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It is well known that the roughness of a wall plays a crucial role in determining the passive earth pressure that is exerted on a rigid wall. While the effects of positive wall roughness have been extensively studied in the past few decades, the study of passive earth pressure with negative wall friction is rarely found in the literature. This study aims to provide a precise solution for negative friction walls under passive wall conditions. The research is initiated by adopting a radial stress field for the cohesionless backfill and employs the concept of stress self-similarity. The problem is then formulated in a way that a statically admissible stress field be developed throughout an analyzed domain using a two-step numerical framework. The framework involves the successful execution of numerical integration, which leads to the exploration of the statically admissible stress field in cohesionless backfills under negative wall friction. This, in turn, helps to shed light on the mechanism of load transfer in such situations so that reliable design charts and tables be provided for practical uses. The study continues with a soft computing model that leads to more robust and effective designs for earth-retaining structures under various negative wall frictions and sloping backfills. [ABSTRACT FROM AUTHOR]

O-Vanillin derived Schiff base 1-[(E)-(2-hydroxy-3-methoxybenzylidene) amino]-4-methylthiosemicarbazone (VCOH) has been synthesized for colorimetric and fluorescence chemosensors towards PO43− ions. A fluorescence 'turn-on' sensing mechanism of VCOH towards PO43− ions has been explained due to emission from the VCO− ion formed upon transfer of the phenolic proton of VCOH to a PO43− ion. The 1 : 1 stoichiometry between the VCOH probe and PO43− ion is confirmed by Job's plot based on UV-vis titration. The limit of detection (LOD) of VCOH towards PO43− ions is found to be 0.49 nM. The PO43− ion sensing property of probe VCOH has been applied to prepare portable paper strips and for the analysis of real water samples. Fluorescence 'turn-on' and 'turn-off' responses of VCOH towards PO43−and H+ respectively have been used to construct a molecular logic gate. Fluorescence based sensing studies in which the concentration of analytes is adjusted over a broad range can be both laborious and expensive. In order to address these challenges, we have utilized various soft computing methods, including artificial neural networks (ANN), fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS), to appropriately model the 'turn-on' and 'turn-off' behaviors of the VCOH probe upon addition of PO43− and H+ respectively as well as to predict the experimental sensing data. [ABSTRACT FROM AUTHOR]

Open Source Software (OSS) is one of the most trusted technologies for implementing industry 4.0 solutions. The study aims to assist a community of OSS developers in quantifying the product's reliability. This research proposes reliability growth models for OSS by incorporating dynamicity in the debugging process. For this, stochastic differential equation-based analytical models are developed to represent the instantaneous rate of error generation. The fault introduction rate is modeled using exponential and Erlang distribution functions. The empirical applications of the proposed methodology are verified using the real-life failure data of the Open Source Software projects, GNU Network Object Model Environment, and Eclipse. A soft computing technique, Genetic Algorithm, is applied to estimate model parameters. Cross-validation is also performed to examine the forecasting efficacy of the model. The predictive power of the developed models is compared with various benchmark studies. The data analysis is conducted using the R statistical computing software. The results demonstrate the proposed models' efficacy in parameter estimation and predictive performance. In addition, the optimal release time policy based on the proposed mathematical models is presented by formulating the optimization model that intends to minimize the total cost of software development under reliability constraints. The numerical illustration and sensitivity analysis exhibit the proposed problem's practical significance. The findings of the numerical analysis exemplify the proposed study's capability of decision-making under uncertainty. [ABSTRACT FROM AUTHOR]

This article presents comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) model for rainfall-runoff (R-R) process. Aim of the present work is to forecast runoff one day ahead at Shivade station of Upper Krishna Basin, India, using 17 years of daily rainfall and discharge data. The R-R modelling can be exercised using various traditional methods which generally require exogenous data in the form of basin parameters. Unavailability of such data becomes major impediment in applying these models at many basins. In such situations, soft computing techniques like ANNs have been extensively applied to model R-R process. Though ANN is now an established tool in hydrology, compared to HEC-HMS, its results are viewed with suspicion owing to its data-driven nature rather than a model-driven nature. In this study, ANN model performed reasonably well, with a higher correlation coefficient (0.87) and the lowest Root Mean Square Error (136.28 m3/s) when compared with HEC-HMS (0.76, 139.8 m3/s) respectively. Novelty of the present work lies in model development using restricted basin data. Both models showed less accuracy in predicting extreme events. Finally, it is concluded that ANN model can be used as a supplementary technique along with HEC-HMS for this phenomenon. [ABSTRACT FROM AUTHOR]

Geo-engineering problems are known for their complexity and high uncertainty levels, requiring precise definitions, past experiences, logical reasoning, mathematical analysis, and practical insight to address them effectively. Soft Computing (SC) methods have gained popularity in engineering disciplines such as mining and civil engineering due to computer hardware and machine learning advancements. Unlike traditional hard computing approaches, SC models use soft values and fuzzy sets to navigate uncertain environments. This study focuses on the application of SC methods to predict backbreak, a common issue in blasting operations within mining and civil projects. Backbreak, which refers to the unintended fracturing of rock beyond the desired blast perimeter, can significantly impact project timelines and costs. This study aims to explore how SC methods can be effectively employed to anticipate and mitigate the undesirable consequences of blasting operations, specifically focusing on backbreak prediction. The research explores the complexities of backbreak prediction and highlights the potential benefits of utilizing SC methods to address this challenging issue in geo-engineering projects. [ABSTRACT FROM AUTHOR]

Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models' performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation. [ABSTRACT FROM AUTHOR]

