Open Science(OS)and Research has reached mixed maturity levels in Finland.The meaning of the national project in the ecosystem of Finnish universities of applied sciences(UAS)is to enhance and elaborate OS and Open Ed...Open Science(OS)and Research has reached mixed maturity levels in Finland.The meaning of the national project in the ecosystem of Finnish universities of applied sciences(UAS)is to enhance and elaborate OS and Open Education(OE)activities.Future actions were defined based on a survey and interviews carried out in the Finnish UAS sector during 2018 and 2019.The aim of both data collections was to evaluate the current status and attitudes towards open Research,Development,and Innovation(RDI)among staff members.Another purpose was to define the need for internal support services concerning open RDI and OE and to identify knowledge gaps.The results revealed several gaps in understanding OS and OE initiatives.Real-life actions were mostly vague,and the respondents experienced the need for support.On the other hand,the attitudes towards open RDI were positive,and the issue aroused questions and reflections.This study revealed gaps in knowledge and actions in Finnish UAS sectors.These results have been the basis of development actions such as joint workshops,educational webinars,and common instructions.The future plan includes the establishment of an experts’network for supporting open RDI and Education.展开更多
In this study 70 male students were participated to determine the thyroid disorder through ultrasonography. Thyroid scan was done in 70 students prospectively with no indicative of thyroid disease (age of 19 - 23 yrs)...In this study 70 male students were participated to determine the thyroid disorder through ultrasonography. Thyroid scan was done in 70 students prospectively with no indicative of thyroid disease (age of 19 - 23 yrs). Thyroid scan for students who participated the study took place in the department of Faculty of Applied Medical Sciences, King Abdulaziz University by using an IU 22 Philips ultrasound machine with a 5 - 12 MHz linear transducer. Among the total number of the subjects, 26% was found with abnormal ultrasound findings, 17% of them with cystic nodule, while solid and mixed nodule represented 4% for each. The high rate of abnormal findings of thyroid gland in the study suggested that screening using ultrasound scan was useful in detecting early thyroid disorders.展开更多
The University of Applied Sciences in Vienna has offered university degree programs in the field of construction for more than twenty years and has thus gained great expertise in developing its curriculum. Founded in ...The University of Applied Sciences in Vienna has offered university degree programs in the field of construction for more than twenty years and has thus gained great expertise in developing its curriculum. Founded in 1996, the department of Building and Design consists of six university degree programs. A major strength of the department is the possibility to adapt to recent challenges in a timely manner. As shown in Figure 1, in the winter term 2008/2009, the master’s degree program, Sustainability in the Construction Industry, was held for the first time;it was transformed into the master’s degree program, Architecture-Green Building, in 2016. In 2013/14 the bachelor’s degree program, Architecture-Green Building, started with the first students graduating in 2016. For ten years the department has focused on sustainability within the building, planning and designing processes.展开更多
Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl m...Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl methylphosphonate(DIMP),a commonly used CWA surrogate,is widely studied to enhance our understanding of CWA behavior.The prevailing thermal decomposition model for DIMP,developed approximately 25 years ago,is based on data collected in nitrogen atmospheres at temperatures ranging from 700 K to 800 K.Despite its limitations,this model continues to serve as a foundation for research across various thermal and reactive environments,including combustion studies.Our recent experiments have extended the scope of decomposition analysis by examining DIMP in both nitrogen and zero air across a lower temperature range of 175℃ to 250℃.Infrared spectroscopy results under nitrogen align well with the established model;however,we observed that catalytic effects,stemming from decomposition byproducts and interactions with stainless steel surfaces,alter the reaction kinetics.In zero air environments,we observed a novel infrared absorption band.Spectral fitting suggests this band may represent a combination of propanal and acetone,while GCMS analysis points to vinyl formate and acetone as possible constituents.Although the precise identity of these new products remains unresolved,our findings clearly indicate that the existing decomposition model cannot be reliably extended to lower temperatures or non-nitrogen environments without further revisions.展开更多
The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant e...The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.展开更多
Aerodynamic research on road cars was reviewed in this work under the thread of reducing drag,with the awareness that this may succeed in effectively decreasing the carbon footprint of transportation.First,a selection...Aerodynamic research on road cars was reviewed in this work under the thread of reducing drag,with the awareness that this may succeed in effectively decreasing the carbon footprint of transportation.First,a selection of studies was presented to focus on the most important aerodynamic features of the flow around realistic car body shapes.Then,the discussion was organized around three pillars related to passive flow control,active flow control and active aerodynamics.Both experimental and numerical investigations were included to provide a comprehensive overview.A clear distinction was made between simplified and realistic car models,as well as production vehicles(within the limits of restricted access information).Moreover,a short essay was dedicated to electric vehicles,for which aerodynamics matters,especially at highway speeds.Last,the impact of aerodynamic principles on the design of current and future vehicle fleet was assessed,honestly admitting that recent market trends must be reversed to turn decarbonization goals into reality and damp the effects of global warming.展开更多
