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Abstract In order to maximize the work efficiency of wireless mobile charger, a payoff‐maximization‐based adaptive hierarchical wireless charging algorithm for mobile charger is proposed. Based on the mesh structure and multi‐node charging technology, the recharging optimization for massive devices is modelled as a problem of payoff maximization. According to energy allocation, anchor point deployment and time allocation, we decompose it into three layers by the hierarchical decomposition method to obtain optimal solution quickly. The process of energy allocation and anchor point deployment in each mesh is optimized in the first two layers based on Karush–Kuhn–Trucker (KKT) condition and greedy strategy, respectively. Based on the feedback of the first two layers, the most complex problem of time allocation in the last layer is solved by our innovative gain recall mechanism. The trade‐off between the number of recharged devices and recharging time in each cycle can be achieved by only charging the devices in the meshes which are without recall gains. The simulation results prove our algorithm can adaptively adjust the ratio of moving time to recharging time in a fixed cycle, and mobile charger can always work in efficient recharging positions, whose effect is exploited utmost.

Massive multiplayer online role-playing games (MMORPG) have been becoming one of the most popular and exciting online games. In recent years, a cheating phenomenon called real money trading (RMT) has arisen and damaged the fantasy world in many ways. RMT is the sale of in-game items, currency, or even characters to earn real money, breaking the balance of the game economy ecosystem and damaging the game experience. Therefore, some studies have emerged to address the problem of RMT detection. However, they cannot well handle the label uncertainty problem in practice, where there are only labeled RMT samples (positive samples) and unlabeled samples, which could either be RMT samples or normal transactions (negative samples). Meanwhile, the trading relationship between RMTers is modeled in a simple way, leading to some normal transactions being falsely classified as RMT. In this article, we propose PU-Detector, a novel framework based on PU learning (learning from positive and unlabeled data) for RMT detection, considering the fact that there are only labeled RMT samples and other unlabeled transactions. We first automatically estimate the likelihood of one transaction being RMT by developing an improved PU learning method and proposing an assessment rule. Sequentially, we use the estimated likelihood as edge weight to construct a trading graph to learn trader representation. Then, with the trader representations and basic trading features, we detect RMT samples by the improved PU learning method. PU-Detector is evaluated on a large-scale real world dataset consisting of 33,809,956 transaction logs generated by 43,217 unique players. Compared with other approaches, it achieves the state-of-the-art performance and demonstrates its advantages in detecting underlying RMT samples. [ABSTRACT FROM AUTHOR]

The article focuses on proposing an efficient architecture, SMLP4Rec, for sequential recommendations, addressing issues in existing self-attention models and MLP4Rec. Topics include changing the cascading structure to parallel mode, integrating normalization layers for efficiency, and improving training speed and prediction accuracy compared to MLP4Rec.

This paper focuses on a multi-objective optimization approach for resource allocation based on sleep strategy, aiming to mitigate the interference and improve the energy efficiency in heterogeneous networks. Then it’s converted into a multi-objective optimization problem. The problem is solved by the nondominated sorting genetic algorithm version II. And the simulations show that the proposed approach is superior to the existing one in the aspect of mitigating the inter-tier interference between dense low power nodes and the macro base station and reducing energy consumption ratio of low power nodes.

To satisfy the continuously high energy consumption and high computational capacity requirements for IoT applications, such as video monitoring, we integrate solar harvesting and multi-access edge computing (MEC) technologies to develop a solar-powered MEC system. Considering the stochastic nature of solar arrivals and channel conditions, we formulate a stochastic optimization problem to maximize network energy efficiency under the constraints of energy queue stability, task queue stability, peak transmission power, and maximum CPU frequency of each sensor. To solve the long-term stochastic optimization problem, we propose a Lyapunov-based online joint computational offloading and resource scheduling optimization algorithm, transforming the long-term stochastic problem into a series of deterministic subproblems in each time slot. Simulation results show that the proposed algorithm can find the optimal solution to tradeoff long-term energy efficiency and queueing backlog without requiring a priori knowledge of the channel state and energy arrival, which is a more realistic solution for practical solar-powered MEC systems. [ABSTRACT FROM AUTHOR]

With the rapid development of the 5G power Internet of Things (IoT), new power systems have higher requirements for data transmission rates, latency, reliability, and energy efficiency. Specifically, the hybrid service of enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC) has brought new challenges to the differentiated service of the 5G power IoT. To solve the above problems, this paper first constructs a power IoT model based on NOMA for the mixed service of URLLC and eMBB. Considering the shortage of resource utilization in eMBB and URLLC hybrid power service scenarios, the problem of maximizing system throughput through joint channel selection and power allocation is proposed. The channel selection algorithm based on matching as well as the power allocation algorithm based on water injection are developed to tackle the problem. Both theoretical analysis and experimental simulation verify that our method has superior performance in system throughput and spectrum efficiency. [ABSTRACT FROM AUTHOR]

