Accuracy Of Artificial Neural Network Research Articles

Utilizing artificial neural network system to predict the residual valve after endoscopic posterior urethral valve ablation.

To build, train, and assess the artificial neural network (ANN) system in estimatingthe residual valve rate after endoscopic valve ablationand compare the data obtained with conventional analysis. In a retrospective cross-sectional study between June 2010 and December 2020, 144 children with a history of posteriorurethral valve (PUV) who underwent endoscopic valve ablation were enrolled in the study. MATLAB software was used to design and train the network in a feed-forward backpropagationerror adjustment scheme. Preoperative and postoperative data from 101 patients (70%) (training set) were utilized to assessthe impact and relative significance of the necessity for repeatedablation. The validated suitably trained ANN was used to predict repeated ablation in the next 33 patients (22.9%) (test set)whose preoperative data were serially input into the system. To assess system accuracy in forecasting the requirement for repeatablation, projected values were compared to actual outcomes. The likelihood of predicting the residual valve was calculatedusing a three-layered backpropagating deep ANN using preoperativeand postoperative information. Of 144 operated cases, 33 (22.9%) had residual valvesand needs to repeated ablation. The ANN accuracy, sensitivity, and specificity for predicting the residual valve were 90.75%,92.73%, and 73.19%, respectively. Younger age at surgery, hyperechogenicity of the renal parenchyma, presence of vesicoureteralreflux (VUR), and grade of reflux before surgery were among the most significant characteristics that affectedpostoperative outcome variables, the need for repeated ablation, and were given the highest relative weight by the ANN system. Conclusions: The ANN is an integrated data-gathering tool for analyzing and finding relationships among variables as a complex non-linear statistical model. The results indicate that ANN is a valuable tool for outcome prediction of the residualvalveafter endoscopic valve ablation in patients with PUV.

Implementation of Artificial Neural Networks on Field-Programmable Gate Arrays

Implementing Artificial Neural Networks (ANNs) on Field-Programmable Gate Arrays (FPGAs) provides a promising solution for achieving high-performance, low-latency, and energy-efficient computations in complex tasks. This paper investigates the methodology for mapping ANNs onto FPGAs, focusing on critical aspects such as architecture selection, hardware design, and optimization techniques. By harnessing the parallel processing capabilities and reconfigurability of FPGAs, neural network computations are significantly accelerated, making them ideal for real-time applications like image processing and embedded systems. The implementation process addresses key considerations, including fixed-point arithmetic, memory management, and dataflow optimization, while employing advanced techniques such as pipelining, quantization, and pruning. The research compares the accuracy and performance speedup of ANNs on CPUs versus FPGAs, revealing that FPGA-based simulations are 4680 times faster than CPU-based simulations using MATLAB, without compromising prediction accuracy.

Optimizing Windowing Techniques to Improve the Accuracy of Artificial Neural Networks in Predicting Outpatient Visits

Hospitals are healthcare institutions that play an important role in providing health services to the community. To optimize the service, hospitals need to predict the number of outpatient visits. The objectives of this research are (1) determine the effect of window size on the accuracy of predicting the number of outpatient visits, and (2) identify the best window size and accuracy of neural networks in predicting the daily number of outpatient visits. To achieve the research objectives, the following steps were undertaken: data collection of outpatient visits at RSUD dr. Soedirman Kebumen from 2018 to 2023, preprocessing, applying different window sizes, modeling neural networks, and testing by calculating the RMSE value for each window size. The test results show that the lowest RMSE for 2018 was 1.267 with a window size of 34, for 2019 was 1.262 with a window size of 34, for 2020 was 1.515 with a window size of 17, for 2021 was 1.81 with a window size of 18, for 2022 was 1.282 with a window size of 20, and for 2023 was 1.263 with a window size of 29. These window sizes indicate the cycle of outpatient visits each year. By understanding these visit cycles, the number of outpatient visits can be predicted at any time.

