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
We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interp...We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interpretations in this evolving field.Given that much research time and financial investment is being given to the study of the effects of eccentric training in both athletic and clinical contexts,it is incumbent on our field to demonstrate whether eccentric contractions are a key(or the key)stimulus for sarcomerogenesis(increases in serial sarcomere number(SSN)).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
Black-box models have demonstrated remarkable accuracy in forecasting building energy loads.However,they usually lack interpretability and do not incorporate domain knowledge,making it difficult for users to trust the...Black-box models have demonstrated remarkable accuracy in forecasting building energy loads.However,they usually lack interpretability and do not incorporate domain knowledge,making it difficult for users to trust their predictions in practical applications.One important and interesting question remains unanswered:is it possible to use intrinsically interpretable models to achieve accuracy comparable to that of black-box models?With an aim of answering this question,this study proposes an intrinsically interpretable machine learning-based method to forecast building energy loads.It creatively combines two intrinsically interpretable machine learning algorithms:clustering decision trees and adaptive multiple linear regression.Clustering decision trees aim to automatically identify various building operation conditions,allowing for the training of multiple models tailored to each condition.It can reduce the complexity of model training data,leading to higher accuracy.Adaptive multiple linear regression is an improved regression algorithm tailored to building energy load prediction.It can adaptively modify regression coefficients according to building operations,enhancing the non-linear fitting capability of multiple linear regression.The proposed method is evaluated utilizing the operational data from an office building.The results indicate that the proposed method exhibits comparable accuracy to both random forests and extreme gradient boosting.Furthermore,it shows significantly superior accuracy,with an average improvement of 10.2%,compared with some popular black-box algorithms such as artificial neural networks,support vector regression,and classification and regression trees.As for model interpretability,the proposed method reveals that historical cooling loads are the most crucial for predicting building cooling loads under most conditions.Additionally,outdoor air temperature has a significant contribution to building cooling load prediction during the daytime on weekdays in summer and transition seasons.In the future,it will be valuable to explore integrating the laws of physics into the proposed method to further enhance its interpretability.展开更多
Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise,inter-modal imbalance,computational bottlenecks,and semantic ambiguity.To address these issues,we propo...Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise,inter-modal imbalance,computational bottlenecks,and semantic ambiguity.To address these issues,we propose SparseMoE-MFN,a novel unified framework that integrates sparse attention with a sparse-activated Mixture of-Experts(MoE)architecture.This framework aims to enhance the efficiency,inferential depth,and interpretability of multimodal fake news detection.Sparse MoE-MFN leverages LLaVA-v1.6-Mistral-7B-HF for efficient visual encoding and Qwen/Qwen2-7B for text processing.The sparse attention module adaptively filters irrelevant tokens and focuses on key regions,reducing computational costs and noise.The sparse MoE module dynamically routes inputs to specialized experts(visual,language,cross-modal alignment)based on content heterogeneity.This expert specialization design boosts computational efficiency and semantic adaptability,enabling precise processing of complex content and improving performance on ambiguous categories.Evaluated on the large-scale,multilingualMR2 dataset,SparseMoEMFN achieves state-of-the-art performance.It obtains an accuracy of 86.7%and a macro-averaged F1 score of 0.859,outperforming strong baselines like MiniGPT-4 by 3.4%and 3.2%,respectively.Notably,it shows significant advantages in the“unverified”category.Furthermore,SparseMoE-MFN demonstrates superior computational efficiency,with an average inference latency of 89.1 ms and 95.4 GFLOPs,substantially lower than existing models.Ablation studies and visualization analyses confirm the effectiveness of both sparse attention and sparse MoE components in improving accuracy,generalization,and efficiency.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
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
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
With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loose...With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loosening faults are difficult to identify through conventional spectrum analysis,and the extreme scarcity of fault data leads to limited training datasets,making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity.This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution.This method proposes specific data processing approaches and a configuration of Bayesian Convolutional Neural Network(BCNN).It can effectively improve the model’s generalization ability.Experimental results demonstrate high detection accuracy and alignment with real-world applications,offering practical significance and reference value for industrial fan bolt loosening detection under data-limited conditions.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
Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,i...Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.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
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We thank Power et al.1 for their interest in our review2 and for contributing to this important scientific discussion.We welcome their commentary and acknowledge the merit of continuing to scrutinize and refine interpretations in this evolving field.Given that much research time and financial investment is being given to the study of the effects of eccentric training in both athletic and clinical contexts,it is incumbent on our field to demonstrate whether eccentric contractions are a key(or the key)stimulus for sarcomerogenesis(increases in serial sarcomere number(SSN)).
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 National Natural Science Foundation of China(No.52161135202).
