Search

Despite progress in understanding diazotrophic distribution in surface soils, few studies have investigated the distribution of diazotrophic in dipper soil layers. Here, we leveraged high-throughput sequencing (HTS) of nifH genes obtained to assess the influence of biochar amended soil (BC) and control (CK), and soil depths (0-20, 20-40 and 40-60 cm) on diazotrophic abundance and community structures, and soil biochemical properties. Although soil physio-chemical properties, enzymes activities, diazotrophic genera and enriched OTUs were significantly influenced across the entire soil profiles, our results also revealed that BC amended soil significantly increased cane stalk height and weight, soil NO3-, NH4+, OM, TC and AK, and enhanced diazotrophic genera in soil depth 0-20 cm compared to CK treatment. Soil TC, TN, OM and NH4+ were the major impact factors shifting diazotrophic community structures in soil depth 0-20 cm. Moreover, multivariate ANOVA analysis revealed that changes in majority of the soil parameters measured were associated with soil depth rather than fertilization. More diazotrophic genera were to a greater extent significantly and positively associated with many soil environmental variables especially in the surface soil 0-20 cm than diazotrophic phyla. Overall, these results were more pronounced in 0-20 cm soil depth in BC than CK treatment.

This work aimed at developing a CRONE Controller based Speed Control of Permanent Magnet Direct Current Motors in order to achieve a robust and more effective speed control. Torque is one of the fundamental factors that affect PMDC motor’s speed control, which generate uncertainty known as disturbance torque on shaft of the motor due to increase in temperature and decrease in frequency of the system. Proportional Integral Derivative (PID) controllers are commonly used in the speed control of PMDC motors, but PID controllers often do not fully reject external disturbances in the system. As such, the CRONE controller based speed control of PMDC motors was developed to address the limitations of PID control. To achieve this, a PMDC motor model with the following system response characteristics was adopted; settling time of 6.2s, rise time of 0.79s and overshot of 30.4%. A second generation CRONE controller was developed and then applied for speed control of the PMDC motor. The performance of the developed controller was then compared with that of a Proportional Integral Derivative – Particle Swamp Algorithm (PIDPSO) controller for speed control of PMDC motor. The result of the second generation CRONE controller for the speed control of PMDC motor obtained a settling time of 0.018s and overshoot of 0.0001%. The PID-PSO controller based speed control of the PMDC motor obtained a settling time and overshoot of 0.2s and 1.87% respectively. The effectiveness of CRONE controller was visualized in frequency domain, with the stability margin of 34.5dB and 900. Simulations were carried out using MATLAB 2016a.

The monitoring, observation and security of a modern-day smart grid system is highly dependent on the accuracy of the selected state estimator. State estimation provides accurate states of a power system in a real-time frame. The conventional meters, which are mostly installed in current smart grid systems are prone to provide wrong measurements to the estimator, which reduces the efficiency of the state estimation results. Over the last two decades, new and reliable meters have been introduced to electric utilities. For example, phasor measurement units (PMU), which are devices that can provide voltage and current phasors with a very high accuracy are readily available. However, they are expensive and need to be optimally placed on the smart grids. This paper presents a technique for optimizing the placement of PMUs, with the objective of improving the estimation performance using Genetic Algorithm (GA). The weighted least absolute value (WLAV) estimator, which is one of the well-established robust estimators, is being selected for simulation. The IEEE-14 and 30-bus power systems are chosen to demonstrate the effectiveness of proposed optimal PMU placement.

