Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders

Functional brain networks demonstrate significant temporal variability and dynamic reconfiguration even in the resting state. Currently, most studies investigate temporal variability of brain networks at the scale of single (micro) or whole-brain (macro) connectivity. However, the mechanism underlying time-varying properties remains unclear, as the coupling between brain network variability and neural activity is not readily apparent when analysed at either micro or macroscales. We propose an intermediate 15 (meso) scale analysis and characterize temporal variability of the functional architecture associated with a particular region. This yields a topography of variability that reflects the whole-brain and, most importantly, creates an analytical framework to establish the fundamental relationship between variability of regional functional architecture and its neural activity or structural connectivity. We find that temporal variability reflects the dynamical reconfiguration of a brain region into distinct functional modules at different times and may be indicative of brain flexibility and adaptability. Primary and unimodal sensory-motor cortices demon- 20 strate low temporal variability, while transmodal areas, including heteromodal association areas and limbic system, demonstrate the high variability. In particular, regions with highest variability such as hippocampus/parahippocampus, inferior and middle temporal gyrus, olfactory gyrus and caudate are all related to learning, suggesting that the temporal variability may indicate the level of brain adaptability. With simultaneously recorded electroencephalography/functional magnetic resonance imaging and functional magnetic resonance imaging/diffusion tensor imaging data, we also find that variability of regional functional architec- 25 ture is modulated by local blood oxygen level-dependent activity and a-band oscillation, and is governed by the ratio of intra- to inter-community structural connectivity. Application of the mesoscale variability measure to multicentre datasets of three mental disorders and matched controls involving 1180 subjects reveals that those regions demonstrating extreme, i.e. highest/lowest variability in controls are most liable to change in mental disorders. Specifically, we draw attention to the identification of diametrically opposing patterns of variability changes between schizophrenia and attention deficit hyperactivity disorder/autism. 30 Regions of the default-mode network demonstrate lower variability in patients with schizophrenia, but high variability in patients with autism/attention deficit hyperactivity disorder, compared with respective controls. In contrast, subcortical regions, especially the thalamus, show higher variability in schizophrenia patients, but lower variability in patients with attention deficit hyperactivity disorder. The changes in variability of these regions are also closely related to symptom scores. Our work provides insights into the dynamic organization of the resting brain and how it changes in brain disorders. The nodal variability measure may also be 35 potentially useful as a predictor for learning and neural rehabilitation

National Identity, Ethnocentrism and Consumer Ethnocentrism, and Effects of Language Choice in Advertising

Identifying with a social group can help a person to define themselves. This self-categorisation process facilitates the transition from “I” to “we”, and encourages people to value their social group memberships. As a result, people are sensitive about their group interests and the symbols that represent their groups - symbols such as their native language. New Zealand is a country with citizens from a diversity of cultural backgrounds. As a result, some local New Zealand businesses may try to attract immigrant consumers by using their native language in advertisements. However, how domestic consumers feel about these advertisements that include foreign languages is unknown. This research seeks to explore the effects of language choices in advertising from a social identity perspective, in particular, whether choice of language in advertisements influences New Zealand consumers’ attitudes towards advertisements and advertised products. Also, this research seeks to explore several social identity-related constructs (national identity, ethnocentrism, and consumer ethnocentrism) that may influence the relationship between choice of language and consumers’ attitudes towards advertisements and advertised products. Thus, two research questions are developed: RQ1. Do language choices in advertising have an impact on consumers’ attitudes towards an advertised product and an advertisement? RQ2. Do consumer ethnocentrism, ethnocentrism and/or national identity moderate the impact of language used in advertising on consumers’ attitude towards an advertisement and an advertised product? Quantitative methodology is utilised to answer the research questions. A pilot study was conducted to finalise the product categories for the main study. The potential moderation effects of the social identity-related constructs were tested in a 3x3 between-subjects factorial experiment, conducted via an online survey throughout New Zealand. In total, the responses from 355 participants were taken into account. The findings of the research indicate that choice of language in advertisements influences consumers’ attitudes towards advertisements and advertised products, when advertisements use the Chinese language compared to English, as well as when the advertisements use Chinese language compared to English + Chinese. Additionally, national identity and ethnocentrism partially moderate the relationship between choice of language and consumers’ attitudes towards advertisements and advertised products, but consumer ethnocentrism does not. The results provide local New Zealand companies with suggestions when they wish to target immigrant consumers through advertising, at the same time avoiding negative responses from native-born New Zealanders

Reliability‐aware service chaining mapping in NFV‐enabled networks

Network function virtualization can significantly improve the flexibility and effectiveness of network appliances via a mapping process called service function chaining. However, the failure of any single virtualized network function causes the breakdown of the entire chain, which results in resource wastage, delays, and significant data loss. Redundancy can be used to protect network appliances; however, when failures occur, it may significantly degrade network efficiency. In addition, it is difficult to efficiently map the primary and backups to optimize the management cost and service reliability without violating the capacity, delay, and reliability constraints, which is referred to as the reliability‐aware service chaining mapping problem. In this paper, a mixed integer linear programming formulation is provided to address this problem along with a novel online algorithm that adopts the joint protection redundancy model and novel backup selection scheme. The results show that the proposed algorithm can significantly improve the request acceptance ratio and reduce the consumption of physical resources compared to existing backup algorithms

