Fragile base-class problem,problem?

The fragile base-class problem (FBCP) has been described in the literature as a consequence of “misusing” inheritance and composition in object-oriented programming when (re)using frameworks. Many research works have focused on preventing the FBCP by proposing alternative mechanisms for reuse, but, to the best of our knowledge, there is no previous research work studying the prevalence and impact of the FBCP in real-world software systems. The goal of our work is thus twofold: (1) assess, in different systems, the prevalence of micro-architectures, called FBCS, that could lead to two aspects of the FBCP, (2) investigate the relation between the detected occurrences and the quality of the systems in terms of change and fault proneness, and (3) assess whether there exist bugs in these systems that are related to the FBCP. We therefore perform a quantitative and a qualitative study. Quantitatively, we analyse multiple versions of seven different open-source systems that use 58 different frameworks, resulting in 301 configurations. We detect in these systems 112,263 FBCS occurrences and we analyse whether classes playing the role of sub-classes in FBCS occurrences are more change and–or fault prone than other classes. Results show that classes participating in the analysed FBCS are neither more likely to change nor more likely to have faults. Qualitatively, we conduct a survey to confirm/infirm that some bugs are related to the FBCP. The survey involves 41 participants that analyse a total of 104 bugs of three open-source systems. Results indicate that none of the analysed bugs is related to the FBCP. Thus, despite large, rigorous quantitative and qualitative studies, we must conclude that the two aspects of the FBCP that we analyse may not be as problematic in terms of change and fault-proneness as previously thought in the literature. We propose reasons why the FBCP may not be so prevalent in the analysed systems and in other systems in general.     

Joint design of clustering and in-cluster data route for heterogeneous wireless sensor networks

A heterogeneous wireless sensor network comprises a number of inexpensive energy constrained wireless sensor nodes which collect data from the sensing environment and transmit them toward the improved cluster head in a coordinated way. Employing clustering techniques in such networks can achieve balanced energy consumption of member nodes and prolong the network lifetimes. In classical clustering techniques, clustering and in-cluster data routes are usually separated into independent operations. Although separate considerations of these two issues simplify the system design, it is often the non-optimal lifetime expectancy for wireless sensor networks. This paper proposes an integral framework that integrates these two correlated items in an interactive entirety. For that, we develop the clustering problems using nonlinear programming. Evolution process of clustering is provided in simulations. Results show that our joint-design proposal reaches the near optimal match between member nodes and cluster heads.     

Performance Analysis of The Auxiliary‐Model‐Based Multi‐Innovation Stochastic Newton Recursive Algorithm for Dual‐Rate Systems

The stochastic Newton recursive algorithm is studied for dual‐rate system identification. Owing to a lack of intersample measurements, the single‐rate model cannot be identified directly. The auxiliary model technique is adopted to provide the intersample estimations to guarantee the recursion process continues. Intersample estimations have a great influence on the convergence of parameter estimations, and one‐step innovation may lead to a large fluctuation or even divergence during the recursion. In the meantime, the sample covariance matrix may appear singular. The recursive process would cease for these reasons. In order to guarantee the recursion process and to also improve estimation accuracy, multi‐innovation is utilized for correcting the parameter estimations. Combining the auxiliary model and multi‐innovation theory, the auxiliary‐model‐based multi‐innovation stochastic Newton recursive algorithm is proposed for time‐invariant dual‐rate systems. The consistency of this algorithm is analyzed in detail. The final simulations confirm the effectiveness of the proposed algorithm.     

Ordering states with Tsallis relative $$\alpha $$-entropies of coherence

In this paper, we study the ordering states with Tsallis relative \(\alpha \)-entropies of coherence and \(l_{1}\) norm of coherence for single-qubit states. Firstly, we show that any Tsallis relative \(\alpha \)-entropies of coherence and \(l_{1}\) norm of coherence give the same ordering for single-qubit pure states. However, they do not generate the same ordering for some high-dimensional states, even though these states are pure. Secondly, we also consider three special Tsallis relative \(\alpha \)-entropies of coherence for \(\alpha =2, 1, \frac{1}{2}\) and show these three measures and \(l_{1}\) norm of coherence will not generate the same ordering for some single-qubit mixed states. Nevertheless, they may generate the same ordering if we only consider a special subset of single-qubit mixed states. Furthermore, we find that any two of these three special measures generate different ordering for single-qubit mixed states. Finally, we discuss the degree of violation of between \(l_{1}\) norm of coherence and Tsallis relative \(\alpha \)-entropies of coherence. In a sense, this degree can measure the difference between these two coherence measures in ordering states.     

Indistinguishability of pure orthogonal product states by LOCC

We construct two sets of incomplete and extendible quantum pure orthogonal product states (POPS) in general bipartite high-dimensional quantum systems, which are all indistinguishable by local operations and classical communication. The first set of POPS is composed of two parts which are \(\mathcal {C}^m\otimes \mathcal {C}^{n_1}\) with \(5\le m\le n_1\) and \(\mathcal {C}^m\otimes \mathcal {C}^{n_2}\) with \(5\le m \le n_2\), where \(n_1\) is odd and \(n_2\) is even. The second one is in \(\mathcal {C}^m\otimes \mathcal {C}^n\) \((m, n\ge 4)\). Some subsets of these two sets can be extended into complete sets that local indistinguishability can be decided by noncommutativity which quantifies the quantumness of a quantum ensemble. Our study shows quantum nonlocality without entanglement.     

