Search

This study posits that the use of artificial intelligence (AI) enables supply chains (SCs) to dynamically react to volatile environments, and alleviate potentially costly decision-makings for small-medium enterprises (SMEs). Building on a resource-based view, this work examines the impact of AI on SC risk management for SMEs. A structural model comprising of AI-risk management capabilities, SC re-engineering capabilities and supply chain agility (SCA) was developed and tested based on data collected from executives, managers and senior managers of SMEs The main methodological approach used in this study is partial least squares-based structural equation modelling (PLS-SEM) and artificial neural network (ANN). The results identified the use of AI for risk management influences SC re-engineering capabilities and agility. Re-engineering capabilities further affect and mediate agility. PLS-SEM and ANN were compared and the results revealed consistency for models A and B. Current levels of demand uncertainties in the SC challenges managers in making complex trade-off decisions that require huge management resources in very limited time. With AI, it is possible to model various scenarios to answer crucial questions that archaic infrastructures are not able to. This study combines a multi-construct agility concept and identified non-linear relationships in the model. [ABSTRACT FROM AUTHOR]

This study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models' performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns. [ABSTRACT FROM AUTHOR]

This research suggests a robust integration of artificial neural networks (ANN) for predicting swell pressure and the unconfined compression strength of expansive soils (PsUCS-ES). Four novel ANN-based models, namely ANN-PSO (i.e., particle swarm optimization), ANN-GWO (i.e., grey wolf optimization), ANN-SMA (i.e., slime mould algorithm) alongside ANN-MPA (i.e., marine predators' algorithm) were deployed to assess the PsUCS-ES. The models were trained using the nine most influential parameters affecting PsUCS-ES, collected from a broader range of 145 published papers. The observed results were compared with the predictions made by the ANN-based metaheuristics models. The efficacy of all these formulated models was evaluated by utilizing mean absolute error (MAE), Nash–Sutcliffe (NS) efficiency, performance index ρ, regression coefficient (R2), root mean square error (RMSE), ratio of RMSE to standard deviation of actual observations (RSR), variance account for (VAF), Willmott's index of agreement (WI), and weighted mean absolute percentage error (WMAPE). All the developed models for Ps-ES had an R significantly > 0.8 for the overall dataset. However, ANN-MPA excelled in yielding high R values for training dataset (TrD), testing dataset (TsD), and validation dataset (VdD). This model also exhibited the lowest MAE of 5.63%, 5.68%, and 5.48% for TrD, TsD, and VdD, respectively. The results of the UCS model's performance revealed that R exceeded 0.9 in the TrD. However, R decreased for TsD and VdD. Also, the ANN-MPA model yielded higher R values (0.89, 0.93, and 0.94) and comparatively low MAE values (5.11%, 5.67, and 3.61%) in the case of PSO, GWO, and SMA, respectively. The UCS models witnessed an overfitting problem because the aforementioned R values of the metaheuristics were 0.62, 0.56, and 0.58 (TsD), respectively. On the contrary, no significant observation was recorded in the VdD of UCS models. All the ANN-base models were also tested using the a-20 index. For all the formulated models, maximum points were recorded to lie within ± 20% error. The results of sensitivity as well as monotonicity analyses depicted trending results that corroborate the existing literature. Therefore, it can be inferred that the recently built swarm-based ANN models, particularly ANN-MPA, can solve the complexities of tuning the hyperparameters of the ANN-predicted PsUCS-ES that can be replicated in practical scenarios of geoenvironmental engineering. [ABSTRACT FROM AUTHOR]

The optimal performance of direct current (DC) motors is intrinsically linked to their mathematical models' precision and their controllers' effectiveness. However, the limited availability of motor characteristic information poses significant challenges to achieving accurate modeling and robust control. This study introduces an approach employing artificial neural networks (ANNs) to estimate critical DC motor parameters by defining practical constraints that simplify the estimation process. A mathematical model was introduced for optimal parameter estimation, and two advanced learning algorithms were proposed to efficiently train the ANN. The performance of the algorithms was thoroughly analyzed using metrics such as the mean squared error, epoch count, and execution time to ensure the reliability of dynamic priority arbitration and data integrity. Dynamic priority arbitration involves automatically assigning tasks in real-time depending on their relevance for smooth operations, whereas data integrity ensures that information remains accurate, consistent, and reliable throughout the entire process. The ANN-based estimator successfully predicts electromechanical and electrical characteristics, such as back-EMF, moment of inertia, viscous friction coefficient, armature inductance, and armature resistance. Compared to conventional methods, which are often resource-intensive and time-consuming, the proposed solution offers superior accuracy, significantly reduced estimation time, and lower computational costs. The simulation results validated the effectiveness of the proposed ANN under diverse real-world operating conditions, making it a powerful tool for enhancing DC motor performance with practical applications in industrial automation and control systems. [ABSTRACT FROM AUTHOR]