Classification of different wheat flour types using hyperspectral imaging and machine learning techniques

Different wheat flour types are used to produce various baked products. Due to the whiteness of the four types, hyperspectral imaging can be used due to receiving infrared wavelength. The technique was applied to distinguish confectionery flour and the flours of Samoun, Sangak, and Tafton breads using a line scanning system in the range of 400–950 nm. Effective wavelengths were selected and different image features were extracted from the corresponding image channels. The selected wavelengths were 601.33, 620.34, 696.41, 730.31, 821.26, and 841.11 nm. The extracted features were used in classification step using linear discriminant analysis, support vector machine, and artificial neural network methods in MATLAB software. The classification accuracy of artificial neural network was higher than the other methods. The efficient features gave higher classification accuracy (98.1 %) than all extracted features (96.9 %). The results showed the high ability of hyperspectral imaging combined with artificial neural network to distinguish different wheat flour types.

Machine learning-based simple and fast approach for the real-time characterization of laser-induced plasma

We report a simple approach to estimate the fast and real-time pulse-to-pulse prediction of plasma parameters, specifically electron temperature and electron number density, using an Artificial Neural network (ANN) in combination with Laser-induced breakdown spectroscopy (LIBS). In a variety of spectroscopic applications, it is essential to have real-time observation of plasma parameters. However, direct measurement of these parameters is challenging and requires complex and time-consuming calculations. Artificial Neural Network (ANN) can be used to model the relation between spectral features from recorded LIBS emission spectra and plasma parameters. The ANN is trained on a suitable preprocessed spectroscopic dataset with corresponding plasma parameters to predict electron temperature and electron number density. The accuracy of Artificial Neural Network (ANN) in predicting the plasma parameters is evaluated, and results are validated with existing conventional methods of calculating plasma parameters, namely the Boltzmann Plot Method for plasma temperature and the Stark Broadening Method for electron number density. The present results show that ANN is an effective method in accurately predicting the plasma parameters directly from the spectral features. The ability to fine-tune plasma in real time enhances control and accuracy in Pulsed Laser Deposition (PLD) process and other plasma coating techniques.

Serving Up Success: Unveiling the Power of Machine Learning for Volleyball League Prediction

This study investigates the efficacy of Artificial Neural Networks (ANN) in predicting volleyball league standings, focusing on the Turkish Volleyball Federation's Sultanlar and Efeler leagues over five seasons (2018-19 to 2022-23). Given the complexity and volume of performance data in volleyball, traditional analysis methods often face challenges such as data overload and high operational costs. ANN models, known for their ability to learn from and generalize data, present a promising solution to these challenges. By analyzing 23 input variables related to match performance, including points scored, services, attacks, and blocks, this study aims to identify the most influential factors on final league standings and provide a more objective, rapid, and economical analysis method. The results indicate significant potential for ANN in sports analytics, demonstrating high accuracy rates in predictions, especially for the Sultanlar League. However, the study also acknowledges limitations such as data quality and model complexity, suggesting areas for future research to enhance predictive accuracy and applicability of ANN in volleyball and other sports analytics.

A neuro‐computational study of viscous dissipation and nonlinear Arrhenius chemical kinetics during the hypodicarbonous acid‐based hybrid nanofluid flow past a Riga plate

AbstractWe examine the flow of Casson hybrid nanofluid (Cu+/) through a Riga plate sensor with perforations that act as an electromagnetic actuator. The hypodicarbonous acid is considered a base fluid. The impact of Arrhenius chemical kinetics and viscous dissipation are taken into account during the dynamics. The problem is formulated by considering the heat and mass transfer. An appropriate scaling is used to reduce the complexity of the problem, and further transform it into a system of ordinary differential equations (ODEs). The reduced system is further set for the first‐order system of equations that are analyzed with the Artificial Neural Network (ANN) which is trained with the Levenberg–Marquardt algorithm. The results for the state variables are displayed through graphs and tables by performing 1000 independent iterations with tolerance and . The Hartman, Casson, and Richardson numbers with their increasing values enhance the velocity profile. The chemical reaction parameter and the Prandtl number decline the thermal and concentration profiles, respectively. The Statistical analysis in the form of regression and histograms is also carried out in each case. The absolute error (AE) ranges up to and validations that range up to are presented for the varying values of each parameter. A comparative analysis of the nanofluid (NF) and hybrid nanofluid (HNF) is performed in each case study. The results for skin friction and Nusselt number are displayed numerically in the form of tables and are compared with the available literature, where the accuracy and performance of ANN are proved.

