天线微小通道冷板金属3D打印成型工艺研究

微小通道液冷散热技术是近年来电子设备热设计研究的热点,已逐渐用于高热流密度电子设备的散热.然而真正实现工程应用仍存在诸多困难,主要原因是与之匹配的工艺技术尚不完善.研究了天线铝合金微小通道冷板的3D打印技术,介绍了工艺流程,加工了多个小尺寸芯片和大尺寸天线阵面的微小通道冷板.经分析,3 D打印基本能实现复杂拓扑结构微小通道冷板的成型,但仍存在通道堵塞、表面凹坑和翘曲等问题.     

Imbalanced SVM‐Based Anomaly Detection Algorithm for Imbalanced Training Datasets

Abnormal samples are usually difficult to obtain in production systems, resulting in imbalanced training sample sets. Namely, the number of positive samples is far less than the number of negative samples. Traditional Support Vector Machine (SVM)‐based anomaly detection algorithms perform poorly for highly imbalanced datasets: the learned classification hyperplane skews toward the positive samples, resulting in a high false‐negative rate. This article proposes a new imbalanced SVM (termed ImSVM)‐based anomaly detection algorithm, which assigns a different weight for each positive support vector in the decision function. ImSVM adjusts the learned classification hyperplane to make the decision function achieve a maximum GMean measure value on the dataset. The above problem is converted into an unconstrained optimization problem to search the optimal weight vector. Experiments are carried out on both Cloud datasets and Knowledge Discovery and Data Mining datasets to evaluate ImSVM. Highly imbalanced training sample sets are constructed. The experimental results show that ImSVM outperforms over‐sampling techniques and several existing imbalanced SVM‐based techniques.     

Inkjet-printed platinum counter electrodes for dye-sensitized solar cells

In this study, inkjet printing method was successfully demonstrated to produce catalytic platinum layers for dye-sensitized solar cells. Our work includes meticulous optical, morphological, and electrocatalytical analyses of precisely inkjet-patterned counter electrodes as well as traditionally drop-cast samples. Similar catalytic performance was obtained with both methods (Rʹ_CT = 1.2 Ω cm² for drop-cast and Rʹ_CT = 1.6 Ω cm² for inkjet-printed) at same Pt loading (ca. 2.5 μg/cm²), and correspondingly almost same cell efficiencies (η_drop-cast = 6.5% and η_printed = 6.7%). All the cells exhibited high stability by keeping their efficiencies after being subjected to a 1000 h aging test under 1 Sun and 35 °C at the open circuit condition. These results highlight the potential of inkjet printing to realize precisely patterned and no-material-wasting counter electrodes by controlled dispensing of the functional solution.     

机械转向器冲击试验台建模与仿真

EPS用机械转向器是实现汽车转向的主要部件之一,为保证车辆的安全性,需要了解其耐冲击性能。机械转向器冲击试验台主要用于对机械转向器的冲击试验。文中针对试验台的阀控非对称缸电液力控制系统进行了研究。通过对控制系统的各个环节的分析,建立各环节的数学模型。进而得到以力为控制量的电液伺服系统数学模型,并利用Simulink仿真平台对系统进行频域和时域分析。分析结果表明,试验台液压系统闭环稳定,满足性能要求。     

Research on Link Quality Estimation Mechanism for Wireless Sensor Networks Based on Support Vector Machine

In the application of Wireless sensor net-works (WSNs), effective estimation for link quality is a basic issue in guarantying reliable data transmission and upper network protocol performance. A link quality es-timation mechanism is proposed, which is based on Sup-port vector machine (SVM) with multi-class classification. Under the analysis of the wireless link characteristics, two physical parameters of communication, Receive sig-nal strength indicator (RSSI) and Link quality indicator (LQI), are chosen as estimation parameters. The link qual-ity is divided into five levels according to Packet recep-tion rate (PRR). A link quality estimation model based on SVM with decision tree is established. The model is built on kernel functions of radial basis and polynomial re-spectively, in which RSSI, LQI are the input parameters. The experimental results show that the model is reason-able. Compared with the recent published link quality es-timation models, our model can estimate the current link quality accurately with a relative small number of probe packets, so that it costs less energy consumption than the one caused by sending a large number of probe packets. So this model which is high efficiency and energy saving can prolong the network life.     

