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We consider a new mathematical model for the COVID-19 disease with Omicron variant mutation. We formulate in details the modeling of the problem with omicron variant in classical differential equations. We use the definition of the Atangana-Baleanu derivative and obtain the extended fractional version of the omicron model. We study mathematical results for the fractional model and show the local asymptotical stability of the model for infection-free case if

The COVID-19 infection which is still infecting many individuals around the world and at the same time the recovered individuals after the recovery are infecting again. This reinfection of the individuals after the recovery may lead the disease to worse in the population with so many challenges to the health sectors. We study in the present work by formulating a mathematical model for SARS-CoV-2 with reinfection. We first briefly discuss the formulation of the model with the assumptions of reinfection, and then study the related qualitative properties of the model. We show that the reinfection model is stable locally asymptotically when R

Solubility data of bioactive compound such as thymoquinone (THQ) are scarce in research community. Therefore, in present study, solubility, solubility parameters, solution thermodynamics and solute-solvent interactions (at molecular level) of THQ in eleven mono solvents including “water (H2O), propylene glycol (PG), polyethylene glycol-400 (PEG-400), methanol (MeOH), ethanol (EtOH), isopropanol (IPA), 1-butanol (1-BuOH), 2-butanol (2-BuOH), ethyl acetate (EA), dimethyl sulfoxide (DMSO) and Transcutol® (THP)” at five different temperatures i.e. “T = 298.2 K, 303.2 K, 308.2 K, 313.2 K and 318.2 K” and constant pressure “p = 0.1 MPa” were studied. The characterization of solid phases of THQ suggested no transformation of THQ into polymorphs/solvates. Measured solubility data of THQ was regressed by “van't Hoff and Apelblat models” with overall deviations of 1.00 and 0.56%, respectively. Experimental solubilities of THQ (expressed in mole fraction) were recorded highest in THP followed by 2-BuOH, 1-BuOH, IPA, PEG-400, EA, DMSO, EtOH, MeOH, PG and H2O at “T = 318.2 K”. Same tendencies were also recorded at each temperature point studied. Ideal/theoretical solubilities of THQ were estimated using its thermal parameters at five different temperatures. Ideal solubilities of THQ were recorded much higher than experimental solubilities of THQ at each temperature. With the help of ideal solubilities of THQ, activity coefficients were estimated. Based on estimated values of activity coefficients, highest interactions at molecular level were recorded in THQ-THP. “Apparent thermodynamic analysis” data showed “endothermic and entropy-driven dissolution” of THQ in each mono solvent studied.

The study explored the 3D numerical solution of an unsteady Ag-MgO/water hybrid nanofluid flow with mass and energy transmission generated by a wavy rotating disc moving up and down. The nanofluid is generated in the context of Ag-MgO nanomaterials. Magnesium oxide and silver nanoparticles have been heavily reported to have broad-spectrum antibacterial operations among metal oxides and metals. Silver nanoparticles are without a doubt the most commonly used inorganic nanoparticles, with numerous innovations in biomaterial’s detection and antimicrobial operations. However, in current paper, the intention of the analysis is to boost thermal energy transmitting rates for a range of industrial implementations. When compared to a flat surface, energy transition is increased up to 15% due to the wavy swirling surface. The problem has been formulated as a system of PDEs, which included the Navier Stokes and Maxwell equations. Following that, the modeled equations are reduced to a dimensionless system of differential equations. The derived equations are then solved numerically using the Parametric Continuation Method (PCM). The findings are displayed graphically and debated. The geometry of a spinning disc is thought to have a positive impact on velocity and heat energy transfer. The insertion of nanostructured materials (silver and magnesium-oxide) increased the carrier fluid’s thermal properties considerably. It is more effective at dealing with low energy transmission.

Carboplatin is amongst the most commonly used anticancer drugs for the management of several human malignancies. However, it has displayed genotoxic properties against normal cells. Evaluation of natural products for their protective effects against chemotherapeutic drug induced toxicity has been growing in recent years. A naturally occurring flavonoid, chrysin, has strong antioxidant abilities and protects against DNA impairment. This study used multiple assays to evaluate the levels of damage to DNA in normal cells and to examine any possible protective role of chrysin against such damage. Male BALB/c mice were administered chrysin orally in two doses of 20 and 40 mg/kg for 10 consecutive days and then a single injection of carboplatin [90 mg/kg body weight (b.w.)] was administered intraperitoneally to induce carboplatin toxicity. 24 h after the carboplatin injection, mice were sacrificed. DNA damage was evaluated using several genotoxicity tests (8-Hydroxydeoxy-guanosine marker, comet assay, micronucleus test, and chromosomal aberration assay) to identify diverse types of damage to the DNA. The results suggest that pretreatment with chrysin significantly decreased the level of DNA damage caused by carboplatin probably due to its potent antioxidant traits. Therefore, chrysin can be considered to be developed as a chemoprotective agent against chemotherapy associated side-effects.

