Interpretation Methods Research Articles (Page 1)

The present study explains our option for New Historicism in a way that avoids the comfort of simply casting our approach within a methodological frame assumed to meet our elective affinities. Instead, we are proceeding along the lines of a compare and contrast discussion of rival perspectives, not only on New Historicism but on the basics of literary theory and criticism. Although the two surveys of contemporary critical theories are didactic in nature, their theoretical assumptions come under our critical examination precisely because they lay the bases of the students’ appropriation of academic protocols. Whereas Mario Klarer (2004) does mention New Historicism defining it in a way which, we think, deserves several amendments, Julian Wolfreys, Editor of Introducing Criticism in the 21st Century (the Second, 2015 Edition of the original 2002 Introducing Criticism at the 21st Century) replaces what he calls the dominant “historicist, contextualist and sociological approach” in universities with a mix of “Space, Place and Memory” Studies including Affect Theory, Space and Place studies, Trauma, Testimony and Memory studies. We can also include here the chapter on Materialities, Immaterialities, (A)materialities, and Realities. The historicist picture is actually decomposed into space which is conceived of, not as static container, but as produced by historical praxis, and permanently emerging as both space of representation (projective, modelled on symbolic configurations) and as representation of space, that is, as an interface of the physical (Materialities), the imaginary (Irrealities), the cultural ((A) materialities), and the actual (Realities of the digital age). A spatialized history of traumatic events and memories will be the outcome of history’s and humanity’s entry into language, which, therefore, is not an objective record but a representation colored by affect and emotional response to historical experience. Among the various methods of interpretation, the author selects four basic approaches according to which most theoretical schools can be classified: text-based approaches, author-based approaches, reader-based approaches and context-based approaches.

This paper employs the methods of trend extrapolation and primary source interpretation to conduct an in-depth analysis of the impact of the Trump administration’s policy changes on Japan’s political and military trends. The article first reviews the traditional U. S. national strategy and the Rimland Theory, highlighting how the United States maintains its global hegemony by containing regional powers on the Eurasian continent. It then examines the shift in the Trump administration’s international political decisions against the backdrop of the Russia-Ukraine war, analyzing its strategic intentions of easing relations with Russia and containing China. Building on this, the paper provides a detailed analysis of Japan’s geopolitical background, arguing that Japan’s strategic choices are deeply influenced by Spykman’s Rimland Theory. Japan is gradually enhancing its influence in East Asia through measures such as“de facto political normalization, ”military transformation, and the construction of multilateral alliances. The study finds that Japan, as a typical rimland state, is seeking a balance between China and the United States by combining economic cooperation with military deterrence. Japan is also accelerating military modernization, strengthening its alliance with the United States, and actively expanding its strategic space in the Indo-Pacific region. However, Japan’s strategy of“dependent autonomy”faces numerous challenges, including over-reliance on U. S. security commitments and uncertainties in domestic politics. The paper concludes that Japan’s strategic choices not only reflect its practice of the Rimland Theory but also demonstrate its long-term vision of reshaping the East Asian order in the era of great power competition. Whether Japan can successfully achieve this goal depends on its ability to balance alliance dependence and strategic autonomy.

Abstract During the exploration and development of Oilfield A, the formation dip revealed by drilling data showed certain differences from the structure map predicted by conventional structural interpretation and mapping methods. This indicated that conventional methods would have difficulty in identifying low-amplitude structures in this area. To solve this geological problem, a comprehensive identification technology system for low-amplitude structural traps is proposed. The system begins with shearlet transform-based seismic data processing to enhance geological response features, followed by a detailed structural interpretation under the guidance of geological models. Innovatively, a shoreface sandbar deposition model is introduced, establishing a “flat base and convex top” interpretation framework, and adopted a technical workflow integrating seismic imaging velocity as soft constraint data with high-resolution vertical well velocity as hard constraint data for joint modeling. Thus, velocity-variant mapping was performed to achieve precise characterization of low-amplitude structures in Oilfield A. Practical applications demonstrated that this technological system significantly improved the consistency between seismic interpretation results and drilling data, providing a reliable geological basis and important technical support for the formulation of subsequent development plans for this oilfield.

