Different Interpretations of Sons and Lovers

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.

Fusing Digital Elevation Models to Improve Hydrological Interpretations

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.

Distinctive Drug-resistant Mutation Profiles and Interpretations of HIV-1 Proviral DNA Revealed by Deep Sequencing in Reverse Transcriptase

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.

On "China-measures related to the exportation of various raw materials" case in terms of DSB's interpretations

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.

New Artistic-Iconological Interpretations in Ecourbarchitecture

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.

On the Effects of Different Interpretations of Stochastic Differential Equations

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.

Study on deep structure in Ailaoshan region based on aeromagnetic interpretations

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.

Textualism, structuralism and originalism： the art of the NPC Standing Committee＇s interpretations of the Basic Law

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.

Lured by Tradition A famous Chinese vlogger is using social media to promote intangible cultural heritage

In the digital age,where traditional culture is in danger of gradually disappearing,one iconic figure has taken it upon herself to reverse the trend by reviving traditional skills.A prominent Chinese vlogger,Li Ziqi,whose birth name is Li Jiajia,has amassed a vast online following by reinterpreting traditions that date back thousands of years.Following a threeyear absence from social media,she made a highly anticipated return with the release of three new videos in mid-November 2024.These videos represent the culmination of several months of behind-the-scenes work.

STUDY ON TEACHING THE INTERPRETATIONS OF CONVERSATIONAL IMPLICATURE IN LISTENING COMPREHENSION

本文讨论了在大学英语听力教学中教授会话含义理解的可行性和有效性。在教学实验中,研究者通过教授基本的语言学理论特别是会话含义理论的相关知识,改革传统听力教学模式,优化课堂教学结构和互动架构。研究表明,学生在掌握相应的会话含义理论之后,经过有指导的学习实践,成功地掌握了教材中听力会话的基本结构以及会话含义的推导过程,进而实现对篇章的理解。因此,在大学英语听力教学中教授会话含义,可以促进学生听力能力的提高。

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

A method to interpret fracture aperture of rock slope using adaptive shape and unmanned aerial vehicle multi-angle nap-of-the-object photogrammetry

The aperture of natural rock fractures significantly affects the deformation and strength properties of rock masses,as well as the hydrodynamic properties of fractured rock masses.The conventional measurement methods are inadequate for collecting data on high-steep rock slopes in complex mountainous regions.This study establishes a high-resolution three-dimensional model of a rock slope using unmanned aerial vehicle(UAV)multi-angle nap-of-the-object photogrammetry to obtain edge feature points of fractures.Fracture opening morphology is characterized using coordinate projection and transformation.Fracture central axis is determined using vertical measuring lines,allowing for the interpretation of aperture of adaptive fracture shape.The feasibility and reliability of the new method are verified at a construction site of a railway in southeast Tibet,China.The study shows that the fracture aperture has a significant interval effect and size effect.The optimal sampling length for fractures is approximately 0.5e1 m,and the optimal aperture interpretation results can be achieved when the measuring line spacing is 1%of the sampling length.Tensile fractures in the study area generally have larger apertures than shear fractures,and their tendency to increase with slope height is also greater than that of shear fractures.The aperture of tensile fractures is generally positively correlated with their trace length,while the correlation between the aperture of shear fractures and their trace length appears to be weak.Fractures of different orientations exhibit certain differences in their distribution of aperture,but generally follow the forms of normal,log-normal,and gamma distributions.This study provides essential data support for rock and slope stability evaluation,which is of significant practical importance.