Quinoa cultivation has expanded from South America to many countries because of its wide adaptability and nutritional value. We evaluated 32 introduced quinoa genotypes using 17 qualitative and 11 quantitative traits under Saudi Arabia conditions during the 2018–2019 season. The quinoa genotypes showed considerable variation during the vegetative and maturity stages. Plant height values varied between 60 and 18 cm, and maturity ranged from 98 to 177 days. Leaf shapes were rhomboidal or triangular, with dentate or serrate margins. Green was the standard color for leaves and panicles at the flowering stage. The leaf granule colors were white, purple, and white-red. At 150 units of the Euclidean distance, the genotypes aggregated into four major groups based on their morphological traits. Twenty-one sequence-related amplified polymorphism (SRAP) primer pair combinations generated 75 amplified fragments (alleles), with a mean of 3.57 alleles per primer pair combination. Unweighted Pair-Group Method with Arithmetic means (UPGMA) clustering analysis showed that the quinoa genotypes were grouped based on origin or according to genetic background. Genotypes from South America presented higher mean values for the average number of alleles, Shannon index (0.411), gene diversity (0.271), and polymorphic percentage (83.95). Analysis of molecular variance (AMOVA) showed that most of the genetic variation was because of differences within populations (86%). The wide variability of the genotypes studied herein is of great importance for quinoa breeders.

Recent technological advances have led to an increase in the adoption of Unmanned Aerial Vehicles (UAVs) in a variety of use-case scenarios. In particular, Departments of Transportation in several states in the United States have been exploring the use of UAVs for bridge and infrastructure inspections to improve safety and reduce the costs of the inspection process. UAVs are remotely piloted from a cockpit or a ground station via radio channels. The UAV's state information and payload information are also transmitted to the cockpit/ground station via radio frequency (RF) signals. The RF channels that are commonly used by most UAVs are 72–73, 902–928 and 2400-2483.5 MHz bands, which is also shared by several other communication protocols such as, WiFi and ZigBee networks, and therefore, the interference effects with the other services on the UAV's operation performance cannot be overlooked, particularly to maintain the minimum distance from the close by surfaces while flying alongside and underneath the bridges to achieve the best results. The loss of signal or even signal strength during such close flights can cause damage to the UAV. Especially while inspecting the bridges located in the urban areas that involve a lot of RF communication around due to presence of sever RC devices providing different services. Conventional Electromagnetic Compatibility (EMC) adherence requirements imposed on electronic systems are not adequate for UAVs due to their airborne nature and the presence of the other RF sources in the environment. Thus, in this work, we investigate the compliance of EMC requirements by designing and conducting field experiments to expose the UAVs to electromagnetic interference and distortions that are likely to be encountered during the UAV operation. The results of this work will enable us to assess the level of RF immunity of the general-purpose UAVs to aid in the selection of a suitable UAV platform for bridge inspection and develop safety procedures for minimizing the impact of RF interference.

The novel coronavirus disease or COVID-19 is still posing an alarming situation around the globe. The whole world is facing the second wave of this novel pandemic. Recently, the researchers are focused to study the complex dynamics and possible control of this global infection. Mathematical modeling is a useful tool and gains much interest in this regard. In this paper, a fractional-order transmission model is considered to study its dynamical behavior using the real cases reported in Saudia Arabia. The classical Caputo type derivative of fractional order is used in order to formulate the model. The transmission of the infection through the environment is taken into consideration. The documented data since March 02, 2020 up to July 31, 2020 are considered for estimation of parameters of system. We have the estimated basic reproduction number ( R 0 ) for the data is 1.2937 . The Banach fixed point analysis has been used for the existence and uniqueness of the solution. The stability analysis at infection free equilibrium and at the endemic state are presented in details via a nonlinear Lyapunov function in conjunction with LaSalle Invariance Principle. An efficient numerical scheme of Adams-Molten type is implemented for the iterative solution of the model, which plays an important role in determining the impact of control measures and also sensitive parameters that can reduce the infection in the general public and thereby reduce the spread of pandemic as shown graphically. We present some graphical results for the model and the effect of the important sensitive parameters for possible infection minimization in the population.

The new coronavirus disease is still a major panic for people all over the world. The world is grappling with the second wave of this new pandemic. Different approaches are taken into consideration to tackle this deadly disease. These approaches were suggested in the form of modeling, analysis of the data, controlling the disease spread and clinical perspectives. In all these suggested approaches, the main aim was to eliminate or decrease the infection of the coronavirus from the community. Here, in this paper, we focus on developing a new mathematical model to understand its dynamics and possible control. We formulate the model first in the integer order and then use the Atangana-Baleanu derivative concept with a non-singular kernel for its generalization. We present some of the necessary mathematical aspects of the fractional model. We use a nonlinear fractional Lyapunov function in order to present the global asymptotical stability of the model at the disease-free equilibrium. In order to solve the model numerically in the fractional case, we use an efficient modified Adams-Bashforth scheme. The resulting iterative scheme is then used to demonstrate the detailed simulation results of the ABC mathematical model to examine the importance of the memory index and model parameters on the transmission and control of COVID-19 infection.

A Correction to this paper has been published: 10.1186/s13662-020-03199-3

Recently, intelligent control techniques have received considerable attention. In most studies, the systems’ model is assumed to be without any delay, and the effects of faults and failure in actuators are ignored. However, in real practice, sensor malfunctioning, mounting limitation, and defects in actuators bring about faults, failure, delay, and disturbances. Consequently, applying controllers that do not consider these problems could significantly deteriorate controllers’ performance. In order to address this issue, in the current paper, we propose a new neural network-based fault-tolerant active control for fractional time-delayed systems. The neural network estimator is integrated with active control to compensate for all uncertainties and disturbances. The suggested method’s stability is achieved based on the concept of active control and the Lyapunov stability theorem. Then, a fractional-order memristor system is investigated, and some characteristics of this chaotic system are studied. Lastly, by applying the proposed control scheme, synchronization results of the fractional time-delayed memristor system in the presence of faults and uncertainties are studied. The simulation results suggest the effectiveness of the proposed control technique for uncertain time-delayed nonlinear systems.

