Homotopy Perturbation Method for Solving System of Generalized Abel’s Integral Equations

In this paper, a user friendly algorithm based on the homotopy perturbation method (HPM) is proposed to solve a system of generalized Abel’s integral equations. The stability of the solution under the influence of noise in the input data is analyzed. It is observed that the approximate solutions converge to the exact solutions. Illustrative numerical examples are given to demonstrate the efficiency and simplicity of the proposed method in solving such types of systems of Abel’s integral equations

Causal Mediation Analysis with Multiple Time-Varying Mediators

In longitudinal studies with time-varying exposures and mediators, the mediational g-formula is an important method for the assessment of direct and indirect effects. However, current methodologies based on the mediational g-formula can deal with only one mediator. This limitation makes these methodologies inapplicable to many scenarios. Hence, we develop a novel methodology by extending the mediational g-formula to cover cases with multiple time-varying mediators. We formulate two variants of our approach that are each suited to a distinct set of assumptions and effect definitions and present nonparametric identification results of each variant. We further show how complex causal mechanisms (whose complexity derives from the presence of multiple time-varying mediators) can be untangled. A parametric method along with a user-friendly algorithm was implemented in R software. We illustrate our method by investigating the complex causal mechanism underlying the progression of chronic obstructive pulmonary disease. We found that the effects of lung function impairment mediated by dyspnea symptoms and mediated by physical activity accounted for 13.7% and 10.8% of the total effect, respectively. Our analyses thus illustrate the power of this approach, providing evidence for the mediating role of dyspnea and physical activity on the causal pathway from lung function impairment to health status

CATSNAP : a user-friendly algorithm for determining the conservation of protein variants reveals extensive parallelisms in the evolution of alternative splicing

Understanding the evolutionary conservation of complex eukaryotic transcriptomes significantly illuminates the physiological relevance of alternative splicing (AS). Examining the evolutionary depth of a given AS event with ordinary homology searches is generally challenging and time-consuming. Here, we present CATSNAP, an algorithmic pipeline for assessing the conservation of putative protein isoforms generated by AS. It employs a machine learning approach following a database search with the provided pair of protein sequences. We used the CATSNAP algorithm for analyzing the conservation of emerging experimentally characterized alternative proteins from plants and animals. Indeed, most of them are conserved among other species. CATSNAP can detect the conserved functional protein isoforms regardless of the AS type by which they are generated. Notably, we found that while the primary amino acid sequence is maintained, the type of AS determining the inclusion or exclusion of protein regions varies throughout plant phylogenetic lineages in these proteins. We also document that this phenomenon is less seen among animals. In sum, our algorithm highlights the presence of unexpectedly frequent hotspots where protein isoforms recurrently arise to carry physiologically relevant functions. The user web interface is available at https://catsnap.cesnet.cz/.peer-reviewe

LoS Coverage Analysis for UAV-based THz Communication Networks: Towards 3D Visualization of Wireless Networks

Terahertz (THz) links require a line-of-sight (LoS) connection, which is hard to be obtained in most scenarios. For THz communications, analyses based on LoS probability are not accurate, and a new real LoS model should be considered to determine the LoS status of the link in a real 3D environment. Considering unmanned aerial vehicle (UAV)-based THz networks, LoS coverage is analyzed in this work, where nodes are accurately determined to be in LoS or not. Specifically, by modeling an environment with 3D blocks, our target is to locate a set of UAVs equipped with directional THz links to provide LoS connectivity for the distributed users among the 3D obstacles. To this end, we first characterize and model the environment with 3D blocks. Then, we propose a user-friendly algorithm based on 3D spatial vectors, which determines the LoS status of nodes in the target area. In addition, using 3D modeling, several meta-heuristic algorithms are proposed for UAVs' positioning under 3D blocks in order to maximize the LoS coverage percentage. In the second part of the paper, for a UAV-based THz communication network, a geometrical analysis-based algorithm is proposed, which jointly clusters the distributed nodes and locates the set of UAVs to maximize average network capacity while ensuring the LoS state of distributed nodes among 3D obstacles. Moreover, we also propose a sub-optimal hybrid k-means-geometrical-based algorithm with a low computational complexity that can be used for networks where the topology continuously changes, and thus, users' clustering and UAVs' positioning need to be regularly updated. Finally, by providing various 3D simulations, we evaluate the effect of various system parameters such as the number and heights of UAVs, as well as the density and height of 3D obstacles on the LoS coverage

How to Choose the Right Inhaler Using a Patient-Centric Approach?

There are many different inhaler devices and medications on the market for the treatment of asthma and chronic obstructive pulmonary disease, with over 230 drug-delivery system combinations available. However, despite the abundance of effective treatment options, the achieved disease control in clinical practice often remains unsatisfactory. In this context, a key determining factor is the match or mismatch of an inhalation device with the characteristics or needs of an individual patient. Indeed, to date, no ideal device exists that fits all patients, and a personalized approach needs to be considered. Several useful choice-guiding algorithms have been developed in the recent years to improve inhaler-patient matching, but a comprehensive tool that translates the multifactorial complexity of inhalation therapy into a user-friendly algorithm is still lacking. To address this, a multidisciplinary expert panel has developed an evidence-based practical treatment tool that allows a straightforward way of choosing the right inhaler for each patient

Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems

[EN] Agent-based algorithms, based on the collective behavior of natural social groups, exploit innate swarm intelligence to produce metaheuristic methodologies to explore optimal solutions for diverse processes in systems engineering and other sciences. Especially for complex problems, the processing time, and the chance to achieve a local optimal solution, are drawbacks of these algorithms, and to date, none has proved its superiority. In this paper, an improved swarm optimization technique, named Grand Tour Algorithm (GTA), based on the behavior of a peloton of cyclists, which embodies relevant physical concepts, is introduced and applied to fourteen benchmarking optimization problems to evaluate its performance in comparison to four other popular classical optimization metaheuristic algorithms. These problems are tackled initially, for comparison purposes, with 1000 variables. Then, they are confronted with up to 20,000 variables, a really large number, inspired in the human genome. The obtained results show that GTA clearly outperforms the other algorithms. To strengthen GTA's value, various sensitivity analyses are performed to verify the minimal influence of the initial parameters on efficiency. It is demonstrated that the GTA fulfils the fundamental requirements of an optimization algorithm such as ease of implementation, speed of convergence, and reliability. Since optimization permeates modeling and simulation, we finally propose that GTA will be appealing for the agent-based community, and of great help for a wide variety of agent-based applications.Meirelles, G.; Brentan, B.; Izquierdo Sebastián, J.; Luvizotto, EJ. (2020). Grand Tour Algorithm: Novel Swarm-Based Optimization for High-Dimensional Problems. Processes. 8(8):1-19. https://doi.org/10.3390/pr8080980S11988Mohamed, A. W., Hadi, A. A., & Mohamed, A. K. (2019). 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NLP-based detection of systematic anomalies among the narratives of consumer complaints

We develop an NLP-based procedure for detecting systematic nonmeritorious consumer complaints, simply called systematic anomalies, among complaint narratives. While classification algorithms are used to detect pronounced anomalies, in the case of smaller and frequent systematic anomalies, the algorithms may falter due to a variety of reasons, including technical ones as well as natural limitations of human analysts. Therefore, as the next step after classification, we convert the complaint narratives into quantitative data, which are then analyzed using an algorithm for detecting systematic anomalies. We illustrate the entire procedure using complaint narratives from the Consumer Complaint Database of the Consumer Financial Protection Bureau