Voice Analysis and Neural Networks as a Clinical Decision Support System for Patients With Lung Diseases

ObjectiveTo analyze the voice of patients with lung diseases, compared with healthy individuals, to detect patterns capable of assessing dyspnea using artificial neural networks (ANNs). Patients and MethodsThis research consists of a cross-sectional prospective pilot study performed in a reference tertiary center, which included a group of patients with lung diseases, compared with a group of healthy individuals. Each patient’s voice was recorded in controlled rooms. The following techniques were applied to extract and select signals’ features: statistical analysis, fast Fourier transform, discrete wavelet transform and Mel-Cepstral analysis. In addition, data augmentation was used to avoid overfitting and improve the ANNs accuracy. ResultsA total of 195 voices were recorded: 131 from lung disease patients and 64 from healthy individuals, separated according to gender and age. Using data augmentation, 751 additional audio samples were generated: 501 from healthy individuals and 445 from patients with lung disease. Among male participants, 133 samples were related to lung diseases and 197 were related to healthy ones. From them, 264 audios were used for ANNs training, obtaining an accuracy of 89%. In the female group, 312 had lung diseases and 304 were healthy. Among them, 492 audios were used for training, resulting in an accuracy of 87.6%. ConclusionSpectral analysis techniques applied to voice recordings using ANNs have reported high accuracy in the efficient diagnosis of lung diseases when compared with healthy individuals.

Comparing the accuracy of ANN with transformer models for sentiment analysis of tweets related to COVID-19 Pfizer vaccines

The current study underscores the critical importance of understanding public sentiment towards vaccination efforts, particularly to achieve the widespread vaccine uptake necessary for public health security, especially in light of pandemics like COVID-19. Despite the clear critical role vaccinations play in mitigating pandemic spread, vaccine hesitancy persists as a formidable challenge. This research focuses on analyzing sentiments related to the Pfizer-BioNTech COVID-19 vaccine using Natural Language Processing (NLP) techniques, offering a comparative assessment of Artificial Neural Network (ANN) frameworks and transformer-based models. The study employs the Valence Aware Dictionary and sEntiment Reasoner (VADER) for sentiment quantification and TensorFlow for text vectorization. Within the ANN domain, both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) models are investigated, alongside the ALBERT and Distilbert models as a representative of transformer-based architectures. The empirical analysis reveals a distinct advantage of transformer-based models over ANN frameworks in accuracy, with the ALBERT model exhibiting exceptional skill in classifying sentiments of tweets concerning the COVID-19 vaccine by Pfizer. The study meticulously employs Receiver Operating Characteristic (ROC) curve analysis to rank the performance of the evaluated models in sentiment classification, establishing ALBERT as the foremost model, followed in order by LSTM-CNN, Distilbert, CNN, and LSTM models. The ALBERT model demonstrates outstanding performance across critical evaluation metrics. The findings advocate for the strategic use of advanced NLP technologies in public health initiatives to better understand and respond to public attitudes towards vaccination, ultimately contributing to improved health outcomes and pandemic preparedness.

Penerapan Machine Learning untuk Mengetahui Kelangsungan Hidup Pasien Gagal Jantung (Kardiovaskular) Menggunakan Kreatinin Serum dan Fraksi Ejeksi

Cardiovascular diseases cause deaths worldwide every year, most of which manifest primarily as heart attacks or heart failure. Heart failure (HF) occurs when the heart cannot pump enough blood to meet the body's needs. Available electronic health records can measure symptoms, physical characteristics, and laboratory values, and perform biostatistical analysis to uncover patterns and relationships unknown to general practitioners. In particular, Machine Learning can predict patient survival based on data and personalize key characteristics of medical records. This article analyzes a data set of 299 heart failure patients by applying machine learning algorithms using an Adaboost-based artificial neural network algorithm to improve further the accuracy of the artificial neural network (ANN) algorithm. In the experimental results of this study, it was found that the accuracy of the AdaBoost-based artificial neural network (ANN) algorithm became very significant with an accuracy result of 81.01%.