Traditional vibration-based damage detection methods often involve human intervention in decision-making, therefore being time-consuming and error-prone. In this study, we propose using Artificial Neural Networks (ANNs) to detect patterns in the structural response and create accurate predictions. The features extracted from the response signal are the Relative Frequency Shifts (RFSs) of the first eight weak-axis bending vibration modes, and the predictions refer to the damage location. To increase the accuracy of the predictions, we propose a novel stacked neural network approach, capable of detecting damage locations with high accuracy. The dataset used for training involves, as input data, the RFSs calculated with an original method for numerous damage locations and severities. The following models were used as building blocks for our stacked approach: Multilayer Perceptron, Recurrent Neural Network, Long Short-term Memory, and Gated Recurrent Units. The entire beam was thus split into segments and each network was trained in this stacked model on one beam segment. All results obtained with the models are also compared to a standard neural network trained on the entire beam. The results obtained show that the model that performs the best contains 14 stacked two-layer feedforward networks. [ABSTRACT FROM AUTHOR]

The present study deals with the application of Electrotopological state (E-State) indices in identifying structural attributes, responsible for exhibiting better antiviral activity for the NNRTI class of the HEPT derivatives. Special thrust is given on the potential of using these simple 2D QSAR models in prefiltering a large chemical database. The relevance of substituents at 1-N, 5-C and thio-aryl ring in enhancing the antiviral activity has been discussed in detail and compared with 3D structural studies. The effects of nearby atomic descriptors on 1-N and 3-N (crucial for the antiviral activity) position have been analyzed with a new insight. Application of similarity-based filtering for data mining of an external database using the best model derived was successfully applied to PubChem dataset, wherein the hits retrieved, not only showed better antiviral activity (predicted), but also complimented with the docking results. [ABSTRACT FROM AUTHOR]

The study presents analysis of 1 year 15 min cycle radon data using artificial neural network (ANN) in an attempt to estimate earthquake prediction time near Indo-Burman subduction line. The region was found to be seismically active and radon anomalies respond well to it, when majority of the radon anomalies peaks and selected earthquakes correlated. The observation also shows that application of non-linear technique ANN to a non-linear data like radon seems a promising approach in predicting geophysical phenomena at one of the most seismically active region of the world. [ABSTRACT FROM AUTHOR]

The resulting fibre orientation when pressing Sheet Moulding Compound (SMC) is essentially dependent on the position and geometry of the resin mat in the mould. It is desirable to adapt the fibre orientation as best as possible to best withstand the applied load. The relationship between the position and geometry of the resin mat and the deflection of the final part under a specific load can be mapped using a numerical simulation; nevertheless, an optimisation is not feasible based on the simulation in an economically reasonable time frame due to the required computing time. For this reason, a metamodel based on an ensemble of artificial neural networks (ANN) was created on the basis of the simulation data, and an optimisation based on these ANN was set up. However, the investigations show that the minimal deflections found on the basis of the metamodel through optimisation have large deviations compared to the simulation. This leads to the hypothesis that the applied optimisation finds weak points of the metamodel instead of actual minima. From this hypothesis, an extension approach of the data set on which the metamodel is based by the weak points found is derived and implemented. This is done in an iterative process. The results show that the goal of increasing the prediction quality of the optimisation approach is achieved by the developed approach. The root mean square error (RMSE) could be reduced from the original deflection of 25 to 7 mm, which corresponds to a reduction of 72%. It is shown that the presented approach is successful in increasing prediction quality; nevertheless, further measures are needed to reach decent prediction quality. [ABSTRACT FROM AUTHOR]

