Purposeful remixing with generative AI: Constructing designer voice in multimodal composing

Voice, the discursive construction of the writer's identity, has been extensively studied and theorized in composition studies. In multimodal writing, students are able to mobilize both linguistic and non linguistic resources to express their real or imagined identities. But at the same time, when students are limited to choose from available online resources, their voices might be compromised due to the incompatibility between their authorial intentions and the existing materials. This study, therefore, investigates whether the use of generative AI tools could help student authors construct a more consistent voice in multimodal writing. In this study, we have designed a photo essay assignment where students recount a story in the form of photo essays and prompt AI image generating tools to create photos for their storytelling. Drawing on interview data, written reflection, written annotation, and multimodal products from seven focal participants, we have identified two remixing practices, through which students attempted to establish a coherent and unique voice in writing. The study sheds light on the intentional and discursive nature of multimodal writing with AI as afforded by the technological flexibility, while also highlighting the practical and ethical challenges that could be attributed to students insufficient prompt and multimodal literacy and the innate limitations of AI systems. This study provides important implications for incorporating AI tools in designing multimodal writing tasks

Development of a Fatigue Life Assessment Model for Pairing Fatigue Damage Prognoses with Bridge Management Systems

Fatigue damage is one of the primary safety concerns for steel bridges reaching the end of their design life. Currently, US federal requirements mandate regular inspection of steel bridges for fatigue cracks; however, these inspections rely on visual inspection, which is subjective to the inspector’s physically inherent limitations. Structural health monitoring (SHM) can be implemented on bridges to collect data between inspection intervals and gather supplementary information on the bridges’ response to loads. Combining SHM with finite element analyses, this paper integrates two analysis methods to assess fatigue damage in the crack initiation and crack propagation periods of fatigue life. The crack initiation period is evaluated using S-N curves, a process that is currently used by the FHWA and AASHTO to assess fatigue damage. The crack propagation period is evaluated with linear elastic fracture mechanic-based finite element models, which have been widely used to predict steady-state crack growth behavior. Ultimately, the presented approach will determine the fatigue damage prognoses of steel bridge elements and damage prognoses are integrated with current condition state classifications used in bridge management systems. A case study is presented to demonstrate how this approach can be used to assess fatigue damage on an existing steel bridge

Improvement of Printing Quality for Laser-induced Forward Transfer based Laser-Assisted Bioprinting Process using a CFD-based numerical model

As one of the three-dimensional (3D) bioprinting techniques with great application potential, laser-induced-forward-transfer (LIFT) based laser assisted bioprinting (LAB) transfers the bioink through a developed jet flow, and the printing quality highly depends on the stability of jet flow regime. To understand the connection between the jet flow and printing outcomes, a Computational Fluid Dynamic (CFD) model was developed for the first time to accurately describe the jet flow regime and provide a guidance for optimal printing process planning. By adopting the printing parameters recommended by the CFD model, the printing quality was greatly improved by forming stable jet regime and organized printing patterns on the substrate, and the size of printed droplet can also be accurately predicted through a static equilibrium model. The ultimate goal of this research is to direct the LIFT-based LAB process and eventually improve the quality of bioprinting

A State-of-the-Art Review of Laser-Assisted Bioprinting and its Future Research Trends

Bioprinting is an additive manufacturing technology with great potential in medical applications. Among available bioprinting techniques, laser-assisted bioprinting (LAB) is a promising technique due to its high resolution, high cell viability, and the capability to deposit high-viscousity bioink. These characteristics allow the LAB technology to control cells precisely to reconstruct living organs. Recent developments of LAB technologies are reviewed in this paper, covering various designs of LAB printers, research progresses in energy-absorbing layer (EAL), the physical phenomenon that triggers the printing process in terms of bubble formation and jet development, printing process parameters, and major factors related to the post-printing cell viability. The latest studies on LAB technologies are highlighted, expounding their advantages and disadvantages, and some potential applications are presented. The potential technical challenges and future research trends for LAB technologies are also discussed

A DC fault current fast-computing method of MMC-HVDC grid with short circuit protection equipment

