Although blockchain empowers the IoT trading market and presents new development opportunities for IoT, scalability issues of blockchain limit its application in this area. Payment Channel Networks (PCNs) have emerged as a promising solution to address the scalability issues. With the help of routing protocols, two users can utilize payment channels to conduct off-chain transactions. However, most Payment Channel Network (PCN) routing protocols overlook the scalability of PCNs, resulting in substantial storage, communication, and computational overhead for lightweight nodes, such as IoT devices. Additionally, frequent utilization of a payment channel can quickly exhaust the channel’s balance, leading to congestion and causing subsequent payments to fail. Channel congestion restricts the throughput of PCNs, yet most PCN routing protocols lack designs for channel congestion control. In this paper, we propose a Cluster-based scalable and high-throughput Routing Protocol (CRP), to enhance the scalability and throughput of PCNs. CRP organizes PCNs into clusters to reduce the average routing table size, thereby alleviating users’ storage, communication, and computational overhead. Furthermore, CRP aims to minimize maximum channel congestion when selecting payment routes, thereby improving throughput. Extensive simulations demonstrate that CRP achieves high scalability and throughput compared to state-of-the-art PCN routing protocols.

BACKGROUND: Depression has been linked to an increased risk of cardiovascular and respiratory diseases; however, its impact on cardiac and lung function remains unclear, especially when accounting for potential gene-environment interactions. METHODS: We developed a novel polygenic and gene-environment interaction risk score (PGIRS) integrating the major genetic effect and gene-environment interaction effect of depression-associated loci. The single nucleotide polymorphisms (SNPs) demonstrating major genetic effect or environmental interaction effect were obtained from genome-wide SNP association and SNP-environment interaction analyses of depression. We then calculated the depression PGIRS for non-depressed individuals, using smoking and alcohol consumption as environmental factors. Using linear regression analysis, we assessed the associations of PGIRS and conventional polygenic risk score (PRS) with lung function (N = 42 886) and cardiac function (N = 1791) in the subjects with or without exposing to smoking and alcohol drinking. RESULTS: We detected significant associations of depression PGIRS with cardiac and lung function, contrary to conventional depression PRS. Among smokers, forced vital capacity exhibited a negative association with PGIRS (β = -0.037, FDR = 1.00 × 10-8), contrasting with no significant association with PRS (β = -0.002, FDR = 0.943). In drinkers, we observed a positive association between cardiac index with PGIRS (β = 0.088, FDR = 0.010), whereas no such association was found with PRS (β = 0.040, FDR = 0.265). Notably, in individuals who both smoked and drank, forced expiratory volume in 1-second demonstrated a negative association with PGIRS (β = -0.042, FDR = 6.30 × 10-9), but not with PRS (β = -0.003, FDR = 0.857). CONCLUSIONS: Our findings underscore the profound impact of depression on cardiac and lung function, highlighting the enhanced efficacy of considering gene-environment interactions in PRS-based studies.

Payment channel networks (PCNs) have emerged as a leading solution to address the scalability problem of blockchain-based cryptocurrencies. Within a PCN, two parties can efficiently conduct path-based off-chain transactions using the funds held in payment channels. Intermediaries involved in transaction routing charge a fee for their services. However, since transaction routing leads to highly dynamic and irreversible changes in channel balances, the success of transaction routing is difficult to guarantee, especially in the presence of concurrent transactions. Existing solutions for PCN routing either overlook the routing of concurrent transactions or impose high fees. In this article, we propose a novel routing protocol Deter-Pay for concurrent PCNs, which improves the success rate of the transaction routing and minimizes transaction fees. Our protocol adopts the idea of the deterministic transaction request declaration and reserved balance feedback. By reserving a sufficient balance during the path probing process, we mitigate the risk of transaction failure caused by fluctuations in channel balances during transaction routing. Additionally, our protocol employs the cheapest path first strategy to select paths, thereby minimizing transaction fees. Extensive simulations show that Deter-Pay can significantly increase the successful transaction volume and reduce the transaction fee compared to the comparison algorithms.