In this study, we present a mathematical model of dengue fever transmission with hospitalization to describe the dynamics of the infection. We estimated the basic reproduction number for the infected cases in East Java Province for the year 2018 is R0≈1.1138. The parameters of the dengue model are estimated by using the confirmed notified cases of East Java province, Indonesia for the year 2018. We formulated the model for dengue with hospitalization and present its dynamics in details. Initially, we present the basic mathematical results and then show briefly the stability results for the model. Further, we formulate an optimal control problem with control functions and obtain the optimal control characterization. The optimal control problem is solved numerically and the results comprised of controls system for different strategies. The controls such as prevention and insecticide could use the best role in the disease eradication from the community. Our results suggest that the prevention of humans from the mosquitoes and the insecticide spray on mosquitoes can significantly reduce the infection of dengue fever and may reduce further spread of infection in the community., Dengue fever modeling; Stability analysis; Real data; Parameters estimations; Optimal control

The heat transfer study of mixed convection flow of the Maxwell fluid is carried out here. The fluid flow is demonstrated by the system of coupled partial differential equations in the dimensionless form firstly. Then, its fractional form is developed by using the new definition of the noninteger-order derivative with the singular kernel (Caputo/C) and nonsingular kernels (Caputo–Fabrizio/CF and Atangana–Baleanu (nonlocal)/ABC). The hybrid-form solutions are obtained by applying the Laplace transform, and for the inverse Laplace transform, the problem is tackled by the numerical algorithms of Stehfest and Tzou. The C, CF, and ABC solution comparison under the effects of considered different parameters is depicted. The physical aspects of the considered problem are well explained by C, CF, and ABC in comparison to the integer-order derivative due to its memory effects. Furthermore, the best fit model to explain the memory effects of velocity is CF. The solutions for the Newtonian fluid and ordinary Maxwell fluid are considered as a special case and found in the literature.

Aerogels are a unique class of materials that possess intriguing properties such as very light weight, low bulk density, high porosity, heat resistance, very high specific surface area, low dielectric constant, and low refractive index. Synthetic polymer-based aerogels have poor biodegradability, toxic precursors along with hazardous degradation products make them inadequate for use. Focus has shifted to the fabrication of aerogels from biological materials. Polysaccharides, proteins, polymers derived from biomass have been exploited to produce bio-aerogels. The biological synthesis of aerogels is advantageous because these materials are biodegradable, biocompatible, abundant in nature, and involve no toxic materials making them candidate of choice for various biomedical and environmental applications. Therefore, this article attempts to review the different types of bio-inspired aerogel materials, there synthesis and environmental applications.

The new emerged infectious disease that is known the coronavirus disease (COVID-19), which is a high contagious viral infection that started in December 2019 in China city Wuhan and spread very fast to the rest of the world. This infection caused million of infected cases globally and still pose an alarming situation for human lives. Pakistan in Asian countries is considered the third country with higher number of cases of coronavirus with more than 200,000. Recently, many mathematical models have been considered to better understand the coronavirus infection. Most of these models are based on classical integer-order derivative which can not capture the fading memory and crossover behavior found in many biological phenomena. Therefore, we study the coronavirus disease in this paper by exploring the dynamics of COVID-19 infection using the non-integer Caputo derivative. In the absence of vaccine or therapy, the role of non-pharmaceutical interventions (NPIs) is examined on the dynamics of theCOVID-19 outbreak in Pakistan. First, we construct the model in integer sense and then apply the fractional operator to have a generalized model. The generalized model is then used to present the detailed theoretical results. We investigate the stability of the model for the case of fractional model using a nonlinear fractional Lyapunov function of Goh-Voltera type. Furthermore, we estimate the values of parameters with the help of least square curve fitting tool for the COVID-19 data recorded in Pakistan since March 1 till June 30, 2020 and show that our considered model give an accurate prediction to the real COVID-19 statistical cases. Finally, numerical simulations are presented using estimated parameters for various values of the fractional order of the Caputo derivative. From the simulation results it is found that the fractional order provides more insights about the disease dynamics.

Green synthesis of metal nanoparticles using plant extracts as capping and reducing agents for the biomedical applications has received considerable attention. Moreover, emergence and spread of multidrug resistance among bacterial pathogens has become a major health concern and lookout for novel alternative effective drugs has gained momentum. In current study, we synthesized gold nanoparticles using the seed extract of Trachyspermum ammi (TA-AuNPs), assessed its efficacy against drug resistant biofilms of Listeria monocytogenes and Serratia marcescens, and evaluated its anticancer potential against HepG2 cancer cell lines. Microwave-assisted green synthesis of gold nanoparticles was carried out and characterization was done using UV-vis spectroscopy, X-ray diffraction (XRD), transmission electron microscopy (TEM), and dynamic light scattering (DLS). Most nanoparticles were observed as spherical and spheroidal with few anisotropies with an average crystalline size of 16.63 nm. Synthesized TA-AuNPs demonstrated significant biofilm inhibitory activity against L. monocytogenes (73%) as well as S. marcescens (81%). Exopolysaccharide (EPS), motility, and CSH, key elements that facilitate the formation and maintenance of biofilm were also inhibited significantly at the tested sub-minimum inhibitory concentrations (sub-MICs). Further, TA-AuNPs effectively obliterated preformed mature biofilms of S. marcescens and L. monocytogenes by 64% and 58%, respectively. Induction of intracellular ROS production in TA-AuNPs treated bacterial cells could be the plausible mechanism for the reduced biofilm formation in test pathogens. Administration of TA-AuNPs resulted in the arrest of cellular proliferation in a concentration-dependent manner. TA-AuNPs decrease the intracellular GSH in HepG2 cancer cell lines, cells become more prone to ROS generation, hence induce apoptosis. Thus, this work proposes a new eco-friendly and rapid approach for fabricating NPs which can be exploited for multifarious biomedical applications.

