Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages ot...Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.展开更多
Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and landslides.Thu...Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and landslides.Thus,this study uses geospatial modeling to produce soil erosion susceptibility maps(SESM)for the Hangu region,Khyber Pakhtunkhwa(KPK),Pakistan.The Hangu region,located in the Kohat Plateau of KPK,Pakistan,is particularly susceptible to soil erosion due to its unique geomorphological and climatic characteristics.Moreover,the Hangu region is characterized by a combination of steep slopes,variable rainfall patterns,diverse land use,and distinct soil types,all of which contribute to the complexity and severity of soil erosion processes.These factors necessitate a detailed and region-specific study to develop effective soil conservation strategies.In this research,we detected and mapped 1013 soil erosion points and prepared 12 predisposing factors(elevation,aspect,slope,Normalized Differentiate Vegetation Index(NDVI),drainage network,curvature,Land Use Land Cover(LULC),rainfall,lithology,contour,soil texture,and road network)of soil erosion using GIS platform.Additionally,GIS-based statistical models like the weight of evidence(WOE)and frequency ratio(FR)were applied to produce the SESM for the study area.The SESM was reclassified into four classes,i.e.,low,medium,high,and very high zone.The results of WOE for SESM show that 16.39%,33.02%,29.27%,and 21.30%of areas are covered by low,medium,high,and very high zones,respectively.In contrast,the FR results revealed that 16.50%,24.33%,35.55%,and 23.59%of the areas are occupied by low,medium,high,and very high classes.Furthermore,the reliability of applied models was evaluated using the Area Under Curve(AUC)technique.The validation results utilizing the area under curve showed that the success rate curve(SRC)and predicted rate curve(PRC)for WOE are 82%and 86%,respectively,while SRC and PRC for FR are 85%and 96%,respectively.The validation results revealed that the FR model performance is better and more reliable than the WOE.展开更多
Manganese was leached from a low-grade manganese ore(LGMO)using banana peel as the reductant in a dilute sulfuric acid medium.The effects of banana peel amount,H2SO4 concentration,reaction temperature,and time on Mn l...Manganese was leached from a low-grade manganese ore(LGMO)using banana peel as the reductant in a dilute sulfuric acid medium.The effects of banana peel amount,H2SO4 concentration,reaction temperature,and time on Mn leaching from the complex LGMO were studied.A leaching efficiency of~98%was achieved at a leaching time of 2 h,banana peel amount of 4 g,leaching temperature of 120°C,manganese ore amount of 5 g,and sulfuric acid concentration of 15vol%.The phase,microstructural,and chemical analyses of LGMO samples before and after the leaching process confirmed the successful leaching of manganese.Furthermore,the leaching process followed the shrinking core model and the leaching rate was controlled by a surface chemical reaction(1−(1−x)^1/3=kt)mechanism with an apparent activation energy of 40.19 kJ·mol^−1.展开更多
On the： basis of wavelet theory, we propose an outlier-detection algorithm for satellite gravity ometry by applying a wavelet-shrinkage-de-noising method to some simulation data with white noise and ers. The result S...On the： basis of wavelet theory, we propose an outlier-detection algorithm for satellite gravity ometry by applying a wavelet-shrinkage-de-noising method to some simulation data with white noise and ers. The result Shows that this novel algorithm has a 97% success rate in outlier identification and that be efficiently used for pre-processing real satellite gravity gradiometry data.展开更多
A sharp increase in economic and human development has multiplied the carbon intensity due to which there is a significant need of effective strategies in order to curb carbon emissions.Thus,the present study aims to ...A sharp increase in economic and human development has multiplied the carbon intensity due to which there is a significant need of effective strategies in order to curb carbon emissions.Thus,the present study aims to examine the effective of green finance,eco-innovation,renewable energy output(REO),renewable energy consumption(REC),and carbon taxes on carbon dioxide(CO_(2))emissions in BRICS countries in the time of 2001-2020.Cross-sectional autoregressive distributed lag(CS ARDL)is used to test the connection among the variables.Empirical estimations of CS-ARDL approach validates the effectiveness of green finance,eco-innovation,REO,REC,carbon taxes,and industrialization as the relationship of these factors with carbon emissions is negative in nature in BRICS economies.Based on the evidences,the study recommends the formulation of environmentally friendly practices and advancement in green finances to mitigate carbon emissions.展开更多
基金Researchers supporting Project Number(RSPD2024R576),King Saud University,Riyadh,Saudi Arabia.
