Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for sp...Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.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
The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can caus...The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.展开更多
A dynamical dq model is proposed for a linear flux-switching permanent magnet（LFSPM） machine which is suitable for high-precision control applications.The operation principle of the prototype machine is analyzed usi...A dynamical dq model is proposed for a linear flux-switching permanent magnet（LFSPM） machine which is suitable for high-precision control applications.The operation principle of the prototype machine is analyzed using the finite element method（FEM）,and the parameters,such as the back electromotive force（EMF） and the phase flux linkage,are calculated.The calculated and measured results reveal that the back EMF and the flux linkage are essentially sinusoidal,and the variation of the phase flux linkage profile of the LFSPM machine is similar to that of the linear surface permanent magnet（LSPM） machine.Based on this,a dynamical dq model and a simulation control model are proposed.The simulation results are compared with the test results obtained from a DSP-based control platform,which verifies that the model is correct and effective.Moreover,the model can be used for design optimization and control development.展开更多
With the progression of photolithography processes,the present technology nodes have attained 3 nm and even 2 nm,necessitating a transition in the precision standards for displacement measurement and alignment methodo...With the progression of photolithography processes,the present technology nodes have attained 3 nm and even 2 nm,necessitating a transition in the precision standards for displacement measurement and alignment methodologies from the nanometer scale to the sub-nanometer scale.Metasurfaces,owing to their superior light field manipulation capabilities,exhibit significant promise in the domains of displacement measurement and positioning,and are anticipated to be applied in the advanced alignment systems of lithography machines.This paper primarily provides an overview of the contemporary alignment and precise displacement measurement technologies employed in photolithography stages,alongside the operational principles of metasurfaces in the context of precise displacement measurement and alignment.Furthermore,it explores the evolution of metasurface systems capable of achieving nano/sub-nano precision,and identifies the critical issues associated with sub-nanometer measurements using metasurfaces,as well as the principal obstacles encountered in their implementation within photolithography stages.The objective is to provide initial guidance for the advancement of photolithography technology.展开更多
BACKGROUND Pancreatic fistula is the most common complication of pancreatic surgeries that causes more serious conditions,including bleeding due to visceral vessel erosion and peritonitis.AIM To develop a machine lear...BACKGROUND Pancreatic fistula is the most common complication of pancreatic surgeries that causes more serious conditions,including bleeding due to visceral vessel erosion and peritonitis.AIM To develop a machine learning(ML)model for postoperative pancreatic fistula and identify significant risk factors of the complication.METHODS A single-center retrospective clinical study was conducted which included 150 patients,who underwent pancreat-oduodenectomy.Logistic regression,random forest,and CatBoost were employed for modeling the biochemical leak(symptomless fistula)and fistula grade B/C(clinically significant complication).The performance was estimated by receiver operating characteristic(ROC)area under the curve(AUC)after 5-fold cross-validation(20%testing and 80%training data).The risk factors were evaluated with the most accurate algorithm,based on the parameter“Importance”(Im),and Kendall correlation,P<0.05.RESULTS The CatBoost algorithm was the most accurate with an AUC of 74%-86%.The study provided results of ML-based modeling and algorithm selection for pancreatic fistula prediction and risk factor evaluation.From 14 parameters we selected the main pre-and intraoperative prognostic factors of all the fistulas:Tumor vascular invasion(Im=24.8%),age(Im=18.6%),and body mass index(Im=16.4%),AUC=74%.The ML model showed that biochemical leak,blood and drain amylase level(Im=21.6%and 16.4%),and blood leukocytes(Im=11.2%)were crucial predictors for subsequent fistula B/C,AUC=86%.Surgical techniques,morphology,and pancreatic duct diameter less than 3 mm were insignificant(Im<5%and no correlations detected).The results were confirmed by correlation analysis.CONCLUSION This study highlights the key predictors of postoperative pancreatic fistula and establishes a robust ML-based model for individualized risk prediction.These findings contribute to the advancement of personalized periop-erative care and may guide targeted preventive strategies.展开更多
Celiac disease(CD)is a common autoimmune disorder where gluten ingestion triggers an immune response,damaging the small intestine in genetically pre-disposed individuals.Affecting around 1%of the global population,CD ...Celiac disease(CD)is a common autoimmune disorder where gluten ingestion triggers an immune response,damaging the small intestine in genetically pre-disposed individuals.Affecting around 1%of the global population,CD presents with diverse symptoms,including gastrointestinal issues like diarrhea and extraintestinal conditions such as anemia and osteoporosis,often complicating diagnosis.Advances in serology,histology,and genetic testing,such as HLA-DQ2/DQ8 analysis,have improved diagnostic accuracy.Precision medicine is transforming CD management by integrating genetic,clinical,and lifestyle data to enable risk prediction,personalized therapies,and improved outcomes.Tools like machine learning enhance early diagnosis,dietary management,and drug discovery,while electronic medical records support comprehensive patient pro-filing and disease monitoring.These technologies facilitate personalized health-care delivery tailored to individual patient profiles.展开更多
