Heat transport in biological tissue is mediated through a variety of phenomenological processes, involving tissue heat exchange, blood-tissue convection, blood perfusion or advection and diffusion across microvascular beds, and metabolic heat production. In recent years, many physicians and engineers have taken an interest in applying computational and mathematical techniques to model biological systems. The objective of the current paper is to provide an analytical solution to the modified Pennes bioheat conduction equation with a single relaxation time. The suggested model is used to examine heat transport in biological tissues as an infinite concentric spherical region during magnetic fluid hyperthermia. This method is used to investigate the influence of heat generation through heat treatment on a skin tumor a spherical layered structure. The present model can explain the effect of different therapeutic approaches such as cryotherapy sessions, laser therapy, and physical occurrences including transfer, metabolism support, blood perfusion, and other similar treatments.Hyperthermia therapy is now being used to treat cancer. However, understanding the pattern of temperature increase in biological tissues during hyperthermia treatment is essential. In recent years, many physicians and engineers have studied the use of computational and mathematical models of heat transfer in biological systems. The rapid progress in computing technology has intrigued many researchers. Many medical procedures also use engineering techniques and mathematical modeling to ensure their safety and assess the risks involved. One such model is the modified Pennes bioheat conduction equation. This paper provides an analytical solution to the modified Pennes bioheat conduction equation with a single relaxation time by incorporating in it the (MGT) equation. The suggested model examines heat transport in biological tissues as forming an infinite concentric spherical region during magnetic fluid hyperthermia. To investigate thermal reactions caused by temperature shock, specifically the influence of heat generation through heat treatment on a skin tumor [AEGP9], the Laplace transformation, and numerical inverse transformation methods are used. This model was able to explain the effects of different therapeutic approaches such as cryotherapy sessions, laser therapy, and physical occurrences, transfer, metabolism support, and blood perfusion. Comparison of the numerical results of the suggested model with those in the literature confirmed the validity of the model’s numerical results. Hyperthermia is a highly interesting topic in medicine. Several studies have been conducted on the use of heat transmission to living tissues for cancer treatment  as well In recent years, hyperthermia therapy has been shown to be useful in treating cancer. Its goal is to increase the heat of diseased tissues to beyond cytotoxic levels (41 °C to 45 °C) while avoiding overexposure of healthy tissues ,4. In thHyperthermia nowadays is a highly interesting topic in medicine. Several studies have been conducted on the use of heat transmission into living tissues, particularly for cancer treatment . In addiHyperthermia treatment can also be performed using a radiofrequency (RF) generator with electrodes and antennas , as wellThese diverse techniques highlight the complexity of heat transmission in biological systems because many physiological functions rely on the spatiotemporal temperature differences in live biological tissues, and heat transmission in biological systems involves a variety of mechanisms that facilitate heat transmission in biological tissues and that must be considered, including convection between the blood and tissues, heat transfer in tissues, blood perfusion or delayed blood perfusion, the vascular structure, diffusion through microvascular beds, metabolic heat generation, and changes in tissue properties depending on physiological conditions, among others ,10,11. TThe study of temporal and geographical variations in temperature is required while investigating bioheat transportation concerns because many physiological functions rely on the spatiotemporal temperature differences in live biological tissues. Many biologists, physicians, mathematicians, and engineers have developed mathematical models of heat transfer in biological tissues Mathematical models are now often used in the study of hyperthermia in tumor treatment, oncology, cryosurgery, and many other applications . There hAn empirical law of thermal conductivity known as “Fourier’s law” describes heat transfer in a continuous medium. Unfortunately, since the Fourier formula produces an infinite speed of heat spread due to the parabolic form of the diffusion equation, it has not been used in cases of fast transient heat transfer such as pulsed laser irradiation. As a result, a finite heat diffusion velocity must be determined. The Pennes model of bioheat transfer is widely used in modern engineering and medical therapy because of its simplicity , althougThe Pennes bioheat equation incorporates the effects of diffusion, advection, volumetric heat production from metabolism, and spatial heating on heat transfer in a biological organism. The thermophysical characteristics of tissues, such as thermal conductivity, density, and specific heat, determine the diffusion and transitory thermal impacts ,18.According to researchers ,20, PennSeveral computational and experimental approaches have been developed to solve the biothermal equation since it is critical to make an accurate calculation of the temperature range across the entire affected region. Kundu  used theJaunich et al.  investigThe impact of the heating method is described by maintaining a consistent temperature for the tumor that is higher than the blood and tissue temperatures. To evaluate a local thermal non-equilibrium (LTNE) bioheat model, Dutta and Kundu  presenteThe use of frameworks based on extended irreversible thermodynamics provides correspondences of the proposed model with experimental models characterized by matching reduced computational loads. In the context of extended irreversible thermodynamics (EIT), Chen and Yeh  proposedMagnetic nanoparticles have the potential to be magnetic contrast agents in biomedical magnetic imaging. Their interference in cellular biological systems such as those of a cell (10–100 nm), virus (20–450 nm), protein (5–50 nm), or gene (2 nm wide-ranging and 10–100 nm) due to their size can be adjusted from a few nanometers to tens of nanometers . Their e2O3) or magnetite (Fe3O4) nanoparticles by cells have been demonstrated for diverse cellular types [The potential of magnetic nanoparticle internalization has been illustrated by different in vitro studies. Internalization of maghemite  equation is famous for being a linear model of wave propagation in viscous thermodynamic fluids. The MGT equation is one of the most prominent models in the world of sound waves in terms of physics. For example, high-intensity ultrasound has been used in medical imaging, treatment, ultrasonic cleaning, and welding. The third-order differential equation, which is combined into the value of the dynamic properties of different fluids, gave rise to this concept. Quintanilla  has deveIn the recent decade, many researchers have focused on using different hyperthermia approaches to apply distinct bioheat models in cancer therapy ,62,63,64This work–study provides an analytical solution to the modified Pennes bioheat equation, which includes the MGT equation, in biological tissues as an infinite concentric spherical area during magnetic fluid hyperthermia. While bioheat transfer has been studied under a variety of conditions, there is relatively little research on bioheat transfer in a multilayered material such as the skin. in this study, the temperature distribution in the skin was calculated numerically, and the complete solution was determined using the interface temperature compatibility criteria and the heat flow compatibility criteria. This solution was intended to investigate the impact of heat generation during thermal treatment of a skin tumor represented as a spherical region. The temperature of the tumor was raised to roughly 42 °C for an hour or more during the hyperthermia.The temperature responses of the generalized and classical MGT bioheat models were compared. The MGT bioheat models were generated from the constitutive MGT model and the Pennes bioheat equation, as well as from the classical Fourier heat transfer model. The model may take into consideration the effects of various therapeutic procedures such as cryotherapy, laser treatment, and other similar therapies, as well as physical processes such as transmission, blood perfusion, and metabolic activity. The model was used to study the effect of different physical factors on temperature profiles. This theoretical approach, as well as the quantitative data presented here, may help to improve our knowledge of bioheat transfer in layered structures such as the skin.Fourier’s law is one of the most famous phenomenological models of mathematical physics, although it is not without flaws. The most well-known of its predictions is that thermal conductivity is a diffusion process in which temperature changes propagate at infinite rates, which means that thermal deflection made at one location in a solid medium is instantly detectable anywhere in the material. The law of heat conduction formulated by Fourier is as follows:The Pennes model  was creaVarious changes to the Pennes bioheat transfer (PBT) equation have been proposed throughout the years to address the inconsistencies. One of these changes is the modified heat flux formula that considers the slow propagation speed of thermal waves in a biological medium ,66. On tCattaneo extended Fourier’s law by adding the relaxation time, The coefficient d sound” ,48. The  tissues .A typical hyperbolic bioheat equation can be derived by combining Equation (1) with the PBT equation, Equation (2). To explain the effect of restricted heat propagation, which must be treated for more realistic settings, Liu et al.  developeGreen and Naghdi  proposedIn this equation, the parameter ntanilla  formulatTaking the derivative of time in Equation (6) yields:A modified Pennes bioheat transfer equation based on the MGT equation is constructed by combining Equations (2) and (7) as follows:By adding the temperature increase Assuming that magnetic nanoparticles are present in abundance in the tumor but not in the surrounding healthy tissue, the directly applied field heats only the tumor. We also assume that the perfusion component, which represents the heat transfer to the blood, is proportional to the volumetric blood flow and the difference between the local tissue temperature and the arterial temperature. Since capillaries are generally more or less evenly distributed in the tissue layer, the blood perfusion is uniform across healthy and injured tissues.As seen in The Pennes bioheat equation in the tumor and normal tissues with constant physiological parameters can be stated as follows.For the tumor  has a general solution that is limited as The general solution of Equation (16), which is bounded by The functions The boundary conditions provided by Equation (19) are utilized to get the constants A numerically reversed technique based on the Riemann sum approximation method is utilized to examine the numerical results for the ultimate solution of the temperature variation. Using this method, any function shown in :(20)θr,t λ≈4.7/t , which aThermal treatments need a precise prediction of thermal damage to skin tissues. One of the most essential aspects of hyperthermia cancer therapy is the assessment of burns. The techniques described by Mortiz and Henriques  can be aWe now present some numerical results to clarify our theoretical conclusions in the previous part and to show the impact of the blood perfusion rate and thermal relaxations on the temperature change. We used the MGT Pennes bioheat equation to examine heat transmission in normal and malignant tissues in the dermal regions of the human body via conduction and temperature-dependent perfusion. The epidermal layer of the skin is not perfused, while its deep tissue consists of the dermis and subcutaneous layers, with perfusion playing a key part in predicting temperature distribution changes. In this model, the role of oscillatory heat flow in predicting the spatial temperature distribution of normal and tumor tissues was investigated. We used the Mathematica software to calculate the numerical values.The unregulated and duplicated development of tumor cells causes abnormal temperature fluctuations in the surrounding normal tissues. Local hyperthermia treatment, which involves delivering a focused beam of heat radiations to the