Summary Pain is not pain because people interpret symptoms differently.Neck pain is one of the most common pains and should not be missing from a study on the effects of pain.Depression does not arise solely from pain...Summary Pain is not pain because people interpret symptoms differently.Neck pain is one of the most common pains and should not be missing from a study on the effects of pain.Depression does not arise solely from pain but is multicausal and often caused by this cumulative effect.展开更多
Scattering obscures information carried by waves by producing speckle patterns,posing a fundamental challenge across diverse fields,from microscopy to astronomy.Although machine learning has recently shown promise in ...Scattering obscures information carried by waves by producing speckle patterns,posing a fundamental challenge across diverse fields,from microscopy to astronomy.Although machine learning has recently shown promise in speckle analysis,existing approaches are hindered by their dependence on large,labeled datasets—a significant bottleneck in many real-world applications.Here,we introduce speckle unsupervised recognition and evaluation(SURE),a groundbreaking unsupervised learning strategy for speckle recognition that eliminates the need for labeled training data.SURE's distinctive feature lies in its ability to extract invariant features through advanced clustering algorithms to enable direct classification of high-level information from speckle patterns without prior knowledge.We demonstrate the transformative potential of this approach in two key applications:(1)a noninvasive glucose monitoring system that accurately tracks glucose concentrations over time without extensive calibration and(2)a high-throughput communication system using multimode fibers,achieving improved performance in dynamic environments.In addition,we showcase SURE's unprecedented capability to classify objects hidden behind obstacles using scattered light,further broadening its scope.This versatile approach opens new frontiers in biomedical diagnostics,quantum network decoupling,and remote sensing,unlocking a transformative new paradigm for extracting information from seemingly random optical patterns.展开更多
The majority of our daily activities and routines are highly dependent on vision.What we experience as our vision arises from the detection and encoding of visual signals in the retina,which are then interpreted in th...The majority of our daily activities and routines are highly dependent on vision.What we experience as our vision arises from the detection and encoding of visual signals in the retina,which are then interpreted in the brain.This interpretation has the benefit of providing a level of constancy to what we experience as vision but also limits our ability to perceive subtle decline in our own vision.展开更多
The forward model of optical fiber strain induced by fractures,together with the associated model resolution matrix,is used to demonstrate the interpretability of fracture parameters once the fracture intersects the f...The forward model of optical fiber strain induced by fractures,together with the associated model resolution matrix,is used to demonstrate the interpretability of fracture parameters once the fracture intersects the fiber.A regularized inversion framework for fracture parameters is established to evaluate the influence of measured data quality on the accuracy of iterative regularized inversion.An interpretation approach for both fracture width and height is proposed,and the synthetic forward data with measurement error and field examples are employed to validate the accuracy of the simultaneous inversion of fracture width and height.The results indicate that,after the fracture contacts the fiber,the strain response is strongly sensitive only to the fracture parameters at the intersection location,whereas the interpretability of parameters at other locations remains limited.The iterative regularized inversion method effectively suppresses the impact of measurement error and exhibits high computational efficiency,showing clear advantages for inversion applications.When incorporating the first-order regularization with a Neumann boundary constraint on the tip width,the inverted fracture-width distribution becomes highly sensitive to fracture height;thus,combined with a bisection strategy,simultaneous inversion of fracture width and height can be achieved.Examination using the model resolution matrix,noisy synthetic data,and field data confirms that the iterative regularized inversion model for fracture width and height provides high interpretive accuracy and can be applied to the calculation and analysis of fracture width,fracture height,net pressure and other parameters.展开更多
0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has...0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.展开更多
1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;...1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;Sun et al.,2024).These capabilities emerge at a time when geoscientific evidence is increasingly informing high-stakes decisions about climate adaptation,resource development,and disaster risk reduction(McGovern et al.,2022).展开更多
Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects exte...Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction.展开更多
To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta ...To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta criterion, hierarchical clustering, and principal component analysis on the original data were performed. Secondly, the prediction results of classic machine learning models of ridge regression, support vector machine, gradient boosting regression (GBR), random forest regression, back-propagation (BP) neural network models, and multi-layer perceptron (MLP) were compared before and after data preprocessing. An improved model was established based on the improved sparrow algorithm and BP using tent chaotic mapping (CSSA-BP). The CSSA-BP model showed the best performance for endpoint carbon prediction with the lowest mean absolute error (MAE) and root mean square error (RMSE) values of 0.01124 and 0.01345 mass% among seven models, respectively. And the lowest MAE and RMSE values of 8.9839 and 10.9321 ℃ for endpoint temperature prediction were obtained among seven models, respectively. Furthermore, the CSSA-BP and GBR models have the smallest error fluctuation range in both endpoint carbon content and temperature predictions. Finally, in order to improve the interpretability of the model, SHapley additive interpretation (SHAP) was used to analyze the results.展开更多
Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and di...Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.展开更多
In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accurac...In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the“black box”effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.展开更多
