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The double-row and double-chamfered aquaculture tank is a special tank structure of the aquaculture ship. The tank sloshing of this structure is coupled with the hull motion, which has an important impact on the safety of the hull motion. In the present study, research on the tank sloshing and hull motion response of aquaculture ships was conducted based on the model seakeeping and tank sloshing tests in regular waves. The test results were compared with the numerical simulation results of solid loading without sloshing. The results showed that the numerical simulation of the pitch motion was consistent with the amplitude-frequency response curve of the experimental results. Under certain transverse wave conditions, a large discrepancy existed between the amplitude-frequency response curve of the heave motion by the numerical simulation and the test results, and the roll motion differed most from the experimental result. Severe roll resonance occurred when the wave length-ship length ratio was 0.6. The roll motion amplitude was increased by 183.2%. Therefore, compared with aquaculture ships without sloshing, the sloshing of the tank has little effect on the pitch but has a great impact on the roll and heave motions, with the most significant effect on the roll motion.

Cardiac hypertrophy is a key factor driving heart failure (HF), yet its pathogenesis remains incompletely elucidated. Mettl1‐catalyzed RNA N7‐methylguanosine (m7G) modification has been implicated in ischemic cardiac injury and fibrosis. This study aims to elucidate the role of Mettl1 and the mechanism underlying non‐ischemic cardiac hypertrophy and HF. It is found that Mettl1 is upregulated in human failing hearts and hypertrophic murine hearts following transverse aortic constriction (TAC) and Angiotensin II (Ang II) infusion. YY1 acts as a transcriptional factor for Mettl1 during cardiac hypertrophy. Mettl1 knockout alleviates cardiac hypertrophy and dysfunction upon pressure overload from TAC or Ang II stimulation. Conversely, cardiac‐specific overexpression of Mettl1 results in cardiac remodeling. Mechanically, Mettl1 increases SRSF9 expression by inducing m7G modification of SRSF9 mRNA, facilitating alternative splicing and stabilization of NFATc4, thereby promoting cardiac hypertrophy. Moreover, the knockdown of SRSF9 protects against TAC‐ or Mettl1‐induced cardiac hypertrophic phenotypes in vivo and in vitro. The study identifies Mettl1 as a crucial regulator of cardiac hypertrophy, providing a novel therapeutic target for HF. [ABSTRACT FROM AUTHOR]

Accurately detecting spine vertebrae plays a crucial role in successful orthopedic surgery. However, identifying and classifying lumbar vertebrae from arbitrary spine X-ray images remains challenging due to their similar appearance and varying sizes among individuals. In this paper, we propose a novel approach to enhance vertebrae detection accuracy by leveraging both global and local spatial relationships between neighboring vertebrae. Our method incorporates a two-stage detector architecture that captures global contextual information using an intermediate heatmap from the first stage. Additionally, we introduce a detection head in the second stage to capture local spatial information, enabling each vertebra to learn neighboring spatial details, visibility, and relative offset. During inference, we employ a fusion strategy that combines spatial offsets of neighboring vertebrae and heatmap from a conventional detection head. This enables the model to better understand relationships and dependencies between neighboring vertebrae. Furthermore, we introduce a new representation of object centers that emphasizes critical regions and strengthens the spatial priors of human spine vertebrae, resulting in an improved detection accuracy. We evaluate our method using two lumbar spine image datasets and achieve promising detection performance. Compared to the baseline, our algorithm achieves a significant improvement of 13.6% AP in the CM dataset and surpasses 6.5% and 4.8% AP in the anterior and lateral views of the BUU dataset, respectively. [ABSTRACT FROM AUTHOR]

This paper describes control methods to improve electric vehicle performance in terms of handling, stability and cornering by adjusting the weight distribution and implementing control systems (e.g., wheel slip control, and yaw rate control). The vehicle is first simulated using the bicycle model to capture the dynamics. Then, a study on the effect of weight distribution on the driving behavior is conducted. The study is performed for three different weight configurations. Moreover, a yaw rate controller and a wheel slip controller are designed and implemented to improve the vehicle's performance for cornering and longitudinal motion under the different loading conditions. The simulation through the bicycle model is compared to the experiments conducted on a rear-wheel driven radio-controlled (RC) electric vehicle. The paper shows how the wheel slip controller contributes to the stabilization of the vehicle, how the yaw rate controller reduces understeering, and how the location of the center of gravity (CoG) affects steering behavior. Lastly, an analysis of the combination of control systems for each weight transfer is conducted to determine the configuration with the highest performance regarding acceleration time, braking distance, and steering behavior. [ABSTRACT FROM AUTHOR]