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The paper introduces the student‐coined term digital instinct which describes students' disposition to resorting to digital technology for solving problems and doing school assignments. Taking cues from the term digital instinct, the paper describes a student perspective on digital literacy emerging from 100 lived experience descriptions and interviews with 37 Danish upper secondary students. The findings show that students' digital practice is characterised by experience‐based, intuitive and familiar use of technologies. Most notably, students employ digital technologies as cognitive partners that help them carry on with assignments that they initially did not understand, but that they were able to complete with the help of the computer. The study examines the nature of this partnership through the words of the students and identifies how technologies expand student agency but fall short of a reflective use of digital technologies. Recognising the strengths and weaknesses of students' digital practices may inform the concept of digital literacy and encourage teachers to acknowledge the digital instinct as a steppingstone to foster students' reflective use of digital technologies. Practitioner notesWhat is already known about this topic Students have inadequate command of digital literacies as described in curricular terminology.Students have positive as well as negative perceptions of the value and usefulness of digital technologies in school.Students both over‐ and underestimate their own digital literacies.What this paper adds Students have a fundamental utilitarian conception of digital technologies that either make schoolwork easier or more difficult, and they do not articulate that their use of digital technologies provides them with digital literacies.Students' conception of a digital instinct describes an intuitive and familiar, albeit unreflected, use of technologies where students employ experience‐based and self‐taught methods for using digital technologies.The digital instinct accounts for a feeling of agency among students, manifested in a widespread confidence that they can do assignments and solve problems when they make use of their computer.Implications for practice and/or policy Curricular terminologies struggle to capture what students can and cannot do with technology, and how much they understand the underlying technology.Teachers can involve and build on students' experience‐based digital practices as a starting point for developing digital literacy among students—also as an entry to a curricular perspective.Teachers should acknowledge students' conception of a 'digital instinct' as an important disposition in its own right. [ABSTRACT FROM AUTHOR]

With the introduction of connected and automated vehicles (CAVs), the integrated control of traffic signals, lane assignments, and vehicle trajectories becomes feasible, offering notable benefits for enhancing intersection operations. However, during the prolonged transition to an entirely CAV environment, how to fully leverage the advantage of CAVs while considering the characteristics of human‐driven vehicles remains a huge challenge. To address this challenge, this paper proposes a joint optimization method for spatiotemporal resources at isolated intersections under mixed‐autonomy traffic conditions. Initially, the lane assignment optimization problem is modeled as a mixed integer linear program model to maximize the reserve capacity. Subsequently, the signal‐vehicle coupled control is formulated as a dynamic programming model with the objective of reducing vehicle travel time. Additionally, criteria are established to assess the need for re‐optimizing lane assignments. Simulations validate the superiority of the proposed control method over adaptive control in terms of traffic efficiency and intersection capacity amid substantial traffic demand fluctuations. Sensitivity analyses reveal that the proposed control method can yield higher benefits under medium traffic demand levels. Furthermore, the proposed algorithm exhibits no significant sensitivity to the CAV market adoption rate, suggesting its applicability throughout the CAV adoption process. [ABSTRACT FROM AUTHOR]

Summary: The next‐generation networks are expected to support diverse latency‐sensitive services, that is, auto‐driving, telemedicine, and so on. Deploying these services in current hardware‐based networks faces significant challenges. The emergence of network function virtualization (NFV) and software defined network (SDN) makes network deployment flexible and agile. In order to meet the strict latency constraints, joint VNF deployment and resource allocation problems are needed. In this paper, we consider the resource‐delay interpendency and formulate the joint VNF deployment and resource allocation problems as linear programming (LP) problems. Discrete spider monkey optimization (DSMO) algorithm is proposed to solve this problem, which imitates spider monkeys' fission and fusion social behavior during foraging. The two‐phase decision update mechanism greatly increases the diversity of the spider monkey decision population and promotes the algorithm to perform a global search of the solution space. Evaluation results show that the proposed solutions achieve superior performance in terms of latency, CPU utilization and service acceptance rate. [ABSTRACT FROM AUTHOR]