The potential toxicity of ionic liquids(ILs)affects their applications;how to control the toxicity is one of the key issues in their applications.To understand its toxicity structure relationship and promote its green...The potential toxicity of ionic liquids(ILs)affects their applications;how to control the toxicity is one of the key issues in their applications.To understand its toxicity structure relationship and promote its greener application,six different machine learning algorithms,including Bagging,Adaptive Boosting(AdaBoost),Gradient Boosting(GBoost),Stacking,Voting and Categorical Boosting(CatBoost),are established to model the toxicity of ILs on four distinct datasets including Leukemia rat cell line IPC-81(IPC-81),Acetylcholinesterase(AChE),Escherichia coli(E.coli)and Vibrio fischeri.Molecular descriptors obtained from the simplified molecular input line entry system(SMILES)are used to characterize ILs.All models are assessed by the mean square error(MSE),root mean square error(RMSE),mean absolute error(MAE)and correlation coefficient(R^(2)).Additionally,an interpretation model based on SHapley Additive exPlanations(SHAP)is built to determine the positive and negative effects of each molecular feature on toxicity.With additional parameters and complexity,the Catboost model outperforms the other models,making it a more reliable model for ILs'toxicity prediction.The results of the model's interpretation indicate that the most significant positive features,SMR_VSA5,PEOE_VSA8,Kappa2,PEOE_VSA6,SMR_VSA5,PEOE_VSA6 and EState_VSA1,can increase the toxicity of ILs as their levels rise,while the most significant negative features,VSA_EState7,EState_VSA8,PEOE_VSA9 and FpDensityMorgan1,can decrease the toxicity as their levels rise.Also,an IL's toxicity will grow as its average molecular weight and number of pyridine rings increase,whereas its toxicity will decrease as its hydrogen bond acceptors increase.This finding offers a theoretical foundation for rapid screening and synthesis of environmentally-benign ILs.展开更多
Based on the Many Worlds Interpretation,I describe reality as a multilayer spacetime,where parallel layers play the role of alternative timelines.I link physics to ethics,arguing that one’s moral choices shape one’s...Based on the Many Worlds Interpretation,I describe reality as a multilayer spacetime,where parallel layers play the role of alternative timelines.I link physics to ethics,arguing that one’s moral choices shape one’s course in the multiverse.I consider one’s ethical decisions as decoherence events,leading to movement between alternative timelines,lighter(higher)or heavier(lower)realities.Sometimes in one’s curvilinear path in spacetime,one can even experience falling toward lower layers,slipping through wormholes.This theory supports free will and the simulation hypothesis.With this background,I explore the idea that a new theory of gravity might open new possibilities to shape matter and change our worldview through the invention of new technology,transforming information into waves and then into solid matter,paving the way for a new Multiverse Aeon for humanity.展开更多
Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-i...Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-invasive geophysical methods,particularly those using passive seismic surface waves,have emerged as viable alternatives for geological profiling and rockhead detection.This study proposes three interpretation methods for rockhead determination using passive seismic surface wave data from Microtremor Array Measurement(MAM)and Horizontal-to-Vertical Spectral Ratio(HVSR)tests.These are:(1)the Wavelength-Normalized phase velocity(WN)method in which a nonlinear relationship between rockhead depth and wavelength is established;(2)the Statistically Determined-shear wave velocity(SD-V_(s))method in which the representative V_(s) value for rockhead is automatically determined using a statistical method;and(3)the empirical HVSR method in which the rockhead is determined by interpreting resonant frequencies using a reliably calibrated empirical equation.These methods were implemented to determine rockhead depths at 28 locations across two distinct geological formations in Singapore,and the results were evaluated using borehole data.The WN method can determine rockhead depths accurately and reliably with minimal absolute errors(average RMSE=3.11 m),demonstrating robust performance across both geological formations.Its advantage lies in interpreting dispersion curves alone,without the need for the inversion process.The SD-V_(s) method is practical in engineering practice owing to its simplicity.The empirical HVSR method reasonably determines rockhead depths with moderate accuracy,benefiting from a reliably calibrated empirical equation.展开更多
The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl...The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl entropy is the Shannon entropy of the Husimi function,and is nonzero even for pure states.This entropy has been extensively studied in mathematical physics.Recent research also suggests a significant connection between the Wehrl entropy and manybody quantum entanglement in spin systems.We investigate the statistical interpretation of the Husimi function and the Wehrl entropy,taking the system of N spin-1/2 particles as an example.Due to the completeness of coherent states,the Husimi function and Wehrl entropy can be explained via the positive operator-valued measurement(POVM)theory,although the coherent states are not a set of orthonormal basis.Here,with the help of the Bayes’theorem,we provide an alternative probabilistic interpretation for the Husimi function and the Wehrl entropy.This interpretation is based on direct measurements of the system,and thus does not require the introduction of an ancillary system as in the POVM theory.Moreover,under this interpretation the classical correspondences of the Husimi function and the Wehrl entropy are just phase-space probability distribution function of N classical tops,and its associated entropy,respectively.Therefore,this explanation contributes to a better understanding of the relationship between the Husimi function,Wehrl entropy,and classical-quantum correspondence.The generalization of this statistical interpretation to continuous-variable systems is also discussed.展开更多
Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for ...Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for evaluating surrounding rock stability,its dynamic evolution are often overlooked in engineering practice.This study systematically summarizes a novel classification framework for stress adjustment types—stabilizing(two-zoned),shallow failure(three-zoned),and deep failure(four-zoned)—characterized by distinct stress adjustment stages.A dynamic interpretation technology system is developed based on microseismic monitoring,integrating key microseismic parameters(energy index EI,apparent stressσa,microseismic activity S),seismic source parameter space clustering,and microseismic paths.This approach enables precise identification of evolutionary stages,stress adjustment types,and failure precursors,thereby elucidating the intrinsic linkage between geomechanical processes(stress redistribution)and failure risks.The study establishes criteria and procedures for identifying stress adjustment types and their associated failure risks,which were successfully applied in the Grand Canyon Tunnel of the E-han Highway to detect 50 instances of disaster risks.The findings offer invaluable insights into understanding the evolution process of stress adjustment and pinpointing the disaster risks linked to hard rock in comparable high geostress tunnels.展开更多
