Optimization Process Research Articles

Body Surface Potential Mapping: A Perspective on High-Density Cutaneous Electrophysiology.

The electrophysiological signals recorded by cutaneous electrodes, known as body surface potentials (BSPs), are widely employed biomarkers in medical diagnosis. Despite their widespread application and success in detecting various conditions, the poor spatial resolution of traditional BSP measurements poses a limit to their diagnostic potential. Advancements in the field of bioelectronics have facilitated the creation of compact, high-quality, high-density recording arrays for cutaneous electrophysiology, allowing detailed spatial information acquisition as BSP maps (BSPMs). Currently, the design of electrode arrays for BSP mapping lacks a standardized framework, leading to customizations for each clinical study, limiting comparability, reproducibility, and transferability. This perspective proposes preliminary design guidelines, drawn from existing literature, rooted solely in the physical properties of electrophysiological signals and mathematical principles of signal processing. These guidelines aim to simplify and generalize the optimization process for electrode array design, fostering more effective and applicable clinical research. Moreover, the increased spatial information obtained from BSPMs introduces interpretation challenges. To mitigate this, two strategies are outlined: observational transformations that reconstruct signal sources for intuitive comprehension, and machine learning-driven diagnostics. BSP mapping offers significant advantages in cutaneous electrophysiology with respect to classic electrophysiological recordings and is expected to expand into broader clinical domains in the future.

INFLUENCE OF HYDROGEN ON COMBUSTION PROCESS AND ON EMISSION PARAMETERS IN THE RCCI ENGINE

Thanks to its carbon-free nature, it is possible to say that hydrogen is a potential fuel for future automotive systems. Its main favourable properties include high burning rate, wide range of flammability and low ignition energy. In combination with hybrid electric technology, the hydrogen combustion engine has the potential for further development with the goal of maximum efficiency in energy conversion. For more efficient hydrogen combustion, a dual fuel injection hybrid system is designed, where one injector is designed for direct hydrogen injection into the engine cylinder and the other injector is supply fuel to the engine's intake pipe. The greatest challenge is development of a control algorithm intended for management of the combustion process in hydrogen engine and optimization of fuel injection divided into individual doses.

Optimization of orange-fleshed sweet potato (SG 18-88-01) flour preparation for maximum carotenoid retention using response surface methodology

Due to the nature of the flour processing methods, the nutritional content of sweet potatoes decreases. Knowing the optimal flour processing conditions is important to maximize the nutrition that one can obtain from sweet potato flour. In this study, optimized flour preparation conditions for maximum β-carotene retention of orangefleshed sweet potato (SG 18-88-01) (OFSP) were determined using response surface methodology. A two-level randomized Box-Behnken design was utilized to assess the effects of blanching temperature (60 to 100°C), blanching time (1 to 3 mins), and drying temperature (50 to 70°C). The optimal conditions were blanching for one minute at 97.75° C and drying at 66.30°C. Using the said conditions, the experimental vitamin A content was reported to be 373.95 IU/g, antioxidant activity was 0.55 mg TE/g, and color values were 82.81 L*, 6.40 a*, 24.92 b*, and 1.32 radians hue angle. The predicted and experimental values were compared using paired t-test and showed no significant differences at p>0.05 and p>0.01. Hence, the model was adequate and had good predicting capacity. The optimized OFSP flour has 5.50±0.80% moisture, 2.14±0.22% ash, 2.51±0.24% crude protein, 3.07±0.09% crude fiber, 0.47±0.03% crude fat, and 86.31% nitrogen-free extract content. The flour has a water absorption index (WAI) of 5.90±0.15 g/g, water solubility index (WSI) of 55.26±0.03%, and swelling power of 13.23±1.34 g/g. Given the results, the generated OFSP flour is ideal to use for food production, particularly in bakery products. It was also revealed that consuming 4-10 g of the flour can meet the Recommended Nutrient Intake in all Filipino age groups.

