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In this paper, a novel medium-voltage (MV) hybrid multilevel inverter with an inner-paralleled (IP) half-bridge (HB) is designed and developed for electric traction drives with a 3-kV dc bus. Within this proposed hybrid configuration, each phase consists of an active-neutral-point-clamped (ANPC) three-level (3-L) cell that adopts 3.3-kV silicon (Si) IGBTs operating at a fundamental frequency, while the IP cell using a custom 3.3-kV H-bridge silicon carbide (SiC) MOSFET operates at a frequency of 50 kHz. With interleaving through line inductors, the output apparent switching frequency has been doubled, e.g., 100 kHz. The comprehensive design and demonstration of the full-scale prototype are intro-duced, including low-inductance laminated busbar architecture optimization, H-bridge power electronics building block (PEBB) design, modular system integration, and proprietary modulation and controls. Extensive simulation and experimental results are presented to validate the overall performance of the studied proto-type systems in steady and dynamic states. The findings show that in contrast to existing all-Si or SiC-based 3-L and five-level (5-L) ANPC, the proposed new 5-L Si+SiC converter is characterized by higher power efficiency and density, better harmonics properties, higher dc bus voltage, lower dv/dt, and build cost, which provides a promising electrified solution for MV traction drives in the trans-portation sector. 

Abstract Testing and validating automated driving systems require carefully designed test cases that capture the complexity of real-world driving conditions. However, the inherent complexity of driving environments and the rarity of safety-critical situations pose significant challenges to developing reliable and efficient validation frameworks. This paper addresses these issues by selecting appropriate test cases from the largest-scale naturalistic driving study. We introduce a Kernel Test Case Sampling method, which selects cases satisfying two key criteria: representativeness, ensuring alignment with real-world scenarios, and coverage, capturing high-risk corner cases. To demonstrate the proposed method, it is applied to large-scale naturalistic driving study data. By selecting a limited number of cases, the method effectively captures long-tailed scenarios while approximating the distribution of naturalistic driving conditions. The sampling framework also enables robust accident-rate estimation, thereby ensuring fair comparisons across human driving performance and multiple systems. The proposed method supports standardized and scalable automated driving system safety validation, facilitating accelerated development and deployment while building public trust and regulatory confidence. 

Mangroves have evolved at least 27 times across ~20 plant families to survive coastal. To environments characterized by high salinity, inundation, intense light, and strong winds survive these extreme conditions, mangroves exhibit a variety of physiological strategies to tolerate the low osmotic potentials associated with saltwater inundation. Because low osmotic potentials are counterbalanced by high turgor pressure, saltwater exposure exerts mechanical demands on cells. Analyzing 34 mangrove species and 33 closely related inland taxa from 17 plant families, we show that compared to their inland relatives, mangroves have unusually small leaf epidermal pavement cells and thicker cell walls, which together confer greater mechanical strength and tolerance to low osmotic potentials. However, mangroves do not exhibit smaller, more numerous stomata that enable higher photosynthetic rates , suggesting selection on biomechanical integrity rather than on gas exchange capacity. Notably, mangroves break the allometric scaling between the sizes of epidermal pavement cells and stomata typically seen in land plants, highlighting that strong selection in saline habitats can override genome size–mediated scaling rules. Phylogenetic comparative analyses revealed repeated convergent evolution of cell traits across independent transitions from inland to coastal habitats. These anatomical changes constitute a simple but effective adaptation to salt stress. Our findings underscore the role of biomechanics in driving convergent evolution of cell traits and suggest that manipulating cell size and wall properties could be a promising strategy to engineering salt-tolerant plants. 

Generative Artificial Intelligence (GAI), such as OpenAI’s ChatGPT, has rapidly emerged as a transformative tool in higher education, offering opportunities to enhance teaching and learning. This paper describes the design and implementation of ChatGPT-integrated curriculum activities, featuring coding learning in psychology and conceptual discussions in physics, and presents the findings of a year-long experimental study in both types of classrooms. Our findings suggest that students generally found ChatGPT easy to use and beneficial to their learning, reporting improved confidence, motivation, and engagement. However, its ability to address individual needs or replace instructors was viewed less favorably. Comparative analyses showed that coding activities in psychology led to higher levels of activity satisfaction and perceived usefulness of ChatGPT compared to the more abstract discussion activities in physics. While graduate students were more enthusiastic about using ChatGPT for skill acquisition than undergraduates, demographic factors such as gender, race, and first-generation college status showed no significant influence on such perceptions. Meanwhile, instructors’ reflections emphasize the importance of thoughtful integration, technical support, and pedagogical balance to maximize GAI’s potential while mitigating its limitations. Recommendations for integrating GAI into teaching practices and future research directions are discussed, contributing to the evolving discourse on GAI’s role in transforming modern classrooms. 

