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Ship Pipe Route Design (SPRD) has always been a challenging and critical aspect in the detailed design for various types of ships. However, existing layout methods are inefficient, existing algorithms lack consideration for collaborative layouts in practical engineering, true intelligent design has not yet been achieved, and the issue of collaborative layout of pipes has been insufficiently researched. Based on the aforementioned research status, this paper proposes a novel collaborative pipe layout method. Firstly, a high-dimensional vector encoding technique to innovate the underlying mathematical representation of pipes is proposed, breaking free from traditional constraints. Secondly, a powerful and engineering-applicable hybrid AA-SPOP algorithm is incorporated, which is based on the heuristic swarm intelligence algorithm known as Artificial Fish Swarm Algorithm (AFSA), exhibits advantages such as high global optimization efficiency and strong adaptability. To mitigate potential issues like suboptimal local optimization and high sensitivity, this paper proposes a parameter adaptive equation and a series of optimization adjustment. Thirdly, a collaborative pipe layout method that considers support equipment is proposed. This paper effectively addresses the lack of research in this area by introducing branch pipe collaboration methods and energy zone guidance methods. Finally, through engineering simulation experiments, the paper demonstrates the efficiency, feasibility, and cutting-edge nature of the proposed collaborative layout optimization method.

The Hurst index is widely used to describe the long memory process of time series in economics, finance, and other fields. The setting for Hurst index estimation has not been thoroughly investigated by current literature. Many scholars choose the estimation settings subjectively without comparing the results of different settings or just choose the settings providing the desirable results. In this research, the impact of various settings such as correlation processing, trend processing, scale range selection, subsequence division, fitting method, and tail-cut method on the Hurst index estimation models, such as R/S and DFA, are evaluated through the Monte Carlo simulation. The results show that the settings have significant impact on the accuracy of the Hurst index estimation. The detailed optimal six setting for Hurst index estimation is summarized for future researchers. In addition, the results show that the comprehensive performance of the DFA estimation is better than the R/S-type estimation. On the basis of the optimal setting, the Hurst indexes of China’s stock market are estimated. The daily volatility in the Chinese stock market show long memory by the Hurst index. Our contribution is to identify the optimal model setting for the Hurst index estimation by comparing all the six optional settings in every step of estimation. Our stepwise analysis for model effectiveness may shed light on challenges for various approaches to market trend analysis.

Tree skeleton extraction plays a fundamental role in reconstructing both biological and structural models of trees. However, traditional approaches can be ineffective and problematic in guaranteeing the topological correctness and centeredness of the tree skeleton when the tree point clouds contain noise and occlusions. To overcome this limitation, we present a tree skeletonization method to generate topologically correct and well-centered tree skeletons. We extract an initial skeleton from the tree point clouds via an octree and level set method, use cylindrical prior constraint (CPC) optimization and the estimated radii of branches to yield corrected positions of improper joints, and finally obtain updated skeletons with improved smoothness. The good centeredness of our proposed method is intrinsically achieved by (1) exploiting the cylindrical shape prior and calculating the CPC in the local neighborhood and (2) feeding the prior knowledge regarding the radii of tree branches into a topology refinement algorithm to yield near-optimal estimates of the positions of the skeleton points. To evaluate our method, we construct a novel tree point cloud data set with known ground truth and propose three quantitative assessment methods: skeleton points deviation (SPD), bifurcation points coverage (BPC) and endpoints coverage (EPC). The quantitative assessment and visual assessment show that the proposed method clearly outperforms traditional ones in terms of topology correctness and centeredness of the extracted tree skeleton.

This paper deals with the analysis of sleep quality, which involves a non-invasive sleep stage detection method with home deployability. Some physiological signals, such as heart rate, heart rate variation, and the number of times the subject rolled over, are collected to determine the sleep stage. A fuzzy inference system is adopted to evaluate the division of sleep stage. Then, a preliminary sleep depth is calculated. Furthermore, a finite-state machine is developed to detect the sleep stage changes. The difference between our research and other existing studies is that, first, both the pressure sensors and the heart rate device are employed; then, the fuzzy inference and a finite-state machine are introduced, which give us a higher precision than the traditional methods to evaluate the sleep stage. The experimental results show that the proposed method can well evaluate the sleep quality that is almost consistent with a polysomnography test. The latter is currently recognized as the best way to measure sleep quality, which, however, requires a variety of monitoring sensors and has to be performed by the nursing staff in a professional setting. The reported approach can be used for monitoring sleep quality or sleep disorder screening at home.

Cell-free (CF) massive multiple-input-multiple-output (MIMO) systems have a large number of individually controllable antennas distributed over a wide area for simultaneously serving a small number of user equipments (UEs). This solution has been considered as a promising next-generation technology due to its ability to offer a similar quality of service to all UEs despite its low-complexity signal processing. In this paper, we provide a comprehensive survey of CF massive MIMO systems. To be more specific, the benefit of the so-called channel hardening and the favorable propagation conditions are exploited. Furthermore, we quantify the advantages of CF massive MIMO systems in terms of their energy- and cost-efficiency. Additionally, the signal processing techniques invoked for reducing the fronthaul burden for joint channel estimation and for transmit precoding are analyzed. Finally, the open research challenges in both its deployment and network management are highlighted.

An aluminum nitride (AlN) Schottky barrier diode (SBD) was fabricated on a nonpolar AlN crystal grown on tungsten substrate by physical vapor transport. The Ni/Au–AlN SBD features a low ideality factor ${n}$ of 3.3 and an effective Schottky barrier height (SBH) of 1.05 eV at room temperature. The ideality factor ${n}$ decreases and the effective SBH increases at high temperatures. The temperature dependences of ${n}$ and SBH were explained using an inhomogeneous model. A mean SBH of 2.105 eV was obtained for the Ni–AlN Schottky junction from the inhomogeneity analysis of the current–voltage characteristics. An equation in which the parameters have explicit physical meanings in thermionic emission theory is proposed to describe the current–voltage characteristics of inhomogeneous SBDs.

In this paper, a quantized adaptive decentralized output feedback control is proposed for a class of interconnected nonlinear systems with quantized input and possible number of hysteretic actuators failure up to infinity. The hysteretic actuators nonlinearities are described by the Prandtl-Ishlinskii model. In order to compensate the effects of the possible number of the hysteretic actuators failures up to infinity and input quantization, a modified backstepping approach is proposed by utilizing the high-gain k-filters, hyperbolic tangent function property, and bound estimation approach. It is proved both mathematically and by numerical simulations that using the proposed controller, all the signals of the closed-loop system are globally bounded despite of the input quantization and possible number of hysteretic actuators failures up to infinity.

Although the massive MIMO enabled heterogeneous networks (HetNets) can intensify the spectral efficiency benefits, the proper user association and resource allocation are both crucial to achieve desirable performance since the power consumption of base station (BS) scales with the large number of antennas. This paper aims to investigate joint design of user association and power allocation with loads constraint and transmit power constraint for the massive MIMO HetNets by considering proportional fairness about the spectral efficiency under imperfect channel state information. First, we derive a closed-form lower bound on the ergodic spectral efficiency with linear zero-forcing beamforming, based on which, a mixed-integer nonlinear programming problem is formulated. It is difficult to efficiently obtain an exact solution since it is non-convex and combinational. To solve this NP-hard problem, an effective algorithm with guaranteed convergence is proposed, where the original problem is decomposed into the corresponding subproblems, which can be solved by low complexity approaches, respectively. Numerical results show that how the number of antennas and the number of BSs affect the spectral efficiency, and our proposed algorithm outperforms other algorithms in terms of the spectral efficiency and load balancing.