In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended t...In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms.展开更多
Objective:To describe the implementation of evidence-based practice(EBP)and identify the associated factors among clinical nurses working at an oncology hospital in Central Vietnam.Methods:A cross-sectional study was ...Objective:To describe the implementation of evidence-based practice(EBP)and identify the associated factors among clinical nurses working at an oncology hospital in Central Vietnam.Methods:A cross-sectional study was conducted with 190 clinical nurses recruited from an oncology hospital in Central Vietnam.The self-administered Evidence-Based Practice Questionnaire(EBPQ)was employed to assess the nurses'knowledge/skills,attitudes,and implementation of EBP.Data analysis utilized descriptive statistics,the Mann-Whitney test,the Kruskal-Wallis test,and Spearman's rho correlation.Results:The mean total score for EBP implementation among the nurses was 29.52(SD=7.14)out of 42 scores.The most frequently undertaken activity was sharing evidence with colleagues,whereas finding relevant evidence was the least performed.The level of EBP implementation significantly varied based on the nurses'role types and their participation in related courses(P<0.05).Moreover,a strong positive correlation was observed between EBP implementation and both knowledge/skills(r=0.703,P<0.001)and attitudes toward EBP(r=0.536,P<0.001).Conclusions:The implementation of EBP by oncology nurses is generally moderate and is significantly positively correlated with their knowledge/skills and attitudes toward EBP.These findings underscore the importance of enhancing educational programs and facilitating suppor tive institutional policies to fur ther encourage the adoption of EBP among nurses.展开更多
Objective:To explore the application of the Registered Nurses'Association of Ontario(RNAO)Best Practice Guideline(BPG)for Developing and Sustaining Nursing Leadership through the lens of nursing students transitio...Objective:To explore the application of the Registered Nurses'Association of Ontario(RNAO)Best Practice Guideline(BPG)for Developing and Sustaining Nursing Leadership through the lens of nursing students transitioning from classroom learning to clinical practice.Through this reflection,the authors hope to inspire and empower future nursing leaders to champion innovation,resilience,and excellence in healthcare.Methods:By integrating evidence-based leadership principles,the authors reflect on their personal experiences,challenges,and successes in implementing these guidelines in real-world settings.Results:The paper highlights the significance of leadership in fostering healthy work environments and improving patient outcomes,emphasizing the role of nursing students as emerging leaders.Practical strategies for applying BPG,the RNAO BPG suggests leadership skills on professional development,and recommendations for embedding leadership principles into nursing education and practice are discussed.Conclusions:Implementing nursing leadership from the beginning of a nursing student's journey is vital for shaping the next generation of healthcare leaders.By integrating leadership principles early in nursing education,students can develop the skills needed to navigate clinical challenges,collaborate effectively with healthcare teams,and advocate for positive change.Starting with leadership guidelines in nursing school ensures that future nurses are equipped to foster supportive work environments,improve patient outcomes,and drive innovation from the very start of their careers,making leadership an inherent part of their professional identity.展开更多
In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown de...In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.展开更多
Globally,mealtimes provide key insights into cultural and social values and practices.We examine mealtime environments and eating practices in two different African settings using quantitative observational and questi...Globally,mealtimes provide key insights into cultural and social values and practices.We examine mealtime environments and eating practices in two different African settings using quantitative observational and questionnaire data.Participating families(N=80)were recruited from two rural locations in Zambia and Kenya.Results following descriptive analysis showed that meals are typically taken as a family in a sociable context,providing opportunities to nurture children’s positive behaviours.In both communities,mothers(71.25%)were most likely to be present and typically prepared meals and provided the food.We observed a few distractions being used during mealtimes,and children finished their food with little to no conflict.Plate sharing varied across the two sites and was more common in Zambia,where we also observed more traditional practices such as eating with hands(as opposed to cutlery)and sitting on mats on the ground(as opposed to seated on chairs or sofas).Overall,our findings suggest more similarities in the cultural and social values across the two African contexts despite slight differences in mealtime practices.In particular,positive mealtime behaviours(little food refusal,lack of conflict)were common across all contexts,regardless of the extent to which traditional practices(such as plate-sharing and sitting on the floor)were used.Findings align with Family Systems theory,which states that the family is understood best by conceptualizing it as a complex,dynamic,and changing collection of parts,subsystems,and family members.展开更多
The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermo...The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.展开更多
