Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of...Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.展开更多
Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining...Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining technologies often struggle to achieve ultra-precision with DMMs resulting from poor surface quality and low processing efficiency.In recent years,field-assisted machining (FAM) technology has emerged as a new generation of machining technology based on innovative principles such as laser heating,tool vibration,magnetic magnetization,and plasma modification,providing a new solution for improving the machinability of DMMs.This technology not only addresses these limitations of traditional machining methods,but also has become a hot topic of research in the domain of ultra-precision machining of DMMs.Many new methods and principles have been introduced and investigated one after another,yet few studies have presented a comprehensive analysis and summarization.To fill this gap and understand the development trend of FAM,this study provides an important overview of FAM,covering different assisted machining methods,application effects,mechanism analysis,and equipment design.The current deficiencies and future challenges of FAM are summarized to lay the foundation for the further development of multi-field hybrid assisted and intelligent FAM technologies.展开更多
Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for st...Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.展开更多
The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,su...The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.展开更多
With the rapid development in advanced industries,such as microelectronics and optics sectors,the functional feature size of devises/components has been decreasing from micro to nanometric,and even ACS for higher perf...With the rapid development in advanced industries,such as microelectronics and optics sectors,the functional feature size of devises/components has been decreasing from micro to nanometric,and even ACS for higher performance,smaller volume and lower energy consumption.By this time,a great many quantum structures are proposed,with not only an extreme scale of several or even single atom,but also a nearly ideal lattice structure with no material defect.It is almost no doubt that such structures play critical role in the next generation products,which shows an urgent demand for the ACSM.Laser machining is one of the most important approaches widely used in engineering and scientific research.It is high-efficient and applicable for most kinds of materials.Moreover,the processing scale covers a huge range from millimeters to nanometers,and has already touched the atomic level.Laser–material interaction mechanism,as the foundation of laser machining,determines the machining accuracy and surface quality.It becomes much more sophisticated and dominant with a decrease in processing scale,which is systematically reviewed in this article.In general,the mechanisms of laser-induced material removal are classified into ablation,CE and atomic desorption,with a decrease in the scale from above microns to angstroms.The effects of processing parameters on both fundamental material response and machined surface quality are discussed,as well as theoretical methods to simulate and understand the underlying mechanisms.Examples at nanometric to atomic scale are provided,which demonstrate the capability of laser machining in achieving the ultimate precision and becoming a promising approach to ACSM.展开更多
Brittle materials are widely used for producing important components in the industry of optics,optoelectronics,and semiconductors.Ultraprecision machining of brittle materials with high surface quality and surface int...Brittle materials are widely used for producing important components in the industry of optics,optoelectronics,and semiconductors.Ultraprecision machining of brittle materials with high surface quality and surface integrity helps improve the functional performance and lifespan of the components.According to their hardness,brittle materials can be roughly divided into hard-brittle and soft-brittle.Although there have been some literature reviews for ultraprecision machining of hard-brittle materials,up to date,very few review papers are available that focus on the processing of soft-brittle materials.Due to the‘soft’and‘brittle’properties,this group of materials has unique machining characteristics.This paper presents a comprehensive overview of recent advances in ultraprecision machining of soft-brittle materials.Critical aspects of machining mechanisms,such as chip formation,surface topography,and subsurface damage for different machining methods,including diamond turning,micro end milling,ultraprecision grinding,and micro/nano burnishing,are compared in terms of tool-workpiece interaction.The effects of tool geometries on the machining characteristics of soft-brittle materials are systematically analyzed,and dominating factors are sorted out.Problems and challenges in the engineering applications are identified,and solutions/guidelines for future R&D are provided.展开更多
BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective pr...BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.展开更多
Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the prope...Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.展开更多
Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning technique...Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.展开更多
Wear of cutting tools is a big concern for industrial manufacturers, because of their acquisition cost as well as the impact on the production lines when they are unavailable. Law of wear is very important in determin...Wear of cutting tools is a big concern for industrial manufacturers, because of their acquisition cost as well as the impact on the production lines when they are unavailable. Law of wear is very important in determining cutting tools lifespan, but most of the existing models don’t take into account the cutting temperature. In this work, the theoretical and experimental results of a dynamic study of metal machining against cutting temperature of a treated steel of grade S235JR with a high-speed steel tool are provided. This study is based on the analysis of two complementary approaches, an experimental approach with the measurement of the temperature and on the other hand, an approach using modeling. Based on unifactorial and multifactorial tests (speed of cut, feed, and depth of cut), this study allowed the highlighting of the influence of the cutting temperature on the machining time. To achieve this objective, two specific approaches have been selected. The first was to measure the temperature of the cutting tool and the second was to determine the wear law using Rayleigh-Ham dimensional analysis method. This study permitted the determination of a law that integrates the cutting temperature in the calculations of the lifespan of the tools during machining.展开更多