This paper examines the ceramic statue set of the Three Great Emperor-Officials (Tam Quan Đại Đế), an antique ensemble of nine statues dating from the early 20th century, produced by the Bửu Nguyên kiln in Đề Ngạn (Saigon). Through artefact analysis, iconographic interpretation, and interdisciplinary methods, the study deciphers the cultural and historical values embodied in the set, from the Han-script inscriptions “堤岸埠” (Đề Ngạn Phụ – De Ngan Port) and “寶源造” (Bửu Nguyên Tạo – Made by Buu Nguyen) to the distinctive ceramic glazing techniques. The paper highlights two dimensions of significance: first, the set as an example of industrial heritage (a ceramic art form), and second, as a symbolic object of belief. The findings suggest that the statues vividly materialise the doctrine of the “celestial bureaucracy,” directly addressing communal needs for “granting blessings, pardoning sins, and praying for health.” Moreover, the set stands as a quintessential testament to the cultural identity of Saigon - Southern Vietnam, characterised by profound religious syncretism (Taoism, Confucianism, and Buddhism) and a dynamic process of creative indigenisation. Ultimately, the study clarifies the close interconnection between Saigon’s ceramic technology and the belief-driven practices of the Southern Vietnam community in the late 19th and early 20th centuries.

Introduction: Environments where people live and work shape resources and opportunities available to them and studying healthcare access in relation to people’s living environments helps in understanding structural factors beyond individual factors. This is especially relevant for many Adivasi communities whose lives are closely connected with forests. Methods: We used the critical interpretive synthesis method, a flexible, critical and iterative approach to literature synthesis. We conceptualised health and healthcare access in relation to neighbourhood environment and used this lens to examine healthcare access in Adivasi communities living in forest neighbourhoods in India. Results: We developed a lens of neighbourhood as a physical and social environment and used it to build a conceptual framework describing forest neighbourhoods in India. We describe forest neighbourhoods in terms of their built and social environment. The availability of mobile networks, condition of roads, flooding of streams during rains and the forms of transport available constitute the built environment. There are two important components of the social environment, first is the connection of the Adivasi people with the forest and second is the institutional environment comprising of different actors working in the forest neighbourhood. The life of Adivasi people is connected with the forest through their livelihood, nutrition, physical and mental well-being and their ecological knowledge about the forest from their lived experience. The institutional environment consists of different actors that shape the built and social environment that comprise the government institutions, private for-profit providers, civil society organisations, traditional healers and the forest department. Conclusion: While working on Adivasi health, it is critical to consider their connection with the forest. Exploring forest neighbourhoods as physical and social environments can help examine distribution of public services and how they are shaped by external policies and actors working in the neighbourhood. This could shift the focus of Adivasi health and healthcare interventions away from the current emphasis on individual-level health interventions.

This study presents a novel deep learning-machine learning fusion network for quantitative and interpretable assessment of chest X-ray positioning, aiming to analyze critical factors in patient positioning layout. In this retrospective study, we analyzed 3300 chest radiographs from a Chinese medical institution, collected between March 2021-December 2022. The dataset was partitioned into the XJ_chest_21 subset for training automated segmentation model and the XJ_chest_22 subset to validate three classification models: Random Forest Fusion Network (RFFN), Threshold Classification (TC), and Multivariate Logistic Regression (MLR). After automatically measuring five positioning indicators in the images, the data were input into the models to assess positioning quality. We compared the performance metrics of the three classification models, including AUC, accuracy, sensitivity, and specificity. SHAP (Shapley Additive Explanations) was utilized to interpret feature importance in the decision-making process of the RFFN model. We evaluated measurement consistency between the Automated Measurement Model (AMM) and radiologists. U-net++ demonstrated significantly superior performance compared to U-net in multi-target segmentation accuracy (mean Dice: 0.926 vs. 0.812). The five positioning metrics showed excellent agreement between AMM and reference standards (r = 0.93). ROC analysis indicated that RFFN performed significantly better in overall image quality classification (AUC, 0.982; 95% CI: 0.963, 0.993) compared to both TC (AUC, 0.959; 95% CI: 0.923, 0.995) and MLR (AUC, 0.953; 95% CI: 0.933, 0.974). Our study introduces a novel segmentation-based random forest fusion network that achieves accurate image positioning classification and identifies critical operational factors. Furthermore, the clinical interpretability of the fusion model was enhanced through the application of the SHAP method. Question How can AI-driven interpretable methods be utilized to assess patient positioning in chest radiography and enhance radiographers' accuracy? Findings The Random Forest Fusion Network (RFFN) outperformed Threshold Classification (TC) and Multivariate Logistic Regression (MLR) in positioning classification (AUC = 0.98). Clinical relevance An integrated framework that combines deep learning and machine learning achieves accurate image positioning classification, identifies critical operational factors, enables expert-level image quality assessment, and delivers automated feedback to radiographers.