To meet the need for cultivating application-oriented talents in local universities,this study introduced a project-based learning approach into the reform of bioinformatics experimental teaching.The course was struct...To meet the need for cultivating application-oriented talents in local universities,this study introduced a project-based learning approach into the reform of bioinformatics experimental teaching.The course was structured around a project titled"Influenza Virus Analysis",comprising four progressive modules:database utilization and information retrieval,sequence alignment and phylogenetic analysis,functional and structural prediction,and omics data analysis.These modules were integrated into a coherent research workflow that connected fragmented knowledge and technical skills.During implementation,flipped classroom and group collaboration methods were employed,alongside the establishment of a diversified assessment system emphasizing process evaluation.Teaching practice indicates that the reform effectively enhances students professional application skills,learning experience,and scientific literacy,facilitating a shift from"tool operation"to"problem-solving"capabilities.This study provides a reference model for the reform of bioinformatics experimental teaching in local universities.展开更多
Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously...Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.展开更多
The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent ...The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.展开更多
In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digesti...In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.展开更多
High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP g...High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP genotyping array for wax gourd was developed using genotyping by target sequencing(GBTS),featuring 10,722 SNPs evenly distributed across all 12 chromosomes,including 278 functional loci associated with key economic traits.To demonstrate its utility,genetic distances among 19 elite inbred lines were calculated from SNP data and correlated with heterosis for single fruit weight.The results revealed that greater genetic distance was associated with higher middle parent heterosis(MPH) for single fruit weight.Furthermore,56 commercial wax gourd cultivars collected from eight regions were selected and genotyped.Population structure analysis,phylogenetic analysis,and principal component analysis(PCA) collectively indicated that these cultivars fall into two major groups.Group I,comprising black or dark green skinned wax gourds,exhibited lower genetic diversity than Group II,which includes green or light green skinned varieties,reflecting shorter genetic distances within Group I.Finally,60 polymorphic SNPs were used to construct DNA fingerprints for distinguishing the 56 cultivars.As the first high-throughput genotyping platform for wax gourd,this SNP array provides an effective and powerful tool for genetic analysis.展开更多
Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely id...Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.展开更多
The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches ...The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.展开更多
Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewa...Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.展开更多
This study investigates how to pedagogically integrate ideological education with competency development in the Intercultural Communication course,a challenge arising from China’s dual reform contexts of the New Libe...This study investigates how to pedagogically integrate ideological education with competency development in the Intercultural Communication course,a challenge arising from China’s dual reform contexts of the New Liberal Arts initiative and the national curriculum ideology policy.As global interactions intensify,cultivating foreign language professionals who possess both firm cultural confidence and sophisticated intercultural competence have become a critical educational imperative.This exploratory study investigates how a three-dimensional“Value-Knowledge-Competency”framework can guide the redesign of course content,task design,and assessment to achieve organic fusion.Drawing on qualitative data from a case study,it analyzes specific implementation pathways,synthesizes teacher and student feedback,and discusses the resultant challenges and broader implications for foreign language curriculum reform.The findings suggest that such an integrated approach can effectively synergize value guidance with skill cultivation,though its success hinges on overcoming issues related to pedagogical naturalness,resource allocation,and standardized evaluation.展开更多
This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loo...This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loop EIV system and its coprime factor(CF)uncertainty description is first derived,based on which the FR measurements suitable for plant CF identification are able to be generated.Different factorizations of a given controller in the closed-loop system can be made best use to adjust right coprime factors(RCFs)of the plant so as to realize an improvement on the signal-to-noise ratio of identification experimental data.Subsequently,a nominal RCF model is estimated by linear matrix inequalities from the applicable FR measurements and its associated worst-case errors are quantified from a priori and a posteriori information on the underlying system.A resulting RCF perturbation model set can then be described by the nominal RCF model and its worst-case error bounds.Such a model set capable of being stabilized by the given controller is ready for its robust stabilizing controller redesign and robust performance analysis.Finally,a numerical simulation is given to show the efficacy of the proposed identification method.展开更多
In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typica...In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.展开更多