tumor’s source, can destroy tumor cells. The constant use of heat may harm the normal tissue cells that surround the tumor. It is therefore critical to investigate the heat distribution in both normal and malignant tissue areas of the human body. A mathematical formula based on the Pennes bioheat equation with certain important parameters was used to evaluate the change in the tissue temperature.Processes ranging from the nanoscale of cell membranes and organelles to the macroscale of the entire body determine heat transport in perfused tissues. The sizes of interest for most clinical applications range from the capillaries, with diameters of a few microns, to anatomical structures such as organs measuring several centimeters or more. In theory, by exposing a well-defined volume of the same composite to an alternating magnetic field, the density of power, The current findings are for a tiny spherical tumor with a radius of utations ,73:ρ1,ρ2Living tissues have significant properties such as metabolic heat production and blood perfusion rates dings in ,75, the dings in  utilizeddings in  calculatAs shown in erved in . As the The efficacy of local tumor heating is stronger for larger tumors than for smaller tumors when there is a considerably decreased perfusion in the tumor relative to the surrounding normal tissues. Thermal diffusion is responsible for this outcome. The bioheat equation may be used to show how the tumor size affects the temperature profile. The effect of a limited tumor volume on the tumor temperature rise  tissues . If the The temperature change in the tumor tissues  model assumes the energy equation for the blood subdomain in this study. The temperature distribution, The classical Pennes bioheat transfer (CPBT) model can be obtained when we set The Cattaneo–Vernotte Pennes bioheat transfer (CVPBT) model can be obtained when we put The Pennes bioheat transfer model based on Green and Naghdi’s theory of type II (GNPBTII) can be obtained when the terms, including The Pennes bioheat transfer model based on Green and Naghdi’s theory of type III (GNPBTIII) can be obtained when the thermal relaxation time is neglected The new MGTPBT model is attained when The following models of the PBT equation can be obtained as special cases from the model derived in Equation (9):The temperature distribution in the tumor and normal tissues is greatly influenced by the thermal factors Including the relaxation coefficient, The predictions of the GNPBTIII and MGTPBT models are incompatible.The magnitude is larger in the case of the GNPBTIII model than in the case of the MGTPBT model, although the graph shows similar results for both models.The thermoelastic results of the GNPBTIII model differ significantly from the GNPBTII model due to energy losses in the case of the first model.In contrast to previous modified bioheat models, the results of the GNPBTIII model of thermoelasticity indicate convergence with the results of the conventional CPBT model, which do not fade in heat rapidly within the tumor and normal tissues, respectively.The profiles of the temperature differences between the MGTPBT and CVPBT models were compared. It is clear from the figure that the behavior and convergence of the results of both models are quite similar, with only slight differences in magnitude.The blood temperature distribution slightly differed between the MGTPBT and GNPBTII models.Heat wave propagation may realistically predict the temperature distribution in living tissues. The cooling function of the blood circulation keeps the tissue temperature from increasing but does not affect the speed of the thermal diffusion. According to this new hypothesis, the relaxation coefficient will become a new measure of the efficiency of the vital heat transfer in living tissues.Transferring thermal energy away from the interface is difficult to apply. As a result, the temperature gradually decreases. It indicates that by lowering the relaxation coefficient, the heat transfer capacity of the medium can be increased.From the graph, it can be seen that:In this paper, the (MGT) equation is included in the modified Pennes bioheat conduction equation with a single relaxation time, and its impacts are shown in thermal treatment applications. The model considers blood circulation, metabolism, and other volumetric heat production processes. According to the research results, the non-Fourier Pennes bioheat model generates a higher temperature rise than the MGT Pennes bioheat model.The conclusions from the main observations are as follows:The rate of change of blood perfusion has a significant effect on the transfer of bioheat in a tumor and normal tissue. Because the skin temperature is higher than the arterial temperature, the blood perfusion acts as a cooling agent. The perfusion rate is proportional to the amount of heat energy extracted from the blood.The results were influenced by the relaxation durations used and the perfusion of the blood. It is also clear that more experimental research is needed to determine the delay times more accurately.It was found that the presence of a thermal relaxation time in the biothermal conduction equation significantly affects the temperature spread in a tumor and in normal tissue over time. As a result, having a thermal relaxation time reduces the temperature drop as well as the tissue depth.This MGT Pennes bioheat model adds some additional dimensions to the investigation of transient heat transfer mechanisms in biological systems.The propagation of thermal waves may provide a realistic prediction of the temperature distribution in living tissue.In the case of the MGT Pennes biothermal model, the temperature spreads with a finite speed in the tumor and the normal tissue instead of an infinite speed in the classical model.The relaxation parameter can be proposed as a novel measure of bioheat transfer efficiency in living tissues in the revolutionary MGT Pennes bioheat model.The findings reported here may be of value for the design of many biomedical and biomechanical application areas, including in healthy and diseased tissues, as well as for the development of theoretical knowledge of bioheat transfer in spherical tissue architecture.


Regression based methods for the detection of publication bias in meta-analysis have been extensively evaluated in literature. When dealing with continuous outcomes, specific hidden factors  may interfere with the test statistics. In this paper we investigate the influence of residual heteroscedasticity on the performance of four tests for publication bias: the Egger test, the Begg-Mazumdar test and two tests based on weighted regression. In the presence of heteroscedasticity, the Egger test and the weighted regression tests highly inflate the Type I error rate, while the Begg-Mazumdar test deflates the Type I error rate. Although all three tests already have low statistical power, heteroscedasticity typically reduces it further. Our results in combination with earlier discussions on publication bias tests lead us to conclude that application of these tests on continuous treatment effects is not warranted. In the presence of publication bias, the treatment effect is associated with factors that affect publication bias, e.g., the size of the standard error of the treatment effect or the size of the study. Thus studies with a lack of statistical significance or smaller studies are less likely to be published.Several methods have been proposed in literature to test for lack of this type of publication bias, e.g., the Egger test , the ranIn case of heteroscedasticity, studies may or may not follow the premises of the test statistics, but when the variance of the outcome is correlated with its mean the resulting treatment effect estimates will be correlated with its standard errors as well. This may lead to the detection of artificial “publication bias” without the presence of a real publication bias process. Since treatment related heteroscedasticity is not testable with aggregated data in a meta-analysis, there is no guarantee that positive tests results for publication bias are truly positive at all. Therefore, it is crucial to better understand the performances of the publication bias test for aggregated data under heteroscedasticity.The objective of this paper is to demonstrate that heteroscedasticity negatively affects the performance of test statistics for publication bias in case of continuous outcomes. We will perform a simulation study, since analytical investigations are complicated. We ignore bias adjustment methods, because we expect that correction of pooled estimates for publication bias is even more difficult when the presence or absence of publication bias is hard to determine. The Egger test, the rank-correlation test and two tests based on a weighted regression model  are consIn Section 22.1An aggregated data meta-analysis usually consists of treatment effect estimates rrors Si . The fouEgger's Test: The Egger test uses a regression model with the standardized effect size t-test, the null hypothesis of no publication bias is rejected if the intercept of the regression model significantly deviates from zero [Weighted regression: A weighted regression method uses t-test, the null hypothesis of no publication bias is rejected if the slope of the independent variable significantly deviates from zero [d method . Using arom zero . This merom zero . Insteadrom zero . We therRank correlation: The rank-correlation test applies Kendall's tau correlation coefficient to the normalized treatment effect ion bias .2.2Let dividual  can thenIt is assumed that The value of The treatment effect per study is given by the raw mean difference Clearly, the introduced random heteroscedasticity 2.3We briefly describe the selection model which will create publication bias at study level ,21. For Note that the selection process of studies will be affected by the random residual heteroscedasticity 3We simulated a meta-analysis study with To introduce publication bias, a selection process or mechanism is simulated according to the selection model described in Section Different simulation settings were considered both with and without publication bias and with Based on the number of studies that remain in the meta-analysis, the four publication bias methods in Section 4The Type I error rate and the power of Egger's test, the rank correlation test (RC), the weighted DL (wDL) and the weighted REML (wREML) test are presented in From When publication bias is introduced, heteroscedasticity influences the statistical power of the four test statistics in a different way than for the Type Ierror rates. The power is increasing with the correlation nism see . Under hnism see . As a co5The performances of the Egger test, the rank correlation test, the weighted DL test and the weighted REML test were investigated for testing publication bias in the presence of residual heteroscedasticity for mean differences. Residual heteroscedasticity for mean differences is plausible and realistic, since it represents heterogeneity in inter-participant variability across studies. Indeed, participants can be more alike in one study than in another study, depending on the implemented selection criteria for the study participants. For instance, pragmatic clinical trials may hardly use any selection criteria, since they focus on efficiency, while other clinical trials may focus on efficacy on a specific set of participants with a particular symptom or disease in a limited age range. Note that the form of heteroscedasticity that we included has been studied in more sophisticated ways in the area of multilevel models .In the simulation study, we found the Type I error of the Egger's test inflated. This is a well-known phenomenon for log odds ratios  and stanHeteroscedasticity renders the four tests unreliable in testing for publication bias in meta-analysis. It causes the Type I error to deviate from the nominal level substantially and also from the level obtained under homoscedasticity if the test was not nominal (Egger's test). For the Egger test, the introduction of heteroscedasticity increases the variability in asticity ,28,29,33We did not include any of the publication bias methods  that could correct the pooled estimate from a meta-analysis ,22. ThesHeteroscedasticity also affected the mechanism of publication bias, making it difficult to disentangle these two mechanisms at an aggregated level. Thus when heteroscedasticity is anticipated from the topic of study, it may be recommended to collect and pool the individual participant data. The heteroscedasticity can then be potentially modeled, although it remains unknown how to address the publication bias into this approach. More research is needed to be able to model the correlation between study effect sizes and its standard error.


