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This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for the training dataset, 80% of the dataset from the sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political optimizer (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used to create an erosion susceptibility model and establishes a unique and original approach. After it was confirmed to be successful, the algorithms were applied to create a susceptibility map for this area, demonstrating the integrity of the results. The AUC values were computed for every optimisation algorithm used in this study. The optimal estimated accuracy indices for populations of 450 were determined to be 0.9846 using the BSA-MLP training databases. The maximum AUC value for the HBO-MLP training databases with different swarm sizes was 0.9736. A swarm size of 350–300 is considered optimal for forecasting erosion susceptibility mapping in hybrid models. With the same swarm size constraints, the AUC value for training in the TLBO-MLP scenario was 0.996. After 150 swarm size conditions were used to train the PO-MLP model, the AUC values were 0.9845. According to these findings, the TLBO-MLP and PO-MLP algorithms worked best with populations of 50 and 150, respectively.

In this paper, we introduce an innovative thermoelectric, photovoltaic hybrid system and investigate its performance under various radiation intensities and heat transfer coefficients outside the cavity. Our findings reveal that the proposed system yields twice the power output compared to a traditional plate thermoelectric, photovoltaic hybrid system. Through economic analysis, we project a 45 % reduction in energy cost with this novel structure compared to a full hybrid system. Notably, positioning the hybrid system at the bottom of the cavity, where maximum radiation occurs, is deemed optimal. Our heat transfer analysis demonstrates a significant increase in power generation due to convection outside the cavity, with approximately 9 % of incoming radiation reflected and a further 59 % reflected without the cavity. Utilizing artificial neural networks, we predict thermal and electrical power generation, achieving a Mean Absolute Error (MAE) below 3 % and an R-squared value exceeding 0.98. Additionally, our model's predictions closely match experimental results, validating its accuracy and practical utility. This comprehensive study advances the field by offering a novel hybrid system design that outperforms existing solutions while providing insights into optimizing placement and enhancing power generation through sophisticated modeling techniques.

In this paper, the energy loss of the green building is optimized based on the thickness and lay-up of the Nano-insulation. As different thicknesses and lay-up of the Nano-insulation have a direct effect on energy consumption of the green building with 1590 square meters, especially with nanomaterial, the machine learning models are employed to represent a new model of the thermal conductivity of the proposed advanced insulation with the precision above 99%. The machine learning models are employed to classify and model the behavior of the heat transfer in the green building due to the complex behavior of the thermal conductivity in the green building. Therefore, 110 data for modeling 20 types of lay-up with 6 different thicknesses are prepared by the machine learning models including Support Vector Machine (SVM), Gaussian Process Regression (GPR), and decision tree. Based on the data analysis and statistical data, thermal conductivity modeling with a decision tree represents the best performance and fitted model. The multi-Disciplinary Optimizing method (MDO) under energy consumption constraint, economical consideration, and environmental effects on insulation properties is performed to enhance the energy efficiency of the green building. The calculated results with the Degree-Day approach reveal that the amount of energy saving for green buildings with Nano insulation is about 40% higher than common insulation in common types of insulations. The proposed insulation characteristics regarding the value of Present Worth Function (PWF) and economic aspects cause energy saving per unit area and decreasing in CO2 emission between 290 kg/m3 to 293 kg/m3 depending on weather conditions, insulation thickness, and lay-up.

In this paper, the energy supply of a zero-energy building with 220 square meters is considered using optimized nanocomposite solar panels with respect to maximum efficiency. An optimized hybrid machine learning method plays a key role in presenting solar panel modeling with over 0.99% accuracy. Predicting the properties of the nanomaterial solar cell in four different seasons is performed by efficient support vector machines (SVM), and k-nearest neighbors (KNN) machine learning algorithms. In addition, the KNN algorithm is optimized by the Particle Swarm Optimization (PSO) method to improve the capabilities of KNN and reveal the best performance criteria for the photovoltaic modeling characteristics. The parameters of the nanocomposite cells are optimized using the proposed novel Multidisciplinary Optimization Method (MDO) to increase the efficiency of the solar panel by up to 170%. Optimization of solar cell performance with nanocomposite material under energy consumption constraints is carried out to propose the best construction of cells with 3 layers. The presented approach as a solution and indicator for the next generation of commercial and residential buildings can increase the potential values of solar cells to at least 70%.