We consider the incremental subgradient algorithm employing dynamic step sizes for minimizing the sum of a large number of component convex functions. The dynamic step size rule was firstly introduced by Goffin and Kiwiel [Math. Program., 1999, 85(1): 207‐211] for the subgradient algorithm, soon later, for the incremental subgradient algorithm by Nedic and Bertsekas in [SIAM J. Optim., 2001, 12(1): 109‐138]. It was observed experimentally that the incremental approach has been very successful in solving large separable optimizations and that the dynamic step sizes generally have better computational performance than others in the literature. In the present paper, we propose two modified dynamic step size rules for the incremental subgradient algorithm and analyse the convergence and complexity properties of them. At last, the assignment problem is considered and the incremental subgradient algorithms employing different kinds of dynamic step sizes are applied to solve the problem. The computational experiments show that the two modified ones converges dramatically faster and more stable than the corresponding one in [SIAM J. Optim., 2001, 12(1): 109‐138]. Particularly, for solving large separable convex optimizations, we strongly recommend the second one (see Algorithm 3.3 in the paper) since it has interesting computational performance and is the simplest one. [ABSTRACT FROM AUTHOR]

Summary: In heterogeneous networks (HetNets), sharply increasing data traffic exacerbates the rapid growth in the network energy consumption and at the same time brings tremendous pressure on the backhaul links. Aiming at improving the network energy efficiency (EE) and relieving backhaul data volume traffic pressure, in this paper, we investigate energy efficient resource allocation which restricts small cell data traffic by imposing different kinds of backhaul capacity constraints (BCCs) on small cells, including small cell BCC (SBCC) and macrocell BCC (MBCC). We formulate the joint resource block (RB) and power allocation problem as a fractional programming problem. By using variable relaxation and replacement, we convert this nonconvex optimization problem into a concave–convex fractional programming problem, which can further be converted into a parametric convex problem and solved by convex optimization theory. Based on the Dinkelbach and dual decomposition methods, we propose iterative resource allocation algorithms under different BCCs, where RB and power are jointly allocated among users in each cell. Numerical results reveal that the proposed algorithms have good convergence performance and also evaluates the impact of both SBCC and MBCC on network performance. [ABSTRACT FROM AUTHOR]

The number of electric vehicles (EVs) and charging facilities is expected to increase significantly in the near future, further coupling the existing transportation system with the power system. This may bring new stresses and risks to such a system of systems. This paper presents a mathematical framework to analyze the resilience of an electrified road network (ERN) subject to potential failures of its supporting fast‐charging stations (FCSs). Within this framework, a novel linear optimization model is proposed for the first time to solve the system optimal dynamic traffic assignment problem of ERN. The characteristics considered in the modeling framework include the location, capacity, and charging speed of FCSs, as well as the driving range, charging time, and state of charge (SoC) of EVs. The linear model is proposed based on the cell transmission model. It is used as the first‐stage model to assign the traffic under normal FCS operations. A second‐stage model is, then, extended to minimize the total travel time after the stochastic occurrence of FCS failures, that is, in the failure and recovery phases. Two metrics are considered to quantify the ERN performance and the impacts of FCS failures. A numerical example is studied to illustrate the usefulness of the proposed framework for analyzing ERN resilience. The results show that deploying FCSs near the highway entrances and maintaining their operation are relevant factors to enhance the system's resilience. The analysis can provide guidelines to the system operators for effective management of the ERN operation and identify resilience‐critical FCSs for system resilience improvement. [ABSTRACT FROM AUTHOR]

