Interpretation Techniques Research Articles

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.

Справедливость для детей и подростков: критическая теория и антиэйджистские веб-сообщества в соцсети «ВКонтакте»

This article discusses the production of a critical anti-ageism project. In this case ‘anti-ageism’ is an emic name for a small-scale web community of several readerships and groups that emerged in 2012 in VK social media. Its participants aim to stand against ageism, meaning discrimination against children and adolescents based on their age. Anti-ageists identify themselves as innovators who produce new ways of imagining children’s roles and challenging the social order. In 2015 their rhetorical strategies changed from producing evidence about personal trauma to formulating texts in the technique of critical social theory. The last ones include condemning ageism as a total system, and various situations and social relations as oppression, exploitation, or domination of children and adolescents, whatever the forms in which they occur. With oppression as a key strategically deployed rhetorical trope the discourse of anti-ageist VK web communities invites comparison with the tendencies of the academic and activist critique of dominant and repressive categorical apparatus. At the same time, the anti-ageists redefine the collective understanding of justice by proposing new visions of children’s agency and scenarios of children’s behaviour. The mechanisms of transfer of interpretive techniques of critical theory, especially the choice of cultural allies of anti-ageism, are presented from the perspective of digital ethnography and placed in the context of sociopolitical changes with attention to the material or technological landscapes and the modes of sociality of specific web fragments. The second part of the article demonstrates how anti-ageism is not only articulated on the internet but also embodied in the social imagination of participants in anti-ageist web communities. Transferring the oppression of children and adolescents from a textual motif into a technique that anti-ageists use in imagining and representing themselves and others opens up the prospect for a discussion about the place of critical theory in the scenes of everyday life.

Exploring the interpretation techniques in Bach’s Cello Suite No. 1 Prelude: A theoretical and practical approach to baroque performance practice

This study investigates the essential strategies for interpreting Johann Sebastian Bach's Cello Suite No. 1 Prelude, with a specific emphasis on historically informed performance practices (HIP). The focus of this study is the intricate implementation of Baroque performance methods, including slurs, detached notes, double stops, cross-string techniques, and ornamentation. These skills are crucial for providing a faithful interpretation of Bach's music. The significance of this research is to connect historical performance techniques with present interpretative approaches, guaranteeing that contemporary performers may give performances that are both historically precise and emotionally captivating. The research methodology employed in this study involves an extensive examination of relevant literature, specifically focusing on the contributions of prominent academics such as Frederick Neumann, David Schulenberg, and Jean Claude Veilhan. These scholars have offered valuable insights into the fundamental aspects of Baroque performance practices. Furthermore, this analysis examines the incorporation of historical knowledge with contemporary performance practices by studying the practical insights of esteemed cellists such as Anner Bylsma and Pieter Wispelwey. This dual approach enables a comprehensive comprehension of both the theoretical foundations and actual implementations of the performance strategies in question. This research emphasizes the crucial function of slurs in producing smooth and connected phrasing, the significance of detached notes for precise rhythm, the utilization of double stops and cross-string techniques to enhance harmonic richness and technical complexity, and the importance of ornamentation in deepening the expressive quality of the Prelude. This research offers a complete framework for interpreting Bach's Cello Suite No. 1 Prelude by combining theoretical analysis with practical performance suggestions. This technique not only respects the artistic authenticity of Baroque music but also guarantees that Bach's enduring masterpiece remains relevant to contemporary audiences, thus contributing to the continuous development of classical music performance.

A Comprehensive Analytical Review of Polyphenols: Evaluating Neuroprotection in Alzheimer's Disease.

