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The purpose of this paper is to investigate the stochastic comparisons of the parallel system with independent heterogeneous Gumbel components and series and parallel systems with independent heterogeneous truncated Gumbel components in terms of various stochastic orderings.

The obtained results in this paper are obtained by using the vector majorization methods and results. First, the components of series and parallel systems are heterogeneous and having Gumbel or truncated Gumbel distributions. Second, multiple-outlier truncated Gumbel models are discussed for these systems. Then, the relationship between the systems having Gumbel components and Weibull components are considered. Finally, Monte Carlo simulations are performed to illustrate some obtained results.

The reversed hazard rate and likelihood ratio orderings are obtained for the parallel system of Gumbel components. Using these results, similar new results are derived for the series system of Weibull components. Stochastic comparisons for the series and parallel systems having truncated Gumbel components are established in terms of hazard rate, likelihood ratio and reversed hazard rate orderings. Some new results are also derived for the series and parallel systems of upper-truncated Weibull components.

To the best of our knowledge thus far, stochastic comparisons of series and parallel systems with Gumbel or truncated Gumble components have not been considered in the literature. Moreover, new results for Weibull and upper-truncated Weibull components are presented based on Gumbel case results.

A critical issue in the design of routing protocols for wireless sensor networks is the efficient utilization of resources such as scarce bandwidth and limited energy supply. Many routing schemes proposed in the literature try to minimize the energy consumed in routing or maximize the lifetime of the sensor network without taking into consideration limited capacities of nodes and wireless links. This can lead to congestion, increased delay, packet losses and ultimately to retransmission of packets, which will waste considerable amount of energy. This paper presents a Minimum‐cost Capacity‐constrained Routing (MCCR) protocol which minimize the total energy consumed in routing while guaranteeing that the total load on each sensor node and on each wireless link does not exceed its capacity. The protocol is derived from polynomial‐time minimum‐cost flow algorithms. Therefore protocol is simple and scalable. The paper improves the routing protocol in (1) to incorporate integrality, node capacity and link capacity constraints. This improved protocol is called Maximum Lifetime Capacity‐constrained Routing (MLCR). The objective of MLCR protocol is to maximize the time until the first battery drains its energy subject to the node capacity and link capacity constraints. A strongly polynomial time algorithm is proposed for a special case of MLCR problem when the energy consumed in transmission by a sensor node is constant. Simulations are performed to analyzed the performance of the proposed protocols.

Wireless sensor networks consist of a large number of nodes with limited battery power and sensing components, which can be used for sensing specified events and gather wanted or interesting information via wireless links. It will enable the reliable monitoring of a variety of environments for both civil and military applications. There is a need of energy‐efficient message collection and power management methods to prolong the lifetime of the sensor network. Many methods, such as clustering algorithm, are investigated for power saving reason, however, they only consider reducing the amount of message deliveries by clustering but not the load balance of the clusters to extend the maximum lifetime of the network. Therefore, in this paper, we propose a fully distributed, randomized, and adaptable clustering mechanism named autonomous clustering and message passing (ACMP) protocol for improving energy efficiency in wireless sensor networks. Sensor nodes, according to ACMP, can cluster themselves autonomously by their remaining energy and dynamically choose a corresponding cluster head (CH) to transfer the collected information. Sensor nodes adjust an appropriate power level to form clusters and use minimum energy to exchange messages. The network topology is changed dynamically depending on the CH's energy. Moreover, by maintaining the remaining energy of each node, the traffic load is distributed to all nodes and thus prolong the network lifetime efficiently. Simulation results show that ACMP can achieve a highly energy saving effect as well as prolong the network lifetime.

In wireless sensor networks, improving the network lifetime is considered as the prime objective that needs to be significantly addressed during data aggregation. Among the traditional data aggregation techniques, cluster-based dominating set algorithms are identified as more effective in aggregating data through cluster heads. But, the existing cluster-based dominating set algorithms suffer from a major drawback of energy deficiency when a large number of communicating nodes need to collaborate for transferring the aggregated data. Further, due to this reason, the energy of each communicating node is gradually decreased and the network lifetime is also decreased. To increase the lifetime of the network, the proposed algorithm uses two sets: Dominating set and hit set.

The proposed algorithm uses two sets: Dominating set and hit set. The dominating set constructs an unequal clustering, and the hit set minimizes the number of communicating nodes by selecting the optimized cluster head for transferring the aggregated data to the base station. The simulation results also infer that the proposed optimized unequal clustering algorithm (OUCA) is greater in improving the network lifetime to a maximum amount of 22% than the existing cluster head selection approach considered for examination.

