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The production of brachytherapy radio-nuclide 169Yb was investigated in the 169Tm(p,n), 170Yb(p,2n), 171Yb(p,3n), 172Yb(p,4n), 169Tm(d,2n), 168Er(α,3n), 167Er(α,2n) and 166Er(α,n) reactions. The cross section of these reactions were calculated using TALYS 1.9 code and compared with the available experimental results. SRIM-2013 code and GEANT4 toolkit were used to calculate the stopping power, range and projectile flux within the target. The optimum target thickness, theoretical and simulation production yield of 169Yb radioisotope in each reaction were calculated. Finally, the 169Tm(p, n) reaction with high production yield was suggested to produce 169Yb radioisotope.

Cloud computing has found an important role in cost-effective and elastic network resource allocation community. Most researchers and practitioners have focused on cloud provider side for efficient resource allocation to end-users and to warrant their service-level agreements (SLAs). Due to heterogeneous user demands which request services from cloud data center networks, simultaneous satisfaction of all users to their pre-defined SLA level while making the cloud provider and all of the data centers to be cost-effective in terms of the financial income and energy-efficiency is a challenging issue. To address this problem, in the current work, the efficient joint provider/customer efficient resource allocation has been first formulated as a multi-objective optimization problem with convex constraint set in terms of virtual machine (VM) parameters (e.g., storage, CPU core, memory, etc.). Then, an evolutionary solution based on cooperative co-evolution algorithm has been proposed that can conduct a divide-and-conquer methodology for solving the complex optimization problem. Numerical results show the prominent feature of the method in terms of VM usage effectiveness and overall joint network cost/user SLA satisfaction.

In this paper, a joint optimization method has been proposed for energy cost and the user's perceived quality-of-experience (QoE) within a content delivery network (CDN). The proposed algorithm has been developed with an objective to minimize the total operational cost using real-time electricity pricing as well as the integration of green energy resources from the smart grid. To solve the joint optimization problem, a linear programming and a differential evolution algorithm have been used to provide a tradeoff between operational cost saving and computational complexity. We also formulate the joint problem as a noncooperative game in which the CDN providers act as players. A group utility function has been defined for the players and it is shown that a Nash equilibrium exists for the joint quality and energy cost saving when each CDN provider has single cluster or multiple clusters. Numerical results are presented to evaluate and validate the aforementioned solutions. These results illustrate significant operational/energy cost reductions while optimizing user's perceived QoE for a CDN or multiple CDN providers with respect to conventional methods.

We propose a Lyapunov drift-plus-penalty- (LDPP-) based algorithm to optimize the average power cost for a data center network. In particular, we develop an algorithm to minimize the operational cost using real-time electricity pricing with the integration of green energy resources from the smart grid. The LDPP technique can achieve significant energy cost savings under quality of service (QoS) constraints. Numerical results are presented to evaluate and validate our solution. These results illustrate significant operational/energy cost reductions for a data center network over the conventional approach which optimizes the predicted values of stochastic parameters under a fixed QoS constraint.

Quality of Experience (QoE) enhancement for a transmitted video sequence in Mobile Ad hoc NETworks (MANETs) is a challenging and important issue in networking research community. Intrinsic high levels of packet error probability in MANETs can cause high levels of distortion based on the position of packet loss in transmitted Intra-coded (I), Predicted (P) and Bi-predictive (B) frames. Hence, exact modeling of the impact of packet loss on the video quality and the resulting distortion is an important task. Many conventional distortion modeling techniques simply consider a linear relationship between the packet loss and distortion which is inaccurate. The main contributions of the current work are twofold. At first, an accurate model has been developed which can capture the exact effect of network packet loss on the video quality performance (and hence on the QoE) with Group Of Picture (GOP)-level granularity. Then, based on this model, an optimal bandwidth allocation strategy is being developed in MANETs which based on some network-specific constraints; the loss-induced distortion associated with a video source is being minimized using some cross-layer design techniques. Finally, the resulting optimal rates can be used as rate-feedbacks for on-line rate adaptation of an appropriate video encoder such as H.264/AVC. Numerical analysis has been presented to validate the theoretical results and to verify the claims.

Many optimization theoretic based bandwidth allocation strategies have been developed for guaranteeing some levels of Quality of Service (QoS) for some classes of competing real-time users in wireless ad hoc networks. By considering different objective functions (such as congestion level, constrained delay and so on), the researchers propose some optimization frameworks by which the problem can be solved. The rapid increase in the development of different real-time applications with stringent maximum packet loss requirements in such environments and the existence of difficulties in satisfying the pre-specified QoS limits, is a great motivation for designing some type of differentiated QoS guaranteeing mechanisms that can satisfy the demands of this class of the real-time traffics. In the current work, a cross-layer technique is being introduced in which, based on the packet loss information from the lower layers, some optimal bandwidth is assigned to the real-time applications which need some levels of the maximum packet loss guaranties. As, each real-time application needs a different level of maximum packet loss guarantee, a weighted aggregate packet loss objective function is being introduced. The weights are proportional with the importance of the packet loss mitigation for a specific application. The simulation results verify the claims.

Optimization theoretic-based rate allocation strategies can be used for the aim of allocating some optimal rates to the competing users in wireless ad hoc networks. By considering different objective functions (such as congestion level, total packet loss, etc.), the researchers propose some optimization framework by which the problem can be solved. Due to the rapid increase in the development of different video applications in such environments and the existence of difficulties in satisfying the prespecified QoS limits, increasing the perceived video quality can be considered as an important and challenging issue. The quality of the received video stream is inversely proportional to the amount of distortion which is being imposed on the video stream by the network packet loss and the video encoder. The main contribution of the current paper is to introduce an optimization theoretic framework in which by optimal rate allocation to some competing video sources, the aggregate distortion associated with all of the sources can be minimized. The numerical results verify the claims.

Fair resource allocation is an important and challenging issue for many telecommunication service providers. Many researchers are investigating fast and efficient algorithms which can provide such fair rates in a distributed manner. It is well-known that, for any rate allocation algorithm, almost all of the bottlenecks occur in the access part of the network. In the current work, we use this inherent feature for designing hierarchical methods of resource allocation and then with the use of the fast Newton method and fuzzy algorithms, we improve the convergence speed of the algorithm in comparison with the conventional ones. An important feature of the proposed method in comparison with the previous fuzzy implementations is its distributed nature, because there is no need for feedback from the core network in developing the desired fuzzy method.