The increasing use of UAV-based LiDAR systems for high-resolution mapping highlights the need for reliable,field-validated accuracy assessment methods.This study presents a practical technique for evaluating geometric...The increasing use of UAV-based LiDAR systems for high-resolution mapping highlights the need for reliable,field-validated accuracy assessment methods.This study presents a practical technique for evaluating geometric and radiometric performance using georeferenced,high-reflectivity foil targets.The method enables precise extraction of target centers and correction of systematic georeferencing errors through 3D transformation.The approach was applied at the Tora Cement Factory in Cairo,Egypt—an industrial site with complex topography—using a DJI Matrice 300 RTK UAV equipped with the Zenmuse L1 LiDAR sensor and Zenmuse P1 photogrammetric camera.Three test flights were performed at altitudes of 50 m(nadir and oblique)and 70 m(oblique),with a high-resolution Structure-from-Motion(SfM)point cloud generated for reference.After transformation,the global RMSE of the LiDAR dataset was reduced to approximately 2.8∼3.2 cm,improving upon the raw uncorrected accuracy of up to 4.6 cm.Surface-wise comparisons showed RMSEs of 3.1 cm on flat areas,3.8 cm on rugged terrain,and 4.5 cm on vertical structures.Additionally,the RGB data embedded in the LiDAR point cloud exhibited a systematic spatial offset between 18 and 43 cm,with an average internal standard deviation near 5 cm,indicating a potential limitation for radiometric applications.The proposed method offers a cost-effective,accurate,and repeatable solution for UAV LiDAR validation and supports operational deployment,quality assurance,and system calibration in real-world scenarios.展开更多
Lithography machines operate in scanning mode for the fabrication of large-scale integrated circuits(ICs),requiring high-precision synchronous motion between the reticle and wafer stages.Disturbances generated by each...Lithography machines operate in scanning mode for the fabrication of large-scale integrated circuits(ICs),requiring high-precision synchronous motion between the reticle and wafer stages.Disturbances generated by each stage during high-acceleration movements are transmitted through the base frame,resulting in degradation of synchronization performance.To address this challenge,this paper proposes a tube-based model predictive control(tube-MPC)approach for synchronization in lithography machines.First,the proposed modeling method accurately characterizes the coupling disturbances and synchronization dynamics.Subsequently,a tube-MPC approach is developed to ensure that the states of the nominal system are constrained within the terminal constraint set.To reduce the complexity of online computations,an approach is employed to transform online optimization problems into offline problems by creating an online lookup table.This enables the determination of optimal control inputs via a simplified online optimization algorithm.The robustness and trajectory tracking performance of the proposed approach are verified through simulation experiments,demonstrating its effectiveness in enhancing the synchronization performance of multiple motion systems.展开更多
Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remai...Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remain elusive.Repetitive transcranial magnetic stimulation(rTMS)has emerged as a promising non-invasive intervention;however,its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding.A recent landmark study published in Brain Stimulation by Chen et al.addressed these challenges by combining a high-dose intermittent theta-burst stimulation(iTBS)protocol with concurrent transcranial magnetic stimulation-electroencephalography(TMS-EEG).This commentary provides an in-depth analysis of their findings,highlighting the restoration of cerebello-cortical inhibition(CBI)as a key therapeutic mechanism.Furthermore,we discuss the broader implications of this work,proposing that future translational research should integrate accelerated iTBS(aiTBS)paradigms,cortical response measurements(CRM),and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.展开更多
We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precisio...We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precision sampling strategy based on prostate cancer’s spatial distribution,aligning with personalized diagnosis trends.展开更多
Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population in...Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population investigation and dynamic monitoring of C.suppressalis populations were conducted in the Meishan region of Sichuan,China,from 2023 to 2024.The optimal timing for insecticide application was estimated,followed by field trials evaluating the efficacy of different insecticides.Results demonstrated that the peak emergence of first-generation adults typically occurred in early July(under the environmental conditions of the Meishan region),with the ambient humidity below 75%and temperature around 29◦C.Pesticide efficacy trials show that insecticide combinations exhibited superior control.Notably,a combined treatment of emamectin benzoate⋅methoxyfenozide+chlorantraniliprole achieved the highest control efficacy(90.05%)and a corresponding yield of 12,491.55 kg/ha.All tested treatments were determined to be safe for rice growth.Furthermore,this optimized strategy resulted in notable economic benefits,including a 50%reduction in pesticide usage and cost savings of 4796.15 CNY compared to conventional practices.This study provides valuable insights into sustainable rice production and pest management and,for the first time,proposes a precision application time window based on intelligent monitoring.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
Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing can...Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.展开更多