Background: We intended to look at results of patients got fruitful fibrinolytic treatment (FT) for ST-section raised myocardial localized necrosis (STEMI) and performed coronary angiography (CAG) inside 24 - 72 h or after 72 h. Methods: Between March 2013 and November 2014, 76 STEMI patients got effective FT and performed CAG > 24 h remembered for the investigation. Patients were separated into two gatherings ac-cording to the time-span from FT admission to CAG performing (Group-1, 24 - 72 h (n = 29), Group-2, > 72 h (n = 47)). Our momentum research directed at Mayo Hospital, Lahore from May 2019 to April 2020. The essential end was major unfriendly cardiovascular occasions (MACE) characterized as cardiovascular passing, non-lethal myocardial localized necrosis, and cardiovascular breakdown. Results: The meantime of patients were 56 ± 11.4 years old (27.6% female). CAG performed inside mean 2.17 ± 0.38 days in the Group-1 and 2.9 ± 11.5 days in the Group 2 (P < 0.001). At present moment development (a half year), MACE rate was higher in Group-2 (21.3%) than Group-1(13.8%), however it was not measurably critical (P = 0.661). The pace of MACE was 37.9% in Group-1 and 38.3% in Group-2 (P = 0.974) in the long haul development (middle: 57 months). In general, cardiovascular death rate was 7.9%, the re-dead tissue rate was 19.7% and cardiovascular breakdown was 17.1% in long haul development, and there was no critical contrast between gatherings. Conclusion: Present examination has demonstrated that presentation of CAG after 24 h of fruitful FT, inside 24 - 74 h or > 72 h, didn't indicated any distinction in term of MACE both in short and long haul development.

Aim: The objective and aim of this evaluation and study is to evaluate graft survival between fat harvested from abdominal and medial thigh for facial contour distortion. Study design: A Randomized Control Trial. Place and duration of the study: In the Department of Plastic Surgery, Ittefaq Hospital Trust, Lahore for two-year duration from March 2018 to February 2020. Methodology: Average fat survival at the start of the study, in the first and twelfth week. Patients meeting the inclusion criteria were randomly divided into two groups. Fat was harvested from medial thigh in Group A and from abdomen in Group Band the results were compared for fat survival. Fat survival> 6.00 mm over the past 12 weeks was considered excellent. ANOVA and chi-square test were used to compare the average fat thickness (mm) between groups. Complication between group with statistical significance p < 0.05. Results: The average final fat thickness in group A was 6030 +0.095 mm and 4.989 ± 1.094 mm in group B (p = 0.001). The average age of the respondents was 27 + 7.44 years (range from 12 to 60 years). Thirty patients (76.9%) in group A, none in group B. showed an excellent response (p = 0.001). Conclusion: Transplant survival with medial thigh fat better evaluation than results from abdominal fat. Key words: Romberg's disease, fat survival, Fat injection, fat grafting, transplant survival, donor site.

It is very pertinent to mention that learn about difference in perception for eye specialists. The importance is in many ways i.e.it helps to find out the problems related to eye, to increase information about Learning and understanding eye and its disorder. So it is decided to check out perception differencein schools of Pakistan. Objective: The main objective and reason of this evaluation and study was to check out that how children of different perception carry out their normal functions. Another reason of this study was to learn more and more about eye. Subject and material: Our current research was conducted at Services Hospital, Lahore from December 2017 to November 2018. Students of age 9 to 17 were taken from schools of Lahore city of Pakistan. These students were quite healthy. Their eyesight wontedness was 20/20. They never suffered by any eye diseases so they never have been referred for eye operations. Wontedness in eyesight at 5% and 2.6% wasServed with Dr.Leas 3.5 meters far wontedness. Conclusion: By doing this process of finding actual sight wontedness we infect obtained normal wontedness values at 100%, 5% and 2.6% difference levels. Keywords: School children, Visual acuity, contrast sensitivity.

Breast cancer is one of the most common life-threatening diseases that affects women globally. Saudi Arabia is also one of the countries that suffer from a serious number of this disease among women. In terms of diagnosis modalities, a mammogram is the first line for detecting breast cancer. In addition, breast cancer can be screened by real-time ultrasound images, which are of relatively less quality and have more impact (noninvasive) images. Therefore, the purpose of this study is to develop image enhancement techniques using deep learning and image processing techniques. The main goal is to improve the ultrasound images in order to help radiologists screen the disease more accurately. For this study, ninety female patients of ages between 15 – 77 years are considered. These patients were already diagnosed using ultrasound with breast lesions. The images are visually graded and evaluated by two trained radiologists, both pre- and post-enhancement. In particular, two parameters were considered; 1) BI-RAD categories, 2) Breast cancer classification. The agreement between radiologists and post-enhancement was assessed using simple kappa and weighted kappa statistics. Moreover, sensitivity and specificity are also calculated.

