Escalating electromagnetic pollution has promoted the advancement of electromagnetic interference(EMI) shielding materials. However, reconciling high shielding effectiveness(SE) with low reflectance(R) is still challenging due to the high conductivity typically induces strong electromagnetic wave(EMW) reflection, resulting in serious secondary pollution. Inspired by the alternate structure of frosting and cream in a millefeuille, we propose an assembly approach for alternating conductive and magnetic layers. Employing sustainable bamboo fibers(BF) and biodegradable polylactic acid(PLA) as basic filler and matrix instead of petroleum products, we fabricated magnetic and conductive layers by compositing copper-plated BF(Cu@BF) and iron-plated BF(Fe@BF) with PLA, respectively. By alternately stacking magnetic and conductive layers and followed by hot pressing molding, a biomass multilayer composite with high EMI SE and low R-value was obtained. More importantly, a linear correlation between alternate layer number, SE, and R-value is established, demonstrating that increasing the alternate layer number could readily tune the SE in the X-band from 45.02dB (3-layers) to 80.2dB (9-layers) and reduce Rmin from 0.40 to 0.25. Furthermore, the 9-layer composite exhibits approximately 75dB SE in 1-18GHz, simultaneously realizing high efficiency, low reflectivity, and broadband shielding. Notably, its excellent conductivity also provides reliable Joule heating performance, reaching 150.35℃ at just 1.5V. The shielding and thermal features of the composite highlight its potential in construction and smart housing heating applications.
Customizing LLMs for a specific task involves distinguishing effective responses from erroneous ones. This skill can be developed using supervised fine-tuning with extensive human preference data. However, obtaining expert-annotated preference data is expensive for most tasks. In this paper, we present a novel method to optimize LLMs using ranking metrics. This method trains the model to prioritize the best responses from a pool of candidates created for a particular task. Rather than a traditional full ordering, we advocate for a partial ordering, as achieving consensus on the perfect order of candidate responses can be challenging. Our partial ordering is more robust, less sensitive to noise, and can be achieved with limited human annotations or through heuristic methods. We test our system's improved response generation ability using benchmark datasets, including the latest multi-document question answering task. We conduct ablation studies to understand crucial factors, such as how to gather candidate responses for specific tasks, determine their most suitable order, and balance supervised fine-tuning with ranking metrics. Our approach, named \textsc{Rescue}, suggests a promising avenue for enhancing LLMs' contextual understanding via response ranking.
Purpose To develop a novel system for quantifying metamorphopsia in patients with myopic traction maculopathy (MTM) and to explore the metamorphopsia pattern of MTM. Design Observational, cross-sectional study. Methods We designed a new system. Results Of the 445 eyes tested, 188 (42.25%) were deemed by patients to have metamorphopsia impacting their daily lives while 257 (57.75%) were considered to have no metamorphopsia symptoms. The Amsler grid, M-CHARTS and METAVISION tests displayed sensitivities for metamorphopsia of 95.74%, 89.89% and 100%, respectively. The specificities of the Amsler grid, M-CHARTS and METAVISION tests are 100%. The metamorphopsia questionnaire and METAVISION scores were highly consistent (average intraclass correlation coefficient=0.951, p<0.001) and strongly correlated (R=0.879, p<0.001). The METAVISION score was highly correlated with the stages of MTM (R=0.837, p<0.001), whereas there was a moderate correlation between the M-CHARTS M-score and the stages of MTM (R=0.679, p<0.001). Conclusions Quantification of metamorphopsia is important and useful for MTM management. The METAVISION is a clinically applicable and comprehensive approach for quantifying metamorphopsia, which can be used in clinical settings.
In order to reveal the effect of exogenous neutral protease addition on the quality of cigar tobacco leaves during fermentation. Taking Sichuan Shifang cigar tobacco leaves as the research object, the changes of conventional chemical composition, amino acid content, organic acids and other substances in tobacco leaves after natural fermentation and fermentation with exogenous enzyme preparations were investigated, and the changes of sensory quality of cigar tobacco leaves before and after fermentation were also investigated. The results show that: 1) After fermentation, the content of total sugar, reducing sugar, total nitrogen, chlorogenic acid and rutin in tobacco leaves decreased, and the content of reducing sugar fermented with enzyme preparation decreased more. 2) After fermentation, the total amount of free amino acids in tobacco leaves increased. With the increase of exogenous neutral protease, the content of amino acids first increased and then decreased. 3) After fermentation, the content of organic acids in tobacco leaves increased. When the amount of enzyme added was 2.5g/kg leaves, the increase of organic acid content was greater than that of natural fermentation, and the other additions of enzyme were close to that of natural fermentation. 4) After fermentation, the content of plastid pigment in tobacco leaves decreased slightly. With the increase of neutral protease, the content of plastid pigment increased. 5) After the fermentation of exogenous enzyme preparation, the sensory quality of tobacco leaves is improved, but too much protease will produce bitter taste.
We introduce an innovative and mathematically rigorous definition for computing common information from multi-view data, drawing inspiration from Gács-Körner common information in information theory. Leveraging this definition, we develop a novel supervised multi-view learning framework to capture both common and unique information. By explicitly minimizing a total correlation term, the extracted common information and the unique information from each view are forced to be independent of each other, which, in turn, theoretically guarantees the effectiveness of our framework. To estimate information-theoretic quantities, our framework employs matrix-based Rényi's α-order entropy functional, which forgoes the need for variational approximation and distributional estimation in high-dimensional space. Theoretical proof is provided that our framework can faithfully discover both common and unique information from multi-view data. Experiments on synthetic and seven benchmark real-world datasets demonstrate the superior performance of our proposed framework over state-of-the-art approaches.
The forecasting skill of numerical weather prediction (NWP) models critically depends on the accurate initial conditions, also known as analysis, provided by data assimilation (DA). Traditional DA methods often face a trade-off between computational cost and accuracy due to complex linear algebra computations and the high dimensionality of the model, especially in nonlinear systems. Moreover, processing massive data in real-time requires substantial computational resources. To address this, we introduce an artificial intelligence-based data assimilation framework (ADAF) to generate high-quality kilometer-scale analysis. This study is the pioneering work using real-world observations from varied locations and multiple sources to verify the AI method's efficacy in DA, including sparse surface weather observations and satellite imagery. We implemented ADAF for four near-surface variables in the Contiguous United States (CONUS). The results indicate that ADAF surpasses the High Resolution Rapid Refresh Data Assimilation System (HRRRDAS) in accuracy by 16% to 33% for near-surface atmospheric conditions, aligning more closely with actual observations, and can effectively reconstruct extreme events, such as tropical cyclone wind fields. Sensitivity experiments reveal that ADAF can generate high-quality analysis even with low-accuracy backgrounds and extremely sparse surface observations. ADAF can assimilate massive observations within a three-hour window at low computational cost, taking about two seconds on an AMD MI200 graphics processing unit (GPU). ADAF has been shown to be efficient and effective in real-world DA, underscoring its potential role in operational weather forecasting.
A new class of luminescent materials based on Sb 3+ –doped 0D Cs 3 GdCl 6 microcrystals featuring a near-unity quantum yield (QY) and good thermal quenching resistance ( I 150°C = 82.4%) are developed and explored for white light-emitting diodes.
Higher plants survive terrestrial water deficiency and fluctuation by arresting cellular activities (dehydration) and resuscitating processes (rehydration). However, how plants monitor water availability during rehydration is unknown. Although increases in hypo-osmolarity-induced cytosolic Ca
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