Globally, effective and efficient healthcare is critical to the wellbeing and standard of living of any society. Unfortunately, several distant communities far from the national grid do not have access to reliable power supply, owing to economic, environmental, and technical challenges. Furthermore, unreliable, unavailable, and uneconomical power supply to these communities contributes significantly to the delivery of substandard or absence of qualitative healthcare services, resulting in higher mortality rates and associated difficulty in attracting qualified healthcare workers to the affected communities. Given these circumstances, this paper aims to conduct a comprehensive review of the status of renewable energy available to rural healthcare clinics around the globe, emphasizing its potential, analysis, procedures, modeling techniques, and case studies. In this light, several renewable energy modeling techniques were reviewed to examine the optimum power supply to the referenced healthcare centers in remote communities. To this end, analytical techniques and standard indices for reliable power supply to the isolated healthcare centers are suggested. Specifically, different battery storage systems that are suitable for rural healthcare systems are examined, and the most economical and realistic procedure for the maintenance of microgrid power systems for sustainable healthcare delivery is defined. Finally, this paper will serve as a valuable resource for policymakers, researchers, and experts in rural power supply to remote healthcare centers globally.

This study explores dynamic analysis and optimal control strategies for modeling the propagation of computer viruses in point-to-group information networks, addressing the significant threat they pose in the modern technological landscape. Grounded in the susceptible-exposed-infected-recovered (SEIR) framework, our mathematical model incorporates a saturated incidence rate to capture the evolving dynamics of virus spread and administrator responses. Our optimal control analysis focuses on three control laws corresponding to antivirus measures targeting susceptible, exposed, and infected populations, with an objective to minimize virus prevalence and associated control costs. Leveraging Pontryagin's Maximum Principle, we derive necessary conditions for optimal control, offering guidance on antivirus measure implementation to mitigate virus impact. Numerical simulations was carried out in other to validate our model and strategies, offering practical insights for network managers to bolster cyber security and minimize economic losses from viral outbreaks. The simulation result shows that by using the optimal control strategies in the model, the population of the infected and exposed computers will reduce to its possible minimal value at a minimum cost. This work establishes a robust framework for studying optimal control of computer virus spread in dynamic networks, merging mathematical modeling with actionable intervention strategies.

A Clinical Data Repository (CDR) is a dynamic database capable of real-time updates with patients' data, organized to facilitate rapid and easy retrieval. CDRs offer numerous benefits, ranging from preserving patients' medical records for follow-up care and prescriptions to enabling the development of intelligent models that can predict, and potentially mitigate serious health conditions. While several research works have attempted to provide state-of-the-art reviews on CDR design and implementation, reviews from 2013 to 2023 cover CDR regulations, guidelines, standards, and challenges in CDR implementation without providing a holistic overview of CDRs. Additionally, these reviews need to adequately address critical aspects of CDR; development and utilization, CDR architecture and metadata, CDR management tools, CDR security, use cases, and artificial intelligence (AI) in CDR design and implementation. The collective knowledge gaps in these works underscore the imperative for a comprehensive overview of the diverse spectrum of CDR as presented in the current study. Existing reviews conducted over the past decade, from 2013 to 2023 have yet to comprehensively cover the critical aspects of CDR development, which are essential for uncovering trends and potential future research directions in Africa and beyond. These aspects include architecture and metadata, security and privacy concerns, tools employed, and more. To bridge this gap, in particular, this study conducts a comprehensive systematic review of CDR, considering critical facets such as architecture and metadata, security and privacy issues, regulations guiding development, practical use cases, tools employed, the role of AI and machine learning (ML) in CDR development, existing CDRs, and challenges faced during CDR development and deployment in Africa and beyond. Specifically, the study extracts valuable discussions and analyses of the different aspects of CDR. Key findings revealed that most architectural models for CDR are still in the theoretical phase, with low awareness and adoption of CDR in healthcare environments, susceptibility to several security threats, and the need to integrate federated learning in CDR systems. Overall, this paper would serve as a valuable reference for designing and implementing cutting-edge clinical data repositories in Africa and beyond.