Application of artificial neural networks in predicting the performance of ice thermal energy storage systems

Efficient prediction of thermal system performance is crucial for optimizing building energy systems. This paper introduces a predictive model to forecast Heating, Ventilation, and Air Conditioning (HVAC) systems' performance with Ice Thermal Energy Storage (ITES). The indicators encompass storage temperature, Coefficient of Performance (COP), cooling thermal load, and chiller power consumption. These forecasts play a pivotal role in cost-effective experimentation, energy optimization, reduced consumption, and minimized operational costs. The study focuses on developing an Artificial Neural Network (ANN) model using a shallow Neural Net Fitting application (nftool). This ANN model predicts necessary ITES system configurations based on nonlinear input variables. Utilizing a comprehensive dataset, incorporating dry bulb temperature, component inlet and outlet temperatures, flow rates, and control valve states, the ANN model is built and validated. Performance metrics, including Mean Squared Errors (MSE) and Regression (R), are employed for training, validation, and testing to predict individual configurations. The results reveal R values ranging from 0.94 to 0.99, with MSE mostly below 20 %. This study exemplifies machine learning's application to ITES cooling systems, serving as a benchmark for evaluating ANN methodologies' effectiveness in prediction. It sheds light on parameter variation's impact on ITES cooling systems and underscores ANN's accuracy in predicting HVAC system parameters with ITES.

Low-cycle fatigue life prediction of austenitic stainless steel alloys: A data-driven approach with identification of key features

A data-driven approach for predicting the low-cycle fatigue (LCF) life of austenitic stainless steel (AusSS) is proposed to address the limitations of classical fatigue life prediction models. This approach integrates four key characteristics of the AusSS alloys, namely chemical compositions, microstructure, material processing parameters, and experimental parameters, to compile a comprehensive fatigue life prediction dataset. By employing the classification and regression technique, the relationship between various input features and LCF life is analyzed in depth, leading to the eventual identification of eight key features. Subsequently, various machine learning (decision tree, random forest, XGBoost, and support vector regression) and artificial neural network (ANN) are utilized to predict the LCF life using the compiled dataset. Comparative analysis of the results reveals higher prediction accuracy of ANN, with respective R2 and mean squared error values of 0.941 and 0.024, showcasing superior performance over the classical Basquin-Coffin-Manson model. This proposed methodology effectively captures the intricate feature-life relationships in AusSS, potentially reducing experimental time and costs of fatigue life estimation and offering promising applications in alloy design and manufacturing optimization.

Strategic Selection of Machine Learning Models for Short-term Trading Optimization

Stock market prediction is a key aspect for a financial analyst and investor, as it facilitates better decisions and improved investment strategies. Towards this end, this project compares how machine learning (ML) models perform in predicting stock market prices with relatively small dataset sizes so as to get the best performing one. This study evaluates the efficiency and accuracy of artificial neural networks, support vector regression, LSTM and decision trees in capturing market trends and forecasting stock movements. The importance of our proposed paper lies in its potential to demystify the complexities of financial markets for students and new entrants in the field of finance. Participants can garner practical insights on how the stock market operates and the application of theoretical ML concepts to financial analytics by identifying the best-performing ML models with limited datasets, which is a common occurrence in real-life situations. This paper aims to analyse the commonly present machine learning models in stock market prediction and choose the most effective model depending upon their performance, which is achieved using limited information (short term data). This would provide an edge in taking small (less-risky) informed financial decisions without extensive study of the stock market.