The multi-terminal modular multi-level converter-based high voltage direct current (MMC-HVDC) grid with short circuit protection equipment (SCPE) is so complex that it is difficult to estimate its fault current and analyze the performance of SCPE by conventional time-domain numerical calculation method, it meets three big obstacles. This paper has made significant progress in overcoming these obstacles. 1). By applying the modern electrical circuit theory, a systematic formulation of the differential equation set for fault current calculation is developed to avoid a lot of complex and cumbersome matrix manual calculations. 2). A novel Y-Delta transformation in the s-domain is proposed to develop an eliminating virtual node approach for a complex MMC-HVDC grid, including the ring, radial, and hybrid topologies. 3). It is difficult to solve the equivalent circuit of MMC-HVDC grid with SCPE since SCPE is a time-variable-nonlinear circuit. A canonical voltage source model of SCPE is established to transform the time-variable-nonlinear circuit into a piecewise linear circuit. Based on the three significant progresses, a DC fault current fast-computing method of MMC-HVDC grid with SCPE is put forward to deal with all kinds of MMC-HVDC grids with several kinds of SCPEs. Then, the performance of several kinds of SCPE is analyzed and compared by this method. Consequently, the proposed DC fault current fast-computing method is a new powerful tool to estimate the fault current of MMC-HVDC grid and analyze the performance of SCPE

Photonic RF Channelization Based on Microcombs

In recent decades, microwave photonic channelization techniques have developed significantly. Characterized by low loss, high versatility, large instantaneous bandwidth, and immunity to electromagnetic interference, microwave photonic channelization addresses the requirements of modern radar and electronic warfare for receivers. Microresonator-based optical frequency combs are promising devices for photonic channelized receivers, enabling full advantage of multicarriers, large bandwidths, and accelerating the integration process of microwave photonic channelized receivers. In this paper, we review the research progress and trends in microwave photonic channelization, focusing on schemes that utilize integrated microcombs. We discuss the potential of microcomb-based RF channelization, as well as their challenges and limitations, and provide perspectives for their future development in the context of on-chip silicon-based photonics.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Pressure drop characteristics of adjustable slotted distributor in fluidized bed

[EN] In this paper, a fluidized bed with a adjustable slotted gas distributor was used to study fluidization in a 230 mm×200 mm rectangular fluidized bed by adjusting the spacing between the two slotted gas distributors. The pressure drop of the distributor at different inlet gas velocities was obtained and the change law between pressure drop and distance between distributors was summarized. This study provides a theoretical basis for the application of adjustable slotted gas distributor fluidized bed.The authors acknowledge Projects supported by the National Natural Science Foundation of China (Grant No. 31571906 & No.21506163).Tong, Z.; Chaoran, L.; Qing, X.; Zhanyong, L.; W., J. (2018). Pressure drop characteristics of adjustable slotted distributor in fluidized bed. En IDS 2018. 21st International Drying Symposium Proceedings. Editorial Universitat Politècnica de València. 1751-1758. https://doi.org/10.4995/IDS2018.2018.7729OCS1751175

Control-free and efficient integrated photonic neural networks via hardware-aware training and pruning

Integrated photonic neural networks (PNNs) are at the forefront of AI computing, leveraging on light's unique properties, such as large bandwidth, low latency, and potentially low power consumption. Nevertheless, the integrated optical components within PNNs are inherently sensitive to external disturbances and thermal interference, which can detrimentally affect computing accuracy and reliability. Current solutions often use complicated control methods, resulting in high hardware complexity impractical for large-scale PNNs. In response, we propose a novel hardware-aware training and pruning approach. The core idea is to train the parameters of a physical neural network towards its noise-robust and energy-efficient region. This innovation enables control-free and energy-efficient photonic computing. Our method is validated across diverse integrated PNN architectures. Through experimental validation, our approach significantly enhances the computing precision of MRR-based PNN, achieving a notable 4-bit improvement without the need for complex device control mechanisms or energy-intensive temperature stabilization circuits. Specifically, it improves the accuracy of experimental handwritten digit classification from 67.0% to 95.0%, nearing theoretical limits and achieved without a thermoelectric controller. Additionally, this approach reduces the energy by tenfold. We further extend the validation to various architectures, such as PCM-based PNN, demonstrating the broad applicability of our approach across different platforms. This advancement represents a significant step towards the practical, energy-efficient, and noise-resilient implementation of large-scale integrated PNNs.Comment: 21 pages, 6 figure