An uncertainty-based approach to discrete-time fault estimation observer design for nonuniformly sampled systems

This paper considers the fault estimation problem of nonuniformly sampled system in which sensor sampling is performed at aperiodic interval. After being discretized at sampling instant, the nonuniformly sampled system is modeled as an equivalent polytopic system with norm bounded uncertainties. A discrete-time time-varying fault estimation observer with multiple design freedom is then constructed, and a sufficient condition given in linear matrix inequality (LMI) is provided to obtain the constant filter gain and ensure not only the asymptotic stability of fault estimation error but also the robustness of uncertainties. Compared with the existing observer designed based on continuous-time delay approach, the proposed one has a better estimation accuracy and less conservatism and is easy for digital implementation. A numerical simulation and a quadruple-tank benchmark are used to demonstrate the effectiveness and superiority of the proposed method.     

Data-driven predictive control for continuous-time linear parameter varying systems with application to wind turbine

A new data-driven predictive control method based on subspace identification for continuous-time linear parameter varying (LPV) systems is presented in this paper. It is developed by reformulating the continuous-time LPV system which utilizes Laguerre filters to obtain the subspace prediction of output. The subspace predictors are derived by QR decomposition of input-output and Laguerre matrices obtained by input-output data. The predictors are then applied to design the model predictive controller. It is shown that the integrated action is incorporated in the control effect to eliminate the steady-state offset. We control the continuous-time LPV systems to obtain the attractive performance with the proposed data-driven predictive control method. The proposed controller is applied to a wind turbine to verify its effectiveness and feasibility.     

Decentralized iterative learning control for large-scale interconnected linear systems with fixed initial shifts

This paper deals with the problem of iterative learning control for large-scale interconnected linear systems in the presence of fixed initial shifts. According to the characteristics of the systems, iterative learning control laws are proposed for such large-scale interconnected linear systems based on the PD-type learning schemes. The proposed controller of each subsystem only relies on local output variables without any information exchanges with other subsystems. Using the contraction mapping method, we show that the schemes can guarantee the output of the system converges uniformly to the corresponding output limiting trajectory over the whole time interval along the iteration axis. Simulation examples illustrate the effectiveness of the proposed method.     

Simultaneous assay of platelet adhesion at multiple shear rates within a single microfluidic channel

Cardiovascular diseases are currently the major causes of mortality in the world, especially in developed nations. As a predominant one, thrombosis is the platelet aggregation induced by a high shear rate. Platelet aggregation assay can clarify the occurrence mechanism of thrombosis, as well as be used as an important tool in the clinical diagnosis, personalized treatment, and screening of anticoagulants. Thus, relevant studies attracted considerable attention. As an important step in platelet aggregation, platelet adhesion and its detection also attract intensive concern. Thus, some analytical methods have been developed for platelet adhesion assay, and the impact of shear rate is one of the focuses. Compared with other devices, biosensors can give a more accurate result within a shorter time. Furthermore, some biosensors can achieve real-time analysis. However, only one or several shear rates can be tested at the same time, which may decrease the analytical efficiency. Meanwhile, in most cases, only the average platelet adhesion effect within a reactor is detected, and the impact of the distribution of shear rates is improperly neglected. In this study, a microfluidic device with a single channel is designed and fabricated for platelet adhesion assay. When the platelet-rich plasma flows through the collagen-modified sensing surface of the channel bottom, the interaction between platelets and collagen molecules on the entire surface can be simultaneously monitored by using a surface plasmon resonance imaging (SPRi) system. A gradient of the shear rate (0–546 s^-1) could be formed within the channel by choosing a suitable depth-to-width ratio (1:5), so platelet adhesion at multiple shear rates could be monitored simultaneously. This method enables the measurement of the adhesion process of unlabeled platelets on the entire sensing surface, in vitro, at multiple shear rates. Such a system can obtain more accurate platelet adhesion result at a given shear rate than traditional methods. Furthermore, in an individual operation, platelet adhesion can be repeatedly tested at multiple points with an equal shear rate, so a much higher analytical efficiency can also be achieved.     

CropStraw Fire Remote Sensing Monitoring and Its Algorithm Implementation

in order to obtain the information and achieve the effective control of crop straw fire spatial distribution in Central China Region.The MODIS L1B remote sensing datasets during 2014 for the main data source in this article,and combined with land use data,the farmland of Central China Region was taken as study region.Based on the enhanced contextual fire remote sensing detection algorithm,and make full use of the theoretical knowledge of quantitative remote sensing and Geospatial Data Abstraction Library (GDAL)and other technical means,to achieve the crop straw fire recognition in Central China Region.Using Ministry of Environmental Protection of the People’s Republic of China release the daily newspaper of crop straw fire in China and the standard fire products (MYD14)of MODIS for the comparative analysis of the quantitative and spatial.The results indicate that the algorithmof this paper can achieve crop straw fire remote sensing monitoring of this study region effectively,and the parameters can be adjusted in real time based on the characteristic of the study region,and improve the automation and working efficiency of crop straw fire monitoring.