 0.66–0.97, rRMSE = 2.83–23.35%) performed better than models based on the plot-level metrics only (CV-R2 = 0.62–0.97, rRMSE = 3.81–27.64%). PLS had a relatively high prediction accuracy for Lorey’s mean height (CV-R2 = 0.97, rRMSE = 2.83%), whereas k-NN performed well for predicting volume (CV-R2 = 0.94, rRMSE = 8.95%) and AGB (CV-R2 = 0.95, rRMSE = 8.81%). For the point cloud density sensitivity analysis, the canopy volume metrics showed a higher dependence on point cloud density than other metrics. ITD results showed a relatively high accuracy (F1-score > 74.93%) when the point cloud density was higher than 10% (16 pts·m−2). The correlations between AGB and the metrics of height percentiles, lower height level of canopy return densities and canopy cover appeared stable across different point cloud densities when the point cloud density was reduced from 50% (80 pts·m−2) to 5% (8 pts·m−2).
A novel type of one-dimensional ordered mesoporous carbon fiber has been prepared via the electrospinning technique by using resol as the carbon source and triblock copolymer Pluronic F127 as the ...template. Sulfur is then encapsulated in this ordered mesoporous carbon fibers by a simple thermal treatment. The interwoven fibrous nanostructure has favorably mechanical stability and can provide an effective conductive network for sulfur and polysulfides during cycling. The ordered mesopores can also restrain the diffusion of long-chain polysulfides. The resulting ordered mesoporous carbon fiber sulfur (OMCF-S) composite with 63% S exhibits high reversible capacity, good capacity retention and enhanced rate capacity when used as cathode in rechargeable lithium–sulfur batteries. The resulting OMCF-S electrode maintains a stable discharge capacity of 690mAh/g at 0.3C, even after 300cycles.
Sodium‐ion energy storage, including sodium‐ion batteries (NIBs) and electrochemical capacitive storage (NICs), is considered as a promising alternative to lithium‐ion energy storage. It is an ...intriguing prospect, especially for large‐scale applications, owing to its low cost and abundance. MoS2 sodiation/desodiation with Na ions is based on the conversion reaction, which is not only able to deliver higher capacity than the intercalation reaction, but can also be applied in capacitive storage owing to its typically sloping charge/discharge curves. Here, NIBs and NICs based on a graphene composite (MoS2/G) were constructed. The enlarged d‐spacing, a contribution of the graphene matrix, and the unique properties of the MoS2/G substantially optimize Na storage behavior, by accommodating large volume changes and facilitating fast ion diffusion. MoS2/G exhibits a stable capacity of approximately 350 mAh g−1 over 200 cycles at 0.25 C in half cells, and delivers a capacitance of 50 F g−1 over 2000 cycles at 1.5 C in pseudocapacitors with a wide voltage window of 0.1–2.5 V.
An expanded MoS2/graphene composite (MoS2/G), prepared by attachment of expanded MoS2 layers onto graphene sheets by a simple hydrothermal method, results in excellent electrochemical properties for both sodium‐ion batteries and pseudocapacitors. The resulting sodium‐ion battery delivers a high capacity of 313 mAh g−1 over 200 cycles at 0.25 C (1 C=400 mA g−1), with capacity retention of 81 %. The sodium‐ion pseudocapacitor shows excellent cycling performance over 2000 cycles with relatively stable capacitance (∼50 F g−1).
High-efficiency blue phosphorescence emission is essential for organic optoelectronic applications. However, synthesizing heavy-atom-free organic systems having high triplet energy levels and ...suppressed non-radiative transitions-key requirements for efficient blue phosphorescence-has proved difficult. Here we demonstrate a simple chemical strategy for achieving high-performance blue phosphors, based on confining isolated chromophores in ionic crystals. Formation of high-density ionic bonds between the cations of ionic crystals and the carboxylic acid groups of the chromophores leads to a segregated molecular arrangement with negligible inter-chromophore interactions. We show that tunable phosphorescence from blue to deep blue with a maximum phosphorescence efficiency of 96.5% can be achieved by varying the charged chromophores and their counterions. Moreover, these phosphorescent materials enable rapid, high-throughput data encryption, fingerprint identification and afterglow display. This work will facilitate the design of high-efficiency blue organic phosphors and extend the domain of organic phosphorescence to new applications.