Advancements in machine learning for predicting phases in high-entropy alloys: a comprehensive review

High entropy alloys (HEAs) are distinguished by their enhanced physicochemical properties, attributed to the formation of various phases such as solid solution (SS), intermetallic (IM), or a combination (SS + IM). These phases contribute distinctively to the microstructure of the alloys. A critical aspect of alloy design revolves around accurately predicting these phases, which has led to the integration of sophisticated data vetting methods and Machine Learning (ML) algorithms in recent research. This review paper aims to provide a comprehensive analysis of the advancements in phase prediction accuracy within HEAs, an essential component in the development of these alloys. HEAs are known for their intricate compositions, offering a wide spectrum of material properties, making them particularly relevant for applications aimed at future sustainability. Phase engineering in HEAs unlocks the potential for creating materials tailored to eco-friendly technologies and energy-efficient solutions. The challenge in predicting phase selection in HEAs is accentuated by the limited data available on these complex materials. This review delves into how advanced data vetting techniques and ML algorithms are being employed to overcome these challenges, thus contributing significantly to sustainable material design. The paper examines various algorithms used in HEA phase prediction, including KNN (K-Nearest Neighbors), SVM (Support Vector Machines), ANN (Artificial Neural Networks), GNB (Gaussian Naive Bayes), and RF (Random Forest). It discusses the testing accuracy of these algorithms in classifying HEA phases, revealing variations in their effectiveness. The review highlights the superior accuracy of ANNs, followed closely by KNN and SVM, while noting the comparatively lower accuracy of GNB. This comprehensive review synthesizes current research efforts in utilizing computational methods to design HEAs, underlining their broader implications in expediting the discovery and development of diverse metal alloys. These efforts are pivotal in meeting the evolving demands of modern engineering applications, thereby contributing to the advancement of materials science.

Artificial Neural Network-Based Prediction of Physical and Mechanical Properties of Concrete Containing Glass Aggregates

This comprehensive study analyzes the use of crushed glass as both fine and coarse aggregate in concrete, as well as the prediction accuracy of Artificial Neural Networks (ANN). The primary objectives are to understand the interactions between concrete’s constituents and to assess the accuracy of ANN models in predicting concrete’s mechanical and physical properties. This is achieved using a two-decade experimental results dataset of concrete’s compressive and tensile strengths, slump, density, and the corresponding mix design proportions, including waste glass aggregate. A series of 70 concrete samples were carefully built and tested, with compressive strengths varying from 12 to 71 MPa and glass aggregate percentages ranging from 0-100%. These samples served as the basis for the creation of an input dataset and ANN targets. The ANN model underwent intensive training, validation, testing, and statistical regression analysis. The ANN models are exceptionally accurate, with a continuously low error margin of roughly 2%, highlighting their usefulness in matching experimental and predicted results. Validation techniques highlight the models' dependability, with consistently high coefficients of determination (R-values), including 0.99484, demonstrating their robustness in replicating complicated concrete properties. The data analysis shows a unique pattern, with optimum glass aggregate percentages in the range of 10–20%. Beyond this range, there is a noticeable decline in concrete properties. Finally, the study confirms the efficacy of ANN in predictive modeling while also validating the potential of crushed glass to replace natural aggregates in concrete. Doi: 10.28991/CEJ-2024-010-05-018 Full Text: PDF

Artificial neural network and discrete wavelet transform for inter-turn short circuit and broken rotor bars faults diagnosis under various operating conditions

