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Any multi-objective optimization algorithms are usually introduced by developing a single-objective algorithm. In the current study, a recently developed physics-based algorithm, equilibrium optimizer (EO), is regarded and is extended into a multi-objective algorithm called MOEO. The optimal control problem for cancer treatment is solved using this novel algorithm to confirm its performance. Considering the cancer treatment as an optimal control problem to reduce both the concentration of cancer cells and the concentration of the drugs during treatment is the multi-objective optimization problem of this research. Due to this, this problem's solution leads to the optimal drug administration protocol to minimize cancer cell concentration and drug concentration. This multi-objective problem is solved using the novel MOEO algorithm, and the results are compared with the previous works. The Pareto front curve obtained from the algorithm offers a set of the most optimal solutions. According to the treatment selection criteria, one of these points is selected as the drug-prescribing protocol. The problem is solved based on three different case studies, where the result comparison shows that the MOEO's performance is great. Considering different cost functions, it shows that using integral of the time-weighted absolute (ITA) objective function gets the fastest response with a higher drug dose while using integral of the absolute (IS) objective function gets lesser drugs. [ABSTRACT FROM AUTHOR]

In the present work, water absorption of lightweight geopolymers produced by fine fly ash, rice husk bark ash and palm oil clinker (POC) aggregates has been modeled based on artificial neural networks. To build the models, training, validating and testing using experimental results from 144 specimens were conducted. The data used in the multilayer feed-forward neural networks models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA+RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the neural networks models, the water absorption of each specimen was predicted. The best value of R2 was 0.9972 and the minimum value of that was 0.8301. The training, validating and testing results in the neural networks model have shown a strong potential for predicting the water absorption of the geopolymer specimens. [ABSTRACT FROM AUTHOR]

In the present work, total specific pore volume of inorganic polymers (geopolymers) made from seeded fly ash and rice husk–bark ash has been predicted by gene expression programming. To build the model, training and testing using experimental results from 120 specimens were conducted. The values for input layers were the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk–bark ash in the ashes mixture, the percentage of coarse rice husk–bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to the input parameters, in the gene expression programming models, the pore volume of each specimen was predicted. The training and testing results in the gene expression programming models have shown a strong potential for predicting the total specific pore volume of the geopolymer specimens. [ABSTRACT FROM AUTHOR]

In this work, mean grain size of synthesized nanomaterials (such as MoSi2–TiC and MoSi2–SiC), which were produced by mechanical alloying, has been modeled by artificial neural networks. A total number of 103 data were gathered from the previous works, trained, validated and tested by the built networks. The used data as inputs were the method of calculation of the mean grain size, milling time, annealing temperature, produced phases after mechanical alloying, vial speed and ball-to-powder ratio. The value of the output layer was the mean grain size. The obtained results from the testing phase of the trained networks showed that the models are capable of predicting the mean grain size of the mechanical-alloyed synthesized materials in the considered range. [ABSTRACT FROM AUTHOR]

In the present study, compressive strength results of geopolymers produced by ordinary Portland cement (OPC) as aluminosilicate source have been modeled by artificial neural networks. Six main factors including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time and water curing regime each at 4 levels were considered for designing. A total of 32 experiments were conducted according to the L32 array proposed by the method. The neural network models were constructed by 10 input parameters including NaOH concentration, water glass to NaOH weight ratio, alkali activator to cement weight ratio, oven curing temperature, oven curing time, water curing regime, water glass content, NaOH content, Portland cement content and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range. [ABSTRACT FROM AUTHOR]

In the present study, compressive strength of lightweight aluminosilicate geopolymers produced by fine fly ash and rice husk bark ash together with palm oil clinker (POC) aggregates has been modeled by gene expression programming. To build the model, training and testing by using experimental results from 144 specimens were conducted. The used data in the models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, in the gene expression programming models, the compressive strength of each specimen was predicted. The best value of R2 and the minimum values of root mean square error (RMSE) and absolute percentage error (MAPE) are 0.9669, 2.583, and 1.984, respectively, all in training phase. The minimum value of R2 and the maximum values of RMSE and MAPE are 0.9456, 3.067, and 2.356, respectively, all in testing phase. The training and testing results in the models have shown a strong potential for predicting the compressive strength of the lightweight geopolymer specimens in the considered range and one may predict them with a tiny error. [ABSTRACT FROM AUTHOR]