The main feature of the present numerical model is to explore the behavior of Maxwell nanoliquid moving within two horizontal rotating disks. The disks are stretchable and subjected to a magnetic field in axial direction. The time dependent characteristics of thermal conductivity have been considered to scrutinize the heat transfer phenomena. The thermophoresis and Brownian motion features of nanoliquid are studied with Buongiorno model. The lower and upper disk's rotation for both the cases, same direction as well as opposite direction of rotation is investigated. The subsequent arrangement of the three dimensional Navier Stoke’s equations along with energy, mass and Maxwell equations are diminished to a dimensionless system of equations through the Von Karman’s similarity framework. The comparative numerical arrangement of modeled equations is further set up by built-in numerical scheme “boundary value solver” (Bvp4c) and Runge Kutta fourth order method (RK4). The various physical constraints, such as Prandtl number, thermal conductivity, magnetic field, thermal radiation, time relaxation, Brownian motion and thermophoresis parameters and their impact are presented and discussed briefly for velocity, temperature, concentration and magnetic strength profiles. In the present analysis, some vital characteristics such as Nusselt and Sherwood numbers are considered for physical and numerical investigation. The outcomes concluded that the disk stretching action opposing the flow behavior. With the increases of magnetic field parameter $$M$$ M the fluid velocity decreases, while improving its temperature. We show a good agreement of the present work by comparing with those published in literature.

In this paper, a time-driven performance-aware mathematical model for trust in the robot is proposed for a Human-Robot Collaboration (HRC) framework. The proposed trust model is based on both the human operator and the robot performances. The human operator’s performance is modeled based on both the physical and cognitive performances, while the robot performance is modeled over its unpredictable, predictable, dependable, and faithful operation regions. The model is validated via different simulation scenarios. The simulation results show that the trust in the robot in the HRC framework is governed by robot performance and human operator’s performance and can be improved by enhancing the robot performance.

Diabetes and its related complications are considered as one of the major causes of morbidity and mortality round the globe [1]. Diabetes is a noncommunicable metabolic syndrome may either characterized by defective or decreased level of insulin secretion [2]. The current estimate on global diabetes epidemiology reveals that roughly under half of billion population is affected by diabetes and associated pathologies and is projected to increase by around 50% by 2045 [3].

Vector-host infectious diseases remain a challenging issue and cause millions of deaths each year globally. In such outbreaks, many countries especially developing or underdevelopment faces a situation where the number of infected individuals is getting larger and the medical facilities are limited. In this paper, we construct an epidemic model to explore the transmission dynamics of vector-borne diseases with nonlinear saturated incidence rate and saturated treatment function. This type of incidence rate, as well as the saturated treatment function, is also known as the Holling type II form and describes the effect of delayed treatment. Initially, we formulate a mathematical model and then present the basic analysis of the model including the positivity and boundedness of the solution. The threshold quantity \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathcal {R}}_{0}$$\end{document}R0 is presented and the stability analysis of the system is carried out for the model equilibria. The global stability results are shown using the Lyapunov function of Goh–Voltera type. The existence of backward bifurcation is discussed using the central manifold theory. Further, the global sensitivity analysis of the model is carried out using the Latin Hypercube sampling and the partial rank correlation coefficient techniques. Moreover, an optimal control problem is formulated and the necessary optimality conditions are investigated in order to eradicate the disease in a community. Four strategies are presented by choosing different set of controls combination for the disease minimization. Finally, the numerical simulations of each strategy are depicted to demonstrate the importance of suggesting control interventions on the disease dynamics and eradication.

COVID-19 or coronavirus is a newly emerged infectious disease that started in Wuhan, China, in December 2019 and spread worldwide very quickly. Although the recovery rate is greater than the death rate, the COVID-19 infection is becoming very harmful for the human community and causing financial loses to their economy. No proper vaccine for this infection has been introduced in the market in order to treat the infected people. Various approaches have been implemented recently to study the dynamics of this novel infection. Mathematical models are one of the effective tools in this regard to understand the transmission patterns of COVID-19. In the present paper, we formulate a fractional epidemic model in the Caputo sense with the consideration of quarantine, isolation, and environmental impacts to examine the dynamics of the COVID-19 outbreak. The fractional models are quite useful for understanding better the disease epidemics as well as capture the memory and nonlocality effects. First, we construct the model in ordinary differential equations and further consider the Caputo operator to formulate its fractional derivative. We present some of the necessary mathematical analysis for the fractional model. Furthermore, the model is fitted to the reported cases in Pakistan, one of the epicenters of COVID-19 in Asia. The estimated value of the important threshold parameter of the model, known as the basic reproduction number, is evaluated theoretically and numerically. Based on the real fitted parameters, we obtained $\mathcal{R}_{0} \approx 1.50$ R 0 ≈ 1.50 . Finally, an efficient numerical scheme of Adams–Moulton type is used in order to simulate the fractional model. The impact of some of the key model parameters on the disease dynamics and its elimination are shown graphically for various values of noninteger order of the Caputo derivative. We conclude that the use of fractional epidemic model provides a better understanding and biologically more insights about the disease dynamics.

