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Brain-derived neurotrophic factor (BDNF) belongs to the family of neurotrophic factors that can potentially increase cancer cell growth, survival, proliferation, anoikis, and migration by tyrosine kinase receptors TrkB and the p75NTR death receptor. The activation of BDNF/TrkB pathways leads to several downstream signaling pathways, including PI3K/Akt, Jak/STAT, PLCγ, Ras-Raf-MEK-ERK, NF-kB, and transactivation of EGFR. The current review aimed to provide an overview of the role of BDNF and its signaling in cancer.We searched a major medical database, PubMed, to identify eligible studies for a narrative synthesis.Pathological examinations demonstrate BDNF overexpression in human cancer, notably involving the prostate, lung, breast, and underlying tissues, associated with a higher death rate and poor prognosis. Therefore, measurement of BDNF, either for identifying the disease or predicting response to therapy, can be helpful in cancer patients. Expression profiling studies have recognized the role of microRNAs (miR) in modulating BDNF/TrkB pathways, such as miR-101, miR-107, miR-134, miR-147, miR-191, miR-200a/c, miR-204, miR-206, miR-210, miR-214, miR-382, miR-496, miR-497, miR-744, and miR-10a-5p, providing a potential biological mechanism by which targeted therapies may correlate with decreased BDNF expression in cancers. Clinical studies investigating the use of agents targeting BDNF receptors and related signaling pathways and interfering with the related oncogenic effect, including Entrectinib, Larotrectinib, Cabozantinib, Repotrectinib, Lestaurtinib, and Selitrectinib, are in progress.The aberrant signaling of BDNF is implicated in various cancers. Well-designed clinical trials are needed to clarify the BDNF role in cancer progression and target it as a therapeutic method.

Glioblastoma (GBM) is the most frequent malignancy among primary brain tumors in adults and one of the worst 5-year survival rates (7%) among all human cancers. Till now, treatments that target particular cell or intracellular metabolism have not improved patients' survival. GBM recruits healthy brain cells and subverts their processes to create a microenvironment that contributes to supporting tumor progression. This microenvironment encompasses a complex network in which malignant cells interact with each other and with normal and immune cells to promote tumor proliferation, angiogenesis, metastasis, immune suppression, and treatment resistance. Communication can be direct via cell-to-cell contact, mainly through adhesion molecules, tunneling nanotubes, gap junctions, or indirect by conventional paracrine signaling by cytokine, neurotransmitter, and extracellular vesicles. Understanding these communication routes could open up new avenues for the treatment of this lethal tumor. Hence, therapeutic approaches based on glioma cells` communication have recently drawn attention. This review summarizes recent findings on the crosstalk between glioblastoma cells and their tumor microenvironment, and the impact of this conversation on glioblastoma progression. We also discuss the mechanism of communication of glioma cells and their importance as therapeutic targets and diagnostic and prognostic biomarkers. Overall, understanding the biological mechanism of specific interactions in the tumor microenvironment may help in predicting patient prognosis and developing novel therapeutic strategies to target GBM.

Abstract: Breast cancer [BC] is one of the most common cancers among women, one of the leading causes of a considerable number of cancer-related death globally. Among all procedures leading to the formation of breast tumors, angiogenesis has an important role in cancer progression and out-comes. Therefore, various anti-angiogenic strategies have been developed so far to enhance treat-ment's efficacy in different types of BC. Vascular endothelial growth factors [VEGFs] and their re-ceptors are regarded as the most well-known regulators of neovascularization. VEGF binding to vas-cular endothelial growth factor receptors [VEGFRs] provides cell proliferation and vascular tissue formation by the subsequent tyrosine kinase pathway. VEGF/VEGFR axis displays an attractive tar-get for anti-angiogenesis and anti-cancer drug design. This review aims to describe the existing litera-ture regarding VEGFR inhibitors, focusing on BC treatment reported in the last two decades.