In the present work, compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nano alumina, and nano silica has been predicted by artificial neural networks. The neural network models were constructed by 12 input parameters including the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO2 nanoparticles content, Al2O3 nanoparticles content, oven curing temperature, oven curing time, and test trial number. The value for the output layer was the compressive strength. According to the input parameters in feed-forward back-propagation algorithm, the constructed networks were trained, validated, and tested. The results indicate that artificial neural networks model is a powerful tool for predicting the compressive strength of the geopolymers in the considered range. [ABSTRACT FROM AUTHOR]

In the present study, the Charpy impact energy of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled in crack divider configuration. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using respectively 174 and 120 experimental data were conducted. A good fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the Charpy impact energy of the corresponding layer. Finally, by applying the rule of mixtures, Charpy impact energy of functionally graded steels in crack divider configuration was found through numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments. [ABSTRACT FROM AUTHOR]

Combined heat and power economic dispatch (CHPED) is introduced as a difficult optimization problem, which provides optimal generation scheduling of heat and power units. The CHPED problem aims to minimize the fuel cost of CHP units with the consideration of operational constraints. In this paper, whale optimization algorithm (WOA) is employed to solve CHPED problem. WOA is a new meta-heuristic optimization technique, which is introduced recently for solving optimization problems. Social behavior of humpback whales is the basic idea of proposal of WOA, where the bubble-net hunting strategy inspires this optimization procedure. Three test systems are considered for evaluation of the performance of the WOA in solving the non-convex nonlinear CHPED problem. The first test instance, which includes 24 units, is studied for evaluating WOA performance in finding the optimal solution of CHPED problem. 84-Unit and 96-unit test systems are introduced for the first time in this paper to show the superiority of WOA in solving non-convex CHPED optimization problem. The second test system contains 40 power-only units, 24 CHP units, and 20 heat-only units. Additionally, the third test instance contains 52 power-only units, 24 CHP units, and 20 heat-only units. The obtained optimal solutions represent WOA efficiency and feasibility and capability of obtaining better solutions with respect to other optimization techniques in terms of operational cost and ability of implementation of WOA on large systems. [ABSTRACT FROM AUTHOR]

Excessive synchronous firing of neurons is the sign of several neurological disorders such as Parkinson and epilepsy. In addition, growing evidence suggests that astrocytes have significant roles in neural synchronization. Drawing on these concepts and based on the latest studies, a bio-inspired stimulator which essentially is a dynamical model of the astrocyte biophysical model is proposed. The performance of the proposed bio-inspired stimulator is investigated on a large-scale, sparsely connected neural network which models a local cortical population. Next, a multiplier-less digital circuit for the bio-inspired stimulator is designed, and finally, the complete digital circuit of the closed-loop system is implemented in hardware on the ZedBoard development kit. Considering software simulations and hardware FPGA implementation, the proposed bio-inspired stimulator is able to prevent the hyper-synchronous neural firing in a network of excitatory and inhibitory neurons. Based on the obtained results, it is demonstrated that the proposed stimulator has a demand-controlled characteristic and can be a good candidate as a new deep brain stimulation (DBS) technique to effectively suppress the hyper-synchronous neural oscillations. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive fuzzy expert system to concurrently estimate and forecast both long-term electricity and natural gas (NG) consumptions with hike in prices. Using a novel procedure, the impact of price hike is incorporated into energy demand modeling. Furthermore, adaptive network-based FIS (ANFIS) is used to model NG consumption in power generation (NGPG). To cope with random uncertainty in small historical data sets, Monte Carlo simulation is used to generate training data for ANFIS. The proposed ANFIS uses electricity consumption data to improve the estimation of total NG consumption. The unique contribution of this paper is three fold. First, it proposes a novel expert system for electricity consumption and NG consumption in end-use sector with hike in prices. Second, it uses ANFIS-Monte Carlo approach for NGPG. Third, electricity consumption is used in ANFIS for improvement of NGPG consumption estimation. A real case study is presented that illustrates the applicability and usefulness of the proposed model where it is applied for joint forecasting of annual electricity and NG consumption with hike in prices. [ABSTRACT FROM AUTHOR]