The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitori...The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitoring,complication observation and management,positioning and mobility away from the bed,and quality assurance.These Guidelines encompass all the phases of intravenous thrombolysis care for patients experiencing acute ischemic stroke.This article aims to elucidate the Guidelines by discussing their developmental background,the designation process,usage recommendations,and the interpretation of evolving perspectives,thereby providing valuable insights for clinical practice.展开更多
The study employs the theoretical framework of Nanosyntax to analyze the generative mechanism of verbal ABAB reduplication pattern in Mandarin Chinese.The research characterizes ABAB reduplication as an inflectional o...The study employs the theoretical framework of Nanosyntax to analyze the generative mechanism of verbal ABAB reduplication pattern in Mandarin Chinese.The research characterizes ABAB reduplication as an inflectional operation involving functional projections of pluractionality and aspect.It distinguishes between event-internal and event-external pluralization,as well as inner and outer aspect in verbal reduplication.Following the One-Function-One-Head Principle in Nanosyntax,verbal ABAB form occurs through the merging of categoryless roots that are categorized by little v,with the RED affix syncretizing multiple functional morphemes.This framework reduces lexical burden and precisely represents the unique syntactic structure of Chinese verbal reduplication.展开更多
Multiple nucleotide variants(MNVs)are frequently misannotated as separate single-nucleotide variants(SNVs)by widely utilized variant-calling pipelines,presenting substantial challenges in genetic testing and research....Multiple nucleotide variants(MNVs)are frequently misannotated as separate single-nucleotide variants(SNVs)by widely utilized variant-calling pipelines,presenting substantial challenges in genetic testing and research.The role of MNVs in genetic diagnosis remains inadequately characterized,particularly within large disease cohorts.In this study,we comprehensively investigate codon-level MNVs(cMNVs)across 157 hearing loss(HL)-related genes in 11,467 HL cases and 7258 controls from the Chinese Deafness Gene Consortium(CDGC)cohort.A total of 116 cMNVs are identified,occurring in 29.07%of HL cases.Among them,56.03%of cMNVs exhibit functional consequences distinct from constituent SNVs.Moreover,amino acid substitutions exclusive to cMNVs cause more substantial physicochemical disruptions than those associated with SNVs.Notably,51 cMNVs show pathogenicity classifications that diverge from at least one constituent SNV,impacting genetic interpretation in 145 cases.Pathogenicity interpretation of cMNV facilitates definitive genetic diagnoses in eight HL cases that would otherwise have been subject to misdiagnoses or missed diagnoses.These findings provide critical insights into the genomic characteristics,functional impacts,and diagnostic implications of cMNVs,underscoring their clinical significance in genetic diagnosis and emphasizing the necessity for comprehensive and accurate detection and interpretation of cMNVs in genetic testing and research.展开更多
The Agadem block is an area of major oil interest located in the large sedimentary basin of Termit,in the south-east of the Republic of Niger.Since the 1950s,this basin has known geological and geophysical research ac...The Agadem block is an area of major oil interest located in the large sedimentary basin of Termit,in the south-east of the Republic of Niger.Since the 1950s,this basin has known geological and geophysical research activities.However,despite the extensive research carried out,we believe that a geophysical contribution in terms of magnetic properties and their repercussions on the structure of the Agadem block allowing the improvement of existing knowledge is essential.The present study aims to study the structural characteristics of the Agadem block associated with magnetic anomalies.For this,after data shaping,several filtering techniques were applied to the aeromagnetic data to identify and map deep geological structures.The reduction to the pole map shows large negative wavelength anomalies in the southeast half of the block and short positive wavelength anomalies in the northwest part embedded in a large positive anomaly occupying the lower northern half of the block.The maps of the total horizontal derivative and tilt angle show lineaments globally distributed along the NW-SE direction in accordance with the structural style of the study area.The resulting map highlights numerous lineaments that may be associated with faults hidden by the sedimentary cover.The calculation of the Euler deconvolution allowed us to locate and estimate the depths of magnetic sources at variable depths of up to 4000 m.The compilation of the results obtained allowed us to locate zones of high and low intensities which correspond respectively to horsts and grabens as major structures of the Agadem block.展开更多
This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Auto...This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Automated Machine Learning(AutoML)to construct an ensemble model to predict the estimated ultimate recovery(EUR)of shale gas wells.To demystify the“black-box”nature of the ensemble model,KernelSHAP,a kernel-based approach to compute Shapley values,is utilized for elucidating the influential factors that affect shale gas production at both global and local scales.Furthermore,a bi-objective optimization algorithm named NSGA-Ⅱ is seamlessly incorporated to opti-mize hydraulic fracturing designs for production boost and cost control.This innovative framework addresses critical limitations often encountered in applying machine learning(ML)to shale gas pro-duction:the challenge of achieving sufficient model accuracy with limited samples,the multidisciplinary expertise required for developing robust ML models,and the need for interpretability in“black-box”models.Validation with field data from the Fuling shale gas field in the Sichuan Basin substantiates the framework's efficacy in enhancing the precision and applicability of data-driven techniques.The test accuracy of the ensemble ML model reached 83%compared to a maximum of 72%of single ML models.The contribution of each geological and engineering factor to the overall production was quantitatively evaluated.Fracturing design optimization raised EUR by 7%-34%under different production and cost tradeoff scenarios.The results empower domain experts to conduct more precise and objective data-driven analyses and optimizations for shale gas production with minimal expertise in data science.展开更多
文摘Summary Pain is not pain because people interpret symptoms differently.Neck pain is one of the most common pains and should not be missing from a study on the effects of pain.Depression does not arise solely from pain but is multicausal and often caused by this cumulative effect.
