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Scintillation is a dynamic phenomenon of the earth's ionosphere that adversely affects satellite-based communication and navigation systems. It is characterized by rapid fluctuations in phase and amplitude of trans -ionospheric radio waves, posing immense risks to systems that operate at radio frequencies, such as the Global Positioning System (GPS). In this regard, harnessing modern technology and state-of-the-art datasets to predict their occurrence is crucial for mitigating their effects. This paper presents a neural network approach to predict ionospheric scintillation using datasets obtained from distributed geodetic receivers in the African region. The motivation for this work is to develop a model backed by an extensive database for scintillation prediction over the region. Eleven years of data from the stations were obtained for magnetically quiet days and the Rate of TEC index (ROTI) was computed as a proxy for scintillation. The model development was backed by data from the ascending to the maximum phases of solar cycle 24 while the years in the descending phase were used for model validation and prediction. Using the solar flux (F10.7 cm), elevation and critical frequency (FoF2) as physical model parameters, the model achieved a prediction accuracy of about 70 %. A control experiment using the wavelet features increased the model's accuracy to about 91 % during the testing phase with an 86 % prediction accuracy. The model was extensively evaluated using metrics such as the Root Mean Square Error (RMSE), statistics from the Residuals and the Wavelet Coherence Analysis (WCA) technique. The standard deviation was used alongside the RMSE to gauge dispersion and ascertain model stability. The developed model demonstrated the ability to reconstruct the ROTI with low errors. [ABSTRACT FROM AUTHOR]

Experimental and numerical analysis have been conducted to examine the heat transfer characteristics of a finned heat pipe based on thermal resistance networks. The present numerical analysis also reports the enhancement of heat transport of heat pipe using the fins. The key simulation parameters considered were three types of fins with circular, square, and hexagonal shapes, the fin length in the range from 19.05 mm to 38.1 mm, the number of fins in the range from 5 to 20, and the fin thickness in the range from 0.25 mm to 1 mm. The heat transfer rate shoots up by 44.7 % in the case of finned heat pipe when compared with the baseline model with respect to the variation in the simulation parameters. An artificial neural network, which is one of the machine learning methods, was used to predict the heat transfer performance obtained from thermal resistance analysis of the finned heat pipe. This paper introduces a novel approach by developing an ANN model that maintains high accuracy over a broader range of operational conditions. The optimized ANN model could predict the heat transfer performance of the finned heat pipe with reasonable accuracy. In addition, the heat transfer rate of the finned heat pipe could be predicted accurately from extrapolated and interpolated data using the optimized ANN model. [ABSTRACT FROM AUTHOR]

This paper offers a comprehensive investigation into the forward and inverse kinematics of a wrist rehabilitation robot, utilizing the Denavit-Hartenberg method for forward kinematics (FK) and a geometric approach, as well as artificial neural networks (ANN) and adaptive Neuro-Fuzzy inference systems (ANFIS) for inverse kinematics (IK) analysis. While the geometric method entails precise parameter measurements and faces uncertainties, ANN and ANFIS are explored as potential remedies to enhance accuracy and robustness. Evaluating 11 different training functions sourced from existing literature, our study conducts a thorough assessment of their performance within an ANN network. We aim to pinpoint the most suitable training function for achieving optimal IK solutions in the context of a wrist rehabilitation robotic. Additionally, the ANFIS model, trained using Fuzzy C-Means (FCM), sets itself apart from Grid Partitioning (GP) and Subtractive Clustering (SC). Among the ANN training functions, Bayesian regularization with 5 hidden layers emerges as the most effective, yielding low root mean square error (RMSE) values of 0.003, 0.004, and 0.007 degrees for pronation/supination (P/S), abduction/adduction (AB/AD), and flexion/extension (F/E), respectively. Conversely, ANFIS, trained with FCM, demonstrates satisfactory yet less precise results, with RMSE values of 0.191, 0.082, and 0.165 degrees for P/S, AB/AD, and F/E, respectively. Despite its adequacy, ANFIS trails behind ANN, showcasing RMSE reductions of 98.4%, 95.1%, and 95.7% for P/S, AB/AD, and F/E angles, respectively. This study contributes to leveraging ANN and ANFIS for IK analysis in wrist rehabilitation robotics, highlighting the efficacy of ANN, particularly when employing Bayesian regularization, to enhance accuracy. [ABSTRACT FROM AUTHOR]

This study aims to investigate the linear and nonlinear (non‐compensation) effects of teacher leadership on teacher well‐being dimensions through the three basic psychological needs dimensions (autonomy, competence, and relatedness). This quantitative cross‐sectional study used partial least squares structural equation modelling and artificial neural network (ANN) for data analysis. Data were collected using survey questionnaires from 728 Malaysian primary school teachers. All the direct (linear) and indirect effects of teacher leadership on teacher well‐being dimensions through teachers' basic psychological needs were significant. The ANN analysis revealed that competence, a dimension of teacher basic psychological needs, was the strongest predictor of workload well‐being and student interaction well‐being. Autonomy was the strongest predictor of organisational well‐being. Implications and future studies are presented. Practitioner Points: The significant influence of teacher leadership on teacher well‐being dimensions suggests practical strategies that empower teachers to assume leadership roles in decision‐making, cultivate a supportive school environment, and foster positive interactions with students.Teacher leadership emerges as a key factor in enhancing teacher well‐being by addressing their basic psychological needs. Practitioners could concentrate on implementing teacher leadership practices aligned with teachers' autonomy, competence, and relatedness.Practitioners can leverage nonlinear insights derived from artificial neural network (ANN) analysis to tailor interventions for a more targeted and effective enhancement of teacher well‐being. [ABSTRACT FROM AUTHOR]

Copper (Cu) nanoparticles (NPs) and polyvinyl alcohol (PVA) are utilized to enhance heat transfer (HT) which is used in the efficiency of solar energy systems. Copper nanoparticles have excellent thermal conductivity (TC) properties that enable them to conduct heat efficiently. In this arrangement, the gap between the two cylindrical channels is settled for the hybrid nanofluids (HNFs) flow in an inclined position that is favourable to sunlight. The nanomaterials consist of a mixture of PVA and Cu nanoparticles (NPs), to execute HNFs. Solar radiation is present on the hot side of the system. The porous gap between the two channels is considered variable which plays a crucial role in enhancing heat transfer and energy conversion. The varying permeability of the gap is adjusted to control the flow resistance and improve the stability of the system. It is observed that higher porosity allows for better convective heat transfer and reduced pressure drop. The transformed equations are solved through an artificial neural network (ANN) while the control volume finite element method (CVFEM) is also used to handle the governing equations. The Cu and PVA (HNF) improves solar radiation absorption and protects components, ultimately increasing the performance and efficiency of the systems. [ABSTRACT FROM AUTHOR]

