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Flexible strain sensors have received extensive attention due to their broad application prospects. However, a majority of present flexible strain sensors may fail to maintain normal sensing performances upon external loads because of their low strength and thus their performances are affected drastically with increasing loads, which severely restricts large-area popularization and application. Scorpions with hypersensitive vibration slit sensilla are coincident with a similar predicament. Herein, it is revealed that scorpions intelligently use risky slits to detect subtle vibrations, and meanwhile, the distinct layered composites of the main body of this organ prevent catastrophic failure of the sensory structure. Furthermore, the extensive use of flexible sensors will generate a mass of electronic waste just as obsoleting silicon-based devices. Considering mechanical properties and environmental issues, a flexible strain sensor based on an elastomer (Ecoflex)-wrapped fabric with the woven structure was designed and fabricated. Note that introducing a "green" basalt fiber (BF) into a degradable elastomer can effectively avoid environmental issues and significantly enhance the mechanical properties of the sensor. As a result, it shows excellent sensitivity (gauge factor (GF) ∼138.10) and high durability (∼40,000 cycles). Moreover, the reduced graphene oxide (RGO)/BF/Ecoflex flexible strain sensor possesses superior mechanical properties (tensile strength ∼20 MPa) and good flexibility. More significantly, the sensor can maintain normal performances under large external tensions, impact loads, and even underwater environments, providing novel design principles for environmentally friendly flexible sensors under extremely harsh environments.

Slit sensillum, a unique sensing organ on the scorpion's legs, is composed of several cracks with curved shapes. In fact, it is just its particular morphological distribution and structure that endows the scorpions with ultrasensitive sensing capacity. Here, a scorpion-inspired flexible strain sensor with an ordered concentric circular curved crack array (CCA) was designed and fabricated by using an optimized solvent-induced and template transfer combined method. The morphology of the cracks can be effectively controlled by the heating temperature and the lasting time. Instead of the nonuniform stress distribution induced by disordered cracks, ordered concentric circle curved structures are introduced to generate a uniform stress distribution and larger deformation, which can significantly improve the performance of the strain sensor. Thus, the CCA sensor exhibits ultrahigh sensitivity (GF ∼ 7878.6), excellent stability (over 16 000 cycles), and fast response time (110 ms). Furthermore, the CCA sensor was demonstrated to be feasible for monitoring human motions and detecting noncontact vibration signals, indicating its great potential in human-health monitoring and vibration signal detection applications.

To promote the development of sports, this study analyzes the results of track-and-field throwing events in a province of China using the methods of literature, comparative analysis, mathematical statistics, and grey prediction, and compares them with the results of track-and-field throwing events in China. The province has made brilliant achievements in track-and-field throwing events in history, but we can also find some problems behind these excellent achievements: the overall results of throwing events in the province are good, relatively outstanding in women’s javelin and men’s discus throw, but still at a low level in women’s shot put and women's hammer throw. In recent years, the throwing events in the province have gradually lost their advantages, especially in women’s shot put and men's hammer throw. Facing the current situation of throwing events in this province, we must clearly understand the characteristics of the performance development of track-and-field throwing events in this province, grasp the advantages of throwing events, and strive to make a breakthrough in other individual events. Only when we have a comprehensive and objective grasp of the development trend and law of achievements, we can fully understand where and how big the gap is with the highest level in China, and then find a breakthrough and catch-up.

Flexible pressure sensors have provided an attractive option for potential applications in wearable fields like human motion monitoring or human-machine interfaces. For the development of flexible pressure sensors, achieving high performance or multifunctions are popular research tendencies in recent years, such as improving their sensitivity, working range, or stability. Sponge materials with porous structures have been demonstrated that they are one of the potential substrates for developing novel and excellent flexible pressure sensors. However, for sponge-based pressure sensors, it is still a great challenge to realize a wide range of pressures from Pa level to hundreds kPa level. And how to achieve mechanical robustness remains unsolved. Here, we develop a flexible pressure sensor based on multicarbon nanotubes (MWCNTs) network-coated porous elastomer sponge with a broad range and robust features for use in wearable applications. Specifically, polyurethane (PU) sponge is used as the substrate matrix while dip-coated PU/MWCNTs composites as a conductive layer, achieving a highly bonding effect between the substrate and the conductive material, hence a great mechanical robust advantage is obtained and the working range also is improved. The pressure sensor show range of up to 350 kPa, while the minimum detection threshold is as low as 150 Pa. And before and after rolling by a bicycle or electric motorcycle, the sensor has the almost same responses, exhibiting great robustness.

The energy management (EM) of active distribution network (ADN) under uncertainties is a stochastic, nonconvex and nonlinear problem, which cannot be solved by traditional algorithms in acceptable time. In this paper, we decouple this computationally intractable problem into a series of subproblems which are easier to handle, and then solve them successively according to an improved approximate dynamic programming (IADP) algorithm. Different from the existing approximate dynamic programming (ADP) algorithms, which need to update value functions iteratively, IADP obtains approximate value functions directly using Galerkin method. Such that the time of updating approximate value functions can be omitted. Furthermore, the influence of each basis function on the approximate value function is evaluated according to the absolute value inequality principle. Then the unimportant basis functions are removed from the basis function set to speed up the algorithm. The historical data can also be embedded into IADP to facilitate the online decision-making and reduce the dependency of real-time forecast information. Numerical simulations on two modified IEEE test systems and a real 682-bus ADN are given to illustrate the effectiveness of the proposed approach.

A strategy of coupling bionics is adopted on a paper-based sensor to achieve these excellent functions simultaneously, including multivariable detection, ultrasensitivity, waterproof and underwater applications.