基金supported by the National Natural Science Foundation of China(Grant Nos.11934011,12074339,62075194,U21A6006,62202418,and U21B2004)the National Key Research and Development Program of China(Grant Nos.2019YFA0308100,2023YFB2806000,and 2022YFA1204700)+4 种基金the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDB28000000)the Leading Innovation and Entrepreneurship Team in Zhejiang Province(Grant No.2020R01001)the Open Program of the State Key Laboratory of Advanced Optical Communication Systems and Networks at Shanghai Jiao Tong University(Grant No.2023GZKF024)the Fundamental Research Funds for the Central Universities,the Information Technology Center and State Key Lab of CAD&CG at the Zhejiang University,the Zhejiang Provincial Key Laboratory of Information Processing,Communication and Networking(IPCAN)the National Institutes of Health(NIH)(Grant Nos.R01GM127696,R01GM152633,R21GM142107,and R21CA269099)。
文摘Scattering obscures information carried by waves by producing speckle patterns,posing a fundamental challenge across diverse fields,from microscopy to astronomy.Although machine learning has recently shown promise in speckle analysis,existing approaches are hindered by their dependence on large,labeled datasets—a significant bottleneck in many real-world applications.Here,we introduce speckle unsupervised recognition and evaluation(SURE),a groundbreaking unsupervised learning strategy for speckle recognition that eliminates the need for labeled training data.SURE's distinctive feature lies in its ability to extract invariant features through advanced clustering algorithms to enable direct classification of high-level information from speckle patterns without prior knowledge.We demonstrate the transformative potential of this approach in two key applications:(1)a noninvasive glucose monitoring system that accurately tracks glucose concentrations over time without extensive calibration and(2)a high-throughput communication system using multimode fibers,achieving improved performance in dynamic environments.In addition,we showcase SURE's unprecedented capability to classify objects hidden behind obstacles using scattered light,further broadening its scope.This versatile approach opens new frontiers in biomedical diagnostics,quantum network decoupling,and remote sensing,unlocking a transformative new paradigm for extracting information from seemingly random optical patterns.
基金supported by Vetenskapsrådet 2022-00799 and the Ulla and Ingemar Dahlberg Foundation(to PAW).
文摘The majority of our daily activities and routines are highly dependent on vision.What we experience as our vision arises from the detection and encoding of visual signals in the retina,which are then interpreted in the brain.This interpretation has the benefit of providing a level of constancy to what we experience as vision but also limits our ability to perceive subtle decline in our own vision.
基金Supported by the Ministry of Education U40 Program(ZYGXONJSKYCXNLZCXM-E19)National Natural Science Foundation of China(52574078)。
文摘The forward model of optical fiber strain induced by fractures,together with the associated model resolution matrix,is used to demonstrate the interpretability of fracture parameters once the fracture intersects the fiber.A regularized inversion framework for fracture parameters is established to evaluate the influence of measured data quality on the accuracy of iterative regularized inversion.An interpretation approach for both fracture width and height is proposed,and the synthetic forward data with measurement error and field examples are employed to validate the accuracy of the simultaneous inversion of fracture width and height.The results indicate that,after the fracture contacts the fiber,the strain response is strongly sensitive only to the fracture parameters at the intersection location,whereas the interpretability of parameters at other locations remains limited.The iterative regularized inversion method effectively suppresses the impact of measurement error and exhibits high computational efficiency,showing clear advantages for inversion applications.When incorporating the first-order regularization with a Neumann boundary constraint on the tip width,the inverted fracture-width distribution becomes highly sensitive to fracture height;thus,combined with a bisection strategy,simultaneous inversion of fracture width and height can be achieved.Examination using the model resolution matrix,noisy synthetic data,and field data confirms that the iterative regularized inversion model for fracture width and height provides high interpretive accuracy and can be applied to the calculation and analysis of fracture width,fracture height,net pressure and other parameters.
