Information Centric Networking (ICN) provides caching strategies to improve network performance based on consumer demands from the intermediate routers. It reduces the load on content server, network ...traffic, and improves end-to-end delay. The content requesters use an Interest packet containing the name of data to express their needs. If such Interest packets are routed efficiently, the end to end delay and throughput of the network could be improved further. This paper describes an efficient method of forwarding Interest packets to retrieve the requested content at the earliest possible time. Here the data source is found and considered as a single player game with content requester as its start state and location of the desired content as final or goal state. The Monte Carlo Tree Search (MCTS) algorithm is used for constructing the path from content requester to concerned data source. For performance evaluation, the proposed scheme is integrated with Leave Copy Down (LCD) and Leave Copy Everywhere (LCE), Cache Less for More (CL4M), and Probability based caching (ProbCache) In ns-3 simulation environment (ndnSim), all these are evaluated in terms of content search latency, server hit ratio, network load, overhead and throughput. Simulation observation reveals that the integration of MCTS significantly improves performance in regard to experimental parameters.
•A novel Interest packet forwarding technique for faster content retrieval in ICN.•Use of Monte Carlo Tree Search (MCTS) method for finding the closest source of searched content.•Mathematical representation of the proposed scheme for better understanding.•Detailed analysis of the proposed scheme with comparative performance evaluation through simulation in ndnSim.
Supporting the ultra-reliable and low-latency communications (URLLCs) has become one of the major goals for future wireless networks. In this paper, we present an analytical reliability model for ...user equipment (UE) connecting multiple base stations (BSs) with multi-stream carrier aggregation (MSCA) technology. We first derive a closed-form expression for reliability characterization using signal-to-interference-plus-noise (SINR) model over Nakagami-m fading. We then formulate a joint resource allocation problem to maximize reliability, considering UE association, sub-carrier assignment and discrete power allocation. With distributed decision making (DDM) theory, we decouple it into two sub-problems, and each sub-problem is formulated as a separate Markov Decision Problem (MDP). We further propose the Monte Carlo Tree Search (MCTS) method to find the optimal solution for each sub-problem. The iterative joint optimization method based on DDM is also proposed. Our simulation results show that the reliability with MSCA increases with increasing the number of sub-carriers, as well as increasing the power allocation. We also show that our algorithm is an effective way in finding the optimal resource allocation for reliability improvement.
•We derive a closed-form expression for the reliability characterization.•We propose DDM to decouple the joint optimization problem into two sub-problems.•We propose the MCTS method to find the optimal results for each sub-problem.•We propose the iterative joint optimization method to find the joint optima.
In this article we try different algorithms, namely Nested Monte Carlo Search and Greedy Best First Search, on AstraZeneca's open source retrosynthetic tool : AiZynthFinder. We compare these ...algorithms to AiZynthFinder's base Monte Carlo Tree Search on a benchmark selected from the PubChem database and by Bayer's chemists. We show that both Nested Monte Carlo Search and Greedy Best First Search outperform AstraZeneca's Monte Carlo Tree Search, with a slight advantage for Nested Monte Carlo Search while experimenting on a playout heuristic. We also show how the search algorithms are bounded by the quality of the policy network, in order to improve our results the next step is to improve the policy network.
The development of marine current energy productions has recently gained attention. However, the variation in marine current will lead to weak fault feature and fault misclassification of marine ...current turbines (MCTs). This paper proposes a new method called extended Concordia transform-latitudinal summation linear discriminant analysis (ECT-LSLDA) to alleviate this problem. First, the extended Concordia transform (ECT) method projects the acquired three-phase currents into the stationary coordinate system to achieve the derivative modulus signals as an effective fault feature. Then, the latitudinal summation linear discriminant analysis (LSLDA) method normalizes and classifies the modulus to realize MCT faults recognition. Experimental results show that the proposed fault diagnosis method achieves high accuracy compare with the state of the art.
