Emotional Intelligence for English Students with Learning Disabilities in Light of Some Variables

This study aimed at determining the level of emotional intelligence for a sample of students with learning disabilities in Irbid province in light of the variables of age, grade and learning disability type. The study sample consisted of (450) learning disabilities students from the 3rd, 4th, 5th, and 6th grades. To achieve the aims of the study, the scale of emotional intelligence was used, and it consisted of 35 clauses distributed into four fields: determining emotions, managing emotions, compassion and social efficiency. Their validity and stability were verified. The study findings revealed that individuals had a moderate level of emotional intelligence on the scale. Regarding the study of articles, the findings revealed the presence of a statistically significant effect in the level of emotional intelligence attributed to age and learning disability type. The study suggested several recommendations. The most significant ones were preparing training programs to develop emotional intelligence for students with learning difficulties, conducting a descriptive and experimental study that undertakes other types of intelligence for students with learning disabilities and other groups of special education and comparing them with normal students

On energy consumption of switch-centric data center networks

Data center network (DCN) is the core of cloud computing and accounts for 40% energy spend when compared to cooling system, power distribution and conversion of the whole data center (DC) facility. It is essential to reduce the energy consumption of DCN to esnure energy-efficient (green) data center can be achieved. An analysis of DC performance and efficiency emphasizing the effect of bandwidth provisioning and throughput on energy proportionality of two most common switch-centric DCN topologies: three-tier (3T) and fat tree (FT) based on the amount of actual energy that is turned into computing power are presented. Energy consumption of switch-centric DCNs by realistic simulations is analyzed using GreenCloud simulator. Power related metrics were derived and adapted for the information technology equipment (ITE) processes within the DCN. These metrics are acknowledged as subset of the major metrics of power usage effectiveness (PUE) and data center infrastructure efficiency (DCIE), known to DCs. This study suggests that despite in overall FT consumes more energy, it spends less energy for transmission of a single bit of information, outperforming 3T

Identification of the Privileged Position in the Imidazo[1,2‑<i>a</i>]pyridine Ring of Phosphonocarboxylates for Development of Rab Geranylgeranyl Transferase (RGGT) Inhibitors

Members of the Rab GTPase family are master regulators of vesicle trafficking. When disregulated, they are associated with a number of pathological states. The inhibition of RGGT, an enzyme responsible for post-translational geranylgeranylation of Rab GTPases represents one way to control the activity of these proteins. Because the number of molecules modulating RGGT is limited, we combined molecular modeling with biological assays to ascertain how modifications of phosphonocarboxylates, the first reported RGGT inhibitors, rationally improve understanding of their structure–activity relationship. We have identified the privileged position in the core scaffold of the imidazo­[1,2-<i>a</i>]­pyridine ring, which can be modified without compromising compounds’ potency. Thus modified compounds are micromolar inhibitors of Rab11A prenylation, simultaneously being inactive against Rap1A/Rap1B modification, with the ability to inhibit proliferation of the HeLa cancer cell line. These findings were rationalized by molecular docking, which recognized interaction of phosphonic and carboxylic groups as decisive in phosphonocarboxylate localization in the RGGT binding site

Exploiting ensemble techniques for automatic virtual machine clustering in cloud systems

Cloud computing has recently emerged as a new paradigm to provide computing services through large-size data centers where customers may run their applications in a virtualized environment. The advantages of cloud in terms of flexibility and economy encourage many enterprises to migrate from local data centers to cloud platforms, thus contributing to the success of such infrastructures. However, as size and complexity of cloud infrastructures grow, scalability issues arise in monitoring and management processes. Scalability issues are exacerbated because available solutions typically consider each virtual machine (VM) as a black box with independent characteristics, which is monitored at a fine-grained granularity level for management purposes, thus generating huge amounts of data to handle. We claim that scalability issues can be addressed by leveraging the similarity between VMs in terms of resource usage patterns. In this paper, we propose an automated methodology to cluster similar VMs starting from their resource usage information, assuming no knowledge of the software executed on them. This is an innovative methodology that combines the Bhattacharyya distance and ensemble techniques to provide a stable evaluation of similarity between probability distributions of multiple VM resource usage, considering both system- and network-related data. We evaluate the methodology through a set of experiments on data coming from an enterprise data center. We show that our proposal achieves high and stable performance in automatic VMs clustering, with a significant reduction in the amount of data collected which allows to lighten the monitoring requirements of a cloud data center