Fabrication and microstructural characterization of silica aerogel by aging additional pressurization

SiO2 aerogel with Light-weight and low thermal conductivity is a promising candidate for thermal insulator used for aerospace vehicles. In this paper, we report the preparation and microstructural characterization of SiO2 aerogel by aging pressurization using supercritical drying method. The results showed that the aging pressurization can rapid increase the bulk density from 0.1g/cm3 to 0.45g/cm3 with the pressure changing from 200Pa to 600Pa. When the pressure increases to 800Pa, the density was increased to 0.46g/cm3 slowly. Further polycondensation is driven by the increasing of contact area between skeleton particles when the aging pressure increased. The grid structure became densification and saturation when the aging pressure approached 800Pa. SEM method gives the evidence of increase of aging pressure, which can help to increase the size of secondary grains. Nitrogen sorption-desorption measurements exhibit an unimodal pore distribution and low specific area and porosity with the increase of aging pressure. Real density test showed that the bulk density increased by pressure. Bulk density, gain size and pore structure distribution can be controlled effectively by aging pressurization

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods

Short-Term Wind Speed Forecasting via Stacked Extreme Learning Machine With Generalized Correntropy

Recently, wind speed forecasting as an effective computing technique plays an important role in advancing industry informatics, while dealing with these issues of control and operation for renewable power systems. However, it is facing some increasing difficulties to handle the large-scale dataset generated in these forecasting applications, with the purpose of ensuring stable computing performance. In response to such limitation, this paper proposes a more practical approach through the combination of extreme-learning machine (ELM) method and deep-learning model. ELM is a novel computing paradigm that enables the neural network (NN) based learning to be achieved with fast training speed and good generalization performance. The stacked ELM (SELM) is an advanced ELM algorithm under deep-learning framework, which works efficiently on memory consumption decrease. In this paper, an enhanced SELM is accordingly developed via replacing the Euclidean norm of the mean square error (MSE) criterion in ELM with the generalized correntropy criterion to further improve the forecasting performance. The advantage of the enhanced SELM with generalized correntropy to achieve better forecasting performance mainly relies on the following aspect. Generalized correntropy is a stable and robust nonlinear similarity measure while employing machine learning method to forecast wind speed, where the outliers may exist in some industrially measured values. Specifically, the experimental results of short-term and ultra-short-term forecasting on real wind speed data show that the proposed approach can achieve better computing performance compared with other traditional and more recent methods

GaussianHair: Hair Modeling and Rendering with Light-aware Gaussians

Hairstyle reflects culture and ethnicity at first glance. In the digital era, various realistic human hairstyles are also critical to high-fidelity digital human assets for beauty and inclusivity. Yet, realistic hair modeling and real-time rendering for animation is a formidable challenge due to its sheer number of strands, complicated structures of geometry, and sophisticated interaction with light. This paper presents GaussianHair, a novel explicit hair representation. It enables comprehensive modeling of hair geometry and appearance from images, fostering innovative illumination effects and dynamic animation capabilities. At the heart of GaussianHair is the novel concept of representing each hair strand as a sequence of connected cylindrical 3D Gaussian primitives. This approach not only retains the hair's geometric structure and appearance but also allows for efficient rasterization onto a 2D image plane, facilitating differentiable volumetric rendering. We further enhance this model with the "GaussianHair Scattering Model", adept at recreating the slender structure of hair strands and accurately capturing their local diffuse color in uniform lighting. Through extensive experiments, we substantiate that GaussianHair achieves breakthroughs in both geometric and appearance fidelity, transcending the limitations encountered in state-of-the-art methods for hair reconstruction. Beyond representation, GaussianHair extends to support editing, relighting, and dynamic rendering of hair, offering seamless integration with conventional CG pipeline workflows. Complementing these advancements, we have compiled an extensive dataset of real human hair, each with meticulously detailed strand geometry, to propel further research in this field

How Many Unicorns Are in This Image? A Safety Evaluation Benchmark for Vision LLMs

This work focuses on the potential of Vision LLMs (VLLMs) in visual reasoning. Different from prior studies, we shift our focus from evaluating standard performance to introducing a comprehensive safety evaluation suite, covering both out-of-distribution (OOD) generalization and adversarial robustness. For the OOD evaluation, we present two novel VQA datasets, each with one variant, designed to test model performance under challenging conditions. In exploring adversarial robustness, we propose a straightforward attack strategy for misleading VLLMs to produce visual-unrelated responses. Moreover, we assess the efficacy of two jailbreaking strategies, targeting either the vision or language component of VLLMs. Our evaluation of 21 diverse models, ranging from open-source VLLMs to GPT-4V, yields interesting observations: 1) Current VLLMs struggle with OOD texts but not images, unless the visual information is limited; and 2) These VLLMs can be easily misled by deceiving vision encoders only, and their vision-language training often compromise safety protocols. We release this safety evaluation suite at https://github.com/UCSC-VLAA/vllm-safety-benchmark.Comment: H.T., C.C., and Z.W. contribute equally. Work done during H.T. and Z.W.'s internship at UCSC, and C.C. and Y.Z.'s internship at UN

Use of non-steroidal anti-inflammatory drugs and adverse outcomes during the COVID-19 pandemic: A systematic review and meta-analysis.