With the continuous advancement of bionanomaterial technology,the design and fabrication strategies of biomimetic nanocarriers have undergone significant strategic transformations and innovations.This article systemat...With the continuous advancement of bionanomaterial technology,the design and fabrication strategies of biomimetic nanocarriers have undergone significant strategic transformations and innovations.This article systematically reviews the evolution from single-cell membrane nanovesicles to hybrid cell membrane nanovesicles integrating multiple cell membranes,culminating in cell membrane hybrid lipid nanoparticles(CM-LNPs)combining natural cell membranes or membrane proteins with engineered synthetic phospholipids.This technological progression enables the synergistic retention of multicellular biological functions and the incorporation of advantageous synthetic material properties,such as enhanced engineering flexibility and surface modifiability.Additionally,the article discusses the advantages and limitations of traditional extrusion and ultrasonication methods in the preparation of cell membrane nanovesicles,highlights the benefits and development prospects of novel microfluidic techniques in the preparation of CM-LNPs,and explores the future application prospects and challenges of CM-LNPs in the biomedical field.展开更多
Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to t...Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to ten times more parameters than traditional sensors,making selection a challenging task even for experts.This study addresses this challenge,focusing on defensive guidance,navigation,and control(GNC)systems where precise and reliable angular velocity measurement is critical to overall performance.A comprehensive mathematical model is introduced to encapsulate all key MEMS parameters,accompanied by discussions on calibration and Allan variance interpretation.For six leading MEMS gyroscope applications,namely inertial navigation,integrated navigation,autopilot systems,rotating projectiles,homing guidance,and north finding,the most critical parameters are identified,distinguishing suitable and unsuitable sensor choices.Special emphasis is placed on inertial navigation systems,where practical rules of thumb for error evaluation are derived using six degrees of freedom motion equations.Rigorous simulations demonstrate the influence of various sensor parameters through real-world case studies,including static navigation,multi-rotor attitude estimation,gimbal stabilization,and north finding via a turntable.This work aims to be a beacon for practitioners across diverse fields,empowering them to make more informed design decisions.展开更多
High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging ...High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).展开更多
Doping is essential for modulating semiconductor conductivity,forming p-n junctions,and reducing contact resistance[1].Notably,as organic semiconductors(OSCs)advance toward high performance,flexibility,and miniaturiza...Doping is essential for modulating semiconductor conductivity,forming p-n junctions,and reducing contact resistance[1].Notably,as organic semiconductors(OSCs)advance toward high performance,flexibility,and miniaturization,achieving precise regionally selective doping becomes critical for building complex,highly integrated devices[2].In inorganic semiconductors(e.g.,silicon),sub-100-nanometer regional doping is achievable through photolithography and ion implantation—techniques foundational to modern complementary metaloxide-semiconductor(CMOS)technology[3].展开更多
Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal ce...Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal cells(MSCs)exhibited greater therapeutic efficacy than CD146-MSCs.These cells enhance epithelial repair through nuclear factor kappa B/cyclooxygenase-2-associated paracrine signaling and secretion of pro-angiogenic factors.We concur that MSCs hold significant promise for ARDS treatment;however,the heterogeneity of cell products is a translational barrier.Phenotype-aware strategies,such as CD146 enrichment,standardized potency assays,and extracellular vesicle profiling,are essential for improving the consistency of these studies.Further-more,advanced preclinical models,such as lung-on-a-chip systems,may provide more predictive insights into the therapeutic mechanisms.This article underscores the importance of CD146+MSCs in ARDS,emphasizes the need for precision in defining cell products,and discusses how integrating subset selection into translational pipelines could enhance the clinical impact of MSC-based therapies.展开更多
Based on the theory of elastic mechanics and material mechanics, the orientation precision of the hohl schaft kegel（HSK） tooling system in static and dynamic states is theoretically and experimentally studied. The r...Based on the theory of elastic mechanics and material mechanics, the orientation precision of the hohl schaft kegel（HSK） tooling system in static and dynamic states is theoretically and experimentally studied. The relation between the clamping force and the shank taper is obtained. And a proper clamping force is found to be essential to assure the axial and radial orientation precisions of the HSK tooling system in high speed machining （HSM）. Analytical results show that the reason why the HSK tooling system can keep high precision at the high rotational speed is that the actual axial clamping force keeps the two surfaces of the shank and the spindle in contact all the time.展开更多
In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,sp...In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.展开更多
With precision agriculture as the base line, using embedded system as technical support, a set of ideas is proposed for solving the serious pesticide poisoning problem, including farmland information collection, exper...With precision agriculture as the base line, using embedded system as technical support, a set of ideas is proposed for solving the serious pesticide poisoning problem, including farmland information collection, experts database analysis and variable pesticide spraying, etc.展开更多
