This research explores machine learning algorithms for reservoir inflow prediction, including long short-term memory (LSTM), random forest (RF), and metaheuristic-optimized models. The impact of feature engineering techniques such as discrete wavelet transform (DWT) and XGBoost feature selection is investigated. LSTM shows promise, with LSTM-XGBoost exhibiting strong generalization from 179.81 m3/s RMSE (root mean square error) in training to 49.42 m3/s in testing. The RF-XGBoost and models incorporating DWT, like LSTM-DWT and RF-DWT, also perform well, underscoring the significance of feature engineering. Comparisons illustrate enhancements with DWT: LSTM and RF reduce training and testing RMSE substantially when using DWT. Metaheuristic models like MLP-ABC and LSSVR-PSO benefit from DWT as well, with the LSSVR-PSO-DWT model demonstrating excellent predictive accuracy, showing 133.97 m3/s RMSE in training and 47.08 m3/s RMSE in testing. This model synergistically combines LSSVR, PSO, and DWT, emerging as the top performers by effectively capturing intricate reservoir inflow patterns.

Quasi-static and dynamic tensile tests on aluminum-hydroxide-enhanced ethylene propylene diene monomer (EPDM) coatings were conducted using a universal testing machine and a Split Hopkinson Tension Bar (SHTB) over a strain rate range of 10-3 to 103 s-1. This comprehensive study explored the tensile performance of enhanced EPDM coatings in solid rocket motors. The results demonstrated a significant impact of strain rate on the mechanical properties of EPDM coatings. To capture the hyperelastic and viscoelastic characteristics of EPDM coatings at large strains, the Ogden hyperelastic model was used to replace the standard elastic component to develop an enhanced Zhu-Wang-Tang (ZWT) nonlinear viscoelastic constitutive model. The model parameters were fitted using a particle swarm optimization (PSO) algorithm. The improved constitutive model\'s predictions closely matched the experimental data, accurately capturing stress-strain responses and inflection points. It effectively predicts the tensile behavior of aluminum-hydroxide-enhanced EPDM coatings within a 20% strain range and a wide strain rate range.

Global energy demand is experiencing a notable surge due to growing energy security. Renewable energy sources, like ethanol, are becoming more viable. In the present study, the application of a PSO-PID (Particle Swarm Optimization - Proportional Integral Derivative) controller with a split-range control strategy was suggested for the regulation of temperature within the fermentation system. To optimize performance, a POS-PID controller with a split-range arrangement utilizing two control valves for hot and cold utilities was constructed. The study began by examining the open-loop dynamic response of the system to inlet temperature and concentration disturbances during ethanol production fermentation. Subsequently, a transfer function model was developed through linearization at the steady-state operating point. The split-range controller structure, implemented by optimizing the PSO-PID controller parameters using PSO, effectively demonstrated temperature control in simulations of a nonlinear model. In this investigation, the ethanol fermentation system was modeled as a CSTR using a modified Monod equation for microbial growth kinetics. Various dynamic behavioral disturbances were explored and verified in the model with plant data in this study. The simulation model results were validated through plant data. The proposed method showed superior closed-loop performance with respect to errors, with the actuators proving to be effective than other reported methods for temperature control.

Laser-arc hybrid additive manufacturing (LAHAM) holds substantial potential in industrial applications, yet ensuring dimensional accuracy remains a major challenge. Accurate prediction and effective control of the geometrical dimensions of the deposited layers are crucial for achieving this accuracy. The width and height of the deposited layers, key indicators of geometric dimensions, directly affect the forming precision. This study conducted experiments and in-depth analysis to investigate the influence of various process parameters on these dimensions and proposed a predictive model for accurate forecasting. It was found that the width of the deposited layers was positively correlated with laser power and arc current and negatively correlated with scanning speed, while the height was negatively correlated with laser power and scanning speed and positively with arc current. Quantitative analysis using the Taguchi method revealed that the arc current had the most significant impact on the dimensions of the deposited layers, followed by scanning speed, with laser power having the least effect. A predictive model based on extreme gradient boosting (XGBoost) was developed and optimized using particle swarm optimization (PSO) for tuning the number of leaf nodes, learning rate, and regularization coefficients, resulting in the PSO-XGBoost model. Compared to models enhanced with PSO-optimized support vector regression (SVR) and XGBoost, the PSO-XGBoost model exhibited higher accuracy, the smallest relative error, and performed better in terms of Mean Relative Error (MRE), Mean Square Error (MSE), and Coefficient of Determination R2 metrics. The high predictive accuracy and minimal error variability of the PSO-XGBoost model demonstrate its effectiveness in capturing the complex nonlinear relationships between process parameters and layer dimensions. This study provides valuable insights for controlling the geometric dimensions of the deposited layers in LAHAM.

