大钟拍声的调整实验

阐明大钟拍声的发生原理,并用大钟振动拍的理论模型说明拍的特性,从而控制清晰度。在实验钟上附着质量,根据附着质量位置的不同,能够改变其非对称程度,进一步调整拍频,同时用有限元分析的结果和等效圆环理论计算的结果对比。引入等效圆环理论,通过建立一个理论上带有附加质量的轴对称圆环模型,达到具有和大钟相同的振动特性。根据模型,得到在大钟特定位置附加的质量,以同时调整所需拍的清晰度和周期。在大钟多个位置进行实验,并进行有限元分析,验证了该理论的有效性。     

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.     

Joint Optimization of Control Strategy and Energy Consumption for Energy Harvesting WSAN

With the rapid development of wireless sensor technology, recent progress in wireless sensor and actuator networks (WSANs) with energy harvesting provide the possibility for various real-time applications. Meanwhile, extensive research activities are carried out in the fields of efficient energy allocation and control strategy design. However, the joint design considering physical plant control, energy harvesting, and consumption is rarely concerned in existing works. In this paper, in order to enhance system control stability and promote quality of service for the WSAN energy efficiency, a novel three-step joint optimization algorithm is proposed through control strategy and energy management analysis. First, the optimal sampling interval can be obtained based on energy harvesting, consumption, and remaining conditions. Then, the control gain for each sampling interval is derived by using a backward iteration. Finally, the optimal control strategy is determined as a linear function of the current plant states and previous control strategies. The application of UAV formation flight system demonstrates that better system performance and control stability can be achieved by the proposed joint optimization design for all poor, sufficient, and general energy harvesting scenarios.     

Thermo-Responsive ZnPc-g-TiO2-g-PNIPAM Photocatalysts Sensitized with Phthalocyanines for Water Purification under Visible Light

A novel thermo-responsive 2,9(10),16(17),23(24)-tetrakis[(3-carboxyacrylamide) phthalocyaninato] zinc (ZnPc)-g-TiO₂-g-poly(N-isopropylacrylamide) (PNIPAM) photocatalyst modified with phthalocyanines was prepared. The photocatalyst exhibited thermo-responsive properties due to the introduction of PNIPAM, which performed recovery for reuse above the lower critical solution temperature (LCST, about 26 °C). ZnPc-g-TiO₂-g-PNIPAM effectively expanded the light response range to the visible light region and inhibited the recombination of electron–hole pairs, which enhanced the performance of the photocatalyst. As expected, ZnPc-g-TiO₂-g-PNIPAM (0.3 g/L) exhibited excellent photocatalytic performance for the removal of Rhodamine B (RhB, 1.0 × 10⁻⁵ mol/L) and methylene blue (MB, 1.0 × 10⁻⁵ mol/L) under visible light, which reached 97.2% and 88.6% at 20 °C within 40 min, respectively. Furthermore, the influence of temperature upon photocatalytic performance was also investigated. When the temperature increased from 20 °C to 45 °C, the removal of RhB decreased by approximately 53.8%. The stability of the photocatalyst demonstrated that the photocatalytic activity was still above 80% for the removal of RhB after 3 cycles. Above all, this work provided an intelligent thermally responsive photocatalyst based on phthalocyanine for water purification under visible light.     

Designable assembly of atomically precise Al4O4 cubane supported mesoporous heterometallic architectures

Heterometallic cluster-based framework materials are of interest in terms of both their porous structures and multi-metallic reactivity. However, such materials have not yet been extensively investigated because of difficulties in their synthesis and structural characterization. Herein, we reported the designable synthesis of atomically precise heterometallic cluster-based framework compounds and their application as catalysts in aldol reactions. By using the synergistic coordination protocol, we successfully isolated a broad range of compounds with the general formula, [Al₄M₄O₄(L)₁₂(DABCO)₂] (L = carboxylates; DABCO = 1,4-diazabicyclo[2.2.2]-octane; M²⁺ = Co²⁺, Mn²⁺, Zn²⁺, Fe²⁺, Cd²⁺). The basic heterometallic building blocks contain unprecedented main-group γ-alumina moieties and surrounding unsaturated transition metal centers. Interestingly, the porosity and interpenetration of these frameworks can be rationally regulated through the unprecedented strategy of increment of the metal radius in addition to general introduction of sterically bulky groups on the ligand. Furthermore, these porous materials are effective catalysts for aldol reactions. This work provides a catalytic molecular model platform with accurate molecular bonding between the supporters and catalytically active metal ions.

