Search

To improve the accuracy and robustness of autonomous vehicle localization in a complex environment, this paper proposes a multi-source fusion localization method that integrates GPS, laser SLAM, and an odometer model. Firstly, fuzzy rules are constructed to accurately analyze the in-vehicle localization deviation and confidence factor to improve the initial fusion localization accuracy. Then, an odometer model for obtaining the projected localization trajectory is constructed. Considering the high accuracy of the odometer's projected trajectory within a short distance, we used the shape of the projected localization trajectory to inhibit the initial fusion localization noise and used trajectory matching to obtain an accurate localization. Finally, the Dual-LSTM network is constructed to predict the localization and build an electronic fence to guarantee the safety of the vehicle while also guaranteeing the updating of short-distance localization information of the vehicle when the above-mentioned fusion localization is unreliable. Under the limited arithmetic condition of the vehicle platform, accurate and reliable localization is realized in a complex environment. The proposed method was verified by long-time operation on the real vehicle platform, and compared with the EKF fusion localization method, the average root mean square error of localization was reduced by 66%, reaching centimeter-level localization accuracy. [ABSTRACT FROM AUTHOR]

The present study aimed to systematically investigate the underlying differences in flesh quality between wild and farmed Monopterus albus. Fifteen healthy M. albus per group with an average body weight of 45 g were sampled to analyze muscle parameters by biochemical indicators, histomorphology, and molecular biology. Compared with the wild fish, the farmed M. albus in flesh had lower crude protein, collagen, lysine, histidine, total amino acids, SFA, n-3 PUFA contents, and n-3/n-6 ratio (p < 0.05), and higher moisture, crude lipid, crude ash, MUFA, n-6PUFA, and total PUFA contents (p < 0.05). The thawing loss, drip loss, steaming loss, and boiling loss in the farmed group were significantly higher, and hardness, springiness, cohesiveness, gumminess, chewiness, and resilience were significantly lower than those in the wild group (p < 0.05). In addition, higher muscle fiber density and lower muscle fiber diameter were observed in wild M. albus (p < 0.05). In muscle transcriptome profiling, differentially expressed genes and enriched pathways are primarily associated with muscle development, protein synthesis, catabolism, lipid metabolism, and immunity. To the best of our knowledge, this is the first investigation that compares the flesh quality between wild and farmed M. albus in terms of biochemistry, histology, and molecular biology levels. Overall, wild M. albus had a higher nutritional value and texture quality than farmed M. albus. [ABSTRACT FROM AUTHOR]

With the deteriorating environment and excessive consumption of primary energy, solar energy has become used in buildings worldwide for renewable energy. Due to the fluctuations of solar radiation, a solar photovoltaic (PV) power system is often combined with a storage battery to improve the stability of a building's energy supply. In addition, the real-time energy consumption pattern of the residual houses fluctuates; a larger size for a PV and battery integrated system can offer more solar energy but also bring a higher equipment cost, and a smaller size for the integrated system may achieve an energy-saving effect. The traditional methods to size a PV and battery integrated system for a detached house are based on the experience method or the traversal algorithm. However, the experience method cannot consider the real-time fluctuating energy demand of a detached house, and the traversal algorithm costs too much computation time. Therefore, this study applies Nondominated Sorting Genetic Algorithm-II (NSGA-II) to size a PV and battery integrated system by optimizing total electricity cost and usage of the grid electricity simultaneously. By setting these two indicators as objectives separately, single-objective genetic algorithms (GAs) are also deployed to find the optimal size specifications of the PV and battery integrated system. The optimal solutions from NSGA-II and single-objective GAs are mutually verified, showing the high accuracy of NSGA-II, and the rapid convergence process demonstrates the time-saving effect of all these deployed genetic algorithms. The robustness of the deployed NSGA-II to various grid electricity prices is also tested, and similar optimal solutions are obtained. Compared with the experience method, the final optimal solution from NSGA-II saves 68.3% of total electricity cost with slightly more grid electricity used. Compared with the traversal algorithm, NSGA-II saves 94% of the computation time and provides more accurate size specifications for the PV and battery integrated system. This study suggests that NSGA-II is suitable for sizing a PV and battery integrated system for a detached house. [ABSTRACT FROM AUTHOR]

