In an ultraprecision turning process for small-diameter optical aspheric workpieces,tool-profile errors induce mid-frequency errors in the workpiece profile,limiting further improvements in precision.In this study,an ...In an ultraprecision turning process for small-diameter optical aspheric workpieces,tool-profile errors induce mid-frequency errors in the workpiece profile,limiting further improvements in precision.In this study,an XZB three-axis linkage ultraprecision machining method is proposed,and the effects of tool-center errors are analyzed.To address residual errors in Z-direction profile-error compensation,a workpiece normal-profile-error compensation method is proposed.After XZB three-axis linkage turning and compensation,the workpiece profile error(PV)reaches 0.086μm,surpassing the precision of XZ two-axis machining,and mid-frequency errors are reduced.Compared with Z-direction profile-error compensation,which results in a profile error of 0.092μm,normal-profile-error compensation reduces PV to 0.047μm,considerably improving aspheric accuracy.Experimental results demonstrate that XZB three-axis linkage machining significantly improves the aspheric workpiece profile,enhancing both its accuracy and surface quality.This method reduces mid-frequency errors,and the subsequent application of normal-profile-error compensation further refines the profile,achieving higher overall accuracy.展开更多
Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has seve...Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision FOGs.The conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under vibration.In this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put forward.Particularly,the loop gain is extracted out by adding a gain-monitoring wave.By demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation depth.The experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 Hz.This technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.展开更多
Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the...Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.展开更多
There has been an increasing emphasis on performing deep neural network(DNN)inference locally on edge devices due to challenges such as network congestion and security concerns.However,as DRAM process technology conti...There has been an increasing emphasis on performing deep neural network(DNN)inference locally on edge devices due to challenges such as network congestion and security concerns.However,as DRAM process technology continues to scale down,the bit-flip errors in the memory of edge devices become more frequent,thereby leading to substantial DNN inference accuracy loss.Though several techniques have been proposed to alleviate the accuracy loss in edge environments,they require complex computations and additional parity bits for error correction,thus resulting in significant performance and storage overheads.In this paper,we propose FeatherGuard,a data-driven lightweight error protection scheme for DNN inference on edge devices.FeatherGuard selectively protects critical bit positions(that have a significant impact on DNN inference accuracy)against bit-flip errors,by considering various DNN characteristics(e.g.,data format,layer-wise weight distribution,actually stored logical values).Thus,it achieves high error tolerability during DNN inference.Since FeatherGuard reduces the bit-flip errors based on only a few simple arithmetic operations(e.g.,NOT operations)without parity bits,it causes negligible performance overhead and no storage overhead.Our experimental results show that FeatherGuard improves the error tolerability by up to 6667×and 4000×,compared to the conventional systems and the state-of-the-art error protection technique for edge environments,respectively.展开更多
Online programming platforms are popular in programming education.However,there has been no research investigating students’real opinions and expectations of the error feedback mechanisms,leaving educators without a ...Online programming platforms are popular in programming education.However,there has been no research investigating students’real opinions and expectations of the error feedback mechanisms,leaving educators without a solid data foundation when attempting to improve the error feedback mechanisms.This paper makes a survey of 834 students across various programming courses and investigates student perceptions of error feedback mechanisms on online programming platforms.It explores the effectiveness of existing feedback,student satisfaction,and preferences for potential improvements,focusing on automatic error localization and program repair mechanisms.Results reveal a significant portion of students are dissatisfied with current feedback due to its limited informativeness.Students also express a clear demand for stronger feedback mechanisms,such as error localization and repair hints.Nevertheless,they prefer feedback that subtly guides them toward solutions,rather than providing direct and explicit answers,valuing the opportunity to enhance their debugging skills.The findings suggest a need for balanced,educational-focused feedback mechanisms that aid learning while promoting independent problem-solving.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