Introduction. This work presents a methodology for detecting inter-turn short circuit (ITSC) and broken rotor bars (BRB) fault in variable speed induction machine controlled by field oriented control. If any of these faults are not detected at an early stage, it may cause an unexpected shutdown of the industrial processes and significant financial losses. Purpose. For these reasons, it is important to develop a new diagnostic system to detect in a precautionary way the ITSC and BRB at various load condition. We propose the application of discrete wavelet transform to overcome the limitation of traditional technique for no-stationary signals. The novelty of the work consists in developing a diagnosis system that combines the advantages of both the discrete wavelet transform (DWT) and artificial neural network (ANN) to identify and diagnose defects, related to both ITSC and BRB faults. Methods. The suggested method involves analyzing the electromagnetic torque signal using DWT to calculate the stored energy at each level of decomposition. Then, this energy is applied to train neural network classifier. The accuracy of ANN based on DWT, was improved by testing different orthogonal wavelet functions on simulated signal. The selection process identified 5 pertinent wavelet energies, concluding that, Daubechies44 (db44) is the best suitable mother wavelet function for effectively detecting and classifying failures in machines. Results. We applied numerical simulations by MATLAB/Simulink software to demonstrate the validity of the suggested techniques in a closed loop induction motor drive. The obtained results prove that this method can identify and classify these types of faults under various loads of the machine. References 31, table 1, figures 9.

Predicted risk factors associated with secondary infertility in women: univariate and multivariate logistic regression analyses.

Infertile women are those who regularly engage in unprotected intercourse for a period of at least 1 year and are unable to become clinically pregnant. Primary infertility means the inability of couples to conceive, without any previous successful pregnancies. Secondary Infertility refers to the inability to get pregnant for 12 months, after having a previous pregnancy for one time at least. The objectives of the current study were to analyze risk factors for secondary infertility and compare the predictive accuracy of artificial neural network (ANN) and multiple logistic regression models. The study was conducted at The University Institute of Public Health collecting data from Gilani Ultrasound Center 18 months after approval of synopsis. A total of 690 women (345 cases and 345 controls) were selected. The women selected for the case group had to be 20-45 years of age, had any parity, and had a confirmed diagnosis of secondary infertility. Multiple logistic regression (MLR) and ANN were used. The chance of secondary infertility was 2.91 times higher in women living in a joint family [odds ratio (OR) = 2.91; 95% confidence interval (CI) (1.91, 4.44)] and was also 2.35 times higher for those women who had relationship difficulties with their husband [OR = 2.35; 95% CI (1.18, 4.70)]. Marriage at an earlier age was associated with secondary infertility with β being negative and OR being < 1 [OR = 0.94; 95% CI (0.88, 0.99)]. For the logistic regression model, the area under the receiver operative characteristic curve (ROC) was 0.852 and the artificial neural network was 0.87, which was better than logistic regression. Identified risk factors of secondary infertility are mostly modifiable and can be prevented by managing these risk factors.

Hybrid AI modeling techniques for pilot scale bubble column aeration: A comparative study

With increased accessibility of process data from the production lines in chemical and biochemical production plants, the use of data-based modeling methods is gaining interest. In this work, three different data-based modeling approaches are applied for modeling aeration in a pilot scale bubble column. In all three modeling approaches the same serial hybrid-model structure is used, combining a species conservation balance based on first-principles with different data-based models for the overall volumetric mass transfer coefficient (KLa). Simple empirical correlations with parameters fit to process data provide transparent models but lack the accuracy of Artificial Neural Networks (ANNs). ANNs provide models with high accuracy within the operation regimes used for training, however, the models are prone to overfitting, and their black-box nature results in models that are difficult to interpret. As an alternative, a symbolic regression-inspired technique is used for discovering symbolic equations, resulting in interpretable models with accuracy that is comparable to that of the ANN.

Differences in running technique between runners with better and poorer running economy and lower and higher milage: An artificial neural network approach.

Prior studies investigated selected discrete sagittal-plane outcomes (e.g., peak knee flexion) in relation to running economy, hereby discarding the potential relevance of running technique parameters during noninvestigated phases of the gait cycle and in other movement planes. Investigate which components of running technique distinguish groups of runners with better and poorer economy and higher and lower weekly running distance using an artificial neural network (ANN) approach with layer-wise relevance propagation. Forty-one participants (22 males and 19 females) ran at 2.78 m∙s-1 while three-dimensional kinematics and gas exchange data were collected. Two groups were created that differed in running economy or weekly training distance. The three-dimensional kinematic data were used as input to an ANN to predict group allocations. Layer-wise relevance propagation was used to determine the relevance of three-dimensional kinematics for group classification. The ANN classified runners in the correct economy or distance group with accuracies of up to 62% and 71%, respectively. Knee, hip, and ankle flexion were most relevant to both classifications. Runners with poorer running economy showed higher knee flexion during swing, more hip flexion during early stance, and more ankle extension after toe-off. Runners with higher running distance showed less trunk rotation during swing. The ANN accuracy was moderate when predicting whether runners had better, or poorer running economy, or had a higher or lower weekly training distance based on their running technique. The kinematic components that contributed the most to the classification may nevertheless inform future research and training.