The machine learning model developed by Shi et al for predicting colorectal polyp recurrence after endoscopic mucosal resection represents a significant advancement in the field of clinical gastroenterology.By integra...The machine learning model developed by Shi et al for predicting colorectal polyp recurrence after endoscopic mucosal resection represents a significant advancement in the field of clinical gastroenterology.By integrating patient-specific factors,such as age,smoking history,and Helicobacter pylori infection,the eXtreme Gradient Boosting algorithm enables precise personalised colonoscopy follow-up planning and risk assessment.This predictive tool offers substantial benefits by optimising surveillance intervals and directing healthcare resources more efficiently toward high-risk individuals.However,real-world implementation requires consideration of the generalisability of our findings across diverse patient populations and clinician training backgrounds.展开更多
Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),a...Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.展开更多
Preparation method of magnetic nanoparticles with core-shell structure was introduced,especially focusing on the preparation principle of sol-gel method,microemulsion method,and self-assembly technique.The application...Preparation method of magnetic nanoparticles with core-shell structure was introduced,especially focusing on the preparation principle of sol-gel method,microemulsion method,and self-assembly technique.The application of core-shell nanoparticles in precision machining was discussed.The Fe_(3)O_(4)@SiO_(2)composite particles were prepared by sol-gel method and were applied to the magnetorheological polishing of titanium alloy plates.Results show that core-shell nanoparticles with higher surface quality can be obtained after processing,compared with those after conventional abrasives.After polishing for 20 min,the surface roughness of the workpiece reaches 23 nm and the scratches are effectively reduced.Finally,the preparation and application of coreshell nanoparticles are summarized and prospected to provide a reference for further research on core-shell nanoparticles.展开更多
BACKGROUND Although chronic-phase chronic myeloid leukemia(CP-CML)is treatable and nearly curable in about 50%of patients,accelerated-phase chronic myeloid leukemia(AP-CML)shows concerning drug resistance,while blast ...BACKGROUND Although chronic-phase chronic myeloid leukemia(CP-CML)is treatable and nearly curable in about 50%of patients,accelerated-phase chronic myeloid leukemia(AP-CML)shows concerning drug resistance,while blast crisis chronic myeloid leukemia(BC-CML)is highly lethal.Advances in whole exome sequencing(WES)reveal pan-cancer mutations in BC-CML,supporting mutation-guided therapies beyond Breakpoint cluster region-Abelson.Artificial intelligence(AI)and machine learning(ML)enable genomic stratification and drug repurposing,addressing overlooked actionable mutations.AIM To stratify BC-CML into molecular subtypes using WES,ML,and AI for precision drug repurposing.METHODS Included 123 CML patients(111 CP-CML,5 AP-CML,7 BC-CML).WES identified pan-cancer mutations.Variants annotated via Ensembl Variant Effect Predictor and Catalogue of Somatic Mutations in Cancer(COSMIC).ML(principal component analysis,K-means)stratified BC-CML.COSMIC signatures and PanDrugs prioritized drugs.Analysis of variance/Kruskal-Wallis validated differences(P<0.05).RESULTS In this exploratory,hypothesis-generating study of BC-CML patients(n=7),we detected over 2500 somatic mutations.ML identified three BC-CML clusters:(1)Cluster 1[breast cancer susceptibility gene 2(BRCA2),TP53];(2)Cluster 2[isocitrate dehydrogenase(IDH)1/2,ten-eleven translocation 2];and(3)Cluster 3[Janus kinase(JAK)2,colony-stimulating factor 3 receptor],with distinct COSMIC signatures.Therapies:(1)Polyadenosinediphosphate-ribose polymerase inhibitors(olaparib);(2)IDH inhibitors(ivosidenib);and(3)JAK inhibitors(ruxolitinib).Mutational burden,signatures,and targets varied significantly across clusters,supporting precision stratification.CONCLUSION This WES-AI-ML framework provides mutation-guided therapies for BC-CML,enabling real-time stratification and Food and Drug Administration-approved drug repurposing.While this exploratory study is limited by its small sample size(n=7),it establishes a methodological framework for precision oncology stratification that warrants validation in larger,multi-center cohorts.展开更多
Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to ...Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to be divided into several groups on an appropriate threshold. Currently, group threshold value is mainly determined by researchers experience. Few studies focus on group threshold in temperature variable grouping. Since the threshold is important in error compensation, this paper arms to find out an optimal threshold to realize temperature variable optimization in thermal error modeling. Firstly, correlation coefficient is used to express membership grade of temperature variables, and the theory of fuzzy transitive closure is applied to obtain relational matrix of temperature variables. Concepts as compact degree and separable degree are introduced. Then evaluation model of temperature variable clustering is built. The optimal threshold and the best temperature variable clustering can be obtained by setting the maximum value of evaluation model as the objective. Finally, correlation coefficients between temperature variables and thermal error are calculated in order to find out optimum temperature variables for thermal error modeling. An experiment is conducted on a precise horizontal machining center. In experiment, three displacement sensors are used to measure spindle thermal error and twenty-nine temperature sensors are utilized to detect the machining center temperature. Experimental result shows that the new method of temperature variable optimization on optimal threshold successfully worked out a best threshold value interval and chose seven temperature variables from twenty-nine temperature measuring points. The model residual of z direction is within 3 μm. Obviously, the proposed new variable optimization method has simple computing process and good modeling accuracy, which is quite fit for thermal error compensation.展开更多
Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision an...Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision and motion precision of machine tool, and the forming motion precision of workpiece surface. For the machine tool with complex forming motion, there is not accurate corresponding relationship between the existing criterion on precision design and the machining precision of workpiece. Therefore, a design scheme on machine tool precision based on error prediction is proposed, which is divided into two-stage digitization precision analysis crucially. The first stage aims at the technology system to complete the precision distribution and inspection from the workpiece to various component parts of technology system and achieve the total output precision of machine tool under the specified machining precision; the second stage aims at the machine tool system to complete the precision distribution and inspection from the output precision of machine tool to the machine tool components. This article serves YK3610 gear hobber as the example to describe the error model of two systems and basic application method, and the practical cutting precision of this machine tool achieves to 5-4-4 grade. The proposed method can provide reliable guidance to the precision design of machine tool with complex forming motion.展开更多
Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are resea...Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.展开更多
Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and c...Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and calculation of the machining tests in the literature are quite difficult and time-consuming.A new method of the machining tests for the trunnion axis of five-axis machine tool is proposed.Firstly,a simple mathematical model of the cradle-type five-axis machine tool was established by optimizing the coordinate system settings based on robot kinematics.Then,the machining tests based on error-sensitive directions were proposed to identify the kinematic errors of the trunnion axis of cradle-type five-axis machine tool.By adopting the error-sensitive vectors in the matrix calculation,the functional relationship equations between the machining errors of the test piece in the error-sensitive directions and the kinematic errors of C-axis and A-axis of five-axis machine tool rotary table was established based on the model of the kinematic errors.According to our previous work,the kinematic errors of C-axis can be treated as the known quantities,and the kinematic errors of A-axis can be obtained from the equations.This method was tested in Mikron UCP600 vertical machining center.The machining errors in the error-sensitive directions can be obtained by CMM inspection from the finished test piece to identify the kinematic errors of five-axis machine tool trunnion axis.Experimental results demonstrated that the proposed method can reduce the complexity,cost,and the time consumed substantially,and has a wider applicability.This paper proposes a new method of the machining tests for the trunnion axis of five-axis machine tool.展开更多
In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with hi...In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with high accuracy, stiffness and efficiency, including whole structure design,key components design, and supporting stiffness design. First, an appropriate structure of five-axis machine tool is determined considering the processing characteristics of aero-engine casing. Then, a dual drive swing head and a compact motorized spindle are designed with enough drive capability and stiffness, and related structure, assembly method, cooling technology, and performance simulation are given in detail. Next, a design method of supporting stiffness of guide is proposed through the deformation prediction of the spindle end. Based on above work, a prototype of machine tool is developed, and some experiments are carried out, including performance tests of swing head and motorized spindle, and machining of a simulated workpiece of aero-engine casing. All experimental results show that the machine tool has satisfactory accuracy, stiffness and efficiency, which meets the machining requirements of aero-engine casing. The main work can be used as references for engineers and technicians, which are meaningful in practice.展开更多
It is concluded from the results of testing the frequency characteristics of the sub micron precision machine tool servo control system, that the existence of several oscillating modalities is the main factor that aff...It is concluded from the results of testing the frequency characteristics of the sub micron precision machine tool servo control system, that the existence of several oscillating modalities is the main factor that affects the performance of the control system. To compensate for this effect,several concave filters are utilized in the system to improve the control accuracy. The feasibility of compensating for several oscillating modalities with a single concave filter is also studied. By applying a modified Butterworth concave filter to the practical system, the maximum stable state output error remains under ±10 nm in the closed loop positioning system.展开更多
Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform tradit...Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.展开更多
