Sentiment Analysis of Tourism Reviews: An exploratory study based on CNNs built on LSTM model

This study is to develop a sentiment analysis system for customers’ review on a scenic site. It is based on Convolutional Neural Networks (CNNs) built on Long Short-Term Memory (LSTM) models for text feature extraction under a deep learning framework. The CNNs built on LSTM models applies convolutional filters of CNNs repeatedly operate on the output matrix of LSTM to obtain robust text feature vector. In this study, the optimal parameter configurations for each component of CNNs and LSTM are given individually in the first place. Then, the entire optimal parameter configuration for the integration recognition frame of the system is identified around the optimum of each component. The results demonstrate that, by employing such a method, the accuracy for sentiment analysis with CNNs built on LSTM model, compared with a single CNNs or LSTM model, is improved by 3.13% and 1.71% respectively

Research on Data Mining of Archives Based on Knowledge Management

Abstract: As the era of knowledge economy has arrived, to implement the knowledge management on digital archives is the requirement of this era, and is the necessity of the archives' own development as well. Data mining provided pre-preparation and technical support for the effective management of knowledge resources for the digital archives. In this paper, a brief introduction has been made on related theories of knowledge management and data mining. And the processes of data mining of archives base on knowledge management has been put forward in this paper

Thermal properties of carbon black aqueous nanofluids for solar absorption

In this article, carbon black nanofluids were prepared by dispersing the pretreated carbon black powder into distilled water. The size and morphology of the nanoparticles were explored. The photothermal properties, optical properties, rheological behaviors, and thermal conductivities of the nanofluids were also investigated. The results showed that the nanofluids of high-volume fraction had better photothermal properties. Both carbon black powder and nanofluids had good absorption in the whole wavelength ranging from 200 to 2,500 nm. The nanofluids exhibited a shear thinning behavior. The shear viscosity increased with the increasing volume fraction and decreased with the increasing temperature at the same shear rate. The thermal conductivity of carbon black nanofluids increased with the increase of volume fraction and temperature. Carbon black nanofluids had good absorption ability of solar energy and can effectively enhance the solar absorption efficiency

Discussion for H

Nonsingular H-matrices and positive stable matrices play an important role in the stability of neural network system. In this paper, some criteria for nonsingular H-matrices are obtained by the theory of diagonally dominant matrices and the obtained result is introduced into identifying the stability of neural networks. So the criteria for nonsingular H-matrices are expanded and their application on neural network system is given. Finally, the effectiveness of the results is illustrated by numerical examples

Bevacizumab loaded CalliSpheres® bronchial arterial chemoembolization combined with immunotherapy and targeted therapy for advanced lung adenocarcinoma

Background: As a new drug delivery and embolization system, drug-eluted bronchial artery chemoembolization (DEB-BACE) can not only embolize the tumor blood supply artery but also load chemotherapy drugs and slowly release them into the local environment. Bevacizumab (BEV) combined with chemotherapy drugs has attained significant achievements in the first-line treatment of advanced non-squamous non-small cell lung cancer (NSCLC). The role of BEV-loaded DEB-BACE combined with immunotherapy and targeted therapy in patients with lung adenocarcinoma (LUAD) is unclear. This study was designed to evaluate the efficacy and safety of bevacizumab-loaded CalliSpheres® bronchial arterial chemoembolization combined with immunotherapy and targeted therapy in patients with lung adenocarcinoma.Methods: Nine patients with LUAD who received BEV-loaded CalliSpheres® BACE combined with immunotherapy and targeted therapy from 1 Jan 2021 to Dec 2021 were included in this study. The primary endpoint was the disease control rate (DCR) and the objective response rate (ORR). The secondary endpoints were the overall survival rates (OS) at 6 months and 12 months. The tumor response was evaluated according to the mRECIST standard. Safety was assessed by the occurrences of adverse events and the severity of the adverse events.Results: All patients received CalliSpheres® BACE loaded with BEV (200 mg) in combination with immunotherapy and targeted therapy. A total of nine patients received the BACE procedures 20 times, four of them received a third session of BACE, three underwent a second session of DEB-BACE, and two underwent one cycle of DEB-BACE. Partial response and stable disease were found in seven (77.8%), and two (22.2%) patients, respectively, 1 month after the last multimodal treatment. The ORR at 1, 3, 6, and 12 months was 77.8%, 66.7%, 44.4%, and 33.3%, respectively, while the DCR was 100%, 77.8%, 44.4%, and 33.3%, respectively. The OS rates at 6-and 12-month were 77.8% and 66.7%, respectively. There were no serious adverse events.Conclusion: BEV-loaded CalliSpheres® transcatheter bronchial arterial chemoembolization combined with immunotherapy and targeted therapy is a promising and well-tolerated treatment for patients with lung adenocarcinoma

