Introduction: Healthcare workers in Mogadishu, Somalia face significant occupational injury risks, particularly needle stick injuries, with 61.1% reporting incidents. This poses a serious threat to their health, leadi...Introduction: Healthcare workers in Mogadishu, Somalia face significant occupational injury risks, particularly needle stick injuries, with 61.1% reporting incidents. This poses a serious threat to their health, leading to infections such as hepatitis B, hepatitis C, and HIV. Despite the high prevalence of injuries, awareness of Post-Exposure Prophylaxis (PEP) accessibility is relatively high, with 84.0% of respondents aware of it. However, there are gaps in knowledge and implementation, as evidenced by variations in availability of PEP. Improving workplace safety measures, providing comprehensive training on injury prevention and PEP protocols, and ensuring consistent availability of PEP in healthcare facilities are crucial steps to safeguard the well-being of healthcare workers in Mogadishu, Somalia. Methods: A cross-sectional study was conducted among hospital workers in Mogadishu, Somalia, focusing on professionals from various healthcare facilities. The study targeted nurses, doctors, laboratory personnel, and pharmacists. Purposive sampling was employed, resulting in a sample size of 383 calculated using Fisher’s sample size formula. Data were collected using coded questionnaires entered into Microsoft Excel 2019 and analyzed with SPSS software to generate frequencies and proportions, presented through frequency tables and pie figures. Results: The study in Mogadishu, Somalia, examined the prevalence of occupational injuries and knowledge of Post-Exposure Prophylaxis (PEP) accessibility among healthcare workers. Findings indicate a high prevalence of injuries, with 61.1% reporting incidents, predominantly needle stick injuries (60.6%). Despite the majority seeking prompt medical attention (72.0%), work-related illnesses affected 53.2% of respondents, notably work-related stress (59.5%). While most received training on injury and illness prevention (68.9%), gaps exist in PEP awareness, with 16.0% unaware of it. Nonetheless, 84.0% were aware, predominantly through health facilities (52.0%). Availability of PEP was reported by 71.3% in healthcare facilities, with variations in shift availability. The majority reported guidelines for PEP use (55.7%). Efforts are needed to bolster PEP awareness and ensure consistent availability in healthcare facilities to safeguard worker health. Conclusion: High prevalence of occupational injuries among healthcare workers, with needle stick injuries being the most common (60.6%). Despite this, 84.0% of respondents were aware of Post-Exposure Prophylaxis (PEP), primarily learning about it from health facilities (52.0%). While 71.3% reported the availability of PEP in their facility, 28.7% noted its unavailability. These results emphasize the need for improved education and accessibility of PEP to mitigate occupational injury risks.展开更多
This paper proposes a method for detecting a helmet for thesafety of workers from risk factors and a mask worn indoors and verifying aworker’s identity while wearing a helmet and mask for security. The proposedmethod...This paper proposes a method for detecting a helmet for thesafety of workers from risk factors and a mask worn indoors and verifying aworker’s identity while wearing a helmet and mask for security. The proposedmethod consists of a part for detecting the worker’s helmet and mask and apart for verifying the worker’s identity. An algorithm for helmet and maskdetection is generated by transfer learning of Yolov5’s s-model and m-model.Both models are trained by changing the learning rate, batch size, and epoch.The model with the best performance is selected as the model for detectingmasks and helmets. At a learning rate of 0.001, a batch size of 32, and anepoch of 200, the s-model showed the best performance with a mAP of0.954, and this was selected as an optimal model. The worker’s identificationalgorithm consists of a facial feature extraction part and a classifier partfor the worker’s identification. The algorithm for facial feature extraction isgenerated by transfer learning of Facenet, and SVMis used as the classifier foridentification. The proposed method makes trained models using two datasets,a masked face dataset with only a masked face, and a mixed face datasetwith both a masked face and an unmasked face. And the model with the bestperformance among the trained models was selected as the optimal model foridentification when using a mask. As a result of the experiment, the model bytransfer learning of Facenet and SVM using a mixed face dataset showed thebest performance. When the optimal model was tested with a mixed dataset,it showed an accuracy of 95.4%. Also, the proposed model was evaluated asdata from 500 images of taking 10 people with a mobile phone. The resultsshowed that the helmet and mask were detected well and identification wasalso good.展开更多
The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this indust...The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.展开更多
With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce ...With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.展开更多
文摘Introduction: Healthcare workers in Mogadishu, Somalia face significant occupational injury risks, particularly needle stick injuries, with 61.1% reporting incidents. This poses a serious threat to their health, leading to infections such as hepatitis B, hepatitis C, and HIV. Despite the high prevalence of injuries, awareness of Post-Exposure Prophylaxis (PEP) accessibility is relatively high, with 84.0% of respondents aware of it. However, there are gaps in knowledge and implementation, as evidenced by variations in availability of PEP. Improving workplace safety measures, providing comprehensive training on injury prevention and PEP protocols, and ensuring consistent availability of PEP in healthcare facilities are crucial steps to safeguard the well-being of healthcare workers in Mogadishu, Somalia. Methods: A cross-sectional study was conducted among hospital workers in Mogadishu, Somalia, focusing on professionals from various healthcare facilities. The study targeted nurses, doctors, laboratory personnel, and pharmacists. Purposive sampling was employed, resulting in a sample size of 383 calculated using Fisher’s sample size formula. Data were collected using coded questionnaires entered into Microsoft Excel 2019 and analyzed with SPSS software to generate frequencies and proportions, presented through frequency tables and pie figures. Results: The study in Mogadishu, Somalia, examined the prevalence of occupational injuries and knowledge of Post-Exposure Prophylaxis (PEP) accessibility among healthcare workers. Findings indicate a high prevalence of injuries, with 61.1% reporting incidents, predominantly needle stick injuries (60.6%). Despite the majority seeking prompt medical attention (72.0%), work-related illnesses affected 53.2% of respondents, notably work-related stress (59.5%). While most received training on injury and illness prevention (68.9%), gaps exist in PEP awareness, with 16.0% unaware of it. Nonetheless, 84.0% were aware, predominantly through health facilities (52.0%). Availability of PEP was reported by 71.3% in healthcare facilities, with variations in shift availability. The majority reported guidelines for PEP use (55.7%). Efforts are needed to bolster PEP awareness and ensure consistent availability in healthcare facilities to safeguard worker health. Conclusion: High prevalence of occupational injuries among healthcare workers, with needle stick injuries being the most common (60.6%). Despite this, 84.0% of respondents were aware of Post-Exposure Prophylaxis (PEP), primarily learning about it from health facilities (52.0%). While 71.3% reported the availability of PEP in their facility, 28.7% noted its unavailability. These results emphasize the need for improved education and accessibility of PEP to mitigate occupational injury risks.
