Blockchain-based energy trading (BBET) systems depend on prosumers to allocate energy betweentradingactivities andblockchain miningoperations. However, inadequate incentive structures lead prosumers to under-contribute to mining, creating throughput bottlenecks and system performance degradation. This paper introduces the Fee and Two-Piece Compensation (FTPC) mechanism to optimize energy allocation and enhance system throughput. We formulate the interaction between the system designer and prosumers as a three-stage Stackelberg game where the system designer establishes the incentive framework in Stage I, while prosumers determine energy allocation in Stage II and set transaction fees in Stage III. Our analysis demonstrates that prosumers' failure to internalize mining's positive externality results in suboptimal throughput investment. Counterintuitively, we show that impatient prosumers may exploit others' mining contributions as free riders. The FTPC mechanism resolves these issues by jointly optimizing transaction fees and compensation structures to align individual incentives with social welfare. We prove that FTPC achieves socially optimal outcomes through fully decentralized decision-making. Numerical evaluation shows FTPC improves social welfare and prosumer payoffs by 88.1% and 87.8%, respectively. Ethereum testbed implementation validates equilibrium convergence through iterative best-response dynamics.

Motif-scaffolding is a fundamental component of protein design, which aims to construct the scaffold structure that stabilizes motifs conferring desired functions. Recent advances in generative models are promising for designing scaffolds, with two main approaches: training-based and sampling-based methods. Training-based methods are resource-heavy and slow, while training-free sampling-based methods are flexible but require numerous sampling steps and costly, unstable guidance. To speed up and improve sampling-based methods, we analyzed failure cases and found that errors stem from the trade-off between generation and guidance. Thus we proposed to exploit the spatial context and adjust the generative direction to be consistent with guidance to overcome this trade-off. Motivated by this, we formulate motif-scaffolding as a Geometric Inverse Design task inspired by the image inverse problem, and present Evolution-ViA-reconstruction (EVA), a novel sampling-based coupled flow framework on geometric manifolds, which starts with a pretrained flow-based generative model. EVA uses motif-coupled priors to leverage spatial contexts, guiding the generative process along a straighter probability path, with generative directions aligned with guidance in the early sampling steps. EVA is 70× faster than SOTA model RFDiffusion with competitive and even better performance on benchmark tests. Further experiments on real-world cases including vaccine design, multi-motif scaffolding and motif optimal placement searching demonstrate EVA's superior efficiency and effectiveness.

The progressions in blockchain technologies have propelled the advancement of the Internet of Things (IoT), particularly the Industrial IoT (IIoT) system, toward a decentralized structure. This shift is driven by the need to address the privacy and security vulnerabilities inherent in centralized organizations. In a blockchain-based IoT system, the IoT nodes are tasked with both mining and data collection activities concurrently. However, due to the limited resources of these nodes, a rational resource allocation mechanism is imperative to maximize social welfare. Insufficient allocation of resources to mining can significantly reduce the overall Hashrate of the blockchain, thereby jeopardizing its security. Conversely, prioritizing resource allocation to mining can diminish the value of collected data, resulting in a decrease in overall social welfare. To tackle these challenges, this article proposes a transaction fee mechanism that levies charges on data consumers based on the resources utilized by the nodes for data collection. The objective is to achieve a balanced allocation of resources between mining and data collection while ensuring the security of the blockchain. Specifically, we model the interactions among the system designer, data consumer, and IoT nodes as a three-stage Stackelberg game. By characterizing the equilibrium of the game, we derive optimal strategies that lead to the maximization of social welfare. The effectiveness of the proposed approach is validated through extensive experimental results.

While large language models excel in a variety of natural language processing (NLP) tasks, to perform well on spoken language understanding (SLU) tasks, they must either rely on off-the-shelf automatic speech recognition (ASR) systems for transcription, or be equipped with an in-built speech modality. This work focuses on the former scenario, where LLM’s accuracy on SLU tasks is constrained by the accuracy of a fixed ASR system on the spoken input. Specifically, we tackle speech-intent classification task, where a high word-error-rate can limit the LLM’s ability to understand the spoken intent. Instead of chasing a high accuracy by designing complex or specialized architectures regardless of deployment costs, we seek to answer how far we can go without substantially changing the underlying ASR and LLM, which can potentially be shared by multiple unrelated tasks. To this end, we propose prompting the LLM with an n-best list of ASR hypotheses instead of only the error-prone 1-best hypothesis. We explore prompt-engineering to explain the concept of n-best lists to the LLM; followed by the finetuning of Low-Rank Adapters [1] on the downstream tasks. Our approach using n-best lists proves to be effective on a device-directed speech detection task as well as on a keyword spotting task, where systems using n-best list prompts outperform those using 1-best ASR hypothesis; thus paving the way for an efficient method to exploit ASR uncertainty via LLMs for speech-based applications.

