Gas stations are important emissions source of benzene (C6H6), toluene (C7H8), ethylbenzene (C8H10), and ortho, meta and para-xylene (C8H10)—better known by the acronym BTEX. The objective of this study was to determ...Gas stations are important emissions source of benzene (C6H6), toluene (C7H8), ethylbenzene (C8H10), and ortho, meta and para-xylene (C8H10)—better known by the acronym BTEX. The objective of this study was to determine the concentrations of BTEX compounds in the ambient air of ten gas stations in the cities of Salvador and Feira de Santana, Bahia, Brazil and evaluate the health risks to workers. Passive samplers diffusive of the Radiello?, containing activated carbon as adsorbent, were used. The samplers were exposed for 8 h and then the analytes were recovered by chemical desorption with CS2 and determined by GC-FID. The BTEX concentrations found in the ambient air of gas stations ranged from 46.72 - 435.43 μg·m?3 for benzene;25.54 - 342.46 μg·m?3 for toluene, 7.10 - 30.07 μg·m?3 for ethylbenzene, 9.36 - 89.73 μg·m?3 for m, p-xylene and 9.79 - 52.29 μg·m?3 for o-xylene. The concentrations of toluene, ethylbenzene and xylenes found in gas stations were lower than the limits recommended by the US NIOSH and NR-15 of the Ministry of Labour of Brazil;however, it should be considered the risks due to chronic exposure of workers. Benzene concentrations in three gas stations were above the exposure limit recommended by NIOSH (3.20 × 102 μg·m?3). Samplings were also held outdoors at 250 m of two gas stations. The total concentrations of the BTEX compounds were equal to 24.97 and 35.51 μg·m?3, and benzene concentrations were about 3 - 4 times higher than the annual pattern of 5.0 μg·m?3 established by Union European, as tolerance limit for outside areas. These data confirm that the next areas of gas stations are subject to the effects of volatilization of these compounds. Additionally, the values found in the 10 gas stations for the cancer risk ranged from 4.06 × 10?5 - 3.78 × 10?4 (mean of 1.82 × 10?4) for workers exposed to benzene for 30 years (acceptable limit equal 1.00 × 10?6). The cancer risk is very high, because the values found are about 40 - 378 times above the acceptable limit and reinforce the need to adopt urgent measures to reduce or eliminate exposure of workers to the BTEX compounds. The average non-cancer risk to benzene, toluene, ethylbenzene and xylenes was 1.84, 5.76 × 10?3, 4.59 × 10?3 and 1.37 × 10?1, respectively (acceptable limit 1). Only to benzene the average value of this risk is above 1, showing that workers are likely the adverse effects health due to exposure to benzene.展开更多
The growing threat of malware,particularly in the Portable Executable(PE)format,demands more effective methods for detection and classification.Machine learning-based approaches exhibit their potential but often negle...The growing threat of malware,particularly in the Portable Executable(PE)format,demands more effective methods for detection and classification.Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance.This research applies deep learning to malware detection,using Convolutional Neural Network(CNN)architectures adapted to work with semantically extracted data to classify malware into malware families.Starting from the Malconv model,this study introduces modifications to adapt it to multi-classification tasks and improve its performance.It proposes a new innovative method that focuses on byte extraction from Portable Executable(PE)malware files based on their semantic location,resulting in higher accuracy in malware classification than traditional methods using full-byte sequences.This novel approach evaluates the importance of each semantic segment to improve classification accuracy.The results revealed that the header segment of PE files provides the most valuable information for malware identification,outperforming the other sections,and achieving an average classification accuracy of 99.54%.The above reaffirms the effectiveness of the semantic segmentation approach and highlights the critical role header data plays in improving malware detection and classification accuracy.展开更多
文摘Gas stations are important emissions source of benzene (C6H6), toluene (C7H8), ethylbenzene (C8H10), and ortho, meta and para-xylene (C8H10)—better known by the acronym BTEX. The objective of this study was to determine the concentrations of BTEX compounds in the ambient air of ten gas stations in the cities of Salvador and Feira de Santana, Bahia, Brazil and evaluate the health risks to workers. Passive samplers diffusive of the Radiello?, containing activated carbon as adsorbent, were used. The samplers were exposed for 8 h and then the analytes were recovered by chemical desorption with CS2 and determined by GC-FID. The BTEX concentrations found in the ambient air of gas stations ranged from 46.72 - 435.43 μg·m?3 for benzene;25.54 - 342.46 μg·m?3 for toluene, 7.10 - 30.07 μg·m?3 for ethylbenzene, 9.36 - 89.73 μg·m?3 for m, p-xylene and 9.79 - 52.29 μg·m?3 for o-xylene. The concentrations of toluene, ethylbenzene and xylenes found in gas stations were lower than the limits recommended by the US NIOSH and NR-15 of the Ministry of Labour of Brazil;however, it should be considered the risks due to chronic exposure of workers. Benzene concentrations in three gas stations were above the exposure limit recommended by NIOSH (3.20 × 102 μg·m?3). Samplings were also held outdoors at 250 m of two gas stations. The total concentrations of the BTEX compounds were equal to 24.97 and 35.51 μg·m?3, and benzene concentrations were about 3 - 4 times higher than the annual pattern of 5.0 μg·m?3 established by Union European, as tolerance limit for outside areas. These data confirm that the next areas of gas stations are subject to the effects of volatilization of these compounds. Additionally, the values found in the 10 gas stations for the cancer risk ranged from 4.06 × 10?5 - 3.78 × 10?4 (mean of 1.82 × 10?4) for workers exposed to benzene for 30 years (acceptable limit equal 1.00 × 10?6). The cancer risk is very high, because the values found are about 40 - 378 times above the acceptable limit and reinforce the need to adopt urgent measures to reduce or eliminate exposure of workers to the BTEX compounds. The average non-cancer risk to benzene, toluene, ethylbenzene and xylenes was 1.84, 5.76 × 10?3, 4.59 × 10?3 and 1.37 × 10?1, respectively (acceptable limit 1). Only to benzene the average value of this risk is above 1, showing that workers are likely the adverse effects health due to exposure to benzene.
文摘The growing threat of malware,particularly in the Portable Executable(PE)format,demands more effective methods for detection and classification.Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance.This research applies deep learning to malware detection,using Convolutional Neural Network(CNN)architectures adapted to work with semantically extracted data to classify malware into malware families.Starting from the Malconv model,this study introduces modifications to adapt it to multi-classification tasks and improve its performance.It proposes a new innovative method that focuses on byte extraction from Portable Executable(PE)malware files based on their semantic location,resulting in higher accuracy in malware classification than traditional methods using full-byte sequences.This novel approach evaluates the importance of each semantic segment to improve classification accuracy.The results revealed that the header segment of PE files provides the most valuable information for malware identification,outperforming the other sections,and achieving an average classification accuracy of 99.54%.The above reaffirms the effectiveness of the semantic segmentation approach and highlights the critical role header data plays in improving malware detection and classification accuracy.