Wear of cutting tools has a major impact on production cost, quality, and efficiency of the machining processes. Tool wear depends on many parameters including cutting parameters and conditions, tool geometry and materials (coating and base materials), and the workpiece material. This study examines and compares the performance of three state-of-the-art milling tools for high-speed end milling while cutting the same material, stainless steel. The tools have the same base material (tungsten carbide — WC-Co), with different geometrical parameters and coatings (TiAlN and AlCrN). Systematic microscopic analysis and finite element (FE) simulations are used to study mechanisms of damage at the cutting edge. Microscopic analyses show that the flank wear is the most critical damage mechanism at the cutting edge. Having the highest material removal rate, the MT-1 tool experiences cutting edge wear faster among the studied tools with a maximum wear size of 420 μm. This tool ran with a radial depth of cut (ae) equal to 0.96 mm and feed per tooth (fz) of 0.15 mm/tooth, which are maximum values among all the tools. The maximum tool stresses from the FE simulations are obtained equal to 1267, 920, and 1145 MPa for MT-1 (ae of 0.96 mm and fz of 0.15), MT-2 (ae of 0.48 mm and fz of 0.12), and MT-3 (ae of 0.6 mm and fz of 0.15) tools, respectively. This indicates that the radial depth of cut and feed per tooth are the key parameters dictating stresses and degree of wear at the cutting edge.

The process of material cutting emerges from a series of nonlinear phenomena including frictional contact, plastic deformation, and fracture. While cutting dominated by shear deformation is of interest to achieve a smooth material removal and a high-quality surface finish, the fracture-induced chip breaking is of equal importance to prevent the formation of long chips. Here we show that discrepant observations and predictions of these two distinct cutting mechanisms can be reconciled into a unified framework. A simple analytical model is developed to predict the mechanism of chip formation in a homogeneous medium as a function of work piece intrinsic material properties, tool geometry, and the process parameters. The model reveals the existence of a critical depth of cut, below which the chip formation is gradually progressed by plastic deformation in the shear plane, and above which chips break off by abrupt crack propagation. The models’ prediction is validated by systematic in situ orthogonal cutting experiments and literature data for a wide range of materials over multiple length scales.

The dynamic load in tubes due to detonation has a number of applications, such as in oil pipeline systems and pulse detonation engines. Various experimental, analytical, and numerical investigations have been conducted to study the mechanical, thermo-mechanical, and fracture behavior of tubes under internal detonation loads. Regarding numerical analysis, different approaches such as interface cohesive element, mesh-free, and extended finite element methods have been used to model the propagation of crack(s) in a tube. This paper presents a review of relevant literature pertaining to numerical and experimental analyses of detonation-driven deformation and fracture, and of studies based on the analytical investigation of moving loads in detonation tubes. The corresponding findings are discussed in detail, and possible avenues for future research are highlighted.

During 2014-2017 Clade 2.3.4.4 H5N8 highly pathogenic avian influenza viruses (HPAIVs) have spread worldwide. In 2016, an epidemic of HPAIV H5N8 in Iran caused mass deaths among wild birds, and several commercial poultry farms and captive bird holdings were affected and continue to experience problems. Several outbreaks were reported in 2017. One of them is related to Hooded crow (Corvus cornix) in a national park in Esfahan province in 2017. Whole genome sequencing and characterization have been done on the detected H5N8 sample. Based on HA sequencing results, it belongs to 2.3.4.4 clade, and the cleavage site is (PLREKRRKR/G). Phylogenetic analysis of the HA gene showed that the Iran 2017 H5N8 virus clustered within subgroup Russia 2016 2.3.4.4 b of group B in H5 clade 2.3.4.4 HPAIV. On the other hand, the NA gene of the virus is placed in group C of Eurasian lineage. Complete genome characterization of this virus revealed probable reassortment of the virus with East-Asian low-pathogenic influenza viruses. Furthermore, the virus possessed some phenotypic markers related to the increased potential for transmission and pathogenicity to mammals at internal segments. This study is the first full genome characterization H5N8 HPAIV in Iran. The data complete the puzzle of molecular epidemiology of H5N8 HPAIV in Iran and the region. Our study provides evidence for fast and continuing reassortment of H5 clade 2.3.4.4 viruses, that might lead to changes in virus structural and functional characteristics such as the route and method of transmission of the virus and virus infective, pathogenic and zoonotic potential.

The solar power tower system (SPTS) has been successfully demonstrated as a favorable candidates to replace fossil fuel energy systems. Although several investigations have been developed to assess the SPTS, the detailed, fundamentals-based, long-term transient simulation capability is limited. Research presented in this chapter is intended to indicate how artificial intelligence (AI) techniques may play an important role to address this need. For this purpose, a method of AI is developed to simulate a solar power tower direct steam (SPTDS) system. As a matter of fact, this hybrid method is developed through the combination of adaptive neurofuzzy inference system with biogeography-based optimization algorithm. The power losses from the receiver, power absorbed by the receiver, receiver thermal efficiency, field optical focus fraction, field optical efficiency, and the cycle electrical power output are simulated and appraised through the intelligent method. A set of input parameters including the meteorological data, solar angles, central receiver features, and heliostat deploy angle are selected as independent variables for sensitivity analysis. The results depict that the intelligent model can successfully recognize the intricate relationship between parameters to predict the targets in a SPTDS system.

