Organic Sulphur Transfers in Coke Oven Gas via Noncatalytic Partial Oxidation

The organic sulfur transformation was studied during coke oven gas to produce syngas via noncatalytic partial oxidation. The concentration of CS2 and thiophene was examined in syngas by sulfide detector. For comparison, the sulfur transfer was also studied in coke oven gas under dry and hydrous conditions. When the ratio of O2 / Gas was 0.32, complete thiophene and about 83% of CS2 in feed gas could be transformed via noncatalytic partial oxidation in the dry condition. It was mainly because of burner nozzle unique structure forming local hyperthemia, which benefited OH, O free radical and active atoms. During steam transforming to produce syngas, the ratio of water to carbon was less than 3, a higher ratio of O2/Gas favored sulfur transformation. However, compared to dry feed, transforming rate of CS2 and thiophene was decreased. This indicates that the steam added was disadvantageous to the transformation of organic sulphur during the production of syngas by noncatalytic partial oxidation, steam and mass H2S in feed gas, resulting in the decrease of local hyperthermia temperature and the formation of organic sulfu

Subsecond total-body imaging using ultrasensitive positron emission tomography.

A 194-cm-long total-body positron emission tomography/computed tomography (PET/CT) scanner (uEXPLORER), has been constructed to offer a transformative platform for human radiotracer imaging in clinical research and healthcare. Its total-body coverage and exceptional sensitivity provide opportunities for innovative studies of physiology, biochemistry, and pharmacology. The objective of this study is to develop a method to perform ultrahigh (100 ms) temporal resolution dynamic PET imaging by combining advanced dynamic image reconstruction paradigms with the uEXPLORER scanner. We aim to capture the fast dynamics of initial radiotracer distribution, as well as cardiac motion, in the human body. The results show that we can visualize radiotracer transport in the body on timescales of 100 ms and obtain motion-frozen images with superior image quality compared to conventional methods. The proposed method has applications in studying fast tracer dynamics, such as blood flow and the dynamic response to neural modulation, as well as performing real-time motion tracking (e.g., cardiac and respiratory motion, and gross body motion) without any external monitoring device (e.g., electrocardiogram, breathing belt, or optical trackers)

Evaluating the Robustness of Text-to-image Diffusion Models against Real-world Attacks

Text-to-image (T2I) diffusion models (DMs) have shown promise in generating high-quality images from textual descriptions. The real-world applications of these models require particular attention to their safety and fidelity, but this has not been sufficiently explored. One fundamental question is whether existing T2I DMs are robust against variations over input texts. To answer it, this work provides the first robustness evaluation of T2I DMs against real-world attacks. Unlike prior studies that focus on malicious attacks involving apocryphal alterations to the input texts, we consider an attack space spanned by realistic errors (e.g., typo, glyph, phonetic) that humans can make, to ensure semantic consistency. Given the inherent randomness of the generation process, we develop novel distribution-based attack objectives to mislead T2I DMs. We perform attacks in a black-box manner without any knowledge of the model. Extensive experiments demonstrate the effectiveness of our method for attacking popular T2I DMs and simultaneously reveal their non-trivial robustness issues. Moreover, we provide an in-depth analysis of our method to show that it is not designed to attack the text encoder in T2I DMs solely

Learnable Descent Algorithm for Nonsmooth Nonconvex Image Reconstruction

We propose a general learning based framework for solving nonsmooth and nonconvex image reconstruction problems. We model the regularization function as the composition of the $l_{2,1}$ norm and a smooth but nonconvex feature mapping parametrized as a deep convolutional neural network. We develop a provably convergent descent-type algorithm to solve the nonsmooth nonconvex minimization problem by leveraging the Nesterov's smoothing technique and the idea of residual learning, and learn the network parameters such that the outputs of the algorithm match the references in training data. Our method is versatile as one can employ various modern network structures into the regularization, and the resulting network inherits the guaranteed convergence of the algorithm. We also show that the proposed network is parameter-efficient and its performance compares favorably to the state-of-the-art methods in a variety of image reconstruction problems in practice

On the Security Bootstrapping in Named Data Networking

By requiring all data packets been cryptographically authenticatable, the Named Data Networking (NDN) architecture design provides a basic building block for secured networking. This basic NDN function requires that all entities in an NDN network go through a security bootstrapping process to obtain the initial security credentials. Recent years have witnessed a number of proposed solutions for NDN security bootstrapping protocols. Built upon the existing results, in this paper we take the next step to develop a systematic model of security bootstrapping: Trust-domain Entity Bootstrapping (TEB). This model is based on the emerging concept of trust domain and describes the steps and their dependencies in the bootstrapping process. We evaluate the expressiveness and sufficiency of this model by using it to describe several current bootstrapping protocols

Digital financial inclusion and the urban–rural income gap in China: empirical research based on the Theil index

This study examined the effect of digital financial inclusion in reducing the urban–rural income inequality in China. Based on citylevel panel data, the results showed that digital financial inclusion narrowed the urban–rural income gap significantly by boosting economic growth. The results were robust when the core explained variables were replaced. Heterogeneity analysis showed that digital financial inclusion indicates regional differences in narrowing the urban–rural income gap. This study puts forward corresponding countermeasures for the development of digital financial inclusion and adds to the research on this very topical subjec