Categorization Method Research Articles

Statistical uncertainty analysis-based precipitation merging (SUPER): A new framework for improved global precipitation estimation

Multi-source merging is an established tool for improving large-scale precipitation estimates. Existing merging frameworks typically use gauge-based precipitation error statistics and neglect the inter-dependence of various precipitation products. However, gauge-observation uncertainties at daily and sub-daily time scales can bias merging weights and yield sub-optimal precipitation estimates, particularly over data-sparse regions. Likewise, frameworks ignoring inter-product error cross-correlation will overfit precipitation observation noise. Here, a Statistical Uncertainty analysis-based Precipitation mERging framework (SUPER) is proposed for addressing these challenges. Specifically, a quadruple collocation analysis is employed to estimate precipitation error variances and covariances for commonly used precipitation products. These error estimates are subsequently used for merging all products via a least-squares minimization approach. In addition, false-alarm precipitation events are removed via a reference rain/no-rain time series estimated by a newly developed categorical variable merging method. As such, SUPER does not require any rain gauge observations to reduce daily random and rain/no-rain classification errors. Additionally, by considering precipitation product inter-dependency, SUPER avoids overfitting measurement noise present in multi-source precipitation products. Results show that the overall RMSE of SUPER-based precipitation is 3.35 mm/day and the daily correlation with gauge observations is 0.71 [−] – metrics that are generally superior to recent precipitation reanalyses and remote sensing products. In this way, we seek to propose a new framework for robustly generating global precipitation datasets that can improve land surface and hydrological modeling skill in data-sparse regions.

Assessing Measurement Error in Hypothetical Questions

The hazard of asking survey participants hypothetical questions is that most often, responses are only an inferential vehicle for understanding preference and may not reflect the truth. Often it is necessary to make a reasoned assumption about the alignment of an individual's attitudes and behaviors. In this paper, we discuss results from a Survey of Doctorate Recipients (SDR) pilot study experiment that found significant response bias to hypothetical questions even when the hypothetical context was unambiguous and included salient properties which were familiar to respondents from prior experience. The experiment asked Treatment group participants to report their reaction to slight alterations in the SDR instrument; meanwhile, those in the Control group were asked hypothetically how they *would react* to the protocol if they had experienced the same changes. We identify the presence of response bias across 10 respondent experience questions using both categorical and quantitative methods. Our results show that the Control group consistently exhibited less preference for the hypothetical protocol than the Treatment group. We believe these differences are representative of the measurement error resulting from the poor correspondence between the Control group's hypothetical attitudes and the Treatment group's reported experience. This work shows that hypothetical bias is a concern in opinion research even when hypothetical conditions may be clearly understood by respondents and similar to their previous experiences.

Translation and cross-cultural adaptation methodology for soundscape attributes – A study with independent translation groups from English to Greek

The use of questionnaires for soundscape evaluation is a key aspect of soundscape research. Since standards and protocols mainly exist in English, using an appropriate translation and cross-cultural adaptation (CCA) methodology is necessary to maintain content equivalence between source and target language. However, many examples can be found in the literature where no appropriate methodology was applied. This study addresses the neglected aspect of the translation and CCA process in soundscape research by selecting, applying and evaluating an appropriate methodology. After a survey of the relevant literature, an approach based on a combined technique of the forward translation, synthesis, back translation, pre-test and a committee approach was selected. Additional translation guidelines drawn from the literature are suggested and implemented. For the case study of the Greek translation of ISO/TS 12913–2:2018 attributes (Method A), the steps of the methodology were applied by four independent translation groups with different compositions according to the biculturalism and bilingualism of the group members. A method for categorization of bilinguals according to the literature is proposed. In order to compare and validate the results, translated and original attributes were used in listening tests with Greek and English participants respectively, and principal component analysis (PCA) was applied. The most important findings of this research are: the results of the bicultural translation group were closer to the PCA results of the English participants for every attribute, translation of bilingual groups may not be always sufficient, translation errors may be misinterpreted for cross-cultural differences without proper application of a translation methodology and the process of back translation can be effective, especially in cases where there are not corresponding words in the target language. Finally, PCA can be used as a validation methodology for comparison of different translations.