Alzheimer's Disease (AD), a prevalent neurodegenerative disorder, is the primary cause of dementia. Despite significant advancements in neuroscience, a definitive cure or treatment for this debilitating disease remains elusive. A notable characteristic of AD is oxidative stress, which has been identified as a potential therapeutic target. Polyphenols, secondary metabolites of plant origin, have attracted attention due to their potent antioxidant properties. Epidemiological studies suggest a correlation between the consumption of polyphenol-rich foods and the prevention of chronic diseases, including neurodegenerative disorders, which underscores the potential of polyphenols as a therapeutic strategy in AD management. Hence, this comprehensive review focuses on the diverse roles of polyphenols in AD, with a particular emphasis on neuroprotective potential. Scopus, ScienceDirect, and Google Scholar were used as leading databases for study selection, from 2018 to late March 2024. Analytical chemistry serves as a crucial tool for characterizing polyphenols, with a nuanced exploration of their extraction methods from various sources, often employing chemometric techniques for a holistic interpretation of the advances in this field. Moreover, this review examines current in vitro and in vivo research, aiming to enhance the understanding of polyphenols' role in AD, and providing valuable insights for forthcoming approaches in this context.

Multiscale and multidisciplinary data-driven reservoir characterization of a fractured carbonate field in Kurdistan

Abstract The combination of traditional subsurface interpretation techniques with advanced data analytics is a key stepping stone for better predicting reservoir quality, especially in heterogeneous and complex geological systems. The Peshkabir oil and gas field, located in the north of the Kurdistan Region of Iraq and within the Tawke Production Sharing Contract, is one such heterogeneous system. Well oil rates vary significantly across the field and cannot be simply correlated to fracture densities measured at the wells. Understanding which fractures matter and what influences reservoir deliverability is a question of major importance for maximizing oil production. The carbonate reservoirs include karstified vuggy zones and hydrothermal dolostones, in addition to an extensively developed fractured network. This paper presents a geological conceptual model for the Peshkabir field, and an application of Python based data science techniques to identify key predictors of reservoir deliverability from drilling, logging and production data. We demonstrate that the major advantage of the application of advanced data analytics is that it can enable the recognition of patterns and associations in a complex, high-dimensional parameter environment whereas traditional interpretation methods typically only allow for the comparison of two or three parameters at a time. This method allows the integration of dynamic and static data effectively and empowers the interpreter to incorporate all the available insights which, coupled with domain knowledge, allows for data-driven decision-making.

Comparative Analysis an Accounting Technique of Analysis and Interpretation of Financial Statement

Comparative Analysis an Accounting Technique of Analysis and Interpretation of Financial Statement

DIRECT: Dual Interpretable Recommendation with Multi-aspect Word Attribution

Recommending products to users with intuitive explanations helps improve the system in transparency, persuasiveness, and satisfaction. Existing interpretation techniques include post-hoc methods and interpretable modeling. The former category could quantitatively analyze input contribution to model prediction but has limited interpretation faithfulness, while the latter could explain model internal mechanisms but may not directly attribute model predictions to input features. In this study, we propose a novel D ual I nterpretable Rec ommenda t ion model called DIRECT, which integrates ideas of the two interpretation categories to inherit their advantages and avoid limitations. Specifically, DIRECT makes use of item descriptions as explainable evidence for recommendation. First, similar to the post-hoc interpretation, DIRECT could attribute the prediction of a user preference score to textual words of the item descriptions. The attribution of each word is related to its sentiment polarity and word importance, where a word is important if it corresponds to an item aspect that the user is interested in. Second, to improve the interpretability of embedding space, we propose to extract high-level concepts from embeddings, where each concept corresponds to an item aspect. To learn discriminative concepts, we employ a concept-bottleneck layer, and maximize the coding rate reduction on word-aspect embeddings by leveraging a word-word affinity graph extracted from a pre-trained language model. In this way, DIRECT simultaneously achieves faithful attribution and usable interpretation of embedding space. We also show that DIRECT achieves linear inference time complexity regarding the length of item reviews. We conduct experiments including ablation studies on five real-world datasets. Quantitative analysis, visualizations, and case studies verify the interpretability of DIRECT. Our code is available at: https://github.com/JacksonWuxs/DIRECT .