Objectives This study aimed to compare the effectiveness of the semi-virtual simulation and traditional simulation teaching models based on the Standards of Best Practice(SOBP)according to the International Nursing As...Objectives This study aimed to compare the effectiveness of the semi-virtual simulation and traditional simulation teaching models based on the Standards of Best Practice(SOBP)according to the International Nursing Association for Clinical Simulation and Learning(INACSL)in the Adult Nursing course.Methods This study used a quasi-experimental design.A total of 94 third-year nursing students from a university in Beijing between November and December 2022 were recruited as participants.An innovative semi-virtual simulation teaching model was designed based on the SOBP established by the INACSL.In the Adult Nursing course,both the semi-virtual and traditional simulation teaching models were implemented.At the end of the simulation sessions,participants completed the Chinese version of the Simulation Effectiveness Tool-Modified(SET-M)to assess the effectiveness of the two teaching models.Results All nursing students completed the simulation sessions.There was no difference(t=−0.93,P=0.353)in the total scores between the semi-virtual simulation teaching model(50.87±5.30)and the traditional simulation teaching model(50.37±5.16).However,there was a statistically significant difference(t=−2.65,P=0.010)in the prebriefing section(semi-virtual simulation:5.60±0.71;traditional simulation:5.33±0.78).In contrast,no statistically significant differences were found for the scenario and debriefing sections(P>0.05).At the individual item level,statistical differences(P<0.05)between the two models were identified for items 1 and 9,but not for the remaining items(P>0.05).By analyzing the open-ended question,it was found that both simulation models were effective,and students’comments were similar.Conclusions The study demonstrated equivalent effectiveness between the semi-virtual and traditional simulation teaching models.Semi-virtual simulation teaching model could offer a more flexible and feasible approach to simulation teaching.展开更多
Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessm...Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessment and human health protection.However,current visual detection methods can only distinguish individual PDA isomers and failed to identify binary or ternary mixtures.Herein,a highly active and ultrastable peroxidase(POD)-like CoPt graphitic nanozyme was used for naked-eye identification and colorimetric/fluorescent(FL)dual-mode quantitative detection of PDA isomers.The CoPt@G nanozyme effectively catalyzed the oxidation of OPD,MPD,PPD,OPD+PPD,OPD+MPD,MPD+PPD and OPD+MPD+PPD into yellow,colorless,lilac,yellow,yellow,wine red and reddish-brown products,respectively,in the presence of H_(2)O_(2).Thus,the MPD,PPD,MPD+PPD and OPD+MPD+PPD were easily identified based on the distinct color of their oxidation products,and the OPD,OPD+PPD,OPD+MPD could be further identified by the additional addition of MPD or PPD.Subsequently,CoPt@G/H_(2)O_(2)-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H_(2)O_(2)-,and MPD/CoPt@G/H_(2)O_(2)-enabled colorimetric/FL dual-mode platforms for the quantitative detection of OPD,MPD and PPD were proposed.The experimental results illustrated that the constructed sensing platforms exhibit satisfactory sensitivity,comparable to that reported in previous studies.Finally,the evaluation of PDAs in water samples was realized,yielding satisfactory recoveries.This work expanded the application prospects of nanozymes in assessing environmental risks and protection of human security.展开更多
基金Supported by Guangxi Degree and Postgraduate Education Reform Project(JGY2023213,JGY2023211).
文摘In order to improve the traditional teaching model of traditional Chinese medicine identification course for graduate students of pharmacy,this paper described the research on constructing and practicing the blended teaching model of"online+offline"based on the teaching concept of TfU by taking the course of"Authentic Medicinal Materials and Quality of Traditional Chinese Medicine"as an example.In the preparatory phase,through resource integration and course content decomposition,it identifies"generative topics"to engage students in pre-class online discussions.During the instructional phase,"comprehension-oriented objectives"are established based on learning analytics,followed by the implementation of"understanding-focused activities"for guided inquiry in offline classrooms.The post-class phase employs online extended materials to conduct"sustained assessment"through evaluations and summaries,thereby continuously optimizing subsequent teaching practices.This pedagogical framework not only effectively cultivates investigative research thinking among graduate students but also enhances standardized management and scientific development of the teaching team.The practical research outcomes and experiences derived from this model can provide valuable references for analogous course reforms.
文摘Objective:To describe the implementation of evidence-based practice(EBP)and identify the associated factors among clinical nurses working at an oncology hospital in Central Vietnam.Methods:A cross-sectional study was conducted with 190 clinical nurses recruited from an oncology hospital in Central Vietnam.The self-administered Evidence-Based Practice Questionnaire(EBPQ)was employed to assess the nurses'knowledge/skills,attitudes,and implementation of EBP.Data analysis utilized descriptive statistics,the Mann-Whitney test,the Kruskal-Wallis test,and Spearman's rho correlation.Results:The mean total score for EBP implementation among the nurses was 29.52(SD=7.14)out of 42 scores.The most frequently undertaken activity was sharing evidence with colleagues,whereas finding relevant evidence was the least performed.The level of EBP implementation significantly varied based on the nurses'role types and their participation in related courses(P<0.05).Moreover,a strong positive correlation was observed between EBP implementation and both knowledge/skills(r=0.703,P<0.001)and attitudes toward EBP(r=0.536,P<0.001).Conclusions:The implementation of EBP by oncology nurses is generally moderate and is significantly positively correlated with their knowledge/skills and attitudes toward EBP.These findings underscore the importance of enhancing educational programs and facilitating suppor tive institutional policies to fur ther encourage the adoption of EBP among nurses.