High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not ...High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not been formally reviewed thus far. This article focuses on the solid mechanics framework of adiabatic shear band(ASB) onset and material metallurgical microstructural evolutions in HSM. The ASB onset is described using partial differential systems. Several factors in HSM were considered in the systems, and the ASB onset conditions were obtained by solving these systems or applying the perturbation method to the systems. With increasing machining speed, an ASB can be depressed and further eliminated by shock pressure. The damage observed in HSM exhibits common features. Equiaxed fine grains produced by dynamic recrystallization widely cause damage to ductile materials, and amorphization is the common microstructural evolution in brittle materials. Based on previous studies, potential mechanisms for the phenomena in HSM are proposed. These include the thickness variation of the white layer of ductile materials. These proposed mechanisms would be beneficial to deeply understanding the various phenomena in HSM.展开更多
The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics an...The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics analysis has always been a research hotspot.The cutting conditions determined by the cutter axis,tool path,and workpiece geometry are complex and changeable,which has made dynamics research a major challenge.For this reason,this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces,and proposes an efficient dynamics analysis model.To simplify the research object,the cutter position points along the tool path were discretized into inclined plane five-axis machining.The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining.These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object.Based on the in-cut cutting edge solved by the space limitation method,the dynamics of the inclined plane five-axis machining unit were studied,and the results were uniformly stored in the abstract space to produce a database.Finally,the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency.Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model.This study has great potential for the online synchronization of intelligent machining of large surfaces.展开更多
The current study of minimum quantity lubrication(MQL)concentrates on its performance improvement.By contrast with nanofluid MQL and electrostatic atomization(EA),the proposed nanofluid composite electrostatic sprayin...The current study of minimum quantity lubrication(MQL)concentrates on its performance improvement.By contrast with nanofluid MQL and electrostatic atomization(EA),the proposed nanofluid composite electrostatic spraying(NCES)can enhance the performance of MQL more comprehensively.However,it is largely influenced by the base fluid of external fluid.In this paper,the lubrication property and machining performance of NCES with different types of vegetable oils(castor,palm,soybean,rapeseed,and LB2000 oil)as the base fluids of external fluid were compared and evaluated by friction and milling tests under different flow ratios of external and internal fluids.The spraying current and electrowetting angle were tested to analyze the influence of vegetable oil type as the base fluid of external fluid on NCES performances.The friction test results show that relative to NCES with other vegetable oils as the base fluids of external fluid,NCES with LB2000 as the base fluid of external fluid reduced the friction coefficient and wear loss by 9.4%-27.7%and 7.6%-26.5%,respectively.The milling test results display that the milling force and milling temperature for NCES with LB2000 as the base fluid of external fluid were 1.4%-13.2%and 3.6%-11.2%lower than those for NCES with other vegetable oils as the base fluids of external fluid,respectively.When LB2000/multi-walled carbon nanotube(MWCNT)water-based nanofluid was used as the external/internal fluid and the flow ratio of external and internal fluids was 2:1,NCES showed the best milling performance.This study provides theoretical and technical support for the selection of the base fluid of NCES external fluid.展开更多
BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are p...BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.展开更多
Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImpr...Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.展开更多
This work demonstrates the viability of the powder-mixed micro-electrochemical discharge machining(PMECDM) process to fabricate micro-holes on C103 niobium-based alloy for high temperature applications.Three processes...This work demonstrates the viability of the powder-mixed micro-electrochemical discharge machining(PMECDM) process to fabricate micro-holes on C103 niobium-based alloy for high temperature applications.Three processes are involved simultaneously i.e.spark erosion,chemical etching,and abrasive grinding for removal of material while the classical electrochemical discharge machining process involves double actions i.e.spark erosion,and chemical etching.The powder-mixed electrolyte process resulted in rapid material removal along with a better surface finish as compared to the classical microelectrochemical discharge machining(MECDM).Further,the results are optimized through a multiobjective optimization approach and study of the surface topography of the hole wall surface obtained at optimized parameters.In the selected range of experimental parameters,PMECDM shows a higher material removal rate(MRR) and lower surface roughness(R_(a))(MRR:2.8 mg/min and R_(a) of 0.61 μm) as compared to the MECDM process(MRR:2.01 mg/min and corresponding Raof 1.11 μm).A detailed analysis of the results is presented in this paper.展开更多
AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a tota...AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a total of 119 patients completed a Patient Satisfaction Questionnaire-18 and a sociodemographic questionnaire.Clinical data was obtained from the case management system.Follow-up adherence was defined as completing each follow-up within±30d of the scheduled time set by ophthalmologists during the study period.RESULTS:Average satisfaction scored 78.65±7,with an average of 4.39±0.58 across the seven dimensions.Age negatively correlated with satisfaction(P=0.008),whilst patients with follow-up duration of 2 or more years reported higher satisfaction(P=0.045).Multivariate logistics regression analysis revealed that longer follow-up durations were associated with lower follow-up adherence(OR=0.97,95%CI,0.95-1.00,P=0.044).Additionally,patients with suspected glaucoma(OR=2.72,95%CI,1.03-7.20,P=0.044)and those with an annual income over 100000 Chinese yuan demonstrated higher adherence(OR=5.57,95%CI,1.00-30.89,P=0.049).CONCLUSION:The case management model proves effective for glaucoma patients,with positive adherence rates.The implementation of this model can be optimized in the future based on the identified factors and extended to glaucoma patients in more hospitals.展开更多
Background:Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease(CHD)care centers.Much less is understood about the loss to follow-up(LTF)after a succ...Background:Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease(CHD)care centers.Much less is understood about the loss to follow-up(LTF)after a successful transition.This is critical too,as patients lost to specialised care are more likely to experience mor-bidity and premature mortality.Aims:To understand the prevalence and reasons for loss to follow-up(LTF)at a large Australian Adult Congenital Heart Disease(ACHD)centre.Methods:Patients with moderate or highly complex CHD and gaps in care of>3 years(defined as LTF)were identified from a comprehensive ACHD data-base.Structured telephone interviews examined current care and barriers to clinic attendance.Results:Overall,407(22%)of ACHD patients(n=1842)were LTF.The mean age at LTF was 31(SD 11.5)years and 54%were male;311(76%)were uncontactable.Compared to adults seen regularly,lost patients were younger,with a greater socio-economic disadvantage,and had less complex CHD(p<0.05 for all).We interviewed 59 patients(14%).The top 3 responses for care absences were“feeling well”(61%),losing track of time(36%),and not needing fol-low-up care(25%).Conclusions:A large proportion of the ACHD population becomes lost to specialised cardiac care,even after a successful transition.This Australian study reports younger age,moderate complexity defects,and socio-economic disadvantage as predictive of loss to follow-up.This study highlights the need for novel approaches to patient-centered service delivery even beyond the age of transition and resources to maintain patient engagement within the ACHD service.展开更多
Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression mode...Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.展开更多
文摘Magnesium alloys have many advantages as lightweight materials for engineering applications,especially in the fields of automotive and aerospace.They undergo extensive cutting or machining while making products out of them.Dry cutting,a sustainable machining method,causes more friction and adhesion at the tool-chip interface.One of the promising solutions to this problem is cutting tool surface texturing,which can reduce tool wear and friction in dry cutting and improve machining performance.This paper aims to investigate the impact of dimple textures(made on the flank face of cutting inserts)on tool wear and chip morphology in the dry machining of AZ31B magnesium alloy.The results show that the cutting speed was the most significant factor affecting tool flank wear,followed by feed rate and cutting depth.The tool wear mechanism was examined using scanning electron microscope(SEM)images and energy dispersive X-ray spectroscopy(EDS)analysis reports,which showed that at low cutting speed,the main wear mechanism was abrasion,while at high speed,it was adhesion.The chips are discontinuous at low cutting speeds,while continuous at high cutting speeds.The dimple textured flank face cutting tools facilitate the dry machining of AZ31B magnesium alloy and contribute to ecological benefits.
基金supported by the National Key Research and Development Project of China (Grant No.2023YFB3407200)the National Natural Science Foundation of China (Grant Nos.52225506,52375430,and 52188102)the Program for HUST Academic Frontier Youth Team (Grant No.2019QYTD12)。
文摘Difficult-to-machine materials (DMMs) are extensively applied in critical fields such as aviation,semiconductor,biomedicine,and other key fields due to their excellent material properties.However,traditional machining technologies often struggle to achieve ultra-precision with DMMs resulting from poor surface quality and low processing efficiency.In recent years,field-assisted machining (FAM) technology has emerged as a new generation of machining technology based on innovative principles such as laser heating,tool vibration,magnetic magnetization,and plasma modification,providing a new solution for improving the machinability of DMMs.This technology not only addresses these limitations of traditional machining methods,but also has become a hot topic of research in the domain of ultra-precision machining of DMMs.Many new methods and principles have been introduced and investigated one after another,yet few studies have presented a comprehensive analysis and summarization.To fill this gap and understand the development trend of FAM,this study provides an important overview of FAM,covering different assisted machining methods,application effects,mechanism analysis,and equipment design.The current deficiencies and future challenges of FAM are summarized to lay the foundation for the further development of multi-field hybrid assisted and intelligent FAM technologies.
