One challenge for gas sensors is humidity interference, as dynamic humidity conditions can cause unpredictable fluctuations in the response signal to analytes, increasing quantitative detection errors. Here, we introduce a concept: Select humidity sensors from a pool to compensate for the humidity signal for each gas sensor. In contrast to traditional methods that extremely suppress the humidity response, the sensor pool allows for more accurate gas quantification across a broader range of application scenarios by supplying customized, high-dimensional humidity response data as extrinsic compensation. As a proof-of-concept, mitigation of humidity interference in colorimetric gas quantification was achieved in three steps. First, across a ten-dimensional variable space, an algorithm-driven high-throughput experimental robot discovered multiple local optimum regions where colorimetric humidity sensing formulations exhibited high evaluations on sensitivity, reversibility, response time, and color change extent for 10-90% relative humidity (RH) in room temperature (25 °C). Second, from the local optimum regions, 91 sensing formulations with diverse variables were selected to construct a parent colorimetric humidity sensor array as the sensor pool for humidity signal compensation. Third, the quasi-optimal sensor subarrays were identified as customized humidity signal compensation solutions for different gas sensing scenarios across an approximately full dynamic range of humidity (10-90% RH) using an ingenious combination optimization strategy, and two accurate quantitative detections were attained: one with a mean absolute percentage error (MAPE) reduction from 4.4 to 0.75% and the other from 5.48 to 1.37%. Moreover, the parent sensor array\'s excellent humidity selectivity was validated against 10 gases. This work demonstrates the feasibility and superiority of robot-assisted construction of a customizable parent colorimetric sensor array to mitigate humidity interference in gas quantification.

Docking methods can be used to predict the orientations of two or more molecules with respect of each other using a plethora of various algorithms, which can be based on the physics of interactions or can use information from databases and templates. The usability of these approaches depends on the type and size of the molecules, whose relative orientation will be estimated. The two most important limitations are (i) the computational cost of the prediction and (ii) the availability of the structural information for similar complexes. In general, if there is enough information about similar systems, knowledge-based and template-based methods can significantly reduce the computational cost while providing high accuracy of the prediction. However, if the information about the system topology and interactions between its partners is scarce, physics-based methods are more reliable or even the only choice. In this chapter, knowledge-, template-, and physics-based methods will be compared and briefly discussed providing examples of their usability with a special emphasis on physics-based protein-protein, protein-peptide, and protein-fullerene docking in the UNRES coarse-grained model.

The purpose of this research is to introduce an approach to assist the diagnosis of Parkinson\'s disease (PD) by classifying functional near-infrared spectroscopy (fNIRS) studies as PD positive or negative. fNIRS is a non-invasive optical signal modality that conveys the brain\'s hemodynamic response, specifically changes in blood oxygenation in the cerebral cortex; and its potential as a tool to assist PD detection deserves to be explored since it is non-invasive and cost-effective as opposed to other neuroimaging modalities. Besides the integration of fNIRS and machine learning, a contribution of this work is that various approaches were implemented and tested to find the implementation that achieves the highest performance. All the implementations used a logistic regression model for classification. A set of 792 temporal and spectral features were extracted from each participant\'s fNIRS study. In the two best performing implementations, an ensemble of feature-ranking techniques was used to select a reduced feature subset, which was subsequently reduced with a genetic algorithm. Achieving optimal detection performance, our approach reached 100 % accuracy, precision, and recall, with an F1 score and area under the curve (AUC) of 1, using 14 features. This significantly advances PD diagnosis, highlighting the potential of integrating fNIRS and machine learning for non-invasive PD detection.

Nanostructured lipid carriers (NLCs) hold significant promise as drug delivery systems (DDS) owing to their small size and efficient drug-loading capabilities. Surface functionalization of NLCs can facilitate interaction with specific cell receptors, enabling targeted cell delivery. Mannosylation has emerged as a valuable tool for increasing the ability of nanoparticles to be recognized and internalized by macrophages. Nevertheless, the design and development of functionalized NLC is a complex task that entails the optimization of numerous variables and steps, making the process challenging and time-consuming. Moreover, no previous studies have been focused on evaluating the functionalization efficiency. In this work, hybrid Artificial Intelligence technologies are used to help in the design of mannosylated drug loaded NLCs. Artificial neural networks combined with fuzzy logic or genetic algorithms were employed to understand the particle formation processes and optimize the combinations of variables for the different steps in the functionalization process. Mannose was chemically modified to allow, for the first time, functionalization efficiency quantification and optimization. The proposed sequential methodology has enabled the design of a robust procedure for obtaining stable mannosylated NLCs with a uniform particle size distribution, small particle size (< 100 nm), and a substantial positive zeta potential (> 20mV). The incorporation of mannose on the surfaces of these DDS following the established protocols achieved > 85% of functionalization efficiency. This high effectiveness should enhance NLC recognition and internalization by macrophages, thereby facilitating the treatment of chronic inflammatory diseases.

In this article, the practical issues connected with guided wave measurement are studied: (1) the influence of gluing of PZT plate actuators (NAC2013) on generated elastic wave propagation, (2) the repeatability of PZT transducers attachment, and (3) the assessment of the possibility of comparing the results of Laser Doppler Vibrometry (LDV) measurement performed on different 2D samples. The consideration of these questions is crucial in the context of the assessment of the possibility of the application of the guided wave phenomenon to structural health-monitoring systems, e.g., in civil engineering. In the examination, laboratory tests on the web of steel I-section specimens were conducted. The size and shape of the specimens were developed in such a way that they were similar to the elements typically used in civil engineering structures. It was proved that the highest amplitude of the generated wave was obtained when the exciters were glued using wax. The repeatability and durability of this connection type were the weakest. Due to this reason, it was not suitable for practical use outside the laboratory. The permanent glue application gave a stable connection between the exciter and the specimen, but the generated signal had the lowest amplitude. In the paper, the new procedure dedicated to objective analysis and comparison of the elastic waves propagating on the surface of different specimens was proposed. In this procedure, the genetic algorithms help with the determination of a new coordinate system, in which the assessment of the quality of wave propagation in different directions is possible.