IoT-based smart agriculture plays a significant role in building a high-yield, sustainable, and intelligent modern agriculture. However, limited battery capacity and low-power processors of sensors cannot accommodate the exponential expansion of data from smart agriculture sensing terminals. To overcome the challenges, we introduced solar harvesting and multiaccess edge computing (MEC) to investigate sustainable monitoring of smart agriculture in solar-powered MEC-enabled WSNs. Considering the cyclical and day-night fluctuations of solar energy, we formulate a joint optimization problem for resource scheduling and computation offloading strategy to maximize the minimum weighted computation capacity across the time slots under solar energy constraints. To solve the mixed-integer nonlinear program (MINLP), we propose a multiply-iterated decoupling optimization algorithm by jointly optimizing a computation offloading strategy, energy provision of the solar-powered hybrid access point (HAP), and local CPU frequency as well as time scheduling. Simulation results show that the proposed algorithm can efficiently use solar energy to balance network calculations, improve network energy efficiency, and realize unmanned and sustainable agricultural WSN. [ABSTRACT FROM AUTHOR]

N6‐methyladenosine (m6A) and alternative polyadenylation (APA) are important regulators of gene expression in eukaryotes. Recently, it was found that m6A is closely related to APA. However, the molecular mechanism of this new APA regulation remains elusive. Here, we show that YTHDC1, a nuclear m6A reader, can suppress proximal APA sites and produce longer 3′ UTR transcripts by binding to their upstream m6A sites. YTHDC1 can directly interact with the 3′ end processing factor FIP1L1 and interfere with its ability to recruit CPSF4. Binding to the m6A sites can promote liquid–liquid phase separation of YTHDC1 and FIP1L1, which may play an important role in their interaction and APA regulation. Collectively, YTHDC1 as an m6A "reader" links m6A modification with pre‐mRNA 3′ end processing, providing a new mechanism for APA regulation. Synopsis: YTHDC1 inhibits proximal poly(A) sites by interfering with FIP1L1 – CPSF4 interaction, which results in mRNA transcripts with longer 3′ UTRs.YTHDC1 inhibits proximal APA sites in an m6A‐dependent manner.YTHDC1 regulates mRNA alternative polyadenylation by interacting with the 3′ processing protein FIP1L1.YTHDC1 interacts with FIP1L1 in nuclear condensates through liquid‐liquid phase separation (LLPS). [ABSTRACT FROM AUTHOR]

Purpose: To explore the impact of a true half dose of [18F]-FDG on image quality in pediatric oncological patients undergoing total-body PET/CT and investigate short acquisition times with half-dose injected activity. Methods: One hundred pediatric oncological patients who underwent total-body PET/CT using the uEXPLORER scanner after receiving a true half dose of [18F]-FDG (1.85 MBq/kg) were retrospectively enrolled. The PET images were first reconstructed using complete 600-s data and then split into 300-s, 180-s, 60-s, 40-s, and 20-s duration groups (G600 to G20). The subjective analysis was performed using 5-point Likert scales. Objective quantitative metrics included the maximum standard uptake value (SUVmax), SUVmean, standard deviation (SD), signal-to-noise ratio (SNR), and SNRnorm of the background. The variabilities in lesion SUVmean, SUVmax, and tumor-to-background ratio (TBR) were also calculated. Results: The overall image quality scores in the G600, G300, G180, and G60 groups were 4.9 ± 0.2, 4.9 ± 0.3, 4.4 ± 0.5, and 3.5 ± 0.5 points, respectively. All the lesions identified in the half-dose images were localized in the G60 images, while 56% of the lesions could be clearly identified in the G20 images. With reduced acquisition time, the SUVmax and SD of the backgrounds were gradually increased, while the TBR values showed no statistically significant differences among the groups (all p > 0.1). Using the half-dose images as a reference, the variability in the lesion SUVmax gradually increased from the G180 to G20 images, while the lesion SUVmean remained stable across all age groups. SNRnorm was highly negatively correlated with age. Conclusion: Total-body PET/CT with a half dose of [18F]-FDG (1.85 MBq/kg, estimated whole-body effective dose: 1.76–2.57 mSv) achieved good performance in pediatric patients, with sufficient image quality and good lesion conspicuity. Sufficient image quality and lesion conspicuity could be maintained at a fast scanning time of 60 s with half-dose activity. [ABSTRACT FROM AUTHOR]

