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.     

Carbene-catalyzed activation of cyclopropylcarbaldehydes for mannich reaction and δ-lactam formation: remote enantioselecitvity control and dynamic kinetic asymmetric transformation

An N-heterocyclic carbene(NHC)-catalyzed enantioselective Mannich reaction of the remote γ-carbon of cyclopropylcarbaldehydes is disclosed for the first time. Diastereo-and enantiomerically enriched multicyclic δ-lactam compound is afforded as the main product from 8 possible stereo-specific isomers through dynamic kinetic asymmetric transformation(DYKAT) processes. Multiple chiral functional molecules can be afforded from the lactam products through simple protocols with retentions of the optical purities.     

The Effects of Charged Amino Acid Side-Chain Length on Diagonal Cross-Strand Interactions between Carboxylate- and Ammonium-Containing Residues in a β-Hairpin

The β-sheet is one of the common protein secondary structures, and the aberrant aggregation of β-sheets is implicated in various neurodegenerative diseases. Cross-strand interactions are an important determinant of β-sheet stability. Accordingly, both diagonal and lateral cross-strand interactions have been studied. Surprisingly, diagonal cross-strand ion-pairing interactions have yet to be investigated. Herein, we present a systematic study on the effects of charged amino acid side-chain length on a diagonal ion-pairing interaction between carboxylate- and ammonium-containing residues in a β-hairpin. To this end, 2D-NMR was used to investigate the conformation of the peptides. The fraction folded population and the folding free energy were derived from the chemical shift data. The fraction folded population for these peptides with potential diagonal ion pairs was mostly lower compared to the corresponding peptide with a potential lateral ion pair. The diagonal ion-pairing interaction energy was derived using double mutant cycle analysis. The Asp2-Dab9 (Asp: one methylene; Dab: two methylenes) interaction was the most stabilizing (−0.79 ± 0.14 kcal/mol), most likely representing an optimal balance between the entropic penalty to enable the ion-pairing interaction and the number of side-chain conformations that can accommodate the interaction. These results should be useful for designing β-sheet containing molecular entities for various applications.     

Spider Silk-Improved Quartz-Enhanced Conductance Spectroscopy for Medical Mask Humidity Sensing

Spider silk is one of the hottest biomaterials researched currently, due to its excellent mechanical properties. This work reports a novel humidity sensing platform based on a spider silk-modified quartz tuning fork (SSM-QTF). Since spider silk is a kind of natural moisture-sensitive material, it does not demand additional sensitization. Quartz-enhanced conductance spectroscopy (QECS) was combined with the SSM-QTF to access humidity sensing sensitively. The results indicate that the resonance frequency of the SSM-QTF decreased monotonously with the ambient humidity. The detection sensitivity of the proposed SSM-QTF sensor was 12.7 ppm at 1 min. The SSM-QTF sensor showed good linearity of ~0.99. Using this sensor, we successfully measured the humidity of disposable medical masks for different periods of wearing time. The results showed that even a 20 min wearing time can lead to a >70% humidity in the mask enclosed space. It is suggested that a disposable medical mask should be changed <2 h.     

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.     

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.     

Linker engineering in metal–organic frameworks for dark photocatalysis

Dark reactions featuring continuous activity under light off conditions play a critical role in natural photosynthesis. However, most artificial photocatalysts are inactive upon the removal of the light source, and the artificial photocatalysts with dark photocatalysis abilities have been rarely explored. Herein, we report a Ti-based metal–organic framework (MOF), MIL-125, exhibiting the capability of dark photocatalytic hydrogen production. Remarkably, the introduction of different functional groups onto the linkers enables distinctly different activities of the resulting MOFs (MIL-125-X, X = NH₂, NO₂, Br). Dynamic and thermodynamic investigations indicate that the production and lifetime of the Ti³⁺ intermediate are the key factors, due to the electron-donating/-withdrawing effect of the functional groups. As far as we know, this is the first report on dark photocatalysis over MOFs, providing new insights into the storage of irradiation energy and demonstrating their great potential in dark photocatalysis due to the great MOF diversity.

A Ti-based MOF with long-lived Ti³⁺ can achieve dark photocatalysis. The different groups on the organic linker modulate electron storage ability and the lifetime of Ti³⁺, significantly regulating dark photocatalytic activity in H₂ production.     

Synthesis,Structure and Theoretical Calculation of Metal Coordination Polymers with Pyrazine-2,3-dicarboxylic Acid and 3-(2-Pyridyl)pyrazole as Co-ligands

A new 2D metal coordination polymer(MCP),[Mn(pzdc)_(0.5)(L)]_n(1,pzdc=pyrazine-2,3-dicarboxylic acid,HL=3-(2-pyridyl)pyrazole),was synthesized under hydrothermal conditions and characterized by single-crystal X-ray diffraction,powder XRD,FT-IR,TG,fluorescence and elemental analysis techniques.Pale yellow crystals crystallize in orthorhombic system,space group Fdd2 with a=11.2368(6),b=38.280(2),c=10.5682(6) A,V=4545.9(4) A~3,C_(11)H_7MnN_4O_2,M_r=282.15,D_c=1.649 g/cm~3,μ(MoKα)=1.159 mm~(-1),F(000)=2272,Z=16,the final R=0.0613 and wR=0.1773 for 2856 observed reflections(I 2σ(I)).It shows a two-dimensional network structure and is further assembled into a three-dimensional supramolecular framework via hydrogen bonds and abundant π-π interactions.In addition,we analyzed natural bond orbital(NBO) of 1 in using the PBE0/LANL2DZ method established in Gaussian 03 Program.There is obvious covalent interaction between the coordinated atoms and Mn(Ⅱ) ions.     

氧化镍纳米晶体的制备及其过程分析

为了开发一种新型纳米氧化镍催化剂,能有效地应用于生物质气化过程中去除焦油,本文采用均匀沉淀法成功地制备了纳米氧化镍晶体,并利用TGA、FTIR、XRD、BET、YEM等分析手段对前驱体和产品的性能进行了表征.同时,对前驱体的分解过程进行了全面的分析.分析结果表明前驱体是水合碱式碳酸镍,其分子式为NiCO3·Ni(OH)2·nH2O,它能在360℃下完全分解转化为纳米NiO,同时煅烧条件对合成纳米NiO的晶体粒径影响很大.实验证实所得纳米NiO颗粒呈球形,分散性好,纯度较高,属立方晶系结构,平均粒径约为7.5nm,其BET表面积为187.98m2/g,这显示纳米NiO晶体具有作为高效催化材料的应用可行性.     

低温制备≥99.5%多晶氧化铝陶瓷

本文通过引入稀土氧化物Y2O3、Tm2O3为烧结助剂低温制备了氧化铝含量大于99.5;的多晶氧化铝陶瓷.实验表明:稀土氧化物的加入能够明显降低99.5;多晶氧化铝陶瓷的烧结温度,提高致密度.Y2O3、Tm2O3混合烧结助剂与单一稀土氧化物的烧结助剂相比能够明显抑制晶粒的生长,促进晶粒的均匀发育.当Y2O3+Tm2O3的含量为0.3;质量分数时,99.5;多晶氧化铝陶瓷的相对密度可达99.2;理论密度,抗弯强度为533MPa,显微硬度为17.2GPa.陶瓷断裂主要以穿晶断裂为主.