Since the publication of Sons and Lovers,it has inspired a wide range of critical interpretation, which testifies to its enduring status as a masterpiece of twentieth-century literature. Most critics analyze and evalu...Since the publication of Sons and Lovers,it has inspired a wide range of critical interpretation, which testifies to its enduring status as a masterpiece of twentieth-century literature. Most critics analyze and evaluate Sons and Lovers by adopting a psychoanalytical or social approach. The discussion either just searches for Oedipus Complex or is confined to the content analysis. This essay attempts to integrate all the theoretical analysis which attach to the novel, Sons and Lovers.展开更多
Improving the accuracy of digital elevation is essential for reducing hydro-topographic derivation errors pertaining to, e.g., flow direction, basin borders, channel networks, depressions, flood forecasting, and soil ...Improving the accuracy of digital elevation is essential for reducing hydro-topographic derivation errors pertaining to, e.g., flow direction, basin borders, channel networks, depressions, flood forecasting, and soil drainage. This article demonstrates how a gain in this accuracy is improved through digital elevation model (DEM) fusion, and using LiDAR-derived elevation layers for conformance testing and validation. This demonstration is done for the Province of New Brunswick (NB, Canada), using five province-wide DEM sources (SRTM 90 m;SRTM 30 m;ASTER 30 m;CDED 22 m;NB-DEM 10 m) and a five-stage process that guides the re-projection of these DEMs while minimizing their elevational differences relative to LiDAR-captured bare-earth DEMs, through calibration and validation. This effort decreased the resulting non-LiDAR to LiDAR elevation differences by a factor of two, reduced the minimum distance conformance between the non-LiDAR and LiDAR-derived flow channels to ± 10 m at 8.5 times out of 10, and dropped the non-LiDAR wet-area percentages of false positives from 59% to 49%, and of false negatives from 14% to 7%. While these reductions are modest, they are nevertheless not only consistent with already existing hydrographic data layers informing about stream and wet-area locations, they also extend these data layers across the province by comprehensively locating previously unmapped flow channels and wet areas.展开更多
Objective To investigate distinctive features in drug-resistant mutations （DRMs） and interpretations for reverse transcriptase inhibitors （RTIs） between proviral DNA and paired viral RNA in HIV-l-infected patients...Objective To investigate distinctive features in drug-resistant mutations （DRMs） and interpretations for reverse transcriptase inhibitors （RTIs） between proviral DNA and paired viral RNA in HIV-l-infected patients. Methods Forty-three HIV-l-infected individuals receiving first-line antiretroviral therapy were recruited to participate in a multicenter AIDS Cohort Study in Anhui and Henan Provinces in China in 2004. Drug resistance genotyping was performed by bulk sequencing and deep sequencing on the plasma and whole blood of 77 samples, respectively. Drug-resistance interpretation was compared between viral RNA and paired proviral DNA. Results Compared with bulk sequencing, deep sequencing could detect more DRMs and samples with DRMs in both viral RNA and proviral DNA. The mutations M1841 and M2301 were more prevalent in proviral DNA than in viral RNA （Fisher＇s exact test, P〈0.05）. Considering ＇majority resistant variants＇, 15 samples （19.48%） showed differences in drug resistance interpretation between viral RNA and proviral DNA, and 5 of these samples with different DRMs between proviral DNA and paired viral RNA showed a higher level of drug resistance to the first-line drugs. Considering ＇minority resistant variants＇, 22 samples （28.57%） were associated with a higher level of drug resistance to the tested RTIs for proviral DNA when compared with paired viral RNA. Conclusion Compared with viral RNA, the distinctive information of DRMs and drug resistance interpretations for proviral DNA could be obtained by deep sequencing, which could provide more detailed and precise information for drug resistance monitoring and the rational design of optimal antiretroviral therapy regimens.展开更多
The Appellate Body report in January 2012 had supported the decision of Panel in the"China-measures related to the exportation of various raw materials"case(WT/DS394,395,398)and affirmed that China's res...The Appellate Body report in January 2012 had supported the decision of Panel in the"China-measures related to the exportation of various raw materials"case(WT/DS394,395,398)and affirmed that China's restrictions(such as tariffs and quota measures)on the exportation of raw materials violated rules put forth by the WTO,which were required to be modified.In this case China's right to invoke Article 20 of GATT1994("general exception")to justify its exemption from the guidelines in Article 11.3 of the WTO Accession Protocol was denied by the Panel and the Appellate Body.This was due to the fact that the phrasing in Article 11.3 of Protocol failed to mention"GATT."This was the consequence of the two interpretation approaches the Dispute Settlement Body(DSB)adopted-a narrow textual interpretation and a subjective presumption of"legislative silence."The inappropriate use of the two methods of interpretation lead to an imbalance between the right and obligation of China under the additional obligations that were imposed upon China by the WTO,which create a negative impact on China's rare earth case and the protection of domestic natural resources.展开更多
