Search results for " Gs"
showing 10 items of 217 documents
Latvijas Vēstures Institūta Žurnāls. 2016, Nr. 2 (99)
2016
Valsts kultūrkapitāla fonds
Tumor and its microenvironment: a synergistic interplay.
2013
The mutual and interdependent interaction between tumor and its microenvironment is a crucial topic in cancer research. Recently, it was reported that targeting stromal events could improve efficacies of current therapeutics and prevent metastatic spreading. Tumor microenvironment is a "complex network" of different cell types, soluble factors, signaling molecules and extracellular matrix components, which orchestrate the fate of tumor progression. As by definition, cancer stem cells (CSCs) are proposed to be the unique cell type able to maintain tumor mass and survive outside the primary tumor at metastatic sites. Being exposed to environmental stressors, including reactive oxygen species …
Late Carnian-Early Norian ammonoids from the GSSP candidate section Pizzo Mondello (Sicani mountain, Sicily).
2012
A small collection of ammonoids from the Upper Triassic Scillato Formation at Pizzo Mondello (Agrigento, Sicily) is studied. The specimens were collected in a framework of a project aimed at providing an integrated high-resolution bio-chronostratigraphic support to the Upper Carnian-Norian magnetostratigraphic scale defined at this site, that is located in an historical area from which G.G. Gemmellaro collected the Upper Triassic of ammonoids monographed at the beginning of the XX century. The specimens from Pizzo Mondello were bed-by-bed sampled and represent the first collection of Upper Triassic ammonoids described from Sicily since Gemmellaro time. Quite several levels of the Pizzo Mond…
Strange and charm mesons at FAIR
2010
Presented at the XXXI Mazurian Lakes Conference on Physics, Piaski, Poland, August 30–September 6, 2009.
Enabling Soft Frequency Reuse and Stienen's Cell Partition in Two-Tier Heterogeneous Networks: Cell Deployment and Coverage Analysis
2021
Heterogeneous cellular networks (HetNets) are one of the key enabling technologies for fifth generation (5 G) networks. In HetNets, the use of small base stations (SBSs) inside the coverage area of a macro base station (MBS) offers higher throughput and improved coverage. However, such multi-tier base station deployment introduces new challenges, e.g., (i) All users experience significant inter-cell interference (ICI) due to frequency reuse, (ii) SBS associated users experience severe MBS-interference due to higher MBS transmit power, and (iii) MBS coverage edge users receive lower signal-to-interference ratio (SIR) due to longer distances. To address the aforementioned challenges, this wor…
How neurophysiological measures can be used to enhance the evaluation of remote tower solutions
2019
New solutions in operational environments are often, among objective measurements, evaluated by using subjective assessment and judgment from experts. Anyhow, it has been demonstrated that subjective measures suffer from poor resolution due to a high intra and inter-operator variability. Also, performance measures, if available, could provide just partial information, since an operator could achieve the same performance but experiencing a different workload. In this study, we aimed to demonstrate: (i) the higher resolution of neurophysiological measures in comparison to subjective ones; and (ii) how the simultaneous employment of neurophysiological measures and behavioral ones could allow a…
Computational Offloading in Mobile Edge with Comprehensive and Energy Efficient Cost Function: A Deep Learning Approach
2021
In mobile edge computing (MEC), partial computational offloading can be intelligently investigated to reduce the energy consumption and service delay of user equipment (UE) by dividing a single task into different components. Some of the components execute locally on the UE while the remaining are offloaded to a mobile edge server (MES). In this paper, we investigate the partial offloading technique in MEC using a supervised deep learning approach. The proposed technique, comprehensive and energy efficient deep learning-based offloading technique (CEDOT), intelligently selects the partial offloading policy and also the size of each component of a task to reduce the service delay and energy …
Energy-Efficient Context-Aware Resource Allocation for Edge-Computing-Empowered Industrial IoT
2020
Edge computing provides a promising paradigm to support the implementation of industrial Internet of Things (IIoT) by offloading computational-intensive tasks from resource-limited machine-type devices (MTDs) to powerful edge servers. However, the performance gain of edge computing may be severely compromised due to limited spectrum resources, capacity-constrained batteries, and context unawareness. In this chapter, we consider the optimization of channel selection which is critical for efficient and reliable task delivery. We aim at maximizing the long-term throughput subject to long-term constraints of energy budget and service reliability. We propose a learning-based channel selection fr…