Increasing evidence strongly links neuroinflammation to Alzheimer’s disease (AD) pathogenesis. Peripheral monocytes are crucial components of the human immune system, but their contribution to AD pathogenesis is still largely understudied partially due to limited human models. Here, we introduce human cortical organoid microphysiological systems (hCO-MPSs) to study AD monocyte-mediated neuroinflammation. By culturing doughnut-shape organoids on 3D-printed devices within standard 96-well plates, we generate hCO-MPSs with reduced necrosis, minimized hypoxia, and improved viability. Using these models, we found that monocytes from AD patients exhibit increased infiltration ability, decreased amyloid-β clearance capacity, and stronger inflammatory response than monocytes from age-matched control donors. Moreover, we observed that AD monocytes induce pro-inflammatory effects such as elevated astrocyte activation and neuronal apoptosis. Furthermore, the marked increase in IL1B and CCL3 expression underscores their pivotal role in AD monocyte-mediated neuroinflammation. Our findings provide insight into understanding monocytes’ role in AD pathogenesis, and our lab-compatible MPS models may offer a promising way for studying various neuroinflammatory diseases. 

Alzheimer's disease (AD) is a progressive and neurodegenerative disease, predominantly causing dementia. Despite increasing clinical evidence suggesting the involvement of peripheral immune cells such as monocytes in AD pathology, the dynamic penetration and infiltration of monocytes crossing blood–brain barrier (BBB) and inducing neuroinflammation is largely understudied in an AD brain. Herein, we engineer BBB-like microphysiological system (BBB-MPS) models for recapitulating the dynamic penetration and infiltration of monocytes in an AD patient's brain. Each BBB-MPS model can be engineered by integrating a functional BBB-like structure on a human cortical organoid using a 3D-printed device within a well of a plate. By coculturing these BBB-MPS models with monocytes from AD patients and age-matched healthy donors, we found that AD monocytes exhibit significantly greater BBB penetration and brain infiltration compared to age-matched control monocytes. Moreover, we also tested the interventions including Minocycline and Bindarit, and found they can effectively inhibit AD monocyte infiltration, subsequently reducing neuroinflammation and neuronal apoptosis. We believe these scalable and user-friendly BBB-MPS models may hold promising potential in modeling and advancing therapeutics for neurodegenerative and neuroinflammatory diseases. 

We integrate quasi-liquid surfaces, nanovibration technology, and capacitive sensing to create an energy-efficient system for detecting and removing frost and ice without complete melting. 

Abstract The leaf economics spectrum (LES) characterizes a tradeoff between building a leaf for durability versus for energy capture and gas exchange, with allocation to leaf dry mass per projected surface area (LMA) being a key trait underlying this tradeoff. However, regardless of the biomass supporting the leaf, high rates of gas exchange are typically accomplished by small, densely packed stomata on the leaf surface, which is enabled by smaller genome sizes. Here, we investigate how variation in genome size‐cell size allometry interacts with variation in biomass allocation (i.e. LMA) to influence the maximum surface conductance to CO₂and the rate of resource turnover as measured by leaf water residence time. We sampled both evergreen and deciduousRhododendron(Ericaceae) taxa from wild populations and botanical gardens, including naturally occurring putative hybrids and artificially generated hybrids. We measured genome size, anatomical traits related to cell sizes, and morphological traits related to water content and dry mass allocation. Consistent with the LES, higher LMA was associated with slower water residence times, and LMA was strongly associated with leaf thickness. Although anatomical and morphological traits varied orthogonally to each other, cell size had a pervasive impact on leaf functional anatomy: for a given leaf thickness, reducing cell size elevated the leaf surface conductance and shortened the mean water residence time. These analyses clarify how anatomical traits related to genome size‐cell size allometry can influence leaf function independently of morphological traits related to leaf longevity and durability.