Capturing outlines is crucial in vectorization of the digital images. An effective and automatic algorithm is presented for outline vectorizing of planar objects within the digital images using trigonometric B-spline. A soft computing optimization technique Genetic Algorithm (GA) is employed to determine the suitable measures of parameter in the description of proposed B-spline. The anticipated scheme is executed on a few raster (bitmap), digital images to validate the robustness of the algorithm. The procedure of vectorizing outlines encompasses a series of stages like boundary detection, corner recognition, break point identification and curve fitting using the proposed B-spline. [ABSTRACT FROM AUTHOR]

The state parameter (ѱ) accounts for both relative density and effective stress, which influence the cyclic stress or liquefaction characteristic of the soil significantly. This study presents a ѱ‐based probabilistic liquefaction evaluation method using six soft computing (SC) techniques. The liquefaction probability of failure (PL) is calculated using the first‐order second moment (FOSM) method based on the cone penetration test (CPT) database. Then, six SC techniques, such as Gaussian process regression (GPR), relevance vector machine (RVM), functional network (FN), genetic programming (GP), minimax probability machine regression (MPMR) and multivariate adaptive regression splines (MARS), are used to predict PL. The performance of these models is examined using nine statistical indices. Additionally, plots such as regression plots, Taylor diagrams, error matrix and rank analysis are shown to assess the SC model's performance. Finally, sensitivity analysis is performed using the cosine amplitude method (CAM) to assess the influence of input parameters on output. The current study demonstrates that SC models based on state parameter predict PL effectively. RVM and MPMR models closely follow the GPR model in terms of performance, which is superior to the other models. Notably, two equations are generated using GP and MARS models to predict PL. The results of the sensitivity analysis reveal the magnitude of earthquake (Mw) as the most sensitive parameter. The outcomes of this research will offer risk evaluations for geotechnical engineering designs and expand the use of state parameter‐based SC models in liquefaction analysis. [ABSTRACT FROM AUTHOR]

The most widely used equation to calculate water saturation or suitable shaly water saturation in clean or shaly formation, respectively, is the modified Archie formula. The quality of Archie parameters including saturation exponent affects the preciseness of water saturation, and thus estimated oil and gas in place. Therefore, estimating the saturation exponent by the soft computation methods deems to be necessary. In this study, intelligent models such as multilayer perceptron neural network, least squares support vector machine, radial basis function neural network, and adaptive neuro-fuzzy inference system are developed to predict saturation exponent in terms of petrophysical data including porosity, absolute permeability, water saturation, true resistivity, and resistivity index by utilizing a databank for middle east oil and gas reservoirs. The introduced models are optimized using particle swarm optimization, genetic algorithm, and levenberg marquardt techniques. Graphical and statistical methods are used to demonstrate the capability of the constructed models. Based on the statistical indexes obtained for each model, it is found that radial basis function neural network, multilayer perceptron neural network, and least squares support vector machine are the most robust models as they possess the smallest mean squared error, root mean squared error and average absolute relative error as well as highest coefficient of determination. Moreover, the sensitivity analysis indicates that water saturation has the most effect and porosity has the least effect on the saturation exponent. The developed models are simple-to-use and time-consuming tools to predict saturation exponent without needing laboratory methods which are tedious and arduous. [ABSTRACT FROM AUTHOR]

In traditional topology optimization, the computing time required to iteratively update the material distribution within a design domain strongly depends on the complexity or size of the problem, limiting its application in real engineering contexts. This work proposes a multi‐stage machine learning strategy that aims to predict an optimal topology and the related stress fields of interest, either in 2D or 3D, without resorting to any iterative analysis and design process. The overall topology optimization is treated as regression task in a low‐dimensional latent space, that encodes the variability of the target designs. First, a fully‐connected model is employed to surrogate the functional link between the parametric input space characterizing the design problem and the latent space representation of the corresponding optimal topology. The decoder branch of an autoencoder is then exploited to reconstruct the desired optimal topology from its latent representation. The deep learning models are trained on a dataset generated through a standard method of topology optimization implementing the solid isotropic material with penalization, for varying boundary and loading conditions. The underlying hypothesis behind the proposed strategy is that optimal topologies share enough common patterns to be compressed into small latent space representations without significant information loss. Results relevant to a 2D Messerschmitt‐Bölkow‐Blohm beam and a 3D bridge case demonstrate the capabilities of the proposed framework to provide accurate optimal topology predictions in a fraction of a second. [ABSTRACT FROM AUTHOR]

This work aims to analyze the impacts of the magnetic field, activation of energy, thermal radiation, thermophoresis, and Brownian effects on the hybrid nanofluid (HNF) (Ag++silicon oil) flow past a porous spinning disk. The pressure loss due to porosity is constituted by the Darcy-Forchheimer relation. The modified Buongiorno model is considered for simulating the flow field into a mathematical form. The modeled problem is further simplified with the new group of dimensionless variables and further transformed into a first-order system of equations. The reduced system is further analyzed with the Levenberg-Marquardt algorithm using a trained artificial neural network (ANN) with a tolerance, step size of 0.001, and 1,000 epochs. The state variables under the impacts of the pertinent parameters are assessed with graphs and tables. It has been observed that when the magnetic parameter increases, the velocity gradient of mono and hybrid nanofluids (NFs) decreases. As the input of the Darcy-Forchheimer parameter increases, the velocity profiles decrease. The result shows that as the thermophoresis parameter increases, temperature and concentration increase as well. When the activation energy parameter increases, the concentration profile becomes higher. For a deep insight into the analysis of the problem, a statistical approach for data fitting in the form of regression lines and error histograms for NF and HNF is presented. The regression lines show that 100% of the data is used in curve fitting, while the error histograms depict the minimal zero error -7.1e6 for the increasing values of Nt. Furthermore, the mean square error and performance validation for each varying parameter are presented. For validation, the present results are compared with the available literature in the form of a table, where the current results show great agreement with the existing one. [ABSTRACT FROM AUTHOR]

