Interpretive Framework Research Articles

A Systematic Review and Conceptual Framework for Natural Resource Performance Management

The topic of "Performance management of natural resources" has drawn more attention in the last few decades. Investigating how to manage natural resource performance more optimally is crucial for the long-term social and economic advancement of a nation. This study reviews the research progress of performance management of natural resources (including water resources) based on the pertinent literature of "Performance management of natural resources" and "performance management and evaluation of nature resources" in Web of Science. Through a review of the literature and inductive analysis, the authors found that the pursuit of sustainable resource consumption and management has emerged as the frontier direction of study. However, there is still room for development in the natural resource performance management evaluation system and methods, as well as the requirement for a comprehensive conceptual interpretation and analytical framework. As the literature on the factors impacting the success of natural resource management is currently lacking in depth, future research should concentrate on bolstering the usage of quantitative models. Enhancing the combination of research and quantitative models is also essential. This study builds research framework for natural resource performance management based on the body of current literature and the real-world experience of nations worldwide. In order to support decision-making for the sustainable use of natural resources, the authors describe the process and mechanism of performance management of natural resources and provide some workable evaluation methods based on a variety of evaluation objectives, subjects, and means.

An Electrical Parameter Characterizing Solute Heterogeneity: The Mixing Factor M

AbstractQuantitative estimates of hydrological state variables using electrical or electromagnetic geophysical methods are systematically biased by overlooked heterogeneity below the spatial scale resolved by the method. We generalize the high‐salinity asymptotic limit of electrical conduction in porous media at the continuous (e.g., Darcy) scale, by introducing a new petrophysical parameter, the mixing factor M, which accounts for the effect of fluid conductivity heterogeneity on the equivalent electrical conductivity tensor; it is expressed in terms of the volume‐average of the product of mean‐removed fluid conductivity and electric fields. We investigate the behavior of M for static and evolving fluid conductivity scenarios. Considering 2‐D ergodic log‐normal random fields of fluid conductivity, we demonstrate, in absence of surface conductivity, that observing the components of the M‐tensor allows univocally determining the variance and anisotropy of the field. Further, time‐series of the M‐tensor under diffusion‐limited mixing allows distinguishing between different characteristic temporal scales of diffusion, which are directly related to the initial integral scales of the salinity field. Under advective‐diffusive transport and for a pulse injection, the time‐series of M have a strong dependence on the Péclet number. Since M is defined in the absence of surface conductivity, we investigate how to correct measurements for surface conductivity effects. The parameter M provides conceptual understanding about the impact of saline heterogeneity on electrical measurements. Further work will investigate how it can be incorporated into hydrogeophysical inverse formulations and interpretative frameworks.

U‐Pb Geochronology of Paleosol Carbonate Cements by LA‐ICP‐MS: A Proof of Concept and Strategy for Dating the Terrestrial Record

AbstractThis study investigates the potential of laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS) U‐Pb dating for carbonate nodules in the Late Triassic Ischigualasto Formation of northwestern Argentina. We establish a fully characterized paragenetic sequence to guide the analysis of three pedogenic carbonates and compare the U‐Pb ages with published geochronology from volcanic ashes within the sedimentary succession. Our findings demonstrate the importance of interpreting U‐Pb data within a well‐defined paragenetic framework for accurate age interpretation of pedogenic carbonates. We observe variations in U‐Pb isotopic signatures across different generations of carbonate precipitates and identify syn‐pedogenic and early burial calcite cements as most suitable for precise dating. Respectively, these two calcite cements are interpreted as microcodium and crack‐lining calcite cements formed early in the paragenetic sequence during pedogenesis to early burial of the paleosols as they transitioned from the unsaturated vadose to saturated phreatic zone below the water table. The U‐Pb ages obtained from the carbonate nodules agree with the radioisotopic ages of volcanic ashes, supporting the validity of our dating strategy. These results contribute to advancing U‐Pb carbonate geochronology and highlight its increased potential for dating sedimentary records in the terrestrial realm. Future research should focus on replicating similar work on different carbonate nodules within the Ischigualasto Fm and expanding the application of LA‐ICP‐MS U‐Pb dating to other carbonate‐bearing formations, especially in successions with limited absolute ages or where volcanic ashes are sparse or absent.