基金supported by National Key R&D Program of China(No.2021YFF0501301)the National Natural Science Foundation of China(No.42172231)。
文摘0 INTRODUCTION Earth science is a natural science concerned with the composition,dynamics,spatiotemporal evolution,and formation mechanisms of Earth materials(Chen and Yang,2023).Traditional Earth science research has largely been discipline-based,relying on field investigations,data collection,experimental analyses,and data interpretation to study individual components of the Earth system.
基金supported by the Natural Science Foundation of Jiangsu Province,China(Grant No.BK20240937)the Natural Science Foundation of Shandong Province(Grant No.ZR2021QE187)+2 种基金the Shandong Higher Education“Young Entrepreneurship Talents Introduction and Cultivation Program”Project(Grant No.ZXQT20221228001)the Natural Science Foundation of China(Grant No.42502273)the Science and Technology Innovation Program of Hunan Province(Grant No.2022RC4028).
文摘1.Introduction Artificial intelligence(AI)is rapidly reshaping geoscience,from Earth observation interpretation and hazard forecasting to subsurface characterisation and Earth system modelling(Kochupillai et al.,2022;Sun et al.,2024).These capabilities emerge at a time when geoscientific evidence is increasingly informing high-stakes decisions about climate adaptation,resource development,and disaster risk reduction(McGovern et al.,2022).
基金supported by the National Natural Science Foundation of China(Grant Nos.52021005,52325904,and 51991391)。
文摘Geological prospecting and the identification of adverse geological features are essential in tunnel construction,providing critical information to ensure safety and guide engineering decisions.As tunnel projects extend into deeper and more mountainous terrains,engineers face increasingly complex geological conditions,including high water pressure,intense geo-stress,elevated geothermal gradients,and active fault zones.These conditions pose substantial risks such as high-pressure water inrush,largescale collapses,and tunnel boring machine(TBM)blockages.Addressing these challenges requires advanced detection technologies capable of long-distance,high-precision,and intelligent assessments of adverse geology.This paper presents a comprehensive review of recent advancements in tunnel geological ahead prospecting methods.It summarizes the fundamental principles,technical maturity,key challenges,development trends,and real-world applications of various detection techniques.Airborne and semi-airborne geophysical methods enable large-scale reconnaissance for initial surveys in complex terrain.Tunnel-and borehole-based approaches offer high-resolution detection during excavation,including seismic ahead prospecting(SAP),TBM rock-breaking source seismic methods,fulltime-domain tunnel induced polarization(TIP),borehole electrical resistivity,and ground penetrating radar(GPR).To address scenarios involving multiple,coexisting adverse geologies,intelligent inversion and geological identification methods have been developed based on multi-source data fusion and artificial intelligence(AI)techniques.Overall,these advances significantly improve detection range,resolution,and geological characterization capabilities.The methods demonstrate strong adaptability to complex environments and provide reliable subsurface information,supporting safer and more efficient tunnel construction.
基金supported by the National Natural Science Foundation of China(Grant No.U1960202)the Science and Technology Commission of Shanghai Municipality(No.19DZ2270200).
文摘To predict the endpoint carbon content and temperature in basic oxygen furnace (BOF), the industrial parameters of BOF steelmaking are taken as input values. Firstly, a series of preprocessing works such as the Pauta criterion, hierarchical clustering, and principal component analysis on the original data were performed. Secondly, the prediction results of classic machine learning models of ridge regression, support vector machine, gradient boosting regression (GBR), random forest regression, back-propagation (BP) neural network models, and multi-layer perceptron (MLP) were compared before and after data preprocessing. An improved model was established based on the improved sparrow algorithm and BP using tent chaotic mapping (CSSA-BP). The CSSA-BP model showed the best performance for endpoint carbon prediction with the lowest mean absolute error (MAE) and root mean square error (RMSE) values of 0.01124 and 0.01345 mass% among seven models, respectively. And the lowest MAE and RMSE values of 8.9839 and 10.9321 ℃ for endpoint temperature prediction were obtained among seven models, respectively. Furthermore, the CSSA-BP and GBR models have the smallest error fluctuation range in both endpoint carbon content and temperature predictions. Finally, in order to improve the interpretability of the model, SHapley additive interpretation (SHAP) was used to analyze the results.
基金Deep-time Digital Earth(DDE)Big Science Program(No.GJ-C03-SGF-2025-004)National Natural Science Foundation of China(No.42394063)Sichuan Science and Technology Program(No.2025ZNSFSC0325).
文摘Topographic maps,as essential tools and sources of information for geographic research,contain precise spatial locations and rich map features,and they illustrate spatio-temporal information on the distribution and differences of various surface features.Currently,topographic maps are mainly stored in raster and vector formats.Extraction of the spatio-temporal knowledge in the maps—such as spatial distribution patterns,feature relationships,and dynamic evolution—still primarily relies on manual interpretation.However,manual interpretation is time-consuming and laborious,especially for large-scale,long-term map knowledge extraction and application.With the development of artificial intelligence technology,it is possible to improve the automation level of map knowledge interpretation.Therefore,the present study proposes an automatic interpretation method for raster topographic map knowledge based on deep learning.To address the limitations of current data-driven intelligent technology in learning map spatial relations and cognitive logic,we establish a formal description of map knowledge by mapping the relationship between map knowledge and features,thereby ensuring interpretation accuracy.Subsequently,deep learning techniques are employed to extract map features automatically,and the spatio-temporal knowledge is constructed by combining formal descriptions of geographic feature knowledge.Validation experiments demonstrate that the proposed method effectively achieves automatic interpretation of spatio-temporal knowledge of geographic features in maps,with an accuracy exceeding 80%.The findings of the present study contribute to machine understanding of spatio-temporal differences in map knowledge and advances the intelligent interpretation and utilization of cartographic information.
