About: Helwan University is a education organization based out in Cairo, Egypt. It is known for research contribution in the topics: Control theory & Population. The organization has 4470 authors who have published 8456 publications receiving 96277 citations.
Papers published on a yearly basis
TL;DR: In this paper, the authors present how renewable energy resources are currently being used, scientific developments to improve their use, their future prospects, and their deployment, and represent the impact of power electronics and smart grid technologies that can enable the proportionate share of renewable resources.
Abstract: Electric energy security is essential, yet the high cost and limited sources of fossil fuels, in addition to the need to reduce greenhouse gasses emission, have made renewable resources attractive in world energy-based economies. The potential for renewable energy resources is enormous because they can, in principle, exponentially exceed the world׳s energy demand; therefore, these types of resources will have a significant share in the future global energy portfolio, much of which is now concentrating on advancing their pool of renewable energy resources. Accordingly, this paper presents how renewable energy resources are currently being used, scientific developments to improve their use, their future prospects, and their deployment. Additionally, the paper represents the impact of power electronics and smart grid technologies that can enable the proportionate share of renewable energy resources.
TL;DR: In this paper, the authors evaluate this argument in the light of the evolution in the structural characteristics of FDI and empirically test the hypothesis that the level of human capital in host countries may affect the geographical distribution of the FDI inflows.
Abstract: Despite the dramatic increase in total foreign direct investment (FDI) flows to developing countries in the last few years, the bulk of the inflows has been directed to only a limited number of countries It has been argued that developing countries might enhance their attractiveness as locations for FDI by pursuing policies that raise the level of local skills and build up human resource capabilities Nevertheless, the empirical evidence in the literature in support of this recommendation for a large sample of developing countries is scant This paper evaluates this argument in the light of the evolution in the structural characteristics of FDI and empirically tests the hypothesis that the level of human capital in host countries may affect the geographical distribution of FDI The empirical findings are: (a) human capital is a statistically significant determinant of FDI inflows; (b) human capital is one of the most important determinants; and (c) its importance has become increasingly greater through time
TL;DR: P predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans are generated.
Abstract: Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.
TL;DR: In this article, a detailed description of the analysis used by the CMS Collaboration in the search for the standard model Higgs boson in pp collisions at the LHC, which led to the observation of a new boson.
Abstract: A detailed description is reported of the analysis used by the CMS Collaboration in the search for the standard model Higgs boson in pp collisions at the LHC, which led to the observation of a new boson. The data sample corresponds to integrated luminosities up to 5.1 inverse femtobarns at sqrt(s) = 7 TeV, and up to 5.3 inverse femtobarns at sqrt(s) = 8 TeV. The results for five Higgs boson decay modes gamma gamma, ZZ, WW, tau tau, and bb, which show a combined local significance of 5 standard deviations near 125 GeV, are reviewed. A fit to the invariant mass of the two high resolution channels, gamma gamma and ZZ to 4 ell, gives a mass estimate of 125.3 +/- 0.4 (stat) +/- 0.5 (syst) GeV. The measurements are interpreted in the context of the standard model Lagrangian for the scalar Higgs field interacting with fermions and vector bosons. The measured values of the corresponding couplings are compared to the standard model predictions. The hypothesis of custodial symmetry is tested through the measurement of the ratio of the couplings to the W and Z bosons. All the results are consistent, within their uncertainties, with the expectations for a standard model Higgs boson.
TL;DR: A novel metaheuristic algorithm named Henry gas solubility optimization (HGSO), which mimics the behavior governed by Henry’s law to solve challenging optimization problems, provides competitive and superior results compared to other algorithms when solving challenging optimize problems.
Abstract: Several metaheuristic optimization algorithms have been developed to solve the real-world problems recently This paper proposes a novel metaheuristic algorithm named Henry gas solubility optimization (HGSO), which mimics the behavior governed by Henry’s law to solve challenging optimization problems Henry’s law is an essential gas law relating the amount of a given gas that is dissolved to a given type and volume of liquid at a fixed temperature The HGSO algorithm imitates the huddling behavior of gas to balance exploitation and exploration in the search space and avoid local optima The performance of HGSO is tested on 47 benchmark functions, CEC’17 test suite, and three real-world optimization problems The results are compared with seven well-known algorithms; the particle swarm optimization (PSO), gravitational search algorithm (GSA), cuckoo search algorithm (CS), grey wolf optimizer (GWO), whale optimization algorithm (WOA), elephant herding algorithm (EHO) and simulated annealing (SA) Additionally, to assess the pairwise statistical performance of the competitive algorithms, a Wilcoxon rank sum test is conducted The experimental results revealed that HGSO provides competitive and superior results compared to other algorithms when solving challenging optimization problems
Showing all 4535 results
|Aboul Ella Hassanien||60||930||16382|
|Mark A. A. Neil||53||229||9536|
|James E. Mark||52||338||12447|
|Suk Ho Chung||50||296||8173|
|Antonio María Pérez-Calero Yzquierdo||48||207||7352|
Related Institutions (5)
55.5K papers, 792.6K citations
26.2K papers, 379.9K citations
Ain Shams University
34.4K papers, 444.5K citations
23K papers, 344.7K citations
United Arab Emirates University
14.1K papers, 321.1K citations