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.

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.

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.

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.

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.

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.

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.

Characterizing structure of cross-disciplinary impact of global disciplines:A perspective of the Hierarchy of Science

Purpose:Interdisciplinary fields have become the driving force of modern science and a significant source of scientific innovation.However,there is still a paucity of analysis about the essential characteristics of disciplines’cross-disciplinary impact.Design/methodology/approach:In this study,we define cross-disciplinary impact on one discipline as its impact to other disciplines,and refer to a three-dimensional framework of variety-balance-disparity to characterize the structure of cross-disciplinary impact.The variety of cross-disciplinary impact of the discipline was defined as the proportion of the high cross-disciplinary impact publications,and the balance and disparity of cross-disciplinary impact were measured as well.To demonstrate the cross-disciplinary impact of the disciplines in science,we chose Microsoft Academic Graph(MAG)as the data source,and investigated the relationship between disciplines’cross-disciplinary impact and their positions in the Hierarchy of Science(HOS).Findings:Analytical results show that there is a significant correlation between the ranking of cross-disciplinary impact and the HOS structure,and that the discipline exerts a greater cross-disciplinary impact on its neighboring disciplines.Several bibliometric features that measure the hardness of a discipline,including the number of references,the number of cited disciplines,the citation distribution,and the Price index have a significant positive effect on the variety of cross-disciplinary impact.The number of references,the number of cited disciplines,and the citation distribution have significant positive and negative effects on balance and disparity,respectively.It is concluded that the less hard the discipline,the greater the cross-disciplinary impact,the higher balance and the lower disparity of cross-disciplinary impact.Research limitations:In the empirical analysis of HOS,we only included five broad disciplines.This study also has some biases caused by the data source and applied regression models.Practical implications:This study contributes to the formulation of discipline-specific policies and promotes the growth of interdisciplinary research,as well as offering fresh insights for predicting the cross-disciplinary impact of disciplines.Originality/value:This study provides a new perspective to properly understand the mechanisms of cross-disciplinary impact and disciplinary integration.

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.

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.

Hyperspectral Image Based Interpretable Feature Clustering Algorithm

Hyperspectral imagery encompasses spectral and spatial dimensions,reflecting the material properties of objects.Its application proves crucial in search and rescue,concealed target identification,and crop growth analysis.Clustering is an important method of hyperspectral analysis.The vast data volume of hyperspectral imagery,coupled with redundant information,poses significant challenges in swiftly and accurately extracting features for subsequent analysis.The current hyperspectral feature clustering methods,which are mostly studied from space or spectrum,do not have strong interpretability,resulting in poor comprehensibility of the algorithm.So,this research introduces a feature clustering algorithm for hyperspectral imagery from an interpretability perspective.It commences with a simulated perception process,proposing an interpretable band selection algorithm to reduce data dimensions.Following this,amulti-dimensional clustering algorithm,rooted in fuzzy and kernel clustering,is developed to highlight intra-class similarities and inter-class differences.An optimized P systemis then introduced to enhance computational efficiency.This system coordinates all cells within a mapping space to compute optimal cluster centers,facilitating parallel computation.This approach diminishes sensitivity to initial cluster centers and augments global search capabilities,thus preventing entrapment in local minima and enhancing clustering performance.Experiments conducted on 300 datasets,comprising both real and simulated data.The results show that the average accuracy(ACC)of the proposed algorithm is 0.86 and the combination measure(CM)is 0.81.

THAPE: A Tunable Hybrid Associative Predictive Engine Approach for Enhancing Rule Interpretability in Association Rule Learning for the Retail Sector

Association rule learning(ARL)is a widely used technique for discovering relationships within datasets.However,it often generates excessive irrelevant or ambiguous rules.Therefore,post-processing is crucial not only for removing irrelevant or redundant rules but also for uncovering hidden associations that impact other factors.Recently,several post-processing methods have been proposed,each with its own strengths and weaknesses.In this paper,we propose THAPE(Tunable Hybrid Associative Predictive Engine),which combines descriptive and predictive techniques.By leveraging both techniques,our aim is to enhance the quality of analyzing generated rules.This includes removing irrelevant or redundant rules,uncovering interesting and useful rules,exploring hidden association rules that may affect other factors,and providing backtracking ability for a given product.The proposed approach offers a tailored method that suits specific goals for retailers,enabling them to gain a better understanding of customer behavior based on factual transactions in the target market.We applied THAPE to a real dataset as a case study in this paper to demonstrate its effectiveness.Through this application,we successfully mined a concise set of highly interesting and useful association rules.Out of the 11,265 rules generated,we identified 125 rules that are particularly relevant to the business context.These identified rules significantly improve the interpretability and usefulness of association rules for decision-making purposes.

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.

Intelligent geochemical interpretation of mass chromatograms:Based on convolution neural network

Gas chromatography-mass spectrometry(GC-MS)is an extremely important analytical technique that is widely used in organic geochemistry.It is the only approach to capture biomarker features of organic matter and provides the key evidence for oil-source correlation and thermal maturity determination.However,the conventional way of processing and interpreting the mass chromatogram is both timeconsuming and labor-intensive,which increases the research cost and restrains extensive applications of this method.To overcome this limitation,a correlation model is developed based on the convolution neural network(CNN)to link the mass chromatogram and biomarker features of samples from the Triassic Yanchang Formation,Ordos Basin,China.In this way,the mass chromatogram can be automatically interpreted.This research first performs dimensionality reduction for 15 biomarker parameters via the factor analysis and then quantifies the biomarker features using two indexes(i.e.MI and PMI)that represent the organic matter thermal maturity and parent material type,respectively.Subsequently,training,interpretation,and validation are performed multiple times using different CNN models to optimize the model structure and hyper-parameter setting,with the mass chromatogram used as the input and the obtained MI and PMI values for supervision(label).The optimized model presents high accuracy in automatically interpreting the mass chromatogram,with R2values typically above 0.85 and0.80 for the thermal maturity and parent material interpretation results,respectively.The significance of this research is twofold:(i)developing an efficient technique for geochemical research;(ii)more importantly,demonstrating the potential of artificial intelligence in organic geochemistry and providing vital references for future related studies.

