Biochar enhances the growth and physiological characteristics of Medicago sativa, Amaranthus caudatus and Zea mays in saline soils.

Biochar is a promising solution to alleviate the negative impacts of salinity stress on agricultural production. Biochar derived from food waste effect was investigated on three plant species, Medicago sativa, Amaranthus caudatus, and Zea mays, under saline environments. The results showed that biochar improved significantly the height by 30%, fresh weight of shoot by 35% and root by 45% of all three species compared to control (saline soil without biochar adding), as well as enhanced their photosynthetic pigments and enzyme activities in soil. This positive effect varied significantly between the 3 plants highlighting the importance of the plant-biochar interactions. Thus, the application of biochar is a promising solution to enhance the growth, root morphology, and physiological characteristics of plants under salt-induced stress.

FOXO inhibition rescues α-defensin expression in human intestinal organoids.

To mediate critical host-microbe interactions in the human small intestine, Paneth cells constitutively produce abundant levels of α-defensins and other antimicrobials. We report that the expression profile of these antimicrobials is dramatically askew in human small intestinal organoids (enteroids) as compared to that in paired tissue from which they are derived, with a reduction of α-defensins to nearly undetectable levels. Murine enteroids, however, recapitulate the expression profile of Paneth cell α-defensins seen in tissue. WNT/TCF signaling has been found to be instrumental in the regulation of α-defensins, yet in human enteroids exogenous stimulation of WNT signaling appears insufficient to rescue α-defensin expression. By stark contrast, forkhead box O (FOXO) inhibitor AS1842856 induced the expression of α-defensin mRNA in enteroids by >100,000-fold, restoring DEFA5 and DEFA6 to levels comparable to those found in primary human tissue. These results newly identify FOXO signaling as a pathway of biological and potentially therapeutic relevance for the regulation of human Paneth cell α-defensins in health and disease.

The impact of surgical weight loss procedures on the risk of metachronous colorectal neoplasia: the differential effect of surgery type, sex, and anatomic location.

Patients with prior colorectal polyps are at high risk for metachronous colorectal neoplasia, especially in the presence of obesity. We assessed the impact of 2 common bariatric surgeries, vertical sleeve gastrectomy and roux-n-Y gastric bypass, on the risk of colorectal neoplasia recurrence. This nationally representative analysis included 1183 postbariatric adults and 3193 propensity score-matched controls, who all had prior colonoscopy with polyps and polypectomy. Colorectal polyps reoccurred in 63.8% of bariatric surgery patients and 71.7% of controls at a mean follow-up of 53.1 months from prior colonoscopy. There was a reduced odds of colorectal polyp recurrence after bariatric surgery compared with controls (odds ratio [OR] = 0.70, 95% confidence interval [CI] = 0.58 to 0.83). This effect was most pronounced in men (OR = 0.58, 95% CI = 0.42 to 0.79), and post roux-n-Y gastric bypass (OR = 0.57, 95% CI = 0.41 to 0.79). However, the risk of rectal polyps or colorectal cancer remained consistent between groups. This study is the first to our knowledge to show a reduction in risk of polyp recurrence following bariatric surgery.

A model for emergency supply management under extended EDAS method and spherical hesitant fuzzy soft aggregation information.

Due to the frequent occurrence of numerous emergency events that have significantly damaged society and the economy, the need for emergency decision-making has been manifest recently. It assumes a controllable function when it is critical to limit property and personal catastrophes and lessen their negative consequences on the natural and social course of events. In emergency decision-making problems, the aggregation method is crucial, especially when there are more competing criteria. Based on these factors, we first introduced some basic concepts about SHFSS, and then we introduced some new aggregation operators such as the spherical hesitant fuzzy soft weighted average, spherical hesitant fuzzy soft ordered weighted average, spherical hesitant fuzzy weighted geometric aggregation, spherical hesitant fuzzy soft ordered weighted geometric aggregation, spherical hesitant fuzzy soft hybrid average, and spherical hesitant fuzzy soft hybrid geometric aggregation operator. The characteristics of these operators are also thoroughly covered. Also, an algorithm is developed within the spherical hesitant fuzzy soft environment. Furthermore, we extend our investigation to the Evaluation based on the Distance from Average Solution method in multiple attribute group decision-making with spherical hesitant fuzzy soft averaging operators. And a numerical illustration for "supply of emergency aid in post-flooding the situation" is given to show the accuracy of the mentioned work. Then a comparison between these operators and the EDAS method is also established in order to further highlight the superiority of the established work.

