基于SSH框架的绿色大学评价支持系统研究

为了满足对绿色大学的系统综合评价要求,以达到能够对绿色大学建设效果进行有效评估的目的,并对绿色大学创建过程中的相关绿色属性进行有效的评价,综合运用J2EE技术,研究了基于轻量级Struts+Spring+Hibernate框架的绿色大学评价支持系统的构架方法,在开发过程中对整个系统进行了逻辑分层设计,表现层运用了Struts技术,业务逻辑层对协调分析方法、层次分析方法的算法进行了实现,并完成了J2EE与Matlab的交互,在数据持久层运用了对象关系映射的Hibernate技术,有效地实现了系统的基于开放Web的经济性、可移植性与可拓展性。     

基于Agent的分布仿真平台原型系统设计与实现

基于Agent的分布仿真是基于Agent的建模与仿真ABMS(Agent-Based Modeling and Simulation)研究的重要组成部分。在提出的基于Agent的分布仿真软件框架和通信系统设计与实现的基础上,利用Java语言和面向对象的方法设计实现了一个基于Agent的分布仿真平台原型系统ADSimE。介绍了该分布式仿真平台的主要实现细节,给出了全系统的主要Agent类的UML设计,重点阐述了保守策略仿真Agent类和乐观策略仿真Agent类的设计、处理流程以及KQML消息解析的实现细节。最后以闭合排队网络为例,说明了怎样在该环境下进行基于Agent的分布仿真应用的开发。     

基于模板法的网页英语试卷自动抽取技术的研究

为解决在线考试系统中建立海量数据库的问题,采用基于模板法的Web信息抽取方法,提取相似网页中的正文内容。并根据包含英文试卷的网页特点,制定正文抽取规则,最终可获得完整的英语试卷及其答案。实验结果表明,该方法具有较高的准确率和提取速度。     

Amine Coordinated Electron-Rich Palladium Nanoparticles for Electrochemical Hydrogenation of Benzaldehyde

Electrocatalytic hydrogenation (ECH) is a burgeoning strategy for the sustainable utilization of hydrogen. However, how to effectively suppress the competitive hydrogen evolution reaction (HER) is a big challenge to ECH catalysis. In this study, amine (NH₂ R)-coordinated Pd nanoparticles loaded on carbon felt (Pd@CF) as a catalyst is successfully synthesized by a one-step solvothermal reduction method using oleylamine as the reducing agent. An exceptional ECH reactivity on benzaldehyde is achieved on the optimal Pd@CF catalyst in terms of a high conversion (89.7%) and selectivity toward benzyl alcohol (89.8%) at −0.4 V in 60 min. Notably, the Faradaic efficiency for producing benzyl alcohol is up to 90.2%, much higher than that catalyzed by Pd@CF-without N-group (41.1%) and thecommercial Pd/C (20.9%). The excellent ECH performance of Pd@CF can be attributed to the enriched electrons on Pd surface resulted from the introduction of NH₂ R groups, which strengthens both the adsorption of benzaldehyde and the adsorbed hydrogen (H_ads) on Pd, preventing the combination of H_ads to form H₂, that is, inhibiting the HER. This study gives a new insight into design principles of highly efficient electrocatalysts for the hydrogenation of unsaturated aldehydes molecules.     

Macrophage Membrane-Coated Nanoparticles Sensitize Glioblastoma to Radiation by Suppressing Proneural–Mesenchymal Transformation in Glioma Stem Cells

Radiotherapy is identified as a crucial treatment for patients with glioblastoma, but recurrence is inevitable. The efficacy of radiotherapy is severely hampered partially due to the tumor evolution. Growing evidence suggests that proneural glioma stem cells can acquire mesenchymal features coupled with increased radioresistance. Thus, a better understanding of mechanisms underlying tumor subclonal evolution may develop new strategies. Herein, data highlighting a positive correlation between the accumulation of macrophage in the glioblastoma microenvironment after irradiation and mesenchymal transdifferentiation in glioblastoma are presented. Mechanistically, elevated production of inflammatory cytokines released by macrophages promotes mesenchymal transition in an NF-κB-dependent manner. Hence, rationally designed macrophage membrane-coated porous mesoporous silica nanoparticles (MMNs) in which therapeutic anti-NF-κB peptides are loaded for enhancing radiotherapy of glioblastoma are constructed. The combination of MMNs and fractionated irradiation results in the blockage of tumor evolution and therapy resistance in glioblastoma-bearing mice. Intriguingly, the macrophage invasion across the blood-brain barrier is inhibited competitively by MMNs, suggesting that these nanoparticles can fundamentally halt the evolution of radioresistant clones. Taken together, the biomimetic MMNs represent a promising strategy that prevents mesenchymal transition and improves therapeutic response to irradiation as well as overall survival in patients with glioblastoma.     

