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Multi-robots simultaneously coverage and tracking (SCAT) is the problem of simultaneously covering area and tracking targets, which is essential for many applications, such as delivery service, environment monitor, traffic surveillance, crime monitor, anti-terrorist mission and so on. The purpose of this paper is to improve the performance of detected target quantity, coverage rate and less deadweight loss by designing a self-organized method for multi-robots SCAT.

A self-organized reciprocal control method is proposed, coupling task assignment, tracking and covering, equipped with collision-avoiding ability naturally. First, SCAT problem is directly modeled as optimal reciprocal coverage velocity (ORCV) in velocity space. Second, the preferred velocity is generated by calculating the best velocity to the center of some robot detected targets. ORCV is given by adjusting the velocity relative to neighbor robots’ toward in optimal coverage velocity (OCV); it is proven that OCV is collision-free assembly. Third, some corresponding algorithms are designed for finding optimal velocity under two situations, such as no detected targets and empty ORCV.

The simulation results of two cases for security robots show that the proposed method has detected more targets with less deadweight loss and decision time and no collisions anytime.

In this paper, a self-organized reciprocal control method is proposed for multi-robots SCAT problem, which is modeled in velocity space directly, different to the traditional method modeling in configuration space. What is more, this method considers the reciprocal of robots that contributes to the better accomplishment of SCAT cooperatively.

The purpose of this paper is to evaluate the risk management benefits provided by the supplemental coverage option (SCO) insurance plan which was created in the 2014 Farm Bill. Specifically, the marginal expected utility benefits are compared with the potential additional subsidy cost introduced by the new program for a stylized example of a corn producer.

The paper uses a stylized simulation model examines the preferred insurance program choice for a typical Midwestern corn farmer. The expected utility of the farmer is calculated under their preferred insurance program choice both with and without the availability of the SCO program, and compared to the case where crop insurance is not available. Scenarios are examined for a range of farmer risk aversion levels, different levels of correlation between farm-level and county-level corn yields, and case with and without insurance premium subsidies.

The SCO program is found to enter into the preferred insurance program choice for risk averse farmers. As risk aversion increases, farmers are estimated to prefer higher coverage levels for individual products along with SCO coverage. While the availability of existing crop insurance programs are shown to substantially increase the expected utility of farmers, the marginal impact of adding SCO to the crop insurance program is relatively small. Furthermore, the additional expected benefits generated by SCO are shown to include both risk management and expected return components. With subsidies removed, the estimated marginal benefits provided by SCO are reduced significantly.

The findings of this paper can help inform the policy debate for future farm bills as agricultural support programs continue to evolve. The results in this paper can also be used to help explain farm-level decision making related to crop insurance program choices.

This paper contributes to the literature by documenting a new, federally supported risk management programs made available to farmers in the 2014 Farm Bill and evaluates the marginal benefits the SCO program offers US crop producers.

Continuous exposure and over‐utilization of sensors in harsh environments can lead some sensors to fail, and thereby not covering the service area effectively and efficiently. The purpose of this paper is to propose a two‐level coverage restoration scheme for the failing sensors by the existing sensors deployed in the immediate neighborhood of the failing sensors. The restoration scheme extends the search process to the set of failed sensors' corner neighbors at a second stage, with non‐available immediate active neighboring sensors at its first stage. Thus, the coverage restoration scheme attempts to sustain a maximum area of coverage with failed sensors.

The authors have considered a wireless sensor network (WSN), comprised of sensors deployed in a grid‐based arrangement in an inaccessible arena. The authors have formulated the coverage restoration problem as an optimization problem, to find the nearest and most apt neighbor sensors to reach solutions of maximizing the coverage area with failed sensors, while minimizing the energy consumption. Simulated annealing has been utilized as a search algorithm to find out the neighboring sensors with maximal energy in the vicinity of the failed node to cover its area.

The experimental results within the optimization algorithm have demonstrated that the restoration scheme shows a better trade‐off in maximizing the coverage area up to 90 per cent with a decrease of 26 per cent lifespan. The performance of the algorithm is further improved with extended search space including the corner neighbors in addition to the immediate neighbors.

The proposed coverage restoration can be embedded within applications using WSN to restore the coverage and maintain its functionality with optimized energy consumption.

The paper employs a novel framework to restore the coverage of the failed sensors by doubling the sensing area of the neighborhood sensors, and it utilizes an optimization scheme to search for neighborhood sensors with maximal energy to extend the lifespan of WSN.

The purpose of this paper is to apply agent-based modeling and simulation concepts in evaluating different approaches to solve ambulance-dispatching decision problems under bounded rationality. The paper investigates the effect of over-responding, i.e. dispatching ambulances even for doubtful high-risk patients, on the performance of equity constrained emergency medical services.

Agent-based modeling and simulation was used to evaluate two different dispatching policies: first, a policy based on maximum reward, and second, a policy based on the Markov decision process formulation. Four equity constraints were used: two from the patients’ side and two from the providers’ side.

The Markov decision process formulation, solved using value iteration method, performed better than the maximum reward method in terms of number of patients served. As the equity constraints conflict with each other, at most three equity constraints could be enforced at a time. The study revealed that it is safe to over-respond if there is uncertainty in the risk level of the patients.

Further research is required to understand the implications of under-responding, where doubtful high-risk patients are denied an ambulance service.

The need for good triage system is apparent as over-responding badly affects the operational budget. The model can be used for evaluating various dispatching policy decisions.

Emergency medical services have to ensure efficient and equitable provision of services, from the perception of both patients and service providers.

