Faculty of Science Engineering and Technology

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2D-Euler Deconvolution and Forward Modeling of Gravity Data of Homa-Hills Geothermal Prospect, Kenya
(2014) Odek, A.; Otieno, A. B.; Githiri, Jg
In order to fully assess the potential of Homa Hills Geothermal prospect, the heat source which is one of the main features of a geothermal system had to be located based on its perturbation on the gravity field. Ground gravity survey was conducted in an area covering about 76 km2 and the data processed to remove all other effects which are not of geological interest. Qualitative interpretation was attempted and cross sections drawn across the anomalous areas on the complete Bouguer anomaly map. Quantitative interpretation attempted involved both Euler Deconvolution and 2‐D Forward modelling. The parameters obtained from Euler Deconvolution were used as the start up parameters for 2‐D Forward modeling. Well clustered Euler solutions were obtained at a shallow depth of approximately 200‐750 m which is consistent with the modeled shallow dike like intrusive probably of carbonatite origin.
2D-Euler Deconvolution and Forward Modeling of Gravity Data of Homa-Hills Geothermal Prospect, Kenya
(2013) Odek, O. 1; Otieno, A.B. 1; Ambusso, W.J. 1; Githiri, J. G 2
In order to fully assess the potential of Homa Hills Geothermal prospect, the heat source which is one of the main features of a geothermal system had to be located based on its perturbation on the gravity field. Ground gravity survey was conducted in an area covering about 76 km2 and the data processed to remove all other effects which are not of geological interest. Qualitative interpretation was attempted and cross sections drawn across the anomalous areas on the complete Bougu er anomaly map. Quantitative interpretation attempted involved both Euler Deconvolution and 2 ‐D Forward modelling. The parameters obtained from Euler Deconvolution were used as the start up parameters for 2 ‐D Forward modeling. Well clustered Euler solutions were obtained at a shallow depth of approximately 200‐750 m which is consistent with the modeled shallow dike like intrusive probably of carbonatite origin
A Backward Regressed Capsule Neural Network for Plant Leaf Disease Detection
(Science Publications, 2022) Jepkoech, J.; Kenduiywo, B. K.; Mugo, D. M.; Too, E. C.
This study investigated the introduction of backward regression coupled with DenseNet features in a Capsule Neural Network (CapsNet) for plant leaf disease classification. Plant diseases are considered one of the main factors influencing food production and therefore fast crop disease detection and recognition are important in enhancing food security interventions. CapsNets have successfully been adopted for plant leaf disease classification however, backpropagation of signals to preceding layers is still a challenge due to low gradient flow. In addition, parameter and computational complexities exist due to complex features. Therefore, this study implemented a loop connectivity pattern to improve gradient flow in the convolution layer and backward regression for feature selection. We observed a 99.7% F1 score with backward regression and 87% F1 score without backward regression accuracy on testing our framework based on the standard Plant Village (PV) dataset comprising ten tomato classes with 9080 images. Additionally, CapsNet with backward regression showed relatively higher and stable accuracy when sensitivity analysis was performed by varying testing and training dataset percentages. In comparison Support Vector Machines (SVM), Artificial Neural Networks (ANN), AlexNet, ResNet, VGGNet, Inception V3, and VGG 16 deep learning approaches scored 84.5, 88.6, 99.3, 97.87, 99.14, and 98.2%, respectively. These findings indicate that the introduction of backward regression of features in the CapsNet model may be a decent and, in most cases superior and less expensive alternative for phrase categorization models based on CNNs and RNNs. Therefore, the accuracy of plant disease detection may be enhanced even further with the aid of the fusion of several classifiers and the integration of a backward regressed capsule neural network.
A Machine Learning-Based Prediction of Malaria Occurrence in Kenya
(American Journal of Theoretical and Applied Statistics, 2024-08-20) Dennis Muriithi* , Victor Wandera Lumumba , Mark Okongo
For many years’ malaria has been a health public concern in Kenya as well as many parts of Africa and other parts of the world. The purpose of this study is to develop and evaluate a supervised machine learning model to predict malaria occurrence (final malaria test results) in Kenya. The study investigated twelve predictor variables on the outcome variable (malaria test results), where five machine learning models namely; k-nearest neighbors, support vector machines, random forest, tree bagging, and boosting, were estimated. During the model evaluation, random forest emerged as the best overall model in the classification and prediction of final malaria test results. The model attained a higher classification accuracy of 97.33%, sensitivity of 71.1%, specificity of 98.4%, balanced accuracy of 84.7% and an area under the curve of 98.3%. From the final model, the presence of plasmodium falciparum emerged most important feature, followed by region, endemic zone and anemic level. The feature with the least importance in predicting final malaria test results was having mosquito nets. In conclusion, employing Machine learning algorithms enhances early detection, optimizing resource allocation for interventions, and ultimately reducing the incidence and impact of malaria in the Kenya. The study recommends allocation of resources and funds to areas with the presence of plasmodium falciparum, region susceptible to malaria, endemic zones and anemic prone areas.