МОДЕЛЬ УПРАВЛІННЯ ЯКІСТЮ ТЕХНІЧНОЇ ПІДГОТОВКИ ВИГОТОВЛЕННЯ КОРПУСІВ БПЛА

Nowadays, the production of unmanned aerial vehicles (UAVs) outside of serial production is an urgent task. There is a need for the manufacture of such devices of various schemes, sizes and intended purpose. Aircraft-type UAVs (monoplanes and biplanes) are more difficult to manufacture. As a rule, they are single-use devices. Therefore, such devices should have a simple design, be light enough, have minimal radio visibility and a number of other properties that are necessary for conventional flying apparatus. Composites (glass and carbon plastics) are materials that largely meet the specified requirements. But for these materials, it is necessary to use special structural and manufacturing solutions (SMS) during the technical preparation of their production (TPP) (production of aerodynamic surfaces, assembly and joining of parts, aggregates into a structure). Quite a lot of such solutions are known, but their optimal selection and combination of individual SMS in the UAV depends significantly on the purpose of the UAV, the volume of production, production conditions and, in general, on the production goals.The purpose of this work is to develop a mathematical model for quality management of technical preparation for the production of UAVs. It is the quality of the technical preparation of the production that determines the quality of the manufactured products and the effect of the production itself.The work gives a general diagram of UAV manufacturing processes. The inextricable connection between the design and technological parts of technical training was noted. The multi-iteration process of developing optimal production processes, which depend on its various goals, is shown. The systematicity, compactness, and reasonableness of the solutions are determined by taking into account many properties of technical processes and the conditions of their implementation. The consideration of possibilities in computer-aided design is sufficiently substantiated. For its implementation, a complex model of quality management of technical training is required. The qualitative approach to model synthesis meets the conditions of complexity and reasonableness.As an example of multi-variability, the SMS of the manufacture of the wing (bearing surfaces) and their connection with the fuselage is considered.The original feature of the proposed mat model is the use as a control parameter of the weighting factor of one or another property of the SMS, which depends on the set goal.The mechanism of distortion of given aerodynamic shapes of bearing surfaces, which are made of polymer composites, when they are supported by various structural elements (spars, stringer frames, fittings) is described. Such residual deformations lead to deterioration of aerodynamic quality and strength.The scientific novelty of the work consists in the synthesis of a mat model of quality management of technical preparation for the manufacture of UAVs. The practical value of the work is determined by the given number of examples of SMS variants of different levels.

Topology Bench: systematic graph-based benchmarking for core optical networks

Topology Bench is a comprehensive topology dataset designed to accelerate benchmarking studies in optical networks. The dataset, focusing on core optical networks, comprises publicly accessible and ready-to-use topologies, including (a) 105 georeferenced real-world optical networks and (b) 270,900 validated synthetic topologies. Prior research on real-world core optical networks has been characterized by fragmented open data sources and disparate individual studies. Moreover, previous efforts have notably failed to provide synthetic data at a scale comparable to our present study. Topology Bench addresses this limitation, offering a unified resource, and represents a 61.5% increase in spatially referenced real-world optical networks. To benchmark and identify the fundamental nature of optical network topologies through the lens of graph-theoretical analysis, we analyze both real and synthetic networks using structural, spatial, and spectral metrics. Our comparative analysis identifies constraints in real optical network diversity and illustrates how synthetic networks can complement and expand the range of topologies available for use. Currently, topologies are selected based on subjective criteria, such as preference, data availability, or perceived suitability, leading to potential biases and limited representativeness. Our framework enhances the generalizability of optical network research by providing a more objective and systematic approach to topology selection. A statistical and correlation analysis reveals the quantitative range of all of these graph metrics and the relationships between them. Finally, we apply unsupervised machine learning to cluster real-world topologies into distinctive groups based on nine optimal graph metrics using K-means. It employs a two-step optimization process: optimal features are selected by maximizing feature uniqueness through principal component analysis, and the optimal number of clusters is determined by maximizing decision boundary distances via support vector machines. We conclude the analysis by providing guidance on how to use such clusters to select a diverse set of topologies for future studies. Topology Bench, openly available via Dataset 1 (https://zenodo.org/records/13921775) and Code 1 (https://github.com/TopologyBench), promotes accessibility, consistency, and reproducibility.

A Scalable Delay‐Expanding Technique for Low‐Area On‐Chip Power‐On Reset Circuits

ABSTRACTA scalable structure is presented and analyzed that can provide delays in a wide range (microseconds to minutes) for the power‐on reset circuit. The proposed structure enables the implementation of these delays entirely on‐chip through the utilization of a provisional oscillator and a set of flip‐flops. The optimal number of flip‐flops can be determined through an area optimization process. The proposed power‐on reset circuit thresholds demonstrate the process, voltage, temperature (PVT) tolerance, facilitated by the use of resistors of the same type. Notably, the thresholds and provided delay for this circuit are supply‐rise‐time independent. The structure has been implemented in a 0.18‐μm CMOS technology, occupying an active area of 48 × 40 μm2.

