Origin and Distribution of Heavy Minerals in the Surficial and Subsurficial Sediments of the Alluvial Nile River Terraces

Origin and distribution of the heavy minerals of surficial and subsurficial sediments has been investigated in the alluvial Nile River terraces, Khartoum North, Sudan. Heavy mineral assemblages in the very fine sand fraction (0.063 - 0.125 mm) of 10 sediment samples were identified using petrography microscope. Results of descriptive statistical parameters revealed that most sediments samples belonged within very poorly sorted to extremely poorly sorted, strongly negative skewed to strongly positive skewed and mesokurtic to very leptokurtic. The quartz was the dominant in the opaque minerals in all sediments. The non-opaque heavy minerals were dominant by zircon, tourmaline, rutile, garnet, sillimanite, and andalusite. Results revealed that the ultrastable minerals (zircon, tourmaline and rutile) were found in all sediments with range from (2% - 47.36%, 2.08% - 29% and 3% - 24.99%), respectively. Garnet, sillimanite and andalusite were also found with range from (5% - 67%, 1% - 9.09% and 1% - 50%), respectively. Heavy mineral assemblage indentifies sources that are not bounded to the local origin. The proportion and presence of heavy minerals from outside source rocks indicated relatively strong reworking of zircon sand from the outer-shelf to inner-shelf as well relatively long distance of transport. Fluvial and Aeolian sediments were the dominant environments in the investigated area. We conclude that most heavy minerals in the study area are originally derived from gneisses and schist metamorphic rocks and some igneous rocks of the Ethiopian plateau.

Genesis and Classification of Some Soils of the River Nile Terraces: A Case Study of Khartoum North, Sudan

Soils developed in the alluvium terraces of the River Nile at Khartoum North, Sudan was analyzed in an attempt to classify it as well as to refer them to their origin. Three river terraces comprising nine profiles were selected to cover the physiographic positions. Lack of B horizon and carbonate accumulation were main pedogenic processes in subsurface horizons, whereas orhric epipedon was developed on top soil surface. The microscopic inspection of heavy sand mineralogy indicated that the origin of the sand was the Ethiopian plateau. The most abundant clay mineral was smectite, followed by illite, kaolinite and chlorite. The presences of micas (illite) and chlorite in all studied soil samples might emphasize that these soils were young from the pedological viewpoint and less weathered. The soils of the River Nile terraces at Khartoum North were classified into: Typic Torrifluvents (1st terrace), Entic Haplocambids (2nd terrace) and Typic Haplocambids (3rd terrace). Mineralogy analysis indicated that the Entisols and Aridisols of the River Nile terraces in the study area had the same origin that of the igneous and metamorphic rocks from Ethiopian plateau.

Hybrid Global Optimization Algorithm for Feature Selection

This paper proposes Parallelized Linear Time-Variant Acceleration Coefficients and Inertial Weight of Particle Swarm Optimization algorithm(PLTVACIW-PSO).Its designed has introduced the benefits of Parallel computing into the combined power of TVAC(Time-Variant Acceleration Coefficients)and IW(Inertial Weight).Proposed algorithm has been tested against linear,non-linear,traditional,andmultiswarmbased optimization algorithms.An experimental study is performed in two stages to assess the proposed PLTVACIW-PSO.Phase I uses 12 recognized Standard Benchmarks methods to evaluate the comparative performance of the proposed PLTVACIWPSO vs.IW based Particle Swarm Optimization(PSO)algorithms,TVAC based PSO algorithms,traditional PSO,Genetic algorithms(GA),Differential evolution(DE),and,finally,Flower Pollination(FP)algorithms.In phase II,the proposed PLTVACIW-PSO uses the same 12 known Benchmark functions to test its performance against the BAT(BA)and Multi-Swarm BAT algorithms.In phase III,the proposed PLTVACIW-PSO is employed to augment the feature selection problem formedical datasets.This experimental study shows that the planned PLTVACIW-PSO outpaces the performances of other comparable algorithms.Outcomes from the experiments shows that the PLTVACIW-PSO is capable of outlining a feature subset that is capable of enhancing the classification efficiency and gives the minimal subset of the core features.