The process is based on the direct adsorption of hydroxytyrosol on selected foodstuff  when placed in contact with olive oil (which is known to contain this phenol) under controlled temperature and pressure conditions of supercritical CO2. The transfer of the nutraceutical to the foodstuff was initially evaluated using a fortified olive oil model and was then applied to commercial olive oil and foodstuff. The yield of the hydroxytyrosol transfer was demonstrated to be quantitative. In order to prove the suitability of the final products, hydroxytyrosol-enriched flour was used to prepare a bread roll, which maintained the nutraceutical characteristics after the cooking. Finally, DPPH based experiments were performed to prove the radical scavenging activity of the functionalized foodstuff.A new original process has been set-up to obtain hydroxytyrosol-enriched food by means of CO This work may open new perspectives for the re-use of aged olive oils, giving, at the same time, the opportunity to develop a new generation of functional foods.A functional food is a product whose consumption is associated with health improvements with respect to its ordinary nutritional properties. Its main aim is to reduce disease risks through prevention . As repoFlour (white bread flour from wheat), whole-wheat flour, sugar (regular fine sugar from beets), and extra virgin olive oil were purchased at a local market .2-hydroxytyrosol was obtained in our laboratory following a method from the literature  × 100, where Acontrol is the absorbance of 800 µL of DPPH solution with 100 µL of DMSO and Asample is the absorbance of DPPH in the presence of food samples. Spectrophotometric analysis was carried out using a spectrophotometer Evolution 600 UV/VIS .DPPH radical scavenging activity was determined spectrophotometrically at 517 nm by measuring the DPPH concentration after the reaction with the antioxidant molecules present in food extracts . Briefly2 supercritical fluid extraction to fortify foodstuffs consumed daily in our diet. Generally, carbon dioxide, which becomes supercritical at temperatures and pressures above 31.2 °C and 72.9 atm, respectively, is used to eliminate unwanted molecules or classes of compounds from a foodstuff; this is the case for decaffeinated coffee, defatted potato chips, etc. At supercritical conditions, CO2 is compressible such as a gas but possesses a density and a solvating power similar to a liquid, having low viscosity and high diffusivity; it diffuses easily through solid materials and provides faster extraction yields. In this application, the extraction process is used to transfer a target polar compound from a fat matrix to another food. The procedure has been optimized to perform a quantitative transfer of hydroxytyrosol from olive oil to foodstuffs in the shortest possible time.Here we present a novel methodology to obtain new functional foods containing hydroxytyrosol, a nutraceutical component naturally found in olive oil, which is well known for its antioxidant properties. This methodology relies on the use of COWe have prepared three different types of hydroxytyrosol-enriched food: flour, whole-wheat flour, and sugar. In general, the starting material  is mixed with olive oil to allow the maximum dispersion of the oil in the food matrix; then, the mixture is submitted to the supercritical fluid process to obtain the final hydroxytyrosol-enriched product after the elimination of the lipid component via the outlet valves of the system. In order to optimize the process, several experiments were conducted on the flour matrix using an olive oil sample spiked with standard hydroxytyrosol at a concentration of 100 mg/L. The experiments were carried out with varying supercritical process conditions, i.e., the temperature, the pressure, and the duration of experiment. It is known that the solubility of an analyte in a solvent is highly influenced by the density of the solvent, while the density of a fluid state is particularly sensitive to small changes in pressure and temperature ,22. The The process was then applied on selected foods using a commercial olive oil containing a concentration of 14 mg/kg of hydroxytyrosol; flour, whole-wheat flour, and sugar were chosen as sample target foods for the hydroxytyrosol transfer. At the conclusion of the transfer process, each sample was extracted and analyzed by HPLC-ESI-MS/MS under MRM conditions following the procedure already reported in the literature . Figure It is worth noting that the analyzed olive oil fraction recovered at the exit of the outer valve has been also analyzed by HPLC-ESI MS/MS in order to control the amount of hydroxytyrosol remaining in the fat. In order to be sure that the enriched food products could have an effective outcome in daily diets, a bread roll was prepared using the hydroxytyrosol-enriched flour. The roll was then cooked in the oven at 180 °C for 30 min.At the end of the cooking process, both the rind and the inner part of the “enriched bread”  were extFinally, the ability of the hydroxytyrosol-enriched novel foods to scavenge free radicals was evaluated. The antioxidant capacity of enriched foods was determined by DPPH assay. This test involved the measurement of DPPH radical absorbance at 517 nm during the reaction with antioxidant compounds present in foods. A DPPH radical is an organic nitrogen radical with a deep purple color, and when mixed with an antioxidant/reducing compound its color changes from purple to yellow, which indicates the formation of hydrazine. For each food sample, two independent assays were performed: the first on original matrices and the second on enriched foods. The experiments were performed with an excess of DPPH in order to run out the H-donating capacity of the antioxidant compounds. The spectrophotometric measurements were performed for 30 min until the reaction reached the equilibrium. The kinetic curves were obtained by plotting the DPPH radical absorbance versus the time of experiment .The kinetic behavior showed a lower DPPH absorbance value for fortified foods at the end of the reaction compared to the original products. In particular, the kinetic curve shows that the disappearance of the DPPH radical is faster in the fortified flour than in the original food sample, and the same effect is observed for the whole-wheat flour sample. For sugar, the absorbance difference between the two samples is much higher, because the original sugar matrix does not show any radical scavenging activity . For all2 fluid extraction to easily enrich common foods with hydroxytyrosol, which is known to possess important antioxidant properties acknowledged by international regulations. Regarding the latter point, the European Union, through the regulation 432/2012 [2 as a carrier. In the process, the supercritical CO2 extraction is used in an unconventional way; in fact, this type of extraction, known for its sustainability in terms of the environment, usually removes undesired components from foodstuff . In this work, on the contrary, the supercritical CO2 works as a medium transfer of hydroxytyrosol from the olive oil to a solid food such as flour or sugar. The transfer is made possible because of the polar nature of hydroxytyrosol, which is preferentially adsorbed on the polar components of the solid foodstuff. This hypothesis has been proved by analyzing the exhausted oil transported out of the column by the CO2 flow; in fact, the oil shows no evidence of the polar phenol , accomplishes a quantitative transfer of hydroxytyrosol from olive oil to foods, leaving their characteristics unaltered. DPPH tests were performed on the enriched foods, showing an increase of their antioxidant activity. Finally, the flour was used to prepare a bread roll which maintained the presence of the nutraceutical after the cooking process.An original method describing the formulation of innovative functionalized foods containing the antioxidant molecule hydroxytyrosol has been developed. The method relies on the CO


Background: Patients with alcohol use disorder (AUD) exhibit deficits in various cognitive domains, including executive functioning, working memory, and learning and memory, which impede the effectiveness of conventional AUD treatment and enhance relapse. Mobile health  services are promising in terms of delivering cognitive training in gamified versions. So far, studies examining the effects of mHealth-based cognitive training in AUD patients have, however, focused on specific rather than multiple cognitive domains and overlooked the importance of clinical outcomes. Furthermore, research has yet to investigate the acceptability and feasibility of this type of cognitive training.Aims: The aims of this pilot study are to examine (1) whether using smartphone-based, multi-domain cognitive training with gamified elements as part of conventional treatment for AUD indicate effect, and (2) whether the intervention is acceptable and feasible as a part of conventional treatment for AUD.Methods: Patients from the alcohol outpatient clinic, Odense Municipality, Denmark will be invited to participate in the study on a consecutive basis until a total of 60 patients have been recruited. The study will be performed as a combined parallel randomized controlled trial (RCT) and qualitative feasibility study. The patients will be randomly assigned to one of two groups. The intervention group (n = 30) will receive smartphone-based, multi-domain cognitive training with gamified elements together with treatment as usual (TAU). The active control group (n = 30) will receive a sham version of the same cognitive training together with TAU. Cognitive outcomes will be assessed via the training application at baseline and post-treatment. Clinical outcomes will be assessed at baseline, post-treatment, and at 6-month follow-up using the Addiction Severity Index. Furthermore, the 30 patients randomized to the intervention group will be invited to participate in the second phase, that is the feasibility study, at post-treatment. A questionnaire inquiring about the use of mHealth treatment in general will be administered. Further, feedback regarding functionality and meaningfulness of the application in addition to other qualitative aspects relating to the use of the application will be collected. The patients will also be asked to provide suggestions about how to improve and potentially implement the tool.Implications: It is anticipated that this pilot study will provide tentative evidence for the effectiveness of smartphone-based, multi-domain cognitive training as well as information about the usability and feasibility of this type of training, including acceptability and compliance. The study will also contribute with feedback derived from the patients about how to improve and implement the tool. If promising, the findings will be used to plan a large-scale RCT. Since cognitive deficits are not addressed in current treatments for AUD, gamified cognitive training delivered through smartphones may increase the effectiveness of current treatment for AUD as well as introduce more mHealth-based treatment that is both accessible and cost-effective. The encumbering nature of alcohol use disorder (AUD) is indisputable—high rates of prevalence, comorbidity with other disorders, and recurrence –5 indicaThe cognitive dysfunctions in AUD involve the domains for processing speed, executive functioning, working memory, and memory , underlining that the whole brain is affected by the neurotoxic effects of alcohol , 12, 13.A direct way of targeting cognitive deficits in AUD could be cognitive training. Several studies have investigated the efficacy of cognitive training in subclinical and clinical AUD samples, but these have mostly focused on cognitive training targeting executive functions and working memory . In subIn clinical samples, several studies have demonstrated improvements in cognitive outcomes after cognitive training . OvThe premise of cognitive training is that the trained cognitive domain(s) can be transferred to neuropsychological tests targeting a similar domain(s)  and/or a