Now vibration isolation of ultra precision machine tool is usually achieved through air springs systems. As far as HCM I sub micro turning machine developed by HIT, an active vibration isolation system that consists o...Now vibration isolation of ultra precision machine tool is usually achieved through air springs systems. As far as HCM I sub micro turning machine developed by HIT, an active vibration isolation system that consists of air springs and electro magnetic actuators was presented. The primary function of air springs is to support the turning machine and to isolate the high frequency vibration. The electro magnetic actuators controlled by fuzzy neural networks isolate the low frequency vibration. The experiment indicates that active vibration isolation system isolates base vibration effectively in all the frequency range. So the vibration of the machine bed is controlled under 10 -6 g and the surface roughness is improved.展开更多
Ultra-precision machine tool is the most important physical tool to machining the workpiece with the frequency domain error requirement, in the design process of which the dynamic accuracy design(DAD) is indispensable...Ultra-precision machine tool is the most important physical tool to machining the workpiece with the frequency domain error requirement, in the design process of which the dynamic accuracy design(DAD) is indispensable and the related research is rarely available. In light of above reasons, a DAD method of ultra-precision machine tool is proposed in this paper, which is based on the frequency domain error allocation.The basic procedure and enabling knowledge of the DAD method is introduced. The application case of DAD method in the ultra-precision flycutting machine tool for KDP crystal machining is described to show the procedure detailedly. In this case, the KDP workpiece surface has the requirements in four different spatial frequency bands, and the emphasis for this study is put on the middle-frequency band with the PSD specifications. The results of the application case basically show the feasibility of the proposed DAD method. The DAD method of ultra-precision machine tool can effectively minimize the technical risk and improve the machining reliability of the designed machine tool. This paper will play an important role in the design and manufacture of new ultra-precision machine tool.展开更多
文摘Artificial intelligence(AI)and machine learning(ML)are transforming spine care by addressing diagnostics,treatment planning,and rehabilitation challenges.This study highlights advancements in precision medicine for spinal pathologies,leveraging AI and ML to enhance diagnostic accuracy through deep learning algorithms,enabling faster and more accurate detection of abnormalities.AIpowered robotics and surgical navigation systems improve implant placement precision and reduce complications in complex spine surgeries.Wearable devices and virtual platforms,designed with AI,offer personalized,adaptive therapies that improve treatment adherence and recovery outcomes.AI also enables preventive interventions by assessing spine condition risks early.Despite progress,challenges remain,including limited healthcare datasets,algorithmic biases,ethical concerns,and integration into existing systems.Interdisciplinary collaboration and explainable AI frameworks are essential to unlock AI’s full potential in spine care.Future developments include multimodal AI systems integrating imaging,clinical,and genetic data for holistic treatment approaches.AI and ML promise significant improvements in diagnostic accuracy,treatment personalization,service accessibility,and cost efficiency,paving the way for more streamlined and effective spine care,ultimately enhancing patient outcomes.
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Projects(U22B2084,52275483,52075142)supported by the National Natural Science Foundation of ChinaProject(2023ZY01050)supported by the Ministry of Industry and Information Technology High Quality Development,China。
文摘The gears of new energy vehicles are required to withstand higher rotational speeds and greater loads,which puts forward higher precision essentials for gear manufacturing.However,machining process parameters can cause changes in cutting force/heat,resulting in affecting gear machining precision.Therefore,this paper studies the effect of different process parameters on gear machining precision.A multi-objective optimization model is established for the relationship between process parameters and tooth surface deviations,tooth profile deviations,and tooth lead deviations through the cutting speed,feed rate,and cutting depth of the worm wheel gear grinding machine.The response surface method(RSM)is used for experimental design,and the corresponding experimental results and optimal process parameters are obtained.Subsequently,gray relational analysis-principal component analysis(GRA-PCA),particle swarm optimization(PSO),and genetic algorithm-particle swarm optimization(GA-PSO)methods are used to analyze the experimental results and obtain different optimal process parameters.The results show that optimal process parameters obtained by the GRA-PCA,PSO,and GA-PSO methods improve the gear machining precision.Moreover,the gear machining precision obtained by GA-PSO is superior to other methods.
基金The National Natural Science Foundation of China (No.41076054)
文摘A dynamical dq model is proposed for a linear flux-switching permanent magnet（LFSPM） machine which is suitable for high-precision control applications.The operation principle of the prototype machine is analyzed using the finite element method（FEM）,and the parameters,such as the back electromotive force（EMF） and the phase flux linkage,are calculated.The calculated and measured results reveal that the back EMF and the flux linkage are essentially sinusoidal,and the variation of the phase flux linkage profile of the LFSPM machine is similar to that of the linear surface permanent magnet（LSPM） machine.Based on this,a dynamical dq model and a simulation control model are proposed.The simulation results are compared with the test results obtained from a DSP-based control platform,which verifies that the model is correct and effective.Moreover,the model can be used for design optimization and control development.