Enhancing Digital Literacy and Technological Integration: A Case Study at Henan's Vocational College

The study focuses on the necessity for vocational instructors to have digital skills to prepare their students for the job. This research intended to improve digital literacy and technology integration among vocational college personnel in Henan Province, China. The key difficulties handled are digital literacy and organizational support gaps that hinder educational technology integration. The study examined approaches to increase digital literacy, organizational support for digital transformation, and teacher digital skills plans. A qualitative case study in an interpretivist framework examined instructors' and stakeholders' subjective experiences. The study included 12 participants consisting of educators, managers, and stakeholders selected via intentional and snowball sampling. Data collection included semi-structured interviews, classroom observations, and document analysis. According to the findings, 70% of the participants have basic digital literacy abilities, but they lack advanced technology integration ability, emphasizing the need for ongoing professional development. Additionally, 60% reported minimal organizational help, emphasizing the necessity for solid infrastructure and coordinated training. Important discussions centered on how these findings may alter vocational college policy and pedagogy. The study stressed the need to match innovative methods to classroom needs. Rogers's Theory of Technological Adoption focused perceived usefulness and ease of use in technology adoption, whereas the Community of Inquiry (CoI) Theory guided cooperative and reflective learning contexts. Also, the Digital Literacy Framework provided a comprehensive approach to learning digital skills, emphasizing advanced training programs. Policymakers and educators should prioritize professional development programs for advanced digital skills and provide appropriate physical and technological infrastructure. The research suggests mentoring programs, hands-on workshops, and contemporary technologies for instructors. Future research should examine the long-term effects of these treatments and adjust similar educational settings to ensure vocational education remains relevant and effective in a digital world.

Liderazgo participante en microempresas

This paper presents some ideas on the concept of participative leadership as a behavioral interpretative framework to understand the specificity of small businesses owner’s organizational role. To that point the paper begins with a review of the general characteristics of Organizational Behavior Management and its analysis and intervention tools. From there, the paper presents an analysis of the way in which the concept of participative leadership can create a conceptual connection to such tools and bring about an understanding of the specificity of leadership in such business. The empirical functions proposed to understand such specificity are: a) innovation actions; b) feedback conditions;and c) operation exemplar. The paper concludes with an emphasis on an equal recognition of such functions and suggests the development of a formula for the quantitative measurement of each function ́s weight on a general parameter of participative leadership viewed as a competence.

Atmospheric oxygenation at the onset of Earth’s Great Oxidation forced enhanced marine anoxia

Abstract Capturing the loss of mass-independent sulphur isotope fractionation (MIF-S), the correlative South African Duitschland and Rooihoogte formations are widely held to bear the isotopic fingerprint of the first atmospheric oxygenation at the onset of the so-called Great Oxidation Event (GOE). Surprisingly, however, while the multiple sulphur isotope systematics of these formations remain central to our understanding of the GOE, until now, comparatively little work has been done to elucidate the repercussions within the marine realm. Here we present chemostratigraphic records from four drill cores covering a large area of the Transvaal Basin, transcending these crucial units and continuing into the overlying Timeball Hill Formation (TBH), that document the immediate, yet counterintuitive, marine response to atmospheric oxygenation. Specifically, irrespective of the interpretative framework employed, our basin-wide redox-sensitive trace element data document an environmental change from oxic/suboxic conditions within the lower and middle parts of the Duitschland and Rooihoogte formations to suboxic/anoxic conditions within their upper reaches. Interestingly, in concert with a ~35‰ negative δ34S excursion that implicates increased sulphate availability and bacterial sulphate reduction, δ98/95Mo3134+0.25 values increase by ~1.0 to 1.5‰. Combining these observations with increased Fe/Mn ratios, elevated total sulphur and carbon contents and a trend towards lower δ13Corg values imply a shift toward less oxygenated conditions across the Transvaal Basin. The combined observations in the mentioned parameters expose a geobiological feedback-driven causality between the earliest oxygenation of the atmosphere and decreased redox potentials of medium to deep marine environments, at least within the Transvaal Basin.