A Machine Learning-Based Prediction of Malaria Occurrence in Kenya
(American Journal of Theoretical and Applied Statistics, 2024-08-20) Dennis Muriithi* , Victor Wandera Lumumba , Mark Okongo
The purpose of this study is to develop and evaluate a supervised machine learning model to predict malaria occurrence (final malaria test results) in Kenya. The study investigated twelve predictor variables on the outcome variable (malaria test results), where five machine learning models namely; k-nearest neighbors, support vector machines, random forest, tree bagging, and boosting, were estimated. During the model evaluation, random forest emerged as the best overall model in the classification and prediction of final malaria test results. The model attained a higher classification accuracy of 97.33%, sensitivity of 71.1%, specificity of 98.4%, balanced accuracy of 84.7% and an area under the curve of 98.3%. From the final model, the presence of plasmodium falciparum emerged most important feature, followed by region, endemic zone and anemic level. The feature with theleast importance in predicting final malaria test results was having mosquito nets. In conclusion, employing Machine learning algorithms enhances early detection, optimizing resource allocation for interventions, and ultimately reducing the incidence and impact of malaria in the Kenya. The study recommends allocation of resources and funds to areas with the presence of plasmodium falciparum, region susceptible to malaria, endemic zones and anemic prone areas.
Ab initio Investigation of the Structural and Electronic Properties of Alkaline Earth Metal - TiO2 Natural Polymorphs
(Hindawi Advances in Materials Science and Engineering, 2022) Mbae, J.K.; Muthui, Z.W.
Titanium (IV) oxide (TiO2) has gained much attention due to its application in technologies such as optoelectronics, electronics, sensors, photocatalysts, and sustainable energy generation. However, its optical absorption falls in the ultraviolet part of the electromagnetic spectrum, resulting in a low absorption ratio of solar light. In addition, rapid electron-hole recombination limits its photocatalytic activity. To extend the application range of TiO2, the structural and chemical properties can be modified by adding various dopants to tune its electronic structure for applications within a wider range of the solar energy spectrum and ideally extend towards the visible region, which forms the dominant part of the solar energy spectrum. In this study, the structural and electronic properties of three polymorphs of TiO2 have been studied using density functional theory (DFT) as implemented in the Quantum ESPRESSO simulation package. )e exchange-correlation potential has been treated with the generalised gradient approximation (GGA). Cationic substitution with non-toxic alkaline earth metal dopants Mg and Ca has been carried out with the aim of modifying the electronic structure of the polymorphs of TiO2. On 1–4% Mg and Ca cationic substitution, there is a slight expansion of the optimal unit cell volume and modulation of the band gap energy by raising the valence band maximum to higher energies. In addition, dopant inter and intra-band states are observed.
Action and variation potential electrical signals in higher plants
(African Journal of Biological Sciences, 2021) Ndung’u, Ruth Wairimu; Kamweru, Paul Kuria; Kirwa, Abraham Tuwei
This review evaluates the types of electrical signals (ESs) in plants, generation and propagation of various ESs, their ways of transmission within the plant body and their corresponding physiological significance. It also outlines abiotic factors, e.g., light, temperature, water content as stimuli on the electrical potential (EP) of the plants. The paper also summarizes a review of the effects of ESs on photosynthesis, the mechanisms of the effects, and its physiological role in plants. Local irritations of plants induce various photosynthetic responses including fast and long-term inactivation of photosynthesis and its activation. The paper also reviews the concept of plant energy harvesting. The measurement techniques used for ESs in plants including extracellular measurement and intracellular measurement are also reviewed. A brief summary of the applications of these methods for investigating ES in plants is also given.