Background: A positive coronary artery calcium (CAC) scan (Agatston score >0) establishes the presence of subclinical coronary artery disease. Currently, atherosclerotic cardiovascular disease (ASCVD) risk scoring tools, such as the Pooled Cohort Equations (PCE) and PREVENT, are not designed for predicting positive CAC. Additionally, the Multi-Ethnic Study of Atherosclerosis (MESA) CAC calculator relies on age, sex, and race/ethnicity, with a particular emphasis on age. Hypothesis: We hypothesized that using an artificial intelligence (AI) model with additional data beyond age, sex and race/ethnicity will enhance the prediction of a positive CAC in the middle-aged population. Method: We analyzed data from MESA baseline men 45-55 and women 45-65 years. Variable selection was conducted using A) a forward feature selection, building on a base logistic regression model with age, sex, race, followed by B) an embedded feature selection technique. We used FasterRisk, an interpretable AI method, to develop a risk score for estimating the likelihood of CAC>0. We compared the AI’s performance with the MESA CAC calculator, PCE, and PREVENT ASCVD, using the area under the receiver operating characteristic curve (AUC), DeLong’s test, and calibration analysis. Results: Among 2,139 MESA middle-aged participants, 581 individuals (27.2%) had a positive CAC. The predictors included age, sex, race, family history of heart attack, hypertension, waist/hip ratio, smoking, non–high-density lipoprotein cholesterol, and use of medications for diabetes, hypertension, or dyslipidemia. The AI achieved an AUC of 0.73 (95% CI: 0.71-0.75), which was significantly higher than that of the MESA CAC calculator (AUC: 0.68, CI: 0.66-0.70, P for difference < 0.001), PCE (AUC: 0.68, CI:0.65-0.70, P < 0.001), and PREVENT ASCVD (AUC: 0.68, CI:0.65-0.70, P < 0.001). Based on the calibration curve, only the MESA CAC calculator and the AI model demonstrated acceptable performance, with the AI outperforming the MESA tool in individuals at higher risk. Conclusion: We have developed an AI model that outperforms the MESA CAC calculator, PCE, and PREVENT risk scores for prediction of a positive CAC in the middle-aged population. These findings must be validated in other cohorts to substantiate their clinical utility. Nonetheless, the AUC range of 0.65 to 0.75 remind us that regardless of risk scoring tools, a large portion of middle-aged CAC positive cases are missed by relying on traditional risk factors.