System identification-based tuning of PID algorithm parameters by using PSO and genetic algorithms

The PID control algorithm is the most used industrial control method. This is due to its simplicity, ease of use, and because it yields stable results. PID parameters vary for each controller; therefore, finding the optimal parameters is crucial for obtaining good results. However, tuning PID parameters for complex systems is not a trivial task and is also somewhat difficult as conventional techniques rely on time and effort manual tuning or rely on simplified statistical estimation techniques of the system’s model yielding sub- optimal results. In this project, we propose to use machine learning for PID parameter tuning on proprietary historical time series operating process control data. The data is processed with the help of computational and machine-learning techniques to better identify the process model and predict the optimal PID parameters. The research methodology consists of three main steps. First, process-model identification is done by using a Radial Basis Function (RBF) neural network. Secondly, Particle Swarm Optimization (PSO) hybrid with Genetic Algorithms (GA) is used for finding the optimal PID parameters. The PID values predicted by PSO, will be fed to GA optimization process as an initial starting point. The final step consists of integrating the identified process model with the PID optimization algorithm in a computer-based simulation environment (Simulink). The experimental simulations are done for various study cases. Results showed that the predicted PID parameters error rate and standard deviation for PID control and process are decreased, which enhances the process controlling and stability.

Robust solutions via optimisation and predictive process monitoring for the scheduling of the interventional radiology procedures

AbstractInterventional radiology (IR) is an increasingly used medical specialty relying on the possibilities offered by medical imaging guidance technologies to perform minimally invasive procedures (both diagnostic and therapeutic) through very small incisions or body orifices. Although the operative context is quite similar to that of the classical operating room (OR) literature, to the best of our knowledge management problems arising in the IR operative context never appeared in the healthcare management literature. This is even more true for studies that combine the OR approach with automatic extraction of information from real hospital health record data as in the present study. Two specific features characterise our case study with respect to the traditional OR literature: due to the Italian legislation, the anaesthetist (usually in a very limited number) must be present for the entire duration of the procedure (), and the IR does not have its own ward but receives inpatients from different wards (). The aim of this paper is to introduce a novel approach to determine a robust solution for our case study problem addressing both features and . Our approach is based on the interplay between optimisation and predictive process monitoring (PPM) models. The obtained results show that the proposed approach produces schedules that achieve higher usage rate, lower overtime and more patients operated on than the original schedule. We also show that the integration of PPM models within the optimisation workflow improves the quality of the output schedule with respect to the standard one‐shot optimisation.

Wind Turbine Enhancement via Active Flow Control Implementation

The present research enhances the efficiency of an airfoil section from the DTU-10MW Horizontal Axis Wind Turbine (HAWT) via Active Flow Control (AFC) implementation and when using synthetic jets (SJ). The flow around two airfoil sections cut along the wind turbine blade and for a wind speed of 10 m/s is initially simulated using the CFD-2D-RANS-Kω-SST turbulence model, from where the time-averaged boundary layer separation point and the associated vortex shedding frequency are obtained. On a second stage of the paper, and considering one of the two airfoil sections, the boundary layer separation point previously determined is used to locate the SJ groove as well as the groove width; the three remaining AFC parameters, momentum coefficient, jet inclination angle, and jet pulsating frequency, are parametrically optimized. Thanks to the energy assessment presented in the final part of the paper, the study shows that a considerable power increase of the airfoil section can be obtained when attaching the former separated boundary layer. The extension of the optimization process to the rest of the blade sections where the boundary layer is separated would lead to an efficiency increase of the HAWT. The Reynolds numbers associated to the respective airfoil sections analyzed in the present manuscript are Re = 14.088×106 and Re = 14.877×106, the characteristic length being the corresponding chord length for each airfoil.

Asymmetric N-Glycosylation in the Tailpiece of Recombinant IgA1.