Comprehensive Summary: TsrM is a cobalamin‐dependent radical S‐adenosylmethionine (SAM) methyltransferase belonging to the Class B radical SAM methylase (RSM) family. This enzyme catalyzes the C‐2 methylation of L‐tryptophan to produce 2‐methyltrytophan (2‐MeTrp), an intermediate involved in the biosynthesis of thiostrepton A. In this work, we report characterization of an unexpected activity of TsrM, which carries out an additional methylation reaction on the product 2‐MeTrp. A series of isotopic labeling studies and assays with different Trp analogs revealed that TsrM is able to transfer a methyl group from SAM to the C4 of 2‐MeTrp to produce 2,4‐dimethyltryptophan. These results reveal the intriguing substrate specificity of TsrM, further expanding the reaction promiscuity of the radical SAM superfamily enzymes. [ABSTRACT FROM AUTHOR]

Solar harvesting, as the most mature environmental energy harvesting technology, can provide a convenient, low-carbon, and off-grid solution for energy-constrained WSN in remote environmental monitoring. However, the stochastic and intermittent nature of solar energy seriously affects network energy efficiency. In this paper, we jointly consider solar harvest deployment and scheduling for clustered WSN collaborating with SWIPT, where the solar-powered cluster (SC) coordinates SWIPT to periodically broadcast energy to a set of wireless charging nodes in full-duplex mode, each member node exhausts the recharged energy for data sensing and forwarding. To maximize network energy efficiency, we formulate a Solar harvesting collaborative SWIPT Energy Scheduling(SS-CoES) optimization problem, which jointly optimizes the deployment of SCs, the energy broadcast power with time allocation, and data sensing size under the constraint of the self-sustaining demand for network energy. Given the combinational nature of the energy deployment with its strong coupling with energy scheduling, we propose a multi-layer iteration decoupling optimization algorithm based on the Dinkelbach method, which decouples the problem into three subproblems and proposes the Hybrid discrete Firefly algorithm, Lagrangian dual subgradient algorithm, and bisection search method to obtain near-optimal feasible solutions. Simulation results show that the proposed SS-CoES can effectively maximize network energy efficiency and eliminate the dependence on the grid and move chargers, achieving rechargeable wireless sensor networks (RWSN) energy self-sustainability in remote areas. [ABSTRACT FROM AUTHOR]

In order to enhance the sustainability of rechargeable wireless sensor networks (RWSN), a deep-q-networks (DQN)-based adaptive dual-mode energy-efficient routing is proposed in this paper. Firstly, the life expectancy of each node is calculated based on multiple related factors, and a multi-hop routing based on forward transmission principle is proposed by using the indicator. Then according to the relationship between the life expectancy of a single node and the average life expectancy of the whole network, an adaptive dual-mode energy-efficient routing is proposed, which combines the multi-hop routing and the direct upload routing. Finally, for reducing the requirement of the single node for the network state information in the process of routing mode selection, a reinforcement learning framework based on DQN is designed, enabling the nodes to learn to judge the above relationship of life expectancy based on partial state information of its local network. Simulation results show that dynamic adjustment of the routing mode enables our algorithm to effectively optimize energy efficiency, so that the network lifetime increases obviously. Based on the limited information, the correct rate of routing mode selection can reach 95%, which ensures the applicability of our algorithm. [ABSTRACT FROM AUTHOR]

The rechargeable wireless sensor networks provide efficient data services for many applications in the Internet of Things (IoT), in which the sensors can be recharged continuously. Wireless recharging technology has been widely studied and applied. Reasonable energy allocation and identification of the nodes which need recharging in each round is key to the technology. According to dynamic recharging planning, it is crucial to improve energy efficiency in networks with excellent Quality of Service (QoS). Hence, a lifespan-balance-based energy-efficient routing is proposed. The optimization problem of maximizing recharging payoff is first designed and it is solved by our heuristic algorithm, which enables dynamic adjustment of the ratio of moving time to recharging time according to energy states of nodes. Then the QoS routing based on multi-hop strategy which integrates multiple related factors is designed. Finally, the indicator which contains energy supplement to quantify lifespan is designed. Combined with our QoS routing, the method of lifespan balance based on the global relay range regulation is proposed to maximize the minimum lifespan of all nodes intelligently. The simulation result shows that our algorithm can improve the energy efficiency with excellent QoS in the studied networks and prolong the network lifetime. [ABSTRACT FROM AUTHOR]