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Low-income households (LIH), exposed to the uncertain modern grid, bear greater energy burdens and face inequitable access to reliable power compared to high-income households (HIH). This paper proposes a two-stage stochastic community-based microgrid planning (CMP) framework to boost energy justice within the system. To reduce the negative impact of income levels, a weighted energy cost model for households within the microgrid (MG) is designed. To address the multisource uncertainty during the operation period, a two-stage stochastic framework is developed. Moreover, to assess the proposed method, the unbalanced IEEE 123 node system is employed and modified as an isolated MG. The analysis reveals the proposed model can achieve a risk-averse solution while economic optimality is guaranteed. Additionally, the designed weighted method improves the LIH’s impact rate to 67.95% and decreases the total planning cost by 22.43%. 

Low-income households (LIH), exposed to the uncertain modern grid, bear greater energy burdens and face inequitable access to reliable power compared to high-income households (HIH). This paper proposes a two-stage stochastic community-based microgrid planning (CMP) framework to boost energy justice within the system. To reduce the negative impact of income levels, a weighted energy cost model for households within the microgrid (MG) is designed. To address the multisource uncertainty during the operation period, a two-stage stochastic framework is developed. Moreover, to assess the proposed method, the unbalanced IEEE 123 node system is employed and modified as an isolated MG. The analysis reveals the proposed model can achieve a risk-averse solution while economic optimality is guaranteed. Additionally, the designed weighted method improves the LIH’s impact rate to 67.95% and decreases the total planning cost by 22.43%. 

We introduce RandAR, a decoder-only visual autoregressive (AR) model capable of generatng images in arbitrary token orders. Unlike previous decoder-only AR models that rely on a predefined generation order, RandAR removes this inductive bias, unlocking new capabilities in decoder-only generation. Our essential design enabling random order is to insert a "position instruction token" before each image token to be predicted, representing the spatial location of the next image token. Trained on randomly permuted token sequences -- a more challenging task than fixed-order generation, RandAR achieves comparable performance to conventional raster-order counterpart. More importantly, decoder-only transformers trained from random orders acquire new capabilities. For the efficiency bottleneck of AR models, RandAR adopts parallel decoding with KV-Cache at inference time, enjoying 2.5x acceleration without sacrificing generation quality. Additionally, RandAR supports in-painting, outpainting and resolution extrapolation in a zero-shot manner.We hope RandAR inspires new directions for decoder-only visual generation models and broadens their applications across diverse scenarios. Our project page is at https://rand-ar.github.io/. 

The upstream mechanobiological pathways that regulate the downstream mineralization rates in periodontal tissues are limitedly understood. Herein, we spatially colocalized and correlated compression and tension strain profiles with the expressions of mechanosensory ion channels (MS-ion) TRPV4 and PIEZO1, biometal zinc, mitochondrial function marker (MFN2), cell senescence indicator (p16), and oxygen status marker hypoxia-inducible factor-1α (HIF-1α) in rats fed hard and soft foods. The observed zinc and related cellular homeostasis in vivo were ascertained by TRPV4 and PIEZO1 agonists and antagonists on human periodontal ligament fibroblasts ex vivo. Four-week-old male Sprague-Dawley rats were fed hard (n= 3) or soft (n= 3) foods for 4 wk (in vivo). Significant changes in alveolar socket and root shapes with decreased periodontal ligament space and increased cementum volume fraction were observed in maxillae on reduced loads (soft food). Reduced loads impaired distally localized compression-stimulated PIEZO1 and mesially localized tension-stimulated TRPV4, decreased mitochondrial function (MFN2), and increased cell senescence in mesial and distal periodontal regions. The switch inHIF-1αfrom hard food–distal to soft food–mesial indicated a plausible effect of shear-regulated blood and oxygen flows in the periodontal complex. Blunting or activating TRPV4 or PIEZO1 MS-ion channels by channel-specific antagonists or agonists in human periodontal ligament fibroblast cultures (in vitro) indicated a positive correlation between zinc levels and zinc transporters but not with MS-ion channel expressions. The effects of reduced chewing loads in vivo were analogous to TRPV4 and PIEZO1 antagonists in vitro. Study results collectively illustrated that tension-induced TRPV4 and compression-induced PIEZO1 activations are necessary for cell metabolism. An increased hypoxic state with reduced functional loads can be a conducive environment for cementum growth. From a practical standpoint, dose rate–controlled loads can modulate tension and compression-specific MS-ion channel activation, cellular zinc, andHIF-1αtranscription. These mechanobiochemical events indicate the plausible catalytic role of biometal zinc in mineralization, periodontal maintenance, and dentoalveolar joint function.