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.

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.

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.

A Reliable and Scalable Internet of Military Things Architecture

Recently,Internet of Things(IoT)technology has provided logistics services to many disciplines such as agriculture,industry,and medicine.Thus,it has become one of the most important scientific research fields.Applying IoT to military domain has many challenges such as fault tolerance and QoS.In this paper,IoT technology is applied on the military field to create an Internet of Military Things(IoMT)system.Here,the architecture of the aforementioned IoMT system is proposed.This architecture consists of four main layers:Communication,information,application,and decision support.These layers provided a fault tolerant coverage communication system for IoMT things.Moreover,it implemented data reduction methods such as filtering,compression,abstraction,and data prioritization queuing system to guarantee QoS for the transmitted data.Furthermore,it used decision support technology and IoMT application unification ideas.Finally,to evaluate the IoMT system,an intensive simulation environment is constructed using the network simulation package,NS3.The simulation results proved that the proposed IoMT system outperformed the traditional military system with regard to the performance metrics,packet loss,end-to-end delay,throughput,energy consumption ratio,and data reduction rate.

Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

Coronavirus 19(COVID-19)can cause severe pneumonia that may be fatal.Correct diagnosis is essential.Computed tomography(CT)usefully detects symptoms of COVID-19 infection.In this retrospective study,we present an improved framework for detection of COVID-19 infection on CT images;the steps include pre-processing,segmentation,feature extraction/fusion/selection,and classification.In the pre-processing phase,a Gabor wavelet filter is applied to enhance image intensities.A marker-based,watershed controlled approach with thresholding is used to isolate the lung region.In the segmentation phase,COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head.DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries.The model was trained using fine-tuned hyperparameters selected after extensive experimentation.Subsequently,the Gray Level Co-occurrence Matrix(GLCM)features and statistical features including circularity,area,and perimeters were computed for each segmented image.The computed features were serially fused and the best features(those that were optimally discriminatory)selected using a Genetic Algorithm(GA)for classification.The performance of the method was evaluated using two benchmark datasets:The COVID-19 Segmentation and the POF Hospital datasets.The results were better than those of existing methods.

Analysis of Public Sentiment regarding COVID-19 Vaccines on the Social Media Platform Reddit

This study undertakes a thorough analysis of the sentiment within the r/Corona-virus subreddit community regarding COVID-19 vaccines on Reddit. We meticulously collected and processed 34,768 comments, spanning from November 20, 2020, to January 17, 2021, using sentiment calculation methods such as TextBlob and Twitter-RoBERTa-Base-sentiment to categorize comments into positive, negative, or neutral sentiments. The methodology involved the use of Count Vectorizer as a vectorization technique and the implementation of advanced ensemble algorithms like XGBoost and Random Forest, achieving an accuracy of approximately 80%. Furthermore, through the Dirichlet latent allocation, we identified 23 distinct reasons for vaccine distrust among negative comments. These findings are crucial for understanding the community’s attitudes towards vaccination and can guide targeted public health messaging. Our study not only provides insights into public opinion during a critical health crisis, but also demonstrates the effectiveness of combining natural language processing tools and ensemble algorithms in sentiment analysis.

Explicit Approximation Solutions and Proof of Convergence of the Space-Time Fractional Advection Dispersion Equations

The space-time fractional advection dispersion equations are linear partial pseudo-differential equations with spatial fractional derivatives in time and in space and are used to model transport at the earth surface. The time fractional order is denoted by β∈ and ?is devoted to the space fractional order. The time fractional advection dispersion equations describe particle motion with memory in time. Space-fractional advection dispersion equations arise when velocity variations are heavy-tailed and describe particle motion that accounts for variation in the flow field over entire system. In this paper, I focus on finding the precise explicit discrete approximate solutions to these models for some values of ?with ?, ?while the Cauchy case as ?and the classical case as ?with ?are studied separately. I compare the numerical results of these models for different values of ?and ?and for some other related changes. The approximate solutions of these models are also discussed as a random walk with or without a memory depending on the value of . Then I prove that the discrete solution in the Fourierlaplace space of theses models converges in distribution to the Fourier-Laplace transform of the corresponding fractional differential equations for all the fractional values of ?and .

