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Mobile edge computing has been developed as a promising technology to extend diverse services to the edge of the Internet of Things system. Motivated by the high flexibility and controllability of unmanned aerial vehicles (UAVs), a multi‐UAVs‐assisted mobile edge computing system is studied to reduce the total consumption of time and energy of terminal equipments. In this system, UAVs act as the computing nodes or relay nodes for process terminal equipment's task. Accordingly, an optimisation problem is formulated to minimise the weighted sum of energy and delay consumption in the edge computing network. To solve the problem, an asynchronous advantage actor–critic (A3C) based deep reinforcement learning algorithm is proposed to obtain the optimal strategy for computation offloading and resource allocation. Experimental results demonstrate that the proposed A3C based algorithm converges fast and outperforms the baseline algorithms in terms of the energy and time consumption of system.

Oil/organic solvent leakage will pose a serious threat to the environment and ecology, and efficient oil/water separation has attracted much attention in recent years. Herein, we report a new conjugated microporous polymers based on tristyryl- s -triazine unit (CMP-TST) through the coupling reaction of 2,4,6 tribromostyryl- s -triazine (TBST) with 1,4 p -phenylenediacetylene monomers. Owning to the highly conjugated structure composed of C  =  C double bonds and C≡C triple bonds, CMP-TST exhibits super-hydrophobicity, rich porous structure as well as excellent chemical stability in hash conditions. Furthermore, CMP-TST can be coated on the skeleton of the polyurethane (PU) sponge, which endows the PU sponge with super-hydrophobicity, high elasticity, rich macroporous structure, and good mechanical stability. Ultimately, the coated PU sponge maintains its original superhydrophobicity even under extremely acidic/alkaline conditions (pH = 1–14). Importantly, the superhydrophobic CMP-TST@PU sponge can absorb low-density grease to saturation within 10 s, and its adsorption saturation capacity can be up to 72 times of its own weight. In addition, CMP-TST@PU can efficiently separate a series of different oil/water mixtures, including floating oil and bottom oil, with oil content greater than 99.8%. Besides, the superhydrophobic CMP-TST@PU sponge can continuously absorb and discharge oil and organic solvents on the water surface by means of vacuum, and the separation efficiency remains unchanged after repeated 10 times, indicating good cycle stability. It is worth mentioning that the sponge can also demulsify the surfactant-stabilized oil-in-water emulsion, and the purity of the recovered oil in the filtrate is about 99.8%. The fast adsorption capacity, excellent absorption capacity, outstanding reproducibility, and good stability make CMP-TST@PU a competitive candidate in dealing with large-scale oil pollution.

Platanus officinalis fibers (PFs) taking advantage of high-availability, eco-friendly and low-cost characteristics have attracted significant focus in the field of biomaterial application. Polyethyleneimine grafted with polydopamine on magnetic Platanus officinalis fibers (PEI-PDA@M-PFs) were prepared through a two-step process of mussel inspiration and the Michael addition reaction, which can work as an effective multifunctional biomass adsorbent for anionic dye with outstanding separation capacity and efficiency. The as-prepared PEI-PDA@M-PFs possess desirable hydrophilicity, magnetism and positive charge, along with abundant amino functional groups on the surface, facilitating efficient adsorption and the removal of Eriochrome Black T (EBT) dyes from water. In addition to the formation mechanism, the adsorption properties, including adsorption isotherms, kinetics, and the reusability of the absorbent, were studied intensively. The as-prepared PEI-PDA@M-PFs achieved a theoretical maximum adsorption capacity of 166.11 mg/g under optimal conditions (pH 7.0), with 10 mg of the adsorbent introduced into the EBT solution. The pseudo-second-order kinetic and Langmuir models were well matched with experimental data. Moreover, thermodynamic data ΔH > 0 revealed homogeneous chemical adsorption with a heat-absorption reaction. The adsorbent remained at high stability and recyclability even after five cycles of EBT adsorption processes. These above findings provide new insights into the adsorption processes and the development of biologic material for sustainable applications.

With the expansion of China's power grid, the scale, transmission distance and load capacity of transmission projects are all growing rapidly. Meanwhile, the problem of transmission line running state detection is also attracting more and more attention. Electric field under high voltage transmission line is taken as the research object in this paper, and the principle of electric field inverse operation is analyzed, two kinds of calculation objects and methods of nondestructive examination are are described in detail. There are two kinds of intelligent algorithms in this paper in order to solve the problem of several unknown solutions in inverse electric field operation. Correspondingly, the fitness function based on voltage value and result value is established, and the inverse electric field operation algorithm with global optimization function is proposed. A calculation example of high-voltage transmission line is also used to verify the inverse problem optimization algorithm. It is respectively proved by calculation results that the effectiveness and accuracy of the optimization method based on the two kinds of intelligent algorithms.

Mobile edge computing (MEC) has been developed to solve the problem of insufficient computing resource of edge user devices. However, it is common that the buildings obstacle information transmission. Motivated by the high flexibility of unmanned aerial vehicles (UAVs), we explore a multi-UAVs-assisted MEC system, in which UAVs play two roles, i.e., offering computation resources or acting as relay nodes, to reduce total consumption of edge computing network in terms of time and energy. Accordingly, an optimization problem is formulated to minimize the total energy consumption of the MEC system. The problem is further formulated as a markov decision process and two reinforcement learning methods: Q-learning based method and dueling deep reinforcement learning based method, are proposed to obtain the optimal policies for computation offloading and resource allocation. Finally, the numerical results in the edge computing network are given to show the effectiveness of the proposed methods.