Bu çalışmada yorum bilgisel (hermeneutik) yaklaşımlar kullanılarak Arap Baharı’ndan sonra Mısır ve Tunus’da gerçekleşen anayasa değişim sürecinin karşılaştırılması yapılacaktır. Bu bağlamda her iki ülkenin anayasalarının değişim sürecinin nasıl başladığı, aktörlerin davranışları ve bu devletlerin amaçlarının farklılıkları ortaya konulacaktır. Gerekli durumlarda iki ülke anayasasının değişim sürecinin neden-sonuç ilişkisine bakılacaktır. Hem Mısır hem de Tunus genel olarak aynı kıta, bölge, kültür, din ve dil ögelerine sahiptir. Ayrıca her iki ülkenin anayasa sürecini tetikleyen neden olarak Arap Baharı görülmektedir. Bu süreç sonunda Mısır otokratik demokrasiye yönelirken; Tunus, liberal demokrasiye yönelmiştir. 2010 yılında Tunus’ta gerçekleşen değişimler, Mısır dâhil birçok Arap ülkesinde yaşanan haksızlıkları, adaletsizlikleri bir nebze de olsa ortadan kaldırmıştır. Bunun ardından yeni oluşan hükümetler yeni bir anayasa oluşturmak için süreç başlatmıştır. Üç yıllık süre içinde Tunus’un siyasi yelpazesindeki partiler ve ilgili taraflar uzun süren görüşmeler yaparak anayasa değişim sürecini gerçekleştirmişken; Mısır aynı sürede siyasi görüşme yapmaksızın üç anayasa değişim süreci geçirmiştir. Böylece Tunus’taki anayasa değişim sürecinin temel amacı, ülkenin otoriter yönetime geri dönmemesi olurken; Mısır, acelecilik duygusuyla güçlü bir liderlik kurma arzusuna kapılmış, ülkede otoriter rejimin ortaya çıkmasının yolu açılmıştır., This research work adopts Hermeneutic (Interpretive) approaches to comparatively analyse the constitutional process between Tunisia and Egypt after the Arab Spring. However, where necessary the cause-effect relationships of the constitutional process will be examined. Even with the fact that the two countries share the same identity, culture, tradition, language, religion and are their constitutional processes were triggered with Arab spring; while Tunisia is tilting towards liberal democracy Egypt is re-falling into autocratic regime. In 2010 the revolution that started in Tunisia spread to other Arab countries and was significant in ending some elements of wanton injustices in the affected countries. Sequel to these developments the new leaders commence a new constitutional process in the affected countries. While in Tunisia with intensive negotiations of political parties and other concerned actors only a single constitutional amendment process was realized; In Egypt within the same time frame three consecutive constitutional amendment processes took place without reasonable deliberation and in a hectic manner. As a result, while in Tunisia`s aim of constitution process was not to return the dictatorial regime; in Egypt due to haphazard process and desire to have a strong leader made the country to slope into dictatorial regime.

“Üçüncü sektör” olarak da bilinen Sivil Toplum Kuruluşları (STK), toplumsal barış, ilerleme ve istikrar için devasa bir araç haline gelmiştir. İttifakı ve birliği yoluyla STK’lar özellikle devletin ulaşmadığı yerlerde toplumdaki temel problemlere çözüm bulmaya katkı sağlamaktadırlar. Günümüz toplumunda STK’ların en önemli rolü terörizmle mücadelesidir. Farkındalık, lanetlenme, motivasyon, değerlendirme, rehabilitasyon, ortaklık, doktrinleştirme ve koordinasyon yaklaşımları ile terör tehdidini ortadan kaldırılmasına yardımcı olmaktadırlar. Bu araştırmada işlevselcilik kuramı kullanılarak Tunus ve Nijerya’da terörün ortadan kaldırmasında STK’ların rolleri karşılaştırmalı bir şekilde analiz edilecektir. Makalede içerik analizi uygulanarak verimli STK’lara sahip olan ülkelerin terörün ortadan kaldırılmasında verimli STK’lara sahip olmayan ülkelere göre daha başarılı olabileceği hipotezi test edilecektir. Makalede şu soruya da cevap verecektir: STK’ların iki ülkede terörle mücadele politikaları ne kadar etkili? Araştırma bulgularına göre, Tunus’taki STK’lar Nijerya’dakilerden daha verimli olmasına rağmen her iki ülke de terör ile mücadelesinde yeteri kadar adım atmamışlardır. Dolayısıyla, bu araştırmanın küresel Sivil Toplumlara önerdiği arabuluculuk, casusluk ve veya sızma gibi daha pratik bir yaklaşıma ihtiyaç olduğudur., Civil Society Organizations (CSOs) otherwise known as the “third sector” have become gargantuan tools for societal peace, progress and stability. Through collaborations and alliances, they are able to intervene in resolving critical problems affecting a given community especially where government’s hand could not have reached. One of the areas where Civil Society takes a bold step in modern society is fighting terrorism. They help in eradicating the menace of terrorism through awareness, damnation, motivation, appraise, rehabilitation, partnership, indoctrination and co-ordinational approaches. Thus, the objective of this paper is to adopt functional approach to critically compare the roles of CSOs in fighting terrorism between Nigeria and Tunisia. The article place more emphasis on content analysis to tests the hypothesis that a country with vibrant CSOs is more likely to eradicate terrorism than the one that has not. The article also responds to question that: how effective are the CSOs’ anti-terrorism policies in the two countries? This research found out that even though Tunisia has more coordinated CSOs than Nigeria, the roles of the CSOs in both countries is not significant enough to help in getting rid of terrorism. So, there is need of more practical approach such as mediation, espionage or infiltration which this research recommends to the global Civil Societies.

