Optimized Base Station Sleeping and Smart Grid Energy Procurement Scheme to Improve Energy Efficiency

In this chapter, the authors propose a joint BS sleeping strategy, resource allocation, and energy procurement scheme to maximize the profit of the network operators and minimize the carbon emission. Then, a joint optimization problem is formulated, which is a mixed-integer programming problem. To solve it, they adopt the bi-velocity discrete particle swarm optimization (BVDPSO) algorithm to optimize the BS sleeping strategy. When the BS sleeping strategy is fixed, the authors propose an optimal algorithm based on Lagrange dual domain method to optimize the power allocation, subcarrier assignment, and energy procurement. Numerical results illustrate the effectiveness of the proposed scheme and algorithm.

The structural repertoire of Fusarium oxysporum f. sp. lycopersici effectors revealed by experimental and computational studies

Plant pathogens secrete proteins, known as effectors, that function in the apoplast and inside plant cells to promote virulence. Effectors can also be detected by cell-surface and cytosolic receptors, resulting in the activation of defence pathways and plant immunity. Our understanding of fungal effector function and detection by immunity receptors is limited largely due to high sequence diversity and lack of identifiable sequence motifs precluding prediction of structure or function. Recent studies have demonstrated that fungal effectors can be grouped into structural classes despite significant sequence variation. Using protein x-ray crystallography, we identify a new structural class of effectors hidden within the secreted in xylem (SIX) effectors from Fusarium oxysporum f. sp. lycopersici (Fol). The recognised effectors Avr1 (SIX4) and Avr3 (SIX1) represent the founding members of the Fol dual-domain (FOLD) effector class. Using AlphaFold ab initio protein structure prediction, benchmarked against the experimentally determined structures, we demonstrate SIX6 and SIX13 are FOLD effectors. We show that the conserved N-domain of Avr1 and Avr3 is sufficient for recognition by their corresponding, but structurally-distinct, immunity receptors. Additional structural prediction and comparison indicate that 11 of the 14 SIX effectors group into four structural families. This revealed that genetically linked effectors are related structurally, and we provide direct evidence for a physical association between one divergently-transcribed effector pair. Collectively, these data indicate that Fol secretes groups of structurally-related molecules during plant infection, an observation that has broad implications for our understanding of pathogen virulence and the engineering of plant immunity receptors.

Dual-Domain Fusion Convolutional Neural Network for Contrast Enhancement Forensics

Contrast enhancement forensics techniques have always been of great interest for the image forensics community, as they can be an effective tool for recovering image history and identifying tampered images. Although several contrast enhancement forensic algorithms have been proposed, their accuracy and robustness against some kinds of processing are still unsatisfactory. In order to attenuate such deficiency, in this paper, we propose a new framework based on dual-domain fusion convolutional neural network to fuse the features of pixel and histogram domains for contrast enhancement forensics. Specifically, we first present a pixel-domain convolutional neural network to automatically capture the patterns of contrast-enhanced images in the pixel domain. Then, we present a histogram-domain convolutional neural network to extract the features in the histogram domain. The feature representations of pixel and histogram domains are fused and fed into two fully connected layers for the classification of contrast-enhanced images. Experimental results show that the proposed method achieves better performance and is robust against pre-JPEG compression and antiforensics attacks, obtaining over 99% detection accuracy for JPEG-compressed images with different QFs and antiforensics attack. In addition, a strategy for performance improvements of CNN-based forensics is explored, which could provide guidance for the design of CNN-based forensics tools.