Learning personalization based on learning style instruments

Adaptive education systems (AES) are considered one of the most interesting research topics in technology-based learning strategies. Since students have different abilities, needs and learning styles, we should fit the curriculum and teaching activities to these different learning styles. This study investigates the impact of using LAES (Libyan Adaptive Education System) on the performance of students. An ALSI (Arabic Learning Style Instrument) was integrated into the LAES system to investigate learning preferences of students. The student models are constructed according to the results obtained using this instrument (ALSI). Three experimental studies were then conducted to investigate the impact of the LAES system on the performance of students. The results reveal the students who have learnt using the LAES system were more successful than others who learnt without, in terms of the knowledge gained

Detection of Crimean-Congo Haemorrhagic Fever cases in a severe undifferentiated febrile illness outbreak in the Federal Republic of Sudan: A retrospective epidemiological and diagnostic cohort study

BACKGROUND: Undifferentiated febrile illness (UFI) is one of the most common reasons for people seeking healthcare in low-income countries. While illness and death due to specific infections such as malaria are often well-quantified, others are frequently uncounted and their impact underappreciated. A number of high consequence infectious diseases, including Ebola virus, are endemic or epidemic in the Federal Republic of Sudan which has experienced at least 12 UFI outbreaks, frequently associated with haemorrhage and high case fatality rates (CFR), since 2012. One of these occurred in Darfur in 2015/2016 with 594 cases and 108 deaths (CFR 18.2%). The aetiology of these outbreaks remains unknown. METHODOLOGY/PRINCIPAL FINDINGS: We report a retrospective cohort study of the 2015/2016 Darfur outbreak, using a subset of 65 of 263 outbreak samples received by the National Public Health Laboratory which met selection criteria of sufficient sample volume and epidemiological data. Clinical features included fever (95.8%), bleeding (95.7%), headache (51.6%) and arthralgia (42.2%). No epidemiological patterns indicative of person-to-person transmission or health-worker cases were reported. Samples were tested at the Public Health England Rare and Imported Pathogens Laboratory using a bespoke panel of likely pathogens including haemorrhagic fever viruses, arboviruses and Rickettsia, Leptospira and Borrelia spp. Seven (11%) were positive for Crimean-Congo haemorrhagic fever virus (CCHFV) by real-time reverse transcription PCR. The remaining samples tested negative on all assays. CONCLUSIONS/SIGNIFICANCE: CCHFV is an important cause of fever and haemorrhage in Darfur, but not the sole major source of UFI outbreaks in Sudan. Prospective studies are needed to explore other aetiologies, including novel pathogens. The presence of CCHFV has critical infection, prevention and control as well as clinical implications for future response. Our study reinforces the need to boost surveillance, lab and investigative capacity to underpin effective response, and for local and international health security

Fog Computing: Strategies for Optimal Performance and Cost Effectiveness

The proliferation of IoT devices has amplified the challenges for cloud computing, causing bottleneck congestion which affects the delivery of the required quality of service. For some services that are delay sensitive, response time is extremely critical to avoid fatalities. Therefore, Cisco presented fog computing in 2012 to overcome such limitations. In fog computing, data processing happens geographically close to the data origin to reduce response time and decrease network and energy consumption. In this paper, a new fog computing model is presented, in which a management layer is placed between the fog nodes and the cloud data centre to manage and control resources and communication. This layer addresses the heterogeneity nature of fog computing and complex connectivity that are considered challenges for fog computing. Sensitivity analysis using simulation is conducted to determine the efficiency of the proposed model. Different cluster configurations are implemented and evaluated in order to reach the optimal clustering method. The results show that the management layer improves QoS, with less bandwidth consumption and execution time

Fog Computing: Strategies for Optimal Performance and Cost Effectiveness

The proliferation of IoT devices has amplified the challenges for cloud computing, causing bottleneck congestion which affects the delivery of the required quality of service. For some services that are delay sensitive, response time is extremely critical to avoid fatalities. Therefore, Cisco presented fog computing in 2012 to overcome such limitations. In fog computing, data processing happens geographically close to the data origin to reduce response time and decrease network and energy consumption. In this paper, a new fog computing model is presented, in which a management layer is placed between the fog nodes and the cloud data centre to manage and control resources and communication. This layer addresses the heterogeneity nature of fog computing and complex connectivity that are considered challenges for fog computing. Sensitivity analysis using simulation is conducted to determine the efficiency of the proposed model. Different cluster configurations are implemented and evaluated in order to reach the optimal clustering method. The results show that the management layer improves QoS, with less bandwidth consumption and execution time