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Network densification (ND) in 5G has been suggested as a solution to improve network capacity. ND has small cell backhaul as its bottleneck in the ensuing ultra-dense network (UDN). Due to the new deployment scenarios of small cells, it becomes necessary to thoroughly investigate the radio-propagation characteristics of the new transmission path between the base station and the small cells. The problem of the impact of small cell height on the backhaul large-scale parameters under typical outdoor-to-indoor (high-rise) and outdoor-to-outdoor (street canyon) scenarios was first investigated. Next, the probability distribution functions of the various parameters were investigated and modeled. Novel use of 5G NR air interface using a deterministic ray-tracing engine to characterize the backhaul at 28 GHz center frequency and 100 MHz bandwidth using 4x4 cross-polarized uniform planar array (UPA) at the base station and 2x2 multiple input, multiple output (MIMO) antennas at the small cells was proposed. New sets of models for root mean square (RMS) delay spread and RMS angular spread suitable for predicting network deployment in the two scenarios and similar environments were presented.

Music, for the longest time, has impacted human lives tremendously. The ability of music to access and activate a wide range of human emotions is sensational. Toward this end, audio features provide a variety of information necessary for sound engineers, music producers, and artists to improve their craft to excite the vast majority of music listeners across the globe. In this paper, analysis of audio features derived using the Spotify web API endpoint and Spotify (Python module for Spotify web servers) is presented. The dataset was curated from audio features of over 160,000 songs released from the year 1921-2020. For clarity, statistical descriptions and probability distribution functions of the audio features are reported. Also, the interrelationship and correlation amongst the various audio features are demonstrated. Overall, the dataset would find useful applications in classical and future music production.

5G millimeter wave (mmWave) application in mobile connectivity to realize high-speed, reliable communication is attributed with high path loss. This paper presents a detailed 3D ray-tracing technique at 28 GHz for Lagos Island to investigate five unique path loss scenarios: path loss, free space path loss with antenna pattern, free space path loss without antenna pattern, excess path loss with antenna pattern, and the excess path loss without antenna pattern for an urban environment. The Close-In (CI) model, Floating Intercept (FI) path loss model, and a root mean square error (RMSE) are used to model and evaluate the best path loss model for Lagos Island. The average achieved FI ( $\alpha,\beta, \sigma $ ) parameters were 189.92352, 0.1654, and 0.66948, While the average CI ( $\eta,X\sigma $ ) parameters were 2.309355 and 56.236425. From all the scenarios evaluated, the lowest path loss exponent achieved was 0.45, while the highest path loss exponent was 3.8. We have established that the FI path loss model accurately characterizes path loss for the Lagos Island environment with the lowest RMSE of 0.0359 dB and the highest RSME of 0.0997 dB. In contrast, the CI model over-predict the path loss at 28 GHz with the lowest RMSE of 0.0495 dB and the highest RMSE of 2.2547 dB. This work opens up a new area of research on mm-Wave at 28 GHz in Lagos Island, and the results obtained from this work can be used to benchmark future studies on mmWave in a similar environment.

This work presents an adaptive neuro-fuzzy inference system (ANFIS) artificial intelligence methodology of predicting the 2nd law efficiency and total irreversibility of a refrigeration system running on LPG/TiO 2 –nano-refrigerants. For this purpose, substractive clustering and grid partition approaches were utilized to train the ANFIS models required in estimating the 2nd law efficiency and total irreversibility using some experimental data. Furthermore, predictions of ANFIS models with subtractive clustering approach was found to be more accurate than ANFIS models predictions with grid partition approach. The predictions of ANFIS models with subtractive clustering approach were also compared with experimental results that were not included in the model training and predictions of already existing ANN models of authors previous publication. The comparison of variance, root mean square error (RMSE), mean absolute percentage error (MAPE) were 0.996–0.999, 0.0296–0.1726 W and 0.108–0.176 % marginal variability values. These results indicate that the ANFIS model with subtractive clustering approach having cluster radii 0.7 and 0.5 can predict the 2nd law efficiency and total irreversibility respectively, with higher accuracy than authors’ previous publication ANN models.

The projected rise in wireless communication traffic has necessitated the advancement of energy-efficient (EE) techniques for the design of wireless communication systems, given the high operating costs of conventional wireless cellular networks, and the scarcity of energy resources in low-power applications. The objective of this paper is to examine the paradigm shifts in EE approaches in recent times by reviewing traditional approaches to EE, analyzing recent trends, and identifying future challenges and opportunities. Considering the current energy concerns, nodes in emerging wireless networks range from limited-energy nodes (LENs) to high-energy nodes (HENs) with entirely different constraints in either case. In view of these extremes, this paper examines the principles behind energy-efficient wireless communication network design. We then present a broad taxonomy that tracks the areas of impact of these techniques in the network. We specifically discuss the preponderance of prediction-based energy-efficient techniques and their limits, and then discuss the trends in renewable energy supply systems for future networks. Finally, we recommend more context-specific energy-efficient research efforts and cross-vendor collaborations to push the frontiers of energy efficiency in the design of wireless communication networks.

This study explored the predictive modelling of the pyrolysis of rice husk to determine the thermal degradation mechanism of rice husk. The study can ensure proper modelling and design of the system, towards optimising the industrial processes. The pyrolysis of rice husk was studied at 10, 15 and 20 °C min−1 heating rates in the presence of nitrogen using thermogravimetric analysis technique between room temperature and 800 °C. The thermal decomposition shows the presence of hemicellulose and some part of cellulose at 225–337 °C, the remaining cellulose and some part of lignin were degraded at 332–380 °C, and lignin was degraded completely at 480 °C. The predictive capability of artificial neural network model was studied using different architecture by varying the number of hidden neurone node, learning algorithm, hidden and output layer transfer functions. The residual mass, initial degradation temperature and thermal degradation rate at the end of the experiment increased with an increase in the heating rate. Levenberg–Marquardt algorithm performed better than scaled conjugate gradient learning algorithm. This result shows that rice husk degradation is best described using nonlinear model rather than linear model. For hidden and output layer transfer functions, ‘log-sigmoid and tan-sigmoid', and ‘tan-sigmoid and tan-sigmoid' transfer functions showed remarkable results based on the coefficient of determination and root mean square error values. The accuracy of the results increases with an increasing number of hidden neurone. This result validates the suitability of an artificial neural network model in predicting the devolatilisation behaviour of biomass.