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.

Sci-tech Simultaneous Interpreter Education Based on Translation Universals Research

The rapid evolution of scientific and technological advancements and industrial changes has profoundly interconnected countries and regions in the digital information era,creating a globalized environment where effective communication is paramount.Consequently,the demand for proficient interpreting skills within the scientific and technology sectors has surged,making effective language communication increasingly crucial.This paper explores the potential impact of translation universals on enhancing sci-tech simultaneous interpreter education.By examining the selection of teaching materials,methods,and activities through the lens of translation universals,this study aims to improve the quality of teaching content,innovate instructional approaches,and ultimately,enhance the effectiveness of interpreter education.The findings of this research are expected to provide valuable insights for curriculum development and pedagogical strategies in interpreter education.

Angular unconformity in Pennsylvanian strata from 3-D seismic interpretation,Goldsmith Field,West Texas

The Pennsylvanian unconformity,which is a detrital surface,separates the beds of the Permian-aged strata from the Lower Paleozoic in the Central Basin Platform.Seismic data interpretation indicates that the unconformity is an angular unconformity,overlying multiple normal faults,and accompanied with a thrust fault which maximizes the region's structural complexity.Additionally,the Pennsylvanian angular unconformity creates pinch-outs between the beds above and below.We computed the spectral decomposition and reflector convergence attributes and analyzed them to characterize the angular unconformity and faults.The spectral decomposition attribute divides the broadband seismic data into different spectral bands to resolve thin beds and show thickness variations.In contrast,the reflector convergence attribute highlights the location and direction of the pinch-outs as they dip south at angles between 2° and 6°.After reviewing findings from RGB blending of the spectrally decomposed frequencies along the Pennsylvanian unconformity,we observed channel-like features and multiple linear bands in addition to the faults and pinch-outs.It can be inferred that the identified linear bands could be the result of different lithologies associated with the tilting of the beds,and the faults may possibly influence hydrocarbon migration or act as a flow barrier to entrap hydrocarbon accumulation.The identification of this angular unconformity and the associated features in the study area are vital for the following reasons:1)the unconformity surface represents a natural stratigraphic boundary;2)the stratigraphic pinch-outs act as fluid flow connectivity boundaries;3)the areal extent of compartmentalized reservoirs'boundaries created by the angular unconformity are better defined;and 4)fault displacements are better understood when planning well locations as faults can be flow barriers,or permeability conduits,depending on facies heterogeneity and/or seal effectiveness of a fault,which can affect hydrocarbon production.The methodology utilized in this study is a further step in the characterization of reservoirs and can be used to expand our knowledge and obtain more information about the Goldsmith Field.

Accountable capability improvement based on interpretable capability evaluation using belief rule base

A new approach is proposed in this study for accountable capability improvement based on interpretable capability evaluation using the belief rule base(BRB).Firstly,a capability evaluation model is constructed and optimized.Then,the key sub-capabilities are identified by quantitatively calculating the contributions made by each sub-capability to the overall capability.Finally,the overall capability is improved by optimizing the identified key sub-capabilities.The theoretical contributions of the proposed approach are as follows.(i)An interpretable capability evaluation model is constructed by employing BRB which can provide complete access to decision-makers.(ii)Key sub-capabilities are identified according to the quantitative contribution analysis results.(iii)Accountable capability improvement is carried out by only optimizing the identified key sub-capabilities.Case study results show that“Surveillance”,“Positioning”,and“Identification”are identified as key sub-capabilities with a summed contribution of 75.55%in an analytical and deducible fashion based on the interpretable capability evaluation model.As a result,the overall capability is improved by optimizing only the identified key sub-capabilities.The overall capability can be greatly improved from 59.20%to 81.80%with a minimum cost of 397.Furthermore,this paper also investigates how optimizing the BRB with more collected data would affect the evaluation results:only optimizing“Surveillance”and“Positioning”can also improve the overall capability to 81.34%with a cost of 370,which thus validates the efficiency of the proposed approach.

Computation Tree Logic Model Checking of Multi-Agent Systems Based on Fuzzy Epistemic Interpreted Systems

Model checking is an automated formal verification method to verify whether epistemic multi-agent systems adhere to property specifications.Although there is an extensive literature on qualitative properties such as safety and liveness,there is still a lack of quantitative and uncertain property verifications for these systems.In uncertain environments,agents must make judicious decisions based on subjective epistemic.To verify epistemic and measurable properties in multi-agent systems,this paper extends fuzzy computation tree logic by introducing epistemic modalities and proposing a new Fuzzy Computation Tree Logic of Knowledge(FCTLK).We represent fuzzy multi-agent systems as distributed knowledge bases with fuzzy epistemic interpreted systems.In addition,we provide a transformation algorithm from fuzzy epistemic interpreted systems to fuzzy Kripke structures,as well as transformation rules from FCTLK formulas to Fuzzy Computation Tree Logic(FCTL)formulas.Accordingly,we transform the FCTLK model checking problem into the FCTL model checking.This enables the verification of FCTLK formulas by using the fuzzy model checking algorithm of FCTL without additional computational overheads.Finally,we present correctness proofs and complexity analyses of the proposed algorithms.Additionally,we further illustrate the practical application of our approach through an example of a train control system.