Dose-dependent protection of reseveratrol against spinal cord ischemic-reperfusion injury in rats

Purpose: To examine the protective effects of resveratrol (RESV) against spinal cord ischemic reperfusion (SCIR) injury.Methods: Forty-eight male rats were divided into six groups: sham-operated (control-I), SCIR-treated (SCIR-II), rats receiving 20 mg/kg of RESV with SCIR (RESV 20+SCIR-III), rats receiving 40 mg/kg of RESV with SCIR (RESV  40+SCIR-IV), rats receiving 60 mg/kg of RESV with SCIR (RESV 60+SCIR-V), and rats receiving 50 mg/kg of methylprednisolone (MP) with SCIR (MP + SCIR-VI), for 7 days prior to IR (pre-treatment) and 7 days after IR (post-treatment).Results: The levels of oxidative markers (TBARS, MPO) and inflammatory markers (IL-1β, IL-6, TNF-α, and NF-p65) were concomitantly suppressed in RESV-treated rats, which showed improved locomotor function. A pronounced increase in the activities of antioxidant enzymes (SOD, CAT and GSH) was noted in the RESV group compared with the MP and SCIR groups. RESV and MP supplementation increased neuronal count with decreased nuclear degeneration. RESV (40 mg) exhibited  greater protective effect than 20 mg and 60 mg of RESV and 50 mg of MP.Conclusion: The results show the neurotherapeutic potential of RESV (40 mg) to attenuate oxidative stress and the inflammatory response to SCIR injury.Keywords: Spinal cord ischemia reperfusion, Resveratrol, Locomotor function,  Antioxidant, Inflammatory marker

Simulation and application of loose tooling forging for heavy grinding roller shaft forgings

The grinding roller shaft is a key part of the grinding roller. It has a step-shaped shaft with different round cross-sections and 1850 mm × 1110 mm rectangular cross-section. If the general method of free forging is used, the upsetting diameter of ingot will reach 2900 mm, and 8400 t hydraulic press current will not be produced so that the loose tooling forging process is to be used. The loose tooling forging process of rectangular flange has been researched by using DEFORM-3D simulation software and establishing a reasonable forging process. The production results reveal that the heavy forgings used as grinding roller shafts can be successfully produced with the present 8400 t capacity hydraulic presses. The eligible forgings have proved the rationality of the technical process

Ridge detection for nonstationary multicomponent signals with time-varying wave-shape functions and its applications

We introduce a novel ridge detection algorithm for time-frequency (TF) analysis, particularly tailored for intricate nonstationary time series encompassing multiple non-sinusoidal oscillatory components. The algorithm is rooted in the distinctive geometric patterns that emerge in the TF domain due to such non-sinusoidal oscillations. We term this method \textit{shape-adaptive mode decomposition-based multiple harmonic ridge detection} (\textsf{SAMD-MHRD}). A swift implementation is available when supplementary information is at hand. We demonstrate the practical utility of \textsf{SAMD-MHRD} through its application to a real-world challenge. We employ it to devise a cutting-edge walking activity detection algorithm, leveraging accelerometer signals from an inertial measurement unit across diverse body locations of a moving subject

Representation Learning with Large Language Models for Recommendation

Recommender systems have seen significant advancements with the influence of deep learning and graph neural networks, particularly in capturing complex user-item relationships. However, these graph-based recommenders heavily depend on ID-based data, potentially disregarding valuable textual information associated with users and items, resulting in less informative learned representations. Moreover, the utilization of implicit feedback data introduces potential noise and bias, posing challenges for the effectiveness of user preference learning. While the integration of large language models (LLMs) into traditional ID-based recommenders has gained attention, challenges such as scalability issues, limitations in text-only reliance, and prompt input constraints need to be addressed for effective implementation in practical recommender systems. To address these challenges, we propose a model-agnostic framework RLMRec that aims to enhance existing recommenders with LLM-empowered representation learning. It proposes a recommendation paradigm that integrates representation learning with LLMs to capture intricate semantic aspects of user behaviors and preferences. RLMRec incorporates auxiliary textual signals, develops a user/item profiling paradigm empowered by LLMs, and aligns the semantic space of LLMs with the representation space of collaborative relational signals through a cross-view alignment framework. This work further establish a theoretical foundation demonstrating that incorporating textual signals through mutual information maximization enhances the quality of representations. In our evaluation, we integrate RLMRec with state-of-the-art recommender models, while also analyzing its efficiency and robustness to noise data. Our implementation codes are available at https://github.com/HKUDS/RLMRec.Comment: Published as a WWW'24 full pape

Distributed Consensus of Linear Multi-Agent Systems with Adaptive Dynamic Protocols

This paper considers the distributed consensus problem of multi-agent systems with general continuous-time linear dynamics. Two distributed adaptive dynamic consensus protocols are proposed, based on the relative output information of neighboring agents. One protocol assigns an adaptive coupling weight to each edge in the communication graph while the other uses an adaptive coupling weight for each node. These two adaptive protocols are designed to ensure that consensus is reached in a fully distributed fashion for any undirected connected communication graphs without using any global information. A sufficient condition for the existence of these adaptive protocols is that each agent is stabilizable and detectable. The cases with leader-follower and switching communication graphs are also studied.Comment: 17 pages, 5 figue

Molecular dynamics simulation of the transformation of Fe-Co alloy by machine learning force field based on atomic cluster expansion

The force field describing the calculated interaction between atoms or molecules is the key to the accuracy of many molecular dynamics (MD) simulation results. Compared with traditional or semi-empirical force fields, machine learning force fields have the advantages of faster speed and higher precision. We have employed the method of atomic cluster expansion (ACE) combined with first-principles density functional theory (DFT) calculations for machine learning, and successfully obtained the force field of the binary Fe-Co alloy. Molecular dynamics simulations of Fe-Co alloy carried out using this ACE force field predicted the correct phase transition range of Fe-Co alloy.Comment: 17 pages, 6 figure

LLMRec: Large Language Models with Graph Augmentation for Recommendation

The problem of data sparsity has long been a challenge in recommendation systems, and previous studies have attempted to address this issue by incorporating side information. However, this approach often introduces side effects such as noise, availability issues, and low data quality, which in turn hinder the accurate modeling of user preferences and adversely impact recommendation performance. In light of the recent advancements in large language models (LLMs), which possess extensive knowledge bases and strong reasoning capabilities, we propose a novel framework called LLMRec that enhances recommender systems by employing three simple yet effective LLM-based graph augmentation strategies. Our approach leverages the rich content available within online platforms (e.g., Netflix, MovieLens) to augment the interaction graph in three ways: (i) reinforcing user-item interaction egde, (ii) enhancing the understanding of item node attributes, and (iii) conducting user node profiling, intuitively from the natural language perspective. By employing these strategies, we address the challenges posed by sparse implicit feedback and low-quality side information in recommenders. Besides, to ensure the quality of the augmentation, we develop a denoised data robustification mechanism that includes techniques of noisy implicit feedback pruning and MAE-based feature enhancement that help refine the augmented data and improve its reliability. Furthermore, we provide theoretical analysis to support the effectiveness of LLMRec and clarify the benefits of our method in facilitating model optimization. Experimental results on benchmark datasets demonstrate the superiority of our LLM-based augmentation approach over state-of-the-art techniques. To ensure reproducibility, we have made our code and augmented data publicly available at: https://github.com/HKUDS/LLMRec.gitComment: WSDM 2024 Oral Presentatio