Adoption of Near Field Communication in Universities in Kenya
(2015-09) Muthengi, Fredrick Mugambi; Njebiu, Victor Mwenda
In our day to day life, the adoption of new technology to new innovation in various sectors is rising. Campus life has shifted from students’ carrying laptops and a bag full of pass cards to carrying Ipads or smart phones. Barcode bars on pass and identification cards have been replaced by near field communication (NFC) instructions. NFC is a technology standard for very-short-range wireless connectivity that enables quick, secure two-way interactions among electronic devices. The level of global interaction and mode of communication is changing and the adoption of Near Flied Communication is on the rise, replacing bar code and QR code. The technology is at advanced stages ranging from file transfer; access controls to paying for goods and services on NFC enabled payment points/devices or cards. The introduction of Near Field Communication in the universities has enhanced the speed of processes as well as simplifying them. The pass cards/ids students carry along and time spent on queues waiting to be served is reduced. This paper explores the advantages of near field communication over magnetic bar codes and QR codes in an academic institution. Near field communication combines several instructions which are read via NFC enabled devices: smart phones or tags. Student card is customized with NFC tag: from library card, access control card to student identification card. NFC is at early stages of implementation in Kenya but has been successfully rolled out in transport industry as Bebapay. With the rise of mobile enabled near field communication devices, its adoption in Kenyan universities will be a success.
Adsorption of Lead (II) Ions from Aqueous Solutions Using Mangroves Roots (Rhizophora Mucronata) Charcoal-Carbon Nanotubes Nanocomposite
(Journal of Multidisciplinary Engineering Science and Technology (JMEST), 2021) Ngugi, Fidelis; Mwangi, Joel; Njagi, Eric; Ombaka, Ochieng
Providing clean and affordable water to meet human needs is a grand challenge of the 21st century. Worldwide, water supply struggles to keep up with the fast growing demand, which is exacerbated by population growth, global climate change, and water quality deterioration. Nanotechnology holds great potential in advancing water treatment to improve water treatment efficiency. In this study, Mangrove Roots Charcoal and Carbon Nanotubes (MRCCNTs) nanocomposite was synthesized and utilized as a novel adsorbent for the removal of lead ions from aqueous solutions. The efficacy of MRC-CNT nanocomposites was investigated in batch mode which involved the effects of pH, temperature, concentration of the lead ions, adsorbent mass and contact time on adsorbates removal. Characterization of the adsorbent was carried out by Scanning Electron Microscopy (SEM) to observe the morphology of the adsorbent and surface area analysis and Energy Dispersive X-ray spectroscopy (EDX) to determine the elemental composition of the adsorbent. Adsorption isotherm models and adsorption kinetic studies were used for data analysis. It was observed that the removal efficiency of Pb (II) ions depended on pH of solution and the maximum efficiency was noticed at pH 7 with adsorption capacity of 3.629 mg/g which was calculated by the Freundlich isotherm model. Kinetic studies were well suited and found in good agreement with pseudo-second order. The results indicated that MRC-CNT nanocomposites would be a promising adsorbent for adsorption of Pb (II) ions from aqueous solutions.
Adsorption of rhodamine b from aqueous solution using mangroves (Rhizophora mucronata) carbon nanotubes nanocomposites
(www.allsubjectjournal.com, 2021) Ngugi, Fidelis; Mwangi, Joel; Njagi, Eric; Ombaka, Ochieng
The use of dyes has increased dramatically and uncontrollably in last few decades. Different types of dyes are frequently employed in plastics, paper, cosmetics, leather, and textile industries for coloring purposes. These dyes are released in water as effluents, which are of low Biological Oxygen Demand (BOD) and high Chemical Oxygen Demand (COD). Some of these dyes also are toxic and carcinogenic in nature. This study report on the synthesis of Mangroves Roots-Carbon Nanotubes (MRC-CNT) nanocomposite as an adsorbent for efficient removal of Rhodamine B (Rh. B) dye from aqueous solution. Effect of contact time, initial concentration of dye, pH, and shaking speed on adsorption behavior were systematically investigated. The data obtained were fitted into Langmuir, Freundlich, Dubinin-Rudishkevich (D-R), and Temkin adsorption isotherm models for evaluation of adsorption parameters. The results indicated that MRC-CNT nanocomposite would be a promising adsorbent for adsorption of Rh. B from aqueous solutions.