基金supported by the National Nat-ural Science Foundation of China(No.52203376)the National Key Research and Development Program of China(No.2023YFB3813200).
文摘Traditional 3Ni weathering steel cannot completely meet the requirements for offshore engineering development,resulting in the design of novel 3Ni steel with the addition of microalloy elements such as Mn or Nb for strength enhancement becoming a trend.The stress-assisted corrosion behavior of a novel designed high-strength 3Ni steel was investigated in the current study using the corrosion big data method.The information on the corrosion process was recorded using the galvanic corrosion current monitoring method.The gradi-ent boosting decision tree(GBDT)machine learning method was used to mine the corrosion mechanism,and the importance of the struc-ture factor was investigated.Field exposure tests were conducted to verify the calculated results using the GBDT method.Results indic-ated that the GBDT method can be effectively used to study the influence of structural factors on the corrosion process of 3Ni steel.Dif-ferent mechanisms for the addition of Mn and Cu to the stress-assisted corrosion of 3Ni steel suggested that Mn and Cu have no obvious effect on the corrosion rate of non-stressed 3Ni steel during the early stage of corrosion.When the corrosion reached a stable state,the in-crease in Mn element content increased the corrosion rate of 3Ni steel,while Cu reduced this rate.In the presence of stress,the increase in Mn element content and Cu addition can inhibit the corrosion process.The corrosion law of outdoor-exposed 3Ni steel is consistent with the law based on corrosion big data technology,verifying the reliability of the big data evaluation method and data prediction model selection.
基金supported by the National Natural Science Foundation of China(Nos.52075255,92160301,52175415,52205475,and 92060203)。
文摘The aerospace community widely uses difficult-to-cut materials,such as titanium alloys,high-temperature alloys,metal/ceramic/polymer matrix composites,hard and brittle materials,and geometrically complex components,such as thin-walled structures,microchannels,and complex surfaces.Mechanical machining is the main material removal process for the vast majority of aerospace components.However,many problems exist,including severe and rapid tool wear,low machining efficiency,and poor surface integrity.Nontraditional energy-assisted mechanical machining is a hybrid process that uses nontraditional energies(vibration,laser,electricity,etc)to improve the machinability of local materials and decrease the burden of mechanical machining.This provides a feasible and promising method to improve the material removal rate and surface quality,reduce process forces,and prolong tool life.However,systematic reviews of this technology are lacking with respect to the current research status and development direction.This paper reviews the recent progress in the nontraditional energy-assisted mechanical machining of difficult-to-cut materials and components in the aerospace community.In addition,this paper focuses on the processing principles,material responses under nontraditional energy,resultant forces and temperatures,material removal mechanisms,and applications of these processes,including vibration-,laser-,electric-,magnetic-,chemical-,advanced coolant-,and hybrid nontraditional energy-assisted mechanical machining.Finally,a comprehensive summary of the principles,advantages,and limitations of each hybrid process is provided,and future perspectives on forward design,device development,and sustainability of nontraditional energy-assisted mechanical machining processes are discussed.
基金supported by the National Natural Science Foundation of China(Nos.52035009,52105475).
文摘With the rapid development in advanced industries,such as microelectronics and optics sectors,the functional feature size of devises/components has been decreasing from micro to nanometric,and even ACS for higher performance,smaller volume and lower energy consumption.By this time,a great many quantum structures are proposed,with not only an extreme scale of several or even single atom,but also a nearly ideal lattice structure with no material defect.It is almost no doubt that such structures play critical role in the next generation products,which shows an urgent demand for the ACSM.Laser machining is one of the most important approaches widely used in engineering and scientific research.It is high-efficient and applicable for most kinds of materials.Moreover,the processing scale covers a huge range from millimeters to nanometers,and has already touched the atomic level.Laser–material interaction mechanism,as the foundation of laser machining,determines the machining accuracy and surface quality.It becomes much more sophisticated and dominant with a decrease in processing scale,which is systematically reviewed in this article.In general,the mechanisms of laser-induced material removal are classified into ablation,CE and atomic desorption,with a decrease in the scale from above microns to angstroms.The effects of processing parameters on both fundamental material response and machined surface quality are discussed,as well as theoretical methods to simulate and understand the underlying mechanisms.Examples at nanometric to atomic scale are provided,which demonstrate the capability of laser machining in achieving the ultimate precision and becoming a promising approach to ACSM.