Various developing countries are confronted with serious environmental difficulties due to excessive resource utilization and insufficient waste management system. In particular, construction and demolition waste poses a grave threat to the environment, contributing to escalating energy consumption, the depletion of landfill capacities, and the generation of harmful noise and dust pollution. Consequently, the research community is tasked with the daunting challenge of devising effective strategies to incorporate this waste material in producing concrete, without compromising the critical strength and durability characteristics. The investigation aims to attain the aforementioned objective by examining the effects of using recycled aggregates as a distinct partial replacement of 0%, 5%, 10%, 15%, and 20% on the compressive and split tensile strength traits, contingent upon 7 and 28 days of age of curing. Experimental test results show that the optimal concrete production is achieved when 10% of coarse aggregate is replaced with recycled aggregate, maintaining 98% of the materials compressive and split tensile strength. To further validate the obtained experimental data, model equations were derived through regression analysis and the framed model equation is further assessed for accuracy using error analysis. In this study, a MATLAB program was utilized for prediction of compressive and split tensile strength with five distinct network types and the Levenberg-Marquardt algorithm is used for optimization. A comparative analysis was conducted between the regression analysis values and the performance of the ANN modelling. The findings demonstrate that the Artificial Neural Network (ANN) serves as a highly effective model, offering significantly improved accuracy in predicting the optimal correlation between compressive strength and split tensile strength of concrete. [ABSTRACT FROM AUTHOR]

To improve the security of the Internet of Medical Things (IoMT) in healthcare, this paper offers a Federated Learning (FL)-guided Intrusion Detection System (IDS) and an Artificial Neural Network (ANN)-based key exchange mechanism inside a blockchain framework. The IDS are essential for spotting network anomalies and taking preventative action to guarantee the secure and dependable functioning of IoMT systems. The suggested method integrates FL-IDS with a blockchain-based ANN-based key exchange mechanism, providing several important benefits: (1) FL-based IDS creates a shared ledger that aggregates nearby weights and transmits historical weights that have been averaged, lowering computing effort, eliminating poisoning attacks, and improving data visibility and integrity throughout the shared database. (2) The system uses edge-based detection techniques to protect the cloud in the case of a security breach, enabling quicker threat recognition with less computational and processing resource usage. FL's effectiveness with fewer data samples plays a part in this benefit. (3) The bidirectional alignment of ANNs ensures a strong security framework and facilitates the production of keys inside the IoMT network on the blockchain. (4) Mutual learning approaches synchronize ANNs, making it easier for IoMT devices to distribute synchronized keys. (5) XGBoost and ANN models were put to the test using BoT-IoT datasets to gauge how successful the suggested method is. The findings show that ANN demonstrates greater performance and dependability when dealing with heterogeneous data available in IoMT, such as ICU (Intensive Care Unit) data in the medical profession, compared to alternative approaches studied in this study. Overall, this method demonstrates increased security measures and performance, making it an appealing option for protecting IoMT systems, especially in demanding medical settings like ICUs. [ABSTRACT FROM AUTHOR]

In this paper, a nonlinear Autoregressive Moving Average (ARMA) time series model is developed for the case where observations are affected by fuzziness. The primary motivation is to address the limitations of ARMA models, specifically their inability to effectively model complex nonlinear relationships, handle long memory processes, and manage non- Gaussian data. To achieve this, a fuzzy ARMA model is estimated using a method based on artificial neural networks. The objective is to construct a robust fuzzy time series model by employing various popular activation functions, such as logistic, hyperbolic tangent, and rectified linear unit. The effectiveness of the proposed model is rigorously evaluated using three well-established performance criteria. Furthermore, to demonstrate the practical benefits and applicability of this new time series model, a comparative analysis using both simulated data and real-world examples is conducted. [ABSTRACT FROM AUTHOR]

BackgroundMethodResultsCardiovascular diseases (CVDs) include abnormal conditions of the heart, diseased blood vessels, structural problems of the heart, and blood clots. Traditionally, CVD has been diagnosed by clinical experts, physicians, and medical specialists, which is expensive, time-consuming, and requires expert intervention. On the other hand, cost-effective digital diagnosis of CVD is now possible because of the emergence of machine learning (ML) and statistical techniques.In this research, extensive studies were carried out to classify CVD via 19 promising ML models. To evaluate the performance and rank the ML models for CVD classification, two benchmark CVD datasets are considered from well-known sources, such as Kaggle and the UCI repository. The results are analysed considering individual datasets and their combination to assess the efficiency and reliability of ML models on the basis of various performance measures, such as precision, kappa, accuracy, recall, and the F1 score. Since some of the ML models are stochastic, we repeated the simulation 50 times for each dataset using each model and applied nonparametric statistical tests to draw decisive conclusions.The nonparametric Friedman – Nemenyi hypothesis test suggests that the Extra Tree Classifier provides statistically superior accuracy and precision compared with all other models. However, the Extreme Gradient Boost (XGBoost) classifier provides statistically superior recall, kappa, and F1 scores compared with those of all the other models. Additionally, the XGBRF classifier achieves a statistically second-best rank in terms of the recall measures. [ABSTRACT FROM AUTHOR]

Diesel Particulate Filter (DPF) in the diesel engine exhaust stream needs frequent regeneration (exotherm) to remove captured particulate matter (PM, or soot) without damaging to the porous DPF structure by controlling the peak temperatures and temperature gradients across the DPF. In this study, temperature distribution in a DPF is measured at 42 strategic locations in the test DPF under various regeneration conditions of exhaust flow rates, regeneration temperatures and soot loads. Then a data-based model with feed-forward neural network architecture is designed to model the thermal gradients and temperature dynamics of the DPF during the regeneration process. The neural network feature vector selection, network architecture, hyperparameter calibration process, measured data preprocessing, and experimental data acquisition procedure are evaluated. Over 7,400 experimental data points at various regeneration temperatures, flow rates and soot loads are used in training and validating the neural network model. It is found that the neural network model can accurately predict the 42 DPF bed temperatures simultaneously at different locations, and the time series analysis of both model-predicted and experimentally measured temperatures shows a good correlation. This indicates that the currently developed neural network model can provide spatial distribution of temperature in the DPF, and comprehend the nonlinearity of the temperature dynamics due to DPF soot load at exothermic conditions. These results demonstrate that the data-based model has capability in predicting thermal gradients within a DPF, aiding in determining a safer DPF regeneration strategy, onboard diagnostics and DPF development. [ABSTRACT FROM AUTHOR]

This research presents a novel hybrid model that integrates a physical-based empirical model with an Artificial Neural Network (ANN) to accurately predict the longitudinal modulus of elasticity for composites under compression. The study focuses on a composite material with a pore inclusion within an ABS plastic matrix, exploring various pore volumes, orientations, and shapes. As part of the proposed hybrid model, a regression-type neural network was trained in MATLAB® to predict and correct discrepancies between the Generalized Stiffness Formulation (GSF) homogenization-based modeling method and the collected compression experimental test results. Using MATLAB® neural network, random error datasets were used to train the feed-forward neural network, and the remaining error datasets were used for validating the performance of the proposed hybrid modeling scheme. The hybrid model demonstrated superior performance, achieving the lowest Mean Error (ME) of 0.1684864, Mean Absolute Error (MAE) of 1.051846, Mean Squared Error (MSE) of 3.500952, and highest R-squared of 0.998797. The proposed hybrid model outperformed both the Generalized Stiffness Formulation (GSF) and standalone ANN models. The significant improvement in prediction accuracy underscores the novelty and robustness of the hybrid approach in composite material modeling. Furthermore, this method can be used to refine any existing physical model by focusing on improving these established models to match experimental results and reducing the discrepancies, which offers a more efficient and attractive strategy for accurate predictions.   • Developed framework to improve accuracy in composites homogenization modeling. • Revealed new insights using AI, analytical, and numerical modeling techniques. • Validated findings with AI, analytical methods, and experimental testing of composites. • Advanced knowledge in modeling through artificial intelligence. • Provided solutions to reduce modeling inaccuracies across various fields. [ABSTRACT FROM AUTHOR]