One of the cleanest and most accessible energy sources is solar energy. However, finding efficient ways to receive this energy source and convert it to electrical power for different applications is still a challenging problem. A parabolic trough solar collector (PTSC) is a type of concentrating solar technology which can be employed for producing electricity and heating simultaneously, which is one of the efficient techniques to produce electrical power from solar energy. After several decades of research, development, and construction of different types of PTSCs, the most recent studies focus on improving the efficiencies of these systems, both from structural/material (solid mechanics) and heat transfer (fluid mechanics) aspects. Improving the PTSC performance has led to enhancement in the electrical energy production. From a heat transfer point of view, there have been many approaches proposed to reach a better efficiency for the PTSC system, such as nanofluids, internal flow inserts, and an external magnetic field acting on the magnetic nanofluid. This chapter discusses PTSCs with detailed explanations on the corresponding heat transfer formulations, and at the end, a numerical example is being analyzed using the ANSYS FLUENT software.

Ovarian Cancer (OC), as the seventh most common cancer throughout the world, is one of the most lethal gynecologic malignancies in women. In addition to well-studied risk and prognostic factors of OC, such as age, obesity, stage, histology, etc., ABO blood groups associations with OC has been attracted the investigators’ focus in the recent decade. The objective of this study is to find any relationships between ABO blood groups and OC through a meta-analysis. The databases of PubMed, Embase, and Web of Science were systematically searched by available MeSh terms for “Ovarian Cancer”, and “ABO blood group” up to August 1, 2020. Hazard ratio (HR) or odds ratio (OR) were extracted and used from studies for outcome estimation whenever appropriate with 95% confident interval (CI). Data were analyzed using CMA v. 2.2.064. Fourteen papers, including 58,193 participants, entered in the systematic review. The meta-analysis indicated a substantially higher risk of developing OC in individuals with blood group A (OR 1.17, 95% CI 1.01–1.36) and AB (OR 1.27, 95% CI 1.02–1.59), but the subject was not significant in individuals with blood group B (OR 1.11, 95% CI 0.81–1.51). No associations were found between OC overall survival and blood group A (HR 1.14, 95% CI 0.79–1.64), AB (HR 1.00, 95% CI 0.69–1.45), and B (HR 0.88, 95% CI 0.58–1.33). Recurrence rates of OC for blood groups A, AB, B, and O were 30.8%, 34.3%, 33.6%, and 32.0%, respectively. Rates of mortality in different blood groups are as follows: A (31.8%), AB (31.1%), B (26.2%), and O (33.8%). This systematic review and meta-analysis revealed a considerably increased risk of developing OC among individuals with blood group A and AB. However, no substantial impacts were found for ABO blood groups on the overall survival of the OC patients.

Generally, the majority of the linear concentrating photovoltaic (LCPV) systems incorporated with active cooling systems use liquid water pump loop or conventional vapor compression refrigeration system. In this study, a new cooling system, so-called two-phase mechanical pumped loop (TMPL), is proposed to be used with LCPV system. The excess heat from photovoltaic (PV) cell is used to heat up water stored in a tank for residential purposes. The case study is Bogota in Colombia and we develop a dynamic simulation model for the LCPV-TMPL system. The design parameters of the TMPL system are the flow volumetric rate and saturation temperature of the refrigerant as well as the tube length of the condenser. Moreover, low global warming potential (GWP) refrigerants such as R1234yf and R1234ze(E) were evaluated, both showing the same performance compared to R134a. The results showed that the use of TMPL system to eliminate the generated heat of PV cell improves its temperature stability and efficiency. It also shows how the proposed model can be used to design the thermal/photovoltaic system for a local, showing also the expected performance before the system installation. In the case study region, the LCPV-TMPL system, using four PV cells with 5 m × 10 mm, produces power with an average monthly of 2 kW with a peak of 5 kW under average and peak monthly solar radiation of 400 W/m2 and 600 W/m2, respectively. The storage tank can heat up 2.2 m³ water per day from 8 °C to approximately 28 °C, in an average sense. This means that the LCPV-TMPL system could save 9000 kWh and 1900 kWh per year in electricity and thermal energies (water heating). This system can be used locally with low or medium solar radiation and cold weather.