基金supported in part by the National Natural Science Foundation(Nos.62271248,62401256)in part by the Natural Science Foundation of Ji-angsu Province(Nos.BK20230090,BK20241384)in part by the Key Laboratory of Land Satellite Remote Sens-ing Application,Ministry of Natural Resources of China(No.KLSMNR-K202303)。
文摘In recent years,deeps learning has been widely applied in synthetic aperture radar(SAR)image processing.However,the collection of large-scale labeled SAR images is challenging and costly,and the classification accuracy is often poor when only limited SAR images are available.To address this issue,we propose a novel framework for sparse SAR target classification under few-shot cases,termed the transfer learning-based interpretable lightweight convolutional neural network(TL-IL-CNN).Additionally,we employ enhanced gradient-weighted class activation mapping(Grad-CAM)to mitigate the“black box”effect often associated with deep learning models and to explore the mechanisms by which a CNN classifies various sparse SAR targets.Initially,we apply a novel bidirectional iterative soft thresholding(BiIST)algorithm to generate sparse images of superior quality compared to those produced by traditional matched filtering(MF)techniques.Subsequently,we pretrain multiple shallow CNNs on a simulated SAR image dataset.Using the sparse SAR dataset as input for the CNNs,we assess the efficacy of transfer learning in sparse SAR target classification and suggest the integration of TL-IL-CNN to enhance the classification accuracy further.Finally,Grad-CAM is utilized to provide visual explanations for the predictions made by the classification framework.The experimental results on the MSTAR dataset reveal that the proposed TL-IL-CNN achieves nearly 90%classification accuracy with only 20%of the training data required under standard operating conditions(SOC),surpassing typical deep learning methods such as vision Transformer(ViT)in the context of small samples.Remarkably,it even presents better performance under extended operating conditions(EOC).Furthermore,the application of Grad-CAM elucidates the CNN’s differentiation process among various sparse SAR targets.The experiments indicate that the model focuses on the target and the background can differ among target classes.The study contributes to an enhanced understanding of the interpretability of such results and enables us to infer the classification outcomes for each category more accurately.
基金funded by Research Platforms and Projects for Higher Education Institutions of Department of Education of Guangdong Province in 2024(2024KTSCX256)2023 Guangdong Province Higher Vocational Education Teaching Quality and Teaching Reform Project(2023JG080).
文摘The potential toxicity of ionic liquids(ILs)affects their applications;how to control the toxicity is one of the key issues in their applications.To understand its toxicity structure relationship and promote its greener application,six different machine learning algorithms,including Bagging,Adaptive Boosting(AdaBoost),Gradient Boosting(GBoost),Stacking,Voting and Categorical Boosting(CatBoost),are established to model the toxicity of ILs on four distinct datasets including Leukemia rat cell line IPC-81(IPC-81),Acetylcholinesterase(AChE),Escherichia coli(E.coli)and Vibrio fischeri.Molecular descriptors obtained from the simplified molecular input line entry system(SMILES)are used to characterize ILs.All models are assessed by the mean square error(MSE),root mean square error(RMSE),mean absolute error(MAE)and correlation coefficient(R^(2)).Additionally,an interpretation model based on SHapley Additive exPlanations(SHAP)is built to determine the positive and negative effects of each molecular feature on toxicity.With additional parameters and complexity,the Catboost model outperforms the other models,making it a more reliable model for ILs'toxicity prediction.The results of the model's interpretation indicate that the most significant positive features,SMR_VSA5,PEOE_VSA8,Kappa2,PEOE_VSA6,SMR_VSA5,PEOE_VSA6 and EState_VSA1,can increase the toxicity of ILs as their levels rise,while the most significant negative features,VSA_EState7,EState_VSA8,PEOE_VSA9 and FpDensityMorgan1,can decrease the toxicity as their levels rise.Also,an IL's toxicity will grow as its average molecular weight and number of pyridine rings increase,whereas its toxicity will decrease as its hydrogen bond acceptors increase.This finding offers a theoretical foundation for rapid screening and synthesis of environmentally-benign ILs.
文摘Based on the Many Worlds Interpretation,I describe reality as a multilayer spacetime,where parallel layers play the role of alternative timelines.I link physics to ethics,arguing that one’s moral choices shape one’s course in the multiverse.I consider one’s ethical decisions as decoherence events,leading to movement between alternative timelines,lighter(higher)or heavier(lower)realities.Sometimes in one’s curvilinear path in spacetime,one can even experience falling toward lower layers,slipping through wormholes.This theory supports free will and the simulation hypothesis.With this background,I explore the idea that a new theory of gravity might open new possibilities to shape matter and change our worldview through the invention of new technology,transforming information into waves and then into solid matter,paving the way for a new Multiverse Aeon for humanity.