Implicit discrete-time fast terminal sliding mode control with disturbance compensation

In this paper, an implicit discrete-time fast terminal sliding mode (DFTSM) control with disturbance compensation is designed and analyzed for uncertain high-order systems. First, a recursive discrete sliding surface is constructed based on implicit Euler technique, which can completely eliminate discretization chattering so as to significantly reduce the boundary layer of sliding mode motion. With the help of a high-order disturbance compensator, the accuracy limitation of implicit DFTSM control systems is overcome by increasing the order of sliding mode. Then the finite-time convergence of implicit DFTSM is proved for the first time, and the influence relationship of control parameters on the convergence speed and control accuracy of the algorithm is established. Finally, two numerical examples are provided to demonstrate the effectiveness and superiority of the proposed design approach.     

Social-path embedding-based transformer for graduation development prediction

As the education of students attracts more and more attention, the task of graduation development prediction has gradually become a hot topic in academia and industry. The task of graduation development prediction aims to predict the employment category of students in advance via academic achievement data, which can help administrators understand students’ learning status and set up a reasonable learning plan. However, existing research ignores the potential impact of social relationships on students’ graduation development choices. To fully explore social relationships among students, we propose a Social-path Embedding-based Transformer Neural Network (SPE-TNN) for the task of graduation development prediction in this paper. Specifically, SPE-TNN is divided into the Social-path selection layer, the Social-path embedding layer, the Transformer layer, and the Multi-layer projection layer. Firstly, the Social-path selection layer is designed to find social relationships that impact graduation development and embed them into the student’s performance features through the Social-path embedding layer. Secondly, the Transformer layer is adopted to balance the weights of the students’ features. Finally, the Multi-layer projection layer is used to achieve the student graduation development prediction. Experimental results on the real-world datasets show that SPE-TNN outperforms the existing popular approaches.

     

Multi-scale Interest Dynamic Hierarchical Transformer for sequential recommendation

Neural Computing and Applications - Existing sequential recommendation methods focus on modeling the temporal relationships of users’ historical behaviors and excel in exploiting users’...     

Reducing Hysteresis and Enhancing Performance of Perovskite Solar Cells Using Low‐Temperature Processed Y‐Doped SnO2 Nanosheets as Electron Selective Layers

Despite the rapid increase of efficiency, perovskite solar cells (PSCs) still face some challenges, one of which is the current–voltage hysteresis. Herein, it is reported that yttrium‐doped tin dioxide (Y‐SnO₂) electron selective layer (ESL) synthesized by an in situ hydrothermal growth process at 95 °C can significantly reduce the hysteresis and improve the performance of PSCs. Comparison studies reveal two main effects of Y doping of SnO₂ ESLs: (1) it promotes the formation of well‐aligned and more homogeneous distribution of SnO₂ nanosheet arrays (NSAs), which allows better perovskite infiltration, better contacts of perovskite with SnO₂ nanosheets, and improves electron transfer from perovskite to ESL; (2) it enlarges the band gap and upshifts the band energy levels, resulting in better energy level alignment with perovskite and reduced charge recombination at NSA/perovskite interfaces. As a result, PSCs using Y‐SnO₂ NSA ESLs exhibit much less hysteresis and better performance compared with the cells using pristine SnO₂ NSA ESLs. The champion cell using Y‐SnO₂ NSA ESL achieves a photovoltaic conversion efficiency of 17.29% (16.97%) when measured under reverse (forward) voltage scanning and a steady‐state efficiency of 16.25%. The results suggest that low‐temperature hydrothermal‐synthesized Y‐SnO₂ NSA is a promising ESL for fabricating efficient and hysteresis‐less PSC.     

One weight $$\mathbb {Z}_2\mathbb {Z}_4$$ additive codes

We study one weight \(\mathbb {Z}_2\mathbb {Z}_4\) additive codes. It is shown that the image of an equidistant \(\mathbb {Z}_2\mathbb {Z}_4\) code is a binary equidistant code and that the image of a one weight \(\mathbb {Z}_2\mathbb {Z}_4\) additive code, with nontrivial binary part, is a linear binary one weight code. The structure and possible weights for all one weight \(\mathbb {Z}_2\mathbb {Z}_4\) additive codes are described. Additionally, a lower bound for the minimum distance of dual codes of one weight additive codes is obtained.