Transplant rejection remains a leading cause of graft failure after organ transplantation.Current immunosuppressive therapies can reduce acute rejection,but their lack of specificity often leads to systemic side effec...Transplant rejection remains a leading cause of graft failure after organ transplantation.Current immunosuppressive therapies can reduce acute rejection,but their lack of specificity often leads to systemic side effects and long-term complications.Therefore,novel strategies for localized and durable immune regulation are urgently needed.Nanomedicine offers a promising approach by enabling the precise delivery of therapeutic agents to specific cells or tissues involved in the rejection process.Through rational design,nanoparticles can be engineered to carry immunosuppressive molecules and selectively target transplanted organs,immune organs such as lymph nodes and spleen,or key immune cells,including dendritic cells,macrophages,and T lymphocytes.These delivery systems improve drug bioavailability,reduce off-target effects,and allow controlled or responsive drug release in complex immune environments.In this review,we summarize recent advances in nanoparticle-based interventions for transplant rejection.We discuss the design and classification of nanoparticles,delivery strategies tailored to different graft types,and therapeutic mechanisms targeting various stages and components of the immune response.Examples of both systemic and local administration routes are presented,demonstrating the versatility of nanomedicine in addressing diverse clinical scenarios.Despite encouraging progress in preclinical studies,several challenges continue to limit clinical translation.These include variability in nanoparticle behavior across species,difficulties in large-scale manufacturing,and the lack of standardized regulatory frameworks.Continued efforts in materials innovation,biological validation,and interdisciplinary collaboration are essential to fully realize the clinical potential of nanomedicine in transplantation.展开更多
This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administratio...This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.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
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
AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A...AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users.展开更多
In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Curre...In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Current research on ultra-short-term wind power prediction often overlooks load characteristics,resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods.To address this limitation,this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics.The methodology involves three key steps:first,deriving interannual prediction error characteristics from ultra-short-term prediction results of wind farm clusters;second,establishing error correction intervals for load peak and valley periods,calculating corresponding correction coefficients,and analyzing the impact of varying correction radii on the final results;third,validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces.The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days,thereby better meeting the operational requirements of power system dispatch.展开更多
Mobile communications are reaching out to every aspect of our daily life,necessitating highefficiency data transmission and support for diverse data types and communication scenarios.Polar codes have emerged as a prom...Mobile communications are reaching out to every aspect of our daily life,necessitating highefficiency data transmission and support for diverse data types and communication scenarios.Polar codes have emerged as a promising solution due to their outstanding error-correction performance and low complexity.Unequal error protection(UEP)involves nonuniform error safeguarding for distinct data segments,achieving a fine balance between error resilience and resource allocation,which ultimately enhancing system performance and efficiency.In this paper,we propose a novel class of UEP rateless polar codes.The codes are designed based on matrix extension of polar codes,and elegant mapping and duplication operations are designed to achieve UEP property while preserving the overall performance of conventional polar codes.Superior UEP performance is attained without significant modifications to conventional polar codes,making it straightforward for compatibility with existing polar codes.A theoretical analysis is conducted on the block error rate and throughput efficiency performance.To the best of our knowledge,this work provides the first theoretical performance analysis of UEP rateless polar codes.Simulation results show that the proposed codes significantly outperform existing polar coding schemes in both block error rate and throughput efficiency.展开更多
Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling ap...Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling approach enhanced by negative learning,employing a Bidirectional Long Short-Term Memory(BiLSTM)network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals.By penalizing the model for accurately reconstructing seismic anomalies,the negative learning approach effectively magnifies the differences between normal and anomalous data.This strategic differentiation enhances the sensitivity of the BiLSTM network,enabling improved detection of subtle geomagnetic anomalies that may serve as earthquake precursors.Experimental validation clearly demonstrated statistically significant higher reconstruction errors for seismic signals compared to non-seismic signals,confirmed through the Mann-Whitney U test with a p-value of 0.0035 for Root Mean Square Error(RMSE).These results provide compelling evidence of the enhanced anomaly detection capability achieved through negative learning.Unlike traditional classification-based methods,negative learning explicitly encourages sensitivity to subtle precursor signals embedded within complex geomagnetic data,establishing a robust basis for further development of reliable earthquake prediction methods.展开更多
Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught...Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught with obstacles.According to the Global Tuberculosis Report 2025,while global incidence has been stabilization,the burden of multidrug-resistant tuberculosis(MDR-TB)and the long-term sequelae facing survivors continue to hinder progress[1].展开更多