Here, we employed a variety of mass spectrometry (MS)-based approaches, both (glyco)peptide-centric and protein-centric, to resolve the complex glycoproteoform landscape of recombinant IgA1 produced in HEK293 cells. These key immunoglobulins harbor several N- and O-glycosylation sites, making them considerably more heterogeneous than their IgG counterparts. We provide quantitative data on the occupancy and glycan composition for each IgA1 glycosylation site. Combining all data, we revealed that IgA1 molecules consist of at least three distinct populations with varying N-glycosylation site occupancies at the C-terminal tailpiece, namely, one with both glycosylation sites occupied, another with both glycosylation sites unoccupied, and a third asymmetric population with one glycosylation site occupied and the other unoccupied, challenging the prevailing acceptance that IgA1 N-glycosylation is symmetrical. This finding is significant, given that the tailpiece is involved in interactions with the J-chain and the Polymeric Immunoglobulin Receptor, and in general as antibody glycosylation is a quality attribute that needs to be carefully monitored, as the presence and nature of these modifications can affect the antibody's efficacy, lifetime, stability, and binding and/or neutralizing capacities. Optimizing strategies to produce recombinant IgA1 requires efficient and specific quality control analytical strategies, as presented here, which is essential for therapeutic IgA1-based antibody development. We expect that the integrated MS-based strategy presented here may be beneficial to comprehensively characterize the glycoproteoform profiles of IgA1-based therapeutics, thereby improving their production and optimization processes and facilitating the pathway to bring more IgA1-based therapeutics into clinical applications.

Optimized Configuration and Operation Of Isolated Microgrid Systems For Rural Electrification: Baron Technopark

Microgrid systems represent a significant advancement in energy supply technologies, particularly for rural communities lacking access to electricity; however, these systems are predominantly reliant on diesel generators (DG). The formulation and selection of suitable configurations, alongside operational patterns, must be meticulously evaluated in the pursuit of economically viable and dependable microgrid systems. Consequently, this research sought to devise an optimal configuration and operational for microgrid systems situated in isolation, utilizing the Baron Techno Park (BTP) in Indonesia as a case study. The optimization process was executed utilizing HOMER software, integrated with an operating cost comparison, with particular emphasis placed on daily load fluctuations, the selection of control algorithms, the reconfiguration of the power supply system, and the regulation of diesel generator operational hours. The proposed microgrid system yielded a surplus energy production of 16.7%, a renewable fraction (RF) of 100%, a levelized cost of electricity (LCOE) of $5.6 per kWh, a net present cost (NPC) of $3,97M. In summary, the study shows that by slightly increasing the capital cost of PV system procurement, it can reduce the operating cost of $629 from the base system in the long term.

TRFSA-HQS: Transformer-based MRI Compressed Sensing Network with Frequency Domain Self-Attention

Compressive sensing (CS) can reconstruct undersampled magnetic resonance images, but its iterative optimization process tends to be computationally intensive and time-consuming. Convolutional neural networks (CNNs) have demonstrated impressive reconstruction performance through non-linear feature extraction and mapping capabilities. However, CNNs often struggle to effectively learn and capture global dependencies in the data. Therefore, constructing a MRI reconstruction method that meets clinical real-time imaging and captures dynamic global correlation is crucial in fast and high-precision MRI tasks. We propose a deep unfolding network (TRFSA-HQS) for fast and accurate CS reconstruction, which combines frequency domain self-attention (FSA) based Transformer and half-quadratic splitting (HQS) iterative optimization scheme. TRFSA-HQS adopts an iterative scheme based on HQS to effectively decouple and update optimization problems. The decoupled data subproblems are updated by minimizing the objective function with competitive terms, while the regularization subproblems are updated using the TRFSA deep prior network. The TRFSA module employs an asymmetric UNet architecture, where the encoder and decoder utilize a frequency-domain discriminative feedforward network (DFFN) and FSA. The DFFN selectively extracts deep features from different frequency components, while the FSA captures global dependencies in the frequency domain. The experiment demonstrate that the proposed model achieves an average reconstruction PSNR of 35.11dB on a 0.2 sampling rate test set on FastMRI knee joint data, with an inference time of 0.74s. It has good reconstruction performance and noise robustness, meeting the clinical real-time imaging requirements.

Numerical optimization of all-inorganic CsSnBr3 perovskite solar cells: the observation of 27% power conversion efficiency

Abstract In this research, we employed SCAPS-1D simulation software to numerically optimize the performance of four CsSnBr3-based perovskite solar cell structures. Specifically, we analyzed the FTO/ZnO/CsSnBr3/rGO/Se, FTO/AlZnO/CsSnBr3/rGO/Se, FTO/LiTiO2/CsSnBr3/rGO/Se, and FTO/WS2/CsSnBr3/rGO/Se configurations. The optimization process focused on adjusting the thicknesses of the electron transport layer, hole transport layer, and perovskite layer, while also evaluating the effects of temperature, series resistance, and shunt resistance on the Jsc, Voc, FF, and PCE. As a result, we achieved PCE of 26.92%, 26.89%, 26.89%, and 26.91% for the FTO/AlZnO, FTO/ZnO, FTO/LiTiO2, and FTO/WS2-based structures, respectively. Furthermore, the PCE obtained for all CsSnBr3-based perovskite solar cell structures outperformed the recently reported ITO/WS2/CsSnBr3/Cu2O/Au perovskite solar cell, which exhibited the highest PCE in the literature, by nearly 5%.

