KEADA: Identifying Key Classes in Software Systems Using Dynamic Analysis and Entropy-Based Metrics

Software maintenance is indispensable in the software development process. Developers need to spend a lot of time and energy to understand the software when maintaining the software, which increases the difficulty of software maintenance. It is a feasible method to understand the software through the key classes of the software. Identifying the key classes of the software can help developers understand the software more quickly. Existing techniques on key class identification mainly use static analysis techniques to extract software structure information. Such structure information may contain redundant relationships that may not exist when the software runs and ignores the actual interaction times between classes. In this paper, we propose an approach based on dynamic analysis and entropy-based metrics to identify key classes in the Java GUI software system, called KEADA (identifying KEy clAsses based on Dynamic Analysis and entropy-based metrics). First, KEADA extracts software structure information by recording the calling relationship between classes during the software running process; such structure information takes into account the actual interaction of classes. Second, KEADA represents the structure information as a weighted directed network and further calculates the importance of each node using an entropy-based metric OSE (One-order Structural Entropy). Third, KEADA ranks classes in descending order according to their OSE values and selects a small number of classes as the key class candidates. In order to verify the effectiveness of our approach, we conducted experiments on three Java GUI software systems and compared them with seven state-of-the-art approaches. We used the Friedman test to evaluate all approaches, and the results demonstrate that our approach performs best in all software systems.     

Amphotericin B and Curcumin Co-Loaded Porous Microparticles as a Sustained Release System against Candida albicans

Amphotericin B (AMB) is an antifungal drug used for serious fungal infections. However, AMB has adverse reactions such as nephrotoxicity, which limit the clinical application of AMB alone or in combination with other antifungal drugs. Nano or micro drug delivery systems (DDS) have been proven to be effective in reducing the toxic and side effects of drugs. Further, the combination of AMB with other compounds with antifungal activity, such as curcumin (CM), may enhance the synergistic effects. Herein, AMB and CM were co-loaded into porous poly (lactic-co-glycolic acid) (PLGA) microparticles (MPs) to prepare AMB/CM-PLGA MPs. The AMB/CM-PLGA MPs showed a remarkably reduced hemolysis (62.2 ± 0.6%) compared to AMB (80.9 ± 1.1%). The nephrotoxicity of AMB/CM-PLGA MPs is significantly lower than that of AMB. In vitro, AMB/CM-PLGA MPs had better inhibitory effects on the adhesion and biofilm formation of Candida albicans compared with AMB. Experiments on mice infected with C. albicans showed that AMB/CM-PLGA MPs have a better therapeutic effect than AMB in vivo. In summary, AMB/CM-PLGA MPs may be a novel and promising therapeutic candidate for fungal infection.     

后疫情背景下武汉创新生态修复与优化研究

2019年底出现的新冠疫情给武汉创新生态带来了明显的冲击,但是同样也带来了新的机遇.本文介绍了创新生态系统的提出和发展历程,梳理了国内外创新生态系统的理论框架及结构相关成果,通过整理分析生态修复相关文献,归纳类比出创新生态系统修复的方法与可行性.采用案例分析方法,通过收集二手数据,分析了武汉光谷创新生态系统受新冠疫情的...     

The Classification of Rice Blast Resistant Seed Based on Ranman Spectroscopy and SVM

Rice blast is a serious threat to rice yield. Breeding disease-resistant varieties is one of the most economical and effective ways to prevent damage from rice blast. The traditional identification of resistant rice seeds has some shortcoming, such as long possession time, high cost and complex operation. The purpose of this study was to develop an optimal prediction model for determining resistant rice seeds using Ranman spectroscopy. First, the support vector machine (SVM), BP neural network (BP) and probabilistic neural network (PNN) models were initially established on the original spectral data. Second, due to the recognition accuracy of the Raw-SVM model, the running time was fast. The support vector machine model was selected for optimization, and four improved support vector machine models (ABC-SVM (artificial bee colony algorithm, ABC), IABC-SVM (improving the artificial bee colony algorithm, IABC), GSA-SVM (gravity search algorithm, GSA) and GWO-SVM (gray wolf algorithm, GWO)) were used to identify resistant rice seeds. The difference in modeling accuracy and running time between the improved support vector machine model established in feature wavelengths and full wavelengths (200–3202 cm⁻¹) was compared. Finally, five spectral preproccessing algorithms, Savitzky–Golay 1-Der (SGD), Savitzky–Golay Smoothing (SGS), baseline (Base), multivariate scatter correction (MSC) and standard normal variable (SNV), were used to preprocess the original spectra. The random forest algorithm (RF) was used to extract the characteristic wavelengths. After different spectral preproccessing algorithms and the RF feature extraction, the improved support vector machine models were established. The results show that the recognition accuracy of the optimal IABC-SVM model based on the original data was 71%. Among the five spectral preproccessing algorithms, the SNV algorithm’s accuracy was the best. The accuracy of the test set in the IABC-SVM model was 100%, and the running time was 13 s. After SNV algorithms and the RF feature extraction, the classification accuracy of the IABC-SVM model did not decrease, and the running time was shortened to 9 s. This demonstrates the feasibility and effectiveness of IABC in SVM parameter optimization, with higher prediction accuracy and better stability. Therefore, the improved support vector machine model based on Ranman spectroscopy can be applied to the fast and non-destructive identification of resistant rice seeds.     

