Objective:To determine the antibiotic resistance profile(ARP)of Staphylococcus(S.)aureus isolates and molecular typing of the methicillin-resistant S.aureus(MRSA)isolates from Tuanku Mizan Armed Forces Hospital(TMAFH)...Objective:To determine the antibiotic resistance profile(ARP)of Staphylococcus(S.)aureus isolates and molecular typing of the methicillin-resistant S.aureus(MRSA)isolates from Tuanku Mizan Armed Forces Hospital(TMAFH),Kuala Lumpur.Methods:The ARP and presence of the pvl gene were determined for 209 S.aureus isolates from clinical specimens.Of these,123 were methicillin-susceptible S.aureus(MSSA)isolates and 86 were MRSA isolates.All MRSA isolates were characterized using SCCmec typing and spa typing.Descriptive analysis was performed to compare the demographic data with the phenotypic and genotypic variables of the S.aureus isolates.Results:No vancomycin-intermediate and-resistant S.aureus(VISA and VRSA,respectively)were detected among the study isolates.The MSSA isolates showed low resistance rates to all tested antibiotics,were commonly invasive(28/42,66.7%),and mostly harboured pvl(35/42,83.3%).Meanwhile,MRSA isolates showed high resistance to penicillin(86/86,100%),ampicillin(86/86,100%),sulbactam/ampicillin(86/86,100%),cefuroxime(81/86,94.19%),cefoperazone(76/86,88.37%),azithromycin(56/86,65.12%),and erythromycin(54/86,62.79%).The majority of MRSA isolates were of SCCmec type IVh(65/86,75.58%),spa type t032(55/85,63.95%),and grouped into spaCC-t022(66/85,77.65%).The t032 type was found to be associated with resistance traits to azithromycin and erythromycin(P<0.05).We also found several spa types that are typically associated with hospital-,community-,and livestock-associated MRSA co-existing in our MRSA population.Conclusions:This study reflected the consistent absence of VISA and VRSA and corroborated the clonal shifting of MRSA isolates in the Malaysian MRSA isolates.展开更多
This study explores the impact of bismuth oxide(Bi_(2)O_(3))on the optical and radiation shielding properties of transparent,lead-free thulium-doped bismuth borotellurite radiation shielding glass.The investigated gla...This study explores the impact of bismuth oxide(Bi_(2)O_(3))on the optical and radiation shielding properties of transparent,lead-free thulium-doped bismuth borotellurite radiation shielding glass.The investigated glass composition follows the formula[(TeO_(2))_(75)(B_(2)O_(3))_(25)]_(98-x)(Bi_(2)O_(3))_x[Tm_(2)O_(3)]_(2),where x=0 mol%,5 mol%,10 mol%,15 mol%,20 mol%,25 mol%,and 30 mol%.All glass samples remain transparent,with an optical bandgap(E_(opt))exceeding 3.1 e V,ensuring visible light transmission.Radiation shielding data from Phy-X and XCom reveal interactions of the photoelectric effect,Compton scattering,and pair production,with minimal relative difference in mass attenuation coefficient(MAC)which is between0.05 and 0.56.At 0.662 Me V photon energy,the 20 mol%and 25 mol%Bi_(2)O_(3)glasses exhibit significantly higher Phy-X MAC values than other samples,except RS 520 glass,which contains 71%Pb O.Despite incorporating only up to 25 mol%Bi_(2)O_(3),these glasses outperform others in density,half-value layer(HVL),and mean free path(MFP).Correlating E_(opt)and MAC,the 20 mol%Bi_(2)O_(3)glass is the best candidate for transparent radiation shielding glass due to its wide optical bandgap which prevents ionization of trapped holes.Significantly,the linkage between MFP and molar refraction was also discovered based on the particle size influence on both parameters.展开更多