In the forthcoming fifth generation (5G) architecture, full dimension MIMO (FD-MIMO) for enabling high network capacity wireless cellular system is been proposed. FD-MIMO provide beam control in azimuth and elevation plane using active antenna system (AAS) to take advantage of 3D-MIMO techniques such as 3D beamforming. It is therefore necessary to characterize and model channel parameters in 3D domain rather than the current 2D domain used for today’s cellular systems. In this work, using the REMCOM Wireless Insite X3D ray tracing engine, we characterize the mmWave MIMO channel at 28 GHz center frequency and 100 MHz bandwidth in terms of the angular spread (AS) using a 4 x 4 cross-polarized uniform planar array (UPA) at the base station and 2 x 2 MIMO antenna at the small cell receivers. Two scenarios were considered which are the street canyon and the high-rise. We provide 3D models for angular spread of arrival (at the receiver) and departure (at the base station). We obtain the cross-correlation coefficients of the parameters and analyze the effect of BS-UE height on AS.

For decades now, a lot of radio wave path loss propagation models have been developed for predictions across different environmental terrains. Amongst these models, empirical models are practically the most popular due to their ease of application. However, their prediction accuracies are not as high as required. Therefore, extensive path loss measurement data are needed to develop novel measurement-oriented path loss models with suitable correction factors for varied frequency, capturing both local terrain and clutter information, this have been found to be relatively expensive. In this paper, a large-scale radio propagation path loss measurement campaign was conducted across the VHF and UHF frequencies. A multi-transmitter propagation set-up was employed to measure the strengths of radio signals from seven broadcasting transmitters (operating at 89.30, 103.5, 203.25, 479.25, 615.25, 559.25 and 695.25 MHz respectively) at various locations covering a distance of 145.5 km within Nigerian urban environments. The measurement procedure deployed ensured that the data obtained strictly reflect the shadowing effects on radio signal propagation by filtering out the small-scale fading components. The paper also, examines the feasibilities of applying Kriging method to predict distanced-based path losses in the VHF and UHF bands. This method was introduced to minimize the cost of measurements, analysis and predictions of path losses in built-up propagation environments., Path loss measurement, Kriging Interpolation Method, prediction model, radio propagation

Embedded systems technology is undergoing a phase of transformation owing to the novel advancements in computer architecture and the breakthroughs in machine learning applications. The areas of applications of embedded machine learning (EML) include accurate computer vision schemes, reliable speech recognition, innovative healthcare, robotics, and more. However, there exists a critical drawback in the efficient implementation of ML algorithms targeting embedded applications. Machine learning algorithms are generally computationally and memory intensive, making them unsuitable for resource-constrained environments such as embedded and mobile devices. In order to efficiently implement these compute and memory-intensive algorithms within the embedded and mobile computing space, innovative optimization techniques are required at the algorithm and hardware levels. To this end, this survey aims at exploring current research trends within this circumference. First, we present a brief overview of compute intensive machine learning algorithms such as hidden Markov models (HMM), k-nearest neighbors (k-NNs), support vector machines (SVMs), Gaussian mixture models (GMMs), and deep neural networks (DNNs). Furthermore, we consider different optimization techniques currently adopted to squeeze these computational and memory-intensive algorithms within resource-limited embedded and mobile environments. Additionally, we discuss the implementation of these algorithms in microcontroller units, mobile devices, and hardware accelerators. Conclusively, we give a comprehensive overview of key application areas of EML technology, point out key research directions and highlight key take-away lessons for future research exploration in the embedded machine learning domain.

Cyber attackers exploit a network of compromised computing devices, known as a botnet, to attack Internet-of-Things (IoT) networks. Recent research works have recommended the use of Deep Recurrent Neural Network (DRNN) for botnet attack detection in IoT networks. However, for high feature dimensionality in the training data, high network bandwidth and a large memory space will be needed to transmit and store the data, respectively in IoT back-end server or cloud platform for Deep Learning (DL). Furthermore, given highly imbalanced network traffic data, the DRNN model produces low classification performance in minority classes. In this paper, we exploit the joint advantages of Long Short-Term Memory Autoencoder (LAE), Synthetic Minority Oversampling Technique (SMOTE), and DRNN to develop a memory-efficient DL method, named LS-DRNN. The effectiveness of this method is evaluated with the Bot-IoT dataset. Results show that the LAE method reduced the dimensionality of network traffic features in the training set from 37 to 10, and this consequently reduced the memory space required for data storage by 86.49%. SMOTE method helped the LS-DRNN model to achieve high classification performance in minority classes, and the overall detection rate increased by 10.94%. Furthermore, the LS-DRNN model outperformed state-of-the-art models.

The structural complexities in lanthanum cuprates family were revisited with the aim of understanding factors that structurally triggers long-range repulsive Coulomb interactions. In this study, polycrystalline samples of La2SrDyCu2Oy (LSDCO) were prepared via solid-state synthesis using high purity chemicals. The X-ray diffraction experiment revealed an unusual structural anomaly in the [2 0 5] and [2 1 3] planes of the crystal lattice. The lattice system was further probed using the Niggli-reduced cell at gamma = 6.0678. It was observed that grain boundaries leading to electron trapping originates from the CuO2 plane while the mesoscopic phase separation is controlled by the cell type and axial value in the x- and y-axes of the crystal lattice. Although the research partly supports popular findings that the main positive lobes of LSDCO are centered on the z-axis, it observed that the negative lobe is located in a ring-like structure along the X Y plane. This result is particularly interesting because it shows the likely origin of broken symmetry in LSDCO sample. The ion bombardment analysis shows that the LSDCO electron-phonon ratio was 8:5. The microstructural analysis of the LSDCO sample was observed to have magnetic field strength of 1.5 kA/m. LSDCO sample possess enormous structural mystery that may interest further studies beyond superconductivity.

