The effect of environmental regulation on firm productivity: evidence from pulp and paper industry in China

The relationship between environmental regulation and firm productivity has been widely debated but inconsistencies in findings across different studies. Using detailed firm-level micro-data from 2000 to 2007, this paper employs difference-in-difference combined with matching based on entropy balancing method to explore the effect of environmental regulation on firm total factor productivity (TFP) in pulp and paper industry in China. Our main findings are as following: Firstly, stricter environmental regulation, as represented by the Wastewater Discharge Standards for Pulp and Paper Industry in Shandong province, increases firm TFP significantly. Moreover, the coefficients of interest are robust to multiple robustness checks. Secondly, dynamic effects estimates reveal that when faced with this phase-in environmental regulation, firms take the foreseeably increasing strictness into account from the very beginning and prefer to take one-step adjustment to reach full compliance. Thirdly, potential mechanism analysis finds that the positive effect mainly comes from the improvement of resource allocation efficiency within firms. Fourthly, the heterogeneity test indicates that the effect of environmental regulation on firm TFP is heterogeneous across firms with different sizes, ages, ownerships, capital intensity, and export status. Finally, this paper provides convincing and insightful evidence that environmental regulation has the potential to achieve the dual goals of environmental sustainability and economic growth and is thus of broader significance for understanding the enforcement of environmental regulation in developing countries

Investigation of wave propagation in piezoelectric helical waveguides with the spectral finite element method

The dispersion behaviors of wave propagation in waveguides of piezoelectric helical structures are investigated. By using the tensor analysis in the helical curve coordinate, the general strain − displacement relationship of piezoelectric helix is firstly considered. This paper's formulation is based on the spectral finite element which just requires the discretization of the cross-section with high-order spectral elements. The eigenvalue matrix of the dispersion relationship between wavenumbers and frequencies is obtained. Numerical examples on PZT5A and Ba2NaNb5O15 helical waveguides of a wide range of lay angles are presented. The effects of the piezoelectric on the dispersive properties and the variation tendency of dispersion curves on helix angles are shown. The mechanism of mode separation in piezoelectric helical waveguides is further analyzed through studying waves structures of the flexural modes

Design of Air Thermal Recovery Experiment Device and Analysis of Thermal Efficiency

AbstractIt was significant for energy save and environment protection that heat recovering high temperature and high humidity air directly discharged into the atmosphere. Based on high temperature and high humidity mine retention tower and return air fluid, the air heat recovery simulation experiment device was designed, and air heat recovery experiment was researched by using spray heat exchanger. Heat exchange efficiency was tested by experiment with certain water spray coefficient and different nozzle number, and experiment result that was error analysis and data correction through compared with theoretical analysis results. Result showed that the air heat recovery simulation device can recycle heat of air fluid stably, and rise temperature of spray cold water to about 15°C. The heat exchange efficiency would be 90% with certain water spray coefficient, which is basically identical with theoretical analysis. Energy saving effect of this device was remarkable with high heat exchange efficiency

Outlier Detection Ensemble with Embedded Feature Selection

Feature selection places an important role in improving the performance of outlier detection, especially for noisy data. Existing methods usually perform feature selection and outlier scoring separately, which would select feature subsets that may not optimally serve for outlier detection, leading to unsatisfying performance. In this paper, we propose an outlier detection ensemble framework with embedded feature selection (ODEFS), to address this issue. Specifically, for each random sub-sampling based learning component, ODEFS unifies feature selection and outlier detection into a pairwise ranking formulation to learn feature subsets that are tailored for the outlier detection method. Moreover, we adopt the thresholded self-paced learning to simultaneously optimize feature selection and example selection, which is helpful to improve the reliability of the training set. After that, we design an alternate algorithm with proved convergence to solve the resultant optimization problem. In addition, we analyze the generalization error bound of the proposed framework, which provides theoretical guarantee on the method and insightful practical guidance. Comprehensive experimental results on 12 real-world datasets from diverse domains validate the superiority of the proposed ODEFS.Comment: 10pages, AAAI202

ODSum: New Benchmarks for Open Domain Multi-Document Summarization

Open-domain Multi-Document Summarization (ODMDS) is a critical tool for condensing vast arrays of documents into coherent, concise summaries. With a more inter-related document set, there does not necessarily exist a correct answer for the retrieval, making it hard to measure the retrieving performance. We propose a rule-based method to process query-based document summarization datasets into ODMDS datasets. Based on this method, we introduce a novel dataset, ODSum, a sophisticated case with its document index interdependent and often interrelated. We tackle ODMDS with the \textit{retrieve-then-summarize} method, and the performance of a list of retrievers and summarizers is investigated. Through extensive experiments, we identify variances in evaluation metrics and provide insights into their reliability. We also found that LLMs suffer great performance loss from retrieving errors. We further experimented methods to improve the performance as well as investigate their robustness against imperfect retrieval. We will release our data and code at https://github.com/yale-nlp/ODSum

The Research on the Effects of the Development of Competencies of Teacher Training Program through Clinical Education Practicum; the comparison by PAC analysis of graduates from master’s program.

This study is to verify the effectiveness of clinical training program in teacher training. The Center of Educational Research and Practice provides variation of practicum opportunities for the graduate students in teacher training program, aimed to enhance their competencies as a teacher. Data was collected by structured interview using PAC (Personal Attitude Construct) analysis to verify difference between three groups of six graduates who had no participation, partial participation, and full participation. The results showed partial and full participation group had built confidence in their assessment skills and intervention skills for children as well as ability to search for methods dealing with children and parents in school settings. Additionally, the full participation group, who experienced providing individual social skills training to children, showed higher confidence by systemizing their experience of training program in practice, and formed positive self-perception about own skills. However, all group showed difficulty in modifying the theory and methods they had learned into their practices

4K-Resolution Photo Exposure Correction at 125 FPS with ~8K Parameters

The illumination of improperly exposed photographs has been widely corrected using deep convolutional neural networks or Transformers. Despite with promising performance, these methods usually suffer from large parameter amounts and heavy computational FLOPs on high-resolution photographs. In this paper, we propose extremely light-weight (with only ~8K parameters) Multi-Scale Linear Transformation (MSLT) networks under the multi-layer perception architecture, which can process 4K-resolution sRGB images at 125 Frame-Per-Second (FPS) by a Titan RTX GPU. Specifically, the proposed MSLT networks first decompose an input image into high and low frequency layers by Laplacian pyramid techniques, and then sequentially correct different layers by pixel-adaptive linear transformation, which is implemented by efficient bilateral grid learning or 1x1 convolutions. Experiments on two benchmark datasets demonstrate the efficiency of our MSLTs against the state-of-the-arts on photo exposure correction. Extensive ablation studies validate the effectiveness of our contributions. The code is available at https://github.com/Zhou-Yijie/MSLTNet.Comment: WACV202

Frequency-astigmatism asymmetric nonlinear conversion of structured light lasers

Nonlinear optics of structured light has recently delivered intriguing fundamental physical phenomena in light-matter interactions and advanced applications from classical imaging to quantum informatics. The mutual interaction between spin, orbital angular momentum (OAM) and wavelength is extensively studied in such cases. In this work, we go beyond only considering OAM and wavelength by taking the nonlinear frequency conversion and transverse mode astigmatism conversion as two building blocks and investigating how single modes and complicated multiplexed modes evolve after them. In particular, We found a generalized law of nonlinear conversion structured light from experiments and theories, that the converted modes are highly related to the sequence of these two blocks, obeying an inherent (non)commutative rule in which. This effect not only creates extended structured laser modes but serve as new rules in nonlinear structured light manipulation