基金supported by the National Natural Science Foundation of China(No.62222511)National Key Research and Devel-opment Program of China(No.2023YFF0613000)+1 种基金Natural Science Foundation of Zhejiang Province China(No.LR22F050006)STI 2030-Major Projects(No.2021ZD0200401).
文摘With the progression of photolithography processes,the present technology nodes have attained 3 nm and even 2 nm,necessitating a transition in the precision standards for displacement measurement and alignment methodologies from the nanometer scale to the sub-nanometer scale.Metasurfaces,owing to their superior light field manipulation capabilities,exhibit significant promise in the domains of displacement measurement and positioning,and are anticipated to be applied in the advanced alignment systems of lithography machines.This paper primarily provides an overview of the contemporary alignment and precise displacement measurement technologies employed in photolithography stages,alongside the operational principles of metasurfaces in the context of precise displacement measurement and alignment.Furthermore,it explores the evolution of metasurface systems capable of achieving nano/sub-nano precision,and identifies the critical issues associated with sub-nanometer measurements using metasurfaces,as well as the principal obstacles encountered in their implementation within photolithography stages.The objective is to provide initial guidance for the advancement of photolithography technology.
文摘BACKGROUND Pancreatic fistula is the most common complication of pancreatic surgeries that causes more serious conditions,including bleeding due to visceral vessel erosion and peritonitis.AIM To develop a machine learning(ML)model for postoperative pancreatic fistula and identify significant risk factors of the complication.METHODS A single-center retrospective clinical study was conducted which included 150 patients,who underwent pancreat-oduodenectomy.Logistic regression,random forest,and CatBoost were employed for modeling the biochemical leak(symptomless fistula)and fistula grade B/C(clinically significant complication).The performance was estimated by receiver operating characteristic(ROC)area under the curve(AUC)after 5-fold cross-validation(20%testing and 80%training data).The risk factors were evaluated with the most accurate algorithm,based on the parameter“Importance”(Im),and Kendall correlation,P<0.05.RESULTS The CatBoost algorithm was the most accurate with an AUC of 74%-86%.The study provided results of ML-based modeling and algorithm selection for pancreatic fistula prediction and risk factor evaluation.From 14 parameters we selected the main pre-and intraoperative prognostic factors of all the fistulas:Tumor vascular invasion(Im=24.8%),age(Im=18.6%),and body mass index(Im=16.4%),AUC=74%.The ML model showed that biochemical leak,blood and drain amylase level(Im=21.6%and 16.4%),and blood leukocytes(Im=11.2%)were crucial predictors for subsequent fistula B/C,AUC=86%.Surgical techniques,morphology,and pancreatic duct diameter less than 3 mm were insignificant(Im<5%and no correlations detected).The results were confirmed by correlation analysis.CONCLUSION This study highlights the key predictors of postoperative pancreatic fistula and establishes a robust ML-based model for individualized risk prediction.These findings contribute to the advancement of personalized periop-erative care and may guide targeted preventive strategies.
文摘Celiac disease(CD)is a common autoimmune disorder where gluten ingestion triggers an immune response,damaging the small intestine in genetically pre-disposed individuals.Affecting around 1%of the global population,CD presents with diverse symptoms,including gastrointestinal issues like diarrhea and extraintestinal conditions such as anemia and osteoporosis,often complicating diagnosis.Advances in serology,histology,and genetic testing,such as HLA-DQ2/DQ8 analysis,have improved diagnostic accuracy.Precision medicine is transforming CD management by integrating genetic,clinical,and lifestyle data to enable risk prediction,personalized therapies,and improved outcomes.Tools like machine learning enhance early diagnosis,dietary management,and drug discovery,while electronic medical records support comprehensive patient pro-filing and disease monitoring.These technologies facilitate personalized health-care delivery tailored to individual patient profiles.
文摘The machine learning model developed by Shi et al for predicting colorectal polyp recurrence after endoscopic mucosal resection represents a significant advancement in the field of clinical gastroenterology.By integrating patient-specific factors,such as age,smoking history,and Helicobacter pylori infection,the eXtreme Gradient Boosting algorithm enables precise personalised colonoscopy follow-up planning and risk assessment.This predictive tool offers substantial benefits by optimising surveillance intervals and directing healthcare resources more efficiently toward high-risk individuals.However,real-world implementation requires consideration of the generalisability of our findings across diverse patient populations and clinician training backgrounds.
