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Waste wool have successfully been used to prepare activated carbon powders (ACPs) via chemical activation and carbonization under air flow. Effects of cuticle layers and experimental parameters on the properties of ACPs were analyzed. Cuticle layers showed little relations with tube formation but affected the thermal, textural and morphological properties of ACPs. While, carbonization temperature was the key factor affecting ACPs properties. High carbonization temperature decreased the tube, pore-size and crystallinity of ACPs but increased their specific surface areas (SBET). The tube-size and wall-thickness of obtained ACPs were controllable by the temperature, which ranged between 2.367–4.500 μm and 10.729–18.833 μm, respectively. ACPs exhibited thermal stableness with less than 50% weight loss. However, functional groups on wool surface changed after carbonization. Among all samples, ACPs activated with 30% ZnCl2 and carbonized at 500 °C under air had a much higher SBET of 533.17 m2 g−1 and larger pore volume of 0.359 cm2 g−1.

Fault localization is one of the most important issues for the modular multilevel converters (MMC) consisting of lots of switches. This article proposes a switch open-circuit fault localization strategy for the MMC, where a sliding-time window (STW) based features extraction algorithm (FEA) is proposed to extract the features of the MMC based on the feature relationship between neighboring STWs. Based on the extracted features, the fault in the MMC can be easily located with the 2-D convolutional neural networks. The proposed STW-FEA-based fault localization strategy can constructs concise low-data-volume features samples for the MMC in both time domain and frequency domain, and accordingly it can locate the fault with short time and high accuracy for the MMC. In addition, it does not require the creation of complex mathematical models and manual setting of empirical thresholds. Simulation and experiment are conducted, and the results confirm the effectiveness of the proposed strategy.

Anisotropy engineering serves as an effective method to achieve unique properties and enhanced performance of materials. Here MOF-71 and derivatives with tuned anisotropy have been achieved via facial tuning the proticity of the solvents, giving rise to nanoflake assemblies into bellows-, boat- and flower-like microstructures. Three anisotropy dimensions including Faceting, Aspect ratio and Arrangement have been proposed to describe the anisotropy of the assemblies, which are further evaluated by heterotype ( H ¯ ), flatness ( F ¯ ) and orderliness ( O ¯ ). Combined off-axis electron holography and electromagnetic scattered field simulation visualize uniform distribution of magnetic lines due to enhanced anisotropy of the bellows-like derivatives. This gives rise to strengthened magnetic loss and electromagnetic wave absorption for the anisotropy evolution from the flower-, boat-to bellows-like structure. As such the work not only provides versatile strategy for anisotropy engineering and reification of the anisotropy for complicated assembled structures, but also insights on impacts of the anisotropy on the distribution of stray field, magnetic dipoles and scattered field for the design of electromagnetic absorbers, which are extendable to various areas including but not limited to magnetics, electrics and optics.

The stability analysis methods of single-bus dc distribution power systems (DPSs) have been widely studied; however, the multivoltage-level dc DPSs, which have been used in data center, all-electric ship, and so on, its stability analysis methods are rarely reported. The main focus of this article is to study the stability analysis methods, especially the impedance ratio of multivoltage-level dc DPSs. From the point of view of whole system, the two-port small-signal models of multivoltage-level dc DPSs are established and the equivalent loop gains are obtained and used to judge the system stability. On the other hand, the multivoltage-level dc DPSs can be regarded as conventional single-bus dc DPSs at different interfacing locations, and the impedance-ratio type stability criteria are obtained. And it is proved that the two stability analysis methods, namely the system equivalent loop gain and impedance-ratio type stability criterion at different interfacing locations, have the same stability results, which clarifies the stability status by considering different interfacing locations. Finally, the analysis is verified with simulations and hardware-based experiments of a three-converter three-stage dc DPS.

In existing ensemble learning algorithms (e.g., random forest), each base learner’s model needs the entire dataset for sampling and training. However, this may not be practical in many real-world applications, and it incurs additional computational costs. To achieve better efficiency, we propose a decentralized framework: Multi-Agent Ensemble. The framework leverages edge computing to facilitate ensemble learning techniques by focusing on the balancing of access restrictions (small sub-dataset) and accuracy enhancement. Specifically, network edge nodes (learners) are utilized to model classifications and predictions in our framework. Data is then distributed to multiple base learners who exchange data via an interaction mechanism to achieve improved prediction. The proposed approach relies on a training model rather than conventional centralized learning. Findings from the experimental evaluations using 20 real-world datasets suggest that Multi-Agent Ensemble outperforms other ensemble approaches in terms of accuracy even though the base learners require fewer samples (i.e., significant reduction in computation costs).

In this paper, we propose a time-efficient indoor navigation and evacuation (TINE) framework to minimize moving time for mobile users based on Internet of Things (IoT) technologies. In normal time, the proposed TINE framework can estimate the density of mobile users in each area and determine the moving speeds to pass through different areas. Based on the determined moving speed of each area, an indoor navigation path can be planned to provide the shortest moving time for a mobile user. In emergent time, TINE can accurately estimate the escaping time for groups of mobile users by jointly considering the length and moving time of passageways, the capacity of passageways/doors/exits, the present distribution and parallel moving of mobile users, and the possible congestion caused by other groups. Based on the estimated escaping time, TINE can efficiently alleviate the congestion of all passageways/exits and evenly distribute the evacuation load among exits to minimize the total escaping time. According to our review of relevant research, this is the first solution that can both provide the fastest navigation path to arbitrary target places and evacuate all groups of mobile users to safe places in the shortest escaping time. Simulation results show that TINE outperforms existing schemes and can significantly reduce the total walking and escaping times of indoor navigation and evacuation, respectively. In particular, an Android-based prototype with iBeacon IoT localization is implemented to verify the feasibility of our TINE system.