In this paper, the author focuses on the ecourbarchitectonic physical structures created after year 2000, whose artistic-esthetic value has an iconological character. An entirely new approach in formation of the facad...In this paper, the author focuses on the ecourbarchitectonic physical structures created after year 2000, whose artistic-esthetic value has an iconological character. An entirely new approach in formation of the facade and roof planes as well as of the forms of structures whose appearance resemble sculptural creations has been analyzed. The buildings from all over the world, with different functions contents, indicate a tendency of a different understanding of interpretation of physical structures and correlation with natural and artifact environment. Water surfaces and vegetative material contribute to an effective, cultural, majestic impression of engineering-technological philosophy of city building. The examples in the paper suggest the obvious need of radical changing of the way of thinking in the application of the design strategy in conceptualization of urban agglomerations, and essentially important, conceptually inspired metabolic of relationships among the spatial structures. The world entered new non-globalization trends of creation of the city memory, of the new iconically, symbolically strong, non-cliché, non-standard forms which define the contemporary cultural-artistic and historical identity of macro-ambient entities. This is a good and encouraging sign.展开更多
This paper addresses the problem of the interpretation of the stochastic differential equations (SDE). Even if from a theoretical point of view, there are infinite ways of interpreting them, in practice only Stratonov...This paper addresses the problem of the interpretation of the stochastic differential equations (SDE). Even if from a theoretical point of view, there are infinite ways of interpreting them, in practice only Stratonovich’s and Itô’s interpretations and the kinetic form are important. Restricting the attention to the first two, they give rise to two different Fokker-Planck-Kolmogorov equations for the transition probability density function (PDF) of the solution. According to Stratonovich’s interpretation, there is one more term in the drift, which is not present in the physical equation, the so-called spurious drift. This term is not present in Itô’s interpretation so that the transition PDF’s of the two interpretations are different. Several examples are shown in which the two solutions are strongly different. Thus, caution is needed when a physical phenomenon is modelled by a SDE. However, the meaning of the spurious drift remains unclear.展开更多
Linear and circular interpretation structure maps of different relative depths are obtained by processing 1:200000 aeromagnetic data to the pole in Ailaoshan region,interpreting upward extension of 4 heights,extractin...Linear and circular interpretation structure maps of different relative depths are obtained by processing 1:200000 aeromagnetic data to the pole in Ailaoshan region,interpreting upward extension of 4 heights,extracting a vertical second derivative line of 0 value and a series of calculations. Concealed boundary of deep magnetic rocks can be delineated according to the maps. On the basis of the conclusions above,a set of economical and practical methods to graph the deep structure are summarized. In addition,the relationship between deep structure and mineralization positions is discussed.展开更多
The method and theoretical system of well logging geology have been widely used in the fields of basic geology,petroleum geology and engineering geology,but the different response sensitivity of different well logging...The method and theoretical system of well logging geology have been widely used in the fields of basic geology,petroleum geology and engineering geology,but the different response sensitivity of different well logging series to geological information and the mismatching between geophysical properties of multiple well logs and geological genesis of rocks frequently result in misunderstandings in the research process of well logging geology.Therefore,it is in an urgent need to analyze the typical misunderstanding cases in the research of well logging geology and explore the corresponding scientific ideas and countermeasures.After analyzing the typical misunderstandings in the research of well logging geology,this paper investigates vertical resolution scale of various logging series and its contradiction with detection depth and illustrates the importance of the integration of different scales of data.In addition,the factor inducing“fake logging data”and its influence on interpretation evaluation are clarified and a set of ideas for well logging evaluation of geological interpretation is put forward.And the following research results are obtained.First,the typical misunderstandings in the research of well logging geology can be classified into two categories,namely geological body