There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reac...There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.展开更多
Density functional theory(DFT)calculations were employed to investigate the adsorption behavior of NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)molecules on both pristine and mono-vacancy phosphorene sheets.The pristine pho...Density functional theory(DFT)calculations were employed to investigate the adsorption behavior of NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)molecules on both pristine and mono-vacancy phosphorene sheets.The pristine phosphorene surface showsweak physisorption with all the gasmolecules,inducing onlyminor changes in its structural and electronic properties.However,the introduction ofmono-vacancies significantly enhances the interaction strength with NH_(3),PH_(3),CO_(2),and CH_(4).These variations are attributed to substantial charge redistribution and orbital hybridization in the presence of defects.The defective phosphorene sheet also exhibits enhanced adsorption energies,along with favorable sensitivity and recovery characteristics,highlighting its potential as a promising gas sensor for NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)at ambient conditions.展开更多
Proton exchange membrane(PEM)is an integral component in fuel cells which enables proton transport for efficient energy conversion.Sulfonated Polyether Ether Ketone(SPEEK)has emerged as a cost-effective option with no...Proton exchange membrane(PEM)is an integral component in fuel cells which enables proton transport for efficient energy conversion.Sulfonated Polyether Ether Ketone(SPEEK)has emerged as a cost-effective option with non-fluorinated aromatic backbones for Proton Exchange Membrane Fuel Cell(PEMFC)applications,even though it exhibits lower proton conductivity compared to Nafion.This work aims to study the influence of Sulfonated Chitosan(SCS)concentrations on proton conductivity of SPEEK-based PEM at room temperature.SPEEK was synthesized using a sulfonation process with concentrated sulfuric acid at room temperature.SCS was synthesized via reflux of CS and 1.2 M H2SO4 with a ratio of 1:35(w/v)at 90℃ for 30 min.The composite membranes of SPEEK-SCS were formed with four different SCS concentrations,using the solution castingmethod,andDimethyl Sulfoxide(DMSO)was used as a solvent.The composite membranes synthesized include pure SPEEK(S0),SPEEK with 1%SCS(S1),SPEEK with 2%SCS(S2),and SPEEK with 3%SCS(S3).Fourier transform infrared spectroscopy(FTIR),X-ray diffraction(XRD),water uptake,degree of swelling,Ionic exchange capacity(IEC)with Electrochemical impedance spectroscopy(EIS)were used to characterize the composite membranes in terms of composition,crystallinity,water absorption,dimensional changes,number of exchangeable ions in membranes,and proton conductivity,respectively.Notably,S3 had the highest water uptake and the lowest degree of swelling.S2 had the highest proton conductivity among the SPEEK-SCS composite membranes at room temperature with 3.44×10^(−2) Scm^(-1).展开更多
Plantago major L.,commonly known as plantain,waybread,or dooryard plantain,is a versatile medicinal plant with multiple therapeutic applications.Traditionally,various parts of the plant have been formulated into syrup...Plantago major L.,commonly known as plantain,waybread,or dooryard plantain,is a versatile medicinal plant with multiple therapeutic applications.Traditionally,various parts of the plant have been formulated into syrups,drops,ointments,vaginal suppositories,gargles,and roasted preparations to treat diverse ailments,such as liver disorders,earaches,epilepsy,asthma,stomachaches,diarrhea,constipation,polymenorrhea,and uterine disorders.The plant contains clinically valuable bioactive compounds,including polysaccharides,flavonoids,lipids,iridoid glycosides,caffeic acid derivatives,terpenoids,alkaloids,and organic acids.These bioactive constituents are the primary contributors to the plant’s broad spectrum of biological activities,including antioxidant,anti-inflammatory,antibacterial,antidiarrheal,hepatoprotective,antiviral,antiphage,antinociceptive,antiulcerogenic,antigenotoxic,and immunomodulatory effects of the plant.This review comprehensively summarizes the phytochemical composition,traditional medicinal applications,and biological properties of this multifunctional medicinal plant.展开更多
This work investigates the effects of deformation mechanisms on the mechanical properties and anisotropy of rolled AZ31B magnesium alloy under uniaxial tension,combining experimental characterization with Visco-Plasti...This work investigates the effects of deformation mechanisms on the mechanical properties and anisotropy of rolled AZ31B magnesium alloy under uniaxial tension,combining experimental characterization with Visco-Plastic Self Consistent(VPSC)modeling.The research focuses particularly on anisotropic mechanical responses along transverse direction(TD)and rolling direction(RD).Experimental measurements and computational simulations consistently demonstrate that prismaticslip activation significantly reduces the strain hardening rate during the initial stage of tensile deformation.By suppressing the activation of specific deformation mechanisms along RD and TD,the tensile mechanical behavior of the magnesium alloy was further investigated.The results show that basalslip has the greatest impact during the initial deformation stage and basalslip activation substantially affects the deformation behavior of AZ31B alloy,causing marked decreases in both yield and tensile strength along RD.Under tensile loading along TD,prismaticslip not only exhibits a synergistic effect on yield strength,but also dominants work hardening during the initial plastic deformation.展开更多
This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on th...This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.展开更多
