Stochastic neuro-swarming intelligence paradigm for the analysis of magneto-hydrodynamic Prandtl-Eyring fluid flow with diffusive magnetic layers effect over an elongated surface

In recent years,the integration of stochastic techniques,especially those based on artificial neural networks,has emerged as a pivotal advancement in the field of computational fluid dynamics.These techniques offer a powerful framework for the analysis of complex fluid flow phenomena and address the uncertainties inherent in fluid dynamics systems.Following this trend,the current investigation portrays the design and construction of an important technique named swarming optimized neuroheuristic intelligence with the competency of artificial neural networks to analyze nonlinear viscoelastic magneto-hydrodynamic Prandtl-Eyring fluid flow model,with diffusive magnetic layers effect along an extended sheet.The currently designed computational technique is established using inverse multiquadric radial basis activation function through the hybridization of a well-known global searching technique of particle swarm optimization and sequential quadratic programming,a technique capable of rapid convergence locally.The most appropriate scaling group involved transformations that are implemented on governing equations of the suggested fluidic model to convert it from a system of nonlinear partial differential equations into a dimensionless form of a third-order nonlinear ordinary differential equation.The transformed/reduced fluid flow model is solved for sundry variations of physical quantities using the designed scheme and outcomes are matched consistently with Adam's numerical technique with negligible magnitude of absolute errors and mean square errors.Moreover,it is revealed that the velocity of the fluid depreciates in the presence of a strong magnetic field effect.The efficacy of the designed solver is depicted evidently through rigorous statistical observations via exhaustive numerical experimentation of the fluidic problem.

Theoretical Assessment of Environmental Factors and Untreated Infectious Individuals in the Transmission Dynamics of Buruli ulcer

Buruli ulcer is the third most common mycobacterial disease worldwide, posing a significant public health burden, especially in impoverished regions of West and Central Africa, such as Benin. The management of Buruli ulcer (BU) in Africa is often hindered by limited resources, delays in treatment, and inadequate medical facilities. Additionally, a portion of the population does not seek hospital care, which facilitates the continued presence of the pathogen in the environment. This paper aims to investigate the role of environmental factors in the transmission of Buruli ulcer. We develop a mathematical model to describe the dynamics of Buruli ulcer transmission, incorporating the presence of the bacterium in the environment. Theoretical results are presented to demonstrate that the model is well-posed. We compute the equilibria, including the disease-free equilibrium and the endemic equilibrium, and study their stability. To achieve this, we derive a threshold parameter called the basic reproduction number ℛ0, which determines whether the disease will persist in a human population. If ℛ0is less than one, the disease will eventually die out;if ℛ0is greater than one, the disease will persist. Sensitivity analysis is performed to understand the impact of various parameters on the dynamics of Buruli ulcer transmission and to identify the parameters that influence the basic reproduction number ℛ0. Finally, numerical simulations are conducted to validate the theoretical results obtained from the mathematical analysis.

Subplanes of PG( 2, q 3 ) and the Ruled Varieties V 2 5 of PG( 6,q )

In this note we study subplanes of order q of the projective plane Π=PG( 2, q 3 ) and the ruled varieties V 2 5 of Σ=PG( 6,q ) using the spatial representation of Π in Σ, by fixing a hyperplane Σ ′ with a regular spread of planes. First are shown some configurations of the affine q-subplanes. Then to prove that a variety V 2 5 of Σ represents a non-affine subplane of order q of Π, after having shown basic incidence properties of it, such a variety V 2 5 is constructed by choosing appropriately the two directrix curves in two complementary subspaces of Σ. The result can be translated into further incidence properties of the affine points of V 2 5 . Then a maximal bundle of varieties V 2 5 having in common one directrix cubic curve is constructed.

Subplanes of PG(2,qr), Ruled Varieties V2r-12 in PG( 2r,q), and Related Codes

In this note we consider ruled varieties V22r−1of PG(2r,q), generalizing some results shown for r=2,3in previous papers. By choosing appropriately two directrix curves, a V22r−1represents a non-affine subplane of order qof the projective plane PG(2,qr)represented in PG(2r,q)by a spread of a hyperplane. That proves the conjecture assumed in [1]. Finally, a large family of linear codes dependent on r≥2is associated with projective systems defined both by V22r−1and by a maximal bundle of such varieties with only an r-directrix in common, then are shown their basic parameters.