Agrobiodiversity conservation enhances food security in subsistence-based farming systems of Eastern Kenya
(2016-09-15) Mburu, Simon Wambui; Koskey, Gilbert; Kimiti, Jacinta Malia; Ombori, Omwoyo; Maingi, John M.; Njeru, Ezekiel Mugendi
Background Globally, there is great concern about expanding agricultural activities due to their impact in the conservation of agrobiodiversity. African continent is known for its richness in biodiversity. In Kenya, there is a continuous unabated expansion of agriculture into natural habitats due to demographic and economic pressures posing a significant threat to biodiversity. Therefore, there is a need to study biodiversity loss and its regain through practices in agricultural landscapes. In this study, we assessed the status of agrobiodiversity and its contribution to food security in four agroecological zones of Eastern Kenya. Sixty households were sampled from two selected agroecological zones (upper and lower midland zones) in Embu and Tharaka-Nithi counties. Structured questionnaires and checklists were used to collect the data. Results Thirty-nine crop species were identified dominated by vegetables, fruits, legumes and cereals with relative densities of 28.8, 20.5, 18.3 and 8.3 %, respectively. Embu Lower Midland and Tharaka-Nithi Lower Midland zones had relatively higher crop species richness of 243 and 240, respectively, and Shannon–Wiener diversity indices (H′) of 3.403 and 3.377, respectively, compared with Embu Upper Midland and Tharaka-Nithi Upper Midland zones with species richness of 229 and 207, respectively, and H′ of 3.298 and 3.204, respectively. Conclusions Households from lower midland zones with high crop diversity and richness were more food secure compared with those from the Upper Midland zones with low crop diversity and richness. These findings suggest that farm production systems with high agrobiodiversity contributed more toward food security among smallholder farmers in the selected sites.
Algorithm for Selection of EAP Authentic
(2016-06) Mwathi, David Gitonga 1; Okello-Odongo, William 2 ,; Opiyo, Elisha 3
ABSTRACT Several recent studies indicate that many implementations of authentication and access control in public WLANs are compromisable. This is because IEEE 802.11 standard leaves the choice of EAP method to use to the discretion of WLAN system implementers due to the fact that IEEE 802.11 standard cannot and does not define the upper layer authentication. Therefore, this paper presents IEEE 802.11 implementation specific issues that may contribute to poor security performance of WLAN authentication and access control implementation. It also analyses various EAP methods and presents an algorithm for selection of an Extensible authentication protocol (EAP) method for a Public WLAN.
Analyses of geophagic materials consumed by pregnant women in Embu, Meru and Chuka towns in eastern province, Kenya
(2011) Mwangi, Gichumbi J.; Ochieng, Ombaka; Mwangi, Gichumbi, J.; Ombaka, Ochieng
Geophagy is the deliberate consumption of soil and clay deposits by animals, including man. During pregnancy all the nourishment needed by the developing fetus comes from the mother, either the food she eats or the supplement she may take. The geochemical and mineralogical composition of the materials which are consumed by pregnant women from Meru, Embu and Chuka open air markets were studied. The geophagic materials were subjected to standard digestion procedures and analyzed for full assay and elemental analysis for Co, Zn, Mg, Cu, Pb and Cd using Atomic absorption spectrometry and Energy-dispersive x-ray fluorescence spectrometry in 30 geophagic samples. The mineralogical composition was investigated using X-ray diffractometry (XRD). The geochemical analysis revealed that the geophagic materials contain high levels of silica from 48.59 to 60.27%. Geophagic materials from Embu showed the highest concentration of Pb at 0.96 ppm. The levels of Pb in all samples exceeded the levels recommended by WHO/FAO limits of 0.01 ppm. The levels obtained for Cd in all the samples did not exceed the WHO/FAO limits of 0.003 ppm. The XRD data showed that the geophagic materials of the area consisted mainly of silica.
Analysis of Microbial Quality of Drinking Water in Njoro Sub-county, Kenya
(Journal of Environment Pollution and Human Health,, 2017) Kirianki P.R; Othira J. O; Kiruki S.
Drinking water should be free of microbial pathogens so as to be regarded as potable water and safe for drinking.However, water is prone to fecal contaminants which are the sources of gastrointestinal illnesses. In Njoro Sub- county, river Njoro and rain water are the primary sources of water which also reduces during dry seasons. Other water sources include boreholes, dams, springs and wells while in other cases, the residents store water in household storage containers for future uses. In this study, various water sources and water stored in different containers in Njoro Sub-County was analyzed for its microbial quality. Various microbial parameters such as total viable colony counts (TVCC), total coliforms (TC) and fecal coliforms (FC) were evaluated by use of the culture methods. Most of the water sources were contaminated. TVCC ranged from 0.47 to 1.76 CFU/1mL in water sources and 0.48 to 2.04 CFU/1mL in domestic storage containers. TC was in the range of between 0.30 to 1.89 CFU/100mL in water sources and 0.59 to 2.47 CFU/100mL in domestic storage containers. The mean FC in water sources ranged from 0.10 to 1.68 CFU/100mL and from 0.81 CFU/100mL domestic storage containers. Therefore frequent water testing should be performed by water authorities as recommended by WHO. At households, the people should employ various water treatment methods and practice safe water handling so as to avoid gastrointestinal infections.