Nowadays, increase in time complexity of applications and decrease in hardware costs are two major contributing drivers for the utilization of high‐performance architectures such as cluster computing systems. Actually, cluster computing environments, in the contemporary sophisticated data centres, provide the main infrastructure to process various data, where the biomedical one is not an exception. Indeed, optimized task scheduling is key to achieve high performance in such computing environments. The most distractive assumption about the problem of task scheduling, made by the state‐of‐the‐art approaches, is to assume the problem as a whole and try to enhance the overall performance, while the problem is actually consisted of two disparate‐in‐nature subproblems, that is, sequencing subproblem and assigning one, each of which needs some special considerations. In this paper, an efficient hybrid approach named ACO‐CLA is proposed to solve task scheduling problem in the mesh‐topology cluster computing environments. In the proposed approach, an enhanced ant colony optimization (ACO) is developed to solve the sequence subproblem, whereas a cellular learning automata (CLA) machine tackles the assigning subproblem. The utilization of background knowledge about the problem (i.e., tasks' priorities) has made the proposed approach very robust and efficient. A randomly generated data set consisting of 125 different random task graphs with various shape parameters, like the ones frequently encountered in the biomedicine, has been utilized for the evaluation of the proposed approach. The conducted comparison study clearly shows the efficiency and superiority of the proposed approach versus traditional counterparts in terms of the performance. From our first metric, that is, the NSL (normalized schedule length) point of view, the proposed ACO‐CLA is 2.48% and 5.55% better than the ETF (earliest time first), which is the second‐best approach, and the average performance of all other competing methods. On the other hand, from our second metric, that is, the speedup perspective, the proposed ACO‐CLA is 2.66% and 5.15% better than the ETF (the second‐best approach) and the average performance of all the other competitors. [ABSTRACT FROM AUTHOR]

DP‐coloring is a relatively new coloring concept by Dvořák and Postle and was introduced as an extension of list‐colorings of (undirected) graphs. It transforms the problem of finding a list‐coloring of a given graph G with a list‐assignment L to finding an independent transversal in an auxiliary graph with vertex set {(v,c)|v∈V(G),c∈L(v)}. In this paper, we extend the definition of DP‐colorings to digraphs using the approach from Neumann‐Lara where a coloring of a digraph is a coloring of the vertices such that the digraph does not contain any monochromatic directed cycle. Furthermore, we prove a Brooks' type theorem regarding the DP‐chromatic number, which extends various results on the (list‐)chromatic number of digraphs. [ABSTRACT FROM AUTHOR]

Summary: Beam space multiple‐input multiple‐output (B‐MIMO) systems operating at mmWave frequency in downlink uses different beam selection techniques for reducing the required number of radio frequency chains without apparent performance loss. In this paper, a joint beam selection and user association scheme with the objective of sum rate maximization is proposed. The proposed work on beam selection is based on a norm and uncorrrelation based approach, which considers channel correlation among the users so as to avoid inter‐user interference. Two different methods for beam user association with one modelled as a balanced assignment problem based on average sum assignment method and the other method that associates an optimal beam to every user by considering their channel gain values are also proposed along with beam selection. The proposed beam assignment methods are less complex and efficient in finding a suitable beam to every user within the cell. Simulation results show that the proposed method accomplish comparable performance in terms of sum rate and energy efficiency with appreciably low computational complexity relative to the existing methods in a correlated environment. [ABSTRACT FROM AUTHOR]

Summary: Network function virtualization (NFV) provides a simple and effective mean to deploy and manage network and telecommunications' services. A typical service can be expressed in the form of a virtual network function–forwarding graph (VNF‐FG). Allocating a VNF‐FG is equivalent to place VNFs and virtual links onto a given substrate network considering resources and quality‐of‐service (QoS) constraints. The deployment of VNF‐FGs in large‐scale networks, such that QoS measures and deployment cost are optimized, is an emerging challenge. Single‐objective VNF‐FGs allocation has been addressed in existing literature; however, there is still a lack of studies considering multiobjective VNF‐FGs allocation. In addition, it is not trivial to obtain optimal VNF‐FGs allocation due to its high computational complexity even in case of single‐objective VNF‐FGs allocation. Genetic algorithms (GAs) have been proved its ability in coping with multiobjective optimization problems; thus, we propose a GA‐based scheme to solve multiobjective VNF‐FGs allocation problem in this paper. The numerical results confirm that the proposed scheme can provide near Pareto‐optimal solutions within a short execution time. [ABSTRACT FROM AUTHOR]

