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The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution's quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture Optimization Algorithm, Dung Beetle Optimization, Whale Optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also performed excellently among various current advanced algorithms for solving function optimization problems, achieving high precision in function optimization. [ABSTRACT FROM AUTHOR]

One of the components to realize the mutual benefit and win-win between electric vehicle (EV) and power grid is to effectively predict the charging load of EVs while the difficulty of charging load prediction is increased because of the randomness of temporal and spatial transfer of EV and a variety of coupling factors in the transfer process. In this paper,a method for predicting the spatial and temporal distribution of EV charging load considering dynamic transfer planning and coupling factors is proposed. Firstly,an individual travel mathematical model with multiple types of EVs is established based on travel chain technology. On this basis,considering the traffic flux,road conditions and temperature,the mathematical model of energy consumption per mileage of EV is constructed. Secondly,based on Markov decision process theory,considering the residual path and road network congestion information,the road network information is dynamically updated and the temporal and spatial transfer path of EVs is randomly planned. Finally,based on an example,the temporal and spatial distribution of EV and its charging load are compared and analyzed under different strategies,functional areas and travel days. The results show that the proposed method can fully reflect the travel decision of EV owners,and the prediction results can truly reflect the differences in the amplitude and distribution of charging load due to EV types and functional areas.

Segmenting clouds and their shadows is a critical challenge in remote sensing image processing. The shape, texture, lighting conditions, and background of clouds and their shadows impact the effectiveness of cloud detection. Currently, architectures that maintain high resolution throughout the entire information-extraction process are rapidly emerging. This parallel architecture, combining high and low resolutions, produces detailed high-resolution representations, enhancing segmentation prediction accuracy. This paper continues the parallel architecture of high and low resolution. When handling high- and low-resolution images, this paper employs a hybrid approach combining the Transformer and CNN models. This method facilitates interaction between the two models, enabling the extraction of both semantic and spatial details from the images. To address the challenge of inadequate fusion and significant information loss between high- and low-resolution images, this paper introduces a method based on ASMA (Axial Sharing Mixed Attention). This approach establishes pixel-level dependencies between high-resolution and low-resolution images, aiming to enhance the efficiency of image fusion. In addition, to enhance the effective focus on critical information in remote sensing images, the AGM (Attention Guide Module) is introduced, to integrate attention elements from original features into ASMA, to alleviate the problem of insufficient channel modeling of the self-attention mechanism. Our experimental results on the Cloud and Cloud Shadow dataset, the SPARCS dataset, and the CSWV dataset demonstrate the effectiveness of our method, surpassing the state-of-the-art techniques for cloud and cloud shadow segmentation. [ABSTRACT FROM AUTHOR]

Soft robotics is closely related to embodied intelligence in the joint exploration of the means to achieve more natural and effective robotic behaviors via physical forms and intelligent interactions. Embodied intelligence emphasizes that intelligence is affected by the synergy of the brain, body, and environment, focusing on the interaction between agents and the environment. Under this framework, the design and control strategies of soft robotics depend on their physical forms and material properties, as well as algorithms and data processing, which enable them to interact with the environment in a natural and adaptable manner. At present, embodied intelligence has comprehensively integrated related research results on the evolution, learning, perception, decision making in the field of intelligent algorithms, as well as on the behaviors and controls in the field of robotics. From this perspective, the relevant branches of the embodied intelligence in the context of soft robotics were studied, covering the computation of embodied morphology; the evolution of embodied AI; and the perception, control, and decision making of soft robotics. Moreover, on this basis, important research progress was summarized, and related scientific problems were discussed. This study can provide a reference for the research of embodied intelligence in the context of soft robotics. [ABSTRACT FROM AUTHOR]

A robot with rolling ability based on spatial octahedron,which consists of 6 vertex modules,12 telescopic unit expansion modules and 1 load module is designed. In order to controlling the robot to roll in a particular direction,the system changes the center of gravity by controlling the amount of expansion and contraction quantity between the telescopic rods. In this study,the process of forward kinematics solution and inverse kinematics solution of the robot are analyzed. According to the analysis,by using the ADAMS software,the dynamic simulation of the movement of the robot,which contain the simulation for gait planning and climbing obstacle capability is finished. Through the result of the dynamic simulation,the correctness of kinematics analysis is validated,and the motion curve of the robot vertices and the centroid is also obtained. The method and simulation results of the kinematics analysis of this study provide an important basis for the optimization design of the space polyhedron rolling robot.