Background: The outbreak of the coronavirus disease-2019 (COVID-19) has become a global public health challenge. Assessing the effect of COVID-19 on liver injury is of great importance. A systematic review and meta-analysis were conducted to establish the characteristics of liver function tests in COVID-19 patients. Methods: A systematic search of publications from December 2019 up to April 2020 in Web of Science, Scopus, and Medline (via PubMed) databases was performed. Both cross-sectional and case series studies reporting an association between liver injury and COVID-19 infection were included. The data were analyzed using the STATA software (version 11.0) and the random-effects model for I2>50% was used to pool the results. Results: In this meta-analysis, 42 articles comprising a total of 6,557 COVID-19 patients were studied. The prevalence of increase in alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels was 30% and 21% in non-severe patients and 38% and 48% in severe patients, respectively. Patients with severe COVID-19 infection were 4.22, 4.96, and 4.13 times more likely to have elevated AST, ALT, and lactate dehydrogenase (LDH) levels, respectively. Conclusion: Elevation in liver function tests was higher in patients with severe than non-severe COVID-19 infection. Given the widespread use of drugs that increases the risk of hepatotoxicity, healthcare providers should be aware of changes in liver enzymes in COVID-19 patients. The inclusion of other studies from outside China could confirm the pattern of elevation in liver function tests in COVID-19 patients across the globe. Preprint of this article is available on medRxiv, https://www.medrxiv.org/content/10.1101/2020.05.20.20108357v1

BackgroundEvidence recommends that vitamin D might be a crucial supportive agent for the immune system, mainly in cytokine response regulation against COVID-19. Hence, we carried out a systematic review and meta-analysis in order to maximize the use of everything that exists about the role of vitamin D in the COVID-19.MethodsA systematic search was performed in PubMed, Scopus, Embase, and Web of Science up to December 18, 2020. Studies focused on the role of vitamin D in confirmed COVID-19 patients were entered into the systematic review.ResultsTwenty-three studies containing 11901participants entered into the meta-analysis. The meta-analysis indicated that 41% of COVID-19 patients were suffering from vitamin D deficiency (95% CI, 29%-55%), and in 42% of patients, levels of vitamin D were insufficient (95% CI, 24%-63%). The serum 25-hydroxyvitamin D concentration was 20.3 ng/mL among all COVID-19 patients (95% CI, 12.1-19.8). The odds of getting infected with SARS-CoV-2 is 3.3 times higher among individuals with vitamin D deficiency (95% CI, 2.5-4.3). The chance of developing severe COVID-19 is about five times higher in patients with vitamin D deficiency (OR: 5.1, 95% CI, 2.6-10.3). There is no significant association between vitamin D status and higher mortality rates (OR: 1.6, 95% CI, 0.5-4.4).ConclusionThis study found that most of the COVID-19 patients were suffering from vitamin D deficiency/insufficiency. Also, there is about three times higher chance of getting infected with SARS-CoV-2 among vitamin D deficient individuals and about 5 times higher probability of developing the severe disease in vitamin D deficient patients. Vitamin D deficiency showed no significant association with mortality rates in this population.

Since 1998, Iran’s poultry industry has faced several outbreaks of low pathogenic avian influenza H9N2. Tissue samples were collected from a broiler flock with respiratory symptoms in autumn 2017. After that, virus isolation and confirmation of H9N2 using RT-PCR, sequencing, and bioinformatic analysis for all eight genes were performed. The phylogenic analysis revealed HA gene of recent Iranian isolate (A/chicken/Mashhad/UT-Barin/2017) which was clustered in G1 sublineage. In addition, all eight genes of the virus were placed with Pakistani isolates of 2015 in separate group. Based on amino acid motif KSSR in HA cleavage site, the UT-Barin is considered as low pathogenic avian influenza with eight HA and seven NA potential N-glycosylated sites. No evidence was detected regarding adamantane and neuraminidase inhibitors’ drug’s resistance. Multiple point mutations were observed in all genes that were responsible for increasing virulence of the virus for avian host and also increasing affinity to mammalian host cells.

