Effect of Sowing Dates, Intra-Row Spacings and Nitrogen Fertilizers of the Productivity of Red Variant Roselle (Hibiscus sabdarifa L)

Field experiments were conducted during the rainy seasons (June - Odober) of 1999 and 2000, at the research farm of Abubakar Tafawa Balewa University, Bauchi, Nigeria to study the effects of some agronomic practices on the growth and yield of roselle. Three agronomic practices (sowing dates, intra-row spacing and nitrogenous fertilizer levels) at three levels each, were factorially combined to give a total of 27 treatment combinations. These were laid out in a randomized complete block design with three replications. Most of the results obtained were highly significant (P50.01). Sowing dates (June) gave the highest mean calyx (2035.15 kg/ha) and seed (2391.19 kg/ha) yields in both years. Intra-row spacing (80 em) gave the highest mean calyx (1651.11 kg/ha) and seed (2024.40 kg/ha) yields. Also, application of 60 kg N/ha gave the highest mean calyx (1671.99 kg/ha) and seed (2067.36 kg/ha) yields. It is evident from the results of this experiment that, the earlier the sowing of dates, the wider the intra-row spacing and the higher the nitrogenous fertilizer level, the higher the productivity of red variant roselle. Sequel to these facts, roselle sown in June at intra-row spacing of 80 em should be applied with 60 kg N / ha for optimum productivity in the study area

Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks

[EN] In this paper, an optimal model is developed for path loss predictions using the Feed-Forward Neural Network (FFNN) algorithm. Drive test measurements were carried out in Canaanland Ota, Nigeria and Ilorin, Nigeria to obtain path loss data at varying distances from 11 different 1,800 MHz base station transmitters. Single-layered FFNNs were trained with normalized terrain profile data (longitude, latitude, elevation, altitude, clutter height) and normalized distances to produce the corresponding path loss values based on the Levenberg-Marquardt algorithm. The number of neurons in the hidden layer was varied (1-50) to determine the Artificial Neural Network (ANN) model with the best prediction accuracy. The performance of the ANN models was evaluated based on different metrics: Mean Absolute error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), standard deviation, and regression coefficient (R). Results of the machine learning processes show that the FNN architecture adopting a tangent activation function and 48 hidden neurons produced the least prediction error, with MAE, MSE, RMSE, standard deviation, and R values of 4.21 dB, 30.99 dB, 5.56 dB, 5.56 dB, and 0.89, respectively. Regarding generalization ability, the predictions of the optimal ANN model yielded MAE, MSE, RMSE, standard deviation, and R values of 4.74 dB, 39.38 dB, 6.27 dB, 6.27 dB, and 0.86, respectively, when tested with new data not previously included in the training process. Compared to the Hata, COST 231, ECC-33, and Egli models, the developed ANN model performed better in terms of prediction accuracy and generalization ability.This work was supported by Covenant University [grant number CUCRID-SMARTCU-000343].Popoola, SI.; Adetiba, E.; Atayero, AA.; Faruk, N.; Tavares De Araujo Cesariny Calafate, CM. (2018). Optimal Model for Path Loss Predictions using Feed-Forward Neural Networks. Cogent Engineering. 5:1-19. https://doi.org/10.1080/23311916.2018.1444345S1195Adetiba, E., Iweanya, V. C., Popoola, S. I., Adetiba, J. N., & Menon, C. (2017). Automated detection of heart defects in athletes based on electrocardiography and artificial neural network. Cogent Engineering, 4(1). doi:10.1080/23311916.2017.1411220Adetiba, E., & Olugbara, O. O. (2015). Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features. The Scientific World Journal, 2015, 1-17. doi:10.1155/2015/786013Adeyemo, Z. K., Ogunremi, O. K., & Ojedokun, I. A. (2016). Optimization of Okumura-Hata Model for Long Term Evolution Network Deployment in Lagos, Nigeria. International Journal on Communications Antenna and Propagation (IRECAP), 6(3), 146. doi:10.15866/irecap.v6i3.9012Akhoondzadeh-Asl, L., & Noori, N. (2007). Modification and Tuning of the Universal Okumura-Hata Model for Radio Wave Propagation Predictions. 2007 Asia-Pacific Microwave Conference. doi:10.1109/apmc.2007.4554925Al Salameh, M. S., & Al-Zu’bi, M. M. (2015). Prediction of radiowave propagation for wireless cellular networks in Jordan.Paper presented at the Knowledge and Smart Technology (KST), 2015 7th International Conference on.Alamoud, M. A., & Schutz, W. (2012). Okumura-hata model tuning for TETRA mobile radio networks in Saudi Arabia. 2012 2nd International Conference on Advances in Computational Tools for Engineering Applications (ACTEA). doi:10.1109/ictea.2012.6462901Armenta, A., Serrano, A., Cabrera, M., & Conte, R. (2011). The new digital divide: the confluence of broadband penetration, sustainable development, technology adoption and community participation. Information Technology for Development, 18(4), 345-353. doi:10.1080/02681102.2011.625925Begovic, P., Behlilovic, N., & Avdic, E. (2012). Applicability evaluation of Okumura, Ericsson 9999 and winner propagation models for coverage planning in 3.5 GHZ WiMAX systems.Erceg, V., Greenstein, L. J., Tjandra, S. Y., Parkoff, S. R., Gupta, A., Kulic, B., … Bianchi, R. (1999). An empirically based path loss model for wireless channels in suburban environments. IEEE Journal on Selected Areas in Communications, 17(7), 1205-1211. doi:10.1109/49.778178Farhoud, M., El-Keyi, A., & Sultan, A. (2013). Empirical correction of the Okumura-Hata model for the 900 MHz band in Egypt. 2013 Third International Conference on Communications and Information Technology (ICCIT). doi:10.1109/iccitechnology.2013.6579585Faruk, N., Adediran, Y. A., & Ayeni, A. A. (2013). Error bounds of empirical path loss models at VHF/UHF bands in Kwara State, Nigeria. Eurocon 2013. doi:10.1109/eurocon.2013.6625043Faruk, N., Ayeni, A., & Adediran, Y. A. (2013). ON THE STUDY OF EMPIRICAL PATH LOSS MODELS FOR ACCURATE PREDICTION OF TV SIGNAL FOR SECONDARY USERS. Progress In Electromagnetics Research B, 49, 155-176. doi:10.2528/pierb13011306Hata, M. (1980). Empirical formula for propagation loss in land mobile radio services. IEEE Transactions on Vehicular Technology, 29(3), 317-325. doi:10.1109/t-vt.1980.23859Hufford, G. A. (1952). An integral equation approach to the problem of wave propagation over an irregular surface. Quarterly of Applied Mathematics, 9(4), 391-404. doi:10.1090/qam/44350Ibhaze, A. E., Ajose, S. O., Atayero, A. A.-A., & Idachaba, F. E. (2016). Developing smart cities through optimal wireless mobile network.Paper presented at the emerging technologies and innovative business practices for the transformation of societies (EmergiTech), IEEE international conference on.Luebbers, R. (1984). Propagation prediction for hilly terrain using GTD wedge diffraction. IEEE Transactions on Antennas and Propagation, 32(9), 951-955. doi:10.1109/tap.1984.1143449Matthews, V. O., Osuoyah, Q., Popoola, S. I., Adetiba, E., & Atayero, A. A. (2017, July 5–7). C-BRIG: A network architecture for real-time information exchange in smart and connected campuses. In Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 398–401). London.Medeisis, A., & Kajackas, A. (s. f.). On the use of the universal Okumura-Hata propagation prediction model in rural areas. VTC2000-Spring. 2000 IEEE 51st Vehicular Technology Conference Proceedings (Cat. No.00CH37026). doi:10.1109/vetecs.2000.851585Mohtashami, V., & Shishegar, A. A. (2012). Modified wavefront decomposition method for fast and accurate ray-tracing simulation. IET Microwaves, Antennas & Propagation, 6(3), 295. doi:10.1049/iet-map.2011.0264Nimavat, V. D., & Kulkarni, G. (2012). Simulation and performance evaluation of GSM propagation channel under the urban, suburban and rural environments.Paper presented at the communication, information & computing technology (ICCICT), 2012 international conference on.. O. F. O. (2014). RADIO FREQUENCY OPTIMIZATION OF MOBILE NETWORKS IN ABEOKUTA, NIGERIA FOR IMPROVED QUALITY OF SERVICE. International Journal of Research in Engineering and Technology, 03(08), 174-180. doi:10.15623/ijret.2014.0308027Phillips, C., Sicker, D., & Grunwald, D. (2013). A Survey of Wireless Path Loss Prediction and Coverage Mapping Methods. IEEE Communications Surveys & Tutorials, 15(1), 255-270. doi:10.1109/surv.2012.022412.00172Popoola, S. I., Atayero, A. A., Badejo, J. A., John, T. M., Odukoya, J. A., & Omole, D. O. (2018). Learning analytics for smart campus: Data on academic performances of engineering undergraduates in Nigerian private university. Data in Brief, 17, 76-94. doi:10.1016/j.dib.2017.12.059Popoola, S. I., Atayero, A. A., & Faruk, N. (2018). Received signal strength and local terrain profile data for radio network planning and optimization at GSM frequency bands. Data in Brief, 16, 972-981. doi:10.1016/j.dib.2017.12.036Popoola, S. I., Atayero, A. A., Faruk, N., & Badejo, J. A. (2018). Data on the key performance indicators for quality of service of GSM networks in Nigeria. Data in Brief, 16, 914-928. doi:10.1016/j.dib.2017.12.005Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Adetiba, E., & Matthews, V. O. (2017, July 5–7). Calibrating the standard path loss model for urban environments using field measurements and geospatial data.Paper presented at the Lecture notes in engineering and computer science: Proceedings of the world congress on engineering 2017 (pp. 513–518). London.Popoola, S. I., Atayero, A. A., Faruk, N., Calafate, C. T., Olawoyin, L. A., & Matthews, V. O. (2017). Standard propagation model tuning for path loss predictions in built-up environments.Paper presented at the International Conference on Computational Science and Its Applications.Popoola, S. I., Atayero, A. A., Okanlawon, T. T., Omopariola, B. I., & Takpor, O. A. (2018). Smart campus: Data on energy consumption in an ICT-driven university. Data in Brief, 16, 780-793. doi:10.1016/j.dib.2017.11.091Popoola, S. I., Badejo, J. A., Ojewande, S. O., & Atayero, A. (2017, October 25–27). Statistical evaluation of quality of service offered by GSM network operators in Nigeria. In Lecture notes in engineering and computer science: Proceedings of the world congress on engineering and computer science 2017 (pp. 69–73). San Francisco.Popoola, S. I., Misra, S., & Atayero, A. A. (2018). Outdoor path loss predictions based on extreme learning machine. Wireless Personal Communications, 1–20.Rath, H. K., Verma, S., Simha, A., & Karandikar, A. (2016). Path Loss model for Indian terrain-empirical approach.Paper presented at the communication (NCC), 2016 twenty second national conference on.Salman, M. A., Popoola, S. I., Faruk, N., Surajudeen-Bakinde, N., Oloyede, A. A., & Olawoyin, L. A. (2017). Adaptive neuro-fuzzy model for path loss prediction in the VHF band.Paper presented at the computing networking and informatics (ICCNI), 2017 international conference on.Schneider, I., Lambrecht, F., & Baier, A. (s. f.). Enhancement of the Okumura-Hata propagation model using detailed morphological and building data. Proceedings of PIMRC ’96 - 7th International Symposium on Personal, Indoor, and Mobile Communications. doi:10.1109/pimrc.1996.567508Sotiroudis, S. P., & Siakavara, K. (2015). Mobile radio propagation path loss prediction using Artificial Neural Networks with optimal input information for urban environments. AEU - International Journal of Electronics and Communications, 69(10), 1453-1463. doi:10.1016/j.aeue.2015.06.014Zelley, C. A., & Constantinou, C. C. (1999). A three-dimensional parabolic equation applied to VHF/UHF propagation over irregular terrain. IEEE Transactions on Antennas and Propagation, 47(10), 1586-1596. doi:10.1109/8.80590

