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Modeling Blood Flow in Veins of Uniform Properties (Giraffe Jugular Vein)

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Open Journal of Biophysics 2024年第2期14卷 132-153页

作者： Rogers Omboga Amenya Johanna Kibet Sigey geoffrey Moriaso Ole Maloiy David mwangi Theuri Department of Mathematics and Physical Sciences Dedan Kimathi University of Technology Nyeri Kenya Department of Pure and Applied Mathematics School of Mathematical Sciences Jomo Kenyatta Unversity of Agriculture and Technology Kisii Kenya Institute of Animal Physiology and Biotechnology Research University of Nairobi Nairobi Kenya

This paper models the giraffe’s jugular veins as a uniform collapsible tube from a rigid skull. The equations governing one-dimensional steady flow through such a tube for various conditions have been developed. The effects of inertial and inclination angles that have not been discussed previously have been included. It has been shown that different flows for a uniform tube (vein) are possible. However, this flow matches that of a jugular vein which is supercritical, and the steady solution has been given by the balance between the driving forces of gravity and the viscous resistance to the flow at the right atrium of the heart must be sub-critical for a fixed right-atrium pressure which means that an elastic jump is required to return the flow to sub-critical from the supercritical flow upstream this type of relationship gives rise to flow limitation at the same time given any right atrium fixed pressure there exists a maximum flow rate which when exceeded the boundary conditions of the flow do not hold boundary conditions at the right atrium are not satisfied hence making the steady flow impossible this mechanism of flow limitation is slightly different from the other one in that causes airways through forced expiration from the observation made it is clearly shown that there is an intravascular pressure difference with a change in height.

关键词： Blood Flow Jugular Vein Cross-Sectional Area Supercritical Subcritical Jump

An annotated checklist of mammals of Kenya

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Zoological Research 2019年第1期40卷 3-52页

作者： Simon Musila Ara Monadjem Paul W.Webala Bruce D.Patterson Rainer Hutterer Yvonne A.De Jong Thomas M.Butynski geoffrey mwangi Zhong-Zheng Chen Xue-Long Jiang Mammalogy Section Department of Zoology National Museums of Kenya Department of Biological Sciences University of Swaziland Mammal Research Institute Department of Zoology & Entomology University of Pretoria Department of Forestry and Wildlife Management Maasai Mara University Integrative Research Center Field Museum of Natural History Zoologisches Forschungsmuseum Alexander Koenig Leibniz-Institut für Biodiversit?t der Tiere Eastern Africa Primate Diversity and Conservation Program School of Natural Resources and Environmental Studies Karatina University Sino-African Joint Research Center Chinese Academy of Sciences Kunming Institute of Zoology Chinese Academy of Sciences

Kenya has a rich mammalian fauna. We reviewed recently published books and papers including the six volumes of Mammals of Africa to develop an up-to-date annotated checklist of all mammals recorded from Kenya. A total of 390 species have been identified in the country, including 106 species of rodents,104 species of bats, 63 species of even-toed ungulates(including whales and dolphins), 36 species of insectivores and carnivores, 19 species of primates,five species of elephant shrews, four species of hyraxes and odd-toed ungulates, three species of afrosoricids, pangolins, and hares, and one species of aardvark, elephant, sirenian and hedgehog. The number of species in this checklist is expected to increase with additional surveys and as the taxonomic status of small mammals(e.g., bats, shrews and rodents) becomes better understood.

关键词： Checklist Rodents Bats Ungulate Carnivores Shrews Kenya

A Lightweight Convolutional Neural Network with Hierarchical Multi-Scale Feature Fusion for Image Classification

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Journal of Computer and Communications 2024年第2期12卷 173-200页

作者： Adama Dembele Ronald Waweru mwangi Ananda Omutokoh Kube Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation Nairobi Kenya Department of Computing School of Computing and Information Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Department of Mathematics and Actuarial Sciences Kenyatta University Nairobi Kenya