Technology Focus: Artificial Lift (March 2024)

The industry is now in its second year beyond the COVID-19 pandemic, and the workforce has changed in many ways, but issues facing production performance remain. Major conferences saw 116 submissions from 28 countries, with 64% regarding electrical submersible pumps (ESPs), and 18% and 8% devoted to gas lift and rod lift, respectively. Many of the papers dealt with case history successes. That is all well and good, but it doesn’t necessarily drive reader interest if it can’t be applied to their asset. The top three papers were selected on the guidelines of clarity of the abstract in addressing the scope, methods/procedures/process, results/observations/conclusions, and novel or additive information. The papers described here focus on novel unconventional horizontal downhole pump card signatures, carbon reduction through alternative energy to pump wells, and predicting downhole-discharge pressure of ESPs. Paper SPE 214723 presents a discussion of the design, evaluation, and field testing of a new type of mechanical gas separator with what the authors term a “hydrohelical” design. The separator, in Permian Basin tests, almost doubled the flow capacity of conventional gas separators with a high level of efficiency. The authors draw particular attention to the improvement of ESP operations under multiphase conditions. Carbon reduction is a hot topic, and paper OTC 32671 does not disappoint as it looks at the use of a 10-MW floating solar photovoltaic power system to meet the energy needed for 10 ESP wells on an oil platform in the Brazil equatorial region. Collaboration with the National Renewable Energy Laboratory and its System Advisor Model was used with extensive meteorological and wind data to examine the sustainability of green energy use for the petroleum industry and offer valuable guidance for future renewable energy projects in regions with similar characteristics. Finally, paper SPE 216598 comes from Sudan and Saudia Arabia and focuses on digital solutions for the challenge of knowing the downhole-discharge pressure of an ESP system. Most ESP systems do not have downhole gauges, so an artificial neural network (ANN) approach is used to predict the ESP discharge pressure for performance optimization. Forty wells and more than 12,000 data points were reduced to 16 inputs plus one hidden layer of seven neurons to provide a high degree of accuracy in prediction of discharge pressure. The authors state that the ability to accurately predict discharge pressure can lead to the early detection of possible anomalies, which can prevent costly failures and reduce downtime. Artificial intelligence technology may improve the accuracy of ANNs, but, for now, ANNs are worth a look to obtain much-needed ESP discharge pressure. Other notable papers dealt with gas separation, ESP reliability, and the increasing challenges of low rates, low pressures, and high gas fractions. Recommended additional reading at OnePetro: www.onepetro.org. SPE 214913 Solved: ESP Shutdowns and Restart Issues—Novel Pump Protection and Active 3D Dispersion System Improve ESP Performance in Multiple Wells by A. Bhattacharjee, SLB, et al. SPE 215474 A New Artificial Lift Approach in Telisa Low-Productivity Reservoir: Utilized Very Low-Rate ESP by Agus Aryanto, Medco E&amp;P Indonesia, et al. SPE 215170 A Comprehensive Study of a Novel Submersible Pump by Hattan Banjar, Saudi Aramco, et al. SPE 214722 Case Study: Predicting Electrical Submersible Pump Failures Using Artificial Intelligence and Physics-Based Hybrid Models by Shejuti Silvia, Baker Hughes, et al. SPE 214733 Gas-Flow-Management Technology Designed To Decrease Downtime and Improve ESP Efficiency by S. Fulwider, ConocoPhillips, et al.