One of the important technologies in present days is Intrusion detection technology. By using the machine learning techniques, researchers were developed different intrusion systems. But, the designed models toughness is affected by the two parameters, in that first one is, high network traffic imbalance in several categories, and another is, non-identical distribution is present in between the test set and training set in feature space. An artificial neural network (ANN) multi-level intrusion detection model with semi-supervised hierarchical k -means method (HSK-means) is presented in this paper. Error rate of intrusion detection is reduced by the ANN's accurate learning so it uses the Grasshopper Optimization Algorithm (GOA) which is analysed in this paper. Based on selection of important and useful parameters as bias and weight, error rate of intrusion detection system is reduced in the GOA algorithm and this is the main objective of the proposed system. Cluster based method is used in the pattern discovery module in order to find the unknown patterns. Here the test sample is treated as unlabelled unknown pattern or the known pattern. Proposed approach performance is evaluated by using the dataset as KDDCUP99. It is evident from the experimental findings that the projected model of GOA based semi supervised learning approach is better in terms of sensitivity, specificity and overall accuracy than the intrusion systems which are existed previously. [ABSTRACT FROM AUTHOR]

Patient-specific studies on vascular flows have significantly increased for hemodynamics due to the need for different observation techniques in clinical practice. In this study, we investigate aortic aneurysms in terms of deformation, stress, and rupture risk. The effect of Ascending Aortic Diameter (AAD) was investigated in different aortic arches (19.81 mm, 42.94 mm, and 48.01 mm) via Computational Fluid Dynamics (CFD), Two-way coupling Fluid–Structure Interactions (FSI) and deep learning. The non-newtonian Carreau viscosity model was utilized with patient-specific velocity waveform. Deformations, Wall Shear Stresses (WSSs), von Mises stress, and rupture risk were presented by safety factors. Results show that the WSS distribution is distinctly higher in rigid cases than the elastic cases. Although WSS values rise with the increase in AAD, aneurysm regions indicate low WSS values in both rigid and elastic artery solutions. For the given AADs, the deformations are 2.75 mm, 6. 82 mm, and 8.48 mm and Equivalent von Mises stresses are 0.16 MPa, 0.46 MPa, and 0.53 MPa. When the rupture risk was evaluated for the arteries, the results showed that the aneurysm with AAD of 48.01 mm poses a risk up to three times more than AAD of 19.81 mm. In addition, an Artificial neural network (ANN) method was developed to predict the rupture risk with a 98.6% accurate prediction by numerical data. As a result, FSI could indicate more accurately the level of rupture risk than the rigid artery assumptions to guide the clinical assessments and deep learning methods could decrease the computational costs according to CFD and FSI. [ABSTRACT FROM AUTHOR]

The vortex-induced vibration (VIV) problem has been of critical concern for the wind-resistance of long-span bridges. Usually there are four types of approach for VIV studies: wind tunnel tests, field monitoring, computational fluid dynamics and mathematical models. However, traditional approaches have shown some limitations, such as high cost and low efficiency. In order to improve the efficiency and accuracy of VIV studies, this article has taken the VIV problem of a split three-box girder in a cable-stayed and cooperative suspension system bridge as an instance, and conducted a series of VIV wind tunnel tests. An approach based on machine learning is described that is able to serve as a complement to the wind tunnel tests. The proposed approach involves two steps: firstly, based on the dataset produced by wind tunnel tests, a clustering algorithm is introduced to separate the VIV signals automatically from other vibrations. Then, an artificial neural network is utilized to recognize the VIV response and aerodynamic force in the lock-in region directly. It is shown that the clustering algorithm can be a good tool for the recognition of VIV signals. Moreover, the proposed artificial neural network models show good ability for recognizing VIV amplitude and aerodynamic lift force. [ABSTRACT FROM AUTHOR]

Human-robot interaction (HRI) which has become the fundamental need of the hour is born out of the necessity for studying the relation between humans and robots. This cutting-edge discipline is a multidisciplinary field that draws from computer science, robotics along with human-computer interaction and psychology. It focuses mainly on designing and programming machines, best known as automated machines or robots, which are used by humans to perform specific tasks in a timely manner and with higher quality. The key problem in HRI is to realize, shape, tune, and modelling the human-robot interaction in a flexible manner. For the sake of reflecting and shaping the interactions between humans and robots, HRI is based on the fusion of the two areas: the people's behaviour and attitudes towards using these robots, as well as the physical, technological, and interactive features of the robots. As the robot has tightly integrated from a set of sensors that collect the data from the environment and send them to the processor which in turn translates the collected data into information that can be used in the robot itself, machine learning (ML) is a well-known research area that focuses on the building of well-stocked knowledge systems by using supervised and unsupervised algorithms. From a conceptual standpoint, this research survey and taxonomy pursue to present an in-depth evaluation and review of the most current state-of-the-art papers that have already been published so far and encompass the use of ML algorithms in the HRI field. Thus, a total of 30research papers devoted to HRI were examined and analysed to give the most ML algorithms implemented in the field of HRI. Evidently, this study shows that the Neural and Reinforcement learning machine algorithms that are used mostly in the recent studies that have an interest in HRI use a machine learning algorithm with a supervised technique in a physical application. There are many challenges facing HRI using ML algorithms, which reduce the use of other ML algorithms such as deep and SVM learning algorithm. Unfortunately, these challenges limit use in social and mobile applications. [ABSTRACT FROM AUTHOR]

Contamination of cereal grain, especially wheat, with fungal infections can cause significant economic impacts and it endangers the health of humans and livestock. This study aims to appraise the UV/VIS–NIR and digital color (RGB) imaging systems and spectroscopic methodology to detect wheat kernels infected by fungi such as Penicillium expansum and Fusarium graminearum. NIR spectra of 190–1100 nm at 10 nm intervals, visible color reflectance images and non‐visible reflectance images of wheat kernels in the ultraviolet and near‐infrared ranges were applied to develop the multi‐layer perceptron (MLP) artificial neural network model. The optimum wavelengths were selected by application of the principal component analysis (PCA) after preprocessing the raw spectra. A confusion matrix was used in the correlation feature selection method (CFS) for the decision tree classifier of selected features. The results showed that the four UV wavelengths of 310, 330, 400, and 410 nm were the best wavelengths using PCA to distinguish healthy and unhealthy wheat kernels. Considering the intensity of the wavelengths as the neural network inputs, samples were classified into healthy and unhealthy categories with an accuracy of 90.9%. Also, 18 features of color images in RGB, LAB, HSV, HSI, YCbCr, and YIQ spaces provided the highest average accuracy of 44.4% in classifying healthy and infected wheat kernels by using a CCD Proline camera in the ultraviolet range. In contrast, other cameras in the visible and invisible range showed low accuracy. Furthermore, the best classification accuracy of the healthy and infected samples by the use of the CFS method was obtained at 88.1%. Based on the findings, spectroscopic methodology proved to be highly effective for detecting, classifying and automatic cleaning of various agricultural seeds, with a particular emphasis on wheat kernals. [ABSTRACT FROM AUTHOR]

