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In developing nations such as Ghana, traditional face-to-face (F2F) mode of education is challenged when physical classroom (academic) resources are not available for all students in a particular tertiary institution. Globally, education modes have improved through learning management systems (LMSs) as a result of technological advancements. Accra Technical University (ATU) in Ghana is currently facing the problem of turning away qualified applicants due to the fact that the academic resources in ATU are not enough to accommodate all qualified applicants. Using a quantitative research instrument (questionnaire) in accordance to the five-point Likert scale and components of the technology acceptance model (TAM) framework, this paper tackled the problem above by proposing and developing an LMS to support the education of students in ATU. Analytical results of data responses from 200 lecturers and 16 students in ATU, showed that majority of these stakeholders are willing to embrace technology and the developed LMS in ATU.

Even though many universities across the world have incorporated internet-based educational and academic systems, the success of their implementation requires an extensive understanding of the end user acceptance process Access to academic resources for teaching and learning using technology (electronic library) has become a popular approach within higher education institutions due to the continuous growth of internet innovations and technologies This case study research, conducted at Accra Technical University uses the technology acceptance model (TAM) as a theoretical framework The study investigates the effects of the system characteristics such as appropriate user interface (UI), software design, and relevance towards the perceived ease of use and perceived usefulness on intention to use the proposed e-library system Two hundred (200) students and sixteen (16) lecturers participated in this quantitative study Results show that improvement of the existing e-library system in ATU will enable students to utilize digital learning resources for effective teaching and learning, especially during the current global COVID-19 pandemic Copyright © 2021, IGI Global Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited

The current proliferation of social networking sites (e.g., Facebook), electronic devices (e.g., smartphones and tablets), and the internet has paved the way for a charge in promoting the phenomenon of internet addiction (IA). This paper analyzed and examined the validity and appropriateness of a well-established instrument for measuring IA among technical education students in Ghana, specifically students of Accra Technical University (ATU). Using a quantitative research method involving questionnaires, data collected from 260 (n=260) students in ATU was used to validate the research objectives and also measure the levels of IA among the students. The principal component tool in statistical package for social sciences (SPSS) was employed to analyze the received data. Analytical results of the study showed that a sizeable majority of students in ATU, especially male students, suffer frequent addiction problems due to the use of the internet. Additionally, results of the study showed that IA psychometric constructs in the Western world differ from those in the African context.

Globally, the superfluity of scholarly research conferences in varying disciplines has introduced the issue of scholarly big data and information overload related to both research papers and conference proceedings/sessions. This evident scholarly expansion in different disciplines has increased the collaborative importance of conferences. Consequently, the problem regarding attendees selecting the right conference session(s) to attend in academic conferences requires further and urgent attention. Using a smart conference scenario, this paper aims to address the problem above by proposing an improved venue recommender algorithm called Socially-Aware Recommendation of Venues and Environments-2 ( SARVE-2 ). Using a closeness centrality approach, SARVE-2 initially employs Breadth First Search (BFS) and Depth First Search (DFS) strategies to search for relevant presenters for a target attendee. Then, the tie strength of the (searched) presenter and target attendee is computed to generate reliable social (conference session) recommendations for the target attendee. Through the utilization of a relevant (real-world) dataset, our benchmark experiments reveal that, in comparison with other contemporary methods, SARVE-2 exhibits better performance in terms of effective social recommendation search, as well as social recommendation quality, coverage and accuracy.

The rapid growth of mobile television (TV), smart TV, and Internet Protocol Television (IPTV) content due to the convergence of broadcasting and the Internet requires effective recommendation methods to select appropriate TV programs/channels. Many previous methods have been proposed to address this issue. However, imperative factors such as the utilization of personality traits and social properties to recommend programs for TV viewers remain a challenge. Consequently, in this paper, we propose a recommender algorithm called Recommendation of Programs via Personality and Social Awareness (ROPPSA) for TV viewers. ROPPSA utilizes normalization and folksonomy procedures to generate group recommendations for TV viewers who have common similarities in terms of personality traits and tie strength with a Target TV Viewer (TTV). Therefore, ROPPSA improves TV viewer cold-start and data sparsity situations by utilizing their personality traits and tie strengths. We conducted extensive experiments on a relevant dataset using standard evaluation metrics to substantiate our ROPPSA recommendation method. Results of our experimentation procedure depict the advantage, recommendation accuracy, and outperformance of ROPPSA in comparison with other contemporary methods in terms of precision, recall, f-measure (F1), and arithmetic mean (AM).

