Wireless sensor network (WSN) nodes are devices with limited power, and rational utilization of node energy and prolonging the network lifetime are the main objectives of the WSN’s routing protocol. However, irrational considerations of heterogeneity of node energy will lead to an energy imbalance between nodes in heterogeneous WSNs (HWSNs). Therefore, in this paper, a routing protocol for HWSNs based on the modified grey wolf optimizer (HMGWO) is proposed. First, the protocol selects the appropriate initial clusters by defining different fitness functions for heterogeneous energy nodes; the nodes’ fitness values are then calculated and treated as initial weights in the GWO. At the same time, the weights are dynamically updated according to the distance between the wolves and their prey and coefficient vectors to improve the GWO’s optimization ability and ensure the selection of the optimal cluster heads (CHs). The experimental results indicate that the network lifecycle of the HMGWO protocol improves by 55.7%, 31.9%, 46.3%, and 27.0%, respectively, compared with the stable election protocol (SEP), distributed energy-efficient clustering algorithm (DEEC), modified SEP (M-SEP), and fitness-value-based improved GWO (FIGWO) protocols. In terms of the power consumption and network throughput, the HMGWO is also superior to other protocols.

An innovational vane extruder made polymeric materials endure an elongation stress that was much larger than the shearing stress in the extrusion process. The operating principle of the vane extruder was completely different than that of conventional screw extruders. As the first stage of polymer processing in the vane extruder, the process of solids conveying was composed of feeding, compacting, and discharging. Most of the energy was consumed in the compacting process of polymer particulate solids in this stage. A mathematical model was developed to analyze the power consumption in the process. The model showed that the power consumption was mainly influenced by the structural parameters of the vane extruder, including the rotor diameter, eccentricity, and axial width of the vane unit. The analysis indicated that more energy was used to generate pressure in the vane extruder than in a screw extruder. The theoretical model was verified by the experimental results. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci., 2013

No abstract

The large-flowered Chinese chrysanthemum is one of the most morphologically complex ornamental plants, and its identification and classification requires a well-defined and reproducible system. The diversity of the capitulum is determined mainly by multiple shapes of ray florets. However, the existing classification systems for ray floret types are incomplete and unsystematic. In this study, 299 ray florets from 151 large-flowered chrysanthemum varieties in China, as well as 12 related traits of ray florets, were selected for quantitative classification. First, as one of the most important indices of ray floret shape, the corolla tube merged degree (CTMD) was defined as the corolla tube length/ray floret length (CTL/RFL). Combined with a probability grading method and linear regression analysis, the CTMD was divided into three groups, flat, spoon, and tubular, of which the CTL/RFL ranged from 0 to 0.20, 0.20 to 0.60, and 0.60 to 1.00, respectively. Second, Q-mode cluster analysis indicated that each group could be further categorized into three types (straight, curved, and atypical), based on other important variables in the ray floret. Finally, the ray floret was classified into nine types, including flat-straight, flat-curve, flat-atypical, spoon-straight, spoon-curve, spoon-atypical, tubular-straight, tubular-curve, and tubular-atypical. This ray floret classification system will be valuable in the classification of capitulum shape and has significance for the identification, breeding, and international standardization of chrysanthemum cultivars.

The salp swarm algorithm (SSA) is a bio-heuristic optimization algorithm proposed in 2017. It has been proved that SSA has competitive results compared to several other well-known meta-heuristic algorithms on various optimization problem. However, like most meta-heuristic algorithms, SSA is prone to problems such as local optimal solution and a slow convergence rate. To solve these problems, a chaotic salp swarm algorithm based on opposition-based learning (OCSSA) is proposed. The application of opposition-based learning (OBL) guarantees a better convergence speed and better develops the search space. The chaotic local search (CLS) method is also introduced, which can improve the performance of the algorithm to obtain the global optimal solution. The performance of OCSSA is compared with that of the original SSA and some other meta-heuristic algorithms on 28 benchmark functions with unimodal or multimodal characteristics. The experimental results show that the performance of OCSSA, with an appropriate chaotic map, is better than or comparable with the SSA and other meta-heuristic algorithms.INDEX TERMS Salp swarm algorithm, global optimization, meta-heuristic algorithms, opposition-based learning, chaotic local search.