To mill fine and well-defined micro-dimpled structures,a machining manner of spiral trajectory tool reciprocating motion,where the tool repeats the process of‘feed milling–retract–cutting feed–feed milling again’...To mill fine and well-defined micro-dimpled structures,a machining manner of spiral trajectory tool reciprocating motion,where the tool repeats the process of‘feed milling–retract–cutting feed–feed milling again’along the spiral trajectory,was proposed.From the kinematics analysis,it is found that the machining quality of micro-dimpled structures is highly dependent on the machining trajectory using spiral trajectory tool reciprocating motion.To reveal this causation,simulation modelling and experimental studies were carried out.A simulation model was developed to quantitatively and qualitatively investigate the influence of the trajectory discretization strategies(constant-angle and constant-arc length)and parameters(discrete angle,discrete arc length,and pitch)on surface texture and residual height of micro-dimpled structures.Subsequently,micro-dimpled structures were milled under different trajectory discretization strategies and parameters with spiral trajectory tool reciprocating motion.A comprehensive comparison between the milled results and simulation analysis was made based on geometry accuracy,surface morphology and surface roughness of milled dimples.Meanwhile,the errors and factors affecting the above three aspects were analyzed.The results demonstrate both the feasibility of the established simulation model and the machining capability of this machining way in milling high-quality micro-dimpled structures.Spiral trajectory tool reciprocating motion provides a new machining way for milling micro-dimpled structures and micro-dimpled functional surfaces.And an appropriate machining trajectory can be generated based on the optimized trajectory parameters,thus contributing to the improvement of machining quality and efficiency.展开更多
High-volume fraction silicon particle-reinforced aluminium matrix composites(Si/Al)are increasingly applied in aerospace,radar communications,and large-scale integrated circuits because of their superior thermal condu...High-volume fraction silicon particle-reinforced aluminium matrix composites(Si/Al)are increasingly applied in aerospace,radar communications,and large-scale integrated circuits because of their superior thermal conductivity,wear resistance,and low thermal expansion coefficient.However,the abrasive and adhesive wear caused by the hard silicon reinforcement and the ductile aluminium matrix leads to significant tool wear,decreased machining efficiency,and compromised surface quality.This study combines theoretical analysis and cutting experiments to investigate polycrystalline diamond(PCD)tool wear during milling of 70 vol%Si/Al composite.A key contribution of this work is the development of a tool wear model that incorporates reinforcement particle characteristics,treating them as ellipsoidal structures,which enhances the accuracy of predicting abrasive and adhesive wear mechanisms.The model is based on abrasive and adhesive wear mechanisms,and can analyze the interaction between silicon particles,aluminium matrix,and tool components,thus providing deeper insights into PCD tool wear processes.Experimental validation of the model shows a good agreement with the results,with a mean deviation of approximately 10%.The findings on the tool wear mechanism reveal that,as tool wear progresses,the proportion of abrasive wear increases from 40%in the running-in stage to 75%in the rapid wear stage,while adhesive wear decreases.The optimal machining parameters of 120 m·min^(–1) cutting speed(v_(c))and 0.04 mm·z^(–1) feed rate(f_(z)),result in tool life of 33 min and surface roughness(S_(a))of 2.2μm.The study uncovers the variation patterns of abrasive and adhesive wear during the tool wear process,and the proposed model offers a robust framework for predicting tool wear during the machining of high-volume fraction Si/Al composites.The research findings also offer key insights for optimizing tool selection and machining parameters,advancing both the theoretical understanding and practical application of PCD tool wear.展开更多
The 2024 development of a precision-engineered retrotransposon system marked a significant milestone in mammalian genome-editing research.As appeared in the July 8 issue of Cell,this methodological breakthrough establ...The 2024 development of a precision-engineered retrotransposon system marked a significant milestone in mammalian genome-editing research.As appeared in the July 8 issue of Cell,this methodological breakthrough established a novel framework for site-specific gene delivery through repurposing ancient viral tools.展开更多
Accuracy allocation is crucial in the accuracy design of machining tools.Current accuracy allocation methods primarily focus on positional deviation,with little consideration for tool direction deviation.To address th...Accuracy allocation is crucial in the accuracy design of machining tools.Current accuracy allocation methods primarily focus on positional deviation,with little consideration for tool direction deviation.To address this issue,we propose a geometric error cost sensitivity-based accuracy allocation method for five-axis machine tools.A geometric error model consisting of 4l error components is constructed based on homogeneous transformation matrices.Volumetric points with positional and tool direction deviations are randomly sampled to evaluate the accuracy of the machine tool.The sensitivity of each error component at these sampling points is analyzed using the Sobol method.To balance the needs of geometric precision and manufacturing cost,a geometric error cost sensitivity function is developed to estimate the required cost.By allocating error components affecting tool direction deviation first and the remaining components second,this allocation scheme ensures that both deviations meet the requirements.We also perform numerical simulation of a BC-type(B-axis and C-axis type)five-axis machine tool to validate the method.The results show that the new allocation scheme reduces the total geometric error cost by 27.8%compared to a uniform allocation scheme,and yields the same positional and tool direction machining accuracies.展开更多
Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefit...Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefits of two calibrations of the Nutrient Expert(NE)tool for rice in Sri Lanka’s Alfisols:the basic calibration(Nutrient Expert Sri Lanka 1,NESL1)and the comprehensive calibration(Nutrient Expert Sri Lanka 2,NESL2).NESL1 was developed by adapting the South Indian version of NE to local conditions,while NESL2 was an updated version,using three years of data from 71 farmer fields.展开更多