文摘Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.
基金The authors extend their appreciation to Researchers Supporting Project number(RSP2024R390),King Saud University,Riyadh,Saudi Arabia.
文摘Soil erosion is a crucial geo-environmental hazard worldwide that affects water quality and agriculture,decreases reservoir storage capacity due to sedimentation,and increases the danger of flooding and landslides.Thus,this study uses geospatial modeling to produce soil erosion susceptibility maps(SESM)for the Hangu region,Khyber Pakhtunkhwa(KPK),Pakistan.The Hangu region,located in the Kohat Plateau of KPK,Pakistan,is particularly susceptible to soil erosion due to its unique geomorphological and climatic characteristics.Moreover,the Hangu region is characterized by a combination of steep slopes,variable rainfall patterns,diverse land use,and distinct soil types,all of which contribute to the complexity and severity of soil erosion processes.These factors necessitate a detailed and region-specific study to develop effective soil conservation strategies.In this research,we detected and mapped 1013 soil erosion points and prepared 12 predisposing factors(elevation,aspect,slope,Normalized Differentiate Vegetation Index(NDVI),drainage network,curvature,Land Use Land Cover(LULC),rainfall,lithology,contour,soil texture,and road network)of soil erosion using GIS platform.Additionally,GIS-based statistical models like the weight of evidence(WOE)and frequency ratio(FR)were applied to produce the SESM for the study area.The SESM was reclassified into four classes,i.e.,low,medium,high,and very high zone.The results of WOE for SESM show that 16.39%,33.02%,29.27%,and 21.30%of areas are covered by low,medium,high,and very high zones,respectively.In contrast,the FR results revealed that 16.50%,24.33%,35.55%,and 23.59%of the areas are occupied by low,medium,high,and very high classes.Furthermore,the reliability of applied models was evaluated using the Area Under Curve(AUC)technique.The validation results utilizing the area under curve showed that the success rate curve(SRC)and predicted rate curve(PRC)for WOE are 82%and 86%,respectively,while SRC and PRC for FR are 85%and 96%,respectively.The validation results revealed that the FR model performance is better and more reliable than the WOE.
文摘Manganese was leached from a low-grade manganese ore(LGMO)using banana peel as the reductant in a dilute sulfuric acid medium.The effects of banana peel amount,H2SO4 concentration,reaction temperature,and time on Mn leaching from the complex LGMO were studied.A leaching efficiency of~98%was achieved at a leaching time of 2 h,banana peel amount of 4 g,leaching temperature of 120°C,manganese ore amount of 5 g,and sulfuric acid concentration of 15vol%.The phase,microstructural,and chemical analyses of LGMO samples before and after the leaching process confirmed the successful leaching of manganese.Furthermore,the leaching process followed the shrinking core model and the leaching rate was controlled by a surface chemical reaction(1−(1−x)^1/3=kt)mechanism with an apparent activation energy of 40.19 kJ·mol^−1.
基金supported by the Director Foundation of the Institute of Seismology,China Earthquake Administration (IS201126025)The Basis Research Foundation of Key laboratory of Geospace Environment & Geodesy Ministry of Education,China (10-01-09)
文摘On the： basis of wavelet theory, we propose an outlier-detection algorithm for satellite gravity ometry by applying a wavelet-shrinkage-de-noising method to some simulation data with white noise and ers. The result Shows that this novel algorithm has a 97% success rate in outlier identification and that be efficiently used for pre-processing real satellite gravity gradiometry data.
文摘A sharp increase in economic and human development has multiplied the carbon intensity due to which there is a significant need of effective strategies in order to curb carbon emissions.Thus,the present study aims to examine the effective of green finance,eco-innovation,renewable energy output(REO),renewable energy consumption(REC),and carbon taxes on carbon dioxide(CO_(2))emissions in BRICS countries in the time of 2001-2020.Cross-sectional autoregressive distributed lag(CS ARDL)is used to test the connection among the variables.Empirical estimations of CS-ARDL approach validates the effectiveness of green finance,eco-innovation,REO,REC,carbon taxes,and industrialization as the relationship of these factors with carbon emissions is negative in nature in BRICS economies.Based on the evidences,the study recommends the formulation of environmentally friendly practices and advancement in green finances to mitigate carbon emissions.
