Flow Field in a Novel Short Residence Time Gas-solid Separator

The gas flow field in a short residence time separator was investigated. The tangential velocity in the separator housing increases with increasing angle to the positive x axis, and decreases with increasing radial position. A swirl of opposite direction to the main current in the separator housing occurs in the gas outlet

Range Anxiety Among Battery Electric Vehicle Users: Both Distance and Waiting Time Matter

Range anxiety is a major concern of battery electric vehicles (BEVs) users or potential users. Previous work has explored the influential factors of distance-related range anxiety. However, time-related range anxiety has rarely been explored. The time cost when charging or waiting to charge the BEVs can negatively impact BEV users' experience. As a preliminary attempt, this survey study investigated time-related anxiety by observing BEV users' charging decisions in scenarios when both battery level and time cost are of concern. We collected and analyzed responses from 217 BEV users in mainland China. The results revealed that time-related anxiety exists and could affect users' charging decisions. Further, users' charging decisions can be a result of the trade-off between distance-related and time-related anxiety, and can be moderated by several external factors (e.g., regions and individual differences). The findings can support the optimization of charge station distribution and EV charge recommendation algorithms.Comment: Accepted by Human Factors and Ergonomics Society International Annual Meeting 202

CFD simulation of hydrodynamic characteristics in a modified internally circulating fluidized bed mixer

A modified internally circulating fluidized bed (MICFB) was proposed as a particle mixer by coupling a pre-mixing section and a modified ICFB section[1]. Four slots were opened at the upside of the draft tube to improve further particle mixing. Hydrodynamics of MICFB was numerically investigated by multi-scale simulation based on a structure–dependent EMMS model[2]. Results showed that strong particle mixing mainly occurred in three regions, the bottom region, the draft tube region and the rectangular slots affected region. At the bottom region, due to the jet and the particles circulating from the annulus, bed density and particle velocity distributed unevenly. A cross-flow occurred in this region, with the circulating particles moving horizontally and the initial bubbles rising vertically. With increasing superficial gas velocity, particle rising velocity and particle circulating mass flow rate increased, leading to better particle mixing. In the slots affected region, radial distribution of bed density seems flat and the rising velocity decreased in the draft tube, while bed density significantly increased in the annulus. Nearly 62 wt. % particles entered the gas-solid separator region and then flowed into the annulus region, while the rest particles directly circulated into the annulus through the slots. A cross-flow of particles was also observed near the slots, with particles from the gas-solid separator region moving downwards and those circulating through slots flowing horizontally. Compared with ICFB with no slots, MICFB had a greater particle circulation mass flow rate with an increase of 20%, which consequently resulted in further particle mixing. Please click Additional Files below to see the full abstract

Notes on lie algebraic analysis of achromats

Normal form technique is a powerful method to analyze the achromat problem. Assume the one cell map M{sub cell} = ARe{sup :h{sub 3}}:{sub e}{sup :h{sub 4}}: A{sup {minus}1}, where h{sub 3},h{sub 4} are the normal forms of the generators of the unit cell map, and A is the nonlinear transformation that brings M{sub cell} into its normal form; then the map of the whole system is M{sub N} = M{sub cell}{sup N} = AR{sup N} A{sup {minus}1} = I, provided that we can set e{sup :h{sub 3}}:, e{sup :h{sub 4}}, and R{sup N} to the identity (or only {delta} dependent) maps. Therefore, the conditions to form an achromat are h{sub 3} and h{sub 4} equal to zero (or {delta} dependent only) and the total linear map is identity. In this report, we will apply these conditions to a FODO array (a simple model system) to make it an achromat. We will start from Hamiltonians and work all the way up to obtain the analytical expressions of the required sextupole and octupole strengths

Attention Is All You Need For Blind Room Volume Estimation

In recent years, dynamic parameterization of acoustic environments has raised increasing attention in the field of audio processing. One of the key parameters that characterize the local room acoustics in isolation from orientation and directivity of sources and receivers is the geometric room volume. Convolutional neural networks (CNNs) have been widely selected as the main models for conducting blind room acoustic parameter estimation, which aims to learn a direct mapping from audio spectrograms to corresponding labels. With the recent trend of self-attention mechanisms, this paper introduces a purely attention-based model to blindly estimate room volumes based on single-channel noisy speech signals. We demonstrate the feasibility of eliminating the reliance on CNN for this task and the proposed Transformer architecture takes Gammatone magnitude spectral coefficients and phase spectrograms as inputs. To enhance the model performance given the task-specific dataset, cross-modality transfer learning is also applied. Experimental results demonstrate that the proposed model outperforms traditional CNN models across a wide range of real-world acoustics spaces, especially with the help of the dedicated pretraining and data augmentation schemes.Comment: 5 pages, 4 figures, submitted ICASSP 202