A prediction model for the bearing capacity estimation of strip and ring footing embedded in layered sand is proposed using soft computing approaches, namely, artificial neural network (ANN) and random forest regression (RFR). The required data for the model preparation were generated by performing lower- and upper-bound finite-elements limit analysis by varying the properties of the top and bottom layers. Two types of layered sand conditions are considered in the study: (a) dense on loose sand; (b) loose on dense sand. The investigation for strip footing was carried out by varying the thickness of the top layer, embedment depth of the foundation and friction angles of top and bottom layers. For a ring footing, the internal-to-external diameter ratio forms an additional variable. In total, 1222 and 4204 data sets were generated for strip and ring footings, respectively. The performance measures obtained during the training and testing phase suggest that the RFR model outperforms the ANN. Also, following the literature, an analytical model was developed to predict the bearing capacity of strip footing on layered sand. The ANN and the generated analytical model predictions agreed with the published experimental data in the literature. [ABSTRACT FROM AUTHOR]

The precise estimation of the shear strength of reinforced concrete walls is critical for structural engineers. This projection, nevertheless, is exceedingly complicated because of the varied structural geometries, plethora of load cases, and highly nonlinear relationships between the design requirements and the shear strength. Recent related design code regulations mostly depend on experimental formulations, which have a variety of constraints and establish low prediction accuracy. Hence, different soft computing techniques are used in this study to evaluate the shear capacity of reinforced concrete walls. In particular, developed models for estimating the shear capacity of concrete walls have been investigated, based on experimental test data accessible in the relevant literature. Adaptive neuro-fuzzy inference system, the integrated genetic algorithms, and the integrated particle swarm optimization methods were used to optimize the fuzzy model's membership function range and the results were compared to the outcomes of random forests (RF) model. To determine the accuracy of the models, the results were assessed using several indices. Outliers in the anticipated data were identified and replaced with appropriate values to ensure prediction accuracy. The comparison of the resulting findings with the relevant experimental data demonstrates the potential of hybrid models to determine the shear capacity of reinforced concrete walls reliably and effectively. The findings revealed that the RF model with RMSE = 151.89, MAE = 111.52, and R 2 = 0.9351 has the best prediction accuracy. Integrated GAFIS and PSOFIS performed virtually identically and had fewer errors than ANFIS. The sensitivity analysis shows that the thickness of the wall (b w) and concrete compressive strength (f c) have the most and the least effects on shear strength, respectively. [ABSTRACT FROM AUTHOR]

High levels of node power in Mobile Wireless Sensor Networks (MWSN) can have an effect on the reliability of a number of different aspects of the service. Due to their inherently low energy efficiency, sensor nodes lose power with every bit of data they transmit. In order to accomplish any data transfer, however, the nodes must engage in cooperative communication. To aid optimal routing in mobile wireless sensor networks, researchers have developed a new method inspired by the work of bee colony optimizers. To enhance service in a mobile wireless sensor network, we present a cluster energy hop-based dynamic route selection (CEH-DRS) that takes into account individual production zones. In this case, sensor nodes can collect data and send it out to the network. They also help with the routing of data packets coming from various origin nodes. Finally, this study optimizes the system's route while still meeting the criterion of selecting the shortest way. This technique improves cluster selection by taking into account the state of affairs in the area and other characteristics (such as throughput, delay and packet delivery ratio). It is also discovered that the soft computing approaches accurately detect and select the best path, whereas the conventional methods cannot. The proposed CEH-DRS method improved network performance in a better way to achieve higher throughput than all approaches like optimized route cache protocol-ad hoc on-demand distance vector, fuzzy and bee colony optimization, selectively turning ON/OFF the sensors, hidden Markov model. [ABSTRACT FROM AUTHOR]

Non-linear optical (NLO) parameter of a series of donor–acceptor substituted N-salicylidene-4-benzenesulfonylaniline (SBSA) derivatives with a contrast of first hyperpolarisability has been investigated using density functional-based theory. The derivative of SBSA with both the donor and acceptor substitution (NMe2-SBSA-NO2) exhibits the highest first hyperpolarisability ($\sim 3 \times$ ∼ 3 × 103 a.u in gas phase) value. Frequency and solvent polarity responsive behaviours of first hyperpolarisability are utilised to demonstrate the variation of NLO as switchable electrical parameter. Each of the studied compounds displays very high NLO response at frequency corresponding to the absorption maxima of their electronic state. Likewise, the increase of solvent polarity induces a substantial increase of first hyperpolarisability. The computation of NLO responsive first hyperpolarisability by varying the frequency and solvent dielectric constant within a wide domain is very tedious and time-consuming. To overcome the lacuna, we implemented machine learning tools such as artificial neural networks (ANNs), fuzzy logic (FL) and adaptive neuro-fuzzy inference system (ANFIS) to predict the computation data of the NMe2-SBSA-NO2 and moderately good results were obtained using ANFIS as soft computing tool. [ABSTRACT FROM AUTHOR]

