BEIKE NLP at SemEval-2022 Task 4: Prompt-Based Paragraph Classification for Patronizing and Condescending Language Detection

PCL detection task is aimed at identifying and categorizing language that is patronizing or condescending towards vulnerable communities in the general media.Compared to other NLP tasks of paragraph classification, the negative language presented in the PCL detection task is usually more implicit and subtle to be recognized, making the performance of common text-classification approaches disappointed. Targeting the PCL detection problem in SemEval-2022 Task 4, in this paper, we give an introduction to our team's solution, which exploits the power of prompt-based learning on paragraph classification. We reformulate the task as an appropriate cloze prompt and use pre-trained Masked Language Models to fill the cloze slot. For the two subtasks, binary classification and multi-label classification, DeBERTa model is adopted and fine-tuned to predict masked label words of task-specific prompts. On the evaluation dataset, for binary classification, our approach achieves an F1-score of 0.6406; for multi-label classification, our approach achieves an macro-F1-score of 0.4689 and ranks first in the leaderboard

Responses of soil respiration and its temperature/moisture sensitivity to precipitation in three subtropical forests in southern China

Both long-term observation data and model simulations suggest an increasing chance of serious drought in the dry season and extreme flood in the wet season in southern China, yet little is known about how changes in precipitation pattern will affect soil respiration in the region. We conducted a field experiment to study the responses of soil respiration to precipitation manipulations – precipitation exclusion to mimic drought, double precipitation to simulate flood, and ambient precipitation as control (abbr. EP, DP and AP, respectively) – in three subtropical forests in southern China. The three forest sites include Masson pine forest (PF), coniferous and broad-leaved mixed forest (MF) and monsoon evergreen broad-leaved forest (BF). Our observations showed that altered precipitation strongly influenced soil respiration, not only through the well-known direct effects of soil moisture on plant and microbial activities, but also by modification of both moisture and temperature sensitivity of soil respiration. In the dry season, soil respiration and its temperature sensitivity, as well as fine root and soil microbial biomass, showed rising trends with precipitation increases in the three forest sites. Contrarily, the moisture sensitivity of soil respiration decreased with precipitation increases. In the wet season, different treatments showed different effects in three forest sites. The EP treatment decreased fine root biomass, soil microbial biomass, soil respiration and its temperature sensitivity, but enhanced soil moisture sensitivity in all three forest sites. The DP treatment significantly increased soil respiration, fine root and soil microbial biomass in the PF only, and no significant change was found for the soil temperature sensitivity. However, the DP treatment in the MF and BF reduced soil temperature sensitivity significantly in the wet season. Our results indicated that soil respiration would decrease in the three subtropical forests if soil moisture continues to decrease in the future. More rainfall in the wet season could have limited effect on the response of soil respiration to the rising of temperature in the BF and MF

Reduction of satellite flywheel microvibration using rubber shock absorbers

Microvibration of flywheels strongly affects the imaging quality of space cameras. A passive vibration method is used in this study to reduce the effect of microvibration. A rubber shock absorber was designed and installed on a satellite. The angular displacement of the second mirror was measured via a fiber optic gyroscopic method. The measured data were imported into MATLAB and analyzed by different methods. The data was plotted as a root-mean-square graph of angular displacement at different speeds along the x-axis, a waterfall plot of the attenuation of force in the x direction, the vibration spectrum between the frequency and displacement amplitude, and the time domain response of the inverse Fourier transform of the spectrum. The results show that the microvibration of the flywheel causes significant vibration of the imaging system, and that adding a rubber shock absorber can reduce the vibration. The proposed method is a new attempt to analyze microvibration, and can be applied to the engineering design of flywheels

Reduction of satellite flywheel microvibration using rubber shock absorbers

Microvibration of flywheels strongly affects the imaging quality of space cameras. A passive vibration method is used in this study to reduce the effect of microvibration. A rubber shock absorber was designed and installed on a satellite. The angular displacement of the second mirror was measured via a fiber optic gyroscopic method. The measured data were imported into MATLAB and analyzed by different methods. The data was plotted as a root-mean-square graph of angular displacement at different speeds along the x-axis, a waterfall plot of the attenuation of force in the x direction, the vibration spectrum between the frequency and displacement amplitude, and the time domain response of the inverse Fourier transform of the spectrum. The results show that the microvibration of the flywheel causes significant vibration of the imaging system, and that adding a rubber shock absorber can reduce the vibration. The proposed method is a new attempt to analyze microvibration, and can be applied to the engineering design of flywheels

A Systematic Literature Review on Performance Evaluation of Power System From the Perspective of Sustainability

Sustainability is a comprehensive concept that integrates at least three dimensions of environment, economy and society. The power system is the primary source of greenhouse gas emissions, adversely impacting environmental sustainability. It also generates necessary energy supplies, which promote economic and social sustainable development. Based on the sustainability nature of power system, this study puts forward an improved methodology, namely “Planning-Searching-Screening-Reporting-Reflecting” (PSSRR Cycle) to review the literature systematically on power system performance evaluation from a sustainability perspective over the past 20 years, with the aim of describing the current state of the whole performance evaluation system including the evaluation framework, evaluation indicators and evaluation methods, and providing research suggestions for future research. This study finds in the current literature that the Triple Bottom Line theory is the most commonly used theoretical evaluation framework; environmental and economic sustainability indicators are more emphasized; the DEA and MCDM methods are the more common evaluation methods. This study presents some future research notes, including improving the Sustainable Balanced Scorecard as a sustainable performance evaluation framework, emphasizing more social sustainability indicators, and using a combination of existing evaluation methods to make performance evaluation more efficient and accurate