Mesoporous heterometallic architectures are designed by the incorporation of the Al₄O₄ cubane and interpenetration regulation.     

A molecular mechanics approach for analyzing tensile nonlinear deformation behavior of single-walled carbon nanotubes

In this paper, by capturing the atomic information and reflecting the behaviour governed by the nonlinear potential function, an analytical molecular mechanics approach is proposed. A constitutive relation for single-walled carbon nanotubes (SWCNT’s) is established to describe the nonlinear stress-strain curve of SWCNT’s and to predict both the elastic properties and breaking strain of SWCNT’s during tensile deformation. An analysis based on the virtual internal bond (VIB) model proposed by P. Zhang et al. is also presented for comparison. The results indicate that the proposed molecular mechanics approach is indeed an acceptable analytical method for analyzing the mechanical behavior of SWCNT’s. The project supported by the National Natural Science Foundation of China (10121202, 90305015 and 10328203), the Key Grant Project of Chinese Ministry of Education (0306) and the Research Grants Council of the Hong Kong Special Administrative Region, China (HKU 7195/04E).     

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.     

PTC: Full and Drift Particle Orbit Tracing Code for α Particles in Tokamak Plasmas

Fusion born α particle confinement is one of the most important issues in burning plasmas,such as ITER and CFETR.However,it is extremely complex due to the nonequilibrium characteristics,and multiple temporal and spatial scales coupling with background plasma.A numerical code using particle orbit tracing method(PTC)has been developed to study energetic particle confinement in tokamak plasmas.Both full orbit and drift orbit solvers are implemented to analyze the Larmor radius effects on α particle confinement.The elastic collisions between alpha particles and thermal plasma are calculated by a Monte Carlo method.A triangle mesh in poloidal section is generated for electromagnetic fields expression.Benchmark between PTC and ORBIT has been accomplished for verification.For CFETR burning plasmas,PTC code is used for α particle source and slowing down process calculation in 2D equilibrium.In future work,3D field like toroidal field ripples,Alfven and magnetohydrodynamics instabilities perturbation inducing α particle transport will be analyzed.     

Characterization of Roasting Time on Sensory Quality,Color, Taste,and Nonvolatile Compounds of Yuan An Yellow Tea

Roasting is crucial for producing Yuan An yellow tea (YAYT) as it substantially affects sensory quality. However, the effect of roasting time on YAYT flavor quality is not clear. To investigate the effect of roasting time on the sensory qualities, chemical components, odor profiles, and metabolic profile of YAYTs produced with 13 min roasting, 16 min roasting, 19 min roasting, 22 min roasting, and 25 min roasting were determined. The YAYTs roasted for 22 min got higher sensory scores and better chemical qualities, such as the content of gallocatechin (GC), gallocatechin gallate (GCG), free amino acids, solutable sugar, meanwhile the lightness decreased, the hue of tea brew color (b) increased, which meant the tea brew got darker and yellower. YAYTs roasted for 22 min also increased the contents of key odorants, such as benzaldehyde, nonanal, β-cyclocitral, linalool, nerol, α-cedrol, β-ionone, limonene, 2-methylfuran, indole, and longiborneol. Moreover, non-targeted metabolomics identified up to 14 differentially expressed metabolites through pair-wise comparisons, such as flavonoids, phenolic acids, sucrose, and critical metabolites, which were the main components corresponding to YAYT roasted for 22 min. In summary, the current results provide scientific guidance for the production of high quality YAYT.     

Rutin Inhibits Ox-LDL-Mediated Macrophage Inflammation and Foam Cell Formation by Inducing Autophagy and Modulating PI3K/ATK Signaling

Atherosclerosis (AS) is one of the leading causes of death among the elderly, and is primarily caused by foam cell generation and macrophage inflammation. Rutin is an anti-inflammatory, anti-oxidant, anti-allergic, and antiviral flavonoid molecule, known to have anti-atherosclerotic and autophagy-inducing properties, but its biological mechanism remains poorly understood. In this study, we uncovered that rutin could suppress the generation of inflammatory factors and reactive oxygen species (ROS) in ox-LDL-induced M2 macrophages and enhance their polarization. Moreover, rutin could decrease foam cell production, as shown by oil red O staining. In addition, rutin could increase the number of autophagosomes and the LC3II/I ratio, while lowering p62 expression. Furthermore, rutin could significantly inhibit the PI3K/ATK signaling pathway. In summary, rutin inhibits ox-LDL-mediated macrophage inflammation and foam cell formation by inducing autophagy and modulating PI3K/ATK signaling, showing potential in treating atherosclerosis.