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ABSTRACT This study utilizes linear elastic fracture mechanics to assess the fatigue criticality of volumetric defects in notched specimens with varying geometries. Contrasting to the existing literature, this study assesses the fatigue criticality of defects, prior to fracture, via a non‐destructive inspection technique, that is, X‐ray computed tomography (XCT). Treating volumetric defects as cracks, based on Murakami's definition, the approach calculates their Mode‐I stress intensity factor (SIF) with their local stresses obtained via linear elastic finite element analysis and utilizes the SIF to represent their criticality. For validation, cylindrical and flat specimens with notch root radii of 5 and 50 mm of AlSi10Mg and 17‐4 precipitation hardened stainless steel were fabricated, XCT scanned, and tested under fatigue loading. All crack initiating defects, observed from fractography, fell within the 99.3 percentile of the defects with the highest stress intensity factor in the respective specimens. 

A highly air- and water-stable Fe(ii) complex with a fluorinated ligand has a strong¹⁹F MRI signal but is a poorT₁-weighted¹H MRI contrast agent. Upon oxidation by H₂O₂, the¹⁹F MRI signal decays as the relaxivity for¹H MRI markedly improves. 

Abstract Aggressive cancers, characterized by high metastatic potential and resistance to conventional therapies, present a significant challenge in oncology. Current treatments often fail to effectively target metastasis, recurrence, and the immunosuppressive tumor microenvironment, while causing significant off‐target toxicity. Here, superparamagnetic copper iron oxide nanoparticles (SCIONs) as a multifunctional platform that integrates magnetic hyperthermia therapy, immune modulation, and targeted chemotherapeutic delivery, aiming to provide a more comprehensive cancer treatment is presented. Specifically, SCIONs generate localized hyperthermia under an alternating magnetic field while delivering a copper‐based anticancer agent, resulting in a synergistic anticancer effect. The hyperthermia induced by SCIONs caused ER stress and ROS production, leading to significant tumor cell death, while the copper complex further enhanced oxidative stress, ferroptosis, and apoptosis. Beyond direct cytotoxicity, SCIONs disrupted the tumor microenvironment by inhibiting cancer‐associated fibroblasts, downregulating epithelial‐mesenchymal transition markers, and reducing cell migration and invasion, thereby limiting metastasis. Additionally, SCION‐based therapy reprogrammed the immune microenvironment by inducing immunogenic cell death and enhancing dendritic cell activation, resulting in increased CD8+ T cell infiltration and amplified antitumor immunity. This integrated approach targets primary and metastatic tumors while mitigating immunosuppression, offering a promising next‐generation therapy for combating cancer with enhanced efficacy and reduced side effects. 

ABSTRACT This paper is concerned with efficient and accurate numerical schemes for the Cahn‐Hilliard‐Navier‐Stokes phase field model of binary immiscible fluids. By introducing two Lagrange multipliers for each of the Cahn‐Hilliard and Navier‐Stokes parts, we reformulate the original model problem into an equivalent system that incorporates the energy evolution process. Such a nonlinear, coupled system is then discretized in time using first‐ and second‐order backward differentiation formulas, in which all nonlinear terms are treated explicitly and no extra stabilization term is imposed. The proposed dynamically regularized Lagrange multiplier (DRLM) schemes are mass‐conserving and unconditionally energy‐stable with respect to the original variables. In addition, the schemes are fully decoupled: Each time step involves solving two biharmonic‐type equations and two generalized linear Stokes systems, together with two nonlinear algebraic equations for the Lagrange multipliers. A key feature of the DRLM schemes is the introduction of the regularization parameters which ensure the unique determination of the Lagrange multipliers and mitigate the time step size constraint without affecting the accuracy of the numerical solution, especially when the interfacial width is small. Various numerical experiments are presented to illustrate the accuracy and robustness of the proposed DRLM schemes in terms of convergence, mass conservation, and energy stability. 

Not AvailableModern Artificial Intelligence (AI) workloads demand computing systems with large silicon area to sustain throughput and competitive performance. However, prohibitive manufacturing costs and yield limitations at advanced tech nodes and die-size reaching the reticle limit restrain us from achieving this. With the recent innovations in advanced packaging technologies, chiplet-based architectures have gained significant attention in the AI hardware domain. However, the vast design space of chiplet-based AI accelerator design and the absence of system and package-level co-design methodology make it difficult for the designer to find the optimum design point regarding Power, Performance, Area, and manufacturing Cost (PPAC). This paper presents Chiplet-Gym, a Reinforcement Learning (RL)-based optimization framework to explore the vast design space of chiplet-based AI accelerators, encompassing the resource allocation, placement, and packaging architecture. We analytically model the PPAC of the chiplet-based AI accelerator and integrate it into an OpenAI gym environment to evaluate the design points. We also explore non-RL-based optimization approaches and combine these two approaches to ensure the robustness of the optimizer. The optimizer-suggested design point achieves 1.52× throughput, 0.27× energy, and 0.89× cost of its monolithic counterpart at iso-area. 

Abstract Detecting and quantifying the global teleconnections with flash droughts (FDs) and understanding their causal relationships is crucial to improve their predictability. This study employs causal effect networks (CENs) to explore the global predictability sources of subseasonal soil moisture FDs in three regions of the United States (US): upper Mississippi, South Atlantic Gulf (SAG), and upper and lower Colorado river basins. We analyzed the causal relationships of FD events with global 2‐m air temperature, sea surface temperature, water deficit (precipitation minus evaporation), and geopotential height at 500 hPa at the weekly timescale over the warm season (April to September) from 1982 to 2018. CENs revealed that the Indian Ocean Dipole, Pacific North Atlantic patterns, Bermuda high‐pressure system, and teleconnection patterns via Rossby wave train and jet streams strongly influence FDs in these regions. Moreover, a strong link from South America suggests that atmospheric circulation forcings could affect the SAG through the low‐level atmospheric flow, reducing inland moisture transport, and leading to a precipitation deficit. Machine learning utilizing the identified causal regions and factors can well predict major FD events up to 4 weeks in advance, providing useful insights for improved subseasonal forecasting and early warnings. 

Abstract Multilayered plastics are widely used in food packaging and other commercial applications due to their tailored functional properties. By layering different polymers, the multilayered composite material can have enhanced mechanical, thermal, and barrier properties compared to a single plastic. However, there is a significant need to recycle these multilayer plastics, but their complex structure offers significant challenges to their successful recycling. Ultimately, the use and recycling of these complex materials requires the ability to characterize the composition and purity as a means of quality control for both production and recycling processes. New advances and availability of low‐field benchtop¹H NMR spectrometers have led to increasing interest in its use for characterization of multicomponent polymers and polymer mixtures. Here, we demonstrate the capability of low‐field benchtop¹H NMR spectroscopy for characterization of three common polymers associated with multilayered packaging systems (low‐density polyethylene [LDPE], ethylene vinyl alcohol [EVOH], and Nylon) as well as their blends. Calibration curves are obtained for determining the unknown composition of EVOH and Nylon in multilayered packaging plastics using both the EVOH hydroxyl peak area and an observed peak shift, both yielding results in good agreement with the prepared sample compositions. Additionally, comparison of results extracted for the same samples characterized by our benchtop spectrometer and a 500‐MHz spectrometer found results to be consistent and within 2 wt% on average. Overall, low‐field benchtop¹H NMR spectroscopy is a reliable and accessible tool for characterization of these polymer systems.