Path loss propagation is a vital concern when designing and planning networks in mobile communication systems. Propagation models such as the empirical, deterministic and theoretical models, which possess complex, inconsistent, time-consuming and non-adaptable features, have proven to be inefficient in designing of wireless systems, thereby resulting in the need for a more reliable model. Artificial Intelligence methods seem to overcome the drawbacks of the propagation models for predicting path loss. In this paper, the ANFIS approach to path loss prediction in the GSM and WCDMA bands is presented for selected urban areas in Nigeria. Furthermore, the effects of the number of Membership Functions (MFs) are investigated. The prediction results indicated that the ANFIS model outperformed the Hata, Cost-231, Egli and ECC-33 models in both Kano and Abuja urban areas. In addition, an increase in the number of MFs conceded an improved RMSE result for the generalized bell-shaped MF. The general performance and outcome of this research work show the efficiency and usefulness of the ANFIS model in improving prediction accuracy over propagation models

The emergence of nanocomposite particulate with the increasing demand for opto-electrical properties for defence application has necessitated this study. In this work, an attempt was made to develop Zn-CeO2/Zn-CeO2-Al2SiO5 thin film composite on A356 mild steel using electrodeposition technique. The developed coating was attained in 2 V for 10 min at a constant current density of 1.5 A/cm2 and pH of 4.5. The mass concentration of Al2SiO5 was varied, ranging from 0 to 15 g. The composite coatings were characterized using Scanning electron microscope equipped with energy dispersive spectrometer (SEM/EDS). The corroding properties of the coated and uncoated sample were examined through potentiodynamic polarization technique via Autolab PGSTAT 101 Metrohm potentiostat/galvanostat with NOVA software of version 2.1.2 in 3.65% NaCl. The electrical characterization was carried out using voltage-ammeter meter and Keithley 2400 series source meter application tester. The opto-electrical investigation was done using a solar simulator with maximum intensity of 1000 W/m2 under an air mass of 1.5 at a working intensity of 750 W/m2. The outcome of various test and characterizations revealed that the electrodeposited Zn-CeO2/Zn-CeO2-Al2SiO5 possessed good stability, improved microstructural qualities, better electrical conductivity and outstanding corrosion resistance.

Widespread adoptions of hydrocarbon-based refrigerants in domestic refrigerators are limited due to lack of economically viable flammability risk reduction methodology. This study presents an experimental investigation of a safe R600a refrigerant mass charge of 30 g and various concentrations of TiO2 nano-lubricants (0.2, 0.4 and 0.6g/L) in a slightly modified R134a domestic refrigerator. Performance parameters investigated at selected evaporator temperatures were instantaneous and mean power consumption, per power ton refrigeration (PPTR), discharge temperature and coefficient of performance (COP) respectively. Findings showed that the mean PPTR and power consumption range were 0.79 - 1.05 and 73 - 86.33 W. In addition, mean discharge temperature and COP range were 50 - 62oC and 3.23 - 4.03 respectively. In conclusion, the adoption of TiO2 nanoparticles enhanced the cooling rate and energy saving potential of the system considerably

It is very important to understand the input features and the neural network parameters required for optimal path loss prediction in wireless communication channels. In this paper, an extensive investigation was conducted to determine the most appropriate neural network parameters for path loss prediction in Very High Frequency (VHF) band. Field measurements were conducted in an urban propagation environment to obtain relevant geographical and network information about the receiving mobile equipment and quantify the path losses of radio signals transmitted at 189.25 MHz and 479.25 MHz. Different neural network architectures were trained with varying kinds of input parameters, number of hidden neurons, activation functions, and learning algorithms to accurately predict corresponding path loss values. At the end of the experimentations, the performance of the developed Artificial Neural Network (ANN) models are evaluated using the following statistical metrics: Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Standard Deviation (SD) and Regression coefficient (R). Results obtained show that the ANN model that yielded the best performance employed four input variables (latitude, longitude, elevation, and distance), nine hidden neurons, hyperbolic tangent sigmoid (tansig) activation function, and the Levenberg-Marquardt (LM) learning algorithm with MAE, MSE, RMSE, SD and R values of 0.58 dB, 0.66 dB, 0.81 dB, 0.56 dB and 0.99 respectively. Finally, a comparative analysis of the developed model with Hata, COST 231, ECC-33 and Egli models showed that ANN-based path loss model has better prediction accuracy and generalization ability than the empirical models.

Nanofluids are being considered as an efficient heat transfer fluid in various thermal applications. In this work, an experimental study was conducted on TiO2 nanoparticle mixed with R600a as the working fluid. TiO2-R600a nano-refrigerant was used in a domestic refrigerator with little system reconstruction. The refrigerator performance was then investigated using test parameters including: Pull down test, specific compressor work-input, refrigeration effect and coefficient of performance (COP) using steady state analysis. The results indicate that TiO2-R600a nano-refrigerant works efficiently and safely in the refrigerator and the performance was better than the pure R600a system. Overall, 0.1g TiO2 in 40g R600a nano-refrigerant mixture had the highest refrigeration effect and COP and gave the least specific compressor work-input within the test rig.

This paper presents an experimental investigation of energy consumption and heat transfer performance characteristics of a safe mass–charge of liquefied petroleum gas refrigerant, enhanced with varying concentrations of TiO2 nano-lubricants (i.e. 0.2 gL−1, 0.4 gL−1 and 0.6 gL−1) in a domestic refrigerator. Performance parameters investigated at steady state included: instantaneous and mean power consumption, cooling capacity, coefficient of performance (COP), discharge thermal conductivity and discharge temperature. Analysis was based on temperature and pressure readings obtained from appropriate gauges attached to the test rig. Refrigerant properties were obtained from Ref-Prop NIST 9.0 software. Findings showed that reductions in mean power consumption were observed to be 14, 9 and 8% at 0.2 gL−1, 0.4 gL−1 and 0.6 gL−1 nano-lubricants respectively; the highest mean power consumption was obtained using pure compressor mineral oil while the lowest was with 0.2 gL−1 TiO2 nano-lubricant. The estimated mean cooling capacities for the various compressor lubricants were found to be higher with 0.4 gL−1 and 0.6 gL−1 nano-lubricants than pure compressor lubricant, and lower with 0.2 gL−1 nano-lubricant when compared with pure mineral oil lubricant. All the TiO2-based nano-lubricants were of higher instantaneous and mean COP values than the pure lubricant. All nano-lubricant mixtures were also found to give lower discharge temperatures than the pure lubricant. In conclusion, selected TiO2-based nano-lubricants improved the efficiency of the domestic refrigeration system considerably.