Inhibition of Cathepsin S Produces Neuroprotective Effects after Traumatic Brain Injury in Mice

Cathepsin S (CatS) is a cysteine protease normally present in lysosomes. It has long been regarded as an enzyme that is primarily involved in general protein degradation. More recently, mounting evidence has shown that it is involved in Alzheimer disease, seizures, age-related inflammatory processes, and neuropathic pain. In this study, we investigated the time course of CatS protein and mRNA expression and the cellular distribution of CatS in a mouse model of traumatic brain injury (TBI). To clarify the roles of CatS in TBI, we injected the mice intraventricularly with LHVS, a nonbrain penetrant, irreversible CatS inhibitor, and examined the effect on inflammation and neurobehavioral function. We found that expression of CatS was increased as early as 1 h after TBI at both protein and mRNA levels. The increased expression was detected in microglia and neurons. Inhibition of CatS significantly reduced the level of TBI-induced inflammatory factors in brain tissue and alleviated brain edema. Additionally, administration of LHVS led to a decrease in neuronal degeneration and improved neurobehavioral function. These results imply that CatS is involved in the secondary injury after TBI and provide a new perspective for preventing secondary injury after TBI

Primary clear cell adenocarcinoma of the bladder with recurrence: a case report and literature review

Clear cell carcinoma of the bladder is a rare tumor of the bladder. There are few reports available on this rare disease, and no cases with recurrence were reported. Here we present a case of 68-year-old woman with primary clear cell carcinoma of the bladder, who underwent repeat TUR-BT and tumor recurrence. We also reviewed the previous treatments and prognoses in previous case reports and evaluate the proper treatment for this disease. Once the diagnosis is determined, the radical surgery should be recommended. The recurrence is not prevented based on post-TUR intravesical therapy

A Case-Based Reasoning Framework for Adaptive Prompting in Cross-Domain Text-to-SQL

Recent advancements in Large Language Models (LLMs), such as Codex, ChatGPT and GPT-4 have significantly impacted the AI community, including Text-to-SQL tasks. Some evaluations and analyses on LLMs show their potential to generate SQL queries but they point out poorly designed prompts (e.g. simplistic construction or random sampling) limit LLMs' performance and may cause unnecessary or irrelevant outputs. To address these issues, we propose CBR-ApSQL, a Case-Based Reasoning (CBR)-based framework combined with GPT-3.5 for precise control over case-relevant and case-irrelevant knowledge in Text-to-SQL tasks. We design adaptive prompts for flexibly adjusting inputs for GPT-3.5, which involves (1) adaptively retrieving cases according to the question intention by de-semantizing the input question, and (2) an adaptive fallback mechanism to ensure the informativeness of the prompt, as well as the relevance between cases and the prompt. In the de-semanticization phase, we designed Semantic Domain Relevance Evaluator(SDRE), combined with Poincar\'e detector(mining implicit semantics in hyperbolic space), TextAlign(discovering explicit matches), and Positector (part-of-speech detector). SDRE semantically and syntactically generates in-context exemplar annotations for the new case. On the three cross-domain datasets, our framework outperforms the state-of-the-art(SOTA) model in execution accuracy by 3.7\%, 2.5\%, and 8.2\%, respectively

Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain

Text-to-SQL aims at generating SQL queries for the given natural language questions and thus helping users to query databases. Prompt learning with large language models (LLMs) has emerged as a recent approach, which designs prompts to lead LLMs to understand the input question and generate the corresponding SQL. However, it faces challenges with strict SQL syntax requirements. Existing work prompts the LLMs with a list of demonstration examples (i.e. question-SQL pairs) to generate SQL, but the fixed prompts can hardly handle the scenario where the semantic gap between the retrieved demonstration and the input question is large. In this paper, we propose a retrieval-augmented prompting method for a LLM-based Text-to-SQL framework, involving sample-aware prompting and a dynamic revision chain. Our approach incorporates sample-aware demonstrations, which include the composition of SQL operators and fine-grained information related to the given question. To retrieve questions sharing similar intents with input questions, we propose two strategies for assisting retrieval. Firstly, we leverage LLMs to simplify the original questions, unifying the syntax and thereby clarifying the users' intentions. To generate executable and accurate SQLs without human intervention, we design a dynamic revision chain which iteratively adapts fine-grained feedback from the previously generated SQL. Experimental results on three Text-to-SQL benchmarks demonstrate the superiority of our method over strong baseline models