In this work, we study the dengue dynamics with fractal-factional Caputo–Fabrizio operator. We employ real statistical data of dengue infection cases of East Java, Indonesia, from 2018 and parameterize the dengue model. The estimated basic reduction number for this dataset is$\mathcal{R}_{0}\approx2.2020$R0≈2.2020. We briefly show the stability results of the model for the case when the basic reproduction number is$\mathcal{R}_{0} R0<1. We apply the fractal-fractional operator in the framework of Caputo–Fabrizio to the model and present its numerical solution by using a novel approach. The parameter values estimated for the model are used to compare with fractal-fractional operator, and we suggest that the fractal-fractional operator provides the best fitting for real cases of dengue infection when varying the values of both operators’ orders. We suggest some more graphical illustration for the model variables with various orders of fractal and fractional.

Image scene geometry recognition is an important task for reconstruct the 3D information of a single image which is beneficial for computer vision applications, such as 3D TV, video categorization. In this paper, a novel architecture for the image scene geometry recognition based on the feature-level fusion of convolutional neural networks (CNN) features and low-level texture gradient features is presented. The main advantages of using low-level features are; simple to extract and contain rich information of image scene geometry. Next, it is evaluated on a novel scene dataset that is constructed by following the twelve different image scene geometries (1000 samples for each category) and experimental results exhibit that proposed system achieves higher accuracy than applying the CNN alone. Additionally, by utilizing the extreme learning machine (ELM) as a classifier, the proposed system achieves 86.29% recognition accuracy that is superior the existing baseline methods.

Pine wilt disease is a lethal tree disease caused by nematodes carried by pine sawyer beetles. Once affected, the trees are destroyed within a few months, resulting in significant environmental and economic losses. The role of asymptomatic carrier trees in the disease dynamics remains unclear. We developed a mathematical model to investigate the effect of asymptomatic carriers on the long-term outcome of the disease. We performed a stability and sensitivity analysis to identify key parameters and used optimal control to examine several intervention options. Our model shows that, with the application of suitable controls, the disease can be eliminated in the vector population and all tree populations except for asymptomatic carriers. Of the possible controls (tree injection, elimination of infected trees, insecticide spraying), we determined that elimination of infected trees is crucial. However, if the costs of insecticide spraying increase, it can be supplemented (although not replaced entirely) by tree injection, so long as some spraying is still undertaken.

Tuberculosis remains a major health challenge for the humans of the whole world, especially in developing countries including Pakistan. Despite being a preventable and curable disease, TB is still the world’s top infectious killer and among the leading diseases causing morbidity and mortality. In this paper, we formulate a deterministic model to study the dynamics and possible control of TB disease. Initially, we develop the model without control variables and present the basic mathematical analysis of the model. The stability results of the model at the disease-free case are presented when $$\mathcal {R}_{0}1$$ . The model biological parameters are estimated using the confirmed TB infected cases reported in Khyber Pakhtunkhwa, Pakistan, from 2002 to 2017. Based on the estimated and fitted parameters, we obtained that $$\mathcal {R}_{0}\approx 1.2928$$ . The model stability is investigated graphically when $$\mathcal {R}_{0}$$ is greater or less than one. Further, the sensitivity analysis of the threshold parameter is performed in order to formulate an appropriate optimal strategy for curtailing the TB infection. Furthermore, we develop the control model using three control variables. The existence of the optimal control problem and the necessary mathematical results for the control characterizations is obtained. Finally, the simulations are presented to highlight the importance and impact of suggested control interventions on the disease dynamics. The proposed set of controls can be used for the elimination of TB infection in a community.

Image-level structural recognition is an important problem for many applications of computer vision such as autonomous vehicle control, scene understanding, and 3D TV. A novel method, using image features extracted by exploiting predefined templates, each associated with individual classifier, is proposed. The template that reflects the symmetric structure consisting of a number of components represents a stage&mdash, a rough structure of an image geometry. The following image features are used: a histogram of oriented gradient (HOG) features showing the overall object shape, colors representing scene information, the parameters of the Weibull distribution features, reflecting relations between image statistics and scene structure, and local binary pattern (LBP) and entropy (E) values representing texture and scene depth information. Each of the individual classifiers learns a discriminative model and their outcomes are fused together using sum rule for recognizing the global structure of an image. The proposed method achieves an 86.25% recognition accuracy on the stage dataset and a 92.58% recognition rate on the 15-scene dataset, both of which are significantly higher than the other state-of-the-art methods.

In the present paper, we formulate a new mathematical model for the dynamics of COVID-19 with quarantine and isolation. Initially, we provide a brief discussion on the model formulation and provide relevant mathematical results. Then, we consider the fractal-fractional derivative in Atangana–Baleanu sense, and we also generalize the model. The generalized model is used to obtain its stability results. We show that the model is locally asymptotically stable if$\mathcal{R}_{0}R0<1. Further, we consider the real cases reported in China since January 11 till April 9, 2020. The reported cases have been used for obtaining the real parameters and the basic reproduction number for the given period,$\mathcal{R}_{0}\approx 6.6361$R0≈6.6361. The data of reported cases versus model for classical and fractal-factional order are presented. We show that the fractal-fractional order model provides the best fitting to the reported cases. The fractional mathematical model is solved by a novel numerical technique based on Newton approach, which is useful and reliable. A brief discussion on the graphical results using the novel numerical procedures are shown. Some key parameters that show significance in the disease elimination from the society are explored.