Ti-6Al-4V is widely used in the aviation industry because of its high strength, and good heat resistance. However, severe tool wear on the rake face occurs during the milling of Ti-6Al-4V,which is caused by intense fr...Ti-6Al-4V is widely used in the aviation industry because of its high strength, and good heat resistance. However, severe tool wear on the rake face occurs during the milling of Ti-6Al-4V,which is caused by intense friction between the tool rake face and the chips. To investigate tool wear in the milling of Ti-6Al-4V, ultrasonic vibration is introduced, and a cutting force prediction model that considers tool-chip contact interface friction behavior in Ultrasonic Longitudinal-Torsional Vibration-Assisted Milling(ULTVAM) is proposed in this paper. First, the tool tip motion trajectory and dynamic cutting thickness under ULTVAM were analyzed calculated, and compared with those in Common Milling(CM). Subsequently, the effects of ultrasonic vibration on the shear force under the ultrasonic softening effect, the friction force, and the friction reversal force on the toolchip contact interface were investigated. A dynamic milling force model under ULTVAM was established before and after friction force reversal caused by ultrasonic longitudinal-torsional vibration. Finally, numerous experiments were conducted to validate the proposed model, and the experimental results indicated that the calculated dynamic milling forces agreed well with the measured values, with errors in the X and Y directions of 5.51% and 10.23%, respectively. In addition, the average roughness of the workpiece surface also decreased(1.08, 0.9, 0.6, 0.7 μm under ultrasonic amplitudes of 0, 1, 2, and 3 μm) and the tool wear state improved on the rake face under ULTVAM.展开更多
Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone t...Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.展开更多
Trochoidal milling is known for its advantages in machining difficult-to-machine materials as it facilitates chip removal and tool cooling.However,the conventional trochoidal tool path presents challenges such as lowe...Trochoidal milling is known for its advantages in machining difficult-to-machine materials as it facilitates chip removal and tool cooling.However,the conventional trochoidal tool path presents challenges such as lower machining efficiency and longer machining time due to its time-varying cutter-workpiece engagement angle and a high percentage of non-cutting tool paths.To address these issues,this paper introduces a parameter-variant trochoidal-like(PVTR)tool path planning method for chatter-free and high-efficiency milling.This method ensures a constant engagement angle for each tool path period by adjusting the trochoidal radius and step.Initially,the nonlinear equation for the PVTR toolpath is established.Then,a segmented recurrence method is proposed to plan tool paths based on the desired engagement angle.The impact of trochoidal tool path parameters on the engagement angle is analyzed and coupled this information with the milling stability model based on spindle speed and engagement angle to determine the desired engagement angle throughout the machining process.Finally,several experimental tests are carried out using the bull-nose end mill to validate the feasibility and effectiveness of the proposed method.展开更多
Recently,tool learning with large language models(LLMs)has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.Despite growing attention and rapid advancements in ...Recently,tool learning with large language models(LLMs)has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.Despite growing attention and rapid advancements in this field,the existing literature remains fragmented and lacks systematic organization,posing barriers to entry for newcomers.This gap motivates us to conduct a comprehensive survey of existing works on tool learning with LLMs.In this survey,we focus on reviewing existing literature from the two primary aspects(1)why tool learning is beneficial and(2)how tool learning is implemented,enabling a comprehensive understanding of tool learning with LLMs.We first explore the“why”by reviewing both the benefits of tool integration and the inherent benefits of the tool learning paradigm from six specific aspects.In terms of“how”,we systematically review the literature according to a taxonomy of four key stages in the tool learning workflow:task planning,tool selection,tool calling,and response generation.Additionally,we provide a detailed summary of existing benchmarks and evaluation methods,categorizing them according to their relevance to different stages.Finally,we discuss current challenges and outline potential future directions,aiming to inspire both researchers and industrial developers to further explore this emerging and promising area.展开更多
Insect-derived traditional Chinese medicine(TCM)constitutes an essential component of TCM,with the earliest records found in“52 Bingfang”(Prescriptions of fifty-two diseases,which is one of the earliest Chinese medi...Insect-derived traditional Chinese medicine(TCM)constitutes an essential component of TCM,with the earliest records found in“52 Bingfang”(Prescriptions of fifty-two diseases,which is one of the earliest Chinese medical prescriptions).展开更多
Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to asse...Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.展开更多
Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of ca...Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.展开更多
Today city planners are confronted with two global trends:on one hand,living space is getting less due to urbanization;on the other hand,demands on living space are constantly rising as for example through stricter cl...Today city planners are confronted with two global trends:on one hand,living space is getting less due to urbanization;on the other hand,demands on living space are constantly rising as for example through stricter climate and energy political objectives based on the Paris Agreement.Therefore,it will be necessary to take into account—near urban planning and social aspects—also the climate compatibility as one central aspect in the construction of buildings,settlements,districts or neighborhoods.To identify and to push successful concepts,Austria has developed a planning tool that allows planning,assessing and ensuring high quality standards of neighborhoods.As the tool has been highly successful,additional planning tools are being developed for specific topics such as“PED—Positive Energy Districts”,“NEB—New European Bauhaus”and“CND—Climate Neutral Districts”.Central quantitative and qualitative criteria—which have been elaborated in the recent years—will be presented in this paper.展开更多