Breast cancer stands as the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality,posing a persistent challenge to global public health1.In recent decades,the landscape of bre...Breast cancer stands as the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality,posing a persistent challenge to global public health1.In recent decades,the landscape of breast cancer care has been profoundly reshaped by the rapid development of precision medicine,targeted therapy,immunotherapy,and clinical translational research.展开更多
Conventional error cancellation approaches separate molecules into smaller fragments and sum the errors of all fragments to counteract the overall computational error of the parent molecules.However,these approaches m...Conventional error cancellation approaches separate molecules into smaller fragments and sum the errors of all fragments to counteract the overall computational error of the parent molecules.However,these approaches may be ineffective for systems with strong localized chemical effects,as fragmenting specific substructures into simpler chemical bonds can introduce additional errors instead of mitigating them.To address this issue,we propose the Substructure-Preserved Connection-Based Hierarchy(SCBH),a method that automatically identifies and freezes substructures with significant local chemical effects prior to molecular fragmentation.The SCBH is validated by the gas-phase enthalpy of formation calculation of CHNO molecules.Therein,based on the atomization scheme,the reference and test values are derived at the levels of Gaussian-4(G4)and M062X/6-31+G(2df,p),respectively.Compared to commonly used approaches,SCBH reduces the average computational error by half and requires only15%of the computational cost of G4 to achieve comparable accuracy.Since different types of local effect structures have differentiated influences on gas-phase enthalpy of formation,substituents with strong electronic effects should be retained preferentially.SCBH can be readily extended to diverse classes of organic compounds.Its workflow and source code allow flexible customization of molecular moieties,including azide,carboxyl,trinitromethyl,phenyl,and others.This strategy facilitates accurate,rapid,and automated computations and corrections,making it well-suited for high-throughput molecular screening and dataset construction for gas-phase enthalpy of formation.展开更多
1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrie...1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrient malnutrition and yield reductions from climate change with sufficient speed or precision[1].Consequently,agriculture is transitioning from selection-based breeding to designbased engineering.Synthetic biology enables the precision modification of metabolic pathways and the construction of novel trait combinations[1,2].This special issue,Synthetic Biology for Crop Improvement,brings together 26 articles that showcase the field’s transition from laboratory curiosity to field-validated agricultural technology.The collection spans 13 plant species,from staple grains and major industrial crops to horticultural and medicinal plants,demonstrating the universal applicability of metabolic engineering.These studies reveal maturation toward field readiness:independent groups achieving reproducible results in identical pathways,greenhouse concepts advancing to multi-season field trials,and engineered traits delivering measurable agronomic value.This progression answers the central question in crop synthetic biology,shifting the paradigm from asking“can it work?”to demonstrating“how it works,and here are the yields”.This transformation is grounded in understanding and manipulating plant metabolism at molecular resolution[3].展开更多
Breast cancer remains a global health challenge with greater than 2.3 million new cases diagnosed annually 1,according to the World Health Organization1.Management of breast cancer is shaped by a complex interplay of ...Breast cancer remains a global health challenge with greater than 2.3 million new cases diagnosed annually 1,according to the World Health Organization1.Management of breast cancer is shaped by a complex interplay of international guidelines,regional adaptations,and the rapidly evolving fields of precision medicine and artificial intelligence(AI).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
基金supported by the National Natural Science Foundation of China(Grant No.52130503)the Science and Technology Innovation Program of Hunan Province(Grants Nos.2023RC1046 and 2023GK2008)+2 种基金the Hunan Provincial Science and Technology Department(Grant No.2021JC0005)the Postgraduate Scientific Research Innovation Project of Hunan Province(Grant No.QL20220088)the Shenzhen Undertakes Major National Science and Technology Projects(Grant No.CJGJZD20220517142406015).
文摘In an ultraprecision turning process for small-diameter optical aspheric workpieces,tool-profile errors induce mid-frequency errors in the workpiece profile,limiting further improvements in precision.In this study,an XZB three-axis linkage ultraprecision machining method is proposed,and the effects of tool-center errors are analyzed.To address residual errors in Z-direction profile-error compensation,a workpiece normal-profile-error compensation method is proposed.After XZB three-axis linkage turning and compensation,the workpiece profile error(PV)reaches 0.086μm,surpassing the precision of XZ two-axis machining,and mid-frequency errors are reduced.Compared with Z-direction profile-error compensation,which results in a profile error of 0.092μm,normal-profile-error compensation reduces PV to 0.047μm,considerably improving aspheric accuracy.Experimental results demonstrate that XZB three-axis linkage machining significantly improves the aspheric workpiece profile,enhancing both its accuracy and surface quality.This method reduces mid-frequency errors,and the subsequent application of normal-profile-error compensation further refines the profile,achieving higher overall accuracy.