interpretation misunderstanding and reservoir property parameter calculation misunderstanding.Second,special geological phenomena,such as high-density and high-resistivity mudstone can lead to logging data ambiguity,so attention shall be paid to petrophysical response mechanisms during geological logging interpretation.Third,to carry out well logging evaluation of unconventional oil and gas,it is necessary to integrate new technologies of electric imaging logging,dipole acoustic logging and nuclear magnetic resonance logging,and the calibration of core data and the integration of geological ideas can improve the interpretation accuracy.Fourth,In the process of borehole structural logging analysis,sedimentary response,geostress evaluation and fracture identification,geological ideas shall be integrated to avoid the logging interpretation misunderstanding caused by the same response of different geological phenomena in well logs.In conclusion,the dialectical and systematic thinking from geology to logging and then to geology,from practice to recognition and then to practice and from“a narrow view”to“a broad view”can provide a scientific ideas for the comprehensive research of well logging geology.展开更多
This paper offers an analysis of the approaches employed in the three interpretations of the Basic Law of the Special Administrative Region of Hong Kong by the Standing Committee of the National People＇s Congress （...This paper offers an analysis of the approaches employed in the three interpretations of the Basic Law of the Special Administrative Region of Hong Kong by the Standing Committee of the National People＇s Congress （NPC） after the return of Hong Kong to China, including textualism, structural reading and originalism. The paper stresses the application of jurisprudential theory in the skilful employment of these methods in the NPC interpretations. In the case of ＂the right of abode＂ in Hong Kong the differences between the interpretations by the Court of Final Appeal of Hong Kong and by the NPC rest mainly in whether a formalist procedural review or a substantivist presumption of intent should be adopted in the process of determining an authoritative text that embodies the original intention of the legislation. That is not just a difference of legal interpretation but also one of jurisprudential theory and political stance. Based on the above considerations, this paper criticizes the common misconception that it is not appropriate for legislators to undertake legal interpretation, and calls for an understanding of the Basic Law in the framework of Chinese constitutional government.展开更多
Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that ...Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.展开更多
Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudi...Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.展开更多
Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse cha...Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks.展开更多
The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unatt...The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.展开更多
Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learni...Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.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
Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning sy...Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability.展开更多
文摘Since the publication of Sons and Lovers,it has inspired a wide range of critical interpretation, which testifies to its enduring status as a masterpiece of twentieth-century literature. Most critics analyze and evaluate Sons and Lovers by adopting a psychoanalytical or social approach. The discussion either just searches for Oedipus Complex or is confined to the content analysis. This essay attempts to integrate all the theoretical analysis which attach to the novel, Sons and Lovers.
文摘Improving the accuracy of digital elevation is essential for reducing hydro-topographic derivation errors pertaining to, e.g., flow direction, basin borders, channel networks, depressions, flood forecasting, and soil drainage. This article demonstrates how a gain in this accuracy is improved through digital elevation model (DEM) fusion, and using LiDAR-derived elevation layers for conformance testing and validation. This demonstration is done for the Province of New Brunswick (NB, Canada), using five province-wide DEM sources (SRTM 90 m;SRTM 30 m;ASTER 30 m;CDED 22 m;NB-DEM 10 m) and a five-stage process that guides the re-projection of these DEMs while minimizing their elevational differences relative to LiDAR-captured bare-earth DEMs, through calibration and validation. This effort decreased the resulting non-LiDAR to LiDAR elevation differences by a factor of two, reduced the minimum distance conformance between the non-LiDAR and LiDAR-derived flow channels to ± 10 m at 8.5 times out of 10, and dropped the non-LiDAR wet-area percentages of false positives from 59% to 49%, and of false negatives from 14% to 7%. While these reductions are modest, they are nevertheless not only consistent with already existing hydrographic data layers informing about stream and wet-area locations, they also extend these data layers across the province by comprehensively locating previously unmapped flow channels and wet areas.