Unsteady hybrid nanofluid ([Formula: see text], MWCNTs/blood) flow between two rotating stretchable disks with chemical reaction and activation energy under the influence of convective boundaries.

Hybrid nanofluids are extensively analyzed in recent studies due to their better performance in numerous areas such as heat and mass transfer enhancement, biological fluid movement, medical equipment, heat exchangers, electronic cooling and automotive industry. In current study the nanoparticle concentration utilized is much important in biomedical industry. Major applications include drug delivery, radio-pharmaceuticals, centrifuging blood to obtain red blood cells and plasma, medical implants, onco therapeutics and photo thermal cancer therapy. In this regard, the primary focus of this study is to simulate a blood based unsteady hybrid nanofluid flow between two rotating, stretching disks and convective boundaries. The two nanoparticles in this study are uranium dioxide [Formula: see text] and multi-walled carbon nanotubes MWCNTs. The hybrid nanofluid is under the influence of magnetohydrodynamic effects and chemical reaction with activation energy. The governing partial differential equations (PDEs) are transformed into ordinary differential equations (ODEs) using suitable similarity transform. Homotopy analysis method is used to solve the non-linear system of ODEs and [Formula: see text]-curves are plotted to find suitable region of [Formula: see text] for convergent series solution. Velocity profile is examined for axial, radial and tangential direction against various fluid parameters. Temperature and concentration profiles are analyzed for both convective and non-convective cases. It is observed that convective boundaries result in elevated temperature when compared with non-convective case. Moreover, skin friction, heat and mass transfer rates are also examined with respect to changing volume fraction [Formula: see text].The results revealed that skin friction and rate of heat transfer increases with increase in volume fraction of both nanoparticles [Formula: see text] and MWCNTs while the mass transfer rate depicts contrasting behavior.

Partial differential equations modeling of bio-convective sutterby nanofluid flow through paraboloid surface.

In this research article, the behavior of 2D non-Newtonian Sutterby nanofluid flow over the parabolic surface is discussed. In boundary region of surface buoyancy-driven flow occurred due to considerable temperature differences produced by the reaction happen between Sutterby nanofluid and catalyst at the surface. Free convection which is sighted easily on the parabolic surface is initiated by reaction on the catalyst surface modeled the 1st order activation energy. Applications of parabolic surfaces are upper cover of bullet, car bonnet, and air crafts. Under discussion flow is modelled mathematically by implementing law of conservation of microorganism's concentration, momentum, mass and heat. The governing equations of the system is of the form of non-linear PDE's. By the use of similarity transform, the governing PDE`s transformed as non-dimensional ODE's. The resultant system of non-dimensional ODE's are numerically solved by built-in function MATLAB package named as 'bvp4c'. Graphical representation shows the influence of different parameters in the concentration, velocity, microorganisms and temperature profiles of the system. In temperature profile, we examined the impact of thermophoresis coefficient Nt (0.1, 0.5, 1.0), Prandtl number Pr (2.0, 3.0, 4.0), and Brownian motion variable Nb (0.1, 0.3, 0.5). Velocity profile depends on the non-dimensional parameters i.e. (Deborah number De & Hartmann number Ha) and found that these numbers (De, Ha) cause downfall in profile. Furthermore, mass transfer, skin friction, and heat transfer rates are numerically computed. The purpose of the study is to enumerate the significance of parabolic surfaces for the transport of heat and mass through the flow of bio-convective Sutterby nanofluid.

Crosswise Stream of Cu-H2O Nanofluid with Micro Rotation Effects: Heat Transfer Analysis.