This paper addressed the robust fuzzy C-Means design for a class of clustering algorithm that are robust against both the plant parameter perturbations with nonlinearity and controller gain variations. Based on the description of Takagi–Sugeno (TS) fuzzy model, the stability and control of nonlinear systems are studied. The recently proposed integral inequality is selected based on the free weight matrix, and the minimum conservative stability criterion is given in the form of linear matrix inequality (LMI). Assuming that the controller and the system have the same premise, this method does not require the number and membership function rules. In addition, the improved control is used as the stability criterion of the closed-loop TS fuzzy system obtained from LMI in large-scale nonlinear systems, and is reorganized for machine learning. The novelty of this paper is to develop a simplified and robust controller design for a class of nonlinear perturbed systems. Moreover, the proposed control process was also ensured by the control criterion derived from the energy function for the stability of the nonlinear system. Finally, a simulation is given and demonstrated the feasibility of the practical application motivated by certain concrete-real problem in vibrated structures.

The formation of public opinions is a complex phenomenon that revolves around the aggregation of individuals’ beliefs. To accurately capture this phenomenon, one needs to build links from individualistic experiences to personal beliefs, which evolve in a social space through information exchange and belief revision. Despite many efforts to model opinion dynamics, the role of personal experiences and beliefs has often been overlooked. In this paper, we address this issue and propose an agent-based model in a social network. We explicitly model belief acquisition as a learning process from experiences that take the form of local data sets. Agents interact through a social network and update their beliefs based on how accurately the belief reflects experiences. Through iterations of interactions, the agents are able to arrive at a unified belief. We then focus on the accuracy of the personal beliefs during their evolution and the impact of the social network structure. On a micro-level, we investigate positional attributes such as the centrality of nodes that affect belief accuracy. On a macro-level, we investigate structural features that affect the overall performance. We then investigate a method to intervene in opinion formation through expert agents. Experiments are performed on real-world and synthetic data sets, which validate a number of important structural insights.

Lower limb injuries occupy a high percentage of the traffic accident injuries among road users. However, the injury outcomes in accident statistics is somehow different from the results in cadaver tests. The purpose of this study is to determine the influence of active muscle function on the kinetics result of lower limb injuries during emergency braking in car frontal collision. Based on a human body numerical model regrading a Chinese 50th percentile male lower extremity with active muscles, frontal collisions under different overlaps were established. Then, by comparing the peak forces and moments of the femur and tibia, the dynamic responses of lower limbs under different load conditions were determined. Furthermore, the distribution of stress indicators was analyzed to determine the influence of active muscle and collision overlaps on the injury outcomes at a micro level. Results show that active muscle has a significant effect on the lower limbs, which are particularly affected by the increase of bending moments. The moment caused by the active muscles in the right tibia is 3.7 times greater than that of the femur. Moreover, the effect of active muscle is more significant in small overlap frontal impact. In the 25% overlap collision, the moment caused by the active muscles of the tibia increase by 1.2 times that of the 40% offset collision. In conclusion, this study can provide an explanation on the differences of lower limb injuries between the cadaveric tests and traffic accidents statistics.

It is well known that there is a design tradeoff between voltage regulation and load current sharing in conventional linear droop control for several parallel-connected distributed sources in a DC microgrid. To improve both performance areas, this article proposes a design method to identify the nonlinear droop curve coefficient with the desired bus voltage regulation and current sharing accuracy in a specified heavy load range, while a linear droop function with a negative droop resistance is used to tighten the bus voltage regulation in the light load range. The design procedure of load boundary and the effect of negative droop resistances on the system stability are analyzed in detail. Also, a boost-up and curve-fitting method can further improve the voltage regulation and achieve smooth transition between light load and heavy load. In this way, no additional voltage compensation loop is needed. Two parallel-connected DC converters are finally built to verify the theoretical analysis.

A modular multilevel converter (MMC)-based multiport power electronic transformer (PET) topology with shared medium-frequency transformer (MFT) is proposed in this brief, where the proposed PET is suitable for the hybrid AC/DC distribution network because of the existence of four essential voltage ports, including medium-voltage AC and DC ports, low-voltage AC and DC ports. Moreover, a multi-frequency modulation and the design of voltage decoupling circuit are introduced. One MFT is shared by three-phase MMC arms, which contributes to the compact structure and reduced cost of PET. The operating principle, applied control strategy and parameter design of proposed PET are introduced in detail. The experimental results are provided to verify the feasibility of PET.

Biomass-based composites are of immerse importance because they allow combining properties in ways that are not found in nature. This work reports facile routs to produce pressure sensor in large scale from biomass kapok fibers and natural rubber. Two different strategies were applied to introduce conductive elements into the pressure sensor and are systemically compared, which include carbonization of the tubular kapok fibers and coating the fibre with conductive polymer. When used as a pressure sensor, the kapok fiber and natural rubber-based composites can respond logarithmically to the loading in the compressive strain range of 0 ~ 15% with excellent recyclability. As a prototype of the concept for practical application, a weight scale was constructed using the kapok fiber and natural rubber-based composites and showed distinct weight-dependent resistance changes, being feasible for measuring weight within a certain weight range. Besides, the strategy based on conductive polymer enables the pressure sensor to be insulative at its original state and become conductive when pressed, hereby making the composite a potential pressure-sensing material and pressure-responsive circuit switch. This study provides a platform for developing biomass-based electronic pressure sensors.