Li–CO2 batteries are an attractive technology for converting CO2 into energy. However, the decomposition of insulating Li2CO3 on the cathode during discharge is a barrier to practical application. ...Here, it is demonstrated that a high loading of single Co atoms (≈5.3%) anchored on graphene oxide (adjacent Co/GO) acts as an efficient and durable electrocatalyst for Li–CO2 batteries. This targeted dispersion of atomic Co provides catalytically adjacent active sites to decompose Li2CO3. The adjacent Co/GO exhibits a highly significant sustained discharge capacity of 17 358 mA h g−1 at 100 mA g−1 for >100 cycles. Density functional theory simulations confirm that the adjacent Co electrocatalyst possesses the best performance toward the decomposition of Li2CO3 and maintains metallic‐like nature after the adsorption of Li2CO3.
Targeted synergy between adjacent Co atoms on graphene oxide is an efficient new electrocatalyst for Li–CO2 batteries. Due to a targeted high mass‐loading, neighboring single Co atoms generate a synergetic interaction and provide continuous catalytic active sites for electrocatalysis of decomposition of Li2CO3 with excellent capacity and cycling stability toward Li–CO2 batteries.
Abstract
Nitrate, the major source of inorganic nitrogen for plants, is a critical signal controlling nutrient transport and assimilation and adaptive growth responses throughout the plant. ...Understanding how plants perceive nitrate and how this perception is transduced into responses that optimize growth are important for the rational improvement of crop productivity and for mitigating pollution from the use of fertilizers. This review highlights recent findings that reveal key roles of cytosolic–nuclear calcium signalling and dynamic protein phosphorylation via diverse mechanisms in the primary nitrate response (PNR). Nitrate-triggered calcium signatures as well as the critical functions of subgroup III calcium-sensor protein kinases, a specific protein phosphatase 2C, and RNA polymerase II C-terminal domain phosphatase-like 3 are discussed. Moreover, genome-wide meta-analysis of nitrate-regulated genes encoding candidate protein kinases and phosphatases for modulating critical phosphorylation events in the PNR are elaborated. We also consider how phosphoproteomics approaches can contribute to the identification of putative regulatory protein kinases in the PNR. Exploring and integrating experimental strategies, new methodologies, and comprehensive datasets will further advance our understanding of the molecular and cellular mechanisms underlying the complex regulatory processes in the PNR.
Unique calcium signalling, specific calcium-sensor protein kinases, and diverse protein phosphorylation mechanisms modulate nutrient transport and assimilation, metabolism, hormone signalling and transcription in the primary nitrate responses in Arabidopsis .
The nonaqueous lithium oxygen battery is a promising candidate as a next‐generation energy storage system because of its potentially high energy density (up to 2–3 kW kg−1), exceeding that of any ...other existing energy storage system for storing sustainable and clean energy to reduce greenhouse gas emissions and the consumption of nonrenewable fossil fuels. To achieve high energy density, long cycling stability, and low cost, the air electrode structure and the electrocatalysts play important roles. Here, a metal‐free, free‐standing macroporous graphene@graphitic carbon nitride (g‐C3N4) composite air cathode is first reported, in which the g‐C3N4 nanosheets can act as efficient electrocatalysts, and the macroporous graphene nanosheets can provide space for Li2O2 to deposit and also promote the electron transfer. The electrochemical results on the graphene@g‐C3N4 composite air electrode show a 0.48 V lower charging plateau and a 0.13 V higher discharging plateau than those of pure graphene air electrode, with a discharge capacity of nearly 17300 mA h g−1
(composite). Excellent cycling performance, with terminal voltage higher than 2.4 V after 105 cycles at 1000 mA h g−1
(composite) capacity, can also be achieved. Therefore, this hybrid material is a promising candidate for use as a high energy, long‐cycle‐life, and low‐cost cathode material for lithium oxygen batteries.
A binder‐free G@CN electrocatalyst electrode with a free‐standing macroporous structure exhibits excellent capacity because of the enormous density of deposition sites for reaction products. The macroporous structured graphene framework is used as an electrocatalyst support with high electronic conductivity. The g‐C3N4 nanosheets are successfully integrated into a composite to catalyze the chemical reaction with high energy.
