Within-species relationship of patchiness to both abundance and occupancy, as exemplified by seagrass macrobenthos

For the first time, intraspecific relationships between the macroecological metrics patchiness (P) and both abundance (A) and occupancy (O) were investigated in a faunal assemblage. As a companion study to recent work on interspecific P, A and O patterns at the same localities, intraspecific patterns were documented within each of the more dominant invertebrates forming the seagrass macrobenthos of warm–temperate Knysna estuarine bay (South Africa) and of sub-tropical Moreton Bay (Australia). As displayed interspecifically, individual species showed strong A–O patterns (mean scaling coefficient − 0.76 and mean R2 > 0.8). All P–O relations were negative and most (67%) were statistically significant, although weaker (mean R2 0.5) than A–O ones; most P–A ones were also negative but fewer (43%) achieved significance, and were even weaker (mean R2 0.4); 33% of species showed no significant interrelations of either O or A with P. No species showed only a significant P–A relationship. Compared with interspecific P–A–O data from the same assemblages, power–law scaling exponents were equivalent, but R2 values were larger. Larviparous species comprised 70% of the total studied, but 94% of those displaying significant patchiness interrelationships; 5 of the 9 showing no P–A or P–O relationships, however, were also larviparous. At Knysna, though not in Moreton Bay, larviparous species also showed higher levels of occupancy than non-larviparous ones, whilst non-larviparous species showed higher levels of patchiness. Dominant Moreton Bay species, but not those at Knysna, exhibited homogeneously sloped P–O relationships.

Stochastic recruitment limited control of shape memory alloy based on Markov chain model

A new control method of Shape Memory Alloy (SMA) actuators based on stochastic recruitment is proposed. From the perspective of bionic control, the physiological basis of stochastic recruitment limited feedback control of artificial muscle is explained. The limited feedback control system of two-state cells and multi-state cells are established, and the limited feedback control law is deduced for these two kinds of systems. In the Simulink environment, two kinds of limited feedback control algorithms are simulated and compared, which verifies the feasibility of the algorithm. Aiming at the key problem of scaling coefficient in the system, the longitudinal and transverse comparative experiments are carried out to analyze the influence of the scaling coefficient on the system.

Apple Fruit Recognition Based on a Deep Learning Algorithm Using an Improved Lightweight Network

HIGHLIGHTSA deep learning algorithm with an improved lightweight network was used to identify apple fruit.Multiscale pooling was used to reduce the image size and enrich the features.Compound scaling was used to scale the basic network.The optimal compound scaling coefficient for apple identification was obtained with the WOA algorithm.The proposed method achieved an average recognition precision rate of 94.43% and a speed of 0.051s.ABSTRACT. Accurate fruit identification is the basis for automating the operation of orchard production. To better apply the identification model in mobile devices so that venue becomes a less restrictive factor for application, this study proposes an apple fruit identification method based on an improved lightweight network named “MobileNetV3-Small.” The whale optimization algorithm was introduced to improve the model by obtaining an optimal compound-scaling coefficient for the MobileNetV3-Small network. A multiscale pooling approach was used for fruit recognition, comprising operations such as lossless scaling and feature extraction on sample images. The obtained images were then inputted into the model for recognition and classification. The experimental process was conducted on an apple data set. The test results show that the multiclass average precision of apple recognition using this model was 94.43% and the running time of recognition was 0.051 s per image. Both indicators outperformed the control network models of “MobileNetV3-Small,” ResNet-50, and VGG-19. This model is 14.63% more accurate and 1.95 times quicker on average in identification than the next best model. These findings indicate that the method can realize high-efficiency and high-precision recognition of apples with high stability and portability, which lays a good foundation for the mechanization of repetitive operations such as orchard yield estimation, fruit labeling, and fruit picking. Keywords: Apple recognition, Compound scaling, Deep learning algorithm, Lightweight network, Yield estimation.