Nowadays, hackers take illegal advantage of distributed resources in a network of computing devices (i.e., botnet) to launch cyberattacks against the Internet of Things (IoT). Recently, diverse Machine Learning (ML) and Deep Learning (DL) methods were proposed to detect botnet attacks in IoT networks. However, highly imbalanced network traffic data in the training set often degrade the classification performance of state-of-the-art ML and DL models, especially in classes with relatively few samples. In this paper, we propose an efficient DL-based botnet attack detection algorithm that can handle highly imbalanced network traffic data. Specifically, Synthetic Minority Oversampling Technique (SMOTE) generates additional minority samples to achieve class balance, while Deep Recurrent Neural Network (DRNN) learns hierarchical feature representations from the balanced network traffic data to perform discriminative classification. We develop DRNN and SMOTE-DRNN models with the Bot-IoT dataset, and the simulation results show that high-class imbalance in the training data adversely affects the precision, recall, F1 score, area under the receiver operating characteristic curve (AUC), geometric mean (GM) and Matthews correlation coefficient (MCC) of the DRNN model. On the other hand, the SMOTE-DRNN model achieved better classification performance with 99.50% precision, 99.75% recall, 99.62% F1 score, 99.87% AUC, 99.74% GM and 99.62% MCC. Additionally, the SMOTE-DRNN model outperformed state-of-the-art ML and DL models.

This study investigates the effect of varying test conditions including ambient temperature (19, 22, and 25 °C), mass charges of R600a refrigerant (40, 50, 60, and 70 g), and concentrations of TiO2 nanolubricant (0, 0.2 and 0.4 g/L) on the performance of a slightly modified 100g R134a domestic refrigeration system. The investigated parameters include evaporator air temperature, energy consumption, coefficient of performance, and second law efficiency of the system. The results showed that the performance of the refrigeration system at 0.2 and 0.4 g/L concentrations of TiO2 nanolubricant, improved at optimum ambient temperature and R600a mass charge conditions. At optimum conditions, the evaporator air temperature and energy consumption reduced within the range 5.26 to 26.32 %, and 0.13 to 14.09 % respectively, while the coefficient of performance and second law efficiency increased within the range 0.05 to 16.32 %, and 2.8 to 16 %, respectively. However, at other conditions (non-optimum), the energy consumption and evaporator air temperature were higher and within the range 0.28 to 8.26 %, and 5 to 40 % respectively, while the coefficient of performance and second law efficiency reduced within the range 2.99 to 10.94 %, and 0.55 to 13.43 % respectively. In conclusion, we observed variations in the performance of the refrigerator with varying test conditions., R600a, Refrigerator, TiO2 nanoparticle, ambient temperature.

Artificial Neural Networks (ANNs) have been recently exploited to develop suitable models for path loss predictions . However, the ANN algorithm that provides the best results has not been well established neither has the models been characterized to limit their performances and applications in the various frequency bands. In this paper, we characterize the propagation path loss in the Very High Frequency Band (VHF) using different ANN learning algorithms and activation functions based on the measurement data collected at 203.25 MHz in an urban environment (Ilorin, Nigeria). The prediction results of Hata, COST 231, ECC-33, and Egli models at varying distances were fed into a feed-forward neural network and mapped to each corresponding measured path loss value. Statistical analysis shows that the ANN model that was trained with hyperbolic tangent activation function (HTAF), Levenberg-Marquardt (LM) algorithm, and 80 neurons in the hidden layer produced the most satisfactory results with Mean Error (ME), Root Mean Square Error (RMSE), Standard Deviation (SD), and coefficient of determination (R2) values of 3.75 dB, 5.10 dB, 3.46 dB, and 0.95. However, the HTAF with Scale Conjugate Gradient (SCG) is more stable even though its prediction errors were slightly higher than that of LM.

Indiscriminate disposal of solid waste is a major issue in urban centers of most developing countries and it poses a serious threat to healthy living of the citizens. Access to reliable data on the state of solid waste at different locations within the city will help both the local authorities and the citizens to effectively manage the menace. In this paper, an intelligent solid waste monitoring system is developed using Internet of Things (IoT) and cloud computing technologies. The fill level of solid waste in each of the containers, which are strategically situated across the communities, is detected using ultrasonic sensors. A Wireless Fidelity (Wi-Fi) communication link is used to transmit the sensor data to an IoT cloud platform known as ThingSpeak. Depending on the fill level, the system sends appropriate notification message (in form of tweet) to alert relevant authorities and concerned citizen(s) for necessary action. Also, the fill level is monitored on ThingSpeak in real-time. The system performance shows that the proposed solution may be found useful for efficient waste management in smart and connected communities.

Iron deficiency (ID) is the most prevalent and severe nutritional disorder globally and is the leading cause of iron deficiency anemia (IDA). IDA often progresses subtly symptomatic in children, whereas prolonged deficiency may permanently impair development. Early detection and frequent screening are, therefore, essential to avoid the consequences of IDA. In order to reduce the production cost and complexities involved in building advanced ID sensors, the devices were fabricated using a home-built patterning procedure that was developed and used for this work instead of lithography, which allows for fast prototyping of dimensions. In this article, we report the development of graphene-based field-effect transistors (GFETs) functionalized with anti-ferritin antibodies through a linker molecule (1-pyrenebutanoic acid, succinimidyl ester), to facilitate specific conjugation with ferritin antigen. The resulting biosensors feature an unprecedented ferritin detection limit of 10 fM, indicating a tremendous potential for non-invasive (e.g., saliva) ferritin detection.