Forecasting river flow is an important stage in reservoir operation, urban water management, and water resource optimization. The goal of this research is to forecast daily river flows for two intermittent and ephemeral rivers. Based on the antecedent river flow, the forecasting approach used stochastic (AR, ARIMA, and SARIMA) and machine learning (ML) techniques. The ML methods consist of three shallow learning models (NARnet, OS-ELM, and NAR-GMDH) and three deep learning models (LSTM, CNN-GRU, and stacked-LSTM). The precision of all the models in the ephemeral river was higher the intermittent river considering both base flow (R2ave = 0.94 vs. 0.87) and peak flow. The study was also extended to forecasting peak river flow values, demonstrating in the superiority of deep learning (RMSEave = 7.6 m3/s) over the shallow (RMSEave = 8.9 m3/s) and stochastic (RMSEave = 9.1 m3/s). Applied models acted similarly in forecasting peak flow in both rivers due to substantial variations in the floods. Moreover, the results demonstrated that the deep learning group models surpass stochastic and shallow learning group models in terms of five evaluation criteria, including RMSE, MAE, mean bias error, correlation coefficient, and agreement index for both rivers. In general, the results indicate that the CNN-GRU outperforms the other models in terms of river flow forecasting and is suggested as a viable model for learning the complicated behavior of streamflow. [ABSTRACT FROM AUTHOR]

The accurate prediction of a lithium-ion battery's State of Health is of critical importance for efficient battery health management. Existing data-driven estimation methodologies grapple with issues such as high model complexity and a dearth of guidance from prior knowledge, which impose constraints on their efficacy. This work introduces a novel cross-modal distillation network for battery State of Health estimation, structured around a TransformerEncoder as the teacher network and a Convolutional Neural Network as the student network. Initially, the teacher model is pre-trained offline using State of Health degradation data to learn the degradation patterns. The directly measurable feature data (such as voltage, temperature, and current) is subsequently fed into the student network for online training and computation of a hard loss. the student network's output is then directed into the pre-trained the teacher network to compute a soft loss, thereby offering prior knowledge of degradation laws and steering the optimization process of the student network. Rigorous experiments are conducted utilizing various datasets, with the outcomes validating the superior estimation accuracy and degradation rule adherence of the model. Notably, among five different models, this model demonstrates the best performance on almost all datasets, achieving an RMSE of 0.0097 and an MAE of 0.0065 on Cell1 of the Oxford dataset. Moreover, the model also demonstrates robust performance across different usage scenarios, inclusive of multi-battery estimation. Furthermore, this paper also introduces a fine tuning method for State of Health predictions only using the first half of the data. Comparative analysis with other models underscores the competitiveness of the proposed model, showcasing its potential for broader application. [ABSTRACT FROM AUTHOR]

Conventional concrete causes significant environmental problems, including resource depletion, high CO2 emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques—Gene Expression Programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models' performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R2). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R2 values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R2 values of 0.97 (training) and 0.93 (testing), GEP with R2 values of 0.85 (training) and 0.87 (testing), and ANN with R2 values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through analysis and well-defined conclusions. [ABSTRACT FROM AUTHOR]

Project management requires the chaotic but important task of estimating software development effort. Several soft computing approaches have been proposed to increase estimation accuracy, and optimization techniques are utilized to concentrate on key aspects. However, a majority of works use data processing that has been found to be unreliable, time-consuming and typically leads to greater error rates. Therefore, the research proposes an efficient software development effort prediction model employing unique deep learning technology to alleviate the existing limitations. Data collection, pre-processing, feature selection and software development effort prediction are only a few of the varied phases of the proposed objective. After data collection, the data are pre-processed, including data cleaning, normalization, missing values and imputation. The expanded archer fish optimization method (Ext_AFO) is used to choose the best features from the pre-processed data. The Multilayer Perceptron Assisted Honey Bidirectional Gated Recurrent Feed Forward Network (Multi-HBiG) is built into this research work to provide an intelligent prediction model for software development effort estimation. The model parameters are adjusted using the Adaptive Honey Badger Optimisation Algorithm (A-Hba) to improve the overall estimation performance. The Albrecht dataset, China dataset, Desharnais dataset, Kemerer dataset, Maxwell dataset, Kitchenham dataset and Cocomos81 dataset are the datasets used in this study. The proposed approach surpassed other models when compared against Mean Relative Error (MRE), Mean Magnitude of Relative Error (MMRE), Mean Balanced Relative Error (MBRE) and Mean Inverted Balanced Relative Error (MIBRE) in the results section. The proposed model was assessed in this study using the MAE in each dataset, and it achieved 0.0753 for the China dataset, 0.0763 for the Cocomos81 dataset, 0.0737 for the Desharnais dataset, 0.0754 for the Kemerer dataset, 0.0759 for the Kitchenham dataset, 0.0734 for the Maxwell dataset and 0.0737 for the Albrecht dataset, respectively. [ABSTRACT FROM AUTHOR]

The business activity of the company expresses the creation of values for customers, owners and society. It implements strategic intentions in ongoing processes and activities. Establishes a link between the sustainability of long-term management and the flexibility of operational management. In Bulgarian literature, it is referred to as „economic activity“, which in turn is also used at the level of the sectoral economy, the national economy and the international economy. In applied research, business activity is usually associated with the turnover of assets and its impact on the efficiency and financial position of the company. In this article is treated as a broad concept integrating the intensity of the use of funds, the newly created value for a given period and the profitability for owners. At the same time, business activity summarizes the factor conditionality of entrepreneurial success from the dynamics of operations, the size of competitive advantages and the degree of capital profitability. The indicators Asset Turnover Ratio, Value Added Level and Return on Equity are used for business activity. The latter in today's uncertain and highly dynamic environment cannot be credibly planned by linear extrapolation of short-term trends into a long-term perspective. They require the development of a useful approach, regardless of non-linearity and uncertainties in economic development. As a solution, this article therefore proposes fuzzy financial indicators – model of scenarios for expected states, metered with fuzzy sets and soft computing. Implement tools from the field of artificial intelligence in strategic business management and analysis. In doing so, plausible alternatives for future estimates of the Asset Turnover Ratio, Value Added Level and Return on Equity are reproduced. Controlled approximation is introduced as an alternative about dealing with uncertainty, rather than classically ignoring it or reducing it to a simple prognosis. Based on the presented opportunity, a fuzzy model is proposed for planning the Temp of sustainable growth of the company's revenues is offered. It is essentially an intelligent platform that belongs to the group of Takagi-Sugeno fuzzy models and offers powerful computing potential with the ability to establish an accurate value result. Among its leading advantages can be mentioned speed, high flexibility and efficiency. It is also applicable for planning the business activity of companies from a large number of industries. [ABSTRACT FROM AUTHOR]

