Hybrid algorithm for the solution of the periodic vehicle routing problem with variable service frequency

This document addresses the problem of scheduling and routing a specific number of vehicles to visit a set of customers in specific time windows during a planning horizon. The vehicles have a homogeneous limited capacity and have their starting point and return in a warehouse or initial node, in addition, multiple variants of the classic VRP vehicle routing problem are considered, where computational complexity increases with the increase in the number of customers to visit, as a characteris-tic of an NP-hard problem. The solution method used consists of two connected phases, the first phase makes the allocation through a mixed-integer linear programming model, from which the visit program and its frequency in a determined plan-ning horizon are obtained. In the second phase, the customers are grouped through an unsupervised learning algorithm, the routing is carried out through an Ant Colony Optimization metaheuristic that includes local heu-ristics to make sure com-pliance with the restrictive factors. Finally, we test our algorithm by performance measures using instances of the literature and a comparative model, and we prove the effectiveness of the proposed algorithm.

Analytical results for the distribution of cover times of random walks on random regular graphs

We present analytical results for the distribution of cover times of random walks (RWs) on random regular graphs consisting of N nodes of degree c (c ≥ 3). Starting from a random initial node at time t = 1, at each time step t ≥ 2 an RW hops into a random neighbor of its previous node. In some of the time steps the RW may visit a new, yet-unvisited node, while in other time steps it may revisit a node that has already been visited before. The cover time TCis the number of time steps required for the RW to visit every single node in the network at least once. We derive a master equation for the distribution Pt(S = s) of the number of distinct nodes s visited by an RW up to time t and solve it analytically. Inserting s = N we obtain the cumulative distribution of cover times, namely the probability P (TC ≤ t) = Pt(S = N) that up to time t an RW will visit all the N nodes in the network. Taking the large network limit, we show that P (TC ≤ t) converges to a Gumbel distribution. We calculate the distribution of partial cover (PC) times P (TPC,k = t), which is the probability that at time t an RW will complete visiting k distinct nodes. We also calculate the distribution of random cover (RC) times P (TRC,k = t), which is the probability that at time t an RW will complete visiting all the nodes in a subgraph of k randomly pre-selected nodes at least once. The analytical results for the distributions of cover times are found to be in very good agreement with the results obtained from computer simulations.

Predictive Sampling Method for Spread Models in Networks

This paper proposes a novel, exploration-based network sampling algorithm called caterpillar quota walk sampling (CQWS) inspired by the caterpillar tree. Network sampling identifies a subset of nodes and edges from a network, creating an induced graph. Beginning from an initial node, exploration-based sampling algorithms grow the induced set by traversing and tracking unvisited neighboring nodes from the original network. Tunable and trainable parameters allow CQWS to maximize the sum of the degrees of the induced graph from multiple trials when sampling dense networks. A network spread model renders effective use in various applications, including tracking the spread of epidemics, visualizing information transmissions through social media, and cell-to-cell spread of neurodegenerative diseases. CQWS generates a spread model as its sample by visiting the highest-degree neighbors of previously visited nodes. For each previously visited node, a top proportion of the highest-degree neighbors fulfills a quota and branches into a new caterpillar tree. Sampling more high-degree nodes constitutes an objective among various applications. Many exploration-based sampling algorithms suffer drawbacks that limit the sum of degrees of visited nodes and thus the number of high-degree nodes visited. Furthermore, a strategy may not be adaptable to volatile degree frequencies throughout the original network architecture, which influences how deep into the original network an algorithm could sample. This paper analyzes CQWS in comparison to four other exploration-based network in tackling these two problems by sampling sparse and dense randomly generated networks.

GATNNCDA: A Method Based on Graph Attention Network and Multi-Layer Neural Network for Predicting circRNA-Disease Associations

Circular RNAs (circRNAs) are a new class of endogenous non-coding RNAs with covalent closed loop structure. Researchers have revealed that circRNAs play an important role in human diseases. As experimental identification of interactions between circRNA and disease is time-consuming and expensive, effective computational methods are an urgent need for predicting potential circRNA–disease associations. In this study, we proposed a novel computational method named GATNNCDA, which combines Graph Attention Network (GAT) and multi-layer neural network (NN) to infer disease-related circRNAs. Specially, GATNNCDA first integrates disease semantic similarity, circRNA functional similarity and the respective Gaussian Interaction Profile (GIP) kernel similarities. The integrated similarities are used as initial node features, and then GAT is applied for further feature extraction in the heterogeneous circRNA–disease graph. Finally, the NN-based classifier is introduced for prediction. The results of fivefold cross validation demonstrated that GATNNCDA achieved an average AUC of 0.9613 and AUPR of 0.9433 on the CircR2Disease dataset, and outperformed other state-of-the-art methods. In addition, case studies on breast cancer and hepatocellular carcinoma showed that 20 and 18 of the top 20 candidates were respectively confirmed in the validation datasets or published literature. Therefore, GATNNCDA is an effective and reliable tool for discovering circRNA–disease associations.