Large increase or hike in energy prices has proven to impact electricity consumption in a way which cannot be drawn from historical data, especially when price elasticity of demand is not significant. This paper proposes an integrated adaptive fuzzy inference system (FIS) to estimate and forecast long-term electricity consumption when prices experience large increase. To this end, first a novel procedure for construction and adaptation of Takagi–Sugeno fuzzy inference system (TS-FIS) is suggested. Logarithmic linear regressions are estimated with historical data and used to construct an initial first-order TS-FIS. Then, in the adaptation phase, expert knowledge is used to define new fuzzy rules which form a new secondary FIS for electricity forecasting. To show the applicability and usefulness of the proposed model, it is applied for forecasting of annual electricity consumption in Iran where removing energy subsidies has resulted in a hike in electricity prices. Gross domestic product (GDP), population and electricity price are three inputs for the initial TS-FIS. A questionnaire survey was conducted to collect the expert estimation on possible change in electricity per capita, change in electricity intensity and the ratio of GDP elasticity to population elasticity when price hikes. Based on the information collected, a fuzzy rule base is formed and used to construct the secondary FIS which is used for electricity forecasting until 2016. Furthermore, the performance of the proposed model of this paper is compared with three other models namely ANFIS, ANN and one-stage regression in terms of their mean absolute percentage error. The comparison shows a superior performance for the proposed FIS model. [ABSTRACT FROM AUTHOR]

The paper presents some results of the research connected with the development of new approach based on the artificial neural network (ANN) of predicting the transformation start temperature of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions (C, Mn, Nb, Mo, Ti, N, Cu, P, S, Si, Al, V), austenitizing temperature, initial austenite grain size and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 138 different experimental data were gathered from the literature. The data used in the ANN model are arranged in a format of fourteen input parameters that cover the chemical compositions, initial austenite grain size and cooling rate, and output parameter which is transformation start temperature. In this model, the training and testing results in the ANN have shown strong potential for prediction of effects of chemical compositions and heat treatments on phase transformation of microalloyed steels. [ABSTRACT FROM AUTHOR]

According to obtained reports, most of customer relationship management (CRM) projects fail. Thus, a structured approach is needed to identify, prioritize and evaluate risks in CRM projects. This helps project managers of CRM implementation to find out how to treat such risks. The nature of CRM is multi-dimensional, and it is one of the most complicated innovations in organizations. In this paper, because of multi-dimensional nature of CRM projects, fuzzy analytic hierarchy process is used to evaluate CRM risk factors based on active project managers’ judgments as experts in organizations which sale CRM software or provide service and consult in Iran. The analyses are performed by the judgments of CRM experts using a questionnaire including 49 pair-wise comparisons. In Iranian organizations, based on experts’ judgments, in the first life cycle phases of CRM, “changes in the stakeholders and top management” has the highest importance among risk factors in CRM projects. According to our experts’ beliefs, management permanence is one of the most important factors for IS projects failure in Iran which is resulted from Iranian organizations’ culture. In the next life cycle phases of CRM, the risk factors such as general factors of classification “dynamic assessment and monitoring” have the lowest priority. [ABSTRACT FROM AUTHOR]

In today’s competitive world, most organizations need to successfully develop new products. The success of new products can be a competitive weapon and advantage for a firm to survive in the current dynamic markets. The most important aspect of the product design is identifying customers’ needs of products. One of the most common methods to satisfy customers’ needs is improvement of customers’ satisfaction. The present paper applies the aspects of the 4P marketing mix (product, price, place, and promotion) for modeling the relationship between customers’ satisfaction and new product design with handling nonlinearity as well as fuzziness. A methodology based on the adaptive neuro-fuzzy inference systems (ANFIS) approach is presented to improve customers’ satisfaction while setting products’ design attributes in a fuzzy environment. The intelligent approach of the present study is then applied to predict customers’ satisfaction and design new product through 4P marketing mix concept in an actual case in the freezer refrigerator industry. A complete sensitivity analysis is run to assess significance or influence of each input variable on customers’ satisfaction. The superiority of ANFIS is proved by error analysis. The proposed approach would help practitioners within the field of marketing and new product design teams to enhance customers’ satisfaction and set new products’ attributes. [ABSTRACT FROM AUTHOR]

