504 research outputs found
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
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