The Surprising Power of Graph Neural Networks with Random Node Initialization

Graph neural networks (GNNs) are effective models for representation learning on relational data. However, standard GNNs are limited in their expressive power, as they cannot distinguish graphs beyond the capability of the Weisfeiler-Leman graph isomorphism heuristic. In order to break this expressiveness barrier, GNNs have been enhanced with random node initialization (RNI), where the idea is to train and run the models with randomized initial node features. In this work, we analyze the expressive power of GNNs with RNI, and prove that these models are universal, a first such result for GNNs not relying on computationally demanding higher-order properties. This universality result holds even with partially randomized initial node features, and preserves the invariance properties of GNNs in expectation. We then empirically analyze the effect of RNI on GNNs, based on carefully constructed datasets. Our empirical findings support the superior performance of GNNs with RNI over standard GNNs.

Is routine neck dissection warranted at salvage laryngectomy?

Background There is controversy regarding management of the neck at salvage laryngectomy. The aim of this study was to perform an analysis to determine the incidence of occult node positivity in this group and analyse factors affecting it. Method A retrospective analysis of 171 patients who underwent salvage total laryngectomy between 2000 and 2015 for recurrent or residual disease following definitive non-surgical treatment and were clinico-radiologically node negative at the time salvage laryngectomy was carried out. Results A total of 171 patients with laryngeal or hypopharyngeal cancers underwent concurrent neck dissection at laryngectomy. There were 162 patients (94.7 per cent) who underwent bilateral neck dissection, and 9 patients (5.3 per cent) who underwent ipsilateral neck dissection. The occult lateral nodal metastasis rate was 10.5 per cent. Of various factors, initial node positive disease was the only factor predicting occult metastasis on univariable and multivariable analysis (p = 0.001). Conclusion Risk of occult metastasis is high in patients who have node positive disease before starting radiotherapy. This group should be offered elective neck dissection.

Genetic algorithm optimized node deployment in IEEE 802.15.4 potato and wheat crop monitoring infrastructure

This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.

Performance Evaluation and Discrimination of AODV and AOMDV VANET Routing Protocols Based on RRSE Technique

The routing protocol is an applied standard to determine the communication scheme of different entities with each other to transfer and process the desired data in considerable time via the best routes from the source to the destination. This paper presents the performance evaluation and discrimination for various parameters of two different routing protocols using the Root Relative Squared Error (RRSE). The two protocols under study are Ad-hoc On-demand Distance Vector (AODV), and Ad-hoc On-demand Multipath Distance Vector (AOMDV). The literature reviews reveals the simulation results of number of nodes that varies approximately between 5 to 100 nodes. Therefore, that he simulation results will be analyzed for two different experiment as follows the first, the effect of initial node energy variation between 50 to 100 Joules at a fixed network size. Whereas in the second, reveals the impact of the network size, which varies between 50 to 450 nodes at constant initial node energy that it is tested between 50 to 100 Joules. The obtained results of the selected parameters prove that the AOMDV protocol is more efficient, robust, and reliable than the AODV protocol for the first experiment, while the RRSE values of AODV are better for the second. Moreover, the proposed technique based on the RRSE algorithm advantageous to compare the two routing protocols.

Modification of Ant Colony Optimization Algorithm to Solve the Traveling Salesman Problem

Traveling salesman problem (TSP) is an optimization problem in determining the optimal route of a number of nodes that will only be passed once with the initial node as the final destination. One method for solving TSP is the Ant Colony Optimization (ACO) Algorithm. ACO is inspired by ant behaviour in searching for food, where ants produce pheromones to find food sources and make a route from the colony to food that will be followed by other ants. However ACO has not been considered as the optimal method for resolving TSP. This is because ACO has several shortcomings in the computational process. Comparisons between pheromones are not yet clear, and slow computing time causes the results of ACO to be not optimal. To correct these deficiencies, modifications will be made to the ACO. Modifications are made by changing some values in the ACO, such as adjusting the number of ants by the node automatically, changing the value in the pheromone renewal, and adding value to the construction of the solution. The outcome of this research is the modification of ACO did not provide shorter computing time with a more accurate final value, thus did not provide an optimal solution. The test results in this study found that the average computation time for the last iteration of each test was 0.54 second, and for the 10 iteration computation time obtained an average of 5.54 second for four tests. The amount of memory used in four tests in this study was 440.11 mb for 10 iterations.

Genetic Algorithm Optimized Node Deployment in IEEE 802.15.4 Potato and Wheat Crop Monitoring Infrastructure

This proposal investigates the effect of vegetation height and density on received signal strength between two sensor nodes communicating under IEEE 802.15.4 wireless standard. With the aim of investigating the path loss coefficient of 2.4 GHz radio signal in an IEEE 802.15.4 precision agriculture monitoring infrastructure, measurement campaigns were carried out in different growing stages of potato and wheat crops. Experimental observations indicate that initial node deployment in the wheat crop experiences network dis-connectivity due to increased signal attenuation, which is due to the growth of wheat vegetation height and density in the grain-filling and physical-maturity periods. An empirical measurement-based path loss model is formulated to identify the received signal strength in different crop growth stages. Further, a NSGA-II multi-objective evolutionary computation is performed to generate initial node deployment and is optimized over increased coverage, reduced over-coverage, and received signal strength. The results show the development of a reliable wireless sensor network infrastructure for wheat crop monitoring.