Inspired by the highly versatile natural motors, artificial micro‐/nanomotors that can convert surrounding energies into mechanical motion and accomplish multiple tasks are devised. In the past few ...years, micro‐/nanomotors have demonstrated significant potential in biomedicine. However, the practical biomedical applications of these small‐scale devices are still at an infant stage. For successful bench‐to‐bed translation, biocompatibility of micro‐/nanomotor systems is the central issue to be considered. Herein, the recent progress in micro‐/nanomotors in biocompatibility is reviewed, with a special focus on their biomedical applications. Through close collaboration between researches in the nanoengineering, material chemistry, and biomedical fields, it is expected that a promising real‐world application platform based on micro‐/nanomotors will emerge in the near future.
The biocompatibility of artificial micro‐/nanomotors is essential for real‐world biomedical applications. Recent progress about biocompatible micro‐/nanomotor systems that are based on biocompatible framework materials, chemical fuels (e.g., water, glucose, urea, and acid), external fields (e.g., magnetic field, light, and ultrasound) and biohybrid, is discussed here.
This paper introduces a new intelligent integration between an IoT platform and deep learning neural network (DNN) algorithm for the online monitoring of computer numerical control (CNC) machines. ...The proposed infrastructure is utilized for monitoring the cutting process while maintaining the cutting stability of CNC machines in order to ensure effective cutting processes that can help to increase the quality of products. For this purpose, a force sensor is installed in the milling CNC machine center to measure the vibration conditions. Accordingly, an IoT architecture is designed to connect the sensor node and the cloud server to capture the real-time machine's status via message queue telemetry transport (MQTT) protocol. To classify the different cutting conditions (i.e., stable cutting and unstable cuttings), an improved model of DNN is designed in order to maintain the healthy state of the CNC machine. As a result, the developed deep learning can accurately investigate if the transmitted data of the smart sensor via the internet is real cutting data or fake data caused by cyber-attacks or the inefficient reading of the sensor due to the environment temperature, humidity, and noise signals. The outstanding results are obtained from the proposed approach indicating that deep learning can outperform other traditional machine learning methods for vibration control. The Contact elements for IoT are utilized to display the cutting information on a graphical dashboard and monitor the cutting process in real-time. Experimental verifications are performed to conduct different cutting conditions of slot milling while implementing the proposed deep machine learning and IoT-based monitoring system. Diverse scenarios are presented to verify the effectiveness of the developed system, where it can disconnect immediately to secure the system automatically when detecting the cyber-attack and switch to the backup broker to continue the runtime operation.
In recent years, the internet of things (IoT) represents the main core of Industry 4.0 for cyber-physic systems (CPS) in order to improve the industrial environment. Accordingly, the application of ...IoT and CPS has been expanded in applied electrical systems and machines. However, cybersecurity represents the main challenge of the implementation of IoT against cyber-attacks. In this regard, this paper proposes a new IoT architecture based on utilizing machine learning techniques to suppress cyber-attacks for providing reliable and secure online monitoring for the induction motor status. In particular, advanced machine learning techniques are utilized here to detect cyber-attacks and motor status with high accuracy. The proposed infrastructure validates the motor status via communication channels and the internet connection with economical cost and less effort on connecting various networks. For this purpose, the CONTACT Element platform for IoT is adopted to visualize the processed data based on machine learning techniques through a graphical dashboard. Once the cyber-attacks signal has been detected, the proposed IoT platform based on machine learning will be visualized automatically as fake data on the dashboard of the IoT platform. Different experimental scenarios with data acquisition are carried out to emphasize the performance of the suggested IoT topology. The results confirm that the proposed IoT architecture based on the machine learning technique can effectively visualize all faults of the motor status as well as the cyber-attacks on the networks. Moreover, all faults of the motor status and the fake data, due to the cyber-attacks, are successfully recognized and visualized on the dashboard of the proposed IoT platform with high accuracy and more clarified visualization, thereby contributing to enhancing the decision-making about the motor status. Furthermore, the introduced IoT architecture with Random Forest algorithm provides an effective detection for the faults on motor due to the vibration under industrial conditions with excellent accuracy of 99.03% that is significantly greater than the other machine learning algorithms. Besides, the proposed IoT has low latency to recognize the motor faults and cyber-attacks to present them in the main dashboard of the IoT platform.
PDF