Myoelectric is a biological signal produced from physiological variations in muscle fibers when they contract and relax. The study of muscle activity through the recording and analysis of myoelectric signals is called electromyography. Electromyography can provide a comprehensive view of the operation and performance of internal muscle groups and cells. The objective of this study is an investigation into hand movement classification using surface electromyography (sEMG) signals. For training and evaluating the proposed model, two data sets from the Ninapro project, a publicly available database for prosthetic hand control, were used: DB5 with low‐cost 16 channels and 200 Hz sampling rate setting and DB10 with 12 channels and 1926 kHz sampling rate setup. First, we divided the EMG data into segments using the windowing technique. These segments were then used to extract a set of time features. Finally, the retrieved feature information was loaded into a simple pattern recognition model: Artificial Neural Network. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Horizontal geometric characteristics have significant impact on vehicle operating speed of vehicles on two-lane rural highways. Majority of the studies have used the conventional approach of modelling, found to he location specific and provide falsejudgement of determining operating speed. Thus, it becomes important to apply methods based on artificial intelligent for predicting operating speed of vehicles on two-lane roads under mixed traffic conditions. Field data was collected on 40 different locations (curves and tangent sections) that includes free speed of vehicles and geometric parameters. Geometric parameters such as curve radius, curve length, deflection angle, degree of curvature and preceding tangent length were measured in the field with total station, whereas free-flow speed data was collected using radar gun at the mid of the horizontal curves. The statistical analysis concluded that the curve radius, curve length, degree of curvature and preceding tangent length are found significant on the operating speed of vehicle type Car, Two-wheeler, Three-wheeler, Light commercial vehicle and Heavy commercial vehicle and developed MLR model. Further, the data driven soft computing methods such as Artificial Neural Network (AN), Adaptive Neuro-fuzzy Inference System (ANFIS) and Support Vector Regression (SVR) are applied to predict the operating speed of these vehicles and results were compared with MLR. The performance of the models evaluated using various goodness-of-fit measures indicates that the SVR model gives better results in prediction of operating speed in compared to other models. As for future research, further investigation could be conducted to explore uncertainties, and the model could be enhanced by utilizing other geometric and traffic parameters, and techniques like random forest, XGBoost etc. [ABSTRACT FROM AUTHOR]

Improving soil strength and reducing the anticipated settlement and construction cost is a great paradox for civil as well as geotechnical engineers. In this paper, these aspects and other suitable types of ground improvement are discussed based on the principles of using geosynthetics for soil reinforcement. A series of load-settlement tests were also performed to compare strength and settlement of the silty sand reinforced with lime and one layer of geotextile. The study finds the maximum insertions of geotextile at 0.2D (3.0 cm) beneath the square footing base, and the lime percentage of 5.0% increases the UBC substantially. The UBC of lime-treated and geotextile-reinforced silty sand was to an optimum of 1,360 kN/m2 that has shown an enhancement of 258% compared to that of untreated and unreinforced silty sand that is approximately 380 kN/m2. Furthermore, comparative analysis between two ANN models was performed to provide improved estimate of the UBC, namely artificial neural network (ANN) and extreme learning machine (ELM). The developed computational models were then compared with experiment data, which proved that such models are more economical and effective than the expensive and time consuming conventional techniques. Consequently, based on the results, it was further validated that ELM possesses better generalization capability compared to ANN for predictive efficiency and thereby proves the efficiency of the model in estimating the ultimate bearing capacity of square footings incorporated with geotextile and lime-treated silty sand. This places the ELM model as a useful tool in the initial conceptual as well as the design for improvement steps of soil reinforcement. [ABSTRACT FROM AUTHOR]

This study investigated the effects of various parameters on the SPL (Sound Pressure Level) levels of rod-airfoil configurations. An experimental study was performed to investigate the effects of the rod parameters, such as the configuration of the rod, the distance between the rod and the airfoil, the diameter effect of the rod, and the geometry of the rod, on the performance of the rod-airfoil configuration. An Artificial Neural Network (ANN) model was then developed and applied to accurately predict the SPL of rod-airfoil configurations. The results of the study revealed that the Levenberg-Marquardt (LM) algorithm with 2 hidden neurons produced the best performance in predicting the SPL level, with a training R-squared value of 0.9998 and a testing R-squared value of 0.998715. The findings also indicated that increasing rod diameter increases sound pressure level while reducing gap width increases SPL levels and decreases frequency values. This method offers a more precise and effective technique to forecast the SPL levels of rod-airfoil designs, allowing designers to enhance their creations and lower noise levels. The findings of this study can also be utilized to direct future research in this area and offer important information for a better understanding of the mechanism of rod-airfoil noise creation. To the best of the authors' knowledge, this is the first study to look into rod-airfoil design predictions made using machine learning approaches. [ABSTRACT FROM AUTHOR]