Irregular nature of renewable energies namely solar and wind entails making use of storage units to improve the reliability of the designed systems and enhance the overall performance and output. In conditions of applying thermal energy technologies like solar thermal power plants and solar collectors, storage systems with ability of storing energy in thermal form are employed. In general, due to some advantages of latent heat thermal energy storage mainly known as Phase Change Material (PCM), like high heat capacity, these types of storage materials are widely used in thermal systems. In addition to energy storage, PCMs are utilizable for some other purposes namely device thermal management like solar PV/Thermal systems. In spite of benefits of PCMs in term of capacity of storage, low thermal conductivity is one the main disadvantages of them. Some techniques and approaches like utilization of nanomaterials, fins and foams are suggested to resolve this issue. The aim of current study is to review the research works on the simultaneous applications of fins and nanomaterials in PCMs. With respect to the findings of the research works it can be stated that there is significant potential in improvement of phase change rate by simultaneous usage of both fins and nanomaterials. The rate of phase change in these cases can be affected by the type of nanomaterials and the characteristics of the fins.

Abstract In this research, the photocatalytic degradation of CIP from aqueous solutions using CQD decorated on N-Cu co-doped titania (NCuTCQD) was made during two synthesis steps by sol–gel and hydrothermal methods. The fabricated catalysts were analyzed using various techniques, including XRD, FT-IR, BET, FESEM, EDX, and DRS. The results showed that N and Cu atoms were doped on TiO2 and CQD was well deposited on NCuT. The investigation of effective operational parameters demonstrated that the complete removal of ciprofloxacin (CIP: 20 mg/L) could be achieved at pH 7.0, NCuTCQD4wt%: 0.8 g/L, and light intensity: 100 mW/cm2 over 60 min reaction time. The O2 •– and OH˙ radicals were identified as the primary reactive species during the decontamination process. The synthesized photocatalyst could be recycled after six consecutive cycles of CIP decomposition with an insignificant decrease in performance. Pharmaceutical wastewater was treated through the optimum degradation conditions which showed the photocatalytic degradation eliminated 89% of COD and 75% of TOC within 180 min. In the effluent toxicity evaluation, the EC50 values for treated and untreated pharmaceutical wastewater increased from 62.50% to 140%, indicating that the NCuTCQD4wt%/Vis system can effectively reduce the toxic effects of pharmaceutical wastewater on aquatic environments.

Abstract Metronidazole (MNZ) is an extensively used antibiotic against bacterial infections for humans and farm animals. Prevention of antibiotics discharge is essential to prevent adverse environmental and health impacts. A member of metal–organic frameworks, zeolite imidazole framework-67 with cobalt sulfate precursor (ZIF-67-SO4) and exceptional physio-chemical properties was prepared via room temperature precipitation to adsorb MNZ. The study framework was designed by Box–Behnken Design to evaluate the effect of pH, ZIF-67-SO4 dose, and contact time on adsorption efficiency. The polynomial model fitted the adsorption system indicated the optimal condition for 97% MNZ removal occurs at pH = 7, adsorbent dosage = 1 g/L, and mixing time = 60 min. The model also revealed that the removal increased with contact time and decreased at strong pH. Equilibrium and kinetic study also indicated the adsorption of MNZ followed the intra-particle diffusion model and the Langmuir isotherm model with a qmax = 63.03 mg/g. The insignificant loss in removal efficacy in use-reuse adsorption cycles reflected the practical viability of ZIF-67-SO4.

Abstract Photocatalytic degradation under ultra-low powered light is a viable advanced oxidation process technique against extensive emerging contaminants. As a new and remarkable class of nanoporous materials, metal-organic frameworks (MOFs), attract interest for the supreme adsorptive and photocatalytic functionalities. An outstanding MOF, MIL-101(Fe) chosen as a photocatalyst template for the synthesis of α-Fe2O3 by a simple thermal modification to improve the structural properties toward methylene blue (MB) eradication. Octahedron-like α-Fe2O3 photocatalyst (Modified MIL-101(Fe), M-MIL-101(Fe)) was superior in dispersion and separation properties in aqueous medium. Moreover, the adsorptive and catalytic performance was increased for modified form by ~ 7.3% and ~ 17.1% compared to pristine MIL-101(Fe), respectively. Synergistic improvement of MB removal achieved by simultaneous adsorption/degradation under 5-W LED irradiation. Parametric study indicated an 18.1% and 44.5% improvement in MB removal was observed by increasing pH from 4 to 10, and M-MIL-101(Fe) dose from 0.2 to 1 g L−1, respectively. MB removal followed the pseudo-second-order kinetics model and the process efficiency dropped by 38% as MB concentration increased from 5 to 20 mg L−1. Radical trapping tests revealed the significant role of $${\mathrm{OH}}^{.}$$ OH . and electron radicals as the major participants in dye degradation. A significant loss in the efficiency of M-MIL-101(Fe) was observed in the reusability tests that is good to study further. In conclusion, a simple thermal post-synthesis modification on MIL-101(Fe) improved its structural, catalytic, and adsorptive properties against MB.