•A stator current measurement-based way solves the problem that the current signal is non -Gauss distribution without additional measurements.•An ECT based on the derivation is studied in the proposed diagnosis method to extract MCT multi-fault features.•An LSLDA classifier is proposed to solve the problem of fault features diversity and classify the MCT faults.
Monocarboxylate transporters (MCTs) play an immense role in metabolically active solid tumors by regulating concentration-dependent transport of different important monocarboxylates including ...pyruvate and lactate and are encoded by the SLC16A family of genes. Given the vast array of functions, these transporters play in oncogenesis, our objective was to look into the association of MCT1 (SLC16A1), MCT2 (SLC16A7), MCT3 (SLC16A8), and MCT4 (SLC16A3) with Epithelial ovarian cancer (EOC) pathophysiology by exploiting various publicly available databases and web resources. Few of the in silico findings were confirmed via in vitro experiments in EOC cell lines, SKOV3 and OAW-42. MCT1 and MCT4 were found to be upregulated at the mRNA level in OC tissues compared to normal. However, only higher level of MCT4 mRNA was found to be associated with poor patient survival. MCT4 was positively correlated with gene families responsible for invasion, migration, and immune modification, proving it to be one of the most important MCTs for therapeutic intervention. We compared the effects of MCT1/2 blocker SR13800 and a broad-spectrum MCT blocker α-Cyano Hydroxy Cinnamic Acid (α-CHCA) and discovered that α-CHCA has a greater effect on diminishing the invasive behavior of the cancer cells than MCT1/2 blocker SR13800. From our study, MCT4 has emerged as a prospective marker for predicting poor patient outcomes and a potential therapeutic target.
Unmanned Aerial Vehicle (UAV) has emerged as a promising technology for the support of human activities, such as target tracking, disaster rescue, and surveillance. However, these tasks require a ...large computation load of image or video processing, which imposes enormous pressure on the UAV computation platform. To solve this issue, in this work, we propose an intelligent Task Offloading Algorithm (iTOA) for UAV edge computing network. Compared with existing methods, iTOA is able to perceive the network’s environment intelligently to decide the offloading action based on deep Monte Calor Tree Search (MCTS), the core algorithm of Alpha Go. MCTS will simulate the offloading decision trajectories to acquire the best decision by maximizing the reward, such as lowest latency or power consumption. To accelerate the search convergence of MCTS, we also proposed a splitting Deep Neural Network (sDNN) to supply the prior probability for MCTS. The sDNN is trained by a self-supervised learning manager. Here, the training data set is obtained from iTOA itself as its own teacher. Compared with game theory and greedy search-based methods, the proposed iTOA improves service latency performance by 33% and 60%, respectively.
The large-scale multiobjective optimization problem (LSMOP) is characterized by simultaneously optimizing multiple conflicting objectives and involving hundreds of decision variables. Many real-world ...applications in engineering can be modeled as LSMOPs; simultaneously, engineering applications require insensitivity in performance. This requirement typically means that the algorithm should not only produce good results in terms of performance for every run but also the performance of multiple runs should not fluctuate too much. However, existing large-scale multiobjective optimization algorithms often focus on improving algorithm performance, but pay little attention to improving the insensitivity characteristic of algorithms. This directly leads to substantial limitations when solving practical problems. In this work, we propose an evolutionary algorithm called large-scale multiobjective optimization algorithm via Monte Carlo tree search, which is based on the Monte Carlo tree search and aims to improve the performance and insensitivity of solving LSMOPs. The proposed method samples decision variables to construct new nodes on the Monte Carlo tree for optimization and evaluation, and it selects nodes with good evaluations for further searches in order to reduce the performance sensitivity caused by large-scale decision variables. We propose two metrics to measure the sensitivity of the algorithm and compare the proposed algorithm with several state-of-the-art designs on different benchmark functions and metrics. The experimental results confirm the effectiveness and performance insensitivity of the proposed design for solving LSMOPs.
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