A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented base...A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented based on the detection system. Firstly, the deviation between the normal vector and the spindle axis is measured by the four laser displacement sensors installed at the head of the multi-function end effector. Then, the robot target attitude is inversely solved according to the auto-normalization algorithm. Finally, adjust the robot to the target attitude via pitch and yaw rotations about the tool center point and the spindle axis is corrected in line with the normal vector simultaneously. To test and verify the auto-normalization algorithm, an experimental platform is established in which the laser tracker is introduced for accurate measurement. The results show that the deviations between the corrected spindle axis and the normal vector are all reduced to less than 0.5°, with the mean value 0.32°. It is demonstrated the detection method and the autonormalization algorithm are feasible and reliable.展开更多
With increasing population, degrading soil health, limited arable land area, and high cost of nitrogen(N) fertilizers, improving nitrogen use efficiency(NUE) of potato is an inevitable approach to save the environment...With increasing population, degrading soil health, limited arable land area, and high cost of nitrogen(N) fertilizers, improving nitrogen use efficiency(NUE) of potato is an inevitable approach to save the environment and achieve sufficient tuber yields with less N fertilizer supply. Recently, we have developed an aeroponics system to study NUE in potato using genomics, physiology, and breeding approaches. This study aims on precision phenotyping of plants of two distinct potato varieties(Kufri Gaurav, N efficient;Kufri Jyoti, N inefficient) in the novel aeroponics system. Plants were grown in aeroponics under controlled conditions with low N(0.75 mmol L^-1NO3^-) and high N(7.5 mmol L^-1NO3^-) levels. Plant biomass, root traits, total chlorophyll content, and plant N were increased with increasing N supply, whereas higher NUE parameters namely NUE, agronomic NUE(Ag NUE), N uptake efficiency(NUp E), harvest index(HI), and N harvest index(NHI) were observed at low N. An NUE efficient cv. Kufri Gaurav showed higher tuber dry weight, fresh tuber yield, tuber number per plant, early start of tuber harvesting, root traits, stolon traits, NUE parameters, and higher amino acid(aspartic acid and asparagine) content at low N supply. Higher expression of nitrate reductase(NR), nitrite reductase(NIR), and asparagine synthetase(AS) genes was observed in the leaf tissues of Kufri Gaurav at high N. Thus, aeroponics-based precision phenotyping enables identification of NUE efficient genotypes based on key traits and genes involved in improving NUE in potato. Further, this study suggests that the potential of aeroponics can be utilized to investigate N biology in potato under different N regimes.展开更多
With regard to precision/ultra-precision motion systems,it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances.In this paper,to overc...With regard to precision/ultra-precision motion systems,it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances.In this paper,to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control(ILC),a novel real-time iterative compensation(RIC)control framework is proposed for precision motion systems without changing the inner closed-loop controller.Specifically,the RIC method can be divided into two parts,i.e.,accurate model prediction and real-time iterative compensation.An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times.In light of predicted errors,a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation.Both the prediction and compen-sation processes are finished in a real-time motion control sampling period.The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions.Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability,which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.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
Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo...Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.展开更多
文摘The increasing use of UAV-based LiDAR systems for high-resolution mapping highlights the need for reliable,field-validated accuracy assessment methods.This study presents a practical technique for evaluating geometric and radiometric performance using georeferenced,high-reflectivity foil targets.The method enables precise extraction of target centers and correction of systematic georeferencing errors through 3D transformation.The approach was applied at the Tora Cement Factory in Cairo,Egypt—an industrial site with complex topography—using a DJI Matrice 300 RTK UAV equipped with the Zenmuse L1 LiDAR sensor and Zenmuse P1 photogrammetric camera.Three test flights were performed at altitudes of 50 m(nadir and oblique)and 70 m(oblique),with a high-resolution Structure-from-Motion(SfM)point cloud generated for reference.After transformation,the global RMSE of the LiDAR dataset was reduced to approximately 2.8∼3.2 cm,improving upon the raw uncorrected accuracy of up to 4.6 cm.Surface-wise comparisons showed RMSEs of 3.1 cm on flat areas,3.8 cm on rugged terrain,and 4.5 cm on vertical structures.Additionally,the RGB data embedded in the LiDAR point cloud exhibited a systematic spatial offset between 18 and 43 cm,with an average internal standard deviation near 5 cm,indicating a potential limitation for radiometric applications.The proposed method offers a cost-effective,accurate,and repeatable solution for UAV LiDAR validation and supports operational deployment,quality assurance,and system calibration in real-world scenarios.