Organic-inorganic metal halides(OIMHs)have emerged as highly promising novel multifunctional optoelectronic materials,owing to their easily adjustable properties from a variety of combinations of different components....Organic-inorganic metal halides(OIMHs)have emerged as highly promising novel multifunctional optoelectronic materials,owing to their easily adjustable properties from a variety of combinations of different components.But it is still difficult and rare to realize highly tunable multicolor luminescence within the same material.In this work,we successfully incorporated three adjustable emission centers in OIMHs to synthesize a novel OIMH(NEA)_(2)MnBr_(4),with each emission center capable of emitting one of the primary colors—red,green,and blue.The green and red emissions originate from the tetrahedron and octahedron structures in the Mn-based frame,while the blue can be attributed to the contribution of organic components.Additionally,to achieve comparable emission intensity among the three primary colors,we enhanced the blue emission performance by optimizing the ratio of organic structure components and incorporating chirality in the OIMHs.The resulting high-quality films can be obtained by spin-coating method with a photoluminescence quantum yields of up to 96%.More interestingly,by the dual manipulation of excitation wavelength and temperature,the sample can be emitted at least seven distinct colors including a standard white luminescence at(0.33,0.33),opening up promising prospects for multicolor luminescence applications such as high-end anti-counterfeiting technology,light-emitting diodes,X-ray imaging,latent fingerprints,humidity detection,and so on.Therefore,based on application scenarios and requirements,our research on this highly tunable luminescent OIMH material lays a solid foundation for further development of various functional properties of related materials.展开更多
Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(...Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.展开更多
Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators,which serve both mechanical and electrical functions.Even a single defectiv...Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators,which serve both mechanical and electrical functions.Even a single defective insulator can lead to equipment breakdown,costly service interruptions,and increased maintenance demands.While unmanned aerial vehicles(UAVs)enable rapid and cost-effective collection of high-resolution imagery,accurate defect identification remains challenging due to cluttered backgrounds,variable lighting,and the diverse appearance of faults.To address these issues,we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced Graph Neural Network(HQGNN).The YOLOv10 module,fine-tuned on domainspecific UAV datasets,improves detection precision,while the HQGNN ensures multi-object tracking and temporal consistency across video frames.This synergy enables reliable and efficient identification of faulty insulators under complex environmental conditions.Experimental results show that the proposed YOLOv10-HQGNN model surpasses existing methods across all metrics,achieving Recall of 0.85 and Average Precision(AP)of 0.83,with clear gains in both accuracy and throughput.These advancements support automated,proactive maintenance strategies that minimize downtime and contribute to a safer,smarter energy infrastructure.展开更多
The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and na...The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.展开更多
Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often f...Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.展开更多
This review provides a comprehensive overview of recent advancements in aluminum-based conductor alloys engineered to achieve superior mechanical strength and thermal stability without sacrificing electrical conductiv...This review provides a comprehensive overview of recent advancements in aluminum-based conductor alloys engineered to achieve superior mechanical strength and thermal stability without sacrificing electrical conductivity.Particular emphasis is placed on the role of microalloying elements—particularly Sc and Zr-in promoting the formation of coherent nanoscale precipitates such as Al_(3)Zr,Al_(3)Sc,and core-shell Al_(3)(Sc,Zr)with metastable L1_(2)crystal structures.These precipitates contribute significantly to high-temperature performance by enabling precipitation strengthening and stabilizing grain boundaries.The review also explores the emerging role of other rare earth elements(REEs),such as erbium(Er),in accelerating precipitation kinetics and improving thermal stability by retarding coarsening.Additionally,recent advancements in thermomechanical processing strategies are examined,with a focus on scalable approaches to optimize the strength-conductivity balance.These approaches involve multi-step heat treatments and carefully controlled manufacturing sequences,particularly the combination of cold drawing and aging treatment to promote uniform and effective precipitation.This review offers valuable insights to guide the development of cost-effective,high-strength,heat-resistant aluminum alloys beyond conductor applications,particularly those strengthened through microalloying with Sc and Zr.展开更多
The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduce...The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.展开更多
Objective:To analyse the prevalence of serotypes,antibiotic resistance,and virulence genes of Group B Streptococcus(GBS)strains isolated from pregnant women at 35-37 weeks of gestation in Ho Chi Minh City,Vietnam,from...Objective:To analyse the prevalence of serotypes,antibiotic resistance,and virulence genes of Group B Streptococcus(GBS)strains isolated from pregnant women at 35-37 weeks of gestation in Ho Chi Minh City,Vietnam,from January 2022 to January 2023.Methods:GBS strains were isolated through selective culture methods and confirmed by PCR.Serotyping,virulence gene detection,and antibiotic susceptibility testing were performed using PCR,gel electrophoresis techniques and Kirby-Bauer test.Results:Totally,61 GBS isolated from 300 participants have been identified including seven GBS serotypes(Ⅰa,Ⅰb,Ⅱ,Ⅲ,Ⅳ,Ⅴ,andⅥ).SerotypesⅦ,Ⅷ,andⅨwere not detected in the study population.Antibiotic resistance patterns varied:13.1%of isolates were fully susceptible,while the majority showed multi-drug resistance,with 34.4%resistant to three antibiotics.SerotypeⅠa demonstrated high susceptibility(35.7%),while serotypeⅢshowed extensive resistance,with 87.5%being resistant to at least three antibiotics.All strains are susceptible to vancomycin andβ-lactams susceptibility also remained high,but resistance to clindamycin,erythromycin,and tetracycline was high(>65%).The virulence genes scpB,cylB,fbsB,and cfb were highly prevalent(90%-100%),indicating their potential for vaccine and diagnostic development.Conclusions:Our findings provide valuable insights into GBS serotypes,resistance,and virulence factors,contributing to community monitoring,preventive measures,diagnostics,and vaccine development.However,the limited sample size necessitates further research.展开更多