In this paper, we consider a blockchain-based energy trading (BBET) system with the proof-of-stake (PoS) protocol. The system designer aims to minimize system cost by considering the prosumers' strategic token allocation between blockchain staking and energy purchase for their applications. This is challenging as the system designer does not know prosumers' private information of impatience levels towards different applications. To this end, we propose an online learning mechanism (OLM), which includes incentive mechanisms to guide both prosumers' private information reporting and staking decisions in two phases. In the exploration phase, we design a randomized staking reward to encourage prosumers' truthful reporting of their private information for the learning of impatience level distributions. Based on the threshold structure of the prosumers' equilibrium staking strategies, in the exploitation phase, we propose a learning-error-based reward to minimize the system cost considering the finite-sample bias. By characterizing the optimal exploration duration, we prove that OLM achieves an asymptotic zero-regret against the complete information benchmark, with the regret bounded by [EQUATION] when operating for T time slots. We implement the corresponding smart contract in Ethereum to demonstrate the feasibility of our approach. Experiment results show that our mechanism reduces regret by an average of 74% compared to the state-of-art mechanism.

Electrically excited synchronous motors (EESMs) without installing slip rings and brushes are drawing increasing attention in the electric vehicle (EV) propulsion systems. To improve the control performance of the EV EESMs with uncertain mutual inductance, which works under low control frequency (LCF), this paper proposes a novel segmented-prediction-based finite control set model predictive current control (FCS-MPCC) strategy. First, a sliding mode (SM) observer is constructed to identify the mutual inductance, with its stability and robustness against parameter mismatch analyzed. By using the estimated mutual inductance, the accurate EESM model used for FCS-MPCC is established, Second, the segmented prediction algorithms are developed to reduce the prediction errors caused by local linearization in the LPF situations. Finally, the proposed mutual inductance identification and high-performance control techniques are verified by experiment, which is conducted on a 580-W EESM drive system.

In current blockchain systems, the transaction fee is often not enough to cover the storage cost, jeopardizing blockchain sustainability in the long run. Such a storage sustainability issue is partially due to miners’ heterogeneous storage costs and users’ low-intensity fee competition. Motivated by these two observations, we propose a Fee and Transaction Expiration Time (FTET) mechanism to alleviate this issue. Specifically, we model the blockchain operation as a three-stage game. In Stage I, the system designer proposes the storage sustainability mechanism. In Stage II, each user decides whether to propose transactions and the corresponding transaction fees. In Stage III, each miner decides which transactions to include in the block. Although the analysis of the heterogeneous miner interaction is technically challenging, we fully solve it in closed-form motivated by how miners select transactions in practice. The equilibrium analysis reveals that high-storage-cost miners admit transactions with fees above a time-increasing threshold. Under the optimal FTET mechanism, the blockchain system can achieve the storage sustainability without any social welfare loss, comparing with the maximum achievable social welfare without the storage sustainability constraint. Moreover, the optimal FTET mechanism achieves a higher social welfare than the fee mechanism in current practice by selectively rejecting some transactions suffering high delays. Finally, we implement a blockchain prototype to compare the performance of the optimal FTET mechanism with the mining round time adjustment (MRTA) mechanism. The optimal FTET mechanism achieves higher social welfare (94.5% on average) and better storage sustainability. We find that more pending transactions may lead to lower transaction fees.

