The induced motion and deformation of a conducting drop suspended in another conducting fluid and subjected to an electric field are studied, paying particular attention to the boundary conditions to be satisfied at the surface of the deformed drop. By satisfying these conditions more accurately than was done in previous work an increase in drop deformation is found which partly explains the discrepancy between theory and experiment. However, the smallness of an expansion parameter ω in practice precludes any great reduction in the discrepancy.

Although prostate cancer is the leading cause of cancer mortality for African men, the vast majority of known disease associations have been detected in European study cohorts. Furthermore, most genome-wide association studies have used genotyping arrays that are hindered by SNP ascertainment bias. To overcome these disparities in genomic medicine, the Men of African Descent and Carcinoma of the Prostate (MADCaP) Network has developed a genotyping array that is optimized for African populations. The MADCaP Array contains more than 1.5 million markers and an imputation backbone that successfully tags over 94% of common genetic variants in African populations. This array also has a high density of markers in genomic regions associated with cancer susceptibility, including 8q24. We assessed the effectiveness of the MADCaP Array by genotyping 399 prostate cancer cases and 403 controls from seven urban study sites in sub-Saharan Africa. Samples from Ghana and Nigeria clustered together, whereas samples from Senegal and South Africa yielded distinct ancestry clusters. Using the MADCaP array, we identified cancer-associated loci that have large allele frequency differences across African populations. Polygenic risk scores for prostate cancer were higher in Nigeria than in Senegal. In summary, individual and populationlevel differences in prostate cancer risk were revealed using a novel genotyping array.Significance: This study presents an Africa-specific genotyping array, which enables investigators to identify novel disease associations and to fine-map genetic loci that are associated with prostate and other cancers.

The internet of things (IoT) and cloud computing are two technologies which have recently changed both the academia and industry and impacted our daily lives in different ways. However, despite their impact, both technologies have their shortcomings. Though being cheap and convenient, cloud services consume a huge amount of network bandwidth. Furthermore, the physical distance between data source(s) and the data centre makes delays a frequent problem in cloud computing infrastructures. Fog computing has been proposed as a distributed service computing model that provides a solution to these limitations. It is based on a para-virtualized architecture that fully utilizes the computing functions of terminal devices and the advantages of local proximity processing. This paper proposes a multi-layer IoT-based fog computing model called IoT-FCM, which uses a genetic algorithm for resource allocation between the terminal layer and fog layer and a multi-sink version of the least interference beaconing protocol (LIBP) called least interference multi-sink protocol (LIMP) to enhance the fault-tolerance/robustness and reduce energy consumption of a terminal layer. Simulation results show that compared to the popular max–min and fog-oriented max–min, IoT-FCM performs better by reducing the distance between terminals and fog nodes by at least 38% and reducing energy consumed by an average of 150 KWh while being at par with the other algorithms in terms of delay for high number of tasks.

PurposeCancer of the prostate (CaP) is the leading cancer among men in sub-Saharan Africa (SSA). A substantial proportion of these men with CaP are diagnosed at late (usually incurable) stages, yet little is known about the etiology of CaP in SSA.MethodsWe established the Men of African Descent and Carcinoma of the Prostate Network, which includes seven SSA centers partnering with five US centers to study the genetics and epidemiology of CaP in SSA. We developed common data elements and instruments, regulatory infrastructure, and biosample collection, processing, and shipping protocols. We tested this infrastructure by collecting epidemiologic, medical record, and genomic data from a total of 311 patients with CaP and 218 matched controls recruited at the seven SSA centers. We extracted genomic DNA from whole blood, buffy coat, or buccal swabs from 265 participants and shipped it to the Center for Inherited Disease Research (Baltimore, MD) and the Centre for Proteomics and Genomics Research (Cape Town, South Africa), where genotypes were generated using the UK Biobank Axiom Array.ResultsWe used common instruments for data collection and entered data into the shared database. Double-entered data from pilot participants showed a 95% to 98% concordance rate, suggesting that data can be collected, entered, and stored with a high degree of accuracy. Genotypes were obtained from 95% of tested DNA samples (100% from blood-derived DNA samples) with high concordance across laboratories.ConclusionWe provide approaches that can produce high-quality epidemiologic and genomic data in multicenter studies of cancer in SSA.

Inefficient healthcare is a major concern among many African nations and can be mitigated by building world-class infrastructure connecting different medical facilities for collaboration and resource sharing. Such infrastructure should support the collection and exchange of medical data among healthcare practitioners for the purpose of accessing expertise not available locally. It should be equipped with the most recent technologies of the fourth Industrial Revolution (4IR), providing decision support to doctors thereby enabling African nations leapfrog from poorly equipped to medically prepared countries. Sadly, world-class healthcare infrastructure are a missing piece in the African public health ecosystem. Medical facilities are either non-existent or prohibitively expensive when they exist. Federated cloud computing can provide a solution to this challenge. Being a model that allows collaboration between multiple Cloud Service Providers (CSPs) by pooling computing resources together with the aim of meeting specific business or technological need; it allows for the execution of tasks on computing resources in a flexible and cost efficient manner. This paper aims to connect unconnected medical facilities in Africa by proposing a cloud federation for healthcare using co-operative and competitive collaboration models. Simulations were carried out to test the efficacy of these models using five different workload allocation schemes: First-Fit-Descending (FFD), Best-Fit-Descending (BFD) and Binary-Search-Best-Fit (BSBF); Genetic Algorithm meta-heuristic and the Stable Roommate Allocation (SRA) economic model for both light and heavy workloads. Results of simulations revealed that the co-operative cloud federation model resulted in lower allocation delays but higher resource utilisation; while the competitive model provided faster service delivery and better Quality of service (QoS) adherence. It also showed that BSBF and BFD gave the best resources utilisation and energy conservation, while FFD was the fastest overall. Finally, deployment considerations and potential business models for the federated cloud for healthcare in Africa were presented.