Analysis of the Hydroelastic Performance of Very Large Floating Structures Based on Multimodules Beam Theory

The hydroelastic behavior of very large floating structures (VLFSs) is investigated based on the proposed multimodules beam theory (MBT). To carry out the analysis, the VLFS is first divided into multiple submodules that are connected through their gravity center by a spatial beam with specific stiffness. The external force exerted on the submodules includes the wave hydrodynamic force as well as the beam bending force due to the relative displacements of different submodules. The wave hydrodynamic force is computed based on three-dimensional potential theory. The beam bending force is expressed in the form of a stiffness matrix. The motion response defined at the gravity center of the submodules is solved by the multibody hydrodynamic control equations; then both the displacement and the structure bending moment of the VLFS are determined from the stiffness matrix equations. To account for the moving point mass effects, the proposed method is extended to the time domain based on impulse response function (IRF) theory. The method is verified by comparison with existing results. Detailed results through the displacement and bending moment of the VLFS are provided to show the influence of the number of the submodules and the influence of the moving point mass

Preliminary screening, identification and biological characteristic analysis of Bacillus probiotics isolated from Cynoglossus semilaevis

To screen local probiotic strains to promote antibiotic-free farming, two potential probiotic strains (S3, S5) were recognized among 89 cultivable bacterial strains isolated from the intestine of healthy Cynoglossus semilaevis. The two potential probiotic isolates were analyzed in terms of their morphology, physiology, biochemistry, the similarity of 16S rDNA sequences, growth characteristics, enzyme production capacity, bacterial antagonism, and safety in C. semilaevis. The results revealed that the bacterial morphology and physiological and biochemical characteristics of S3 and S5 were similar to those of Bacillus subtilis. The 16S rDNA sequences had 99.9 % similarity to that of Bacillus subtilis MH 145363.1. Therefore, S3 and S5 were identified as B. subtilis. In addition, we found that S3 and S5 had a strong ability to secrete amylase, protease, and lipase. During the safety tests of S3 and S5 in C. semilaevis with high concentrations, C. semilaevis in immersion, injection, and feeding groups remained in good condition without falling ill or dying. Moreover, we found that S3 and S5 exhibited superior growth at 25~50℃, salinities of 10 to 40, and pH values of 5 to 9. Furthermore, S3 and S5 had significant bacteriostatic activity against Vibrio anguillarum, Aeromonas salmonicida, and Shewanella algae, which are the main pathogenic bacteria of mariculture fish. In summary, S3 and S5 showed superb inhibition of the pathogenic bacteria of marine fish, rapid growth, eurythermal and euryhaline features, and suitability for the intestinal environment of C. semilaevis. Thus, strains S3 and S5 have excellent commercial development potential. These results provide a basis for ecological disease prevention strategies and are also valuable for developing and utilizing probiotics

Deep Reinforcement Learning with Multitask Episodic Memory Based on Task-Conditioned Hypernetwork

Deep reinforcement learning algorithms are usually impeded by sampling inefficiency, heavily depending on multiple interactions with the environment to acquire accurate decision-making capabilities. In contrast, humans rely on their hippocampus to retrieve relevant information from past experiences of relevant tasks, which guides their decision-making when learning a new task, rather than exclusively depending on environmental interactions. Nevertheless, designing a hippocampus-like module for an agent to incorporate past experiences into established reinforcement learning algorithms presents two challenges. The first challenge involves selecting the most relevant past experiences for the current task, and the second challenge is integrating such experiences into the decision network. To address these challenges, we propose a novel method that utilizes a retrieval network based on task-conditioned hypernetwork, which adapts the retrieval network's parameters depending on the task. At the same time, a dynamic modification mechanism enhances the collaborative efforts between the retrieval and decision networks. We evaluate the proposed method on the MiniGrid environment.The experimental results demonstrate that our proposed method significantly outperforms strong baselines

Внедрение зарплатного проекта с использованием банковских пластиковых карточек в ОАО «Белинвестбанк»