Plant diseases are one of the factors that lead to yield and economic losses, which have a direct effect on national and international food production systems. One of the most essential ways to avoid agricultural product loss or reduction in amount is to diagnose plant diseases promptly and accurately. Hence, the diagnosis process for plants is crucial and should be conducted accurately. Moreover, this study focuses on this process by constructing an Artificiality Diagnostics Framework (ADF) to serve the study's objectives which entailed conducting diagnosis for plants in a professional and precise manner over uncertain environments. Thus, neutrosophic theory is considered the principal ingredient in our ADF. Due to the ability of neutrosophic to divide images into Truth (T), Falsity(F), and Indeterminacy (I). Also, deep learning (DL) is considered another principal ingredient in treating vast samples of datasets. Our comparative analysis of the leaves of potatoes is conducted whether leveraging neutrosophic and without utilizing Neutrosophic. ResNet50, ResNet152, and Mobile Net are the principal ingredients for the training dataset. The findings of implementing these networks indicated that ResNet50 achieved the highest accuracy of 0.915 in the T domain, ResNet152 achieved the highest accuracy of 0.905 in the True(T) domain, and Mobile Net achieved the highest accuracy of 0.915 in Truth(T) domain. Accuracy of 0.863 in Indeterminate(I). [ABSTRACT FROM AUTHOR]

The Internet of Things, or IoT, is a rapidly expanding technology, and one of its most important application areas is sustainable transportation. Choosing the right IoT service provider is a complex process that is considered a multi-criteria decision issue. The multi-criteria decision-making (MCDM) methodology deals with massive criteria. This study proposed an MCDM methodology for selecting the best IoT service provider under different sustainable criteria. The Multi-Objective Optimization Ratio Analysis (MOORA) method is an MCDM methodology used to rank the alternatives. The MOORA method is integrated with a type 2 neutrosophic set to deal with uncertain information. The proposed MCDM methodology applied to six criteria and 13 alternatives. The results show that alternative 3 is the best and alternative 5 is the worst. The sensitivity analysis was conducted to show the stability of the rank. The sensitivity analysis was performed under seven cases; the results show that the rank of alternatives was stable under different cases. The comparative study was conducted to show the effectiveness of the proposed methodology. [ABSTRACT FROM AUTHOR]

The novelty of this work is that it is the first to introduce complex-valued suprametric spaces and apply it to Fractal Generation and mixed Volterra–Fredholm Integral Equations. In the realm of fuzzy logic, complex-valued suprametric spaces provide a robust framework for quantifying the similarity between fuzzy sets; for instance, utilizing a complex-valued suprametric approach, we compared the similarity between fuzzy sets represented by complex-valued feature vectors, yielding quantitative measures of their relationships. Thereafter, we establish related fixed point results and their applications in algorithmic and numerical contexts. The study then delves into the generation of fractals, exemplified by the Barnsley Fern fractal, utilizing sequences of affine transformations within complex-valued suprametric spaces. Moreover, this article presents two algorithms for soft computing and fractal generation. The first algorithm uses complex-valued suprametric similarity for fuzzy clustering, iteratively assigning fuzzy sets to clusters based on similarity and updating cluster centers until convergence. The distinctive pattern of the Barnsley Fern fractal is produced by the second algorithm's repetitive affine transformations, which are chosen at random. These techniques demonstrate how well complex numbers cluster and how simple procedures can create complicated fractals. Moving beyond fractal generation, the paper addresses the solution of mixed Volterra–Fredholm integral equations in the complex plane using our results, demonstrating numerical illustrations of complex-valued integral equations. [ABSTRACT FROM AUTHOR]

This study aims to achieve the densest possible state of soil for constructing dams and roads. This requires assessing the compaction characteristics, Optimum Moisture Content (OMC), and Maximum Dry Density (MDD) to determine the soil's suitability for earthworks. However, this process is resource-intensive and time-consuming. To streamline the assessment, the study incorporates six parameters: gravel (G), sand (S), fine (F) contents, plastic limit (PL), liquid limit (LL), and plasticity index (PI). Four different models are used to predict compaction characteristics: artificial neural network (ANN), nonlinear regression (NLR), linear regression (LR), and multilinear regression (MLR). The study utilized a substantial dataset of 2162 entries, considering various soil gradation and plasticity properties as input variables. To evaluate the models' effectiveness, several statistical measures, including coefficient of determination (R2), scatter index (SI), root mean squared error (RMSE), mean absolute error (MAE), a20-index, and Objective (OBJ) value, were employed. The ANN model outperformed other models in predicting OMC, with RMSE, MAE, OBJ, SI, a20-index, and R2 values of 3.51, 2.31, 4.26, 0.202, 0.7, and 0.92%, respectively. However, for predicting MDD, the ANN model had the highest R2 value (R2 = 0.87), but the minimum RMSE (1.01), MAE (0.8), a20-index (0.998), and OBJ (1.07) were obtained from the MLR and LR models. Furthermore, sensitivity analyses revealed that the plastic limit significantly influences the OMC, while the gravel content plays a dominant role in predicting MDD. [ABSTRACT FROM AUTHOR]

