A Blockchain-Based Reward Mechanism for Mobile Crowdsensing

Mobile crowdsensing (MCS) is a novel sensing scenario of cyber-physical-social systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger because of malicious task initiators/participants and hackers. This article proposes a novel blockchain-based MCS framework that preserves privacy and secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this article has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthly-pay participants and instant-pay participants. By analyzing two different kinds of participants and the task initiator, this article proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a 10% improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants while achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, this article discusses the future challenges of blockchain-based MCS

Neural Multi-network Diffusion towards Social Recommendation

Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommendation suffer from serious problems of generalization and oversmoothness, because of the underexplored negative sampling method and the direct implanting of the off-the-shelf GNN models. In this paper, we propose a succinct multi-network GNN-based neural model (NeMo) for social recommendation. Compared with the existing methods, the proposed model explores a generative negative sampling strategy, and leverages both the positive and negative user-item interactions for users' interest propagation. The experiments show that NeMo outperforms the state-of-the-art baselines on various real-world benchmark datasets (e.g., by up to 38.8% in terms of NDCG@15)

Mechanisms of Stress Tolerance in Xerophyte \u3cem\u3eZygophyllum xanthoxylum\u3c/em\u3e and Their Application in Genetic Improvement of Legume Forages

Xerophytes, naturally growing in desert areas, have evolved multiple protective mechanisms to survive and grow well in harsh environments. Zygophyllum xanthoxylum, a succulent xerophyte with excellent adaptability to adverse arid environments and a fodder shrub with high palatability and nutrient value, colonizes arid areas in China and Mongolia. In this study, we found that Z. xanthoxylum grew better responding to salt condition with a typical feature for halophytes and became more tolerant to drought in the presence of moderate salinity (50 mM NaCl); 50 mM NaCl alleviated deleterious impacts of drought on the growth of Z. xanthoxylum by improving the relative water content, inducing a significant drop in leaf water potential and, concomitantly, increasing leaf turgor pressure and chlorophyll concentrations resulting in an enhancement of overall plant photosynthetic activity. Subsequently, co-expression of genes encoding the tonoplast Na+/H+ antiporter (ZxNHX) and H+-PPase (ZxVP1-1) which involve in leaf Na+ accumulation under stress condition by compartmentalizing Na+ into vacuoles in Z. xanthoxylum significantly improved both drought and salt tolerance in legume forages, Lotus corniculatus L. and Medicago sativa L

Tera-sample-per-second arbitrary waveform generation in the synthetic dimension

The synthetic dimension opens new horizons in quantum physics and topological photonics by enabling new dimensions for field and particle manipulations. The most appealing property of the photonic synthetic dimension is its ability to emulate high-dimensional optical behavior in a unitary physical system. Here we show that the photonic synthetic dimension can transform technical problems in photonic systems between dimensionalities, providing unexpected solutions to technical problems that are otherwise challenging. Specifically, we propose and experimentally demonstrate a photonic Galton board (PGB) in the temporal synthetic dimension, in which the temporal high-speed challenge is converted into a spatial fiber-optic length matching problem, leading to the experimental generation of tera-sample-per-second arbitrary waveforms. Limited by the speed of the measurement equipment, waveforms with sampling rates of up to 341.53 GSa/s are recorded. Our proposed PGB operating in the temporal synthetic dimension breaks the speed limit in a physical system, bringing arbitrary waveform generation into the terahertz regime. The concept of dimension conversion offers possible solutions to various physical dimension-related problems, such as super-resolution imaging, high-resolution spectroscopy, time measurement, etc

Inhibition of Proteasomal Degradation of Rpn4 Impairs Nonhomologous End-Joining Repair of DNA Double-Strand Breaks

BACKGROUND: The proteasome homeostasis in Saccharomyces cerevisiae is regulated by a negative feedback circuit in which the transcription factor Rpn4 induces the proteasome genes and is rapidly degraded by the assembled proteasome. The integrity of the Rpn4-proteasome feedback loop is critical for cell viability under stressed conditions. We have demonstrated that inhibition of Rpn4 degradation sensitizes cells to DNA damage, particularly in response to high doses of DNA damaging agents. The underlying mechanism, however, remains unclear. METHODOLOGY/PRINCIPAL FINDINGS: Using yeast genetics and biochemical approach we show that inhibition of Rpn4 degradation displays a synthetic growth defect with deletion of the MEC1 checkpoint gene and sensitizes several checkpoint mutants to DNA damage. In addition, inhibition of Rpn4 degradation leads to a defect in repair of double-strand breaks (DSBs) by nonhomologous end-joining (NHEJ). The expression levels of several key NHEJ genes are downregulated and the recruitment of Yku70 to a DSB is reduced by inhibition of Rpn4 degradation. We find that Rpn4 and the proteasome are recruited to a DSB, suggesting their direct participation in NHEJ. Inhibition of Rpn4 degradation may result in a concomitant delay of release of Rpn4 and the proteasome from a DSB. CONCLUSION/SIGNIFICANCE: This study provides the first evidence for the role of proteasomal degradation of Rpn4 in NHEJ