In this paper, lifetime of the network is prolonged by constructing an unequal cluster using the dominating set and electing an optimized cluster head using hit set. The dominator set chooses the dominator based on the remaining energy and its node degree of each node. The optimized cluster head is chosen by the hit set to minimize the number of communicating nodes in the network. The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit set. The simulation result confirms that the proposed algorithm prolonging the lifetime of the network efficiently when compared with the existing algorithms.

The proposed algorithm effectively constructs the clusters with a minimum number of communicating nodes using the dominating and hit sets. The simulation result confirms that the proposed algorithm is prolonging the lifetime of the network efficiently when compared with the existing algorithms.

Multimedia applications such as digital audio and video have stringent quality of service (QoS) requirement in mobile ad hoc network. To support wide range of QoS, complex routing protocols with multiple QoS constraints are necessary. In QoS routing, the basic problem is to find a path that satisfies multiple QoS constraints. Moreover, mobility, congestion and packet loss in dynamic topology of network also leads to QoS performance degradation of protocol.

In this paper, the authors proposed a multi-path selection scheme for QoS aware routing in mobile ad hoc network based on fractional cuckoo search algorithm (FCS-MQARP). Here, multiple QoS constraints energy, link life time, distance and delay are considered for path selection.

The experimentation of proposed FCS-MQARP is performed over existing QoS aware routing protocols AOMDV, MMQARP, CS-MQARP using measures such as normalized delay, energy and throughput. The extensive simulation study of the proposed FCS-based multipath selection shows that the proposed QoS aware routing protocol performs better than the existing routing protocol with maximal energy of 99.1501 and minimal delay of 0.0554.

This paper presents a hybrid optimization algorithm called the FCS algorithm for the multi-path selection. Also, a new fitness function is developed by considering the QoS constraints such as energy, link life time, distance and delay.

The purpose of this paper is to investigate conditional and unconditional lifetime sequence of wireless sensor networks (WSN) that have many important practical applications. A significant limitation for WSN is its short lifetime due to the limited capacity of the battery. Renewable energy can significantly extend the lifetime of WSN. In this paper, we investigate the whole sequence of lifetimes of every sensor in WSN, as different application scenarios have different requirement on how many sensors can die until the WSN is no longer functional.

Linear programming formulation was used to investigate both the conditional and unconditional lifetime sequence of WSN. The lifetime sequences of WSN without and with differ levels of solar power were studied.

This investigation of lifetime sequences discovered three interesting phenomena: the sensors that die first are on the peripheral of the network, rather close to the base station; multiple sensors tend to die simultaneously; and the lifetimes of sensors that die later can be extended by renewable energy much more significantly than those that die early, which is very good news to applications that can tolerate the death of a fraction of sensors.

In this paper, the first optimization formulation for maximizing both unconditional and conditional lifetime sequences of WSNs with renewable energy sources was provided. Only the conditional lifetime sequence has been investigated in a previous work, but this method runs n-times faster than the previous work, with n being the number of nodes in the WSN.

The purpose of this paper is to review and analyze the perishable inventory models along various dimensions such as its evolution, scope, demand, shelf life, replenishment policy, modeling techniques and research gaps.

In total, 418 relevant and scholarly articles of various researchers and practitioners during 1990-2016 were reviewed. They were critically analyzed along author profile, nature of perishability, research contributions of different countries, publication along time, research methodologies adopted, etc. to draw fruitful conclusions. The future research for perishable inventory modeling was also discussed and suggested.

There are plethora of perishable inventory studies with divergent objectives and scope. Besides demand and perishable rate in perishable inventory models, other factors such as price discount, allow shortage or not, inflation, time value of money and so on were found to be combined to make it more realistic. The modeling of inventory systems with two or more perishable items is limited. The multi-echelon inventory with centralized decision and information sharing is acquiring lot of importance because of supply chain integration in the competitive market.

Only peer-reviewed journals and conference papers were analyzed, whereas the manuals, reports, white papers and blood-related articles were excluded. Clustering of literature revealed that future studies should focus on stochastic modeling.

Stress had been laid to identify future research gaps that will help in developing realistic models. The present work will form a guideline to choose the appropriate methodology(s) and mathematical technique(s) in different situations with perishable inventory.