The accurate prediction of scour depth in culverts is crucial to ensure public safety and due to the uncertainties in classical empirical/deterministic equations, it remains challenging. This study presents new ensemble multi-models to predict scour depth and quantify model bias and uncertainty. In this study, culvert scour was predicted by gene expression programming (GEP) and least square support vector machine (LSSVM), firstly based on dimensionless parameters. In the multi-model ensemble strategy, four ensemble approaches, namely SM, WM, LSSVM, and BMA were applied. The LSSVM showed overall best skill for the multi-model approach, that reduced the error by 61.3% and increased the coefficient of determination by 52.1%. The results showed the proposed approach can satisfy the requirements of simplicity, applicability, and accuracy at the same time. For application purposes, we provided a simple code to obtain scour depth with great accuracy using LSSVM. BMA and LSSVM were used to quantify the scour culvert uncertainty. The results of the uncertainty analysis showed that the LSSVM predicts scour depth quite well and generates high-quality prediction intervals. The results revealed that a multi-model ensemble strategy improved accuracy, certainty and reliability for culvert scour compared to empirical equations. [ABSTRACT FROM AUTHOR]

Mass transfer-based models are among the extensively applied reference evapotranspiration (ETo) estimation approaches that have been proven to provide reliable estimates under certain climatic and geographical conditions. A major issue with such approaches is the high variations in wind speed time series, which is a governing meteorological parameter in mass transfer-based models. few studies have focused on applying wavelet transform as a robust pre-processing technique for ETo estimation. A major question with coupling wavelet transforms with soft computing methods would be the proper choice of the wavelet function, vanishing moments and the decomposition levels. The present study aims at assessing different wavelet functions coupled with boosted regression tree (BT) models, i.e. wavelet-BT (WBT), in estimating ETo values (through mass transfer parameters) in both coastal and island regions, where it is supposed that wind speed variations are affected by sea–inland interactions and so has the sharp variations (unlike ETo). The obtained results showed that the coupled WBT model could improve the single BT model results to a great extent, and meanwhile, the Daubechies wavelet function with six vanishing moments provided the most accurate results in all the locations. Further, the best simulation outcomes with each wavelet function were obtained when the maximum possible decomposition level was used with each function. [ABSTRACT FROM AUTHOR]

A groundwater reservoir is either a solitary aquifer or a network of interconnected aquifers. A particular aquifer's groundwater purity evaluation could be time-consuming and costly. This study quantified the properties of Na%, SO42−, Cl, Na+, Mg2+, Ca2+, HCO3−, K+, and pH to predict the water quality parameter known as total hardness (as CaCO3). Groundwater quality data for the Shiraz Plain from 2002 to 2018 was utilized to accomplish this objective. The paper contrasts a hybrid methodology that combines Teaching Learning-Based Optimization (TLBO), Multiverse Optimizer (MVO), the Cuckoo Optimization Algorithm (COA), and the Evaporation Rate-based Water Cycle Algorithm (ER-WCA) with Artificial Neural Networks (ANN) this was done to design an optimal network for groundwater quality with conventional ANN. In comparison to all other TLBO-ANN, MVO-ANN, and COA-ANN developed models, the ER-WCA-ANN technique (with a population size of 500 and eight neurons in each hidden layer) provided the most accurate prediction for the TH with R2 values of 0.9983 and 0.98261, and RMSE values of 0.03698 and 0.00611, respectively, in the training and testing datasets. A comparison of the findings for the forecasting of groundwater quality showed that the ER-WCA-ANN hybrid model might increase prediction accuracy. These findings may have significant implications for future groundwater quality assessments. [ABSTRACT FROM AUTHOR]

To avoid or mitigate the unwanted water and gas content, inflow control devices (ICDs) are designed and installed in the well to disturb the water and gas breakthrough which are trying to overtake the oil inflow, water and gas coning and sand production. Smart wells with permanent downhole valves such as ICDs are used to balance production and injection in wells. A paramount issue regarding using downhole control devices is determining the required cross-sectional area of them for control of the imposed pressure drop across the device to stabilize the fluid flow. Current methods for calculating the opening size of the ICDs are mainly based on sensitivity analysis of the ICD flow area or optimization algorithms coupled with simulation models. Although these approaches are quite effective in oil field cases, they tend to be time-consuming and require demanding system models. This paper presents a fast analytical method to determine the ICD flow area validated by a genetic algorithm (GA). Analytically, a closed-form expression is introduced by manipulating Darcy's law applicable to multi-layer injection wells with different layer properties to balance the injection profile in the reservoir pay zone, based on equalizing injected front velocity in layers with different permeability. Considering various scenarios of analytical technique, GA optimization, and sensitivity analysis scenarios for ICD cross-sectional area determination, results for oil recovery, water production, water breakthrough time, and net present value (NPV) are discussed and compared. NPV values obtained by both analytical and GA approaches are virtually identical and greater than those of other scenarios. Compared to the base field case, the analytical method improved the oil recovery by almost 1%, reduced water production by almost 91%, and synchronized the water breakthrough time of high- and low-permeability layers (from a ratio of 1.76–1.06). The proposed analytical solution proved to be capable of providing desirable results with only one reservoir simulation run in contrast to GA and sensitivity analysis scenarios which require iterative simulation runs. The proposed analytical solution outperformed the GA as it is less computationally demanding in addition to its success in case of lowering water production for the field data. The findings of this study can help for a better understanding of the situation where water injection into the oil reservoir is problematic as the layers present different permeabilities which can induce problems such as early water breakthrough from the more permeable layer and hinder the success of the water injection process. Using ICDs and a faster and more accurate approach to calculate its cross-sectional area such as the analytical method that was used in this study can greatly increase the success rate of water injection in case of oil recovery and lower the amount of the produced water. [ABSTRACT FROM AUTHOR]

Abstract It is well known that the roughness of a wall plays a crucial role in determining the passive earth pressure that is exerted on a rigid wall. While the effects of positive wall roughness have been extensively studied in the past few decades, the study of passive earth pressure with negative wall friction is rarely found in the literature. This study aims to provide a precise solution for negative friction walls under passive wall conditions. The research is initiated by adopting a radial stress field for the cohesionless backfill and employs the concept of stress self-similarity. The problem is then formulated in a way that a statically admissible stress field be developed throughout an analyzed domain using a two-step numerical framework. The framework involves the successful execution of numerical integration, which leads to the exploration of the statically admissible stress field in cohesionless backfills under negative wall friction. This, in turn, helps to shed light on the mechanism of load transfer in such situations so that reliable design charts and tables be provided for practical uses. The study continues with a soft computing model that leads to more robust and effective designs for earth-retaining structures under various negative wall frictions and sloping backfills.

