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China is in the stage of rapid development of new urbanization, cities are more vulnerable when facing various risks, and the construction of urban resilience (UR) makes cities more secure. At the same time, the allocation of digital factors has a vital impact on the construction of UR under energy constraints. Based on the panel data of cities in China from 2011 to 2020, this paper studies the influence and mechanism of digital factor allocation (DFA) on UR under energy constraints through panel fixed effect model (FE), mediation effect model, and panel threshold model. The main empirical results include: (1) The misallocation of digital factors has a negative impact on UR. In the case of differences in the division of urban agglomeration, the impact of digital factor misallocation (DFM) on UR will also be different. (2) The misallocation of digital factors has an impact on UR through the intermediary effects of capital allocation efficiency, green innovation and industrial structure upgrading. (3) There is a nonlinear relationship between the DFM and UR. [ABSTRACT FROM AUTHOR]

This paper addresses a scheduling problem with a cumulative continuous resource and energy constraints. Given a set of non-preemptive tasks, each task requires a continuously-divisible resource. The instantaneous resource usage of a task is limited by a minimum and maximum resource requirement. Its processing has to lie within a time-window and the total energy received obtained by integrating a function f of the instantaneous resource usage over the processing interval must reach a required value (where f is a non-decreasing, continuous function). The problem is to find a feasible schedule of the tasks, which satisfies all the constraints. This problem, which is a generalization of the well-known cumulative scheduling problem, is NP-complete. For the case where all functions f are linear, we exhibit structural properties of the feasible solutions and we present a Mixed Integer Linear Program (MILP) based on an event-based formulation. We also adapt the famous 'left-shift/right-shift' satisfiability test (energetic reasoning) and the associated time-window adjustments to our problem. To achieve this test, we present three different ways for computing the relevant intervals. Finally, we present a hybrid branch-and-bound method to solve the problem, which performs, at each node, the satisfiability test and time-window adjustments and, when the domains of all start and end times are small enough, the remaining solution space is searched via the event-based MILP. [ABSTRACT FROM AUTHOR]

The advancements of technology in the field of communication made WSN based IoT attractive and applicable to various areas. It is comprised IoT nodes that work on limited battery supplies. Hence, a high-performance routing protocol is essential for routing in such networks to overcome the energy constraint problems. In this paper, an energy efficient routing algorithm Fractional Gravitational Grey Wolf Optimization (FGGWO) is proposed for multipath data transmission. This work is motivated by the Ant Colony Optimization (ACO) algorithm that discovered multipaths based on clustering technique. The proposed algorithm improves the routing process of ACO in a two stage process. At first, the cluster heads are selected by utilizing the previous work Fractional Gravitational Search Algorithm (FGSA). Secondly, multiple paths are generated from the source to the destination using FGGWO, which modifies Grey Wolf Optimization by integrating FGSA in the algorithm. Objectives, such as, energy, inter and intra-cluster distance, delay and lifetime, considered in the fitness function provide optimal paths for the transmission. The experimental results show that the proposed FGSA + FGGWO algorithm has higher performance regarding energy and alive nodes, in comparison with the existing ABC + ACO, FABC + EACO, and Threshold + ACO techniques. The maximum number of alive nodes and energy estimated in FGSA + FGGWO is 25 and 0.1298 for 50 nodes; and 27 and 0.0876, for 100 nodes. [ABSTRACT FROM AUTHOR]

This paper is concerned with the weighted observation fusion Kalman filtering problem for a class of multi-sensor fusion systems with random measurement delays and energy constraints. To reduce the energy consumptions, each sensor intermittently sends information to the fusion center in a random way. By using the full rank decomposition approach, the observation fusion equation is derived. Without resorting to the augmentation technique, optimal weighted observation fusion Kalman filter (WOFKF) is given, and it is proved that the performance of the WOFKF is equivalent to that of the centralized fusion Kalman filter. Simulations show the effectiveness of the proposed fusion methods. [ABSTRACT FROM AUTHOR]

For data collection systems, it is important to rationally allocate link capacity to each source node so that the base station can receive fresh data. In recent years, a metric called age of information (AoI) has appeared to measure the freshness of the received information. In this paper, we build a model in which multiple heterogeneous source nodes with energy constraints transmit samples to a base station via a shared capacity-constrained channel. Then, with the objective of minimizing average weighted AoI in data collection systems, we propose a strategy where each source node has two buffers to store its latest partially transmitted sample and its complete latest collected sample, respectively. We establish the AoI model, sample transmission model and energy consumption model for the two buffers, and design a scheduling algorithm two-buffers strategy algorithm in this strategy. Finally, the proposed algorithm has been compared with the other four scheduling algorithms by simulation. The results show that the proposed algorithm performs better than them in terms of the average weighted AoI. [ABSTRACT FROM AUTHOR]