Recent studies for nanofluids, did not pay enough attention to the effect of the nanoparticle size and nanofluid stability on the heat transfer rate. In this study, the thermal conductivity (TC) coefficient of silica/deionized water (DW) nanofluid with various sizes of nanoparticles and various rates of nanofluid stability was investigated. Validation experiments, including X-Ray diffraction analysis (XRD), FE-Scanning Electron Microscopy (FE-SEM), Dynamic Light Scattering (DLS), and Zeta Potential (Zp), were carried out to examine the particle sizes and the nanofluid stability. The results illustrated that increasing the temperature leads to the augmentation of the TC coefficient of the SiO2/deionized water in all nanoparticle sizes and all volume concentrations. Also, increasing the volume concentration of the nanofluid leads to the augmentation of the TC coefficient of the nanofluid in all nanoparticle sizes and all temperatures. Eventually, increasing the nanoparticle sizes causes the TC coefficient of the nanofluid to be decreased in all volume concentrations. For the 11 nm nanoparticle size, the amount of the Relative TC (RTC) coefficient enhancement by increasing the temperature was 1.2%, 2.5%, 4.7%, and 6.8% for concentrations of 0.0.1%, 0.1%, 0.5%, and 1%, respectively. For the 50 nm nanoparticle size, this enhancement was from 0.7% to 6.5%, and for the 70 nm nanoparticle size, it was from 0.5% to 5.7%. Finally, Artificial Intelligence (AI) optimization was done to find the optimized size and stability.

The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings’ heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R2 and RMSE). Model performance of PSO-MLP is shown by R2 amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R2 amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance.

Today, wound healing is an important clinical problem that is often affected by microbial infection and not paying attention to this problem can cause irreparable damage to people. Biosynthesis of metal nanoparticles (NPs) has created a huge revolution in the field of nanomedicine due to their non-toxic, biocompatible, and stable characteristics. For this purpose, in this study, Petroselinum crispum seed extract (PCS) was applied to preparation of silver nanoparticles (AgNPs@PCS). The optimization of the silver nanoparticle synthesis involved adjusting the concentration of the silver salt, as well as the time and temperature parameters. After identifying the fabricated nanoparticles by FESEM, XRD, FT-IR, UV–Vis, TEM and EDS, their biological activity (like antibacterial, antifungal, antioxidant, anticancer, and wound treatment) was determined. The green synthesis of AgNPs was confirmed by several characteristics, including the surface plasmon response with a peak around 420 nm, the presence of both regular and heterogeneous sizes in the sample, and the observed color change from a clear solution to a brown color. The antimicrobial properties of the biofabricated AgNPs were investigated against fungal and various bacteria. The anticancer property of AgNPs@PCS was demonstrated by an IC50 value of 200 µg/ml after a 24-hour exposure on the MCF-7 cell line. Also, a dose-dependent antioxidant performance of AgNPs was found against the DPPH free radical. Histopathological evaluations of AgNPs@PCS ointment illustrated significant decrease in inflammatory cells. The results showed that vaseline ointment containing AgNPs@PCS prevented inflammation in the wound area and increased the number of fibroblast cells, which led to accelerated wound healing. Furthermore, in-vivo investigation showed the higher percentage of wound closure on days 7 and 14 was than the control group (Vaseline). Interestingly, the wound was completely closed after 21 days. Thus, we concluded that Petroselinum crispum-mediated silver nanoparticles show potential biological activity which can be used as nano-drug in clinical treatment.

In this paper, a compact filtering coupler is presented using a new microstrip configuration. To improve the frequency response, achieve good phase balance and reduce the losses of the proposed coupler, an optimization method is used along with a mathematical analysis method. Our coupler works at 1.9 GHz, which is suitable for 5G and GSM applications. The working frequency range of our designed coupler is from 1.84 GHz to 2.11 GHz, where it has a fractional bandwidth (FBW) of 13.7%. The phase and magnitude imbalances are only $0.029^{o}$ and 0.08 dB, respectively, making it suitable for applications in microwave components, communication and radar systems. The maximum group delays at the direct and coupling ports are 1.9 ns and 2.3 ns, respectively. This is an advantage because most of the previously reported microstrip couplers have not addressed the group delay improvement. The other advantages of our coupler are its filtering frequency response and a good isolation factor of -42 dB around the operating frequency. The proposed structure is mathematically analyzed and optimized. It is fabricated and measured to verify the simulation results and the proposed mathematical analysis. The results show that all mathematical analysis, simulation and measurement results are in good agreement. The experimental results confirm its performance in terms of low losses, low group delay and low phase and magnitude imbalances. Therefore, the presented design offers a significant improvement over the traditional couplers, which generally suffer from the large size, high group delay and poor phase and magnitude balances.