Investigation on the Peroxidase-like Activity of Vitamin B6 and Its Applications in Colorimetric Detection of Hydrogen Peroxide and Total Antioxidant Capacity Evaluation

The peroxidase-like activity of vitamin B6 (VB6) was firstly demonstrated by catalyzing the peroxidase chromogenic substrate 3,3′,5,5′-tetramethylbenzidine (TMB) at the existence of H₂O₂. The influence of different factors on the catalytic property of VB6, including pH, temperature, VB6 concentration, and incubation time, were investigated. The steady-state kinetic study results indicate that VB6 possesses higher affinity to H₂O₂ than natural horseradish peroxidase and some other peroxidase mimics. Besides, the radical quenching experiment results confirm that hydroxyl radical (•OH) accounts for the catalytic process. Based on the excellent peroxidase-like catalytic activity of VB6, the colorimetric methods for H₂O₂ and gallic acid (GA) detection were developed by measuring the absorbance variance of the catalytic system. Under the optimal conditions, the linear ranges of the methods for H₂O₂ and GA determination with good selectivity are 50.0–600.0 μM and 10.0–50.0 μM, respectively. In addition, the developed method was applied in the detection of H₂O₂ in milk samples and evaluation of total antioxidant capacity of different tea infusions. This study may broaden the application prospect of VB6 in environmental and biomedical analysis fields, contribute to profound insight of the physiological functions of VB6, as well as lay foundation for further excavation of small-molecule peroxidase mimics.     

广义D4模

作为D4模的真推广,引入了广义D4模的概念.研究了这类模的基本性质,给出了广义D4模的一些等价条件.进一步引入了拟D4模的概念,证明了环R是半单环当且仅当每个R-模是拟D4模.     

埋地热油管道正常运行的数值模拟研究

对高凝原油管道输送的水力热力问题进行分析研究,掌握管线运行规律,保证管线安全经济运行有着重要意义.本文建立了埋地热油管道正常运行的数学模型,采用非结构化网格和有限容积法对该问题进行了研究,计算结果与实验测量值吻合良好.     

JY633抢险挖掘机辅助燃油系统设计

持续工作时间是各类抢险挖掘机最重要的性能指标之一,文中对JY633型抢险挖掘机辅助燃油系统进行了结构设计和控制分析.首先分析了原燃油供给系统结构原理以及对挖掘机持续工作时间的影响,然后再不改变原结构空间前提下设计了辅助燃油系统的总体结构,电气布置与控制程序.并对两种结构系统进行了对比分析.研究结论表明通过在JY633抢险挖掘机增设辅助燃油供给及控制系统,可使挖掘机结构得到优化,空间利用率提高,持续工作时间延长一倍以上,大幅提升了JY633抢险挖掘机的工作效率及持续工作时间.     

基于Web的实验中心物资管理系统的开发

设计开发了BS结构Web系统的实验中心物资管理系统,介绍了该系统的模块构成及设计实现方法,讨论了系统在开发中需解决的关键技术.该系统通过网络管理实验物资确保了物资数据的实时性、准确性,提高了物资管理效率.系统采用了基于Java的J2EE相关技术,如EJB(Enterprise Java Bean)、JSF (JavaServer Faces).利用UML(Unified Modeling Language)技术进行面向对象的分析和设计,因此系统有良好的可维护性和可扩展性.     

探空火箭图像系统设计方案

作为一种理想的实验平台,探空火箭在微重力科学领域得到了广泛应用.利用探空火箭进行微重力实验,首要问题是如何将实验过程以图像形式记录下来,并实时传回地面台站.因此,箭上图像的实时采集、编码与传输对实验具有重要作用.根据探空火箭微重力实验图像的特点,提出了一种适合于探空火箭图像系统的设计方案.按照该方案,图像系统可根据微重力实验的科学需求.实时调节实验图像的分辨率、帧速率和压缩比.该系统中,箭上的小波压缩单是核心模块,它通过基于硬件级的小波自适应压缩算法实现图像的箭上实时压缩,解码和实时显示则由与之对应的地面小波解码单元完成.