The present study focuses on a crosswise stream of liquid-holding nano-sized particles over an elongating (stretching) surface. Tiny particles of copper are added into base liquid (water). The influence of the micro rotation phenomenon is also considered. By means of appropriate transformations non-linear coupled ordinary differential equations are attained that govern the flow problem. The Runge-Kutta-Fehlberg scheme, together with the shooting method, is engaged to acquire results numerically. Micropolar coupling parameter, microelements concentration and nanoparticles volume fraction effects are examined over the profiles of velocity, temperature and micro-rotation. Moreover, heat flux and shear stress are computed against pertinent parameters and presented through bar graphs. Outcomes revealed that material constant has increasing effects on normal components of flow velocity; however, it decreasingly influences the tangential velocity, micro-rotation components and temperature profile. Temperature profile appeared to be higher for weak concentration of microelements. It is further noticed that normal velocity profile is higher in magnitude for the case of strong concentration (n = 0) of microelements, whereas tangential velocity profile is higher near the surface for the case of weak concentration (n = 0.5) of microelements. An increase of 3.74% in heat flux is observed when the volume fraction of nanoparticles is increased from 1 to 5%.

The restoration and erection of the world's first elevated obelisk.

Obelisks presented an important element in the architecture of ancient Egypt. This research is concerned with the re-erection of an obelisk that belongs to the famous Pharoah Ramses II. It was found broken and was transported to the Grand Egyptian Museum for restoration and display. An observation of Ramses II Cartouche at the bottom side of the obelisk base inspired the authorities to provide an innovative architectural design to display the obelisk elevated. The supporting structure was designed to allow the visitors to walk underneath the obelisk and observe Ramses II's signature. The idea of elevating the obelisk presented several challenges including evaluating the obelisk's current condition, restoration and fixation methodology, structural stability, and uncertainties of material characteristics, amongst others. To control the obelisk deformations under lateral loading, state-of-the-art base isolators were introduced. For the task to be achieved, a multidisciplinary team including historians, conservators, archaeologists, architects, and engineers with different specialties was appointed. The team performed the task successfully and currently, the obelisk stands at the entrance piazza of the Grand Egyptian Museum representing the world's first elevated obelisk.

A computational supervised neural network procedure for the fractional SIQ mathematical model.

The purpose of the current work is to provide the numerical solutions of the fractional mathematical system of the susceptible, infected and quarantine (SIQ) system based on the lockdown effects of the coronavirus disease. These investigations provide more accurateness by using the fractional SIQ system. The investigations based on the nonlinear, integer and mathematical form of the SIQ model together with the effects of lockdown are also presented in this work. The impact of the lockdown is classified into the susceptible/infection/quarantine categories, which is based on the system of differential models. The fractional study is provided to find the accurate as well as realistic solutions of the SIQ model using the artificial intelligence (AI) performances along with the scale conjugate gradient (SCG) design, i.e., AI-SCG. The fractional-order derivatives have been used to solve three different cases of the nonlinear SIQ differential model. The statics to perform the numerical results of the fractional SIQ dynamical system are 7% for validation, 82% for training and 11% for testing. To observe the exactness of the AI-SCG procedure, the comparison of the numerical attained performances of the results is presented with the reference Adam solutions. For the validation, authentication, aptitude, consistency and validity of the AI-SCG solver, the computing numerical results have been provided based on the error histograms, state transition measures, correlation/regression values and mean square error. Supplementary Information: The online version contains supplementary material available at 10.1140/epjs/s11734-022-00738-9.

Radio Frequency Identification Temperature/CO2 Sensor Using Carbon Nanotubes.

In the world of digitization, different objects cooperate with the Internet of Things (IoT); these objects also amplify using sensing and data processing structures. Radio frequency identification (RFID) has been identified as a key enabler technology for IoT. RFID technology has been used in different conventional applications for security, goods storage, transportation and asset management. In this paper, a fully inkjet-printed chipless radio frequency identification (RFID) sensor tag is presented for the wireless identification of tagged objects. The dual polarized tag consists of two resonating structures functioning wirelessly. One resonator works for encoding purpose and other resonator is used as a CO2/temperature sensor. The sensing behavior of the tag relies on the integration of a meandered structure comprising of multi-wall carbon nanotubes (MWCNT). The MWCNT is highly sensitive to CO2 gas. The backscattered response of the square-shaped cascaded split ring resonators (SRR) is analyzed through a radar cross-section (RCS) curve. The overall tag dimension is 42.1 mm × 19.5 mm. The sensing performance of the tag is examined and optimized for two different flexible substrates, i.e., PET and Kapton®HN. The flexible tag structure has the capability to transmit 5-bit data in the frequency bands of 2.36-3.9 GHz and 2.37-3.89 GHz, for PET and Kapton®HN, respectively. The proposed chipless RFID sensor tag does not require any microchip or a power source, so it has a great potential for low-cost and automated temperature/CO2 sensing applications.