In the current work, a DWT (Discrete Wavelet Transform) was linked to ANN, MLR, and RF to develop hybrid models WANN, WMLR, and WRF, respectively, for Brahmaputra River flow forecasting. We used ten-year daily flow data from Pandu Station, which was decomposed (up to five levels) into multiresolution time series using DWT and Daubechies wavelets db1, db2, db3, db8, and db10. The predicted discharge values for multiple lead times (2, 3, 4, 7, and 14 days) have been then obtained by feeding multiresolution time series data as the input to MLR, ANN, and RF. It was discovered that the WMLR-db10 model outperformed the WANN and WRF models for all lead times. Throughout the testing phase, the values of Nash-Sutcliffe efficiency (NS) along with RMSE (shown in bracket) for the WMLR-db10 model for lead times 2, 3, 4, 7 and 14 days have been observed to be, respectively, 0.998 (415.18 m3/s), 0.998 (514.21 m3/s), 0.996 (713.62 m3/s), 0.991 (1030.83 m3/s), and 0.977 (1638.64 m3/s). Additionally, it has been observed that WANN performed better for low-order wavelets (db1, db2, db3), WMLR performed better for high-order wavelets (db8, db10), and WRF performed worst of all the wavelets. [ABSTRACT FROM AUTHOR]

This investigation employed different ANN infrastructures for predicting the quality of sugarcane juice under varying microfluidization pressures (50–200 MPa) and cycles (1–7) which was previously unexplored. Two hidden layer (HL) activation functions (tansigmoid, logsigmoid) and learning algorithms (LM, GDX) with varying hidden layer neurons (HLNs) were tested to predict the color, total phenol content, total flavonoid content, chlorophyll content, total and reducing sugars, polyphenol oxidase activity, peroxidase activity, sucrose neutral invertase activity, aerobic plate count, yeast and mold count, particle size, sensory score and sedimentation rate of sugarcane juice under different microfluidization processing conditions. Results showed that the combination of LM + logsigmoid, GDX + logsigmoid and GDX + tansigmoid produced > 90% prediction accuracy. Among these models, GDX + tansigmoid exhibited 91.7% accuracy on training, and 96% accuracy on testing using relatively lower number of neurons (10 HLNs), and was therefore selected to predict the quality characteristics of sugarcane juice. [ABSTRACT FROM AUTHOR]

ABSTRACT Currently, healthcare systems operate under conventional management practices and entail storing and processing substantial medical data. Integrating the Internet of Things (IoT) and wireless sensor networks (WSNs) technologies has facilitated the development of IoT‐enabled healthcare, which possesses advanced data processing capabilities and extensive data storage. This paper proposes a WSN and IoT framework for patient monitoring in high‐speed 5G communications. Based on an artificial neural network (ANN), an intelligent health monitoring system was developed using IoT technology to monitor a person's blood pressure, heart rate, oxygen level, and temperature. Furthermore, the system helps the elderly being in critical cases in their homes to communicate and update their medical condition with the hospital, especially in critical cases, to be treated as soon as possible, especially in remote areas. The experimental results showed the superiority and effectiveness of the proposed system. Moreover, relying on ANNs to extract the basic features, the accuracy reached 96%. The proposed system was implemented practically, and the results were displayed in real time and compared with commercial medical devices. Maximum relative errors are heart rate (2.19), body temperature (2.94), systolic blood pressure (3.4), diastolic blood pressure (2.89), and SpO2 (1.05). On the other hand, the proposed system is much faster than other wireless communication methods, regardless of the detection quality. [ABSTRACT FROM AUTHOR]