Asymptotic Normality of the Nelson-Aalen and the Kaplan-Meier Estimators in Competing Risks

This paper studies the asymptotic normality of the Nelson-Aalen and the Kaplan-Meier estimators in a competing risks context in presence of independent right-censorship. To prove our results, we use Robelledo’s theorem which makes it possible to apply the central limit theorem to certain types of particular martingales. From the results obtained, confidence bounds for the hazard and the survival functions are provided.

An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

Today,due to the pandemic of COVID-19 the entire world is facing a serious health crisis.According to the World Health Organization(WHO),people in public places should wear a face mask to control the rapid transmission of COVID-19.The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places.Therefore,it is very difficult to manually monitor people in overcrowded areas.This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places,by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19.This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked.The proposed framework is built by fine-tuning the state-of-the-art deep learning model,Faster-RCNN,and has been validated on a publicly available dataset named Face Mask Dataset(FMD)and achieving the highest average precision(AP)of 81%and highest average Recall(AR)of 84%.This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces.Moreover,this work applies to real-time and can be implemented in any public service area.

Chaos Behavior and Estimation of the Unknown Parameters of Stochastic Lattice Gas for Prey-Predator Model with Pair-Approximation

In this paper, the problem of chaos, stability and estimation of unknown parameters of the stochastic lattice gas for prey-predator model with pair-approximation is studied. The result shows that this dynamical system exhibits an oscillatory behavior of the population densities of prey and predator. Using Liapunov stability technique, the estimators of the unknown probabilities are derived, and also the updating rules for stability around its steady states are derived. Furthermore the feedback control law has been as non-linear functions of the population densities. Numerical simulation study is presented graphically.

Study of the 1D Saint-Venant Equations and Application to the Simulation of a Flood Problem

In this paper we are concerned with the mathematical and numerical analysis of the one-dimensional Saint-Venant equations. Thus, we prove the existence of a weak solution for any fixed time and with low regularity on the data. For the numerical approach we use the Rusanov scheme to approximate the flux and the hydrostatic reconstruction method which consists of decentering the source term at the interface. A numerical test of the proposed resolution is performed on a non-uniform topography.

Updated Lithological Map in the Forest Zone of the Centre, South and East Regions of Cameroon Using Multilayer Perceptron Neural Network and Landsat Images

The Multilayer Perceptron Neural Network (MLPNN) induction technique has been successfully applied to a variety of machine learning tasks, including the extraction and classification of image features. However, not much has been done in the application of MLPNN on images obtained by remote sensing. In this article, two automatic classification systems used in image feature extraction and classification from remote sensing data are presented. The first is a combination of two models: a MLPNN induction technique, integrated under ENVI (Environment for Visualizing Images) platform for classification, and a pre-processing model including dark subtraction for the calibration of the image, the Principal Components Analysis (PCA) for band selections and Independent Components Analysis (ICA) as blind source separator for feature extraction of the Landsat image. The second classification system is a MLPNN induction technique based on the Keras platform. In this case, there was no need for pre-processing model. Experimental results show the two classification systems to outperform other typical feature extraction and classification methods in terms of accuracy for some lithological classes including Granite1 class with the highest class accuracies of 96.69% and 92.69% for the first and second classification system respectively. Meanwhile, the two classification systems perform almost equally with the overall accuracies of 53.01% and 49.98% for the first and second models respectively though the keras model has the advantage of not integrating the pre-processing model, hence increasing its efficiency. The application of these two systems to the study area resulted in the generation of an updated geological mapping with six lithological classes detected including the Gneiss, the Micaschist, the Schist and three versions of Granites (Granite1, Granite2 and Granite3).

An Output Stabilization Problem of Distributed Linear Systems Approaches and Simulations

The goal of this paper is to study an output stabilization problem: the gradient stabilization for linear distributed systems. Firstly, we give definitions and properties of the gradient stability. Then we characterize controls which stabilize the gradient of the state. We also give the stabilizing control which minimizes a performance given cost. The obtained results are illustrated by simulations in the case of one-dimensional distributed systems.