Contextualizing Islamic Inheritance Law in Indonesia: Addressing Negative Stigma

Islamic inheritance law is stigmatized due to its rigidity and conservativity. The rigidity and conservatism of Islamic inheritance can be seen in the view that Islamic inheritance rules cannot be changed or modified. In addition, the assumption of Islamic inheritance rules does not consider the social developments that occur over time. This research aims to explore how Islamic inheritance law is applied in Indonesia and how contextualization is carried out in response to existing stigma. This research employs a qualitative study with a normative juridical approach. The research data is derived from documents and texts on Islamic inheritance law and concrete cases of contextualization of Islamic inheritance law in Indonesia in different situations and contexts. The data are analyzed using descriptive interpretative techniques with the application of Amina Wadud's hermeneutics of tauhid. The research findings indicate that Islamic inheritance law in Indonesia has undergone contextualization, thus negating the negative stigma attached to Islamic inheritance. Contextualization is achieved by understanding and advocating Islamic inheritance law in the Indonesian context, reconstructing the basic concepts of Islamic inheritance law according to the Indonesian context, and promoting the resolution of inheritance conflicts through consultation and mediation. This research offers a model of inheritance division applied in Indonesia that is contextual and flexible based on Amina Wadud's hermeneutics of tauhid.

Militant Rule of Law and Not-so-Bad Law

The article provides intellectual arguments and tools from legal dogmatics that can help to counter the rule of law backlash. It argues that resilience can be boosted by a systemic militant rule of law approach. When it comes to restoring the rule of law, legal theory turns to the Radbruch formula (supra-statutory law). This approach remains contested by lawyers who are convinced – following the tradition of positivist legal theory – that invoking this formula is unacceptable because it violates a fundamental requirement of the rule of law, namely that of legality. Irrespective of the value of this concern, Radbruch’s formula is not applicable to the current demise of the rule of law, as the law resulting from cheating and abuse in illiberal regimes does not result in evil law (though it may facilitate such developments). Instead of evil law, we face not-so-bad law. Legal imperfections exist in every legal system, and militant rule of law necessitates the systemic revision of these shortcomings in order to preempt the abuses of an anti-formalistic populist regime. In illiberal regimes, the self-corrective mechanisms of the rule of law are gradually eliminated, but the name of the game remains the rule of law. It means that judges still have (some) power to counter the backlash using extant interpretive techniques (for a while). This article will begin by introducing the concept of not-so-bad (NSB) law as an imperfection of the rule of law. In Part Two, the validity of NSB laws is discussed by relying on the source theory. It argues that even if validity is a matter of conformity to the source, the source can be understood to contain a legal merit component as determined by the rule of law, and falling short on this legal merit component can constitute a ground for declaring the norm’s invalid. Part Three describes the abuses of the rule of law in illiberal democracies and describes how the NSB law of illiberal regimes does not satisfy the validity requirements of legal positivism. Part Four discusses the opportunities open to judges for resisting or undoing NSB law using existing techniques of legal interpretation and without violating rule of law principles.

EPIC THINKING NARRATIVE TECHNIQUE IS AN IMPORTANT FACTOR

In the article, the uniqueness of narrative technique in epic interpretation is summarized in the example of Abdulla Qadiri's novel "Scorpion from the Altar" and its universal essence and individuality. It observes the nature of poetic speech, the integrity of image and expression in the language of an artistic work, the task and purpose of revealing the hero's character. In fact, literary language is based on universal criteria, while artistic speech provides a method of personal expression.

Community Empowerment Design Through the Pentahelix Model in Tourism Development

The tourism sector has become a development sector that has really developed in recent years. Tourism development has three dimensions of development, namely government, academics and entrepreneurs, but this model is modified into five dimensions, namely government as regulator, academics as conceptualizer, tourism business as investor, community working in the tourism sector as accelerator and media as catalyst. These five dimensions are called the pentahelix model. Through this model, it is hoped that the tourism sector can contribute to the community's economy through an empowerment process. So this research aims firstly, to explore tourism development using the pentahelix model, secondly, to design community empowerment using tourism development using the pentahelix model. This goal was achieved with a qualitative approach, a type of case study on Ijen Geopark Bondowoso tourism. This location is one of the biggest tourist attractions in Bondowoso. Data was collected by means of observation, interviews, documentation and FGD. Data were analyzed using descriptive and interpretive techniques and the validity of the data was tested using triangulation of sources and techniques. This research results, firstly, tourism developed using the pentahelix model is more successful than that developed using the triple helix model, because the dimensions used are more comprehensive, secondly, the community empowerment design through the pentahelix model is comprehensive and can absorb a high level of participation in tourism development. Thus, people's income has increased and unemployment has decreased in the Ijen Geopark tourist area because the community participates in tourism development. So, this pentahelix model can be used in designing sustainable community empowerment.