The main aim of this study is to examine the effects of electrical discharge machining (EDM) parameters on the surface roughness (SR) and material removal rate (MRR) of magnesium composites. The composites are produced with AZ91 as matrix, graphene (Gr) and aluminum oxide (Al2O3) as reinforcements using stir casting process. In this work, one material factor (i.e., Material Type (MT)) and four machining factors (i.e., voltage (V), flushing pressure (P), pulse on time (TON) and peak current (I)) were considered to examine their impact on the MRR and SR. The experimental design and analysis was carried out using Taguchi method. From the analysis, it is noticed that peak current (I) is the most influencing factor on both MRR and SR, followed by V, P, TON, and MT for MRR and TON, MT, P, and V for SR. The optimal conditions for output responses are as follows: MT = T1, V = 40 V, P = 0.75 kg/cm2, TON = 20 µs, and I = 30 A for MRR and are MT = T1, V = 40 V, P = 0.50 kg/cm2, TON = 20 µs and I = 10 A for SR. SEM micrographs showed the surface characteristics of machined surfaces at higher and lower MRR conditions. A hybrid neural network model (ANN-TLBO) was developed to predict the output responses at the corresponding input variables. The neuron and population independency tests were performed to get ideal values for ANN-TLBO characteristics such as neuron number and population size. The developed model was trained and tested with the EDM experimental data. Confirmation tests are conducted under optimal conditions, and these results are examined with the results of ANN-TLBO. Results showed that the hybrid algorithm predicts the MRR and SR with more accuracy (for majority of the data the prediction error was within ± 10%). [ABSTRACT FROM AUTHOR]

To what extent does a firm's digital readiness influence its competence in implementing digital initiatives? This study employs a deep-learning-based dual-stage approach using Partial Least Squares Structural Equation Modelling (PLS-SEM) and Artificial Neural Network (ANN) to demonstrate and quantify this relationship. Data were sourced from a questionnaire survey involving 428 architecture, engineering and construction (AEC) firms in New Zealand. The PLS-SEM analysis confirmed the positive correlation between the digital readiness of an organization and its competence towards seven types of DT, including immersive technologies, sensing technology, robotics, 3D printing, digital fabrication, artificial intelligence and big data. The ANN analysis further quantified the importance of the investigated readiness indicators in influencing digital competence. The results highlighted four most significant readiness attributes influencing the digital competence of AEC firms: (1) organizational culture, (2) perception of the leadership team, (3) hardware & software systems and (4) strategy plans. The findings can serve as a baseline for developing effective change management strategies and contribute to reducing the digital divide within AEC organizations, facilitating the effectiveness of organizational digital transformation. [ABSTRACT FROM AUTHOR]

This research aims to emphasize on Dynamic Capability View and Human Capital Framework to analyze the effect of HR Analytics capabilities on organizational performance with innovative capability as a mediator. Further, the study will evaluate the role Technological Turbulence as a moderator. An empirical test utilizing a sample of 418 Indian HR professionals through structural equation modeling and Artificial Neural Network (ANN) was conducted. The findings illustrates that HR Analytics capabilities (intangible resources, human skills and tangible resources) have a significant impact on organizational performance. Innovative capability acts as a significant mediator between HR analytics capabilities and organizational performance. The relationship between organizational performance and innovative capability is positively moderated by technological turbulence. Further the mediated moderation impact of Innovative capability and Technological turbulence was significant between HR analytics capabilities and organizational performance. This research makes recommendations for HR directors and executives on how to effectively utilize their resources while keeping innovation in mind. The influence of technological turbulence encourages organizations to innovate and enhance business performance. [ABSTRACT FROM AUTHOR]

Purpose: Mirroring global trends, artificial intelligence advances in medicine, notably urolithiasis. It promises accurate diagnoses, effective treatments, and forecasting epidemiological risks and stone passage. This systematic review aims to identify the types of AI models utilised in urolithiasis studies and evaluate their effectiveness. Methods: The study was registered with PROSPERO. Pubmed, EMBASE, Google Scholar, and Cochrane Library databases were searched for relevant literature, using keywords such as 'urology,' 'artificial intelligence,' and 'machine learning.' Only original AI studies on urolithiasis were included, excluding reviews, unrelated studies, and non-English articles. PRISMA guidelines followed. Results: Out of 4851 studies initially identified, 71 were included for comprehensive analysis in the application of AI in urolithiasis. AI showed notable proficiency in stone composition analysis in 12 studies, achieving an average precision of 88.2% (Range 0.65–1). In the domain of stone detection, the average precision remarkably reached 96.9%. AI's accuracy rate in predicting spontaneous ureteral stone passage averaged 87%, while its performance in treatment modalities such as PCNL and SWL achieved average accuracy rates of 82% and 83%, respectively. These AI models were generally superior to traditional diagnostic and treatment methods. Conclusion: The consolidated data underscores AI's increasing significance in urolithiasis management. Across various dimensions—diagnosis, monitoring, and treatment—AI outperformed conventional methodologies. High precision and accuracy rates indicate that AI is not only effective but also poised for integration into routine clinical practice. Further research is warranted to establish AI's long-term utility and to validate its role as a standard tool in urological care. [ABSTRACT FROM AUTHOR]

The increasing rates of illicit behaviors, particularly financial crimes, e.g., bank fraud and tax evasion, adversely affect national economies. In such cases, using nondestructive methods, scientists must evaluate relevant documents carefully to preserve their value as evidence. When forensic laboratories analyze paper as evidence, they typically investigate its origin and date of manufacture. If a document's date is earlier than the earliest availability of the paper used in its creation, then this anachronism indicates that the document has been backdated. This study investigated weave marks and drainage marks for forensic purposes. Machine learning models for forensic document examination were developed and evaluated. The partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and artificial neural network (ANN) classification models achieved F1-scores of 0.903, 0.952, and 0.931, respectively. In addition, to enhance model effectiveness and construct a robust model, variables were selected using the VIP scores generated by the PLS-DA model. As a result, the SoftMax classifier in the ANN model maintained its performance with an F1-score of 0.951 even with a 50% reduction in the number of input variables. [ABSTRACT FROM AUTHOR]