Feature selection and response prediction on a suspension bridge due to wind effect by machine learning

This research introduces an efficient machine learning approach for predicting the structural health of long-span suspension bridges, focusing on their responses to dynamic environmental load like earthquakes and wind. Unlike traditional analytical methods that rely on simplifications, this study utilizes real-world data from anemometers and accelerometers to enhance prediction accuracy and efficiency. Also instead of using whole signals, the study uses fewer time frame signal. The main contribution of this paper is the application of support vector regression (SVR), optimized through Observer-Teacher-Learner-Based-Optimization (OTLBO) for parameter tuning. The optimization process is further refined using Multi-Objective Observer-Teacher-Learner-Based-Optimization (MOOTLBO), targeting feature selection and dimension reduction to improve model performance significantly. Hardanger Bridge serves as a case study, demonstrating the model's capability to accurately predict acceleration responses to wind. By inputting wind sensor data and analyzing acceleration outputs, the enhanced SVR model, through OTLBO and MOOTLBO, shows remarkable predictive accuracy, validated against six performance indices. This methodological advancement suggests that the machine learning-based model could potentially supplant traditional finite element models, offering a more adaptable, efficient, and accurate tool for structural health monitoring (SHM). This advancement addresses critical discrepancies in SHM systems, such as data gaps or sensor malfunctions, by providing a reliable prediction method that ensures the ongoing safety and integrity of bridge structures.

Optimal design of Vivaldi antenna with corner reflector: a Multiobjective Minimax Method

The paper presents the optimization of a reflector-backed Vivaldi antenna which was designed to operate at the 1.8, 2.1, and 3.5 GHz frequencies used in the fourth and fifth generation of wireless systems. Reducing antenna back radiation is the primary goal of the optimization; at the same time, however, antenna impedance matching has to be preserved for the considered set of frequencies, this way generating a conflict of goals. The proposed design method is based on a minimax formulation of each goal against frequency and a Pareto-like tradeoff of solutions. Both goals were achieved as a consequence of the optimization process, and the antenna now exhibits a voltage standing wave ratio below 3, front-to-back ratio of 14 dB and a gain in the front direction not smaller than 8 dBi.

Model Adequacy in Assessing the Predictive Performance of Regression Models in Pharmaceutical Product Optimization: The Bedaquiline Solid Lipid Nanoparticle Example

This study aimed to assess the predictive performance of first- and second-order regression models in optimizing bedaquiline (BQ) solid lipid nanoparticle (SLN) formulations. A three-step central composite design and graphical optimization process was employed. A design of experiments method was used to evaluate the impact of BQ, Tween 80 (T80), polyethylene glycol (PEG), and lecithin on the formulations’ response variables, including Z-average (PSD), polydispersibility index (PdI), and Zeta potential (ZP). Secondly, we quantified the relationship between experimental variables using the regression model coefficients. Lastly, we predicted the responses and verified the models’ adequacies to ensure accurate representation and effective optimization. The first-order polynomial showed poor model adequacy and required further refinement due to its lack of explanatory power and significant predictors. Conversely, the second-order models provided superior fitness, sensitivity to variability, complexity, and prediction consistency. The optimized formulation achieved a desirability value of 0.9998, indicating alignment with the desired criteria. Specifically, the levels of BQ (19.4 mg), T80 (25.2 mg), PEG (39.2 mg), and lecithin (200 mg) corresponded to PdI (0.41), PSD (250.99 nm), and ZP (−25.95 mV). Maintaining a BQ concentration between 10 and 20% and T80 between 15 and 18% is vital for maximizing ZP and minimizing PdI and PSD, ensuring stable SLN formulations. This study underscores the significance of precise model selection and statistical analysis in pharmaceutical formulation optimization for enhanced drug delivery systems.

