Maximizing Revenue With Adaptive Modulation and Multiple FECs in Flexible Optical Networks

Flexible optical networks (FONs) are being adopted to accommodate the increasingly heterogeneous traffic in today's Internet. However, in presence of high traffic load, not all offered traffic can be satisfied at all time. As carried traffic load brings revenues to operators, traffic blocking due to limited spectrum resource leads to revenue losses. In this study, given a set of traffic requests to be provisioned, we consider the problem of maximizing operator's revenue, subject to limited spectrum resource and physical layer impairments (PLIs), namely amplified spontaneous emission noise (ASE), self-channel interference (SCI), cross-channel interference (XCI), and node crosstalk. In FONs, adaptive modulation, multiple FEC, and the tuning of power spectrum density (PSD) can be effectively employed to mitigate the impact of PLIs. Hence, in our study, we propose a universal bandwidth-related impairment evaluation model based on channel bandwidth, which allows a performance analysis for different PSD, FEC and modulations. Leveraging this PLI model and a piecewise linear fitting function, we succeed to formulate the revenue maximization problem as a mixed integer linear program. Then, to solve the problem on larger network instances, a fast two-phase heuristic algorithm is also proposed, which is shown to be near-optimal for revenue maximization. Through simulations, we demonstrate that using adaptive modulation enables to significantly increase revenues in the scenario of high signal-to-noise ratio (SNR), where the revenue can even be doubled for high traffic load, while using multiple FECs is more profitable for scenarios with low SNR

Throughput Maximization Leveraging Just-Enough SNR Margin and Channel Spacing Optimization

Flexible optical network is a promising technology to accommodate high-capacity demands in next-generation networks. To ensure uninterrupted communication, existing lightpath provisioning schemes are mainly done with the assumption of worst-case resource under-provisioning and fixed channel spacing, which preserves an excessive signal-to-noise ratio (SNR) margin. However, under a resource over-provisioning scenario, the excessive SNR margin restricts the transmission bit-rate or transmission reach, leading to physical layer resource waste and stranded transmission capacity. To tackle this challenging problem, we leverage an iterative feedback tuning algorithm to provide a just-enough SNR margin, so as to maximize the network throughput. Specifically, the proposed algorithm is implemented in three steps. First, starting from the high SNR margin setup, we establish an integer linear programming model as well as a heuristic algorithm to maximize the network throughput by solving the problem of routing, modulation format, forward error correction, baud-rate selection, and spectrum assignment. Second, we optimize the channel spacing of the lightpaths obtained from the previous step, thereby increasing the available physical layer resources. Finally, we iteratively reduce the SNR margin of each lightpath until the network throughput cannot be increased. Through numerical simulations, we confirm the throughput improvement in different networks and with different baud-rates. In particular, we find that our algorithm enables over 20\% relative gain when network resource is over-provisioned, compared to the traditional method preserving an excessive SNR margin.Comment: submitted to IEEE JLT, Jul. 17th, 2021. 14 pages, 8 figure

Throughput Maximization in Multi-Band Optical Networks with Column Generation

Multi-band transmission is a promising technical direction for spectrum and capacity expansion of existing optical networks. Due to the increase in the number of usable wavelengths in multi-band optical networks, the complexity of resource allocation problems becomes a major concern. Moreover, the transmission performance, spectrum width, and cost constraint across optical bands may be heterogeneous. Assuming a worst-case transmission margin in U, L, and C-bands, this paper investigates the problem of throughput maximization in multi-band optical networks, including the optimization of route, wavelength, and band assignment. We propose a low-complexity decomposition approach based on Column Generation (CG) to address the scalability issue faced by traditional methodologies. We numerically compare the results obtained by our CG-based approach to an integer linear programming model, confirming the near-optimal network throughput. Our results also demonstrate the scalability of the CG-based approach when the number of wavelengths increases, with the computation time in the magnitude order of 10 s for cases varying from 75 to 1200 wavelength channels per link in a 14-node network.Comment: 6 pages, 4 figures, submitted to IEEE International Conference on Communications 2024 (ICC2024). (Note on arXiv: for beginners in the area of column generation, please refer to the example computation in the file . I have uploaded it to this arXiv project along with other source files.

Strand Displacement Amplification for Multiplex Detection of Nucleic Acids

The identification of various targets such as bacteria, viruses, and other cells remains a prerequisite for point-of-care diagnostics and biotechnological applications. Nucleic acids, as encoding information for all forms of life, are excellent biomarkers for detecting pathogens, hereditary diseases, and cancers. To date, many techniques have been developed to detect nucleic acids. However, most of them are based on polymerase chain reaction (PCR) technology. These methods are sensitive and robust, but they require expensive instruments and trained personnel. DNA strand displacement amplification is carried out under isothermal conditions and therefore does not need expensive instruments. It is simple, fast, sensitive, specific, and inexpensive. In this chapter, we introduce the principles, methods, and updated applications of DNA strand displacement technology in the detection of infectious diseases. We also discuss how robust, sensitive, and specific nucleic acid detection could be obtained when combined with the novel CRISPR/Cas system