Population structure and evolutionary history of the greater cane rat (Thryonomys swinderianus) from the Guinean Forests of West Africa

Grasscutter (Thryonomys swinderianus) is a large-body old world rodent found in sub-Saharan Africa. The body size and the unique taste of the meat of this major crop pest have made it a target of intense hunting and a potential consideration as a micro-livestock. However, there is insufficient knowledge on the genetic diversity of its populations across African Guinean forests. Herein, we investigated the genetic diversity, population structures and evolutionary history of seven Nigerian wild grasscutter populations together with individuals from Cameroon, Republic of Benin, and Ghana, using five mitochondrial fragments, including D-loop and cytochrome b (CYTB). D-loop haplotype diversity ranged from 0.571 (± 0.149) in Republic of Benin to 0.921 (± 0.013) in Ghana. Within Nigeria, the haplotype diversity ranged from 0.659 (± 0.059) in Cross River to 0.837 (± 0.075) in Ondo subpopulation. The fixation index (FST), haplotype frequency distribution and analysis of molecular variance revealed varying levels of population structures across populations. No significant signature of population contraction was detected in the grasscutter populations. Evolutionary analyses of CYTB suggests that South African population might have diverged from other populations about 6.1 (2.6–10.18, 95% CI) MYA. Taken together, this study reveals the population status and evolutionary history of grasscutter populations in the region

