Objective:To examine how nursing aides in nursing homes perceived their caring work.Methods:Twenty-four nursing aides from one public and one private nursing home in Fuzhou,Fujian Province,China were selected and inte...Objective:To examine how nursing aides in nursing homes perceived their caring work.Methods:Twenty-four nursing aides from one public and one private nursing home in Fuzhou,Fujian Province,China were selected and interviewed in focus groups.Phenomenological analysis was performed.Results:Two themes(positive and negative working experiences)and six sub-themes were drawn:companionship,happiness,trust,achievement,hard work,and grievance.Conclusion:A reasonable work arrangement,positive psychological intervention,and the strengthening of professional,medical and social supports are recommended to improve the work quality and satisfaction of nursing aides in elderly homes.展开更多
Background:The ergogenic effects of caffeine intake on exercise performance are well-established,even if differences exist among individuals in response to caffeine intake.The genetic variation of a specific gene,huma...Background:The ergogenic effects of caffeine intake on exercise performance are well-established,even if differences exist among individuals in response to caffeine intake.The genetic variation of a specific gene,human cytochrome P450 enzyme 1A2(CYP1A2)(rs762551),may be one reason for this difference.This systematic review and meta-analysis aimed to comprehensively evaluate the influence of CYP1A2 gene types on athletes’exercise performance after caffeine intake.Methods:A literature search through 4 databases(Web of Science,PubMed,Scopus,and China National Knowledge Infrastructure)was conducted until March 2023.The effect size was expressed as the weighted mean difference(WMD)by calculating fixed effects meta-analysis if heterogeneity was not significant(I^(2)≤50%and p≥0.1).Subgroup analyses were performed based on AA and AC/CC genotype of CYP1A2.Results:The final number of studies meeting the inclusion criteria was 12(n=666 participants).The overall analysis showed that the cycling time trial significantly improved after caffeine intake(WMD=-0.48,95%confidence interval(95%CI):-0.83 to-0.13,p=0.007).In subgroup analyses,acute caffeine intake improved cycling time trial only in individuals with the A allele(WMD=-0.90,95%CI:-1.48 to-0.33,p=0.002),but not the C allele(WMD=-0.08,95%CI:-0.32 to 0.17,p=0.53).Caffeine supplementation did not influence the Wingate(WMD=8.07,95%CI:-22.04 to 38.18,p=0.60)or countermovement jump test(CMJ)performance(WMD=1.17,95%CI:-0.02 to 2.36,p=0.05),and these outcomes were not influenced by CYP1A2 genotype.Conclusion:Participants with the CYP1A2 genotype with A allele improved their cycling time trials after caffeine supplementation.However,compared to placebo,acute caffeine supplementation failed to increase the Wingate or CMJ performance,regardless of CYP1A2 genotype.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
Objective This study aimed to determine the current epidemiological status of PLWHA aged≥50 years in China from 2018 to 2021.It also aimed to recommend targeted interventions for the prevention and treatment of HIV/A...Objective This study aimed to determine the current epidemiological status of PLWHA aged≥50 years in China from 2018 to 2021.It also aimed to recommend targeted interventions for the prevention and treatment of HIV/AIDS in elderly patients.Methods Data on newly reported cases of PLWHA,aged≥50 years in China from 2018 to 2021,were collected using the CRIMS.Trend tests and spatial analyses were also conducted.Results Between 2018 and 2021,237,724 HIV/AIDS cases were reported among patients aged≥50 years in China.The main transmission route was heterosexual transmission(91.24%).Commercial heterosexual transmission(CHC)was the primary mode of transmission among males,while non-marital non-CHC([NMNCHC];60.59%)was the prevalent route in women.The proportion of patients with CHC decreased over time(Z=67.716,P<0.01),while that of patients with NMNCHC increased(Z=153.05,P<0.01).The sex ratio varied among the different modes of infection,and it peaked at 17.65 for CHC.The spatial analysis indicated spatial clustering,and the high-high clustering areas were mainly distributed in the southwestern and central-southern provinces.Conclusion In China,PLWHA,aged≥50 years,were predominantly infected through heterosexual transmission.The primary modes of infection were CHC and NMNCHC.There were variations in the sex ratio among different age groups,infected through various sexual behaviors.HIV/AIDS cases exhibited spatial clustering.Based on these results,the expansion of HIV testing,treatment,and integrated behavioral interventions in high-risk populations is recommended to enhance disease detection in key regions.展开更多
Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these d...Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.展开更多
The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are ins...The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.展开更多
基金This study was supported by grants from the National key clinical specialist construction Programs of China(NO.2010)Fujian Province Science and Technology Plan Key Projects(NO.2012Y0013).
文摘Objective:To examine how nursing aides in nursing homes perceived their caring work.Methods:Twenty-four nursing aides from one public and one private nursing home in Fuzhou,Fujian Province,China were selected and interviewed in focus groups.Phenomenological analysis was performed.Results:Two themes(positive and negative working experiences)and six sub-themes were drawn:companionship,happiness,trust,achievement,hard work,and grievance.Conclusion:A reasonable work arrangement,positive psychological intervention,and the strengthening of professional,medical and social supports are recommended to improve the work quality and satisfaction of nursing aides in elderly homes.