The rapid progression of the Internet of Things(IoT)technology enables its application across various sectors.However,IoT devices typically acquire inadequate computing power and user interfaces,making them susceptibl...The rapid progression of the Internet of Things(IoT)technology enables its application across various sectors.However,IoT devices typically acquire inadequate computing power and user interfaces,making them susceptible to security threats.One significant risk to cloud networks is Distributed Denial-of-Service(DoS)attacks,where attackers aim to overcome a target system with excessive data and requests.Among these,low-rate DoS(LR-DoS)attacks present a particular challenge to detection.By sending bursts of attacks at irregular intervals,LR-DoS significantly degrades the targeted system’s Quality of Service(QoS).The low-rate nature of these attacks confuses their detection,as they frequently trigger congestion control mechanisms,leading to significant instability in IoT systems.Therefore,to detect the LR-DoS attack,an innovative deep-learning model has been developed for this research work.The standard dataset is utilized to collect the required data.Further,the deep feature extraction process is executed using the Residual Autoencoder with Sparse Attention(ResAE-SA),which helps derive the significant feature required for detection.Ultimately,the Adaptive Dense Recurrent Neural Network(ADRNN)is implemented to detect LR-DoS effectively.To enhance the detection process,the parameters present in the ADRNN are optimized using the Renovated Random Attribute-based Fennec Fox Optimization(RRA-FFA).The proposed optimization reduces the False Discovery Rate and False Positive Rate,maximizing the Matthews Correlation Coefficient from 23,70.8,76.2,84.28 in Dataset 1 and 70.28,73.8,74.1,82.6 in Dataset 2 on EPC-ADRNN,DPO-ADRNN,GTO-ADRNN,FFA-ADRNN respectively to 95.8 on Dataset 1 and 91.7 on Dataset 2 in proposed model.At batch size 4,the accuracy of the designed RRA-FFA-ADRNN model progressed by 9.2%to GTO-ADRNN,11.6%to EFC-ADRNN,10.9%to DPO-ADRNN,and 4%to FFA-ADRNN for Dataset 1.The accuracy of the proposed RRA-FFA-ADRNN is boosted by 12.9%,9.09%,11.6%,and 10.9%over FFCNN,SVM,RNN,and DRNN,using Dataset 2,showing a better improvement in accuracy with that of the proposed RRA-FFA-ADRNN model with 95.7%using Dataset 1 and 94.1%with Dataset 2,which is better than the existing baseline models.展开更多
基金The study was funded by the UPNM Short Term Grant (UPNM/2019/GPJP/SP/1).
文摘Objective:To determine the antibiotic resistance profile(ARP)of Staphylococcus(S.)aureus isolates and molecular typing of the methicillin-resistant S.aureus(MRSA)isolates from Tuanku Mizan Armed Forces Hospital(TMAFH),Kuala Lumpur.Methods:The ARP and presence of the pvl gene were determined for 209 S.aureus isolates from clinical specimens.Of these,123 were methicillin-susceptible S.aureus(MSSA)isolates and 86 were MRSA isolates.All MRSA isolates were characterized using SCCmec typing and spa typing.Descriptive analysis was performed to compare the demographic data with the phenotypic and genotypic variables of the S.aureus isolates.Results:No vancomycin-intermediate and-resistant S.aureus(VISA and VRSA,respectively)were detected among the study isolates.The MSSA isolates showed low resistance rates to all tested antibiotics,were commonly invasive(28/42,66.7%),and mostly harboured pvl(35/42,83.3%).Meanwhile,MRSA isolates showed high resistance to penicillin(86/86,100%),ampicillin(86/86,100%),sulbactam/ampicillin(86/86,100%),cefuroxime(81/86,94.19%),cefoperazone(76/86,88.37%),azithromycin(56/86,65.12%),and erythromycin(54/86,62.79%).The majority of MRSA isolates were of SCCmec type IVh(65/86,75.58%),spa type t032(55/85,63.95%),and grouped into spaCC-t022(66/85,77.65%).The t032 type was found to be associated with resistance traits to azithromycin and erythromycin(P<0.05).We also found several spa types that are typically associated with hospital-,community-,and livestock-associated MRSA co-existing in our MRSA population.Conclusions:This study reflected the consistent absence of VISA and VRSA and corroborated the clonal shifting of MRSA isolates in the Malaysian MRSA isolates.