文摘Objective:The increasing global prevalence of mental health disorders highlights the urgent need for the development of innovative diagnostic methods.Conditions such as anxiety,depression,stress,bipolar disorder(BD),and autism spectrum disorder(ASD)frequently arise from the complex interplay of demographic,biological,and socioeconomic factors,resulting in aggravated symptoms.This review investigates machine intelligence approaches for the early detection and prediction of mental health conditions.Methods:The preferred reporting items for systematic reviews and meta-analyses(PRISMA)framework was employed to conduct a systematic review and analysis covering the period 2018 to 2025.The potential impact of machine intelligence methods was assessed by considering various strategies,hybridization of algorithms,tools,techniques,and datasets,and their applicability.Results:Through a systematic review of studies concentrating on the prediction and evaluation of mental disorders using machine intelligence algorithms,advancements,limitations,and gaps in current methodologies were highlighted.The datasets and tools utilized in these investigations were examined,offering a detailed overview of the status of computational models in understanding and diagnosing mental health disorders.Recent research indicated considerable improvements in diagnostic accuracy and treatment effectiveness,particularly for depression and anxiety,which have shown the greatest methodological diversity and notable advancements in machine intelligence.Conclusions:Despite these improvements,challenges persist,including the need for more diverse datasets,ethical issues surrounding data privacy and algorithmic bias,and obstacles to integrating these technologies into clinical settings.This synthesis emphasizes the transformative potential of machine intelligence in enhancing mental healthcare.
基金National Natural Science Foundation of China(52265056)Lanzhou Youth Talent Project(2023-QN-38)Hongliu Youth Fund of Lanzhou University of Technology(07/062004)。
文摘Preparation method of magnetic nanoparticles with core-shell structure was introduced,especially focusing on the preparation principle of sol-gel method,microemulsion method,and self-assembly technique.The application of core-shell nanoparticles in precision machining was discussed.The Fe_(3)O_(4)@SiO_(2)composite particles were prepared by sol-gel method and were applied to the magnetorheological polishing of titanium alloy plates.Results show that core-shell nanoparticles with higher surface quality can be obtained after processing,compared with those after conventional abrasives.After polishing for 20 min,the surface roughness of the workpiece reaches 23 nm and the scratches are effectively reduced.Finally,the preparation and application of coreshell nanoparticles are summarized and prospected to provide a reference for further research on core-shell nanoparticles.
文摘BACKGROUND Although chronic-phase chronic myeloid leukemia(CP-CML)is treatable and nearly curable in about 50%of patients,accelerated-phase chronic myeloid leukemia(AP-CML)shows concerning drug resistance,while blast crisis chronic myeloid leukemia(BC-CML)is highly lethal.Advances in whole exome sequencing(WES)reveal pan-cancer mutations in BC-CML,supporting mutation-guided therapies beyond Breakpoint cluster region-Abelson.Artificial intelligence(AI)and machine learning(ML)enable genomic stratification and drug repurposing,addressing overlooked actionable mutations.AIM To stratify BC-CML into molecular subtypes using WES,ML,and AI for precision drug repurposing.METHODS Included 123 CML patients(111 CP-CML,5 AP-CML,7 BC-CML).WES identified pan-cancer mutations.Variants annotated via Ensembl Variant Effect Predictor and Catalogue of Somatic Mutations in Cancer(COSMIC).ML(principal component analysis,K-means)stratified BC-CML.COSMIC signatures and PanDrugs prioritized drugs.Analysis of variance/Kruskal-Wallis validated differences(P<0.05).RESULTS In this exploratory,hypothesis-generating study of BC-CML patients(n=7),we detected over 2500 somatic mutations.ML identified three BC-CML clusters:(1)Cluster 1[breast cancer susceptibility gene 2(BRCA2),TP53];(2)Cluster 2[isocitrate dehydrogenase(IDH)1/2,ten-eleven translocation 2];and(3)Cluster 3[Janus kinase(JAK)2,colony-stimulating factor 3 receptor],with distinct COSMIC signatures.Therapies:(1)Polyadenosinediphosphate-ribose polymerase inhibitors(olaparib);(2)IDH inhibitors(ivosidenib);and(3)JAK inhibitors(ruxolitinib).Mutational burden,signatures,and targets varied significantly across clusters,supporting precision stratification.CONCLUSION This WES-AI-ML framework provides mutation-guided therapies for BC-CML,enabling real-time stratification and Food and Drug Administration-approved drug repurposing.While this exploratory study is limited by its small sample size(n=7),it establishes a methodological framework for precision oncology stratification that warrants validation in larger,multi-center cohorts.
