Mercury is ranked 3^(rd)as a global pollutant because of its long persistence in the environment. Approximately 65% of its anthropogenic emission (Hg^(0)) to the atmosphere is from coal-thermal power plants. Thus, the...Mercury is ranked 3^(rd)as a global pollutant because of its long persistence in the environment. Approximately 65% of its anthropogenic emission (Hg^(0)) to the atmosphere is from coal-thermal power plants. Thus, the Hg^(0)emission control from coal-thermal power plants is inevitable. Therefore, multiple sorbent materials were synthesized using a one-step pyrolysis method to capture the Hg^(0)from simulated coal syngas. Results showed, the Hg^(0)removal performance of the sorbents increased by the citric acid/ultrasonic application.T5CUF_(0.3)demonstrated the highest Hg^(0)capturing performance with an adsorption capacity of 106.81 μg/g within 60 min at 200 °C under complex simulated syngas mixture (20% CO,20% H_(2), 10 ppm V HCl, 6% H_(2)O, and 400 ppm V H_(2)S). The Hg^(0)removal mechanism was proposed, revealing that the chemisorption governs the Hg^(0)removal process. Besides, the active Hg^(0)removal performance is attributed to the high dispersion of valence Fe_(3)O_(4)and lattice oxygen (α) contents over the T5CUF_(0.3)surface. In addition, the temperature programmed desorption (TPD) and XPS analysis confirmed that H_(2)S/HCl gases generate active sites over the sorbent surface, facilitating high Hg^(0)adsorption from syngas. This work represented a facile and practical pathway for utilizing cheap and eco-friendly tea waste to control the Hg^(0)emission.展开更多
Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still ...Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still lacking.Unlike other SLs,the visuals of the Urdu Language are different.This study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this purpose.Unlike existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited resources.We conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and prediction.To enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise filtering.Comparative analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of 0.95.Additionally,our model exhibited superior performance in Precision,Recall,and F1-score evaluations.This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.展开更多
基金supported by the Natural Science Basic Research Plan in Shaanxi Province of China (No. 2019JLM-13)。
文摘Mercury is ranked 3^(rd)as a global pollutant because of its long persistence in the environment. Approximately 65% of its anthropogenic emission (Hg^(0)) to the atmosphere is from coal-thermal power plants. Thus, the Hg^(0)emission control from coal-thermal power plants is inevitable. Therefore, multiple sorbent materials were synthesized using a one-step pyrolysis method to capture the Hg^(0)from simulated coal syngas. Results showed, the Hg^(0)removal performance of the sorbents increased by the citric acid/ultrasonic application.T5CUF_(0.3)demonstrated the highest Hg^(0)capturing performance with an adsorption capacity of 106.81 μg/g within 60 min at 200 °C under complex simulated syngas mixture (20% CO,20% H_(2), 10 ppm V HCl, 6% H_(2)O, and 400 ppm V H_(2)S). The Hg^(0)removal mechanism was proposed, revealing that the chemisorption governs the Hg^(0)removal process. Besides, the active Hg^(0)removal performance is attributed to the high dispersion of valence Fe_(3)O_(4)and lattice oxygen (α) contents over the T5CUF_(0.3)surface. In addition, the temperature programmed desorption (TPD) and XPS analysis confirmed that H_(2)S/HCl gases generate active sites over the sorbent surface, facilitating high Hg^(0)adsorption from syngas. This work represented a facile and practical pathway for utilizing cheap and eco-friendly tea waste to control the Hg^(0)emission.
文摘Deaf people or people facing hearing issues can communicate using sign language(SL),a visual language.Many works based on rich source language have been proposed;however,the work using poor resource language is still lacking.Unlike other SLs,the visuals of the Urdu Language are different.This study presents a novel approach to translating Urdu sign language(UrSL)using the UrSL-CNN model,a convolutional neural network(CNN)architecture specifically designed for this purpose.Unlike existingworks that primarily focus on languageswith rich resources,this study addresses the challenge of translating a sign language with limited resources.We conducted experiments using two datasets containing 1500 and 78,000 images,employing a methodology comprising four modules:data collection,pre-processing,categorization,and prediction.To enhance prediction accuracy,each sign image was transformed into a greyscale image and underwent noise filtering.Comparative analysis with machine learning baseline methods(support vectormachine,GaussianNaive Bayes,randomforest,and k-nearest neighbors’algorithm)on the UrSL alphabets dataset demonstrated the superiority of UrSL-CNN,achieving an accuracy of 0.95.Additionally,our model exhibited superior performance in Precision,Recall,and F1-score evaluations.This work not only contributes to advancing sign language translation but also holds promise for improving communication accessibility for individuals with hearing impairments.