基金supported by National Natural Science Foundation of China(52375530,52075132)Natural Science Foundation of Heilongjiang Province(YQ2022E025)+2 种基金State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment(Guangdong University of Technology)(JMDZ202312)Fundamental Research Funds for the Central Universities(HIT.OCEF.2024034)Space Drive and Manipulation Mechanism Laboratory of BICE and National Key Laboratory of Space Intelligent Control(BICE-SDMM-2024-01).
文摘Lithography machines operate in scanning mode for the fabrication of large-scale integrated circuits(ICs),requiring high-precision synchronous motion between the reticle and wafer stages.Disturbances generated by each stage during high-acceleration movements are transmitted through the base frame,resulting in degradation of synchronization performance.To address this challenge,this paper proposes a tube-based model predictive control(tube-MPC)approach for synchronization in lithography machines.First,the proposed modeling method accurately characterizes the coupling disturbances and synchronization dynamics.Subsequently,a tube-MPC approach is developed to ensure that the states of the nominal system are constrained within the terminal constraint set.To reduce the complexity of online computations,an approach is employed to transform online optimization problems into offline problems by creating an online lookup table.This enables the determination of optimal control inputs via a simplified online optimization algorithm.The robustness and trajectory tracking performance of the proposed approach are verified through simulation experiments,demonstrating its effectiveness in enhancing the synchronization performance of multiple motion systems.
基金supported by grants from the Open Research Fund of the Zhejiang Key Laboratory of Precision Psychiatry(2025A2)the Natural Science Foundation of Zhejiang Province(LY23C090002)。
文摘Machado-Joseph disease,or spinocerebellar ataxia type 3(SCA3),represents the most common autosomal dominant cerebellar ataxia worldwide.Despite its progressive and debilitating nature,disease-modifying therapies remain elusive.Repetitive transcranial magnetic stimulation(rTMS)has emerged as a promising non-invasive intervention;however,its clinical application has been hindered by inconsistent protocols and a lack of mechanistic understanding.A recent landmark study published in Brain Stimulation by Chen et al.addressed these challenges by combining a high-dose intermittent theta-burst stimulation(iTBS)protocol with concurrent transcranial magnetic stimulation-electroencephalography(TMS-EEG).This commentary provides an in-depth analysis of their findings,highlighting the restoration of cerebello-cortical inhibition(CBI)as a key therapeutic mechanism.Furthermore,we discuss the broader implications of this work,proposing that future translational research should integrate accelerated iTBS(aiTBS)paradigms,cortical response measurements(CRM),and individualized neuro-navigation to establish a new era of precision neuromodulation for ataxia.
文摘We read with great interest Deng et al.’s study 1 comparing sextant(6-core)and 12-core systematic biopsy in theMRI-targeted era,which valuably challenges the“more cores=higher accuracy”dogma by proposing a precision sampling strategy based on prostate cancer’s spatial distribution,aligning with personalized diagnosis trends.
基金funded by the National Key R&D Project‘Innovation and Integration of Key Technologies for Integration of Agricultural Machinery and Agronomy in Weak Links of Hybrid Mid-season Rice in Hilly Areas of Southwest China’(2023YFD2301901).