文摘Objective:To explore the application of the Registered Nurses'Association of Ontario(RNAO)Best Practice Guideline(BPG)for Developing and Sustaining Nursing Leadership through the lens of nursing students transitioning from classroom learning to clinical practice.Through this reflection,the authors hope to inspire and empower future nursing leaders to champion innovation,resilience,and excellence in healthcare.Methods:By integrating evidence-based leadership principles,the authors reflect on their personal experiences,challenges,and successes in implementing these guidelines in real-world settings.Results:The paper highlights the significance of leadership in fostering healthy work environments and improving patient outcomes,emphasizing the role of nursing students as emerging leaders.Practical strategies for applying BPG,the RNAO BPG suggests leadership skills on professional development,and recommendations for embedding leadership principles into nursing education and practice are discussed.Conclusions:Implementing nursing leadership from the beginning of a nursing student's journey is vital for shaping the next generation of healthcare leaders.By integrating leadership principles early in nursing education,students can develop the skills needed to navigate clinical challenges,collaborate effectively with healthcare teams,and advocate for positive change.Starting with leadership guidelines in nursing school ensures that future nurses are equipped to foster supportive work environments,improve patient outcomes,and drive innovation from the very start of their careers,making leadership an inherent part of their professional identity.
基金supported by the National Natural Science Foundation of China(62371465)Taishan Scholar Project of Shandong Province(ts201511020)。
文摘In wireless sensor networks,ensuring communication security via specific emitter identification(SEI)is crucial.However,existing SEI methods are limited to closed-set scenarios and lack the ability to detect unknown devices and perform classincremental training.This study proposes a class-incremental open-set SEI approach.The open-set SEI model calculates radiofrequency fingerprints(RFFs)prototypes for known signals and employs a self-attention mechanism to enhance their discriminability.Detection thresholds are set through Gaussian fitting for each class.For class-incremental learning,the algorithm freezes the parameters of the previously trained model to initialize the new model.It designs specific losses:the RFFs extraction distribution difference loss and the prototype transformation distribution difference loss,which force the new model to retain old knowledge while learning new knowledge.The training loss enables learning of new class RFFs.Experimental results demonstrate that the open-set SEI model achieves state-of-theart performance and strong noise robustness.Moreover,the class-incremental learning algorithm effectively enables the model to retain old device RFFs knowledge,acquire new device RFFs knowledge,and detect unknown devices simultaneously.
基金funded by UKRI Global Challenges Research Fund through the Economic and Social Research Council(grant reference:ES/T004959/1)with additional partial support from the National Research Foundation of South Africa(grant CSUR230503101533)。
文摘Globally,mealtimes provide key insights into cultural and social values and practices.We examine mealtime environments and eating practices in two different African settings using quantitative observational and questionnaire data.Participating families(N=80)were recruited from two rural locations in Zambia and Kenya.Results following descriptive analysis showed that meals are typically taken as a family in a sociable context,providing opportunities to nurture children’s positive behaviours.In both communities,mothers(71.25%)were most likely to be present and typically prepared meals and provided the food.We observed a few distractions being used during mealtimes,and children finished their food with little to no conflict.Plate sharing varied across the two sites and was more common in Zambia,where we also observed more traditional practices such as eating with hands(as opposed to cutlery)and sitting on mats on the ground(as opposed to seated on chairs or sofas).Overall,our findings suggest more similarities in the cultural and social values across the two African contexts despite slight differences in mealtime practices.In particular,positive mealtime behaviours(little food refusal,lack of conflict)were common across all contexts,regardless of the extent to which traditional practices(such as plate-sharing and sitting on the floor)were used.Findings align with Family Systems theory,which states that the family is understood best by conceptualizing it as a complex,dynamic,and changing collection of parts,subsystems,and family members.
基金supported by the State Grid Southwest Branch Project“Research on Defect Diagnosis and Early Warning Technology of Relay Protection and Safety Automation Devices Based on Multi-Source Heterogeneous Defect Data”.
文摘The reliable operation of power grid secondary equipment is an important guarantee for the safety and stability of the power system.However,various defects could be produced in the secondary equipment during longtermoperation.The complex relationship between the defect phenomenon andmulti-layer causes and the probabilistic influence of secondary equipment cannot be described through knowledge extraction and fusion technology by existing methods,which limits the real-time and accuracy of defect identification.Therefore,a defect recognition method based on the Bayesian network and knowledge graph fusion is proposed.The defect data of secondary equipment is transformed into the structured knowledge graph through knowledge extraction and fusion technology.The knowledge graph of power grid secondary equipment is mapped to the Bayesian network framework,combined with historical defect data,and introduced Noisy-OR nodes.The prior and conditional probabilities of the Bayesian network are then reasonably assigned to build a model that reflects the probability dependence between defect phenomena and potential causes in power grid secondary equipment.Defect identification of power grid secondary equipment is achieved by defect subgraph search based on the knowledge graph,and defect inference based on the Bayesian network.Practical application cases prove this method’s effectiveness in identifying secondary equipment defect causes,improving identification accuracy and efficiency.