Beyond the veil: Popular culture, morality and the afterlife in contemporary society

Ongoing interest in the afterlife serves to navigate existential crises by offering solace or amplifying anxieties about death. Popular cultural narratives, deeply intertwined with religious doctrines, serve as interpretative frameworks for understanding human experiences, ethical conduct and the mysteries of death. This Special Issue, then, explores how, in an increasingly secular world, the afterlife is portrayed in media narratives, which, while primarily aimed at entertainment, also respond to deep-seated existential and moral questions. It discusses how these portrayals offer a mix of hope, happiness and moral guidance, reflecting broader societal values and beliefs about life, death and what lies beyond. This collection of scholarly contributions aims to dissect the portrayal of morality and religion in media representations of the afterlife, examining how these portrayals reflect, critique or reinforce societal norms and values. By focusing on themes such as digital immortality and the supernatural, the Special Issue not only highlights the evolving nature of afterlife narratives but also underscores the role of popular culture in navigating and negotiating the existential dilemmas of the twenty-first century.

Deconstructing racial code words

AbstractRacism has become more covert in post-civil rights America. Yet, measures to combat it are hindered by inadequate general knowledge on what “colorblind” race talk says and does and what makes it effective. We deepen understanding of covert racism by investigating one type of discourse – racial code words, which are (1) indirect signifiers of racial or ethnic groups that contain (2) at least one positive or negative value judgment and (3) contextually implied or salient meanings. Through a thematic analysis of 734 racial code words from 97 scholarly texts, we develop an interpretive framework that explains their tropes, linguistic mechanisms and unique roles in perpetuating racism, drawing from race, linguistic and cultural studies. Racial code words promote tropes of White people’s respectability and privilege and Racial/Ethnic Minorities’ pathology and inferiority in efficient, adaptable, plausibly deniable and almost always racially stratifying ways, often through euphemism, metonymy and othering. They construct a “colorblind” discursivity and propel both “epistemic racism” (racism in knowledge) and systemic racism (racism in action). We further strengthen applications of Critical Race Theory in sociolegal studies of race by presenting a “racial meaning decoding tool” to assist legal and societal measures to detect coded racism.

A Secure and Interpretable AI for Smart Healthcare System: A Case Study on Epilepsy Diagnosis Using EEG Signals.

The efficient patient-independent and interpretable framework for electroencephalogram (EEG) epileptic seizure detection (ESD) has informative challenges due to the complex pattern of EEG nature. Automated detection of ES is crucial, while Explainable Artificial Intelligence (XAI) is urgently needed to justify the model detection of epileptic seizures in clinical applications. Therefore, this study implements an XAI-based computer-aided ES detection system (XAI-CAESDs), comprising three major modules, including of feature engineering module, a seizure detection module, and an explainable decision-making process module in a smart healthcare system. To ensure the privacy and security of biomedical EEG data, the blockchain is employed. Initially, the Butterworth filter eliminates various artifacts, and the Dual-Tree Complex Wavelet Transform (DTCWT) decomposes EEG signals, extracting real and imaginary eigenvalue features using frequency domain (FD), time domain (TD) linear feature, and Fractal Dimension (FD) of non-linear features. The best features are selected by using Correlation Coefficients (CC) and Distance Correlation (DC). The selected features are fed into the Stacking Ensemble Classifiers (SEC) for EEG ES detection. Further, the Shapley Additive Explanations (SHAP) method of XAI is implemented to facilitate the interpretation of predictions made by the proposed approach, enabling medical experts to make accurate and understandable decisions. The proposed Stacking Ensemble Classifiers (SEC) in XAI-CAESDs have demonstrated 2% best average accuracy, recall, specificity, and F1-score using the University of California, Irvine, Bonn University, and Boston Children's Hospital-MIT EEG data sets. The proposed framework enhances decision-making and the diagnosis process using biomedical EEG signals and ensures data security in smart healthcare systems.