Efficiency appraisal of the companies in stock exchange and selecting the type of industries and companies is an important subject for financial managers and also investors because it can reduce the risk of investment, noticeably. In these situations, investors need information to facilitate the investing process. One of the ways that can be used in this regard is evaluation and ranking of the companies. The goal of this study is to do a comprehensive appraising and ranking of the companies in a stock exchange. To this end, the indices needed to appraise the efficiency of exchange companies in the literature are reviewed. Then, using field research, those indices are recognized as mainly affecting the efficiency of companies existing in Tehran stock exchange. Since the financial status of a company is not fixed throughout time, there is a source of uncertainty in data of such problem. To address this uncertainty, fuzzy data envelopment analysis (FDEA) is applied to rank companies. Finally, the results with the ranking given by the organization in charge are compared. Results show that FDEA is a suitable and efficient approach to model the fluctuations and uncertainties existing in financial data and factors affecting the efficiency of a corporation. [ABSTRACT FROM AUTHOR]

In the present work, the effect of SiO and AlO nanoparticles on compressive strength of ash-based geopolymers with different mixtures of rice husk ash, fly ash, nanoalumina and nanosilica has been predicted by gene expression programming. The models were constructed by 12 input parameters, namely the water curing time, the rice husk ash content, the fly ash content, the water glass content, NaOH content, the water content, the aggregate content, SiO nanoparticle content, AlO nanoparticle content, oven curing temperature, oven curing time and test trial number. The value for the output layer was the compressive strength. According to the input parameters in gene expression programming models, the data were trained and tested, and the effects of SiO and AlO nanoparticles on compressive strength of the specimens were predicted with a tiny error. The results indicate that gene expression programming model is a powerful tool for predicting the effect of nanoparticles on compressive strength of the geopolymers in the considered range. [ABSTRACT FROM AUTHOR]

In the present work, compressive strength of lightweight inorganic polymers (geopolymers) produced by fine fly ash and rice husk–bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled based on fuzzy logic. To build the model, training, validating and testing were conducted using experimental results from 144 specimens. The used data in the ANFIS models were arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the model, the compressive strength of each specimen was predicted. The training, validating and testing results in the model have shown a strong potential for predicting the compressive strength of the geopolymer specimens in the considered range. [ABSTRACT FROM AUTHOR]

In the present study, the tensile strength of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled by artificial neural networks. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using respectively 174 and 120 experimental data were conducted. According to the input parameters, in the neural networks model, the Vickers microhardness of each layer was predicted. A good-fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic- and austenitic-graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the yield stress of the corresponding layer and by assuming Holloman relation for stress-strain curve of each layer, they were acquired. Finally, by applying the rule of mixtures, tensile strength of functionally graded steels configuration was found through a numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments. [ABSTRACT FROM AUTHOR]

In this work, Charpy impact energy of Al6061-SiC nanocomposites produced by mechanical alloying has been modeled by artificial neural networks in both crack divider and crack arrester configurations. Al6061 powders were mixed by 2, 3, and 5 vol % of SiC nanoparticles and ball-milled for 45 min. Afterward, the produced powders were hot-pressed in aluminum cans and then were extruded to produce a dense bulk. Charpy impact specimens were prepared from the produced samples in layered form with different adhesives and thickness of the layers. To build the model, training, validating, and testing was performed using 171 pair input-target data. The used data in the multilayer feed-forward neural networks models were arranged in a format of six input parameters including the thickness of layers, the number of layers, the adhesive type, the crack tip configuration, the content of SiC nanoparticles, and the test trial number. The output parameter was Charpy impact energy of the nanocomposites. The training, validating, and testing results in the neural network models have shown a strong potential for predicting Charpy impact energy of Al6061/SiC nanocomposites in the considered range of input-target values. [ABSTRACT FROM AUTHOR]