dissimilar cognitive domain(s) . Most oTo date, little is known about the effects of cognitive training on clinical outcomes . Only one clinical study has examined the effects of WM-specific cognitive training on alcohol consumption, which found no effect of the training . HoweverCurrent options for delivering cognitive training are based on standardized neuropsychological tasks with poor ecological validity . It has Although a few studies examining cognitive training have applied SG-elements , 22, theThe aims of this pilot study are to examine (1) whether using smartphone-based, multi-domain cognitive training with gamified elements as part of conventional treatment for AUD shows some effect, and (2) whether the intervention is acceptable and feasible as part of conventional treatment for AUD.This pilot study will be conducted in two phases: (1) a parallel small-scale randomized controlled trial (RCT) and (2) a feasibility study.The study will be carried out at the outpatient alcohol clinic, Odense Municipality, Denmark. Outpatient treatment is publicly financed and accessible to individuals AUD with varying levels of severity or other alcohol-related problems. Individuals with AUD and comorbid disorders such as psychotic or affective disorders or individuals with other substance-related disorders are presented with treatment options localized at different facilities . FurtherBefore the primary treatment is offered, it is possible for patients to receive a personalized, pharmacological detoxification program at the clinic. Here the patients will be administered the benzodiazepine, chlordiazepoxide, and the specific dosage and duration is adapted to the needs of each individual patient. In 2019, the outpatient facility received 230 patients, where 18.26%  had undergone pharmacological detoxification.Psychotherapy and pharmacotherapy are used as the primary treatment either alone or in combination. The former includes motivational interviewing and CBT administered as eight individual or group-based sessions with the option for extension, and the latter often encompasses treatment with acamprosate, disulfiram, or naltrexone. The treatment lasts for 3 months, and it is conducted by therapists, nurses, and social workers. Psychiatrists monitor the progression of patients during the treatment. For the treatment to be attuned to the individual patient, both the therapist and patient co-plan the course of the treatment, but typically the patient receives psychoeducation, is instructed in adaptive coping strategies , and functional analyses are conducted for drinking scenarios.Patients from the alcohol outpatient clinic will be invited to participate on a consecutive basis until a total of 60 patients have been recruited. The clinic receives 600 patients over the course of 1 year.To be eligible for participation in the pilot and feasibility study, patients must: (1) have a confirmed AUD diagnosis; (2) agree to participate in the study and provide verbal and written informed consent; (3) be aged between 18 and 60 years; (4) speak Danish; (5) have completed detoxification (if needed); (6) not have any sensory or motor deficits complicating the provision of the intervention ; (7) not meet diagnostic criteria for other substance use disorders (SUD); (8) not have a severe psychiatric or neurological illness  or terminal somatic illness; (9) own or be able to acquire a smartphone or tablet with internet access.Shortly after completing a personalized, pharmacological detoxification program  at the outpatient facility and prior to starting primary treatment, the patients will be briefly informed of the study by the health care providers at the outpatient facility and asked if they would be willing to meet with a research assistant from the project for provision of full information on the study. The patients will also be informed about the possibility of bringing a visitor, either a friend, family member, or therapist at the clinic, for the meeting with the research assistant.Before recruiting the patient, the research assistant will read out a standardized manuscript with information regarding the study, and the patients will also be provided with written information about the study. After this initial introduction, the research assistant will ask the patients whether they would be interested in participating in a baseline interview earliest the next day.n = 30) will receive smartphone-based, multi-domain cognitive training with gamified elements together with treatment as usual (TAU), and the active control group (n = 30) will receive a sham version of the same cognitive training together with TAU.Upon receiving informed consent from the patients, the baseline interview will be administered, where the history of the AUD will be assessed . The first 60 patients fulfilling the eligibility criteria will shortly after the baseline interview be randomized to one of two groups. The intervention group  and will be based on a computer-generated list of random numbers.The intervention consists of using Brain+ Recover, a gamified smartphone application developed by Brain+ ApS, which is available to the public and can be downloaded to iOS and Android devices. In the application, the patient has access to various cognitive training games, each targeting a set of complementary cognitive functions see . The cogThe active control group will be provided with the same Brain+ Recover application as the experimental group. However, the difficulty of the tasks will be kept constant at a low level. Because of the fixed level of difficulty, the active control group will not have access to a brain profile. The active control group will receive the same instructions as the intervention group. This type of sham-cognitive training has been used in previous studies evaluating WM-specific serious gaming, which found no differences between the experimental and active control groups in terms of study completers , 27.All patients will be helped with downloading the Brain+ Recover application on their own smartphone or tablet. All the games can be accessed at any time during the entire study period. The patients will be recommended to use the application for at least 20 min a day, 5 days a week, for 1 month . This recommendation is based on previous studies on gamified cognitive training reporting improvements in cognitive outcomes after 2–5 sessions per week (each session varying between 45 and 60 min) with a total of 4–5 weeks of training . Actvia the Brain+ Recover application at baseline and post-treatment. Clinical outcome measures will be assessed at baseline, post-treatment, and at 6-months follow-up.The baseline interview will yield data on sociodemographic characteristics and AUD diagnosis and severity of the AUD . Cognitive outcomes will be assessed The Mini-International Neuropsychiatric Interview (MINI) is a structured interview and will be used to confirm the diagnosis of AUD according to the criteria from the fifth version of the Diagnostic and Statistical Manual of Mental Disorders . The coThe Addiction Severity Index (ASI), a standardized international assessment instrument, will be used to generate an addiction severity profile for each patient. The profile covers seven areas of the patient's life: medical status, employment, drug use, alcohol use, legal status, family/social status, and psychiatric status . Based oRepeated-measures Analysis of Variance (ANOVA), with group as the between-subjects factor and time of assessment as the within-subjects factor, will be used to test the effectiveness of the intervention across the assessment points, which will be indicated by a significant Time x Study Group interaction. If there is an overall significant interaction effect between any time-point and group, contrasts will be used to examine whether the change over time differs between the groups and the time-point. If the assumptions for the repeated measures ANOVA are violated, then a non-parametric equivalent will be used to address this issue. An intention-to-treat analysis and a completer analysis  will be conducted for each outcome. The intention-to-treat analyses will be carried out for all patients, irrespective of whether they have completed the training or were re-interviewed. The significance level in the models will be set at α = 0.05, and two-tailed tests will be conducted. Effect sizes will be reported in accordance with the statistical modeling. All data will be analyzed in Stata version 16.Since this is a pilot study, no power calculation has been conducted. Nonetheless, prior small-scale studies have been able to detect effects on cognitive and clinical outcomes in similar sample sizes (include references).The 30 patients randomized to the intervention group will be invited to participate in the feasibility study. After completing the training, the patients will be asked to complete a short questionnaire focusing on their general experience with using the application , which elements of the cognitive games they found the most engaging , how they think the application can be improved , and what could motivate them to use the application more . In addition, the validated System Usability Scale (SUS) will be used to evaluate how the user-interface is perceived by the patients .n = 252).Furthermore, three focus group interviews will be performed, for which 30 patients will be invited to participate. The themes of the focus group interviews will be inspired by the data recorded in the application and the questionnaires, and in particular address (1) patients' experience of the training; (2) possible improvements; and (3) aspects of importance when implementing the cognitive training as an adjunct to conventional AUD treatment. Furthermore, the patients that did not follow their daily training schedule or who refrained from using the application, will be asked whether they experienced any technical difficulties over the course of the study. Based on the feedback provided by the patients, consisting of both quantitative and qualitative data, the Brain+ Recover application will be modified and subsequently renamed Brain+ AlcoRecover before implementing it in the subsequent large-scale RCT . If this is not achieved in the first trial, a further 60 patients will be recruited and invited to participate in another pilot study using an adapted version of Brain+ AlcoRecover.The data collected during the baseline interview and during the 6-month follow up will be treated as strictly confidential and managed by Odense Patient Data Explorative Network (OPEN). After the patients have been randomized to either of the two groups, they are assigned an ID-number, which ensures that the data collected through the Brain+ Recover application is anonymous so that the data cannot be traced back to any of the personal information of the patient. Treatment of data will comply with the Data Protection Regulation and the Data Protection Act. No analysis or publication will contain information that allows person identification.Since this is a study examining a psychological intervention expected to cause no damage and, in the worst-case scenario, only cause transient and minor discomfort, there is no economical compensation system linked to the trial.The study was developed on the initiative of the Unit of Clinical Alcohol Research and is unconditionally funded by the Psychiatric Research Foundation, Region of Southern Denmark.All the patients in this study will be offered either pharmacological or psychological treatment at the outpatient alcohol clinic. There are no known harmful effects of the smartphone-based training program. Hence, the current study does not pose any ethical problems, and it will adhere to the World Medical Association Declaration of Helsinki. In addition, the protocol has been approved by the Research Ethics Committee for Southern Denmark (Project ID: S-20200199) and will be conducted in accordance with the General Data Protection Regulation. Before signing the consent agreement for participation in the research project, the patients are informed that they at any given time