A new microstrip triplexer with a compact size of $0.007~\lambda _{\mathrm {g}}^{2}$ is proposed in this work, where designing a microstrip triplexer with a size less than $0.01~\lambda _{\mathrm {g}}^{2}$ is a big challenge. In addition to the compact size, it shows a high performance i.e. its channels are wide with low insertion losses. Moreover, the channels are flat with three low group delays (GDs) of 0.65, 0.95 and 1.17 ns, while most of the previously reported triplexers have not considered this important parameter. The proposed resonator is mathematically analyzed to find a method to get rid of harmonics and tune the resonance frequency. Meanwhile, the $1^{st}$ , $2^{nd}$ , $3^{rd}$ and $4^{th}$ harmonics are suppressed above the first channel. The designed triplexer is suitable for 5G mid-band applications. The proposed triplexer is exactly fabricated and then measured to confirm the designing method and simulation results. The obtained results show that all the simulation, mathematical analysis and experimental results are in good agreement. Therefore, the proposed triplexer can be easily used in designing high-performance RF communication systems.

Ground vibration induced by blasting operations is considered one of the most common environmental effects of mining projects. A strong ground vibration can destroy buildings and structures, hence its prediction and minimization are of high importance. The aim of this study is to estimate the ground vibration through a hybrid soft computing (SC) method, called RSM-SVR, which comprises two main regression techniques: the response surface model (RSM) and support vector regression (SVR). The RSM-SVR model applies an RSM in the first calibrating process and an SVR in the second calibrating process to improve the accuracy of the ground vibration predictions. The predicted results of an RSM, which are obtained using the input data of problems, are used as the input dataset for the regression process of an SVR. The effectiveness and agreement of the RSM-SVR model were compared to those of an SVR optimized with the particle swarm optimization (PSO) and genetic algorithm (GA), RSM, and multivariate linear regression (MLR) based on several statistical factors. The findings confirmed that the RSM-SVR model was considerably superior to other models in terms of accuracy. The amounts of coefficient of determination (R2) were 0.896, 0.807, 0.782, 0.752, 0.711, and 0.664 obtained from the RSM-SVR, PSO-SVR, GA-SVR, MLR, SVR, and RSM models, respectively.

Symmetry in designing a microstrip coupler is crucial because it ensures balanced power division and minimizes unwanted coupling between the coupled lines. In this paper, a filtering branch-line coupler (BLC) with a simple symmetrical microstrip structure was designed, analyzed and fabricated. Based on a mathematical design procedure, the operating frequency was set at 5.2 GHz for WLAN and 5G applications. Moreover, an optimization method was used to improve the performance of the proposed design. It occupied an area of 83.2 mm2. Its harmonics were suppressed up to 15.5 GHz with a maximum level of −15 dB. Meanwhile, the isolation was better than −28 dB. Another advantage of this design was its high phase balance, where the phase difference between its output ports was 270° ± 0.1°. To verify the design method and simulation results, the proposed coupler was fabricated and measured. The results show that all the simulation, design methods, and experimental results are in good agreement. Therefore, the proposed design can be easily used in designing high-performance microstrip-based communication systems.

The accurate estimation of rock strength is an essential task in almost all rock-based projects, such as tunnelling and excavation. Numerous efforts to create indirect techniques for calculating unconfined compressive strength (UCS) have been attempted. This is often due to the complexity of collecting and completing the abovementioned lab tests. This study applied two advanced machine learning techniques, including the extreme gradient boosting trees and random forest, for predicting the UCS based on non-destructive tests and petrographic studies. Before applying these models, a feature selection was conducted using a Pearson’s Chi-Square test. This technique selected the following inputs for the development of the gradient boosting tree (XGBT) and random forest (RF) models: dry density and ultrasonic velocity as non-destructive tests, and mica, quartz, and plagioclase as petrographic results. In addition to XGBT and RF models, some empirical equations and two single decision trees (DTs) were developed to predict UCS values. The results of this study showed that the XGBT model outperforms the RF for UCS prediction in terms of both system accuracy and error. The linear correlation of XGBT was 0.994, and its mean absolute error was 0.113. In addition, the XGBT model outperformed single DTs and empirical equations. The XGBT and RF models also outperformed KNN (R = 0.708), ANN (R = 0.625), and SVM (R = 0.816) models. The findings of this study imply that the XGBT and RF can be employed efficiently for predicting the UCS values.