文摘Brittle materials are widely used for producing important components in the industry of optics,optoelectronics,and semiconductors.Ultraprecision machining of brittle materials with high surface quality and surface integrity helps improve the functional performance and lifespan of the components.According to their hardness,brittle materials can be roughly divided into hard-brittle and soft-brittle.Although there have been some literature reviews for ultraprecision machining of hard-brittle materials,up to date,very few review papers are available that focus on the processing of soft-brittle materials.Due to the‘soft’and‘brittle’properties,this group of materials has unique machining characteristics.This paper presents a comprehensive overview of recent advances in ultraprecision machining of soft-brittle materials.Critical aspects of machining mechanisms,such as chip formation,surface topography,and subsurface damage for different machining methods,including diamond turning,micro end milling,ultraprecision grinding,and micro/nano burnishing,are compared in terms of tool-workpiece interaction.The effects of tool geometries on the machining characteristics of soft-brittle materials are systematically analyzed,and dominating factors are sorted out.Problems and challenges in the engineering applications are identified,and solutions/guidelines for future R&D are provided.
基金Supported by Science and Technology Support Program of Qiandongnan Prefecture,No.Qiandongnan Sci-Tech Support[2021]12Guizhou Province High-Level Innovative Talent Training Program,No.Qiannan Thousand Talents[2022]201701.
文摘BACKGROUND Intensive care unit-acquired weakness(ICU-AW)is a common complication that significantly impacts the patient's recovery process,even leading to adverse outcomes.Currently,there is a lack of effective preventive measures.AIM To identify significant risk factors for ICU-AW through iterative machine learning techniques and offer recommendations for its prevention and treatment.METHODS Patients were categorized into ICU-AW and non-ICU-AW groups on the 14th day post-ICU admission.Relevant data from the initial 14 d of ICU stay,such as age,comorbidities,sedative dosage,vasopressor dosage,duration of mechanical ventilation,length of ICU stay,and rehabilitation therapy,were gathered.The relationships between these variables and ICU-AW were examined.Utilizing iterative machine learning techniques,a multilayer perceptron neural network model was developed,and its predictive performance for ICU-AW was assessed using the receiver operating characteristic curve.RESULTS Within the ICU-AW group,age,duration of mechanical ventilation,lorazepam dosage,adrenaline dosage,and length of ICU stay were significantly higher than in the non-ICU-AW group.Additionally,sepsis,multiple organ dysfunction syndrome,hypoalbuminemia,acute heart failure,respiratory failure,acute kidney injury,anemia,stress-related gastrointestinal bleeding,shock,hypertension,coronary artery disease,malignant tumors,and rehabilitation therapy ratios were significantly higher in the ICU-AW group,demonstrating statistical significance.The most influential factors contributing to ICU-AW were identified as the length of ICU stay(100.0%)and the duration of mechanical ventilation(54.9%).The neural network model predicted ICU-AW with an area under the curve of 0.941,sensitivity of 92.2%,and specificity of 82.7%.CONCLUSION The main factors influencing ICU-AW are the length of ICU stay and the duration of mechanical ventilation.A primary preventive strategy,when feasible,involves minimizing both ICU stay and mechanical ventilation duration.
基金This work has been supported by the Conselleria de Inno-vación,Universidades,Ciencia y Sociedad Digital de la Generalitat Valenciana(CIAICO/2021/335).
文摘Jet grouting is one of the most popular soil improvement techniques,but its design usually involves great uncertainties that can lead to economic cost overruns in construction projects.The high dispersion in the properties of the improved material leads to designers assuming a conservative,arbitrary and unjustified strength,which is even sometimes subjected to the results of the test fields.The present paper presents an approach for prediction of the uniaxial compressive strength(UCS)of jet grouting columns based on the analysis of several machine learning algorithms on a database of 854 results mainly collected from different research papers.The selected machine learning model(extremely randomized trees)relates the soil type and various parameters of the technique to the value of the compressive strength.Despite the complex mechanism that surrounds the jet grouting process,evidenced by the high dispersion and low correlation of the variables studied,the trained model allows to optimally predict the values of compressive strength with a significant improvement with respect to the existing works.Consequently,this work proposes for the first time a reliable and easily applicable approach for estimation of the compressive strength of jet grouting columns.