Mango is a highly diverse tropical fruit with numerous varieties that differ in flavor, texture, and chemical composition. Consequently, identifying fraudulent substitutions of mango varieties poses a significant challenge using traditional techniques. Therefore, there is an increasing need for new methods to discriminate between mango varieties. Hyperspectral imaging coupled with machine learning techniques presents a promising approach for varietal discrimination. In this study, mango samples of eleven varieties were collected from a germplasm bank, with four slices obtained from each sample. Hyperspectral images were acquired in the Vis–NIR and NIR ranges for each slice, and spectral profiles were extracted and pretreated. Three discrimination models, linear discriminant analysis, K-nearest neighbor, and artificial neural networks, were implemented and validated using relevant wavelengths selected through a covering array feature selection algorithm. The performance of these models was evaluated using precision, accuracy, and F-score metrics. The average spectral profiles of the studied varieties exhibited a similar behavior with slight differences, which could be used for classification within the evaluated ranges. The optimal number of variables selected to refine the models was 17 for the UV–Vis–NIR range and 21 for the NIR range, with an accuracy ranging between 0.752 and 0.972. This study concludes that hyperspectral imaging combined with machine learning techniques can effectively discriminate between different varieties of mango. [ABSTRACT FROM AUTHOR]

Bridges are vital assets of transport infrastructure, systems, and communities. Damage characterization is critical in ensuring safety and planning adaptation measures. Nondestructive methods offer an efficient means towards assessing the condition of bridges, without causing harm or disruption to transport services, and these can deploy measurable evidence of bridge deterioration, e.g., deflections due to tendon loss. This paper presents an enhanced input-doubling technique and the Artificial Neural Network (ANN)-based cascade ensemble method for bridge damage state identification and is exclusively relying on small datasets, that are common in structural assessments. A new data augmentation scheme rooted in the principles of linearizing response surfaces is introduced, which significantly boosts the efficiency of intelligent data analysis when faced with limited volumes of data. Furthermore, improvements to a two-step ANN-based ensemble method, designed for solving the stated task, are presented. By adding the improved input-doubling methods as simple predictors in the first part of the cascade ensemble and optimizing it, we significantly boost accuracy (7%, 0.5%, and 8% based on R2 in predicting tendon losses for three critical zones that were defined across the deck of a real deteriorated prestressed balanced cantilever bridge). This improvement is strong evidence of the accuracy of the proposed method for the task at hand that is proven to be more accurate than other methods available in the international literature. [ABSTRACT FROM AUTHOR]

Industrial wastes have found great use in the built environment due to the role they play in the sustainable infrastructure development especially in green concrete production. In this research investigation, the impact of wastes from the industry on the compressive strength of concrete incorporating fly ash (FA) and silica fume (SF) as additional components alongside traditional concrete mixes has been studied through the application of machine learning (ML). A green concrete database comprising 330 concrete mix data points has been collected and modelled to estimate the unconfined compressive strength behaviour. Considering the concerning environmental ramifications associated with concrete production and its utilization in construction activities, there is a pressing need to perform predictive model exercise. Furthermore, given the prevalent reliance of concrete production professionals on laboratory experiments, it is imperative to propose smart equations aimed at diminishing this dependency. These equations should be applicable for use in the design, construction, and performance assessment of concrete infrastructure, thereby reflecting the multi-objective nature of this research endeavour. It has been proposed by previous research works that the addition of FA and SF in concrete has a reduction impact on the environmental influence indicators due to reduced cement use. The artificial neural network (ANN) and the M5P models were applied in this exercise to predict the compressive strength of FA- and SF-mixed concrete also considering the impact of water reducing agent in the concrete. A sensitivity analysis was also conducted to determine the impact of the concrete components on the strength of the concrete. At the end, closed-form equations were proposed by the ANN and M5P with performance indices which outperformed previous models conducted on the same database size. The result of the sensitivity analysis showed that FA is most impactful of all the studied components thereby emphasizing the importance of adding industrial wastes in concrete production for improved mechanical properties and reduced carbon footprint in the concrete construction activities. Also, the M5P and ANN models with R2 of 0.99 showed a potential for use as decisive models to predict the compressive strength of FA- and SF-mixed concrete. [ABSTRACT FROM AUTHOR]