A mathematical model of coronavirus transmission by using the heuristic computing neural networks.

In this study, the nonlinear mathematical model of COVID-19 is investigated by stochastic solver using the scaled conjugate gradient neural networks (SCGNNs). The nonlinear mathematical model of COVID-19 is represented by coupled system of ordinary differential equations and is studied for three different cases of initial conditions with suitable parametric values. This model is studied subject to seven class of human population N(t) and individuals are categorized as: susceptible S(t), exposed E(t), quarantined Q(t), asymptotically diseased IA (t), symptomatic diseased IS (t) and finally the persons removed from COVID-19 and are denoted by R(t). The stochastic numerical computing SCGNNs approach will be used to examine the numerical performance of nonlinear mathematical model of COVID-19. The stochastic SCGNNs approach is based on three factors by using procedure of verification, sample statistics, testing and training. For this purpose, large portion of data is considered, i.e., 70%, 16%, 14% for training, testing and validation, respectively. The efficiency, reliability and authenticity of stochastic numerical SCGNNs approach are analysed graphically in terms of error histograms, mean square error, correlation, regression and finally further endorsed by graphical illustrations for absolute errors in the range of 10-05 to 10-07 for each scenario of the system model.

Numerical performances through artificial neural networks for solving the vector-borne disease with lifelong immunity.

The current study is related to solve a nonlinear vector-borne disease with a lifelong immunity model (VDLIM) by designing a computational stochastic framework using the strength of artificial Levenberg-Marquardt backpropagation neural network (ALMBNN). The detail of the nonlinear VDLIM is provided along with its five classes. The numerical performances of the results have been presented using the ALMBNN by taking three different cases to solve the nonlinear VDLIM using the training, sample data, testing and authentication. The selection of the statics is selected as 80% for training, while the data for both testing and validations is applied 10%. The results of the nonlinear VDLIM are performed using the ALMBNN and the correctness of the scheme is observed to compare the results with the reference solutions. The calculated performance of the results to solve the nonlinear VDLIM is applied for the reduction of the mean square error. In order to check the competence, efficacy, exactness and reliability of the ALMBNN, the numerical investigations using the proportional procedures based on the MSE, correlation, regression and error histograms are presented.

Exocrine pancreatic insufficiency after bariatric surgery: a bariatric surgery center of excellence experience.

INTRODUCTION: Gastrointestinal symptoms such as diarrhea, bloating, abdominal pain, and nausea are common after bariatric surgery (BS) and can lead to significant morbidity. While many diagnoses can explain these symptoms, post-bariatric exocrine pancreatic insufficiency (EPI) is becoming increasingly recognized as contributor to gastrointestinal symptoms. The frequency and outcomes of EPI after BS are not well understood. We investigated the prevalence and outcomes of EPI over 18 years at a tertiary bariatric referral center. METHODS: A retrospective review of patients who underwent primary or revisional BS from 2002 to 2020 was performed. Patients were included if they were suspected of having EPI or underwent fecal elastase testing (FE-1). EPI diagnosis was defined as positive FE-1 testing or improvement with empiric pancreatic enzyme replacement therapy (PERT). RESULTS: EPI was suspected in 261 patients, and 190 were tested via FE-1 (89.5%) or empirically treated (10.5%). EPI was diagnosed in 79 (41.6%) patients and was associated with older age and lower BMI. Therapeutic PERT was given to 65 patients diagnosed with EPI, and 56 (86.2%) patients reported improved symptoms. Patients who underwent RYGB and BPD-DS were more likely to have EPI than those after SG (47.9% and 70.0% vs 17.4%, p < 0.01). EPI diagnosis was associated with a history chronic pancreatitis. While diarrhea and abdominal pain were the most common symptoms prompting FE-1 testing, no symptoms were significantly associated with EPI. EPI was also associated with abnormal fecal fat results and treatment with bile acid sequestrants, but not small intestinal bacterial overgrowth. CONCLUSION: This study highlights that exocrine pancreatic insufficiency can account to for previously unexplained GI complaints after bariatric surgery. Therefore, bariatric surgery programs should consider this diagnosis in symptomatic patients, especially following RYGB and BPD-DS. Further work to define patient factors that should prompt evaluation, optimal treatment, and prevention is necessary.

Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model.