during the study have the option to withdraw their consent without it having any implications for their current or future treatment options. If the patients choose to withdraw their consent, they are informed that all their data will be erased.Since the present study relies on feedback regarding modifications to the Brain+ Recover application, patients with AUD will take active part in the development of the final version of Brain+ AlcoRecover. Collaboration with the patients will ensure that the smartphone-based cognitive training is well-suited to the requirements of patients with AUD. Thus, this study will uncover how compliance and adherence to gamified and smartphone-based cognitive training can be optimized for it to be a putative instrument for clinical practice in terms of the future treatment for AUD. In keeping with this, to bridge the gap between research and clinical practice even further and to maximize the effectiveness of the intervention, the current study will also include patients who underwent pharmacological detoxification recently  well-knowing that such treatment with benzodiazepines have an impact on cognitive functions .In this study the mHealth-based cognitive training is delivered as adjunctive treatment, therefore, it is hypothesized that it will be less disruptive for the conventional treatment, which in turn would make it easier to adapt it to the clinical facilities. If the Brain+ Recover shows a trend toward effectiveness and feasibility, this pilot study will constitute the fundament for a future large-scale RCT in which the effects of smartphone-based, multi-domain cognitive training with gamified elements delivered as an adjunct to TAU will be compared to sham-control training in combination with TAU as well as TAU only. Given the scarcity of evidence on SG and cognitive training as adjunctive treatment delivered through smartphone applications, which could create the groundwork for future research to explore the effects of mHealth-based cognitive training in patients with AUD. The cognitive heterogeneity of patients with AUD emphasizes the need of this type of mHealth focused research, as it could create the groundwork for more motivating personalized treatment options. Future smartphone based personalized cognitive training could also give patients the opportunity to be more in charge of their own treatment, making it more anonymous and less stigmatizing for the patients.The original contributions presented in the study are included in the article/The studies involving human participants were reviewed and approved by the Research Ethics Committee for Southern Denmark (Project ID: S-20200199) and will be conducted in accordance with the General Data Protection Regulation. The patients/participants provided their written informed consent to participate in this study.AM and NM wrote the manuscript. All authors were responsible for the design of the whole study, wrote the protocol, supported the manuscript preparation, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Fluidic oscillators are often used to modify the forces fluid generates on any given bluff body; they can also be used as flow, pressure or acoustic sensors, with each application requiring a particular oscillator configuration. Regarding the fluidic oscillators’ main performance, a problem which is not yet clarified is the understanding of the feedback channel effect on the oscillator outlet mass flow frequency and amplitude, especially under compressible flow conditions. In order to bring light to this point, a set of three-dimensional Direct Numerical Simulations under compressible flow conditions are introduced in the present paper; four different feedback channel lengths and two inlet Reynolds numbers  Fluidic oscillators (FO) have a wide range of applications. They can be used to enhance mixing ,2, as seOne of the first analyses of the internal flow in an angled fluidic oscillator was undertaken by Bobusch et al. . ParticlEmploying the same fluidic oscillator configuration previously analyzed in , GartleiGokoglu et al.  analyzedThe same configuration previously employed in , althougWoszidlo et al.  studied The fluidic oscillator outlet frequency and amplitude, whenever the feedback channel and the mixing chamber lengths were modified, was studied using a 2D numerical model by Seo et al. ; the fluVia using stereoscopic PIV, the velocity field properties emitted by an FO rounded configuration were studied by Ostermann et al.  at ReynoAn experimental and numerical study of a fluidic oscillator which could generate a wide range of frequencies (50–300 Hz), was studied by Wang et al. . Their sThe present paper is presenting a 3D-DNS numerical evaluation of the same fluidic oscillator configuration employed in . The effV is the spatial averaged fluid velocity; in the present paper all these parameters were defined at the FO power nozzle, see T being the fluid temperature, R the gas constant and y is the distance between the first mesh cell and the wall, f being the dimensional frequency. The advective time is given according to the following equation—T defines the dimensional time.Despite the fact that some of the graphs introduced in the present paper are presented in dimensional form, it is necessary to define the different parameters used for non-dimensionalization as well as the non-dimensional numbers used in the present manuscript. The Reynolds number definition used to characterize the main flow was M associated to the fluid at the (FC’s) outlet, see P are, respectively, the mass flow, the spatial averaged fluid velocity, the surface where the momentum needs to be evaluated and the instantaneous spatial averaged static pressure; Another parameter particularly useful in the present manuscript is the momentum As the flow is considered as compressible , the instantaneous momentum equation to be applied at any given surface (FC’s outlet in the present paper) needs to be discretized and evaluated at each grid cell belonging to the corresponding surface.i denotes any parameter associated to the generic mesh cell belonging to the FC outlet surface under evaluation. The term n defines the total number of cells corresponding to any of the two FC outlet surfaces.The subindex Based on the previous equation, it can be seen that the momentum associated to the flow at any given section consists of two parts, the momentum due to the fluid mass flow and the one associated to the static pressure. In order to evaluate the mass flow momentum term, it is necessary to know the instantaneous fluid density and velocity associated to each mesh cell section through which the fluid flows. In the present paper, the instantaneous momentum term due to the mass flow was obtained simply by adding the elementary momentum terms of each mesh cell belonging to the chosen surface. The momentum pressure term was obtained when multiplying the instantaneous static pressure acting on each cell by the cell surface, and then adding the elemental momentum pressure terms corresponding to the surface under study.The two instantaneous momentum terms were obtained at each of the two feedback channel outlets. The instantaneous net-momentum, characterizing the overall forces acting on the main jet lateral surfaces (the two FC outlets), considers the instantaneous pressure and mass flow momentum terms acting on both feedback channels’ outlets.To be able to compare the relation between several parameters triggering the oscillations, some of the graphs presented were made non-dimensional. The non-dimensionalization was done by dividing each variable by the maximum value of the same variable measured at the same location and obtained from the baseline case oscillator at the smallest power nozzle velocity studied, 65 m/s. Two power nozzle velocities of 65 m/s and 97 m/s are reported in the present manuscript.In the present manuscript, the three-dimensionalcompressible Navier–Stokes equations are employed as the governing equations. In Cartesian coordinates, such equations take the following form.x, y and z components of an inviscid flux, respectively. The vectors x, y and z components of a viscous flux, respectively. The definition of all these vectors is presented next.The term u, v and w represent the fluid velocity components in the x, y and z directions, respectively. x, y and z directions, respectively.The variable e is defined in terms of the following equation of state for ideal gas.p and The stress tensor Direct Numerical Simulation (DNS) of turbulent flow takes this set of equations as a starting point and develops a transient solution on a sufficiently fine spatial mesh with sufficiently small time steps to resolve even the smallest turbulent eddies and the fastest fluctuations. The feasibility of these requirements is evaluated in the next section.M = 0.188 and M = 0.282. Based on these Mach numbers, the fluid could nearly be considered as incompressible, yet as it will be presented in results section, the maximum Mach number observed inside the mixing chamber leave no doubt regarding the need to consider the fluid as compressible.The different boundary conditions employed in all simulations were Dirichlet boundary condition (BC) for velocity and temperature at the FO inlet, Neumann BC for pressure. At the FO outlet, Neumann BC for temperature and velocity as well as Dirichlet BC for pressure were used. At the walls, Neumann BC for pressure and temperature as well as Dirichlet BC for velocity were chosen. The time step used for all simulations was of To to the authors’ knowledge, on the present fluidic oscillator configuration, no previous 3D-DNS simulations considering the fluid as compressible have been undertaken. In the present paper, and knowing that for the operating conditions chosen no comparisons with previous experimental or numerical results are possible, initially a mesh independence study using the baseline fluidic oscillator configuration for a velocity of 97 m/s measured at the fluidic oscillator power nozzle, see As compressible three-dimensional Direct Numerical Simulations (3D-DNS) are employed in all the cases studied, and in order to prove the simulations can be considered as DNS, A final point to consider is the viability of employing a buffer zone. In previous studies , a buffeIn the present subsection, the main flow parameters associated to the FO working under compressible flow conditions and at the two Mach numbers evaluated are introduced. The initial analysis is focused on the baseline case. The main vortical structures inside the FO mixing and external chambers, given for a period of oscillation ented in ,39. The The feedback channels are alternatively pressurized, but due to the fluid compressibility, it can be seen that just parts of the feedback channels are pressurized; in other words, pressure waves can be seen moving along the FCs. The videos presented in Pressure waves originate at the MC converging walls and are due to the stagnation pressure points appearing alternatively at these surfaces. When carefully looking at Pressure waves generated at the MC converging walls do not appear to be moving downstream towards the external chamber; it seems that the main jet flowing across the mixing chamber outlet width blocks them from displacing downstream. The same happens to the pressure waves generated at the FC outlets’ internal vertical walls. The waves propagate along the FC but not towards the MC, the main jet entering the MC appears to be acting as a barrier to the pressure wave propagation. On the other hand, some high-frequency waves are likely to dissipate into heat. The fluid expansion suffered at the EC tends to help in dissipating the pressure waves effect in this chamber. The Q criterion plots presented in When studding the same FO configuration under incompressible flow conditions and at Reynolds numbers up to 32,000, refs. ,41 estabIn order to determine the origin of the forces responsible of the jet oscillations inside the MC, When the power nozzle inlet velocity is of 65 m/s, As the power nozzle velocity increases to 97 m/s, the stagnation pressure