文摘Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.
文摘Wear of cutting tools is a big concern for industrial manufacturers, because of their acquisition cost as well as the impact on the production lines when they are unavailable. Law of wear is very important in determining cutting tools lifespan, but most of the existing models don’t take into account the cutting temperature. In this work, the theoretical and experimental results of a dynamic study of metal machining against cutting temperature of a treated steel of grade S235JR with a high-speed steel tool are provided. This study is based on the analysis of two complementary approaches, an experimental approach with the measurement of the temperature and on the other hand, an approach using modeling. Based on unifactorial and multifactorial tests (speed of cut, feed, and depth of cut), this study allowed the highlighting of the influence of the cutting temperature on the machining time. To achieve this objective, two specific approaches have been selected. The first was to measure the temperature of the cutting tool and the second was to determine the wear law using Rayleigh-Ham dimensional analysis method. This study permitted the determination of a law that integrates the cutting temperature in the calculations of the lifespan of the tools during machining.
基金support of the Shenzhen Science and Technology Innovation Commission under Project Numbers KQTD20190929172505711,JSGG20210420091802007, and JCYJ20210324115413036Guangdong Provincial Department of Science and Technology under Project Number K22333004。
文摘High-speed machining(HSM) has been studied for several decades and has potential application in various industries, including the automobile and aerospace industries. However,the underlying mechanisms of HSM have not been formally reviewed thus far. This article focuses on the solid mechanics framework of adiabatic shear band(ASB) onset and material metallurgical microstructural evolutions in HSM. The ASB onset is described using partial differential systems. Several factors in HSM were considered in the systems, and the ASB onset conditions were obtained by solving these systems or applying the perturbation method to the systems. With increasing machining speed, an ASB can be depressed and further eliminated by shock pressure. The damage observed in HSM exhibits common features. Equiaxed fine grains produced by dynamic recrystallization widely cause damage to ductile materials, and amorphization is the common microstructural evolution in brittle materials. Based on previous studies, potential mechanisms for the phenomena in HSM are proposed. These include the thickness variation of the white layer of ductile materials. These proposed mechanisms would be beneficial to deeply understanding the various phenomena in HSM.
基金Supported by National Natural Science Foundation of China(Grant Nos.52005078,U1908231,52075076).
文摘The equipment used in various fields contains an increasing number of parts with curved surfaces of increasing size.Five-axis computer numerical control(CNC)milling is the main parts machining method,while dynamics analysis has always been a research hotspot.The cutting conditions determined by the cutter axis,tool path,and workpiece geometry are complex and changeable,which has made dynamics research a major challenge.For this reason,this paper introduces the innovative idea of applying dimension reduction and mapping to the five-axis machining of curved surfaces,and proposes an efficient dynamics analysis model.To simplify the research object,the cutter position points along the tool path were discretized into inclined plane five-axis machining.The cutter dip angle and feed deflection angle were used to define the spatial position relationship in five-axis machining.These were then taken as the new base variables to construct an abstract two-dimensional space and establish the mapping relationship between the cutter position point and space point sets to further simplify the dimensions of the research object.Based on the in-cut cutting edge solved by the space limitation method,the dynamics of the inclined plane five-axis machining unit were studied,and the results were uniformly stored in the abstract space to produce a database.Finally,the prediction of the milling force and vibration state along the tool path became a data extraction process that significantly improved efficiency.Two experiments were also conducted which proved the accuracy and efficiency of the proposed dynamics analysis model.This study has great potential for the online synchronization of intelligent machining of large surfaces.
基金Supported by National Natural Science Foundation of China(Grant Nos.52175411 and 51205177)Jiangsu Provincial Natural Science Foundation(Grant Nos.BK20171307 and BK2012277).
