Most carbon Emission Trading Systems (ETS) rely on a centralized system to manage the transactional tasks, and are vulnerable to security threats. This paper proposes a Blockchain-enabled Distributed ETS (BD-ETS) to improve the security and efficiency of the system. The BD-ETS transforms the centralized Carbon Emissions Permit (CEP) trading mode to a distributed trading system in which the trading mode is based on a smart contract performed in Hyperledger Fabric. In a smart contract, every transaction considers both the offer price and reputation value of the emitting enterprises. The voting power of the emitting enterprise is determined by its reputation value, which stems from their contributions to carbon emission reduction. To achieve consistency of every node in the CEP transactions, we propose a Delegated Proof of Reputation (DPoR) consensus mechanism. Compared to the enhanced Delegated Proof of Stake, the DPoR decreases the attack intention of malicious enterprises and performs better in finding malicious miners faster, thus improving the security of the BD-ETS. A case study and numerical simulations are developed to illustrate how the CEP trading functions, and to validate the DPoR mechanism.
Visual memes have become an important mechanism through which ideologically potent and hateful content spreads on today's social media platforms. At the same time, they are also a mechanism through which we convey much more mundane things, like pictures of cats with strange accents. Little is known, however, about the relative percentage of visual memes shared by real people that fall into these, or other, thematic categories. The present work focuses on visual memes that contain superimposed text. We carry out the first large-scale study on the themes contained in the text of these memes, which we refer to as image-with-text memes. We find that 30% of the image-with-text memes in our sample which have identifiable themes are politically relevant, and that these politically relevant memes are shared more often by Democrats than Republicans. We also find disparities in who expresses themselves via image-with-text memes, and images in general, versus other forms of expression on Twitter. The fact that some individuals use images with text to express themselves, instead of sending a plain text tweet, suggests potential consequences for the representativeness of analyses that ignore text contained in images.
The John H. Chafee Foster Care Program for Successful Transition to Adulthood (CFCIP) allocates funding to provide services to youth who are likely to age out of foster care. These services, covering everything from mentoring to financial aid, are expected to be distributed in ways that prepare youth for life after care. However, surprisingly little is known about which youth receive which services. The present work makes use of the National Youth in Transition Database (NYTD), a large-scale administrative dataset that tracks services allocated to youth that use CFCIP funds. Specifically, we conduct a forensic social science analysis of the NYTD data. To do so, we first use computational methods to help us uncover the most important factors associated with service receipt. Doing so helps us to identify three major factors-youth age, youth time in care, and the state in which a youth is in care-that are most heavily associated with service receive. We then conduct an analysis that links existing theory to these factors, expanding our understanding of how services are allocated and paving the way to future work to understand why such associations exist.
We introduce an analytic pipeline to model and simulate youth trajectories through the New York state foster care system. Our goal in doing so is to forecast how proposed interventions may impact the foster care system's ability to achieve it's stated goals before these interventions are actually implemented and impact the lives of thousands of youth. Here, we focus on two specific stated goals of the system: racial equity, and, as codified most recently by the 2018 Family First Prevention Services Act (FFPSA), a focus on keeping all youth out of foster care. We also focus on one specific potential intervention-a predictive model, proposed in prior work and implemented elsewhere in the U.S., which aims to determine whether or not a youth is in need of care. We use our method to explore how the implementation of this predictive model in New York would impact racial equity and the number of youth in care. While our findings, as in any simulation model, ultimately rely on modeling assumptions, we find evidence that the model would not necessarily achieve either goal. Primarily, then, we aim to further promote the use of data-driven simulation to help understand the ramifications of algorithmic interventions in public systems.CCS Concepts: • Computing methodologies → Model verification and validation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.