An oxidative damage model of human proximal renal epithelial cells (HK-2) was established using oxalate damage. The repair effects of Astragalus polysaccharide (APS) and selenized APS (Se-APS) on damaged HK-2 cells were investigated. Differences in the adhesion and endocytosis of HK-2 cells to calcium oxalate dihydrate crystals with a size of approximately 100 nm before and after APS and Se-APS repair were also explored. The results showed that after being repaired by APS and Se-APS, HK-2 cells exhibited increased cell viability, restored cell morphology, reduced reactive oxygen species level, increased mitochondrial membrane potential, reduced phosphatidylserine eversion, and osteopontin expression. Moreover, the amount of adherent crystals on the cell surface decreased, but the amount of endocytic crystals increased. At the same concentration, Se-APS exhibited better repair effects on the damaged HK-2 cells than APS. All these findings revealed that Se-APS may be a potential drug candidate for inhibiting the formation of kidney stones.

Global navigation satellite system (GNSS) based passive radar has been applied in the detection of moving targets. However, the low signal power of GNSS on the earth's surface limits the application of this technology for the long-range or low-observable target detection. Increasing the observation time can effectively improve the detection capability. But the target motion involves the range cell migration (RCM) and the Doppler frequency migration (DFM) over the long observation time, which results in the integration gain loss and lower the detection performance. This article proposes a new hybrid coherent and noncoherent integration method named the keystone transform and Lv's distribution. The proposed method not only compensate the RCM and the DFM but also provide coherent and noncoherent integration gains to increase the signal-to-noise ratio. The simulated results and the field trial results demonstrate that the detection performance of the proposed method is superior to the other two known moving target detection methods. And the analysis of the computational complexity shows that the proposed method and the other two methods are in the same order of ${\mathrm O}({{N^3}{\rm{log}}N})$ .

A universal and general quantum simultaneous secret distribution (QSSD) protocol is put forward based on the properties of the one-dimensional high-level cluster states, in which one sender dispatches different high-level classical secret messages to many users at the same time. Due to the idea of quantum dense coding, the sender can send different two-dit classical messages (two d-level classical numbers) to different receivers simultaneously by using a one-dimensional d-level cluster state, which means that the information capacity is up to the maximal. To estimate the security of quantum channels, a new eavesdropping check strategy is put forward. Meanwhile, a new attack model, the general individual attack is proposed and analyzed. It is shown that the new eavesdropping check strategy can effectively prevent the traditional attacks including the general individual attack. In addition, multiparty quantum secret report (MQSR, the same as quantum simultaneous secret submission (QSSS)) in which different users submit their different messages to one user simultaneously can be gotten if the QSSD protocol is changed a little.

Under China’s vigorous development of integrated energy services, the Integrated Energy Service Agency (IESA) is responsible for purchasing energy from external markets and selling energy to multi-energy users (MEUs). Currently, an increase in the various forms of energy in industrial parks has caused great uncertainty for MEUs participating in an integrated demand response (IDR) but has also provided an opportunity for industrial parks to optimize energy conservation. Therefore, determining how to build an elastic energy cloud model with IDR uncertainty and add the uncertainty and randomness of the cloud model to the optimal scheduling of an industrial park integrated energy system is a key problem. In this paper, an optimal economic dispatch model of an industrial park is proposed and considers the uncertain elastic energy of IDR. In this model, the IESA is responsible for the reasonable scheduling of equipment for optimal operation, the establishment of integrated energy retail prices for MEUs, and the goal of maximizing the net income of the IESA. First, the functional relationship among the self-elastic coefficient, retail energy prices, and IDR variation is considered. A cloud model of the self-elastic coefficient is constructed to indirectly represent the multiple uncertainties of the elastic energy in the industrial park. Second, this paper compares and analyzes the economic benefits and IDR potential of the industrial park by considering only single power users in different intervals and the selection of cloud drop elements of MEUs in all intervals. Finally, a new scene random sampling method based on interval contributions (SRS-IC) is employed to solve the optimization model, and a typical example is used to demonstrate that the model and method can guarantee the overall economy of the industrial park, improve the computational efficiency, and explore the IDR potential of MEUs.

The global navigation satellite system (GNSS) is widely used in smartphone positioning, but its performance can be degraded in urban environments because of signal reflections or blockages. To address these GNSS outages, pedestrian dead reckoning (PDR) is commonly used due to its significant improvements in both the stability and continuity of positioning, which are dependent on three key factors: continuous absolute position, heading and step information. Signals of opportunity are commonly used in positioning, whereas the installation of Bluetooth low energy (BLE) sensors on lampposts can provide an opportunity for positioning and heading estimation in urban canyons. In this article, a system integrating the GNSS, PDR, and BLE techniques is implemented in smartphones to provide a real-time positioning solution for pedestrians, which includes a new position correction method based on BLE heading, a reliable heading estimation integrating BLE and inertial sensors, an unconstrained step detection method with high accuracy, and an extended Kalman filter (EKF) to integrate multiple sensors and techniques. In several field experiments, with improvements in availability and robustness, the heading accuracy of the proposed fusion approach could reach approximately 3 degrees; the positioning accuracy achieved between 2.7 m and 4.2 m, compared with a 30 m error from the GNSS alone. Simultaneously, this system could achieve a high positioning accuracy of 2.4 m with unconstrained smartphones in a mixed environment. The proposed system has been demonstrated to perform well in urban canyons.

The purpose of this study is to determine the effect of active muscle on lower limb injury in frontal vehicles collision, and to provide corresponding biomechanical parameters for the protective de...

Achieving consensus is a crucial problem for large scale drone swam to perform collaboration tasks. There are some critical issues for the consensus of large-scale drone swarms, such as limited-time convergence and high robustness against drone failure due to stringent requirements of mission cycle and hostile environment. Note that traditional consensus cannot guarantee limited-time convergence and a robust consensus against node failure. Existing finite-time consensus faces challenges of strict prerequisites and high complexity. Moreover, leaderless and leader-follower consensus adopts different models respectively. Toward these ends, a unified consensus model for both leaderless and leader-follower modes is proposed. It achieves limited-time convergence by using distributed energy minimization. A dynamic spanning tree algorithm is designed to ensure consensus under dynamic topology. Furthermore, a robust method against node failure is proposed by combining grey prediction, average consensus, and Molly-Reed criterion. Simulation results show that the proposed methods can be adopted in both leaderless and leader-follower situations with advantages of limited-time convergence and high robustness.