Summary: To comprehensively consider the actual spatial‐temporal transfer process of electric vehicles (EVs) and enhance the computation efficiency of scheduling, this article proposes a spatial‐temporal transfer model of EVs and an improved Lagrange dual relaxation method (ILDRM) for the decentralized scheduling of a charging‐storage station (CSS). Specifically, with the application of trip chain technology, Monte Carlo, and Markov decision process (MDP), the spatial‐temporal transfer model of EVs is constructed, taking into account multiple factors including temperatures, traffic conditions, and transfer randomness. Subsequently, by introducing ILDRM, a decentralized optimization model is proposed which converts the traditional centralized optimization model into a set of sub‐problems. Moreover, the optimization model aims to maximize the profit of CSS under the constraints of vehicle‐to‐grid behavior and the operation of both CSS and distribution network. To validate the proposed spatial‐temporal transfer model and the decentralized optimization method for CSS, a series of simulations in various scenarios are performed regarding the load curve and computation efficiency. The comprehensive and systematical study indicates that the proposed spatial‐temporal transfer model enables to reflect EVs transfer randomness and it is more factually practical than the classical Dijkstra algorithm. Besides, ILDRM can provide a high computationally efficient solution to the operation of CSS. [ABSTRACT FROM AUTHOR]

Flaviviruses, such as Zika virus (ZIKV), Japanese encephalitis virus (JEV), Dengue virus (DENV), and West Nile virus (WNV), are important arthropod-borne pathogens that present an immense global health problem. Their unpredictable disease severity, unusual clinical features, and severe neurological manifestations underscore an urgent need for antiviral interventions. Furin, a host proprotein convertase, is a key contender in processing flavivirus prM protein to M protein, turning the inert virus to an infectious particle. For this reason, the current study was planned to evaluate the antiviral activity of decanoyl-Arg-Val-Lys-Arg-chloromethylketone, a specific furin inhibitor, against flaviviruses, including ZIKV and JEV. Analysis of viral proteins revealed a significant increase in the prM/E index of ZIKV or JEV in dec-RVKR-cmk-treated Vero cells compared to DMSO-treated control cells, indicating dec-RVKR-cmk inhibits prM cleavage. Plaque assay, qRT-PCR, and immunofluorescence assay revealed a strong antiviral activity of dec-RVKR-cmk against ZIKV and JEV in terms of the reduction in virus progeny titer and in viral RNA and protein production in both mammalian cells and mosquito cells. Time-of-drug addition assay revealed that the maximum reduction of virus titer was observed in post-infection treatment. Furthermore, our results showed that dec-RVKR-cmk exerts its inhibitory action on the virus release and next round infectivity but not on viral RNA replication. Taken together, our study highlights an interesting antiviral activity of dec-RVKR-cmk against flaviviruses. [ABSTRACT FROM AUTHOR]

The integrated optical 90-degree hybrid is a crucial component for coherent receivers. Here, we simulate and fabricate a 4 × 4 multimode interference coupler as a 90-degree hybrid using thin film lithium niobate (TFLN). The device features low loss (0.37 dB), high common mode rejection ratio (over 22 dB), compact footprint, and small phase error (below 2°) within the whole C-band experimentally, which is promising for integration with coherent modulators and photodetectors for TFLN-based high-bandwidth optical coherent transceivers.

Heart sound is a physiological parameter with individual characteristics generated by heart beat. To do the individual classification and recognition, in this paper, we present our study of using wavelet transform in the signal denoising, with the Mel-Frequency cepstrum coefficients (MFCC) as the feature parameters, and propose a research of reducing the dimensionality through principal components analysis (PCA). We have done the preliminary study to test the feasibility of biometric identification method using heart sound. The results showed that under the selected experimental conditions, the system could reach a 90% recognition rate. This study can provide a reference for further research.

As a novel BioChip technique, LiquiChip technique uses fluorescent polystyrene beads as the carrriers of various probes, thus the related reactions of bio-molecules are in liquid system. LiquiChip technique is of use in the methods to detect bio-macromolecules, e. g. DNA detection, immunoassay, cytokine assay, hormone assay, environmental survey and analysis. In comparison to conventional biochip, LiquiChip presents the advantages of high-throughput, high sensitivity, high accuracy, fine repeatability, wide linear range, etc. Now LiquiChip technique is widely used in biomedical engineering field.