Addressing Wealth Inequality Problem in Blockchain-Enabled Knowledge Community with Reputation-Based Incentive Mechanism

An increasing number of online knowledge communities have started incorporating the cut-edge FinTech, such as the tokenbased incentive mechanism running on blockchain, into their ecosystems. However, the improper design of incentive mechanisms may result in reward monopoly, which has been observed to harm the ecosystems of exiting communities. This study is aimed to ensure that the key factors involved in users’ reward distribution can truly reflect their contributions to the community so as to increase the equity of wealth distribution. It is one of the first to comprehensively balance a user’s historical and current contributions in reward distribution, which has not received sufficient attention from extant research. The simulation analysis demonstrates that the proposed solution of amending the existing incentive mechanism by incorporating a refined reputation indicator significantly increases the equity of rewards distribution and effectively enlarges the cost of achieving reward monopoly

A Roles Approach to Conflict Strategies: Modeling the Effects of Self- and Other-Role Enactment on Conflict Strategies Through Goals and Emotion

This dissertation addresses how, in a conflict situation, individuals enact different roles and how their responses to the other party's role enactment affect the strategies they choose to handle the conflict. A model is proposed to delineate the cognitive and emotional process through which the focal individual and the other party's role enactment affect the focal individual's conflict strategies. The model was first examined using the data based on participants' recall of a past conflict and their answers to questions that assessed behaviors (N = 265). Next, a laboratory experiment was used to test a model in which a conflict was induced and each participant interacted with a confederate to complete a decision making task (N = 261). The focal person's obligation to his or her general role and the other party's expectation violations were manipulated. Participants' embracement of their situated roles, perceived goal importance, emotion, and the use of four types of conflict strategies were measured. Results indicated that obligation predicted the use of relational-protective strategies through the mediating effect of relational goal importance. Embracement of the situated role was found to directly predict the use of a relational-protective confronting strategy but indirectly predict the use of a relational-disruptive confronting strategy through situated goal importance. The other's expectation violation changed the perceived goal importance and the emotion of the focal individual, which predicted the use of relational-disruptive strategies. However, the main reason for the effect of expectation violation on relational-disruptive strategies was individuals' direct reaction to the other's behavior rather than anger. Interpretations and implications of the results, the limitations of the study, theoretical and methodological contributions of the study, and future directions were discussed

The Opaqueness of Structured Bonds: Evidence from the U.S. Insurance Industry

It has been argued that the opaqueness of structured bonds, such as mortgage-backed securities, asset-backed securities and collateral debt obligations, was one of the major causes of the recent financial crisis that started in late 2007. We analyse the evolving nature of information asymmetry inherent in various types of structured bonds by examining the U.S. insurers’ assets. We show that, prior to 2004, structured bonds were not associated with greater information asymmetry; however, holding more multi-class structured bonds, especially privately placed bonds, increased the information asymmetry when evaluating insurers’ assets post-2004. The effect of information asymmetry was more significant with life insurers than with non-life insurers. In addition, by investigating the rating grades of such structured bonds, we find that the market views higher-grade, privately placed, multi-class structured bonds as having the highest information asymmetry among all types of structured bonds post 2004, an effect which is, again, more significant with life insurers. This result shows that structuring complexities and unreliable ratings make structured bonds more opaque than just securitisation itself

Essays on migration

Migration is one of the main forces shaping our society as we know it. Focusing on the determinants of migration and its influence on local communities, this dissertation consists of three chapters. Chapter 1 provides a brief introduction to the thesis, covering the motivation for the research, the methodologies used, and policy implications. Chapter 2 estimates the impact of education on two key outcomes: migration probability and distance. Migration greatly affects the regional economy, and hence, the out-migration of highly educated workers has raised serious concerns for regional development. The OLS estimator indicates a small but positive effect of education on both outcomes, which is similar to other studies. However, using compulsory schooling law changes as an instrumental variable, the 2SLS estimator suggests that education increases migration distance but decreases the probability to migrate. To guide the analysis, this paper expands the basic migration model to include distance as another element in people's decisions. The intuition is that by searching broader distances, people could obtain higher expected incomes, but must also pay higher costs. The overall effect of education on migration is determined by the trade-off between the cost and benefits of migrating longer distances. Chapter 3 estimates the influence of immigration on local housing prices. Housing price is crucial to people's well-being, as it not only affects their living conditions, but also affects homeowners' investment values. Both the OLS and 2SLS results suggest that on average immigration has a slight positive effect on housing prices. However, if we use quantile regression, we observe quite significant but heterogeneous effects of different neighborhoods. For census tracts with expensive housing, immigrants increase housing prices. For census tracts with cheap housing, immigrants reduce housing prices. Lower housing prices make housing more affordable for tenants, but reduces homeowners' total wealth. We also look at possible sources of heterogeneity from both the supply side and demand side. In poor neighborhoods, for example, immigrants might drive natives to neighborhoods with better amenities while increasing housing supply in the area, hence reducing housing prices