This paper addresses a scheduling problem with a continuously divisible, cumulative and renewable resource with limited capacity. During its processing, each task consumes a part of this resource, which lies between a minimum and a maximum requirement. A task is finished when a certain amount of energy is received by it within its time window. This energy is received via the resource and an amount of resource is converted into an amount of energy with a non-decreasing and continuous function. The goal is to find a feasible schedule, which is already NP-complete, and then to minimize the resource consumption. For the case where all functions are linear, we present two new mixed-integer linear programs (MILP), as well as improvements of an existing formulation. We also present a detailed version of the adaptation of the well-known 'left-shift/right-shift' satisfiability test for the cumulative constraint and the associated time-window adjustments to our problem. For this test, three ways of computing relevant intervals are described. Finally, a hybrid branch-and-bound using both the satisfiability test and the MILP is presented with a new heuristic for choosing the variable on which the branching is done. Computational experiments on randomly generated instances are reported in order to compare all of these solution methods. [ABSTRACT FROM AUTHOR]

In this paper, we study algorithmic problems for quantitative models that are motivated by the applications in modeling embedded systems. We consider two-player games played on a weighted graph with mean-payoff objective and with energy constraints. We present a new pseudopolynomial algorithm for solving such games, improving the best known worst-case complexity for pseudopolynomial mean-payoff algorithms. Our algorithm can also be combined with the procedure by Andersson and Vorobyov to obtain a randomized algorithm with currently the best expected time complexity. The proposed solution relies on a simple fixpoint iteration to solve the log-space equivalent problem of deciding the winner of energy games. Our results imply also that energy games and mean-payoff games can be reduced to safety games in pseudopolynomial time. [ABSTRACT FROM AUTHOR]

We consider a cumulative scheduling problem where a task duration and resource consumption are not fixed. The consumption profile of the task, which can vary continuously over time, is a decision variable of the problem to be determined and a task is completed as soon as the integration over its time window of a non-decreasing and continuous processing rate function of the consumption profile has reached a predefined amount of energy. The goal is to find a feasible schedule, which is an NP-hard problem. For the case where functions are concave and piecewise linear, we present two propagation algorithms. The first one is the adaptation to concave functions of the variant of the energetic reasoning previously established for linear functions. Furthermore, a full characterization of the relevant intervals for time-window adjustments is provided. The second algorithm combines a flow-based checker with time-bound adjustments derived from the time-table disjunctive reasoning for the cumulative constraint. Complementarity of the algorithms is assessed via their integration in a hybrid branch-and-bound and computational experiments on small-size instances. [ABSTRACT FROM AUTHOR]

The fusion estimation issue of sensor networks is investigated for nonlinear time-varying systems with energy constraints, time delays as well as packet loss. For the addressed problem, some local estimations are first obtained by using the designed Luenberger-type local estimator and then transmitted to a fusion center (FC) to generate a desired fusion value. A novel transmission model with energy constraints is proposed, where part information is reliably transmitted and the other is randomly determined whether to be transmitted. Furthermore, a diagonal matrix is utilized to describe the communication scheduling. With the help of the Lyapunov stability theory, sufficient conditions are established to ensure the predetermined local and fused H ∞ performances over a finite horizon. Furthermore, by virtue of the well-known Schur complement lemma, the desired gains of local estimators and the suboptimal fusion weight matrices are obtained in light of the solution of linear matrix inequalities. It should be pointed out that the developed scheme is a two-step process under which the design of fusion weight matrices is based on the obtained estimator gains. Finally, a simulation example for sensor networks is performed to check the effectiveness of the proposed fusion scheme. [ABSTRACT FROM AUTHOR]

The fundamental objective of deploying wireless sensors is to monitor a physical condition(s) within the desired region and relay the measured data to the interested parties through a gateway. However, achieving maximum coverage over the desired area after the initial random deployment of nodes is likely impossible. Arbitrary paths become exposed, uncovered by any of the sensor nodes questioning the quality of service of the network. In our proposed coverage maximization scheme, exposure profiles of sensor nodes were used by the sink node to compute the minimal exposure path. A combination of the computed minimal exposed path and the ratio of covered to uncovered grids were used as the function fitness in our particle swarm optimization algorithm. Bearing in mind the energy constraints of sensor nodes, randomly deployed nodes were only repositioned in the optimization process when their energy levels are above the predefined threshold. The accuracy and efficiency of our scheme are established via simulations. We compared the results of our scheme with a similar optimization scheme. The results showed our scheme achieved a higher coverage degree with the same number of deployed nodes over a region of interest of the same size. [ABSTRACT FROM AUTHOR]

We performed a food provisioning experiment in a population of Pied Flycatchers Ficedula hypoleuca breeding at high altitude in central Spain to test if food availability before and during laying determines clutch size. Food was provided to one of two pairs with the same date of initiation of nest-building (15 dyads of subsequently reproducing pairs were thus created). Food provisioning began on the day of initiation of nest-building and ended on the day after the last egg was laid. Although laying date was unaffected by the experiment, clutch size in the experimental treatment was significantly larger. This result could indicate that food availability at laying (1) proximately constrained clutch size or (2) that females evaluated future conditions for incubating eggs and feeding nestlings based on food availability at laying. Reproductive success (proportion of eggs that resulted in fledged young) was significantly reduced in the experimental treatment. This effect suggest that supplemented females were tricked by the experiment into laying more eggs than the number of eggs they were able to incubate with success and the number of nestlings they were able to feed, a source of error in clutch size adjustment which could be common in non-experimental situations. [ABSTRACT FROM AUTHOR]