基金based on the work done in the “open RDI, learning, and the innovation ecosystem of Finnish UAS” projectco-funded by the Ministry of Education and Culture of Finland
文摘Open Science(OS)and Research has reached mixed maturity levels in Finland.The meaning of the national project in the ecosystem of Finnish universities of applied sciences(UAS)is to enhance and elaborate OS and Open Education(OE)activities.Future actions were defined based on a survey and interviews carried out in the Finnish UAS sector during 2018 and 2019.The aim of both data collections was to evaluate the current status and attitudes towards open Research,Development,and Innovation(RDI)among staff members.Another purpose was to define the need for internal support services concerning open RDI and OE and to identify knowledge gaps.The results revealed several gaps in understanding OS and OE initiatives.Real-life actions were mostly vague,and the respondents experienced the need for support.On the other hand,the attitudes towards open RDI were positive,and the issue aroused questions and reflections.This study revealed gaps in knowledge and actions in Finnish UAS sectors.These results have been the basis of development actions such as joint workshops,educational webinars,and common instructions.The future plan includes the establishment of an experts’network for supporting open RDI and Education.
文摘In this study 70 male students were participated to determine the thyroid disorder through ultrasonography. Thyroid scan was done in 70 students prospectively with no indicative of thyroid disease (age of 19 - 23 yrs). Thyroid scan for students who participated the study took place in the department of Faculty of Applied Medical Sciences, King Abdulaziz University by using an IU 22 Philips ultrasound machine with a 5 - 12 MHz linear transducer. Among the total number of the subjects, 26% was found with abnormal ultrasound findings, 17% of them with cystic nodule, while solid and mixed nodule represented 4% for each. The high rate of abnormal findings of thyroid gland in the study suggested that screening using ultrasound scan was useful in detecting early thyroid disorders.
文摘The University of Applied Sciences in Vienna has offered university degree programs in the field of construction for more than twenty years and has thus gained great expertise in developing its curriculum. Founded in 1996, the department of Building and Design consists of six university degree programs. A major strength of the department is the possibility to adapt to recent challenges in a timely manner. As shown in Figure 1, in the winter term 2008/2009, the master’s degree program, Sustainability in the Construction Industry, was held for the first time;it was transformed into the master’s degree program, Architecture-Green Building, in 2016. In 2013/14 the bachelor’s degree program, Architecture-Green Building, started with the first students graduating in 2016. For ten years the department has focused on sustainability within the building, planning and designing processes.
基金sponsored by the Department of Defense,Defense Threat Reduction Agency under the Materials Science in Extreme Environments University Research Alliance,HDTRA1-20-2-0001。
文摘Chemical warfare agents(CWAs)remain a persistent hazard in many parts of the world,necessitating a deeper exploration of their chemical and physical characteristics and reactions under diverse conditions.Diisopropyl methylphosphonate(DIMP),a commonly used CWA surrogate,is widely studied to enhance our understanding of CWA behavior.The prevailing thermal decomposition model for DIMP,developed approximately 25 years ago,is based on data collected in nitrogen atmospheres at temperatures ranging from 700 K to 800 K.Despite its limitations,this model continues to serve as a foundation for research across various thermal and reactive environments,including combustion studies.Our recent experiments have extended the scope of decomposition analysis by examining DIMP in both nitrogen and zero air across a lower temperature range of 175℃ to 250℃.Infrared spectroscopy results under nitrogen align well with the established model;however,we observed that catalytic effects,stemming from decomposition byproducts and interactions with stainless steel surfaces,alter the reaction kinetics.In zero air environments,we observed a novel infrared absorption band.Spectral fitting suggests this band may represent a combination of propanal and acetone,while GCMS analysis points to vinyl formate and acetone as possible constituents.Although the precise identity of these new products remains unresolved,our findings clearly indicate that the existing decomposition model cannot be reliably extended to lower temperatures or non-nitrogen environments without further revisions.
文摘The modern world remains vulnerable to natural disasters,including floods,earthquakes,wildfires,and others.These events remain unpredictable and inevitable,and recovering quickly and effectively requires significant effort and expense.Monitoring is becoming more efficient thanks to technologies such as Unmanned Aerial Vehicles(UAVs),which can access hard-to-reach areas and provide real-time data.However,in disaster-affected areas,these monitoring systems may encounter many obstacles when communicating with servers or transmitting monitored data.This paper proposes an adaptive communication model to overcome the challenges faced in disaster-affected areas.A base station is responsible for collecting data(such as images and videos)captured by UAVs performing surveillance within its communication range.This station is typically a tower providing fixed cellular network service.However,in the absence of such a tower,a selected UAV may serve as the station,depending on the situation.If surveillance needs to be performed outside the coverage area,it can continue to communicate via nearby UAVs through cooperative communication.UAVs with internet support,known as the Internet of Flying Things(IoFT),will also be utilized to enhance communication capacity and efficiency.The proposed communication model is validated through experiments,showing superior data transmission performance and higher throughput.Analysis indicates it outperforms traditional systems,even in rural areas,with or without internet access.