Deep Learning-Based Classification of Fruit Diseases:An Application for Precision Agriculture

Agriculture is essential for the economy and plant disease must be minimized.Early recognition of problems is important,but the manual inspection is slow,error-prone,and has high manpower and time requirements.Artificial intelligence can be used to extract fruit color,shape,or texture data,thus aiding the detection of infections.Recently,the convolutional neural network(CNN)techniques show a massive success for image classification tasks.CNN extracts more detailed features and can work efficiently with large datasets.In this work,we used a combined deep neural network and contour feature-based approach to classify fruits and their diseases.A fine-tuned,pretrained deep learning model(VGG19)was retrained using a plant dataset,from which useful features were extracted.Next,contour features were extracted using pyramid histogram of oriented gradient(PHOG)and combined with the deep features using serial based approach.During the fusion process,a few pieces of redundant information were added in the form of features.Then,a“relevance-based”optimization technique was used to select the best features from the fused vector for the final classifications.With the use of multiple classifiers,an accuracy of up to 99.6%was achieved on the proposed method,which is superior to previous techniques.Moreover,our approach is useful for 5G technology,cloud computing,and the Internet of Things(IoT).

Use of Cataract Surgery in Urban Beijing: a Post Screening Follow-up of the Elderly with Visual Impairment due to Age-related Cataract

Objective To understand the perception for the use of cataract surgical services in a population acceptors and non-acceptors of cataract surgery in urban Beijing. Methods From a community-based screening program a total of 158 patients with presenting visual acuity of less than 6/18 on either eye due to age-related cataract were informed about the possibility of surgical treatment. These patients were interviewed and re-examined 36 to 46 months after initial screening. The main reasons for not accepting surgery were obtained using a questionnaire. Vision function and vision-related quality of life scores were assessed in those who received and did not receive surgery. Results At the follow-up examination 116 of the 158 patients were available and 36 （31.0%） had undergone cataract surgery. Cases who chose surgery had higher education level than those who did not seek surgery （OR=2.64, 95% CI： 1.08-6.63, P=0.02）. There were no significant differences in vision function （P=0.11） or quality of life scores （P=0.16） between the surgery group and the non-surgery group. Main reasons for not having surgery included no perceived need （50.0%）, feeling of being ＂too old＂ （19.2%）, and worry about the quality of surgery （9.6%）. Cost was seeking surgery. cited by 1 （1.9%） subject as the main reason for not

Association of C(-106)T Polymorphism in Aldose Reductase Gene with Diabetic Retinopathy in Chinese Patients with Type 2 Diabetes Mellitus

Objective To identify the possible association between C（-106）T polymorphism of the aldose reductase （ALR） gene and diabetic retinopathy （DR） in a cohort of Chinese patients with type 2 diabetes mellitus （T2DM）. Methods From November 2009 to September 2010, patients with T2DM were recruited and assigned to DR group or diabetic without retinopathy （DWR） group according to the duration of diabetes and the grading of 7-field fundus color photographs of both eyes. Genotypes of the C（-106）T polymorphism （rs759853） in ALR gene were analyzed using the MassARRAY genotyping system and an association study was performed. Results A total of 268 T2DM patients （129 in the DR group and 139 in the DWR group） were included in this study. No statistically significant differences were observed between the 2 groups in the age of diabetes onset （P=0.10） and gender （P=0.78）. The success rate of genotyping for the study subjects was 99.6% （267/268）, with one case of failure in the DR group. The frequencies of the T allele in the C（-106）T polymorphism were 16.0% （41/256） in the DR group and 19.4% （54/278） in the DWR group （P=0.36）. There was no signit^cant difference in the C（-106）T genotypes between the 2 groups （P=0.40）. Compared with the wild-type genotype, odds ratio （OR） for the risk of DR was 0.7 （95% CI, 0.38-1.3） for the heterozygous CT genotype and 0.76 （95% CI, 0.18-3.25） for the homozygous TT genotype. The risk of DR was positively associated with microalbuminuria （OR=4.61; 95% CI, 2.34-9.05） and insulin therapy （OR=3.43; 95% CI, 1.94-6.09）. Conclusions Microalbuminuria and insulin therapy are associated with the risk of DR in Chinese patients with T2DM. C（-106）T polymorphism of the ALR gene may not be significantly associated with DR in Chinese patients with T2DM.