Prediction of cyanotic and acyanotic congenital heart disease using machine learning models

BACKGROUND Congenital heart disease is most commonly seen in neonates and it is a major cause of pediatric illness and childhood morbidity and mortality.AIM To identify and build the best predictive model for predicting cyanotic and acyanotic congenital heart disease in children during pregnancy and identify their potential risk factors.METHODS The data were collected from the Pediatric Cardiology Department at Chaudhry Pervaiz Elahi Institute of Cardiology Multan,Pakistan from December 2017 to October 2019.A sample of 3900 mothers whose children were diagnosed with identify the potential outliers.Different machine learning models were compared,and the best-fitted model was selected using the area under the curve,sensitivity,and specificity of the models.RESULTS Out of 3900 patients included,about 69.5%had acyanotic and 30.5%had cyanotic congenital heart disease.Males had more cases of acyanotic(53.6%)and cyanotic(54.5%)congenital heart disease as compared to females.The odds of having cyanotic was 1.28 times higher for children whose mothers used more fast food frequently during pregnancy.The artificial neural network model was selected as the best predictive model with an area under the curve of 0.9012,sensitivity of 65.76%,and specificity of 97.23%.CONCLUSION Children having a positive family history are at very high risk of having cyanotic and acyanotic congenital heart disease.Males are more at risk and their mothers need more care,good food,and physical activity during pregnancy.The best-fitted model for predicting cyanotic and acyanotic congenital heart disease is the artificial neural network.The results obtained and the best model identified will be useful for medical practitioners and public health scientists for an informed decision-making process about the earlier diagnosis and improve the health condition of children in Pakistan.

Intelligent Detection and Identification of Attacks in IoT Networks Based on the Combination of DNN and LSTM Methods with a Set of Classifiers

Internet of Things (IoT) networks present unique cybersecurity challenges due to their distributed and heterogeneous nature. Our study explores the effectiveness of two types of deep learning models, long-term memory neural networks (LSTMs) and deep neural networks (DNNs), for detecting attacks in IoT networks. We evaluated the performance of six hybrid models combining LSTM or DNN feature extractors with classifiers such as Random Forest, k-Nearest Neighbors and XGBoost. The LSTM-RF and LSTM-XGBoost models showed lower accuracy variability in the face of different types of attack, indicating greater robustness. The LSTM-RF and LSTM-XGBoost models show variability in results, with accuracies between 58% and 99% for attack types, while LSTM-KNN has higher but more variable accuracies, between 72% and 99%. The DNN-RF and DNN-XGBoost models show lower variability in their results, with accuracies between 59% and 99%, while DNN-KNN has higher but more variable accuracies, between 71% and 99%. LSTM-based models are proving to be more effective for detecting attacks in IoT networks, particularly for sophisticated attacks. However, the final choice of model depends on the constraints of the application, taking into account a trade-off between accuracy and complexity.

Control of the Dust Vertical Distribution over Western Africa by Convection and Scavenging

Saharan dust represents more than 50%of the total desert dust emitted around the globe and its radiative effect significantly affects the atmospheric circulation at a continental scale.Previous studies on dust vertical distribution and the Saharan Air Layer(SAL)showed some shortcomings that could be attributed to imperfect representation of the effects of deep convection and scavenging.The authors investigate here the role of deep convective transport and scavenging on the vertical distribution of mineral dust over Western Africa.Using multi-year(2006-2010)simulations performed with the variable-resolution(zoomed)version of the LMDZ climate model.Simulations are compared with aerosol amounts recorded by the Aerosol Robotic Network(AERONET)and with vertical profiles of the Cloud-Aerosol Lidar with Orthogonal Polarization(CALIOP)measurements.LMDZ allows a thorough examination of the respective roles of deep convective transport,convective and stratiform scavenging,boundary layer transport,and advection processes on the vertical mineral dust distribution over Western Africa.The comparison of simulated dust Aerosol Optical Depth(AOD)and distribution with measurements suggest that scavenging in deep convection and subsequent re-evaporation of dusty rainfall in the lower troposphere are critical processes for explaining the vertical distribution of desert dust.These processes play a key role in maintaining a well-defined dust layer with a sharp transition at the top of the SAL and in establishing the seasonal cycle of dust distribution.This vertical distribution is further reshaped offshore in the Inter-Tropical Convergence Zone(ITCZ)over the Atlantic Ocean by marine boundary layer turbulent and convective transport and wet deposition at the surface.