Fault identification of double-circuit transmission lines on the same pole based on EEMD energy ratio

In order to improve the sensitivity and reliability of traveling wave protection, on the basis of analyzing the relationship of the anti-traveling wave current amplitude in the window after the internal/external failure of the double circuit line on the same tower, a fault identification method based on EEMD energy ratio is proposed. Use EEMD decomposition to decompose the anti-traveling wave current in a time window after the fault into 7 scales, and extracts the EEMD energy ratio at each scale at both ends to form a feature vector. Then it is sent to the particle swarm optimization support vector machine (PSO-SVM) for training and testing, and the internal and external faults are identified. Experiments show that the algorithm has good fault identification ability, the fault accuracy is 95% and the method sensitivity is high

Design of emergency UAV network identity authentication protocol based on Beidou

In view of the bad environment and intermittent communication of UAV network, based on the short message communication function of Beidou satellite navigation system, which can provide all-weather and no blind area communication service, a UAV network identity authentication protocol under emergency state when conventional means cannot communicate is designed

Infrared image-based detection method of electrical equipment overheating area in substation

For the detection of overheated areas of electrical equipment, in order to accurately segment out the overheated areas and reduce the fault detection range, this paper proposes a new overheated area detection algorithm. Firstly, the Ostu algorithm is used to remove the background and segment the general area of the electrical equipment area; secondly, the active contour model is used to refine the edge of the target area to remove the redundant edge points; finally, FCM clustering algorithm is used to suppress over segmentation and accurately divide the overheated area. The experiment proves that the algorithm can accurately divide the overheated area, and has certain practical value

A Review of Power System Fault Diagnosis with Spiking Neural P Systems

With the advancement of technologies it is becoming imperative to have a stable, secure and uninterrupted supply of power to electronic systems as well as to ensure the identification of faults occurring in these systems quickly and efficiently in case of any accident. Spiking neural P system (SNPS) is a popular parallel distributed computing model. It is inspired by the structure and functioning of spiking neurons. It belongs to the category of neural-like P systems and is well-known as a branch of the third generation neural networks. SNPS and its variants can perform the task of fault diagnosis in power systems efficiently. In this paper, we provide a comprehensive survey of these models, which can perform the task of fault diagnosis in transformers, power transmission networks, traction power supply systems, metro traction power supply systems, and electric locomotive systems. Furthermore, we discuss the use of these models in fault section estimation of power systems, fault location identification in distribution network, and fault line detection. We also discuss a software tool which can perform the task of fault diagnosis automatically. Finally, we discuss future research lines related to this topic

High level of intraspecific divergence and low frequency of RNA editing in the chloroplast genome sequence of Tagetes erecta

Tagetes erecta L. is an important commercial and medicinal plant. In this study, we reported the complete chloroplast genome sequence of T. erecta. The genome has a circular structure of 152,076 bp containing a large single-copy region (LSC) of 83,914 bp, a small copy region (SSC) of 18,064 bp, and two inverted repeats (IR) of 25,049 bp by each. It harbors 111 unique genes, including 79 protein-coding genes, 4 ribosomal RNA genes, and 28 transfer RNA genes. A total of 41 microsatellite, 20 tandem, and 37 interspersed repeats were detected in the genome. The phylogenomic analysis shows that T. erecta is a single phylogenetic cluster. The complete chloroplast genome of T. erecta lays the foundation for the phylogenetic, evolutionary, and conservation studies of the genus Tagetes. Furthermore, the intergenic region of atpB-rbcL was variable among the species T. erecta. This suggests that this region might be a mutation hotspot and will be useful for phylogenetic study and the development of molecular markers. At last, we systematically identified the RNA editing sites in the chloroplast genome of T. erecta based on the transcriptome downloaded from the SRA database. This study identified the characteristics of the T. erecta chloroplast genome, SNPs, and RNA editing sites, which will facilitate species identification and phylogenetic analysis within T. erecta

A Dyna-Q-Based Solution for UAV Networks Against Smart Jamming Attacks

Unmanned aerial vehicle (UAV) networks have a wide range of applications, such as in the Internet of Things (IoT), 5G communications, and so forth. However, the communications between UAVs and UAVs to ground control stations mainly use radio channels, and therefore these communications are vulnerable to cyberattacks. With the advent of software-defined radio (SDR), smart attacks that can flexibly select attack strategies according to the defender’s state information are gradually attracting the attention of researchers and potential attackers of UAV networks. The smart attack can even induce the defender to take a specific defense strategy, causing even greater damage. Inspired by symmetrical thinking, a solution using a software-defined network (SDN) to combat software-defined radio was proposed. We propose a network architecture which uses dual controllers, including a UAV flight controller and SDN controller, to achieve collaborative decision-making. Built on the top of the SDN, the state information of the whole network converges quickly and is fitted to an environment model used to develop an improved Dyna-Q-based reinforcement learning algorithm. The improved algorithm integrates the power allocation and track planning of UAVs into a unified action space. The simulation data showed that the proposed communication solution can effectively avoid smart jamming attacks and has faster learning efficiency and higher convergence performance than the compared algorithms