ABSTRACT Carbon‐inclusive policy is regarded as an incentive measure to personal low‐carbon actions. However, its impacts are various for different parties under the government‐led (including government and consumer) mode and the enterprise‐led (including government, consumer and the enterprise) mode, while few studies reveal their difference and give reasonable implications. To fill these gaps, taking consumer's low‐carbon travel as an example, this study develops two evolutionary game models—a two‐party model (based on government‐led adoption) and a tripartite model (based on enterprise‐led adoption)—to investigate the effects of carbon‐inclusive policy. The findings show that (1) the policy benefits all parties in both models, but the participation of the enterprise enhances the effectiveness of the policy; (2) the enterprise‐led mode, that is, the operation of the carbon‐inclusive platform by the enterprise is preferred because all parties have higher payoff, compared with the government‐led mode; and (3) subsidies from the government has a greater impact for consumers' low‐carbon behaviours. However, it has a less impact for the enterprise, which indicates the strategic action of the government is to establish a reasonable consumer subsidy system while reducing subsidies for the enterprise. This study offers a novel perspective on the effects of the carbon‐inclusive policy on consumers' low‐carbon behaviour, and enriches the practice of personal carbon trading.

The Payment Channel Network (PCN) provides an off-chain payment model to alleviate the problem of limited blockchain throughput. However, PCNs typically rely on third-party regulators to monitor the blockchain states continuously to prevent honest users’ funds from being lost, which increases user overhead and compromises transaction privacy. To overcome these challenges, we propose a highly compatible bidirectional Self-Regulatory Virtual Channel (SRVC) that eliminates third-party reliance while enhancing privacy and reducing overhead. By leveraging absolute time locks, we establish a self-regulatory mechanism where users only need to monitor the blockchain online for a short time window to ensure the security of their funds, effectively removing the need for third-party monitoring. Additionally, we introduce a novel payment paradigm and a punishment mechanism based on adaptor signatures across underlying channels to ensure transaction privacy and security, while also reducing transaction overhead. We formalize the security properties of SRVC as an ideal functionality and prove that SRVC is secure in the Universal Composability framework. Performance analysis demonstrates that, compared to other virtual channel protocols based on Lightning channels, SRVC reduces communication overhead by approximately 46.8% in the open operation and 62.9% in the update operation. In high-concurrency payment scenarios, SRVC further decreases communication overhead by about 57.8% and reduces the number of transactions by around 52.8% compared to Sleepy Channel’s Virtual Channel (SCVC) implementation.

The payment channel network aims to solve the problems of long payment confirmation time and limited throughput in cryptocurrencies through off-chain payments. Hash Time-Lock Contract (HTLC) is an off-chain payment protocol that Lightning Network (LN) adopted. Unfortunately, when performing high-valued payments off-chain, due to the impact of payment channel capacity, it is often necessary to split a single payment, which increases the transaction fees and time. Therefore, we propose LightPay, an atomic off-chain multi-path payment protocol based on adapter signature and discrete logarithm problem. Among different conditions encoded in the multi-path contract, the multi-path transmission of a single high-valued payment can be realized under the premise of the unlinkability of partial payments. We construct an ideal functionality in the Universal Composability framework and demonstrate that LightPay UC-realizes it, thereby providing proof of its security and privacy. Experimental results indicate that the payment success rate of LightPay can be increased by 11.08% in 0.0025 BTC payments compared with the single-path payment protocol Multihop HTLC in LN. Additionally, compared with the multi-path payment protocol CryptoMaze, the communication overhead required by LightPay is reduced to about 55.6% on average in the simulated network. Overall, LightPay has advantages regarding payment success rate and overhead.

Abstract Unmanned aerial vehicles (UAVs) are envisioned to provide a variety of sensing applications in the cellular systems, which is known as the cellular Internet of UAVs. This paper considers a cellular Internet of UAVs, in which the sensory data collected by UAVs can be transmitted to the base station by cellular communications, or to the corresponding mobile devices directly by overlaying UAV‐to‐device communications. The authors first propose a joint sensing and transmission protocol to schedule UAV sensing and transmission, and then investigate the energy utility (EU) maximisation problem by jointly optimising the trajectory, speed, and transmit power of UAVs in the system. As this problem is non‐convex and difficult to be solved, the authors decouple it into three subproblems , i.e. trajectory optimisation subproblem, speed optimisation subproblem, and transmit power optimisation subproblem. Then, the authors propose a joint trajectory, speed, and transmit power optimisation algorithm to obtain the suboptimal solution of the original problem. Simulation results show that the authors' proposed algorithm can efficiently improve the EU in the system as compared with other benchmark schemes.

Summary Customer‐to‐customer (C2C) interaction is a basic characteristic of social commerce, which is potentially to generate substantial economic benefits. The C2C interaction quality is employed to describe the impact on consumers' purchasing intention. This paper presents a Kano method–based C2C interaction model to study different factors involved with the communication process. Further, with the analysis of consumers' communication factors, a support vector machine classification system is established and learns to identify the quality of C2C interaction. Our results prove that the proposed methodology is feasible for C2C interaction quality distinguishing and highlights the importance of basic communication properties for supporting the purchase decisions.

MOTIVATION: The use of allosteric modulators as preferred therapeutic agents against classic orthosteric ligands has colossal advantages, including higher specificity, fewer side effects and lower toxicity. Therefore, the computational prediction of allosteric sites in proteins is receiving increased attention in the field of drug discovery. Allosite is a newly developed automatic tool for the prediction of allosteric sites in proteins of interest and is now available through a web server. AVAILABILITY: The Allosite server and tutorials are freely available at http://mdl.shsmu.edu.cn/AST CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.