文摘Chilo suppressalis(Walker)is one of the most important rice pests worldwide,posing a significant challenge to effective control.To develop a precision-timed,eco-friendly management strategy,overwintering population investigation and dynamic monitoring of C.suppressalis populations were conducted in the Meishan region of Sichuan,China,from 2023 to 2024.The optimal timing for insecticide application was estimated,followed by field trials evaluating the efficacy of different insecticides.Results demonstrated that the peak emergence of first-generation adults typically occurred in early July(under the environmental conditions of the Meishan region),with the ambient humidity below 75%and temperature around 29◦C.Pesticide efficacy trials show that insecticide combinations exhibited superior control.Notably,a combined treatment of emamectin benzoate⋅methoxyfenozide+chlorantraniliprole achieved the highest control efficacy(90.05%)and a corresponding yield of 12,491.55 kg/ha.All tested treatments were determined to be safe for rice growth.Furthermore,this optimized strategy resulted in notable economic benefits,including a 50%reduction in pesticide usage and cost savings of 4796.15 CNY compared to conventional practices.This study provides valuable insights into sustainable rice production and pest management and,for the first time,proposes a precision application time window based on intelligent monitoring.
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.
文摘Gastrointestinal(GI)cancers remain a leading cause of cancer-related morbidity and mortality worldwide.Artificial intelligence(AI),particularly machine learning and deep learning(DL),has shown promise in enhancing cancer detection,diagnosis,and prognostication.A narrative review of literature published from January 2015 to march 2025 was conducted using PubMed,Web of Science,and Scopus.Search terms included"gastrointestinal cancer","artificial intelligence","machine learning","deep learning","radiomics","multimodal detection"and"predictive modeling".Studies were included if they focused on clinically relevant AI applications in GI oncology.AI algorithms for GI cancer detection have achieved high performance across imaging modalities,with endoscopic DL systems reporting accuracies of 85%-97%for polyp detection and segmentation.Radiomics-based models have predicted molecular biomarkers such as programmed cell death ligand 2 expression with area under the curves up to 0.92.Large language models applied to radiology reports demonstrated diagnostic accuracy comparable to junior radiologists(78.9%vs 80.0%),though without incremental value when combined with human interpretation.Multimodal AI approaches integrating imaging,pathology,and clinical data show emerging potential for precision oncology.AI in GI oncology has reached clinically relevant accuracy levels in multiple diagnostic tasks,with multimodal approaches and predictive biomarker modeling offering new opportunities for personalized care.However,broader validation,integration into clinical workflows,and attention to ethical,legal,and social implications remain critical for widespread adoption.
基金financially supported by the National Natural Science Foundation of China(32371475)。
文摘With the continuous advancement of bionanomaterial technology,the design and fabrication strategies of biomimetic nanocarriers have undergone significant strategic transformations and innovations.This article systematically reviews the evolution from single-cell membrane nanovesicles to hybrid cell membrane nanovesicles integrating multiple cell membranes,culminating in cell membrane hybrid lipid nanoparticles(CM-LNPs)combining natural cell membranes or membrane proteins with engineered synthetic phospholipids.This technological progression enables the synergistic retention of multicellular biological functions and the incorporation of advantageous synthetic material properties,such as enhanced engineering flexibility and surface modifiability.Additionally,the article discusses the advantages and limitations of traditional extrusion and ultrasonication methods in the preparation of cell membrane nanovesicles,highlights the benefits and development prospects of novel microfluidic techniques in the preparation of CM-LNPs,and explores the future application prospects and challenges of CM-LNPs in the biomedical field.
文摘Microelectromechanical systems(MEMS)technology has gained significant attention over the past decade for measuring inertial angular velocity.However,due to inherent complexity,MEMS gyroscopes typically feature up to ten times more parameters than traditional sensors,making selection a challenging task even for experts.This study addresses this challenge,focusing on defensive guidance,navigation,and control(GNC)systems where precise and reliable angular velocity measurement is critical to overall performance.A comprehensive mathematical model is introduced to encapsulate all key MEMS parameters,accompanied by discussions on calibration and Allan variance interpretation.For six leading MEMS gyroscope applications,namely inertial navigation,integrated navigation,autopilot systems,rotating projectiles,homing guidance,and north finding,the most critical parameters are identified,distinguishing suitable and unsuitable sensor choices.Special emphasis is placed on inertial navigation systems,where practical rules of thumb for error evaluation are derived using six degrees of freedom motion equations.Rigorous simulations demonstrate the influence of various sensor parameters through real-world case studies,including static navigation,multi-rotor attitude estimation,gimbal stabilization,and north finding via a turntable.This work aims to be a beacon for practitioners across diverse fields,empowering them to make more informed design decisions.
