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With the ongoing promotion and adoption of electric vehicles, intelligent and connected technologies have been continuously advancing. Electrical control systems implemented in electric vehicles have emerged as a critical research direction. Various drive-by-wire chassis systems, including drive-by-wire driving and braking systems and steer-by-wire systems, are extensively employed in vehicles. Concurrently, unavoidable issues such as conflicting control system objectives and execution system interference emerge, positioning integrated chassis control as an effective solution to these challenges. This paper proposes a model predictive control-based longitudinal dynamics integrated chassis control system for pure electric commercial vehicles equipped with electro–mechanical brake (EMB) systems, centralized drive, and distributed braking. This system integrates acceleration slip regulation (ASR), a braking force distribution system, an anti-lock braking system (ABS), and a direct yaw moment control system (DYC). This paper first analyzes and models the key components of the vehicle. Then, based on model predictive control (MPC), it develops a controller model for integrated stability with double-layer torque distribution. The required driving and braking torque for each wheel are calculated according to the actual and desired motion states of the vehicle and applied to the corresponding actuators. Finally, the effectiveness of this strategy is verified through simulation results from Matlab/Simulink. The simulation shows that the braking deceleration of the braking condition is increased by 32% on average, and the braking distance is reduced by 15%. The driving condition can enter the smooth driving faster, and the time is reduced by 1.5 s~5 s. The lateral stability parameters are also very much improved compared with the uncontrolled vehicles. [ABSTRACT FROM AUTHOR]

Advancements in robotics and mapping technology have spotlighted the development of Simultaneous Localization and Mapping (SLAM) systems as a key research area. However, the high cost of advanced SLAM systems poses a significant barrier to research and development in the field, while many low-cost SLAM systems, operating under resource constraints, fail to achieve high-precision real-time mapping and localization, rendering them unsuitable for practical applications. This paper introduces a cost-effective SLAM system design that maintains high performance while significantly reducing costs. Our approach utilizes economical components and efficient algorithms, addressing the high-cost barrier in the field. First, we developed a robust robotic platform based on a traditional four-wheeled vehicle structure, enhancing flexibility and load capacity. Then, we adapted the SLAM algorithm using the LiDAR-inertial Odometry framework coupled with the Fast Iterative Closest Point (ICP) algorithm to balance accuracy and real-time performance. Finally, we integrated the 3D multi-goal Rapidly exploring Random Tree (RRT) algorithm with Nonlinear Model Predictive Control (NMPC) for autonomous exploration in complex environments. Comprehensive experimental results confirm the system's capability for real-time, autonomous navigation and mapping in intricate indoor settings, rivaling more expensive SLAM systems in accuracy and efficiency at a lower cost. Our research results are published as open access, facilitating greater accessibility and collaboration. [ABSTRACT FROM AUTHOR]

Advancements in electric vehicle technology have promoted the development trend of smart and low-carbon environmental protection. The design and optimization of electric vehicle braking systems faces multiple challenges, including the reasonable allocation and control of braking torque to improve energy economy and braking performance. In this paper, a multi-source braking force system and its control strategy are proposed with the aim of enhancing braking strength, safety, and energy economy during the braking process. Firstly, an ENMPC (explicit nonlinear model predictive control)-based braking force control strategy is proposed to replace the traditional ABS strategy in order to improve braking strength and safety while providing a foundation for the participation of the drive motor in ABS (anti-lock braking system) regulation. Secondly, a grey wolf algorithm is used to rationally allocate mechanical and electrical braking forces, with power consumption as the fitness function, to obtain the optimal allocation method and provide potential for EMB (electro–mechanical brake) optimization. Finally, simulation tests verify that the proposed method can improve braking strength, safety, and energy economy for different road conditions, and compared to other methods, it shows good performance. [ABSTRACT FROM AUTHOR]

