太赫兹辐射对小鼠脑神经发生及认知能力的影响

为探究太赫兹(THz)辐射对成年小鼠海马齿状回亚颗粒区(SGZ)神经发生及对老年小鼠认知能力的影响,用0.14 THz波照射实验小鼠头部,10 min/次,2次/d,连续21 d,对照组小鼠进行相同时间的麻醉。采用5-溴脱氧尿嘧啶核苷(BrdU)染色评价新生细胞存活、BrdU/神经元细胞核(NeuN)染色评价细胞分化、Morris水迷宫(MWM)实验分析认知能力变化。实验结果表明：与对照组相比,0.14 THz 10 min/次,2次/d,连续照射21 d后,可有效促进成年小鼠SGZ神经发生,成年小鼠SGZ新生细胞存活明显改善(P<0.05),新生细胞向神经元的分化无明显改变。老年小鼠逃避潜伏期,运动寻找轨迹无显著变化,THz辐射没有影响老年小鼠的空间认知能力。     

基于改进最小叉熵法的区域综合能源系统经济效益评估

区域综合能源系统（regional integrated energy system,RIES）作为一种可充分利用多种能源的有效网络形式,其组成方案的经济效益评估意义重大。文章首先分析了RIES的架构,搭建了RIES经济性评价指标体系。为提高指标权重融合的合理性,本文提出可反映主客观权重内在联系的改进最小叉熵法;并结合优劣解距离法（Technique for order preference by similarity to ideal solution,TOPSIS）和秩和比法（Rank-sum ratio,RSR）,进行RIES投资方案的多准则评价排序并选出最优方案。最后,选取某工业园区不同系统组成方案作为算例进行经济效益评估验证,结果证明了该方法的工程实用性,对RIES规划、改造等项目的经济效益评价具有一定的参考价值。     

一种具有不同功分比的同相平衡至单端式功分器北大核心CSCD

提出了一种具有同相输出和不同功分比的平衡至单端式功分器。该功分器由±90°和180°传输线以及一个电阻构成,能够实现对差模信号的不同功率分配。其电路参数通过S参数的约束条件进行求解。为了减少尺寸并增大最大功分比至11.6∶1,提出了两种耦合结构来实现-90°传输线结构。最终设计了功分比为1∶1和7∶1的两种原型,最小插入损耗为0.3~0.4 dB,并与现有设计进行了比较。实测工作带宽(S_(ddAA)<-20 dB)分别为14.3%和11.1%。在工作频带内,隔离和共模抑制均高于20 dB,两个输出端口之间的相位差为0°±5°。因此,所提出的具有不同功分比的设计具有尺寸小、共模抑制、易于同时与平衡和单端电路集成等优点。     

基于霍尼韦尔WEBs的智能照明监控实训系统设计与实现

从建筑智能化专业楼宇自控系统工程技术课程的实训教学实际需要出发,以模拟实际智能建筑现场为背景,开发了智能照明监控实训系统。该系统使用霍尼韦尔WEBs为系统软件平台,应用霍尼韦尔Spyder DDC控制器PUB6438S作为系统的控制核心,利用人体红外感应、定时时间和光照度感应等方法进行实时控制。学生利用该实训教学系统可以更加深刻理解楼控系统软硬件开发的整个生命周期,对楼控项目设计和编程调试等应用技术进行系统学习和实训,从而提升职业院校学生的工程实践能力。     

Friendly spectrum jamming against MIMO eavesdropping

Wireless Networks - Friendly spectrum jamming is a flexible scheme to establish secure communications among heterogeneous wireless devices without the need of encryption. Previous works have...     

Magnetically coupled oscillators applicable to high-sensitivity mass detection

Microsystem Technologies - In this paper, a system of magnetically coupled oscillators consisting of a Π-shaped horizontal cantilever and a rectangular vertical cantilever with a frequency...     

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.