GCMD: Genetic Correlation Multi-Domain Virtual Network Embedding algorithm

With the increase of network scale and the complexity of network structure, the problems of traditional Internet have emerged. At the same time, the appearance of network function virtualization (NFV) and network virtualization technologies has largely solved this problem, they can effectively split the network according to the application requirements, and flexibly provide network functions when needed. During the development of virtual network, how to improve network performance, including reducing the cost of embedding process and shortening the embedding time, has been widely concerned by the academia. Combining genetic algorithm with virtual network embedding problem, this paper proposes a genetic correlation multi-domain virtual network embedding algorithm (GCMD-VNE). The algorithm improves the natural selection stage and crossover stage of genetic algorithm, adds more accurate selection formula and crossover conditions, and improves the performance of the algorithm. Simulation results show that, compared with the existing algorithms, the algorithm has better performance in terms of embedding cost and embedding time.Postprint (published version

Optical Orbital Angular Momentum Demultiplexing and Channel Equalization by Using Equalizing Dammann Vortex Grating

A novel equalizing Dammann vortex grating (EDVG) is proposed as orbital angular momentum (OAM) multiplexer to realize OAM signal demultiplexing and channel equalization. The EDVG is designed by suppressing odd diffraction orders and adjusting the grating structure. The light intensity of diffraction is subsequently distributed evenly in the diffraction orders, and the total diffraction efficiency can be improved from 53.22% to 82%. By using the EDVG, OAM demultiplexing and channel equalization can be realized. Numerical simulation shows that the bit error rate (BER) of each OAM channel can decrease to 10-4 when the bit SNR is 22 dB, and the intensity is distributed over the necessary order of diffraction evenly

Inferring plant–plant interactions using remote sensing

Rapid technological advancements and increasing data availability have improved the capacity to monitor and evaluate Earth's ecology via remote sensing. However, remote sensing is notoriously ‘blind’ to fine-scale ecological processes such as interactions among plants, which encompass a central topic in ecology. Here, we discuss how remote sensing technologies can help infer plant–plant interactions and their roles in shaping plant-based systems at individual, community and landscape levels. At each of these levels, we outline the key attributes of ecosystems that emerge as a product of plant–plant interactions and could possibly be detected by remote sensing data. We review the theoretical bases, approaches and prospects of how inference of plant–plant interactions can be assessed remotely. At the individual level, we illustrate how close-range remote sensing tools can help to infer plant–plant interactions, especially in experimental settings. At the community level, we use forests to illustrate how remotely sensed community structure can be used to infer dominant interactions as a fundamental force in shaping plant communities. At the landscape level, we highlight how remotely sensed attributes of vegetation states and spatial vegetation patterns can be used to assess the role of local plant–plant interactions in shaping landscape ecological systems. Synthesis. Remote sensing extends the domain of plant ecology to broader and finer spatial scales, assisting to scale ecological patterns and search for generic rules. Robust remote sensing approaches are likely to extend our understanding of how plant–plant interactions shape ecological processes across scales—from individuals to landscapes. Combining these approaches with theories, models, experiments, data-driven approaches and data analysis algorithms will firmly embed remote sensing techniques into ecological context and open new pathways to better understand biotic interactions

Research on High Performance Milling of Engineering Ceramics from the Perspective of Cutting Variables Setting

The setting of cutting variables for precision milling of ceramics is important to both the machined surface quality and material removal rate (MRR). This work specifically aims at the performance of corner radius PCD (polycrystalline diamond) end mill in precision milling of zirconia ceramics with relatively big cutting parameters. The characteristics of the cutting zone in precision milling ceramics with corner radius end mill are analyzed. The relationships between the maximum uncut chip thickness (hmax) and the milling parameters including feed per tooth (fz), axial depth of cut (ap) and tool corner radius (rε) are discussed. Precision milling experiments with exploratory milling parameters that cause uncut chip thickness larger than the critical value were carried out. The material removal mechanism was also analyzed. According to the results, it is advisable to increase fz appropriately during precision milling ZrO2 ceramics with corner radius end mill. There is still a chance to obtain ductile processed surface, as long as the brittle failure area is controlled within a certain range. The appropriate increasing of ap, not only can prevent the brittle damage from affecting the machined surface, but also could increase the MRR. The milling force increases with increasing MRR, but the surface roughness can still be stabilized within a certain range