Background There are concerns that the use of non-steroidal anti-inflammatory drugs (NSAIDs) may increase the risk of adverse outcomes among patients with coronavirus COVID-19. This study aimed to synthesize the evidence on associations between the use of NSAIDs and adverse outcomes. Methods A systematic search of WHO COVID-19 Database, Medline, the Cochrane Library, Web of Science, Embase, China Biology Medicine disc, China National Knowledge Infrastructure, and Wanfang Database for all articles published from January 1, 2020, to November 7, 2021, as well as a supplementary search of Google Scholar. We included all comparative studies that enrolled patients who took NSAIDs during the COVID-19 pandemic. Data extraction and quality assessment of methodology of included studies were completed by two reviewers independently. We conducted a meta-analysis on the main adverse outcomes, as well as selected subgroup analyses stratified by the type of NSAID and population (both positive for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or not). Findings Forty comparative studies evaluating 4,867,795 adult cases were identified. Twenty-eight (70%) of the included studies enrolled patients positive to SARS-CoV-2 tests. The use of NSAIDs did not reduce mortality outcomes among people with COVID-19 (number of studies [N] = 29, odds ratio [OR] = 0.93, 95% confidence interval [CI]: 0.75 to 1.14, I2  = 89%). Results suggested that the use of NSAIDs was not significantly associated with higher risk of SARS-CoV-2 infection in patients with or without COVID-19 (N = 10, OR = 0.96, 95% CI: 0.86 to 1.07, I2  = 78%; N = 8, aOR = 1.01, 95% CI: 0.94 to 1.09, I2  = 26%), or an increased probability of intensive care unit (ICU) admission (N = 12, OR = 1.28, 95% CI: 0.94 to 1.75, I2  = 82% ; N = 4, aOR = 0.89, 95% CI: 0.65 to 1.22, I2  = 60%), requiring mechanical ventilation (N = 11, OR = 1.11, 95% CI: 0.79 to 1.54, I2  = 63%; N = 5, aOR = 0.80, 95% CI: 0.52 to 1.24, I2  = 66%), or administration of supplemental oxygen (N = 5, OR = 0.80, 95% CI: 0.52 to 1.24, I2  = 63%; N = 2, aOR = 1.00, 95% CI: 0.89 to 1.12, I2  = 0%). The subgroup analysis revealed that, compared with patients not using any NSAIDs, the use of ibuprofen (N = 5, OR = 1.09, 95% CI: 0.50 to 2.39; N = 4, aOR = 0.95, 95% CI: 0.78 to 1.16) and COX-2 inhibitor (N = 4, OR = 0.62, 95% CI: 0.35 to 1.11; N = 2, aOR = 0.73, 95% CI: 0.45 to 1.18) were not associated with an increased risk of death. Interpretation Data suggests that NSAIDs such as ibuprofen, aspirin and COX-2 inhibitor, can be used safely among patients positive to SARS-CoV-2. However, for some of the analyses the number of studies were limited and the quality of evidence was overall low, therefore more research is needed to corroborate these findings. Funding There was no funding source for this study

Immunization expands B cells specific to HIV-1 V3 glycan in mice and macaques.

Broadly neutralizing monoclonal antibodies protect against infection with HIV-1 in animal models, suggesting that a vaccine that elicits these antibodies would be protective in humans. However, it has not yet been possible to induce adequate serological responses by vaccination. Here, to activate B cells that express precursors of broadly neutralizing antibodies within polyclonal repertoires, we developed an immunogen, RC1, that facilitates the recognition of the variable loop 3 (V3)-glycan patch on the envelope protein of HIV-1. RC1 conceals non-conserved immunodominant regions by the addition of glycans and/or multimerization on virus-like particles. Immunization of mice, rabbits and rhesus macaques with RC1 elicited serological responses that targeted the V3-glycan patch. Antibody cloning and cryo-electron microscopy structures of antibody-envelope complexes confirmed that immunization with RC1 expands clones of B cells that carry the anti-V3-glycan patch antibodies, which resemble precursors of human broadly neutralizing antibodies. Thus, RC1 may be a suitable priming immunogen for sequential vaccination strategies in the context of polyclonal repertoires

Using aquatic animals as partners to increase yield and maintain soil nitrogen in the paddy ecosystems

Whether species coculture can overcome the shortcomings of crop monoculture requires additional study. Here, we show how aquatic animals (i.e. carp, crabs, and softshell turtles) benefit paddy ecosystems when cocultured with rice. Three separate field experiments and three separate mesocosm experiments were conducted. Each experiment included a rice monoculture (RM) treatment and a rice-aquatic animal (RA) coculture treatment; RA included feed addition for aquatic animals. In the field experiments, rice yield was higher with RA than with RM, and RA also produced aquatic animal yields that averaged 0.52–2.57 t ha-1. Compared to their corresponding RMs, the three RAs had significantly higher apparent nitrogen (N)-use efficiency and lower weed infestation, while soil N contents were stable over time. Dietary reconstruction analysis based on 13C and 15N showed that 16.0–50.2% of aquatic animal foods were from naturally occurring organisms in the rice fields. Stable-isotope-labeling (13C) in the field experiments indicated that the organic matter decomposition rate was greater with RA than with RM. Isotope 15N labeling in the mesocosm experiments indicated that rice used 13.0–35.1% of the aquatic animal feed-N. All these results suggest that rice-aquatic animal coculture increases food production, increases N-use efficiency, and maintains soil N content by reducing weeds and promoting decomposition and complementary N use. Our study supports the view that adding species to monocultures may enhance agroecosystem functions