Evaluation of the Sustainable Forest Management Performance in Forestry Enterprises Based on a Hybrid Multi-Criteria Decision-Making Model: A Case Study in China

Sustainable Forest Management (SFM) can fully use forest resources and improve the economic, environmental, and social sustainability of forest areas. Forestry enterprises play a crucial role in the implementation of SFM. However, the previous literature on SFM pays little attention to the subject of forestry enterprises. This paper aims to extend research on SFM from a macro perspective to the micro level of forestry enterprises. Taking the Triple Bottom Line (TBL) as a theoretical framework and the Montreal Process Criteria and Indicators (MP C&Is) as a basis, this paper constructs an indicator system to evaluate the performance of SFM of forestry enterprises from economic, social, and environmental aspects. Then, we apply the hybrid Multi-Criteria Decision-Making (MCDM) methods, i.e., the Best–Worst Method (BWM) and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, to construct the methodological system for SFM performance evaluation of forestry enterprises. Through a questionnaire survey of 68 academics and researchers, we determine the weights of the SFM indicators and select a representative forestry enterprise as a case study. The effectiveness of this SFM performance evaluation model is then demonstrated through its application to the case study of forestry enterprises in China. Through the application of the model, this paper evaluates the enterprise’s SFM performance over the five-year period 2017–2021 and proposes appropriate policy recommendations and improvements. It is found that environmental factors are the primary factors of SFM in forestry enterprises. Forestry enterprises should not only pay attention to economic benefits but also to the use of forest resources and the protection of forest ecosystems to better achieve SFM

A New Probabilistic Transformation in Generalized Power Space

AbstractThe mapping from the belief to the probability domain is a controversial issue, whose original purpose is to make (hard) decision, but for contrariwise to erroneous widespread idea/claim, this is not the only interest for using such mappings nowadays. Actually the probabilistic transformations of belief mass assignments are very useful in modern multitarget multisensor tracking systems where one deals with soft decisions, especially when precise belief structures are not always available due to the existence of uncertainty in human being's subjective judgments. Therefore, a new probabilistic transformation of interval-valued belief structure is put forward in the generalized power space, in order to build a subjective probability measure from any basic belief assignment defined on any model of the frame of discernment. Several examples are given to show how the new transformation works and we compare it to the main existing transformations proposed in the literature so far. Results are provided to illustrate the rationality and efficiency of this new proposed method making the decision problem simpler

Changing rainfall frequency rather than drought rapidly alters annual soil respiration in a tropical forest

Tropical forests play an important role in global carbon (C) cycling due to high primary productivity and rapid litter and soil organic C decomposition. However, it is still unclear how changing rainfall will influence soil CO2 losses (i.e. via soil respiration) in tropical forests. Here, using a rainfall and litter manipulation experiment in a tropical forest, we show that enhanced litter-leached dissolved organic carbon (DOC) production with increased rainfall frequency drives substantial CO2 loss via soil respiration. A 50% increase in rainfall frequency (no change in total rainfall amount) enhanced inputs of DOC by 28%, total dissolved nitrogen (TDN) by 17%, and total dissolved phosphorus (TDP) by 34% through leaching from litter layer to soil surface likely due to faster litter decomposition rate, and stimulated soil respiration by similar to 17% (about 1.16 t C ha(-1) yr(-1)). Soil respiration responded to altered rainfall frequency with limited when litter layer was removed. Accordingly, soil microbial biomass C (MSC) and fine root biomass were increased by 23% and 20%, respectively only in the plots with litter layer. A 50% reduction in total rainfall (no change in rainfall frequency) did not change litter-leached DOC and nutrients fluxes, soil MBC, fine root biomass, or annual mean soil respiration rates. The new finding - that enhanced leached-DOC production with increased rainfall frequency drives profound increases in soil respiration in tropical forests - suggests that future climate changes may have significant impacts on soil C dynamics and global C budget, and argues for the importance of incorporating this underappreciated feedback into prognostic models used to predict future C-climate interactions

Changing rainfall frequency rather than drought rapidly alters annual soil respiration in a tropical forest

Tropical forests play an important role in global carbon (C) cycling due to high primary productivity and rapid litter and soil organic C decomposition. However, it is still unclear how changing rainfall will influence soil CO2 losses (i.e. via soil respiration) in tropical forests. Here, using a rainfall and litter manipulation experiment in a tropical forest, we show that enhanced litter-leached dissolved organic carbon (DOC) production with increased rainfall frequency drives substantial CO2 loss via soil respiration. A 50% increase in rainfall frequency (no change in total rainfall amount) enhanced inputs of DOC by 28%, total dissolved nitrogen (TDN) by 17%, and total dissolved phosphorus (TDP) by 34% through leaching from litter layer to soil surface likely due to faster litter decomposition rate, and stimulated soil respiration by ∼17% (about 1.16 t C ha−1 yr−1). Soil respiration responded to altered rainfall frequency with limited when litter layer was removed. Accordingly, soil microbial biomass C (MBC) and fine root biomass were increased by 23% and 20%, respectively only in the plots with litter layer. A 50% reduction in total rainfall (no change in rainfall frequency) did not change litter-leached DOC and nutrients fluxes, soil MBC, fine root biomass, or annual mean soil respiration rates. The new finding – that enhanced leached-DOC production with increased rainfall frequency drives profound increases in soil respiration in tropical forests – suggests that future climate changes may have significant impacts on soil C dynamics and global C budget, and argues for the importance of incorporating this underappreciated feedback into prognostic models used to predict future C-climate interactions