基金supported by grants from the State Key Laboratory of Infectious Disease Prevention and Control(2011SKLID102)the National Nature Science Foundation of China(81172733 and 81561128006)the 12th Five-Year National Science and Technology Major Project(2013ZX10001-006)
文摘Objective To investigate distinctive features in drug-resistant mutations （DRMs） and interpretations for reverse transcriptase inhibitors （RTIs） between proviral DNA and paired viral RNA in HIV-l-infected patients. Methods Forty-three HIV-l-infected individuals receiving first-line antiretroviral therapy were recruited to participate in a multicenter AIDS Cohort Study in Anhui and Henan Provinces in China in 2004. Drug resistance genotyping was performed by bulk sequencing and deep sequencing on the plasma and whole blood of 77 samples, respectively. Drug-resistance interpretation was compared between viral RNA and paired proviral DNA. Results Compared with bulk sequencing, deep sequencing could detect more DRMs and samples with DRMs in both viral RNA and proviral DNA. The mutations M1841 and M2301 were more prevalent in proviral DNA than in viral RNA （Fisher＇s exact test, P〈0.05）. Considering ＇majority resistant variants＇, 15 samples （19.48%） showed differences in drug resistance interpretation between viral RNA and proviral DNA, and 5 of these samples with different DRMs between proviral DNA and paired viral RNA showed a higher level of drug resistance to the first-line drugs. Considering ＇minority resistant variants＇, 22 samples （28.57%） were associated with a higher level of drug resistance to the tested RTIs for proviral DNA when compared with paired viral RNA. Conclusion Compared with viral RNA, the distinctive information of DRMs and drug resistance interpretations for proviral DNA could be obtained by deep sequencing, which could provide more detailed and precise information for drug resistance monitoring and the rational design of optimal antiretroviral therapy regimens.
文摘The Appellate Body report in January 2012 had supported the decision of Panel in the"China-measures related to the exportation of various raw materials"case(WT/DS394,395,398)and affirmed that China's restrictions(such as tariffs and quota measures)on the exportation of raw materials violated rules put forth by the WTO,which were required to be modified.In this case China's right to invoke Article 20 of GATT1994("general exception")to justify its exemption from the guidelines in Article 11.3 of the WTO Accession Protocol was denied by the Panel and the Appellate Body.This was due to the fact that the phrasing in Article 11.3 of Protocol failed to mention"GATT."This was the consequence of the two interpretation approaches the Dispute Settlement Body(DSB)adopted-a narrow textual interpretation and a subjective presumption of"legislative silence."The inappropriate use of the two methods of interpretation lead to an imbalance between the right and obligation of China under the additional obligations that were imposed upon China by the WTO,which create a negative impact on China's rare earth case and the protection of domestic natural resources.
文摘In this paper, the author focuses on the ecourbarchitectonic physical structures created after year 2000, whose artistic-esthetic value has an iconological character. An entirely new approach in formation of the facade and roof planes as well as of the forms of structures whose appearance resemble sculptural creations has been analyzed. The buildings from all over the world, with different functions contents, indicate a tendency of a different understanding of interpretation of physical structures and correlation with natural and artifact environment. Water surfaces and vegetative material contribute to an effective, cultural, majestic impression of engineering-technological philosophy of city building. The examples in the paper suggest the obvious need of radical changing of the way of thinking in the application of the design strategy in conceptualization of urban agglomerations, and essentially important, conceptually inspired metabolic of relationships among the spatial structures. The world entered new non-globalization trends of creation of the city memory, of the new iconically, symbolically strong, non-cliché, non-standard forms which define the contemporary cultural-artistic and historical identity of macro-ambient entities. This is a good and encouraging sign.
文摘This paper addresses the problem of the interpretation of the stochastic differential equations (SDE). Even if from a theoretical point of view, there are infinite ways of interpreting them, in practice only Stratonovich’s and Itô’s interpretations and the kinetic form are important. Restricting the attention to the first two, they give rise to two different Fokker-Planck-Kolmogorov equations for the transition probability density function (PDF) of the solution. According to Stratonovich’s interpretation, there is one more term in the drift, which is not present in the physical equation, the so-called spurious drift. This term is not present in Itô’s interpretation so that the transition PDF’s of the two interpretations are different. Several examples are shown in which the two solutions are strongly different. Thus, caution is needed when a physical phenomenon is modelled by a SDE. However, the meaning of the spurious drift remains unclear.