The present study investigates the ultimate bearing capacity (UBC) of a footing subjected to an eccentric load situated above an unlined horseshoe-shaped tunnel in the rock mass, following the Generalized Hoek-Brown (GHB) failure criterion. A reduction factor (Rf) is introduced to investigate the impact of the tunnel on the UBC of the footing. Rf is determined using upper and lower bound analyses with adaptive finite-element limit analysis. The study examines the influence of several independent variables, including normalized load eccentricity (e/B), normalized vertical and horizontal distances (δ/B and H/B) of the footing from the tunnel, tunnel size (W/B), and other rock mass parameters. It was found that all these parameters significantly affect the behavior of tunnel-footing interaction depending on the range of varying parameters. The findings of the study indicate that the critical depth (when Rf is nearly 1) of the tunnel decreases with increasing load eccentricity. The critical depth is found to be δ/B ≥ 2 for e/B ≤ 0.2 and δ/B ≥ 1.5 for e/B ≥ 0.3, regardless of H/B ratios. Additionally, the GHB parameters of the rock mass significantly influence the interaction between the tunnel and the footing. Moreover, this study identifies some typical potential failure modes depending on the tunnel position. The typical potential failure modes of the footing include punching failure, cylindrical shear wedge failure, and Prandtl-type failure. This study also incorporates soft computing techniques and formulates empirical equations to predict Rf using artificial neural networks (ANNs) and multiple linear regression (MLR). [ABSTRACT FROM AUTHOR]

The present work deals with evaluating the nonlinear deflections of the smart sandwich plate with agglomerated Carbon Nanotubes (CNTs) porous core and piezo-magneto-electric (PME) facings, using a novel finite element method (FEM) – artificial neural network (ANN) approach. For the first time, an ANN-based computational tool that integrates the effects of agglomeration of CNTs, porosity and pyro-coupling of the PME materials is presented. Firstly, an in-house finite element (FE) computational tool is proposed and developed using the principle of virtual work in association with higher-order shear deformation theory (HSDT) and von-Karman's nonlinearity. The data points owing to the nonlinear deflections are collected using the proposed FE formulation, which trains the ANN model using Levenberg–Marquardt algorithm. The externally applied thermal loads are assumed to vary uniformly and linearly across the thickness of the plate. The primary focus of this work is to assess the variation in the degree of pyro-coupling associated with agglomeration and porosity. Two states of agglomeration, such as partial and complete; three forms of porosity, such as uniformly distributed, and two variants of functionally graded porosity, are considered for investigation. Numerical examples are solved to understand the interrelated effects of these material properties. A significant variation in the deflection of the plate, which refers to its actuation capability, is witnessed when the parameters of agglomeration and porosity change. [ABSTRACT FROM AUTHOR]

The need for accurate solar energy forecasting is paramount as the global push towards renewable energy intensifies. We aimed to provide a comprehensive analysis of the latest advancements in solar energy forecasting, focusing on Machine Learning (ML) and Deep Learning (DL) techniques. The novelty of this review lies in its detailed examination of ML and DL models, highlighting their ability to handle complex and nonlinear patterns in Solar Irradiance (SI) data. We systematically explored the evolution from traditional empirical, including machine learning (ML), and physical approaches to these advanced models, and delved into their real-world applications, discussing economic and policy implications. Additionally, we covered a variety of forecasting models, including empirical, image-based, statistical, ML, DL, foundation, and hybrid models. Our analysis revealed that ML and DL models significantly enhance forecasting accuracy, operational efficiency, and grid reliability, contributing to economic benefits and supporting sustainable energy policies. By addressing challenges related to data quality and model interpretability, this review underscores the importance of continuous innovation in solar forecasting techniques to fully realize their potential. The findings suggest that integrating these advanced models with traditional approaches offers the most promising path forward for improving solar energy forecasting. [ABSTRACT FROM AUTHOR]

A significant advancement in wireless communication has occurred over the past couple of decades. Nowadays, people rely more on services offered by the Internet of Things, cloud computing, and big data analytics-based applications. Higher data rates, faster transmission/reception times, more coverage, and higher throughputs are all necessary for these emerging applications. 5G technology supports all these features. Antennas, one of the most crucial components of modern wireless gadgets, must be manufactured specifically to meet the market's growing demand for fast and intelligent goods. This study reviews various 5G antenna types in detail, categorizing them into two categories: conventional design approaches and machine learning-assisted optimization approaches, followed by a comparative study on various 5G antennas reported in publications. Machine learning (ML) is receiving a lot of emphasis because of its ability to identify optimal outcomes in several areas, and it is expected to be a key component of our future technology. ML is demonstrating an evident future in antenna design optimization by predicting antenna behavior and expediting optimization with accuracy and efficiency. The analysis of performance metrics used to evaluate 5G antenna performance is another focus of the assessment. Open research problems are also investigated, allowing researchers to fill up current research gaps. [ABSTRACT FROM AUTHOR]

This study has two main objectives: (i) to determine the physicochemical and rheological properties of different flours and (ii) to estimate the alveograph parameters obtained as a result of experimental studies. In this context, physicochemical (protein, ash, falling number, wet gluten, gluten index, Zeleny, and delayed sedimentation) and alveograph parameters (P, L, G, W, P/L, and IE) of 150 different bread and pastry flours were determined. Multiple regression analysis (MRA) and artificial neural network (ANN) methods were then used to predict alveograph results from this experimentally obtained data set. Root mean square error (RMSE), mean absolute error (MAE), Nash‐Sutcliffe (NSEC), and relative error (RE) performance statistics were used to evaluate the CS prediction capabilities of the methods. It was found that the flours were in the range of 11.01%–13.82% protein, 325–403 s falling number, and 30–61 mL Zeleny and delayed sedimentation values. The ANN method showed better predictive performance than the regression‐based method. W was the best estimated parameter in the ANN model. This was followed by G, L, Ie, P/L, and P values. Considering the RMSE value of the W parameter, it was observed that the ANN method provided an improvement of 5.16, 1.76, and 2.15 times compared to the regression method for the training, validation, and test sets, respectively. [ABSTRACT FROM AUTHOR]

Sea surface salinity (SSS) is crucial to the marine ecosystem. Soil Moisture and Ocean Salinity (SMOS) establishes a geophysical modeling function (GMF) between sea surface brightness temperature (BT) and SSS, which incorporates sea surface wind speed and significant wave height (SWH) to retrieve the SSS. However, the relationship between sea surface BT and SSS is complex and influenced by a variety of factors, making it challenging to accurately characterize this relationship using GMF. Spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) observations directly respond to sea surface roughness and offer low cost and high spatiotemporal resolution advantages. Therefore, in this study, for the first time, spaceborne GNSS-R observations from the Cyclone GNSS (CYGNSS) have been incorporated into the SMOS SSS retrieval. Additionally, an empirical model between SMOS BT and Argo SSS was developed using an artificial neural network (ANN). Compared to the conventional SMOS SSS retrieval method, the proposed method in this study reduces the root mean square error (RMSE) of the retrieved SSS from 1.17 to 0.76 psu and increases the correlation coefficient (R) from 0.55 to 0.66. Furthermore, comparisons were made with ground truth measurements from the National Data Buoy Center (NDBC) buoys, which indicated that the proposed method decreases the RMSE of the retrieved SSS from 0.87 to 0.62 psu and reduces the absolute mean deviation from 0.66 to 0.48 psu. These provide references for the future application of spaceborne GNSS-R in SSS retrieval. [ABSTRACT FROM AUTHOR]