基金Fundamental Research Funds for the Central Universities(YWF-23-L-1225)National Natural Science Foundation of China(62201025)Chinese Aeronautical Establishment(2022Z037051001)。
文摘Vibration-induced bias deviation,which is generated by intensity fluctuations and additional phase differences,is one of the vital errors for fiber optic gyroscopes(FOGs)operating in vibration environment and has severely restricted the applications of high-precision FOGs.The conventional methods for suppressing vibration-induced errors mostly concentrate on reinforcing the mechanical structure and optical path as well as the compensation under some specific operation parameters,which have very limited effects for high-precision FOGs maintaining performances under vibration.In this work,a technique of suppressing the vibration-induced bias deviation through removing the part related to the varying gain from the rotation-rate output is put forward.Particularly,the loop gain is extracted out by adding a gain-monitoring wave.By demodulating the loop gain and the rotation rate simultaneously under distinct frequencies and investigating their quantitative relationship,the vibrationinduced bias error is compensated without limiting the operating parameters or environments,like the applied modulation depth.The experimental results show that the proposed method has achieved the reduction of bias error from about 0.149°/h to0.014°/h during the random vibration with frequencies from20 Hz to 2000 Hz.This technique provides a feasible route for enhancing the performances of high-precision FOGs heading towards high environmental adaptability.
文摘Due to the inability of manufacturing a single monolithic mirror at the 10-meter scales,segmented mirrors have become indispensable tools in modern astronomical research.However,to match the imaging performance of the monolithic counterpart,the sub-mirrors must maintain precise co-phasing.Piston error critically degrades segmented mirror imaging quality,necessitating efficient and precise detection.To ad-dress the limitations that the conventional circular-aperture diffraction with two-wavelength algorithm is sus-ceptible to decentration errors,and the traditional convolutional neural networks(CNNs)struggle to capture global features under large-range piston errors due to their restricted local receptive fields,this paper pro-poses a method that integrates extended Young’s interference principles with a Vision Transformer(ViT)to detect piston error.By suppressing decentration error interference through two symmetrically arranged aper-tures and extending the measurement range to±7.95μm via a two-wavelength(589 nm/600 nm)algorithm.This approach exploits ViT’s self-attention mechanism to model global characteristics of interference fringes.Unlike CNNs constrained by local convolutional kernels,the ViT significantly improves sensitivity to inter-ferogram periodicity.The simulation results demonstrate that the proposed method achieves a measurement accuracy of 5 nm(0.0083λ0)across the range of±7.95μm,while maintaining an accuracy exceeding 95%in the presence of Gaussian noise(SNR≥15 dB),Poisson noise(λ≥9 photons/pixel),and sub-mirror gap er-ror(Egap≤0.2)interference.Moreover,the detection speed shows significant improvement compared to the cross-correlation algorithm.This study establishes an accurate,robust framework for segmented mirror error detection,advancing high-precision astronomical observation.
基金the“Convergence and Open sharing System”Project,supported by the Ministry of Education and National Research Foundation of Korea.
文摘There has been an increasing emphasis on performing deep neural network(DNN)inference locally on edge devices due to challenges such as network congestion and security concerns.However,as DRAM process technology continues to scale down,the bit-flip errors in the memory of edge devices become more frequent,thereby leading to substantial DNN inference accuracy loss.Though several techniques have been proposed to alleviate the accuracy loss in edge environments,they require complex computations and additional parity bits for error correction,thus resulting in significant performance and storage overheads.In this paper,we propose FeatherGuard,a data-driven lightweight error protection scheme for DNN inference on edge devices.FeatherGuard selectively protects critical bit positions(that have a significant impact on DNN inference accuracy)against bit-flip errors,by considering various DNN characteristics(e.g.,data format,layer-wise weight distribution,actually stored logical values).Thus,it achieves high error tolerability during DNN inference.Since FeatherGuard reduces the bit-flip errors based on only a few simple arithmetic operations(e.g.,NOT operations)without parity bits,it causes negligible performance overhead and no storage overhead.Our experimental results show that FeatherGuard improves the error tolerability by up to 6667×and 4000×,compared to the conventional systems and the state-of-the-art error protection technique for edge environments,respectively.
