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The quality of service (QoS) is an important and considerable issue in designing survivable dense wavelength division multiplexing (DWDM) backbones for IP networks. This paper investigates the effect of network topology on QoS delivering in survivable DWDM optical transport networks using bandwidth/load ratio and design flexibility metrics. The dedicated path protection architecture is employed to establish diverse working and spare lightpaths between each node pair in demand matrix for covering a single link failure model. The simulation results, obtained for the Pan-European and ARPA2 test bench networks, demonstrate that the network topology has a great influence on QoS delivering by network at optical layer for different applications. The Pan-European network, a more connected network, displays better performance than ARPA2 network for both bandwidth/load ratio and design flexibility metrics.

Design of fault tolerant dense wavelength division multiplexing (DWDM) backbones is a major issue for service provision in the presence of failures. The problem is an NP-hard problem. This paper presents a genetic algorithm based approach for designing fault tolerant DWDM optical networks in the presence of a single link failure. The working and spare lightpaths are encoded into variable length chromosomes. Then the best lightpaths are found by use of a fitness function and these are assigned the minimum number of wavelengths according to the problem constraints using first-fit (FF) algorithm. The proposed approach has been evaluated for dedicated path protection architecture. The results, obtained from the ARPA2 test bench network, show that the method is well suited to tackling this complex and multi-constraint problem.

In this article, we give a brief overview of security and management issues that arise in all-optical networks (AONs). Then we present an outline of the multiple attack localization and identification (MALI) algorithm that can participate in some of the tasks for fault management in AONs. Consequently, we discuss a hardware-based control unit that can be embedded in AON nodes to accelerate the performance of the MALI algorithm. We conclude the article with a discussion concerning the applicability and implementation of this device in AON management systems.

Liquid crystals (LCs) have been widely used as sensitive elements to construct LC biosensors based on the principle that specific bonding events between biomolecules can affect the orientation of LC molecules. On the basis of the sensing interface of LC molecules, LC biosensors can be classified into three types: LC–solid interface sensing platforms, LC–aqueous interface sensing platforms, and LC–droplet interface sensing platforms. In addition, as a signal amplification method, the combination of LCs and whispering gallery mode (WGM) optical microcavities can provide higher detection sensitivity due to the extremely high quality factor and the small mode volume of the WGM optical microcavity, which enhances the interaction between the light field and biotargets. In this review, we present an overview of the basic principles, the structure, and the applications of LC biosensors. We discuss the important properties of LC and the principle of LC biosensors. The different geometries of LCs in the biosensing systems as well as their applications in the biological detection are then described. The fabrication and the application of the LC-based WGM microcavity optofluidic sensor in the biological detection are also introduced. Finally, challenges and potential research opportunities in the development of LC-based biosensors are discussed.

Pepper yellow leaf curl virus (PYLCV) is a threat to chili plants and can significantly reduce yields. This study aimed as a pilot project to detect PYLCV by analyzing compounds emitted by chili plants using gas chromatography-mass spectrometry (GC-MS). The samples investigated in this research were PYLCV-infected and PYLCV-undetected chili plants taken from commercial chili fields. The infection status was validated by using a polymerase chain reaction (PCR) test. A headspace technique was used to extract the volatile organic compounds emitted by plants. The analysis of GC-MS results began with pre-processing, analyzing sample compound variability with a boxplot analysis, and sample classification by using a multivariate technique. Unsupervised multivariate technique principal component analysis (PCA) was performed to discover whether GC-MS could identify PYLCV-infected or not. The results showed that PYLCV-infected and PYLCV-undetected chili plants could be differentiated, with a total percent variance of the first three principal components reaching 91.32%, and successfully discriminated between PYLCV-infected and PYLCV-undetected chili plants. However, more comprehensive studies are needed to find the potential biomarkers of the infected plants.

Abstract The complexity of social-ecological systems (SES) is rooted in the outcomes of node activities connected by network topology. Thus far, in network dynamics research, the connectivity degree (CND), indicating how many nodes are connected to a given node, has been the dominant concept. However, connectivity focuses only on network topology, neglecting the crucial relation to node activities, and thereby leaving system outcomes largely unexplained. Inspired by the phenomenon of “consensus of wills and coordination of activities” often observed in disaster risk management, we propose a new concept of network characteristic, the consilience degree (CSD), aiming to measure the way in which network topology and node activities together contribute to system outcomes. The CSD captures the fact that nodes may assume different states that make their activities more or less compatible. Connecting two nodes with in/compatible states will lead to outcomes that are un/desirable from the perspective of the SES in question. We mathematically prove that the CSD is a generalized CND, and the CND is a special case of CSD. As a general, fundamental concept, the CSD can facilitate the development of a new framework of network properties, models, and theories that allows us to understand patterns of network behavior that cannot be explained in terms of connectivity alone. We further demonstrate that a co-evolutionary mechanism can naturally improve the CSD. Given the generality of co-evolution in SES, we argue that the CSD is an inherent attribute rather than an artificial concept, which underpins the fundamental importance of the CSD to the study of SES.

A wireless body-centric nano-network consists of various nano-sized sensors with the purpose of healthcare application. One of the main challenges in the network is caused by the very limited power that can be stored in nano-batteries in comparison with the power required to drive the device for communications. Recently, novel rectifying antennas (rectennas) based on carbon nanotubes (CNTs), metal and graphene have been proposed. At the same time, research on simultaneous wireless information and power transfer (SWIPT) schemes has progressed apace. Body-centric nano-networks can overcome their energy bottleneck using these mechanisms. In this Letter, a nano-rectenna energy harvesting model is developed. The energy harvesting is realised by a nano-antenna and an ultra-high-speed rectifying diode combined as a nano-rectenna. This device can be used to power nanosensors using part of the terahertz (THz) information signal without any other system external energy source. The broadband properties of nano-rectennas enable them to generate direct current (DC) electricity from inputs with THz to optical frequencies. The authors calculate the output power generated by the nano-rectenna and compare this with the power required for nanosensors to communicate in the THz band. The calculation and analysis suggest that the nano-rectenna can be a viable approach to provide power for nanosensors in body-centric nano-networks.