Urban-rural linkages are an integral part of fostering development in both urban and rural communities. However, the focus on development tends to have an urban bias toward Sub-Saharan Africa (SSA) with an increase in urbanization. Ghana is one of the fastest urbanizing countries in SSA. This paper sought to identify the challenges of urban-rural linkages, their corresponding solutions, and contributions to Agenda 2030 that are context-specific to Ghana through a stepwise solution scanning technique. In step 1, the study applied a scoping process to identify the urban-rural challenges. In step 2, a solution for the identified urban-rural challenges in step 1 was co-designed. In step 3, each identified solution was assessed based on its potential to contribute to all three pillars of sustainability by ranking and prioritization. In step 4, the Sustainable Development Goals (SDGs) interlinkages of the top five prioritized potential solutions were analyzed. The identified challenges were (1) inequality and gender gaps, (2) poor and lack of basic and economic infrastructures, (3) the limited effectiveness of decentralization, and (4) food and nutrition security dynamics. The prioritized potential solutions were gender inclusiveness, investment in infrastructures, sustainable agricultural systems, effective decentralization, and financial inclusion. This study recommends maximizing the synergies and minimizing the trade-offs between the SDGs of the potential solutions identified.

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In this article our goal is to apply the mobile agent technology to provide a better scheduling to MPI applications executing in a cluster configuration. This approach could represent in a distributed cluster environment an enhancement on the load balancing of the parallel processes. MPI in cluster of heterogeneous machines could lead parallel programmers to obtain frustrated results, mainly because of the lack of an even distribution of the workload in the cluster. As a result, before submitting a MPI application to a cluster, we use our JOTA mobile agent approach to acquire a more precise information of machine´s workload. Therefore, with a more precise knowledge of the load and characteristics in each machine, we are ready to gather lightweight workstations to form a cluster. Our empirical results indicates that it is possible to spend less elapsed time when considering the execution of a parallel application using the agent approach in comparison to an ordinary MPI environment.

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