文摘Aerodynamic research on road cars was reviewed in this work under the thread of reducing drag,with the awareness that this may succeed in effectively decreasing the carbon footprint of transportation.First,a selection of studies was presented to focus on the most important aerodynamic features of the flow around realistic car body shapes.Then,the discussion was organized around three pillars related to passive flow control,active flow control and active aerodynamics.Both experimental and numerical investigations were included to provide a comprehensive overview.A clear distinction was made between simplified and realistic car models,as well as production vehicles(within the limits of restricted access information).Moreover,a short essay was dedicated to electric vehicles,for which aerodynamics matters,especially at highway speeds.Last,the impact of aerodynamic principles on the design of current and future vehicle fleet was assessed,honestly admitting that recent market trends must be reversed to turn decarbonization goals into reality and damp the effects of global warming.
基金supported by the Korea Institute of Energy Technology Evaluation and Planning(KETEP)and the Ministry of Trade,Industry&Energy(MOTIE)of the Republic of Korea(No.RS-2025-02315209).
文摘There is a need for accurate prediction of heat and mass transfer in aerodynamically designed,non-Newtonian nanofluids across aerodynamically designed,high-flux biomedical micro-devices for thermal management and reactive coating processes,but existing work is not uncharacteristically remiss regarding viscoelasticity,radiative heating,viscous dissipation,and homogeneous–heterogeneous reactions within a single scheme that is calibrated.This research investigates the flow of Williamson nanofluid across a dynamically wedged surface under conditions that include viscous dissipation,thermal radiation,and homogeneous-heterogeneous reactions.The paper develops a detailed mathematical approach that utilizes boundary layers to transform partial differential equations into ordinary differential equations using similarity transformations.RK4 is the technique for gaining numerical solutions,but with the addition of ANNs,there is an improvement in prediction accuracy and computational efficiency.The study investigates the influence of wedge angle parameter,along with Weissenberg number,thermal radiation parameter and Brownian motion parameter,and Schmidt number,on velocity distribution,temperature distribution,and concentra-tion distribution.Enhanced Weissenberg numbers enhance viscoelastic responses that modify velocity patterns,but radiation parameters and thermophoresis have key impacts on thermal transfer phenomena.This research develops findings that are of enormous application in aerospace,biomedical(artificial hearts and drug delivery),and industrial cooling technology applications.New findings on non-Newtonian nanofluids under full flow systems are included in this work to enhance heat transfer methods in novel fluid-based systems.
基金financial support to conduct this research from the Science and Engineering Research Board(SERB)through a state university research excellence(SURE)grant(SUR/2022/004935).
文摘Density functional theory(DFT)calculations were employed to investigate the adsorption behavior of NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)molecules on both pristine and mono-vacancy phosphorene sheets.The pristine phosphorene surface showsweak physisorption with all the gasmolecules,inducing onlyminor changes in its structural and electronic properties.However,the introduction ofmono-vacancies significantly enhances the interaction strength with NH_(3),PH_(3),CO_(2),and CH_(4).These variations are attributed to substantial charge redistribution and orbital hybridization in the presence of defects.The defective phosphorene sheet also exhibits enhanced adsorption energies,along with favorable sensitivity and recovery characteristics,highlighting its potential as a promising gas sensor for NH_(3),AsH_(3),PH_(3),CO_(2),and CH_(4)at ambient conditions.
文摘Proton exchange membrane(PEM)is an integral component in fuel cells which enables proton transport for efficient energy conversion.Sulfonated Polyether Ether Ketone(SPEEK)has emerged as a cost-effective option with non-fluorinated aromatic backbones for Proton Exchange Membrane Fuel Cell(PEMFC)applications,even though it exhibits lower proton conductivity compared to Nafion.This work aims to study the influence of Sulfonated Chitosan(SCS)concentrations on proton conductivity of SPEEK-based PEM at room temperature.SPEEK was synthesized using a sulfonation process with concentrated sulfuric acid at room temperature.SCS was synthesized via reflux of CS and 1.2 M H2SO4 with a ratio of 1:35(w/v)at 90℃ for 30 min.The composite membranes of SPEEK-SCS were formed with four different SCS concentrations,using the solution castingmethod,andDimethyl Sulfoxide(DMSO)was used as a solvent.The composite membranes synthesized include pure SPEEK(S0),SPEEK with 1%SCS(S1),SPEEK with 2%SCS(S2),and SPEEK with 3%SCS(S3).Fourier transform infrared spectroscopy(FTIR),X-ray diffraction(XRD),water uptake,degree of swelling,Ionic exchange capacity(IEC)with Electrochemical impedance spectroscopy(EIS)were used to characterize the composite membranes in terms of composition,crystallinity,water absorption,dimensional changes,number of exchangeable ions in membranes,and proton conductivity,respectively.Notably,S3 had the highest water uptake and the lowest degree of swelling.S2 had the highest proton conductivity among the SPEEK-SCS composite membranes at room temperature with 3.44×10^(−2) Scm^(-1).