Liquid phase transalkylation of mixture of xylene and benzene was investigated over ZnO/SiO2 catalyst in a batch reactor under continuous stirring using a magnetic stirrer with a temperature regulator. The prepared ZnO/SiO2 was characterized using Brunauer-Emmet-Teller Surface Area Analysis (BET), X-Ray Diffraction (XRD), and Fourier Transfom InfraRed Spectroscopy (FTIR). Also, the effect of catalyst loading, reaction temperature and time were studied. The BET results obtained shows that there is an increase in the surface area of ZnO/SiO2 when compared to that of the parent SiO2. Impregnation of ZnO unto the SiO2 does not affect the parent structure of the SiO2 as depicted by the XRD result. Ethylbenzene is the product obtained from the transalkylation reaction and its yield is favored at a lower catalyst loading of 0.4 wt% as compared to 0.7 wt% and 1.0 wt% weight of xylene. A study of the effect of reaction temperature at 45 minutes on the yield shows that the yield is likely to increase with temperature. The maximum ethylbenzene yield obtained was approximately 62% at 60 oC and 75 minutes.Keywords: Transalkylation, ZnO/SiO2, Catalysis, Aromatics, Zinc Oxide, Silica

Luminescent liquid Crystal (LC) material is regarded as the most promising material for polarized organic light emission due to their intrinsic characteristics including orderly alignment and luminescence. Nevertheless, the optical extraction efficiency of LC based organic light emitting diodes (OLEDs) devices still requires significant effort and innovation towards real-world applications. In this paper, we propose the design of a highly linearly polarized light-emission from OLEDs with integrated refractive index nanograting in the emissive layer (EML) based on photo aligned luminescent liquid crystal material. The simulation results indicate that the geometrically optimized polarized device yields an external quantum efficiency (EQE) up to 47% with a polarized ratio up to 28 dB at a 550 nm emission wavelength. This conceptual design offers a new opportunity to achieve efficient polarized organic luminescence, and it is (to the best of our knowledge) the first approach that enhances the light extraction of OLEDs based on luminescent liquid crystal via index grating in the EML.

The k-NN algorithm is one of the most renowned ML algorithms widely used in the area of data classification research. With the emergence of big data, the performance and the efficiency of the traditional k-NN algorithm is fast becoming a critical issue. The traditional k-NN algorithm is inefficient to solve the high volume multi-categorical training datasets Traditional k-NN algorithm has a constraint in filtering the training dataset to yield training data that are most relevant to the intended or the targeted test dataset/file. It has to scan through all the training datasets categories to classify the intended/targeted data. As such, traditional k-NN is considered not intelligent and consequently is suffering poor accuracy performance with high computational complexity. A Semantic-kNN (Sk-NN) algorithm for ML is thus proposed in this paper to address the limitations in the traditional k-NN. The proposed Sk-NN deploys a process by leveraging on the semantic itemization and bigram model to filter the training dataset in accordance with the relevant information engaged in the test dataset. It is aimed for general security applications such as finding (the confidentiality level of the data when the algorithm is trained with multiple training categories during the data classification phase. Ultimately, Sk-NN is to elevate the ML performance in pattern extraction and labeling in the big data context.