Water scarcity is a critical global issue, necessitating efficient water purification and desalination methods. Membrane separation methods are environmentally friendly and consume less energy, making them more economical compared to other desalination and purification methods. This survey explores the application of artificial intelligence (AI) to predict membrane behaviour in water purification and desalination processes. Various AI platforms, including machine learning (ML) and artificial neural networks (ANNs), were utilised to model water flux, predict fouling behaviour, simulate micropollutant dynamics and optimise operational parameters. Specifically, models such as convolutional neural networks (CNNs), recurrent neural networks (RNNs) and support vector machines (SVMs) have demonstrated superior predictive capabilities in these applications. This review studies recent advancements, emphasising the superior predictive capabilities of AI models compared to traditional methods. Key findings include the development of AI models for various membrane separation techniques and the integration of AI concepts such as ML and ANNs to simulate membrane fouling, water flux and micropollutant behaviour, aiming to enhance wastewater treatment and optimise treatment and desalination processes. In conclusion, this review summarised the applications of AI in predicting the behaviour of membranes as well as their strengths, weaknesses and future directions of AI in membranes for water purification and desalination processes. [ABSTRACT FROM AUTHOR]

Predicting crop yields before harvest is important for making and carrying out policies about food safety, transportation costs, import-export, storage, and selling of agricultural goods. The weather is a key factor in crop growth and its development. Therefore, models that include meteorological variables can predict reliable forecasts for crop output; however, selecting the appropriate model for use in agricultural production forecasting can be challenging. This study investigates the development of wheat yield prediction models using various multivariate analysis techniques and weather indices derived from meteorological data collected over 22 years in Punjab, India. Five different modeling approaches, including stepwise multiple linear regression (SMLR), LASSO, elastic net (ELNET), artificial neural network (ANN), and ridge regression, were employed and compared for their effectiveness in predicting wheat yield. The models were calibrated using data from 17 years (2000–01 to 2016–17) and validated using data from the subsequent 5 years (2017–18 to 2021–22). Evaluation metrics such as R2, root mean square error (RMSE), normalized root mean square error (NRMSE), mean biased error (MBE), and modeling efficiency (EF) were utilized to assess model performance. The results indicate varying degrees of performance across districts and modeling techniques. ANN demonstrated the highest performance during both calibration and validation periods, followed closely by LASSO and ELNET. However, certain districts showed discrepancies in model fit, with some models performing better than others depending on the specific district. Overall, ANN emerged as the most reliable approach for wheat yield prediction in Punjab followed by ELNET and LASSO, offering valuable insights for agricultural planning and management. This comprehensive analysis provides valuable contributions to the field of crop yield prediction, enhancing understanding of the complex interactions between weather variables and agricultural outcomes. [ABSTRACT FROM AUTHOR]

Resistive Random Access Memory (RRAM) has gained considerable momentum due to its non-volatility and energy efficiency. Material and device scientists have been proposing novel material stacks that can mimic the "ideal memristor" which can deliver performance, energy efficiency, reliability and accuracy. However, designing RRAM-based systems is challenging. Engineering a new material stack, designing a device, and experimenting takes significant time for material and device researchers. Furthermore, the acceptability of the device is ultimately decided at the system level. We see a gap here where there is a need for facilitating material and device researchers with a "push button" modeling framework that allows to evaluate the efficacy of the device at system level during early device design stages. Speed, accuracy, and adaptability are the fundamental requirements of this modelling framework. In this paper, we propose a digital twin (DT)-like modeling framework that automatically creates RRAM device models from device measurement data. Furthermore, the model incorporates the peripheral circuit to ensure accurate energy and performance evaluations. We demonstrate the DT generation and DT usage for multiple RRAM technologies and applications and illustrate the achieved performance of our GPU implementation. We conclude with the application of our modeling approach to measurement data from two distinct fabricated devices, validating its effectiveness in a neural network processing an Electrocardiogram (ECG) dataset and incorporating Fault Aware Training (FAT). [ABSTRACT FROM AUTHOR]

In this study artificial neural network (ANN) models were developed for predicting the effects of wood species, density, modifying time, and temperature on the equilibrium moisture content (EMC) and swelling of six different thermally modified hardwood species, as previously published by the authors. Lumber of Yellow-poplar (Liriodendron tulipifera), red oak (Quercus borealis), white ash (Fraxinus americana), red maple (Acer rubrum), hickory (Carya glabra), and black cherry (Prunus serotina) were selected. Treatment type, species, temperature, time, and density were used as inputs for the models. Using Keras and Pytorch libraries in Python, different feed forward and back propagation multilayer ANN models were created and tested. The best prediction models, determined based on the errors in training iterations, were selected and used for testing. Based on the performance analysis, the prediction ANN models were accurate, reliable, and effective tools in terms of time and cost-effectiveness, for predicting the EMC and swelling characteristics of thermally modified wood. The multiple-input model was more accurate than the single-input model and it provided a prediction with R2 of 0.9975, 0.92, and MAPE of 1.36, 7.77 for EMC and swelling. [ABSTRACT FROM AUTHOR]