基金Project supported by National Key Technology R &D Program (No.2006BAB01B10)
文摘Linear and circular interpretation structure maps of different relative depths are obtained by processing 1:200000 aeromagnetic data to the pole in Ailaoshan region,interpreting upward extension of 4 heights,extracting a vertical second derivative line of 0 value and a series of calculations. Concealed boundary of deep magnetic rocks can be delineated according to the maps. On the basis of the conclusions above,a set of economical and practical methods to graph the deep structure are summarized. In addition,the relationship between deep structure and mineralization positions is discussed.
文摘The method and theoretical system of well logging geology have been widely used in the fields of basic geology,petroleum geology and engineering geology,but the different response sensitivity of different well logging series to geological information and the mismatching between geophysical properties of multiple well logs and geological genesis of rocks frequently result in misunderstandings in the research process of well logging geology.Therefore,it is in an urgent need to analyze the typical misunderstanding cases in the research of well logging geology and explore the corresponding scientific ideas and countermeasures.After analyzing the typical misunderstandings in the research of well logging geology,this paper investigates vertical resolution scale of various logging series and its contradiction with detection depth and illustrates the importance of the integration of different scales of data.In addition,the factor inducing“fake logging data”and its influence on interpretation evaluation are clarified and a set of ideas for well logging evaluation of geological interpretation is put forward.And the following research results are obtained.First,the typical misunderstandings in the research of well logging geology can be classified into two categories,namely geological body interpretation misunderstanding and reservoir property parameter calculation misunderstanding.Second,special geological phenomena,such as high-density and high-resistivity mudstone can lead to logging data ambiguity,so attention shall be paid to petrophysical response mechanisms during geological logging interpretation.Third,to carry out well logging evaluation of unconventional oil and gas,it is necessary to integrate new technologies of electric imaging logging,dipole acoustic logging and nuclear magnetic resonance logging,and the calibration of core data and the integration of geological ideas can improve the interpretation accuracy.Fourth,In the process of borehole structural logging analysis,sedimentary response,geostress evaluation and fracture identification,geological ideas shall be integrated to avoid the logging interpretation misunderstanding caused by the same response of different geological phenomena in well logs.In conclusion,the dialectical and systematic thinking from geology to logging and then to geology,from practice to recognition and then to practice and from“a narrow view”to“a broad view”can provide a scientific ideas for the comprehensive research of well logging geology.
文摘This paper offers an analysis of the approaches employed in the three interpretations of the Basic Law of the Special Administrative Region of Hong Kong by the Standing Committee of the National People＇s Congress （NPC） after the return of Hong Kong to China, including textualism, structural reading and originalism. The paper stresses the application of jurisprudential theory in the skilful employment of these methods in the NPC interpretations. In the case of ＂the right of abode＂ in Hong Kong the differences between the interpretations by the Court of Final Appeal of Hong Kong and by the NPC rest mainly in whether a formalist procedural review or a substantivist presumption of intent should be adopted in the process of determining an authoritative text that embodies the original intention of the legislation. That is not just a difference of legal interpretation but also one of jurisprudential theory and political stance. Based on the above considerations, this paper criticizes the common misconception that it is not appropriate for legislators to undertake legal interpretation, and calls for an understanding of the Basic Law in the framework of Chinese constitutional government.
基金supported by the Deanship of Scientific Research,Vice Presidency for Graduate Studies and Scientific Research,King Faisal University,Saudi Arabia Grant No.KFU253765.
