An Optimal Algorithm for Resource Allocation in D2D Communication

The number of mobile devices accessing wireless networks is skyrocketing due to the rapid advancement of sensors and wireless communication technology. In the upcoming years, it is anticipated that mobile data traffic would rise even more. The development of a new cellular network paradigm is being driven by the Internet of Things, smart homes, and more sophisticated applications with greater data rates and latency requirements. Resources are being used up quickly due to the steady growth of smartphone devices and multimedia apps. Computation offloading to either several distant clouds or close mobile devices has consistently improved the performance of mobile devices. The computation latency can also be decreased by offloading computing duties to edge servers with a specific level of computing power. Device-to-device (D2D) collaboration can assist in processing small-scale activities that are time-sensitive in order to further reduce task delays. The task offloading performance is drastically reduced due to the variation of different performance capabilities of edge nodes. Therefore, this paper addressed this problem and proposed a new method for D2D communication. In this method, the time delay is reduced by enabling the edge nodes to exchange data samples. Simulation results show that the proposed algorithm has better performance than traditional algorithm.     

Genomics and Epigenomics of Gestational Diabetes Mellitus: Understanding the Molecular Pathways of the Disease Pathogenesis

One of the most common complications during pregnancy is gestational diabetes mellitus (GDM), hyperglycemia that occurs for the first time during pregnancy. The condition is multifactorial, caused by an interaction between genetic, epigenetic, and environmental factors. However, the underlying mechanisms responsible for its pathogenesis remain elusive. Moreover, in contrast to several common metabolic disorders, molecular research in GDM is lagging. It is important to recognize that GDM is still commonly diagnosed during the second trimester of pregnancy using the oral glucose tolerance test (OGGT), at a time when both a fetal and maternal pathophysiology is already present, demonstrating the increased blood glucose levels associated with exacerbated insulin resistance. Therefore, early detection of metabolic changes and associated epigenetic and genetic factors that can lead to an improved prediction of adverse pregnancy outcomes and future cardio-metabolic pathologies in GDM women and their children is imperative. Several genomic and epigenetic approaches have been used to identify the genes, genetic variants, metabolic pathways, and epigenetic modifications involved in GDM to determine its etiology. In this article, we explore these factors as well as how their functional effects may contribute to immediate and future pathologies in women with GDM and their offspring from birth to adulthood. We also discuss how these approaches contribute to the changes in different molecular pathways that contribute to the GDM pathogenesis, with a special focus on the development of insulin resistance.     

Microwave-Induced Chemotoxicity of Polydopamine-Coated Magnetic Nanocubes

Polydopamine-coated FeCo nanocubes (PDFCs) were successfully synthesized and tested under microwave irradiation of 2.45 GHz frequency and 0.86 W/cm² power. These particles were found to be non-toxic in the absence of irradiation, but gained significant toxicity upon irradiation. Interestingly, no increase in relative heating rate was observed when the PDFCs were irradiated in solution, eliminating nanoparticle (NP)-induced thermal ablation as the source of toxicity. Based on these studies, we propose that microwave-induced redox processes generate the observed toxicity.     

Storing and analysing voice of the market data in the corporate data warehouse

Web opinion feeds have become one of the most popular information sources users consult before buying products or contracting services. Negative opinions about a product can have a high impact in its sales figures. As a consequence, companies are more and more concerned about how to integrate opinion data in their business intelligence models so that they can predict sales figures or define new strategic goals. After analysing the requirements of this new application, this paper proposes a multidimensional data model to integrate sentiment data extracted from opinion posts in a traditional corporate data warehouse. Then, a new sentiment data extraction method that applies semantic annotation as a means to facilitate the integration of both types of data is presented. In this method, Wikipedia is used as the main knowledge resource, together with some well-known lexicons of opinion words and other corporate data and metadata stores describing the company products like, for example, technical specifications and user manuals. The resulting information system allows users to perform new analysis tasks by using the traditional OLAP-based data warehouse operators. We have developed a case study over a set of real opinions about digital devices which are offered by a wholesale dealer. Over this case study, the quality of the extracted sentiment data is evaluated, and some query examples that illustrate the potential uses of the integrated model are provided.     