Security for authentication is required to give a superlative secure users' personal information. This paper presents a model of the Graphical password scheme under the impact of security and ease of use for user authentication. We integrate the concept of recognition with re-called and cued-recall based schemes to offer superior security compared to existing schemes. Click Symbols (CS) Alphabet combine into one entity: Alphanumeric (A) and Visual (V) symbols (CS-AV) is Captcha-based password scheme, we integrate it with recall-based $n \times n$ grid points, where a user can draw the shape or pattern by the intersection of the grid points as a way to enter a graphical password. Next scheme, the combination of CS-AV with grid cells allows very large password space ( $2.4 \times 10^{4}$ bits of entropy) and provides reasonable usability results by determining an empirical study of memorable password space. Proposed schemes support most applicable platform for input devices and promising strong resistance to shoulder surfing attacks on a mobile device which can be occurred during unlocking (pattern) the smartphone.

Novel, safe, and effective antilisterial agents are required in order to prevent

There is grave necessity to counter the menace of drug-resistant biofilms of pathogens using nanomaterials. Moreover, we need to produce nanoparticles (NPs) using inexpensive clean biological approaches that demonstrate broad-spectrum inhibition of microbial biofilms and cytotoxicity against HepG2 cell lines. In the current research work, titanium dioxide (TiO2) NPs were fabricated through an environmentally friendly green process using the root extract of Withania somnifera as the stabilizing and reducing agent to examine its antibiofilm and anticancer potential. Further, X-ray diffraction (XRD), Fourier transform infrared (FTIR), scanning electron microscopy (SEM), transmission electron micrograph (TEM), energy-dispersive X-ray spectroscopy (EDS), dynamic light scattering (DLS), thermogravimetric analysis (TGA), and Brunauer-Emmett-Teller (BET) techniques were used for determining the crystallinity, functional groups involved, shape, size, thermal behavior, surface area, and porosity measurement, respectively, of the synthesized TiO2 NPs. Antimicrobial potential of the TiO2 NPs was determined by evaluating the minimum inhibitory concentration (MIC) against Escherichia coli, Pseudomonas aeruginosa, methicillin-resistant Staphylococcus aureus, Listeria monocytogenes, Serratia marcescens, and Candida albicans. Furthermore, at levels below the MIC (0.5 × MIC), TiO2 NPs demonstrated significant inhibition of biofilm formation (43–71%) and mature biofilms (24–64%) in all test pathogens. Cell death due to enhanced reactive oxygen species (ROS) production could be responsible for the impaired biofilm production in TiO2 NP–treated pathogens. The synthesized NPs induced considerable reduction in the viability of HepG2 in vitro and could prove effective in controlling liver cancer. In summary, the green synthesized TiO2 NPs demonstrate multifarious biological properties and could be used as an anti-infective agent to treat biofilm-based infections and cancer.

In this paper, a driver's intention prediction near a road intersection is proposed. Our approach uses a deep bidirectional Long Short-Term Memory (LSTM) with an attention mechanism model based on a hybrid-state system (HSS) framework. As intersection is considered to be as one of the major source of road accidents, predicting a driver's intention at an intersection is very crucial. Our method uses a sequence to sequence modeling with an attention mechanism to effectively exploit temporal information out of the time-series vehicular data including velocity and yaw-rate. The model then predicts ahead of time whether the target vehicle/driver will go straight, stop, or take right or left turn. The performance of the proposed approach is evaluated on a naturalistic driving dataset and results show that our method achieves high accuracy as well as outperforms other methods. The proposed solution is promising to be applied in advanced driver assistance systems (ADAS) and as part of active safety system of autonomous vehicles., Comment: IEEE Intelligent Vehicles Symposium 2020 (IEEE IV 2020)

The purpose of the proposed paper is to analyze the dynamics of an epidemic model with fractional and fractal–fractional order by considering two different numerical approaches. Initially, we consider an epidemic model in fractional Atangana–Baleanu derivative and then obtain the necessary results associated with the model. Stability results for the model are obtained and showed that the model is stable when the basic reproduction number is less than unity. Then, we apply the new idea of fractal–fractional to the influenza model in the sense of Atangana–Baleanu fractional operator. The model with fractional and fractal–fractional operators is solved with numerical techniques. We present graphical results for fractional model with many values of $$\theta $$. For the fractal–fractional model, we present a different set of fractal and fractional order to obtain graphical results. Further, we provide a comparison among the operators with novel numerical procedure considering different orders of $$\theta $$. We conclude that the idea of fractal–fractional provides powerful results than that of fractional and integer-order derivative.

Probiotics are live microorganisms or bacterial cultures that can have beneficial effects for the host when ingested. Probiotics must survive stressful conditions of the gastrointestinal tract by tolerating acid, bile, and gastric enzymes and must adhere to intestinal epithelial cells to colonize the gut. Moreover, probiotics should have antimicrobial effects against pathogenic microorganisms and desirable antibiotic susceptibility patterns. Many gastrointestinal diseases, such as diarrhea, irritable bowel syndrome, and chronic inflammatory bowel disease are caused by intestinal microflora imbalance, which is an important factor in bacterial translocation and infection. The current treatment of intestinal microbiota imbalance is using antibiotics; however, misuse or overuse of antibiotics contributes to resistance, which is one of the major public health problems worldwide. Another concern is the decreasing efficacy of antibiotics in treating human and animal infections because of the biofilm formation of pathogenic bacteria. Bacterial cells in biofilms are highly protected, less subjected to mutation, represent low metabolic activity, and become resistant to antibiotics. Therefore, the probiotic strains having both antimicrobial and antibiofilm properties may be expected to be therapeutically more effective. The antimicrobial probiotics having biofilm-dispersive properties can yield better clinical benefits as a therapeutic agent. Keeping in focus the promise shown by probiotics, current review is aimed to highlight the antibiofilm potential of probiotic microorganisms against drug-resistant pathogens.