文摘The current study of minimum quantity lubrication(MQL)concentrates on its performance improvement.By contrast with nanofluid MQL and electrostatic atomization(EA),the proposed nanofluid composite electrostatic spraying(NCES)can enhance the performance of MQL more comprehensively.However,it is largely influenced by the base fluid of external fluid.In this paper,the lubrication property and machining performance of NCES with different types of vegetable oils(castor,palm,soybean,rapeseed,and LB2000 oil)as the base fluids of external fluid were compared and evaluated by friction and milling tests under different flow ratios of external and internal fluids.The spraying current and electrowetting angle were tested to analyze the influence of vegetable oil type as the base fluid of external fluid on NCES performances.The friction test results show that relative to NCES with other vegetable oils as the base fluids of external fluid,NCES with LB2000 as the base fluid of external fluid reduced the friction coefficient and wear loss by 9.4%-27.7%and 7.6%-26.5%,respectively.The milling test results display that the milling force and milling temperature for NCES with LB2000 as the base fluid of external fluid were 1.4%-13.2%and 3.6%-11.2%lower than those for NCES with other vegetable oils as the base fluids of external fluid,respectively.When LB2000/multi-walled carbon nanotube(MWCNT)water-based nanofluid was used as the external/internal fluid and the flow ratio of external and internal fluids was 2:1,NCES showed the best milling performance.This study provides theoretical and technical support for the selection of the base fluid of NCES external fluid.
文摘BACKGROUND Liver transplantation(LT)is a life-saving intervention for patients with end-stage liver disease.However,the equitable allocation of scarce donor organs remains a formidable challenge.Prognostic tools are pivotal in identifying the most suitable transplant candidates.Traditionally,scoring systems like the model for end-stage liver disease have been instrumental in this process.Nevertheless,the landscape of prognostication is undergoing a transformation with the integration of machine learning(ML)and artificial intelligence models.AIM To assess the utility of ML models in prognostication for LT,comparing their performance and reliability to established traditional scoring systems.METHODS Following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines,we conducted a thorough and standardized literature search using the PubMed/MEDLINE database.Our search imposed no restrictions on publication year,age,or gender.Exclusion criteria encompassed non-English studies,review articles,case reports,conference papers,studies with missing data,or those exhibiting evident methodological flaws.RESULTS Our search yielded a total of 64 articles,with 23 meeting the inclusion criteria.Among the selected studies,60.8%originated from the United States and China combined.Only one pediatric study met the criteria.Notably,91%of the studies were published within the past five years.ML models consistently demonstrated satisfactory to excellent area under the receiver operating characteristic curve values(ranging from 0.6 to 1)across all studies,surpassing the performance of traditional scoring systems.Random forest exhibited superior predictive capabilities for 90-d mortality following LT,sepsis,and acute kidney injury(AKI).In contrast,gradient boosting excelled in predicting the risk of graft-versus-host disease,pneumonia,and AKI.CONCLUSION This study underscores the potential of ML models in guiding decisions related to allograft allocation and LT,marking a significant evolution in the field of prognostication.
文摘Cutting parameters have a significant impact on the machining effect.In order to reduce the machining time and improve the machining quality,this paper proposes an optimization algorithm based on Bp neural networkImproved Multi-Objective Particle Swarm(Bp-DWMOPSO).Firstly,this paper analyzes the existing problems in the traditional multi-objective particle swarm algorithm.Secondly,the Bp neural network model and the dynamic weight multi-objective particle swarm algorithm model are established.Finally,the Bp-DWMOPSO algorithm is designed based on the established models.In order to verify the effectiveness of the algorithm,this paper obtains the required data through equal probability orthogonal experiments on a typical Computer Numerical Control(CNC)turning machining case and uses the Bp-DWMOPSO algorithm for optimization.The experimental results show that the Cutting speed is 69.4 mm/min,the Feed speed is 0.05 mm/r,and the Depth of cut is 0.5 mm.The results show that the Bp-DWMOPSO algorithm can find the cutting parameters with a higher material removal rate and lower spindle load while ensuring the machining quality.This method provides a new idea for the optimization of turning machining parameters.
文摘This work demonstrates the viability of the powder-mixed micro-electrochemical discharge machining(PMECDM) process to fabricate micro-holes on C103 niobium-based alloy for high temperature applications.Three processes are involved simultaneously i.e.spark erosion,chemical etching,and abrasive grinding for removal of material while the classical electrochemical discharge machining process involves double actions i.e.spark erosion,and chemical etching.The powder-mixed electrolyte process resulted in rapid material removal along with a better surface finish as compared to the classical microelectrochemical discharge machining(MECDM).Further,the results are optimized through a multiobjective optimization approach and study of the surface topography of the hole wall surface obtained at optimized parameters.In the selected range of experimental parameters,PMECDM shows a higher material removal rate(MRR) and lower surface roughness(R_(a))(MRR:2.8 mg/min and R_(a) of 0.61 μm) as compared to the MECDM process(MRR:2.01 mg/min and corresponding Raof 1.11 μm).A detailed analysis of the results is presented in this paper.