Embracing technology in EFL pre-university classrooms: A qualitative study on EFL learners' perceptions of intensive and extensive reading approaches

This study attempts to explore the perceptions of Pakistani pre-university EFL learners about the intensive reading (IR) and extensive reading (ER) approaches and the use of technology in these two approaches. This is a qualitative study using a purposive sampling technique. The participants were 10 pre-university undergraduate students enrolled in the foundation program of a public sector university in Pakistan. Researchers used semi-structured interviews and reflective diaries as data collection tools. Thematic analysis and personal interpretation techniques were used to analyze the results. The results showed that after participating in the reading classes, the students had positive perceptions of the IR and ER approaches and found them useful and interesting. The results also showed that the learners felt that their language skills improved significantly after participating in the IR and ER-related activities. Students also found these reading approaches to be complementary to each other. The results also indicated that teachers' use of intensive reading and extensive reading approaches could make learners skilled and autonomous readers. Ultimately, their reading problems could be solved with these approaches. Moreover, the learners also perceived the use of technology in these two reading approaches as of great benefit to them. The researchers recommend incorporating IR and ER activities into the EFL classroom using digital resources to maximize EFL learners' reading abilities and other language skills.

Explainable artificial intelligence in transport Logistics: Risk analysis for road accidents

Automobile traffic accidents represent a significant threat to global public safety, resulting in numerous injuries and fatalities annually. This paper introduces a comprehensive, explainable artificial intelligence (XAI) artifact design, integrating accident data for utilization by diverse stakeholders and decision-makers. It proposes responsible, explanatory, and interpretable models with a systems-level taxonomy categorizing aspects of driver-related behaviors associated with varying injury severity levels, thereby contributing theoretically to explainable analytics. In the initial phase, we employed various advanced techniques such as data missing at random (MAR) with Bayesian dynamic conditional imputation for addressing missing records, synthetic minority oversampling technique for data imbalance issues, and categorical boosting (CatBoost) combined with SHapley Additive exPlanations (SHAP) for determining and analyzing the importance and dependence of risk factors on injury severity. Additionally, exploratory feature analysis was conducted to uncover hidden spatiotemporal elements influencing traffic accidents and injury severity levels. We developed several predictive models in the second phase, including eXtreme Gradient Boosting (XGBoost), random forest (RF), deep neural networks (DNN), and fine-tuned parameters. Using the SHAP approach, we employed model-agnostic interpretation techniques to separate explanations from models. In the final phase, we provided an analysis and summary of the system-level taxonomy across feature categories. This involved classifying crash data into high-level causal factors using aggregate SHAP scores, illustrating how each risk factor contributes to different injury severity levels.

An explainable integrated machine learning model for mapping soil erosion by wind and water in a catchment with three desiccated lakes

Soil erosion by water and wind is a critical challenge for sustainable management of catchments in drylands and accurate spatial information can help in mitigation of its destructive consequences. Here, seven machine learning (ML) models were applied to map simultaneously the water and wind erosions in the Bakhtegan catchment, south Iran, with three dried lakes in its southern part and three dams established in upstream parts of the lakes. The analysis identified 10 and 11 effective variables controlling water and wind erosions, among 20 and 17 potential variables, respectively, via the MARS feature selection algorithm. According to the most accurate ML models (artificial neural network for water erosion, and Cubist for wind erosion), an integrated model was developed to map soil erosion by water and wind simultaneously. Permutation feature importance (PFI) and Shapley additive exPlanation (SHAP) interpretation techniques were employed to explain the model outputs, revealing that 19.7 % of the total area belonged to high and very high susceptibility classes to soil erosion by water and wind. The PFI plot revealed that the slope and wind speed were the most influencing factors for water and wind erosion, respectively. According to SHAP decision plot, slope had the highest contribution on the predictive water erosion model’s output, whereas vegetation cover exhibited the highest contribution on the predictive wind erosion model’s output. Due to climate change and intensified drought during the recent years, as well as due to construction of dams upstream of the lakes, the southern part of the study area was converted to a source of sand and dust storms. The hotspots with severe water erosion are distributed all over the study area, rendering it quite vulnerable to adverse climate change projections.