A path loss estimation model that is both computationally efficient, and precise is required for link budgeting, system performance optimization, base station selection, and coverage analysis. The limitations of empirical and deterministic models, on the other hand, necessitate the incorporation of computational intelligence into the path loss channel models development for multi-frequency band propagation channels. The principle and technique of Deep Neural Network (DNN) were applied in this paper for the modelling and development of a multi-frequency Convolutional Neural Network (CNN)-based path loss estimation model. The CNN architecture employed is a one-dimensional convolution - consisting of a convolution layer, a flattened layer, a dense layer and two fully connected layers. Filter sizes and epochs were varied to examine the effect on accuracy with respect to RMSE, and MSE during training. Results from the CNN model show that there is a significant impact of the training parameters in the development of the CNN model.

Signal propagation in a particular region differs from another due to differences in atmospheric, climatic and environmental properties, distinct terrain and clutter features. Adequate analysis is essential to understand the radio propagation behavior in a particular region. The ITU-R designated four rain regions, M, N, P, and Q, for Nigeria representing the rain rate distribution and also provided further classifications based on ground conductivity, among other salient parameters. Based on these classifications, this paper utilized the EDX Signal Pro software® to model and simulate a typical Point-to-Point (P2P), Non-Line of Sight (NLOS) link scenarios for 5G networks. The objective was to estimate and compare the total loss, excess loss, and flat fade margins for each rain region in Nigeria. Results obtained from the comparison showed that signals propagating in region N experience the highest level of losses and fading, while, region Q has the least losses and fading.

Terrestrial Radio Propagation (TRP) involves radio wave propagation from one station to another over the surface of the earth. Radio communication systems have been deployed for broadcasting, mobile cellular, and public safety. Radio propagation planning plays a crucial role in designing and deploying terrestrial radio networks. Radio propagation models (Path loss Models) are utilized during the link budget, coverage, and interference estimations. The models guide radio network engineers in choosing appropriate placements of radio network equipment, such as the base stations. However, the high cost attributed to TRP data collection, lack of open access, and accurate TRP datasets hinders the development of path loss models that can reliably predict outcomes for different use cases. To address this problem, this paper aims to present a robust TRP repository that provides a platform for hosting and disseminating TRP datasets, which the research community could use for path loss modeling. The repository was implemented using the latest technologies, and its performance was evaluated. The system has been deployed for public access.

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

ITU-R categorized Nigeria into four rain regions (i.e., M, N, P, and Q) depending on the atmospheric conditions. Previous works that have conducted rain and attenuation modeling and simulations have assumed similar signal propagation behavior and parameters within locations categorized under the same region. This paper aims to explore these assumptions by conducting an extensive radio propagation simulations of point-to-point microwave links with a clear line-of-sight in order to estimate the total path loss (attenuation), excess path loss, and flat fade margins for each of the locations within the M-regions, i.e., Kano, Sokoto, and Adamawa. Results obtained showed that in all the locations, the loss monotonically increases with distance, with Sokoto having the highest level of signal losses and fading, while, Adamawa has the lowest. The deviation for both total loss, excess loss, and flat fading margin was found to be between 15 dB across the region.

The rapid increase in data traffic caused by the proliferation of smart devices has spurred the demand for extremely large-capacity wireless networks. Thus, faster data transmission rates and greater spectral efficiency have become critical requirements in modern-day networks. The ubiquitous 5G is an end-to-end network capable of accommodating billions of linked devices and offering high-performance broadcast services due to its several enabling technologies. However, the existing review works on 5G wireless systems examined only a subset of these enabling technologies by providing a limited coverage of the system model, performance analysis, technology advancements, and critical design issues, thus requiring further research directions. In order to fill this gap and fully grasp the potential of 5G, this study comprehensively examines various aspects of 5G technology. Specifically, a systematic and all-encompassing evaluation of the candidate 5G enabling technologies was conducted. The evolution of 5G, the progression of wireless mobile networks, potential use cases, channel models, applications, frequency standardization, key research issues, and prospects are discussed extensively. Key findings from the elaborate review reveal that these enabling technologies are critical to developing robust, flexible, dependable, and scalable 5G and future wireless communication systems. Overall, this review is useful as a resource for wireless communication researchers and specialists.

