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The efficacy of saving energy standards depends on the ability to anticipate the heat loss of buildings. Environmentally friendly materials, also known as eco-friendly or sustainable materials, have a minimal negative impact on the environment throughout their life cycle. These materials are designed to conserve resources, reduce pollution, and promote sustainability. The characteristics of non-stationary and non-linear heat loss through environmentally friendly materials make it challenging to anticipate accurately. At the same time, many of the industry's presently accessible computational models have been created with this in mind; the majority call for powerful computers and time-consuming computations. The artificial neural network (ANN) has been utilized for prediction, and ground-breaking research has shown the viability of this strategy. This research proposes an artificial neural network (ANN) prototype to estimate construction cooling load usage. ANN is integrated with the vortex search algorithm (VS), stochastic fractal search (SFS), and multi-verse optimizer (MVO) models to compare the three models' outcomes and suggest a more accurate strategy. These techniques make a linear mapping among the output and input parameters, often utilized for modeling and regression. The value of the multiple determination coefficient is also determined. The values of the training R 2 (coefficient of multiple determination) are 0.9464, 0.99827, and 0.99522 for VS-MLP, SFS-MLP, and MVO-MLP, respectively, with an unknown dataset which is acceptable. The training RMSE amounts for VS-MLP, SFS-MLP, and MVO-MLP are 0.06433, 0.00619, and 0.01028 for the unknown dataset, which is acceptable. According to the MAE values of 0.0082902, 0.0047834, and 0.0076534 in the training phase for VS-MLP, SFS-MLP, and MVO-MLP approaches and the values of testing MAE error of 0.029107, 0.018167, and 0.029212 for VS-MLP, SFS-MLP, and MVO-MLP approaches, respectively, it is obtained that the SFS-MLP has a lower MAE value. The lowest RMSE value and the higher R 2 value indicate the favorable accuracy of the SFS-MLP technique., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background and Objective: In this work, using Sisko model, blood flow is simulated inside an artery which have cone shape of stenosis with different angles of φ = 0.25, φ = 0.5, φ = 0.75, φ = 1 and φ = 1.25 degree, respectively., Methods: In the first step, an artery radius of 0.002 m is fixed to study the effects of cone shape of arterial stenosis on the flow behavior. Then, stenosis angle of φ = 0.5 degree is fixed to study the effects of different Artery radii of 0.002 m, 0.0025 m, 0.003m, and 0.0035 m orderly on the flow behavior. For simulation the blood flow, Sisko model is used. Afterward, stenosis angle of φ = 0.5 degrees with a radius of 0.002m is fixed for investigating the influences of different behavior of blood fluid by manipulation of constant parameters of the Sisko model., Results: It is reported that with increasing arterial stenosis angle, maximum blood flow velocity is sharply increased in central region of artery from 0.12 m/s to 0.16 m/s, 0.25 m/s, 0.36 m/s and 0.56 m/s in order of increasing stenosis angles from φ = 0.25 to φ = 0.5, φ = 0.75, φ = 1 and φ = 1.25 degree, respectively. Also, maximum shear stress of artery wall are as much as 64 Pa, 42 Pa, 24 Pa, 18 Pa and 16Pa respectively in order of stenosis angles of φ = 0.25, φ = 0.5, φ = 0.75, φ = 1 and φ = 1.25 degree. On the other side, the effect of increasing artery radius is against the influences of stenosis angle, and contradiction of these parameters is affected by the stress tension and viscosity of blood., Conclusions: Variations of blood behavior from non-Newtonian to Newtonian shows that shear stress in blood stream in the stenosis artery with non-Newtonian blood is higher than that of Newtonian blood due to differences in their viscous behaviors and reactions in exposure of stenosis and artery wall effects., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Background and Objective: In this work, a numerical study is done on the blood flow inside an artery with a cone shape of stenosis. An artery has different stenosis angles. Also, blood flow is energized by constant heat flux which is applied on the wall. The finite volume method is employed to determine blood properties on the basis of a Sisko fluid model with different constant parameters., Methods: Firstly, the effects of applying constant heat flux of q'' = 4 W/m 2 are studied on the velocity profile of blood flow inside an artery with stenosis angles of φ = 0.5°. Afterward, effects of different stenosis angles of φ = 0.25, φ = 0.5, φ = 0.75, φ = 1 and φ = 1.25 is studied on blood flow temperature profile. Then, different values of the Sisko model are employed to investigate influences of Newtonian and non-Newtonian behaviors of blood fluid on the temperature profiles of blood flow inside an artery with stenosis angle of φ = 0.5°., Results: It is reported that with increasing stenosis angles, blood flow temperature is decreased due to velocity enhancement. This phenomenon is convinced by non-slip condition and sticking non-Newtonian blood fluid to the internal surface of artery wall which reduce velocity from wall surface region to central region of vessel. Also, it is concluded that changing behavior of blood fluid from non-Newtonian to Newtonian behaviors can empower the capability of blood in thermal energy transfer inside human body vessels., Conclusions: Any treatment by balloon angioplasty, percutaneous transluminal angioplasty, and serum injection, which changes the behavior of blood flow inside body veins, can be effective on blood flow temperature., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