文摘Plantago major L.,commonly known as plantain,waybread,or dooryard plantain,is a versatile medicinal plant with multiple therapeutic applications.Traditionally,various parts of the plant have been formulated into syrups,drops,ointments,vaginal suppositories,gargles,and roasted preparations to treat diverse ailments,such as liver disorders,earaches,epilepsy,asthma,stomachaches,diarrhea,constipation,polymenorrhea,and uterine disorders.The plant contains clinically valuable bioactive compounds,including polysaccharides,flavonoids,lipids,iridoid glycosides,caffeic acid derivatives,terpenoids,alkaloids,and organic acids.These bioactive constituents are the primary contributors to the plant’s broad spectrum of biological activities,including antioxidant,anti-inflammatory,antibacterial,antidiarrheal,hepatoprotective,antiviral,antiphage,antinociceptive,antiulcerogenic,antigenotoxic,and immunomodulatory effects of the plant.This review comprehensively summarizes the phytochemical composition,traditional medicinal applications,and biological properties of this multifunctional medicinal plant.
基金supported by the National Nature Science Foundation of China(52275356).
文摘This work investigates the effects of deformation mechanisms on the mechanical properties and anisotropy of rolled AZ31B magnesium alloy under uniaxial tension,combining experimental characterization with Visco-Plastic Self Consistent(VPSC)modeling.The research focuses particularly on anisotropic mechanical responses along transverse direction(TD)and rolling direction(RD).Experimental measurements and computational simulations consistently demonstrate that prismaticslip activation significantly reduces the strain hardening rate during the initial stage of tensile deformation.By suppressing the activation of specific deformation mechanisms along RD and TD,the tensile mechanical behavior of the magnesium alloy was further investigated.The results show that basalslip has the greatest impact during the initial deformation stage and basalslip activation substantially affects the deformation behavior of AZ31B alloy,causing marked decreases in both yield and tensile strength along RD.Under tensile loading along TD,prismaticslip not only exhibits a synergistic effect on yield strength,but also dominants work hardening during the initial plastic deformation.
文摘This survey presents a comprehensive examination of sensor fusion research spanning four decades,tracing the methodological evolution,application domains,and alignment with classical hierarchical models.Building on this long-term trajectory,the foundational approaches such as probabilistic inference,early neural networks,rulebasedmethods,and feature-level fusion established the principles of uncertainty handling andmulti-sensor integration in the 1990s.The fusion methods of 2000s marked the consolidation of these ideas through advanced Kalman and particle filtering,Bayesian–Dempster–Shafer hybrids,distributed consensus algorithms,and machine learning ensembles for more robust and domain-specific implementations.From 2011 to 2020,the widespread adoption of deep learning transformed the field driving some major breakthroughs in the autonomous vehicles domain.A key contribution of this work is the assessment of contemporary methods against the JDL model,revealing gaps at higher levels-especially in situation and impact assessment.Contemporary methods offer only limited implementation of higher-level fusion.The survey also reviews the benchmark multi-sensor datasets,noting their role in advancing the field while identifying major shortcomings like the lack of domain diversity and hierarchical coverage.By synthesizing developments across decades and paradigms,this survey provides both a historical narrative and a forward-looking perspective.It highlights unresolved challenges in transparency,scalability,robustness,and trustworthiness,while identifying emerging paradigms such as neuromorphic fusion and explainable AI as promising directions.This paves the way forward for advancing sensor fusion towards transparent and adaptive next-generation autonomous systems.
基金supported by supported by the Basic Research Project of State Key Laboratory of Photovoltaic Science and Technology(No.202401020302)funding support from the National Natural Science Foundation of China(No.62274040 and No.62304046)Shanghai science and technology innovation action plan(No.24DZ3001200)。
文摘Organic-inorganic metal halides(OIMHs)have emerged as highly promising novel multifunctional optoelectronic materials,owing to their easily adjustable properties from a variety of combinations of different components.But it is still difficult and rare to realize highly tunable multicolor luminescence within the same material.In this work,we successfully incorporated three adjustable emission centers in OIMHs to synthesize a novel OIMH(NEA)_(2)MnBr_(4),with each emission center capable of emitting one of the primary colors—red,green,and blue.The green and red emissions originate from the tetrahedron and octahedron structures in the Mn-based frame,while the blue can be attributed to the contribution of organic components.Additionally,to achieve comparable emission intensity among the three primary colors,we enhanced the blue emission performance by optimizing the ratio of organic structure components and incorporating chirality in the OIMHs.The resulting high-quality films can be obtained by spin-coating method with a photoluminescence quantum yields of up to 96%.More interestingly,by the dual manipulation of excitation wavelength and temperature,the sample can be emitted at least seven distinct colors including a standard white luminescence at(0.33,0.33),opening up promising prospects for multicolor luminescence applications such as high-end anti-counterfeiting technology,light-emitting diodes,X-ray imaging,latent fingerprints,humidity detection,and so on.Therefore,based on application scenarios and requirements,our research on this highly tunable luminescent OIMH material lays a solid foundation for further development of various functional properties of related materials.
