Performance analysis of cache-enabled millimeter wave small cell networks

CCBY Millimeter wave (mmWave) small-cell networks can provide high regional throughput, but the backhaul requirement has become a performance bottleneck. This paper proposes a hybrid system that combines traditional backhaul-connected small base stations (SBSs) and cache-enabled SBSs to achieve the maximum area spectral efficiency (ASE) while saving backhaul consumption in mmWave small cell networks. We derive and compare the ASE results for both the traditional and hybrid networks, and also show that the optimal content placement to maximize ASE is to cache the most popular contents. Numerical results demonstrate the performance improvement of deploying cache-enabled SBSs. Furthermore, given a total caching capacity, it is revealed that there is a tradeoff between the cache-enabled SBSs density and individual cache size to maximize the ASE

A new look at physical layer security, caching, and wireless energy harvesting for heterogeneous ultra-dense networks

Heterogeneous ultra-dense networks enable ultra-high data rates and ultra-low latency through the use of dense sub-6 GHz and millimeter-wave small cells with different antenna configurations. Existing work has widely studied spectral and energy efficiency in such networks and shown that high spectral and energy efficiency can be achieved. This article investigates the benefits of heterogeneous ultra-dense network architecture from the perspectives of three promising technologies, physical layer security, caching, and wireless energy harvesting, and provides an enthusiastic outlook toward application of these technologies in heterogeneous ultra-dense networks. Based on the rationale of each technology, opportunities and challenges are identified to advance the research in this emerging network

Western Blotting Using Microchip Electrophoresis Interfaced to a Protein Capture Membrane

Western blotting is a commonly used assay for proteins. Despite the utility of the method, it is also characterized by long analysis times, manual operation, and lack of established miniaturized counterpart. We report a new way to Western blot that addresses these limitations. In the method, sodium dodecyl sulfate (SDS)-protein complexes are separated by sieving electrophoresis in a microfluidic device or chip. The chip is interfaced to a moving membrane so that proteins are captured in discrete zones as they migrate from the chip. Separations of SDS-protein complexes in the molecular weight range of 11–155 kDa were completed in 2 min with 4 × 10⁴ theoretical plates at 460 V/cm. Migration time and peak area relative standard deviations were 3–6% and 0.2%, respectively. Detection limit for actin was 0.7 nM. Assays for actin, AMP-kinase, carbonic anhydrase, and lysozyme are shown to demonstrate versatility of the method. Total analysis time including immunoassay was 22–32 min for a single sample. Because processing membrane for immunoassay is the slow step of the assay, sequential injections from different reservoirs on the chip and capture in different tracks on the same membrane allow increased throughput. As a demonstration, 9 injections were collected on one membrane and analyzed in 43 min (∼5 min/sample). Further improvements in throughput are possible with more reservoirs or parallel channels

Tuning of Thermally Induced Sol-to-Gel Transitions of Moderately Concentrated Aqueous Solutions of Doubly Thermosensitive Hydrophilic Diblock Copolymers Poly(methoxytri(ethylene glycol) acrylate)-<i>b</i>-poly(ethoxydi(ethylene glycol) acrylate-<i>co</i>-acrylic acid)

