Chinese Undergraduate Women's Perspectives on Interethnic Dating

Honors (Bachelor's)SociologySociologyUniversity of Michiganhttp://deepblue.lib.umich.edu/bitstream/2027.42/91848/1/juliasw.pd

Case Study: CUP between L1 and L2

This case study project explores the English language learning process of a student named Abby*(name changed for confdentiality), an adult beginning learner studied at Abram Friedman Occupational Center (AFOC). The paper focuses on analyzing under the theoretical concept of Common Underlying Profciency model (CUP), especially the language transfer with the data collected from ten weeks of observation, interview with the case study student and her teacher, the chat with her classmates, and the samples of the student’s work. Data is acquired from the community, school, and classroom, to include all related factor that may influence and reflect the relationship between the student’s first language (L1) - Spanish and her second language (L2) - English. The paper intends to analyze the L1 of the case study student plays a positive role during the process of learning English via three sub-claims. Finally, the paper provides recommendations to support the student to use her L1 to promote English learning comprehensively and end with reflections on the case study process

Treatment for a Full Weathering Rock Dam Foundation

The main dam for the upper reservoir of the Tianhuanping pumped storage power station is a rockfill dam with an asphalt concrete impervious lining on the upstream face constructed on a full weathering rock foundation. In this paper, we present the case study on the treatment for this full weathering rock dam foundation. The treatment includes the partial excavation of the full weathering rock at the main dam foundation, the increase of the transition curvature at the parts where the lining is extended from the upstream face to the reservoir bottom and turned to both the left and the right banks, and the reinforcement for the asphalt concrete impervious lining with a layer of polyester mesh at the parts where the tensile strain of the lining is large. A 3D FEM analysis is carried out for the main dam. The calculated results provide a good basis for the above compound treatment method. So far, this project has operated well for more than three years, illustrating the success of the treatment for the full weathering rock dam foundation

Soft mechanical sensors for wearable and implantable applications

Wearable and implantable sensing of biomechanical signals such as pressure, strain, shear, and vibration can enable a multitude of human-integrated applications, including on-skin monitoring of vital signs, motion tracking, monitoring of internal organ condition, restoration of lost/impaired mechanoreception, among many others. The mechanical conformability of such sensors to the human skin and tissue is critical to enhancing their biocompatibility and sensing accuracy. As such, in the recent decade, significant efforts have been made in the development of soft mechanical sensors. To satisfy the requirements of different wearable and implantable applications, such sensors have been imparted with various additional properties to make them better suited for the varied contexts of human-integrated applications. In this review, focusing on the four major types of soft mechanical sensors for pressure, strain, shear, and vibration, we discussed the recent material and device design innovations for achieving several important properties, including flexibility and stretchability, bioresorbability and biodegradability, self-healing properties, breathability, transparency, wireless communication capabilities, and high-density integration. We then went on to discuss the current research state of the use of such novel soft mechanical sensors in wearable and implantable applications, based on which future research needs were further discussed. This article is categorized under: Diagnostic Tools > Biosensing Diagnostic Tools > Diagnostic Nanodevices Implantable Materials and Surgical Technologies > Nanomaterials and Implants</p

AN EXPERIMENTAL STUDY OF THE EFFECTS OF ELECTRICAL STIMULATION ON STRENGTH AND FLEXIBILITY

Electrical stimulation (ES) in muscles has widely applied in muscle strength training as a training method. It was proven to greatly enhance muscle strength. The purpose of this study was to examine the changes in muscle flexibility in the training of muscle strength with the use of electrical stimulation. The experiment demonstrated that the use of electrical-stimulation in training (EST) could obtain the effects of the improvement in both of muscle strength and flexibility

DetectGPT-SC: Improving Detection of Text Generated by Large Language Models through Self-Consistency with Masked Predictions

General large language models (LLMs) such as ChatGPT have shown remarkable success, but it has also raised concerns among people about the misuse of AI-generated texts. Therefore, an important question is how to detect whether the texts are generated by ChatGPT or by humans. Existing detectors are built on the assumption that there is a distribution gap between human-generated and AI-generated texts. These gaps are typically identified using statistical information or classifiers. In contrast to prior research methods, we find that large language models such as ChatGPT exhibit strong self-consistency in text generation and continuation. Self-consistency capitalizes on the intuition that AI-generated texts can still be reasoned with by large language models using the same logical reasoning when portions of the texts are masked, which differs from human-generated texts. Using this observation, we subsequently proposed a new method for AI-generated texts detection based on self-consistency with masked predictions to determine whether a text is generated by LLMs. This method, which we call DetectGPT-SC. We conducted a series of experiments to evaluate the performance of DetectGPT-SC. In these experiments, we employed various mask scheme, zero-shot, and simple prompt for completing masked texts and self-consistency predictions. The results indicate that DetectGPT-SC outperforms the current state-of-the-art across different tasks.Comment: 7 pages, 3 figure

A Robust and Constrained Multi-Agent Reinforcement Learning Framework for Electric Vehicle AMoD Systems

Electric vehicles (EVs) play critical roles in autonomous mobility-on-demand (AMoD) systems, but their unique charging patterns increase the model uncertainties in AMoD systems (e.g. state transition probability). Since there usually exists a mismatch between the training and test (true) environments, incorporating model uncertainty into system design is of critical importance in real-world applications. However, model uncertainties have not been considered explicitly in EV AMoD system rebalancing by existing literature yet and remain an urgent and challenging task. In this work, we design a robust and constrained multi-agent reinforcement learning (MARL) framework with transition kernel uncertainty for the EV rebalancing and charging problem. We then propose a robust and constrained MARL algorithm (ROCOMA) that trains a robust EV rebalancing policy to balance the supply-demand ratio and the charging utilization rate across the whole city under state transition uncertainty. Experiments show that the ROCOMA can learn an effective and robust rebalancing policy. It outperforms non-robust MARL methods when there are model uncertainties. It increases the system fairness by 19.6% and decreases the rebalancing costs by 75.8%.Comment: 8 page