Graphene Oxide-Based Biosensors

In this chapter, the latest developments in graphene oxide-based biosensors are presented. These biosensors are complexes of graphene oxide and biomacromolecules, including enzymes such as glucose oxidase, horseradish peroxidase, laccase, and nucleic acids such as DNA and RNA. The structure, design and preparation process (immobilization process) of the above graphene oxide-biomacromolecule composites were summarized. Some typical examples of immobilization of biological macromolecules are described. The immobilization efficiency and electrochemical performance of immobilized biomolecules based on graphene oxide were discussed, which may guide designing better graphene oxide-based biosensors

Structural Engineering of Hierarchical Micro‐nanostructured Ge-C Framework by Controlling the Nucleation for Ultralong Life Li Storage

The rational design of a proper electrode structure with high energy and power densities, long cycling lifespan, and low cost still remains a significant challenge for developing advanced energy storage systems. Germanium is a highly promising anode material for high-performance lithium ion batteries due to its large specific capacity and remarkable rate capability. Nevertheless, poor cycling stability and high price significantly limit its practical application. Herein, a facile and scalable structural engineering strategy is proposed by controlling the nucleation to fabricate a unique hierarchical micro-nanostructured Ge-C framework, featuring high tap density, reduced Ge content, superb structural stability, and a 3D conductive network. The constructed architecture has demonstrated outstanding reversible capacity of 1541.1 mA h g −1 after 3000 cycles at 1000 mA g −1 (with 99.6% capacity retention), markedly exceeding all the reported Ge-C electrodes regarding long cycling stability. Notably, the assembled full cell exhibits superior performance as well. The work paves the way to constructing novel metal-carbon materials with high performance and low cost for energy-related applications

Grazing exclusion alters soil methane flux and methanotrophic and methanogenic communities in alpine meadows on the Qinghai–Tibet Plateau

Grazing exclusion (GE) is an effective measure for restoring degraded grassland ecosystems. However, the effect of GE on methane (CH4) uptake and production remains unclear in dominant bacterial taxa, main metabolic pathways, and drivers of these pathways. This study aimed to determine CH4 flux in alpine meadow soil using the chamber method. The in situ composition of soil aerobic CH4-oxidizing bacteria (MOB) and CH4-producing archaea (MPA) as well as the relative abundance of their functional genes were analyzed in grazed and nongrazed (6 years) alpine meadows using metagenomic methods. The results revealed that CH4 fluxes in grazed and nongrazed plots were −34.10 and −22.82 μg‧m−2‧h−1, respectively. Overall, 23 and 10 species of Types I and II MOB were identified, respectively. Type II MOB comprised the dominant bacteria involved in CH4 uptake, with Methylocystis constituting the dominant taxa. With regard to MPA, 12 species were identified in grazed meadows and 3 in nongrazed meadows, with Methanobrevibacter constituting the dominant taxa. GE decreased the diversity of MPA but increased the relative abundance of dominated species Methanobrevibacter millerae from 1.47 to 4.69%. The proportions of type I MOB, type II MOB, and MPA that were considerably affected by vegetation and soil factors were 68.42, 21.05, and 10.53%, respectively. Furthermore, the structural equation models revealed that soil factors (available phosphorus, bulk density, and moisture) significantly affected CH4 flux more than vegetation factors (grass species number, grass aboveground biomass, grass root biomass, and litter biomass). CH4 flux was mainly regulated by serine and acetate pathways. The serine pathway was driven by soil factors (0.84, p < 0.001), whereas the acetate pathway was mainly driven by vegetation (−0.39, p < 0.05) and soil factors (0.25, p < 0.05). In conclusion, our findings revealed that alpine meadow soil is a CH4 sink. However, GE reduces the CH4 sink potential by altering vegetation structure and soil properties, especially soil physical properties

A magnetic biocatalyst based on mussel-inspired polydopamine and its acylation of dihydromyricetin

A support made of mussel-inspired polydopamine-coated magnetic iron oxide nanoparticles (PD-MNPs) was prepared and characterized. The widely used Aspergillus niger lipase (ANL) was immobilized on the PD-MNPs (ANL@PD-MNPs) with a protein loading of 138 mg/g and an activity recovery of 83.6% under optimized conditions. For the immobilization, the pH and immobilization time were investigated. The pH and thermal and storage stability of the ANL@PD-MNPs significantly surpassed those of free ANL. The ANL@PD-MNPs had better solvent tolerance than free ANL. The secondary structure of free ANL and ANL@PD-MNPs was analyzed by infrared spectroscopy. A kinetic study demonstrated that the ANL@PD-MNPs had enhanced enzyme-substrate affinity and high catalytic efficiency. The ANL@PD-MNPs was applied as a biocatalyst for the regioselective acylation of dihydromyricetin (DMY) in DMSO and gave a conversion of 79.3%, which was higher than that of previous reports. The ANL@PD-MNPs retained over 55% of its initial activity after 10 cycles of reuse. The ANL@PD-MNPs were readily separated from the reaction system by a magnet. The PD-MNPs is an excellent support for ANL and the resulting ANL@PD-MNPs displayed good potential for the efficient synthesis of dihydromyricetin-3-acetate by enzymatic regioselective acylation