Micro-grinding has been widely used in aerospace and other industry.However,the small diameter of the micro-grinding tool has limited its machining performance and efficiency.In order to solve the above problems,micro...Micro-grinding has been widely used in aerospace and other industry.However,the small diameter of the micro-grinding tool has limited its machining performance and efficiency.In order to solve the above problems,micro-structure has been applied on the micro-grinding tool.A morphology modeling has been established in this study to characterize the surface of microstructured micro-grinding tool,and the grinding performance of micro-structured micro-grinding tool has been analyzed through undeformed chip thickness,abrasive edge width,and effective distance between abrasives.Then deviation analysis,path optimization and parameter optimization of microchannel array precision grinding have been finished to improve processing quality and efficiency,and the deflection angle has the most obvious effects on the rectangular slot depth,micro-structured micro-grinding tool could reduce 10%surface roughness and 20%grinding force compared to original micro-grinding tool.Finally,the microchannel array has been machined with a size deviation of 2μm and surface roughness of 0.2μm.展开更多
Understanding and strengthening community-level resilience to natural hazard-induced disasters is critical for the management of adverse impacts of such events and the growth of community well-being.A key gap in achie...Understanding and strengthening community-level resilience to natural hazard-induced disasters is critical for the management of adverse impacts of such events and the growth of community well-being.A key gap in achieving this is limited standardized and validated disaster resilience measurement frameworks that operate at local levels and are universally applicable.The Flood Resilience Measurement for Communities(FRMC)is a foremost tool for community flood resilience assessment.It follows a structured approach to comprehensively assess community flood resilience across five classes of capacities(capitals)to support strategic investment in resilience strengthening initiatives.The FRMC is a further development of an earlier version(the FRMT,the Flood Resilience Measurement Tool).The FRMT has been developed and applied between 2015 and 2017 in 118 flood prone communities across nine countries.It has been validated in terms of content and face validity as well as in terms of reliability.To reduce redundancy and survey eff ort,the FRMC holds a lesser number of indicators(44 versus 88)and has now been applied in over 320 communities across 20 countries.We examine the validation for the revised resilience construct and the new community applications and present a comprehensive overview of the statistical and user validation process and outcomes in both practical and scientific terms.The results confirm the validity,reliability as well as usefulness of the FRMC framework and tool.Furthermore,our approach and results provide insights for other resilience measurement approaches and their validation eff orts.We also present a comprehensive discussion about the dynamic aspects of flood resilience at community level,and the many validation aspects that need to be incorporated both in terms of quantification eff orts as well as usability on the ground.展开更多
Bioinformatics analysis often requires the filtering of multi-datasets,based on frequency or frequency of occurrence,for decisions on retention or deletion.Existing tools for this purpose often present a challenge wit...Bioinformatics analysis often requires the filtering of multi-datasets,based on frequency or frequency of occurrence,for decisions on retention or deletion.Existing tools for this purpose often present a challenge with complex installation,which necessitate custom coding,thereby impeding efficient data processing activities.To address this issue,Filterx,a user-friendly command line tool that written in C language,was developed that supports multi-condition filtering,based on frequency or occurrence.This tool enables users to complete the data processing tasks through a simple command line,greatly reducing both workload and data processing time.In addition,future development of this tool could facilitate its integration into various bioinformatics data analysis pipelines.展开更多
In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic op...In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings,highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime.However,varying conditions induce feature distribution shifts,and scarce fault samples limitmodel generalization.Therefore,this paper proposes a causal-Transformer-based meta-learning(CTML)method for bearing fault diagnosis in CNC machine tools,comprising three core modules:(1)the original bearing signal is transformed into a multi-scale time-frequency feature space using continuous wavelet transform;(2)a causal-Transformer architecture is designed to achieve feature extraction and fault classification based on the physical causal law of fault propagation;(3)the above mechanisms are integrated into a model-agnostic meta-learning(MAML)framework to achieve rapid cross-condition adaptation through an adaptive gradient pruning strategy.Experimental results using the multiple bearing dataset show that under few-shot cross-condition scenarios(3-way 1-shot and 3-way 5-shot),the proposed CTML outperforms benchmark models(e.g.,Transformer,domain adversarial neural networks(DANN),and MAML)in terms of classification accuracy and sensitivity to operating conditions,while maintaining a moderate level of model complexity.展开更多
BACKGROUND Nosocomial fever of unknown origin(nFUO)is a frequent and challenging diagnostic entity,encompassing diverse infectious and non-infectious etiologies.Timely identification is crucial,yet evidence on the dia...BACKGROUND Nosocomial fever of unknown origin(nFUO)is a frequent and challenging diagnostic entity,encompassing diverse infectious and non-infectious etiologies.Timely identification is crucial,yet evidence on the diagnostic accuracy of commonly employed sepsis screening tools and biomarkers remains sparse.We hypothesized that these tools and biomarkers measured at fever onset could distinguish infectious from non-infectious causes of nFUO in critically ill adults.AIM To evaluate the diagnostic utility of sepsis tools and biomarkers in identifying infectious causes of nFUO.METHODS This prospective observational study included patients admitted to the Acute Care Emergency Medicine Unit,Postgraduate Institute of Medical Education and Research,Chandigarh,India(July 2023 to December 2024).nFUO was defined by Durack and Street criteria.Diagnostic performance of sepsis screening tools(systemic inflammatory response syndrome,Sequential Organ Failure Assessment,quick Sequential Organ Failure Assessment,National Early Warning Score,and Modified Early