Negative resistance devices have attracted much attention in the wireless communication industry because of their low cost, better performance, high speed, and reduced power requirements. Although negative resistance circuits are non-linear circuits, they are associated with distortion, which may either be amplitude-to-amplitude distortion or amplitude-to-phase distortion. In this paper, a unique way of realizing a negative resistance amplifier is proposed using a single metal-semiconductor field-effect transistor (MESFET). Intermodulation distortion test (IMD) is performed to evaluate the characteristic response of the negative resistance circuit amplifier to different bias voltages using the harmonic balance (HB) of the advanced designed software (ADS 2016). The results obtained are compared to those of a conventional distributed amplifier. The findings of this study showed that the negative resistance amplifier spreads over a wider frequency output with reduced power requirements while the conventional distributed amplifier has a direct current (DC) offset with output voltage of 32.34 dBm.

The reliability and accuracy of radio propagation models depends on the unique localized features in the area under study. In this paper, we calibrate empirical radio propagation models for 1800 MHz cellular network planning in Lagos Metropolis, Nigeria. Drive test are conducted to obtain measured data within suburban and dense urban propagation environment. Received Signal Strength (RSS) and path loss values of radio signals in 1800 MHz cellular networks are recorded for model calibration and evaluation. COST 231–Hata model achieved the closest prediction results relative to the field measurement. Mean Error (ME), Standard Deviation (SD) and Root Mean Square (RMS) results are 11.004 dB, 12.194 dB and 16.43 dB respectively in dense suburban, while the corresponding results are 9.151 dB, 8.151 dB and 12.254 dB in dense urban. ME of all the calibrated propagation prediction models reduced to nearly zero (\(\approx 0\) dB). Also, the SD and the RMS fall within the calibration quality target with ME as less than 1 dB and SD is less than 8.5 dB for each of the calibrated models. In conclusion, the proposed calibrated path loss models achieved minimum mean error and standard deviation. Prediction results improved when terrain type and clutter data were taken into account during path loss calculations.

Path Loss (PL) propagation models are important for accurate radio network design and planning. In this paper, we propose a new radio propagation model for PL predictions in urban environment using Support Vector Machine (SVM). Field measurement campaigns are conducted in urban environment to obtain mobile network and path loss information of radio signals transmitted at 900, 1800 and 2100 MHz frequencies. SVM model is trained with field measurement data to predict path loss in urban propagation environment. Performance of SVM model is evaluated using Mean Absolute Error (MAE), Mean Square Error (MSE), Root Mean Square Error (RMSE) and Standard Error Deviation (SED). Results show that SVM achieve MAE, MSE, RMSE and SED of 7.953 dB, 99.966 dB, 9.998 dB and 9.940 dB respectively. SVM model outperforms existing empirical models (Okumura-Hata, COST 231, ECC-33 and Egli) with relatively low prediction error.

The material selection process for producing a horizontal axis wind turbine blade for sustainable energy generation is a vital issue when using Nigeria as a case study. Due to the challenge faced with the low wind speed variations. However, this paper focuses on implementing MCDM for the material selection process for a suitable material for developing a horizontal wind turbine blade. This paper used a quantitative research approach using AHP and TOPSIS multi-criteria decision method. The study put into consideration the environmental conditions for the material selection process when designing the questionnaire. The authors extracted the data used for the selection process from the 130 research questionnaire distributed to materials engineers and renewable energy professionals. This research considered four alternatives that is, aluminum alloy, stainless steel, glass fiber, and mild steel to determine the best material for the wind turbine blade. Also, the model has four criteria and eight sub-criteria used for developing the pair-wise matrix and the performance score used for the ranking process of the alternatives. The result shows that a consistency index of 0.056 and a consistency ratio of 0.062 gotten via the AHP method is workable for material selection practice. 78%, 43%, 67%, and 25% are the performance scores for the four alternatives via the TOPSIS techniques. In conclusion, aluminum alloy is the best material, followed by glass fibre. Therefore, the decision-makers recommended aluminum alloy; hence, manufacturers should apply aluminum alloy to develop the wind turbine blade for sustainable energy generation., Materials science; Energy; Mechanical engineering; Environmental science; Wind turbine blade; Multi-criteria decision method; Analytical hierarchy process; TOPSIS techniques.

This article presents the frequency of distribution of editorial members of Dove Medical press, across the world based on their official stated affiliations. Uneven distributions across the six continents were observed and this was confirmed by the Chi-square test of goodness of fit. Further research can focus on data on the gender composition, distribution of the affiliations of the first or corresponding authors of the respective journals, citation and editorial board composition based on the abstraction and indexation of the journals. Keywords: Dove Medical Press, Bibliometrics, Data analysis, Random, Smart campus, Ranking analytics, Statistics

In this data article, an analysis on the strategies for talent retention in Covenant University and the corresponding effects on employees’ attitude to work was presented. The study population included the academic staff of Covenant University, which has a population of 530 employees, but a sample size was determined using Yamen׳s formula. The data obtained through survey questionnaires were analysed using Statistical Package for Social Sciences (SPSS). Linear regression was used to model the effect of talent retention strategy on employees’ attitude to work. This information is made publicly available to aid empirical researches on the subject of talent management in organizations. Keywords: Talent Retention, Employee, Attitude to work, Organizations