文摘Black-box models have demonstrated remarkable accuracy in forecasting building energy loads.However,they usually lack interpretability and do not incorporate domain knowledge,making it difficult for users to trust their predictions in practical applications.One important and interesting question remains unanswered:is it possible to use intrinsically interpretable models to achieve accuracy comparable to that of black-box models?With an aim of answering this question,this study proposes an intrinsically interpretable machine learning-based method to forecast building energy loads.It creatively combines two intrinsically interpretable machine learning algorithms:clustering decision trees and adaptive multiple linear regression.Clustering decision trees aim to automatically identify various building operation conditions,allowing for the training of multiple models tailored to each condition.It can reduce the complexity of model training data,leading to higher accuracy.Adaptive multiple linear regression is an improved regression algorithm tailored to building energy load prediction.It can adaptively modify regression coefficients according to building operations,enhancing the non-linear fitting capability of multiple linear regression.The proposed method is evaluated utilizing the operational data from an office building.The results indicate that the proposed method exhibits comparable accuracy to both random forests and extreme gradient boosting.Furthermore,it shows significantly superior accuracy,with an average improvement of 10.2%,compared with some popular black-box algorithms such as artificial neural networks,support vector regression,and classification and regression trees.As for model interpretability,the proposed method reveals that historical cooling loads are the most crucial for predicting building cooling loads under most conditions.Additionally,outdoor air temperature has a significant contribution to building cooling load prediction during the daytime on weekdays in summer and transition seasons.In the future,it will be valuable to explore integrating the laws of physics into the proposed method to further enhance its interpretability.
基金supported by the National Social Science Fund of China(20BXW101).
文摘Detecting fake news in multimodal and multilingual social media environments is challenging due to inherent noise,inter-modal imbalance,computational bottlenecks,and semantic ambiguity.To address these issues,we propose SparseMoE-MFN,a novel unified framework that integrates sparse attention with a sparse-activated Mixture of-Experts(MoE)architecture.This framework aims to enhance the efficiency,inferential depth,and interpretability of multimodal fake news detection.Sparse MoE-MFN leverages LLaVA-v1.6-Mistral-7B-HF for efficient visual encoding and Qwen/Qwen2-7B for text processing.The sparse attention module adaptively filters irrelevant tokens and focuses on key regions,reducing computational costs and noise.The sparse MoE module dynamically routes inputs to specialized experts(visual,language,cross-modal alignment)based on content heterogeneity.This expert specialization design boosts computational efficiency and semantic adaptability,enabling precise processing of complex content and improving performance on ambiguous categories.Evaluated on the large-scale,multilingualMR2 dataset,SparseMoEMFN achieves state-of-the-art performance.It obtains an accuracy of 86.7%and a macro-averaged F1 score of 0.859,outperforming strong baselines like MiniGPT-4 by 3.4%and 3.2%,respectively.Notably,it shows significant advantages in the“unverified”category.Furthermore,SparseMoE-MFN demonstrates superior computational efficiency,with an average inference latency of 89.1 ms and 95.4 GFLOPs,substantially lower than existing models.Ablation studies and visualization analyses confirm the effectiveness of both sparse attention and sparse MoE components in improving accuracy,generalization,and efficiency.
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.
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 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.
基金funded by the Zhejiang Provincial Key Science and Technology“LingYan”Project Foundation,grant number 2023C01145Zhejiang Gongshang University Higher Education Research Projects,grant number Xgy22028.
文摘With the deep integration of smart manufacturing and IoT technologies,higher demands are placed on the intelligence and real-time performance of industrial equipment fault detection.For industrial fans,base bolt loosening faults are difficult to identify through conventional spectrum analysis,and the extreme scarcity of fault data leads to limited training datasets,making traditional deep learning methods inaccurate in fault identification and incapable of detecting loosening severity.This paper employs Bayesian Learning by training on a small fault dataset collected from the actual operation of axial-flow fans in a factory to obtain posterior distribution.This method proposes specific data processing approaches and a configuration of Bayesian Convolutional Neural Network(BCNN).It can effectively improve the model’s generalization ability.Experimental results demonstrate high detection accuracy and alignment with real-world applications,offering practical significance and reference value for industrial fan bolt loosening detection under data-limited conditions.
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.
基金supported by the Meteorological Joint Funds of the National Natural Science Foundation of China(Grant No.U2142211)the National Natural Science Foundation of China(Grant Nos.42075141,42341202 and 62088101)+1 种基金the National Key Research and Development Program of China(Grant No.2020YFA0608000)the Shanghai Municipal Science and Technology Major Project(Grant No.2021SHZDZX0100).
文摘Accurate forecasting of tropical cyclone(TC)tracks and intensities is essential.Although the TianXing large weather model,a six-hourly forecasting model surpassing operational forecasts,exhibits superior performance,its TC forecasts still require enhancement.Prediction errors persist due to biases in the training data and smoothing effects in data-driven methods.To address this,we introduce CycloneBCNet,a deep-learning model designed to correct TianXing’s TC forecast biases by leveraging spatial and temporal data.CycloneBCNet utilizes the SimVP(simpler yet better video prediction)framework with spatial attention to highlight cyclone core regions in forecast fields.It also incorporates TC trend information(center position,maximum wind speed,and minimum sea level pressure)via an LSTM(long short-term memory)module.These TC vectors are derived from post-processed TianXing forecasts.By fusing features from forecast fields and TC vectors,CycloneBCNet corrects biases across multiple lead times.At a 96-h lead time,the track error reduces from 162.4 to 86.4 km,the wind speed error from 17.2 to 6.69 m s^(-1),and the pressure error from 22.2 to 9.36 hPa.Interpretability analysis shows that CycloneBCNet adjusts its attention across forecast lead times.Intensity corrections prioritize inner-core dynamics,particularly the eye and eyewall,while track corrections shift from lower-level variables and the cyclone’s core to broader environmental factors and mid-to upper-level features as the forecast duration increases.These findings demonstrate that CycloneBCNet effectively captures key TC dynamics consistent with meteorological principles,including the dominance of near-surface conditions for intensity and the increasing influence of steering currents on track prediction.
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.