Warning Score)and biomarkers[procalcitonin(PCT),C-reactive protein(CRP)]at fever onset was assessed using receiver operating characteristic curve analysis.RESULTS Of 80 cases(mean age 42.9±16.5 years;80% male),42.5% had infectious causes,38.7% non-infectious,and 18.8% remained undiagnosed.Pneumonia(26.2%)and bloodstream infections(11.2%)were the most common infectious etiologies,while central fever and thrombophlebitis(each 7.5%)were predominant among non-infectious causes.Sepsis tools showed poor diagnostic accuracy,with area under the receiver operating characteristic curve(AUC)values close to 0.5.PCT demonstrated modest performance(AUC=0.61;optimal cut-off:0.85μg/L),while CRP was paradoxically higher in non-infectious cases(AUC=0.45).Overall mortality was 20% and was highest among undiagnosed patients(33.3%).Fever duration and hospitalization length were significantly greater in infectious cases.CONCLUSION Sepsis tools,PCT,and CRP have limited utility in identifying infectious causes of nFUO in critically ill adults and should not solely guide initial decision-making.展开更多
Objective:This study aimed to systematically evaluate the measurement characteristics and methodological quality of childbirth experience assessment tools,with a view to informing the selection of healthcare professio...Objective:This study aimed to systematically evaluate the measurement characteristics and methodological quality of childbirth experience assessment tools,with a view to informing the selection of healthcare professionals who can provide high-quality assessment tools.Method:A systematic search was performed on specific databases:PubMed,Web of Science,Embase,CINAHL,SinoMed,China National Knowledge Infrastructure(CNKI),and Wanfang,from inception to February 29,2024.The researchers retrieved studies on the measurement attributes of the childbirth experience assessment tool,and traced back the references of the included studies to supplement relevant literature.According to the inclusion and exclusion criteria,screening and data extraction were independently undertaken by two reviewers.Two researchers individually used the Consensus-based Standards for the Selection of Health Measurement Instruments(COSMIN)Risk of Bias Checklist to assess the methodological quality of the scale,applied the COSMIN criteria to evaluate the measurement properties of the scale,and used a modified Grading of Recommendations,Assessment,Development,and Evaluation(GRADE)system to assess the certainty of evidence.Result:A total of 15 studies were included to evaluate the psychometric properties of 11 childbirth experience assessment tools(including different language versions).Eight studies’methodological quality of content validity was doubtful,and the remaining studies did not report content validity.None of the tools reported measurement error,cross-cultural validity,or responsiveness.In light of the questionable or unreported content validity of the tools,the evidence quality was deemed moderate or below.Consequently,the 11 assessment tools were recommended as grade B.Conclusion:In contrast,the Questionnaire for Assessing the Childbirth Experience(QACE)is recommended for provisional use,given its relatively good methodological and measurement attributes and appropriate content for evaluation.However,further validation of other measurement properties is needed.展开更多
Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that e...Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that enhance the platform's chances of surviving different scenarios.Such scenarios can involve various types of threats that can affect the platform's survivability.Among such,blast waves impacting the platform's structure represent critical conditions that have not yet been studied in detail.That is,frameworks for vulnerability assessment that can deal with blast loading have not been presented yet.In this context,this work presents a fast-running engineering tool that can quantify the risk that a structure fails when it is subjected to blast loading from the detonation of high explosive-driven threats detonating at various distances from the structure itself.The tool has been implemented in an in-house software that calculates vulnerability to various impacting objects,and its capabilities have been shown through a simplified,yet realistic,case study.The novelty of this research lies in the development of an integrated computational environment capable of calculating the platform's vulnerability to blast waves,without the need for running expensive finite element simulations.In fact,the proposed tool is fully based on analytical models integrated with a probabilistic approach for vulnerability calculation.展开更多
基金co-supported the National Natural Science Foundation of China(No.52235010)the Heilongjiang Postdoctoral Fund(No.LBH-Z22136)the New Era Longjiang Excellent Master and Doctoral Dissertation Fund(No.LJYXL2022-057).
文摘To mill fine and well-defined micro-dimpled structures,a machining manner of spiral trajectory tool reciprocating motion,where the tool repeats the process of‘feed milling–retract–cutting feed–feed milling again’along the spiral trajectory,was proposed.From the kinematics analysis,it is found that the machining quality of micro-dimpled structures is highly dependent on the machining trajectory using spiral trajectory tool reciprocating motion.To reveal this causation,simulation modelling and experimental studies were carried out.A simulation model was developed to quantitatively and qualitatively investigate the influence of the trajectory discretization strategies(constant-angle and constant-arc length)and parameters(discrete angle,discrete arc length,and pitch)on surface texture and residual height of micro-dimpled structures.Subsequently,micro-dimpled structures were milled under different trajectory discretization strategies and parameters with spiral trajectory tool reciprocating motion.A comprehensive comparison between the milled results and simulation analysis was made based on geometry accuracy,surface morphology and surface roughness of milled dimples.Meanwhile,the errors and factors affecting the above three aspects were analyzed.The results demonstrate both the feasibility of the established simulation model and the machining capability of this machining way in milling high-quality micro-dimpled structures.Spiral trajectory tool reciprocating motion provides a new machining way for milling micro-dimpled structures and micro-dimpled functional surfaces.And an appropriate machining trajectory can be generated based on the optimized trajectory parameters,thus contributing to the improvement of machining quality and efficiency.
基金supported by the National Natural Science Foundation of China(Grant No.52075255)the Jiangsu Provincial Science and Technology Plan(Grant No.BZ2023005).