Exploring the Forbidden Forest Haze: An Ecocritical Analysis of Environmental Themes in the Short Story “Tragedi Asap”

Purpose: This study aims to explore the phenomenon of the forbidden forest haze in the short story Tragedi Asap by Gigih Suroso. Method: This study uses a qualitative method. Data are presented descriptively by using Greg Garrard's ecocritical approach. The research data is in the form of words, phrases, clauses, and sentences in short stories. The data source is from the short story Tragedi Asap which was published by Analisa Newspaper. Data analysis using software Nvivo 12 and reseacher was carried out using interactive and interpretive model analysis techniques consisting of data condensation, data presentation, and concluding/verification. Result and Conclusion: The findings research, there are six aspects presented in the short story, namely pollution, wilderness, apocalypse, dwelling, animals, and earth. Environmental education found is protecting nature, disaster mitigation, and preserving nature as a mandate from God. This short story also features an arcadia construction in the form of a prohibition on the transfer of forest land because it is considered holy and sacred. Implication of the research: The study contributes to public policy by providing an ecocritical analysis of the environmental themes within the short story 'Tragedi Asap'. It sheds light on the portrayal of the natural environment and its ecological implications, offering a nuanced understanding of how literature intersects with environmental issues. This research adds to the broader discourse on environmental literature and its socio-political implications. Originality/value: The research novelty lies in the application of ecocritical analysis to the specific cultural and environmental context depicted in the short story 'Tragedi Asap', offering a unique perspective on the intersection of literature, society, and ecological concerns in a particular cultural setting.

Using an interpretable deep learning model for the prediction of riverine suspended sediment load.

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed's hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine systems. This paper investigates the abilities of four DL models, including dense deep neural networks (DDNN), long short-term memory (LSTM), gated recurrent unit (GRU), and simple recurrent neural network (RNN) models for the prediction of daily SSL using river discharge and rainfall data at a daily time scale in the Taleghan River watershed, northwestern Tehran, Iran. The performance of models was evaluated by using several quantitative and graphical criteria. The effect of parameter settings on the performance of deep models on SSL prediction was also investigated. The optimal optimization algorithms, maximum iteration (MI), and batch size (BC) were obtained for modeling daily SSL, and structure of the model impact on prediction remarkably. The comparison of prediction accuracy of the models illustrated that DDNN (with R2 = 0.96, RMSE = 333.46) outperformed LSTM (R2 = 0.75, RMSE = 786.20), GRU (R2 = 0.73, RMSE = 825.67), and simple RNN (R2 = 0.78, RMSE = 741.45). Furthermore, the Taylor diagram confirmed that DDNN has the highest performance among other models. Interpretation techniques can address the black-box nature of models, and here, SHAP was applied to develop an interpretable DL model to interpret of DL model's output. The results of SHAP showed that river discharge has the strongest impact on the model's output in estimating SSL. Overall, we conclude that DL models have great potential in watersheds to predict SSL. Therefore, different interpretation techniques as tools to interpret DL model's output (DL model is as black-box model) are recommended in future research.