Материалы VI Междунар. межвуз. науч.-техн. конф. студентов, аспирантов и молодых ученых, Гомель, 4–5 мая 2006 г

Antidiabetic retinopathy effect of Fufang Danshen Mingmu in rats

Purpose: To investigate the effect of Fufang Danshen Mingmu (FDM) on streptozotocin-induced diabetic retinopathy rats.Methods: Diabetic retinopathy model rats were prepared using a single intraperitoneal injection of a freshly prepared solution of streptozotocin (50 mg/kg). The rats were randomly divided into 6 groups of ten rats each: negative control group, control group, reference group (glibenclamide, 1 mg/kg) as well as FDM groups, (50, 100 and 200 mg/kg body weight). Blood glucose and plasma insulin levels were determined. Oxidative stress was evaluated in liver and kidney as lipid peroxidation (LPO), superoxide dismutase (SOD), reduced glutathione (GSH), glutathione peroxidase (GPx) and catalase (CAT). Blood serum levels of creatinine and urea were determined in both diabetic control and treated rats.Results: Compared with diabetic rats, oral administration of FDM at a dose of 200 mg/kg daily for 30 days resulted in a significant decrease in fasting blood glucose (120.21 ± 3.37 mg/dL, p < 0.05) and increased insulin level (13.31 ± 0.67 uU/mL, p < 0.05).  Furthermore, it significantly reduced biochemical parameters (serum creatinine, 0.86 ±0.24 mg/dL, p < 0.05) and serum urea 41.86±1.59 mg/dL, p <0.05).Conclusion: The results indicate that FDM normalizes impaired antioxidant status in streptozotocin induced diabetic retinopathy rats, and also exerts a protective effect against lipid peroxidation by scavenging free radicals

InternEvo: Efficient Long-sequence Large Language Model Training via Hybrid Parallelism and Redundant Sharding

Large language models (LLMs) with long sequences begin to power more and more fundamentally new applications we use every day. Existing methods for long-sequence LLM training are neither efficient nor compatible with commonly-used training algorithms such as FlashAttention. We design InternEvo to address these issues. InternEvo decouples all of the sharding dimensions into a new hierarchical space, and systematically analyzes the memory and communication cost of LLM training. Then, it generates an effective hybrid parallelism strategy. We design a new selective overlap mechanism to mitigate the communication overhead introduced by the hybrid parallelism. We also implement memory management techniques to reduce GPU memory fragmentation. Evaluation results show that InternEvo generates parallelization strategies that match or outperform existing methods in model FLOPs utilization

JARVIS-1: Open-World Multi-task Agents with Memory-Augmented Multimodal Language Models

Achieving human-like planning and control with multimodal observations in an open world is a key milestone for more functional generalist agents. Existing approaches can handle certain long-horizon tasks in an open world. However, they still struggle when the number of open-world tasks could potentially be infinite and lack the capability to progressively enhance task completion as game time progresses. We introduce JARVIS-1, an open-world agent that can perceive multimodal input (visual observations and human instructions), generate sophisticated plans, and perform embodied control, all within the popular yet challenging open-world Minecraft universe. Specifically, we develop JARVIS-1 on top of pre-trained multimodal language models, which map visual observations and textual instructions to plans. The plans will be ultimately dispatched to the goal-conditioned controllers. We outfit JARVIS-1 with a multimodal memory, which facilitates planning using both pre-trained knowledge and its actual game survival experiences. JARVIS-1 is the existing most general agent in Minecraft, capable of completing over 200 different tasks using control and observation space similar to humans. These tasks range from short-horizon tasks, e.g., "chopping trees" to long-horizon tasks, e.g., "obtaining a diamond pickaxe". JARVIS-1 performs exceptionally well in short-horizon tasks, achieving nearly perfect performance. In the classic long-term task of $\texttt{ObtainDiamondPickaxe}$, JARVIS-1 surpasses the reliability of current state-of-the-art agents by 5 times and can successfully complete longer-horizon and more challenging tasks. The project page is available at https://craftjarvis.org/JARVIS-1Comment: update project pag