For the direct matrix converter (DMC) applied to permanent-magnet synchronous motor (PMSM) drives, a novel modulation scheme is proposed in this article, which could reduce the control complexity and improve the efficiency while achieving zero common-mode voltage (CMV). Only three counterclockwise or clockwise rotating vectors are adopted to operate the DMC. The proposed scheme has the inherent ability to generate sinusoidal input currents without explicit control in the modulation. The duty cycle expressions for the three vectors are quite simple without any trigonometric operations. The switching pattern is fixed, saving the need for sector and sequence determination. Therefore, compared with the conventional modulation scheme which also uses only rotating vectors, the proposed scheme could reduce the execution time by over 80%. In addition, it achieves higher efficiency benefiting from the reduced switching actions. Its validity is verified by experimental results on a 2.4-kW DMC-fed PMSM drive prototype.

In order to improve the power density of dc transformer (DCT), this article proposes a novel three-phase dual-active-bridge (DAB) dc–dc converter, which possesses triple input voltage of conventional full-bridge DAB converter without increasing the voltage stress of switches. By adopting it as the DCT submodule (SM), the number of SMs in DCT is reduced markedly and power density can be improved. Based on the analysis of operation principle, design procedure, basic control strategy and startup scheme of the proposed three-phase triple-voltage DAB (T2-DAB) converter are presented. Then, the feasibility of proposed converter is verified with simulation and a 2 kW, 600/200 V prototype. Finally, the superiority of the DCT scheme based on proposed T2-DAB converter is verified by a comparison with some existing DAB schemes.

Recently, more occasions with high input voltage and/or high output voltage applications are emerging. However, the existing switching devices are limited by the voltage stress, e.g., the available maximum blocking voltage of insulated-gate bipolar transistors is 6.5 kV. Therefore, several methods are provided to solve this issue, including series connection of switches, multilevel converters, and modular series–parallel structures, where the modular series–parallel systems are the most popular choice due to the advantages of low cost, simple design process, high modularity, reliability, and redundancy. The main objective of series–parallel systems is to ensure the sharing of input/output voltage/current. Based on this, a comprehensive review on the available voltage/current sharing methods for series–parallel systems, which include input-series output-parallel, input-parallel output-series, and input-series output-series structures is provided in this article. Moreover, the relationships between the input voltage/current sharing and output voltage/current sharing of series–parallel systems are explained.

When the vortices shedding from an upstream rod impinge upon the surface of a downstream airfoil, the resultant vortex–body interaction noise can be significant. The use of “natural rod-base blowing” to reduce this interaction noise is investigated numerically using a 3D hybrid computational aeroacoustics approach. The natural blowing is generated through an internal slot that interconnects the stagnation and base region of the rod. Numerical simulation is performed for a straight blowing at blowing rates (BRs) between 6.4% and 16.3% and an oblique blowing with slot–incidence angles ( θ ) between 0 ° and 15 ° , respectively. Far-field noise evaluation demonstrates that the natural rod-base blowing under tested BRs can reduce significantly the noise emission, whereas loses its efficiencies gradually with the increase of angle θ . The most effective case is the straight blowing ( θ = 0 ° ) at BR ≥ 13.6% where the far-field tonal noise associated with the steady-periodic von Karman vortex shedding is annihilated. The changes in transient flow structures indicate that the natural blowing gradually attenuates the von Karman vortex shedding when BR increases, resulting in a mitigation of vortex–body​ interaction on the airfoil leading surface and hence a reduction of the unsteady lift. At BR=13.6%, as the angle θ reaches 15 ° from below, a new von Karman vortex street reforms in the flow field, leading to another tonal noise. Linear stability analysis based on a local concept of absolute/convective instability suggests that the absolutely unstable region in the near-wake of the rod elongates first and then shrinks with the increase of BR, while the local absolute growth rate decreases monotonously.

The relationship between the number of detonation waves and the evolution process of the flow field in a rotating detonation engine was investigated through a numerical analysis. The simulations were based on the Euler equation and a detailed chemical reaction model. In the given engine model, the flow-field evolution became unstable when a single detonation wave was released. New detonation waves formed spontaneously, changing the operational mode from single-wave to four-wave. However, when two or three detonation waves were released, the flow field evolved in a quasi-steady manner. Further study revealed that the newly formed detonation wave resulted from an accelerated chemical reaction on the contact surface between the detonation products and the reactive mixture. To satisfy the stable propagation requirements of detonation waves, we proposed a parameter called NL, which can be compared with the number of detonation waves in the combustor to predict the evolution (quasi-stable or unstable) of the flow field. Finally, we verified the effectiveness of NL in a redesigned engine. This study may assist the operational mode control in rotating detonation engine experiments.

The Autonomous Navigation (AutoNav) mode of a Global Navigation Satellite System (GNSS) utilizes Inter-Satellite Links (ISLs) to maintain satellite operations without the service from the ground control segment. The newly launched Beidou satellites are capable of conducting ISLs for orbit determination to improve their autonomy, integrity, reliability, and robustness. However, the satellite orbit errors can increase over time due to various force model errors, particularly that of Solar Radiation Pressure (SRP). Accelerometers aboard satellites can measure non-conservative forces directly and have been successfully used in satellite missions for gravity recovery and atmosphere study (i.e., GRACE, CHAMP, and GOCE). This study investigates the feasibility to use accelerometers aboard Beidou satellites to improve AutoNav accuracy and service span. The results show that in a simulated 180-day AutoNav period, the orbit accuracy with the aid of accelerometers is two times better than that of using ISL data only.