point appearing at the FC outlet internal vertical wall reaches a smaller value than in the previous case, compare From the comparison of To be able to further understand the effect of the FC length on the FO outlet mass flow frequency and amplitude,  made by .In fact, and regardless of the velocity studied, the curves showing the FO outlet mass flow become scattered as FC length increases. It appears as if instead of clean oscillations, the main jet undergoes some vibrational movement, although still maintaining a low amplitude oscillatory displacement, see Once the main FO outlet flow characteristics are evaluated, it is interesting to analyze the fluid evolution inside the MC and the feedback channels. For the two power nozzle velocities and the four FC lengths, the overall forces acting on the main jet at the MC inlet, expressed as the net-momentum acting onto the FC’s outlets are presented in In order to prove that the origin of FO oscillations is the stagnation pressure temporal evolution at the MC converging walls, the FFT of the stagnation pressure signal measured at the MC lower converging wall is presented in Another important point to clarify is which of the two terms characterizing the net momentum acting onto the jet is the dominant one. In a previous numerical work done by Baghaei and Bergada ,41, usinIn order to visualize the flow topology for a FC length of 9L, instantaneous velocity, pressure, density and temperature fields and for the two power nozzle velocities of 65 m/s and 97 m/s are presented in The relation between the maximum and minimum pressure observed in Another question which may arise is whether the maximum and minimum values of the velocity, pressure, density and temperature are affected by the feedback channel length. Despite the full information generated not being presented in this manuscript, it can be concluded that the maximum and minimum values of all these parameters remain very stable for all different feedback channel lengths studied, their variation was just found to depend on the power nozzle velocity.The effect of the FC length in an angled FO geometry and under compressible subsonic flow conditions has been carefully studied. As feedback channel length increases, the outlet mass flow peak-to-peak amplitude and frequency decrease. The oscillation would eventually stop if a certain feedback channel length is overcome. See Pressure waves are generated when the main jet impinges alternatively at the mixing chamber converging walls. Under some conditions, pressure waves are also alternatively generated at the feedback channels outlet vertical internal walls. When this happens, the dynamic net-momentum, feedback channel mass flow and stagnation pressure signals are particularly scattered, this is due to the pressure waves appearing simultaneously on both sides of the feedback channels. See At high feedback channel lengths, the typical jet oscillatory movement is almost gone, the jet mostly undergoes random fluctuations despite still performing low amplitude, low frequency oscillations. See At small feedback channel lengths, the flow dynamics of all parameters are well-correlated, the stagnation pressure oscillations at the mixing chamber convergent walls drive the rest of the flow parameters, see The different high frequencies associated to the fluctuations appearing inside the MC and the FCs are not fully transferred to the flow at the FO outlets. It seems that the main jet is acting as a barrier preventing the displacement of these flow fluctuations to the external chamber. Compare Under all conditions, the fluid suffers an expansion inside the mixing chamber. Vortical structures inside the MC and FCs are clearly observed, their sign changes alternatively with the jet oscillation inside the mixing chamber. Vortical structures can be observed in For the present fluidic oscillator configuration operating under compressible flow conditions, the oscillations are pressure-driven. The forces due to the mass flow flowing along the FCs and acting on the main jet at the feedback channels outlets, are an order of magnitude smaller than the ones due to the pressure. See The authors strongly recommend to employ the methodology presented in this paper to determine in any feedback channel fluidic oscillator if oscillations are pressure- or mass flow-driven.


This allows us to find more complex pattern from the image. To overcome the overfitting problems due to small samples, an augmentation approach is utilized. The results demonstrated that the proposed framework achieved precision 96%, recall 97%, F score, average surface distance (ASD) of 2.8 ± 0.3 mm, and volume overlap error (VOE) of 5.6 ± 1.2%.The COVID-19 pandemic is a global, national, and local public health concern which has caused a significant outbreak in all countries and regions for both males and females around the world. Automated detection of lung infections and their boundaries from medical images offers a great potential to augment the patient treatment healthcare strategies for tackling COVID-19 and its impacts. Detecting this disease from lung CT scan images is perhaps one of the fastest ways to diagnose patients. However, finding the presence of infected tissues and segment them from CT slices faces numerous challenges, including similar adjacent tissues, vague boundary, and erratic infections. To eliminate these obstacles, we propose a two-route convolutional neural network (CNN) by extracting global and local features for detecting and classifying COVID-19 infection from CT images. Each pixel from the image is classified into the normal and infected tissues. For improving the classification accuracy, we used two different strategies including fuzzy  Since December 2019, the world has been experiencing a new disease caused by SARS-CoV-2, which can cause asthma symptoms, acute respiratory malfunctioning, and even permanent changes to the biology of the lungs in patients regardless of their age limit. This disease was reported for the first time in Wuhan, Hubei province of China, and became a pandemic all over the world , 4. As tInflammation growths in the lung can pose significant risks to human health. The increasing occurrence of infected people among the population demands more effective treatments along with a cost-efficient procedure that relies on its primary diagnosis. Providing prompt and precise recognition of the infected tissue plays a key role in effective patient treatment and survival , 9.A CT scan or computed tomography scan as a routine tool and a high sensitivity for the diagnosis of COVID-19 are broadly employed in hospitals and can perform early screening for the defected tissue to recognize them precisely –12. DoctImage segmentation is a complex and challenging area of the biomedical engineering task that is affected by numerous aspects, including illumination, low contrast, noise, and irregularity of the objects. Segmentation refers to partitioning an image into different parts or regions based on similar characteristics in neighboring proximity.Deep learning systems, as a prominent segment of the rising artificial intelligence (AI) technology in recent years, have been reported with significantly improved diagnostic accuracy in medical imaging , 17. TheMinaee et al.  investigA 3D deep convolutional neural network (DeCoVNet) proposed in  for deteEarly-phase detection of Coronavirus proposed by  which emTo overcome the limitations of previous works, a new hybrid algorithm for finding the location and boundary of the infected tissue from clinical CT images which takes advantage of clustering, local descriptor, and convolutional neural network is introduced. It is broadly considered to be challenging to find the exact location of the lesions inside the lung and extract their borders precisely due to the impact of the COVID-19 which caused the much similar intensity values across the lung. The growing progress of deep learning in all areas of image processing was a great motivation for this study. This work is interested to investigate the power of a CNN model for detecting and segmenting the infected regions inside the lung due to the COVID-19.Z score normalization technique is represented. In The remaining parts of this paper are organized as follows. In As indicated in , due to As the stochastic noise is the principal source of noise in these kinds of imaging, the bad effects can be diminished during the imaging procedure by increasing the amount of photons . However, in obtained images from any hospital or medical center, a significant amount of noise is observed which needs to be removed before starting the process of the segmentation.Z score normalization technique is employed so that all the nonzero values inside the image have a unit variance and zero mean  or automated recognize parts (such as mean-shift method) based on certain criteria such as differences in the color, magnitude, and location [c-means (FCM) algorithm used in our work is an unsupervised data dividing/splitting strategy. In this method, data is split into n predefined natural groupings, namely, the so-called clusters such that every single pixel in the dataset be owned by at least two clusters with dissimilar weights. In this fuzzy partitioning technique, finding the cluster center of each segment and related pixels are accomplished through an iterative optimization of the objective function [k shows the center of the kth cluster and pixeli illustrates the ith sample of I, μki outlines the membership value of the ith sample with respect to the kth cluster which is linked inversely to the distance from pixeli to the cluster center centerk, m defines the number of clusters, t refers to the level of cluster fuzziness, and N denotes the number of pixels image pixels I.After detecting the borders of the lungs with high accuracy, we need to recognize the volume and border of the infected areas inside the lungs more efficiently. The image of the detected lungs achieved from the previous stage has to be clustered to segment the infected areas from the other organs (background tissue). Clustering can be outlined as an unsupervised strategy that is aimed at fragmenting the input data  into the predefined segments  and local binary pattern (LBP) feature descriptors are easy to implement and be influenced by the change of the pixel intensity of nearest-neighbor  in clockwise or counterclockwise to alter (encoding) the low-level information of a spot, edges, curve, and line inside an image and calculate the outcome as a binary value , 38. OwiIn our work, the first phase for encoding the chest images is to define the location and value of all significant edges. This is implemented by operating 8 directions of Kirsch kernels (filters) that are rotated by 45° in 8 main compass directions . These nAutomated recognition of patterns in data by computers based on knowledge already obtained is called pattern recognition. It has applications in image analysis, information retrieval, signal processing, bioinformatics, data compression, statistical data analysis, computer graphics, and machine learning , 41–44.In machine learning approaches and applications, the convolutional neural network (CNN) structures demonstrate a high capability to extract and classify some key features and bridging the gap between the capabilities of machines and humans –47. The This neuron-based pipeline that captures temporal and spatial dependencies has a grid-like topology and permits us for extracting characteristics powerfully from the 1D or 2D input data by passing through a stack of convolution layers with the predefined dimension of the filters , 50, 51.This structure is able to extract high-level features automatically from raw input features, which are considerably powerful than human-designed features. The core building block of a CNN is outlined as the convolutional layer which calculates the dot product between input data and a set of learnable filters, much like a traditional neural network , 52–54. Mostly, the spatial-temporal dependencies at various scales are able to be effectively obtained