Porous Porphyrin‐Based Organosilica Nanoparticles for NIR Two‐Photon Photodynamic Therapy and Gene Delivery in Zebrafish

Periodic mesoporous organosilica nanoparticles emerge as promising vectors for nanomedicine applications. Their properties are very different from those of well‐known mesoporous silica nanoparticles as there is no silica source for their synthesis. So far, they have only been synthesized from small bis‐silylated organic precursors. However, no studies employing large stimuli‐responsive precursors have been reported on such hybrid systems yet. Here, the synthesis of porphyrin‐based organosilica nanoparticles from a large octasilylated metalated porphyrin precursor is described for applications in near‐infrared two‐photon‐triggered spatiotemporal theranostics. The nanoparticles display unique interconnected large cavities of 10–80 nm. The framework of the nanoparticles is constituted with J‐aggregates of porphyrins, which endows them with two‐photon sensitivity. The nanoparticle efficiency for intracellular tracking is first demonstrated by the in vitro near‐infrared imaging of breast cancer cells. After functionalization of the nanoparticles with aminopropyltriethoxysilane, two‐photon‐excited photodynamic therapy in zebrafish is successfully achieved. Two‐photon photochemical internalization in cancer cells of the nanoparticles loaded with siRNA is also performed for the first time. Furthermore, siRNA targeting green fluorescent protein complexed with the nanoparticles is delivered in vivo in zebrafish embryos, which demonstrates the versatility of the nanovectors for biomedical applications.     

Modeling optimizes PEM fuel cell performance using three-dimensional multi-phase computational fluid dynamics model

This paper presents the results of an optimization study using a comprehensive three-dimensional, multi-phase, non-isothermal model of a PEM fuel cell that incorporates the significant physical processes and the key parameters affecting fuel cell performance. The model accounts for both the gas and liquid phase in the same computational domain and, thus, allows for the implementation of phase change inside the gas diffusion layers. The model includes the transport of gaseous species, liquid water, protons, energy and water dissolved in the ion conducting polymer. Water is assumed to be exchanged among three phases; liquid, vapor and dissolved, and equilibrium among these phases is assumed. The model features an algorithm that allows a more realistic representation of the local activation overpotentials, which leads to improved prediction of the local current density distribution. This model also takes into account convection and diffusion of different species in the channels as well as in the porous gas diffusion layer, heat transfer in the solids as well as in the gases and electrochemical reactions. The results showed that the present multi-phase model is capable of identifying important parameters for the wetting behavior of the gas diffusion layers and can be used to identify conditions that might lead to the onset of pore plugging, which has a detrimental effect on the fuel cell performance. This model is used to study the effects of several operating, design and material parameters on fuel cell performance. Detailed analyses of the fuel cell performance under various operating conditions have been conducted and examined.     

Lattice Transformation from 2D to Quasi 1D and Phonon Properties of Exfoliated ZrS2 and ZrSe2

Recent reports on thermal and thermoelectric properties of emerging 2D materials have shown promising results. Among these materials are Zirconium-based chalcogenides such as zirconium disulfide (ZrS₂), zirconium diselenide (ZrSe₂), zirconium trisulfide (ZrS₃), and zirconium triselenide (ZrSe₃). Here, the thermal properties of these materials are investigated using confocal Raman spectroscopy. Two different and distinctive Raman signatures of exfoliated ZrX₂ (where X = S or Se) are observed. For 2D-ZrX₂, Raman modes are in alignment with those reported in literature. However, for quasi 1D-ZrX₂, Raman modes are identical to exfoliated ZrX₃ nanosheets, indicating a major lattice transformation from 2D to quasi-1D. Raman temperature dependence for ZrX₂ are also measured. Most Raman modes exhibit a linear downshift dependence with increasing temperature. However, for 2D-ZrS₂, a blueshift for A1g mode is detected with increasing temperature. Finally, phonon dynamics under optical heating for ZrX₂ are measured. Based on these measurements, the calculated thermal conductivity and the interfacial thermal conductance indicate lower interfacial thermal conductance for quasi 1D-ZrX₂ compared to 2D-ZrX₂, which can be attributed to the phonon confinement in 1D. The results demonstrate exceptional thermal properties for Zirconium-based materials, making them ideal for thermoelectric device applications and future thermal management strategies.