In the present work, water absorption of lightweight geopolymers produced by fine fly ash and rice husk-bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled by adaptive network-based fuzzy inference systems (ANFIS). Different specimens made from a mixture of fine fly ash and rice husk-bark ash with and without POC were subjected to water absorption tests at 2, 7, and 28 days of curing. The specimens were oven cured for 36 h at 80 °C and then cured at room temperature until 2, 7, and 28 days. The results showed that high amount of POC particles improve the percentage of water absorption at the early age of curing. In addition, the ratio of 'the percentage of water absorption' to 'weight' of the POC-contained specimens at all ages of curing was much higher than that of POC-free specimens, which make them suitable for lightweight applications. To build the model, training, validating, and testing using experimental results from 144 specimens were conducted. The used data in the ANFIS models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing, and the test trial number. According to these input parameters, the water absorption of each specimen was predicted. The training, validating, and testing results in the ANFIS models showed a strong potential for predicting the water absorption of the geopolymer specimens. [ABSTRACT FROM AUTHOR]

In this research, a method has been presented to predict the science production share of the Islamic Azad University in Iran at the end of 2017 and 2025 by artificial neural networks and adaptive network-based fuzzy inference systems. The used data were scientific publications under affiliations entitled 'Azad University' or 'University of Azad' gathered from Scopus databases. To build the model for training, testing and validation the network, scientific researches of the recent 24 years were extracted and used as inputs. The output of the obtained models was the science production share of the Islamic Azad University in scientific research of Iran. The built models showed an excellent potential to predict the science production share of the Islamic Azad University. A good fit equation that correlates the science production share of the Islamic Azad University in Iran to its number of publication at a specific year was presented. By extrapolation method, the scientific share of the Islamic Azad University among all of the Iranian's scientific institutions was predicted equal to about 50 and 78% at the years 2017 and 2025, respectively. [ABSTRACT FROM AUTHOR]

In this work, compressive strength of lightweight geopolymers produced by fine fly ash and rice husk-bark ash together with palm oil clinker (POC) aggregates has been investigated experimentally and modeled based on artificial neural networks. Different specimens made from a mixture of fine fly ash and rice husk-bark ash with and without POC were subjected to compressive strength tests at 2, 7, and 28 days of curing. A model based on artificial neural networks for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 144 specimens were conducted. The data used in the multilayer feed-forward neural networks models are arranged in a format of six input parameters that cover the quantity of fine POC particles, the quantity of coarse POC particles, the quantity of FA + RHBA mixture, the ratio of alkali activator to ashes mixture, the age of curing and the test trial number. According to these input parameters, in the neural networks model, the compressive strength of each specimen was predicted. The training and testing results in the neural networks model have shown a strong potential for predicting the compressive strength of the geopolymer specimens in the considered range. [ABSTRACT FROM AUTHOR]

In the present work, compressive strength of inorganic polymers (geopolymers) made from seeded fly ash and rice husk bark ash has been predicted by artificial neural networks. Different specimens were subjected to compressive strength tests at 7 and 28 days of curing. One set of the specimens were cured at room temperature until reaching to 7 and 28 days, and the other sets were oven-cured for 36 h at the range of 40-90 °C and then room cured until 7 and 28 days. A model based on artificial neural networks for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The data used in the multilayer feed-forward neural networks models are arranged in a format of six input parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk bark ash in the ashes mixture, the percentage of coarse rice husk bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to these input parameters, in the neural networks model, the compressive strength of each specimen was predicted. The training and testing results in the neural networks model have shown a strong potential for predicting the compressive strength of the geopolymer specimens. [ABSTRACT FROM AUTHOR]

The paper presents some results of the research connected with the development of new approach based on the fuzzy logic of predicting the Vickers microhardness of the phase constituents occurring in five steels after continuous cooling. The independent variables in the model are chemical compositions, initial austenite grain size, and cooling rate over the temperature range of the occurrence of phase transformations. For purpose of constructing these models, 114 different experimental data were gathered from the literature. The data used in the fuzzy logic model are arranged in a format of twelve input parameters that cover the chemical compositions, initial austenite grain size, and cooling rate, and output parameter which is Vickers microhardness. In this model, the training and testing results in the fuzzy logic systems have shown strong potential for prediction of effects of chemical compositions and heat treatments on hardness of microalloyed steels. [ABSTRACT FROM AUTHOR]