Convolutional neural networks (CNNs) are widely used in image classification tasks, but their increasing model size and computation make them challenging to implement on embedded systems with constrained hardware resources. To address this issue, the MobileNetV1 network was developed, which employs depthwise convolution to reduce network complexity. MobileNetV1 employs a stride of 2 in several convolutional layers to decrease the spatial resolution of feature maps, thereby lowering computational costs. However, this stride setting can lead to a loss of spatial information, particularly affecting the detection and representation of smaller objects or finer details in images. To maintain the trade-off between complexity and model performance, a lightweight convolutional neural network with hierarchical multi-scale feature fusion based on the MobileNetV1 network is proposed. The network consists of two main subnetworks. The first subnetwork uses a depthwise dilated separable convolution (DDSC) layer to learn imaging features with fewer parameters, which results in a lightweight and computationally inexpensive network. Furthermore, depthwise dilated convolution in DDSC layer effectively expands the field of view of filters, allowing them to incorporate a larger context. The second subnetwork is a hierarchical multi-scale feature fusion (HMFF) module that uses parallel multi-resolution branches architecture to process the input feature map in order to extract the multi-scale feature information of the input image. Experimental results on the CIFAR-10, Malaria, and KvasirV1 datasets demonstrate that the proposed method is efficient, reducing the network parameters and computational cost by 65.02% and 39.78%, respectively, while maintaining the network performance compared to the MobileNetV1 baseline.

关键词： MobileNet Image Classification Lightweight Convolutional Neural Network Depthwise Dilated Separable Convolution Hierarchical Multi-Scale Feature Fusion

Advancing Type II Diabetes Predictions with a Hybrid LSTM-XGBoost Approach

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Journal of Data Analysis and Information Processing 2024年第2期12卷 163-188页

作者： Ayoub Djama Waberi Ronald Waweru mwangi Richard Maina Rimiru Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Department of Computing Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya School of Computing and Information Technology (SCIT) Jomo Kenyatta University of Agriculture and Technology (JKUAT) Nairobi Kenya

In this paper, we explore the ability of a hybrid model integrating Long Short-Term Memory (LSTM) networks and eXtreme Gradient Boosting (XGBoost) to enhance the prediction accuracy of Type II Diabetes Mellitus, which is caused by a combination of genetic, behavioral, and environmental factors. Utilizing comprehensive datasets from the Women in Data Science (WiDS) Datathon for the years 2020 and 2021, which provide a wide range of patient information required for reliable prediction. The research employs a novel approach by combining LSTM’s ability to analyze sequential data with XGBoost’s strength in handling structured datasets. To prepare this data for analysis, the methodology includes preparing it and implementing the hybrid model. The LSTM model, which excels at processing sequential data, detects temporal patterns and trends in patient history, while XGBoost, known for its classification effectiveness, converts these patterns into predictive insights. Our results demonstrate that the LSTM-XGBoost model can operate effectively with a prediction accuracy achieving 0.99. This study not only shows the usefulness of the hybrid LSTM-XGBoost model in predicting diabetes but it also provides the path for future research. This progress in machine learning applications represents a significant step forward in healthcare, with the potential to alter the treatment of chronic diseases such as diabetes and lead to better patient outcomes.

关键词： LSTM XGBoost Hybrid Models Machine Learning. Deep Learning

Enhancing Pneumonia Detection in Pediatric Chest X-Rays Using CGAN-Augmented Datasets and Lightweight Deep Transfer Learning Models

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Journal of Data Analysis and Information Processing 2024年第1期12卷 1-23页

作者： Coulibaly Mohamed Ronald Waweru mwangi John M. Kihoro Department of Mathematics Pan African University Institute for Basic Sciences Technology and Innovation (PAUSTI) Nairobi Kenya Department of Computing Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya School of Computing and Mathematics The Cooperative University of Kenya Nairobi Kenya

