Rescue and mosaic analysis experiments suggested that HLB-1 regulated synaptic functions in a cell nonautonomously way.Moreover, HLB-1 expression was not required for the presynaptic active zone morphology. Genetic evidence further demonstrated that hlb-1 acted in a parallel pathway with syd-2 to regulate the synaptic functions. Conclusion HLB-1 appeared as a new regulator for the organization and function of neuromuscular junctions in C. elegans.
Extracting the charging load pattern of residential electric vehicle (REV) will help grid operators make informed decisions in terms of scheduling and demand-side response management. Due to the multi-state and high-frequency characteristics of integrated residential appliances from the residential perspective, it is difficult to achieve accurate extraction of the charging load pattern. To deal with that, this paper presents a novel charging load extraction method based on residential smart meter data to noninvasively extract REV charging load pattern. The proposed algorithm harnesses the low-frequency characteristics of the charging load pattern and applies a two-stage decomposition technique to extract the characteristics of the charging load. The two-stage decomposition technique mainly includes: the trend component of the charging load being decomposed by seasonal and trend decomposition using loess (STL) method, and the low-frequency approximate component being decomposed by discrete wavelet technology (DWT). Furthermore, based on the extracted characteristics, event monitoring, and dynamic time warping (DTW) is applied to estimate the closest charging interval and amplitude. The key features of the proposed algorithm include (1) significant improvement in extraction accuracy; (2) strong noise immunity; (3) online implementation of extraction. Experiments based on ground truth data validate the superiority of the proposed method compared to the existing ones.
By taking into account not only the node specifics but also the benefits of mobile edge computing, an integrated strategy for energy saving of Internet of Things (IoT) devices is proposed in this article. This strategy consists of two algorithms at both the end and the edge. Considering the changeable battery level and downlink communication traffic of the battery‐powered wireless nodes, an energy efficient automatic mode switching algorithm is designed at the end. Three different kinds of working modes are designed based on the features of the end nodes and the various application requirements. This algorithm tends to enable the end nodes to automatically select and switch to the proper working mode according to the actual conditions. At the edge server with much stronger processing and computing capabilities which belongs to a higher layer, the dynamic sampling rate adjustment algorithm is designed. It can adaptively adjust the sampling frequencies of the end nodes and thus reduce their working durations. The proposed integrated solution aims to decrease the energy consumption of IoT devices as much as possible and thus prolong their battery lives. The simulation results have shown both the effectiveness and the efficiency of the proposed end and edge integrated strategy in terms of energy consumption.
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