UNASSIGNED: Healthcare is crucial to patient care because it provides vital services for maintaining and restoring health. As healthcare technology evolves, cutting-edge tools facilitate faster diagnosis and more effective patient treatment. In the present age of pandemics, the Internet of Things (IoT) offers a potential solution to the problem of patient safety monitoring by creating a massive quantity of data about the patient through the linked devices around them and then analyzing it to estimate the patient\'s current status. Utilizing the IoT-based meta-heuristic algorithm allows patients to be remotely monitored, resulting in timely diagnosis and improved care. Meta-heuristic algorithms are successful, resilient, and effective in solving real-world enhancement, clustering, predicting, and grouping. Healthcare organizations need an efficient method for dealing with big data since the prevalence of such data makes it challenging to analyze for diagnosis. The current techniques used in medical diagnostics have limitations due to imbalanced data and the overfitting issue.
UNASSIGNED: This study introduces the particle swarm optimization and convolutional neural network to be used as a meta-heuristic optimization method for extensive data analysis in the IoT to monitor patients\' health conditions.
UNASSIGNED: Particle Swarm Optimization is used to optimize the data used in the study. Information for a diabetes diagnosis model that includes cardiac risk forecasting is collected. Particle Swarm Optimization and Convolutional Neural Networks (PSO-CNN) results effectively make illness predictions. Support Vector Machine has been used to predict the possibility of a heart attack based on the classification of the collected data into projected abnormal and normal ranges for diabetes.
UNASSIGNED: The results of the simulations reveal that the PSO-CNN model used to predict diabetic disease increased in accuracy by 92.6%, precision by 92.5%, recall by 93.2%, F1-score by 94.2%, and quantization error by 4.1%.
UNASSIGNED: The suggested approach could be applied to identify cancer cells.

Currently, the use of acoustic echo cancellers (AECs) plays a crucial role in IoT applications, such as voice control appliances, hands-free telephony and intelligent voice control devices, among others. Therefore, these IoT devices are mostly controlled by voice commands. However, the performance of these devices is significantly affected by echo noise in real acoustic environments. Despite good results being achieved in terms of echo noise reductions using conventional adaptive filtering based on gradient optimization algorithms, recently, the use of bio-inspired algorithms has attracted significant attention in the science community, since these algorithms exhibit a faster convergence rate when compared with gradient optimization algorithms. To date, several authors have tried to develop high-performance AEC systems to offer high-quality and realistic sound. In this work, we present a new AEC system based on the grey wolf optimization (GWO) and particle swarm optimization (PSO) algorithms to guarantee a higher convergence speed compared with previously reported solutions. This improvement potentially allows for high tracking capabilities. This aspect has special relevance in real acoustic environments since it indicates the rate at which noise is reduced.

The effectiveness of an aquaponic system significantly relies on the habitat provided for both the fish and plants. As an integral component of aquaponics, hydroponic cultivation benefits greatly from the controlled environment of a greenhouse. Within this environment, factors such as temperature, carbon dioxide levels, humidity, and light can be carefully adjusted to maximize plant growth and development. This precise regulation ensures an ideal growing environment, fostering the flourishing of plants and contributing to the overall success of the aquaponic ecosystem. This study presented a control approach for an aquaponic greenhouse system. It aims to keep the greenhouse climate parameters (temperature, CO2 concentration, and humidity) at their ideal levels. The proposed control strategy is a two-layered mechanism in which the first layer presents an optimization framework using particle swarm optimization (PSO) algorithm to give the setpoints for the controller, and the second layer demonstrates a constrained discrete model predictive control (CDMPC) strategy to maintain the desired trajectories received from the optimization layer. To validate the results obtained using PSO, this study incorporates genetic algorithms (GA) and assesses their performance in comparison. Given similar computational efficiency and low computational time for both algorithms, the optimal values identified by particle swarm optimization (PSO) are adopted as the setpoints. Two performance criteria, relative average deviation (RAD) and mean relative deviation (MRD), are derived to evaluate the tracking performance of the proposed CDMPC controller under external disturbances. A comparison of the proposed CDMPC with the PI controller is also offered. According to the comparison results, our proposed CDMPC performs better than the PI controller with lower RAD values (temperature, 1.1315; CO2 concentration, 0.9225; humidity, 2.547) and MRD values (temperature, 0.315; CO2 concentration, 0.25; humidity: 1.013). The controller is validated to be efficient by its strong control performance, highlighted by robustness, efficient setpoint tracking, and adequate disturbance rejection. This novel approach might prove to be a useful technique for developing environmental control strategies that can be used for potentially boosting production rates of aquaponic greenhouse systems, maximizing profitability, and reducing labor needs. By maintaining optimal conditions, it can enhance ecosystem health, improve yields, and streamline operations, paving the way for greater system performance and sustainability.