Pneumonia ranks as a leading cause of mortality, particularly in children aged five and under. Detecting this disease typically requires radiologists to examine chest X-rays and report their findings to physicians, a task susceptible to human error. The application of Deep Transfer Learning (DTL) for the identification of pneumonia through chest X-rays is hindered by a shortage of available images, which has led to less than optimal DTL performance and issues with overfitting. Overfitting is characterized by a model’s learning that is too closely fitted to the training data, reducing its effectiveness on unseen data. The problem of overfitting is especially prevalent in medical image processing due to the high costs and extensive time required for image annotation, as well as the challenge of collecting substantial datasets that also respect patient privacy concerning infectious diseases such as pneumonia. To mitigate these challenges, this paper introduces the use of conditional generative adversarial networks (CGAN) to enrich the pneumonia dataset with 2690 synthesized X-ray images of the minority class, aiming to even out the dataset distribution for improved diagnostic performance. Subsequently, we applied four modified lightweight deep transfer learning models such as Xception, MobileNetV2, MobileNet, and EfficientNetB0. These models have been fine-tuned and evaluated, demonstrating remarkable detection accuracies of 99.26%, 98.23%, 97.06%, and 94.55%, respectively, across fifty epochs. The experimental results validate that the models we have proposed achieve high detection accuracy rates, with the best model reaching up to 99.26% effectiveness, outperforming other models in the diagnosis of pneumonia from X-ray images.

关键词： Pneumonia Detection Pediatric Radiology CGAN (Conditional Generative Adversarial Networks) Deep Transfer Learning Medical Image Analysis

Development and Application of Integrated Indicators for Assessing Healthcare Waste Management Systems in Kenyan Hospitals

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Open Journal of Applied Sciences 2024年第4期14卷 1080-1120页

作者： Muriithi Jackson Githinji Paul mwangi Njogu Zipporah Nganga Mohamed Karama Institute of Tropical Medicine and Infectious Diseases (ITROMID) Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Institute of Energy and Environmental Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Academic Research and Student Affairs South Eastern Kenya University Kitui Kenya School of Public Health Amref International University Nairobi Kenya

Healthcare waste management (HCWM) is an important aspect of healthcare delivery globally because of its hazardous and infectious components that have potential for adverse health and environmental impacts. The paper introduces a set of indicators for assessing HCWM systems in hospitals. These indicators are: HCWM policies and standard operating procedures, management and oversight, logistics and budget support, training and occupational health and safety, and treatment, disposal and waste treatment equipment housing. By plotting a mark on a continuum which is defined as good and poor on the extremes and is connected with all other marks in a spoke arrangement, it’s possible to describe a baseline for HCWM in any specific hospital. This baseline can be used to improve awareness of the actors and policy-makers, compare the same hospital at a different point in time, to compare observations by different evaluators and to track improvements. Results suggest that in Kenya, the application of such indicators is useful for evaluating which priorities should be addressed to improve outcomes in HCWM systems. Systematic sampling technique was used to identify and collect data by use of observational checklist, interviews, visual verification and review of documents and a HCWM assessment tool. The objective is to suggest an integrated management tool as a method to identify prevailing problems with a HCWM system. The method can be replicated in other contexts worldwide, with a focus on the developing world. The integrated indicators focus on management of HCW and not its potential impact on human health and environment, an area recognized to be critical for future research.

关键词： Developing Countries HCW Management Indicators Management Tools

Levels of Polycyclic Aromatic Hydrocarbons (PAHs) in Healthcare Waste Incinerators’ Bottom Ash from Five County Hospitals in Kenya

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Journal of Environmental Protection 2024年第3期15卷 318-337页

作者： Muriithi Jackson Githinji Paul mwangi Njogu Zipporah Nganga Mohamed Karama Institute of Tropical Medicine and Infectious Diseases (ITROMID) Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Institute of Energy and Environmental Technology Jomo Kenyatta University of Agriculture and Technology Nairobi Kenya Academic Research and Student Affairs South Eastern Kenya University Kitui Kenya Public Health and Epidemiology Umma University Kajiado Kenya