Distributed generation presents the potential solution to the power deficit problem faced in developing counties by encouraging local generation with renewable energy resources that are abundantly available in some part of the world, especially in Ghana. Distributed generations on the other hand are closely linked to Smart Grid (SG) that uses information technology to manage electricity networks. A plethora of problems relating to safety, reliability and affordability of Smart grid solutions remains challenging today. In Africa, especially in Ghana, the adoption of smart grid has been very slow, poorly managed, and almost inexistent in some countries despite the crucial need for electricity provision. This slow adoption can be attributed to many limiting factors that demand a thorough investigation. This paper aims at investigating the barriers to the adoption of smart grids in Africa and also develops and evaluates countermeasures to hasten the adoption process. A stratified sampling approach was used to collect comprehensive data in Ghana that were handled with SmartPLS software. The evaluation of factors was done through inferential statistics. The impact of the proposed measures was tested using a partial least structural equation model analysis. Findings revealed some factors that strongly influence the adoption of SG including cost, education, government policies; other factors like culture, Grid stability, had slight influence on the adoption process, nevertheless, societal perception had a low impact on the adoption process and therefore attracts less attention in fostering the SG acceptance in Ghana. Regarding SG prospects, the study identified several factors that can foster the prospect of smart grid adoption in Ghana namely education on smart grid technology, government policies and consumers behaviour. It is believed that, if careful attention is given to the factors listed above, the adoption of SG in Ghana will move at a faster speed.

This paper seeks to propose the implementation of a mobile agriculture (m-agriculture) system for Poms Poultry Farm (PPF) to solve problems such as low patronage of farm products and customer relationship management among farmers and customers. To corroborate the successful implementation of an m-agriculture system at PPF, we employed a questionnaire research instrument concerning the Technology Acceptance Model (TAM) model as a theoretical framework. Closed-ended questionnaires were administered to a sample size of 130 PPF customers. Based on the responses received, we established optimistic responses towards technological acceptance which paved the way for us to propose and develop a suitable m-agriculture application for PPF. It is envisaged that successful implementation of our proposed system will practically increase productivity and help PPF reach out to their target customers.

Ad-hoc Social Network (ASNET), which explores social connectivity between users of mobile devices, is becoming one of the most important forms of today's internet. In this context, maximum bandwidth utilization of intermediate nodes in resource scarce environments is one of the challenging tasks. Traditional Transport Control Protocol (TCP) uses the round trip time mechanism for sharing bandwidth resources between users. However, it does not explore socially-aware properties between nodes and cannot differentiate effectively between various types of packet losses in wireless networks. In this paper, a socially-aware congestion avoidance protocol, namely TIBIAS, which takes advantage of similarity matching social properties among intermediate nodes, is proposed to improve the resource efficiency of ASNETs. TIBIAS performs efficient data transfer over TCP. During the course of bandwidth resource allocation, it gives high priority for maximally matched interest similarity between different TCP connections on ASNET links. TIBIAS does not require any modification at lower layers or on receiver nodes. Experimental results show that TIBIAS performs better as compared against existing protocols, in terms of link utilization, unnecessary reduction of the congestion window, throughput and retransmission ratio., 10 pages, 10 figures

Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.