Health Information Exchange (HIE) exhibits remarkable benefits for patient care such as improving healthcare quality and expediting coordinated care. The Office of the National Coordinator (ONC) for Health Information Technology is seeking patient-centric HIE designs that shift data ownership from providers to patients. There are multiple barriers to patient-centric HIE in the current system, such as security and privacy concerns, data inconsistency, timely access to the right records across multiple healthcare facilities. After investigating the current workflow of HIE, this paper provides a feasible solution to these challenges by utilizing the unique features of blockchain, a distributed ledger technology which is considered "unhackable". Utilizing the smart contract feature, which is a programmable self-executing protocol running on a blockchain, we developed a blockchain model to protect data security and patients' privacy, ensure data provenance, and provide patients full control of their health records. By personalizing data segmentation and an "allowed list" for clinicians to access their data, this design achieves patient-centric HIE. We conducted a large-scale simulation of this patient-centric HIE process and quantitatively evaluated the model's feasibility, stability, security, and robustness.

Up-to-date digital elevation model (DEM) products are essential in many fields such as hazards mitigation and urban management. Airborne and low-earth-orbit (LEO) space-borne interferometric synthetic aperture radar (InSAR) has been proven to be a valuable tool for DEM generation. However, given the limitations of cost and satellite repeat cycles, it is difficult to generate or update DEMs very frequently (e.g., on a daily basis) for a very large area (e.g., continental scale or greater). Geosynchronous synthetic aperture radar (GEOSAR) satellites fly in geostationary earth orbits, allowing them to observe the same ground area with a very short revisit time (daily or shorter). This offers great potential for the daily DEM generation that is desirable yet thus far impossible with space-borne sensors. In this work, we systematically analyze the quality of daily GEOSAR DEM. The results indicate that the accuracy of a daily GEOSAR DEM is generally much lower than what can be achieved with typical LEO synthetic aperture radar (SAR) sensors; therefore, it is important to develop techniques to mitigate the effects of errors in GEOSAR DEM generation.

The quantitative determination of crack damage stress is of significant importance for investigating rock deformation and failure. In this study, a new quantitative method for determining the crack damage stress of rock materials was proposed based on acoustic emission (AE) signal detection. Ten rock materials were subjected to uniaxial compression testing, during which real-time AE signals and axial, lateral, and volumetric strain values were recorded. The AE cumulative count curves of the different rock materials were calculated based on the AE counts. These curves were divided into three stages as the axial stress increased: the slowly increasing stage, the steady stage, and the sharply increasing stage. The point where the sharply increasing stage began was defined as the PR point and could be accurately determined based on a fitting optimization method. Then, the damage stress was calculated by using the crack volumetric strain model. Finally, the relationship between the R stress (axial stress corresponding to the PR point) and the crack damage stress was investigated for the ten rock materials. The results showed that there was a good correspondence between the R stress and the crack damage stress. Hence, the R stress can be used to determine the crack damage stress and the starting point of the unstable crack growth stage in rock materials.

Gas production during the electrochemical treatment of waste liquids has potential safety risks. Herein, hydrogen (H2) production in such treatment was taken as the control target. Electrode combin...

The characteristics of in-depth hardness distribution in the high-temperature carburized case of Nb-microalloyed gear steel before and after cryogenic treatment were studied by nano-indentation method and Vickers hardness method. The results showed that there was no significant difference in Vickers hardness of the carburized case before and after cryogenic treatment, while the nano-hardness of the carburized case was increased after cryogenic treatment. The change of nano-hardness could be related to the decrease in retained austenite content. The retained austenite content in the surface of the carburized case was 26%, which was reduced to 18% after cryogenic treatment at − 196 °C, with the nano-hardness increased from 11.8 to 12.6 GPa. Nano-indentation can be used to reliably measure the properties of the high-temperature carburized case with fine retained austenite for Nb-microalloyed gear steel.

The rapid development of Space-Air-Ground integrated network, IoT, and swarm-based robotic systems has promoted the transformation of traditional single drone toward drone swarm. Compared to the traditional single drone, drone swarm can collaboratively complete complex tasks with higher efficiency and lower cost, especially in harsh environments. Communication and networking techniques are essential to enabling collaborate information sharing, coordinating multiple drones, and achieving autonomous drone swarm. However, the traditional communication technologies on fixed networks or slowly moving networks cannot address the unique characteristics of drone swarm, such as high dynamic topology, intermittent links and capability constraints. Two kinds of networking techniques fit for different drone swarm tasks are investigated, and the performance indexes of several wireless technologies suitable for drone swarm are also analyzed. Considering that drone swarm would usually be deployed in dire circumstances and the network may get frequently partitioned, the robustness of drone swarm becomes crucial. Based on the Molloy-Reed criterion, a swarm intelligent robust solution for drone swarm is proposed by using the consensus method and grey prediction, which has advantages of small overhead and local information exchanging. The simulation results corroborate that the robustness to node failure of drone swarm can be effectively improved by the proposed method.