An expert‐in‐the‐loop method for domain‐specific document categorization based on small training data

AbstractAutomated text categorization methods are of broad relevance for domain experts since they free researchers and practitioners from manual labeling, save their resources (e.g., time, labor), and enrich the data with information helpful to study substantive questions. Despite a variety of newly developed categorization methods that require substantial amounts of annotated data, little is known about how to build models when (a) labeling texts with categories requires substantial domain expertise and/or in‐depth reading, (b) only a few annotated documents are available for model training, and (c) no relevant computational resources, such as pretrained models, are available. In a collaboration with environmental scientists who study the socio‐ecological impact of funded biodiversity conservation projects, we develop a method that integrates deep domain expertise with computational models to automatically categorize project reports based on a small sample of 93 annotated documents. Our results suggest that domain expertise can improve automated categorization and that the magnitude of these improvements is influenced by the experts' understanding of categories and their confidence in their annotation, as well as data sparsity and additional category characteristics such as the portion of exclusive keywords that can identify a category.

A descriptive study of samples sizes used in agreement studies published in the PubMed repository

IntroductionA sample size justification is required for all studies and should give the minimum number of subjects to be recruited for the study to achieve its primary objective. The aim of this review is to describe sample sizes from agreement studies with continuous or categorical endpoints and different methods of assessing agreement, and to determine whether sample size justification was provided.MethodsData were gathered from the PubMed repository with a time interval of 28th September 2018 to 28th September 2020. The search returned 5257 studies of which 82 studies were eligible for final assessment after duplicates and ineligible studies were excluded.ResultsWe observed a wide range of sample sizes. Forty-six studies (56%) used a continuous outcome measure, 28 (34%) used categorical and eight (10%) used both. Median sample sizes were 50 (IQR 25 to 100) for continuous endpoints and 119 (IQR 50 to 271) for categorical endpoints. Bland–Altman limits of agreement (median sample size 65; IQR 35 to 124) were the most common method of statistical analysis for continuous variables and Kappa coefficients for categorical variables (median sample size 71; IQR 50 to 233). Of the 82 studies assessed, only 27 (33%) gave justification for their sample size.ConclusionsDespite the importance of a sample size justification, we found that two-thirds of agreement studies did not provide one. We recommend that all agreement studies provide rationale for their sample size even if they do not include a formal sample size calculation.

A coloristic image of the modern northern city (the example of Norilsk)

In this paper, mutual influence of the color and image of the city in the perception of the residents is studied with the example of the city of Norilsk located 300 km north of the Arctic Circle. The chronological span is limited to the present moment, however, part of the study concerns the period since the foundation of the city (1935). The aim of the study is to determine how the coloristics supports the image of the city in the minds of its residents. Concerning the methods, the study was divided into several blocks. In the first part (the actual palette of the city), the method of categorization of color carriers and method of generalization of color shades were used. In the sec-ond part (hypotheses about the contours of group images), the content-analysis of the social networks and media was used. In the third part (hypotheses testing), the methods of questionnaires and quota sampling were em-ployed. In the fourth block, in-depth interviews with Photo-Voice were conducted. As a result, it was found that coloristics is closely interconnected with the image of the territory. In particular, the use of bright colors in the ar-chitecture of the 1950s in Leninsky Prospect accurately identifies the historical events associated with the builders of the city (political prisoners of GULAG). The architecture of other historical periods is less contemptable, thus the events are not integrated into group images. Therefore, the city is associated with the talent and resilience of the prisoners and supports a sense of self-continuity of the exiled intellectuals among the inhabitants. The life in the city is still thought of through the categories of ‘heroism’, whereas all subsequent events are perceived as imposed (industrial exploitation of resources, type-design practice in building). Therefore, when the residents con-trapose the beauty of Leninsky Prospekt to the rest of the city, actually, the talent, intelligence, and respect to the nature are opposed to the degradation of the territory. The presence of the blue-colored buildings and symbolism of the city-forming company reinforces the image of Norilsk as a center of metallurgy, since the production is pre-sent in the city in the form of symbols. The image of Norilsk as an isolated city is enhanced by the difference in color between the back yard and street facades. The city is perceived by the residents as a decoration, its prob-lems are not interesting to external observers. The perception of the city as a larger category of the Far North is reflected in the coloristic image of the city.