基金supported by a grant (20015427)of Regional Customized Disaster-Safety R&D Programfunded by Ministry of Interior and Safety (MOIS,Korea)was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education (No.2022R1A6A1A03052954).
文摘This paper proposes a method for detecting a helmet for thesafety of workers from risk factors and a mask worn indoors and verifying aworker’s identity while wearing a helmet and mask for security. The proposedmethod consists of a part for detecting the worker’s helmet and mask and apart for verifying the worker’s identity. An algorithm for helmet and maskdetection is generated by transfer learning of Yolov5’s s-model and m-model.Both models are trained by changing the learning rate, batch size, and epoch.The model with the best performance is selected as the model for detectingmasks and helmets. At a learning rate of 0.001, a batch size of 32, and anepoch of 200, the s-model showed the best performance with a mAP of0.954, and this was selected as an optimal model. The worker’s identificationalgorithm consists of a facial feature extraction part and a classifier partfor the worker’s identification. The algorithm for facial feature extraction isgenerated by transfer learning of Facenet, and SVMis used as the classifier foridentification. The proposed method makes trained models using two datasets,a masked face dataset with only a masked face, and a mixed face datasetwith both a masked face and an unmasked face. And the model with the bestperformance among the trained models was selected as the optimal model foridentification when using a mask. As a result of the experiment, the model bytransfer learning of Facenet and SVM using a mixed face dataset showed thebest performance. When the optimal model was tested with a mixed dataset,it showed an accuracy of 95.4%. Also, the proposed model was evaluated asdata from 500 images of taking 10 people with a mobile phone. The resultsshowed that the helmet and mask were detected well and identification wasalso good.
文摘The construction industry has always remained the economic and social backbone of any country in the world where occupational health and safety(OHS)is of prime importance.Like in other developing countries,this industry pays very little,rather negligible attention to OHS practices in Pakistan,resulting in the occurrence of a wide variety of accidents,mishaps,and near-misses every year.One of the major causes of such mishaps is the non-wearing of safety helmets(hard hats)at construction sites where falling objects from a height are unavoid-able.In most cases,this leads to serious brain injuries in people present at the site in general and the workers in particular.It is one of the leading causes of human fatalities at construction sites.In the United States,the Occupational Safety and Health Administration(OSHA)requires construction companies through safety laws to ensure the use of well-defined personal protective equipment(PPE).It has long been a problem to ensure the use of PPE because round-the-clock human monitoring is not possible.However,such monitoring through technological aids or automated tools is very much possible.The present study describes a systema-tic strategy based on deep learning(DL)models built on the You-Only-Look-Once(YOLOV5)architecture that could be used for monitoring workers’hard hats in real-time.It can indicate whether a worker is wearing a hat or not.The proposed system usesfive different models of the YOLOV5,namely YOLOV5n,YOLOv5s,YOLOv5 m,YOLOv5l,and YOLOv5x for object detection with the support of PyTorch,involving 7063 images.The results of the study show that among the DL models,the YOLOV5x has a high performance of 95.8%in terms of the mAP,while the YOLOV5n has the fastest detection speed of 70.4 frames per second(FPS).The proposed model can be successfully used in practice to recognize the hard hat worn by a worker.
基金supported by the National Outstanding Youth Science Fund Project of National Natural Science Foundation of China(Projects No.52202012)the National Natural Science Foundation of China(Projects No.51834007)。
文摘With the development of ordnance technology,the survival and safety of individual combatants in hightech warfare are under serious threat,and the Personal Protective Equipment(PPE),as an important guarantee to reduce casualties and maintain military combat effectiveness,is widely developed.This paper systematically reviewed various PPE based on individual combat through literature research and comprehensive discussion,and introduced in detail the latest application progress of PPE in terms of material and technology from three aspects:individual integrated protection system,traditional protection equipment,and intelligent protection equipment,respectively,and discussed in depth the functional improvement and optimization status brought by advanced technology for PPE,focusing on the achievements of individual equipment technology application.Finally,the problems and technical bottlenecks in the development of PPE were analyzed and summarized,and the development trend of PPE were pointed out.The results of the review will provide a forward-looking reference for the current development of individual PPE,and are important guidance for the design and technological innovation of advanced equipment based on the future technological battlefield.