基金partially supported by the Singapore Ministry of National Development and the National Research Foundation,Prime Minister’s Office,Singapore,under the Land and Liveability National Innovation Challenge(L2 NIC)Research Program(Grant No.L2NICCFP2-2015-1)by the National Research Foundation(NRF)of Singapore,under the Virtual Singapore program(Grant No.NRF2019VSG-GMS-001).
文摘Accurate determination of rockhead is crucial for underground construction.Traditionally,borehole data are mainly used for this purpose.However,borehole drilling is costly,time-consuming,and sparsely distributed.Non-invasive geophysical methods,particularly those using passive seismic surface waves,have emerged as viable alternatives for geological profiling and rockhead detection.This study proposes three interpretation methods for rockhead determination using passive seismic surface wave data from Microtremor Array Measurement(MAM)and Horizontal-to-Vertical Spectral Ratio(HVSR)tests.These are:(1)the Wavelength-Normalized phase velocity(WN)method in which a nonlinear relationship between rockhead depth and wavelength is established;(2)the Statistically Determined-shear wave velocity(SD-V_(s))method in which the representative V_(s) value for rockhead is automatically determined using a statistical method;and(3)the empirical HVSR method in which the rockhead is determined by interpreting resonant frequencies using a reliably calibrated empirical equation.These methods were implemented to determine rockhead depths at 28 locations across two distinct geological formations in Singapore,and the results were evaluated using borehole data.The WN method can determine rockhead depths accurately and reliably with minimal absolute errors(average RMSE=3.11 m),demonstrating robust performance across both geological formations.Its advantage lies in interpreting dispersion curves alone,without the need for the inversion process.The SD-V_(s) method is practical in engineering practice owing to its simplicity.The empirical HVSR method reasonably determines rockhead depths with moderate accuracy,benefiting from a reliably calibrated empirical equation.
基金supported by the National Key Research and Development Program of China[Grant No.2022YFA1405300(PZ)]the Innovation Program for Quantum Science and Technology(Grant No.2023ZD0300700)。
文摘The Husimi function(Q-function)of a quantum state is the distribution function of the density operator in the coherent state representation.It is widely used in theoretical research,such as in quantum optics.The Wehrl entropy is the Shannon entropy of the Husimi function,and is nonzero even for pure states.This entropy has been extensively studied in mathematical physics.Recent research also suggests a significant connection between the Wehrl entropy and manybody quantum entanglement in spin systems.We investigate the statistical interpretation of the Husimi function and the Wehrl entropy,taking the system of N spin-1/2 particles as an example.Due to the completeness of coherent states,the Husimi function and Wehrl entropy can be explained via the positive operator-valued measurement(POVM)theory,although the coherent states are not a set of orthonormal basis.Here,with the help of the Bayes’theorem,we provide an alternative probabilistic interpretation for the Husimi function and the Wehrl entropy.This interpretation is based on direct measurements of the system,and thus does not require the introduction of an ancillary system as in the POVM theory.Moreover,under this interpretation the classical correspondences of the Husimi function and the Wehrl entropy are just phase-space probability distribution function of N classical tops,and its associated entropy,respectively.Therefore,this explanation contributes to a better understanding of the relationship between the Husimi function,Wehrl entropy,and classical-quantum correspondence.The generalization of this statistical interpretation to continuous-variable systems is also discussed.
基金supported by the National Natural Science Foundation of China(Nos.42177173,U23A20651 and 42130719)and the Outstanding Youth Science Fund Project of Sichuan Provincial Natural Science Foundation(No.2025NSFJQ0003)。
文摘Dynamic stress adjustment in deep-buried high geostress hard rock tunnels frequently triggers catastrophic failures such as rockbursts and collapses.While a comprehensive understanding of this process is critical for evaluating surrounding rock stability,its dynamic evolution are often overlooked in engineering practice.This study systematically summarizes a novel classification framework for stress adjustment types—stabilizing(two-zoned),shallow failure(three-zoned),and deep failure(four-zoned)—characterized by distinct stress adjustment stages.A dynamic interpretation technology system is developed based on microseismic monitoring,integrating key microseismic parameters(energy index EI,apparent stressσa,microseismic activity S),seismic source parameter space clustering,and microseismic paths.This approach enables precise identification of evolutionary stages,stress adjustment types,and failure precursors,thereby elucidating the intrinsic linkage between geomechanical processes(stress redistribution)and failure risks.The study establishes criteria and procedures for identifying stress adjustment types and their associated failure risks,which were successfully applied in the Grand Canyon Tunnel of the E-han Highway to detect 50 instances of disaster risks.The findings offer invaluable insights into understanding the evolution process of stress adjustment and pinpointing the disaster risks linked to hard rock in comparable high geostress tunnels.
文摘The Interpretation of Nursing Guidelines for Intravenous Thrombolysis in Acute Ischemic Stroke offers comprehensive recommendations across five key domains:hospital organizational management,patient condition monitoring,complication observation and management,positioning and mobility away from the bed,and quality assurance.These Guidelines encompass all the phases of intravenous thrombolysis care for patients experiencing acute ischemic stroke.This article aims to elucidate the Guidelines by discussing their developmental background,the designation process,usage recommendations,and the interpretation of evolving perspectives,thereby providing valuable insights for clinical practice.