Analysis of Microbial Quality of Drinking Water in Njoro Sub-county, Kenya
(2019-10) Kirianki, Philip; Othira, J.O.; Kiruki, Silas;
Drinking water should be free of microbial pathogens so as to be regarded as potable water and safe for drinking. However, water is prone to fecal contaminants which are the sources of gastrointestinal illnesses. In Njoro Sub-county, river Njoro and rain water are the primary sources of water which also reduces during dry seasons. Other water sources include boreholes, dams, springs and wells while in other cases, the residents store water in household storage containers for future uses. In this study, various water sources and water stored in different containers in Njoro Sub-County was analyzed for its microbial quality. Various microbial parameters such as total viable colony counts (TVCC), total coliforms (TC) and fecal coliforms (FC) were evaluated by use of the culture methods. Most of the water sources were contaminated. TVCC ranged from 0.47 to 1.76 CFU/1mL in water sources and 0.48 to 2.04 CFU/1mL in domestic storage containers. TC was in the range of between 0.30 to 1.89 CFU/100mL in water sources and 0.59 to 2.47 CFU/100mL in domestic storage containers. The mean FC in water sources ranged from 0.10 to 1.68 CFU/100mL and from 0.81 CFU/100mL domestic storage containers. Therefore frequent water testing should be performed by water authorities as recommended by WHO. At households, the people should employ various water treatment methods and practice safe water handling so as to avoid gastrointestinal infections.
Analysis of Volatility of Real Exchange Rate and Exports in Kenya using the Garch Model: 2005:2012
(2015) Mohammed, Mustapha Wasseja *; Mwenda, Samwel N. 1; Musundi, Sammy W., 2; Njoroge, Elizabeth 2
Abstract The real exchange rate has proven to be an important factor in international trade because it is expected that exports respond to real exchange rate movements with respect to the characteristics of the importing and exporting countries. Exchange rate volatility increases uncertainty of profits on contracts denominated in foreign currency and subsequently dampens trade and economic growth. This study investigated how real exchange rate volatility affected exports of key Kenyan commodities to the European Union and United Kingdom, namely; tea, coffee and horticulture to the European Union. The presence of exchange rate volatility was determined using the GARCH model. A Bounds testing and Autoregressive Distributed Lag model was used to establish the presence of a long run relationship between exchange rate volatility and commodity exports. Findings revealed that exchange rate volatility affected tea exports to the UK and horticulture exports to the European Union. Foreign income played an important role in explaining tea and coffee exports to the UK and EU respectively. (PDF) ANALYSIS OF THE VOLATILITY OF REAL EXCHANGE RATE AND EXPORTS IN KENYA USING THE GARCH MODEL: 2005-2012.. Available from: https://www.researchgate.net/publication/298212379_ANALYSIS_OF_THE_VOLATILITY_OF_REAL_EXCHANGE_RATE_AND_EXPORTS_IN_KENYA_USING_THE_GARCH_MODEL_2005-2012 [accessed Dec 04 2019].
Analysis of Volatility of Real Exchange Rate and Exports in Kenya using the Garch Model: 2005:2012
(2015-07) Mustapha, Wasseja; Musundi, Sammy Wabomba; Njoroge, Elizabeth; Ngugi, Mwenda
The real exchange rate has proven to be an important factor in international trade because it is expected that exports respond to real exchange rate movements with respect to the characteristics of the importing and exporting countries. Exchange rate volatility increases uncertainty of profits on contracts denominated in foreign currency and subsequently dampens trade and economic growth. This study investigated how real exchange rate volatility affected exports of key Kenyan commodities to the European Union and United Kingdom, namely; tea, coffee and horticulture to the European Union. The presence of exchange rate volatility was determined using the GARCH model. A Bounds testing and Autoregressive Distributed Lag model was used to establish the presence of a long run relationship between exchange rate volatility and commodity exports. Findings revealed that exchange rate volatility affected tea exports to the UK and horticulture exports to the European Union. Foreign income played an important role in explaining tea and coffee exports to the UK and EU respectively.