基金Supported by the Key Innovation and Guidance Program of the Eye Hospital,School of Ophthalmology&Optometry,Wenzhou Medical University(No.YNZD2201903)the Scientific Research Foundation of the Eye Hospital,School of Ophthalmology&Optometry,Wenzhou Medical University(No.KYQD20180306)the Nursing Project of the Eye Hospital of Wenzhou Medical University(No.YNHL2201908).
文摘AIM:To assess glaucoma patient satisfaction and follow-up adherence in case management and identify associated predictors to improve healthcare quality and patient outcomes.METHODS:In this cross-sectional study,a total of 119 patients completed a Patient Satisfaction Questionnaire-18 and a sociodemographic questionnaire.Clinical data was obtained from the case management system.Follow-up adherence was defined as completing each follow-up within±30d of the scheduled time set by ophthalmologists during the study period.RESULTS:Average satisfaction scored 78.65±7,with an average of 4.39±0.58 across the seven dimensions.Age negatively correlated with satisfaction(P=0.008),whilst patients with follow-up duration of 2 or more years reported higher satisfaction(P=0.045).Multivariate logistics regression analysis revealed that longer follow-up durations were associated with lower follow-up adherence(OR=0.97,95%CI,0.95-1.00,P=0.044).Additionally,patients with suspected glaucoma(OR=2.72,95%CI,1.03-7.20,P=0.044)and those with an annual income over 100000 Chinese yuan demonstrated higher adherence(OR=5.57,95%CI,1.00-30.89,P=0.049).CONCLUSION:The case management model proves effective for glaucoma patients,with positive adherence rates.The implementation of this model can be optimized in the future based on the identified factors and extended to glaucoma patients in more hospitals.
文摘Background:Much has been written about the loss to follow-up in the transition between pediatric and adult Congenital Heart Disease(CHD)care centers.Much less is understood about the loss to follow-up(LTF)after a successful transition.This is critical too,as patients lost to specialised care are more likely to experience mor-bidity and premature mortality.Aims:To understand the prevalence and reasons for loss to follow-up(LTF)at a large Australian Adult Congenital Heart Disease(ACHD)centre.Methods:Patients with moderate or highly complex CHD and gaps in care of>3 years(defined as LTF)were identified from a comprehensive ACHD data-base.Structured telephone interviews examined current care and barriers to clinic attendance.Results:Overall,407(22%)of ACHD patients(n=1842)were LTF.The mean age at LTF was 31(SD 11.5)years and 54%were male;311(76%)were uncontactable.Compared to adults seen regularly,lost patients were younger,with a greater socio-economic disadvantage,and had less complex CHD(p<0.05 for all).We interviewed 59 patients(14%).The top 3 responses for care absences were“feeling well”(61%),losing track of time(36%),and not needing fol-low-up care(25%).Conclusions:A large proportion of the ACHD population becomes lost to specialised cardiac care,even after a successful transition.This Australian study reports younger age,moderate complexity defects,and socio-economic disadvantage as predictive of loss to follow-up.This study highlights the need for novel approaches to patient-centered service delivery even beyond the age of transition and resources to maintain patient engagement within the ACHD service.
文摘Machine learning(ML) is well suited for the prediction of high-complexity,high-dimensional problems such as those encountered in terminal ballistics.We evaluate the performance of four popular ML-based regression models,extreme gradient boosting(XGBoost),artificial neural network(ANN),support vector regression(SVR),and Gaussian process regression(GP),on two common terminal ballistics’ problems:(a)predicting the V50ballistic limit of monolithic metallic armour impacted by small and medium calibre projectiles and fragments,and(b) predicting the depth to which a projectile will penetrate a target of semi-infinite thickness.To achieve this we utilise two datasets,each consisting of approximately 1000samples,collated from public release sources.We demonstrate that all four model types provide similarly excellent agreement when interpolating within the training data and diverge when extrapolating outside this range.Although extrapolation is not advisable for ML-based regression models,for applications such as lethality/survivability analysis,such capability is required.To circumvent this,we implement expert knowledge and physics-based models via enforced monotonicity,as a Gaussian prior mean,and through a modified loss function.The physics-informed models demonstrate improved performance over both classical physics-based models and the basic ML regression models,providing an ability to accurately fit experimental data when it is available and then revert to the physics-based model when not.The resulting models demonstrate high levels of predictive accuracy over a very wide range of projectile types,target materials and thicknesses,and impact conditions significantly more diverse than that achievable from any existing analytical approach.Compared with numerical analysis tools such as finite element solvers the ML models run orders of magnitude faster.We provide some general guidelines throughout for the development,application,and reporting of ML models in terminal ballistics problems.