Ushuni as a method of sound production, performance practice and categorisation in the philosophical and intellectual foundation of Umaskandi

The focus of this article is to demonstrate and explain how employing linguistic apparatus, the epistemological and metaphysical world of omaskandi can be explored and understood. It does so by focusing on ushuni as the method of sound production, performance practice and categorisation method. The focus on ushuni demonstrates that the indigenous African languages Isizulu in particular, which plays a significant role as it dominates the conceptual apparatus beyond its ordinary communicative function, ought to be fundamental in rationalising and theorising about umaskandi. Isizulu language, therefore, should accordingly enable and guarantee access to umaskandi’s profound conceptual levels. The study has assumed the qualitative research paradigm as it offers an in-depth analysis of reality. Primarily, the investigation into the nature of umaskandi was conducted through fieldwork, and equally important was the consideration of the known history of the genre through earlier documentation. This article, therefore, attempts to unpack Isizulu indigenous music-making principles, practices, and contexts in which umaskandi exists. It is envisaged that it will be more beneficial to umaskandi genre, omaskandi and formal music education hence contributing to knowledge production about this musical phenomenon.

Simplicial Sets in Topology, Category Theory, and Beyond

The notion of a simplicial set originated in algebraic topology and has also been utilized extensively in category theory, but until relatively recently was not used outside of those fields. However, with the increasing prominence of higher categorical methods in a wide range of applications, it is important for researchers in a range of fields to have a good working knowledge of them. This paper is intended as an introduction to simplicial sets, both as an overview of their development from other concepts and as a user’s guide for someone wanting to read modern literature that makes use of them.

“Who Is the FAIRest of Them All?” Authors, Entities, and Journals Regarding FAIR Data Principles

The perceived need to improve the infrastructure supporting the re-use of scholarly data since the second decade of the 21st century led to the design of a concise number of principles and metrics, named FAIR Data Principles. This paper, part of an extended study, intends to identify the main authors, entities, and scientific journals linked to research conducted within the FAIR Data Principles. The research was developed by means of a qualitative approach, using documentary research and a constant comparison method for codification and categorization of the sampled data. The sample studied showed that most authors were located in the Netherlands, with Europe accounting for more than 70% of the number of authors considered. Most of these are researchers and work in higher education institutions. These entities can be found in most of the territorial-administrative areas under consideration, with the USA being the country with more entities and Europe being the world region where they are more numerous. The journal with more texts in the used sample was Insights, with 2020 being the year when more texts were published. Two of the most prominent authors present in the sample texts were located in the Netherlands, while the other two were in France and Australia.

Can Multi-Label Classifiers Help Identify Subjectivity? A Deep Learning Approach to Classifying Cognitive Presence in MOOCs