Artificial intelligence-driven radiomics study in cancer:the role of feature engineering and modeling

Modern medicine is reliant on various medical imaging technologies for non-invasively observing patients’anatomy.However,the interpretation of medical images can be highly subjective and dependent on the expertise of clinicians.Moreover,some potentially useful quantitative information in medical images,especially that which is not visible to the naked eye,is often ignored during clinical practice.In contrast,radiomics performs high-throughput feature extraction from medical images,which enables quantitative analysis of medical images and prediction of various clinical endpoints.Studies have reported that radiomics exhibits promising performance in diagnosis and predicting treatment responses and prognosis,demonstrating its potential to be a non-invasive auxiliary tool for personalized medicine.However,radiomics remains in a developmental phase as numerous technical challenges have yet to be solved,especially in feature engineering and statistical modeling.In this review,we introduce the current utility of radiomics by summarizing research on its application in the diagnosis,prognosis,and prediction of treatment responses in patients with cancer.We focus on machine learning approaches,for feature extraction and selection during feature engineering and for imbalanced datasets and multi-modality fusion during statistical modeling.Furthermore,we introduce the stability,reproducibility,and interpretability of features,and the generalizability and interpretability of models.Finally,we offer possible solutions to current challenges in radiomics research.

An improved deep dilated convolutional neural network for seismic facies interpretation

With the successful application and breakthrough of deep learning technology in image segmentation,there has been continuous development in the field of seismic facies interpretation using convolutional neural networks.These intelligent and automated methods significantly reduce manual labor,particularly in the laborious task of manually labeling seismic facies.However,the extensive demand for training data imposes limitations on their wider application.To overcome this challenge,we adopt the UNet architecture as the foundational network structure for seismic facies classification,which has demonstrated effective segmentation results even with small-sample training data.Additionally,we integrate spatial pyramid pooling and dilated convolution modules into the network architecture to enhance the perception of spatial information across a broader range.The seismic facies classification test on the public data from the F3 block verifies the superior performance of our proposed improved network structure in delineating seismic facies boundaries.Comparative analysis against the traditional UNet model reveals that our method achieves more accurate predictive classification results,as evidenced by various evaluation metrics for image segmentation.Obviously,the classification accuracy reaches an impressive 96%.Furthermore,the results of seismic facies classification in the seismic slice dimension provide further confirmation of the superior performance of our proposed method,which accurately defines the range of different seismic facies.This approach holds significant potential for analyzing geological patterns and extracting valuable depositional information.

Characterization and spatial analysis of coseismic landslides triggered by the Luding Ms 6.8 earthquake in the Xianshuihe fault zone, Southwest China

On September 5, 2022, a magnitude Ms 6.8 earthquake occurred along the Moxi fault in the southern part of the Xianshuihe fault zone located in the southeastern margin of the Tibetan Plateau,resulting in severe damage and substantial economic loss. In this study, we established a coseismic landslide database triggered by Luding Ms 6.8 earthquake, which includes 4794 landslides with a total area of 46.79 km^(2). The coseismic landslides primarily consisted of medium and small-sized landslides, characterized by shallow surface sliding. Some exhibited characteristics of high-position initiation resulted in the obstruction or partial obstruction of rivers, leading to the formation of dammed lakes. Our research found that the coseismic landslides were predominantly observed on slopes ranging from 30° to 50°, occurring at between 1000 m and 2500 m, with slope aspects varying from 90° to 180°. Landslides were also highly developed in granitic bodies that had experienced structural fracturing and strong-tomoderate weathering. Coseismic landslides concentrated within a 6 km range on both sides of the Xianshuihe and Daduhe fault zones. The area and number of coseismic landslides exhibited a negative correlation with the distance to fault lines, road networks, and river systems, as they were influenced by fault activity, road excavation, and river erosion. The coseismic landslides were mainly distributed in the southeastern region of the epicenter, exhibiting relatively concentrated patterns within the IX-degree zones such as Moxi Town, Wandong River basin, Detuo Town to Wanggangping Township. Our research findings provide important data on the coseismic landslides triggered by the Luding Ms 6.8 earthquake and reveal the spatial distribution patterns of these landslides. These findings can serve as important references for risk mitigation, reconstruction planning, and regional earthquake disaster research in the earthquake-affected area.