Stability of a Delayed Stochastic Epidemic COVID-19 Model with Vaccination and with Differential Susceptibility

In this paper, we treat the spread of COVID-19 using a delayed stochastic SVIRS (Susceptible, Infected, Recovered, Susceptible) epidemic model with a general incidence rate and differential susceptibility. We start with a deterministic model, then add random perturbations on the contact rate using white noise to obtain a stochastic model. We first show that the delayed stochastic differential equation that describes the model has a unique global positive solution for any positive initial value. Under the condition R0 ≤ 1, we prove the almost sure asymptotic stability of the disease-free equilibrium of the model.

Asymptotic Analysis of a Stochastic Model of Mosquito-Borne Disease with the Use of Insecticides and Bet Nets

Ross’ epidemic model describing the transmission of malaria uses two classes of infection, one for humans and one for mosquitoes. This paper presents a stochastic extension of a deterministic vector-borne epidemic model based only on the class of human infectious. The consistency of the model is established by proving that the stochastic delay differential equation describing the model has a unique positive global solution. The extinction of the disease is studied through the analysis of the stability of the disease-free equilibrium state and the persistence of the model. Finally, we introduce some numerical simulations to illustrate the obtained results.

Supervised Learning Method: Critical Analysis and Updating of Cubing Rate Formulas for Determining Bark Masses of Prunus africana (Hook. f.) Kalkman (Rosaceae) in Cameroon

The stem barks of Prunus africana are used in the treatment of the benign prostate. Cameroon is one of the important exporters of the barks. Despite the important measures adopted in Cameroon for sustaining its harvesting, some many chalenges still remain. The objective of this work is to refine the forest management parameters in relation to P. africana in the regions of Adamaoua and the South-West by developing a volume rate which makes it possible to estimate the production for a new stem. The work took place in two phases: in the South-West in 2010 and in Adamaoua in 2011. Data collection used the semi-direct method, while the cubing equation was deduced by the multiple linear regression method. Two models for volume estimation and three models for mass prediction were developed. The predictive parameters retained are diameter, height of the bole and thickness of the bark. Results show that the average mass of the dry bark for a given P. africana tree species is 27.55 ± 14.44 kg and this varies according to the site. The strong adjusted coefficient of determination (adjusted R2) observed illustrates the reliability of the proposed models. These models provide a reliable tool that can be adopted as a standard in Cameroon for P. africana.

Curtailing the spread of COVID-19 in Nigeria using digital framework and data driven approaches

The onset of the coronavirus disease 2019(COVID-19)pandemic had profound economic and societal repercussions worldwide.Some nations managed to mitigate the spread of the virus sufficiently early through implementing digital frameworks and other containment measures.However,many countries in Africa faced significant challenges due to limited technological infrastructure for infection control.This study investigated the efficacy of digital frameworks,with a specific focus on contact tracing apps,in stemming the spread of COVID-19 within Nigeria.It investigated the potential impact of the use of these apps on reducing infection rates and improving public health strategies.Additionally,this study provided insights into the practical implementation of such frameworks and the challenges associated with their nationwide deployment.A structured user-oriented questionnaire was distributed to participants across different geopolitical zones in Nigeria.The study employed association rule mining and descriptive statistics,setting a support threshold of 0.001 and confidence of 0.8,to uncover patterns of interest among respondents.Cluster analysis using Barnes–Hut t-Distributed Stochastic Neighbor Embedding was conducted to derive reliable inferences from the data.The findings revealed adoption rates of 69.7%and 93.8%for the minimum and maximum,respectively,in favor of using a COVID-19 contact tracing app.Furthermore,71.6%of participants expressed concerns about the privacy of their personal information,whereas 74%showed an interest in receiving daily reports on estimated COVID-19 infection in their communities.This study presents empirical evidence highlighting the potential of contact tracing apps as valuable tools for monitoring and curbing the spread of COVID-19 and related diseases in Nigeria.

Multilinear commutators of BMO functions and multilinear singular integrals with non-smooth kernels

This paper is concerned with certain multilinear commutators of BMO functions and multilinear singular integral operators with non-smooth kernels. By the sharp maximal functions estimates, the weighted norm inequalities for this kind of commutators are established.