Introduction:The outbreak of new coronavirus has become a global public health challenge. Given a consequential liver function, and the high risk of death coming from liver disorders, the assessment of Novel Coronavirus Disease on liver function is importance. Hence, we carried out this meta-analysis to heightening insight into the occult features of COVID 19, which is likely to affect liver function. Method:This study was performed using databases of Web of Science, Scopus, and PubMed. We considered English cross-sectional and case-series papers, which reported available findings on the association between liver injury and COVID-19 infection. We used the STATA v.11 and random effect model for data analysis. Result:In this present meta-analysis, 52 papers, including 8,463 COVID-19 patients, were studied. The prevalence of increased liver enzymes among the patients, including Alanine aminotransferase, Aspartate aminotransferase, were 30% and 21% in non-severe patients, respectively, which were 38% and 48% in severe patients. The prevalence of increasing C-reactive protein, Lactate dehydrogenase, D-dimer, and Bilirubin were 55%, 39%, 28%, and 10% in non-severe patients respectively, which were 78%, 75%, 79% and 17% in sever patients.The prevalence of liver toxicity as a complication of COVID-19 was 20%.Also patients who have severe condition are 5.54, 4.22, 4.96, 4.13 and 4.34 times more likely to have elevated CRP, ALT, AST, LDH, D-dimer enzymes retrospectively. Conclusion:Elevation of some liver markers were higher in patients with severe COVID-19 infection. All to gather, we assumed that abnormal liver markers could act as a prognostic factor for a better survey of COVID-19.

A parabolic trough is defined as a type of solar thermal collector that is straight in one dimension and curved as a parabola in the other two, lined with a polished metal mirror. Enhancing the thermal efficiency of this collectors is one of the major challenges of developing and growing of parabolic trough solar thermal power plants. Ferrofluids were proposed as a novel working fluid for industrial applications, due to their thermal performances. In this study, the convective heat transfer of Fe3O4-Therminol 66 ferrofluid under magnetic field (0-500 G) is evaluated using computational fluid dynamics. The ferrofluid with different volume fraction (1-4%) and the Therminol 66 (as the base fluid) are considered as the working fluids for a parabolic trough solar collector. Numerical analysis first validated using theoretical results, and then a detailed study is conducted in order to analyze the effect of the magnetic field on different parameters. The result demonstrated that using magnetic field can increase the local heat transfer coefficient of the collector tube, thermal efficiency as well as output temperature of the collector. In addition, increasing the volume fraction of nanoparticle in the base fluid and intensity of magnetic field increased the collector performance. (C) 2018 Elsevier Ltd. All rights reserved.

Generalized or extended finite element method (G/XFEM) uses enrichment functions that holds a priori knowledge about the problem solution. These enrichment functions are mostly limited to two-dimensional problems. A well-established solution for problems without any specific types of analytically derived enrichment functions is to use numerically-build functions in which they are called global-local enrichment functions. These functions are extracted from the solution of boundary value problems defined around the region of interest discretized by a fine mesh. Such solution is used to enrich the global solution space through the partition of unity framework of the G/XFEM. Here it is presented a two-scale/global-local G/XFEM approach to model crack propagation in plane stress/strain and Reissner–Mindlin plate problems. Discontinuous functions along with the asymptotic crack-tip displacement fields are used to represent the crack without explicitly represent its geometries. Under the linear elastic fracture mechanics approach, the stress intensity factor (obtained from a domain-based interaction energy integral) can be used to either determine the crack propagation direction or propagation status, i.e., the crack can start to propagate or not. The proposed approach is presented in detail and validated by solving several linear elastic fracture mechanics problems for both plane stress/strain and Reissner–Mindlin plate cases to demonstrate its the robustness and accuracy.

Ferrofluid is defined as a magnetic fluid which is composed of magnetic nanoparticles immersed in the base fluid such as water and oil. Nanofluids under magnetic field were proposed as a novel working fluid for industrial applications. In this study, the convective heat transfer of Fe3O4/water ferrofluid under constant magnetic field is evaluated. For this purpose, computational fluid dynamics (CFD) simulation and adaptive neuro-fuzzy inference system optimized with particle swarm optimization (ANFIS-PSO) are applied. To develop the ANFIS-PSO model, inlet temperature of ferrofluid, volume fraction of nanoparticle (Fe3O4), Reynolds number and intensity of magnetic field are considered as input variables of the model and heat transfer coefficient (HTC) of Fe3O4/water ferrofluid is considered to be the target. The results demonstrated that the developed ANFIS-PSO model can successfully predict the HTC of ferrofluid in laminar and turbulent flows in terms of the correlation coefficient (R), root mean square error (RMSE) and mean absolute percentage error (MAPE) respectively with 0.9992, 117.19 (W/m2K) and 2.44% for testing phase of the network. Also, CFD simulation and ANFIS-PSO model illustrated that the amount of the HTC of ferrofluid increases by increasing in intensity of magnetic field and inlet temperature of ferrofluid.