This paper investigates using multi-label deep learning approach to extending the understanding of cognitive presence in MOOC discussions. Previous studies demonstrate the challenges of subjectivity in manual categorisation methods. Training automatic single-label classifiers may preserve this subjectivity. Using a triangulation approach, we developed a multi-label, fine-tuning BERT classifier to analyse cognitive presence to enrich results with state-of-the-art, single-label classifiers. We trained the multi-label classifiers on the MOOC discussion messages that were categorised into the same phase of cognitive presence by the expert coders, and tested the best-performing classifiers on the messages that the coders categorised into different phases. The results suggest that multi-label classifiers slightly outperformed the single-label classifiers, and the multi-label classifiers predicted the discussion messages as either one category or two adjacent categories of cognitive presence. No messages were tagged as non-adjacent categories by the multi-label classifier. This is an improvement compared to manual categorisation by our expert coders, who obtained non-adjacent categories and even three categories of cognitive presence in one message. In addition to the fully correct prediction, parts of messages were partially correctly predicted by the multi-label classifier. We report an in-depth quantitative and qualitative analysis of these messages in the paper. The automatic categorisation results suggest that the multi-label classifiers have the potential to help educators and researchers identify research subjectivity and tolerate the multiplicity in cognitive presence categorisation. This study contributes to extending the literature on understanding cognitive presence in MOOC discussions.

Concurrent Learning of Patch and Image-Based Features by CNN for Diabetic Retinopathy Detection

Diabetic Retinopathy (DR) is a disorder of the eyes allied with prolonged hyperglycemia and is the leading cause of blindness globally. The earliest signs of DR include the presence of microaneurysms (MA). This research proposes a paradigm that includes patch-level lesion classification and DR severity assessment. A framework is presented that utilizes a multi-task learning approach centered on a single data source and is capable of predicting two outcomes. The proposed method for patch-level MA categorization consists of only three stages and is based on the combination of auto feature extraction by deep learning and classification by the machine learning algorithm. After preprocessing, for feature descriptor learning, GoogLeNet is employed, followed by SVM for classification. A comprehensive approach is provided to use the patch and image-level feature vectors to grade DR severity. To assess the suggested technique for patch-level classification, an experiment was undertaken by using two widely known public datasets, namely E-ophtha and IDRiD datasets. The developed system achieved 98.67% accuracy, a sensitivity of 99.2%, specificity of 99.06% on e-ophtha-MA database, while a trained model is validated on the IDRiD database, it achieves an accuracy of 70.6%, the sensitivity of 67.62%, specificity of 73.58%. The MESSIDOR dataset is used to evaluate the performance of DR grading as well. The proposed system achieves an accuracy of 89.39%, a sensitivity of 91.1%, specificity of 79.31% for two-stage grade DR severity.

Using the ecosystem concept in the development of smart cities

This article discusses the main aspects of the implementation of the ecosystem concept in smart cities. The author defines the concept of "smart city", identifies its main characteristic features, and provides the evolution of development. The role of the smart city as a center of attraction in the modern economy, smoothing social inequality, providing digital services for all categories of the population, and improving the quality of business processes is emphasized. This study is based on the idea that the ecosystem concept is the most effective approach in the formation and development of smart cities. To prove this thesis, the author used a number of general scientific research methods: analysis, synthesis, classification, categorization, comparison, graphical and tabular methods of presenting information, and others. The ecosystem in this study is considered as one of the most promising forms of organization of innovation activity. The components and structure of the smart city ecosystem are described. The possibilities of applying the ecosystem approach in a smart city are determined. The synergetic effect of the smart city ecosystem in solving emerging problems is shown. A smart city is considered as a point of innovative growth, the economic indicators of which are significantly higher in quantitative and qualitative terms compared to an ordinary city that does not have the characteristics inherent in a smart city. The author concludes that a smart city is a complex ecosystem of people, processes, policies, technologies and other factors working together to achieve results. Successful and sustainable smart cities use a programmatic approach to engage stakeholders across the ecosystem.

Estimates of species-level tolerance of urban habitat in North American birds.

