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Vibration protection is a reliable mean for safe operation of rotating machinery. In recent years, vibration protection of pumped storage unit gets more and more complicated, resulting in an increasing risk of vibration protection system failure. Focusing on vibration characteristics of pumped-storage units, the inverse-time vibration protection method for of pumped-storage unit vibration was proposed based on material fatigue damage curve and linear damage accumulation theory. Difficulties of determining damage level were provided on applying damage accumulation method to vibration protection. Simplified inverse-time protection method for vibration of pumped-storage unit was proposed, and research showed that the inverse-time vibration protection threshold should be set according to vibration accumulation that avoids all unit transient processes, the reliability coefficient can be determined from 1.5 to 2.0, the refresh periods of unit vibration characteristic values do not affect the cumulative calculation results, and the inverse-time vibration protection has characteristic of self-returning. The vibration protection method based on inverse-time has the advantages of reliable principle, simple logic, and easy implementation. It has the characteristics of reliability, selectivity, sensitivity, and rapidity as relay protection in electric engineering. It solves the problem of dual parameter coordination in the traditional protection strategy with fixed threshold and fixed time delay. The method can be extended to the vibration protection of other types of rotating machinery.

The fractal property has been regarded as a fundamental property of complex networks, characterizing the self-similarity of a network. Such a property is usually numerically characterized by the fractal dimension metric, and it not only helps the understanding of the relationship between the structure and function of complex networks but also finds a wide range of applications in complex systems. The existing literature shows that class-level software networks (i.e., class dependency networks) are complex networks with the fractal property. However, the fractal property at the feature (i.e., methods and fields) level has never been investigated, although it is useful for measuring class complexity and predicting bugs in classes. Furthermore, existing studies on the fractal property of software systems were all performed on un-weighted software networks and have not been used in any practical quality assurance tasks such as bug prediction. Generally, considering the weights on edges can give us more accurate representations of the software structure and thus help us obtain more accurate results. The illustration of an approach's practical use can promote its adoption in practice. In this article, we examine the fractal property of classes by proposing a new metric. Specifically, we build a Feature-Level Software Network (FLSN) for each class to represent the methods/fields and their couplings (including coupling frequencies) within the class and propose a new metric, Fractal Dimension for Classes (FDC), to numerically describe the fractal property of classes using FLSNs, which captures class complexity. We evaluate FDC theoretically against Weyuker's nine properties, and the results show that FDC adheres to eight of the nine properties. Empirical experiments performed on a set of 12 large open source Java systems show that (i) for most classes (larger than \(96\%\)), there exists the fractal property in their FLSNs, (ii) FDC is capable of capturing additional aspects of class complexity that have not been addressed by existing complexity metrics, (iii) FDC significantly correlates with both the existing class-level complexity metrics and the number of bugs in classes, and (iv) FDC, when used together with existing class-level complexity metrics, can significantly improve bug prediction in classes in three scenarios (i.e., bug-count, bug-classification, and effort-aware) of the cross-project context, but in the within-project context, it cannot. [ABSTRACT FROM AUTHOR]

The performance of corrosion-induced cracking of reinforced concrete members under transverse constraints was studied. Based on the theory of elastic-plastic mechanics and the hypothesis of uniform corrosion of a steel bar, a three-layer hollow cylinder model was established to predict the critical corrosion of the steel bar at the time of the cracking of the concrete cover. Taking the constraint of stirrups on surrounding concrete into consideration, it can be used to predict the corrosion rate of members with stirrups at the time of the cracking of the concrete cover, which further expands the application range of the corrosion-induced cracking models of concrete. On this basis, the critical corrosion rate of concrete under different stirrup ratios at the time of cracking was measured. The calculated results of the model are in accordance with experimental data. For corner steel bars, when the stirrup spacing is less than 100 mm, the existence of stirrups can effectively delay the occurrence of rust expansion cracks and enhance the durability of the structure. On the basis of this study, the problem of corrosion expansion and cracking of the concrete cover caused by non-uniform corrosion of steel bars along longitudinal and radial directions needs to be further studied in the future. [ABSTRACT FROM AUTHOR]

