State of Eye Protection for Metal Welders in the Workshops of Conakry

Introduction: Welders are an occupational group at high risk for eye injuries. The aim of this study was to investigate the state of eye protection of metal welders in the workshops of Conakry. Material and Methods: The study was cross-sectional, descriptive, and included 180 welders from 45 welding workshops in the city of Conakry for a period of three months. It involved all welding professionals working in an informal unit selected by the study and who had agreed to participate in the study. Results: The average age of the participants was 33.9 ± 13.4 years, with extremes of 15 and 68 years. The study found that 99% of the welders owned glasses, 27% owned face shields, and 49% owned welding masks. Goggles were used regularly by 86% of the welders but were not suitable for welding (98%). All welders had reported having had an eye injury at least once. Foreign bodies were cited in 81%, arc strike in 65%;eye burns in 61%. However, approximately 81% of welders did not have first aid kits at their work sites. Conclusion: The use of protective equipment during welding remains very low in the workshops of Conakry, which is the cause of great ocular morbidity among welders.

China Eyes Closer Environmental Cooperation with Arab States

China is seeking closer environmental cooperation with Arab States driven by the strategy of 'One Belt and One Road'.'Co-building and greenization of ’One Belt and One Road’ will provide new opportunities and contents for China-Arab states cooperation。

Ensemble Deep Learning with Chimp Optimization Based Medical Data Classification

Eye state classification acts as a vital part of the biomedical sector,for instance,smart home device control,drowsy driving recognition,and so on.The modifications in the cognitive levels can be reflected via transforming the electro-encephalogram(EEG)signals.The deep learning(DL)models automated extract the features and often showcased improved outcomes over the conventional clas-sification model in the recognition processes.This paper presents an Ensemble Deep Learning with Chimp Optimization Algorithm for EEG Eye State Classifi-cation(EDLCOA-ESC).The proposed EDLCOA-ESC technique involves min-max normalization approach as a pre-processing step.Besides,wavelet packet decomposition(WPD)technique is employed for the extraction of useful features from the EEG signals.In addition,an ensemble of deep sparse autoencoder(DSAE)and kernel ridge regression(KRR)models are employed for EEG Eye State classification.Finally,hyperparameters tuning of the DSAE model takes place using COA and thereby boost the classification results to a maximum extent.An extensive range of simulation analysis on the benchmark dataset is car-ried out and the results reported the promising performance of the EDLCOA-ESC technique over the recent approaches with maximum accuracy of 98.50%.