Abstract Drought is deemed a major natural disaster that can lead to severe economic and social implications. Drought indices are utilized worldwide for drought management and monitoring. However, as a result of the inherent complexity of drought phenomena and hydroclimatic condition differences, no universal drought index is available for effectively monitoring drought across the world. Therefore, this study aimed to develop a new meteorological drought index to describe and forecast drought based on various artificial intelligence (AI) models: decision tree (DT), generalized linear model (GLM), support vector machine, artificial neural network, deep learning, and random forest. A comparative assessment was conducted between the developed AI-based indices and nine conventional drought indices based on their correlations with multiple drought indicators. Historical records of five drought indicators, namely runoff, along with deep, lower, root, and upper soil moisture, were utilized to evaluate the models’ performance. Different combinations of climatic datasets from Alice Springs, Australia, were utilized to develop and train the AI models. The results demonstrated that the rainfall anomaly drought index was the best conventional drought index, scoring the highest correlation (0.718) with the upper soil moisture. The highest correlation between the new and conventional indices was found between the DT-based index and the rainfall anomaly index at a value of 0.97, whereas the lowest correlation was 0.57 between the GLM and the Palmer drought severity index. The GLM-based index achieved the best performance according to its high correlations with conventional drought indicators, e.g., a correlation coefficient of 0.78 with the upper soil moisture. Overall, the developed AI-based drought indices outperformed the conventional indices, hence contributing effectively to more accurate drought forecasting and monitoring. The findings emphasized that AI can be a promising and reliable prediction approach for achieving better drought assessment and mitigation.

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]

This study proposes a novel system for accurately predicting grout's uniaxial compressive strength (UCS) in fully grouted rock bolting systems. To achieve this, a database comprising 73 UCS values with varying water-to-grout (W/G) ratios ranging from 22 to 42%, curing times from 1 to 28 days, the admixture of fly ash contents ranging from 0 to 30%, and two Australian commercial grouts, Stratabinder HS, and BU-100, was built after conducting comprehensive series of experimental tests. After building the dataset, a metaheuristic technique, the jellyfish search (JS) algorithm was employed to determine the weight of base models in the ensemble system. This system combined various data and modelling techniques to enhance the accuracy of the UCS predictions. What sets this technique apart is the comprehensive database and the innovative use of the JS algorithm to create a weighted averaging ensemble model, going beyond traditional methods for predicting grout strength. The proposed ensemble model was called the weighted averaging ensemble model (WAE-JS), in which the obtained results of several soft computing models such as multi-layer perceptron (MLP), Bayesian regularized (BR) neural networks, generalized feed-forward (GFF) neural networks, classification and regression tree (CART), and random forest (RF) were weighted based on JS and the new results were then generated. Eventually, the result of WAE-JS was compared to other models, including MLP, BR, GFF, CART, and RF, based on some statistical parameters, such as R-squared coefficients, RMSE, and VAF as indices for evaluating the performance and capability of the proposed model. The results suggested the superiority of the ensemble WAE-JS system over the base models. In addition, the proposed WAE-JS model effectively improved the predicting accuracy achieved from the MLP, BR, GFF, CART, and RF. Furthermore, the sensitivity analysis revealed that the W/G had the most significant impact on the grout's UCS values. [ABSTRACT FROM AUTHOR]

Oncolytic viral immunotherapy is gaining considerable prominence in the realm of chronic diseases treatment and rehabilitation. Oncolytic viral therapy is an intriguing therapeutic approach due to its low toxicity and dual function of immune stimulations. This work aims to design a soft computing approach using stupendous knacks of neural networks (NNs) optimized with Bayesian regularization (BR), i.e. NNs-BR, procedure. The constructed NNs-BR technique is exploited in order to determine the approximate numerical treatment of the nonlinear multi-delayed tumor virotherapy (TVT) models in terms of the dynamic interactions between the tumor cells free of viruses, tumor cells infected by viruses, viruses, and cytotoxic T-lymphocytes (CTLs). The strength of state-of-the-art numerical approach is incorporated to develop the reference dataset for the variation in the infection rate for tumor cells, virus-free tumor cell clearance rate by CTLs, CTLs clearance rate for infectious tumor cells, the natural lifecycle of infectious tumor cells, the natural lifecycle of viral cell, the natural lifecycle of CTLs cells, tumor cells free of viruses' maximum proliferation rate, production of tumor cells with an infection, CTLs simulated ratio for infectious tumor cells, CTLs simulated ratios for virus-free cells and delay in time. The dataset is randomly chosen/segmented for training-testing-validation samples to construct the NNs models optimized with backpropagated BR representing the approximate numerical solutions of the dynamic interactions in the TVT model. The performance of the designed NNs-BR technique is accessed/evaluated and outcomes are found in good agreement with the reference solutions having the range of accuracy from 10 − 9 to 10 − 1 6 . The efficacy of NNs-BR paradigm is further substantiated after rigorous analysis on regression metrics, learning curves on MSE, and error histograms for the dynamics of TVT model. [ABSTRACT FROM AUTHOR]

Fuzzy Time Series (Fts) models are experiencing an increase in popularity due to their effectiveness in forecasting and modelling diverse and intricate time series data sets. Essentially these models use membership functions and fuzzy logic relation functions to produce predicted outputs through a defuzzification process. In this study, we suggested using a Second Order Type-1 fts (S-O T-1 F-T-S) forecasting model for the analysis of time series data sets. The suggested method was compared to the state-of-theart First Order Type 1 Fts method. The suggested approach demonstrated superior performance compared to the First Order Type 1 Fts method when applied to household consumption data from the Magene Regency in Indonesia, as measured by absolute percentage error rate (APER).