This study compares bio-inspired optimization algorithms for enhancing an ANN-based Maximum Power Point Tracking (MPPT) forecast system under partial shading conditions in photovoltaic systems. Four algorithms—grey wolf optimizer (GWO), particle swarm optimization (PSO), squirrel search algorithm (SSA), and cuckoo search (CS)—were evaluated, with the dataset augmented by perturbations to simulate shading. The standard ANN performed poorly, with 64 neurons in Layer 1 and 32 in Layer 2 (MSE of 159.9437, MAE of 8.0781). Among the optimized approaches, GWO, with 66 neurons in Layer 1 and 100 in Layer 2, achieved the best prediction accuracy (MSE of 11.9487, MAE of 2.4552) and was computationally efficient (execution time of 1198.99 s). PSO, using 98 neurons in Layer 1 and 100 in Layer 2, minimized MAE (2.1679) but had a slightly longer execution time (1417.80 s). SSA, with the same neuron count as GWO, also performed well (MSE 12.1500, MAE 2.7003) and was the fastest (987.45 s). CS, with 84 neurons in Layer 1 and 74 in Layer 2, was less reliable (MSE 33.7767, MAE 3.8547) and slower (1904.01 s). GWO proved to be the best overall, balancing accuracy and speed. Future real-world applications of this methodology include improving energy efficiency in solar farms under variable weather conditions and optimizing the performance of residential solar panels to reduce energy costs. Further optimization developments could address more complex and larger-scale datasets in real-time, such as integrating renewable energy sources into smart grid systems for better energy distribution. [ABSTRACT FROM AUTHOR]

This paper presents deep learning using LSTM, machine learning employing RF and GB algorithms, and the rating curve (RC) that can be used for estimating daily streamflow at the outlet of river basins. The Kone River basin in Vietnam is selected as an example for demonstrating the ability of these approaches. Hydro-meteorological data, including rainfall at Vinh Kim as well as water level and streamflow at Binh Tuong, were collected in the long period from 1/1/1979 to 31/12/2018. Multiple approaches mentioned above are implemented and applied for estimating daily streamflow at Binh Tuong in the Kone River basin. Firstly, coefficients and hyper-parameters in each approach are carefully determined using available hydro-meteorological data from 1/1/1979 to 31/12/2009 and dimensional and dimensionless error indexes. The results revealed that deep learning using LSTM presents the most suitable performance of the observed streamflow, with correlation coefficient r and NSE being close unity, while RMSE and MAE are less than 1.5% of the observed magnitude of streamflow. The RC and machine learning employing RF and GB algorithms procedures acceptably the observed streamflow, with r and NSE varying between 0.77 and 0.98, and RMSE and MAE ranging from 0.4 to 6.0% of the observed magnitude of streamflow. Secondly, multiple approaches are also applied for estimating daily streamflow from 1/1/2010 to 31/12/2018, revealing consistent statistical characteristics of streamflow in the river basin. Finally, the impacts of input data on output streamflow are discussed. [ABSTRACT FROM AUTHOR]

Corn syrup is a cost-effective sweetener ingredient for the food industry. In producing syrup from corn, process control to enhance and/or maintain a constant dextrose equivalent value (DE) is a constant challenge, especially in semi-automated/batch production settings, which are common in small to medium-size factories. Existing work has focused on continuous process control to keep parameter values within a setpoint. The machine learning method applied is for time series data. This study focuses on building process control models to enable semi-automation in small to medium-size factories in which the data are not as time dependent. Correlation coefficients were used to identify key process parameters that contribute to feed pH value and DE. Artificial neural network (ANN), support vector machine (SVM), and linear regression (LR) models were built to predict feed pH and DE. The results suggest (1) model accuracy ranges from 91% to 96%; (2) the ANN models yielded about 1% to 3% higher accuracy than the SVM and LR models and the prediction accuracy is robust even with as few as six data sets; (3) both the SVM and ANN models have noise tolerant properties, but ANN has a higher noise tolerance than SVM; (4) SVM performance can be hindered when using high-dimensional data sets; (5) the LR model yields higher variation in accuracy prediction than ANN and SVM; (6) distribution fitting is a good approach for generating data; however, fidelity of fitting can greatly impact accuracy; and (7) multi-stage models yield higher accuracy than single-stage models, but there are pros and cons to each approach. [ABSTRACT FROM AUTHOR]