The present study explores the bioenergy potential of animal manure based on the physicochemical and thermogravimetric analysis. This study examines the thermal degradation behaviour of cattle manure using different model-free approaches. Further, the artificial neural network (ANN) was used to train the recorded TGA data. The average activation energy was 159.47, 158.08, 159.89, 158.66, 158.36, and 161.65 kJ/mol using FWO, KAS, Friedman, Starink, Tang, and Boswell methods. Further, Criado master plots were also used to determine the reaction mechanism, and the Coats–Redfern (CR) method was used for the multi-component modelling. The best fit of the model was R2 of 0.990 at a heating rate of 20 °C/min as compared to other heating rates. R2 was determined to be the most appropriate mechanism at all heating rates, and it represents the second-order random nucleation. The several mechanisms that best fit the experimental data at 20 °C /min are as follows: R2 > R1.5 > D5 > R2.5 > R1 > N1.5 > R3 > N2 > D3 > G2 > R3.25 > D4 > N3 > R0.5 > D2 > R4 > D1 > N4 > P2/3 > R0. Also, activation energies were calculated from six different iso-conversional methods to determine the effect on thermodynamic parameters, namely, enthalpy, Gibbs free energy, and entropy change during the pyrolysis of cattle manure. This study develops an ANN-predicted modelling for determining the thermal behaviour of cattle manure at unknown heating rates and overcomes the challenges related to the effects of network parameters on the model. [ABSTRACT FROM AUTHOR]

The global energy demand of buildings is on the rise, driven by factors such as rapid population growth, increasing comfort, technological advances, and ongoing developments in building construction. This escalating energy consumption in buildings is a major contributor to the energy crisis and climate change. Accurate prediction of building energy consumption is essential for gaining insight into energy utilization, reducing waste, and enhancing comfort conditions. This study aimed to introduce a reliable technique for predicting and optimizing the energy consumption of residential buildings, with a focus on a case study in Kuwait. A robust Artificial Neural Network (ANN) was developed, meticulously trained, and rigorously tested to provide accurate energy consumption predictions. Subsequently, an innovative variant of the Velocity Pausing Particle Swarm Optimization (VPPSO) algorithm was employed to identify optimal energy consumption solutions. This novel optimization technique can achieve significant reductions in building energy consumption, with potential savings of up to 43%. Additionally, a sensitivity analysis was performed using the Garson method to assess the impact of input parameters on energy utilization. The results reveal that the insulation and cooling setpoint exert the greatest influence on the objective function, followed by the outdoor airflow. The proposed model, which combines the power of ANN with VPPSO, can be applied to similar buildings, offering precise predictions and optimizing energy consumption. This approach holds promise in addressing the pressing challenges of energy efficiency in building environments. [ABSTRACT FROM AUTHOR]

This pioneering work explores the immense potential of young coconut waste, a continuously marginalized residue of the food and beverage industry, to serve as an indispensable feedstock in the production of biochar. Through an examination of the key carbonization factors that include time, temperature, and concentrations of the activating agent, KOH, the outcomes offer relevant insights that could be leveraged to maximize biochar production for tailored applications. This study stands out for its innovative use of Artificial Neural Network (ANN) approaches for predictive modeling. Fifty datasets, supplemented with secondary data obtained from the literature and experiments, were utilized for the purposes of training, testing, and validating the neural network model. Here, the datasets were processed utilizing the Deep Neural Network (DNN) framework, which was designed and implemented with the minimal loss function framework feasible. The architectural configuration comprises the following; an input layer, four hidden layers (128-neuron dense layer, batch normalization, and 64-neuron dense layer, batch normalization), a dropout layer, and an output layer. With an R2 of 0.8238 for biochar yield and 0.7324 for iodine number, the trained DNN model showed a relatively high degree of accuracy in making predictions. [ABSTRACT FROM AUTHOR]

Abstract DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

ABSTRACT Vertical axis wind turbine (VAWT) has a rotating axis perpendicular to the wind direction. This type of wind turbine that is suitable for urban environments has low wind direction dependency and noise. In this research, a novel surrogated approach for optimizing a VAWT is proposed, used, tested, and verified, which is not reported in literature. The proposed method consisted of 3D computational fluid dynamics (CFD) analysis of wind flow through the wind turbine with FLUENT software by solving the unsteady turbulent equations. However, 3D CFD analysis was time and cost consuming to obtain the output result (power coefficient) from input values (airfoil chord length, pitch angle, and tip speed ratio as turbine design variables). Thus, artificial neural network (ANN) was applied to obtain weight functions to correlate FLUENT software inputs and outputs after learning process. Finally, genetic algorithm was used for maximizing the turbine power coefficient considering three defined design variables. The optimum value of power coefficient was improved to 0.244, and the optimum values of design variables for blade chord length, blade pitch angle, and blade tip speed ratio were 0.218, −0.453, and 1.24, respectively. This novel surrogated method reduced the computational time and cost of VAWT optimizing considerably.

In this study, an Artificial Neural Network-Genetic Algorithm (ANN-GA) approach was successfully applied to optimise the physicochemical factors influencing the synthesis of unsaturated fatty acids (UFAs) in the microalgae P. kessleri UCM 001. The optimized model recommended specific cultivation conditions, including glucose at 29 g/L, NaNO3 at 2.4 g/L, K2HPO4 at 0.4 g/L, red LED light, an intensity of 1000 lx, and an 8:16-h light-dark cycle. Through ANN-GA optimisation, a remarkable 66.79% increase in UFAs production in P. kessleri UCM 001 was achieved, compared to previous studies. This underscores the potential of this technology for enhancing valuable lipid production. Sequential variations in the application of physicochemical factors during microalgae culture under mixotrophic conditions, as optimized by ANN-GA, induced alterations in UFAs production and composition in P. kessleri UCM 001. This suggests the feasibility of tailoring the lipid profile of microalgae to obtain specific lipids for diverse industrial applications. The microalgae were isolated from a high-mountain lake in Colombia, highlighting their adaptation to extreme conditions. This underscores their potential for sustainable lipid and biomaterial production. This study demonstrates the effectiveness of using ANN-GA technology to optimise UFAs production in microalgae, offering a promising avenue for obtaining valuable lipids. The microalgae's unique origin in a high-mountain environment in Colombia emphasises the importance of exploring and harnessing microbial resources in distinctive geographical regions for biotechnological applications.

The field capacity (FC) and permanent wilting point (PWP) are fundamental hydrological properties critical for assessing water availability within soils, rather than direct measures of soil health. Due to the challenges associated with their field measurement, alternative assessment methods are necessary. In this study, global-scale accessible soil data were retrieved from the world soil database called the World Soil Information Service (WoSIS), and artificial neural network (ANN) and gene-expression programming (GEP) algorithms were used to predict soil FC and PWP based on easily obtainable parameters from the database. The best-fit variable combination for FC (longitude, latitude, altitude, sand content, silt content, clay content, and electrical conductivity) and PWP (best-fit FC combination plus pH) modeling was determined. Both ANN and GEP showed greater accuracy than linear-based models in simulating the FC and PWP from the best-fit variables. The mean absolute error (MAE) was reduced by 51.54% for the FC and 56.38% for the PWP by the ANN model, compared with the linear model used in the previous literature. The normalized root mean square error (NRMSE) evaluation indicated that the ANN model performed best for PWP prediction (NRMSE of 19.9%), while the GEP model was superior for FC prediction (NRMSE of 29.9%). Between the ANN and GEP models, the ANN model showed a slightly higher model of interpretability; however, the GEP model exhibited a similar or better ability to avoid large error, based on the error distribution. Overall, our results demonstrated that machine learning is effective in predicting the FC and PWP from easily accessible data from WoSIS, and the GEP model is more preferable for FC and PWP modeling.