Fault diagnosis of rolling bearing based on parameter-adaptive re-constraint VMD optimized by SABO

Abstract Variational mode decomposition (VMD) is widely used in fault-bearing vibration-signal processing. Nonetheless, VMD remains a challenging task because of the difficulty in finding the optimal combination of parameters and excessive fault information in the residual term. The optimal parameter combination plays a balancing role in the optimization process, controlling the error between the reconstructed signal and the original signal while suppressing interference between modes. To address these defects, a parameter-adaptive re-constrained VMD method based on a subtraction average-based optimizer (SABO) is proposed. In this method, exponential functions are first used to build filters to implement a re-constrained VMD. Focusing on the fault information and minimizing it in the residuals. Then, SABO was employed to find the best parameter combination for subsequent signal processing. Finally, the signal is decomposed, and envelope spectral analysis is performed on each component to extract the fault frequencies, thereby identifying the specific fault type. Numerical simulations and real experimental data were used to demonstrate the effectiveness of the proposed method. In addition, the generalization ability of the proposed method was tested using 40 sets of sample data, and the average accuracy of this method reached 97.5%. Compared with other commonly used signal decomposition methods, the superiority of this method in rolling bearing fault feature extraction is proved.

A multi-stage balancing scheduling method for flexible loads of large-scale electric vehicles

To address the degradation of grid quality and charging efficiency associated with the large-scale integration of electric vehicles (EVs), a multi-stage balanced flexible load scheduling method is proposed. This approach is designed to facilitate peak shaving and valley filling, balance intermittent energy fluctuations, and provide auxiliary services, thereby significantly altering system load characteristics, smoothing energy fluctuations, reducing operational costs, and enhancing the regulatory capabilities of power grid dispatching operations. A multi-objective optimization mathematical model is developed, focusing on key indicators that impact the scheduling process, including network loss, operational cost, and user satisfaction. A multi-stage flexible load scheduling framework is introduced within the competitive swarm optimization (CSO) algorithm, resulting in the design of an advanced CSO algorithm. This algorithm is distinguished from traditional methods by the implementation of advanced learning based on grouping after a random competitive learning phase, which enhances the efficiency of particle swarm learning while ensuring stable population convergence throughout the optimization process. Furthermore, the CSO framework is maintained to ensure effective population diversity, greatly improving the optimization performance. Simulation results indicate that the voltage fluctuation index of the proposed algorithm is 1.8% lower than that of the standard CSO algorithm, while network loss and operational costs are reduced by 2.83 and 5.81%, respectively, thereby validating the effectiveness and efficiency of the proposed approach.

A novel hybrid method for predicting maglev train-induced environmental vibrations combining field measurement, 2.5D FEM modelling and multi-objective optimization

The maglev system has been developed rapidly in recently years since its advantage in mass transportation, ride comfort and energy efficiency, which also caused an increasing concern on the maglev train-induced environmental vibrations. This paper presents a novel hybrid framework for predicting high-speed maglev train-induced environmental vibrations combining field measurement, 2.5D FEM simulation and multi-objective optimization. First, a maglev train-guideway-soil coupled model considering guideway irregularities was proposed to calculate the ground vibration using 2.5D FEM. Then, the guideway irregularity spectrum and soil damping are inverted by the optimization process using the NSGA3 genetic algorithm, in which the differences of measured and calculated vertical vibrations of measuring points are chosen as objective functions. With the inverted guideway irregularity spectrum and soil damping, the ground vibrations were calculated using the 2.5D FEM model and compared with the field measurements. It was found that the inverted PSD spectrum of guideway irregularity generally decreases as the spatial frequency increases, exhibiting a similar trend as the measured ones. The results obtained by the proposed hybrid prediction method agree well with the field measurement in both time domain and frequency domain. The prediction error for the vertical vibration level of all measuring points induced by five maglev train passing are mainly less than 10 %, indicating that the proposed hybrid method provided a reliable and effective method for vibration prediction combining field measurement and numerical simulation.

Entrepreneurship' sustainability of the popular and solidarity economy in Ecuador

The popular and solidarity economy (PSE) plays a vital role in the economic and social development of Ecuador, fostering inclusion, employment, and the sustainability of local economies. This study aims to develop an index that assesses the sustainability levels of PSE organizations in Ecuador by evaluating several factors, including organizational processes, productivity, innovation, income generation, democracy, cooperation, and environmental practices. Using data from the Unique Registry of PSE Organizations in Ecuador, we employ Variational Autoencoders (VAE) network for modeling, combined with a Bayesian optimization process and K-means clustering to categorize sustainability levels by province. Findings indicate a high level of sustainability in Santa Elena, Pastaza, and Guayas, although challenges remain in terms of innovation and environmental practices. This research provides a framework for policy interventions designed to enhance sustainable growth within the PSE sector in Ecuador.