Environmental investigation of bio-modification of steel slag through microbially induced carbonate precipitation

Steel slag (SS) is one of byproduct of steel manufacture industry. The environmental concerns of SS may limit their re-use in different applications. The goal of this study was to investigate the leaching behavior of metals from SS before and after treated by microbially induced carbonate precipitation (MICP). Toxicity characteristic leaching procedure, synthetic precipitation leaching procedure and water leaching tests were performed to evaluate the leaching behavior of major elements (Fe, Mg and Ca) and trace elements (Ba, Cu and Mn) in three scenarios. The concentrations of leaching metals increased with the content of SS. After it reached the peak concentration, the leaching concentration decreased with the content of SS. The leachability of all elements concerned in this study was below 0.5%. The carbonate generated from the MICP process contributed to the low leachability of metals. After bio-modified by MICP process, the leaching concentrations of Ba from TCLP, SPLP and WLT tests were below 2.0 mg/L, which was the limit in drinking water regulated by U.S. EPA. The concentrations of Cu leached out from MICP-treated SS-sand samples were below 1.3 mg/L which is the limit regulated by national secondary drinking water. Compared with the regulations of U.S.EPA and Mississippi Department of Environment Quality (MDEQ), MICP-treated samples were classified as non-hazardous materials with respects to the leaching of metals. Meanwhile, maximum contaminant limits regulated by U.S.EPA states that MICP-treated SS are eco-friendly materials that can be reused as construction materials

MVControl: Adding Conditional Control to Multi-view Diffusion for Controllable Text-to-3D Generation

We introduce MVControl, a novel neural network architecture that enhances existing pre-trained multi-view 2D diffusion models by incorporating additional input conditions, e.g. edge maps. Our approach enables the generation of controllable multi-view images and view-consistent 3D content. To achieve controllable multi-view image generation, we leverage MVDream as our base model, and train a new neural network module as additional plugin for end-to-end task-specific condition learning. To precisely control the shapes and views of generated images, we innovatively propose a new conditioning mechanism that predicts an embedding encapsulating the input spatial and view conditions, which is then injected to the network globally. Once MVControl is trained, score-distillation (SDS) loss based optimization can be performed to generate 3D content, in which process we propose to use a hybrid diffusion prior. The hybrid prior relies on a pre-trained Stable-Diffusion network and our trained MVControl for additional guidance. Extensive experiments demonstrate that our method achieves robust generalization and enables the controllable generation of high-quality 3D content. Code available at https://github.com/WU-CVGL/MVControl/.Comment: Project page: https://lizhiqi49.github.io/MVControl

A Weakly-Supervised Streaming Multilingual Speech Model with Truly Zero-Shot Capability

In this paper, we introduce our work of building a Streaming Multilingual Speech Model (SM2), which can transcribe or translate multiple spoken languages into texts of the target language. The backbone of SM2 is Transformer Transducer, which has high streaming capability. Instead of human labeled speech translation (ST) data, SM2 models are trained using weakly supervised data generated by converting the transcriptions in speech recognition corpora with a machine translation service. With 351 thousand hours of anonymized speech training data from 25 languages, SM2 models achieve comparable or even better ST quality than some recent popular large-scale non-streaming speech models. More importantly, we show that SM2 has the truly zero-shot capability when expanding to new target languages, yielding high quality ST results for {source-speech, target-text} pairs that are not seen during training.Comment: submitted to ICASSP 202

A Discrete-Time Algorithm for Stiffness Extraction from sEMG and Its Application in Antidisturbance Teleoperation

© 2016 Peidong Liang et al. We have developed a new discrete-time algorithm of stiffness extraction from muscle surface electromyography (sEMG) collected from human operator's arms and have applied it for antidisturbance control in robot teleoperation. The variation of arm stiffness is estimated from sEMG signals and transferred to a telerobot under variable impedance control to imitate human motor control behaviours, particularly for disturbance attenuation. In comparison to the estimation of stiffness from sEMG, the proposed algorithm is able to reduce the nonlinear residual error effect and to enhance robustness and to simplify stiffness calibration. In order to extract a smoothing stiffness enveloping from sEMG signals, two enveloping methods are employed in this paper, namely, fast linear enveloping based on low pass filtering and moving average and amplitude monocomponent and frequency modulating (AM-FM) method. Both methods have been incorporated into the proposed stiffness variance estimation algorithm and are extensively tested. The test results show that stiffness variation extraction based on the two methods is sensitive and robust to attenuation disturbance. It could potentially be applied for teleoperation in the presence of hazardous surroundings or human robot physical cooperation scenarios

USB-NeRF: Unrolling Shutter Bundle Adjusted Neural Radiance Fields

Neural Radiance Fields (NeRF) has received much attention recently due to its impressive capability to represent 3D scene and synthesize novel view images. Existing works usually assume that the input images are captured by a global shutter camera. Thus, rolling shutter (RS) images cannot be trivially applied to an off-the-shelf NeRF algorithm for novel view synthesis. Rolling shutter effect would also affect the accuracy of the camera pose estimation (e.g. via COLMAP), which further prevents the success of NeRF algorithm with RS images. In this paper, we propose Unrolling Shutter Bundle Adjusted Neural Radiance Fields (USB-NeRF). USB-NeRF is able to correct rolling shutter distortions and recover accurate camera motion trajectory simultaneously under the framework of NeRF, by modeling the physical image formation process of a RS camera. Experimental results demonstrate that USB-NeRF achieves better performance compared to prior works, in terms of RS effect removal, novel view image synthesis as well as camera motion estimation. Furthermore, our algorithm can also be used to recover high-fidelity high frame-rate global shutter video from a sequence of RS images

Token-Level Serialized Output Training for Joint Streaming ASR and ST Leveraging Textual Alignments

In real-world applications, users often require both translations and transcriptions of speech to enhance their comprehension, particularly in streaming scenarios where incremental generation is necessary. This paper introduces a streaming Transformer-Transducer that jointly generates automatic speech recognition (ASR) and speech translation (ST) outputs using a single decoder. To produce ASR and ST content effectively with minimal latency, we propose a joint token-level serialized output training method that interleaves source and target words by leveraging an off-the-shelf textual aligner. Experiments in monolingual (it-en) and multilingual (\{de,es,it\}-en) settings demonstrate that our approach achieves the best quality-latency balance. With an average ASR latency of 1s and ST latency of 1.3s, our model shows no degradation or even improves output quality compared to separate ASR and ST models, yielding an average improvement of 1.1 WER and 0.4 BLEU in the multilingual case

The Protective Effects of Trypsin Inhibitor on Hepatic Ischemia-Reperfusion Injury and Liver Graft Survival

The aim of this study was to explore the protective effects of ulinastatin (urinary trypsin inhibitor, UTI) on liver ischemia-reperfusion injury (IRI) and graft survival. We employed mouse liver cold IRI and orthotopic liver transplantation (OLTx) models. UTI was added to lactated Ringer’s (LR) solution for liver perfusion and preservation in vitro or combined with UTI injection intraperitoneally to the liver graft recipient. Our results indicated that UTI supplementation protected the liver from cold IRI in a dose-dependent manner and prolonged liver graft survival from extended cold preserved liver donors significantly. The underlying mechanism of UTI on liver IRI may be mediated by inhibition of proinflammatory cytokine release, increasing the expression of the antiapoptotic gene Bcl-2 and decreasing the expression of the proapoptosis genes of Caspase-3 and Bax, and further protects hepatocytes from apoptotic death and improves liver function