基金supported and funded by the Deanship of Scientific Research at Imam Mohammad Ibn Saud Islamic University(IMSIU)(grant number IMSIU-DDRSP2601).
文摘Automated detection of Motor Imagery(MI)tasks is extremely useful for prosthetic arms and legs of stroke patients for their rehabilitation.Prediction of MI tasks can be performed with the help of Electroencephalogram(EEG)signals recorded by placing electrodes on the scalp of subjects;however,accurate prediction of MI tasks remains a challenge due to noise that is incurred during the EEG signal recording process,the extraction of a feature vector with high interclass variance,and accurate classification.The proposed method consists of preprocessing,feature extraction,and classification.First,EEG signals are denoised using a bandpass filter followed by Independent Component Analysis(ICA).Multiple channels are combined to form a single surrogate channel.Short Time Fourier Transform(STFT)is then applied to convert time domain EEG signals into the frequency domain.Handcrafted and automated features are extracted from EEG signals and then concatenated to form a single feature vector.We propose a customized two-dimensional Convolutional Neural Network(CNN)for automated feature extraction with high interclass variance.Feature selection is performed using Particle Swarm Optimization(PSO)to obtain optimal features.The final feature vector is passed to three different classifiers:Support Vector Machine(SVM),Random Forest(RF),and Long Short-Term Memory(LSTM).The final decision is made using the Model-Agnostic Meta Learning(MAML).The Proposed method has been tested on two datasets,including PhysioNet and BCI Competition IV-2a,and it achieved better results in terms of accuracy and F1 score than existing state-of-the-art methods.The proposed framework achieved an accuracy and F1 score of 96%on the PhysioNet dataset and 95.5%on the BCI Competition IV,dataset 2a.We also present SHapley Additive exPlanations(SHAP)and Gradient-weighted Class Activation Mapping(Grad-CAM)explainable techniques to enhance model interpretability in a clinical setting.
基金supported by Ho Chi Minh City Open University,Vietnam and Suan Sunandha Rajabhat Univeristy,Thailand.
文摘Ensuring the reliability of power transmission networks depends heavily on the early detection of faults in key components such as insulators,which serve both mechanical and electrical functions.Even a single defective insulator can lead to equipment breakdown,costly service interruptions,and increased maintenance demands.While unmanned aerial vehicles(UAVs)enable rapid and cost-effective collection of high-resolution imagery,accurate defect identification remains challenging due to cluttered backgrounds,variable lighting,and the diverse appearance of faults.To address these issues,we introduce a real-time inspection framework that integrates an enhanced YOLOv10 detector with a Hybrid Quantum-Enhanced Graph Neural Network(HQGNN).The YOLOv10 module,fine-tuned on domainspecific UAV datasets,improves detection precision,while the HQGNN ensures multi-object tracking and temporal consistency across video frames.This synergy enables reliable and efficient identification of faulty insulators under complex environmental conditions.Experimental results show that the proposed YOLOv10-HQGNN model surpasses existing methods across all metrics,achieving Recall of 0.85 and Average Precision(AP)of 0.83,with clear gains in both accuracy and throughput.These advancements support automated,proactive maintenance strategies that minimize downtime and contribute to a safer,smarter energy infrastructure.
基金supported by the Academic Research Projects of Beijing Union University(ZK20202204)the National Natural Science Foundation of China(12250005,12073040,12273059,11973056,12003051,11573037,12073041,11427901,11572005,11611530679 and 12473052)+1 种基金the Strategic Priority Research Program of the China Academy of Sciences(XDB0560000,XDA15052200,XDB09040200,XDA15010700,XDB0560301,and XDA15320102)the Chinese Meridian Project(CMP).
文摘The solar cycle(SC),a phenomenon caused by the quasi-periodic regular activities in the Sun,occurs approximately every 11 years.Intense solar activity can disrupt the Earth’s ionosphere,affecting communication and navigation systems.Consequently,accurately predicting the intensity of the SC holds great significance,but predicting the SC involves a long-term time series,and many existing time series forecasting methods have fallen short in terms of accuracy and efficiency.The Time-series Dense Encoder model is a deep learning solution tailored for long time series prediction.Based on a multi-layer perceptron structure,it outperforms the best previously existing models in accuracy,while being efficiently trainable on general datasets.We propose a method based on this model for SC forecasting.Using a trained model,we predict the test set from SC 19 to SC 25 with an average mean absolute percentage error of 32.02,root mean square error of 30.3,mean absolute error of 23.32,and R^(2)(coefficient of determination)of 0.76,outperforming other deep learning models in terms of accuracy and training efficiency on sunspot number datasets.Subsequently,we use it to predict the peaks of SC 25 and SC 26.For SC 25,the peak time has ended,but a stronger peak is predicted for SC 26,of 199.3,within a range of 170.8-221.9,projected to occur during April 2034.