Average Energy Transfer Characteristics and Control Strategy of Active Feedback Sound Insulation for Water-Filled Acoustic System Based on Double-Layer Plate Structure

The double-layer plate structure in passive sound insulation systems can improve the high-frequency sound insulation performance, but it is still not ideal in the low-frequency region. The actuator of the active sound insulation system can adjust the stiffness and damping in real time, with strong adaptability and adjustability. Therefore, in this paper, active actuators and feedback control strategy are applied to a double-layer plate structure to improve the low-frequency sound insulation performance of a water-filled acoustic cavity system. The theoretical model of a sound insulation system with a double-layer plate structure and active feedback control strategy is established for a water-filled acoustic cavity. The average energy transfer is used as an evaluation index for the active sound insulation effect of the system, and the calculation method of this index is derived. Then, the MATLAB numerical simulation is used to analyze the effect of six feedback control parameters on the average energy transfer of the system. Finally, it is concluded that when the feedback parameters are within the effective range, all six feedback control methods can produce significant effects on the low-frequency sound insulation of the system, but the effective range of some parameters is narrow

Average Energy Transfer Characteristics and Control Strategy of Active Feedback Sound Insulation for Water-Filled Acoustic System Based on Double-Layer Plate Structure

The double-layer plate structure in passive sound insulation systems can improve the high-frequency sound insulation performance, but it is still not ideal in the low-frequency region. The actuator of the active sound insulation system can adjust the stiffness and damping in real time, with strong adaptability and adjustability. Therefore, in this paper, active actuators and feedback control strategy are applied to a double-layer plate structure to improve the low-frequency sound insulation performance of a water-filled acoustic cavity system. The theoretical model of a sound insulation system with a double-layer plate structure and active feedback control strategy is established for a water-filled acoustic cavity. The average energy transfer is used as an evaluation index for the active sound insulation effect of the system, and the calculation method of this index is derived. Then, the MATLAB numerical simulation is used to analyze the effect of six feedback control parameters on the average energy transfer of the system. Finally, it is concluded that when the feedback parameters are within the effective range, all six feedback control methods can produce significant effects on the low-frequency sound insulation of the system, but the effective range of some parameters is narrow

Raman-encoded microbeads for spectral multiplexing with SERS detection

Simultaneous detection of multiple molecular targets can greatly facilitate early diagnosis and drug discovery. Encoding micron-sized beads with optically active tags is one of the most popular methods to achieve multiplexing. Noble metal nanoparticle labels for optical detection by surface-enhanced Raman spectroscopy (SERS) exhibit narrow bandwidths, high photostability and intense Raman signals. In this study, we demonstrate the feasibility of spectral multiplexing by SERS using micron-sized polystyrene (PS) beads loaded with SERS-active nanoparticles. The silica-encapsulated SERS nanotags comprise gold nanocrystals with a self-assembled monolayer (SAM) of aromatic thiols as Raman reporter molecules for spectral identification. SERS microspectroscopic images of single Raman-encoded PS microbeads indicate the homogeneous spatial distribution of the SERS-active nanoparticles on the surface of the beads. By using up to five different Raman reporters, 31 spectrally distinct micron-sized beads were encoded and characterized spectroscopically at the single-bead level

Time-Aware Service Ranking Prediction in the Internet of Things Environment

With the rapid development of the Internet of things (IoT), building IoT systems with high quality of service (QoS) has become an urgent requirement in both academia and industry. During the procedures of building IoT systems, QoS-aware service selection is an important concern, which requires the ranking of a set of functionally similar services according to their QoS values. In reality, however, it is quite expensive and even impractical to evaluate all geographically-dispersed IoT services at a single client to obtain such a ranking. Nevertheless, distributed measurement and ranking aggregation have to deal with the high dynamics of QoS values and the inconsistency of partial rankings. To address these challenges, we propose a time-aware service ranking prediction approach named TSRPred for obtaining the global ranking from the collection of partial rankings. Specifically, a pairwise comparison model is constructed to describe the relationships between different services, where the partial rankings are obtained by time series forecasting on QoS values. The comparisons of IoT services are formulated by random walks, and thus, the global ranking can be obtained by sorting the steady-state probabilities of the underlying Markov chain. Finally, the efficacy of TSRPred is validated by simulation experiments based on large-scale real-world datasets