This experimental work studies the performance of varying concentrations of SiO2 nanoparticle in mineral oil lubricant using LPG refrigerant, as a retrofit to R134a in a domestic refrigeration system. The test rig is fitted with thermocouple K, pressure gauges and a watt-meter to monitor the suction, discharge, the condensing temperatures, pressures and power consumption in accordance with ISO 8187 recommendations. Performance parameters investigated included: pull down time, compressor power consumption and input, coefficient of performance and thermal conductivity and viscosity. Findings showed that all the selected charges of LPG refrigerant infused with varying concentrations of nano-lubricants, achieved equal values of -3 °C (ISO 8187), or lower values of evaporator air temperatures at lower refrigerant charges, than the baseline R134a refrigerant. All the selected nano-lubricants based refrigerants resulted in improved Coefficient of Performance (COP) than R134a refrigerant, with COP values ranging from 2.05 with 50 g charge of LPG using 0.4 g/L nano-lubricant to 2.65 with 60 g of LPG using 0.2 g/L SiO2 based lubricant. Lower power input was recorded by the compressor for all the selected charges of SiO2-lubricant based LPG than R134a refrigerants, having 28.81 W (with 60 g charge of LPG using 0.2 g/L) and 39.21 W (with 100 g charge of R134a refrigerant using pure compressor oil lubricant). In addition, at low concentration of nanoparticle in the lubricants based LPG refrigerant, reduction in power consumption of the compressor was observed, whereas higher concentration of nanoparticle in the lubricant, resulted in a rise in power consumption. Furthermore, thermal conductivity values were found to be low at the suction and discharge ends of the compressor for R134a using pure mineral oil lubricant, whereas the values were high for retrofit working fluid (pure LPG refrigerant, and LPG with varying concentrations of nano-lubricants), at the suction and discharge ends of the compressor.

Emerging economies are an increasingly significant part of the global market, and the potential for technology integration and innovation in such areas cannot be overstated. Smart electrical meters are a key component for monitoring and controlling energy consumption, but the installation of such meters assumes the existence of basic smart infrastructures. This paper, however, focuses on the use of home-based smart meters to allow for the remote switching of loads to achieve energy conservation. The design of a smart metering infrastructure coupled with load control capabilities is presented, achieving effective energy management with built-in load control. When deployed, this metering architecture will promote efficient home energy management.

The statistical analysis of editorial board composition, Citescore and percentile of 180 Hindawi journals currently indexed in Scopus are presented in this data article. The three indicators (editorial board composition, Citescore and percentile) can be helpful for researchers to make informed decision about the impact of Hindawi journals. The last two indicators are components of Scopus Citescore metrics. Keywords: Hindawi, Bibliometrics, Data analysis, Scopus, Percentile, Smart campus, Ranking analytics, Statistics, Citescore

This article explores the editorial board composition (across the six continents) of Hindawi journals indexed in PubMed. The dataset used is the official affiliation of the board members available at the various webpages of Hindawi journal website and not the countries of origin of the editorial board members. Summary statistics were presented and the raw dataset was provided for further analysis by interested scholars. The percentage of the editorial board composition across the continents was presented, the dataset of Hindawi journals indexed in both Hindawi and Scopus were also presented and measured in terms of Citescore and percentiles. The dataset can be used in journal evaluation, auditing, bibliometric analysis, management of smart campus; ranking and the analysis can be extended to other journal indexations. Keywords: Hindawi, Bibliometrics, Data analysis, PubMed, PubMed Central, Random, Smart campus, Ranking analytics, Statistics

The data are descriptions of the responses of the students that attended the ENACTUS (ENtrepreneurial ACTion US) Nigeria leadership conference 2018, which held from February 19 to 21, 2018. Hence, this data article describes the age range of the students, the amount of ENACTUS projects they have been involved in, their willingness to start their ventures, when they would like to launch their ventures and the industries they would like to venture into. The Google Doc Online Form was used to design the questionnaire used for sourcing the data. 109 respondents, of 333 that attended the conference, completed the questionnaire.

Path loss models are veritable tools for estimation of expected large-scale signal fading in a specific propagation environment during wireless network design and optimization. In this paper, the capability of Adaptive Neuro-Fuzzy Inference System (ANFIS) to establish non-linear relationship between related variables was explored for path loss predictions at Very High Frequency (VHF) band in a typical urban propagation environment. Drive test measurements were conducted along various routes in the urban area to obtain terrain profile data and path losses of radio signals transmitted at 92.3 MHz and 189.25 MHz frequencies. ANFIS was modelled to predict the magnitude of large-scale signal fading (i.e. path loss) based on the longitude, latitude, distance and elevation of the receiver’s location. Fuzzy Inference System (FIS) was generated based on Fuzzy C-Means (FCM) and subtractive clustering methods. Model performance evaluation results showed that the ANFIS model developed based on FCM clustering method yielded the least prediction errors with a Root Mean Squared Error (RMSE) value of 0.88 dB. Whereas, the International Telecommunications Union Radiocommunication (ITU-R) had earlier set a maximum allowable RMSE value of 6 dB for urban propagation environments. Thus, ANFIS technique produced a very efficient largescale signal fading prediction model for VHF network design and optimization in urban areas.

In this data article, a robust data exploration is performed on daily Internet data traffic generated in a smart university campus for a period of twelve consecutive (12) months (January–December, 2017). For each day of the one-year study period, Internet data download traffic and Internet data upload traffic at Covenant University, Nigeria were monitored and properly logged using required application software namely: FreeRADIUS; Radius Manager Web application; and Mikrotik Hotspot Manager. A comprehensive dataset with detailed information is provided as supplementary material to this data article for easy research utility and validation. For each month, descriptive statistics of daily Internet data download traffic and daily Internet data upload traffic are presented in tables. Boxplot representations and time series plots are provided to show the trends of data download and upload traffic volume within the smart campus throughout the 12-month period. Frequency distributions of the dataset are illustrated using histograms. In addition, correlation and regression analyses are performed and the results are presented using a scatter plot. Probability Density Functions (PDFs) and Cumulative Distribution Functions (CDFs) of the dataset are also computed. Furthermore, Analysis of Variance (ANOVA) and multiple post-hoc tests are conducted to understand the statistical difference(s) in the Internet traffic volume, if any, across the 12-month period. The robust data exploration provided in this data article will help Internet Service Providers (ISPs) and network administrators in smart campuses to develop empirical model for optimal Quality of Service (QoS), Internet traffic forecasting, and budgeting. Keywords: Smart campus, Internet Protocol, Internet data traffic, Nigerian university, Smart education