Symmetry Reductions, Exact Solutions and Conservation Laws of Asymmetric Nizhnik-Novikov-Veselov Equation

By applying a direct symmetry method, we get the symmetry of the asymmetric Nizhnik-Novikov-Veselov equation （ANNV）. Taking the special case, we have a finite-dimensional symmetry. By using the equivalent vector of the symmetry, we construct an eight-dimensional symmetry algebra and get the optimal system of group-invariant solutions. To every case of the optimal system, we reduce the ANNV equation and obtain some solutions to the reduced equations. Furthermore, we find some new explicit solutions of the ANNV equation. At last, we give the conservation laws of the ANNV equation.

An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM

New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government can control the pandemic by using the corresponding policies.However,the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction,and even have large estimation errors.To address this issue,we propose an improved method for predicting confirmed cases based on LSTM(Long-Short Term Memory)neural network.This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models(such as Logistic and Hill equations)with the real data as reference.Furthermore,this work uses the goodness of fitting to evaluate the fitting effect of the improvement.Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect.Compared with the previous forecasting methods,the contributions of our proposed improvement methods are mainly in the following aspects:1)we have fully considered the spatiotemporal characteristics of the data,rather than single standardized data.2)the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting.3)we consider the impact of the epidemic stage and conduct reasonable data processing for different stage.

EXISTENCE AND UNIQUENESS RESULTS FOR BOUNDARY VALUE PROBLEMS OF HIGHER ORDER FRACTIONAL INTEGRO-DIFFERENTIAL EQUATIONS INVOLVING GRONWALL'S INEQUALITY IN BANACH SPACES

We study boundary value problems for fractional integro-differential equations involving Caputo derivative of order α∈ （n-1, n） in Banach spaces. Existence and uniqueness results of solutions are established by virtue of the Holder＇s inequality, a suitable singular Cronwall＇s inequality and fixed point theorem via a priori estimate method. At last, examples are given to illustrate the results.

The Controllability, Observability, and Stability Analysis of a Class of Composite Systems with Fractional Degree Generalized Frequency Variables

This paper is concerned with fundamental properties of a class of composite systems with fractional degree generalized frequency variables, including controllability, observability and stability. Firstly, some necessary and sufficient conditions are given to guarantee controllability and observability of such composite systems. Then we prove that the stability problem of such composite systems can be reduced to judging whether a fractional degree polynomial is stable. Finally, the stability analysis result is applied in the supervisory control of fractional-order multi-agent systems, and an example is provided to illustrate the effectiveness of the proposed methods.

On the Strong Laws for Weighted Sums of m-negatively Asso ciated Random Variables

In this article, the author establishes the strong laws for linear statistics that are weighted sums of a m-negatively associated(m-NA) random sample. The obtained results extend and improve the result of Qiu and Yang in [1] to m-NA random variables.

Stability and Bifurcation Analysis of Man-machine System with Time Delay

A mathematical model of man-machine system is considered.Based on the reference [4],the direction and stability of the Hopf bifurcation are determined using the normal form method and the center manifold theory.Furthermore,the existence of Hopf-zero bifurcation is discussed.In the end,some numerical simulations are carried out to illustrate the results found.

Stability analysis and modeling for the three-dimensional Darcy-Forchheimer stagnation point nanofluid flow towards a moving surface

In this research,the three-dimensional(3D)steady and incompressible laminar Homann stagnation point nanofluid flow over a porous moving surface is addressed.The disturbance in the porous medium has been characterized by the Darcy-Forchheimer relation.The slip for viscous fluid is considered.The energy equation is organized in view of radiative heat flux which plays an important role in the heat transfer rate.The governing flow expressions are first altered into first-order ordinary ones and then solved numerically by the shooting method.Dual solutions are obtained for the velocity,skin friction coefficient,temperature,and Nusselt number subject to sundry flow parameters,magnetic parameter,Darcy-Forchheimer number,thermal radiation parameter,suction parameter,and dimensionless slip parameter.In this research,the main consideration is given to the engineering interest like skin friction coefficient(velocity gradient or surface drag force)and Nusselt number(temperature gradient or heat transfer rate)and discussed numerically through tables.In conclusion,it is noticed from the stability results that the upper branch solution(UBS)is more reliable and physically stable than the lower branch solution(LBS).

Complete Moment Convergence for Arrays of Rowwise Negatively Associated Random Variables

In this paper, the authors present some new results on complete moment convergence for arrays of rowwise negatively associated random variables. These results improve some previous known theorems.