Species vary in their responses to urban habitat; most species avoid these environments, whereas others tolerate or even thrive in them. To better characterize the extent to which species vary in their responses to urban habitat (from this point forwards "urban tolerance"), we used several methods to quantify these responses at a continental scale across all birds. Using open access community science-derived data from the eBird Status and Trends Products and two different types of high-resolution geospatial data that quantify urbanization of landscapes, we calculated urban tolerance for 432 species with breeding ranges that overlap large cities in Canada or the USA. We developed six different calculations to characterize species-level urban tolerance, allowing us to assess how each species' relative abundance across their breeding range varied with estimates of urban habitat use and intensity. We assessed correlations among these six indices, then compressed the two best-performing indices into a single principal component (multivariate urban tolerance index) that captured variation in urban tolerance among species. We assessed the accuracy of our single and multivariate urban tolerance indices using 24 test species that have been well characterized for their tolerance or avoidance of the urban habitat, as well as with previously published, independent urban tolerance estimates. Here, we provide this new dataset of species-level urban tolerance estimates that improves upon previous metrics by incorporating continental-scale, continuous estimates that better differentiate species' tolerance of urban habitat compared with existing, categorical methods. These refined metrics can be used to test hypotheses that link ecological, life history, and behavioral traits to avian urban tolerance. The dataset is licensed as CC-By Attribution 4.0 International. Users must appropriately cite the data paper and dataset if used in publications and scientific presentations.

The order of goals in financial monitoring: History and modernity

Aim. Theoretical study of the development of financial monitoring in world and Russian practice, development of trends and conclusions about the development of financial monitoring and the impact of economic processes on it. Tasks. To analyze the development of financial monitoring in world and Russian practice, to consider the evolution of the object in the categories of the method “The order of goals”, to consider the possibilities of the impact of financial monitoring on the activities of an industrial enterprise, to study the basic state of financial monitoring. Methods. In the work, the author uses methods of generalization, comparison, critical analysis, tabular, categorical method — “The order of goals”. Results. The conclusion about the evolutionary development of financial monitoring is formulated. A list of trends in the development of financial monitoring in world and Russian practice has been developed, a conclusion on the change in financial monitoring has been formulated; recommendations for further study of financial monitoring for use in the industrial sector of the economy have been given. Conclusions. The conducted research shows that the development of financial monitoring can be compared with the change in financial management, the evolution of financial monitoring depended on an increase in the complexity of financial processes and the required assessments of the organization's activities, the results of the study will contribute to further study of financial monitoring.

An anti-attack method for emotion categorization from images

Emotion categorization has become an important area of research due to the increasing number of intelligent systems, such as robots interacting with humans. This includes deep learning models, which have performed remarkably well on many classification-based tasks. However, due to their homogeneous representation of knowledge, the deep learning models are vulnerable to different kinds of attacks. The hypothesis is that emotions displayed in facial images are more than patterns of pixels. Thus, the objective of this work is to propose a novel heterogeneous facial landmark-based emotion categorization (LEmo) method that will show robustness to distractor and adversarial attacks. Moreover, we compared the proposed LEmo method with seven state-of-the-art methods, including neural networks, (i.e. the residual neural network (ResNet), the Visual Geometry Group (VGG), and the Inception-ResNet models), emotion categorization tools (i.e. Py-Feat and LightFace), as well as anti-attack-based methods (i.e. Adv-Network and DLP-CNN). To test the robustness of the LEmo method, three different types of adversarial attacks, and a distractor attack were launched at the data. Unlike other methods that have exhibited large performance decreases (up to 79%), the LEmo method was strongly resistant to all attacks, achieving high accuracy with only little (< 9.3%) or no decrease with different changes made to the images of CK+ and the KDEF databases. Furthermore, the LEmo method has shown a considerably lower execution time compared to all other methods.