基金supported by the National Natural Science Foundation of China under Grant No.92582204,No.62577007,and No.62177003the Fundamental Research Funds for the Central Universities under Grant No.JKF-2025011975129.
文摘Online programming platforms are popular in programming education.However,there has been no research investigating students’real opinions and expectations of the error feedback mechanisms,leaving educators without a solid data foundation when attempting to improve the error feedback mechanisms.This paper makes a survey of 834 students across various programming courses and investigates student perceptions of error feedback mechanisms on online programming platforms.It explores the effectiveness of existing feedback,student satisfaction,and preferences for potential improvements,focusing on automatic error localization and program repair mechanisms.Results reveal a significant portion of students are dissatisfied with current feedback due to its limited informativeness.Students also express a clear demand for stronger feedback mechanisms,such as error localization and repair hints.Nevertheless,they prefer feedback that subtly guides them toward solutions,rather than providing direct and explicit answers,valuing the opportunity to enhance their debugging skills.The findings suggest a need for balanced,educational-focused feedback mechanisms that aid learning while promoting independent problem-solving.
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.
基金supported by the Natural Science Foundation of China(82151316,82171964,82230066,82202234,12326619)the Natural Science Foundation of Wuhan(2024040801020350)the Natural Science Foundation of Hubei(2021CFA046 and 2023AFB753).
文摘Transplant rejection remains a leading cause of graft failure after organ transplantation.Current immunosuppressive therapies can reduce acute rejection,but their lack of specificity often leads to systemic side effects and long-term complications.Therefore,novel strategies for localized and durable immune regulation are urgently needed.Nanomedicine offers a promising approach by enabling the precise delivery of therapeutic agents to specific cells or tissues involved in the rejection process.Through rational design,nanoparticles can be engineered to carry immunosuppressive molecules and selectively target transplanted organs,immune organs such as lymph nodes and spleen,or key immune cells,including dendritic cells,macrophages,and T lymphocytes.These delivery systems improve drug bioavailability,reduce off-target effects,and allow controlled or responsive drug release in complex immune environments.In this review,we summarize recent advances in nanoparticle-based interventions for transplant rejection.We discuss the design and classification of nanoparticles,delivery strategies tailored to different graft types,and therapeutic mechanisms targeting various stages and components of the immune response.Examples of both systemic and local administration routes are presented,demonstrating the versatility of nanomedicine in addressing diverse clinical scenarios.Despite encouraging progress in preclinical studies,several challenges continue to limit clinical translation.These include variability in nanoparticle behavior across species,difficulties in large-scale manufacturing,and the lack of standardized regulatory frameworks.Continued efforts in materials innovation,biological validation,and interdisciplinary collaboration are essential to fully realize the clinical potential of nanomedicine in transplantation.
基金supported by the National Key R&D Program of China [grant number 2023YFC3008004]。
文摘This study presents a comprehensive evaluation of tropical cyclone(TC)forecast performance in the western North Pacific from 2013 to 2022,based on operational forecasts issued by the China Meteorological Administration.The analysis reveals systematic improvements in both track and intensity forecasts over the decade,with distinct error characteristics observed across various forecast parameters.Track forecast errors have steadily decreased,particularly for longer lead times,while error magnitudes have increased with longer forecast lead times.Intensity forecasts show similar progressive enhancements,with maximum sustained wind speed errors decreasing by 0.26 m/s per year for 120 h forecasts.The study also identifies several key patterns in forecast performance:typhoon-grade or stronger TCs exhibit smaller track errors than week or weaker systems;intensity forecasts systematically overestimate weaker TCs while underestimating stronger systems;and spatial error distributions show greater track inaccuracies near landmasses and regional intensity biases.These findings highlight both the significant advances in TC forecasting capability achieved through improved modeling and observational systems,and the remaining challenges in predicting TC changes and landfall behavior,providing valuable benchmarks for future forecast system development.
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Supported by Ongoing Research Funding Program(ORFFT-2025-054-1),King Saud University,Riyadh,Saudi Arabia.
