Leader-following consensus for a class of second-order nonlinear multi-agent systems

This paper deals with the leader-following consensus problem for a class of multi-agent systems with nonlinear dynamics and directed communication topology. The control input of the leader agent is assumed to be unknown to all follower agents. A distributed adaptive nonlinear control law is constructed using the relative state information between neighboring agents, which achieves leader-following consensus for any directed communication graph that contains a spanning tree with the root node being the leader agent. Compared with previous results, the nonlinear functions are not required to satisfy the globally Lipschitz or Lipschitz-like condition and the adaptive consensus protocol is in a distributed fashion. A numerical example is given to verify our proposed protocol.     

Choosing small sets of policy-relevant scenarios by combining vulnerability and diversity approaches

Computer simulation models can generate large numbers of scenarios, far more than can be effectively utilized in most decision support applications. How can one best select a small number of scenarios to consider? One approach calls for choosing scenarios that illuminate vulnerabilities of proposed policies. Another calls for choosing scenarios that span a diverse range of futures. This paper joins these two approaches for the first time, proposing an optimization-based method for choosing a small number of relevant scenarios that combine both vulnerability and diversity. The paper applies the method to a real case involving climate resilient infrastructure for three African river basins (Volta, Orange and Zambezi). Introducing selection criteria in a stepwise manner helps examine how different criteria influence the choice of scenarios. The results suggest that combining vulnerability- and diversity-based criteria can provide a systematic and transparent method for scenario selection.     

Physical topology optimization of infrastructure health monitoring sensor network for high-speed rail

With the rapid development of high-speed rail (HSR) systems, the security and safety of these huge systems are becoming the primary concerns for passengers. HSR infrastructure plays an important role in HSR systems, making the maintenance of security and safety of the HSR infrastructure especially important. Meanwhile, sensor network technologies allow the realization of real-time and all-weather monitoring of HSR infrastructure. This paper analyzes the application requirements and characteristics of infrastructure health monitoring sensor network (IHMSN) through construction of a three-layer IHMSN which is composed of end devices, repeater points, and access points. The physical topology optimization goal of IHMSN is to set the optimal number of network nodes (namely, minimum cost) as well as the best physical connections. Given types and amount of the end devices, a multiple knapsack model is established which converts the physical topology optimization problems into multiple knapsack problems. Based on the different needs of practical application, three different cases (basis case, adding devices case and weight-based case) are proposed, and the corresponding models are built. Some artificial intelligence algorithms and a traditional dynamic programming algorithm are presented to solve the problems. In addition, a general algorithmic finite state machine is proposed to describe the solving process. After comparing these algorithms in execution time, memory, and optimal results, the genetic algorithm and particle swarm optimization algorithm stand out when used to solve the basic case as well as the extended cases. The numerical results show that these proposed models and algorithms can effectively solve the physical topology optimization problem of IHMSN for HSR systems. Moreover, these methods can effectively reduce network costs and provide a theoretical basis for network communication link optimization.     

Construction of a pulse-coupled dipole network capable of fear-like and relief-like responses

The challenge for neuroscience as an interdisciplinary programme is the integration of ideas among the disciplines to achieve a common goal. This paper deals with the problem of deriving a pulse-coupled neural network that is capable of demonstrating behavioural responses (fear-like and relief-like). Current pulse-coupled neural networks are designed mostly for engineering applications, particularly image processing. The discovered neural network was constructed using the method of minimal anatomies approach. The behavioural response of a level-coded activity-based model was used as a reference. Although the spiking-based model and the activity-based model are of different scales, the use of model-reference principle means that the characteristics that is referenced is its functional properties. It is demonstrated that this strategy of dissection and systematic construction is effective in the functional design of pulse-coupled neural network system with nonlinear signalling. The differential equations for the elastic weights in the reference model are replicated in the pulse-coupled network geometrically. The network reflects a possible solution to the problem of punishment and avoidance. The network developed in this work is a new network topology for pulse-coupled neural networks. Therefore, the model-reference principle is a powerful tool in connecting neuroscience disciplines. The continuity of concepts and phenomena is further maintained by systematic construction using methods like the method of minimal anatomies.     

Topology optimization for nonlinear dynamic problems: Considerations for automotive crashworthiness

Crashworthiness of automotive structures is most often engineered after an optimal topology has been arrived at using other design considerations. This study is an attempt to incorporate crashworthiness requirements upfront in the topology synthesis process using a mathematically consistent framework. It proposes the use of equivalent linear systems from the nonlinear dynamic simulation in conjunction with a discrete-material topology optimizer. Velocity and acceleration constraints are consistently incorporated in the optimization set-up. Issues specific to crash problems due to the explicit solution methodology employed, nature of the boundary conditions imposed on the structure, etc. are discussed and possible resolutions are proposed. A demonstration of the methodology on two-dimensional problems that address some of the structural requirements and the types of loading typical of frontal and side impact is provided in order to show that this methodology has the potential for topology synthesis incorporating crashworthiness requirements.     