This paper performs a technoeconomic comparison of two hybrid renewable energy supplies (HRES) for a specific location in Ghana and suggests the optimal solution in terms of cost, energy generation capacity, and emissions. The two HRES considered in this paper were wind/hydrogen/fuel-cell and wind/battery storage, respectively. The necessity of this study was derived from the rise and expansion of hybrid renewable energy supply in a decentralised network. The readiness to embrace these new technologies is apparently high, but the best combination for a selected location that brings optimum benefits is not obvious and demands serious technical knowledge of their technical and economic models. In the methodology, an analytical model of energy generation by the various RE sources was first established, and data were collected about a rural-urban community in Doderkope, Ghana, to test the models. HOMER software was used to design the two hybrid systems based on the same load profiles, and results were compared. It turns out that the HRES 1 (wind/hydrogen/fuel-cell) had the lowest net present cost (NPC) and levelized cost of electricity (COE) over the project life span of 25 years. The energy reserve with the HRES 2 (wind/battery storage) was huge compared to that with the HRES 1, about 270% bigger. Furthermore, with respect to the emissions, the HRES 2 was environmentally friendlier than the HRES 1. Even though the battery storage seems to be more cost-effective than the hydrogen fuel-cell technology, the latter presents some merits regarding system capacity and emission that deserve greater attention as the world looks into more sustainable energy storage systems.

As a result of the importance of academic collaboration at smart conferences, various researchers have utilized recommender systems to generate effective recommendations for participants. Recent research has shown that the personality traits of users can be used as innovative entities for effective recommendations. Nevertheless, subjective perceptions involving the personality of participants at smart conferences are quite rare and haven't gained much attention. Inspired by the personality and social characteristics of users, we present an algorithm called Socially and Personality Aware Recommendation of Participants (SPARP). Our recommendation methodology hybridizes the computations of similar interpersonal relationships and personality traits among participants. SPARP models the personality and social characteristic profiles of participants at a smart conference. By combining the above recommendation entities, SPARP then recommends participants to each other for effective collaborations. We evaluate SPARP using a relevant dataset. Experimental results confirm that SPARP is reliable and outperforms other state-of-the-art methods., Comment: 12 pages, 13 figures

This paper shows A 2.4GHz two-way directional Antenna of Wi-Fi Systems for Underground Mine-Tunnel. The designed antenna enhances wireless transmission of signals in an underground mine-tunnel in a two-way direction. The research was carried-out through scientific design and simulation-using high frequency structure simulator (HFSS). This antenna has fabricated and tested. The antenna is in the shape of a rectangle, partitioned into 12 sub-rectangular rings hooked together, that work in a standing-wave mode. The high-gain two-way radiation property realized by the currents distribution in-phase on the vertical edges. The dimensions of the desired ring are 230 mm, 22 mm and 0.8 mm $(1.84\lambda, 0.17\lambda $ and $ 0.006\lambda $ at 2.4 GHz). The simulated and measured -10-dB bandwidth is about 340 MHz (2.320–2.66 GHz), the simulated peak gain is 8.4 dBi. Presentation and discussion of the simulated results showed that the proposed antenna in two-way direction application is of high directional radiation gain, low profile and easy to build.

This paper proposes a model of PCB traces for high speed digital systems. The adopted approach involves predetermined geometry using direct discretization of transmission lines. Initially, the proposed methodology involves computing the line propagation delay by employing its geometry with associated empirical equations. The initial procedure paves the way to design a Lattice diagram which depicts multiple reflections that the signal underwent due to impedance mismatches between transmission lines and loads. Subsequent computations of electrical model parameters were further done. Simulation results using Multisim software illustrated a favorable performance with a time delay of 1.42 ns and an equivalent electrical model of 10 lumped LC cells. The time delay between input and output signal obtained from the simulation was approximately 15.152 ns corresponding to the time it took for a transmitted signal to reach a steady state which further signifies good performance of our proposed method.