Solar radiation data is an essential pre-requisite for the designing, sizing and performing evaluation of any solar energy conversion system in any part of the globe, even though solar radiation data are not readily available for many location of many developing countries such as Nigeria, hence the needs to rely on empirical models. There are many developed models across the globe for the estimation of solar radiation, to identify the optimum performing model for locations such as Katsina requires the comparism between the various models. In this study, performance evaluation of six selected models for the estimation of global solar radiation was carried out for Katsina location, Nigeria. The models were formed from different combinations of some meteorological parameters (sunshine hours, relative humidity and temperatures) obtained for a period of ten years (2006-2015) from Nigerian Meteorological Agency (NIMET). Monthly average extraterrestrial global solar radiation was evaluated for the locations. The data was analysed to find the empirical constants for all the selected models in the locations. Estimated values of global solar radiation was obtained from the six selected models. The estimated values were then compared using statistical parameters (mean bias error (MBE), mean percentage error (MPE), root mean square error (RMSE) and coefficient of determinant (R2). Model 6 was found to be the optimum model for Katsina because it fitted the measured data most for each month of year based on various statistical parameters used for the analysis.

A new enhanced reversible logic circuit synthesis method was developed using reversible gates that include NOT, CNOT (Feynman), Toffoli, Fredkin, Swap, and Peres gates. The synthesis method was done using newly developed genetic programming. Usually previous synthesis methods that uses genetic algorithms or other similar evolutionary algorithms suffers a problem known as blotting which is a sudden uncontrolled growth of an individual (circuit), which may render the synthesis inefficient because of memory utilization, making the algorithm difficult to continue running and eventually stack in a local minima, there for an optimized reversible circuit may not be generated. In this method the algorithm used was blot free, the blotting was carefully controlled by fixing a suitable length and size of the individuals in the population. Following this approach, the cost of generating circuits was greatly reduced giving the algorithm to reach the end of the last designated generation to give out optimal or near optimal results. The results of the circuits generated using this method were compared with some of the results already in the literature, and in many cases, our results appeared to be better in terms of gate count and quantum cost metrics.

This paper presents a novel data and timed control routing protocol which is Flying Adhoc Network (FANET) specific. The developed FANET specific routing protocol laid emphasis on the route connectivity in the network by considering the captured data size, minimum allowable distance between randomly moving nodes and connection time. The performance of the proposed FANET specific routing protocol was simulated using NS3. The obtained throughput value for the routing protocol fluctuated between 742.064kbps and 755.083kbps as data are exchanged between nodes. This showed that when all the UAVs are on the network and communicating with one another, the throughput is flatline and not plummet. This implies consistency as nodes join and leave the network. The packet delivery ratio obtained for the FSRP during simulation was 96.13%. These results implied that data is successfully transmitted between the UAV acting as server and UAV acting as client on the network.

The unique and rich details present in human fingerprints has nowadays made fingerprint verification common in a number of authentication systems. However, fraudulent fingerprints made in order to imitate the live ones with a view to deceiving these systems are now common. It is therefore important to develop a robust authentication system capable of detecting or recognizing liveness in any fingerprints presented to the authentication system. This paper therefore developed a software-based fingerprint spoof detection model using deep convolutional neural network (DCNN) features extracted from a set of live and spoof fingerprint images. The extracted features are then separately fed to two trainable classifiers namely logistic regression (LR) and support vector machine (SVM). Three variants of the SVM were used based on the training kernel: radial basis function (RBF), linear and polynomial kernels). Experiments performed on the Biometrika datasets obtained from the LivDet2009 database yielded a true accept rate (TAR) of 97.96%, a true reject rate (TRR) of 85.95%, and an average classification accuracy (ACA) of 91.94% for the LR classifier. An accuracy of 100% was obtained for the SVM trained using RBF kernel while an accuracy of 96.16% was obtained using the linear kernel. SVM trained using polynomial kernels of orders 2 and 3 yielded accuracies of 99.6163% and 99.4244% respectively. The model's accuracy was observed to decrease as the polynomials order increased. All experiments were carried out in MATLAB programming environment.