To highlight how Longitudinal Experiential Concepts can be used as conceptual anchors within Longitudinal Interpretative Phenomenological Analysis to gain temporal interpretative phenomenological insights, a lack of which can be a criticism levelled at novice nurse or midwife researchers utilising phenomenological research methods. Longitudinal Experiential Concepts were utilised as a novel methodological adjunct to Longitudinal Interpretative Phenomenological Analysis in a study of the lived experience of perinatal anxiety by a midwife researcher. Longitudinal Experiential Concepts were identified following assimilation of Group Experiential Themes and while building the interpretative narrative account across all three data collection time points, with reflexive annotations facilitating their formulation. Within a longitudinal vertical (by time point) analysis, Longitudinal Experiential Concepts can add a horizontal view, giving a contemporaneous and dynamic perspective on the experiential threads woven throughout the temporal experience. Use of these conceptual anchors, enabled with reflexive prompts, can prevent the fragmentation that potentially occurs when examining moments in time in Longitudinal Qualitative Research, facilitate clarity in the temporal view of the whole phenomenon and enable phenomenological insights. A novel addition to the Longitudinal Interpretative Phenomenological Analysis method, Longitudinal Experiential Concepts as conceptual anchors can encourage deeper holistic thinking about the less immediately obvious facets of experience and temporal progression and give the novice nurse or midwife researcher a means to robustly access the phenomenological attitude. These principles may be applicable more broadly within other Longitudinal Qualitative Research approaches. The use of Longitudinal Experiential Concepts in Longitudinal Interpretative Phenomenological Analysis can enable nurses, midwives, and other clinical health researchers to produce high-quality, robust longitudinal phenomenological research. This is important due to the popular use and value of these methods aiming to generate new understanding of health conditions and improve patient care. Patients and members of the public were involved in the design of the original research study. Their contributions included reviewing study plans, ensuring the research was in line with the priorities of women experiencing poor perinatal mental health, guiding the researchers on the acceptability of the proposed approach to recruitment and data collection and reviewing participant information and study marketing materials. We gratefully acknowledge Get Me Out the Four Walls, Norfolk, for their support enabling this.

The housing prices are crucial to the sustainable development of the real estate market. Nowadays, few academic attempts have focused on the impact of multi-dimensional accessibility on housing prices in a large-scale area. This study utilized machine learning methods to extract indicators of the visual environment from street view images. The indicators were combined with multiple sources of spatiotemporal geographic big data, such as second-hand housing data and online map POIs, to quantify the factors of housing prices. Both the hedonic price model and random forest were constructed, with Shapley additive explanations applied to interpret the results. Our work took Shanghai as a case study, and the results indicate that the random forest exhibits superior performance compared to the hedonic price model. The location accessibility (e.g., distance to the CBD) is paramount, and functional accessibility (e.g., to subways and finance facilities) exhibits nonlinear thresholds. We further uncovered the characteristics of the nonlinear relationship between visual environmental factors and housing prices. Our findings can deepen the understanding of housing price variation in the spatial dimension and provide the theoretical basis for ensuring the optimization of urban planning.

Introduction: Accurate subtyping of cardiac amyloidosis (CA) into light - chain (AL) or transthyretin (ATTR) forms is essential for targeted therapy but often relies on biopsy or ^99mTc - PYP scintigraphy - tests that are invasive or involve ionizing radiation. Cardiac MRI (CMR) provides rich morphologic and tissue - characterisation data, yet its complex multi - sequence interpretation limits routine subtype assignment. Research Questions: A deep learning model integrating multimodal CMR (cine, LGE, T1/T2 maps), demographic data, and key laboratory values, including comprehensive serum and urine light chains could enable high - accuracy, non-invasive CA subtype classification with improved interpretability. Methods: We developed a deep learning model using data from 122 CA patients from the SCMR Registry (61 AL, 61 ATTR; mean age 70.2 ± 11.0 y; 24 % female) confirmed per society guidelines. Sequence - specific encoders included an xLSTM for cine, 3D CNNs for LGE, and 2D CNNs for parametric maps. Demographic data (age as Fourier features, sex as embedding) and labs (light chains, M - protein; continuous as Fourier features, categorical as embeddings) were processed via MLPs. These non-imaging embeddings were integrated with aggregated CMR features using a cross - attention mechanism. Interpretability was provided by (1) Monte - Carlo dropout for uncertainty, (2) modality - gate weights, and (3) Grad - CAM. Five - fold cross - validation (patient - level splits) evaluated performance; operating thresholds were chosen by maximising the Youden index (J = sensitivity + specificity – 1). Results: Across 5 - fold cross - validation, the model achieved a mean ROC - AUC of 0.92 ± 0.05. At the fold - specific Youden thresholds it reached sensitivity 0.82 ± 0.05 and specificity 0.93 ± 0.05 for ATTR. Confidence scores derived from Monte - Carlo dropout averaged 0.68, likely scaled down due to a combination of aggressive dropout during training with cross entropy loss, enabled automatic flagging of uncertain cases while other interpretability methods provided insights into model decision - making. Conclusions: Integrating CMR, demographics, and light chains into an interpretable multimodal deep - learning framework enables accurate, non - invasive CA subtyping while quantifying predictive confidence. By coupling strong performance with uncertainty - aware triage and transparent saliency cues, the model could reduce reliance on scintigraphy or biopsy and expedite subtype - directed care pending prospective validation.

