Optimum Operating Conditions Confirmation and Effectiveness Analysis Based on Research of the Coagulation and Precipitation Integrated Process

AbstractAiming at the increasing small-scale water supply projects, the increasingly serious pollution of the water resource and stringent water quality standards, the coagulation and precipitation integrated process on the basis of quiescent precipitation was proposed in this study. By experiments in the integrated reactor, the optimum process operating conditions were confirmed. It is verified that the optimal dosage of PAC was 16mg/L in the optimum temperature and pH range. The repeated utilization volume of the floc mud from the former precipitation period was the same as 6% of the water volume in the next processing period, and the corresponding optimal dosage of PAC was 8mg/L with 50% reduction of the flocculants dosage, while the residual turbidity was less than 1.0NTU, which could reach the standard after simple filtration and disinfection procedure. With low energy consumption, little land occupation, low cost, high efficiency of the water production and strong anti shock loading capability, this process could guarantee the safety of drinking water supply, and deserve popularization and application

Successful radiofrequency ablation of a right posteroseptal accessory pathway through an anomalous inferior vena cava and azygos continuation in a patient with incomplete situs inversus

We present a 43-year-old patient with paroxysmal supraventricular tachycardia. In the process of catheter ablation, we found interruption of the inferior vena cava with azygos continuation with incomplete situs inversus. In this patient, we adopted the lower approach via the anomalous inferior vena cava and azygos continuation to achieve stability of radiofrequency catheter for right posteroseptal accessory pathway, and successfully abolished the preexcitation

M2DF: Multi-grained Multi-curriculum Denoising Framework for Multimodal Aspect-based Sentiment Analysis

Multimodal Aspect-based Sentiment Analysis (MABSA) is a fine-grained Sentiment Analysis task, which has attracted growing research interests recently. Existing work mainly utilizes image information to improve the performance of MABSA task. However, most of the studies overestimate the importance of images since there are many noise images unrelated to the text in the dataset, which will have a negative impact on model learning. Although some work attempts to filter low-quality noise images by setting thresholds, relying on thresholds will inevitably filter out a lot of useful image information. Therefore, in this work, we focus on whether the negative impact of noisy images can be reduced without modifying the data. To achieve this goal, we borrow the idea of Curriculum Learning and propose a Multi-grained Multi-curriculum Denoising Framework (M2DF), which can achieve denoising by adjusting the order of training data. Extensive experimental results show that our framework consistently outperforms state-of-the-art work on three sub-tasks of MABSA.Comment: Accepted by EMNLP 202

Examining the Factors Influencing Tourists’ Destination: A Case of Nanhai Movie Theme Park in China

The present study used a stimulus-organism-response (S-O-R) theoretical framework to examine the relationship between theme park tourists’ experience, brand identity, brand satisfaction, and brand loyalty in China. By using the structural equation model (CB-SEM), this paper illustrates the process of forming destination brand loyalty for sustainable tourism on theme parks. The results suggested a second-order structure of tourism experience. The first-order four factors have different impacts on the second-order tourism experience. Activity experience is the most important factor influencing tourism experience, followed by environment experience, then facility experience, and finally interaction experience. In terms of tourism experience, individual brand identity-brand satisfaction-brand loyalty is the most important path of a theme park on tourists’ behavioral intention, among which brand satisfaction plays the most significant partial mediation effect in the relationship between individual identity and destination loyalty. It is expected that the results of this study provide a reference for improving tourists’ brand loyalty to achieve sustainable development of theme parks

The effect of strategic synergy between local and neighborhood environmental regulations on green innovation efficiency: The perspective of industrial transfer

Considering the environmental governance dilemma caused by environmental decentralization, this study aims to explore whether the strategic synergy between local and neighborhood environmental regulations can be an essential tool to improve green innovation efficiency and achieve sustainable development. Using the data of industrial firms from 2005 to 2019, and employing network slack-based measure and Tobit regression, this study provides empirical evidence that (1) the green innovation efficiency shows an upward trend in fluctuations but still has great room for improvement; (2) the direct impact of local environmental regulation on green innovation is positive, but the indirect impact through forcing firms to transfer into the neighborhood with loose regulation is negative, that is, the industrial transfer plays a suppression effect; (3) the strategic synergy of environmental regulations has U-shaped and direct effect on green innovation and also has a positive indirect effect through inhibiting the firm's behavior transferring into the neighborhood. This study reveals the influence mechanism of the strategic synergy of local-neighborhood environmental regulations and offers empirical evidence to explain the reason why synergistic environmental governance can effectively promote green innovation, which provides the theoretical guidance for government to formulate environmental policies and construct an environmental governance system

GPT-NER: Named Entity Recognition via Large Language Models

Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised baselines. This is due to the gap between the two tasks the NER and LLMs: the former is a sequence labeling task in nature while the latter is a text-generation model. In this paper, we propose GPT-NER to resolve this issue. GPT-NER bridges the gap by transforming the sequence labeling task to a generation task that can be easily adapted by LLMs e.g., the task of finding location entities in the input text "Columbus is a city" is transformed to generate the text sequence "@@Columbus## is a city", where special tokens @@## marks the entity to extract. To efficiently address the "hallucination" issue of LLMs, where LLMs have a strong inclination to over-confidently label NULL inputs as entities, we propose a self-verification strategy by prompting LLMs to ask itself whether the extracted entities belong to a labeled entity tag. We conduct experiments on five widely adopted NER datasets, and GPT-NER achieves comparable performances to fully supervised baselines, which is the first time as far as we are concerned. More importantly, we find that GPT-NER exhibits a greater ability in the low-resource and few-shot setups, when the amount of training data is extremely scarce, GPT-NER performs significantly better than supervised models. This demonstrates the capabilities of GPT-NER in real-world NER applications where the number of labeled examples is limited

Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images

Optical coherence microscopy (OCM) imaging of th