We report in this article a method to tune the sol-to-gel transitions of moderately concentrated aqueous solutions of doubly thermosensitive hydrophilic diblock copolymers that consist of two blocks exhibiting distinct lower critical solution temperatures (LCSTs) in water. A small amount of weak acid groups is statistically incorporated into the lower LCST block so that its LCST can be tuned by varying solution pH. Well-defined diblock copolymers, poly­(methoxytri­(ethylene glycol) acrylate)-<i>b</i>-poly­(ethoxydi­(ethylene glycol) acrylate-<i>co</i>-acrylic acid) (PTEGMA-<i>b</i>-P­(DEGEA-<i>co</i>-AA)), were prepared by reversible addition–fragmentation chain transfer polymerization and postpolymerization modification. PTEGMA and PDEGEA are thermosensitive water-soluble polymers with LCSTs of 58 and 9 °C, respectively, in water. A 25 wt % aqueous solution of PTEGMA-<i>b</i>-P­(DEGEA-<i>co</i>-AA) with a molar ratio of DEGEA to AA units of 100:5.2 at pH = 3.24 underwent multiple phase transitions upon heating, from a clear, free-flowing liquid (<15 °C) to a clear, free-standing gel (15–46 °C) to a clear, free-flowing hot liquid (47–56 °C), and a cloudy mixture (≥57 °C). With the increase of pH, the sol-to-gel transition temperature (<i>T</i>_sol–gel) shifted to higher values, while the gel-to-sol transition (<i>T</i>_gel–sol) and the clouding temperature (<i>T</i>_clouding) of the sample remained essentially the same. These transitions and the tunability of <i>T</i>_sol–gel originated from the thermosensitive properties of two blocks of the diblock copolymer and the pH dependence of the LCST of P­(DEGEA-<i>co</i>-AA), which were confirmed by dynamic light scattering and differential scanning calorimetry studies. Using the vial inversion test method, we mapped out the C-shaped sol–gel phase diagrams of the diblock copolymer in aqueous buffers in the moderate concentration range at three different pH values (3.24, 5.58, and 5.82, all measured at ∼0 °C). While the upper temperature boundaries overlapped, the lower temperature boundary shifted upward and the critical gelation concentration increased with the increase of pH. The AA content in PTEGMA-<i>b</i>-P­(DEGEA-<i>co</i>-AA) was found to have a significant effect on the pH dependence of <i>T</i>_sol–gel. For PTEGMA-<i>b</i>-P­(DEGEA-<i>co</i>-AA) with a molar ratio of DEGEA to AA units of 100:10, the <i>T</i>_sol–gel of its 25 wt % aqueous solution increased faster with the increase of pH than that of PTEGMA-<i>b</i>-P­(DEGEA-<i>co</i>-AA) with a DEGEA-to-AA molar ratio of 100:5.2

Peptidic Inhibitors for in Vitro Pentamer Formation of Human Papillomavirus Capsid Protein L1

A new 14 peptide, originating essentially from the helix 5 of HPV 16L1, illustrates an IC₅₀ of 19.38 nM for the inhibition of HPV 16 L1 pentamer formation, which is highly efficient for targeting a specific protein segment. In addition, mechanism studies reveal that the length, sequence, and the folding of the peptide are critical factors for its inhibition. Particularly, the peptide shows similar inhibition against the pentamer formation of HPV 58L1, although it is designed specially for HPV 16 L1. This study opens a way for the development of high-efficiency, broad-spectrum inhibitors as a new class of anti-HPV agents, which could be extended to the treatment of other virus types

The interplay between artificial intelligence and fog radio access networks

The interplay between artificial intelligence (AI) and fog radio access networks (F-RANs) is investigated in this work from two perspectives: how F-RANs enable hierarchical AI to be deployed in wireless networks and how AI makes F-RANs smarter to better serve mobile devices. Due to the heterogeneity of processing capability, the cloud, fog, and device layers in F-RANs provide hierarchical intelligence via centralized, distributed, and federated learning. In addition, cross-layer learning is also introduced to further reduce the demand for the memory size of the mobile devices. On the other hand, AI provides F-RANs with technologies and methods to deal with massive data and make smarter decisions. Specifically, machine learning tools such as deep neural networks are introduced for data processing, while reinforcement learning (RL) algorithms are adopted for network optimization and decisions. Then, two examples of AI-based applications in F-RANs, i.e., health monitoring and intelligent transportation systems, are presented, followed by a case study of an RL-based caching application in the presence of spatiooral unknown content popularity to showcase the potential of applying AI to F-RANs

Logistic Regression analysis for the clinical and radiological parameters as predictors of WHO non-grade I meningioma.

<p>Logistic Regression analysis for the clinical and radiological parameters as predictors of WHO non-grade I meningioma.</p

The supplement information from Facile synthesis of hierarchical CNF/SnO₂/Ni nanostructures via self-assembly process as anode materials for lithium ion batteries

The supplement informatio

Bar-graph demonstrating the survival time in group I, II and III in relation to WHO grades of meningioma.

<p>Bar-graph demonstrating the survival time in group I, II and III in relation to WHO grades of meningioma.</p

Summary of the preoperative radiological classification.

<p>*Each “Yes” scores 1 point; the lowest possible score is 0 and the highest is 5.</p><p>^# Group I—score 0–1; group II-score 2–3; group III—score 4–5.</p><p>Summary of the preoperative radiological classification.</p