Spontaneous combustion of coal increases the temperature in adjoining overburden strata of coal seams and poses a challenge when loading blastholes. This condition, known as hot-hole blasting, is dangerous due to the increased possibility of premature explosions in loaded blastholes. Thus, it is crucial to load the blastholes with an appropriate amount of explosives within a short period to avoid premature detonation caused by high temperatures of blastholes. Additionally, it will help achieve the desired fragment size. This study tried to ascertain the most influencial variables of mean fragment size and their optimum values adopted for blasting in a fiery seam. Data on blast design, rock mass, and fragmentation of 100 blasts in fiery seams of a coal mine were collected and used to develop mean fragmentation prediction models using soft computational techniques. The coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), mean square error (MSE), variance account for (VAF) and coefficient of efficiency in percentage (CE) were calculated to validate the results. It indicates that the random forest algorithm (RFA) outperforms the artificial neural network (ANN), response surface method (RSM), and decision tree (DT). The values of R2, RMSE, MAE, MSE, VAF, and CE for RFA are 0.94, 0.034, 0.027, 0.001, 93.58, and 93.01, respectively. Multiple parametric sensitivity analyses (MPSAs) of the input variables showed that the Schmidt hammer rebound number and spacing-to-burden ratio are the most influencial variables for the blast fragment size. The analysis was finally used to define the best blast design variables to achieve optimum fragment size from blasting. The optimum factor values for RFA of S/B, ld/B and ls/ld are 1.03, 1.85 and 0.7, respectively.

The authors have investigated the axisymmetric and three-dimensional, steady, incompressible, and bioconvective flow of Ag-TiO2/water hybrid nanofluid between two infinite and parallel rotating disks. Practical uses of flows between two rotating disks include brake systems in vehicles, engines, disks in computers, atomizers, rotating air cleaners, gas turbines, and evaporators. This study was conducted within a Darcy-Forchheimer porous medium and considered the impact of a magnetic field, heat source, and thermal radiation. The governing mathematical equations are transformed into coupled and nonlinear ordinary differential equations through similarity transformations. Subsequently, these equations are numerically solved using MATLAB's built-in function "bvp4c". A multilayer perceptron based artificial neural network (ANN) model has been formulated to predict the Nusselt number (heat transfer rate) on both the lower and upper surfaces of the disk. The model utilizes the Levenberg-Marquardt training algorithm, renowned for its exceptional learning capability, as the training method for the ANN. Moreover, the authors generated a dataset consisting of 84 data points for each case using numerical methods to construct the proposed Multilayer Perceptron Artificial Neural Network. The computed mean squared error values for the developed ANN model, targeting Nusselt number predictions, were found to be 2×10−6, 5×10−6, 9×10−6, and 3×10−6. Additionally, the regression (R2) values, serving as an additional performance parameter, were determined as 0.999317, 0.997672, 0.999963, and 0.999840, respectively. A comprehensive assessment of these outcomes, strongly affirms that the ANN model has been crafted with a high degree of accuracy for predicting Nusselt numbers.

The deposition and dynamics of smoke layers in large-space building fires are governed by a complex interplay of factors, making the prediction of such dynamics using traditional mathematical models challenging. In response, this study introduces a Back Propagation (BP) neural network model, devised from field simulation data, to efficiently forecast the temporal progression of smoke layers. It was observed that the model exhibits minimal training errors and high processing speeds, thereby fulfilling the stringent accuracy demands of fire engineering. Specifically, the model achieved a minimum relative error of 0.0005 and a maximum of 0.0845 across various prediction points, underscoring its reliability and precision. The ability of this BP neural network model to predict smoke layer changes significantly enhances the design optimization of smoke control systems swiftly and accurately in large buildings and supports rapid, informed decision-making during fire emergencies. Moreover, the model facilitates the development of engineering calculation models tailored for the quick prediction of fire smoke dynamics, which are essential for both theoretical research and practical applications. This approach not only conserves experimental resources but also advances the implementation of scientific, effective rescue operations in the event of large space building fires.

Abstract Microgrids have emerged as a promising solution for enhancing energy sustainability and resilience in localized energy distribution systems. Efficient energy management and accurate load forecasting are one of the critical aspects for improving the operation of microgrids. Various approaches for energy prediction and load forecasting using statistical models are discussed in the literature. In this work, a novel energy management framework that incorporates machine learning (ML) techniques is presented for an accurate prediction of solar and wind energy generation. The anticipated approach also emphasizes time series-based load forecasting in microgrids with precise estimation of State of Charge (SoC) of battery. A unique feature of the proposed framework is that utilizes historical load data and employs time series analysis coupled with different ML models to forecast the load demand in a commercial microgrids scenario. In this work, Long Short-Term Memory (LSTM) and Linear Regression (LR) models are employed for an experimental analysis to study the proposed framework under three different cases, such as (i) prediction of energy generation, (ii) load demand forecasting and, (iii) prediction of SoC of battery. The results show that the Random Forest (RF) and LSTM models performs well for energy prediction and load forecasting respectively. On the other hand, the Artificial Neural Network (ANN) model exhibited superior accuracy in terms of SoC estimation. Further, in this work, a Graphical User Interface (GUI) is developed for evaluating the efficacy of the proposed energy management framework.

Abstract Highway shoulder rocks are exposed to continuous erosion force due to extreme rainfall that could be caused by global warming to some extent. However, the logical design method for erosion-resistant highway shoulder is not well-researched yet. This study utilized a large-scale UNLETB (University of Nebraska Lincoln–Erosion Testing Bed) with a 7.6 cm nozzle width and a 4000 cm3/sec flow rate to study the erosion characteristics of highway shoulder rocks. Test results showed that different shoulder materials currently used had vastly diverse erosion resistance. However, the clear criteria between the erosion-resistant gradation and other gradation could not be determined easily. Then, this study trained ANN (Artificial Neural Network) with test results to conveniently distinguish the erosion resistance of rocks from other rocks. The ANN predicted the acceptable/non-acceptable erosion characteristics of shoulder rocks with close to 99% accuracy based on the three gradation parameters (D10, D30, and D60).

Abstract This study aims to develop two models for thermodynamic data on hydrogen generation from the combined processes of dimethyl ether steam reforming and partial oxidation, applying artificial neural networks (ANN) and response surface methodology (RSM). Three factors are recognized as important determinants for the hydrogen and carbon monoxide mole fractions. The RSM used the quadratic model to formulate two correlations for the outcomes. The ANN modeling used two algorithms, namely multilayer perceptron (MLP) and radial basis function (RBF). The optimum configuration for the MLP, employing the Levenberg–Marquardt (trainlm) algorithm, consisted of three hidden layers with 15, 10, and 5 neurons, respectively. The ideal RBF configuration contained a total of 80 neurons. The optimum configuration of ANN achieved the best mean squared error (MSE) performance of 3.95e−05 for the hydrogen mole fraction and 4.88e−05 for the carbon monoxide mole fraction after nine epochs. Each of the ANN and RSM models produced accurate predictions of the actual data. The prediction performance of the ANN model was 0.9994, which is higher than the RSM model's 0.9771. The optimal condition was obtained at O/C of 0.4, S/C of 2.5, and temperature of 250 °C to achieve the highest H2 production with the lowest CO emission.