Classication of COVID-19 CT Scans via Extreme Learning Machin

Here,we use multi-type feature fusion and selection to predict COVID-19 infections on chest computed tomography(CT)scans.The scheme operates in four steps.Initially,we prepared a database containing COVID-19 pneumonia and normal CT scans.These images were retrieved from the Radiopaedia COVID-19 website.The images were divided into training and test sets in a ratio of 70:30.Then,multiple features were extracted from the training data.We used canonical correlation analysis to fuse the features into single vectors;this enhanced the predictive capacity.We next implemented a genetic algorithm(GA)in which an Extreme Learning Machine(ELM)served to assess GA tness.Based on the ELM losses,the most discriminatory features were selected and saved as an ELM Model.Test images were sent to the model,and the best-selected features compared to those of the trained model to allow nal predictions.Validation employed the collected chest CT scans.The best predictive accuracy of the ELM classier was 93.9%;the scheme was effective.

MULTISCALE HOMOGENIZATION OF NONLINEAR HYPERBOLIC EQUATIONS WITH SEVERAL TIME SCALES

We study the multiscale homogenization of a nonlinear hyperbolic equation in a periodic setting.We obtain an accurate homogenization result.We also show that as the nonlinear term depends on the microscopic time variable,the global homogenized problem thus obtained is a system consisting of two hyperbolic equations.It is also shown that in spite of the presence of several time scales,the global homogenized problem is not a reiterated one.

Lightweight Transfer Learning Models for Ultrasound-Guided Classification of COVID-19 Patients

Lightweight deep convolutional neural networks(CNNs)present a good solution to achieve fast and accurate image-guided diagnostic procedures of COVID-19 patients.Recently,advantages of portable Ultrasound(US)imaging such as simplicity and safe procedures have attracted many radiologists for scanning suspected COVID-19 cases.In this paper,a new framework of lightweight deep learning classifiers,namely COVID-LWNet is proposed to identify COVID-19 and pneumonia abnormalities in US images.Compared to traditional deep learning models,lightweight CNNs showed significant performance of real-time vision applications by using mobile devices with limited hardware resources.Four main lightweight deep learning models,namely MobileNets,ShuffleNets,MENet and MnasNet have been proposed to identify the health status of lungs using US images.Public image dataset(POCUS)was used to validate our proposed COVID-LWNet framework successfully.Three classes of infectious COVID-19,bacterial pneumonia,and the healthy lung were investigated in this study.The results showed that the performance of our proposed MnasNet classifier achieved the best accuracy score and shortest training time of 99.0%and 647.0 s,respectively.This paper demonstrates the feasibility of using our proposed COVID-LWNet framework as a new mobilebased radiological tool for clinical diagnosis of COVID-19 and other lung diseases.

Determination of the Root Canal Length of Teeth of Bantu Patients Attending the Teaching Hospital of Kinshasa University

Background: There is paucity of literature on the determination of the root canal length of Bantu subjects in dental professional practicing in Africa and Democratic Republic of Congo in particular. Aims: The aim of the present study was to determine the root canal length of teeth of Bantu patients extracts attending the Teaching Hospital of Kinshasa University. Methods and Material: Prospective cross-sectional study was carried out in the service of Conservative Dentistry. The patients suffering with pulpitis of permanent teeth which were selected for root canal treatment during the period of January 2014 to December 2016 were included. All patients whose main root canals were inaccessible, teeth carrying prosthesis, teeth with large coronal decay, teeth having periapical periodontitis, supernumerary teeth, wisdom and primary teeth were excluded. Results: The upper canines presented some significant longer canals compared to the lower canine (23.4 ± 2.3 mm and 21.6 ± 1.8 mm). Palatal canals of the first and second molar were respectively longer as compared to the superior teeth canals (21.5 ± 1 mm, 21.3 ± 2 mm). The distal canals of the first and second molar were the longest in the mandibular arch respectively measuring 20.7 ± 2.0 mm and 21.5 ± 1.7 mm. Conclusion: Data obtained from Bantu patients show slightly shorter roots compared to some European populations, but longer than some Asian populations.