Simple Summary: Mitochondrial genomes have become a powerful tool for studying molecular genetics and phylogeny of mollusks. In this study, the complete mitochondrial genome of Mactra grandis was characterized for the first time. The newly sequenced mitochondrial genome fits the typical composition pattern of mollusks with 37 functional genes. Among the Mactridae species with reported mitochondrial genomes, Mactra grandis has the closest relationship with Mactra cygnus. The gene arrangement, genetic distance, and selective pressure of protein-coding genes among Mactra species were also analyzed. This study provides a molecular basis for taxonomy and germplasm research on Mactridae species. Mitochondrial genomes are playing an increasingly important role in molluscan taxonomy, germplasm, and evolution studies. The first complete mitochondrial genome of the commercial big brown mactra clam, Mactra grandis, was characterized using Illumina next-generation sequencing in this study. The 17,289 bp circular genome has a typical gene organization of 13 protein-coding genes (PCGs), 2 rRNAs, and 22 tRNAs, with an obvious (A + T)-bias of 64.54%. All PCGs exhibited a homogeneous bias in nucleotide composition with a (A + T)-bias, a positive GC skew, and a negative AT skew. Results of phylogenetic analysis showed that Mactra grandis was most closely related to Mactra cygnus. The functional gene arrangement of the two species was identical but different from other Mactra species. The congeneric relationships among Mactra species were demonstrated by genetic distance analysis. Additionally, the selective pressure analysis suggested that cox1 was highly efficient for discriminating closely related species in genus Mactra, while nad2 was the most appropriate marker for population genetic analysis. [ABSTRACT FROM AUTHOR]

Introduction: Previous research has found that incidental emotions of different valences (positive/negative/neutral) influence risky decision‐making. However, the mechanism of their influence on psychological expectations of decision outcomes remains unclear. Methods: We explored the effects of different incidental emotions on the behavioral, psychological, and electrophysiological responses of individuals in risky decision‐making through a money gambling task using a one‐way (emotion type: positive, negative, neutral emotions) between‐subjects experimental design. Results: Individuals with positive emotions had significantly greater risk‐seeking rates than those with negative emotions during the decision selection phase (p <.01). In the feedback stage of decision outcomes, individuals showed stronger perceptions of uncertainty in the decision environment under gain and loss feedback compared with neutral feedback, as evidenced by a more positive P2 component (i.e., the second positive component of an event‐related potential). Positive emotions produced greater than expected outcome bias than neutral emotions, as evidenced by a more negative FRN component (i.e., the feedback‐related negativity component). Conclusion: Our results suggest that positive emotions increase individuals' psychological expectations of decision outcomes. This study provides new empirical insights to understand the influence of incidental emotions on risky decision outcome expectations. [ABSTRACT FROM AUTHOR]

Background Antimicrobial resistance (AMR) in Neisseria gonorrhoeae is a global health challenge. Limitations to AMR surveillance reporting, alongside reduction in culture-based susceptibility testing, has resulted in a need for rapid diagnostics and strain detection. We investigated Nanopore sequencing time, and depth, to accurately identify closely related N. gonorrhoeae isolates, compared to Illumina sequencing. Methods N. gonorrhoeae strains collected from a London sexual health clinic were cultured and sequenced with MiSeq and MinION sequencing platforms. Accuracy was determined by comparing variant calls at 68 nucleotide positions (37 resistance-associated markers). Accuracy at varying MinION sequencing depths was determined through retrospective time-stamped read analysis. Results Of 22 MinION-MiSeq pairs reaching sufficient sequencing depth, agreement of variant call positions passing quality control criteria was 185/185 (100%; 95% confidence interval [CI], 98.0%–100.0%), 502/503 (99.8%; 95% CI, 98.9%–99.9%), and 564/565 (99.8%; 95% CI, 99.0%–100.0%) at 10x, 30x, and 40x MinION depth, respectively. Isolates identified as closely related by MiSeq, within one yearly evolutionary distance of ≤5 single nucleotide polymorphisms, were accurately identified via MinION. Conclusions Nanopore sequencing shows utility as a rapid surveillance tool, identifying closely related N. gonorrhoeae strains, with just 10x sequencing depth, taking a median time of 29 minutes. This highlights its potential for tracking local transmission and AMR markers. [ABSTRACT FROM AUTHOR]