Abstract The accurate estimation of gas viscosity remains a pivotal concern for petroleum engineers, exerting substantial influence on the modeling efficacy of natural gas operations. Due to their time-consuming and costly nature, experimental measurements of gas viscosity are challenging. Data-based machine learning (ML) techniques afford a resourceful and less exhausting substitution, aiding research and industry at gas modeling that is incredible to reach in the laboratory. Statistical approaches were used to analyze the experimental data before applying machine learning. Seven machine learning techniques specifically Linear Regression, random forest (RF), decision trees, gradient boosting, K-nearest neighbors, Nu support vector regression (NuSVR), and artificial neural network (ANN) were applied for the prediction of methane (CH4), nitrogen (N2), and natural gas mixture viscosities. More than 4304 datasets from real experimental data utilizing pressure, temperature, and gas density were employed for developing ML models. Furthermore, three novel correlations have developed for the viscosity of CH4, N2, and composite gas using ANN. Results revealed that models and anticipated correlations predicted methane, nitrogen, and natural gas mixture viscosities with high precision. Results designated that the ANN, RF, and gradient Boosting models have performed better with a coefficient of determination (R2) of 0.99 for testing data sets of methane, nitrogen, and natural gas mixture viscosities. However, linear regression and NuSVR have performed poorly with a coefficient of determination (R2) of 0.07 and − 0.01 respectively for testing data sets of nitrogen viscosity. Such machine learning models offer the industry and research a cost-effective and fast tool for accurately approximating the viscosities of methane, nitrogen, and gas mixture under normal and harsh conditions.

Biofilter technology has played a significant role over several decades in providing clean air through removal of Volatile Organic Compounds (VOCs) and odor causing chemicals such as hydrogen sulfide from industrial polluted airstreams. Biofilters where biological oxidation process takes place are designed and installed in numerous industrial facilities including chemical manufacturing, food processing, solid waste recycling and wastewater treatment plants to control emissions of VOCs, and odors in order to comply with the air emission regulations and to provide clean breathable air. Biofilter mathematical models under steady-state and transient conditions are essential in order to design, scale-up and predict biofilter performance under different operating conditions. Similarly, Artificial Intelligence (AI) through the use of Artificial Neural Network (ANN) modeling of biofiltration process is also becoming important. This research provides a detailed discussion and review of the recent (i.e., the last two decades) and important studies related to ANN, steady-state and transient biofilter models.

Drying is one of the most common and effective techniques for preserving the quantitative and qualitative characteristics of medicinal plants in the post-harvest phase. Therefore, in this research, the effect of the new refractance window (RW) technology on the kinetics, thermodynamics, greenhouse gasses, color indices, bioactive properties, and percentage of mint leaf essential oil was investigated in five different water temperatures in the form of a completely randomized design. This process was modeled by the methods of mathematical models and artificial neural networks (ANNs) with inputs (drying time and water temperature) and an output (moisture ratio). The results showed that with the increase in temperature, the rate of moisture removal from the samples increased and as a result, the drying time, specific energy consumption, CO2, NOx, enthalpy, and entropy decreased significantly (p < 0.05). In addition, the drying water temperature had a significant effect on the rehydration ratio, color indices, bioactive properties, and essential oil percentage of the samples (p < 0.05). The highest value of rehydration ratio was obtained at 80 °C. By increasing temperature, the main color indices such as b*, a*, L*, and Chroma decreased significantly compared to the control (p < 0.05). However, with the increase in temperature, the overall color changes (ΔE) and L* first had a decreasing trend and then an increasing trend, and this trend was the opposite for the rest of the indicators. The application of drying water temperature from 50 to 70 °C increased antioxidant, phenol content, and flavonoid content, and higher drying temperatures led to a significant decrease in these parameters (p < 0.05). On the other hand, the efficiency of the essential oil of the samples was in the range of 0.82 to 2.01%, and the highest value was obtained at the water temperature of 80 °C. Based on the analysis performed on the modeled data, a perceptron artificial neural network with 2-15-14-1 structure with explanation coefficient (0.9999) and mean square error (8.77 × 10−7) performs better than the mathematical methods for predicting the moisture ratio of mint leaves. [ABSTRACT FROM AUTHOR]

Waste materials contribute to a wide range of environmental and economic problems. To minimize their effects, a safe strategy for reducing such negative impact is required. Recycling and reusing waste materials have proved to be effective measures in this respect. In this study, an eco-friendly treatment is investigated based on using waste powdered glass (WGP) and EPS beads (EPSb) as mechanical and chemical admixers in soils. For this purpose, Atterberg limit, standard proctor, free swell, and unconfined compression tests are performed on soil samples with different ratios of waste materials at their optimum moisture contents. The obtained test results indicate that adding WGP to cohesive soils increases the unconfined compressive strength (UCS) and reduces free swell (FS). In contrast, using EPSb reduces both FS and UCS of the treated soil samples. An optimum combination of both waste materials is determined for the improvement of the properties of high plasticity clay used in this study. Furthermore, multiple linear regression (MLR) and artificial neural network (ANN) methods are used to predict the FS and UCS of the clayey soils based on the data obtained here and the experimental test results reported in the literature. Once the FS and UCS values of untreated soil and additive percentages are defined as independent variables, both methods are shown to predict the FS and UCS values of the treated soil samples on a satisfactory level with the coefficient of correlation ( R 2 ) values greater than 0.926. Additionally, when only the index properties (liquid limit, plastic limit, and plasticity index) of the soil samples with waste materials are used as dependent variables, the R 2 values obtained by the ANN method are 0.968 and 0.974 for FS and UCS, respectively. The results of the untreated soil samples' FS and UCS tests are known, and the linear regression and ANN techniques yield similar results. Lastly, the ANN method is used to predict the FS and UCS of the treated samples in accordance to the limited predictors (e.g., only the Atterberg limits of the soil sample). [ABSTRACT FROM AUTHOR]

Conventional direct torque control (DTC) improves the dynamic performance of the five-phase induction machine (FPIM). Nevertheless, it suffers from significant drawbacks of high stator flux and electromagnetic torque ripples. Moreover, the DTC technique relies on an open-loop estimator for accurate stator flux module and position knowledge. However, this method is subjected to substandard performance, mainly during the low-speed operation range. Therefore, a sliding mode sensorless stator flux and rotor speed DTC based on an artificial neural network (DTC-ANN) for two parallel-connected FPIMs is discussed to tackle the problems above. This approach optimizes the DTC performance by replacing the two hysteresis controllers (HC) and the look-up table. As for the poor estimation drawback, the sliding mode observer (SMO) offers a robust estimation and reconstruction of the FPIM variables and eliminates the need for additional sensors, increasing the system's reliability. The present results verify and compare the performance of the control scheme [ABSTRACT FROM AUTHOR]