Control and Optimization in Applied Mathematics
Volume:8 Issue: 2, Summer-Autumn 2023

Every year‎, ‎extensive experimental analysis is conducted to evaluate the anti-cancer properties of plants‎. ‎Developing a well-ranked list of potential anti-cancer plants based on verified anti-cancer metabolites can significantly reduce the time and cost required for plant evaluation‎. This paper proposes a method for generating such a ranked list by analyzing biological graphs of plant-metabolite interactions‎. ‎In this approach‎, ‎graph nodes are ranked based on specific graph features‎. ‎However‎, ‎a challenge arises in selecting the most informative graph features that ensure the resulting ranked plant list is more relevant‎, ‎prioritizing plants with greater anti-cancer properties at the top‎. ‎To address this challenge‎, ‎we propose the use of the Average Precision metric commonly used in information retrieval and recommender systems‎, ‎to compare different ranked lists‎. By constructing a network that captures the similarities between plants based on their shared metabolites‎, ‎and ranking plants using different combinations of graph features‎, ‎we can identify the subset of features that yields a ranked list with a higher Average Precision score‎. ‎This subset of features can then be considered the most suitable for recommending anti-cancer plants‎. ‎ The proposed method can be used to select the best graph features for screening unverified plant lists for anti-cancer properties‎, ‎increasing the likelihood of identifying plants with higher scores in the list that possess anti-cancer properties‎.

This paper presents a novel approach using artificial neural networks to solve the SEIR (Susceptible‎, ‎Exposed‎, ‎Infected‎, ‎and Recovered) model of infectious diseases based on dynamical systems‎. ‎Optimal control techniques are employed to determine a vaccination schedule for a standard SEIR epidemic model‎. ‎The multilayer perceptron is utilized to approximate the state and co-state functions of the SEIR model and to solve the optimal control problem by utilizing a nonlinear programming approach‎. By constructing a loss function and using Pontryagin's Minimum Principle (PMP) for the SEIR model, a minimization problem is defined, ‎a minimization problem is defined‎, ‎and the approximate solution of the Hamiltonian system is computed‎. ‎This method is compared with the fourth-order Runge-Kutta method. The proposed approach's effectiveness is demonstrated through illustrative examples.

This paper is motivated by high dose rate brachytherapy treatment planning problems which involve the specification of the movement schedule of a radiation source so that the target volumes are adequately covered with sufficient doses and organs at risk are not radiated beyond the clinical acceptance threshold. It utilizes four powerful multi-objective evolutionary algorithms (MOEA), which create a set of equally-weighted Pareto optimal solutions instead of only one and produce better results compared to other optimization methods. These algorithms include non-dominated sorting genetic algorithms, Pareto envelope-based selection algorithm, non-dominated ranking genetic algorithm, and strength Pareto evolutionary algorithm. The results indicate that the last algorithm uses the dependency between decision variables to solve them efficiently and is the best type of MOEA both in terms of convergence criteria and solution diversity maintenance for the brachytherapy problems.

This paper addresses a non-smooth multi-objective semi-infinite programming problem that involves a feasible set defined by inequality constraints‎. ‎Our focus is on introducing a new weak Slater constraint qualification and deriving the necessary and sufficient conditions for (weakly‎, ‎properly) efficient solutions to the problem using (weak and strong) Karush-Kuhn-Tucker types‎. ‎Additionally‎, ‎we present two duals of the Mond-Weir type for the problem and provide (weak and strong) duality results for them‎. ‎All of the results are given in terms of Clarke subdifferential‎.

This paper discusses the challenges facing the logistics industry in the global business environment, including issues related to tracking transactions, preserving transaction privacy, and ensuring the security of logistics information. We propose a method for addressing these challenges using a blockchain-based system that employs a smart contract to control the behaviors of all participants in the logistics and trade process. ‎‎In the experiment‎, ‎we‎ ‎use the Solidity programming language to develop a smart contract on‎ ‎Ethereum and tested it for common logistics and transaction uses‎. ‎The‎ ‎results of related programming and coding in Remix IDE show that the‎ ‎proposed algorithm is highly implementable‎. ‎To test the smart contract‎ ‎code and validation‎, ‎we test four main functionalities‎, ‎which include‎ ‎successful collateral deposit after the customer requests a document‎, ‎unique token generation‎, ‎successful payment settlement‎, ‎and a refund‎ ‎based on dispute handling by the arbitrator‎. ‎Oyente vulnerability‎ ‎analysis also shows that the code does not suffer security bugs‎. ‎Therefore‎, ‎the proposed method ‎‎can‎ ‎effectively decrease the risk of the logistics and trade process.

The paper discusses the limitations of emotion recognition in Persian speech due to inefficient feature extraction and classification tools‎. ‎To address this‎, ‎we propose a new method for detecting hidden emotions in Persian speech with higher recognition accuracy‎. ‎The method involves four steps‎: ‎preprocessing‎, ‎feature description‎, ‎feature extraction‎, ‎and classification‎. ‎The input signal is normalized in the preprocessing step using single-channel vector conversion and signal resampling‎. ‎Feature descriptions are performed using Mel-Frequency Cepstral Coefficients and Spectro-Temporal Modulation techniques‎, ‎which produce separate feature matrices‎. ‎These matrices are then merged and used for feature extraction through a Convolutional Neural Network‎. ‎Finally‎, ‎a Support Vector Machine with a linear kernel function is used for emotion classification‎. ‎The proposed method is evaluated using the Sharif Emotional Speech dataset and achieves an average accuracy of 80.9% in classifying emotions in Persian speech‎.