基金Supported by Undergraduate Higher Education Teaching Quality and Reform Projects of Guangdong Province(Yuejiao Gao Han[2024]No.9,Yuejiao Gao Han[2024]No.30)Guangdong Basic and Applied Basic Research Foundation(2023A1515110973)+1 种基金Guangdong Provincial Young Innovative Talents Project of General Colleges and Universities(2023KQNCX089)Quality Engineering and Teaching Reform Projects of Zhaoqing University(zlgc202239,zlgc202207,zlgc2024005,zlgc2024038).
文摘To meet the need for cultivating application-oriented talents in local universities,this study introduced a project-based learning approach into the reform of bioinformatics experimental teaching.The course was structured around a project titled"Influenza Virus Analysis",comprising four progressive modules:database utilization and information retrieval,sequence alignment and phylogenetic analysis,functional and structural prediction,and omics data analysis.These modules were integrated into a coherent research workflow that connected fragmented knowledge and technical skills.During implementation,flipped classroom and group collaboration methods were employed,alongside the establishment of a diversified assessment system emphasizing process evaluation.Teaching practice indicates that the reform effectively enhances students professional application skills,learning experience,and scientific literacy,facilitating a shift from"tool operation"to"problem-solving"capabilities.This study provides a reference model for the reform of bioinformatics experimental teaching in local universities.
基金supported in part by the National Natural Science Foundation of China (62431018)in part by the Guangzhou Municipal Science and Technology Bureau (SL2023A04J00435)in part by the One Hundred Youth Project of Guangdong University of Technology (263113873)。
文摘Selectivity remains a significant challenge for gas sensors. In contrast to conventional gas sensors that depend solely on conductivity to detect gases, we exploited a single NiO-doped SnO_(2) sensor to simultaneously monitor transient changes in both sensor conductivity and temperature. The distinct response profiles of H_(2) and NH_(3) gases were attributed to differences in their redox rates and enthalpy changes during chemical reactions, which provided an opportunity for gas identification using machine learning(ML) algorithms. The test results indicate that preprocessing the extracted calorimetric and chemi-resistive parameters using the principal component analysis(PCA), followed by the application of ML classifiers for identification,enables a 100% accuracy for both target analytes. This work presents a facile gas identification method that enhances chiplevel sensor applications while minimizing the need for complex sensor arrays.
基金Supported by the Autonomous Region-level Research and Practice Projects for New Engineering,New Medicine,New Agriculture,and New Humanities of Guangxi Department of Education(XNK202409)the Undergraduate Teaching Reform Project of Guangxi Higher Education(2024JGB332+1 种基金2024JGA304)the Guangxi Degree and Graduate Education Reform Project(JGY2025382).
文摘The deep integration of artificial intelligence technology and agricultural industry has pushed smart agriculture into a new stage of"AI+scenario",and put forward a transformation requirement for the talent cultivation of smart agriculture major in universities from"technology application"to"intelligent innovation".In response to the problems of insufficient AI integration,lack of contextualization,and insufficient collaboration between industry and education in the traditional"technology+"practical course system,this paper takes the smart agriculture major at Yulin Normal University as an example to construct a"AI+agriculture"practical course reconstruction framework and propose a four-dimensional transformation path of"goal-content-mode-evaluation".Through the practical exploration of modular curriculum design,scenario based practical design,integration of industry and education,and intelligent evaluation reform,a practical teaching system with local application-oriented university characteristics has been formed,providing a reference example for the cultivation of smart agriculture professionals under the background of new agricultural science.
文摘In the fast-paced living environment, changes in dietary patterns have led to a continuous increase in the incidence and mortality rates of colorectal cancer (CRC), making it a prevalent malignant tumor of the digestive system worldwide. Currently, CRC clinical diagnosis and treatment face challenges such as high costs and persistently high recurrence rates. Traditional quantification of tumor-infiltrating lymphocytes (TILs) relies on manual analysis and judgment, resulting in low diagnostic efficiency and susceptibility to subjective factors, leading to missed or misdiagnosed cases. To enhance the efficiency and quality of CRC clinical diagnosis and treatment, this study explores domestic and international research on the automatic identification of CRC cells using machine learning strategies. It analyzes the morphological heterogeneity and prognostic value in the application of this strategy, aiming to deepen the understanding of intelligent tool applications in precise diagnosis, treatment, and prognostic evaluation of colorectal cancer, comprehend the current research status and development trends, and provide references for addressing and addressing the gaps in related research.