基金supported by Jiangsu Provincial Prospective Joint Research Foundation for Industry-University-Research of China (Grant No. BY2009102)Henan Provincial Major Scientific and Technological Projects of China (Grant No. 102102210050)
文摘Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to be divided into several groups on an appropriate threshold. Currently, group threshold value is mainly determined by researchers experience. Few studies focus on group threshold in temperature variable grouping. Since the threshold is important in error compensation, this paper arms to find out an optimal threshold to realize temperature variable optimization in thermal error modeling. Firstly, correlation coefficient is used to express membership grade of temperature variables, and the theory of fuzzy transitive closure is applied to obtain relational matrix of temperature variables. Concepts as compact degree and separable degree are introduced. Then evaluation model of temperature variable clustering is built. The optimal threshold and the best temperature variable clustering can be obtained by setting the maximum value of evaluation model as the objective. Finally, correlation coefficients between temperature variables and thermal error are calculated in order to find out optimum temperature variables for thermal error modeling. An experiment is conducted on a precise horizontal machining center. In experiment, three displacement sensors are used to measure spindle thermal error and twenty-nine temperature sensors are utilized to detect the machining center temperature. Experimental result shows that the new method of temperature variable optimization on optimal threshold successfully worked out a best threshold value interval and chose seven temperature variables from twenty-nine temperature measuring points. The model residual of z direction is within 3 μm. Obviously, the proposed new variable optimization method has simple computing process and good modeling accuracy, which is quite fit for thermal error compensation.
基金supported by National Natural Science Foundation of China (Grant No. 51075419)Chongqing Municipal Natural Science Foundation of China (Grant No. CSTC,2009BB3234)
文摘Digitization precision analysis is an important tool to ensure the design precision of machine tool currently. The correlative research about precision modeling and analysis mainly focuses on the geometry precision and motion precision of machine tool, and the forming motion precision of workpiece surface. For the machine tool with complex forming motion, there is not accurate corresponding relationship between the existing criterion on precision design and the machining precision of workpiece. Therefore, a design scheme on machine tool precision based on error prediction is proposed, which is divided into two-stage digitization precision analysis crucially. The first stage aims at the technology system to complete the precision distribution and inspection from the workpiece to various component parts of technology system and achieve the total output precision of machine tool under the specified machining precision; the second stage aims at the machine tool system to complete the precision distribution and inspection from the output precision of machine tool to the machine tool components. This article serves YK3610 gear hobber as the example to describe the error model of two systems and basic application method, and the practical cutting precision of this machine tool achieves to 5-4-4 grade. The proposed method can provide reliable guidance to the precision design of machine tool with complex forming motion.
基金Supported by National Natural Science Foundation of China(Grant No.51305244)Shandong Provincal Natural Science Foundation of China(Grant No.ZR2013EEL015)
文摘Aiming at the problem of low machining accu- racy and uncontrollable thermal errors of NC machine tools, spindle thermal error measurement, modeling and compensation of a two turntable five-axis machine tool are researched. Measurement experiment of heat sources and thermal errors are carried out, and GRA（grey relational analysis） method is introduced into the selection of tem- perature variables used for thermal error modeling. In order to analyze the influence of different heat sources on spindle thermal errors, an ANN （artificial neural network） model is presented, and ABC（artificial bee colony） algorithm is introduced to train the link weights of ANN, a new ABC- NN（Artificial bee colony-based neural network） modeling method is proposed and used in the prediction of spindle thermal errors. In order to test the prediction performance of ABC-NN model, an experiment system is developed, the prediction results of LSR （least squares regression）, ANN and ABC-NN are compared with the measurement results of spindle thermal errors. Experiment results show that the prediction accuracy of ABC-NN model is higher than LSR and ANN, and the residual error is smaller than 3 pm, the new modeling method is feasible. The proposed research provides instruction to compensate thermal errors and improve machining accuracy of NC machine tools.