Sarc-frailty: towards an integrated view of ageing

ObjectivesSarcopenia and frailty have both been related to adverse events in ageing, but have long been studied in parallel. The purpose of this study was to provide a better depiction of ageing by taking the advantages of both entities. Study design2532 participants were enrolled from the National Health and Nutrition Examination Survey 1999–2002. A new diagnostic entity, sarc-frailty, was established by merging the diagnostic elements of both sarcopenia and frailty. Main outcome measuresCox proportional hazard models, Kaplan-Meier curve, and receiver operating characteristic (ROC) curve analysis were utilized to compare the hazard ratios and predictive power in relation to mortality of sarcopenia, frailty, and sarc-frailty. Two different classification strategies, categorical and scoring, were used as alternative assessment methods. ResultsThe median follow-up duration was 67.49 months, and no participants were lost to follow-up. ROC analysis revealed the highest area under curves (AUC) in sarc-frailty by both categorical and scoring classification (AUC = 0.660, p value <0.001 and AUC = 0.697, p value <0.001, respectively), indicating the best predictive ability in relation to mortality. Kaplan-Meier analysis also demonstrated the shortest overall survival for sarc-frailty with both classifications (p value both <0.001). In addition, hazard ratios (HRs) of sarc-frailty with both classifications were higher than their counterparts for sarcopenia and frailty (HR = 12.599, 95 % CI = 7.780 to 20.403, p < 0.001 by categorical method, and HR = 20.121, 95 % CI = 8.101 to 49.973, p < 0.001 by scoring classification). Moreover, the scoring classification revealed a more delicate hierarchical structure of mortality levels than the categorical classification. ConclusionsSarc-frailty had a better predictive ability in relation to mortality. Considering that ageing well is a holistic health-care issue, our new entity, along with the scoring method, provides clinicians with more effective tools in geriatric assessment.

Treatment Tolerance of Cetuximab versus Alternative Chemotherapy Agents in Non-Cisplatin Candidates with Head and Neck Cancer Receiving Concurrent Chemoradiotherapy

Introduction: Standard of care for radiosensitization in head and neck squamous cell carcinoma (HNSCC) is concurrent chemoradiotherapy (CCRT) with high-dose cisplatin. The optimal chemoradiation regimen for patients medically unfit for cisplatin is unclear. We compared our experience with concurrent cetuximab (CTX) versus other cytotoxic non-cisplatin agents. Methods: We reviewed 53 patients between 2011 and 2017 with HNSCC treated with CCRT ineligible for cisplatin. Chemotherapy and radiotherapy treatment tolerance was evaluated in those receiving CTX versus non-CTX chemotherapy (NCC). Of the NCC regimens, the majority were carboplatin/paclitaxel and were dosed at an area under the curve (AUC) of 2 and 45–50 mg/m², respectively. Standard radiation dosing was 70 Gray (Gy) in the definitive setting and 60–66 Gy in the postoperative setting. Patient characteristics and treatment toxicities were evaluated using categorical methods. Results: Patients were well balanced overall including differences between performance status and the comorbidity score. NCC patients experienced more radiation treatment breaks (52.4% vs. 21.9%, p = 0.022), radiation delays >1 week (33.3% vs. 3.1%, p < 0.01), and chemotherapy dose-limiting toxicity (61.9% vs. 28.1%, p = 0.015) compared to CTX patients. Nutritional dependence on a PEG tube was more likely in the NCC cohort (52.4% vs. 22.6%, p = 0.027). Conclusion: Our results suggest decreased treatment tolerance in non-cisplatin cytotoxic chemotherapy compared to cetuximab. Further prospective study is needed to clarify optimal chemotherapy in patients unable to receive cisplatin.

Prognostic Diagnosis for Breast Cancer Patients Using Probabilistic Bayesian Classification.