The present study is to investigate the Gudermannian neural networks (GNNs) using the optimization procedures of genetic algorithm and active-set approach (GA-ASA) to solve the three-species food chain nonlinear model. The three-species food chain nonlinear model is dependent upon the prey populations, top-predator, and specialist predator. The design of an error-based fitness function is presented using the sense of the three-species food chain nonlinear model and its initial conditions. The numerical results of the model have been obtained by exploiting the GNN-GA-ASA. The obtained results through the GNN-GA-ASA have been compared with the Runge-Kutta method to substantiate the correctness of the designed approach. The reliability, efficacy and authenticity of the proposed GNN-GA-ASA are examined through different statistical measures based on single and multiple neurons for solving the three-species food chain nonlinear model.

Thermal and solute aspects among two viscosity models in synovial fluid inserting suspension of tri and hybrid nanomaterial using finite element procedure.

Inclusion of nanoparticles boosts thermal performance and is essential for thermal transport. The current investigation has been made to conduct research on heat mass transport in synovial material with the mixing of hybrid and tri-hybrid comprising variable viscosity past over a heated surface having constant density and a steady environment. The conservation laws have been considered in the presence of Lorentz force, heat generation/absorption, modified heat and mass fluxes together with chemical reaction. The mathematical model is developed in Cartesian coordinate in the form of coupled partial differential equation (PDEs). The derived PDEs are simplified by a boundary layer approach (BLA) and reduced PDEs have been converted into ordinary differential equation (ODEs) using scaling group Similarity transformation. The converted ODEs are highly nonlinear and have been solved numerically by finite elements scheme (FES). The used scheme is effective for nonlinear problem and can be frequently utilized to tackle nonlinear problems arising in mathematical physics.

A neuro swarm procedure to solve the novel second order perturbed delay Lane-Emden model arising in astrophysics.

The current work provides a mathematical second order perturbed singular delay differential model (SO-PSDDM) by using the standard form of the Lane-Emden model. The inclusive structures based on the delay terms, singular-point and perturbation factor and shape forms of the SO-PSDDM are provided. The novel form of the SO-PSDDM is numerically solved by using the procedures of artificial neural networks (ANNs) along with the optimization measures based on the swarming procedures (PSO) and interior-point algorithm (IPA). An error function is optimized through the swarming PSO procedure along with the IPA to solve the SO-PSDDM. The precision, substantiation and validation are observed for three problems of the SO-PSDDM. The exactness of the novel SO-PSDDM is observed by comparing the obtained and exact solutions. The reliability, stability and convergence of the proposed stochastic algorithms are observed for 30 independent trials to solve the novel SO-PSDDM.

Utilization of Galerkin finite element strategy to investigate comparison performance among two hybrid nanofluid models.

The utilization of Fourier's law of heat conduction provides the parabolic partial differential equation of thermal transport, which provides the information regarding thermal transport for the initial time, but during many practical applications, this theory is not applicable. Therefore, the utilization of modified heat flux model is to be used. This work discusses the utilization of non-Fourier heat flux model to investigate thermal performance of tri-hybrid nanoparticles mixture immersed in Carreau Yasuda material past over a Riga plate by using Hamilton Crosser and Yamada Ota models considering the variable thermos-physical characteristics. The phenomenon presenting the transport of momentum and energy are developed in the form of coupled partial differential equations, which are complex and then transformed into ordinary differential equations by using an appropriate transformation. The transformed equations have been tackled numerically via finite element scheme and the authenticity of obtained solution is shown with the help of comparative analysis of present results with those are available in open literature.

Swarming morlet wavelet neural network procedures for the mathematical robot system.

The task of this work is to present the solutions of the mathematical robot system (MRS) to examine the positive coronavirus cases through the artificial intelligence (AI) based Morlet wavelet neural network (MWNN). The MRS is divided into two classes, infected I ( θ ) and Robots R ( θ ) . The design of the fitness function is presented by using the differential MRS and then optimized by the hybrid of the global swarming computational particle swarm optimization (PSO) and local active set procedure (ASP). For the exactness of the AI based MWNN-PSOIPS, the comparison of the results is presented by using the proposed and reference solutions. The reliability of the MWNN-PSOASP is authenticated by extending the data into 20 trials to check the performance of the scheme by using the statistical operators with 10 hidden numbers of neurons to solve the MRS.