Nigerian Biomass for Bioenergy Applications:A Review on the Potential and Challenges

Nigeria,often referred to as“the giant of Africa,”boasts a sizable population,a thriving economy,and abundant energy resources.Nevertheless,Nigeria has yet to fully harness its renewable energy potential,despite its enormous capacity in this field.The goal of this review paper is to thoroughly examine the difficulties and untapped opportunities in utilizing biomass for bioenergy production in Nigeria.Notably,Nigeria generates substantial volumes of biomass annually,primarily in the form of agricultural waste,which is often either discarded or burned inefficiently,resulting in significant ecological and environmental damage.Therefore,an efficient approach to reducing pollution and transforming waste into wealth involves converting these biomass resources into energy.This work critically examines the status of biomass utilization for energy applications in Nigeria and highlights the bottlenecks that impede its widespread adoption.The review emphasizes the economic and ecological advantages of biomass utilization over traditional waste treatment methods.Additionally,it underscores the appeal of biomass as an industrial fuel source,particularly considering the current high cost of fossil fuels in contemporary Nigeria.Relevant literature on biomass,energy,agricultural waste,fossil fuel,and calorific value in the context of Nigeria was reviewed by utilizing a thorough search technique in key scientific databases.The analysis did not include any non-English publications.The findings of this research provide valuable insights into the challenges faced in maximizing Nigeria’s biomass potential and offer strategic recommendations to promote the use of biomass for bioenergy development.This review paper will assist a wide range of local and international readers,as well as industries interested in green and bioenergy,in making informed decisions regarding the most suitable types of biomass for biofuel production.

An Efficient Encryption and Compression of Sensed IoT Medical Images Using Auto-Encoder

Healthcare systems nowadays depend on IoT sensors for sending data over the internet as a common practice.Encryption ofmedical images is very important to secure patient information.Encrypting these images consumes a lot of time onedge computing;therefore,theuse of anauto-encoder for compressionbefore encodingwill solve such a problem.In this paper,we use an auto-encoder to compress amedical image before encryption,and an encryption output(vector)is sent out over the network.On the other hand,a decoder was used to reproduce the original image back after the vector was received and decrypted.Two convolutional neural networks were conducted to evaluate our proposed approach:The first one is the auto-encoder,which is utilized to compress and encrypt the images,and the other assesses the classification accuracy of the image after decryption and decoding.Different hyperparameters of the encoder were tested,followed by the classification of the image to verify that no critical information was lost,to test the encryption and encoding resolution.In this approach,sixteen hyperparameter permutations are utilized,but this research discusses three main cases in detail.The first case shows that the combination of Mean Square Logarithmic Error(MSLE),ADAgrad,two layers for the auto-encoder,and ReLU had the best auto-encoder results with a Mean Absolute Error(MAE)=0.221 after 50 epochs and 75%classification with the best result for the classification algorithm.The second case shows the reflection of auto-encoder results on the classification results which is a combination ofMean Square Error(MSE),RMSprop,three layers for the auto-encoder,and ReLU,which had the best classification accuracy of 65%,the auto-encoder gives MAE=0.31 after 50 epochs.The third case is the worst,which is the combination of the hinge,RMSprop,three layers for the auto-encoder,and ReLU,providing accuracy of 20%and MAE=0.485.

A Framework for Introducing Precision Agriculture Technologies in Egypt

Precision Agriculture(PA)has been used in many countries and serving the agricultural sectors.The use of PA solutions intervened with many agricultural businesses and supported decision making using data analytics.Precision Agriculture depends on weather,soil,plants,and water information that are essential for farming.Precision Agriculture depends on the use of several technologies such as image sensors,vision machines,drones,robots,machine learning,and artificial intelligence.The use of Precision Agriculture Technologies(PAT)depends on integration between devices,sensors,and systems to ensure the proper implementation of activities.This paper is generated from research on the applicability of PA in in Egypt that ended with a proposed framework for proper implementation of it.The conducted research depended on a survey,focus group discussions,and an online questionnaire that reached 271 respondents from 19 Egyptian governorates.The framework has been developed to enhance the role of an initiative leader to promote PAT through collaboration with other stakeholders in the agricultural sector.The proposed framework can be used by governmental,non-governmental entities,universities and private sector institutions and could be used at countries facing issues with land fragmentation,limited access to information,limited access to agricultural extension services,and increase in agricultural input’s prices.

