Data-Augmented and Retrieval-Augmented Context Enrichment in Chinese Media Bias Detection

With the increasing pursuit of objective reports, automatically understanding media bias has drawn more attention in recent research. However, most of the previous work examines media bias from Western ideology, such as the left and right in the political spectrum, which is not applicable to Chinese outlets. Based on the previous lexical bias and informational bias structure, we refine it from the Chinese perspective and go one step further to craft data with 7 fine-grained labels. To be specific, we first construct a dataset with Chinese news reports about COVID-19 which is annotated by our newly designed system, and then conduct substantial experiments on it to detect media bias. However, the scale of the annotated data is not enough for the latest deep-learning technology, and the cost of human annotation in media bias, which needs a lot of professional knowledge, is too expensive. Thus, we explore some context enrichment methods to automatically improve these problems. In Data-Augmented Context Enrichment (DACE), we enlarge the training data; while in Retrieval-Augmented Context Enrichment (RACE), we improve information retrieval methods to select valuable information and integrate it into our models to better understand bias. Extensive experiments are conducted on both our dataset and an English dataset BASIL. Our results show that both methods outperform our baselines, while the RACE methods are more efficient and have more potential

Optimizing the thermal performance of building envelopes for energy saving in underground office buildings in various climates of China

This article investigates the influence of the thermal performance of building envelopes on annual energy consumption in a ground-buried office building by means of the dynamic building energy simulation, aiming at offering reasonable guidelines for the energy efficient design of envelopes for underground office buildings in China. In this study, the accuracy of dealing with the thermal process for underground buildings by using the Designer's Energy Simulation Tool (DeST) is validated by measured data. The analyzed results show that the annual energy consumptions for this type of buildings vary significantly, and it is based on the value of the overall heat transfer coefficient (U-value) of the envelopes. Thus, it is necessary to optimize the U-value for underground buildings located in various climatic zones in China. With respect to the roof, an improvement in its thermal performance is significantly beneficial to the underground office building in terms of annual energy demand. With respect to the external walls, the optimized U-values completely change with the distribution of the climate zones. The recommended optimal values for various climate zones of China are also specified as design references for public office building in underground in terms of the building energy efficiency

IndiVec: An Exploration of Leveraging Large Language Models for Media Bias Detection with Fine-Grained Bias Indicators

This study focuses on media bias detection, crucial in today's era of influential social media platforms shaping individual attitudes and opinions. In contrast to prior work that primarily relies on training specific models tailored to particular datasets, resulting in limited adaptability and subpar performance on out-of-domain data, we introduce a general bias detection framework, IndiVec, built upon large language models. IndiVec begins by constructing a fine-grained media bias database, leveraging the robust instruction-following capabilities of large language models and vector database techniques. When confronted with new input for bias detection, our framework automatically selects the most relevant indicator from the vector database and employs majority voting to determine the input's bias label. IndiVec excels compared to previous methods due to its adaptability (demonstrating consistent performance across diverse datasets from various sources) and explainability (providing explicit top-k indicators to interpret bias predictions). Experimental results on four political bias datasets highlight IndiVec's significant superiority over baselines. Furthermore, additional experiments and analysis provide profound insights into the framework's effectiveness

Impacts of urban microclimate on summertime sensible and latent energy demand for cooling in residential buildings of Hong Kong

The urban heat island (UHI) and urban moisture island (UMI) effect can be significant in Hong Kong due to its high-density land utilization, and this can strongly affect building energy performance. While the UHI’ energy impact has been rather intensively studied recently, the UMI effect on latent energy is still underexplored, especially for humid subtropical climate like Hong Kong. This study investigated the intensity of UHI and UMI in Hong Kong, and its impacts on the sensible and latent cooling demand of residential buildings in summer. Firstly, a ten-year weather dataset from 2004 to 2013 for the six stations selected based on the local climate zone (LCZ) scheme was analysed. The results show that the urban area of Hong Kong appears as both a heat and moisture island during summer nights but as cooling and dry islands during daytime, and the nocturnal UHI and UMI intensity vary significantly with different LCZs. Furthermore, the energy performance of a typical residential building in Hong Kong was simulated with measured weather data for the selected stations as an input. The urban building shows a higher sensible cooling demand, approximately twice that of the comparative rural one, and the latent cooling demand could be up to 96% higher. Both sensible and latent cooling energy demand decrease with increasing LCZ grades. Our study highlights that both UHI and UMI effect should be considered in the estimation of building energy in Hong Kong due to their significant impacts on the cooling energy demand

PA-MSHA improves prognosis of patients undergoing radical cystectomy: a retrospective cohort study using inverse probability of treatment weighting

