Design and fabrication of whisker hybrid ceramic membranes with narrow pore size distribution and high permeability via co-sintering process

Ceramic microfiltration membranes (MF) with narrow pore size distribution and high permeability are widely used for the preparation of ceramic ultrafiltration membranes (UF) and in wastewater treatment. In this work, a whisker hybrid ceramic membrane (WHCM) consisting of a whisker layer and an alumina layer was designed to achieve high permeability and narrow pore size distribution based on the relative resistance obtained using the Hagen-Poiseuille and Darcy equations. The whisker layer was designed to prevent the penetration of alumina particles into the support and ensure a high porosity of the membrane, while the alumina layer provided a smooth surface and narrow pore size distribution. Mass transfer resistance is critical to reduce the effect of the membrane layers. It was found that the resistance of the WHCM depended largely on the alumina layer. The effect of the support and whisker layer on the resistance of the WHCM was negligible. This was consistent with theoretical calculations. The WHCM was co-sintered at 1000 °C, which resulted in a high permeability of ~ 645 L m−1 h−1 ;bar−1 and a narrow pore size distribution of ~ 100 nm. Co-sintering was carried out on a macroporous ceramic support (just needed one sintering process), which greatly reduced the preparation cost and time. The WHCM (as the sub-layer) also showed a great potential to be used for the fabrication of ceramic UF membranes with high repeatability. Hence, this study provides an efficient approach for the fabrication of advanced ceramic MF membranes on macroporous supports, allowing for rapid prototyping with scale-up capability

TDANet: A Novel Temporal Denoise Convolutional Neural Network With Attention for Fault Diagnosis

Fault diagnosis plays a crucial role in maintaining the operational integrity of mechanical systems, preventing significant losses due to unexpected failures. As intelligent manufacturing and data-driven approaches evolve, Deep Learning (DL) has emerged as a pivotal technique in fault diagnosis research, recognized for its ability to autonomously extract complex features. However, the practical application of current fault diagnosis methods is challenged by the complexity of industrial environments. This paper proposed the Temporal Denoise Convolutional Neural Network With Attention (TDANet), designed to improve fault diagnosis performance in noise environments. This model transforms one-dimensional signals into two-dimensional tensors based on their periodic properties, employing multi-scale 2D convolution kernels to extract signal information both within and across periods. This method enables effective identification of signal characteristics that vary over multiple time scales. The TDANet incorporates a Temporal Variable Denoise (TVD) module with residual connections and a Multi-head Attention Fusion (MAF) module, enhancing the saliency of information within noisy data and maintaining effective fault diagnosis performance. Evaluation on two datasets, CWRU (single sensor) and Real aircraft sensor fault (multiple sensors), demonstrates that the TDANet model significantly outperforms existing deep learning approaches in terms of diagnostic accuracy under noisy environments

Facile co-sintering process to fabricate sustainable antifouling silver nanoparticles (AgNPs)-enhanced tight ceramic ultrafiltration membranes for protein separation

Protein separation in chemical industry applications using tight ceramic ultrafiltration (UF) membranes with multilayer asymmetric structures is hindered by challenges in their fabrication and fouling phenomenon. In this study, a facile co-sintering method for fabrication of silver nanoparticles (AgNPs)-enhanced tight ceramic ultrafiltration membranes was comprehensively investigated. The introduction of AgNPs into the membrane layer not only controlled the membrane surface charge properties, but also alleviated the sintering stress in the co-sintering process, ensuring a complete membrane layer owing to the higher ductility. The AgNPs obtained from silver nitrate were introduced before the formation of boehmite nucleation, providing a uniform distribution of AgNPs within boehmite owing to the electric double layer. The final UF membranes prepared by the co-sintering process exhibited a molecular weight cut-off of 9000 Da and permeance of 62 Lm−2h−1bar−1. Furthermore, the isoelectric point of the membrane surface could be controlled by the AgNPs (from 9.0 to 2.7), providing sustainable antifouling properties for protein purification owing to the electrostatic repulsion force. The AgNPs-enhanced ceramic membrane material also exhibits a higher stability without silver leakage due to the thermal treatment at 1000 °C. The proposed facile co-sintering process for fabrication of antifouling ceramic UF membranes with the assistance of AgNPs could decrease the sintering time and energy consumption, and thus is promising for industrial protein separation applications

Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects

We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additional data and can handle glossy objects or bright lighting. It is a progressive inverse rendering approach, which consists of three stages. First, we reconstruct the scene radiance and signed distance function (SDF) with our novel regularization strategy for specular reflections. Our approach considers both the diffuse and specular colors, which allows for handling complex view-dependent lighting effects for surface reconstruction. Second, we distill light visibility and indirect illumination from the learned SDF and radiance field using learnable mapping functions. Third, we design a method for estimating the ratio of incoming direct light represented via Spherical Gaussians reflected in a specular manner and then reconstruct the materials and direct illumination of the scene. Experimental results demonstrate that the proposed method outperforms the current state-of-the-art in recovering surfaces, materials, and lighting without relying on any additional data.Comment: 12 pages, 10 figures. Project page: https://authors-hub.github.io/Factored-Neu