by the convolutional layers. The dimension of the kernel which defines the dimension of the receptive field needs to be selected based on the depth of the applied 1D, 2D, or 3D data. Also, stride defines how much the convolution filter can be moved at each step. Moreover, the bigger strides lead to less overlap between the receptive fields  .The high-level features are extracted  in the deeper convolutional layers of the model, while the first convolutional layers are responsible for mining the low-level information including curves, edges, and points. It should be mentioned that the numbers of columns and rows for each filter need to be an odd number, for instance, 9 × 9, 7 × 7, and 3 × 3 .It is noteworthy that the dimension of the extracted features in the last convolutional layer is greatly smaller than the input matrix (1D or 2D matrix). The diminution in the width and height of the image relies upon the length of the strides and the filter size employed for the convolution procedure.The output of the convolution layer is fed to the activation layer in order to help the network learn complex patterns . This laIn this study, the nonlinearity (ReLU) activation function has been employed to shift the negative values to zero. The ReLU act as a linear function for the positive and zero values. As all negative values change to the zero number, it leads some nodes to completely die and not learn anything. It means fewer neurons in the model would activate because of the limitations imposed by this layer.Train deep networks: the architecture with large labeled datasets is able to reach the best performance on purely supervised tasksLinear behavior: the procedure of decreasing the cost function (optimization) in the CNN is much easier if their behavior could be close to a linear mannerRepresentational sparsity: as the ReLU layer shift the negative input values to the zero values, it causes some of the neurons in the hidden layers in neural networks to have zero values. In other words, by removing the effect of some neurons with zero weight, an accelerating in the learning process can be achieved which is called a sparse representationh and Sigmoid activation functions, ReLU consists of only simple operations in terms of computation so that computing the exponential function in activations can be eliminated and therefore much more practicable to implement in modelsComputational simplicity: dissimilar to the tanSome of the most important benefits of the ReLU layer can be expressed as follows –60:TraiThe ReLU layer does not cause the vanishing gradient problem and avoid easy saturation. Also, due to overcoming the vanishing gradient issue, models are permitted to learn faster and perform better. Equation  outlinesx demonstrates the input value and f(x) is its related output.where Since in object recognition applications, there is evidence that demonstrates the form, dimension, color, or position of the object has no matter, only the spatial variances need to be investigated. In order to accomplish this, a downsampling layer is applied by summarizing the key information in patches of each feature map without losing any details that lead to a good classification. In contrast to the convolution operation, the pooling layer has no parameters and only slides a window over its input, and simply takes the predefined value  in the window. Furthermore, as the quantity of pixels in this layer (in both row and column) is dropped, it leads to shortening the training time and combats overfitting , 61–63.An appropriate technique for dimensionality reduction of feature maps is to reduce the number of parameters and computation in the network so that the model can be robust to alter the high-frequency information (key information) and preserves vital features . This diIn a CNN structure , since the receptive field in the last convolutional layer does not cover the entire spatial dimension of the image, the generated features by the last convolutional layer correspond to a section of the input image. Therefore, one or some FC layers are obligatory in such a scenario. A fully connected layer (FC) allows the model to learn the nonlinear combinations of the high-level features in an input image.Each node in the fully connected layer produces a single output with its learnable corresponding weight that is linked to all the activations in the previous nodes . It is nFor the training step, since we are not working with a big dataset with hundreds of different samples from many patients, it is enormously easy for the CNN-based models to converge or to be specialized according to its reliability level and application area (to be less intelligent). To overcome this issue, there are two main strategies: (1) transform learning and (2) data augmentation.The transform learning method is utilized to bring some trained biases and weights into any pipeline rather than select them randomly at the first step. Data augmentation is a popular method for artificially boosting the number of training examples , 70.As mentioned before, CNNs are used to explore significant details from an input of raw pixels more efficiently. Hence, in this study, we investigated the probability of the presence of the lesions caused by CVOID-19 using a novel model based on the combination of global and local features. Moreover, to maximize the segmentation accuracy for even small damaged healthy tissue, the proposed approach concludes three distinct input images instead of a single one. The three input images include original image, fuzzy clustered image, and encoded image (LDN). These three different inputs enable our model to handle many types of variability in the raw input pixels. The flowchart of the proposed structure is shown in detail in When we use CNNs for automatic feature extracting that are effective for various tumor or lesion detection problems, the need for preprocessing and highlighting the suspect regions is significantly reduced. This is due to the fact that the CNN-based structures have millions of parameters that are able to produce the best suited feature maps for expressing the class probability. Although numerous CNN pipelines have been recommended for lesion segmentation in recently published papers, none of them has concentrated on applying the combined the textural encoding algorithm, fuzzy clustered, and raw image pixels as an input to a CNN structure. Since miscellaneous texture or images definitely encompass complementary and detailed information (features), our experimental outcomes for small samples (data) imply that this complex two-path strategy is effective to enhance the score of the evaluation indexes.While analyzing the complex texture of our input images, due to many similarities of the lesion (infected area) with normal tissue in the margin of the lesion, semiglobal and local features must be taken into account. Moreover, the lesions may appear anywhere on the lung since COVID-19 has a multifocal distribution that for gaining better results need to have knowledge of neighbor information in a little further of each analyzing pixel location. As is clearly indicated in In contrast to some other recently published methods such as studies by Hu et al. , Wang etIn our model, two distinct routes are employed; the first one (upper path) comprises of the five convolutional layers for extracting the global features. The other path (bottom path) utilizes two convolutional layers for extracting the local features. The local and global investigation windows (patches) are 25 × 25 and 60 × 60, respectively.The semiglobal patches are employed for providing key details about the analogous touching textures with scar tissues, while the local patches are applied more for recognizing inflammation in the tiny air sacs. Moreover, the outcome of our strategy for inflammation detection highly depends on information extracted from the global windows. In The size of the local region is 25 × 25 × 3, which three implies three distinct input images. The selected regions are convolved using 64 kernels to generate the feature maps based on the 3 × 3 receptive field. In the next layer, the number of filters is changed to 128 with the same receptive field. After producing feature maps in the first layer, the max pooling layer is not, while after the second layer, max pooling decreases the dimension of the produced feature maps.Unlike the local features extraction path, in the global feature extraction procedure, five convolutional layers are employed. In this path, only two intermediate layers are employed that are using the max pooling approach. All extracted feature maps with the size of 9 × 9 at the end of each route are concatenated to create 384 feature maps in order to use in the next convolutional layer. After the concatenation step, 128 kernels are applied to these feature maps, and then, a max-pooling layer changes the all dimensions to the 4 × 4. Then, all created feature maps are transformed into a 2048 × 1 feature vector. Lastly, by applying a Softmax layer, all extracted data are tagged to one of two expected classes .For minimizing the cross-entropy loss, the proposed CNN structure with two routes was learned through stochastic gradient descent (SGD) in 1000 epochs with a batch size of 128 , in Equai implies the loss value for training data i, and UK demonstrates the raw production score  for the reference class K.where lossL. Since two output neurons are presented at the output layer, in the above equation, L is equal to 2.The unnormalized production score is generated by multiplying the outputs from the previous FC layer with the parameters of the corresponding logistic unit. To find the normalized scores for each class between 0 and 1, the denominator aggregates the scores for all the logistic units https://github.com/UCSD-AI4H/COVID-C. To segment the corrupted tissues accurately, four experienced specialists segmented the borders manually. It is noteworthy that by employing an augmentation strategy to increase the number of data, a lot of new samples are generated. Also, 70% of data for training, 10% for validating, and 20% for testing are used. Data augmentations are useful approaches to decrease the validation and training errors. The augmentation methods artificially inflate the training dataset size by either data oversampling or warping. When in the augmentation process, the labels of the existing images are preserved; this process is called data warping augmentations. This method includes augmentations such as color and geometric transformations, adversarial training, random erasing, and neural style transfer. Oversampling augmentations generate synthetic samples and add them to the training set [The proposed novel technique and three recently published models were investigated on a public chest dataset  to evaluning set .Six approaches of data augmentation are utilized in this paper to increase efficiency, namely, flipping, color space, rotation, translation, noise injection, color space transformations, and random erasing.n × m patch of an image is randomly selected and masking it with zero values.In flipping, a horizontal axis flipping is used. In the color space, contrast enhancing is employed. In rotation, 180 degrees is selected. In translation, left, right, up, and down are applied. In noise injection, a Gaussian distribution is utilized. In the color space transformations, decreasing and increasing the pixel values by a constant value are applied. In random erasing, an F score, ASD (average surface distance), RVD (relative volume difference), RMSD (root mean square symmetric surface distance), MSD (maximum surface distance), VOE (volume overlap error), and DICE (Dice similarity) [In this study, the following nine measures were calculated by comparing the segmentation results with that of lesions segmented by the experts to appraise the proposed architecture's efficiency. The promising accuracy of the proposed two-path architecture was assessed using recall, precision, ilarity) , 75–77. Ms1 and Ms2 denote the result of segmentation using our strategy and ground-truth