This data article presents data of academic performances of undergraduate students in Science, Technology, Engineering and Mathematics (STEM) disciplines in Covenant University, Nigeria. The data shows academic performances of Male and Female students who graduated from 2010 to 2014. The total population of samples in the observation is 3046 undergraduates mined from Biochemistry (BCH), Building technology (BLD), Computer Engineering (CEN), Chemical Engineering (CHE), Industrial Chemistry (CHM), Computer Science (CIS), Civil Engineering (CVE), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mathematics (MAT), Microbiology (MCB), Mechanical Engineering (MCE), Management and Information System (MIS), Petroleum Engineering (PET), Industrial Physics-Electronics and IT Applications (PHYE), Industrial Physics-Applied Geophysics (PHYG) and Industrial Physics-Renewable Energy (PHYR). The detailed dataset is made available in form of a Microsoft Excel spreadsheet in the supplementary material of this article. Keywords: Learning analytics, STEM students, STEM, STEM education, Gender roles, Undergraduates, Education data mining, Smart campus, Nigerian university

Empirical measurement, monitoring, analysis, and reporting of learning outcomes in higher institutions of developing countries may lead to sustainable education in the region. In this data article, data about the academic performances of undergraduates that studied engineering programs at Covenant University, Nigeria are presented and analyzed. A total population sample of 1841 undergraduates that studied Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) within the year range of 2002–2014 are randomly selected. For the five-year study period of engineering program, Grade Point Average (GPA) and its cumulative value of each of the sample were obtained from the Department of Student Records and Academic Affairs. In order to encourage evidence-based research in learning analytics, detailed datasets are made publicly available in a Microsoft Excel spreadsheet file attached to this article. Descriptive statistics and frequency distributions of the academic performance data are presented in tables and graphs for easy data interpretations. In addition, one-way Analysis of Variance (ANOVA) and multiple comparison post-hoc tests are performed to determine whether the variations in the academic performances are significant across the seven engineering programs. The data provided in this article will assist the global educational research community and regional policy makers to understand and optimize the learning environment towards the realization of smart campuses and sustainable education. Keywords: Smart campus, Learning analytics, Sustainable education, Nigerian university, Education data mining, Engineering

This data article contains the statistical analysis of the total, percentage and distribution of editorial board composition of 111 Hindawi journals indexed in Emerging Sources Citation Index (ESCI) across the continents. The reliability of the data was shown using correlation, goodness-of-fit test, analysis of variance and statistical variability tests. Keywords: Hindawi, Bibliometrics, Data analysis, ESCI, Random, Smart campus, Web of science, Ranking analytics, Statistics

Path loss models are often used by radio network engineers to predict signal coverage, optimize limited network resources, and perform interference feasibility studies. However, the propagation mechanisms of electromagnetic waves depend on the physical characteristics of the wireless channel. Therefore, efficient radio network planning and optimization requires detailed information about the specific propagation environment. In this data article, the path loss data and the corresponding information that are needed for modeling radio wave propagation in smart campus environment are presented and analyzed. Extensive drive test measurements are performed along three different routes (X, Y, and Z) within Covenant University, Ota, Ogun State, Nigeria (Latitude 6°40′30.3″N, Longitude 3°09′46.3″E) to record path loss data as the mobile receiver moves away from each of the three 1800 MHz base station transmitters involved. Also, the longitude, latitude, elevation, altitude, clutter height, and the distance information, which describes the smart campus environment, are obtained from Digital Terrain Map (DTM) in ATOLL radio network planning tool. Results of the first-order descriptive statistics and the frequency distributions of all the seven parameters are presented in tables and graphs respectively. In addition, correlation analyses are performed to understand the relationships between the network parameters and the terrain information. For ease of reuse, the comprehensive data are prepared in Microsoft Excel spreadsheet and attached to this data article. In essence, the availability of these data will facilitate the development of path loss models for efficient radio network planning and optimization in smart campus environment. Keywords: Path loss, Radio propagation, Wireless communications, GSM networks, Smart campus

In Nigerian universities, enrolment into any engineering undergraduate program requires that the minimum entry criteria established by the National Universities Commission (NUC) must be satisfied. Candidates seeking admission to study engineering discipline must have reached a predetermined entry age and met the cut-off marks set for Senior School Certificate Examination (SSCE), Unified Tertiary Matriculation Examination (UTME), and the post-UTME screening. However, limited effort has been made to show that these entry requirements eventually guarantee successful academic performance in engineering programs because the data required for such validation are not readily available. In this data article, a comprehensive dataset for empirical evaluation of entry requirements into engineering undergraduate programs in a Nigerian university is presented and carefully analyzed. A total sample of 1445 undergraduates that were admitted between 2005 and 2009 to study Chemical Engineering (CHE), Civil Engineering (CVE), Computer Engineering (CEN), Electrical and Electronics Engineering (EEE), Information and Communication Engineering (ICE), Mechanical Engineering (MEE), and Petroleum Engineering (PET) at Covenant University, Nigeria were randomly selected. Entry age, SSCE aggregate, UTME score, Covenant University Scholastic Aptitude Screening (CUSAS) score, and the Cumulative Grade Point Average (CGPA) of the undergraduates were obtained from the Student Records and Academic Affairs unit. In order to facilitate evidence-based evaluation, the robust dataset is made publicly available in a Microsoft Excel spreadsheet file. On yearly basis, first-order descriptive statistics of the dataset are presented in tables. Box plot representations, frequency distribution plots, and scatter plots of the dataset are provided to enrich its value. Furthermore, correlation and linear regression analyses are performed to understand the relationship between the entry requirements and the corresponding academic performance in engineering programs. The data provided in this article will help Nigerian universities, the NUC, engineering regulatory bodies, and relevant stakeholders to objectively evaluate and subsequently improve the quality of engineering education in the country. Keywords: Smart campus, Learning analytics, Sustainable education, Nigerian university, Education data mining, Engineering