Fatigue is a process in engineering materials in which damage accumulates due to the fluctuating loading. One solution for a component under the fatigue process is to arrest the crack propagation before the final failure using different available retardation methods, such as drilling/stop-hole technique. In addition, structural components may also suffer from the existence of micro-cracks or voids due to their forming process or service lives. These micro-cracks/voids are very critical to study, since they can effectively play an important role in the behavior of the existing main crack in a component. This article aims to investigate the effect of the stop-hole retardation technique and multiple micro-cracks/voids with different characteristic lengths and geometries on the fatigue crack propagation in a compact tension specimen. A modified Forman equation, the so-called NASGRO equation is used to define the transition between crack initiation and crack growth period. Also, the extended finite element method is adapted in the crack propagation phase in order to model crack path in the geometry eliminating the need for remeshing procedure. The whole analyses are conducted in a commercial package through a user-written code that handles all fatigue crack growth analysis. The reference solutions from the literature are used to compare and to validate results obtained from current work.

One of the main issues with the cylindrical tubes is the propagation of cracks during their operational life wherein most of the cracks happen along the axial direction of the tube. This paper reports a 3D finite element simulation of the mechanical responses of an aluminum tube containing a longitudinal crack repaired with composite patches . After several loading-unloading cycles of the dynamic load, mechanical stresses at the crack tips may result in unstable crack propagation . Therefore, it is necessary to use a repair process to prevent the structure from final rupture. Glass/epoxy and boron/epoxy composite patches, with different number of patch layers, are considered in the analysis of the composite material repair effect on the crack tips stress distribution, evaluating the possibilities of crack propagation and patch delamination onset. Numerical simulations were partially compared with literature results to verify the modeling procedure, while a parametric study on the patch thickness was performed to verify the repair effectiveness. In general, boron and longer (140 mm) repair patches presented better performance than glass and shorter (70 mm) patches. Although thinner patches (2 layers) do not present the required resistance, thicker patches (16 layers) may precipitate delamination onset and thus should be avoided.

Generalized or extended finite element method (G/XFEM) models the crack by enriching functions of partition of unity type with discontinuous functions that represent well the physical behavior of the problem. However, this enrichment functions are not available for all problem types. Thus, one can use numerically-built (global-local) enrichment functions to have a better approximate procedure. This paper investigates the effects of micro-defects/inhomogeneities on a main crack behavior by modeling the micro-defects/inhomogeneities in the local problem using a two-scale G/XFEM. The global-local enrichment functions are influenced by the micro-defects/inhomogeneities from the local problem and thus change the approximate solution of the global problem with the main crack. This approach is presented in detail by solving three different linear elastic fracture mechanics problems for different cases: two plane stress and a Reissner–Mindlin plate problems. The numerical results obtained with the two-scale G/XFEM are compared with the reference solutions from the analytical, numerical solution using standard G/XFEM method and ABAQUS as well, and from the literature.

Purpose This paper aims to present a computational framework to generate numeric enrichment functions for two-dimensional problems dealing with single/multiple local phenomenon/phenomena. The two-scale generalized/extended finite element method (G/XFEM) approach used here is based on the solution decomposition, having global- and local-scale components. This strategy allows the use of a coarse mesh even when the problem produces complex local phenomena. For this purpose, local problems can be defined where these local phenomena are observed and are solved separately by using fine meshes. The results of the local problems are used to enrich the global one improving the approximate solution. Design/methodology/approach The implementation of the two-scale G/XFEM formulation follows the object-oriented approach presented by the authors in a previous work, where it is possible to combine different kinds of elements and analyses models with the partition of unity enrichment scheme. Beside the extension of the G/XFEM implementation to enclose the global–local strategy, the imposition of different boundary conditions is also generalized. Findings The generalization done for boundary conditions is very important, as the global–local approach relies on the boundary information transferring process between the two scales of the analysis. The flexibility for the numerical analysis of the proposed framework is illustrated by several examples. Different analysis models, element formulations and enrichment functions are used, and the accuracy, robustness and computational efficiency are demonstrated. Originality/value This work shows a generalize imposition of different boundary conditions for global–local G/XFEM analysis through an object-oriented implementation. This generalization is very important, as the global–local approach relies on the boundary information transferring process between the two scales of the analysis. Also, solving multiple local problems simultaneously and solving plate problems using global–local G/XFEM are other contributions of this work.