Isotope Hydrograph Separation Reveals Rainfall on the Glaciers Will Enhance Ice Meltwater Discharge to the Himalayan Rivers

AbstractThe Indian Summer Monsoon (ISM) and meltwater from the Himalayan are the two most important sources of water in the Indian subcontinent. However, the impact of ISM on Himalayan glaciers and subsequent stream hydrology remains largely unknown. To provide new insight into the impact of rainfall on glacial hydrology, here we present hydro‐meteorological and time‐series observations of meltwater stable water isotope compositions from the snout of the Chorabari glacier in the Upper Ganga Basin, Central Himalayas across the ablation season corresponding to 2019. We observe that rainfall events (>2 mm d−1) on the glacier enhance discharge driven by ice meltwater in River Mandakini. Energy balance calculations reveal that one of the drivers behind enhanced ice meltwater contribution could be rain‐induced melting of the glacier where rainfall on the ice surface melts the glacier producing up to 13% of the total discharge at the glacier snout. Further, rainfall on glacier surface have other control on glacial processes—for example, snow metamorphism, ice flow dynamics such as short‐term acceleration in ice speed flow, and reorganization of the englacial and subglacial drainage network—that are poorly studied and needs further investigation. We conclude rainfall events on the glacier have a complex control on mountain hydrology. This study, therefore, provides an interpretative framework that calls for additional assessments of the direct and indirect impact of rainfall in glacial hydrology.

A high-precision interpretable framework for marine dissolved oxygen concentration inversion

A high-precision interpretable framework for marine dissolved oxygen concentration inversion

Thermodynamically informed graph for interpretable and extensible machine learning: Martensite start temperature prediction

A common challenge in accelerated material design is to apply machine learning (ML) methods that can handle data with different structures and dimensions, and also provide physical interpretability. Unfortunately, most existing ML methods are ‘black box’ models incapable of providing physical interpretation or dealing with missing dimensions data that are often encountered in materials science. To overcome this challenge, we propose an interpretable and extensible machine learning framework based on thermodynamically informed graphs and deep data mining from graph neural networks. We demonstrate our framework on the problem of predicting the martensite start (Ms) temperature, which depends on various factors (composition, austenite grain size, and outfield conditions). We construct a thermodynamically informed graph that captures the quantitative relationships between these factors and the Ms temperature using limited and incomplete data. The prediction results indicate that our framework provides clear physical insights because the thermodynamic mechanisms are embedded in the thermodynamic representation graph. Our framework has several advantages: 1) it incorporates thermodynamic mechanisms into the graph structure, 2) it can handle missing dimensions data by filling in the gaps with graph information, and 3) it can be easily extended to new features without requiring much additional data for training. Moreover, we derive a general empirical equation for the Ms temperature prediction from the trained graph neural networks for practical applications.

Katachi (形): Decoding the Imprints of Past Star Formation on Present-day Morphology in Galaxies with Interpretable CNNs* * https://github.com/juanpabloalfonzo/Katachi