The rapid advancement of networking and manufacturing technologies has facilitated the proliferation of Internet of Things (IoT) devices connecting to networks. Just like humans, these devices, spanning various domains worldwide, necessitate interconnection. Therefore, cross domain identity authentication becomes crucial to ensure secure communication and mitigate cyber threats. Blockchain, as an emerging decentralized ledger technology, has garnered widespread attention due to its ability to effectively address the issue of single point of failure in traditional cross-domain identity authentication solutions. However, most cross-domain identity authentication solutions based on blockchain are limited by their reliance on existing single-chain blockchain architectures, which significantly restricts the performance of cross-domain authentication. In this paper, we present a splitchain based efficient authentication mechanism for cross-domain IoT. Specifically, we design an innovative split-chain blockchain structure that enhances the efficiency of authentication through parallelization. Additionally, we have developed a consensus algorithm that guarantees the security and fairness of the authentication system. Extensive experiments have been conducted to demonstrate that our scheme increases throughput by approximately 40% compared to MCCA, one of the most recent existing cross-domain solutions.

The Payment Channel Network (PCN) has emerged as a prominent solution for addressing the scalability limitations of cryptocurrencies. A critical aspect of PCN transactions is the selection of appropriate routing paths between parties. However, the success rate of payment routing in PCNs is significantly influenced by channel capacity and other payments, making path selection in PCNs more challenging than in traditional networks. In this paper, we present a novel path selection algorithm for PCNs that jointly considers path delay, channel capacity, and inter-path impact to achieve high throughput. Our simulation results demonstrate that the proposed algorithm improves throughput by 24.2% compared to the traditional widest path algorithm, and by 5.3% compared to the traditional shortest path algorithm.

Federated Learning (FL) has received widespread attention in 5G mobile edge networks (MENs) due to its ability to facilitate collaborative learning of machine learning models without revealing user privacy data. However, FL training is both time and energy consuming. Constrained by the instability and limited resources of clients in MENs, it is challenging to optimize both learning time and energy consumption for FL. This paper studies the problem of client selection and resource allocation to minimize the energy consumption and learning time of multiple FL jobs competing for resources. Because minimizing learning time and minimizing energy consumption are conflicting objectives, we design a decoupling algorithm to optimize them separately and efficiently. Simulations based on popular models and learning datasets show the effectiveness of our approach, reducing up to 75.7% energy consumption and 38.5% learning time compared to prior work.

We describe the implementation of a short answer extraction system.It consists of a simple sentence selection front-end and a two phase approach to answer extraction from a sentence.In the first phase sentence classification is performed with a classifier trained with the passive aggressive algorithm utilizing the UIUC dataset and taxonomy and a feature set including word vectors.This phase outperforms the current best published results on that dataset.In the second phase, a sieve algorithm consisting of a series of increasingly general extraction rules is applied, using WordNet to find word types aligned with the UIUC classifications determined in the first phase.Some very preliminary performance metrics are presented.

The Long Branch attraction (LBA) artefact induced by fast evolving Urochordata had hindered the interpretation of relationships among 3 subphyla of Chordata. Although Olfactores hypothesis which placed Urochordata rather than Cephalochordata as the closest relatives to Craniata was gradually accepted, every step of phylogenetic reconstruction had to be treated prudential to minimize LBA phenomenon. MiRNAs (microRNAs) are well known for their 1) adherence to organism development, 2) high conservation, and 3) rarity of secondary loss, parallel evolution, and convergence among metazoan. Therefore we suppose miRNAs to be promising candidates to dispel LBA phenomenon. We performed a phylogenetic study upon 35 pre-miRNA datasets and reconstruct Chordata phylogeny which supported Olfactores hypothesis in a more toilless way by applying fewer datasets and unspecified substitution model. This is the first attempt to apply miRNA sequences in interpreting Chordata phylogeny, and we reckon miRNAs as promising phylogenetic markers for illuminating deuterostome evolution.