Adding sustainable sources to the Saudi Arabian electricity sector

With power plants in Saudi Arabia consuming 27% of the kingdom’s oil and 100% of its natural gas, a host of strategic projects are under way to boost the prominence of alterative resources in the overall mix. The nation is targeting 54?GW of renewable energy and 17?GW of nuclear power by 2040.     

Deep context of citations using machine-learning models in scholarly full-text articles

Information retrieval systems for scholarly literature rely heavily not only on text matching but on semantic- and context-based features. Readers nowadays are deeply interested in how important an article is, its purpose and how influential it is in follow-up research work. Numerous techniques to tap the power of machine learning and artificial intelligence have been developed to enhance retrieval of the most influential scientific literature. In this paper, we compare and improve on four existing state-of-the-art techniques designed to identify influential citations. We consider 450 citations from the Association for Computational Linguistics corpus, classified by experts as either important or unimportant, and further extract 64 features based on the methodology of four state-of-the-art techniques. We apply the Extra-Trees classifier to select 29 best features and apply the Random Forest and Support Vector Machine classifiers to all selected techniques. Using the Random Forest classifier, our supervised model improves on the state-of-the-art method by 11.25%, with 89% Precision-Recall area under the curve. Finally, we present our deep-learning model, the Long Short-Term Memory network, that uses all 64 features to distinguish important and unimportant citations with 92.57% accuracy.     

Saving lives using social media: Analysis of the role of twitter for personal blood donation requests and dissemination

Social media has an impact on many aspects of human life ranging from sharing personal information to revolutionizing political systems of entire countries. One not so well studied aspect of social media is analyzing its usage and efficacy in healthcare, particularly in developing countries which lack state-of-the-art healthcare systems and processes. In such countries, social media may be used to facilitate patient-centric healthcare by involving the patient for fulfilling personal healthcare needs. This article provides an in-depth analysis of one such need, that is, how people use social media to request for blood donations. We study the request and dissemination behavior of people using social media to fulfill blood donation requests. We focus on twitter, and blood donation accounts in India. Our study reveals that each of the seven twitter accounts we studied have a large followership of more than 35,000 users on an average and receive a substantial number (more than 900) of donation requests in a day on an average. We analyze the requests in various ways to present an outlook for healthcare providers to make their systems more patient-centric through a better understanding of the needs of people requesting for blood donations. Our study also identifies areas where future social media enabled automated healthcare systems can focus on the needs of individual patients. These systems can provide support for saving more lives by reducing the gap between blood donors and the people in need.     

Virtual learning environment to predict withdrawal by leveraging deep learning

The current evolution in multidisciplinary learning analytics research poses significant challenges for the exploitation of behavior analysis by fusing data streams toward advanced decision-making. The identification of students that are at risk of withdrawals in higher education is connected to numerous educational policies, to enhance their competencies and skills through timely interventions by academia. Predicting student performance is a vital decision-making problem including data from various environment modules that can be fused into a homogenous vector to ascertain decision-making. This research study exploits a temporal sequential classification problem to predict early withdrawal of students, by tapping the power of actionable smart data in the form of students' interactional activities with the online educational system, using the freely available Open University Learning Analytics data set by employing deep long short-term memory (LSTM) model. The deployed LSTM model outperforms baseline logistic regression and artificial neural networks by 10.31% and 6.48% respectively with 97.25% learning accuracy, 92.79% precision, and 85.92% recall.     

Improved Metaheuristic Based Failure Prediction with Migration Optimization in Cloud Environment

Cloud data centers consume high volume of energy for processing and switching the servers among different modes. Virtual Machine (VM) migration enhances the performance of cloud servers in terms of energy efficiency, internal failures and availability. On the other end, energy utilization can be minimized by decreasing the number of active, underutilized sources which conversely reduces the dependability of the system. In VM migration process, the VMs are migrated from underutilized physical resources to other resources to minimize energy utilization and optimize the operations. In this view, the current study develops an Improved Metaheuristic Based Failure Prediction with Virtual Machine Migration Optimization (IMFP-VMMO) model in cloud environment. The major intention of the proposed IMFP-VMMO model is to reduce energy utilization with maximum performance in terms of failure prediction. To accomplish this, IMFP-VMMO model employs Gradient Boosting Decision Tree (GBDT) classification model at initial stage for effectual prediction of VM failures. At the same time, VMs are optimally migrated using Quasi-Oppositional Artificial Fish Swarm Algorithm (QO-AFSA) which in turn reduces the energy consumption. The performance of the proposed IMFP-VMMO technique was validated and the results established the enhanced performance of the proposed model. The comparative study outcomes confirmed the better performance of the proposed IMFP-VMMO model over recent approaches.     

Deficiency in the Treatment Description of mTOR Inhibitor Resistance in Medulloblastoma,a Systematic Review

Medulloblastoma is a common fatal pediatric brain tumor. More treatment options are required to prolong survival and decrease disability. mTOR proteins play an essential role in the disease pathogenesis, and are an essential target for therapy. Three generations of mTOR inhibitors have been developed and are clinically used for immunosuppression and chemotherapy for multiple cancers. Only a few mTOR inhibitors have been investigated for the treatment of medulloblastoma and other pediatric tumors. The first-generation mTOR, sirolimus, temsirolimus, and everolimus, went through phase I clinical trials. The second-generation mTOR, AZD8055 and sapanisertib, suppressed medulloblastoma cell growth; however, limited studies have investigated possible resistance pathways. No clinical trials have been found to treat medulloblastoma using third-generation mTOR inhibitors. This systematic review highlights the mechanisms of resistance of mTOR inhibitors in medulloblastoma and includes IDO1, T cells, Mnk2, and eIF4E, as they prolong malignant cell survival. The findings promote the importance of combination therapy in medulloblastoma due to its highly resistant nature.     

An in-text citation classification predictive model for a scholarly search system

Scientometrics - We argue that citations in scholarly documents do not always perform equivalent functions or possess equal importance. To address this problem, we worked with a corpus of over...     

Tweet Coupling: a social media methodology for clustering scientific publications

Scientometrics - We argue that classic citation-based scientific document clustering approaches, like co-citation or Bibliographic Coupling, lack to leverage the social-usage of the scientific...