Clinico-Epidemiological Characteristics and Survival Outcomes of COVID-19 Infection in Kassala, Eastern Sudan

Introduction: Severe acute respiratory syndrome coronavirus2 (SARS-CoV-2) represents a major health problem worldwide. Thus, early detection and appropriate management of the virus will influence the outcome of the disease. This study aimed to investigate the epidemiological characteristics and survival outcomes of patients with COVID-19 infection in Kassala, Eastern Sudan. Methods: A cross-sectional hospital-study was conducted among patients visiting Kassala teaching hospital with suspicion of COVID-19 infection. A structured questionnaire was used to gather clinical and socio-demo- graphic information from COVID-19 patients. Nasopharyngeal specimens and blood samples were collected and tested to confirm the diagnosis of COVID-19 infection using RT-PCR. Results: A total of 371 patients were enrolled in the study from September 2020 to January 2021, with mean age ± SD was 42.9 ± 19.9. The prevalence of COVID-19 infection was estimated at 61.7%. The majority were males 159 (69.4%), of university-level education, 96 (49.7%), and urban residents, 175 (9.7%). The most common symptoms were fever 215 (93.9%), cough 188 (82.1%), headache 179 (78.2%), and shortness of breath 154 (67.2%). Overall all mortality was reported as 16%. Older age group with the age ≥ 70, P P = 0.020, diabetes mellitus P = 0.029 were significantly associated with high case fatality. Conclusion: This study demonstrated that older age, male gender, laboratory tests (leukocytosis, lymphopenia, low Hemoglobin and high CRP) and various comorbid conditions significantly increase the disease severity and mortality. Therefore, attention should be paid to preventive measures to reduce the considerable impacts of the disease.

Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design strolls.ML is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data science.An advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture design.The algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole antenna.The proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and T-Test.It offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.

Early detection of hepatocellular carcinoma co-occurring with hepatitis C virus infection: A mathematical model

AIM: To develop a mathematical model for the early detection of hepatocellular carcinoma (HCC) with a panel of serum proteins in combination with α-fetoprotein (AFP).METHODS: Serum levels of interleukin (IL)-8, soluble intercellular adhesion molecule-1 (sICAM-1), soluble tumor necrosis factor receptor II (sTNF-RII), proteasome, and β-catenin were measured in 479 subjects categorized into four groups: (1) HCC concurrent with hepatitis C virus (HCV) infection (n = 192); (2) HCV related liver cirrhosis (LC) (n = 96); (3) Chronic hepatitis C (CHC) (n = 96); and (4) Healthy controls (n = 95). The R package and different modules for binary and multi-class classifiers based on generalized linear models were used to model the data. Predictive power was used to evaluate the performance of the model. Receiver operating characteristic curve analysis over pairs of groups was used to identify the best cutoffs differentiating the different groups.RESULTS: We revealed mathematical models, based on a binary classifier, made up of a unique panel of serum proteins that improved the individual performance of AFP in discriminating HCC patients from patients with chronic liver disease either with or without cirrhosis. We discriminated the HCC group from the cirrhotic liver group using a mathematical model (-11.3 + 7.38 × Prot + 0.00108 × sICAM + 0.2574 × β-catenin + 0.01597 × AFP) with a cutoff of 0.6552, which achieved 98.8% specificity and 89.1% sensitivity. For the discrimination of the HCC group from the CHC group, we used a mathematical model [-10.40 + 1.416 × proteasome + 0.002024 × IL + 0.004096 × sICAM-1 + (4.251 × 10-4) × sTNF + 0.02567 × β-catenin + 0.02442 × AFP] with a cutoff 0.744 and achieved 96.8% specificity and 89.7% sensitivity. Additionally, we derived an algorithm, based on a binary classifier, for resolving the multi-class classification problem by using three successive mathematical model predictions of liver disease status.CONCLUSION: Our proposed mathematical model may be a useful method for the early detection of different statuses of liver disease co-occurring with HCV infection.