Health-care waste contains potentially harmful microorganisms and compounds which can infect and affect hospital patients, healthcare workers, the general public and environment. Therefore, management of health care waste requires safe handling, treatment and disposal procedures. While incineration reduces the volume and quantity of waste for final disposal, it leads to the production of fly and bottom ashes laden with toxic incomplete combustion products such as Polycyclic Aromatic Hydrocarbons (PAHs), dioxins, furans and heavy metals. This exposes workers who handle and dispose the bottom ashes, hospital patients, the general public and environment. The goal of this study was to determine the total and individual levels of 16 most prevalent and toxic PAHs. Bottom ash samples were collected from incinerators in five county hospitals in Kenya, namely;Moi-Voi, Narok, Kitale, Makindu and Isiolo. Bottom ash samples were collected over a period of six months from the five hospitals. The samples were then sieved, homogenised and stored at 4°C in amber coloured glass containers. The PAHs were extracted using 30 ml of a hexane-acetone solvent (1:1) mixture by ultrasonication at room temperature (23°C) for 45 minutes. The PAHs were then analyzed with a GC-MS spectrophotometer model (Shimadzu GCMS-QP2010 SE) connected to a computer work station was used for the PAHs analysis. The GC-MS was equipped with an SGE BPX5 GC capillary column (30 m × 0.25 mm × 0.25 μm) for the separation of compounds. Helium was used as the carrier gas at a flow rate of 15.5 ml/minute and 14.5 psi. 1 μl of the sample was injected at 280°C, split mode (10:1). The oven programming was set for a total runtime of 40 minutes, which included: 100°C (2-minute hold);10°C /min rise to 200°C;7°C /min rise to 249°C;3°C /min rise to 300°C (2-minute hold). The interface temperature was set at 290°C. Analysis was done in Selected Ion Monitoring (SIM) mode and the peak areas of each of the PAHs were collected from

关键词： PAHs GC-MS Healthcare Wastes Disposal Incinerator Bottom Ash

Mathematical Modeling of HIV Investigating the Effect of Inconsistent Treatment

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Journal of Applied Mathematics and Physics 2024年第4期12卷 1063-1078页

作者： Sr Mary Nyambura mwangi Virginia M. Kitetu Isaac O. Okwany Department of Mathematics and Actuarial Science Catholic University of Eastern Africa Nairobi Kenya

HIV is a retrovirus that infects and impairs the cells and functions of the immune system. It has caused a great challenge to global public health systems and leads to Acquired Immunodeficiency Syndrome (AIDS), if not attended to in good time. Antiretroviral therapy is used for managing the virus in a patient’s lifetime. Some of the symptoms of the disease include lean body mass and many opportunistic infections. This study has developed a SIAT mathematical model to investigate the impact of inconsistency in treatment of the disease. The arising non-linear differential equations have been obtained and analyzed. The DFE and its stability have been obtained and the study found that it is locally asymptotically stable when the basic reproduction number is less than unity. The endemic equilibrium has been obtained and found to be globally asymptotically stable when the basic reproduction number is greater than unity. Numerical solutions have been obtained and analyzed to give the trends in the spread dynamics. The inconsistency in treatment uptake has been analyzed through the numerical solutions. The study found that when the treatment rate of those infected increases, it leads to an increase in treatment population, which slows down the spread of HIV and vice versa. An increase in the rate of treatment of those with AIDS leads to a decrease in the AIDS population, the reverse happens when this rate decreases. The study recommends that the community involvement in advocating for consistent treatment of HIV to curb the spread of the disease.

关键词： HIV Modeling Mathematical Modeling Reproduction Number Inconsistent Treatment

Mathematical Modeling of the Co-Infection Dynamics of HIV and Tuberculosis Incorporating Inconsistency in HIV Treatment

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Journal of Applied Mathematics and Physics 2024年第5期12卷 1744-1768页

作者： Sr Mary Nyambura mwangi Virginia M. Kitetu Isaac O. Okwany Department of Mathematics and Actuarial Science Catholic University of Eastern Africa Nairobi Kenya

A non-linear HIV-TB co-infection has been formulated and analyzed. The positivity and invariant region has been established. The disease free equilibrium and its stability has been determined. The local stability was determined and found to be stable under given conditions. The basic reproduction number was obtained and according to findings, co-infection diminishes when this number is less than unity, and persists when the number is greater than unity. The global stability of the endemic equilibrium was calculated. The impact of HIV on TB was established as well as the impact of TB on HIV. Numerical solution was also done and the findings indicate that when the rate of HIV treatment increases the latent TB increases while the co-infected population decreases. When the rate of HIV treatment decreases the latent TB population decreases and the co-infected population increases. Encouraging communities to prioritize the consistent treatment of HIV infected individuals must be emphasized in order to reduce the scourge of HIV-TB co-infection.

关键词： Co-Infection Modeling HIV-TB Co-Infection Mathematical Modeling Reproduction Number Inconsistent Treatment