Women are underrepresented within academic general practice, particularly after mid-career. To explore the lived experiences of early- and mid-career female academic GPs and inform ways to reduce attrition through the GP academic career path. This was an in-depth qualitative interview study within the UK. Adapted biographical narrative interpretive method interviews were utilised, analysed thematically using Braun and Clarke's reflexive method, and informed by Bourdieu's theory of practice. Composite narratives were developed as part of the analysis to identify key biographical storylines and to present findings. In total, 39 interviews with a diverse sample of 13 female academic GPs were conducted. Five composite narratives reflecting corresponding themes were generated. Participants described: challenges in 'thriving? or surviving?' in academic general practice; 'feeling on the cliff edge' with precarious careers balanced against fulfilment and creativity; the cumulative burdens of 'doing the juggle'; and living between 'two worlds'. Women who followed conventional academic career pathways appeared more positive within their careers than those who did not. Women who entered later in their GP careers and those who experienced multiple forms of disadvantage reported additional barriers. Participants described their efforts managing practical and ethical tensions between their clinical, academic, and personal responsibilities. Women academic GPs live complex and demanding lives. Different strands of their unfolding life narratives - as clinicians, academics, and partners and/or carers - generate recurrent tensions and conflicting pressures. Experiences are varied. Academic support structures should address (among other things) the career-limiting impact of short-term contracts, mentorship, and inequity in navigating the field.

To address the urgent need for safety maintenance of remote reclaimed islands, we propose a novel monitoring framework integrating PPP, leveling, and InSAR technologies to comprehensively capture slow surface deformations across point, line, and area dimensions. This study also details the data interpretation methods and critical processing workflow, using Shandong Haiyang Junzi-Lianli island as a case study. The monitoring results revealed maximum annual displacements of 2 mm for PPP reference points, 5 mm elevation variations for leveling benchmarks, and an average InSAR deformation rate of −0.34 mm/yr with peak deformation reaching 18.60 mm/yr. Meanwhile, cross-validation was performed on the results obtained from these three different techniques. The discrepancy between the benchmark PPP observation and the InSAR measurement was 3.81 mm. For the common monitoring points, the differences between leveling and InSAR ranged from 0.57 mm to 5.41 mm. The deformation trends observed in PPP reference points, leveling benchmarks, and corresponding InSAR time-series data demonstrated good consistency, indicating overall stability of the reclamation island. The proposed methodology accurately identifies minute surface deformations at different spatial scales (point, linear, and areal) of the artificial island, overcoming the limitations of single-technique approaches, thus proving to be an effective means for subsidence assessment of offshore artificial island structures. This study advances the technical framework for reclaimed island stability monitoring, offering data and solutions to identify subsidence risks and enhance disaster prevention.

Validation of HPV tests usable in cervical cancer screening require demonstration of noninferior clinical accuracy and sufficient reproducibility. The Papilloplex HR-HPV assay [version1] (Genefirst, UK) has been validated only for clinical accuracy. Here, we assessed its reproducibility, aiming to complete the validation process. A panel of 550 cryopreserved cervical cell samples collected from women attending the cervical cancer screening program in Belgium was used to assess the intra- and inter-laboratory reproducibility of Papilloplex HR-HPV [version1], a full genotyping assay that identifies separately 14 high-risk HPV (hrHPV) types using multiple probe amplification technology. We assessed whether the reproducibility fulfils validation criteria (lower 95% confidence interval [CI] bound ≥ 87% and κ ≥ 0.50). Subsequently, we compared the concordance between version1 and a new version6 of the assay and between three analysis methods for PCR curve interpretation. Papilloplex HR-HPV version1 assay showed an excellent reproducibility for hrHPV (97.5% [CI: 95.8%-98.7%], κ = 0.94 for intra- and 93.5% [CI: 91.0%-95.4%], κ = 0.85 for inter-laboratory reproducibility). Concordance analyses exhibited an excellent agreement between two assay versions and between three PCR curve analysis methods. Papilloplex HR-HPV version1 assay exhibited excellent reproducibility, completing the international validation criteria. Papilloplex HR-HPV version6 showed excellent concordance with version1 but still lacks clinical validation.