文摘High-volume fraction silicon particle-reinforced aluminium matrix composites(Si/Al)are increasingly applied in aerospace,radar communications,and large-scale integrated circuits because of their superior thermal conductivity,wear resistance,and low thermal expansion coefficient.However,the abrasive and adhesive wear caused by the hard silicon reinforcement and the ductile aluminium matrix leads to significant tool wear,decreased machining efficiency,and compromised surface quality.This study combines theoretical analysis and cutting experiments to investigate polycrystalline diamond(PCD)tool wear during milling of 70 vol%Si/Al composite.A key contribution of this work is the development of a tool wear model that incorporates reinforcement particle characteristics,treating them as ellipsoidal structures,which enhances the accuracy of predicting abrasive and adhesive wear mechanisms.The model is based on abrasive and adhesive wear mechanisms,and can analyze the interaction between silicon particles,aluminium matrix,and tool components,thus providing deeper insights into PCD tool wear processes.Experimental validation of the model shows a good agreement with the results,with a mean deviation of approximately 10%.The findings on the tool wear mechanism reveal that,as tool wear progresses,the proportion of abrasive wear increases from 40%in the running-in stage to 75%in the rapid wear stage,while adhesive wear decreases.The optimal machining parameters of 120 m·min^(–1) cutting speed(v_(c))and 0.04 mm·z^(–1) feed rate(f_(z)),result in tool life of 33 min and surface roughness(S_(a))of 2.2μm.The study uncovers the variation patterns of abrasive and adhesive wear during the tool wear process,and the proposed model offers a robust framework for predicting tool wear during the machining of high-volume fraction Si/Al composites.The research findings also offer key insights for optimizing tool selection and machining parameters,advancing both the theoretical understanding and practical application of PCD tool wear.
文摘The 2024 development of a precision-engineered retrotransposon system marked a significant milestone in mammalian genome-editing research.As appeared in the July 8 issue of Cell,this methodological breakthrough established a novel framework for site-specific gene delivery through repurposing ancient viral tools.
基金supported by the Key R&D Program of Zhejiang Province(Nos.2023C01166 and 2024SJCZX0046)the Zhejiang Provincial Natural Science Foundation of China(Nos.LDT23E05013E05 and LD24E050009)the Natural Science Foundation of Ningbo(No.2021J150),China.
文摘Accuracy allocation is crucial in the accuracy design of machining tools.Current accuracy allocation methods primarily focus on positional deviation,with little consideration for tool direction deviation.To address this issue,we propose a geometric error cost sensitivity-based accuracy allocation method for five-axis machine tools.A geometric error model consisting of 4l error components is constructed based on homogeneous transformation matrices.Volumetric points with positional and tool direction deviations are randomly sampled to evaluate the accuracy of the machine tool.The sensitivity of each error component at these sampling points is analyzed using the Sobol method.To balance the needs of geometric precision and manufacturing cost,a geometric error cost sensitivity function is developed to estimate the required cost.By allocating error components affecting tool direction deviation first and the remaining components second,this allocation scheme ensures that both deviations meet the requirements.We also perform numerical simulation of a BC-type(B-axis and C-axis type)five-axis machine tool to validate the method.The results show that the new allocation scheme reduces the total geometric error cost by 27.8%compared to a uniform allocation scheme,and yields the same positional and tool direction machining accuracies.
基金supported by the National Research Council of Sri Lanka(Grant No.NRC TO 16-07).
文摘Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefits of two calibrations of the Nutrient Expert(NE)tool for rice in Sri Lanka’s Alfisols:the basic calibration(Nutrient Expert Sri Lanka 1,NESL1)and the comprehensive calibration(Nutrient Expert Sri Lanka 2,NESL2).NESL1 was developed by adapting the South Indian version of NE to local conditions,while NESL2 was an updated version,using three years of data from 71 farmer fields.
基金the National Natural Science Foundation of China(No.52475516,52005166,91960203)the Young Core Instructor Project in the Higher Education Institutions of Henan Province(No.2023GGJS051)the National Science Fund for Distinguished Young Scholars of Henan Polytechnic University(No.J2022-5).
文摘Ti-6Al-4V is widely used in the aviation industry because of its high strength, and good heat resistance. However, severe tool wear on the rake face occurs during the milling of Ti-6Al-4V,which is caused by intense friction between the tool rake face and the chips. To investigate tool wear in the milling of Ti-6Al-4V, ultrasonic vibration is introduced, and a cutting force prediction model that considers tool-chip contact interface friction behavior in Ultrasonic Longitudinal-Torsional Vibration-Assisted Milling(ULTVAM) is proposed in this paper. First, the tool tip motion trajectory and dynamic cutting thickness under ULTVAM were analyzed calculated, and compared with those in Common Milling(CM). Subsequently, the effects of ultrasonic vibration on the shear force under the ultrasonic softening effect, the friction force, and the friction reversal force on the toolchip contact interface were investigated. A dynamic milling force model under ULTVAM was established before and after friction force reversal caused by ultrasonic longitudinal-torsional vibration. Finally, numerous experiments were conducted to validate the proposed model, and the experimental results indicated that the calculated dynamic milling forces agreed well with the measured values, with errors in the X and Y directions of 5.51% and 10.23%, respectively. In addition, the average roughness of the workpiece surface also decreased(1.08, 0.9, 0.6, 0.7 μm under ultrasonic amplitudes of 0, 1, 2, and 3 μm) and the tool wear state improved on the rake face under ULTVAM.