In the present study, the Vickers microhardness profile of ferritic and austenitic functionally graded steel produced by electroslag remelting process has been modeled by adaptive network-based fuzzy inference system (ANFIS). To produce functionally graded steels, a spot-welded electrode that consists of two slices of plain carbon steel and austenitic stainless steel was used. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with ANFIS. To build the model for graded ferritic and austenitic steels, training, testing and validation using, respectively, 174 and 120 experimental data were conducted. According to the input parameters, the Vickers microhardness of each layer was predicted. The training, testing and validation results in the ANFIS models have shown a strong potential for predicting microhardness profile of both graded ferritic and austenitic steels. It was shown that the Vickers microhardness can be predicted by ANFIS in the range of the examined data. [ABSTRACT FROM AUTHOR]

In the present study, an artificial neural networks-based model (ANNs) was developed to predict the Vickers microhardness of low-carbon Nb microalloyed steels. Fourteen parameters affecting the Vickers microhardness were considered as inputs, including the austenitizing temperature, cooling rate, initial austenite grain size, different chemical compositions and Nb in solution. The network was then trained to predict the Vickers microhardness amounts as outputs. A Multilayer feed-forward back-propagation network was developed and trained using experimental data form literatures. Five low-carbon Nb microalloyed steels and one low-carbon steel without Nb were investigated. The effects of austenitizing temperature (900-1,100°C) and subsequent cooling rate (0.15-227°C/s) and initial austenite grain size (5-130 μm) on the Vickers microhardness of steels were modeled by ANNs as well. The predicted values are in very good agreement with the measured ones, indicating that the developed model is very accurate and has the great ability for predicting the Vickers microhardness. [ABSTRACT FROM AUTHOR]

In the present work, compressive strength of geopolymers made from seeded fly ash and rice husk-bark ash has been predicted by adaptive network-based fuzzy inference systems (ANFIS). Different specimens, made from a mixture of fly ash and rice husk-bark ash in fine and coarse forms and a mixture of water glass and NaOH mixture as alkali activator, were subjected to compressive strength tests at 7 and 28 days of curing. The curing regimes were different: one set of the specimens were cured in water at room temperature until 7 and 28 days and the other sets were oven-cured for 36 h at the range of 40-90°C and then cured at room temperature until 7 and 28 days. A model based on ANFIS for predicting the compressive strength of the specimens has been presented. To build the model, training and testing using experimental results from 120 specimens were conducted. The used data as the inputs of ANFIS models are arranged in a format of six parameters that cover the percentage of fine fly ash in the ashes mixture, the percentage of coarse fly ash in the ashes mixture, the percentage of fine rice husk-bark ash in the ashes mixture, the percentage of coarse rice husk-bark ash in the ashes mixture, the temperature of curing, and the time of water curing. According to these input parameters in the ANFIS models, the compressive strength of each specimen was predicted. The training and testing results in ANFIS models showed a strong potential for predicting the compressive strength of the geopolymeric specimens. [ABSTRACT FROM AUTHOR]

In the present work, total specific pore volume of inorganic polymers (geopolymers) made from seeded fly ash and rice husk bark ash has been predicted by artificial neural networks. Different specimens were subjected to porosimetry tests at 7 and 28 days of curing. One set of the specimens were cured at room temperature until reaching to 7 and 28 days, and the other sets were oven-cured for 36 h at the range of 40-90°C and then cured at room temperature until 7 and 28 days. To build the neural network models, training and testing using experimental results from 120 specimens were conducted. According to these input parameters, in the neural networks models, the pore volume of each specimen was predicted. The training and testing results in the neural networks model have shown a strong potential for predicting the total specific pore volume of the geopolymer specimens in the examined range. [ABSTRACT FROM AUTHOR]