Miners in a blockchain system are suffering from ever-increasing storage costs, which in general have not been properly compensated by the users’ transaction fees. This reduces the incentives for the miners’ participation and may jeopardize the blockchain security. To mitigate this blockchain insufficient fee issue, we propose a Fee and Waiting Tax (FWT) mechanism, which explicitly considers the two types of negative externalities in the system. Specifically, we model the interactions between the protocol designer, users, and miners as a three-stage Stackelberg game. By characterizing the equilibrium of the game, we find that miners neglecting the negative externality in transaction selection cause they are willing to accept insufficient-fee transactions. This leads to the insufficient storage fee issue in the existing protocol (i.e., deployed in Bitcoin and Ethereum). Moreover, our proposed optimal FWT mechanism can motivate users to pay sufficient transaction fees to cover the storage costs and achieve the unconstrained social optimum. Numerical results show that the optimal FWT mechanism guarantees sufficient transaction fees and achieves an average social welfare improvement of 51.43% or more over the existing protocol. Furthermore, the optimal FWT mechanism reduces the average waiting time of low-fee transactions and all transactions by 68.49% and 61.56%, respectively.

Miners in a blockchain system are suffering from the ever-increasing storage costs, which in general have not been properly compensated by the users' transaction fees. In the long run, this may lead to less participation of miners and jeopardize the blockchain security. In this paper, we study the economics of blockchain storage and identify the incentive issues related to this storage cost problem. More specifically, we model the interactions among users (who generate transactions) and miners in two stages, where the users set the transaction fees in Stage 1, and the miners select which transactions to include in Stage 2. Through characterizing the Nash equilibrium of the two-stage game, we find that the transaction fees indeed cannot cover the storage costs under the current practice in general, due to the negative externality and the unfair delay-based pricing. We also identify that a longer block interval can alleviate the concern by raising the transactions fees at the expense of larger delay.

The airborne laser scanner (ALS) provides great potential for mapping the leaf area index (LAI) at the landscape scale using grid cell statistics, while its application is restricted by the lack of clumping information, which has been an unsolved issue highlighted for a long time. ALS generally provides an effective LAI because its footprint is too large to capture small gaps to apply traditional ground-based clumping correction methods. Here, we present a grid cell method based on path length distribution model to calculate the clumping-corrected LAI using ALS data without the requirement of additional field measurements. We separated the within- and between-crown areas to consider between-crown clumping, and used the path length distribution as estimated by local canopy height distribution to consider 3-D foliage profile and within-crown clumping. The path length distribution model takes advantage of the 3-D information rather than the gap size distribution, thus avoiding the limitation of large ALS footprint. With the 0.4-m-footprint ALS data, the results are generally promising and a multilevel clumping analysis is consistent with landscape flown. The ALS LAIs of different resolutions are consistent, with a difference of less than 5% from 5- to 250-m resolutions. Due to its consistency and simple configuration, the method provides an opportunity to map the clumping-corrected LAI operationally and strengthens the ability of airborne lidar to monitor vegetation change and validate the satellite product. This grid cell method based on path length distribution is worth further testing and application using more recent laser technology.

With the explosive growth of Internet of Vehicles (IoV), it is undoubted that vehicular demands for real-time Internet access would get a surge in the near future. Therefore, it is foreseeable that the cars within the IoV will generate enormous data. On the one hand, the huge volume of data mean we could get much information (e.g., vehicle's condition and real-time traffic distribution) through the big data analysis. On the other hand, the huge volume of data will overload the cellular network since the cellular infrastructure still represents the dominant access methods for ubiquitous connections. The vehicular ad hoc network (VANET) offloading is a promising solution to alleviate the conflict between the limited capacity of cellular network and big data collection. In a vehicular heterogeneous network formed by cellular network and VANET, an efficient network selection is crucial to ensure vehicles' quality of service. To address this issue, we develop an intelligent network recommendation system supported by traffic big data analysis. Firstly, the traffic model for network recommendation is built through big data analysis. Secondly, vehicles are recommended to access an appropriate network by employing the analytic framework which takes traffic status, user preferences, service applications and network conditions into account. Furthermore an Android application is developed, which enables individual vehicle to access network automatically based on the access recommender. Finally, extensive simulation results show that our proposal can effectively select the optimum network for vehicles, and network resource is fully utilized at the same time.

This paper suggests that information geometry may form a natural framework to deal with the unknown part of the boundary of entropic region. An application of information geometry shows that distributions associated with Shannon facets can be associated, in the right coordinates, with affine collections of distributions. This observation allows an information geometric reinterpretation of the Shannon-type inequalities as arising from a Pythagorean style relationship. The set of distributions which violate Ingleton's inequality, and hence are linked with the part of the entropic region which is yet undetermined, is shown also to have a surprising affine information geometric structure in a special case involving four random variables and a certain support. These facts provide strong evidence for the link between information geometry and characterizing the boundary of the entropic region.