Fabrication of polymer microsphere particle standards containing trace explosives using an oil/water emulsion solvent extraction piezoelectric printing process

We present a methodology for fabricating polymer microspheres using inkjet printing of a biodegradable polymer containing either high explosives or high explosive simulant. Poly(dl-lactide/glycolide) 85:15 (PLGA) microsphere production is based on an oil/water emulsion solvent extraction process. The inkjet printing process allows for precise control of the microsphere diameter and the chemical composition. The microspheres can be used as calibrants or verification standards for explosives trace detection instruments. Gas chromatography/mass spectrometry analysis demonstrated that the composition of the microspheres was consistent with predicted concentrations based on the amount of analyte incorporated into the polymer solution and the inkjet operating parameters. We have demonstrated that the microspheres can be fabricated with a mass fraction of 70% of an analyte compound.     

Prediction of protein structural class using novel evolutionary collocation-based sequence representation

Knowledge of structural classes is useful in understanding of folding patterns in proteins. Although existing structural class prediction methods applied virtually all state-of-the-art classifiers, many of them use a relatively simple protein sequence representation that often includes amino acid (AA) composition. To this end, we propose a novel sequence representation that incorporates evolutionary information encoded using PSI-BLAST profile-based collocation of AA pairs. We used six benchmark datasets and five representative classifiers to quantify and compare the quality of the structural class prediction with the proposed representation. The best, classifier support vector machine achieved 61-96% accuracy on the six datasets. These predictions were comprehensively compared with a wide range of recently proposed methods for prediction of structural classes. Our comprehensive comparison shows superiority of the proposed representation, which results in error rate reductions that range between 14% and 26% when compared with predictions of the best-performing, previously published classifiers on the considered datasets. The study also shows that, for the benchmark dataset that includes sequences characterized by low identity (i.e., 25%, 30%, and 40%), the prediction accuracies are 20-35% lower than for the other three datasets that include sequences with a higher degree of similarity. In conclusion, the proposed representation is shown to substantially improve the accuracy of the structural class prediction. A web server that implements the presented prediction method is freely available at http://biomine.ece.ualberta.ca/Structural_Class/SCEC.html.     

Using support vector machine to predict beta- and gamma-turns in proteins

By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta- and gamma-turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the beta- and gamma-turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7-fold cross-validation are 79.8% and 0.47, respectively, for the beta-turns. The overall prediction accuracy in 5-fold cross-validation is 61.0% for the gamma-turns. These results are significantly higher than the other algorithms in the prediction of beta- and gamma-turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the beta- and gamma-turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns' prediction. To ensure the ability of the SVM method to correctly classify beta-turn and non-beta-turn (gamma-turn and non-gamma-turn), the receiver operating characteristic threshold independent measure curves are provided.     

分子机器的研究进展

结合分子机器的基本概念、合成方法、驱动方式以及研究的意义, 综述了近几年分子机器的研究进展, 探讨了下一步研究的发展方向.     

Cloud-Enabled Handheld NIR Spectroscopy: A Transformative Approach for Real-Time Forensic Analysis of Cannabis Specimens

In the past few years, there has been significant interest within the forensic community regarding the deployment of portable solutions that provide real-time results. This article introduces an innovative technology or technology architecture that enables the integration of a handheld device, specifically, Viavi MicroNIR, with a cloud-based system. This cloud system encompasses a server responsible for data processing and a mobile application acting as a user interface. To demonstrate the transformative impact of this technology on field operators, the analysis of cannabis specimens has been utilized. System's capacity to distinguish between CBD-type and THC-type cannabis has been particularly highlighted, along with the remarkable congruence observed between the near-infrared (NIR) spectra and the reference analytical method involving ultra-high-performance liquid chromatography (UHPLC) The article will present the advantages of this application primarily focusing on its potential to alleviate the burden on laboratories by expediting routine illicit drug analysis. Viavi MicroNIR technology provides laboratory personnel with additional time to handle more complex cases, thereby enhancing overall efficiency.