This paper analyzed the conventional one-vehicle look-ahead convoy with the view to improve the overall operation of the system. A vehicle was first modelled from first principle taking the effect of aerodynamic drag into consideration to mitigate the effects of disturbance. The proposed vehicle model was used to develop the improved topology of two-vehicle look-ahead and rear-vehicle convoy. To maximize the operating region of the convoy system, constant headway time (CHT) policy was utilized for the inter-vehicular spacing. Homogeneous vehicle convoy was assumed and it was compared with the conventional one-vehicle look-ahead to ascertain the improvement achieved. It was observed that the proposed model provides 5 m of inter-vehicular spacing with no chattering over the period of 80 s. The control vehicle's speed depends on the neighboring vehicles speeds with an improvement in reduction on acceleration and jerk of 0. 0213 ms−2 and 0.02109 ms−3 respectively.

We have defined a new method for automatic construction of reversible logic circuits by using the genetic programming approach. The choice of the gate library is 100% dynamic. The algorithm is capable of accepting all possible combinations of the following gate types: NOT TOFFOLI, NOT PERES, NOT CNOT TOFFOLI, NOT CNOT SWAP FREDKIN, NOT CNOT TOFFOLI SWAP FREDKIN, NOT CNOT PERES, NOT CNOT SWAP FREDKIN PERES, NOT CNOT TOFFOLI PERES and NOT CNOT TOFFOLI SWAP FREDKIN PERES. Our method produced near optimum circuits in some cases when a particular subset of gate types was used in the library. Meanwhile, in some cases, optimal circuits were produced due to the heuristic nature of the algorithm. We compared the outcomes of our method with several existing synthesis methods, and it was shown that our algorithm performed relatively well compared to the previous synthesis methods in terms of the output efficiency of the algorithm and execution time as well.

This paper proposed a modification of the Artificial Fish Swarm Algorithm (AFSA) using adaptive behaviour base combination of normative and situational knowledge inherent in cultural algorithm. Four variations (wCAFSA_ Ns, wCAFSA_Sd, wCAFSA_NsSd and wCAFSA_NsNd) of the AFSA called weighted Cultural Artificial Fish Swarm Algorithm (wCAFSA) were then proposed with the hope of reducing the chance of falling into local minima by the standard AFSA. The performances of these were first evaluated using a collection of seven optimizations benchmark functions. Thereafter, all the variants were used to design an optimized PID controller for the dc motor of deep space antenna azimuth position control with the hope of minimizing the cost function and maximizing the steady state responses. Simulation results demonstrated the effectiveness of the algorithms in determining efficiently the optimized PID controller gains. Even though all the developed algorithms demonstrated their effectiveness, the wCAFSA_NsSd appears to be more efficient.

NoSQL is a database used to store high volume of data. NoSQL databases are horizontally scalable, distributed, open source and non-relational. High performance is a major concern for practically every data-driven system. NoSQL databases claim to deliver faster performance than the popular Relational database systems in various use cases, most notably those involving huge data. While this is always the case, it should be understood that not all NoSQL databases are created alike where performance is concerned. This being the case, IT professionals works hard to ensure that the database they select is optimized for the success of their application use cases. Such selection can be made in-house, based on tests with academic database benchmarks. We present the Yahoo! Cloud Serving Benchmark (YCSB) framework, with the goal of facilitating performance comparisons of the new generation of NoSQL databases in an environment where resources are limited. Unlike many previous benchmarks that considered a cluster or distributed system that NoSQL is known for, we limit out experiment to a single PC assuming a cluster with a single node or a distributed system with a single PC. We define a core set of benchmarks and report results for four widely used systems: MongoDB, ElasticSearch, Redis, and OrientDB implementation.