Scaling Laws for Phonotactic Complexity in Spoken English Language Data

Two prominent statistical laws in language and other complex systems are Zipf’s law and Heaps’ law. We investigate the extent to which these two laws apply to the linguistic domain of phonotactics—that is, to sequences of sounds. We analyze phonotactic sequences with different lengths within words and across word boundaries taken from a corpus of spoken English (Buckeye). We demonstrate that the expected relationship between the two scaling laws can only be attested when boundary spanning phonotactic sequences are also taken into account. Furthermore, it is shown that Zipf’s law exhibits both high goodness-of-fit and a high scaling coefficient if sequences of more than two sounds are considered. Our results support the notion that phonotactic cognition employs information about boundary spanning phonotactic sequences.

Mixing efficiency in large-eddy simulations of stratified turbulence

The irreversible mixing efficiency is studied using large-eddy simulations (LES) of stratified turbulence, where three different subgrid-scale (SGS) parameterizations are employed. For comparison, direct numerical simulations (DNS) and hyperviscosity simulations are also performed. In the regime of stratified turbulence where $Fr_{v}\sim 1$, the irreversible mixing efficiency $\unicode[STIX]{x1D6FE}_{i}$ in LES scales like $1/(1+2Pr_{t})$, where $Fr_{v}$ and $Pr_{t}$ are the vertical Froude number and turbulent Prandtl number, respectively. Assuming a unit scaling coefficient and $Pr_{t}=1$, $\unicode[STIX]{x1D6FE}_{i}$ goes to a constant value $1/3$, in agreement with DNS results. In addition, our results show that the irreversible mixing efficiency in LES, while consistent with this prediction, depends on SGS parameterizations and the grid spacing $\unicode[STIX]{x1D6E5}$. Overall, the LES approach can reproduce mixing efficiency results similar to those from the DNS approach if $\unicode[STIX]{x1D6E5}\lesssim L_{o}$, where $L_{o}$ is the Ozmidov scale. In this situation, the computational costs of numerical simulations are significantly reduced because LES runs require much smaller computational resources in comparison with expensive DNS runs.

Study of the Similarities in Scale Models of a Single-Layer Spherical Lattice Shell Structure under the Effect of Internal Explosion

The similarity of each scale model is verified based on the theory of similarity, deriving the similarity law of internal explosions in a single-layer spherical lattice shell structure via dimensional theory, calculated based on models with scaling coefficients of 1, 0.8, 0.6, 0.4, 0.2, and 0.1. The results show that the shock wave propagation characteristics, the distribution of the overpressure on the inner surface, the maximum dynamic response position, and the position at which the earliest explosion venting occurs are all similar to those of the original model. With the decrease of scaling coefficients, the overpressure peak value of the shock waves of each scale model, and the specific action time of the positive pressure zone, as well as specific impulse are increasingly deviated from the original model values; when the scaling coefficient is 0.1, the maximum relative error between the overpressure peak value at the measurement point and the specific action time of the positive pressure zone as well as the specific impulse and the original model value is 4.9%. Thus, it is feasible to forecast the internal explosion effect of the original structure size model by using the experiment results of the scale model with scaling coefficient λ≥0.1.

The Modified Power Method for Solving the Eigenvalue Problem with the Use of Idempotent Matrix

The work presents a new approach to the power method serving the purpose of solving the eigenvalue problem of a matrix. Instead of calculating the eigenvector corresponding to the dominant eigenvalue from the formula , the idempotent matrix B associated with the given matrix A is calculated from the formula , where m stands for the method’s rate of convergence. The scaling coefficient ki is determined by the quotient of any norms of matrices Bi and or by the reciprocal of the Frobenius norm of matrix Bi. In the presented approach the condition for completing calculations has the form. Once the calculations are completed, the columns of matrix B are vectors parallel to the eigenvector corresponding to the dominant eigenvalue, which is calculated from the Rayleigh quotient. The new approach eliminates the necessity to use a starting vector, increases the rate of convergence and shortens the calculation time when compared to the classic method.

Effect of SiO2 nanoparticles on the reaction-induced phase separation in dynamically asymmetric epoxy/PEI blends

The scaling coefficient α decreases significantly when the epoxy/PEI blend is filled with certain concentration of MEK–SiO2 nanoparticles, implying that the nanoparticles are forcing the coarsening mechanism towards the diffusion-controlled regime.