Loading system mechanism for dielectric elastomer generators with equi-biaxial state of deformation

Dielectric Elastomer Generators (DEGs) are devices that employ a cyclically variable membrane capacitor to produce electricity from oscillating sources of mechanical energy. Capacitance variation is obtained thanks to the use of dielectric and conductive layers that can undergo different states of deformation including: uniform or non-uniform and uni- or multi-axial stretching. Among them, uniform equi-biaxial stretching is reputed as being the most effective state of deformation that maximizes the amount of energy that can be extracted in a cycle by a unit volume of Dielectric Elastomer (DE) material. This paper presents a DEG concept, with linear input motion and tunable impedance, that is based on a mechanical loading system for inducing uniform equi-biaxial states of deformation. The presented system employs two circular DE membrane capacitors that are arranged in an agonist-antagonist configuration. An analytical model of the overall system is developed and used to find the optimal design parameters that make it possible to tune the elastic response of the generator over the range of motion of interest. An apparatus is developed for the equi-biaxial testing of DE membranes and used for the experimental verification of the employed numerical models

Active Keyword Selection to Track Evolving Topics on Twitter

How can we study social interactions on evolving topics at a mass scale? Over the past decade, researchers from diverse fields such as economics, political science, and public health have often done this by querying Twitter's public API endpoints with hand-picked topical keywords to search or stream discussions. However, despite the API's accessibility, it remains difficult to select and update keywords to collect high-quality data relevant to topics of interest. In this paper, we propose an active learning method for rapidly refining query keywords to increase both the yielded topic relevance and dataset size. We leverage a large open-source COVID-19 Twitter dataset to illustrate the applicability of our method in tracking Tweets around the key sub-topics of Vaccine, Mask, and Lockdown. Our experiments show that our method achieves an average topic-related keyword recall 2x higher than baselines. We open-source our code along with a web interface for keyword selection to make data collection from Twitter more systematic for researchers.Comment: 10 pages, 3 figure

A Note on the Unconditional Bias of the Nadaraya-Watson Regression Estimator

In this note we investigate the order of the unconditional bias of the Nadarya-Watson (Nadaraya, 1964; Watson, 1964) estimator for a multivariate regression. Surprisingly, previous attempts in establishing this result are either imprecise and technically deficient, or of limited use given the assumptions imposed (see inter alia, Glad (1998), Mack and Müller (1988), Pagan and Ullah (1999), and Scott (2015)). The results are also often conflicting (see inter alia, Choi et al. (2000), Chu and Marron (1991), Collomb (1981), and Glad (1998)). Unfortunately, our result here is incomplete, but we highlight the issues and suggest further ideas to resolve them

Stochastic Water Balance Dynamics of Passive and Controlled Stormwater Basins

Urbanization and changing rainfall intensities affect the performance of urban stormwater infrastructure, creating the necessity to design resilient stormwater systems. One proposed method to increase the resilience of stormwater infrastructure is the active control of system flows. To improve the understanding of actively-controlled urban water infrastructure function under variable hydro-climate, we develop a stochastic water balance model for stormwater retention and detention basins with both passive and actively-controlled outflow structures. Under active outflow control, the outflow valve is closed until the water level in the basin reaches a specified maximum at which point the valve opens and the basin empties. Using the stochastic water balance model, we develop analytical expressions for the steady-state probability density functions (PDFs) of water level and valve closure time, as well as the joint PDF of water level and valve closure time. These PDFs then are used to define water level and flow duration curves that provide a probabilistic description of the full range of basin performance. The model accurately predicts the water level PDF estimated from data collected at a retention basin with a passive outflow structure. The model provides a basis for evaluating how changes in the rainfall-runoff process, affected by land use and climate change, will impact the variability of stormwater basin water storage and pollutant removal function. We find that this variability can be managed through the adaptive updating of the active control rule for the outflow structure

Towards Reliable Misinformation Mitigation: Generalization, Uncertainty, and GPT-4

Misinformation poses a critical societal challenge, and current approaches have yet to produce an effective solution. We propose focusing on generalization, soft classification, and leveraging recent large language models to create more practical tools in contexts where perfect predictions remain unattainable. We begin by demonstrating that GPT-4 and other language models can outperform existing methods in the literature. Next, we explore their generalization, revealing that GPT-4 and RoBERTa-large exhibit critical differences in failure modes, which offer potential for significant performance improvements. Finally, we show that these models can be employed in soft classification frameworks to better quantify uncertainty. We find that models with inferior hard classification results can achieve superior soft classification performance. Overall, this research lays groundwork for future tools that can drive real-world progress on misinformation

Open, Closed, or Small Language Models for Text Classification?

Recent advancements in large language models have demonstrated remarkable capabilities across various NLP tasks. But many questions remain, including whether open-source models match closed ones, why these models excel or struggle with certain tasks, and what types of practical procedures can improve performance. We address these questions in the context of classification by evaluating three classes of models using eight datasets across three distinct tasks: named entity recognition, political party prediction, and misinformation detection. While larger LLMs often lead to improved performance, open-source models can rival their closed-source counterparts by fine-tuning. Moreover, supervised smaller models, like RoBERTa, can achieve similar or even greater performance in many datasets compared to generative LLMs. On the other hand, closed models maintain an advantage in hard tasks that demand the most generalizability. This study underscores the importance of model selection based on task requirementsComment: 14 pages, 15 Tables, 1 Figur

The Rayleigh-Lamb wave propagation in dielectric elastomer layers subjected to large deformations

The propagation of waves in soft dielectric elastomer layers is investigated. To this end incremental motions superimposed on homogeneous finite deformations induced by bias electric fields and pre-stretch are determined. First we examine the case of mechanically traction-free layer, which is an extension of the Rayleigh-Lamb problem in the purely elastic case. Two other loading configurations are accounted for too. Subsequently, numerical examples for the dispersion relations are evaluated for a dielectric solid governed by an augmented neo-Hookean strain energy. It is found that the the phase speeds and frequencies strongly depend on the electric excitation and pre-stretch. These findings lend themselves at the possibility of controlling the propagation velocity as well as filtering particular frequencies with suitable choices of the electric bias field