文摘Background:The ergogenic effects of caffeine intake on exercise performance are well-established,even if differences exist among individuals in response to caffeine intake.The genetic variation of a specific gene,human cytochrome P450 enzyme 1A2(CYP1A2)(rs762551),may be one reason for this difference.This systematic review and meta-analysis aimed to comprehensively evaluate the influence of CYP1A2 gene types on athletes’exercise performance after caffeine intake.Methods:A literature search through 4 databases(Web of Science,PubMed,Scopus,and China National Knowledge Infrastructure)was conducted until March 2023.The effect size was expressed as the weighted mean difference(WMD)by calculating fixed effects meta-analysis if heterogeneity was not significant(I^(2)≤50%and p≥0.1).Subgroup analyses were performed based on AA and AC/CC genotype of CYP1A2.Results:The final number of studies meeting the inclusion criteria was 12(n=666 participants).The overall analysis showed that the cycling time trial significantly improved after caffeine intake(WMD=-0.48,95%confidence interval(95%CI):-0.83 to-0.13,p=0.007).In subgroup analyses,acute caffeine intake improved cycling time trial only in individuals with the A allele(WMD=-0.90,95%CI:-1.48 to-0.33,p=0.002),but not the C allele(WMD=-0.08,95%CI:-0.32 to 0.17,p=0.53).Caffeine supplementation did not influence the Wingate(WMD=8.07,95%CI:-22.04 to 38.18,p=0.60)or countermovement jump test(CMJ)performance(WMD=1.17,95%CI:-0.02 to 2.36,p=0.05),and these outcomes were not influenced by CYP1A2 genotype.Conclusion:Participants with the CYP1A2 genotype with A allele improved their cycling time trials after caffeine supplementation.However,compared to placebo,acute caffeine supplementation failed to increase the Wingate or CMJ performance,regardless of CYP1A2 genotype.
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.
文摘Objective This study aimed to determine the current epidemiological status of PLWHA aged≥50 years in China from 2018 to 2021.It also aimed to recommend targeted interventions for the prevention and treatment of HIV/AIDS in elderly patients.Methods Data on newly reported cases of PLWHA,aged≥50 years in China from 2018 to 2021,were collected using the CRIMS.Trend tests and spatial analyses were also conducted.Results Between 2018 and 2021,237,724 HIV/AIDS cases were reported among patients aged≥50 years in China.The main transmission route was heterosexual transmission(91.24%).Commercial heterosexual transmission(CHC)was the primary mode of transmission among males,while non-marital non-CHC([NMNCHC];60.59%)was the prevalent route in women.The proportion of patients with CHC decreased over time(Z=67.716,P<0.01),while that of patients with NMNCHC increased(Z=153.05,P<0.01).The sex ratio varied among the different modes of infection,and it peaked at 17.65 for CHC.The spatial analysis indicated spatial clustering,and the high-high clustering areas were mainly distributed in the southwestern and central-southern provinces.Conclusion In China,PLWHA,aged≥50 years,were predominantly infected through heterosexual transmission.The primary modes of infection were CHC and NMNCHC.There were variations in the sex ratio among different age groups,infected through various sexual behaviors.HIV/AIDS cases exhibited spatial clustering.Based on these results,the expansion of HIV testing,treatment,and integrated behavioral interventions in high-risk populations is recommended to enhance disease detection in key regions.
基金supported by the Institute of Information&communications Technology Planning&Evaluation(IITP)grant Funded by the Korean government(MSIT)(2021-0-00755,Dark Data Analysis Technology for Data Scale and Accuracy Improvement)This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2024R407)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Thyroid disorders represent a significant global health challenge with hypothyroidism and hyperthyroidism as two common conditions arising from dysfunction in the thyroid gland.Accurate and timely diagnosis of these disorders is crucial for effective treatment and patient care.This research introduces a comprehensive approach to improve the accuracy of thyroid disorder diagnosis through the integration of ensemble stacking and advanced feature selection techniques.Sequential forward feature selection,sequential backward feature elimination,and bidirectional feature elimination are investigated in this study.In ensemble learning,random forest,adaptive boosting,and bagging classifiers are employed.The effectiveness of these techniques is evaluated using two different datasets obtained from the University of California Irvine-Machine Learning Repository,both of which undergo preprocessing steps,including outlier removal,addressing missing data,data cleansing,and feature reduction.Extensive experimentation demonstrates the remarkable success of proposed ensemble stacking and bidirectional feature elimination achieving 100%and 99.86%accuracy in identifying hyperthyroidism and hypothyroidism,respectively.Beyond enhancing detection accuracy,the ensemble stacking model also demonstrated a streamlined computational complexity which is pivotal for practical medical applications.It significantly outperformed existing studies with similar objectives underscoring the viability and effectiveness of the proposed scheme.This research offers an innovative perspective and sets the platform for improved thyroid disorder diagnosis with broader implications for healthcare and patient well-being.
文摘The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leadsto wide losses for various organizations. These dangers have proven that signature-based approaches are insufficientto prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious ExecutableDetection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE)files in hosts using Windows operating systems through collecting PE headers and applying machine learningmechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031benign files and 179,071 malware samples from diverse sources to ensure the efficiency of RMED approach.The most effective PE headers that can highly differentiate between benign and malware files were selected totrain the model on 15 PE features to speed up the classification process and achieve real-time detection formalicious executables. The evaluation results showed that RMED succeeded in shrinking the classification timeto 91 milliseconds for each file while reaching an accuracy of 98.42% with a false positive rate equal to 1.58. Inconclusion, this paper contributes to the field of cybersecurity by presenting a comprehensive framework thatleverages Artificial Intelligence (AI) methods to proactively detect and prevent cyber-attacks.