In the present study, the Charpy impact energy of ferritic and austenitic functionally graded steel produced by electroslag remelting has been modeled in crack divider configuration. To produce functionally graded steels, two slices of plain carbon steel and austenitic stainless steels were spot welded and used as electroslag remelting electrode. Functionally graded steel containing graded layers of ferrite and austenite may be fabricated via diffusion of alloying elements during remelting stage. Vickers microhardness profile of the specimen has been obtained experimentally and modeled with artificial neural networks. To build the model for graded ferritic and austenitic steels, training, testing and validation using, respectively, 174 and 120 experimental data were conducted. According to the input parameters, in the neural networks model, the Vickers microhardness of each layer was predicted. A good fit equation that correlates the Vickers microhardness of each layer to its corresponding chemical composition was achieved by the optimized network for both ferritic and austenitic graded steels. Afterward, the Vickers microhardness of each layer in functionally graded steels was related to the yield stress of the corresponding layer and by assuming Holloman relation for stress-strain curve of each layer, the area under each stress-strain curve was acquired. Finally, by applying the rule of mixtures, Charpy impact energy of functionally graded steels in crack divider configuration was found through numerical method. The obtained results from the proposed model are in good agreement with those acquired from the experiments. [ABSTRACT FROM AUTHOR]

In the present paper, two models based on artificial neural networks and genetic programming for predicting split tensile strength and percentage of water absorption of concretes containing AlO nanoparticles have been developed at different ages of curing. For purpose of building these models, training and testing using experimental results for 144 specimens produced with 16 different mixture proportions were conducted. The data used in the multilayer feed-forward neural networks models and input variables of genetic programming models are arranged in a format of eight input parameters that cover the cement content, nanoparticle content, aggregate type, water content, the amount of superplasticizer, the type of curing medium, Age of curing and number of testing try. According to these input parameters, in the neural networks and genetic programming models, the split tensile strength and percentage of water absorption values of concretes containing AlO nanoparticles were predicted. The training and testing results in the neural network and genetic programming models have shown that every two models have strong potential for predicting the split tensile strength and percentage of water absorption values of concretes containing AlO nanoparticles. It has been found that NN and GEP models will be valid within the ranges of variables. In neural networks model, as the training and testing ended when minimum error norm of network gained, the best results were obtained, and in genetic programming model, when 4 gens was selected to construct the model, the best results were acquired. Although neural network have predicted better results, genetic programming is able to predict reasonable values with a simpler method rather than neural network. [ABSTRACT FROM AUTHOR]

A correction to this paper has been published: https://doi.org/10.1007/s00521-021-06075-7 [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a large number of articles that were under consideration at the same time, including [2, 3], and previously published articles, including [4, 5, 6]. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with number of articles including those that were under consideration at the same time [2] and previously published articles [3–6]. Additionally, the article shows evidence of peer review manipulation. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article because it significantly overlaps with a number of previously published articles. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including those that were under consideration at the same time [2] and previously published articles [3–6]. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including those that were under consideration at the same time [2–4] and previously published articles [5, 6]. Additionally, the article shows evidence of peer review manipulation. The authors have not responded to any correspondence regarding this retraction. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this articl [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a large number of articles that were under consideration at the same time, including [2,3, 4] and previously published articles, including [5,6]. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a large number of articles that were under consideration at the same time, including [2, 3] and previously published articles, including [4–6]. Additionally, the article shows evidence of peer review manipulation. The author has not responded to any correspondence regarding this retraction. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of previously published articles including [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including those that were under consideration at the same time [2, 3] and previously published articles [4-6]. Additionally, the article shows evidence of peer review manipulation. The authors have not responded to any correspondence regarding this retraction. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including those that were consideration at the same time [2] and previously published articles [3–6]. Additionally, the article shows evidence of peer review manipulation. The authors have not responded to any correspondence regarding this retraction. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including those that were under consideration at the same time [2], and previously published articles [3-6]. Additionally, the article shows evidence of peer review manipulation. The author has not responded to any correspondence regarding this retraction. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a number of articles including those that were under consideration at the same time [2, 3, 4] and previously published articles [5, 6]. Additionally, the article shows evidence of peer review manipulation. The authors have not responded to any correspondence regarding this retraction. [ABSTRACT FROM AUTHOR]

The Editor-in-Chief has retracted this article [1] because it significantly overlaps with a large number of articles that were under consideration at the same time, including [2–5], and previously published articles, including [6]. [ABSTRACT FROM AUTHOR]