基金supported by the Science and Technology Talent Support Project of Hunan Province,China (2022TJ-N15)the Hunan Agricultural Science and Technology Innovation Fund,China (2024CX90 and 2024CX65)the Science and Technology Innovation Program of Hunan Province,China (2021NK1006)。
文摘High-throughput single nucleotide polymorphism(SNP) arrays have emerged as essential genotyping tools,significantly accelerating breeding programs and advancing basic research.In this study,a high-throughput 10K SNP genotyping array for wax gourd was developed using genotyping by target sequencing(GBTS),featuring 10,722 SNPs evenly distributed across all 12 chromosomes,including 278 functional loci associated with key economic traits.To demonstrate its utility,genetic distances among 19 elite inbred lines were calculated from SNP data and correlated with heterosis for single fruit weight.The results revealed that greater genetic distance was associated with higher middle parent heterosis(MPH) for single fruit weight.Furthermore,56 commercial wax gourd cultivars collected from eight regions were selected and genotyped.Population structure analysis,phylogenetic analysis,and principal component analysis(PCA) collectively indicated that these cultivars fall into two major groups.Group I,comprising black or dark green skinned wax gourds,exhibited lower genetic diversity than Group II,which includes green or light green skinned varieties,reflecting shorter genetic distances within Group I.Finally,60 polymorphic SNPs were used to construct DNA fingerprints for distinguishing the 56 cultivars.As the first high-throughput genotyping platform for wax gourd,this SNP array provides an effective and powerful tool for genetic analysis.
基金supported by the National Natural Science Foundation of China(Grant Nos.42130719 and 42177173)the Doctoral Direct Train Project of Chongqing Natural Science Foundation(Grant No.CSTB2023NSCQ-BSX0029).
文摘Underground engineering projects such as deep tunnel excavation often encounter rockburst disasters accompanied by numerous microseismic events.Rapid interpretation of microseismic signals is crucial for the timely identification of rockbursts.However,conventional processing encompasses multi-step workflows,including classification,denoising,picking,locating,and computational analysis,coupled with manual intervention,which collectively compromise the reliability of early warnings.To address these challenges,this study innovatively proposes the“microseismic stethoscope"-a multi-task machine learning and deep learning model designed for the automated processing of massive microseismic signals.This model efficiently extracts three key parameters that are necessary for recognizing rockburst disasters:rupture location,microseismic energy,and moment magnitude.Specifically,the model extracts raw waveform features from three dedicated sub-networks:a classifier for source zone classification,and two regressors for microseismic energy and moment magnitude estimation.This model demonstrates superior efficiency compared to traditional processing and semi-automated processing,reducing per-event processing time from 0.71 s to 0.49 s to merely 0.036 s.It concurrently achieves 98%accuracy in source zone classification,with microseismic energy and moment magnitude estimation errors of 0.13 and 0.05,respectively.This model has been well applied and validated in the Daxiagu Tunnel case in Sichuan,China.The application results indicate that the model is as accurate as traditional methods in determining source parameters,and thus can be used to identify potential geomechanical processes of rockburst disasters.By enhancing the signal processing reliability of microseismic events,the proposed model in this study presents a significant advancement in the identification of rockburst disasters.
基金supported by the National Natural Science Foundation of China(No.52207228)the Beijing Natural Science Foundation,China(No.3224070)the National Natural Science Foundation of China(No.52077208).
文摘The growing use of lithium-ion batteries in electric transportation and grid-scale storage systems has intensified the need for accurate and highly generalizable state-of-health(SOH)estimation.Conventional approaches often suffer from reduced accuracy under dynamically uncertain state-of-charge(SOC)operating ranges and heterogeneous aging stresses.This study presents a unified SOH estimation framework that integrates physics-informed modeling,subspace identification,and Transformer-based learning.A reduced-order model is derived from simplified electrochemical dynamics,providing an interpretable and computationally efficient representation of battery behavior.Subspace identification across a wide SOC and SOH range yields degradation-sensitive features,which the Transformer uses to capture long-range aging dynamics via multi-head self-attention.Experiments on LiFePO4 cells under joint-cell training show consistently accurate SOH estimation,with a maximum error of 1.39%,demonstrating the framework’s effectiveness in decoupling SOC and SOH effects.In cross-cell validation,where training and validation are performed on different cells,the model maintains a maximum error of 2.06%,confirming strong generalization to unseen aging trajectories.Comparative experiments on LiFePO_(4)and public LiCoO_(2)datasets confirm the framework’s cross-chemistry applicability.By extracting low-dimensional,physically interpretable features via subspace identification,the framework significantly reduces training cost while maintaining high SOH estimation accuracy,outperforming conventional data-driven models lacking physical guidance.