Key class identification approaches aim at identifying the most important classes to help developers, especially newcomers, start the software comprehension process. So far, many supervised and unsupervised approaches have been proposed; however, they have not considered the effort to comprehend classes. In this article, we identify the challenge of "effort-aware key class identification"; to partially tackle it, we propose an approach, EASE, which is implemented through a modification to existing unsupervised key class identification approaches to take into consideration the effort to comprehend classes. First, EASE chooses a set of network metrics that has a wide range of applications in the existing unsupervised approaches and also possesses good discriminatory power. Second, EASE normalizes the network metric values of classes to quantify the probability of any class to be a key class and utilizes Cognitive Complexity to estimate the effort required to comprehend classes. Third, EASE proposes a metric, RKCP, to measure the relative key-class proneness of classes and further uses it to sort classes in descending order. Finally, an effort threshold is utilized, and the top-ranked classes within the threshold are identified as the cost-effective key classes. Empirical results on a set of 18 software systems show that (i) the proposed effort-aware variants perform significantly better in almost all (≈98.33%) the cases, (ii) they are superior to most of the baseline approaches with only several exceptions, and (iii) they are scalable to large-scale software systems. Based on these findings, we suggest that (i) we should resort to effort-aware key class identification techniques in budget-limited scenarios; and (ii) when using different techniques, we should carefully choose the weighting mechanism to obtain the best performance. [ABSTRACT FROM AUTHOR]

The lines optimisation of an underwater vehicle based on a dynamic surrogate model is studied. Four performances including rapidity, manoeuverability, energy consumption and structure of the underwater vehicle are considered in the optimisation framework constructed by a generalised collaborative optimisation method. Expert knowledge based analytic hierarchy process is conducted to obtain the optimisation object that involves the four performances. Numerical simulation is performed to accurately analyze the rapidity, manoeuverability and structure performances of the underwater vehicle. To reduce the calculation burden, dynamic surrogate models are proposed to replace numerical simulation in the optimisation framework. To guarantee the optimisation efficiency and accuracy, a synthetic minority oversampling technique (SMOTE) and adaptive minimise lower confidence bound (LCB) are combined in constructing the dynamic surrogate models. The proposed optimisation strategy is applied to the SUBOFF model and compared with other dynamic surrogate models. Comparison results prove the advantages of the proposed dynamic surrogate model. [ABSTRACT FROM AUTHOR]

The original design of a software system is rarely prepared for every new requirement. Software systems should be updated frequently, which is usually accompanied by the decline in software modularity and quality. Although many approaches have been proposed to improve the quality of software, a majority of them are guided by metrics defined on the local properties of software. In this article, we propose to use a global metric borrowed from the network science to detect the moving method refactoring. First, our approach uses a bipartite network to represent classes, features (i.e., methods and fields), and their dependencies. Second, a new metric is introduced to quantify the modularity of a software system as a whole. Finally, a crossover-only evolutionary algorithm that uses the metric as its fitness function is introduced to optimize the class structure of a software system and detect the methods that should be moved. Empirical results on the benchmark Java projects show that our approach can find meaningful methods that should be moved with a high stability. The advantages of our approach are illustrated in comparison with some other approaches, specifically one refactoring approach, namely search-based refactoring approach (SBRA), and two community detection algorithms, namely a graph theoretic clustering algorithm (MCODE) and a fast algorithm for community detection (FG). Our approach provides a new way to do refactoring from the perspective of software structure.

Identifying important entities in software systems has many implications for effective resource allocation. Complex network research opens new opportunities for identifying important entities from software networks. However, the existing methods only focus on identifying important classes. Little work has been done on the identification of important packages. Moreover, the metrics they used to quantify the class importance are only designed for unweighted software networks and cannot fit in with the weighted software networks. To overcome these limitations, in this article, we introduce the weighted k-core decomposition method (Wk-core) to identify the important packages. First, we use a weighted software network to describe packages and their internal dependencies. Second, we use Wk-core to partition a software network into a layered structure. Then, the packages that are denoted by the nodes within the main core are the identified important packages. To evaluate our method, we use a variant of the susceptible–infectious–recovered model to examine the spreading influence of the nodes in six real weighted software networks. The results show that our method can well identify influential nodes, better than other four methods (i.e., original k-core decomposition, degree centrality, closeness centrality, and betweenness centrality methods). Furthermore, we demonstrate our method on two software networks and show that the important packages identified by our method are more meaningful from a software engineering perspective when compared with the other methods.

With a view to using solar energy, the exploitation of near‐infrared (NIR) light, which constitutes about 50 % of solar energy, in photocatalytic H2O2 synthesis remains challenging. In this study, resorcinol−formaldehyde (RF), which has a relatively low bandgap and high conductivity, is introduced for photothermal catalytic generation of H2O2 under ambient conditions. Owing to the promoted surface charge transfer rate under high temperature, the photosynthetic yield reaches roughly 2000 μm within 40 min under 400 mW cm−2 irradiation with a solar‐to‐chemical conversion (SCC) efficiency of up to 0.19 % at 338 K under ambient conditions, exceeding the rate of photocatalysis with a cooling system by a factor of about 2.5. Notably, the H2O2 produced by RF during photothermal process was formed via a two‐channel pathway, leading to the overall promotion of H2O2 formation. The resultant H2O2 can be applied in situ for pollutant removal. This work offers a sustainable and economical route for the efficient formation of H2O2. [ABSTRACT FROM AUTHOR]