Pharmacokinetics of mequindox after intravenous and intramuscular administration to goat

Pharmacokinetics and bioavailability of mequindox were determined after single intravenous (i.v.) or intramuscular (i.m.) administrations of 7 mg/kg body weight (b.w.) to 10 healthy adult goats. Plasma mequindox concentrations were measured by high performance liquid chromatography. Pharmacokinetics were best described by a two-compartment open model and an one-compartment open model for i.v. and i.m. groups, respectively. The elimination half-life and volume of distribution after i.v. and i.m. administrations were statistically different (t1/2β, 1.8 to 1.5 h, P < 0.05 and Vd, 0.35 to 0.45 L·kg-1, P < 0.05, respectively). Mequindox was rapidly (t1/2a, 0.28 h) and almost completely absorbed (F, 99.8%) after i.m. administration. In conclusion, 2~3 times daily i.v. and i.m. administration of mequindox (7 mg/kg b.w.) in goats may be useful in treatment of infectious diseases caused by sensitive pathogens. The plasma disposition kinetics of mequindox in goats is reported for the first time.Key words: Mequindox, pharmacokinetics, high performance liquid chromatography (HPLC), goats

UKnow: A Unified Knowledge Protocol for Common-Sense Reasoning and Vision-Language Pre-training

This work presents a unified knowledge protocol, called UKnow, which facilitates knowledge-based studies from the perspective of data. Particularly focusing on visual and linguistic modalities, we categorize data knowledge into five unit types, namely, in-image, in-text, cross-image, cross-text, and image-text. Following this protocol, we collect, from public international news, a large-scale multimodal knowledge graph dataset that consists of 1,388,568 nodes (with 571,791 vision-related ones) and 3,673,817 triplets. The dataset is also annotated with rich event tags, including 96 coarse labels and 9,185 fine labels, expanding its potential usage. To further verify that UKnow can serve as a standard protocol, we set up an efficient pipeline to help reorganize existing datasets under UKnow format. Finally, we benchmark the performance of some widely-used baselines on the tasks of common-sense reasoning and vision-language pre-training. Results on both our new dataset and the reformatted public datasets demonstrate the effectiveness of UKnow in knowledge organization and method evaluation. Code, dataset, conversion tool, and baseline models will be made public

The Emerging of Hydrovoltaic Materials as a Future Technology: A Case Study for China

Water contains tremendous energy in various forms, but very little of this energy has yet been harvested. Nanostructured materials can generate electricity by water-nanomaterial interaction, a phenomenon referred to as hydrovoltaic effect, which potentially extends the technical capability of water energy harvesting. In this chapter, starting by describing the fundamental principle of hydrovoltaic effect, including water-carbon interactions and fundamental mechanisms of harvesting water energy with nanostructured materials, experimental advances in generating electricity from water flows, waves, natural evaporation, and moisture are then reviewed. We further discuss potential applications of hydrovoltaic technologies, analyze main challenges in improving the energy conversion efficiency and scaling up the output power, and suggest prospects for developments of the emerging technology, especially in China

Logic Diffusion for Knowledge Graph Reasoning

Most recent works focus on answering first order logical queries to explore the knowledge graph reasoning via multi-hop logic predictions. However, existing reasoning models are limited by the circumscribed logical paradigms of training samples, which leads to a weak generalization of unseen logic. To address these issues, we propose a plug-in module called Logic Diffusion (LoD) to discover unseen queries from surroundings and achieves dynamical equilibrium between different kinds of patterns. The basic idea of LoD is relation diffusion and sampling sub-logic by random walking as well as a special training mechanism called gradient adaption. Besides, LoD is accompanied by a novel loss function to further achieve the robust logical diffusion when facing noisy data in training or testing sets. Extensive experiments on four public datasets demonstrate the superiority of mainstream knowledge graph reasoning models with LoD over state-of-the-art. Moreover, our ablation study proves the general effectiveness of LoD on the noise-rich knowledge graph.Comment: 10 pages, 6 figure