基金funded by the National Defence University of Malaysia(Grant No.UPNM/2022/GPJP/SG/3)My Brain Sc Scholarship 2023。
文摘This study explores the impact of bismuth oxide(Bi_(2)O_(3))on the optical and radiation shielding properties of transparent,lead-free thulium-doped bismuth borotellurite radiation shielding glass.The investigated glass composition follows the formula[(TeO_(2))_(75)(B_(2)O_(3))_(25)]_(98-x)(Bi_(2)O_(3))_x[Tm_(2)O_(3)]_(2),where x=0 mol%,5 mol%,10 mol%,15 mol%,20 mol%,25 mol%,and 30 mol%.All glass samples remain transparent,with an optical bandgap(E_(opt))exceeding 3.1 e V,ensuring visible light transmission.Radiation shielding data from Phy-X and XCom reveal interactions of the photoelectric effect,Compton scattering,and pair production,with minimal relative difference in mass attenuation coefficient(MAC)which is between0.05 and 0.56.At 0.662 Me V photon energy,the 20 mol%and 25 mol%Bi_(2)O_(3)glasses exhibit significantly higher Phy-X MAC values than other samples,except RS 520 glass,which contains 71%Pb O.Despite incorporating only up to 25 mol%Bi_(2)O_(3),these glasses outperform others in density,half-value layer(HVL),and mean free path(MFP).Correlating E_(opt)and MAC,the 20 mol%Bi_(2)O_(3)glass is the best candidate for transparent radiation shielding glass due to its wide optical bandgap which prevents ionization of trapped holes.Significantly,the linkage between MFP and molar refraction was also discovered based on the particle size influence on both parameters.
基金funded by the Ministry of Higher Education Malaysia,Fundamental Research Grant Scheme(FRGS),FRGS/1/2024/ICT07/UPNM/02/1.
文摘The rapid progression of the Internet of Things(IoT)technology enables its application across various sectors.However,IoT devices typically acquire inadequate computing power and user interfaces,making them susceptible to security threats.One significant risk to cloud networks is Distributed Denial-of-Service(DoS)attacks,where attackers aim to overcome a target system with excessive data and requests.Among these,low-rate DoS(LR-DoS)attacks present a particular challenge to detection.By sending bursts of attacks at irregular intervals,LR-DoS significantly degrades the targeted system’s Quality of Service(QoS).The low-rate nature of these attacks confuses their detection,as they frequently trigger congestion control mechanisms,leading to significant instability in IoT systems.Therefore,to detect the LR-DoS attack,an innovative deep-learning model has been developed for this research work.The standard dataset is utilized to collect the required data.Further,the deep feature extraction process is executed using the Residual Autoencoder with Sparse Attention(ResAE-SA),which helps derive the significant feature required for detection.Ultimately,the Adaptive Dense Recurrent Neural Network(ADRNN)is implemented to detect LR-DoS effectively.To enhance the detection process,the parameters present in the ADRNN are optimized using the Renovated Random Attribute-based Fennec Fox Optimization(RRA-FFA).The proposed optimization reduces the False Discovery Rate and False Positive Rate,maximizing the Matthews Correlation Coefficient from 23,70.8,76.2,84.28 in Dataset 1 and 70.28,73.8,74.1,82.6 in Dataset 2 on EPC-ADRNN,DPO-ADRNN,GTO-ADRNN,FFA-ADRNN respectively to 95.8 on Dataset 1 and 91.7 on Dataset 2 in proposed model.At batch size 4,the accuracy of the designed RRA-FFA-ADRNN model progressed by 9.2%to GTO-ADRNN,11.6%to EFC-ADRNN,10.9%to DPO-ADRNN,and 4%to FFA-ADRNN for Dataset 1.The accuracy of the proposed RRA-FFA-ADRNN is boosted by 12.9%,9.09%,11.6%,and 10.9%over FFCNN,SVM,RNN,and DRNN,using Dataset 2,showing a better improvement in accuracy with that of the proposed RRA-FFA-ADRNN model with 95.7%using Dataset 1 and 94.1%with Dataset 2,which is better than the existing baseline models.