文摘The study employs the theoretical framework of Nanosyntax to analyze the generative mechanism of verbal ABAB reduplication pattern in Mandarin Chinese.The research characterizes ABAB reduplication as an inflectional operation involving functional projections of pluractionality and aspect.It distinguishes between event-internal and event-external pluralization,as well as inner and outer aspect in verbal reduplication.Following the One-Function-One-Head Principle in Nanosyntax,verbal ABAB form occurs through the merging of categoryless roots that are categorized by little v,with the RED affix syncretizing multiple functional morphemes.This framework reduces lexical burden and precisely represents the unique syntactic structure of Chinese verbal reduplication.
基金supported by the Key Project of the National Natural Science Foundation of China(82030030)the National Natural Science Foundation of China(82171836)+1 种基金the Science and Technology Department of Sichuan Province(2024NSFSC0648)the 1·3·5 Project for Disciplines of Excellence,West China Hospital,Sichuan University(ZYJC20002).
文摘Multiple nucleotide variants(MNVs)are frequently misannotated as separate single-nucleotide variants(SNVs)by widely utilized variant-calling pipelines,presenting substantial challenges in genetic testing and research.The role of MNVs in genetic diagnosis remains inadequately characterized,particularly within large disease cohorts.In this study,we comprehensively investigate codon-level MNVs(cMNVs)across 157 hearing loss(HL)-related genes in 11,467 HL cases and 7258 controls from the Chinese Deafness Gene Consortium(CDGC)cohort.A total of 116 cMNVs are identified,occurring in 29.07%of HL cases.Among them,56.03%of cMNVs exhibit functional consequences distinct from constituent SNVs.Moreover,amino acid substitutions exclusive to cMNVs cause more substantial physicochemical disruptions than those associated with SNVs.Notably,51 cMNVs show pathogenicity classifications that diverge from at least one constituent SNV,impacting genetic interpretation in 145 cases.Pathogenicity interpretation of cMNV facilitates definitive genetic diagnoses in eight HL cases that would otherwise have been subject to misdiagnoses or missed diagnoses.These findings provide critical insights into the genomic characteristics,functional impacts,and diagnostic implications of cMNVs,underscoring their clinical significance in genetic diagnosis and emphasizing the necessity for comprehensive and accurate detection and interpretation of cMNVs in genetic testing and research.
文摘The Agadem block is an area of major oil interest located in the large sedimentary basin of Termit,in the south-east of the Republic of Niger.Since the 1950s,this basin has known geological and geophysical research activities.However,despite the extensive research carried out,we believe that a geophysical contribution in terms of magnetic properties and their repercussions on the structure of the Agadem block allowing the improvement of existing knowledge is essential.The present study aims to study the structural characteristics of the Agadem block associated with magnetic anomalies.For this,after data shaping,several filtering techniques were applied to the aeromagnetic data to identify and map deep geological structures.The reduction to the pole map shows large negative wavelength anomalies in the southeast half of the block and short positive wavelength anomalies in the northwest part embedded in a large positive anomaly occupying the lower northern half of the block.The maps of the total horizontal derivative and tilt angle show lineaments globally distributed along the NW-SE direction in accordance with the structural style of the study area.The resulting map highlights numerous lineaments that may be associated with faults hidden by the sedimentary cover.The calculation of the Euler deconvolution allowed us to locate and estimate the depths of magnetic sources at variable depths of up to 4000 m.The compilation of the results obtained allowed us to locate zones of high and low intensities which correspond respectively to horsts and grabens as major structures of the Agadem block.
基金funded by the National Natural Science Foundation of China(42050104).
文摘This study introduces a comprehensive and automated framework that leverages data-driven method-ologies to address various challenges in shale gas development and production.Specifically,it harnesses the power of Automated Machine Learning(AutoML)to construct an ensemble model to predict the estimated ultimate recovery(EUR)of shale gas wells.To demystify the“black-box”nature of the ensemble model,KernelSHAP,a kernel-based approach to compute Shapley values,is utilized for elucidating the influential factors that affect shale gas production at both global and local scales.Furthermore,a bi-objective optimization algorithm named NSGA-Ⅱ is seamlessly incorporated to opti-mize hydraulic fracturing designs for production boost and cost control.This innovative framework addresses critical limitations often encountered in applying machine learning(ML)to shale gas pro-duction:the challenge of achieving sufficient model accuracy with limited samples,the multidisciplinary expertise required for developing robust ML models,and the need for interpretability in“black-box”models.Validation with field data from the Fuling shale gas field in the Sichuan Basin substantiates the framework's efficacy in enhancing the precision and applicability of data-driven techniques.The test accuracy of the ensemble ML model reached 83%compared to a maximum of 72%of single ML models.The contribution of each geological and engineering factor to the overall production was quantitatively evaluated.Fracturing design optimization raised EUR by 7%-34%under different production and cost tradeoff scenarios.The results empower domain experts to conduct more precise and objective data-driven analyses and optimizations for shale gas production with minimal expertise in data science.
