EC Analysis of Multi-Antenna System over 5G and Beyond Networks and its Application to IRS-Assisted Wireless Systems

Wireless Personal Communications - Recently, the theory of effective rate has attracted much attention, since it can take the delay aspect into account when performing channel capacity analysis. In...     

Simple Sequence Repeat Markers in Genetic Divergence and Marker-Assisted Selection of Rice Cultivars: A Review

Sequencing of rice genome has facilitated the understanding of rice evolution and has been utilized extensively for mining of DNA markers to facilitate marker-assisted breeding. Simple sequence repeat (SSR) markers that are tandemly repeated nucleotide sequence motifs flanked by unique sequences are presently the maker of choice in rice improvement due to their abundance, co-dominant inheritance, high levels of allelic diversity, and simple reproducible assay. The current level of genome coverage by SSR markers in rice is sufficient to employ them for genotype identification and marker-assisted selection in breeding for mapping of genes and quantitative trait loci analysis. This review provides comprehensive information on the mapping and applications of SSR markers in investigation of rice cultivars to study their genetic divergence and marker-assisted selection of important agronomic traits.     

Physicochemical and Functional Properties of Freeze-Dried and Oven Dried Corn Gluten Meals

The physicochemical and functional properties of convection oven- and freeze-dried gluten meals of two corn varieties were evaluated. The physicochemical properties (water solubility index, water absorption index, Hunter color parameters, and bulk density) and functional properties (water absorption, oil absorption, least gelation concentration, protein solubility, and emulsification properties) of convection-oven and freeze-dried corn gluten meals were compared with each other and soy flour. Freeze-dried corn gluten meals was observed to have lower bulk density (0.244-0.263 kg/m³) and was lighter in color (high L and ΔE) compared to their counterpart convection oven-dried gluten meals. Freeze-dried gluten meals from both corn varieties showed significantly higher oil absorption, water absorption, pH, emulsification, and protein solubility compared to oven-dried corn gluten meals. The gluten meals from both corn varieties had lower water absorption and bulk density but higher oil absorption than soy flour, suggesting the hydrophobic nature of corn proteins. Corn gluten meals formed thin (pourable) emulsions compared to soy flour emulsions, which were thick salad dressing type. Freeze- and convection oven-dried corn gluten meals showed significantly lower protein solubility measured at different pH than soy flour.     

Pathway to health: Cluster-randomized trail to increase fruit and vegetable consumption among smokers in public housing.

Objectives: Examine the effectiveness of an intervention to increase fruits and vegetables (FV) consumption among smokers. Design: Cluster-randomized trial of 20 public housing developments; 10 randomly assigned to an FV intervention and 10 to a smoking cessation intervention. Main outcome measures: Usual (past 7 days) and past 30 days change in daily FV intake at 8 weeks and 6 months postbaseline. Results: Greater increases were seen in the FV group. At Week 8 and Month 6, the FV group had consumed 1.58 (p = .001) and 0.78 (p = .04), respectively, more daily FV servings in the past 7 days than the cessation group. At the same time points, the FV group had consumed 3.61 (p = .01) and 3.93 (p = .01), respectively, more FV servings in the past 30 days than the cessation group. Completing more motivational interviewing sessions (p = .02) and trying more recipes (p = .02) led to significantly greater increases at Month 6 among FV participants. Conclusions: Motivational interviewing counseling and lifestyle modification through trying out healthy recipes may be effective in helping a high-risk population increase their FV intake. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Monolithically integrated laser/rear-facet monitor arrays withV-groove for passive optical fiber alignment

An InGaAsP/InP laser monolithically integrated with a rear facet monitor and a fiber V-groove has been demonstrated for the first time. The integrated device incorporates an etched-facet laser fabricated using an in situ, multistep, reactive ion etch process. The integrated V-groove, which is etched directly into the InP substrate, is designed to enable passive alignment of an optical fiber to the active region of the laser. Passive coupling efficiencies of 18% and 8% have been obtained using cleaved multimode and single mode fibers, respectively. Responsivities of the rear facet monitor were as high as 0.49 A/W     

Study of automatic text summarization approaches in different languages

Artificial Intelligence Review - Nowadays we see huge amount of information is available on both, online and offline sources. For single topic we see hundreds of articles are available, containing...     

Densely connected convolutional networks-based COVID-19 screening model

The extensively utilized tool to detect novel coronavirus (COVID-19) is a real-time polymerase chain reaction (RT-PCR). However, RT-PCR kits are costly and consume critical time, around 6 to 9 hours to classify the subjects as COVID-19(+) or COVID-19(-). Due to the less sensitivity of RT-PCR, it suffers from high false-negative results. To overcome these issues, many deep learning models have been implemented in the literature for the early-stage classification of suspected subjects. To handle the sensitivity issue associated with RT-PCR, chest CT scans are utilized to classify the suspected subjects as COVID-19 (+), tuberculosis, pneumonia, or healthy subjects. The extensive study on chest CT scans of COVID-19 (+) subjects reveals that there are some bilateral changes and unique patterns. But the manual analysis from chest CT scans is a tedious task. Therefore, an automated COVID-19 screening model is implemented by ensembling the deep transfer learning models such as Densely connected convolutional networks (DCCNs), ResNet152V2, and VGG16. Experimental results reveal that the proposed ensemble model outperforms the competitive models in terms of accuracy, f-measure, area under curve, sensitivity, and specificity.