文摘Modern manufacturing processes have become more reliant on automation because of the accelerated transition from Industry 3.0 to Industry 4.0.Manual inspection of products on assembly lines remains inefficient,prone to errors and lacks consistency,emphasizing the need for a reliable and automated inspection system.Leveraging both object detection and image segmentation approaches,this research proposes a vision-based solution for the detection of various kinds of tools in the toolkit using deep learning(DL)models.Two Intel RealSense D455f depth cameras were arranged in a top down configuration to capture both RGB and depth images of the toolkits.After applying multiple constraints and enhancing them through preprocessing and augmentation,a dataset consisting of 3300 annotated RGB-D photos was generated.Several DL models were selected through a comprehensive assessment of mean Average Precision(mAP),precision-recall equilibrium,inference latency(target≥30 FPS),and computational burden,resulting in a preference for YOLO and Region-based Convolutional Neural Networks(R-CNN)variants over ViT-based models due to the latter’s increased latency and resource requirements.YOLOV5,YOLOV8,YOLOV11,Faster R-CNN,and Mask R-CNN were trained on the annotated dataset and evaluated using key performance metrics(Recall,Accuracy,F1-score,and Precision).YOLOV11 demonstrated balanced excellence with 93.0%precision,89.9%recall,and a 90.6%F1-score in object detection,as well as 96.9%precision,95.3%recall,and a 96.5%F1-score in instance segmentation with an average inference time of 25 ms per frame(≈40 FPS),demonstrating real-time performance.Leveraging these results,a YOLOV11-based windows application was successfully deployed in a real-time assembly line environment,where it accurately processed live video streams to detect and segment tools within toolkits,demonstrating its practical effectiveness in industrial automation.The application is capable of precisely measuring socket dimensions by utilising edge detection techniques on YOLOv11 segmentation masks,in addition to detection and segmentation.This makes it possible to do specification-level quality control right on the assembly line,which improves the ability to examine things in real time.The implementation is a big step forward for intelligent manufacturing in the Industry 4.0 paradigm.It provides a scalable,efficient,and accurate way to do automated inspection and dimensional verification activities.
基金supported by the National Natural Science Foundation of China(Grant Nos.U22A20202 and 52275477).
文摘Trochoidal milling is known for its advantages in machining difficult-to-machine materials as it facilitates chip removal and tool cooling.However,the conventional trochoidal tool path presents challenges such as lower machining efficiency and longer machining time due to its time-varying cutter-workpiece engagement angle and a high percentage of non-cutting tool paths.To address these issues,this paper introduces a parameter-variant trochoidal-like(PVTR)tool path planning method for chatter-free and high-efficiency milling.This method ensures a constant engagement angle for each tool path period by adjusting the trochoidal radius and step.Initially,the nonlinear equation for the PVTR toolpath is established.Then,a segmented recurrence method is proposed to plan tool paths based on the desired engagement angle.The impact of trochoidal tool path parameters on the engagement angle is analyzed and coupled this information with the milling stability model based on spindle speed and engagement angle to determine the desired engagement angle throughout the machining process.Finally,several experimental tests are carried out using the bull-nose end mill to validate the feasibility and effectiveness of the proposed method.
基金funded by the National Key R&D Program of China(2023YFA1008704),the National Natural Science Foundation of China(Grant No.62377044)Beijing Key Laboratory of Big Data Management and Analysis Methods,Major Innovation&Planning Interdisciplinary Platform for the“Double-First Class”Initiative,funds for building world-class universities(disciplines)of Renmin University of China,and PCC@RUC.The authors would like to extend their sincere gratitude to Yankai Lin for his constructive feedback throughout the development of this work.
文摘Recently,tool learning with large language models(LLMs)has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.Despite growing attention and rapid advancements in this field,the existing literature remains fragmented and lacks systematic organization,posing barriers to entry for newcomers.This gap motivates us to conduct a comprehensive survey of existing works on tool learning with LLMs.In this survey,we focus on reviewing existing literature from the two primary aspects(1)why tool learning is beneficial and(2)how tool learning is implemented,enabling a comprehensive understanding of tool learning with LLMs.We first explore the“why”by reviewing both the benefits of tool integration and the inherent benefits of the tool learning paradigm from six specific aspects.In terms of“how”,we systematically review the literature according to a taxonomy of four key stages in the tool learning workflow:task planning,tool selection,tool calling,and response generation.Additionally,we provide a detailed summary of existing benchmarks and evaluation methods,categorizing them according to their relevance to different stages.Finally,we discuss current challenges and outline potential future directions,aiming to inspire both researchers and industrial developers to further explore this emerging and promising area.
基金funded by the National Natural Science Foundation of China(Grant Nos.:82222068,82070423,82270348,and 82173779)the Innovation Team and Talents Cultivation Pro-gram of National Administration of Traditional Chinese Medicine,China(Grant No:ZYYCXTD-D-202206)+1 种基金Fujian Province Science and Technology Project,China(Grant Nos.:2021J01420479,2021J02058,2022J011374,and 2022J02057)Fundamental Research Funds for the Chinese Central Universities,China(Grant No.:20720230070).
文摘Insect-derived traditional Chinese medicine(TCM)constitutes an essential component of TCM,with the earliest records found in“52 Bingfang”(Prescriptions of fifty-two diseases,which is one of the earliest Chinese medical prescriptions).
文摘Developmental and reproductive toxicity(DART)endpoint entails a toxicological assessment of all developmental stages and reproductive cycles of an organism.In silico tools to predict DART will provide a method to assess this complex toxicity endpoint and will be valuable for screening emerging pollutants as well as for m anaging new chemicals in China.Currently,there are few published DART prediction models in China,but many related research and development projects are in progress.In 2013,WU et al.published an expert rule-based DART decision tree(DT).This DT relies on known chemical structures linked to DART to forecast DART potential of a given chemical.Within this procedure,an accurate DART data interpretation is the foundation of building and expanding the DT.This paper excerpted case studies demonstrating DART data curation and interpretation of four chemicals(including 8-hydroxyquinoline,3,5,6-trichloro-2-pyridinol,thiacloprid,and imidacloprid)to expand the existing DART DT.Chemicals were first selected from the database of Solid Waste and Chemicals Management Center,Ministry of Ecology and Environment(MEESCC)in China.The structures of these 4 chemicals were analyzed and preliminarily grouped by chemists based on core structural features,functional groups,receptor binding property,metabolism,and possible mode of actions.Then,the DART conclusion was derived by collecting chemical information,searching,integrating,and interpreting DART data by the toxicologists.Finally,these chemicals were classified into either an existing category or a new category via integrating their chemical features,DART conclusions,and biological properties.The results showed that 8-hydroxyquinoline impacted estrous cyclicity,s exual organ weights,and embryonal development,and 3,5,6-trichloro-2-pyridinol caused central nervous system(CNS)malformations,which were added to an existing subcategory 8e(aromatic compounds with multi-halogen and nitro groups)of the DT.Thiacloprid caused dystocia and fetal skeletal malformation,and imidacloprid disrupted the endocrine system and male fertility.They both contain 2-chloro-5-methylpyridine substituted imidazolidine c yclic ring,which were expected to create a new category of neonicotinoids.The current work delineates a t ransparent process of curating toxicological data for the purpose of DART data interpretation.In the presence of sufficient related structures and DART data,the DT can be expanded by iteratively adding chemicals within the a pplicable domain of each category or subcategory.This DT can potentially serve as a tool for screening emerging pollutants and assessing new chemicals in China.