Distribution transformers are generally provided with advanced control measures that ensure their operation and safety at a high reliability because their failure usually leads to long power outage, increase maintenance cost and unreliable provision of electricity to customers. Despite the protection measures that accompany a distribution transformer, there is still a need to provide extra remote monitoring systems to increase the reliability of operation and the confidence of instantaneous fault reporting. This paper deals with the design and construction of automatic monitoring system for power transformer parameters. An Arduino board coupled with XBEE module were programmed to enable the monitoring of voltage, current and temperature on a typical power transformer. The system was implemented and tested successfully. Results showed that the voltage versus temperature as well as current versus voltage characteristic obtained, were closed to datasheet specifications and therefore confirming the reliability of the proposed design. This system is therefore recommended as an additional protection that should be added to Power Transformers.

obile learning (m-learning) is a field that involves the use of mobile computing and wireless technology to enable learning occur anytime, anyplace and anywhere. The implementation strategies of mobile learning are very important for every nation due to the advantages that come with it, e.g. ease of access to education and enhanced interaction between instructors and learners. However, a lot of challenges come with mobile learning implementation, especially with respect to developing nations. This paper highlights on some mobile learning implementation challenges with a focus on Ghana, Africa and developing nations. Furthermore, the paper describes and summarizes how best to strategically overcome the enumerated challenges.

Teaching and Learning through ICT in Higher Educational Institutions (HEIs) has attracted global ICT investments of various Governments. However the results of these investments in ICT, that has a target to enhance quality of education remains to be seen and realized. Various factors such as ICT adoption and penetration, ICT infrastructure and equipment and professional development to enhance education using ICT have been limited. Using Wollo University in Ethiopia and Accra Polytechnic in Ghana as case studies, this article reviews institutional, personal and technological factors that can encourage stakeholders, teachers and students to use ICT to enhance the quality of education in these HEIs. The article reviews these factors and concludes that knowing the extent to which the enumerated barriers affect teachers and students of Wollo University and Accra Polytechnic, decisions concerning enhancing ICT quality of education through ICT can be made to the benefit of these HEIs as well as other ones.

Due to the rapid growth of internet broadband access and proliferation of modern mobile devices, various types of multimedia (e.g. text, images, audios and videos) have become ubiquitously available anytime. Mobile device users usually store and use multimedia contents based on their personal interests and preferences. Mobile device challenges such as storage limitation have however introduced the problem of mobile multimedia overload to users. In order to tackle this problem, researchers have developed various techniques that recommend multimedia for mobile users. In this survey paper, we examine the importance of mobile multimedia recommendation systems from the perspective of three smart communities, namely, mobile social learning, mobile event guide and context-aware services. A cautious analysis of existing research reveals that the implementation of proactive, sensor-based and hybrid recommender systems can improve mobile multimedia recommendations. Nevertheless, there are still challenges and open issues such as the incorporation of context and social properties, which need to be tackled in order to generate accurate and trustworthy mobile multimedia recommendations.

Although existing replica allocation protocols perform well in most cases, some challenges still need to be addressed to further improve their performance. The success of such protocols for Ad-hoc Social Networks (ASNETs) depends on the performance of data accessibility and on the easy consistency management of available replica. We contribute to this line of research with replication protocol for maximizing availability of a data. Essentially, we propose ComPAS, a community-partitioning aware replica allocation method. Its goals include integration of social relationship for placing copy of the data in the community to achieve better efficiency and consistency by keeping the replica read cost, relocation cost and traffic as low as possible.

Ad-hoc social network (ASNET) services deal with the dynamic nature and resource constraints of mobile nodes to support various applications. The availability, accessibility and reliability of these services can be assured by data management approaches such as replica allocation methods. Data replication is used to increase data availability by replicating data items locally or nearby. In ASNETs, replication helps to avoid data losses in case of an unpredictable community or network partition and also aids in reducing the number of hops when a data is transmitted from source to destination. A new data replication method called ComPAS (community-partition aware replica allocation method for ASNETs) is proposed in this paper. This method can significantly improve a social network's efficiency by taking into account social relationships and properties of its data while replicating in the community to achieve better load-balance. This type of replica allocation method will increase the availability of different data items in a partitioned social community. Evaluation results verify the effectiveness of the method.