A radical cyclization and ring-opening of oxime esters with diselenides was developed.

An iridium-catalyzed selenium-directed ortho-C–H borylation of benzyl selenide derivatives was successfully developed. This is the first example where selenium is used as a directing group in C–H b...

A general and efficient method for copper-catalyzed transfer hydrogenation of isoquinolines with an oxazaborolidine-BH3 complex, under mild reaction conditions, is successfully developed. A broad range of isoquinolines has been reduced to the corresponding products with 61-85% yields. The method is applied to the synthesis of biologically active tetrahydrosioquinoline alkaloid (±)-norlaudanosine in 62% yield, which is the key precursor for the preparation of (±)-laudanosine, (±)-N-methyl-laudanosine, and (±)-xylopinine in one or two steps.

For advanced flexible strain sensors, it is not difficult to achieve high sensitivity only. However, integrating high sensitivity, high stability, and high durability into one sensor still remains a great challenge. Fortunately, natural creatures with diversified excellent performances have given us a lot of ready-made solutions. Here, scorpion and spiderweb are selected as coupling bionic prototypes, which are famous for their ultrasensitive sensing capacity and excellent structural durability, respectively. Based on that, a bioinspired strain sensor is successfully fabricated. The results demonstrate that the bioinspired strain sensor has a sensitivity of 940.5 in the strain range of 0-1.5% and a sensitivity of 2742.3 between 1.5 and 2.5%. Meantime, this sensor with a spiderweb-like reticular structure has a great improvement in stability and durability. Specifically, the sensor exhibits excellent stability during bending and stretching cycles over 80,000 times. Moreover, the response time and recovery time of the sensor are 169 and 195 ms, respectively. Besides, the sensor also has functions such as vibrating frequency identification due to its low hysteresis. Based on the excellent performance, the sensor can be applied to monitor human body motions serving as wearable electronics.

Sensors are widely used in various fields, among which flexible strain sensors that can sense minuscule mechanical signals and are easy to adapt to many irregular surfaces are attractive for structure health monitoring, early detection, and failure prevention in humans, machines, or buildings. In practical applications, subtle and abnormal vibrations generated from any direction are highly desired to detect and even orientate their directions initially to eliminate potential hazards. However, it is challenging for flexible strain sensors to achieve hypersensitivity and omnidirectionality simultaneously due to the restrictions of many materials with anisotropic mechanical/electrical properties and some micro/nanostructures they employed. Herein, it is revealed that the vision-degraded scorpion detects subtle vibrations spatially and omnidirectionally using a slit sensillum with fan-shaped grooves. A bioinspired flexible strain sensor consisting of curved microgrooves arranged around a central circle is devised, exhibiting an unprecedented gauge factor of over 18 000 and stability over 7000 cycles. It can sense and recognize vibrations of diverse input waveforms at different locations, bouncing behaviors of a free-falling bead, and human wrist pulses regardless of sensor installation angles. The geometric designs can be translated to other material systems for potential applications including human health monitoring and engineering failure detection.

With the promotion and application of smart grid, the technology of non-intrusive load monitoring (NILM) has gained more and more attention in recent years. Different from direct device monitoring, it identifies the type of appliances using the aggregated load profile measured at a single metering point, which is more convenient and flexible, and thus has the potential to be extended to all households equipped with smart meters. This paper proposes an extensible and comprehensive model to solve the load disaggregation problem, which employs the additive factorial approximate maximum a posteriori (AFAMAP) based on iterative fuzzy ${c}$ -means (IFCM). To make sure that the model is adaptive to other households, the hidden Markov models (HMMs) are applied to obtain the independent load model of each appliance, and the IFCM is used to determine the number of hidden states adaptively. Finally, the AFAMAP is utilized to decompose the aggregated power consumption based on the independent load models built by HMM. Simulation studies are conducted on the open database of Almanac of Minutely Power dataset (AMPds), and the results have demonstrated that the proposed model is more accurate in comparison with the other models.

This paper proposes a deep reinforcement learning (DRL) based algorithm for the stochastic dynamic microgrid energy management. First, we consider AC power flow constraints which makes the problem non-convex and uncertainties in load, renewable generation and real-time electricity price which makes the problem stochastic. Then, the original multi-period optimization problem, which was difficult to deal with, is decomposed into several single-period optimization problems by the proposed deep reinforcement learning based method. Our method can obtain an approximate optimal policy for the original multi-period problem without long-term forecast and optimization or distribution knowledge of the uncertainty by learning from the results of single-period problems. Last, the superiority and effectiveness of the proposed method are demonstrated through several case studies.

An iridium-catalyzed selenium-directed

As wind power becomes an important part of power systems, more accurate forecast is critical to high-efficiency application of wind power. The paper proposes a novel forecast model of MHF-LSH-SVR based on morphological high-frequency filter (MHF) and local-sensitive hashing algorithms (LSH), focusing on short-term wind power forecast. To mitigate the effect of high volatility of the raw wind power time series on forecast, the MHF filter is used to extract the mean trend time series, which indicates the mean tendency. In order to perform local forecast, the mean trend time series is reconstructed into mean trend segments in the higher-dimension phase space. Afterwards, to obtain more accurate forecast result, the LSH algorithm is applied to pick out the similar segments of the forecast mean trend segment, which are then employed to perform local forecast by the algorithm of support vector regression (SVR). Simulation studies are carried out on the raw wind power time series collected from a real wind farm, and the final results demonstrate that the MHF-LSH-SVR model outperforms the models of persistence (Per.), SVR and MHF-SVR in terms of both accuracy and stability.