文摘AIM:To evaluate the efficacy of the total computer vision syndrome questionnaire(CVS-Q)score as a predictive tool for identifying individuals with symptomatic binocular vision anomalies and refractive errors.METHODS:A total of 141 healthy computer users underwent comprehensive clinical visual function assessments,including evaluations of refractive errors,accommodation(amplitude of accommodation,positive relative accommodation,negative relative accommodation,accommodative accuracy,and accommodative facility),and vergence(phoria,positive and negative fusional vergence,near point of convergence,and vergence facility).Total CVS-Q scores were recorded to explore potential associations between symptom scores and the aforementioned clinical visual function parameters.RESULTS:The cohort included 54 males(38.3%)with a mean age of 23.9±0.58y and 87 age-matched females(61.7%)with a mean age of 23.9±0.53y.The multiple regression model was statistically significant[R²=0.60,F=13.28,degrees of freedom(DF=17122,P<0.001].This indicates that 60%of the variance in total CVS-Q scores(reflecting reported symptoms)could be explained by four clinical measurements:amplitude of accommodation,positive relative accommodation,exophoria at distance and near,and positive fusional vergence at near.CONCLUSION:The total CVS-Q score is a valid and reliable tool for predicting the presence of various nonstrabismic binocular vision anomalies and refractive errors in symptomatic computer users.
基金supported by the National Key R&D Program of China(Technology and application of wind power/photovoltaic power prediction for promoting renewable energy consumption(2018YFB0904200).
文摘In recent years,the global installed capacity of wind power has grown rapidly,making the enhancement of wind power prediction accuracy crucial for facilitating the integration and consumption of renewable energy.Current research on ultra-short-term wind power prediction often overlooks load characteristics,resulting in an inability to adequately address grid connection requirements and load dispatching demands across different time periods.To address this limitation,this study proposes a novel approach to ultra-short-term wind power prediction error correction that incorporates load peak-valley characteristics.The methodology involves three key steps:first,deriving interannual prediction error characteristics from ultra-short-term prediction results of wind farm clusters;second,establishing error correction intervals for load peak and valley periods,calculating corresponding correction coefficients,and analyzing the impact of varying correction radii on the final results;third,validating the proposed method through empirical analysis of wind farm clusters in three northeastern provinces.The results demonstrate that this approach not only improves wind power prediction accuracy but also significantly reduces the occurrence of harmful error days,thereby better meeting the operational requirements of power system dispatch.
基金supported by National Natural Science Foundation of China(No.62301008)China Postdoctoral Science Foundation(No.2022M720272)New Cornerstone Science Foundation through the XPLORER PRIZE。
文摘Mobile communications are reaching out to every aspect of our daily life,necessitating highefficiency data transmission and support for diverse data types and communication scenarios.Polar codes have emerged as a promising solution due to their outstanding error-correction performance and low complexity.Unequal error protection(UEP)involves nonuniform error safeguarding for distinct data segments,achieving a fine balance between error resilience and resource allocation,which ultimately enhancing system performance and efficiency.In this paper,we propose a novel class of UEP rateless polar codes.The codes are designed based on matrix extension of polar codes,and elegant mapping and duplication operations are designed to achieve UEP property while preserving the overall performance of conventional polar codes.Superior UEP performance is attained without significant modifications to conventional polar codes,making it straightforward for compatibility with existing polar codes.A theoretical analysis is conducted on the block error rate and throughput efficiency performance.To the best of our knowledge,this work provides the first theoretical performance analysis of UEP rateless polar codes.Simulation results show that the proposed codes significantly outperform existing polar coding schemes in both block error rate and throughput efficiency.
基金funded by the Ministry of Higher Education through Universiti Putra Malaysia(UPM)under Grant FRGS/1/2023/STG07/UPM/02/4.