One of the most studied topics in analytical chemistry and physics is to develop bio-sensors. Aptamers are small single-stranded RNA or DNA oligonucleotides (5-25 kDa), which have advantages in comparison to their antibodies such as physicochemical stability and high binding specificity. They are able to integrate with proteins or small molecules, including intact viral particles, plant lectins, gene-regulation factor, growth factors, antibodies and enzymes. The aptamers have reportedly shown some unique characteristics, including long shelf-life, simple modification to provide covalent bonds to material surfaces, minor batch variation, cost-effectiveness and slight denaturation susceptibility. These features led important efforts toward the development of aptamer-based sensors, known as apta-sensors classified into optical, electrical and mass-sensitive based on the signal transduction mode. This review provided a number of current advancements in selecting, development criteria, and aptamers application with the focus on the effect of apta-sensors, specifically for disease-associated analyses. The review concentrated on the current reports of apta-sensors that are used for evaluating different food and environmental pollutants., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background and Objective: Hemodynamic blood flow analysis in the cerebrovascular is has become one of the important research topics in the bio-mechanic in recent decades. The primary duty of the cerebral blood vessel is supplying Glucose and oxygen for the brain., Methods: In this investigation, the non-Newtonian blood flow in the cerebral blood vessels studied. For modeling the geometry of this problem, we used Magnetic Resonance Image (MRI) approach to take Digital Imaging and Communications in Medicine (DICOM) images and using an open-source software package to construct the geometry, which is a complicated one. The power-law indexes, heat flux, and Reynolds number range in the investigation are 0.6 ≤ n ≤ 0.8, 5 ≤ q ≤ 15Wm -2 and 160≤Re≤310. Effects of Reynolds number, power-law indexes and heat fluxes are investigated., Results: We found that the pressure drop increase with increasing the Reynolds number and power-law index. The maximum Nusselt number in the cerebral blood vessels accrued in the running position of the body in n = 0.8. Also, the highest average wall shear stress occurs in maximum power-law indexes and Reynolds number., Conclusion: By increasing the power-law index and Reynolds number, the wall shear stress increases., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Background and Objective: In this paper, different behaviors of blood flow are simulated inside the artery under applying a constant heat flux on the artery boundary walls., Methods: To simulate the blood flow, the Sisko model is employed. Then, the temperature and Nusselt number of blood flow are reported for different Sisko parameters. Afterward, the effects of different artery radiuses are studied on the Nusselt number., Results: Medical treatment by replenishes fluid and electrolytes in the body vessels can change blood flow properties from non-Newtonian behavior to Newtonian behavior, which increases heat transfer in blood flow and causes to reduce blood flow temperature. In this research, the maximum temperature of Newtonian blood fluid flow is reported as much as 310.0045 K, whereas; maximum flow temperature in non-Newtonian blood fluid is 310.007 K. These results emphasize the effects of the type of Newtonian and non-Newtonian fluid model on the thermal behavior of blood inside body vessels. Since medical science does not permit body temperature to be changed from the normal condition, this small variation can be noticeable and sensible on the health. Hence, medical scientific research centers and institutes of vaccine and serum have to be careful in the mechanical design of drugs for blood fluid., Conclusions: The results of this research show the application of mechanical engineering for some of the medical concerns in designing the drugs which are effective on the behavior of human body blood., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Using single layer microchannels accompanied by nanofluids is one of the most practical solutions in thermal management of high power density devices. The main challenge in cooling systems of electronic devices is to provide a uniform temperature distribution. In the present study, fluid flow and heat transfer in a fractal microchannel heatsink have been simulated employing the computational fluid dynamics (CFD) method. The fractal microchannel is used to achieve uniform temperature distribution. Thermal performance of single-walled carbon nanotubes (SWCNT) and multi-walled carbon nanotubes (MWCNT) dispersed in the two base fluids of water and kerosene in a fractal microchannel at Reynolds (Re) numbers of 1500 to 3000 are investigated. It should be noted that the nanofluids have been simulated by the two-phase mixture model. The results indicated that the use of fractals silicon microchannel leads to having a uniform temperature distribution. Based on the results, at maximum Re number when the working fluid is water, Nu number and pumping power are 20.9 and 0.033 W whereas, in kerosene flow at the same condition, Nu number and pumping power are 6 and 0.054 W, respectively. According to the obtained results, using the SWCNT nanoparticle compared with the MWCNT nanoparticle leads to a significant enhancement in the Nusselt (Nu) number. This difference is more pronounced by increasing the Re number and nanoparticle volume fraction. In addition, the results indicated that at the same Re number and nanoparticle volume fraction, the performance evaluation criterion of the water-based nanofluid is 4 times higher than that of the kerosene-based nanofluid. So the use of the water as the working fluid with the SWCNT nanoparticle for cooling in the fractal silicon microchannel is recommended.