Although altimeters have been widely used to monitor the spatiotemporal variation of sea-ice thickness, they are unable to separate sea-ice freeboard from snow depth. We use a floating GPS deployed on sea ice to derive the freeboard and snow depth near China's Zhongshan Station. Our results show that the standalone floating GPS can monitor freeboard with a precision of 4.2 cm. If time-varying dynamic ocean topography provided by, for example, a bottom pressure gauge is available, then the precision of GPS-derived freeboard can improve to 1.3 cm. The daily snow depth inverted by GPS interferometric reflectometry captures three precipitation events during our experiment, showing that the floating GPS can monitor the variation in snow depth and observe the freeboard variation at the same time. By studying the relationship between freeboard, snow depth and sea-ice thickness, we find that sea-ice thickness will be greatly underestimated by the negative single-point freeboard under the assumption of hydrostatic equilibrium. As a supplement to existing technologies, the GPS-derived freeboard and snow depth can be used both to evaluate the altimeter observations directly and to improve our understanding of the real-time variation of freeboard and snow depth in the experimental area.

The modular multilevel converter (MMC) is an attractive option for high-voltage and high-power applications. Fault localization is one of the most important challenges for the MMC consisting of a large number of power electronic switches. Existing literature has not provided a complete fault localization strategy for the MMC under submodule (SM) lower switch open-circuit fault, which only considers the MMC in inverter mode and neglects the MMC in rectifier mode. This article proposes a fault localization strategy for the MMC under SM lower switch open-circuit fault, which is suitable for the MMC not only in inverter mode but also in rectifier mode. This article analyzes the MMC fault characteristics under SM lower switch open-circuit fault, which considers the MMC in inverter and rectifier mode, respectively. Based on two modes' common fault characteristic that the capacitor is charged all the time during the period when the arm current is positive, the faulty SM can be effectively located by the proposed fault localization strategy. The proposed strategy is not only implemented in simulation with professional tool PSCAD/EMTDC but also verified with a downscale MMC prototype in the laboratory. The results confirm the effectiveness of the proposed strategy.

$$l_{q}$$ ( $$0< q < 1$$ ) regularization is a dominating strategy for matrix completion problems. The main goal of nonconvex $$l_{q}$$ regularization based algorithm is to find a so-called low-rank solution.Unfortunately, most existing algorithms suffer from full singular value decomposition (SVD), and thus become inefficient for large-scale matrix completion problems. To alleviate this limitation, in this paper we propose an accelerated inexact algorithm to handle such problem. The key idea is to employ the closed-form q-thresholding ( $$q = 1/2, 2/3$$ ) operator to approximate the rank of a matrix. The power method and the special “sparse plus low-rank” structure of the matrix iterates are adopted to allow efficient SVD. Besides, we employ Nesterov’s accelerated gradient method and continuation technique to further accelerate the convergence speed of our proposed algorithm. A convergence analysis shows that the sequence $$\{X_{t}\}$$ generated by our proposed algorithm is bounded and has at least one accumulation point. Extensive experiments have been conducted to study its recovery performance on synthetic data, image recovery and recommendation problems. All results demonstrate that our proposed algorithm is able to achieve comparable recovery performance, while being faster and more efficient than state-of-the-art methods.

Impedance-based methods are effective for stability assessment of cascaded systems. However, the dc distribution power system (DPS) is becoming increasingly complex, incorporating multivoltage levels, many dc buses, and numerous converters. Consequently, conventional stability criteria, such as Middlebrook criterion and its extensions, may not have the ability to judge stability very succinctly. Thus, more research work related to stability evaluation of dc DPS with multivoltage levels needs to be carried out. This article proposes an impedance-based stability assessment methodology. First of all, a unified form of the system is obtained regardless of its structures and operating modes, based on classification of any converter as either a bus voltage-controlled converter or a bus current-controlled converter. Then, according to the two-port small-signal model, the equivalent loop gain of the system is derived precisely. It should be noted that intermediate bus converter could adopt output current or output voltage control modes, which will have influence on the equivalent loop gain expression. After that, the stability requirement of the system is given by introducing the Nyquist criterion to the equivalent loop gain. Finally, an experimental prototype is established to validate the effectiveness of the proposed criterion.

The half bridge sub-module (HBSM)-based modular multilevel converter (MMC) is widely used in high voltage direct current (HVDC) transmission systems. However, it cannot block the dc fault current due to the uncontrolled freewheeling diodes in IGBT modules. The existing sub-modules with inherent dc fault current blocking capability still suffer from large power device amounts and complex control schemes. To solve these issues, this brief proposed a novel double reverse blocking sub-module (DRBSM), which has the fewer power devices than the existing sub-modules. With the same output voltage level, DRBSM has the same dc fault current blocking capability as full bridge sub-module (FBSM) and the same control logic circuit as HBSM. In order to further reduce the amount of power devices, a hybrid MMC based on HBSM and DRBSM is also proposed and the design principle is given in detail. Two study cases are built in simulation to verify the proposed DRBSM-based MMC and hybrid MMC well.

Leakage inductance is an important parameter in a high-frequency transformer for its influence on power transmission performance and the soft switching of converters. Nowadays, Litz wire is widely used to control winding loss and reach high efficiency. However, it is not an easy task to accurately calculate its leakage inductance. This article presents a detailed model of leakage energy in Litz wire, and proposes an accurate closed-form expression for calculating the leakage inductance in a high-frequency transformer. The skin effect and the proximity effect of Litz wire are taken into account in two-dimensional polar coordinates, and the filling factor of Litz wire is also considered. A global sensitivity analysis is performed to assess the influence of each parameter on the overall leakage inductance. Finally, two transformer prototypes are made to verify the accuracy of the proposed analytical expression.

The measurement of the right and wrong results of cognitive tasks has important applications in many commercial and educational areas such as the drivers’ training system, the simulation training and online learning system. This Letter aims to distinguish the heartbeat pattern of cognitively wrong responses to that of cognitively right responses based on the electrocardiogram (ECG) through 36 subjects with different professional backgrounds. The experimental design methods were double-digit and five-digit addition/subtraction, which were blindly selected by subjects from a black box. Through the R–R interval (RRI) series obtained from the ECG data, some linear, nonlinear and moment features were extracted to evaluate the cognitive task results by using pattern recognition methods. The binary classification of RRI datasets indicated that autonomic nerve patterns of the right and wrong cognitive heartbeat responses were distinguishable.

