Transforming low-quality sand into construction materials under 110℃ and Recycling of the Waste Solution

A strong and eco-friendly material was transformed from low-quality sand via sol-gel method with ethanol as the solvent. 110oC was chosen as a target temperature because it is the average day temperature of the moon, which may be the first place for extraterrestrial development. The appropriate KOH content and ethanol concentration can improve the reaction degree and limit the side reaction. The main results indicated that the highest compressive strength (38 MPa) of the produced material could be obtained by using 20 mass% KOH and 90 V/V% ethanol. According to XRD and FTIR analysis, the formation of sanidine, zeolite, and tetraethoxysilane is the main reason for strength enhancement. Sanidine and zeolite could fill the gap between sand particles and tetraethoxysilane is a good consolidate. Excess ethanol in the waste solution can be reused with recycle rate above 65%. The total carbon emission is 197 kg CO2 eq/m2 after recycling waste solution, which is 35.82% of that produced by normal concrete. Therefore, a tough construction material can be synthesized from lowquality sand, which can partially substitute concrete. This material can address the shortage of raw materials for concrete and can be utilised for extra-terrestrial construction

Nature of the spin resonance mode in CeCoIn5_5

Spin-fluctuation-mediated unconventional superconductivity can emerge at the border of magnetism, featuring a superconducting order parameter that changes sign in momentum space. Detection of such a sign-change is experimentally challenging, since most probes are not phase-sensitive. The observation of a spin resonance mode (SRM) from inelastic neutron scattering is often seen as strong phase-sensitive evidence for a sign-changing superconducting order parameter, by assuming the SRM is a spin-excitonic bound state. Here, we show that for the heavy fermion superconductor CeCoIn$_5$, its SRM defies expectations for a spin-excitonic bound state, and is not a manifestation of sign-changing superconductivity. Instead, the SRM in CeCoIn$_5$ likely arises from a reduction of damping to a magnon-like mode in the superconducting state, due to its proximity to magnetic quantum criticality. Our findings emphasize the need for more stringent tests of whether SRMs are spin-excitonic, when using their presence to evidence sign-changing superconductivity.Comment: accepted for publication in Communications Physic

Robust Upward Dispersion of the Neutron Spin Resonance in the Heavy Fermion Superconductor Ce1−x_{1-x}Ybx_{x}CoIn5_5

The neutron spin resonance is a collective magnetic excitation that appears in copper oxide, iron pnictide, and heavy fermion unconventional superconductors. Although the resonance is commonly associated with a spin-exciton due to the $d$($s^{\pm}$)-wave symmetry of the superconducting order parameter, it has also been proposed to be a magnon-like excitation appearing in the superconducting state. Here we use inelastic neutron scattering to demonstrate that the resonance in the heavy fermion superconductor Ce$_{1-x}$Yb$_{x}$CoIn$_5$ with $x=0,0.05,0.3$ has a ring-like upward dispersion that is robust against Yb-doping. By comparing our experimental data with random phase approximation calculation using the electronic structure and the momentum dependence of the $d_{x^2-y^2}$-wave superconducting gap determined from scanning tunneling microscopy for CeCoIn$_5$, we conclude the robust upward dispersing resonance mode in Ce$_{1-x}$Yb$_{x}$CoIn$_5$ is inconsistent with the downward dispersion predicted within the spin-exciton scenario.Comment: Supplementary Information available upon reques

Valley: Video Assistant with Large Language model Enhanced abilitY

Large language models (LLMs), with their remarkable conversational capabilities, have demonstrated impressive performance across various applications and have emerged as formidable AI assistants. In view of this, it raises an intuitive question: Can we harness the power of LLMs to build multimodal AI assistants for visual applications? Recently, several multi-modal models have been developed for this purpose. They typically pre-train an adaptation module to align the semantics of the vision encoder and language model, followed by fine-tuning on instruction-following data. However, despite the success of this pipeline in image and language understanding, its effectiveness in joint video and language understanding has not been widely explored. In this paper, we aim to develop a novel multi-modal foundation model capable of comprehending video, image, and language within a general framework. To achieve this goal, we introduce Valley, a Video Assistant with Large Language model Enhanced abilitY. The Valley consists of a LLM, a temporal modeling module, a visual encoder, and a simple projection module designed to bridge visual and textual modes. To empower Valley with video comprehension and instruction-following capabilities, we construct a video instruction dataset and adopt a two-stage tuning procedure to train it. Specifically, we employ ChatGPT to facilitate the construction of task-oriented conversation data encompassing various tasks, including multi-shot captions, long video descriptions, action recognition, causal relationship inference, etc. Subsequently, we adopt a pre-training-then-instructions-tuned pipeline to align visual and textual modalities and improve the instruction-following capability of Valley. Qualitative experiments demonstrate that Valley has the potential to function as a highly effective video assistant that can make complex video understanding scenarios easy

Machine learning reveals neutrophil-to-lymphocyte ratio as a crucial prognostic indicator in severe Japanese encephalitis patients

Japanese encephalitis (JE) is a severe infectious disease affecting the central nervous system (CNS). However, limited risk factors have been identified for predicting poor prognosis (PP) in adults with severe JE. In this study, we analyzed clinical data from thirty-eight severe adult JE patients and compared them to thirty-three patients without organic CNS disease. Machine learning techniques employing branch-and-bound algorithms were used to identify clinical risk factors. Based on clinical outcomes, patients were categorized into two groups: the PP group (mRs ≥ 3) and the good prognosis (GP) group (mRs ≤ 2) at three months post-discharge. We found that the neutrophil-to-lymphocyte ratio (NLR) and the percentage of neutrophilic count (N%) were significantly higher in the PP group compared to the GP group. Conversely, the percentage of lymphocyte count (L%) was significantly lower in the PP group. Additionally, elevated levels of aspartate aminotransferase (AST) and blood glucose were observed in the PP group compared to the GP group. The clinical parameters most strongly correlated with prognosis, as indicated by Pearson correlation coefficient (PCC), were NLR (PCC 0.45) and blood glucose (PCC 0.45). In summary, our findings indicate that increased serum NLR, N%, decreased L%, abnormal glucose metabolism, and liver function impairment are risk factors associated with poor prognosis in severe adult JE patients

High-energy magnetic excitations from heavy quasiparticles in CeCu2_2Si2_2

Magnetic fluctuations is the leading candidate for pairing in cuprate, iron-based and heavy fermion superconductors. This view is challenged by the recent discovery of nodeless superconductivity in CeCu$_2$Si$_2$, and calls for a detailed understanding of the corresponding magnetic fluctuations. Here, we mapped out the magnetic excitations in \ys{superconducting (S-type)} CeCu$_2$Si$_2$ using inelastic neutron scattering, finding a strongly asymmetric dispersion for $E\lesssim1.5$~meV, which at higher energies evolve into broad columnar magnetic excitations that extend to $E\gtrsim 5$ meV. While low-energy magnetic excitations exhibit marked three-dimensional characteristics, the high-energy magnetic excitations in CeCu$_2$Si$_2$ are almost two-dimensional, reminiscent of paramagnons found in cuprate and iron-based superconductors. By comparing our experimental findings with calculations in the random-phase approximation,we find that the magnetic excitations in CeCu$_2$Si$_2$ arise from quasiparticles associated with its heavy electron band, which are also responsible for superconductivity. Our results provide a basis for understanding magnetism and superconductivity in CeCu$_2$Si$_2$, and demonstrate the utility of neutron scattering in probing band renormalization in heavy fermion metals