文摘Accurate prediction of concrete compressive strength is fundamental for optimizing mix designs,improving material utilization,and ensuring structural safety in modern construction.Traditional empirical methods often fail to capture the non-linear relationships among concrete constituents,especially with the growing use of supple-mentary cementitious materials and recycled aggregates.This study presents an integrated machine learning framework for concrete strength prediction,combining advanced regression models—namely CatBoost—with metaheuristic optimization algorithms,with a particular focus on the Somersaulting Spider Optimizer(SSO).A comprehensive dataset encompassing diverse mix proportions and material types was used to evaluate baseline machine learning models,including CatBoost,XGBoost,ExtraTrees,and RandomForest.Among these,CatBoost demonstrated superior accuracy across multiple performance metrics.To further enhance predictive capability,several bio-inspired optimizers were employed for hyperparameter tuning.The SSO-CatBoost hybrid achieved the lowest mean squared error and highest correlation coefficients,outperforming other metaheuristic approaches such as Genetic Algorithm,Particle Swarm Optimization,and Grey Wolf Optimizer.Statistical significance was established through Analysis of Variance and Wilcoxon signed-rank testing,confirming the robustness of the optimized models.The proposed methodology not only delivers improved predictive performance but also offers a transparent framework for mix design optimization,supporting data-driven decision making in sustainable and resilient infrastructure development.
文摘This review provides a comprehensive overview of recent advancements in aluminum-based conductor alloys engineered to achieve superior mechanical strength and thermal stability without sacrificing electrical conductivity.Particular emphasis is placed on the role of microalloying elements—particularly Sc and Zr-in promoting the formation of coherent nanoscale precipitates such as Al_(3)Zr,Al_(3)Sc,and core-shell Al_(3)(Sc,Zr)with metastable L1_(2)crystal structures.These precipitates contribute significantly to high-temperature performance by enabling precipitation strengthening and stabilizing grain boundaries.The review also explores the emerging role of other rare earth elements(REEs),such as erbium(Er),in accelerating precipitation kinetics and improving thermal stability by retarding coarsening.Additionally,recent advancements in thermomechanical processing strategies are examined,with a focus on scalable approaches to optimize the strength-conductivity balance.These approaches involve multi-step heat treatments and carefully controlled manufacturing sequences,particularly the combination of cold drawing and aging treatment to promote uniform and effective precipitation.This review offers valuable insights to guide the development of cost-effective,high-strength,heat-resistant aluminum alloys beyond conductor applications,particularly those strengthened through microalloying with Sc and Zr.
基金supported by Ho Chi Minh City Open University,Vietnam under grant number E2024.02.1CD and Suan Sunandha Rajabhat University,Thailand.
文摘The Financial Technology(FinTech)sector has witnessed rapid growth,resulting in increasingly complex and high-volume digital transactions.Although this expansion improves efficiency and accessibility,it also introduces significant vulnerabilities,including fraud,money laundering,and market manipulation.Traditional anomaly detection techniques often fail to capture the relational and dynamic characteristics of financial data.Graph Neural Networks(GNNs),capable of modeling intricate interdependencies among entities,have emerged as a powerful framework for detecting subtle and sophisticated anomalies.However,the high-dimensionality and inherent noise of FinTech datasets demand robust feature selection strategies to improve model scalability,performance,and interpretability.This paper presents a comprehensive survey of GNN-based approaches for anomaly detection in FinTech,with an emphasis on the synergistic role of feature selection.We examine the theoretical foundations of GNNs,review state-of-the-art feature selection techniques,analyze their integration with GNNs,and categorize prevalent anomaly types in FinTech applications.In addition,we discuss practical implementation challenges,highlight representative case studies,and propose future research directions to advance the field of graph-based anomaly detection in financial systems.
文摘Objective:To analyse the prevalence of serotypes,antibiotic resistance,and virulence genes of Group B Streptococcus(GBS)strains isolated from pregnant women at 35-37 weeks of gestation in Ho Chi Minh City,Vietnam,from January 2022 to January 2023.Methods:GBS strains were isolated through selective culture methods and confirmed by PCR.Serotyping,virulence gene detection,and antibiotic susceptibility testing were performed using PCR,gel electrophoresis techniques and Kirby-Bauer test.Results:Totally,61 GBS isolated from 300 participants have been identified including seven GBS serotypes(Ⅰa,Ⅰb,Ⅱ,Ⅲ,Ⅳ,Ⅴ,andⅥ).SerotypesⅦ,Ⅷ,andⅨwere not detected in the study population.Antibiotic resistance patterns varied:13.1%of isolates were fully susceptible,while the majority showed multi-drug resistance,with 34.4%resistant to three antibiotics.SerotypeⅠa demonstrated high susceptibility(35.7%),while serotypeⅢshowed extensive resistance,with 87.5%being resistant to at least three antibiotics.All strains are susceptible to vancomycin andβ-lactams susceptibility also remained high,but resistance to clindamycin,erythromycin,and tetracycline was high(>65%).The virulence genes scpB,cylB,fbsB,and cfb were highly prevalent(90%-100%),indicating their potential for vaccine and diagnostic development.Conclusions:Our findings provide valuable insights into GBS serotypes,resistance,and virulence factors,contributing to community monitoring,preventive measures,diagnostics,and vaccine development.However,the limited sample size necessitates further research.