文摘Most predictive maintenance studies have emphasized accuracy but provide very little focus on Interpretability or deployment readiness.This study improves on prior methods by developing a small yet robust system that can predict when turbofan engines will fail.It uses the NASA CMAPSS dataset,which has over 200,000 engine cycles from260 engines.The process begins with systematic preprocessing,which includes imputation,outlier removal,scaling,and labelling of the remaining useful life.Dimensionality is reduced using a hybrid selection method that combines variance filtering,recursive elimination,and gradient-boosted importance scores,yielding a stable set of 10 informative sensors.To mitigate class imbalance,minority cases are oversampled,and class-weighted losses are applied during training.Benchmarking is carried out with logistic regression,gradient boosting,and a recurrent design that integrates gated recurrent units with long short-term memory networks.The Long Short-Term Memory–Gated Recurrent Unit(LSTM–GRU)hybrid achieved the strongest performance with an F1 score of 0.92,precision of 0.93,recall of 0.91,ReceiverOperating Characteristic–AreaUnder the Curve(ROC-AUC)of 0.97,andminority recall of 0.75.Interpretability testing using permutation importance and Shapley values indicates that sensors 13,15,and 11 are the most important indicators of engine wear.The proposed system combines imbalance handling,feature reduction,and Interpretability into a practical design suitable for real industrial settings.
文摘Mortality prediction in respiratory health is challenging,especially when using large-scale clinical datasets composed primarily of categorical variables.Traditional digital twin(DT)frameworks often rely on longi-tudinal or sensor-based data,which are not always available in public health contexts.In this article,we propose a novel proto-DT framework for mortality prediction in respiratory health using a large-scale categorical biomedical dataset.This dataset contains 415,711 severe acute respiratory infection cases from the Brazilian Unified Health System,including both COVID-19 and non-COVID-19 patients.Four classification models—extreme gradient boosting(XGBoost),logistic regression,random forest,and a deep neural network(DNN)—are trained using cost-sensitive learning to address class imbalance.The models are evaluated using accuracy,precision,recall,F1-score,and area under the curve(AUC)related to the receiver operating characteristic(ROC).The framework supports simulated interventions by modifying selected inputs and recalculating predicted mortality.Additionally,we incorporate multiple correspondence analysis and K-means clustering to explore model sensitivity.A Python library has been developed to ensure reproducibility.All models achieve AUC-ROC values near or above 0.85.XGBoost yields the highest accuracy(0.84),while the DNN achieves the highest recall(0.81).Scenario-based simulations reveal how key clinical factors,such as intensive care unit admission and oxygen support,affect predicted outcomes.The proposed proto-DT framework demonstrates the feasibility of mortality prediction and intervention simulation using categorical data alone.This framework provides a foundation for data-driven explainable DTs in public health,even in the absence of time-series data.
基金funded by Guangdong Basic and Applied Basic Research Foundation(2023B1515120064)National Natural Science Foundation of China(62273097).
文摘Deep learning has become integral to robotics,particularly in tasks such as robotic grasping,where objects often exhibit diverse shapes,textures,and physical properties.In robotic grasping tasks,due to the diverse characteristics of the targets,frequent adjustments to the network architecture and parameters are required to avoid a decrease in model accuracy,which presents a significant challenge for non-experts.Neural Architecture Search(NAS)provides a compelling method through the automated generation of network architectures,enabling the discovery of models that achieve high accuracy through efficient search algorithms.Compared to manually designed networks,NAS methods can significantly reduce design costs,time expenditure,and improve model performance.However,such methods often involve complex topological connections,and these redundant structures can severely reduce computational efficiency.To overcome this challenge,this work puts forward a robotic grasp detection framework founded on NAS.The method automatically designs a lightweight network with high accuracy and low topological complexity,effectively adapting to the target object to generate the optimal grasp pose,thereby significantly improving the success rate of robotic grasping.Additionally,we use Class Activation Mapping(CAM)as an interpretability tool,which captures sensitive information during the perception process through visualized results.The searched model achieved competitive,and in some cases superior,performance on the Cornell and Jacquard public datasets,achieving accuracies of 98.3%and 96.8%,respectively,while sustaining a detection speed of 89 frames per second with only 0.41 million parameters.To further validate its effectiveness beyond benchmark evaluations,we conducted real-world grasping experiments on a UR5 robotic arm,where the model demonstrated reliable performance across diverse objects and high grasp success rates,thereby confirming its practical applicability in robotic manipulation tasks.
