Optimal Demand Shut-offs of AC Microgrid using AO-SBQP Method

Microgrids are increasingly being utilized to improve the resilience and operational flexibility of power grids, and act as a backup power source during grid outages. However, it necessitates that the microgrid itself could provide power to the critical loads. This paper presents an algorithm named alternating optimization based sequential boolean quadratic programming tailored for solving optimal demand shut-offs problems arising in microgrids. Moreover, we establish local superlinear convergence of the proposed approximate Boolean quadratic programming method over nonconvex problems. In the end, the performance of the proposed method is illustrated on the modified IEEE 30-bus case study

Synthesis and fungicidal activity of pyrazole derivatives containing 1,2,3,4-tetrahydroquinoline

Additional file 3. Structural information (CIF) for Compound 10g

Removal of Hsf4 leads to cataract development in mice through down-regulation of γS-crystallin and Bfsp expression

<p>Abstract</p> <p>Background</p> <p>Heat-shock transcription factor 4 (HSF4) mutations are associated with autosomal dominant lamellar cataract and Marner cataract. Disruptions of the <it>Hsf4 </it>gene cause lens defects in mice, indicating a requirement for HSF4 in fiber cell differentiation during lens development. However, neither the relationship between HSF4 and crystallins nor the detailed mechanism of maintenance of lens transparency by HSF4 is fully understood.</p> <p>Results</p> <p>In an attempt to determine how the underlying biomedical and physiological mechanisms resulting from loss of HSF4 contribute to cataract formation, we generated an <it>Hsf4 </it>knockout mouse model. We showed that the <it>Hsf4 </it>knockout mouse (<it>Hsf4</it>^-/-) partially mimics the human cataract caused by HSF4 mutations. Q-PCR analysis revealed down-regulation of several cataract-relevant genes, including <it>γS-crystallin (Crygs) </it>and lens-specific beaded filament proteins 1 and 2 (<it>Bfsp1 </it>and <it>Bfsp2</it>), in the lens of the <it>Hsf4</it>^-/-mouse. Transcription activity analysis using the dual-luciferase system suggested that these cataract-relevant genes are the direct downstream targets of HSF4. The effect of HSF4 on <it>γS-crystallin </it>is exemplified by the cataractogenesis seen in the <it>Hsf4</it>^-/-,<it>rncat </it>intercross. The 2D electrophoretic analysis of whole-lens lysates revealed a different expression pattern in 8-week-old <it>Hsf4</it>^-/-mice compared with their wild-type counterparts, including the loss of some αA-crystallin modifications and reduced expression of γ-crystallin proteins.</p> <p>Conclusion</p> <p>Our results indicate that HSF4 is sufficiently important to lens development and disruption of the <it>Hsf4 </it>gene leads to cataracts via at least three pathways: 1) down-regulation of <it>γ-crystallin</it>, particularly <it>γS-crystallin</it>; 2) decreased lens beaded filament expression; and 3) loss of post-translational modification of αA-crystallin.</p

Learning with Noisy Labels Using Collaborative Sample Selection and Contrastive Semi-Supervised Learning

Learning with noisy labels (LNL) has been extensively studied, with existing approaches typically following a framework that alternates between clean sample selection and semi-supervised learning (SSL). However, this approach has a limitation: the clean set selected by the Deep Neural Network (DNN) classifier, trained through self-training, inevitably contains noisy samples. This mixture of clean and noisy samples leads to misguidance in DNN training during SSL, resulting in impaired generalization performance due to confirmation bias caused by error accumulation in sample selection. To address this issue, we propose a method called Collaborative Sample Selection (CSS), which leverages the large-scale pre-trained model CLIP. CSS aims to remove the mixed noisy samples from the identified clean set. We achieve this by training a 2-Dimensional Gaussian Mixture Model (2D-GMM) that combines the probabilities from CLIP with the predictions from the DNN classifier. To further enhance the adaptation of CLIP to LNL, we introduce a co-training mechanism with a contrastive loss in semi-supervised learning. This allows us to jointly train the prompt of CLIP and the DNN classifier, resulting in improved feature representation, boosted classification performance of DNNs, and reciprocal benefits to our Collaborative Sample Selection. By incorporating auxiliary information from CLIP and utilizing prompt fine-tuning, we effectively eliminate noisy samples from the clean set and mitigate confirmation bias during training. Experimental results on multiple benchmark datasets demonstrate the effectiveness of our proposed method in comparison with the state-of-the-art approaches

The mechanism of NLRP3 inflammasome activation and its pharmacological inhibitors

NLRP3 (NOD-, LRR-, and pyrin domain-containing protein 3) is a cytosolic pattern recognition receptor (PRR) that recognizes multiple pathogen-associated molecular patterns (PAMPs) and damage-associated molecular patterns (DAMPs). Once activated, NLRP3 initiates the inflammasome assembly together with the adaptor ASC and the effector caspase-1, leading to caspase-1 activation and subsequent cleavage of IL-1β and IL-18. Aberrant NLRP3 inflammasome activation is linked with the pathogenesis of multiple inflammatory diseases, such as cryopyrin­associated periodic syndromes, type 2 diabetes, non-alcoholic steatohepatitis, gout, and neurodegenerative diseases. Thus, NLRP3 is an important therapeutic target, and researchers are putting a lot of effort into developing its inhibitors. The review summarizes the latest advances in the mechanism of NLRP3 inflammasome activation and its pharmacological inhibitors