An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their performance.Antenna size affects the quality factor and the radiation loss of the antenna.Metamaterial antennas can overcome the limitation of bandwidth for small antennas.Machine learning(ML)model is recently applied to predict antenna parameters.ML can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated antenna.The accuracy of the prediction depends mainly on the selected model.Ensemble models combine two or more base models to produce a better-enhanced model.In this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial Antenna.Two base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the ensemble.Dynamic Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base models.The proposed model is compared with three based models and the average ensemble model.The results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

Enhancing Parkinson’s Disease Diagnosis Accuracy Through Speech Signal Algorithm Modeling

Parkinson’s disease(PD),one of whose symptoms is dysphonia,is a prevalent neurodegenerative disease.The use of outdated diagnosis techniques,which yield inaccurate and unreliable results,continues to represent an obstacle in early-stage detection and diagnosis for clinical professionals in the medical field.To solve this issue,the study proposes using machine learning and deep learning models to analyze processed speech signals of patients’voice recordings.Datasets of these processed speech signals were obtained and experimented on by random forest and logistic regression classifiers.Results were highly successful,with 90%accuracy produced by the random forest classifier and 81.5%by the logistic regression classifier.Furthermore,a deep neural network was implemented to investigate if such variation in method could add to the findings.It proved to be effective,as the neural network yielded an accuracy of nearly 92%.Such results suggest that it is possible to accurately diagnose early-stage PD through merely testing patients’voices.This research calls for a revolutionary diagnostic approach in decision support systems,and is the first step in a market-wide implementation of healthcare software dedicated to the aid of clinicians in early diagnosis of PD.

Initiation of Basal Insulin in Patients with Uncontrolled Type 2 Diabetes Mellitus

Type 2 diabetes mellitus is a growing health problem, characterized by insulin resistance progressing to beta cell dysfunction and insulin deficiency, most of these patients will need intensification of treatment and initiation of insulin to delay or prevent diabetic complications. Glycemic control is the most important aspect of management, and in reducing morbidity and mortality of the diseases. Control of plasma glucose in patients with diabetes can be assessed by HbA1c, FPG, PPG, but still HbA1c% remains the gold standard for assessment of glycemic control and follow up of diabetic patients. The aim of this study is to assess HbA1c% in patients on oral anti-diabetic drugs, with poor glycemic control before and after adding basal insulin, with titration of the dose of insulin depending on fasting blood sugar. 82 patients with uncontrolled type 2 diabetes (43.9% male, 56.1% female), with HbA1c more than 9%, on two types of oral diabetic medication or more, were started on basal insulin (glargine, lantus) and followed for three to six months. Overall 82 patients with type 2 diabetes mellitus were included in the study. The mean age of the study population was 58.4 years, the mean duration of the disease range was 13.4 years. All patients with HbA1c more than 9%, without organ failure, were included in the study. The mean HbA1c overall had decreased from mean of 11.15% before starting basal insulin to the mean of 8.43% within 3 to 6 month, after initiating basal insulin, this difference was significant at p < 0.001. There was no adverse effect on this medication in any of the study group. The addition of basal insulin to oral anti-diabetic medication in uncontrolled insulin-naïve type 2 diabetic patients resulted in significant improvement of glycemic control, with improved HbA1c level, without adverse effects.

Optimum Location of Field Hospitals for COVID-19: A Nonlinear Binary Metaheuristic Algorithm

Determining the optimum location of facilities is critical in many fields,particularly in healthcare.This study proposes the application of a suitable location model for field hospitals during the novel coronavirus 2019(COVID-19)pandemic.The used model is the most appropriate among the three most common location models utilized to solve healthcare problems(the set covering model,the maximal covering model,and the P-median model).The proposed nonlinear binary constrained model is a slight modification of the maximal covering model with a set of nonlinear constraints.The model is used to determine the optimum location of field hospitals for COVID-19 risk reduction.The designed mathematical model and the solution method are used to deploy field hospitals in eight governorates in Upper Egypt.In this case study,a discrete binary gaining–sharing knowledge-based optimization(DBGSK)algorithm is proposed.The DBGSK algorithm is based on how humans acquire and share knowledge throughout their life.The DBGSK algorithm mainly depends on two junior and senior binary stages.These two stages enable DBGSK to explore and exploit the search space efficiently and effectively,and thus it can solve problems in binary space.