The proliferation of wireless technologies, particularly the advent of 5G networks, has ushered in transformative possibilities for enhancing vehicular communication systems, particularly in the context of autonomous driving. Leveraging sensory data and mapping information downloaded from base stations using I2V links, autonomous vehicles in these networks present the promise of enabling distant perceptual abilities essential to completing various tasks in a dynamic environment. However, the efficient down-link transmission of vehicular network data via base stations, often relying on spectrum sharing, presents a multifaceted challenge. This paper addresses the intricacies of spectrum allocation in vehicular networks, aiming to resolve the thorny issues of cross-station interference and coupling while adapting to the dynamic and evolving characteristics of the vehicular environment. A novel approach is suggested involving the utilization of a multi-agent option-critic reinforcement learning algorithm. This algorithm serves a dual purpose: firstly, it learns the most efficient way to allocate spectrum resources optimally. Secondly, it adapts to the ever-changing dynamics of the environment by learning various policy options tailored to different situations. Moreover, it identifies the conditions under which a switch between these policy options is warranted as the situation evolves. The proposed algorithm is structured in two layers, with the upper layer consisting of policy options that are shared across all agents, and the lower layer comprising intra-option policies executed in a distributed manner. Through experimentation, we showcase the superior spectrum efficiency and communication quality achieved by our approach. Specifically, our approach outperforms the baseline methods in terms of training average reward convergence stability and the transmission success rate. Control-variable experiments also reflect the better adaptability of the proposed method as the environmental conditions change, underscoring the significant potential of the proposed method in aiding successful down-link transmissions in vehicular networks. [ABSTRACT FROM AUTHOR]

Stress-induced anisotropy in calcareous sand can cause an uneven displacement in island reef engineering. In this study, stiffness, as a quantitative indicator, is explored to reveal the stress-induced anisotropy in calcareous sand. Based on the discrete element method, the stiffness anisotropic characteristics of calcareous sand during shearing, as well as the impact of particle breakage, are investigated by numerical simulations. Both the macro and micro responses, i.e., the maximum shear modulus, contact normal, strong and weak contact normal force, and the direction of particle breakage, are explored for calcareous sand with different particle breakage ratios. The results show that calcareous sand exhibits notable anisotropy during shearing, with the maximum shear modulus in the vertical direction (deviatoric stress direction) being significantly greater than that in the horizontal direction. Moreover, the higher the particle breakage rate, the lower the stiffness and its anisotropy. The micro-mechanism results indicate that the primary particle breakage during the shearing process occurs in the vertical direction. That is, the particle breakage weakens the strong contact force in the vertical direction, leading to a redistribution of the strong contact forces from the vertical direction to other directions. This redistribution mainly manifests in a decrease in the anisotropy of contact normal and contact vector within the sample, as well as a decrease in the proportion of strong contact forces in the overall contacts. This, in turn, reduces the shear strength and stiffness of calcareous sand, particularly in the vertical direction, and results in a decrease in the maximum shear modulus and its anisotropy. The maximum reduction can be up to 50% of the original value. These insights can provide a certain theoretical support for the uneven displacement and long-term stability of calcareous sand for islands and reefs. [ABSTRACT FROM AUTHOR]

All-silicon terahertz absorbers have attracted considerable interest. We present a design and numerical study of an all-silicon polarization-insensitive terahertz metamaterial absorber. The meta-atoms of the metamaterial absorber are square silicon rings which can be viewed as gratings. By properly optimizing the structure of the meta-atom, we achieve a broadband absorptivity that is above 90% ranging from 0.77 THz to 2.53 THz, with a relative bandwidth of 106.7%. Impedance matching reduces the reflection of the terahertz waves and the (0, ±1)-order diffraction induce the strong absorption. The absorption of this absorber is insensitive to the polarization of the terahertz wave and has a large incident angle tolerance of up to 60 degrees. The all-silicon metamaterial absorber proposed here provides an effective way to obtain broadband absorption in the terahertz regime. Metamaterial absorbers have outstanding applications in terahertz communication and imaging. [ABSTRACT FROM AUTHOR]