Neuron with Steady Response Leads to Better Generalization

Regularization can mitigate the generalization gap between training and inference by introducing inductive bias. Existing works have already proposed various inductive biases from diverse perspectives. However, none of them explores inductive bias from the perspective of class-dependent response distribution of individual neurons. In this paper, we conduct a substantial analysis of the characteristics of such distribution. Based on the analysis results, we articulate the Neuron Steadiness Hypothesis: the neuron with similar responses to instances of the same class leads to better generalization. Accordingly, we propose a new regularization method called Neuron Steadiness Regularization (NSR) to reduce neuron intra-class response variance. Based on the Complexity Measure, we theoretically guarantee the effectiveness of NSR for improving generalization. We conduct extensive experiments on Multilayer Perceptron, Convolutional Neural Networks, and Graph Neural Networks with popular benchmark datasets of diverse domains, which show that our Neuron Steadiness Regularization consistently outperforms the vanilla version of models with significant gain and low additional computational overhead.Comment: Accepted by NeurIPS'2

Size Effect on the Compressive Strength of Laminated Bamboo Lumber

The size effect on the axial compressive performance of laminated bamboo lumber is studied through compression tests on three groups of short columns with different heights and section sizes. The failure modes, bearing capacity, strain distribution, and deformation capacity were analyzed. Based on the test results, three groups of stress-strain models of laminated bamboo lumber with different sizes are presented. The simulated results were in good agreement with the test results. The slope method and the parameter method were used to calculate the size effect coefficient and the results showed that the linear regression parameter analysis method is more efficient for analyzing the size effect. It is concluded that the size effect coefficients of compressive strength, ultimate load, elastic modulus, ductility, and compressibility are 0.043 (1/23.26), 0.6676 (1/1.52), 0.064 (1/15.63), 0.0529 (1/18.90), and 0.133 (1/7.52), respectively

Label-free Node Classification on Graphs with Large Language Models (LLMS)

In recent years, there have been remarkable advancements in node classification achieved by Graph Neural Networks (GNNs). However, they necessitate abundant high-quality labels to ensure promising performance. In contrast, Large Language Models (LLMs) exhibit impressive zero-shot proficiency on text-attributed graphs. Yet, they face challenges in efficiently processing structural data and suffer from high inference costs. In light of these observations, this work introduces a label-free node classification on graphs with LLMs pipeline, LLM-GNN. It amalgamates the strengths of both GNNs and LLMs while mitigating their limitations. Specifically, LLMs are leveraged to annotate a small portion of nodes and then GNNs are trained on LLMs' annotations to make predictions for the remaining large portion of nodes. The implementation of LLM-GNN faces a unique challenge: how can we actively select nodes for LLMs to annotate and consequently enhance the GNN training? How can we leverage LLMs to obtain annotations of high quality, representativeness, and diversity, thereby enhancing GNN performance with less cost? To tackle this challenge, we develop an annotation quality heuristic and leverage the confidence scores derived from LLMs to advanced node selection. Comprehensive experimental results validate the effectiveness of LLM-GNN. In particular, LLM-GNN can achieve an accuracy of 74.9% on a vast-scale dataset \products with a cost less than 1 dollar.Comment: The code will be available soon via https://github.com/CurryTang/LLMGN