High dynamic topology and limited bandwidth of the airborne network make it difficult to provide reliable information interaction services for diverse combat mission of aviation swarm operations. Therefore, it is necessary to identify the elephant flows in the network in real time to optimize the process of traffic control and improve the performance of airborne network. Aiming at this problem, a timeliness-enhanced traffic identification method based on machine learning Bayesian network model is proposed. Firstly, the data flow training subset is obtained by preprocessing the original traffic dataset, and the sub-classifier is constructed based on Bayesian network model. Then, the multi-window dynamic Bayesian network classifier model is designed to enable the early identification of elephant flow. The simulation results show that compared with the existing elephant flow identification method, the proposed method can effectively improve the timeliness of identification under the condition of ensuring the accuracy of identification.

A series of nanocomposites based on cellulose nanocrystals (CNCs) and polymethyl acrylate (PMA) with covalently incorporated 1,2-dioxetane as a luminescent mechanophore were prepared. Through surface-initiated single-electron transfer radical polymerization, the CNCs nanofiller offers good compatibility with polymer matrix. As a consequence, all the composite materials exhibit reinforced mechanical properties with increased stiffness and strength. Most importantly, 1,2-dioxetane is demonstrated as a sensitive platform to characterize the dissipation pathway of fracture energy, as well as the polymer chain scission in the Mullins effect within these polymer nanocomposites. The combined use of mechanical macroscopic testing and molecular bond scission data herein provides detailed information on how force distributes and failure occurs in complex soft materials.

The three-phase buck rectifier (3ph-BR) is suitable for applications where a voltage step down function is required. In this paper, an improved 3ph-BR topology is proposed to reduce the voltage stress on the transistors. The freewheeling diode in the conventional topology is split into two diodes in series and the input neutral point is connected to the common point of the two diodes. With the proposed topology and the correspondingly modified modulation scheme, the transistors only need to withstand the input phase voltage instead of the line-to-line voltage, bringing about the significant reduction of voltage stress. The proposed topology enables a more cost-efficient and flexible selection of the transistors. Experimental results have verified the validity of the modified topology and associated modulation scheme.

Data-driven fault diagnosis is essential for the reliability and safety of industry equipment. However, the lack of real labeled fault data make the machine learning-based diagnosis methods difficult to carry out. To solve this problem, this article proposes a new fault diagnosis framework called multilabel one-dimensional (1-D) generation adversarial network (ML1-D-GAN). In our method, Auxiliary Classifier GAN is utilized first for real damage data generation. Then the generated and real damage data are both used to train the fault classifier. Experimental results reveal that the generated data is applicable, and ML1-D-GAN improves the diagnosing accuracy for real bearing faults from 95% to 98% when trained with the generated data. The scalability of the learning model is also proven in the experiment.

We studied the protection of degraded Porphyra yezoensis polysaccharide (PYP) on human proximal tubular epithelial cells (HK-2) from cytotoxicity of nano-calcium oxalate monohydrate (COM) crystal, and the regulation of adhesion and endocytosis of the COM crystal. Four degraded fractions, namely, PYP1, PYP2, PYP3, and PYP4, were successfully obtained, with molecular weights (Mws) of 576.2, 49.5, 12.6, and 4.02 kDa, respectively. PYP protection reduced the crystal toxicity, prevented the destruction of cell morphology and cytoskeleton, inhibited the production of reactive oxygen species and the decline of lysosomal integrity, and reduced the expression of osteopontin and transmembrane protein (CD44). PYPi inhibited the adhesion and endocytosis of HK-2 cells by nano-COM. Endocytic COM crystals were accumulated in the lysosomes. With decreasing molecular weight, the ability of PYP to reduce cell damage and inhibit cell adhesion and endocytosis increased. PYP4, which has the smallest molecular weight, weaker intramolecular hydrogen bonds and more reducing groups, showed the best biological activity. PYPi can reduce the oxidative damage of the crystal to the cell, inhibit the adhesion and endocytosis of the crystal, and reduce the risk of kidney stone formation. Therefore, PYP, especially PYP4, has potential for use as a green drug to inhibit the formation and recurrence of calcium oxalate stones.

In this communication, a paramagnetic bifunctional manganese(II) chelate ([Mn(Dopa-EDTA)]2−) containing a catechol group is designed and synthesized. The catechol can bind iron ions on the surface of superparamagnetic iron oxide (SPIO) nanocrystals to form core–shell nanoparticles. Both 4 and 7 nm SPIO@[Mn(Dopa-EDTA)]2− show good water solubility, single-crystal dispersion, and low cytotoxicity. The study of the interplay between the longitudinal and transverse relaxation revealed that 4 nm SPIO@[Mn(Dopa-EDTA)]2− with lower r2/r1 = 1.75 at 0.5 T tends to be a perfect T1 contrast agent while 7 nm SPIO@[Mn(Dopa-EDTA)]2− with a higher r2/r1 = 15.0 at 3.0 T tends to be a T2 contrast agent. Interestingly, 4 nm SPIO@[Mn(Dopa-EDTA)]2− with an intermediate value of r2/r1 = 5.26 at 3.0 T could act as T1–T2 dual-modal contrast agent. In vivo imaging with the 4 nm SPIO@[Mn(Dopa-EDTA)]2− nanoparticle shows unique imaging features: (1) long-acting vascular imaging and different signal intensity changes between the liver parenchyma and blood vessels with the CEMRA sequence; (2) the synergistic contrast enhancement of hepatic imaging with the T1WI and T2WI sequence. In summary, these Fe/Mn hybrid core–shell nanoparticles, with their ease of synthesis, good biocompatibility, and synergistic contrast enhancement ability, may provide a useful method for tissue and vascular MR imaging.

