Detection of protein-protein interactions (PPIs) plays a vital role in molecular biology. Particularly, pathogenic infections are caused by interactions of host and pathogen proteins. It is important to identify host-pathogen interactions (HPIs) to discover new drugs to counter infectious diseases. Conventional wet lab PPI detection techniques have limitations in terms of cost and large-scale application. Hence, computational approaches are developed to predict PPIs. This study aims to develop machine learning models to predict inter-species PPIs with a special interest in HPIs. Specifically, we focus on seeking answers to three questions that arise while developing an HPI predictor: (1) How should negative training examples be selected? (2) Does assigning sample weights to individual negative examples based on their similarity to positive examples improve generalization performance? and, (3) What should be the size of negative samples as compared to the positive samples during training and evaluation? We compare two available methods for negative sampling: random versus DeNovo sampling and our experiments show that DeNovo sampling offers better accuracy. However, our experiments also show that generalization performance can be improved further by using a soft DeNovo approach that assigns sample weights to negative examples inversely proportional to their similarity to known positive examples during training. Based on our findings, we have also developed an HPI predictor called HOPITOR (Host-Pathogen Interaction Predictor) that can predict interactions between human and viral proteins. The HOPITOR web server can be accessed at the URL: http://faculty.pieas.edu.pk/fayyaz/software.html#HoPItor .

This research aims to contribute in developing a mathematical model for the composite probabilistic energy emissions dispatch (CPEED) with renewable energy systems, and it proposes a novel framework, based on an existing astute black widow optimization (ABWO) algorithm. Renewable energy power generation technology has contributed to pollution reduction and sustainable development. Therefore, this research aims to explore the CPEED problem in the context of renewable energy generation systems to enhance the energy and climate benefits of the power systems. Five benchmark test systems, combined with conventional thermal power plants and renewable energy sources such as wind and solar, are considered herein to obtain the optimum solution for cost and pollutant emission by using the ABWO approach. The ascendancy is not limited to environmental impacts, but it also provides the diversification of energy supply and reduction of reliance on imported fuels. As a result, the research findings contribute in lowering the cost of fuel and pollutant emissions, correlated with electricity generation systems, while increasing the renewable energy usage and penetration. Finally, the performance and efficacy of the designed scheme have been fully validated by comprehensive experimental results and statistical analyses.

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The reformations of the electrical power sector have resulted in very dynamic and competitive market that has changed many elements of the power industry. Excessive demand of energy, depleting the fossil fuel reserves of planet and releasing the toxic air pollutant, has been causing harm to earth habitats. In this new situation, insufficiency of energy supplies, rising power generating costs, high capital cost of renewable energy equipment, environmental concerns of wind power turbines, and ever-increasing demand for electrical energy need efficient economic dispatch. The objective function in practical economic dispatch (ED) problem is nonlinear and non-convex, with restricted equality and inequality constraints, and traditional optimization methods are incapable of resolving such non-convex problems. Over the recent decade, meta-heuristic optimization approaches have acquired enormous reputation for obtaining a solution strategy for such types of ED issues. In this paper, a novel soft computing optimization technique is proposed for solving the dynamic economic dispatch problem (DEDP) of complex non-convex machines with several constraints. Our premeditated framework employs the genetic algorithm (GA) as an initial optimizer and sequential quadratic programming (SQP) for the fine tuning of the pre-optimized run of GA. The simulation analysis of GA-SQP performs well by acquiring less computational cost and finite time of execution, while providing optimal generation of powers according to the targeted power demand and load, whereas subject to valve point loading effect (VPLE) and multiple fueling option (MFO) constraints. The adequacy of the presented strategy concerning accuracy, convergence as well as reliability is verified by employing it on ten benchmark case studies, including non-convex IEEE bus system at the same time also considering VPLE of thermal power plants. The potency of designed optimization seems more robust with fast convergence rate while evaluating the hard bounded DEDP. Our suggested hybrid method GA-SQP converges to achieve the best optimal solution in a confined environment in a limited number of simulations. The simulation results demonstrate applicability and adequacy of the given hybrid schemes over conventional methods.

This paper investigates the distributed set-membership filtering problem for discrete-time nonlinear systems subject to one-sided Lipschitz (OSL) and quadratic inner-boundedness (QIB) constraints over wireless sensor networks (WSNs). The investigation takes into account both system and sensor nonlinearities under unknown but bounded (UBB) perturbations. Moreover, a novel non-uniform event-triggering condition is introduced to achieve resource conservation while maintaining filter accuracy. The proposed triggering condition is more general and can be customized as per design requirements. Furthermore, a recursive condition for set-membership filter design is derived that guarantees the existence of optimal bounding ellipsoids for true system states. In contrast to the existing methods, the proposed approach considers set-membership filtering for discrete-time systems subject to OSL system and sensor nonlinearities over a WSN and deals with non-uniform amplitude-based triggering. Finally, a numerical example is presented to validate filter’s efficacy.