How Much Versus Who: Which Social Norms Information Is More Effective?

We conduct an experiment to investigate how different types of information about social norms affect individuals’ stated contributions to a specific pro-environment program, a student ‘green fee’, in the context of a referendum. Compared to students that receive no information about peer contributions, on average, students that receive information about the dollar value range of contributions at peer institutions contribute less while students that learn about the high percentage of students voting ‘yes’ on green fee programs at peer institutions contribute more. The results are economically significant as the absolute values of both effects represent approximately 25% of average contributions. These results suggest that information about participation rates can be more effective than information about dollar amounts in encouraging contributions to environmental initiatives. Of interest to stated preference researchers, we find that results do not change when controlling for self-selection into survey completion

Evaluating Conformity and Reciprocity in University Alumni Donation

Alumni donation has a significant impact on the function of liberal arts institutions across the U.S. Specific factors relating to alumni donation behavior have been identified in previous research; however few studies systematically utilize existing theories of motivation for voluntary contributions to evaluate the effectiveness of alumni donation factors. This research classifies specific factors into reciprocity and conformity and surveys Ohio Wesleyan University (OWU) alumni about donation attitudes. The logistic regression model and the linear regression model complement each other and provide support for the hypothesis that the more one subjects to conformity, the more likely one tends to donate to OWU. This research also adds to the existing literature in focusing on the relative income and perceived information rather than on absolute information. The research results provide policy suggestions to improve university fundraising strategies

Endogenous Skills and Labor Income Inequality

How much does inequality in life depend on conditions established at age 18? What role does post-18 higher education play? I use an education choice model with exogenous conditions from family wealth, established human capital at age 18 and shocks to human capital to examine these questions. Family wealth and established human capital at age 18 determine the post-18 education choices. Education builds up human capital and reduces future earnings volatility. Absent this transmission channel, previous studies dramatically underestimate the importance of initial family wealth in explaining lifetime earnings inequality. My model finds that family wealth at age 18 explains up to 15% of lifetime earnings inequalities, and human capital at age 18 explains 72%. Policy counterfacutals that encourage college education by providing financial aid reduce inequality and improve welfare

General Equilibrium Evaluation of Temporary Employment

This paper studies the response of firms in an environment with heightened idiosyncratic risk and dual labor markets, a regular market with firing rigidity and a frictionless temporary labor market. I find that firing rigidity induces firms to switch from regular employment to temporary employment, and heightened risks amplify such behavior. Efficiency and welfare loss from friction and risk, though alleviated by a small extent, cannot be fully compensated by having temporary employment. This study also first extends the literature of temporary employment by examining its impact in the U.S. labor market

General Equilibrium Evaluation of Temporary Employment

This paper studies the response of firms in an environment with heightened idiosyncratic risk and dual labor markets, a regular market with firing rigidity and a frictionless temporary labor market. I find that firing rigidity induces firms to switch from regular employment to temporary employment, and heightened risks amplify such behavior. Efficiency and welfare loss from friction and risk, though alleviated by a small extent, cannot be fully compensated by having temporary employment. This study also first extends the literature of temporary employment by examining its impact in the U.S. labor market

Endogenous Skills and Labor Income Inequality

How much does inequality in life depend on conditions established at age 18? What role does post-18 higher education play? I use an education choice model with exogenous conditions from family wealth, established human capital at age 18 and shocks to human capital to examine these questions. Family wealth and established human capital at age 18 determine the post-18 education choices. Education builds up human capital and reduces future earnings volatility. Absent this transmission channel, previous studies dramatically underestimate the importance of initial family wealth in explaining lifetime earnings inequality. My model finds that family wealth at age 18 explains up to 15% of lifetime earnings inequalities, and human capital at age 18 explains 72%. Policy counterfacutals that encourage college education by providing financial aid reduce inequality and improve welfare

Overview of Upgrading of Pyrolysis Oil of Biomass

AbstractPyrolysis oil, obtained from fast pyrolysis of biomass, is a promising renewable energy source which has received widespread interests for its characteristics as combustion fuels used in boiler, engines or gas turbines and resources in chemical industries. However, the pyrolysis oil as a fuel has many unfavourable properties due to its chemical composition, making it corrosive, viscose and thermally instability. Therefore, bio-oil must be properly upgraded to produce high quality biofuel for using as transportation fuels. In this review article, various types of upgrading processes have been discussed in detail including physical refining routes, chemical refining and total pyrolysis refined routes. Finally, a new upgrading route, Physical-Chemical Refining (PCR) is proposed, which will be a very promising refining route of bio-oil

A Survey on Semantic Processing Techniques

Semantic processing is a fundamental research domain in computational linguistics. In the era of powerful pre-trained language models and large language models, the advancement of research in this domain appears to be decelerating. However, the study of semantics is multi-dimensional in linguistics. The research depth and breadth of computational semantic processing can be largely improved with new technologies. In this survey, we analyzed five semantic processing tasks, e.g., word sense disambiguation, anaphora resolution, named entity recognition, concept extraction, and subjectivity detection. We study relevant theoretical research in these fields, advanced methods, and downstream applications. We connect the surveyed tasks with downstream applications because this may inspire future scholars to fuse these low-level semantic processing tasks with high-level natural language processing tasks. The review of theoretical research may also inspire new tasks and technologies in the semantic processing domain. Finally, we compare the different semantic processing techniques and summarize their technical trends, application trends, and future directions.Comment: Published at Information Fusion, Volume 101, 2024, 101988, ISSN 1566-2535. The equal contribution mark is missed in the published version due to the publication policies. Please contact Prof. Erik Cambria for detail

Application of Artificial Intelligence in Drilling and Completion

In this chapter, we will delve into the applications of Artificial Intelligence (AI) in drilling and completion engineering within the oil and gas industry. The scope of this chapter will include the fundamentals of machine learning and deep learning, the essential algorithms, and the workflow of AI in drilling and completion engineering, from data collection to implementation and optimization. Furthermore, we will discuss various AI application areas, such as drilling parameter optimization, downhole environment detection, intelligent completion design, and more. Lastly, we will address the challenges and prospects of AI in drilling and completion engineering, examining issues related to data quality, model accuracy, reliability, and future development trends. This comprehensive exploration aims to provide readers with a solid understanding of the potential and limitations of AI in the drilling and completion engineering domain

A human‐robot collaboration method for uncertain surface scanning

Robots are increasingly expected to replace humans in many repetitive and high‐precision tasks, of which surface scanning is a typical example. However, it is usually difficult for a robot to independently deal with a surface scanning task with uncertainties in, for example the irregular surface shapes and surface properties. Moreover, it usually requires surface modelling with additional sensors, which might be time‐consuming and costly. A human‐robot collaboration‐based approach that allows a human user and a robot to assist each other in scanning uncertain surfaces with uniform properties, such as scanning human skin in ultrasound examination is proposed. In this approach, teleoperation is used to obtain the operator's intent while allowing the operator to operate remotely. After external force perception and friction estimation, the orientation of the robot end‐effector can be autonomously adjusted to keep as perpendicular to the surface as possible. Force control enables the robotic manipulator to maintain a constant contact force with the surface. And hybrid force/motion control ensures that force, position, and pose can be regulated without interfering with each other while reducing the operator's workload. The proposed method is validated using the Elite robot to perform a mock B‐ultrasound scanning experiment