Research and development in the applied sciences at the atomic or molecular level is the order of the day under the domain of nanotechnology or nano-science with enormous influence on nearly all areas of human health and activities comprising diverse medical fields such as pharmacological studies, clinical diagnoses, and supplementary immune system. The field of nano-dentistry has emerged due to the assorted dental applications of nano-technology. This review provides a brief introduction to the general nanotechnology field and a comprehensive overview of the synthesis features and dental uses of nano-materials including current innovations and future expectations with general comments on the latest advancements in the mechanisms and the most significant toxicological dimensions., Competing Interests: There are no conflicts to declare., (This journal is © The Royal Society of Chemistry.)

Forests are important dynamic systems which are widely affected by fire worldwide. Due to the complexity and non-linearity of the forest fire problem, employing hybrid evolutionary algorithms is a logical task to achieve a reliable approximation of this environmental threat. Three fuzzy-metaheuristic ensembles, based on adaptive neuro-fuzzy inference systems (ANFIS) incorporated with genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE) evolutionary algorithms are used to produce the forest fire susceptibility map (FFSM) of a fire-prone region in Iran. A sensitivity analysis is also executed to evaluate the effectiveness of the proposed ensembles in terms of time and complexity. The results revealed that all models produce FFSMs with acceptable accuracy. However, the superiority of the GA-ANFIS was shown in both recognizing the pattern (AUROC train  = 0.912 and Error = 0.1277) and predicting unseen fire events (AUROC test  = 0.850 and Error = 0.1638). The optimized structures of the proposed GA-ANFIS and PSO-ANFIS ensembles could be good alternatives to traditional forest fire predictive models, and their FFSMs can be promisingly used for future planning and decision making in the proposed area., Competing Interests: Declaration of competing interest The authors declare no conflict of interest., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In the current paper, the main aim is to fabricate the BaMnO 3 nanostructures via the sonochemical route. The various factor, including precursors, reaction time and power of sonication can affect the shape, size, and purity of the samples. We utilized X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), Fourier transform infrared spectroscopy (FT-IR), and X-ray energy dispersive spectroscopy (EDS) to characterize the BaMnO 3 nanostructures. The optical property of BaMnO 3 nanostructures was explored by Ultraviolet-visible spectroscopy (UV-vis) and the energy gap was suitable for catalytic activity (about 2.75 eV). Changing the precursor can affect the size, nanoparticle shape, architectures, and uniformity of the samples. We employed the BaMnO 3 nanostructures for O 2 evolution reaction as catalysts. It can observe that increasing the homogeneity of the catalysts can increase the efficiency of the Oxygen evolution reaction. The maximum amount of the O 2 evolution and the highest TOF and TON are related to nanoplate disc using barium salicylate as a precursor of barium. As a result, we can nominate the BaMnO 3 nanostructures as an effective and novel catalyst for water-splitting reaction., (Copyright © 2019 Elsevier B.V. All rights reserved.)

In the present work, LaFeO 3 perovskite was prepared via ultrasonic probe with power of 60 W and frequency of 18 KHz. LaFeO 3 nanorods were formed when sonication time was 20 min. In this research, green materials including corn, starch, and rice were used to control the size, morphology, and purity of final products. As-prepared LaFeO 3 nanostructures were used to purify water containing organic contaminants. LaFeO 3 nanostructures prepared by using corn, starch, and rice showed higher photocatalytic activity compare to LaFeO 3 nanostructures without natural capping agents. Using corn increased degradation efficiency by 65% under visible light. XRD results show that Fe 2 O 3 appeared as an impurity when starch was used to prepare LaFeO 3 nanostructures. This impurity significantly boosts the degradation efficiency under UV light. Fe 2 O 3 under UV light act as co-absorbent and boost efficiency by 43%. LaFeO 3 nanostructures were characterized by XRD, EDX, SEM, CV, BET, TEM, DRS and FT-IR., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Regular optimization techniques have been widely used in landslide-related problems. This paper outlines two novel optimizations of artificial neural network (ANN) using grey wolf optimization (GWO) and biogeography-based optimization (BBO) metaheuristic algorithms in the Ardabil province, Iran. To this end, these algorithms are synthesized with a multi-layer perceptron (MLP) neural network for optimizing its computational parameters. The used spatial database consists of fourteen landslide conditioning factors, namely elevation, slope aspect, land use, plan curvature, profile curvature, soil type, distance to river, distance to road, distance to fault, rainfall, slope degree, stream power index (SPI), topographic wetness index (TWI) and lithology. 70% of the identified landslides are randomly selected to train the proposed models and the remaining 30% is used to evaluate the accuracy of them. Also, the frequency ratio theory is used to analyze the spatial interaction between the landslide and conditioning factors. Obtained values of area under the receiver operating characteristic curve, as well as mean square error and mean absolute error showed that both GWO and BBO hybrid algorithms could efficiently improve the learning capability of the MLP. Besides, the BBO-based ensemble surpasses other implemented models.