Introduction and Significance: Burn injury (BI) is a considerable health issue which is responsible for around 300,000 deaths and affecting about 11 million people every year worldwide. In Saudi Arabia, the prevalence...Introduction and Significance: Burn injury (BI) is a considerable health issue which is responsible for around 300,000 deaths and affecting about 11 million people every year worldwide. In Saudi Arabia, the prevalence of BIs array from 112 to 518 per 100,000 per year. The appropriate awareness of performing first aid could facilitate to improve the outcomes of burns. Purpose and Objectives: To appraise the community that acknowledges burns, first aid, and associated factors among the community population in Jazan City, Saudi Arabia. The paper aims to identify limitations to encourage additional research and persuade legislators to develop improved burn-injury care recommendations and training programs. Materials and Methods: An observational-based sample survey was conducted among the people who live in Jazan City aging 13 years or more, during April 5 to May 5, 2023. Data collection was done by a validated online self-administrated questionnaire sent randomly to community members in different parts of Jazan City via social media platforms. Collected data were coded and cleaned by an excel program, and finally exported on SPSS 26.0 software. The variables were analyzed using descriptive statistics like frequencies and percentages. Also, the Chi-square test was used to investigate the relation between different variables, with a significance value of P Results: This study included 243 participants (about 62%) among them were mostly male participants (151) having a university degree. The majority of participants 75% did not take any form of BFA training in the past. This study shows that 69.9% of the participants have inadequate awareness, despite 72% having a constructive attitude towards burn first aid. Previous burn-related first aid training was significantly associated with participants’ knowledge of BFA at a p-value less than 0.05. Conclusion: This study indicates a high frequency of Jazan population having inadequate knowledge of burn first aid despite the high prevalence of a favorable attitude. There is a need to develop an effective nationwide burn prevention program and early burn first aid treatment in Saudi Arabia and promote a consistent guideline for burn first aid.展开更多
Dear Editor,This letter deals with the tracking problem for non-cooperative maneuvering targets based on the underwater sensor networks. Considering the acoustic intensity feature of underwater targets, a feature-aide...Dear Editor,This letter deals with the tracking problem for non-cooperative maneuvering targets based on the underwater sensor networks. Considering the acoustic intensity feature of underwater targets, a feature-aided multi-model tracking method for maneuvering targets is proposed.展开更多
Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on l...Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.展开更多
文摘Introduction and Significance: Burn injury (BI) is a considerable health issue which is responsible for around 300,000 deaths and affecting about 11 million people every year worldwide. In Saudi Arabia, the prevalence of BIs array from 112 to 518 per 100,000 per year. The appropriate awareness of performing first aid could facilitate to improve the outcomes of burns. Purpose and Objectives: To appraise the community that acknowledges burns, first aid, and associated factors among the community population in Jazan City, Saudi Arabia. The paper aims to identify limitations to encourage additional research and persuade legislators to develop improved burn-injury care recommendations and training programs. Materials and Methods: An observational-based sample survey was conducted among the people who live in Jazan City aging 13 years or more, during April 5 to May 5, 2023. Data collection was done by a validated online self-administrated questionnaire sent randomly to community members in different parts of Jazan City via social media platforms. Collected data were coded and cleaned by an excel program, and finally exported on SPSS 26.0 software. The variables were analyzed using descriptive statistics like frequencies and percentages. Also, the Chi-square test was used to investigate the relation between different variables, with a significance value of P Results: This study included 243 participants (about 62%) among them were mostly male participants (151) having a university degree. The majority of participants 75% did not take any form of BFA training in the past. This study shows that 69.9% of the participants have inadequate awareness, despite 72% having a constructive attitude towards burn first aid. Previous burn-related first aid training was significantly associated with participants’ knowledge of BFA at a p-value less than 0.05. Conclusion: This study indicates a high frequency of Jazan population having inadequate knowledge of burn first aid despite the high prevalence of a favorable attitude. There is a need to develop an effective nationwide burn prevention program and early burn first aid treatment in Saudi Arabia and promote a consistent guideline for burn first aid.
基金supported by the National Natural Science Foundation of China (62173299, U1909206)the Zhejiang Provincial Natural Science Foundation of China (LZ23F030006)+1 种基金the Joint Fund of Ministry of Education for Pre-research of Equipment (8091B022147)the Fundamental Research Funds for the Central Universities (xtr072022001)。
文摘Dear Editor,This letter deals with the tracking problem for non-cooperative maneuvering targets based on the underwater sensor networks. Considering the acoustic intensity feature of underwater targets, a feature-aided multi-model tracking method for maneuvering targets is proposed.
基金the Key Project of Zhejiang Provincial Natural Science Foundation under Grants LD21F020001,Z20F020022the National Natural Science Foundation of China under Grants 62072340,62076185the Major Project of Wenzhou Natural Science Foundation under Grants 2021HZSY0071,ZS2022001.
文摘Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global features.In contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel dimensions.However,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is disregarded.To tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features simultaneously.It combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification performance.Moreover,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer structure.Finally,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC task.This demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic tasks.Furthermore,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet performance.Besides,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.