Determining the optimal sizing of a solar power tower system (SPTS) with a thermal energy storage system is subject to finding the optimum values of design parameters including the solar multiple (SM), design direct normal irradiance (DNI) and thermal storage hours. These design parameters are determined for each station separately and have remarkable effects on the thermo-economic performance of the system. This paper aims to demonstrate how artificial intelligence (AI) techniques may play an important role in addressing the above-mentioned need and help determine the optimum design parameters for different stations. For this purpose, we developed a thermo-economic model of a 100 MW SPTS with a molten salt storage system for five stations (two stations in India, and one each in Bangladesh, Pakistan, and Afghanistan). A method-based AI is utilized in this paper to ascertain the design parameters of the system. Additionally, a novel hybrid method based on adaptive neuro-fuzzy inference system optimized with a combination of genetic algorithm and teaching-learning-based optimization algorithm (ANFIS-GATLBO) is employed. The input parameters are latitude, longitude, design point DNI and SM, while the annual energy produced, levelized cost of energy and capacity factor are the target variables. The results of the study show that although the annual energy produced by SPTS rises by increasing the SM and decreasing design point DNI, optimum design parameters should be determined by the economic factors. In addition, it was found that the ANFIS-GATLBO method used in this study successfully predicted the targets with a correlation coefficient close to 1.

Dynamic load in tubes due to detonation has plenty of applications such as the oil pipeline system and pulse detonation engine. Various experimental, analytical, and numerical investigations have been made to study the elastic-dynamic behaviors of tubes under internal moving pressure, with few studies dedicated to elastic–plastic analysis of this kind of problem. The investigation of elastic–plastic response of a tube made of homogeneous and nonhomogeneous (functionally graded material—FGM) materials subjected to the internal moving pressure (gaseous detonation) is the specific objective of this study. FGM is a newly well-established type of composite materials that its material contents are varying gradually along a user-defined direction. In this work, the effect of non-homogeneity on the vibrational response of the tube was studied using an axisymmetric FE model, which developed by Abaqus® commercial package accompanied by VUMAT, VDLOAD, and VUSDFLD user-written subroutines. Different detonation load velocities were considered based on the tube material properties, i.e., according to the critical velocities. The presented results showed that the variation of stress/strain fields against time was affected by the gradation of FGM material. In addition, FGM material can help to withstand against the detonation loads, specifically when there are multiple sequential detonations or a high-pressure detonation load.

Solar energy is among the cleanest and most adaptable compared to other renewable energy sources. The major challenge is how to get this energy in efficient way to make it available for industrial applications such as electricity generation. One of the most efficient techniques to harvest solar energy and transform it into electrical energy is parabolic trough solar collector (PTSC), which is a type of concentrating solar power generation system. This system operates by concentrating solar irradiance onto a tubular receiver in which this centralized energy is absorbed by a heat transfer fluid and transported to the power cycle. Improving the performance of the PTSC can enhance efficiency as well as power generation of a PTS power plant. Hence, this issue has been considered as one of the major challenges for scholars in this field. One promising solution is finding more efficient heat transfer working fluids. In recent years, ferrofluids due to their heat transfer characteristics are proposed as working fluids for engineering systems. In this research, Fe3O4/Therminol 66 and CuO/Therminol 66 nanofluids as working fluids for a PTSC are proposed and appraised. The repercussion of the magnetic field on the thermal efficiency of the collector is carried out using computational fluid dynamics (CFD). To improve the heat transfer characteristics of the collector, receiver is designed with internal fins. This assessment is done by considering the different nanoparticle sizes on the friction factor, thermal efficiency, performance evaluation criteria (PEC) and convective heat transfer. The results depict that reducing the particle size and enhancing the nanoparticles volume fraction increase the convective heat transfer coefficient, Nusselt number, PEC and the collector efficiency. In addition, the collector efficiency rises in the attendance of the magnetic field and maximum efficiency of the collector was obtained for 4% Fe3O4/Therminol 66 working fluid.