基金supported by the Scientific and Technological Innovation Project of the China Academy of Chinese Medical Sciences(CI2021A04013)the National Natural Science Foundation of China(82204610)+1 种基金the Qihang Talent Program(L2022046)the Fundamental Research Funds for the Central Public Welfare Research Institutes(ZZ15-YQ-041 and L2021029).
文摘Background:The medicinal material known as Os Draconis(Longgu)originates from fossilized remains of ancient mammals and is widely used in treating emotional and mental conditions.However,fossil resources are nonrenewable,and clinical demand is increasingly difficult to meet,leading to a proliferation of counterfeit products.During prolonged geological burial,static pressure from the surrounding strata severely compromises the microstructural integrity of osteons in Os Draconis,but Os Draconis still largely retains the structural features of mammalian bone.Methods:Using verified authentic Os Draconis samples over 10,000 years old as a baseline,this study summarizes the ultrastructural characteristics of genuine Os Draconis.Employing electron probe microanalysis and optical polarized light microscopy,we examined 28 batches of authentic Os Draconis and 31 batches of counterfeits to identify their ultrastructural differences.Key points for ultrastructural identification of Os Draconis were compiled,and a new identification approach was proposed based on these differences.Results:Authentic Os Draconis exhibited distinct ultrastructural markers:irregularly shaped osteons with traversing fissures,deformed/displaced Haversian canals,and secondary mineral infill(predominantly calcium carbonate).Counterfeits showed regular osteon arrangements,absent traversal fissures,and homogeneous hydroxyapatite composition.Lab-simulated samples lacked structural degradation features.EPMA confirmed calcium carbonate infill in fossilized Haversian canals,while elemental profiles differentiated lacunae types(void vs.mineral-packed).Conclusion:The study established ultrastructural criteria for authentic Os Draconis identification:osteon deformation,geological fissures penetrating bone units,and heterogenous mineral deposition.These features,unattainable in counterfeits or modern processed bones,provide a cost-effective,accurate identification method.This approach bridges gaps in TCM material standardization and supports quality control for clinical applications.
基金Funding Source:2024 Heilongjiang Province Higher Education Teaching Reform Key Research Project.Project Name:Practice and Exploration of Curriculum-Based Political Education in the Intercultural Communication Course Under the New Liberal Arts Framework(Grant No.SJGZB2024063).
文摘This study investigates how to pedagogically integrate ideological education with competency development in the Intercultural Communication course,a challenge arising from China’s dual reform contexts of the New Liberal Arts initiative and the national curriculum ideology policy.As global interactions intensify,cultivating foreign language professionals who possess both firm cultural confidence and sophisticated intercultural competence have become a critical educational imperative.This exploratory study investigates how a three-dimensional“Value-Knowledge-Competency”framework can guide the redesign of course content,task design,and assessment to achieve organic fusion.Drawing on qualitative data from a case study,it analyzes specific implementation pathways,synthesizes teacher and student feedback,and discusses the resultant challenges and broader implications for foreign language curriculum reform.The findings suggest that such an integrated approach can effectively synergize value guidance with skill cultivation,though its success hinges on overcoming issues related to pedagogical naturalness,resource allocation,and standardized evaluation.
文摘This paper proposes a robust control-oriented identification method for errors-in-variables(EIV)systems in output feedbacks using frequency-response(FR)experimental data.An important relation between such a closed-loop EIV system and its coprime factor(CF)uncertainty description is first derived,based on which the FR measurements suitable for plant CF identification are able to be generated.Different factorizations of a given controller in the closed-loop system can be made best use to adjust right coprime factors(RCFs)of the plant so as to realize an improvement on the signal-to-noise ratio of identification experimental data.Subsequently,a nominal RCF model is estimated by linear matrix inequalities from the applicable FR measurements and its associated worst-case errors are quantified from a priori and a posteriori information on the underlying system.A resulting RCF perturbation model set can then be described by the nominal RCF model and its worst-case error bounds.Such a model set capable of being stabilized by the given controller is ready for its robust stabilizing controller redesign and robust performance analysis.Finally,a numerical simulation is given to show the efficacy of the proposed identification method.
基金supported in part by the National Natural Science Foundation of China(62373070 and 52272388)in part by the Chongqing Natural Science Foundation(CSTB2024NSCQ-QCXMX0054,CSTB2022NSCQ-MSX1225 and CSTC2024YCJH-BGZXM0042)in part by the Key Research and Development Project of Anhui Province(202304a05020060).