The diagnosis and treatment of patients in the healthcare industry are greatly aided by data analytics. Massive amounts of data should be handled using machine learning approaches to provide tools for prediction and categorization to support practitioner decision-making. Based on the kind of tumor, disorders like breast cancer can be categorized. The difficulties associated with evaluating vast amounts of data should be overcome by discovering an efficient method for categorization. Based on the Bayesian method, we analyzed the influence of clinic pathological indicators on the prognosis and survival rate of breast cancer patients and compared the local resection value directly using the lymph node ratio (LNR) and the overall value using the LNR differences in effect between estimates. Logistic regression was used to estimate the overall LNR of patients. After that, a probabilistic Bayesian classifier-based dynamic regression model for prognosis analysis is built to capture the dynamic effect of multiple clinic pathological markers on patient prognosis. The dynamic regression model employing the total estimated value of LNR had the best fitting impact on the data, according to the simulation findings. In comparison to other models, this model has the greatest overall survival forecast accuracy. These prognostic techniques shed light on the nodal survival and status particular to the patient. Additionally, the framework is flexible and may be used with various cancer types and datasets.

A Review of Proxy Modeling Highlighting Applications for Reservoir Engineering

Numerical models can be used for many purposes in oil and gas engineering, such as production optimization and forecasting, uncertainty analysis, history matching, and risk assessment. However, subsurface problems are complex and non-linear, and making reliable decisions in reservoir management requires substantial computational effort. Proxy models have gained much attention in recent years. They are advanced non-linear interpolation tables that can approximate complex models and alleviate computational effort. Proxy models are constructed by running high-fidelity models to gather the necessary data to create the proxy model. Once constructed, they can be a great choice for different tasks such as uncertainty analysis, optimization, forecasting, etc. The application of proxy modeling in oil and gas has had an increasing trend in recent years, and there is no consensus rule on the correct choice of proxy model. As a result, it is crucial to better understand the advantages and disadvantages of various proxy models. The existing work in the literature does not comprehensively cover all proxy model types, and there is a considerable requirement for fulfilling the existing gaps in summarizing the classification techniques with their applications. We propose a novel categorization method covering all proxy model types. This review paper provides a more comprehensive guideline on comparing and developing a proxy model compared to the existing literature. Furthermore, we point out the advantages of smart proxy models (SPM) compared to traditional proxy models (TPM) and suggest how we may further improve SPM accuracy where the literature is limited. This review paper first introduces proxy models and shows how they are classified in the literature. Then, it explains that the current classifications cannot cover all types of proxy models and proposes a novel categorization based on various development strategies. This new categorization includes four groups multi-fidelity models (MFM), reduced-order models (ROM), TPM, and SPM. MFMs are constructed based on simplifying physics assumptions (e.g., coarser discretization), and ROMs are based on dimensional reduction (i.e., neglecting irrelevant parameters). Developing these two models requires an in-depth knowledge of the problem. In contrast, TPMs and novel SPMs require less effort. In other words, they do not solve the complex underlying mathematical equations of the problem; instead, they decouple the mathematical equations into a numeric dataset and train statistical/AI-driven models on the dataset. Nevertheless, SPMs implement feature engineering techniques (i.e., generating new parameters) for its development and can capture the complexities within the reservoir, such as the constraints and characteristics of the grids. The newly introduced parameters can help find the hidden patterns within the parameters, which eventually increase the accuracy of SPMs compared to the TPMs. This review highlights the superiority of SPM over traditional statistical/AI-based proxy models. Finally, the application of various proxy models in the oil and gas industry, especially in subsurface modeling with a set of real examples, is presented. The introduced guideline in this review aids the researchers in obtaining valuable information on the current state of PM problems in the oil and gas industry.

The Connection between the Risk of Terrorism and Its Economic and Political Determinants, Analysis, and Application for Forecasting

ABSTRACT The paper is focused on studying the relationship between the risk of terrorism and economic and political development of 13 European countries, quantified using a set of socio-economic and political indicator variables. Numbers of terrorist attacks are used as a terrorist activity indicator. A method of categorization of the numbers of terrorist attacks into a level of risk of terrorism index is presented. Proportional odds multinomial logit generalized linear model is used to describe the connection between the level of risk and the socio-economic and political indicator variables. The presented findings imply that the connection is significant for 9 out of the 13 studied countries. Furthermore, a possible application of the model for obtaining short time predictions of the probability of the levels of risk is presented using a real data set.