The paper applies agent-based modeling to equity constrained emergency medical services and highlights findings that are not reported in the existing literature.

One of the problems in post-earthquake disaster management in developing countries, such as Iran, is the prediction of the residual network available for disaster relief operations. Therefore, it is important to use methods that are executable in such countries given the limited amount of accurate data. The purpose of this paper is to present a multi-objective model that seeks to determine the set of roads of a transportation network that should preserve its role in carrying out disaster relief operations (i.e. known as “emergency road network” (ERN)) in the aftermath of earthquakes.

In this paper, the total travel time of emergency trips, the total length of network and the provision of coverage to the emergency demand/supply points have been incorporated as three important metrics of ERN into a multi-objective mixed integer linear programming model. The proposed model has been solved by adopting the e-constraint method.

The results of applying the model to Tehran’s highway network indicated that the least possible length for the emergency transportation network is about half the total length of its major roads (freeways and major arterials).

Gathering detailed data about origin-destination pair of emergency trips and network characteristics have a direct effect on designing a suitable emergency network in pre-disaster phase.

To become solvable in a reasonable time, especially in large-scale cases, the problem has been modeled based on a decomposing technique. The model has been solved successfully for the emergency roads of Tehran within about 10 min of CPU time.

Using China’s key audit matters (KAMs) data, this study aims to examine whether negative press coverage alleviates boilerplate KAMs.

This study uses Levenshtein edit distance (LVD) to calculate the horizontal boilerplate of KAMs and investigates how boilerplate changes under different levels of the perceived legal risk.

The findings indicate that auditors of firms exposed to substantial negative press coverage will reduce the boilerplate of KAMs. This association is more significant for auditing firms with lower market share and client firms with higher financial distress. Additionally, the authors find that negative press coverage is more likely to alleviate the boilerplate disclosure of KAMs related to managers’ subjective estimation and material transactions and events. Furthermore, the association between negative press coverage and boilerplate KAMs varies with the source of negative news.

The findings suggest that upon exposure to negative press coverage, reducing the boilerplate of KAMs has a disclaimer effect for auditors.

Software vulnerabilities have been the greatest threat to the software industry for a long time. Many detection techniques have been developed to address this kind of issue, such as Fuzzing, but mere Fuzz Testing is not good enough, because the Fuzzing only alters the input of program randomly, and does not consider the basic semantics of the target software. The purpose of this paper is to introduce a new vulnerability exploring system, called “SEVE” to explore the target software more deeply and to generate more test cases with more accuracy.

Symbolic execution is the core technique of SEVE. The user can just input a standard input, and the SEVE system will record the execution path, alter the critical branches of it, and generate a totally different test case which will make the software under test execute a different path. In this way, some potential bugs or defects, even the exploitable vulnerabilities will be discovered. To alleviate path explosion, the authors propose heuristic method and function abstraction, which in turn improve the performance of SEVE even further.

We evaluate SEVE system to record critical data about its efficiency and performance. We have tested some real‐world vulnerabilities, from which the underlying file‐input programs suffer. After that, the results show that SEVE is not only re‐creating the discovery of these vulnerabilities, but also at a higher performance level than traditional techniques.

The paper proposes a new vulnerability exploring system, called “SEVE” to explore the target software and generate test cases automatically and also heuristic method and function abstraction to handle path explosion.

Routing protocol for low-power lossy network (RPL) being the de facto routing protocol used by low power lossy networks needs to provide adequate routing service to mobile nodes (MNs) in the network. As RPL is designed to work under constraint power requirements, its route updating frequency is not sufficient for MNs in the network. The purpose of this study is to ensure that MNs enjoy seamless connection throughout the network with minimal handover delay.

This study proposes a load balancing mobility aware secure hybrid – RPL in which static node (SN) identifies route using metrics like expected transmission count, and path delay and parent selection are further refined by working on remaining energy for identifying the primary route and queue availability for secondary route maintenance. MNs identify route with the help of smart timers and by using received signal strength indicator sampling of parent and neighbor nodes. In this work, MNs are also secured against rank attack in RPL.

This model produces favorable result in terms of packet delivery ratio, delay, energy consumption and number of living nodes in the network when compared with different RPL protocols with mobility support. The proposed model reduces packet retransmission in the network by a large margin by providing load balancing to SNs and seamless connection to MNs.

In this work, a novel algorithm was developed to provide seamless handover for MNs in network. Suitable technique was developed to provide load balancing to SNs in network by maintaining appropriate secondary route.

A growing body of evidence points to the influence of location and allocation decisions on the structure of healthcare networks. The authors introduced a three-level hierarchical facility location model to minimize travel time in the healthcare system under uncertainty.

Most healthcare networks are hierarchical and, as a result, the linkage between their levels makes it difficult to specify the location of the facilities. In this article, the authors present a hybrid approach according to data envelopment analysis and robust programming to design a healthcare network. In the first phase, the efficiency of each potential location is calculated based on the non-radial range-adjusted measure considering desirable and undesirable outputs based on a number of criteria such as the target area's population, proximity to earthquake faults, quality of urban life, urban decrepitude, etc. The locations deemed suitable are then used as candidate locations in the mathematical model. In the second phase, based on the proposed robust optimization model, called light robustness, the location and allocation decisions are adopted.

The developed model is evaluated using an actual-world case study in District 1 of Tehran, Iran and relevant results and different sensitivity analyses were presented as well. When the percentage of referral parameters changes, the value of the robust model's objective function increases.

The contributions of this article are listed as follows: Considering desirable and undesirable criteria to selecting candidate locations, providing a robust programming model for building a service network and applying the developed model to an actual-world case study.