文摘High-throughput transcriptomics has evolved from bulk RNA-seq to single-cell and spatial profiling,yet its clinical translation still depends on effective integration across diverse omics and data modalities.Emerging foundation models and multimodal learning frameworks are enabling scalable and transferable representations of cellular states,while advances in interpretability and real-world data integration are bridging the gap between discovery and clinical application.This paper outlines a concise roadmap for AI-driven,transcriptome-centered multi-omics integration in precision medicine(Figure 1).
文摘Doping is essential for modulating semiconductor conductivity,forming p-n junctions,and reducing contact resistance[1].Notably,as organic semiconductors(OSCs)advance toward high performance,flexibility,and miniaturization,achieving precise regionally selective doping becomes critical for building complex,highly integrated devices[2].In inorganic semiconductors(e.g.,silicon),sub-100-nanometer regional doping is achievable through photolithography and ion implantation—techniques foundational to modern complementary metaloxide-semiconductor(CMOS)technology[3].
基金the Scientific and Technological Research Council of Türkiye(TÜBİTAK)Under the International Postdoctoral Research Fellowship Program(2219),No.1059B192400980the National Postdoctoral Research Fellowship Program(2218),No.122C158.
文摘Acute respiratory distress syndrome(ARDS)is a life-threatening condition that is characterized by high mortality rates and limited therapeutic options.Notably,Zhang et al demonstrated that CD146+mesenchymal stromal cells(MSCs)exhibited greater therapeutic efficacy than CD146-MSCs.These cells enhance epithelial repair through nuclear factor kappa B/cyclooxygenase-2-associated paracrine signaling and secretion of pro-angiogenic factors.We concur that MSCs hold significant promise for ARDS treatment;however,the heterogeneity of cell products is a translational barrier.Phenotype-aware strategies,such as CD146 enrichment,standardized potency assays,and extracellular vesicle profiling,are essential for improving the consistency of these studies.Further-more,advanced preclinical models,such as lung-on-a-chip systems,may provide more predictive insights into the therapeutic mechanisms.This article underscores the importance of CD146+MSCs in ARDS,emphasizes the need for precision in defining cell products,and discusses how integrating subset selection into translational pipelines could enhance the clinical impact of MSC-based therapies.
文摘Based on the theory of elastic mechanics and material mechanics, the orientation precision of the hohl schaft kegel（HSK） tooling system in static and dynamic states is theoretically and experimentally studied. The relation between the clamping force and the shank taper is obtained. And a proper clamping force is found to be essential to assure the axial and radial orientation precisions of the HSK tooling system in high speed machining （HSM）. Analytical results show that the reason why the HSK tooling system can keep high precision at the high rotational speed is that the actual axial clamping force keeps the two surfaces of the shank and the spindle in contact all the time.
基金Supported by National"863"High-tech Project(2006AA10A309)Jilin Technology Gallery Key Project(20060213)~~
文摘In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.
基金Supported by Education Science " Eleventh Five-Year" Assistance Fund Project in Hebei Province(06130044)Hebei Hengshui City Association of Social Sciences 2009 Social Science Research Projects (0907B)Hengshui University 2009 Class Project(2009016)~~
文摘With precision agriculture as the base line, using embedded system as technical support, a set of ideas is proposed for solving the serious pesticide poisoning problem, including farmland information collection, experts database analysis and variable pesticide spraying, etc.
基金co-supported by Key Technology Research and Development Program of Jiangsu Province, China (No. BE2011178)the Aviation Industry Innovation Fund (No. AC2011214)
文摘A novel approach is proposed to detect the normal vector to product surface in real time for the robotic precision drilling system in aircraft component assembly, and the auto-normalization algorithm is presented based on the detection system. Firstly, the deviation between the normal vector and the spindle axis is measured by the four laser displacement sensors installed at the head of the multi-function end effector. Then, the robot target attitude is inversely solved according to the auto-normalization algorithm. Finally, adjust the robot to the target attitude via pitch and yaw rotations about the tool center point and the spindle axis is corrected in line with the normal vector simultaneously. To test and verify the auto-normalization algorithm, an experimental platform is established in which the laser tracker is introduced for accurate measurement. The results show that the deviations between the corrected spindle axis and the normal vector are all reduced to less than 0.5°, with the mean value 0.32°. It is demonstrated the detection method and the autonormalization algorithm are feasible and reliable.