The current review analyzed 419 research papers available in the literature on perishable inventory modeling to summarize its current status and identify its potential future directions. Also the future research gaps were uncovered. This systemic review is strongly felt to fill the gap in the perishable inventory literature and help in formulating effective strategies to design of an effective and efficient inventory management system for perishable items.

In the past few decades, the wireless sensor network (WSN) has become the more vital one with the involvement of the conventional WSNs and wireless multimedia sensor networks (WMSNs). The network that is composed of low-power, small-size, low-cost sensors is said to be WSN. Here, the communication information is handled using the multiple hop and offers only a simple sensing data, such as humidity, temperature and so on, whereas WMSNs are referred as the distributed sensing networks that are composed of video cameras, which contain the sector sense area. These WMSNs can send, receive and process the video information data, which is more intensive and complicated by wrapping with wireless transceiver. The WSNs and the WMSNs are varied in terms of their characteristic of turnablity and directivity.

The main intention of this paper is to maximize the lifetime of network with reduced energy consumption by using an advanced optimization algorithm. The optimal transmission radius is achieved by optimizing the system parameter to transmit the sensor information to the consequent sensor nodes, which are contained within the range. For this optimal selection, this paper proposes a new modified lion algorithm (LA), the so-called cub pool-linked lion algorithm (CLA). The next contribution is on the optimal selection of cluster head (CH) by the proposed algorithm. Finally, the performance of proposed model is validated and compared over the other traditional methods in terms of network energy, convergence rate and alive nodes.

The proposed model's cost function relies in the range of 74–78. From the result, it is clear that at sixth iteration, the proposed model’s performance attains less cost function, that is, 11.14, 9.78, 7.26, 4.49 and 4.13% better than Genetic Algorithm (GA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO) and Firefly (FF), correspondingly. The performance of the proposed model at eighth iteration is 14.15, 7.96, 4.36, 7.73, 7.38 and 3.39% superior to GA, DA, PSO, GSO, FF and LA, correspondingly with less convergence rate.

This paper presents a new optimization technique for increasing the network lifetime with reduced energy consumption. This is the first work that utilizes CLA for optimization problems.

This study aims to introduce a novel approach in form of a comprehensive software suite to help understanding and optimizing the build orientation toward maximizing the fatigue lifetime of complex geometries. The objective is to find an optimized build orientation under a given in-service loading state, which brings on smoother surfaces in stressed regions, mitigated roughness-induced stress concentration and deferred crack initiation stage. The solution addresses scenarios that no post-build surface treatment can be applied.

To account for the surface topography, the staircase induced surface roughness is registered as a function of build angle using the white light interferometry characterization, based on which the stress concentration factor (k_t) is calculated. Thereafter, the developed module in “Fatlab toolbox” is used to find the optimum build angle, considering the integrated surface orientations and stress analysis under a given loading condition.

Surface topography creates local stress concentrations upon loading, directly influencing the fatigue lifetime. It is a well-established fact that the conditions of the staircase geometry and surface roughness affect the magnitude of the stress concentration upon loading, which is influenced by the build orientation of the component. The proposed solution suggests the best build orientation that mitigates staircase-related surface roughness.

The suggested numerical approach assists the designers with positioning of the part on the build plate to minimize the build orientation-induced surface roughness and improve the as-built fatigue lifetime of the component.

The purpose of this paper is to establish a theoretical foundation for seemingly contradictory empirical findings about child labor and the families and the children's well‐being.

The systemic yoyo model is employed as justification and road map for exploring some issues related to child labor and its efficiency.

Among many interesting results obtained in this paper, it is particularly shown that: first, as long as children's disutility or utility of labor or formal schooling are concerned, the laissez faire triple of child labor, formal schooling, and level of maturity, which maximizes the parents' utility function, can never be efficient; second, if the efficiency of parent's chosen (for their children) levels of child labor, formal schooling, and level of maturity, is determined by the impact on the children's lifetime earnings potential, then the laissez faire triple of child labor, formal schooling, and level of maturity, is efficient, either if the parents' savings and bequests are interior when the capital markets are imperfect, or if the parents' bequests are interior and the capital markets are perfect. Third, if a government regulation is introduced to impose a marginal ban on child labor, then such a ban could be either welfare reducing for both the child and the parents or a Pareto improvement for both the child and the parents, under different sets of specific conditions.

To take advantage of the methods and results of calculus, the assumption of continuity and differentiability is implied. So, all the findings of this work are limited by these conditions.

It is shown that by using systems analysis, seemingly contradictory empirical findings regarding the efficiency of child labor can be explained by using a unified theory.