The performance of heat pump systems for heating and cooling heavily relies on the thermal conditions of their reservoirs. This study introduces a novel thermal reservoir, detailing a 2017 project where the Municipality of Stavanger installed a heat exchanger system on the wall of a main wastewater tunnel beneath the city center. It provides a comprehensive account of the system's design, installation, and performance, and presents an Artificial Neural Network (ANN) model that predicts heat pump capacity, electricity consumption, and outlet temperature across seasonal variations in wastewater temperatures. By integrating domain knowledge with the ANN, this study demonstrates the model's capability to detect anomalies in heat pump operations effectively. The network also confirms the consistent performance of the heat exchangers from 2020 to 2024, indicating minimal fouling impacts. This study establishes wastewater heat exchangers as a safe, effective, and virtually maintenance-free solution for heat extraction and rejection. [ABSTRACT FROM AUTHOR]

The daily air quality indices (AQIs) for pollutants, including particulate matter (PM10 and PM2.5), carbon monoxide (CO), nitrogen oxides (NO2), ozone (O3), and sulfur dioxide (SO2), were evaluated for the period of 2019–2022 in Al-Jahra City, Kuwait. This study is designed to (1) evaluate overall and seasonal changes in pollutants, (2) investigate the correlation for PM10 and PM2.5 and other pollutants during each season, and (3) examine the best model for prediction of air pollutant concentration of PM10 and PM2.5. An assessment of air quality indices was carried out by using different algorithms models, including random forest (RF), an artificial neural network (ANN), and extreme gradient boosting (XGBoost). The overall level of PM10, PM2.5, and SO2 pollutants shows an increasing trend from 2019 to 2022, reaching their highest in 2022 with a significant decrease in 2020 during COVID-19 lockdown restrictions. The pollutants CO and O3 reached their peak in 2021. The obtained results showed that the PM10 and O3 levels are higher in the summer, whereas PM2.5, NO2, and CO were recorded at the highest levels during spring, autumn, and winter respectively due to a variation in the meteorologic condition. Furthermore, during the winter season, the PM10 is positively correlated with CO (rp = 0.401), NO2 (rp = 0.121), and SO2 (rp = 0.119) AQIs while PM2.5 is significantly positively correlated with the CO (rp = 0.198), O3 (rp = 0.310), and SO2 (rp = 0.129) AQIs. In contrast, the performance of XGBoost is a more reliable predictor for PM10 and PM2.5 levels. [ABSTRACT FROM AUTHOR]

The service life of downstream dams, river hydraulics, waterworks construction, and reservoir management is significantly affected by the amount of sediment load (SL). This study combined models such as the artificial neural network (ANN) algorithm with the Firefly algorithm (FA) and Artificial Bee Colony (ABC) optimization techniques for the estimation of monthly SL values in the Çoruh River in Northeastern Turkey. The estimation of SL values was achieved using inputs of previous SL and streamflow values provided to the models. Various statistical metrics were used to evaluate the accuracy of the established hybrid and stand-alone models. The hybrid model is a novel approach for estimating sediment load based on various input variables. The results of the analysis determined that the ABC-ANN hybrid approach outperformed others in SL estimation. In this study, two combinations, M1 and M2, with different input variables, were used to assess the model's accuracy, and the best-performing model for monthly SL estimation was identified. Two scenarios, Q(t) and Q(t − 1), were coupled with the ABC-ANN algorithm, resulting in a highly effective hybrid approach with the best accuracy results (R2 = 0.90, RMSE = 1406.730, MAE = 769.545, MAPE = 5.861, MBE = − 251.090, Bias Factor = − 4.457, and KGE = 0.737) compared to other models. Furthermore, the utilization of FA and ABC optimization techniques facilitated the optimization of the ANN model parameters. The significant results demonstrated that the optimization and hybrid techniques provided the most effective outcomes in forecasting SL for both combination scenarios. As a result, the prediction outputs achieved higher accuracy than those of a stand-alone ANN model. The findings of this study can provide essential resources to various managers and policymakers for the management of water resources. [ABSTRACT FROM AUTHOR]