The management of blood bank is an integral part of health delivery system and it is centred on the donation and transfusion of blood. The four major classification of blood is considered in the formulation of Markov model with a first- order dependence. The data of blood transfused in a city hospital was studied and the result revealed that the blood that is donated and received by the same blood group constitute the major blood needed in the hospital with 100%, 99.1%, 98.3%, and 98.5% for blood groups AB,B,A and O respectively. There will be a little variation in the blood needed for transfusion in the future on the basis of the present when the initial values of the transition probabilities P12 (t), P13 (t), P14 (t), P24 (t), and P34 (t) (t = 0) are 0.00354, 0.00202, 0.00865, 0.01653 and 0.00884 respectively. The information about blood bank inventory is important for a successful health care delivery. If such information is available in every city hospital, it may be possible to move blood from one hospital to another during emergency. Mathematics Subject Classification: 60J28

Straw retention, an alternative to artificial fertilization, commonly mitigates soil degradation and positively affects soil fertility. In this study, we investigated the succession of soil bacteria during two sugarcane straw retention treatments (control (CK) and sugarcane straw retention (SR)) and at four depths (0&ndash, 10, 10&ndash, 20, 20&ndash, 30, and 30&ndash, 40 cm) in fallow soil in a sugarcane cropping system. Using an Illumina MiSeq (16S rRNA) and soil enzyme activity, we explored the SR influence on soil bacterial communities and enzyme activities and its inclusive impact on soil fertility, with an emphasis on topsoil (0&ndash, 10 cm) and subsoil (10&ndash, 40 cm). Our results show that SR effectively improved soil fertility indicators (C, N, and P), including enzyme activities (C and N cycling), throughout the soil profile: these soil parameters greatly improved in the topsoil compared to the control. Sugarcane straw retention and soil depth (0&ndash, 10 cm vs. 10&ndash, 40 cm) were associated with little variation in bacterial species richness and alpha diversity throughout the soil profile. Subsoil and topsoil bacterial communities differed in composition. Compared to the CK treatment, SR enriched the topsoil with Proteobacteria, Verrucomicrobia, Actinobacteria, Chloroflexi, and Nitrospirae, while the subsoil was depleted in Nitrospirae and Acidobacteria. Similarly, SR enriched the subsoil with Proteobacteria, Verrucomicrobia, Actinobacteria, Chloroflexi, Gemmatimonadetes, and Bacteroidetes, while the topsoil was depleted in Acidobacteria, Gemmatimonadetes, and Planctomycetes compared to the CK. At the genus level, SR enriched the topsoil with Gp1, Gp2, Gp5, Gp7, Gemmatimonas, Kofleria, Sphingomonas, and Gaiella, which decompose lignocellulose and contribute to nutrient cycling. In summary, SR not only improved soil physicochemical properties and enzyme activities but also enriched bacterial taxa involved in lignocellulosic decomposition and nutrient cycling (C and N) throughout the soil profile. However, these effects were stronger in topsoil than in subsoil, suggesting that SR enhanced fertility more in topsoil than in subsoil in fallow land.

Meteorological parameters namely; relative sunshine duration, maximum ambient temperature and relative humidity were used to develop an empirical model for the analysis of global solar radiation in Maiduguri. Response surface methodology was used for the analysis. The predicted and measured values of the global solar radiation have shown good agreement. A coefficient of determination of 99.8% was recorded with maximum percentage error of 0.0558 and root mean square error of0.2396. The model has appreciable advantage in comparison with existing models.

In this paper, we developed a new mathematical model for the dynamics of hepatitis B virus (HBV) transmission in a population with vital dynamics, incorporating vertical transmission and sexual maturity. We obtained the basic reproduction number, 0 R , proof the local and global stability of the disease-free equilibrium of the model. Sensitivity analysis of 0 R with respect to the model parameters were carried out. Our result shows that birth rate, death removal rate, HBV sexual transmission probability per contact rate, and the average total sexual contacts rate are highly sensitive parameters that affect the transmission dynamics of HBV in any population. Thus, vaccination, condom usage and reduced-average sexual partner(s) are good strategies that can lead to controlling HBV transmission.

