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We develop a comprehensive framework for storing, analyzing, forecasting, and visualizing industrial energy systems consisting of multiple devices and sensors. Our framework models complex energy systems as a dynamic knowledge graph, utilizes a novel machine learning (ML) model for energy forecasting, and visualizes continuous predictions through an interactive dashboard. At the core of this framework is A-RNN, a simple yet efficient model that uses dynamic attention mechanisms for automated feature selection. We validate the model using datasets from two manufacturers and one university testbed containing hundreds of sensors. Our results show that A-RNN forecasts energy usage within 5% of observed values. These enhanced predictions are as much as 50% more accurate than those produced by standard RNN models that rely on individual features and devices. Additionally, A-RNN identifies key features that impact forecasting accuracy, providing interpretability for model forecasts. Our analytics platform is computationally and memory efficient, making it suitable for deployment on edge devices and in manufacturing plants. 

Gilded hollow-core fibers, embedded with gold nanoparticles, offer a unique combination of efficient optical transmission and surface heating, enabling applications in light-driven catalysis and improved laser-ignition in internal combustion engines. 

The structural control of the monofilament fiber cross‐sectional architecture is a well‐established method for imparting its active functionality. Resulting from a thermal draw, the fiber device, until recently, is expected to be a cross‐sectionally scaled‐down and axially scaled‐up replica of its preform. However, thermal draw is a melt‐shaping process in which the preform is heated to a viscous liquid to be scaled into a fiber. Thus, it is prone to capillary instabilities on the interfaces between preform cladding and materials it encapsulates, distorting the fiber‐embedded architecture and complicating preform‐to‐fiber geometry translation. Traditionally, capillary instabilities are suppressed by performing the draw at a high‐viscosity, large‐feature‐size regime, such that the scaling of the preform into the fiber happens faster than a pronounced instability can develop. It is discovered recently that highly nonlinear, at times even chaotic capillary instabilities, in some fluid dynamic regimes, become predictable and thus engineerable. Driven by ever‐growing demand for enhancing the fiber‐device functionality, piggybacking on a capillary instability, instead of suppressing it, establishes itself as a new material processing strategy to achieve fiber‐embedded systems with user‐engineered architecture in all 3D, including the axial. Considering this development, the notable emerging methodologies are cross‐compared for designing 3D fiber‐embedded architectures. 

Abstract Capillary breakup of cores is an exclusive approach to fabricating fiber-integrated optoelectronics and photonics. A physical understanding of this fluid-dynamic process is necessary for yielding the desired solid-state fiber-embedded multimaterial architectures by design rather than by exploratory search. We discover that the nonlinearly complex and, at times, even chaotic capillary breakup of multimaterial fiber cores becomes predictable when the fiber is exposed to the spatiotemporal temperature profile, imposing a viscosity modulation comparable to the breakup wavelength. The profile acts as a notch filter, allowing only a single wavelength out of the continuous spectrum to develop predictably, following Euler-Lagrange dynamics. We argue that this understanding not only enables designing the outcomes of the breakup necessary for turning it into a technology for materializing fiber-embedded functional systems but also positions a multimaterial fiber as a universal physical simulator of capillary instability in viscous threads. 

Abstract Antibiotic‐resistant infections caused by bacterial pathogens pose a serious threat to public health, hampering wound healing and causing significant morbidities worldwide. A biomedical fiber‐device that functions as a drugless antiseptic is introduced as a solution to this problem. Through stitching, piercing, or topical application to the wound, this fiber slows down the proliferation of pathogenic bacteria, thereby reducing the risks associated with inflammation and inhibiting infections. The fiber's bacterial proliferation inhibition function is based on the galvanic effect, which disturbs bacterial quorum sensing. Detailed herein are the fiber design optimization, scalable fabrication approach, electrical function characterization, and antiseptic function verification in cultures of typical wound pathogens. Such a fiber—mechanically and environmentally resilient, insensitive to harsh storage conditions with nominally infinite shelf‐life, resulting from machining rather than pharmacochemical fabrication— provides a cost‐effective and widely available alternative to current antibiotic treatments of physical injury.