Model-based interpretation of bottomhole pressure records during matrix treatments in layered formations

During injection treatments, bottomhole pressure measurements may significantly mismatch modeling results. We devise a computationally effective technique for interpretation of fluid injection in a wellbore interval with multiple geological layers based on the bottomhole pressure measurements. The permeability, porosity and compressibility in each layer are initially setup, while the skin factor and partitioning of injected fluids among the zones during the injection are found as a solution of the problem. The problem takes into account Darcy flow and chemical interactions between the injected acids, diverter fluids and reservoir rock typical in modern matrix acidizing treatments. Using the synchronously recorded injection rate and bottomhole pressure, we evaluate skin factor changes in each layer and actual fluid placement into the reservoir during different pumping jobs: matrix acidizing, water control, sand control, scale squeezes and water flooding. The model is validated by comparison with a simulator used in industry. It gives opportunity to estimate efficiency of a matrix treatment job, role of every injection stage, and control fluid delivery to each layer in real time. The presented interpretation technique significantly improves accuracy of matrix treatments analysis by coupling the hydrodynamic model with records of pressure and injection rate during the treatment.

Directly predicting N_(2) electroreduction reaction free energy using interpretable machine learning with non-DFT calculated features

Electrocatalytic nitrogen reduction to ammonia has garnered significant attention with the blooming of single-atom catalysts(SACs),showcasing their potential for sustainable and energy-efficient ammonia production.However,cost-effectively designing and screening efficient electrocatalysts remains a challenge.In this study,we have successfully established interpretable machine learning(ML)models to evaluate the catalytic activity of SACs by directly and accurately predicting reaction Gibbs free energy.Our models were trained using non-density functional theory(DFT)calculated features from a dataset comprising 90 graphene-supported SACs.Our results underscore the superior prediction accuracy of the gradient boosting regression(GBR)model for bothΔg(N_(2)→NNH)andΔG(NH_(2)→NH_(3)),boasting coefficient of determination(R^(2))score of 0.972 and 0.984,along with root mean square error(RMSE)of 0.051 and 0.085 eV,respectively.Moreover,feature importance analysis elucidates that the high accuracy of GBR model stems from its adept capture of characteristics pertinent to the active center and coordination environment,unveilling the significance of elementary descriptors,with the colvalent radius playing a dominant role.Additionally,Shapley additive explanations(SHAP)analysis provides global and local interpretation of the working mechanism of the GBR model.Our analysis identifies that a pyrrole-type coordination(flag=0),d-orbitals with a moderate occupation(N_(d)=5),and a moderate difference in covalent radius(r_(TM-ave)near 140 pm)are conducive to achieving high activity.Furthermore,we extend the prediction of activity to more catalysts without additional DFT calculations,validating the reliability of our feature engineering,model training,and design strategy.These findings not only highlight new opportunity for accelerating catalyst design using non-DFT calculated features,but also shed light on the working mechanism of"black box"ML model.Moreover,the model provides valuable guidance for catalytic material design in multiple proton-electron coupling reactions,particularly in driving sustainable CO_(2),O_(2),and N_(2) conversion.

Towards trustworthy multi-modal motion prediction:Holistic evaluation and interpretability of outputs

Predicting the motion of other road agents enables autonomous vehicles to perform safe and efficient path planning.This task is very complex,as the behaviour of road agents depends on many factors and the number of possible future trajectories can be consid-erable(multi-modal).Most prior approaches proposed to address multi-modal motion prediction are based on complex machine learning systems that have limited interpret-ability.Moreover,the metrics used in current benchmarks do not evaluate all aspects of the problem,such as the diversity and admissibility of the output.The authors aim to advance towards the design of trustworthy motion prediction systems,based on some of the re-quirements for the design of Trustworthy Artificial Intelligence.The focus is on evaluation criteria,robustness,and interpretability of outputs.First,the evaluation metrics are comprehensively analysed,the main gaps of current benchmarks are identified,and a new holistic evaluation framework is proposed.Then,a method for the assessment of spatial and temporal robustness is introduced by simulating noise in the perception system.To enhance the interpretability of the outputs and generate more balanced results in the proposed evaluation framework,an intent prediction layer that can be attached to multi-modal motion prediction models is proposed.The effectiveness of this approach is assessed through a survey that explores different elements in the visualisation of the multi-modal trajectories and intentions.The proposed approach and findings make a significant contribution to the development of trustworthy motion prediction systems for autono-mous vehicles,advancing the field towards greater safety and reliability.