文摘The integration of machine learning(ML)into geohazard assessment has successfully instigated a paradigm shift,leading to the production of models that possess a level of predictive accuracy previously considered unattainable.However,the black-box nature of these systems presents a significant barrier,hindering their operational adoption,regulatory approval,and full scientific validation.This paper provides a systematic review and synthesis of the emerging field of explainable artificial intelligence(XAI)as applied to geohazard science(GeoXAI),a domain that aims to resolve the long-standing trade-off between model performance and interpretability.A rigorous synthesis of 87 foundational studies is used to map the intellectual and methodological contours of this rapidly expanding field.The analysis reveals that current research efforts are concentrated predominantly on landslide and flood assessment.Methodologically,tree-based ensembles and deep learning models dominate the literature,with SHapley Additive exPlanations(SHAP)frequently adopted as the principal post-hoc explanation technique.More importantly,the review further documents how the role of XAI has shifted:rather than being used solely as a tool for interpreting models after training,it is increasingly integrated into the modeling cycle itself.Recent applications include its use in feature selection,adaptive sampling strategies,and model evaluation.The evidence also shows that GeoXAI extends beyond producing feature rankings.It reveals nonlinear thresholds and interaction effects that generate deeper mechanistic insights into hazard processes and mechanisms.Nevertheless,several key challenges remain unresolved within the field.These persistent issues are especially pronounced when considering the crucial necessity for interpretation stability,the demanding scholarly task of reliably distinguishing correlation from causation,and the development of appropriate methods for the treatment of complex spatio-temporal dynamics.
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
基金funded by Ongoing Research Funding Program for Project number(ORF-2025-648),King Saud University,Riyadh,Saudi Arabia.
文摘Heart disease remains a leading cause of mortality worldwide,emphasizing the urgent need for reliable and interpretable predictive models to support early diagnosis and timely intervention.However,existing Deep Learning(DL)approaches often face several limitations,including inefficient feature extraction,class imbalance,suboptimal classification performance,and limited interpretability,which collectively hinder their deployment in clinical settings.To address these challenges,we propose a novel DL framework for heart disease prediction that integrates a comprehensive preprocessing pipeline with an advanced classification architecture.The preprocessing stage involves label encoding and feature scaling.To address the issue of class imbalance inherent in the personal key indicators of the heart disease dataset,the localized random affine shadowsampling technique is employed,which enhances minority class representation while minimizing overfitting.At the core of the framework lies the Deep Residual Network(DeepResNet),which employs hierarchical residual transformations to facilitate efficient feature extraction and capture complex,non-linear relationships in the data.Experimental results demonstrate that the proposed model significantly outperforms existing techniques,achieving improvements of 3.26%in accuracy,3.16%in area under the receiver operating characteristics,1.09%in recall,and 1.07%in F1-score.Furthermore,robustness is validated using 10-fold crossvalidation,confirming the model’s generalizability across diverse data distributions.Moreover,model interpretability is ensured through the integration of Shapley additive explanations and local interpretable model-agnostic explanations,offering valuable insights into the contribution of individual features to model predictions.Overall,the proposed DL framework presents a robust,interpretable,and clinically applicable solution for heart disease prediction.
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the King Salman center for Disability Research for funding this work through Research Group No.KSRG-2024-050.
文摘Artificial Intelligence(AI)is changing healthcare by helping with diagnosis.However,for doctors to trust AI tools,they need to be both accurate and easy to understand.In this study,we created a new machine learning system for the early detection of Autism Spectrum Disorder(ASD)in children.Our main goal was to build a model that is not only good at predicting ASD but also clear in its reasoning.For this,we combined several different models,including Random Forest,XGBoost,and Neural Networks,into a single,more powerful framework.We used two different types of datasets:(i)a standard behavioral dataset and(ii)a more complex multimodal dataset with images,audio,and physiological information.The datasets were carefully preprocessed for missing values,redundant features,and dataset imbalance to ensure fair learning.The results outperformed the state-of-the-art with a Regularized Neural Network,achieving 97.6%accuracy on behavioral data.Whereas,on the multimodal data,the accuracy is 98.2%.Other models also did well with accuracies consistently above 96%.We also used SHAP and LIME on a behavioral dataset for models’explainability.