The physical processes responsible for shaping how galaxies form and quench over time leave imprints on both the spatial (galaxy morphology) and temporal (star formation history; SFH) tracers that we use to study galaxies. While the morphology–SFR connection is well studied, the correlation with past star formation activity is not as well understood. To quantify this, we present Katachi (形), an interpretable convolutional neural network framework that learns the connection between the factors regulating star formation in galaxies on different spatial and temporal scales. Katachi is trained on 9904 galaxies at 0.02 < z < 0.1 in the SDSS-IV MaNGA DR17 sample to predict stellar mass (M *; root mean square error (RSME) 0.22 dex), current star formation rate (SFR; RMSE 0.31 dex), and half-mass time (t 50; RMSE 0.23 dex). This information allows us to reconstruct nonparametric SFHs for each galaxy from gri imaging alone. To quantify the morphological features informing the SFH predictions, we use SHAP (SHapley Additive exPlanations). We recover the expected trends of M * governed by the growth of galaxy bulges, as well as those of SFR correlating with spiral arms and other star-forming regions. We also find that the SHAP maps of D4000 are more complex than those of M * and SFR, and that morphology is correlated with t 50 even at fixed mass and SFR. Katachi serves as a scalable public framework to predict galaxy properties from large imaging surveys including Rubin, Roman, and Euclid, with large data sets of high signal-to-noise ratio imaging across limited photometric bands.

Az apaszerep funkciói és az engedelmesség foka a kora újkori államelméletekben

In the early modern theories of the state, thinking about governance started from the idea of body politic, through the introduction of passions and the family, to the patriarchal theory of the state, in which the monarch governs the people as a family as head of the family. To the head of the family, like to the monarch, the members of the family, like the people, owe obedience according to the laws of nature. The family has become the interpretative framework for the description of society, in which the monarch, by right of his supremacy, has been transformed into the head of the family. At the same time, the concept of the head of the family has undergone a significant change. The change in the role of the father has been accompanied by a change in the content of obedience as well.

SPIN: sex-specific and pathway-based interpretable neural network for sexual dimorphism analysis.

Sexual dimorphism in prevalence, severity and genetic susceptibility exists for most common diseases. However, most genetic and clinical outcome studies are designed in sex-combined framework considering sex as a covariate. Few sex-specific studies have analyzed males and females separately, which failed to identify gene-by-sex interaction. Here, we propose a novel unified biologically interpretable deep learning-based framework (named SPIN) for sexual dimorphism analysis. We demonstrate that SPIN significantly improved the C-index up to 23.6% in TCGA cancer datasets, and it was further validated using asthma datasets. In addition, SPIN identifies sex-specific and -shared risk loci that are often missed in previous sex-combined/-separate analysis. We also show that SPIN is interpretable for explaining how biological pathways contribute to sexual dimorphism and improve risk prediction in an individual level, which can result in the development of precision medicine tailored to a specific individual's characteristics.

An interpretable framework for high-precision flatness prediction in strip cold rolling

Flatness plays a crucial role in determining the quality of products in strip cold rolling. Data driven methods have shown promise in flatness prediction by effectively capturing the nonlinearities and strong coupling present in cold rolling processing, surpassing the capability of conventional methods. However, existing data driven models remain restricted by a lack of rolling theory guidance, a black-box nature of predictive processes, and gradient conflict of multi-channel flatness. To overcome these limitations, this paper proposes an interpretable mechanism guided multi-channel distributed meta learning framework for flatness prediction. Initially, significant physic-based parameters, such as theoretical rolling force deviation and tension deviation, and controller parameters are calculated to guide data driven modeling. Subsequently, a distributed meta learning framework is modeled for multi-channel flatness to eliminate gradient conflict. Furthermore, eXplainable Artificial Intelligence (XAI) technique is implemented to ensure the transparent predictive processes of multi-channel flatness. The analysis results present that theoretical parameters and controller parameters effectively improve the performance of flatness prediction. In addition, the comparative results demonstrate that the proposed framework outperforms the existing flatness prediction methods and other state-of-the-art machine learning methods by 4.24%. Importantly, the XAI-based explanation of the proposed framework effectively enhances the credibility of data driven flatness prediction.