Interpretable equipment health state assessment method based on evidential reasoning with statistical feature

An interpretable deep transfer learning method for fault diagnosis of nuclear power plants under multiple power level conditions

An interpretable dynamic Bayesian network method for time-varying seismic liquefaction uplift risk assessment of underground rectangular structures

Future and present susceptibility to thermokarst hazards in the Northern Hemisphere using an interpretable CNN method

Leveraging explainable artificial intelligence for transparent and trustworthy cancer detection systems.

Research Problem: This article examines the enduring land conflict between the Tiv and Jukun communities in Taraba State, Nigeria. The conflict centers on land as a resource essential to survival, identity, and political relevance. Despite multiple peace initiatives, the dispute recurs, highlighting the limitations of existing interventions and the neglect of deeper structural issues. Methods/Theory: The article employs a qualitative approach, using documentary analysis and interpretive methods to explore the causes and peacebuilding responses. It is grounded in Realistic Conflict Theory, which links intergroup conflict to competition over scarce resources, and uses the conflict tree model to analyze immediate, underlying, and structural causes. Results: Findings reveal that land represents more than a resource; it symbolizes power, belonging, and recognition. The conflict follows a cyclical pattern, re-emerging with heightened intensity despite efforts at resolution. Traditional mechanisms and state-led peacebuilding have proven inadequate, often failing to address the root causes or provide long-term stability. Conclusion: The Tiv-Jukun conflict illustrates the failure of superficial interventions to resolve deeply embedded disputes. Structural inequalities and historical grievances must be addressed to achieve lasting peace. Key Contribution to Knowledge: By applying Realistic Conflict Theory and the conflict tree, the article offers a fresh perspective on the persistence of ethnic land conflicts in Nigeria, emphasizing the roles of resource competition and identity. Recommendation: The article advocates for inclusive, community-driven peacebuilding, and mobilization of social and intellectual resources to tackle structural causes and build trust.

The relevance of this paper stems from the need to identify the lexical descriptors associated with the “CAREER” biogram. This biogram aims to explore Steve Jobs’ motives for image creation and creative self-expression. The project aims to analyze Steve Jobs’ memories as well as his colleagues, with the “CAREER” biogram serving as a case study to illuminate this iconic figure’s self-identification. The subject of this paper is Steve Jobs’ self-identification throughout his conscious life. Lexical units — such as words and phrases — are utilised as descriptors in the literary biography that encapsulate the essence of the “CAREER” biogram. They serve as the constituents of Steve Jobs’ portrait as a reflection of his personality. These semantic connections reflect the nuances of Steve Jobs’ self-identification during his mindful life. The biogram “CAREER” is represented by two structural components (“The Apple I” and “The Apple II and Xerox”) as they both brightly outline Steve Jobs’ formation of professional identity. The study employs general scientific methods, including analysis, synthesis, and generalisation, along with contextual analysis, interpretive methods, and discourse analysis. The novelty of the research lies in both the subject being studied and the perspective of the analysis, which provides a solid foundation for a deeper exploration of Steve Jobs’ psychological profile as well as his communication style. The results. Through the biogram “CAREER”, which encompasses the chapters “The APPLE I,” “The APPLE II,” and “XEROX and LISA” from W. Isaacson’s literary biography, we have identified particular lexical descriptors that collectively illustrate Steve Jobs’ psychological profile, showcasing his self-perception as an INSPIRING VISIONARY, as well as a GENIUS, CREATOR, AUTHORITARIAN LEADER, and REVOLUTIONARY. It has been established that five strategies — self-presentation, forecasting, control, criticism, and appealing to authorities — play a role in shaping the two motives of self-identity, which reflect the development and transformation of a person’s character. These motives help build Steve Jobs’ persona and serve as the driving force behind his image creation and artistic self-expression.