Graph Transformer for Communities Detection in Social Networks

Graphs are used in various disciplines such as telecommunication,biological networks,as well as social networks.In large-scale networks,it is challenging to detect the communities by learning the distinct properties of the graph.As deep learning hasmade contributions in a variety of domains,we try to use deep learning techniques to mine the knowledge from large-scale graph networks.In this paper,we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs.The advantages of neural attention are widely seen in the field of NLP and computer vision,which has low computational complexity for large-scale graphs.The contributions of the paper are summarized as follows.Firstly,a transformer is utilized to downsample the first-order proximities of the graph into a latent space,which can result in the structural properties and eventually assist in detecting the communities.Secondly,the fine-tuning task is conducted by tuning variant hyperparameters cautiously,which is applied to multiple social networks(Facebook and Twitch).Furthermore,the objective function(crossentropy)is tuned by L0 regularization.Lastly,the reconstructed model forms communities that present the relationship between the groups.The proposed robust model provides good generalization and is applicable to obtaining not only the community structures in social networks but also the node classification.The proposed graph-transformer shows advanced performance on the social networks with the average NMIs of 0.67±0.04,0.198±0.02,0.228±0.02,and 0.68±0.03 on Wikipedia crocodiles,Github Developers,Twitch England,and Facebook Page-Page networks,respectively.

Comparison and Evaluation of Two Analytical Methods for Cation Exchange Capacity and Exchangeable Sodium Percentage of Five Soil Types in Central Sudan

Twenty-six soil samples were collected from five soil profiles at different climatological and ecological regions in central Sudan. Soil profile was dug in each studied area and morphological profile description was carried out for different horizons. All samples were analyzed using two different methods to determine Cation Exchange Capacity (CEC) and exchangeable sodium percentage (ESP). Statistical analysis (T-test) was used in order to investigate the differences between soil samples for the studied locations. Significant differences appeared when compared the two methods for CEC determination at Gedaref area, Wad Medani and Nile flood plain and that appeared in evaluation of ESP at Nile flood plain and Shambat area. The results also revealed that, the developed method used in this study was more practical, simple and reliable for determination of CEC and ESP as the currently used in most soil laboratories. In addition, it will be safer than the other methods in some problematic soils. The adoption of this developed method is advisable because it is less time consuming as it omits the washing step. In contrast, the old method cannot be a good substitute in laboratories which have no possibility to determine sodium by using flame photometer. We conclude that when the developed method is used to determine CEC and ESP time will be saved, that fewer amounts of chemicals will be used and that accurate results will be achieved.

Seismic Analysis of a Ten-Storey Reinforced Concrete Building in Jazan Area, KSA

Jazan area is located in the most active seismic zone region of the Kingdom of Saudi Arabia where there is a complicated geological structures and tectonics. This project reviews the seismic activities occurred in Jazan area together with reviewing the Saudi Building (Seismic) Code (SBC-301-2007) [1]. A multi-story reinforced concrete building, in Jazan city, was seismically analyzed using the Equivalent Lateral Force Procedure with the aid of STAAD PRO software. The building, which was Ordinary Reinforced Concrete Moment Resisting Frame (ORCMRF), was analyzed in compliance with the provisions of (SBC-301-2007) [1]. The most important parameters governing the analysis of this frame were dead load, live load and seismic loads. Seismic loads were computed as pairs of accelerations versus times. The damping ratio was taken as 0.05 (5% of the critical damping). The ground accelerations versus time periods were calculated using SBC-301-2007 together with parameters necessary to be used as input data for the program to calculate the seismic parameters, i.e., reactions, displacements, base shear, bending moments, shearing forces, drifts. The obtained results show effects of earthquake ground motions on building studied herein are so greater for the higher increases of the values of outputs resulting from seismic loads comparing to that due to static load only. Finally, the results obtained, clearly, show the importance of taking the Saudi seismic code provisions into account when analyzing and designing multi-story buildings in Jazan area.