Landslides represent a growing threat among the various morphological processes that cause damage to territories. To address this problem and prevent the associated risks, it is essential to quickly find adequate methodologies capable of predicting these phenomena in advance. The following study focuses on the implementation of an experimental WebGIS infrastructure designed and built to predict the susceptibility index of a specific presumably at-risk area in real time (using specific input data) and in response to extreme weather events (such as heavy rain). The climate data values are calculated through an innovative and experimental atmospheric simulator developed by the authors, which is capable of providing data on meteorological variables with high spatial precision. To this end, the terrain is represented through cellular automata, implementing a suitable neural network useful for producing the desired output. The effectiveness of this methodology was tested on two debris flow events that occurred in the Calabria region, specifically in the province of Reggio Calabria, in 2001 and 2005, which caused extensive damage. The (forecast) results obtained with the proposed methodology were compared with the (known) historical data, confirming the effectiveness of the method in predicting (and therefore signaling the possibility of an imminent landslide event) a higher susceptibility index than the known one and one provided (to date) by the Higher Institute for Environmental Protection and Research (ISPRA), validating the result obtained through the actual subsequent occurrence of a landslide event in the area under investigation. Therefore, the method proposed today is not aimed at predicting the local movement of a small landslide area, but is primarily aimed at predicting the change or improving the variation of the landslide susceptibility index to compare the predicted value with the current one provided by the relevant bodies (ISPRA), thus signaling an alert for the entire area under investigation.

A Mixture of factor analyzer (MFA) model is a powerful tool to reduce the number of free parameters in high-dimensional data through the factor-analyzer technique based on the covariance matrices. This model also prepares an efficient methodology to determine latent groups in data. In this paper, we use an MFA model with a rich and flexible class of distributions called hidden truncation hyperbolic (HTH) distribution and a Bayesian structure with several computational benefits. The MFA based on the HTH family allows the factor scores and the error component can be skewed and heavy-tailed. Therefore, using the HTH family leads to the robustness of the MFA in modeling asymmetrical datasets with/without outliers. Furthermore, the HTH family, because of several desired properties, including analytical flexibility, provides steps in the estimation of parameters that are computationally tractable. In the present study, the advantages of MFA based on the HTH family have been discussed and the suitable efficiency of the introduced MFA model has been demonstrated by using real data examples and simulation.

The Internet of Things, or IoT, is a rapidly expanding technology, and one of its most important application areas is sustainable transportation. Choosing the right IoT service provider is a complex process that is considered a multi-criteria decision issue. The multi-criteria decision-making (MCDM) methodology deals with massive criteria. This study proposed an MCDM methodology for selecting the best IoT service provider under different sustainable criteria. The Multi-Objective Optimization Ratio Analysis (MOORA) method is an MCDM methodology used to rank the alternatives. The MOORA method is integrated with a type 2 neutrosophic setto deal with uncertain information. The proposed MCDM methodology applied to six criteria and 13 alternatives. The results show that alternative 3 is the best and alternative 5 is the worst. The sensitivity analysis was conducted to show the stability of the rank. The sensitivity analysis was performed under seven cases; the results show that the rank of alternatives was stable under different cases. The comparative study was conducted to show the effectiveness of the proposed methodology.

Abstract Concrete-filled steel tube columns (CFSTCs) are important elements in the construction sector and predictive analysis of their behavior is essential. Recent works have revealed the potential of metaheuristic-assisted approximators for this purpose. The main idea of this paper, therefore, is to introduce a novel integrative model for appraising the axial compression capacity (P u ) of CFSTCs. The proposed model represents an artificial neural network (ANN) supervised by satin bowerbird optimizer (SBO). In other words, this metaheuristic algorithm trains the ANN optimally to find the best contribution of input parameters to the P u . In this sense, column length and the compressive strength of concrete, as well as the characteristics of the steel tube (i.e., diameter, thickness, yield stress, and ultimate stress), are considered input data. The prediction results are compared to five ANNs supervised by backtracking search algorithm (BSA), earthworm optimization algorithm (EWA), social spider algorithm (SOSA), salp swarm algorithm (SSA), and wind-driven optimization. Evaluating various accuracy indicators showed that the proposed model surpassed all of them in both learning and reproducing the P u pattern. The obtained values of mean absolute percentage error of the SBO-ANN was 2.3082% versus 4.3821%, 17.4724%, 15.7898%, 4.2317%, and 3.6884% for the BSA-ANN, EWA-ANN, SOSA-ANN, SSA-ANN and WDA-ANN, respectively. The higher accuracy of the SBO-ANN against several hybrid models from earlier literature was also deduced. Moreover, the outcomes of principal component analysis on the dataset showed that the yield stress, diameter, and ultimate stress of the steel tube are the three most important factors in P u prediction. A predictive formula is finally derived from the optimized SBO-ANN by extracting and organizing the weights and biases of the ANN. Owing to the accurate estimation shown by this model, the derived formula can reliably predict the P u of concrete-filled steel tube columns.