Iterative multireference configuration interaction (IMRCI) is proposed. It is exploited to compute the electronic energies of H $_2$ O and CH $_2$ (singlet and triplet states) at equilibrium and non-equilibrium geometries. The potential energy curves of H $_2$ O, CH $_2$ (singlet and triplet states) and N $_2$ have also been calculated with IMRCI as well as the Moller Plesset perturbation theory (MP2, MP3, and MP4), the coupled cluster method with single and double substitutions (CCSD), and CCSD with perturbative triples correction (CCSD(T)). These calculations demonstrate that IMRCI results are independent of the initial guess of configuration functions in the reference space and converge quickly to the results of the full configuration interaction. The IMRCI errors relative to the full configuration interaction results are at the order of magnitude of 10 $^{-5}$ hartree within just 2-4 iterations. Further, IMRCI provides an efficient way to find on the potential energy surface the leading electron configurations which, as correct reference states, will be very helpful for the single-reference and multireference theoretical models to obtain accurate results.

Yb/Ln (Ln=Er, Tm) doped TeO2-based glasses containing CsPbBr3 perovskite quantum dots were successfully prepared via in-situ glass crystallization. The nanocomposites yield typical green downshifting luminescence attributing to CsPbBr3 exciton recombination under UV excitation, and produce Er3+ green, Er3+ red and Tm3+ blue upconversion emissions under 980 nm laser excitation. Impressively, specific Ln3+ emissions will be quenched with the precipitation of CsPbBr3 in glass, enabling to finely tune upconversion emitting color. Spectroscopic characterizations evidence that the luminescence quenching is originated from non-radiative reabsorption effect induced by the precipitation of CsPbBr3 rather than energy transfers from Ln3+ to CsPbBr3. Finally, these nanocomposites are demonstrated to exhibit superior water resistance due to the effective protecting role of dense structural glass, particularly, about 95% downshifting luminescence of CsPbBr3 and upconversion luminescence of Er3+ related to pristine ones are retained after immersing the products in water up to 30 days.

A monitoring system for smart machinery has been considered to be one of the most important goals in recent enterprises. This monitoring system will encounter huge difficulties, such as more data uploaded by smart machines, and the available internet bandwidth will influence the transmission speed of data and the reliability of the equipment monitoring platform. This paper proposes reducing the periodical information that has been uploaded to the monitoring platform by setting an upload event through the traits of production data from machines. The proposed methods reduce bandwidth and power consumption. The monitoring information is reconstructed by the proposed methods, so history data will not reduce storage in the cloud server database. In order to reduce the halt time caused by machine error, the proposed system uses machine-learning technology to model the operating status of machinery for fault prediction. In the experimental results, the smart machinery monitoring system using the Industrial Internet of Things reduces the volume of information uploaded by 54.57% and obtains a 98% prediction accuracy.

Bidirectional isolated DC–AC inverters are widely used in industrial applications, such as grid-connected uninterruptible power supply, renewable power energy system, electric vehicles and so on. In order to improve the conversion efficiency, realising soft switching will become the essential technology in the research of DC–AC inverters. Based on the commonly used two-stage isolated inverter, this study proposed a novel DC–AC inverter that combines dual-active-bridge (DAB) converter, switched capacitor and full-bridge inverter. Utilising the strategy of phase-shift shoot-through control, DAB will generate a high-frequency pulse DC link cooperated with switched capacitor. As a result, during the shoot-through period, when the DC link is in zero-voltage-stage, the full-bridge inverter could realise zero-voltage switching (ZVS) through discrete pulse modulation. Principles, characteristics and implementations of the proposed inverter and its control strategy are analysed in detail. A further analysis on the operation modes and ZVS constraint of DAB's secondary bridge and switched capacitor is completed. A 250 VDC to 150 VAC (peak value) 1.13 kW simulation and experimental prototype is presented to validate the ZVS of full-bridge inverter and the analysis of DAB converter and switched capacitor.

The seatbelt restraint load is one of the primary sources of occupant chest injury. Thus, studying the different biomechanical responses of chest by varying the seatbelt loads will result in a significant improvement in seatbelt protection performance. Based on the high-biofidelity mechanical dummy model, a sled-dummy test was conducted to investigate the differences in chest injury outcomes caused by the variation of seatbelt load paths or load processes. The chest kinematics and kinetics are compared to determine the influence of load factors on these biomechanical outcomes. Results show that chest injury severity has a positive nonlinear correlation with impact speed. However, the injury risk is mainly determined by the seatbelt peak load in the chest deflection analysis. The results of this study can provide a reference to seatbelt safety design and optimization. The model and method can be used in other research works on the biomechanics of frontal impact.

In recent years, with the development of deep neural networks, object detection has been widely used in many fields such as video surveillance, face recognition, and unmanned aerial vehicles (UAVs). However, small objects detection is still a challenge for the most of existing methods due to its low resolution and limited information. Here, the authors use an effective object detector single shot multi-box detector (SSD) as base architecture to detect small vehicles shot by UAV platform. First, in order to improve the performance for small objects, an extended-layer feature method is proposed for introducing more small-scale information. Second, the shapes of default boxes on specific feature maps are adjusted to balance the performance loss caused by adding feature maps. Experimental results show that this method reached the accuracy of 78.6% on PASCAL VOC object-detection dataset, which has a 1.8% bonus compared with the baseline. Moreover, in order to show the effectiveness on small vehicles, an additional experiment is conducted on the UAV123 dataset, which gets a 3.1% bonus. The speed of the proposed method has a 39 fps on a NVIDIA GTX Titan X.