Physiologically based kinetic (PBK) models are widely used in pharmacology and toxicology for predicting the internal disposition of substances upon exposure, voluntarily or not. Due to their complexity, a large number of model parameters need to be estimated, either through in silico tools, in vitro experiments, or by fitting the model to in vivo data. In the latter case, fitting complex structural models on in vivo data can result in overparameterization and produce unrealistic parameter estimates. To address these issues, we propose a novel parameter grouping approach, which reduces the parametric space by co-estimating groups of parameters across compartments. Grouping of parameters is performed using genetic algorithms and is fully automated, based on a novel goodness-of-fit metric. To illustrate the practical application of the proposed methodology, two case studies were conducted. The first case study demonstrates the development of a new PBK model, while the second focuses on model refinement. In the first case study, a PBK model was developed to elucidate the biodistribution of titanium dioxide (TiO2) nanoparticles in rats following intravenous injection. A variety of parameter estimation schemes were employed. Comparative analysis based on goodness-of-fit metrics demonstrated that the proposed methodology yields models that outperform standard estimation approaches, while utilizing a reduced number of parameters. In the second case study, an existing PBK model for perfluorooctanoic acid (PFOA) in rats was extended to incorporate additional tissues, providing a more comprehensive portrayal of PFOA biodistribution. Both models were validated through independent in vivo studies to ensure their reliability.

The precise placement of antennas is essential to ensure effective coverage, service quality, and network capacity in wireless communications, particularly given the exponential growth of mobile connectivity. The antenna positioning problem (APP) has evolved from theoretical approaches to practical solutions employing advanced algorithms, such as evolutionary algorithms. This study focuses on developing innovative web tools harnessing genetic algorithms to optimize antenna positioning, starting from propagation loss calculations. To achieve this, seven empirical models were reviewed and integrated into an antenna positioning web tool. Results demonstrate that, with minimal configuration and careful model selection, a detailed analysis of antenna positioning in any area is feasible. The tool was developed using Java 17 and TypeScript 5.1.6, utilizing the JMetal framework to apply genetic algorithms, and features a React-based web interface facilitating application integration. For future research, consideration is given to implementing a server capable of analyzing the environment based on specific area selection, thereby enhancing the precision and objectivity of antenna positioning analysis.

This study explores the efficacy of metaheuristic-based feature selection in improving machine learning performance for diagnosing sarcopenia. Extraction and utilization of features significantly impacting diagnosis efficacy emerge as a critical facet when applying machine learning for sarcopenia diagnosis. Using data from the 8th Korean Longitudinal Study on Aging (KLoSA), this study examines harmony search (HS) and the genetic algorithm (GA) for feature selection. Evaluation of the resulting feature set involves a decision tree, a random forest, a support vector machine, and naïve bayes algorithms. As a result, the HS-derived feature set trained with a support vector machine yielded an accuracy of 0.785 and a weighted F1 score of 0.782, which outperformed traditional methods. These findings underscore the competitive edge of metaheuristic-based selection, demonstrating its potential in advancing sarcopenia diagnosis. This study advocates for further exploration of metaheuristic-based feature selection\'s pivotal role in future sarcopenia research.

This study provides a comprehensive analysis of the combination of Genetic Algorithms (GA) and XGBoost, a well-known machine-learning model. The primary emphasis lies in hyperparameter optimization for fraud detection in smart grid applications. The empirical findings demonstrate a noteworthy enhancement in the model\'s performance metrics following optimization, particularly emphasizing a substantial increase in accuracy from 0.82 to 0.978. The precision, recall, and AUROC metrics demonstrate a clear improvement, indicating the effectiveness of optimizing the XGBoost model for fraud detection. The findings from our study significantly contribute to the expanding field of smart grid fraud detection. These results emphasize the potential uses of advanced metaheuristic algorithms to optimize complex machine-learning models. This work showcases significant progress in enhancing the accuracy and efficiency of fraud detection systems in smart grids.

Additive manufacturing technologies such as directed energy deposition use powder as their raw material, and it must be deposited in a precise and controlled manner. Venturi injectors could be a solution for the highly precise transport of particulate material. They have been studied from different perspectives, but they are always under high-pressure conditions and mostly fed by gravity. In the present study, an optimization of the different dimensional parameters needed for the manufacturing of a Venturi injector in relation to a particle has been carried out to maximize the amount of powder capable of being sucked and transported for a specific flow in a low-pressure system with high precision in transport. For this optimization, simulations of Venturi usage were performed using the discrete element method, generating different variations proposed by a genetic algorithm based on a preliminary design of experiments. Statistical analysis was also performed to determine the most influential design variables on the objective, with these being the suction diameter (D3), the throat diameter (d2), and the nozzle diameter (d1). The optimal dimensional relationships were as follows: a D3 34 times the particle diameter, a d2 26.5 times the particle diameter, a d1 40% the d2, a contraction angle alpha of 18.73°, and an expansion angle beta of 8.28°. With these proportions, an 85% improvement in powder suction compared to the initial attempts was achieved, with a maximum 2% loss of load.