Dust particles and sand storms can cause attenuation and cross-polarization of electromagnetic wave propagation, especially at high frequencies above 10 GHz. Dust attenuation has been the focus of many research works, mainly with the deployment of a 5G wireless network in the FR-2 band (mmWave band, 23-53 GHz with TDD). This has led to the development of novel models to accurately predict and estimate attenuation. However, the existing review works have not adequately provided extensive taxonomies for these models to show the state-of-art and future research directions. This paper aims to bridge this gap by providing a comprehensive review of all electromagnetic scattering models in terms of their strengths, weaknesses, and applications. Lessons learned from the detailed survey have been stated and discussed extensively. Key findings from this review indicate that all the models developed were limited to the region where they were developed, with frequency and visibility levels as the two main parameters. The survey across regions showed no model was developed for Region 2, including the Americas, Greenland, and some of the eastern Pacific Islands. Among the dry regions of the globe, where dust and sand storms can occur either occasionally or frequently, it can be seen that only a few parts of these desert regions of Africa (Region 1) and Asia (Region 3) have been considered by authors for the development of prediction models for attenuation due to dust storms. Thus, this also shows the limitations of the overall deterministic models and presents the crucial need to develop new models or modify existing models to accurately predict dust attenuation in other regions, particularly in Africa.

This paper presents Multi-robot co-operative hunting behavior using Differential game approach. Two robots were used as pursuers while another robot is used as evader, the two robots (pursuers) try to search and surround the prey (evader) robot. The aim of the game is for the two robots to detect the evader at the minimum possible time while at the same time the evader dodged the pursuer to the maximum possible time. Differential game approach was used to construct the problem using system of ordinary differential equation. We give the required conditions for the two pursuers to catch the evader. It was also shown that the evader maximize the capture time, while the pursuers minimize the capture time.

Underwater robots have the ability to go down the sea up to several meters of height without any fear of loss of human lives. These robots need autonomous control systems and guidance to carry out their tasks. One of the main objectives of any underwater robot is to reach a given depth under the water and also be able to maintain that depth throughout the operation period. In this paper a simple full state feedback controller was designed to control the underwater robot's depth, despite all the external forces and disturbances during a given mission.

In this paper a fractional optimal control problem was formulated for the outbreak of COVID-19 using a mathematical model with fractional order derivative in the Caputo sense. The state and co-state equations were given and the best strategy to significantly reduce the spread of COVID-19 infections was found by introducing two time-dependent control measures, u1(t)(which represents the awareness campaign, lockdown, and all other measures that reduce the possibility of contacting the disease in susceptible human population) and u2(t)(which represents quarantine, monitoring and treatment of infected humans). Numerical simulations were carried out using RK-4 to show the significance of the control functions. The exposed population in susceptible population is reduced by the factor (1−u1(t)) due to the awareness and all other measures taken. Likewise, the infected population is reduced by a factor of (1−u2(t)) due to the monitoring and treatment by health professionals.

In this paper, we developed a model that suggests the use of robots in identifying COVID-19-positive patients and which studied the effectiveness of the government policy of prohibiting migration of individuals into their countries especially from those countries that were known to have COVID-19 epidemic. Two compartmental models consisting of two equations each were constructed. The models studied the use of robots for the identification of COVID-19-positive patients. The effect of migration ban strategy was also studied. Four biologically meaningful equilibrium points were found. Their local stability analysis was also carried out. Numerical simulations were carried out, and the most effective strategy to curtail the spread of the disease was shown.

Control of Malaria is very difficult due to anti malarial drug resistant diseases. Many control measures exist such as; insecticides treated bed net (ITNs) and drug treatments. Most mathematical models in literature used constant control measures which is not realistic. Here we use optimal control as a measure in curtailing the disease spread. The control function is added in the sensitive strain. Analysis of the controller was carried out.