This paper proves the following result. Assume G is a triangle‐free planar graph, X is an independent set of G. If L is a list assignment of G such that |L(v)|=4 for each vertex v∈V(G)−X and |L(v)|=3 for each vertex v∈X, then G is L‐colorable. [ABSTRACT FROM AUTHOR]

In recent years, the smart inverter that is an advanced inverter has been investigated as a solution to voltage problem in the distribution network in which a lot of photovoltaic(PV)s are introduced Among multiple functions of the smart inverter, voltage‐reactive power control (Volt‐Var) and voltage‐active power control (Volt‐Watt) have been attracted attention because of their effectiveness. However, the problem is how to determine optimal curves setting of Volt‐Var and Volt‐Watt. In this paper, we suggest the determination method of optimal Volt‐Var and Volt‐Watt curves, and verify the effectiveness of the optimal curves on distribution network. [ABSTRACT FROM AUTHOR]

This paper presents a decomposition scheme to find near‐optimal solutions to a cell transmission model‐based system optimal dynamic traffic assignment problem with multiple origin‐destination pairs. A linear and convex formulation is used to define the problem characteristics. The decomposition is designed based on the Dantzig–Wolfe technique that splits the set of decision variables into subsets through the construction of a master problem and subproblems. Each subproblem includes only a single origin‐destination pair with significantly less computational burden compared to the original problem. The master problem represents the coordination between subproblems through the design of interactive flows between the pairs. The proposed methodology is implemented in two case study networks of 20 and 40 intersections with up to 25 origin‐destination pairs. The numerical results show that the decomposition scheme converges to the optimal solution, within 2.0% gap, in substantially less time compared to a benchmark solution, which confirms the computational efficiency of the proposed algorithm. Various network performance measures have been assessed based on different traffic state scenarios to draw managerial insights. [ABSTRACT FROM AUTHOR]

In this paper, we address the problem of robust characteristic polynomial assignment for LTI systems whose parameters are assumed to belong to a semialgebraic uncertainty region. The objective is to design a dynamic fixed-order controller in order to constrain the coefficients of the closed-loop characteristic polynomial within prescribed intervals. First, necessary conditions on the plant parameters for the existence of a robust controller are reviewed, and it is shown that such conditions are satisfied if and only if a suitable Sylvester matrix is nonsingular for all possible values of the uncertain plant parameters. The problem of checking such a robust nonsingularity condition is formulated in terms of a nonconvex optimization problem. Then, the set of all feasible robust controllers is sought through the solution to a suitable robust diophantine equation. Convex relaxation techniques based on sum-of-square decomposition of positive polynomials are used to efficiently solve the formulated optimization problems by means of semidefinite programming. The presented approach provides a generalization of the results previously proposed in the literature on the problem of assigning the characteristic polynomial in the presence of plant parametric uncertainty. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This paper extends the recently introduced assignment flow approach for supervised image labeling to unsupervised scenarios where no labels are given. The resulting self‐assignment flow takes a pairwise data affinity matrix as input data and maximizes the correlation with a low‐rank matrix that is parametrized by the variables of the assignment flow, which entails an assignment of the data to themselves through the formation of latent labels (feature prototypes). A single user parameter, the neighborhood size for the geometric regularization of assignments, drives the entire process. By smooth geodesic interpolation between different normalizations of self‐assignment matrices on the positive definite matrix manifold, a one‐parameter family of self‐assignment flows is defined. Accordingly, our approach can be characterized from different viewpoints, e.g. as performing spatially regularized, rank‐constrained discrete optimal transport, or as computing spatially regularized normalized spectral cuts. Regarding combinatorial optimization, our approach successfully determines completely positive factorizations of self‐assignments in large‐scale scenarios, subject to spatial regularization. Various experiments including the unsupervised learning of patch dictionaries using a locally invariant distance function, illustrate the properties of the approach. [ABSTRACT FROM AUTHOR]