Microcellulose materials are increasingly considered multifunctional candidates for emerging energy applications. Microcellulose fibers (MCF) are a kind of bio-based reinforcement in composites, and their hydrophilic character hinders their wide application in industry. Thus, in the present work, MCF was hybrid-modified by amino silicone oil-phosphorylated to fabricate hydrophobic, thermal stability, and flame-retardant microcellulose fibers for potential application in vehicle engineering. The results showed that the amino silicone oil-phosphorylated (ASOP) hybrid modification could transform the surface property of microcellulose from hydrophilic to hydrophobic and improve the compatibility between MCF and resin matrix. Meanwhile, the ASOP treatment led to the formation of an amino silicone oil film layer on the surface of the microcellulose, which improved the thermal stability of the MCF. Furthermore, the ASOP hybrid modification microcellulose fibers paper (100% microcellulose fibers paper) was transformed from flammable to flame-retardant and showed self-extinguishing behavior after burning under flame for 2 s. The flame-retardant mechanism was attributed to the formation of the char layer in the condensed phase and the production of non-combustible gases in the gaseous phase. [ABSTRACT FROM AUTHOR]

Click-through rate (CTR) prediction plays a crucial role in online services and applications, such as online shopping and advertising. The performance of CTR prediction can have a direct impact on user experience and the revenue of the online platforms. For CTR prediction models, self-attention-based methods have been widely applied to this field. Recent works generally adopted the Transformer architecture, where the self-attention mechanism can capture the global dependencies of the user's historical interactions and predict the next item. Despite the effectiveness of self-attention methods in modeling sequential user behaviors, most sequential recommenders hardly exploit feature interaction techniques to extract high-order feature combinations. In this paper, we propose a Feature-Interaction-Enhanced Sequence Model (FESeq), which integrates feature interaction and the sequential recommendation model in a cascading structure. Specifically, the interacting layer in FESeq is an automatic feature engineering step for the Transformer model. Then, we add a linear time interval embedding layer and a positional embedding layer to the Transformer in the sequence-refiner layer to learn both the time intervals and the position information in the user's sequence behaviors. We also design an attention-based sequence pooling layer that can model the relevance of the user's historical behaviors and the target item representation through scaled bilinear attention. Our experiments show that the proposed method beats all the baselines on both public and industrial datasets. [ABSTRACT FROM AUTHOR]

Over the last three years, the pandemic of COVID-19 has had a significant impact on people's lives and the global economy. The incessant emergence of variant strains has compounded the challenges associated with the management of COVID-19. As the predominant variant from late 2021 to the present, Omicron and its sublineages, through continuous evolution, have demonstrated iterative viral fitness. The comprehensive elucidation of the biological implications that catalyzed this evolution remains incomplete. In accordance with extant research evidence, we provide a comprehensive review of subvariants of Omicron, delineating alterations in immune evasion, cellular infectivity, and the cross-species transmission potential. This review seeks to clarify the underpinnings of biology within the evolution of SARS-CoV-2, thereby providing a foundation for strategic considerations in the post-pandemic era of COVID-19. [ABSTRACT FROM AUTHOR]

The mechanical response and deformation characteristics in calcareous sand foundations during pile driving and setup were studied using model tests combined with the technical methods of tactile pressure sensors and close-range photogrammetry. Different types of piles were considered, including a pipe pile, square pile and semi-closed steel pipe pile. The test results show that during pile driving, the pile tip resistance of different piles increases with an increase in the pile insertion depth, and an obvious fluctuation is also obtained due to the particle breakage of the calcareous sand and energy dissipation. Different degrees of particle breakage generated by different type piles make the internal stress variations different, as with the pile tip resistance. The pile tip resistance of model pile A, which simulates a pipe pile, is the highest, followed by model pile B, the simulated square pile. Model pile C, which simulates a semi-closed steel pipe pile, has the smallest pile tip resistance because its particle breakage is the most obvious and the pile tip energy cannot be continuously accumulated. The induced deformation such as sag or uplift on the surface and the associated influence range for the calcareous sand foundation are the smallest for model pile C, followed by model pile B and then model pile A. Model pile A has the most obvious pile driving effect. During the pile setup process after piling, the increase in the total internal stress of model pile B is the largest, and the improvement of the potential bearing capacity is the most obvious, followed by model pile A and model pile C. During the pile setup, the induced uplift deformation in pile driving is recovered and the potential bearing capacity increases due the redistribution and uniformity of the vertical and radial stress distributions in the calcareous sand foundation. Considering the potential bearing capacity of different model piles, the influence range of pile driving, foundation deformation and the pile setup effect, it is suggested to use a pointed square pile corresponding to model pile B in pile engineering in calcareous sand foundations. [ABSTRACT FROM AUTHOR]