Analyzing the Risks in Highway Projects Using the Markov Chain Approach
(sciepub, 2018-12-05) DM,Obare,M M,Muraya
Theissuesassociatedtohighwayprojectsusuallyaffectaspectslikenatureoftheproject,costandtime. Riskeventsthatarenotusualalwaysgiverisetopositiveornegativefeedbackandnormallycausevariationsfrom project designs especially inclined to risk construction projects. However, more investigations have been done on riskmanagementassociatedtohighwayprojects,thereislimitedliteraturemoresototheriskoftheproject.These projectscanbeoverseenundersomedubiousconditionbyapplyingtheriskmanagementtechnique.Theaimofthis research was to demonstrate pertinence of the Markov Chain approaches in diminishing the risks of the highway projectsbyutilizationofdatafromtheprojects.OtherCommonlyutilizedproceduresdon'tinvestigatepotentialrisks successfully and subsequently the utilization of Markov chains. This project has made utilization of the Markov chainproceduretoevaluatetheshortandlong-termpotentialrisks.Thisresearchprojectutilizedriskdataacquired fromexpertsthroughquestionnaires.ThedatawasthenanalyzedutilizingtheOctavesoftware.
Anti-inflammatory activities of dichloromethane-methanolic leaf and stem bark extracts of Ximenia americana in mice models
(2020) Gaichu, Daniel M.1; Nthiga, Peter M. 1; Kariuki, Duncan M. 1; Ngugi, Matthew P. 1; Mburu, David N. 1
Introduction: Ximenia americana is a highly branched shrub mainly found in tropics of Asia, Africa, New Zealand, Central and South America among others. In most parts of Africa, X. americana is used in folklore to treat various disorders such as oedema, pain, fever, helminthiasis, diarrhea and burns among others. There is no published data on anti-inflammatory activities of organic extracts of X. americana. It is against this background that this research was carried out. The study tested for the anti-inflammatory activities of dichloromethane-methanolic (DCMMeOH) leaf and stem bark extracts of X. americana in rats. Methods: The plant materials were collected from Mbeere North sub-county, Embu county, Kenya. Methanol and dichloromethane in the ratio of 1:1 was used to extract the active compounds. Five to 6 weeks old Swiss Albino mice were employed for the anti-inflammatory studies. Animals were divided into 6 groups of 5 mice each: normal, negative, reference and three experimental groups (50, 100 and 150 mg/kg body weight). Inflammation was induced experimentally using carrageenan. The experimental groups were treated with predetermined dose quantities of prepared extracts. Diclofenac was used as the reference drug. Data was analyzed using one-way analysis of variance (ANOVA). Results: The extracts from the leaves reduced hind paw circumference by between 0.91% and 16.90% while the stem bark extracts reduced hind paw circumference by between 5.84% and 29.00%. Diclofenac reduced right hind paw circumference by 1.32%-29.60%. Qualitative phytochemical screening showed presence of alkaloids, flavonoids, steroids, saponins, cardiac glycosides, phenolics and terpenoids in the extract. Conclusion: The study established that the DCM-MeOH leaf and stem bark extracts of X. americana is effective in management of inflammation and therefore it can be explored as a possible bio-resource in the development of herbal medicines.
Antibacterial and Antifungal activities of Novel hydroxytriazenes.
(2012-04-02) Ombaka, A.O *; Muguna, A. T.; Gichumbi, J. M.
In a search for new leads potent antimicrobial agents, an array of novel hydroxytriazenes i-xi were synthesized and characterized through their melting point, crystal shape, colour and elemental analysis. In vitro microbiological evaluations were carried out for all the synthesized compounds against both bacterial and fungi using the turbidimetric method. The reagent number x and xi showed significant antibacterial activities against two gram positive [Streptococcus feacalis, Staphylococcus aureus, penicillin resistance (2500 units)]. All synthesized hydroxytriazenes except reagent number viii showed antifungal activities against five fungi [Candida albicans, Cryptococcus neoformans, Sporotrichum schenckii, Trichophyton mantagrophytes, Aspergillus fumigatus}. The minimum inhibitory concentration (mic) values against these bacteria and fungi ranged from <12.5 to 50 μg/ml. Some hydroxytriazenes (x, xi) showed an unusual combined antibacterial and antifungal action, which suggest that hydroxytriazenes revealed a wide range of antimicrobial activity.

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