BACKGROUND: Magnetoencephalography (MEG) and magnetic resonance imaging (MRI) are non-invasive imaging techniques that offer effective means for disease diagnosis. A more straightforward and optimized method is presented for designing gradient coils which are pivotal parts of the above imaging systems.
OBJECTIVE: A novel design method based on stream function combining an optimization algorithm is proposed to obtain highly linear gradient coil.
METHODS: Two-dimensional Fourier expansion of the current field on the surface where the coil is located and the equipotential line of the expansion term superposition according to the number of turns of the coil are used to represent the coil shape. Particle swarm optimization is utilized to optimize the coil shape while linearity and field uniformity are used as parameters to evaluate the coil performance. Through this method, the main parameters such as input current distribution region, coil turns, desired magnetic field strength, expansion order and iteration times can be combined in a given solution space to optimize coil design.
RESULTS: Simulation results show that the maximum linearity spatial deviation of the designed bi-planar x-gradient coil compared with that of target field method is reduced from 14% to 0.54%, and that of the bi-planar z-gradient coil is reduced from 8.98% to 0.52%. Similarly, that of the cylindrical x-gradient coil is reduced from 2% to 0.1%, and that of the cylindrical z-gradient coil is reduced from 0.87% to 0.45%. The similar results are found in the index of inhomogeneity error. Moreover, it has also been verified experimentally that the result of measured magnetic field is consist with simulated result.
CONCLUSIONS: The proposed method provides a straightforward way that simplifies the design process and improves the linearity of designed gradient coil, which could be beneficial to realize better magnetic field in engineering applications.

The identification of slag inclusion defects in welds is of the utmost importance in guaranteeing the integrity, safety, and prolonged service life of welded structures. Most research focuses on different kinds of weld defects, but branch research on categories of slag inclusion material is limited and critical for safeguarding the quality of engineering and the well-being of personnel. To address this issue, we design a simulated method using ultrasonic testing to identify the inclusion of material categories in austenitic stainless steel. It is based on a simulated experiment in a water environment, and six categories of cubic specimens, including four metallic and two non-metallic materials, are selected to simulate the slag materials of the inclusion defects. Variational mode decomposition optimized by particle swarm optimization is employed for ultrasonic signals denoising. Moreover, the phase spectrum of the denoised signal is utilized to extract the phase characteristic of the echo signal from the water-slag specimen interface. The experimental results show that our method has the characteristics of appropriate decomposition and good denoising performance. Compared with famous signal denoising algorithms, the proposed method extracted the lowest number of intrinsic mode functions from the echo signal with the highest signal-to-noise ratio and lowest normalized cross-correlation among all of the comparative algorithms in signal denoising of weld slag inclusion defects. Finally, the phase spectrum can ascertain whether the slag inclusion is a thicker or thinner medium compared with the weld base material based on the half-wave loss existing or not in the echo signal phase.

Feature selection (FS) is a critical step in many data science-based applications, especially in text classification, as it includes selecting relevant and important features from an original feature set. This process can improve learning accuracy, streamline learning duration, and simplify outcomes. In text classification, there are often many excessive and unrelated features that impact performance of the applied classifiers, and various techniques have been suggested to tackle this problem, categorized as traditional techniques and meta-heuristic (MH) techniques. In order to discover the optimal subset of features, FS processes require a search strategy, and MH techniques use various strategies to strike a balance between exploration and exploitation. The goal of this research article is to systematically analyze the MH techniques used for FS between 2015 and 2022, focusing on 108 primary studies from three different databases such as Scopus, Science Direct, and Google Scholar to identify the techniques used, as well as their strengths and weaknesses. The findings indicate that MH techniques are efficient and outperform traditional techniques, with the potential for further exploration of MH techniques such as Ringed Seal Search (RSS) to improve FS in several applications.