Dynamic Random Walk and Dynamic Opposition Learning for Improving Aquila Optimizer: Solving Constrained Engineering Design Problems.

One of the most important tasks in handling real-world global optimization problems is to achieve a balance between exploration and exploitation in any nature-inspired optimization method. As a result, the search agents of an algorithm constantly strive to investigate the unexplored regions of a search space. Aquila Optimizer (AO) is a recent addition to the field of metaheuristics that finds the solution to an optimization problem using the hunting behavior of Aquila. However, in some cases, AO skips the true solutions and is trapped at sub-optimal solutions. These problems lead to premature convergence (stagnation), which is harmful in determining the global optima. Therefore, to solve the above-mentioned problem, the present study aims to establish comparatively better synergy between exploration and exploitation and to escape from local stagnation in AO. In this direction, firstly, the exploration ability of AO is improved by integrating Dynamic Random Walk (DRW), and, secondly, the balance between exploration and exploitation is maintained through Dynamic Oppositional Learning (DOL). Due to its dynamic search space and low complexity, the DOL-inspired DRW technique is more computationally efficient and has higher exploration potential for convergence to the best optimum. This allows the algorithm to be improved even further and prevents premature convergence. The proposed algorithm is named DAO. A well-known set of CEC2017 and CEC2019 benchmark functions as well as three engineering problems are used for the performance evaluation. The superior ability of the proposed DAO is demonstrated by the examination of the numerical data produced and its comparison with existing metaheuristic algorithms.

Using the Grey Wolf Aquila Synergistic Algorithm for Design Problems in Structural Engineering.

The Aquila Optimizer (AO) is a metaheuristic algorithm that is inspired by the hunting behavior of the Aquila bird. The AO approach has been proven to perform effectively on a range of benchmark optimization issues. However, the AO algorithm may suffer from limited exploration ability in specific situations. To increase the exploration ability of the AO algorithm, this work offers a hybrid approach that employs the alpha position of the Grey Wolf Optimizer (GWO) to drive the search process of the AO algorithm. At the same time, we applied the quasi-opposition-based learning (QOBL) strategy in each phase of the Aquila Optimizer algorithm. This strategy develops quasi-oppositional solutions to current solutions. The quasi-oppositional solutions are then utilized to direct the search phase of the AO algorithm. The GWO method is also notable for its resistance to noise. This means that it can perform effectively even when the objective function is noisy. The AO algorithm, on the other hand, may be sensitive to noise. By integrating the GWO approach into the AO algorithm, we can strengthen its robustness to noise, and hence, improve its performance in real-world issues. In order to evaluate the effectiveness of the technique, the algorithm was benchmarked on 23 well-known test functions and CEC2017 test functions and compared with other popular metaheuristic algorithms. The findings demonstrate that our proposed method has excellent efficacy. Finally, it was applied to five practical engineering issues, and the results showed that the technique is suitable for tough problems with uncertain search spaces.

A Neurological Complication in Rheumatoid Arthritis - A Scenario of Catastrophic Proportions.

Background: Rheumatoid Arthritis (RA) is a common systemic inflammatory disease that can present with a plethora of extraarticular manifestations. Many patients with RA from low- and middle-income countries do not get timely and adequate treatment with disease-modifying therapies. This results in the perpetuation of a chronic inflammatory state. Focus: Rheumatoid vasculitis (RV) is one of the most aggressive complications of RA resulting from a prolonged proinflammatory milieu. Usually, it has the involvement of multiple organ systems, with cutaneous manifestations being the most common. Neurological presentation is uncommon but severe when present. Highlight: We present a case of severe RV presenting with an unexpected neurological complication consisting of cranial and peripheral neuropathy with small vessel disease and intracerebral haemorrhage. We intend to highlight the morbidity and long-term consequences of inadequately treated RA, the most common inflammatory disease of the connective system especially in light of the neurological presentation.

High flow nasal cannula in acute hypercapnic exacerbation of chronic obstructive pulmonary disease: an emerging utility.

High flow nasal cannula (HFNC) provides warmed and humidified air with flow rates up to 60 liters/min with relatively fixed oxygen content (FiO2). It has been extensively evaluated for hypoxemic respiratory failure and has been used in mild acute respiratory distress syndrome, pre-intubation, bronchoscopy and pediatric obstructive sleep apnea. Recent data has suggested a role in stable hypercapnic chronic obstructive pulmonary disease (COPD) and even in acute exacerbations, though, the use has not been advocated by any guidelines yet. We present a case of acute hypercapnic exacerbation of COPD, intolerant to non-invasive ventilation, showing response and improvement on use of HFNC. This case highlights this potential mechanisms and prospects for the same.

Understanding the mechanism of atovaquone drug resistance in Plasmodium falciparum cytochrome b mutation Y268S using computational methods.

The rapid appearance of resistant malarial parasites after introduction of atovaquone (ATQ) drug has prompted the search for new drugs as even single point mutations in the active site of Cytochrome b protein can rapidly render ATQ ineffective. The presence of Y268 mutations in the Cytochrome b (Cyt b) protein is previously suggested to be responsible for the ATQ resistance in Plasmodium falciparum (P. falciparum). In this study, we examined the resistance mechanism against ATQ in P. falciparum through computational methods. Here, we reported a reliable protein model of Cyt bc1 complex containing Cyt b and the Iron-Sulphur Protein (ISP) of P. falciparum using composite modeling method by combining threading, ab initio modeling and atomic-level structure refinement approaches. The molecular dynamics simulations suggest that Y268S mutation causes ATQ resistance by reducing hydrophobic interactions between Cyt bc1 protein complex and ATQ. Moreover, the important histidine contact of ATQ with the ISP chain is also lost due to Y268S mutation. We noticed the induced mutation alters the arrangement of active site residues in a fashion that enforces ATQ to find its new stable binding site far away from the wild-type binding pocket. The MM-PBSA calculations also shows that the binding affinity of ATQ with Cyt bc1 complex is enough to hold it at this new site that ultimately leads to the ATQ resistance.