In this paper the pole placement problem for singular systems via state feedback is studied. We give a complete solution to this problem for systems without row minimal indices. As a corollary, the eigenvalue assignment problem is solved for singular systems in the case they are regularizable. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of disconnecting a graph by removing as few vertices as possible, such that no component of the disconnected graph has more than a given number of vertices. We give applications of this problem, present a formulation for it, and describe some polyhedral results. Furthermore, we establish ties with other polytopes and show how these relations can be used to obtain facets of our polytope. Finally, we give some computational results. [ABSTRACT FROM AUTHOR]

This paper introduces models of robustness in flight gate assignments at airports. We briefly repeat the general flight gate assignment problem and disruptions occurring in airline scheduling. Recovery strategies and robust scheduling are surveyed as the main methods in disruption management. We present a non-robust flight gate assignment model and incorporate two approaches of robustness. [ABSTRACT FROM AUTHOR]

In this paper, we invoke probability constrained optimization to establish a framework for allocating means and tolerances in design for quality that focuses on customer satisfaction at predictable cost levels. The optimal allocation minimizes the production costs while ensuring that responses conform probabilistically to their specification limits. An overall system probability of conformance is obtained from a quality policy (e.g. defect rate, process capability index). Probabilities are evaluated using limit-state functions and fast integration methods. The three quality metrics (i.e. target/larger/smaller-is-best) and robustness are addressed naturally. The methodology is developed in detail and compared with the traditional minimum total cost approach. Optimal means and tolerances are found for an electro-mechanical servo system and a power division circuit to illustrate the practicality and potential of the approach. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

An efficient scheme for establishing a multicast route in packet networks is constructing a tree-shaped path in which multicast packets are replicated at specific branching nodes, called “copy nodes,” by the packet copy function and are forwarded to downstream nodes. This scheme leads to the efficient usage of network resources, but needs multicast routing control. To realize rapid multicast routing, it is suitable and realistic to restrict the number of copy nodes in calculating the multicast route. However, even in this approach we need to adequately select a limit on the number of copy nodes which satisfies the conditions of both priority for multicasting and geographical distribution. In this paper, a new copy-node allocation algorithm is proposed. This heuristic algorithm makes some “copy node functions” move on the network map sequentially according to attraction and repulsion determined by several conditions and finally makes them converge on the switching nodes suitable as copy nodes. In addition, the performance of this algorithm is evaluated by a computer simulation. This algorithm is applicable to various other server assignment problems. © 1999 Scripta Technica, Electron Comm Jpn Pt 1, 83(1): 87–96, 2000 [ABSTRACT FROM AUTHOR]

Summary: Recently, the routing problem in vehicular ad hoc networks is one of the most vital research. Despite the variety of the proposed approaches and the development of communications technologies, the routing problem in VANET suffers from the high speed of vehicles and the repetitive failures in communications. In this paper, we adjusted the well‐known K‐medoids clustering algorithm to improve the network stability and to increase the lifetime of all established links. First, the number of clusters and the initial cluster heads will not be selected randomly as usual, but based on mathematical formula considering the environment size and the available transmission ranges. Then the assignment of nodes to clusters in both k‐medoids phases will be carried out according to several metrics including direction, relative speed, and proximity. To the best of our knowledge, our proposed model is the first that introduces the new metric named "node disconnection frequency." This metric prevents nodes with volatile and suspicious behavior to be elected as a new CH. This screening ensures that the new CH retains its property as long as possible and thus increases the network stability. Empirical results confirm that in addition to the convergence speed that characterizes our adjusted K‐medoids clustering algorithm (AKCA), the proposed model achieves more stability and robustness when compared with most recent approaches designed for the same objective. [ABSTRACT FROM AUTHOR]