The Community of Practice: An Essential and Elegant Framework for Archaeological Interpretation

The Community of Practice: An Essential and Elegant Framework for Archaeological Interpretation

‘The means of life’: Civilizational witnessing in Write to Life’s Souvenirs

Discourses underpinning UK policies such as Fundamental British Values and the Hostile Environment draw justification from framing asylum seekers as threats to western civilizational values and the British way of life. In this article, I examine Write to Life’s play Souvenirs as a remarkable example of ethically informed theatre with a conceptually refined understanding of the practicalities of civilization. Using Elaine Scarry’s theorization of the sheltering room as vital facilitator of civilization, the article explores how this testimonial play intervenes in mainstream discourses that work to hamper the two-way exchange that Kelly Oliver identifies as the foundational witnessing structure of subjectivity. I argue that this framework for interpretation highlights Souvenirs’s framing concepts of the garden and the souvenir scar as illustrating collective possibilities for co-creating civilization based on negotiation rather than enforced legibility.

An interpretable deep learning model predicts RNA–small molecule binding sites

The intricate interplay between RNA molecules and small ligands plays a pivotal role in regulating biological processes, underscoring the necessity for accurate prediction models of RNA–small molecule binding sites. In this study, we develop CapBind, a novel interpretive deep learning framework utilizing a Capsule Network architecture, to precisely identify RNA–small molecule binding sites. This framework stands out for its multifaceted approach, which leverages both the sequence and structural information of RNA as input features, allowing for a thorough understanding of the complex interactions between RNA and small molecules. Leveraging multi-source bioinformatics data, including the distribution of RNA secondary structures, our model characterizes sequence and structural features with unparalleled depth. Through a unified alignment of these multi-source feature representations, we achieve dimensional homogeneity in the potential feature maps for both types of biological data. The crux of our methodology lies in the deployment of a multi-level Capsule Network, engineered to identify the nuanced interactions of RNA sequences with small molecules. The essence of our approach resides in the implementation of a multi-layered Capsule Network. Utilizing a sophisticated routing mechanism, this network demonstrates unparalleled efficacy in learning and identifying the intricate interactions between RNA sequences and small molecules. This mechanism enables dynamic adjustment of information flow across multiple levels, thereby capturing subtle features and patterns that might elude conventional neural network architectures. The experimental results demonstrate the superior performance of our model, particularly highlighting its progress in evaluation metrics compared to current state-of-the-art models. Case studies further validate the model’s precision in identifying binding sites, offering potential insights into gene expression pathways and novel therapeutic avenues for neurodegenerative diseases. Moreover, an extensive interpretability analysis of our silicon model reveals the internal interaction mechanisms between specific RNA regions and small molecules, contributing valuable perspectives for drug design and molecular biology research. These findings underscore the model’s predictive accuracy and biological applicability, establishing our approach as a significant leap forward in the computational prediction of RNA–small molecule binding sites.

MTG_CD: Multi-scale learnable transformation graph for fault classification and diagnosis in microservices

The rapid advancement of microservice architecture in the cloud has led to the necessity of effectively detecting, classifying, and diagnosing run failures in microservice applications. Due to the high dynamics of cloud environments and the complex dependencies between microservices, it is challenging to achieve robust real-time system fault identification. This paper proposes an interpretable fault diagnosis framework tailored for microservice architecture, namely Multi-scale Learnable Transformation Graph for Fault Classification and Diagnosis(MTG_CD). Firstly, we employ multi-scale neural transformation and graph structure adjacency matrix learning to enhance data diversity while extracting temporal-structural features from system monitoring metrics Secondly, a graph convolutional network (GCN) is utilized to fuse the extracted temporal-structural features in a multi-feature modeling approach, which helps to improve the accuracy of anomaly detection. To identify the root cause of system faults, we finally conduct a coarse-grained level diagnosis and exploration after obtaining the results of classifying the fault data. We evaluate the performance of MTG_CD on the microservice benchmark SockShop, demonstrating its superiority over several baseline methods in detecting CPU usage overhead, memory leak, and network delay faults. The average macro F1 score improves by 14.05%.