文摘Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.
文摘Today city planners are confronted with two global trends:on one hand,living space is getting less due to urbanization;on the other hand,demands on living space are constantly rising as for example through stricter climate and energy political objectives based on the Paris Agreement.Therefore,it will be necessary to take into account—near urban planning and social aspects—also the climate compatibility as one central aspect in the construction of buildings,settlements,districts or neighborhoods.To identify and to push successful concepts,Austria has developed a planning tool that allows planning,assessing and ensuring high quality standards of neighborhoods.As the tool has been highly successful,additional planning tools are being developed for specific topics such as“PED—Positive Energy Districts”,“NEB—New European Bauhaus”and“CND—Climate Neutral Districts”.Central quantitative and qualitative criteria—which have been elaborated in the recent years—will be presented in this paper.
基金co-supported by the Enterprise Innovation and Development Joint Program of the National Natural Science Foundation of China(No.U20B2032)Open Project Funding of State Key Laboratory for High Performance Tools(GXNGJSKL-2024-08)+1 种基金Open Foundation of the State Key Laboratory of Intelligent Manufacturing Equipment and Technology(IMETKF2023005)Introduced Innovative Scientific Research Team Project of Zhongshan(the tenth batch)(CXTD2023008)。
文摘Micro-grinding has been widely used in aerospace and other industry.However,the small diameter of the micro-grinding tool has limited its machining performance and efficiency.In order to solve the above problems,micro-structure has been applied on the micro-grinding tool.A morphology modeling has been established in this study to characterize the surface of microstructured micro-grinding tool,and the grinding performance of micro-structured micro-grinding tool has been analyzed through undeformed chip thickness,abrasive edge width,and effective distance between abrasives.Then deviation analysis,path optimization and parameter optimization of microchannel array precision grinding have been finished to improve processing quality and efficiency,and the deflection angle has the most obvious effects on the rectangular slot depth,micro-structured micro-grinding tool could reduce 10%surface roughness and 20%grinding force compared to original micro-grinding tool.Finally,the microchannel array has been machined with a size deviation of 2μm and surface roughness of 0.2μm.
基金funded by the Z Zurich Foundation,Zurich,Switzerland as a contribution to the Zurich Climate Resilience Alliance。
文摘Understanding and strengthening community-level resilience to natural hazard-induced disasters is critical for the management of adverse impacts of such events and the growth of community well-being.A key gap in achieving this is limited standardized and validated disaster resilience measurement frameworks that operate at local levels and are universally applicable.The Flood Resilience Measurement for Communities(FRMC)is a foremost tool for community flood resilience assessment.It follows a structured approach to comprehensively assess community flood resilience across five classes of capacities(capitals)to support strategic investment in resilience strengthening initiatives.The FRMC is a further development of an earlier version(the FRMT,the Flood Resilience Measurement Tool).The FRMT has been developed and applied between 2015 and 2017 in 118 flood prone communities across nine countries.It has been validated in terms of content and face validity as well as in terms of reliability.To reduce redundancy and survey eff ort,the FRMC holds a lesser number of indicators(44 versus 88)and has now been applied in over 320 communities across 20 countries.We examine the validation for the revised resilience construct and the new community applications and present a comprehensive overview of the statistical and user validation process and outcomes in both practical and scientific terms.The results confirm the validity,reliability as well as usefulness of the FRMC framework and tool.Furthermore,our approach and results provide insights for other resilience measurement approaches and their validation eff orts.We also present a comprehensive discussion about the dynamic aspects of flood resilience at community level,and the many validation aspects that need to be incorporated both in terms of quantification eff orts as well as usability on the ground.
基金supported by grant CNTC-110202101039(JY-16)and YNTC-2022530000241008.
文摘Bioinformatics analysis often requires the filtering of multi-datasets,based on frequency or frequency of occurrence,for decisions on retention or deletion.Existing tools for this purpose often present a challenge with complex installation,which necessitate custom coding,thereby impeding efficient data processing activities.To address this issue,Filterx,a user-friendly command line tool that written in C language,was developed that supports multi-condition filtering,based on frequency or occurrence.This tool enables users to complete the data processing tasks through a simple command line,greatly reducing both workload and data processing time.In addition,future development of this tool could facilitate its integration into various bioinformatics data analysis pipelines.
基金the National Key Research and Development Program of China(Grant No.2022YFB3302700)the National Natural Science Foundation of China(Grant No.52375486)the Shanghai Rising-Star Program(Grant No.22QB1404200).