PERFORMANCE ANALYSIS AND EVALUATION OF ENERGY EFFICIENCY RESOURCES IN CELLULAR MOBILE NETWORKS

The aim of this paper is to provide an in-depth analysis of network power characteristics, in order to be able to address the basic power estimation and behavior under extreme load conditions, these being the main resources in a cellular network. Currently, one of the biggest challenge is continuous growth of energy consumption by cellular infrastructure equipment, especially base station which take up to 80% in total of input energy into cellular networks. A set of good metric were proposed that significantly reflect the energy efficiency and allow for performance to be evaluated. These metrics include Energy Consumption Index (ECI) that measures efficiency of power utilization for a base station, its lower value indicate better energy efficiency and Performance Indicator (PI) measures power consumption per coverage area, its higher values indicates better energy efficiency. The effect of traffic, interference and path loss exponent on the energy efficiency of cellular network were investigated and the analysis results obtained by Monte-Carlo (MC) simulations were discussed based on the proposed energy efficient model. When considering heaviness index and minimum traffic rate, the numerical results show that there exist a maximum value for energy efficiency under each parameter configuration. The maximal energy values are 0.52, 0.43.0.24 and 0.22 bits/Hz/Joule corresponding to the intensity ratio of MSs to BSs of 105, 78, 120 and 82, respectively. The proposed energy efficient metrics provide effective means for design objective and evaluating the performance efficiency of components of cellular network

MODIFICATION OF LOG-NORMAL PREDICTION MODEL FOR HSPA NETWORK USING ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM

The transmission of radio signals over a channel for proper path-loss prediction is a core aspect of planning in wireless communication. Some conventional path-loss prediction models such as Log-normal, Okumura-Hata and COST 231 models are not appropriate for predicting the path-loss values due to differences in frequencies of operation which, therefore, need adaptation before employing. This paper, therefore, modifies the Log-normal prediction model for High Speed Packet Access (HSPA) using Adaptive Neuro-fuzzy Inference System (ANFIS). The modification is carried out by measuring the Received Signal Strength (RSS) using drive test at Ayetoro area of Lagos, Nigeria on (Longitude 3.19647E and Latitude 6.59167 N). The drive test equipment consists of a computer system integrated with Test Equipment for Mobile System (TEMS) software, Ericson TEMS phone and Global Positioning System (GPS). Suitability of the conventional models is determined using Base Station (BS) parameters of the network after which the modification of Log-normal prediction model is carried out by obtaining the path-loss exponent. The path-loss exponent is used to determine the deviation for proper modification. The modified model is further enhanced using ANFIS model which is developed by training five layer ANFIS architecture for adaptation. The models are evaluated using path-loss values and Root Mean Square Error (RMSE) to determine the performances. The results obtained show that ANFIS, COST 231, and modified Log-normal models give the lowest RMSE values with their path-loss values closest to the measured values. Therefore, these models are suitable for predicting the HSPA signal in this area and can be used for future planning of wireless network

Burden of Unsafe Abortion Among Young Ladies in Edo State, South-South Nigeria

Background: Unsafe abortion is a preventable cause of maternal mortality. It is common among the teenagers and young females mainly due to a combination of socio-economic vulnerability, teenage pregnancy, and inadequate access to healthcare services. This research was done to highlight some of the burdens of unsafe abortion among our very young females.Methods: This was a cross-sectional study. A total of 423 young sexually active females aged 15-29 years, who consented to join in the study were recruited from 4 communities (2 urban and 2 rural) in Edo state of Nigeria using multistage sampling technique. Information were obtained from them using a pre-tested semi-structured interviewer administered questionnaire. Data were analysed using Epi Info 3.5.4 Statistical software.Results: Participants were between 17-29 years, mostly from low socio-economic class, and also students with tertiary level of education. The mean age of sexual debut was 19 years, with majority (70.2%) having multiple sexual partners, and 77.1% not using any form of contraceptive. Majority of the respondents (67.4%) have had abortions. About 75.4% of the respondents had had unsafe abortions. The relationship between abortion and the following were statistically significant: increasing age, not married, socio-economic status, and reproductive tract infection.Conclusion: Unsafe abortion was found in this study to be high among adolescents and young women in Edo State, South-South Nigeria. Factors like socioeconomic vulnerability, teenage pregnancy, and inadequate access to healthcare services combine to leave large numbers of women at risk of unsafe abortion and abortion-related death. Keywords: Abortion, Maternal, Mortality, Adolescents, Healthcar