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Enhanced proton-feeding kinetics of metal-organic framework toward industrial-level H2O2 electrosynthesis for sustainable bleaching
Genomic characterization of novel lytic phage vB_Sal_S6 with putative host FhuA interaction and its application for Salmonella biocontrol in milk
Anti-PD-L2 immunotherapy is efficacious against melanoma in aged hosts through IL-17 and IFNγ signalling
Lightweight high-precision monitoring of damage locations in Dunhuang murals based on improved YOLACT
Inferring asymptomatic carriers of antimicrobial-resistant organisms in hospitals using genomic, microbiological and patient mobility data
Feasibility on diverse biochars as supplementary cementitious materials
Hexokinase 2-mediated histone H3K18la promotes PAI-1-dependent thrombosis in acute myeloid leukemia via tumor-endothelial crosstalk
Learning robust parameter inference and density reconstruction in flyer plate impact experiments
Ultrahigh-speed optical encryption enabled by spatiotemporal noise chaffing
Effect of green space in the workplace on hearing thresholds in noise exposed population
Flame-retardant electrolytes with electrochemically-inert and weakly coordinating dichloroalkane diluents for practical lithium metal batteries
Abstract The next-generation of lithium metal batteries urgently require electrolytes that simultaneously possess low-cost, high-safety, wide-temperature operating range, high electrochemical stability and good electrode-electrolyte interphases formation ability. Here we present a flame-retardant electrolyte by introducing electrochemically-inert and weakly coordinating dichloroalkane diluents in triethyl phosphate-based high-concentration electrolyte. We systematically investigate the effects of dichloroalkane diluents with diverse carbon chain lengths on the Li + solvation structure, redox behavior, and lithium metal interfacial chemistry in the electrolyte. Consequently, 1,3-dichloropropane, which shows the favorable electrochemical inertness, weakly coordinating ability and wide liquid temperature range (−99 to +120 °C), is chosen as an ideal diluent in electrolyte to form robust anions-derived inorganic-rich electrode-electrolyte interphases on electrodes and improve the Li + transport/de-solvation capability. The developed electrolyte exhibits significant improvement in safety, cycling stability, rate capability and wide temperature operation capability of high-voltage lithium metal batteries. Particularly, the practical Li (50 μm)||LiNi 0.83 Co 0.12 Mn 0.05 O 2 (NCM83, 5.6 mAh cm −2 ) pouch cells exhibit stable cycling performance over 100 cycles with a high capacity retention rate of 94.1% at 0.1 C charge/0.2 C discharge under 25 °C, and deliver a promising application potential within a broad temperature range of −60 to +60 °C.
How risk communication shapes individual response to climate change: an experimental study
In vivo conformational space and defects of misfolded CFTR variants by covalent protein painting
Abstract In vivo characterization of protein structures and structural changes after perturbation is still a major challenge and has impacted our understanding of the molecular events involved in protein misfolding diseases. To identify the true conformational space occupied by proteins in their native state in vivo, we recently developed a structural proteomics method named Covalent Protein Painting (CPP). Here, we show how CPP can be used to identify and quantify the conformational defects of proteins in the misfolding disease Cystic Fibrosis. We first report the discovery of a previously unreported opening mechanism for the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) as well as its conformational changes during biogenesis. Then we further reveal how misfolding of different CFTR variants in Cystic Fibrosis disturbs these conformational changes even upon treatment with current approved drugs and suggest possibilities to stabilize misfolded CFTR variants not or less responsive to these drugs such as N1303K CFTR.
Revolutionizing the way students learn photographic arts through experiential education using AI and AR systems
Abstract The evolution of educational environments has seen a shift from conventional classrooms to technology-enhanced smart classrooms, driven by the rapid advancement of digital tools. The integration of traditional art education and modern technologies lacks interactivity and personalized feedback, which limits student engagement and creative progression. The objective of this research is to assess how AI and AR can be combined to improve student engagement, creativity, academic performance, and aesthetic understanding in art education. Data were collected from smart classroom sessions involving educational videos and interactive AR applications focused on photography. The pre-processing stage automatically filters low-quality images, retaining those with high saliency and clarity scores to ensure meaningful input for analysis. Using a TensorFlow-based experimental framework, a Deep Recurrent Neural Network (DRNN) algorithm was employed for intelligent image synthesis and feedback, allowing real-time analysis of composition and augmented visual storytelling. Results indicated notable improvements in student, Accuracy (97.18%), precision (97.33%), recall (96.95%), F1 score (97%). Students responded positively to the immersive experience, showing increased appreciation for cultural and visual diversity. In conclusion, the study demonstrates that integrating AI and AR in smart classroom environments can redefine art education by fostering experiential learning and providing dynamic, student-centered educational opportunities.
Hole migration enables efficient and ultra-bright green quantum dot LEDs
Exploring the role of hepatic lipid droplets in mouse liver toxicity induced by 2,3,7,8-tetrachlorodibenzo-p-dioxin: sequestration, biochemical alterations and gene regulation
Reconstructing interfacial electric double layer for efficient sulfur conversion reaction in aqueous zinc sulfur batteries
Experimental analysis of time difference of arrival estimates based on inexactly reconstructed signals
Abstract Abstract: Time Difference of Arrival (TDOA) estimation is a pivotal technique with extensive applications across various domains, including passive detection and indoor positioning. For signals characterized by unknown modulation types and originating from non-cooperative sources, achieving high-precision TDOA estimation traditionally necessitates a substantial volume of sampling data. Conventional approaches, such as cross-correlation, require high sampling rates and extended durations, posing significant challenges in terms of data acquisition, transmission, and storage. To surmount these obstacles, this paper delves into an enhanced inexact reconstruction-based compressed sensing method for TDOA estimation (EIRCS), presents an optimized algorithmic procedure that further reduces the computational complexity of the EIRCS method. Experimental results substantiate that the EIRCS method is an unbiased estimation technique. It accomplishes high-precision TDOA estimation concurrent with efficient data compression. These insights suggest that the EIRCS method can yield dependable TDOA estimates with minimal error, even at elevated compression ratios. Its capacity to sustain high accuracy across a range of compression ratios renders it particularly apt for applications demanding efficient data processing and reliable TDOA estimation.