Hierarchical Correlation Clustering in Multiple 2D Scalar Fields

Sets of multiple scalar fields can be used to model many types of variation in data, such as uncertainty in measurements and simulations or time‐dependent behavior of scalar quantities. Many structural properties of such fields can be explained by dependencies between different points in the scalar field. Although these dependencies can be of arbitrary complexity, correlation, i.e., the linear dependency, already provides significant structural information. Existing methods for correlation analysis are usually limited to positive correlation, handle only local dependencies, or use combinatorial approximations to this continuous problem. We present a new approach for computing and visualizing correlated regions in sets of 2‐dimensional scalar fields. This paper describes the following three main contributions: (i) An algorithm for hierarchical correlation clustering resulting in a dendrogram, (ii) a generalization of topological landscapes for dendrogram visualization, and (iii) a new method for incorporating negative correlation values in the clustering and visualization. All steps are designed to preserve the special properties of correlation coefficients. The results are visualized in two linked views, one showing the cluster hierarchy as 2D landscape and the other providing a spatial context in the scalar field's domain. Different coloring and texturing schemes coupled with interactive selection support an exploratory data analysis.     

Multi-leader coordination algorithm for networks with switching topology and quantized information

This paper analyzes the stability and convergence properties of a proportional–integral protocol for coordination of a network of agents with dynamic information flow and quantized information exchange. In the setup adopted, each agent is only required to exchange its coordination state with its neighboring agents, and the desired reference rate is only available to a group of leaders. We show that the integral term of the protocol allows the agents to learn the reference rate, rather than have it available a priori, and also provides disturbance rejection capabilities. The paper addresses the case where the graph that captures the underlying network topology is not connected during some interval of time or even fails to be connected at all times.     

New design of nucleotide excision repair (NER) inhibitors for combination cancer therapy

Many cancer chemotherapy agents act by targeting the DNA of cancer cells, causing substantial damage within their genome and causing them to undergo apoptosis. An effective DNA repair pathway in cancer cells can act in a reverse way by removing these drug-induced DNA lesions, allowing cancer cells to survive, grow and proliferate. In this context, DNA repair inhibitors opened a new avenue in cancer treatment, by blocking the DNA repair mechanisms from removing the chemotherapy-mediated DNA damage. In particular, the nucleotide excision repair (NER) involves more than thirty protein–protein interactions and removes DNA adducts caused by platinum-based chemotherapy. The excision repair cross-complementation group 1 (ERCC1)-xeroderma pigmentosum, complementation group A (XPA) protein (XPA–ERCC1) complex seems to be one of the most promising targets in this pathway. ERCC1 is over expressed in cancer cells and the only known cellular function so far for XPA is to recruit ERCC1 to the damaged point. Here, we build upon our recent advances in identifying inhibitors for this interaction and continue our efforts to rationally design more effective and potent regulators for the NER pathway. We employed in silico drug design techniques to: (1) identify compounds similar to the recently discovered inhibitors, but more effective at inhibiting the XPA–ERCC1 interactions, and (2) identify different scaffolds to develop novel lead compounds. Two known inhibitor structures have been used as starting points for two ligand/structure-hybrid virtual screening approaches. The findings described here form a milestone in discovering novel inhibitors for the NER pathway aiming at improving the efficacy of current platinum-based therapy, by modulating the XPA–ERCC1 interaction.     

Controlled automated discovery of collections of business process models

Automated process discovery techniques aim at extracting process models from information system logs. Existing techniques in this space are effective when applied to relatively small or regular logs, but generate spaghetti-like and sometimes inaccurate models when confronted to logs with high variability. In previous work, trace clustering has been applied in an attempt to reduce the size and complexity of automatically discovered process models. The idea is to split the log into clusters and to discover one model per cluster. This leads to a collection of process models – each one representing a variant of the business process – as opposed to an all-encompassing model. Still, models produced in this way may exhibit unacceptably high complexity and low fitness. In this setting, this paper presents a two-way divide-and-conquer process discovery technique, wherein the discovered process models are split on the one hand by variants and on the other hand hierarchically using subprocess extraction. Splitting is performed in a controlled manner in order to achieve user-defined complexity or fitness thresholds. Experiments on real-life logs show that the technique produces collections of models substantially smaller than those extracted by applying existing trace clustering techniques, while allowing the user to control the fitness of the resulting models.