Text-Independent Algorithm for Source Printer Identification Based on Ensemble Learning

Because of the widespread availability of low-cost printers and scanners,document forgery has become extremely popular.Watermarks or signatures are used to protect important papers such as certificates,passports,and identification cards.Identifying the origins of printed documents is helpful for criminal investigations and also for authenticating digital versions of a document in today’s world.Source printer identification(SPI)has become increasingly popular for identifying frauds in printed documents.This paper provides a proposed algorithm for identifying the source printer and categorizing the questioned document into one of the printer classes.A dataset of 1200 papers from 20 distinct(13)laser and(7)inkjet printers achieved significant identification results.A proposed algorithm based on global features such as the Histogram of Oriented Gradient(HOG)and local features such as Local Binary Pattern(LBP)descriptors has been proposed for printer identification.For classification,Decision Trees(DT),k-Nearest Neighbors(k-NN),Random Forests,Aggregate bootstrapping(bagging),Adaptive-boosting(boosting),Support Vector Machine(SVM),and mixtures of these classifiers have been employed.The proposed algorithm can accurately classify the questioned documents into their appropriate printer classes.The adaptive boosting classifier attained a 96%accuracy.The proposed algorithm is compared to four recently published algorithms that used the same dataset and gives better classification accuracy.

Assessment of Calcimetric and Titrimetric Methods for Calcium Carbonate Estimation of Five Soil Types in Central Sudan

Despite the CaCO3 estimation using titration method was not reliable, but up to the present time, some soil laboratories in Sudan still used this method. The objective of this study was to compare and assess the results of calcimetric and titrimetric methods of quantitative estimation for soil calcium carbonate of different soils in Sudan. 26 soil samples from five soil profiles were collected from different climatological and ecological regions in central Sudan. CaCO3 equivalent was estimated using calcimeter and titration methods in order to find accurate, rapid and suitable method for soils of Sudan. The results revealed that there are no significant differences between calcimeter and titration methods for calcium carbonate estimation in all studied samples except in samples from Gedaref area. We concluded that when the Calcimeter method used for CaCO3 estimation, the differences between one person and another in detecting titration end point would be avoided, rapid and accurate results would be obtained compared to titration method. Additionally, time would be saved;fewer amounts of chemicals would be used. From this study, we highly recommend using calcimeter method for CaCO3 estimation for soils of Sudan.

Will Africa experience a spike in COVID-19 cases?

Due to increase in trade and air travel between countries affected by the novel coronavirus disease 2019(COVID-19)epidemic and Africa[1],Africa stands to be at a high risk of a possible surge in the number of COVID-19 cases.The biggest concern for the continent and public health experts is whether Africa will experience an uncontrollable spike in the spread of the disease,as it has recently been designated a pandemic by World Health Organization(WHO).This concern may be attributed to the deficiency in health systems in Africa.So far,the number of confirmed COVID-19 cases in Africa is relatively low.Altogether,700 confirmed cases and 17 deaths have been reported on the continent[2].

Bitter taste receptor agonists to abate the bitterness of genital infections: A hype or hope?

Dear Editor,Genital infections are associated with high level of morbidity,economic and psychosocial burden.Several millions of new cases are reported annually among people aged 15-49 years.The complications of genital infections include chronic neurological diseases,end stage renal disease in adults,ectopic pregnancy,premature delivery and stillbirth[1].Indeed,genital infections account for about 15%-35%and 50%-75%of infertility cases in males and females respectively[2].Despite the awareness campaign and substantial improvement in medical treatment,the incidence of genital infections has continued to increase over the past years.Without doubt,this negative trend corroborates the necessity to search for alternative areas that may lead to discovery of novel therapy for genital infections.