ObjectiveTo observe the effect of Pseudomonas aeruginosa mannose-sensitive hemagglutinin (PA-MSHA) on the prognosis and the incidence of lymphatic leakage in patients undergoing radical cystectomy (RC).MethodA total of 129 patients who underwent RC in Lanzhou University Second Hospital from 2013 to 2022 were enrolled in this study. They were divided into 43 patients treated with PA-MSHA and 86 patients in the control group. Inverse probability of treatment weighting (IPTW) was applied to reduce potential selection bias. Kaplan-Meier method and Cox regression analysis were used to analyze the effect of PA-MSHA on the survival of patients and the incidence of postoperative lymphatic leakage.ResultsThe PA-MSHA group exhibited improved overall survival (OS) and cancer-specific survival (CSS) rates compared to the control group. The 3-year and 5-year overall survival (OS) rates for the PA-MSHA group were 69.1% and 53.2%, respectively, compared to 55.6% and 45.3% for the control group (Log-rank=3.218, P=0.072). The 3-year and 5-year cancer-specific survival (CSS) rates for the PA-MSHA group were 73.3% and 56.5%, respectively, compared to 58.0% and 47.3% for the control group (Log-rank=3.218, P=0.072). Additionally, the 3-year and 5-year progression-free survival (PFS) rates for the PA-MSHA group were 74.4% and 56.8%, respectively, compared to 57.1% and 52.2% for the control group (Log-rank=2.016, P=0.156). Multivariate Cox regression analysis indicates that lymph node metastasis and distant metastasis are poor prognostic factors for patients, while the use of PA-MSHA can improve patients’ OS (HR: 0.547, 95%CI: 0.304–0.983, P=0.044), PFS (HR: 0.469, 95%CI: 0.229–0.959, P=0.038) and CSS (HR: 0.484, 95%CI: 0.257–0.908, P=0.024). The same trend was observed in the cohort After IPTW adjustment. Although there was no significant difference in the incidence of postoperative lymphatic leakage [18.6% (8/35) vs. 15.1% (84.9%), P=0.613] and pelvic drainage volume [470 (440) ml vs. 462.5 (430) ml, P=0.814] between PA-MSHA group and control group, PA-MSHA could shorten the median retention time of drainage tube (7.0 d vs 9.0 d) (P=0.021).ConclusionPA-MSHA may improve radical cystectomy in patients with OS, PFS, and CSS, shorten the pelvic drainage tube retention time

An Adaptive Multi-Sensor Data Fusion Method Based on Deep Convolutional Neural Networks for Fault Diagnosis of Planetary Gearbox

A fault diagnosis approach based on multi-sensor data fusion is a promising tool to deal with complicated damage detection problems of mechanical systems. Nevertheless, this approach suffers from two challenges, which are (1) the feature extraction from various types of sensory data and (2) the selection of a suitable fusion level. It is usually difficult to choose an optimal feature or fusion level for a specific fault diagnosis task, and extensive domain expertise and human labor are also highly required during these selections. To address these two challenges, we propose an adaptive multi-sensor data fusion method based on deep convolutional neural networks (DCNN) for fault diagnosis. The proposed method can learn features from raw data and optimize a combination of different fusion levels adaptively to satisfy the requirements of any fault diagnosis task. The proposed method is tested through a planetary gearbox test rig. Handcraft features, manual-selected fusion levels, single sensory data, and two traditional intelligent models, back-propagation neural networks (BPNN) and a support vector machine (SVM), are used as comparisons in the experiment. The results demonstrate that the proposed method is able to detect the conditions of the planetary gearbox effectively with the best diagnosis accuracy among all comparative methods in the experiment

Optimization for energy efficiency of underground building envelope thermal performance in different climate zones of China

The object of this article is to investigate the influence of thermal performance of envelopes in shallow-buried buildings on energy consumption for different climate zones of China. For the purpose of this study, an effective building energy simulation tool (DeST) developed by Tsinghua University was chosen to model the heat transfer in underground buildings. Based on the simulative results, energy consumption for heating and cooling for the whole year was obtained. The results showed that the relationship between energy consumption and U-value of envelopes for underground buildings is different compared with above-ground buildings: improving thermal performance of exterior walls cannot reduce energy consumption, on the contrary, may result in more energy cost. Besides, it is can be derived that optimized U-values of underground building envelopes vary with climate zones of China in this study. For severe cold climate zone, the optimized U-value of underground building envelopes is 0.8W/(m2·K); for cold climate zone, the optimized U-value is 1.5W/(m2·K); for warm climate zone, the U-value is 2.0W/(m2·K)

Optimization for energy efficiency of underground building envelope thermal performance in different climate zones of China

The object of this article is to investigate the influence of thermal performance of envelopes in shallow-buried buildings on energy consumption for different climate zones of China. For the purpose of this study, an effective building energy simulation tool (DeST) developed by Tsinghua University was chosen to model the heat transfer in underground buildings. Based on the simulative results, energy consumption for heating and cooling for the whole year was obtained. The results showed that the relationship between energy consumption and U-value of envelopes for underground buildings is different compared with above-ground buildings: improving thermal performance of exterior walls cannot reduce energy consumption, on the contrary, may result in more energy cost. Besides, it is can be derived that optimized U-values of underground building envelopes vary with climate zones of China in this study. For severe cold climate zone, the optimized U-value of underground building envelopes is 0.8W/(m2·K); for cold climate zone, the optimized U-value is 1.5W/(m2·K); for warm climate zone, the U-value is 2.0W/(m2·K)

The role of configural processing in face classification by race: an ERP study

The current study investigated the time course of the other-race classification advantage (ORCA) in the subordinate classification of normally configured faces and distorted faces by race. Slightly distorting the face configuration delayed even more the categorization of own-race faces having no conspicuous effect on other race faces. The N170 was not sensitive to configural distortions and faces’ races. The P3 was enhanced for other-race than own-race faces and reduced by configural manipulation only for own-race faces. We suggest that the source of ORCA is the configural analysis applied by default while processing own-race faces