Compressed air energy storage (CAES) is a hopeful technology to overcome the intermittency of renewable energy systems and meet the high peak load demand. The objective of this study is to propose a double pipe heat exchanger (DPHX) working with CuO/water nanofluid in order to cool the compressed air before cavern in a small-scale CAES system. A new design of DPHX by considering different internal tube geometry (nine configurations) is proposed. To achieve these targets, a transient model for simulating the technical demeanor of the CAES system is developed. After simulating the behavior of the CAES system, DPHX is modeled by computational fluid dynamics (CFD) to evaluate the outcome of nanofluid as well as geometry design on the DPHX performance. The pressure drop is unchanged for all finned tube at higher Reynolds numbers. The numerical analysis through mathematical modeling of the charging process of the cavern denotes the effect of length and mass flow rate of the secondary fluid in the DPHX. The results illustrate that by enhancing the mass flow of the secondary fluid, the cavern temperature declines. The pressure inside the cavern has a small dependence on its temperature. The cavern pressure is invariant by increasing the secondary fluid flow. For proposed DPHX, the convective heat transfer coefficient increased up to 22% for cold fluid considering tube with four fins (air/nanofluid + finned tube (w = 3.5 mm and H = 1.0 mm)) and compared to the smooth tube. In addition, around 17% enhancement in convective heat transfer coefficient was achieved using tube with eight fins and with air/nanofluid as the working fluid (case with w = 3.5 mm and H = 1.0 mm), compared to tube with four fins. This shows the capability of the proposed finned tube along with the utilization of the nanofluid to increase the heat exchanger performance.

In this study, thermal resistance of a closed-loop oscillating heat pipe (OHP) is investigated using experimental tests and artificial intelligence methods. For this target, γFe2O3 and Fe3O4 nanoparticles are mixed with the base fluid. Also, intelligent models are developed to predict the thermal resistance of the OHP. These models are developed based on the heat input into evaporator section, the thermal conductivity of working fluids, and the ratio of the inner diameter to length of OHP. The intelligent methods are multilayer feed-forward neural network (MLFFNN), adaptive neuro-fuzzy inference system (ANFIS) and group method of data handling (GMDH) type neural network. Thermal resistance of the heat pipe (as a measure of thermal performance) is considered as the target. The results showed that using the nanofluids as working fluid in the OHP decreased the thermal resistance, where this decrease for Fe3O4/water nanofluid was more than that of γFe2O3/water. The intelligent models also predicted successfully the thermal resistance of OHP with a correlation coefficient close to 1. The root-mean-square error (RMSE) for MLFFNN, ANFIS, and GMDH models was obtained as 0.0508, 0.0556, and 0.0569 (°C/W) (for the test data), respectively.

Adaptation to the loss of O2 is regulated via the activity of hypoxia-inducible factors such as Hypoxia-Inducible Factor-1 (HIF-1). HIF-1 acts as a main transcriptional mediator in the tissue hypoxia response that regulates over 1000 genes related to low oxygen tension. The role of HIF-1α in oncogenic processes includes angiogenesis, tumor metabolism, cell proliferation, and metastasis, which has been examined in various malignancies, such as melanoma. Melanoma is accompanied by a high death rate and a cancer type whose incidence has risen over the last decades. The linkage between O2 loss and melanogenesis had extensively studied over decades. Recent studies revealed that HIF-1α contributes to melanoma progression via different signaling pathways such as PI3K/Akt/mTOR, RAS/RAF/MEK/ERK, JAK/STAT, Wnt/β-catenin, Notch, and NF-κB. Also, various microRNAs (miRs) are known to mediate the HIF-1α role in melanoma. Therefore, HIF-1α offers a diagnostic/prognostic biomarker and a candidate for targeted therapy in melanoma.

Fracture of interlocking asperities during sliding contact plays a central role in determining friction and wear of brittle materials. The local force to break surface asperities and subsurface crack propagation directly affect the frictional force, wear rate and evolution of surface roughness. Here, we study the fracture failure of an isolated asperity under a lateral contacting force using a systematic set of experiments and numerical simulations. Combining the generalized elastic fracture mechanics and the Euler-Bernoulli beam theory, we also develop an analytical model to predict the critical force at the onset of asperity fracture as a function of the asperity's shape and material properties. The developed model can be used to develop physics-based friction and wear models for brittle solids.