Regarding evaluating disaster risks in Iran’s West Kurdistan area, the multi-layer perceptron (MLP) neural network was upgraded with two novel techniques: backtracking search algorithm (BSA) and biogeography-based optimization (BBO). Utilizing 16 landslide conditioning elements such as elevation (aspect), plan (curve), profile (curvature), geology, NDVI (land use), slope (degree), stream power index (SPI), topographic wetness index (TWI), rainfall, and sediment transport index (STI), and 504 landslides as target variables, a large geographic database is constructed. Applying the techniques mentioned above to the synthesis of the MLP results in the suggested BBO-MLP and BSA-MLP ensembles. As accuracy standards, we benefit from mean absolute error, mean square error, and area under the receiving operating characteristic curve to assess the utilized models, we have also designed a scoring system. The MLP’s accuracy increases thanks to the application of the BBO and BSA algorithms. Comparing the BBO with the BSA, we find that the former achieves higher average MLP optimization ranks (20, 15, and 14). A further finding showed that the BBO is superior to the BSA at maximizing the MLP.

Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. Given the importance of the thermal loads’ analysis in energy-efficiency buildings, a new optimizer method, i.e., the teaching–learning based optimization (TLBO) approach, has been developed and compared with alternative techniques in the present paper to predict the heating loads (HLs). This model is applied to the adaptive neuro–fuzzy interface system (ANFIS) in order to overcome its computational deficiencies. A literature-based dataset acquired for residential buildings is used to feed these models. According to the results, all the applied models can appropriately predict and analyze the heating load pattern. Based on the value of R2 calculated for both testing and training (0.98933, 0.98931), teaching–learning-based optimization can help the adaptive neuro–fuzzy interface system to enhance the results’ correlation. Also, the high R2 value means that the model has high accuracy in the HL prediction. In addition, according to the estimated RMSE, the training error of TLBO–ANFIS in the testing and training stages was 0.07794 and 0.07984, respectively. The low value of root–mean–square error (RMSE) indicates that the TLBO–ANFIS method acts favorably in the estimation of the heating load for residential buildings.

This research aims to forecast, using various criteria, the flow of soil erosion that will occur at a particular geographical location. As for the training dataset, 80% of the dataset from the sample sites, four hybrid algorithms, namely heap-based optimizer (HBO), political optimizer (PO), teaching-learning based optimization (TLBO), and backtracking search algorithm (BSA) combined with artificial neural network (ANN) was used to create an erosion susceptibility model and establishes a unique and original approach. After it was confirmed to be successful, the algorithms were applied to create a susceptibility map for this area, demonstrating the integrity of the results. The AUC values were computed for every optimisation algorithm used in this study. The optimal estimated accuracy indices for populations of 450 were determined to be 0.9846 using the BSA-MLP training databases. The maximum AUC value for the HBO-MLP training databases with different swarm sizes was 0.9736. A swarm size of 350-300 is considered optimal for forecasting erosion susceptibility mapping in hybrid models. With the same swarm size constraints, the AUC value for training in the TLBO-MLP scenario was 0.996. After 150 swarm size conditions were used to train the PO-MLP model, the AUC values were 0.9845. According to these findings, the TLBO-MLP and PO-MLP algorithms worked best with populations of 50 and 150, respectively. [ABSTRACT FROM AUTHOR]

The attainment of energy sustainability in the building sector can be realised by implementing a green building programme, which has grown significantly over the last thirty years. Green building is considered a technical and management strategy within the building and construction industries. Many different prediction methods, both complex and simple, have been put out in recent years and used to solve a wide variety of issues. Several case studies have highlighted factors that impede energy and resource usage in green buildings. The utilisation, trends, and consequences of wall and thermal insulation materials are examined. The main scope of this investigation is to predict buildings' heat loss by applying artificial neural networks according to the heat transfer coefficients of walls and coating materials, as well as indoor, outdoor, and external surface temperatures. The data has been normalised and presented to two selected neural networks (Harmony search (HS) and particle swarm optimisation are used and contrasted (PSO)). For evaluating the accuracy of models, two statistical indexes are used (R² and RMSE). Model performance of PSO-MLP is shown by R² amounts of 0.97055 and 0.87381, respectively, and RMSE amounts of 0.02534 and 0.09685. Similarly, HS-MLP model accuracy is also indicated by R² amounts of 0.93839 and 0.84176 and RMSE amounts of 0.03635 and 0.10753. The analysis in this paper shows that PSO-MLP predicts heat loss with higher accuracy and improved performance. [ABSTRACT FROM AUTHOR]