Along with the progress of electric network scale, power load and transmission lines are also growing rapidly in China. In addition, the transmission state detection is getting more and more attention. The paper explored the electric field under the HV (high voltage) line of transmission and researched the inverse operation principle of electric field. To solve the multiple parameter solutions in electric field inversion, the paper proposed two smart algorithms. Firstly, the fitness function of voltage value is extracted, therefore inversion algorithm global optimization function of the electric field is proposed. Secondly, an example of HV transmission line verified the effectiveness of the inverse optimization algorithm. Last, the case results indicate that the optimization methods are effective and accurate.

Background RNA, an essential component of the central dogma of molecular biology, plays versatile roles in all cellular processes. RNA large language models (LLMs) are emerging as powerful methods in RNA research to decipher its intricate network of function and regulation. However, previous RNA LLMs were based on the Transformer model and pre-trained on short segment of non-coding RNAs, which limits their general usability. Here we present the first full-length RNA foundation model, HydraRNA, which is based on a hybrid architecture of bidirectional state space model and multi-head attention mechanism. Results HydraRNA is pre-trained on a large amount of both protein-coding mRNAs and non-coding RNAs. Despite being pre-trained with the fewest parameters and the least GPU resources, HydraRNA learns better RNA representations and outperforms the existing foundation models on a variety of mRNA-related tasks, including coding/non-coding RNA classification, prediction of RNA secondary structure, RBP binding sites, splicing and polyadenylation sites, mRNA stability and translation efficiency. Furthermore, HydraRNA can accurately predict the effect of mutations and estimate the relative contributions of different mRNA regions to the RNA stability and translation. Conclusions Our results demonstrate that the hybrid architecture outperforms the pure Transformer architectures in RNA language modeling. We anticipate that HydraRNA will enable dissecting the diverse properties of mRNA, accelerating the research of mRNA regulation and facilitating the optimal design of mRNA therapeutics.

Artificial spin ice (ASI) consisting patterned array of nano-magnets with frustrated dipolar interactions offers an excellent platform to study frustrated physics using direct imaging methods. Moreover, ASI often hosts a large number of nearly degenerated and non-volatile spin states that can be used for multi-bit data storage and neuromorphic computing. The realization of the device potential of ASI, however, critically relies on the capability of transport characterization of ASI, which has not been demonstrated so far. Using a tri-axial ASI system as the model system, we demonstrate that transport measurements can be used to distinguish the different spin states of the ASI system. Specifically, by fabricating a tri-layer structure consisting a permalloy base layer, a Cu spacer layer and the tri-axial ASI layer, we clearly resolve different spin states in the tri-axial ASI system using lateral transport measurements. We have further demonstrated that the tri-axial ASI system has all necessary required properties for reservoir computing, including rich spin configurations to store input signals, nonlinear response to input signals, and fading memory effect. The successful transport characterization of ASI opens up the prospect for novel device applications of ASI in multi-bit data storage and neuromorphic computing. Artificial spin ices consist of small magnets arranged in a lattice. Their simplicity belies their rich behaviour; they allowed for the investigation of effective magnetic monopoles, and more recently have been suggested as promising platforms for neuromorphic computing. For this latter function, efficient readout of the artificial spin ice state is critical. In this manuscript, Hu et al succeed in distinguishing artificial spin ice states using simple transport measurements.

This paper proposes a two-phase evolutionary algorithm framework for solving multi-objective optimization problems (MOPs), which allows different users to flexibly handle MOPs with different existing algorithms. In the first phase, a specific multi-objective evolutionary algorithm (MOEA) with a smaller population size is adopted to fast obtain a population converging to the true Pareto front. Then, in the second phase, a simple environmental selection mechanism based on a measure function and a well-designed crowdedness function is used to promote the uniformity of population in the objective space. Based on the proposed framework, we form four instantiations by embedding four distinct MOEAs into the first phase of the proposed framework. In the experimental study, different experiments are conducted on a variety of well-known benchmark problems from 3 to 10 objectives, and experimental results demonstrate the effect of the proposed framework. Furthermore, compared with several state-of-the-art multi-objective evolutionary algorithms, the four instantiations of the proposed framework have better performance and can obtain well-distributed solution sets. In short, the proposed framework has the strong ability to promote the performance of existing algorithms.

Background RNA metabolism is tightly regulated by cis -elements and trans -acting factors. Most information guiding such regulation is encoded in RNA sequences. Deciphering the regulatory rules is critical for RNA biology and therapeutics; however, the prediction of diverse regulation from RNA sequences remains a formidable challenge. Results Considering the similarities in semantic and syntactic features between RNAs and human language, we present LAMAR, a transformer-based foundation LAnguage Model for RNA Regulation, to decipher general rules underlying RNA processing. The model is pretrained on approximately 15 million sequences from both genome and transcriptome of 225 mammals and 1569 viruses, and further fine-tuned with labeled datasets for various tasks. The resulting fine-tuned models outperform the state-of-the-art methods in predicting mRNA translation efficiency and mRNA half-life, while achieving comparable accuracy to specifically designed methods in predicting splice sites of pre-mRNAs and internal ribosome entry sites (IRESs). The fine-tuned LAMAR is further applied to predict mutational effects of cis -regulatory elements and reveals known and novel regulatory elements that modulate RNA degradation. The fine-tuned LAMAR is also applied in an in silico screen of novel IRESs, resulting in the identifications of highly active IRESs that promote circRNA translation. Conclusions Our results indicate that a single foundation language model is applicable in the comprehensive analysis of different aspects of RNA regulation and predictive identification of novel regulatory elements, providing new insight into the design and optimization of RNA drugs.