基金the Competent Authority, Indian Council of Agricultural Research (ICAR)-Central Potato Research Institute (CPRI), Shimla, Himachal Pradesh, India for necessary supports under the Biotechnology Program and the CABin Scheme (ICAR) (HORTCPRICIL 201500300131)
文摘With increasing population, degrading soil health, limited arable land area, and high cost of nitrogen(N) fertilizers, improving nitrogen use efficiency(NUE) of potato is an inevitable approach to save the environment and achieve sufficient tuber yields with less N fertilizer supply. Recently, we have developed an aeroponics system to study NUE in potato using genomics, physiology, and breeding approaches. This study aims on precision phenotyping of plants of two distinct potato varieties(Kufri Gaurav, N efficient;Kufri Jyoti, N inefficient) in the novel aeroponics system. Plants were grown in aeroponics under controlled conditions with low N(0.75 mmol L^-1NO3^-) and high N(7.5 mmol L^-1NO3^-) levels. Plant biomass, root traits, total chlorophyll content, and plant N were increased with increasing N supply, whereas higher NUE parameters namely NUE, agronomic NUE(Ag NUE), N uptake efficiency(NUp E), harvest index(HI), and N harvest index(NHI) were observed at low N. An NUE efficient cv. Kufri Gaurav showed higher tuber dry weight, fresh tuber yield, tuber number per plant, early start of tuber harvesting, root traits, stolon traits, NUE parameters, and higher amino acid(aspartic acid and asparagine) content at low N supply. Higher expression of nitrate reductase(NR), nitrite reductase(NIR), and asparagine synthetase(AS) genes was observed in the leaf tissues of Kufri Gaurav at high N. Thus, aeroponics-based precision phenotyping enables identification of NUE efficient genotypes based on key traits and genes involved in improving NUE in potato. Further, this study suggests that the potential of aeroponics can be utilized to investigate N biology in potato under different N regimes.
基金This work was supported in part by the National Nature Science Foundation of China(51922059)in part by the Beijing Natural Science Foundation(JQ19010)in part by the China Postdoctoral Science Foundation(2021T140371).
文摘With regard to precision/ultra-precision motion systems,it is important to achieve excellent tracking performance for various trajectory tracking tasks even under uncertain external disturbances.In this paper,to overcome the limitation of robustness to trajectory variations and external disturbances in offline feedforward compensation strategies such as iterative learning control(ILC),a novel real-time iterative compensation(RIC)control framework is proposed for precision motion systems without changing the inner closed-loop controller.Specifically,the RIC method can be divided into two parts,i.e.,accurate model prediction and real-time iterative compensation.An accurate prediction model considering lumped disturbances is firstly established to predict tracking errors at future sampling times.In light of predicted errors,a feedforward compensation term is developed to modify the following reference trajectory by real-time iterative calculation.Both the prediction and compen-sation processes are finished in a real-time motion control sampling period.The stability and convergence of the entire control system after real-time iterative compensation is analyzed for different conditions.Various simulation results consistently demonstrate that the proposed RIC framework possesses satisfactory dynamic regulation capability,which contributes to high tracking accuracy comparable to ILC or even better and strong robustness.
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.
基金supported by Beijing Natural Science Foundation(No.4142017)International Cooperation Project of National Natural Science Foundation of China(No.61120106009)Beijing Science and Technology Commission Precision Machinery Projects(No.Z121100001612007)
文摘Nowadays, high-precision motion controls are needed in modern manufacturing industry. A data-driven nonparametric model adaptive control(NMAC) method is proposed in this paper to control the position of a linear servo system. The controller design requires no information about the structure of linear servo system, and it is based on the estimation and forecasting of the pseudo-partial derivatives(PPD) which are estimated according to the voltage input and position output of the linear motor. The characteristics and operational mechanism of the permanent magnet synchronous linear motor(PMSLM) are introduced, and the proposed nonparametric model control strategy has been compared with the classic proportional-integral-derivative(PID) control algorithm. Several real-time experiments on the motion control system incorporating a permanent magnet synchronous linear motor showed that the nonparametric model adaptive control method improved the system s response to disturbances and its position-tracking precision, even for a nonlinear system with incompletely known dynamic characteristics.