文摘Detecting geomagnetic anomalies preceding earthquakes is a challenging yet promising area of research that has gained increasing attention in recent years.This study introduces a novel reconstruction-based modeling approach enhanced by negative learning,employing a Bidirectional Long Short-Term Memory(BiLSTM)network explicitly trained to accurately reconstruct non-seismic geomagnetic signals while intentionally amplifying reconstruction errors for seismic signals.By penalizing the model for accurately reconstructing seismic anomalies,the negative learning approach effectively magnifies the differences between normal and anomalous data.This strategic differentiation enhances the sensitivity of the BiLSTM network,enabling improved detection of subtle geomagnetic anomalies that may serve as earthquake precursors.Experimental validation clearly demonstrated statistically significant higher reconstruction errors for seismic signals compared to non-seismic signals,confirmed through the Mann-Whitney U test with a p-value of 0.0035 for Root Mean Square Error(RMSE).These results provide compelling evidence of the enhanced anomaly detection capability achieved through negative learning.Unlike traditional classification-based methods,negative learning explicitly encourages sensitivity to subtle precursor signals embedded within complex geomagnetic data,establishing a robust basis for further development of reliable earthquake prediction methods.
文摘Tuberculosis(TB)remains one of the most persistent and formidable public health challenges globally.Despite the ambitious targets set by the World Health Organization End TB Strategy,the path to elimination is fraught with obstacles.According to the Global Tuberculosis Report 2025,while global incidence has been stabilization,the burden of multidrug-resistant tuberculosis(MDR-TB)and the long-term sequelae facing survivors continue to hinder progress[1].
文摘Breast cancer stands as the most prevalent malignancy among women worldwide and a leading cause of cancer-related mortality,posing a persistent challenge to global public health1.In recent decades,the landscape of breast cancer care has been profoundly reshaped by the rapid development of precision medicine,targeted therapy,immunotherapy,and clinical translational research.
基金the support of the National Natural Science Foundation of China(22575230)。
文摘Conventional error cancellation approaches separate molecules into smaller fragments and sum the errors of all fragments to counteract the overall computational error of the parent molecules.However,these approaches may be ineffective for systems with strong localized chemical effects,as fragmenting specific substructures into simpler chemical bonds can introduce additional errors instead of mitigating them.To address this issue,we propose the Substructure-Preserved Connection-Based Hierarchy(SCBH),a method that automatically identifies and freezes substructures with significant local chemical effects prior to molecular fragmentation.The SCBH is validated by the gas-phase enthalpy of formation calculation of CHNO molecules.Therein,based on the atomization scheme,the reference and test values are derived at the levels of Gaussian-4(G4)and M062X/6-31+G(2df,p),respectively.Compared to commonly used approaches,SCBH reduces the average computational error by half and requires only15%of the computational cost of G4 to achieve comparable accuracy.Since different types of local effect structures have differentiated influences on gas-phase enthalpy of formation,substituents with strong electronic effects should be retained preferentially.SCBH can be readily extended to diverse classes of organic compounds.Its workflow and source code allow flexible customization of molecular moieties,including azide,carboxyl,trinitromethyl,phenyl,and others.This strategy facilitates accurate,rapid,and automated computations and corrections,making it well-suited for high-throughput molecular screening and dataset construction for gas-phase enthalpy of formation.
文摘1.Introduction Crop breeding is transitioning to engineering by synthetic biology.Conventional breeding,constrained by limited genetic variation and lengthy development cycles,cannot meet the challenges of micronutrient malnutrition and yield reductions from climate change with sufficient speed or precision[1].Consequently,agriculture is transitioning from selection-based breeding to designbased engineering.Synthetic biology enables the precision modification of metabolic pathways and the construction of novel trait combinations[1,2].This special issue,Synthetic Biology for Crop Improvement,brings together 26 articles that showcase the field’s transition from laboratory curiosity to field-validated agricultural technology.The collection spans 13 plant species,from staple grains and major industrial crops to horticultural and medicinal plants,demonstrating the universal applicability of metabolic engineering.These studies reveal maturation toward field readiness:independent groups achieving reproducible results in identical pathways,greenhouse concepts advancing to multi-season field trials,and engineered traits delivering measurable agronomic value.This progression answers the central question in crop synthetic biology,shifting the paradigm from asking“can it work?”to demonstrating“how it works,and here are the yields”.This transformation is grounded in understanding and manipulating plant metabolism at molecular resolution[3].
文摘Breast cancer remains a global health challenge with greater than 2.3 million new cases diagnosed annually 1,according to the World Health Organization1.Management of breast cancer is shaped by a complex interplay of international guidelines,regional adaptations,and the rapidly evolving fields of precision medicine and artificial intelligence(AI).
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).
