Investigations of wavelength division multiplexing-orthogonal frequency division multiplexing (WDM-OFDM) system with 50 Gb/s optical access

In the present work, orthogonal frequency division multiplexing (OFDM) is combined with wavelength division multiplexing-passive optical networks (WDMPONs) to obtain the combined benefits of both technologies for overall system improvement. performance of five OFDM signals using WDM is analyzed. An optical system is designed using the OFDM signal, and its performance is analyzed using various matrices like bit error rate (BER) and Q-factor under the effect of varying laser power. Mapping of OFDM signals is done using 16-QAM modulation. The system shows an enhancement in the performance by increasing the laser power but up to some limit, and efficient results are obtained in the 70–130 mW laser power range.

Using Data to Support Networks’ Theory of Change

Funders, communities, and network leaders all recognize the value of data. This chapter argues that effective data-use practices should be used to support the theory of change that networks employ. It prescribes program evaluation for project-based social impact and continuous quality improvement for learning-based models. It suggests public opinion polling and power mapping for policy-based mechanisms. For catalyst-based social impact, it advocates for gathering implementation fidelity data. Finally, it suggests using data to identify gaps in service and leaky pipelines for systems alignment-based mechanisms. It also includes instructions for using data for network management and community empowerment. The chapter uses examples of best practices from case studies. It also includes a tool for assessing how project-based networks are using data and instructions for using pivot tables for systems alignment.

Machine learning deployment for arms dynamics pattern recognition in Southeast Asia region

<p>Finding the most significant determinant variable of arms dynamic is highly required due to strategic policies formulations and power mapping for academics and policy makers. Machine learning is still new or underdiscussed among the study of politics and international relations. Existing literature have much focus on using advanced quantitative methods by applying various types of regression analysis. This study analyzed the arms dynamic in Southeast Asia countries along with its some strategic partners such as United States, China, Russia, South Korea, and Japan by using ‘Decision Tree’ of machine learning algorithm. This study conducted a machine learning analysis on 55 variable items which is classified into 8 classes of variables videlicet defense budget, arms trade exports, arms trade imports, political posture, economic posture, security posture and defense priority, national capability, and direct contact,. The results suggest three findings: (1) state who perceives maritime as strategic drivers and forces will seek more power for its maritime defense posture which is translated to defense budget, (2) big size countries tend to be an arms exporter country, and (3) state’s energy dependence often leads to a higher volume of arms transfers between countries.</p>

Cost-Effective Mapping of Genetic Interactions in Mammalian Cells

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2,359 interactions (FDR &lt; 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲$50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

Cost-effective mapping of genetic interactions in mammalian cells

Comprehensive maps of genetic interactions in mammalian cells are daunting to construct because of the large number of potential interactions, ~ 2 × 108 for protein coding genes. We previously used co-inheritance of distant genes from published radiation hybrid (RH) datasets to identify genetic interactions. However, it was necessary to combine six legacy datasets from four species to obtain adequate statistical power. Mapping resolution was also limited by the low density PCR genotyping. Here, we employ shallow sequencing of nascent human RH clones as an economical approach to constructing interaction maps. In this initial study, 15 clones were analyzed, enabling construction of a network with 225 genes and 2359 interactions (FDR < 0.05). Despite its small size, the network showed significant overlap with the previous RH network and with a protein-protein interaction network. Consumables were ≲ $50 per clone, showing that affordable, high quality genetic interaction maps are feasible in mammalian cells.

How partners mediate platform power: Mapping business and data partnerships in the social media ecosystem

Social media platforms’ digital advertising revenues depend considerably on partnerships. Business partnerships are endemic and essential to the business of platforms, yet their role remains relatively underexplored in the literature on platformisation and platform power. This article considers the significance of partnerships in the social media ecosystem to better understand how industry platforms, and the infrastructure they build, mediate and shape platform power and governance. We argue that partners contribute to ‘platformisation’ through their collective development of business-to-business platform infrastructures. Specifically, we examine how partners have integrated social media platforms with what we call the audience economy – an exceptionally complex global and interconnected marketplace of intermediaries involved in the creation, commodification, analysis, and circulation of data audiences for purposes including but not limited to digital advertising and marketing. We determined which relationships are involved, which are exclusive or shared, and identified key ecosystem partners. Further, we found that partners build and integrate extensive infrastructures for data-sourcing and media distribution, surfacing infrastructural and strategic sources and locations, or ‘nodes’, of power in this ecosystem. The empirical findings thus highlight the significance of partnerships and partner integrations and draw attention to the powerful industry players and intermediaries that remain largely invisible.