Efficacy of co-administration of oxiracetam and butylphthalide in the treatment of elderly patients with hypertensive intracerebral hemorrhage, and its effect on NIHSS and ADL scores

Purpose: To investigate the effect of combined use of oxiracetam and butylphthalide on hypertensive intracerebral hemorrhage (HICH) in elderly patients, and its influence on NIHSS and activities of daily living (ADL) scores of patients.Methods: Ninety (90) elderly patients with HICH who were admitted to Renmin Hospital of Wuhan University, Wuhan, China served as study subjects, and were randomly assigned to control and study groups, with 45 patients per group. The patients in the control group were treated with oxiracetam alone, while patients in the study group received a combination of oxiracetam and butylphthalide. Clinical efficacy, undesirable side effects and serum indices were determined. The NIHSS and ADL rating scales were used to evaluate cerebral nerve function and ADL score before and after treatment.Results: There were significantly higher total treatment effectiveness and lower incidence of adverse reactions in the study group than in control group, while tissue inhibitor of metalloproteinase-1 (TIMP-1) index, matrix metalloproteinase-9 (MMP-9) index and NIHSS score were reduced in study patients, relative to controls (p < 0.001). However, ADL score in the study group was higher than that in the control group (p < 0.001).Conclusion: Treatment of elderly patients with HICH using a combination of oxiracetam and butylphthalide improves various serum indices, cerebral nerve function and ADL, as well as clinical efficacy. Further research on the combined medication will help to establish a reliable treatment plan for these patients

Enhancing thermoelectric figure-of-merit by low-dimensional electrical transport in phonon-glass crystals

Low-dimensional electronic and glassy phononic transport are two important ingredients of highly-efficient thermoelectric material, from which two branches of the thermoelectric research emerge. One focuses on controlling electronic transport in the low dimension, while the other on multiscale phonon engineering in the bulk. Recent work has benefited much from combining these two approaches, e.g., phonon engineering in low-dimensional materials. Here, we propose to employ the low-dimensional electronic structure in bulk phonon-glass crystal as an alternative way to increase the thermoelectric efficiency. Through first-principles electronic structure calculation and classical molecular dynamics simulation, we show that the $\pi$-$\pi$ stacking Bis-Dithienothiophene molecular crystal is a natural candidate for such an approach. This is determined by the nature of its chemical bonding. Without any optimization of the material parameter, we obtain a maximum room-temperature figure of merit, $ZT$, of $1.48$ at optimal doping, thus validating our idea.Comment: Nano Lett.201

Research on Personalized Recommender System for Tourism Information Service

Since the development in the 1990s, Recommender system has been widely applied in various fields. The conflict between the expansion of tourism information and difficulty of tourists obtaining tourism information allows Tourism Information Recommender System to have a practical significance. Based on the existing online tourism information service and the mature recommendation algorithms, Personal Recommender System can be used to solve present problems of the key recommendation algorithms. In the first place, this research presents an overview of researches on this issue both at home and abroad, and analyzes the applications of main stream recommendation algorithms. Secondly, a comparative study of domestic and international tourism information service websites is conducted. Drawbacks in their applications are defined and advantages are adopted in the settings of Recommender System. Finally, this research provides the framework of Recommender System, which combines the design and test of algorithms and the existing tourism information recommendation websites. This system allows customers to broaden experience of tourism information service and make tourism decisions more accurately and rapidly. Keywords: Tourism information service, Personalized recommendation, Intelligence recommendation module, Apriori algorith

Back-stepping variable structure controller design for off-road intelligent vehicle

In this paper, off-road path recognition and navigation control method are studied to realize intelligent vehicle autonomous driving in unstructured environment. Firstly, the traversable path is achieved by vision and laser sensors. The vehicle steering and driving coupled dynamic model is established. Secondly, a coordinated controller for steering and driving is proposed via the back-stepping variable structure control method, which can be used to deal with the unmatched uncertainties of the control system model. To reduce the chattering phenomenon caused by variable structure, the boundary layer approach is introduced. The results of simulation and off-road experiment show the effectiveness and robustness of the proposed controller

RecExplainer: Aligning Large Language Models for Recommendation Model Interpretability

Recommender systems are widely used in various online services, with embedding-based models being particularly popular due to their expressiveness in representing complex signals. However, these models often lack interpretability, making them less reliable and transparent for both users and developers. With the emergence of large language models (LLMs), we find that their capabilities in language expression, knowledge-aware reasoning, and instruction following are exceptionally powerful. Based on this, we propose a new model interpretation approach for recommender systems, by using LLMs as surrogate models and learn to mimic and comprehend target recommender models. Specifically, we introduce three alignment methods: behavior alignment, intention alignment, and hybrid alignment. Behavior alignment operates in the language space, representing user preferences and item information as text to learn the recommendation model's behavior; intention alignment works in the latent space of the recommendation model, using user and item representations to understand the model's behavior; hybrid alignment combines both language and latent spaces for alignment training. To demonstrate the effectiveness of our methods, we conduct evaluation from two perspectives: alignment effect, and explanation generation ability on three public datasets. Experimental results indicate that our approach effectively enables LLMs to comprehend the patterns of recommendation models and generate highly credible recommendation explanations.Comment: 12 pages, 8 figures, 4 table