基金Supported by National Nature Science Foundation of China(Grant No.51175461)Science Fund for Creative Research Groups of National Natural Science Foundation of China(Grant No.51221004)Program for Zhejiang Leading Team of S&T Innovation of China(Grant No.2009R50008)
文摘Compared with the traditional non-cutting measurement,machining tests can more accurately reflect the kinematic errors of five-axis machine tools in the actual machining process for the users.However,measurement and calculation of the machining tests in the literature are quite difficult and time-consuming.A new method of the machining tests for the trunnion axis of five-axis machine tool is proposed.Firstly,a simple mathematical model of the cradle-type five-axis machine tool was established by optimizing the coordinate system settings based on robot kinematics.Then,the machining tests based on error-sensitive directions were proposed to identify the kinematic errors of the trunnion axis of cradle-type five-axis machine tool.By adopting the error-sensitive vectors in the matrix calculation,the functional relationship equations between the machining errors of the test piece in the error-sensitive directions and the kinematic errors of C-axis and A-axis of five-axis machine tool rotary table was established based on the model of the kinematic errors.According to our previous work,the kinematic errors of C-axis can be treated as the known quantities,and the kinematic errors of A-axis can be obtained from the equations.This method was tested in Mikron UCP600 vertical machining center.The machining errors in the error-sensitive directions can be obtained by CMM inspection from the finished test piece to identify the kinematic errors of five-axis machine tool trunnion axis.Experimental results demonstrated that the proposed method can reduce the complexity,cost,and the time consumed substantially,and has a wider applicability.This paper proposes a new method of the machining tests for the trunnion axis of five-axis machine tool.
基金co-supported by the Natural Science Foundation of Beijing(No.3214043)the Project of State Key Lab of Tribology of Tsinghua University(No.SKLT2021D16)the National Natural Science Foundation of China(No.51975319)。
文摘In order to satisfy the machining requirements of aero-engine casing in modern aviation industry, this paper investigates three main issues during the design and development process of a five-axis machine tool with high accuracy, stiffness and efficiency, including whole structure design,key components design, and supporting stiffness design. First, an appropriate structure of five-axis machine tool is determined considering the processing characteristics of aero-engine casing. Then, a dual drive swing head and a compact motorized spindle are designed with enough drive capability and stiffness, and related structure, assembly method, cooling technology, and performance simulation are given in detail. Next, a design method of supporting stiffness of guide is proposed through the deformation prediction of the spindle end. Based on above work, a prototype of machine tool is developed, and some experiments are carried out, including performance tests of swing head and motorized spindle, and machining of a simulated workpiece of aero-engine casing. All experimental results show that the machine tool has satisfactory accuracy, stiffness and efficiency, which meets the machining requirements of aero-engine casing. The main work can be used as references for engineers and technicians, which are meaningful in practice.
文摘It is concluded from the results of testing the frequency characteristics of the sub micron precision machine tool servo control system, that the existence of several oscillating modalities is the main factor that affects the performance of the control system. To compensate for this effect,several concave filters are utilized in the system to improve the control accuracy. The feasibility of compensating for several oscillating modalities with a single concave filter is also studied. By applying a modified Butterworth concave filter to the practical system, the maximum stable state output error remains under ±10 nm in the closed loop positioning system.
基金The authors extend their appreciation to the Deanship of Scientific Research at King Khalid University for funding this work under grant number(RGP 2/209/42).
文摘Agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable agriculture.The use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision agriculture.To achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be employed.With this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision agriculture.The proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for irrigation.The proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and light.Besides,the sensed data are transmitted to the cloud server for processing and decision making.Moreover,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for irrigation.Furthermore,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly increased.The performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.
文摘Now vibration isolation of ultra precision machine tool is usually achieved through air springs systems. As far as HCM I sub micro turning machine developed by HIT, an active vibration isolation system that consists of air springs and electro magnetic actuators was presented. The primary function of air springs is to support the turning machine and to isolate the high frequency vibration. The electro magnetic actuators controlled by fuzzy neural networks isolate the low frequency vibration. The experiment indicates that active vibration isolation system isolates base vibration effectively in all the frequency range. So the vibration of the machine bed is controlled under 10 -6 g and the surface roughness is improved.
基金Supported by Zhejiang Provincial Natural Science Foundation of China(Grant No.LQ16E050012)National Natural Science Foundation of China(Grant Nos.51705462 and 51275115)International Science and Technology Cooperation Program of China(Grant No.2015DFA70630)
文摘Ultra-precision machine tool is the most important physical tool to machining the workpiece with the frequency domain error requirement, in the design process of which the dynamic accuracy design(DAD) is indispensable and the related research is rarely available. In light of above reasons, a DAD method of ultra-precision machine tool is proposed in this paper, which is based on the frequency domain error allocation.The basic procedure and enabling knowledge of the DAD method is introduced. The application case of DAD method in the ultra-precision flycutting machine tool for KDP crystal machining is described to show the procedure detailedly. In this case, the KDP workpiece surface has the requirements in four different spatial frequency bands, and the emphasis for this study is put on the middle-frequency band with the PSD specifications. The results of the application case basically show the feasibility of the proposed DAD method. The DAD method of ultra-precision machine tool can effectively minimize the technical risk and improve the machining reliability of the designed machine tool. This paper will play an important role in the design and manufacture of new ultra-precision machine tool.