文摘In intelligentmanufacturing processes such as aerospace production,computer numerical control(CNC)machine tools require real-time optimization of process parameters to meet precision machining demands.These dynamic operating conditions increase the risk of fatigue damage in CNC machine tool bearings,highlighting the urgent demand for rapid and accurate fault diagnosis methods that can maintain production efficiency and extend equipment uptime.However,varying conditions induce feature distribution shifts,and scarce fault samples limitmodel generalization.Therefore,this paper proposes a causal-Transformer-based meta-learning(CTML)method for bearing fault diagnosis in CNC machine tools,comprising three core modules:(1)the original bearing signal is transformed into a multi-scale time-frequency feature space using continuous wavelet transform;(2)a causal-Transformer architecture is designed to achieve feature extraction and fault classification based on the physical causal law of fault propagation;(3)the above mechanisms are integrated into a model-agnostic meta-learning(MAML)framework to achieve rapid cross-condition adaptation through an adaptive gradient pruning strategy.Experimental results using the multiple bearing dataset show that under few-shot cross-condition scenarios(3-way 1-shot and 3-way 5-shot),the proposed CTML outperforms benchmark models(e.g.,Transformer,domain adversarial neural networks(DANN),and MAML)in terms of classification accuracy and sensitivity to operating conditions,while maintaining a moderate level of model complexity.
文摘BACKGROUND Nosocomial fever of unknown origin(nFUO)is a frequent and challenging diagnostic entity,encompassing diverse infectious and non-infectious etiologies.Timely identification is crucial,yet evidence on the diagnostic accuracy of commonly employed sepsis screening tools and biomarkers remains sparse.We hypothesized that these tools and biomarkers measured at fever onset could distinguish infectious from non-infectious causes of nFUO in critically ill adults.AIM To evaluate the diagnostic utility of sepsis tools and biomarkers in identifying infectious causes of nFUO.METHODS This prospective observational study included patients admitted to the Acute Care Emergency Medicine Unit,Postgraduate Institute of Medical Education and Research,Chandigarh,India(July 2023 to December 2024).nFUO was defined by Durack and Street criteria.Diagnostic performance of sepsis screening tools(systemic inflammatory response syndrome,Sequential Organ Failure Assessment,quick Sequential Organ Failure Assessment,National Early Warning Score,and Modified Early Warning Score)and biomarkers[procalcitonin(PCT),C-reactive protein(CRP)]at fever onset was assessed using receiver operating characteristic curve analysis.RESULTS Of 80 cases(mean age 42.9±16.5 years;80% male),42.5% had infectious causes,38.7% non-infectious,and 18.8% remained undiagnosed.Pneumonia(26.2%)and bloodstream infections(11.2%)were the most common infectious etiologies,while central fever and thrombophlebitis(each 7.5%)were predominant among non-infectious causes.Sepsis tools showed poor diagnostic accuracy,with area under the receiver operating characteristic curve(AUC)values close to 0.5.PCT demonstrated modest performance(AUC=0.61;optimal cut-off:0.85μg/L),while CRP was paradoxically higher in non-infectious cases(AUC=0.45).Overall mortality was 20% and was highest among undiagnosed patients(33.3%).Fever duration and hospitalization length were significantly greater in infectious cases.CONCLUSION Sepsis tools,PCT,and CRP have limited utility in identifying infectious causes of nFUO in critically ill adults and should not solely guide initial decision-making.
文摘Objective:This study aimed to systematically evaluate the measurement characteristics and methodological quality of childbirth experience assessment tools,with a view to informing the selection of healthcare professionals who can provide high-quality assessment tools.Method:A systematic search was performed on specific databases:PubMed,Web of Science,Embase,CINAHL,SinoMed,China National Knowledge Infrastructure(CNKI),and Wanfang,from inception to February 29,2024.The researchers retrieved studies on the measurement attributes of the childbirth experience assessment tool,and traced back the references of the included studies to supplement relevant literature.According to the inclusion and exclusion criteria,screening and data extraction were independently undertaken by two reviewers.Two researchers individually used the Consensus-based Standards for the Selection of Health Measurement Instruments(COSMIN)Risk of Bias Checklist to assess the methodological quality of the scale,applied the COSMIN criteria to evaluate the measurement properties of the scale,and used a modified Grading of Recommendations,Assessment,Development,and Evaluation(GRADE)system to assess the certainty of evidence.Result:A total of 15 studies were included to evaluate the psychometric properties of 11 childbirth experience assessment tools(including different language versions).Eight studies’methodological quality of content validity was doubtful,and the remaining studies did not report content validity.None of the tools reported measurement error,cross-cultural validity,or responsiveness.In light of the questionable or unreported content validity of the tools,the evidence quality was deemed moderate or below.Consequently,the 11 assessment tools were recommended as grade B.Conclusion:In contrast,the Questionnaire for Assessing the Childbirth Experience(QACE)is recommended for provisional use,given its relatively good methodological and measurement attributes and appropriate content for evaluation.However,further validation of other measurement properties is needed.
文摘Assessing the vulnerability of a platform is crucial in its design.In fact,the results obtained from vulnerability analyses provide valuable information,leading to precise design choices or corrective solutions that enhance the platform's chances of surviving different scenarios.Such scenarios can involve various types of threats that can affect the platform's survivability.Among such,blast waves impacting the platform's structure represent critical conditions that have not yet been studied in detail.That is,frameworks for vulnerability assessment that can deal with blast loading have not been presented yet.In this context,this work presents a fast-running engineering tool that can quantify the risk that a structure fails when it is subjected to blast loading from the detonation of high explosive-driven threats detonating at various distances from the structure itself.The tool has been implemented in an in-house software that calculates vulnerability to various impacting objects,and its capabilities have been shown through a simplified,yet realistic,case study.The novelty of this research lies in the development of an integrated computational environment capable of calculating the platform's vulnerability to blast waves,without the need for running expensive finite element simulations.In fact,the proposed tool is fully based on analytical models integrated with a probabilistic approach for vulnerability calculation.