Ultrahydrophobicity of Polydimethylsiloxanes-Based Multilayered Thin Films

The formation of polydimethylsiloxanes (PDMSs)-based layer-by-layer multilayer ultrathin films on charged surfaces prepared from water and phosphate buffer solutions has been investigated. The multilayer films prepared under these conditions showed different surface roughness. Nanoscale islands and network structures were observed homogeneously on the multilayer film prepared from pure water solutions, which is attributing to the ultrahydrobic property of the multilayer film. The formation of nanoscale islands and network structures was due to the aggregation of PDMS-based polyelectrolytes in water. This work provides a facile approach for generating ultrahydrophobic thin films on any charged surfaces by PDMS polyelectrolytes

Optimized hydrophobic magnetic nanoparticles stabilized pickering emulsion for enhanced oil recovery in complex porous media of reservoir

With an extensive application of flooding technologies in oil recovery, traditional emulsion flooding has seen many limits due to its poor stability and easy demulsification. Pursuing a new robust emulsion plays a fundamental role in developing highly effective emulsion flooding technology. In this work, a novel Pickering emulsion with special magnetic nanoparticles Fe3O4@PDA@Si was designed and prepared. To disclose the flooding mechanism from magnetic nanoparticles, the physico-chemical characterization of Fe3O4@PDA@Si was systematically examined. Meanwhile, the flooding property of the constructed Pickering emulsion was evaluated on the basis of certain downhole conditions. The results showed that the synthesis of Fe3O4@PDA@Si nanoparticles was found to have a hydrophobic core-shell structure with a diameter of 30 nm. Pickering emulsions based on Fe3O4@PDA@Si nanoparticles at an oil-to-water ratio of 5:5, 50°C, the water separation rate was only 6% and the droplet diameter of the emulsion was approximately 15 μm in the ultra-depth-of-field microscope image. This demonstrates the excellent stability of Pickering emulsions and improves the problem of easy demulsification. We further discussed the oil displacement mechanism and enhanced oil recovery effect of this type of emulsion. The microscopic flooding experiment demonstrated that profile control of the Pickering emulsion played a more important role in enhanced recovery than emulsification denudation, with the emulsion system increasing oil recovery by 10.18% in the micro model. Core flooding experiments have established that the incremental oil recovery of the Pickering emulsion increases with decreasing core permeability, from 12.36% to 17.39% as permeability drops from 834.86 to 219.34 × 10−3 μm2. This new Pickering emulsion flooding system stabilized by Fe3O4@PDA@Si nanoparticles offers an option for enhanced oil recovery (EOR)

Myrica rubra Extracts Protect the Liver from CCl4-Induced Damage

The relationship between the expression of mitochondrial voltage-dependent anion channels (VDACs) and the protective effects of Myrica rubra Sieb. Et Zucc fruit extract (MCE) against carbon tetrachloride (CCl4)-induced liver damage was investigated. Pretreatment with 50 mg kg−1, 150 mg kg−1 or 450 mg kg−1 MCE significantly blocked the CCl4-induced increase in both serum aspartate aminotransferase (sAST) and serum alanine aminotransferase (sALT) levels in mice (P < .05 or .01 versus CCl4 group). Ultrastructural observations of decreased nuclear condensation, ameliorated mitochondrial fragmentation of the cristae and less lipid deposition by an electron microscope confirmed the hepatoprotection. The mitochondrial membrane potential dropped from −191.94 ± 8.84 mV to −132.06 ± 12.26 mV (P < .01) after the mice had been treated with CCl4. MCE attenuated CCl4-induced mitochondrial membrane potential dissipation in a dose-dependent manner. At a dose of 150 or 450 mg kg−1 of MCE, the mitochondrial membrane potentials were restored (P < .05). Pretreatment with MCE also prevented the elevation of intra-mitochondrial free calcium as observed in the liver of the CCl4-insulted mice (P < .01 versus CCl4 group). In addition, MCE treatment (50–450 mg kg−1) significantly increased both transcription and translation of VDAC inhibited by CCl4. The above data suggest that MCE mitigates the damage to liver mitochondria induced by CCl4, possibly through the regulation of mitochondrial VDAC, one of the most important proteins in the mitochondrial outer membrane

Assessing rice chlorophyll content with vegetation indices from hyperspectral data

Abstract. Leaf chlorophyll content is not only an important biochemical parameter for determinating the capacity of rice photosynthesis, but also a good indicator of crop stress, nutritional state. Due to the reliable, operational and non-destructive advantages, hyperspectral remote sensing plays a significant role for assessing and monitoring chlorophyll content. In the study, a few of typical vegetation indices (VI) with the combination of 670nm and 800nm band reflectance, Normalized Difference Vegetation Index (NDVI), Modified Simple Ratio index (MSR), Modified Chlorophyll Absorption Ratio Index (MCARI), Transformed Chlorophyll Absorption Ratio Index (TCARI), and Optimized Soil-Adjusted Vegetation Index (OSAVI) are modified by using 705nm and 750nm band reflectance so as to reduce the effect of spectral saturation in 660-680nm absorptive band region, and then used to assess the rice chlorophyll content. The result shows that the five mentioned VIs have better correlation with rice chlorophyll content while using 705nm and 750nm. In addition, in the study the Weight optimization combination (WOC) principle is utilized to further assess the capacity of the five modified VIs for estimating rice chlorophyll content, it is proved that OSAVI and MSR display the better performance