Human activity recognition using a single-photon direct time-of-flight sensor

Single-Photon Avalanche Diode (SPAD) direct Time-of-Flight (dToF) sensors provide depth imaging over long distances, enabling the detection of objects even in the absence of contrast in colour or texture. However, distant objects are represented by just a few pixels and are subject to noise from solar interference, limiting the applicability of existing computer vision techniques for high-level scene interpretation. We present a new SPAD-based vision system for human activity recognition, based on convolutional and recurrent neural networks, which is trained entirely on synthetic data. In tests using real data from a 64×32 pixel SPAD, captured over a distance of 40 m, the scheme successfully overcomes the limited transverse resolution (in which human limbs are approximately one pixel across), achieving an average accuracy of 89% in distinguishing between seven different activities. The approach analyses continuous streams of video-rate depth data at a maximal rate of 66 FPS when executed on a GPU, making it well-suited for real-time applications such as surveillance or situational awareness in autonomous systems.

Critical factors for capacity building of NGOs working in the intellectual disability sector in India: a TISM-MICMAC approach

PurposeThis study aims to identify the critical factors contributing to India-based non-governmental organizations (NGOs) capacity building and value creation for beneficiaries.Design/methodology/approachA total interpretive structural modeling technique has been used to develop a hierarchical model of critical factors and understand their direct and indirect interrelationships. The driving force and dependence force of these factors were determined by using cross-impact matrix multiplication applied to classification analysis.FindingsThis study identifies 12 critical factors influencing NGO capacity building in India’s intellectual disability sector across four dimensions. Internal organizational capabilities include infrastructure, staff qualifications, fundraising, vocational activities and technical resources. Second, coordination and stakeholder engagement highlight government and agency collaboration, dedicated board members and stakeholder involvement. Third, adaptability and responsiveness emphasize adjusting to external trends and seizing opportunities. Finally, impact and value creation emphasis on improving value for persons with disabilities (PWDs).Practical implicationsThe findings of this study have practical implications for Indian NGOs working for PWDs. The study provides NGOs with a structural model for improving organizational capacity by identifying and categorizing critical factors into the strategic model.Originality/valueThere is a scarcity of literature on capacity building for disability-focused NGOs in India. This study seeks to identify critical factors and develop a hierarchical model of those factors to assist policymakers in India in building the capacity of NGOs.

Study on the Mechanism of Natural Gas Hydrate Decomposition and Seabed Seepage Triggered by Mass Transport Deposits

Previous studies indicate that mass transport deposits are related to the dynamic accumulation of natural gas hydrates and gas leakage. This research aims to elucidate the causal mechanism of seabed seepage in the western region of the southeastern Qiongdongnan Basin through the application of seismic interpretation and attribute fusion techniques. The mass transport deposits, bottom simulating reflector, submarine mounds, and other phenomena were identified through seismic interpretation techniques. Faults and fractures were identified by utilizing variance attribute analysis. Gas chimneys were identified using instantaneous frequency attribute analysis. Free gas and paleo-seepage points were identified using sweetness attributes, enabling the analysis of fluid seepage pathways and the establishment of a seepage evolution model. Research has shown that in areas where the mass transport deposits develop thicker layers, there is a greater uplift of the bottom boundary of the gas hydrate stability zone, which can significantly alter the seafloor topography. Conversely, the opposite is true. The research indicates that the upward migration of the gas hydrate stability zone, induced by the mass transport deposits in the study area, can result in the rapid decomposition of gas hydrates. The gas generated from the decomposition of gas hydrates is identified as the principal factor responsible for inducing seabed seepage. Moderate- and low-speed natural gas seepage can create spiny seamounts and domed seamounts, respectively.

Using Euler Deconvolution as Part of a Mineral Exploration Project

Mineral exploration projects can make considerable use of a variety of geophysical techniques and datasets, including magnetic and gravity data. The interpretation of large quantities of data can be very time consuming, so semi-automatic interpretation techniques are often used to provide initial estimates of the parameters (primarily the location and depth) of the sources of anomalies. Euler deconvolution is a commonly used interpretation method for potential fields which has a number of advantages over many other techniques, such as working in the presence of remanent magnetisation, and not being restricted to a particular model such as a contact. A second-order version of Euler’s equation is introduced here, which is much less affected by trends in the data than the standard method and additionally produces depth parabolas, which simplify the interpretation of results. The method was applied to aeromagnetic data from a mineral exploration project in Southern Africa and provided plausible results.