In this data article, the Key Performance Indicators (KPIs) for Quality of Service (QoS) of Global System for Mobile Communications (GSM) networks in Nigeria are provided and analyzed. The data provided in this paper contain the Call Setup Success Rate (CSSR), Drop Call Rate (DCR), Stand-alone Dedicated Channel (SDCCH) congestion, and Traffic Channel (TCH) congestion for the four GSM network operators in Nigeria (Airtel, Etisalat, Glo, and MTN). These comprehensive data were obtained from the Nigerian Communications Commission (NCC). Significant differences in each of the KPIs for the four quarters of each year were presented based on Analysis of Variance (ANOVA). The values of the KPIs were plotted against the months of the year for better visualization and understanding of data trends across the four quarters. Multiple comparisons of the mean-quarterly differences of the KPIs were also presented using Tukey's Post Hoc test. Public availability and further interpretation and discussion of these useful information will assist the network providers, Nigerian government, local and international regulatory bodies, policy makers, and other stakeholders in ensuring access of people, machines, and things to high quality telecommunications services. Keywords: Quality of service, GSM networks, Call setup success rate, Drop call rate, Stand-alone dedicated channel congestion, Traffic channel congestion

In this data article, we present a comprehensive dataset on electrical energy consumption in a university that is practically driven by Information and Communication Technologies (ICTs). The total amount of electricity consumed at Covenant University, Ota, Nigeria was measured, monitored, and recorded on daily basis for a period of 12 consecutive months (January–December, 2016). Energy readings were observed from the digital energy meter (EDMI Mk10E) located at the distribution substation that supplies electricity to the university community. The complete energy data are clearly presented in tables and graphs for relevant utility and potential reuse. Also, descriptive first-order statistical analyses of the energy data are provided in this data article. For each month, the histogram distribution and time series plot of the monthly energy consumption data are analyzed to show insightful trends of energy consumption in the university. Furthermore, data on the significant differences in the means of daily energy consumption are made available as obtained from one-way Analysis of Variance (ANOVA) and multiple comparison post-hoc tests. The information provided in this data article will foster research development in the areas of energy efficiency, planning, policy formulation, and management towards the realization of smart campuses. Keywords: Smart campus, Energy consumption, Energy efficiency, Load forecasting, Energy management

This data article presents comparisons of energy generation costs from gas-fired turbine and diesel-powered systems of distributed generation type of electrical energy in Covenant University, Ota, Nigeria, a smart university campus driven by Information and Communication Technologies (ICT). Cumulative monthly data of the energy generation costs, for consumption in the institution, from the two modes electric power, which was produced at locations closed to the community consuming the energy, were recorded for the period spanning January to December 2017. By these, energy generation costs from the turbine system proceed from the gas-firing whereas the generation cost data from the diesel-powered generator also include data on maintenance cost for this mode of electrical power generation. These energy generation cost data that were presented in tables and graphs employ descriptive probability distribution and goodness-of-fit tests of statistical significance as the methods for the data detailing and comparisons. Information details from this data of energy generation costs are useful for furthering research developments and aiding energy stakeholders and decision-makers in the formulation of policies on energy generation modes, economic valuation in terms of costing and management for attaining energy-efficient/smart educational environment. Keywords: Smart campus, Energy consumption, Energy efficiency, Load forecasting, Energy management, Learning analytics, Nigerian university, Education data mining

In a typical outdoor environment, the propagation of radio waves is usually random in nature, to the extent that the characterization of the wireless channel often becomes very difficult. Several models have been developed to predict the average Received Signal Strength (RSS) for specified distance ranges. However, the use of deterministic models requires high computational efficiency while the prediction results of empirical models may not be as accurate as required. On machine learning approach, the performances of multi-layered feed-forward network models are limited by slow convergence and local minimum, such that a global optimal solution is not guaranteed. In this paper, Extreme Learning Machine (ELM) algorithm is considered in the development of an optimal path loss prediction model for outdoor propagation scenario. Single Hidden Layer Feed-forward Neural Networks (SHLFNNs) are trained and tested with the path loss data that were computed based on the RSS data of a commercial 1800 MHz base station located along Lagos-Badagry highway in Nigeria. The training speed, learning effectiveness, and the generalization ability of Artificial Neural Network Back-Propagation (ANN-BP) and ELM algorithms are analysed. Experimental results show that ELM models are 140 times faster to train than the ANN-BP models. On prediction accuracy, the outputs of ELM, ANN-BP, Okumura–Hata, and COST-231 models have Root Mean Squared Error (RMSE) values of 2.896, 2.449, 7.456, and 6.116 dB respectively; and regression coefficient (R) values of 0.959, 0.973, 0.935, and 0.935 respectively, when compared to the target variable of the training dataset. When the models were tested with new input data that were excluded from the training process, RMSE values of 4.250, 6.622, 8.732, and 7.087 respectively; and R values of 0.893, 0.876, 0.904, and 0.904 respectively are obtained. In conclusion, the findings of this study confirm that ELM algorithm guarantees an optimal path loss model with fast training convergence, high prediction accuracy, and good generalization ability for radio network planning and optimization in outdoor environments.

Objectives: System capacity has become a major concern while machine-to-machine (M2M) traffic evacuation has become a paramount interest due to the traffic congestion as well as overloading at the radio access and core network of the human-to-human (H2H) and machine-to-machine (M2M) communication framework of the 5G network. We therefore review the various solutions to the evacuation of M2M traffic with the aim of eliminating the said congestion. Methods/Statistical Analysis: We review various technologies including the low range radio technologies, and the long range wide area technologies available for internet of things (IoT) traffic otherwise called M2M traffic for decongesting the network and alleviating the M2M communication degradation effect on H2H communication. We also considered the application of massive MIMO in Heterogeneous networks for massive evacuation of the M2M traffic leading to greater separation of the H2H and M2M traffic and the eventual reduction of the network congestion. Findings: The application of massive MIMO for backhauling in macro cells for M2M traffic evacuation and which also provides 40 per cent capacity improvement via small cells placed indoor in a spatial densification heterogeneous network (HetNet) was considered as a good option for solving the above problems. Application/Improvement: A comprehensive survey on the use of the various techniques for traffic evacuation was not presented in the literature which we achieved in this paper.