Ultrasound Assisted Synthesis of Size-Controlled Aqueous Colloids for the Fabrication of Nanoporous Zirconia Membrane

Permeation and separation efficiency of ceramic membranes are strongly dependent on their nanoporous structures, especially on the pore size. In this work, ultrasound is employed to form the size-controlled ZrO2 nanoparticles, and a ceramic membrane is prepared with tunable pore size. Under the ultrasound treatment, H+ from water plays a key role in the synthesis process. The cavitation caused by ultrasound promotes the hydrolysis of the precursor in water, which produces a large number of H+. These H+ will react with precipitant added and generate cyclic tetrameric units. Excess H+ can peptize cyclic tetrameric units and form an electrical double layer, resulting in a stable sol. Unlike ultrasound treatment, precipitant will react directly with the precursor and generate precipitation if there is no ultrasound added. Moreover, cavitation is good for the dispersion of cyclic tetrameric units. The particle size of Zr-based colloidal sol can be tuned in the ranges of 1.5 to 120 nm by altering the molar ratio of precursor to precipitant, ultrasonic power density and radiation time. Meanwhile, ultrasonic power density and radiation time have effects on grain size and the crystalline transition temperature of particles which influence performance of the ceramic membrane. As a result, membranes exhibit high performance together with high permeability and desirable rejection. To develop such a simple and controllable method for tuning particle size is extremely important in the preparation of nanoporous ceramic membranes

SARS Pandemic Exposure Impaired Early Childhood Development in China

Social and mental stressors associated with the pandemic of a novel infectious disease, e.g., COVID-19 or SARS may promote long-term effects on child development. However, reports aimed at identifying the relationship between pandemics and child health are limited. A retrospective study was conducted to associate the SARS pandemic in 2003 with development milestones or physical examinations among longitudinal measurements of 14,647 children. Experiencing SARS during childhood was associated with delayed milestones, with hazard ratios of 3.17 (95% confidence intervals CI: 2.71, 3.70), 3.98 (3.50, 4.53), 4.96 (4.48, 5.49), or 5.57 (5.00, 6.20) for walking independently, saying a complete sentence, counting 0–10, and undressing him/herself for urination, respectively. These results suggest relevant impacts from COVID-19 on child development should be investigated

MA4DIV: Multi-Agent Reinforcement Learning for Search Result Diversification

The objective of search result diversification (SRD) is to ensure that selected documents cover as many different subtopics as possible. Existing methods primarily utilize a paradigm of "greedy selection", i.e., selecting one document with the highest diversity score at a time. These approaches tend to be inefficient and are easily trapped in a suboptimal state. In addition, some other methods aim to approximately optimize the diversity metric, such as $\alpha$-NDCG, but the results still remain suboptimal. To address these challenges, we introduce Multi-Agent reinforcement learning (MARL) for search result DIVersity, which called MA4DIV. In this approach, each document is an agent and the search result diversification is modeled as a cooperative task among multiple agents. This approach allows for directly optimizing the diversity metrics, such as $\alpha$-NDCG, while achieving high training efficiency. We conducted preliminary experiments on public TREC datasets to demonstrate the effectiveness and potential of MA4DIV. Considering the limited number of queries in public TREC datasets, we construct a large-scale dataset from industry sources and show that MA4DIV achieves substantial improvements in both effectiveness and efficiency than existing baselines on a industrial scale dataset

FLM-101B: An Open LLM and How to Train It with $100K Budget

Large language models (LLMs) have achieved remarkable success in NLP and multimodal tasks, among others. Despite these successes, two main challenges remain in developing LLMs: (i) high computational cost, and (ii) fair and objective evaluations. In this paper, we report a solution to significantly reduce LLM training cost through a growth strategy. We demonstrate that a 101B-parameter LLM with 0.31T tokens can be trained with a budget of 100K US dollars. Inspired by IQ tests, we also consolidate an additional range of evaluations on top of existing evaluations that focus on knowledge-oriented abilities. These IQ evaluations include symbolic mapping, rule understanding, pattern mining, and anti-interference. Such evaluations minimize the potential impact of memorization. Experimental results show that our model, named FLM-101B, trained with a budget of 100K US dollars, achieves performance comparable to powerful and well-known models, e.g., GPT-3 and GLM-130B, especially on the additional range of IQ evaluations. The checkpoint of FLM-101B is released at https://huggingface.co/CofeAI/FLM-101B