文摘In this paper,we consider a multiple-input single-output(MISO)Hammerstein system whose inputs and output are disturbed by unknown Gaussian white measurement noises.The parameter estimation of such a system is a typical errors-in-variables(EIV)nonlinear system identification problem.This paper proposes a bias-correction least squares(BCLS)identification methods to compute a consistent estimate of EIV MISO Hammerstein systems from noisy data.To obtain the unbiased parameter estimates of EIV MISO Hammerstein system,the analytical expression of estimated bias for the standard least squares(LS)algorithm is derived first,which is a function about the variances of noises.And then a recursive algorithm is proposed to estimate the unknown term of noises variances from noisy data.Finally,based on bias estimation scheme,the bias caused by the correlation between the input–output signals exciting the true system and the corresponding measurement noise,resulting in unbiased parameter estimates of the EIV MISO Hammerstein system.The performance of the proposed method is demonstrated through a simulation example and a chemical continuously stirred tank reactor(CSTR)system.
文摘Objectives This study aimed to compare the effectiveness of the semi-virtual simulation and traditional simulation teaching models based on the Standards of Best Practice(SOBP)according to the International Nursing Association for Clinical Simulation and Learning(INACSL)in the Adult Nursing course.Methods This study used a quasi-experimental design.A total of 94 third-year nursing students from a university in Beijing between November and December 2022 were recruited as participants.An innovative semi-virtual simulation teaching model was designed based on the SOBP established by the INACSL.In the Adult Nursing course,both the semi-virtual and traditional simulation teaching models were implemented.At the end of the simulation sessions,participants completed the Chinese version of the Simulation Effectiveness Tool-Modified(SET-M)to assess the effectiveness of the two teaching models.Results All nursing students completed the simulation sessions.There was no difference(t=−0.93,P=0.353)in the total scores between the semi-virtual simulation teaching model(50.87±5.30)and the traditional simulation teaching model(50.37±5.16).However,there was a statistically significant difference(t=−2.65,P=0.010)in the prebriefing section(semi-virtual simulation:5.60±0.71;traditional simulation:5.33±0.78).In contrast,no statistically significant differences were found for the scenario and debriefing sections(P>0.05).At the individual item level,statistical differences(P<0.05)between the two models were identified for items 1 and 9,but not for the remaining items(P>0.05).By analyzing the open-ended question,it was found that both simulation models were effective,and students’comments were similar.Conclusions The study demonstrated equivalent effectiveness between the semi-virtual and traditional simulation teaching models.Semi-virtual simulation teaching model could offer a more flexible and feasible approach to simulation teaching.
基金supported by the National Key Research and Development Program of China(No.2022YFC2403500)the National Natural Science Foundation of China(No.22225401)+1 种基金the Science and Technology Innovation Program of Hunan Province(No.2020RC4017)the Guizhou Provincial Science and Technology Projects(No.ZK[2023]293).
文摘Simultaneous identification and quantitative detection of phenylenediamine(PDA)isomers,including o-phenylenediamine(OPD),m-phenylenediamine(MPD),and p-phenylenediamine(PPD),are essential for environmental risk assessment and human health protection.However,current visual detection methods can only distinguish individual PDA isomers and failed to identify binary or ternary mixtures.Herein,a highly active and ultrastable peroxidase(POD)-like CoPt graphitic nanozyme was used for naked-eye identification and colorimetric/fluorescent(FL)dual-mode quantitative detection of PDA isomers.The CoPt@G nanozyme effectively catalyzed the oxidation of OPD,MPD,PPD,OPD+PPD,OPD+MPD,MPD+PPD and OPD+MPD+PPD into yellow,colorless,lilac,yellow,yellow,wine red and reddish-brown products,respectively,in the presence of H_(2)O_(2).Thus,the MPD,PPD,MPD+PPD and OPD+MPD+PPD were easily identified based on the distinct color of their oxidation products,and the OPD,OPD+PPD,OPD+MPD could be further identified by the additional addition of MPD or PPD.Subsequently,CoPt@G/H_(2)O_(2)-,a 3,3′,5,5′-tetramethylbenzidine(TMB)/CoPt@G/H_(2)O_(2)-,and MPD/CoPt@G/H_(2)O_(2)-enabled colorimetric/FL dual-mode platforms for the quantitative detection of OPD,MPD and PPD were proposed.The experimental results illustrated that the constructed sensing platforms exhibit satisfactory sensitivity,comparable to that reported in previous studies.Finally,the evaluation of PDAs in water samples was realized,yielding satisfactory recoveries.This work expanded the application prospects of nanozymes in assessing environmental risks and protection of human security.
