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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%. 

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. 

Transformer models have been widely investigated in different domains by providing long-range dependency handling and global contextual awareness, driving the development of popular AI applications such as ChatGPT, Gemini, and Alexa. State Space Models (SSMs) have emerged as strong contenders in the field of sequential modeling, challenging the dominance of Transformers. SSMs incorporate a selective mechanism that allows for dynamic parameter adjustment based on input data, enhancing their performance. However, this mechanism also comes with increasing computational complexity and bandwidth demands, posing challenges for deployment on resource-constraint mobile devices. To address these challenges without sacrificing the accuracy of the selective mechanism, we propose a sparse learning framework that integrates architecture-aware compiler optimizations. We introduce an end-to-end solution–C 4 n kernel sparsity, which prunes n elements from every four contiguous weights, and develop a compiler-based acceleration solution to ensure execution efficiency for this sparsity on mobile devices. Based on the kernel sparsity, our framework generates optimized sparse models targeting specific sparsity or latency requirements for various model sizes. We further leverage pruned weights to compensate for the remaining weights, enhancing downstream task performance. For practical hardware acceleration, we propose C 4 n -specific optimizations combined with a layout transformation elimination strategy. This approach mitigates inefficiencies arising from fine-grained pruning in linear layers and improves performance across other operations. Experimental results demonstrate that our method achieves superior task performance compared to other semi-structured pruning methods and achieves up-to 7→ speedup compared to llama.cpp framework on mobile devices.