The availability of complex rotating machines is vital for the prevention of catastrophic failures in a significant number of industrial operations. Reliability engineering theories stipulate that optimising the mean-time-to-repair (MTTR) for failed machines can immensely boost availability. In practice, however, a significant amount of time is taken to accurately detect and classify rotor-related anomalies which often negate the drive to achieve a truly robust maintenance decision-making system. Earlier studies have attempted to address these limitations by classifying the poly coherent composite spectra (pCCS) features generated at different machine speeds using principal components analysis (PCA). As valuable as the observations obtained were, the PCA-based classifications applied are linear which may or may not limit their applicability to some real-life machine vibration data that are often associated with certain degrees of non-linearities due to faults. Additionally, the PCA-based faults classification approach used in earlier studies sometimes lack the capability to self-learn which implies that routine machine health classifications would be done manually. The initial parts of the current paper were presented in the form of a thorough search of the literature related to the general concept of data fusion approaches in condition monitoring (CM) of rotation machines. Based on the potentials of pCCS features, the later parts of the article are concerned with the application of the same features for the exploration of a simplified two-staged artificial neural network (ANN) classification approach that could pave the way for the automatic classification of rotating machines faults. This preliminary examination of the classification accuracies of the networks at both stages of the algorithm offered encouraging results, as well as indicates a promising potential for this enhanced approach during field-based condition monitoring of critical rotating machines.

We newly formed universal gate library, that includes NOT, CNOT (Feyman), Toffoli, Fredkin, Swap, Peres gates and a special gate called G gate. The gate G on its own is a universal gate, but using it alone in a library will result in large circuit realization. G gate combines the operations of Generalized Toffoli gates. For example a gate called G3 combines the operations of NOT, CNOT and T3 (3 - bit Toffoli) gates all in one place. The new library was used in synthesizing reversible circuits. The experiment was done using Genetic programming algorithm that is capable of allowing the choice of any type of gate library and optimizing the circuit. The results were promising because the gate complexity in the circuits were drastically reduced compared to previously attempted synthesis.

The university system is an organized anarchy. It is a system that brings different components from different dimensions with different focus sometimes conflicting one another in one place, in order to achieve a particular goal. This paper examines the university as a system that comprises different faculties, departments and units managing different disciplines and courses/subjects. Chaos and complexity nature of university was examined and results show that each faculty and department is taking a different direction without caring about others, and in the same faculty there are different departments handling different courses. And lecturers are using different methods in the conduct of their lessons and researches, and all these struggles are made towards achieving university objectives. The challenges faced by university system and possible solutions to overcome those challenges were discussed.

This paper presents a novel data and timed control routing protocol which is Flying Adhoc Network (FANET) specific. The developed FANET specific routing protocol laid emphasis on the route connectivity in the network by considering the captured data size, minimum allowable distance between randomly moving nodes and connection time. The performance of the proposed FANET specific routing protocol was simulated using NS3. The obtained throughput value for the routing protocol fluctuated between 742.064kbps and 755.083kbps as data are exchanged between nodes. This showed that when all the UAVs are on the network and communicating with one another, the throughput is flatline and not plummet. This implies consistency as nodes join and leave the network. The packet delivery ratio obtained for the FSRP during simulation was 96.13%. These results implied that data is successfully transmitted between the UAV acting as server and UAV acting as client on the network.

A Markov decision model is applied to determine the optimal cost of drugs in the treatment of leprosy disease. The result shows that the minimum cost is obtained if the Doctor is visited and the patient is placed on the MDT instead of traditional treatment and Dopsone monotherapy. The paper presents a theoretical framework to study the cost of treatment of leprosy where the relevant data could be obtained.

This paper presents the use of Semi-Markov model in discrete state and discrete time to study the condition of leprosy patients. Leprosy is considered to be controlled if is possible to maximize recovery and consequently minimize relapse and death. The model takes into account the concept of effectiveness of the treatment. The result shows that it is possible to attain a zero probabilities for a relapse and death from leprosy when the treatment is very effective. The model could be used to predict the cases of leprosy disease. The predictions could be used for planning resources for the control of leprosy.

The work presents in this paper proposes to solve the existing issue of initial qubit (primary key) lost due to an attack by eavesdropper, causing the quantum bit error rate (QBER) to be high which may leaked enough information to the eavesdropper during secret key sharing in network communication. We intend to greatly reduce the QBER to a reasonable percentage that will make the key sharing communication more secured and effective. We use the dual quantum channels against the traditional single quantum channel. The dual channels give an upper hand to reduce the chances of error caused by eavesdropper. Simulations were conducted for varying the noise factor which is a measure of the presence of an eavesdropper. The results where compared between our proposed method and the one quantum channel model. Our method shows an almost half of the QBER reduced during the secret key sharing session.