mask, respectively. Also, BMs1 and BMs2  imply the borders result of our segmentation technique and ground-truth image, respectively. Moreover, the FN, FP, and TP represent false negative, false positive, and true positive, respectively [where ectively , 78.Dice similarity coefficient (DSC) is defined as one for a perfect segmentation and is a statistical tool for measuring the similarity between two sets of data. MSD measures the distance between the borders of each segmented object from its corresponding border in the groundruth image. Measuring the difference between the segmented object and related object in the groundtruth image can be calculated by RVD, in which the positive value implies oversegmentation and the negative value represents the undersegmentation result. It means that the best value is zero that indicates the segmented object is equal to the groundtruth image.Our two-path architecture was implemented in Python, and the experiments were run on an Intel(R) Core(TM)i7-3.4 GHz + GEFORCE GTX 1080 Ti GPU+16 gigabytes of RAM under the windows 10 (64-bit) operating system. The results of our pipeline using 3 distinct input images were appraised utilizing the corresponding ground-truths and reported in Tables Z score normalization technique, and CNN framework to accurate estimating borders, For exemplifying the significance of utilizing the grouping of the LDN encoding approach, Z score normalization and fuzzy clustering methods, our approach is more capable of enhancing the contrast near the border of the lung to obtain more accuracy in the distinction of an infected region and vague border of the lung. Considering the heterogeneous textures, opaque appearance of the infected tissue, misalignment of the infection boundaries, unclear borders, and different dimensions of the infection regions, it is more evident that our pipeline suitably finds a pattern most similar to the infected area, which demonstrates its robust performance under realistic scenarios on countless infection outlines. It worth mentioning that in all methods, the white tissue (pulmonary nodules) near the infected area cannot be properly recognized due to much similarity between both tissue values. The results may get better if the amount of training data is increased.As indicated in The proposed two-path CNN structure achieved a higher segmentation performance than the other three evaluated methods when other representations of the lung images are applied; meaning more substantial features are available to achieve the best distinction between classes. The efficiency of our technique on different CT infected lungs was assessed using the Dice similarity index, as illustrated in Tables F score are highlighted in bold. The outcomes of every first five assessment criteria are demonstrated by standard deviation and mean values in c-means algorithms are less than 0. Also, adding the LDN method to the proposed CNN model leads to observe the positive value in the RVD result. The RMS score imply that the proposed CNN+fuzzy c-means+LDN and proposed CNN+LDN methods produced the best outcomes among the seven structures. Also, the DenseNet201 technique gains the highest mean score of RMS.In addition, the mean value of MSD and VOE of the models employed by DenseNet201 and weakly supervised framework were outstandingly higher as compared to our outcomes. Moreover, both the weakly supervised deep learning and the weakly supervised framework models show a large standard deviation in the RVD; however, a major standard deviation in MSD score is obtained in the DenseNet201 method. The observed results in the ASD and VOE indicate that adding LDN and fuzzy clustering methods to our CNN model can significantly improve our model accuracy.c-means+LDN, consistently performs the best among all approaches. The F score, precision, and recall of the DenseNet201 and weakly supervised deep learning structures are highly similar to the proposed CNN algorithm; however, by adding the LDN or fuzzy clustering approach, these three criteria are highly increased. Also, the DenseNet201 approach gains the worst results and our architecture obtains the competitive performance on lesions segmentation in all evaluation metrics.The results in Z score normalization technique to obtain a more distinguishable lung border from the original image. Then, by using a fuzzy clustering method, all tissues in the image are clustered and obtain a distinct pixel value for all pixels corresponding to each cluster. This approach helps the CNN pipeline for decreasing the convolutional layers for extracting some key features and leads to a drop in the training time of the pipeline and increase the final efficiency.In this study, we implemented a two-path CNN pipeline that incorporates the three distinct input images, to automatically segment the infected tissues inside the lung caused due to the COVID-19 from CT images. For a better demonstration of the tissues to extract more key features inside the CNN model, we showed the input CT image represented in the two other different ways which each of them includes some unique information. Due to inflammation inside the lung because of COVID-19, infected areas near the border of the lung are highly difficult to segment. So, our algorithm first employed a Then, an LDN encoding approach was implemented for representing the information of the images in another form to extract more essential details from the input image. This strategy roots in the fact that sometimes by changing the representation domain (like frequency domain rather than the time domain) some other substantial features can be observed.We also represented a new two-route CNN model that considered semiglobal and local information to categorize each pixel in the input image to one of the two normal and infected tissues. The number of the convolutional layers in the global route is more than the local route, while the kernel size for all convolutional layers is the same. To overcome the overfitting problems and boost efficiency, using data augmentation methods, the number of samples has been increased. Lastly, using the CT image and two obtained images, our CNN structure was trained.The suggested two-route segmentation pipeline was appraised on a public dataset which 70% of data for training, 10% for validating, and 20% for testing were used. Our significant findings demonstrate that our CNN pipeline and three distinct input images gained the following: (1) acceptable performance even if the infected area shared an extended border with touching tissues, (2) appropriately robust as indicated by the negligible standard deviations which show the uniformity of the values for all the nine criteria, and (3) accomplished well in the detection and segmentation process even for the intricate cases with numerous unalike categories of the infection, which had the amoeboid shapes and analogous thicknesses.The proposed architecture satisfactorily overcomes the difficulty of failing in accurate detection of the lesions at the presence of the similar adjacent tissues and identification of an uneven border where it seemed to not properly appear to exist with an aim to reach superior outcomes. In addition, the employed technique does not require more extra parameters for feeding into the algorithm apart from one CT image to define the position of the lesions and border detection. But the functional limitation of this architecture is that the white matter (pulmonary nodules) inside the normal lung near the border of a lesion cannot properly be recognized from the infected tissue. We think that by increasing the training samples this problem can be solved.Tables


Quantum computers are invaluable tools to explore the properties of complex quantum systems. We show that dynamical localization of the quantum sawtooth map, a highly sensitive quantum coherent phenomenon, can be simulated on actual, small-scale quantum processors. Our results demonstrate that quantum computing of dynamical localization may become a convenient tool for evaluating advances in quantum hardware performances. The simulation of complex systems is expected to be one of the main applications of future quantum computers . The speDynamical localization is one of the most interesting phenomena that characterize the quantum behavior of classically chaotic systems: quantum interference effects suppress the diffusion taking place in the underlying classical model, leading to exponentially localized wave functions. This phenomenon, first discovered in the quantum kicked-rotor model ,18, has In this paper, we simulate dynamical localization with three qubits by means of several IBM quantum processors, with the aim of analyzing the performances of current noisy quantum hardware, freely available for cloud quantum computing. In particular, we use the known quantum algorithm for the quantum sawtooth map ,27, whicHere we show that it is possible to detect quantum localization with The paper is organized as follows. In T. The evolution from time t-th kick) to time U acting on the wave function The most convenient model to simulate dynamical localization on a quantum computer is the quantum sawtooth map. This map describes the dynamics of a periodically driven system, as derived from the HamiltonianU, introduced in Equation  applied to qubit or model ,18, auxiodel [Uk , thus inI representation is obtained by means of the quantum Fourier transform (QFT), which requires n Hadamard (H) gates and The change from the I representation, the operator In the We return to the initial Overall, this quantum algorithm requires N. With the above discussed improvements of the quantum circuit, we need P gates are obtained by changing order of the gates and combining them. The explicit formulas for the angles are given as the following:The map  can be eEquation , is the cuit see  avoids St of map iterations,The classical limit of the quantum sawtooth map is obtained for D. However, after a break time m singles out the momentum eigenstates t⋆ and ℓ lima quantum processor. This machine has a nominal quantum volume We simulate dynamical localization with formance ), which As we can see in lima, belem, and quito . In lima processor). On the other hand, the worst performances are obtained for smaller yorktown processor). However, large fluctuations around this average behavior can be noticed. This is expected, since the data from the Qiskit simulator that are obtained by assuming the same nominal parameters used to define In order to quantitatively relate the effectiveness of the dynamical localization quantum algorithm to the error rate, we compare the height of the localization peak to the noise level in each of the analyzed devices, as measured from the day-to-day calibrations . As a measure of the quality of the qubits, we define an average decoherence time ibmqx2 - yorktown quantum processor the peak at lima quantum processor the peak is visible up to ibmx2-yorktown) and in 10–15 kicks for lima. Overall, the data of Finally, with the aim of further testing the capabilities of quantum processors to simulate dynamical localization, we show in n of qubits for which the peak height after one kick is reproduced with a given accuracy. Since the number of elementary quantum gates required to simulate a single step of the map scales as n. The results of this work show that, for We have simulated the dynamical localization phenomenon of the quantum sawtooth map by means of several freely available IBM quantum processors, remotely accessed through cloud quantum programming. The localization peak emerges from noise, even though significantly reduced with respect to the noiseless dynamics, with up to three steps of the sawtooth map model for three qubits. Our results demonstrate a significant improvement of the performances of the available quantum processors over the past few years. This study shows that quantum computing of dynamical localization may be employed as a fast and convenient benchmark to check the performance of future generations of quantum processors. In particular, we could envision studying the maximum number