Cognitive radio (CR) technology offers the possibility of an increase in spectrum utilization efficiency to resolve the prevalent spectrum scarcity problem. The economic survival of secondary spectrum markets (SSMs) is heavily dependent on the sharing of both the licensed spectrum and spectrum infrastructure by primary licensed operators (PLOs). In this research, an automated pricing model using a blockchain token called the spectrum dollar has been implemented for secondary radio spectrum trade. The use of spectrum dollars enables noncash-based secondary spectrum trade among PLOs based on a floor-and-trade rule. The pricing of spectrum dollars and the associated revenue shares are based on the underlying secondary spectrum trading behaviours of PLOs. PLOs that do not contribute enough secondary spectra to the SSM (to satisfy demand) suffer a loss proportional to the difference between their earned revenues and the specified floor value in the SSM. The secondary spectrum trade is assumed to be centrally managed by a spectrum broker, which announces the floor value for each bidding period while ensuring nonnegative revenue for the market itself. The use of the spectrum dollar along with the floor-and-trade methodology eliminates the possibilities for economic malpractice by PLOs that could increase spectrum reuse costs. In addition, the floor value provides automatic regulatory control to ensure the economic viability and prevent the technological hijacking of future SSMs.

The flow of common fluids (water, oils and ethylene glycol etc.) is diluted by adding different small particles of metals, and their oxides are more powerful to reduce the scientific issues related to quicker heat transfer. According to this indication, we have contemplated finite film of $$\gamma {\hbox {Al}}_2 {\hbox {O}}_3$$ – $${\hbox {H}}_2 {\hbox {O}}$$ and $$\gamma {\hbox {Al}}_2 {\hbox {O}}_3$$ – $${\hbox {C}}_2 {\hbox {H}}_6 {\hbox {O}}_2 $$ nanoliquid sprayed on an extending cylinder. In this scenario, uniform magnetic field $$B_0$$ and constant reference temperature are employed on the stream of thin film nanofluid. The impact of effective Prandtl number, viscosity and thermal conductivity is derived from the experimental data (Sheikhzadeh et al. in J Appl Fluid Mech 10:209–219, 2017; Lee et al. in J Heat transf 121:280–289, 1992; Wang et al. in J Thermo Phys Heat Transf 13:474–480, 1999; Hamilton and Crosser in Ind Eng Chem Fundam 1:187–191, 1962; Maiga et al. in Super Lattices Microstruct 35:543–55, 2004; Hayat et al. in J Mol Liq. https://doi.org/10.1016/j.molliq.2018.06.029 , 2018). The model problem is excellently converted into a set of proper self-comparable forms with the assistance of possible transformations. Analytical results of velocity and thermal profile are computed using homotopy analysis method for both $$\gamma {\hbox {Al}}_2 {\hbox {O}}_3$$ – $${\hbox {H}}_2 {\hbox {O}}$$ and $$\gamma {\hbox {Al}}_2 {\hbox {O}}_3$$ – $${\hbox {C}}_2 {\hbox {H}}_6 {\hbox {O}}_2 $$ nanoliquid. Furthermore, during coating analysis, rate of spray, pressure distribution, skin friction coefficient (surface drag force) $$C_{\mathrm{f}} $$ and Nusselt number (the rate of heat transfer) Nu for both nanofluids are also intended. The impact of additional ingrained quantities like magnetic parameter M, volume fraction of nanoparticles $$\varphi $$ , Grashof number Gr, fluid thickness parameter $$\beta $$ , Prandtl number Pr and Reynolds number Re is portrayed numerically and graphically for both alumina particles. The key observation indicates that the temperature of $$\gamma {\hbox {Al}}_2 {\hbox {O}}_3$$ – $${\hbox {C}}_2 {\hbox {H}}_6 {\hbox {O}}_2$$ nanoliquid leading on $$\gamma {\hbox {Al}}_2 {\hbox {O}}_3$$ – $${\hbox {H}}_2 {\hbox {O}}$$ nanoliquid during the study. Due to greater viscosity and thermal conductivity, $${\hbox {C}}_2 {\hbox {H}}_6 {\hbox {O}}_2$$ -based nanofluid is observed as upgraded common base fluid assimilated to $${\hbox {H}}_2$$ O.

In this paper, we present a nonlinear fractional order model in the Caputo sense to explore and simulate the TB dynamics. Using the TB confirmed notified cases from the year 2002 to 2017 in Khyber Pakhtunkhwa, Pakistan, we estimate the model parameters and demonstrate that the proposed fractional model provides a good fit to the real data. Initially, we compute the basic reproduction number and the model equilibria. Then, the existence and uniqueness of the model are shown via generalized mean value theorem. Further, we explore the local and global stability of the disease free equilibria in fractional environment. Finally, numerical results are obtained in order to validate the importance of the arbitrary order derivative and theoretical results. We conclude that the fractional epidemic model is more generalized than the classical model, which give more information about the disease dynamics and give a good agreement to the real data of TB infection.

A Caputo–Fabrizio type fractional order mathematical model for the dynamics of pine wilt disease (FPWD) is presented. The basic properties of the model are investigated. The existence and uniqueness of the solution for the proposed FPWD model are given via the fixed point theorem. The numerical simulations for the model are obtained by using particular parameter values. The non-integer order derivative provides more flexible and deeper information about the complexity of the dynamics of the proposed FPWD model than the integer order models established before.