Classification Scheme Research Articles

Depth estimation of pipe wall thinning using multifrequency reflection coefficients of T(0,1) mode-guided waves with supervised multilayer perceptron

This study entailed the development of a novel method for estimating the depth of wall thinning of pipes using multifrequency (30–65 kHz) reflection coefficients (MRCs) of the T(0,1) mode-guided waves and a multilayer perceptron (MLP). First, this study established why MRCs are a critical feature of the input layer of the MLP for the defect depth estimation of wall thinning. Further, a mathematical model that can quickly collect large amounts of training data was used to calculate the reflection waveforms. The depths of artificial and actual wall thinning were estimated using the MLP based on the MRCs and the mathematical model. Experiments were conducted using the T(0,1) mode-guided waves to obtain the MRCs for 21 artificial and 6 actual wall thinnings to estimate the defect depths. A maximum of 8347 training data points were prepared using the mathematical model. Because the optimization of the MLP strongly depended on the initial weights and biases, 100 random initial values were prepared to evaluate the average estimations and their standard deviations. The classification scheme of the MLP was used, with classification step widths of 0.5 and 0.25 mm. The correct answer rates for the 21 artificial defects were 93%, with a tolerance of ±0.5 mm for the 0.5 mm classification scheme; those for the 0.25 mm classification scheme were 89%. For the six actual defects, the correct answer rates were 100% with a tolerance of ±0.5 mm for both the 0.5 and 0.25 mm classification schemes. Sufficiently high correct answer rates were obtained in all the cases.

Exploring optimal features and image analysis methods for crop type classification from the perspective of crop landscape heterogeneity

Agricultural landscape structure (e.g., the shape of fields, crop diversity, and landscape heterogeneity) greatly influences the selection of methods for large-scale crop mapping using remote sensing data. However, in-depth assessments of its impacts on crop mapping remain infrequent in the existing literature. This study investigated the optimal crop identification features and image analysis methods including pixel- and object-based approaches on crop classification, through the integration of spectral and textural features across various quantitative agricultural landscapes. In the experiments, crop fields were initially delineated into four distinct landscapes using the K-means clustering algorithm based on analyzing 13 selected landscape metrics such as PLAND, LSI and SHDI. Both pixel- and object-based approaches were then employed to conduct crop classification was then conducted using 48 selected features including 9 band reflectance, 23 vegetation indices (VIs), and 16 textures) and two image analysis methods. Specifically, five classification schemes for the different combinations of feature datasets and image analysis methods were explored to assess the impacts of crop heterogeneity on crop classification. Results indicated the five landscape metrics (e.g., SPLIT, SHEI, Average distance, etc.) performed best in assessing crop heterogeneity. In general, spectral bands and VIs had a higher contribution in the compositional heterogeneity, while textural features and VIs played a more important role in the configurational heterogeneity. VIs in the object-based approach and texture features in the pixel-based approach can improved crop classification accuracy in configurational landscapes. The findings provide a theoretical basis on selecting optimal features and image analysis methods for crop classification in complex agricultural landscapes.

A novel adapted MRI-based scheme for Dejour classification of trochlear dysplasia.

To elaborate an optimized scheme for the Dejour classification of trochlear dysplasia based on axial and sagittal MR images and to evaluate its intra- and inter-reader reliability. Over a period of 20months patients with a knee MRI and the diagnosis of trochlear dysplasia were retrospectively included. Exclusion criteria were incomplete examination, qualitatively non-diagnostic examination, post trochlear surgery, missing informed consent for research purposes. Three independent evaluations were performed by two radiologists: first using an established description of the Dejour classification (types A-D) and then two evaluations using a new adapted scheme (types A-D). The adapted scheme includes a shallow trochlea, in type A no spur/no cliff, in type B with spur/no cliff, in type C no spur/with cliff, and in type D with spur/with cliff. One hundred seventy-one knee MRIs (female:65.5%; left side:52.6%) were included with a median age of 34.3years (range:11.3-79.2). Inter-reader reliability using the established description was fair for the four-type-classification (kappa(k) = 0.23; 95%CI:0.11-0.34), fair for differentiation low-grade versus high-grade dysplasia (k = 0.28;0.13-0.43), slight for differentiation spur versus no-spur types (k = 0.20;0.05-0.34). Inter-reader reliability using the adapted scheme was substantial (k = 0.79;0.75-0.83) for the four-type-classification, substantial for differentiation low-grade versus high-grade dysplasia (k = 0.80;0.75-0.85), substantial for differentiation spur versus no-spur presence (k = 0.76;0.71-0.81). Intra-reader reliability was almost perfect for the adapted scheme (k-values: 0.88-0.95; 95%CIs: 0.84-0.98). The novel adapted scheme for Dejour classification shows an almost perfect intra-reader reliability and a substantially higher inter-reader reliability. It may become a helpful tool in the daily diagnostic work of radiologists.

Preserving isohydricity: vertical environmental variability explains Amazon forest water use strategies.

Increases in hydrological extremes, including drought, are expected for Amazon forests. A fundamental challenge for predicting forest responses lies in identifying ecological strategies which underlie such responses. Characterization of species-specific hydraulic strategies for regulating water use, thought to be arrayed along an 'isohydric-anisohydric' spectrum, is a widely used approach. However, recent studies have questioned the usefulness of this classification scheme, because its metrics are strongly influenced by environments, hence can lead to divergent classifications even within the same species. Here, we propose an alternative approach positing that individual hydraulic regulation strategies emerge from the interaction of environments with traits. Specifically, we hypothesize that the vertical forest profile represents a key gradient in drought-related environments (atmospheric vapor pressure deficit, soil water availability) that drives divergent tree water use strategies for coordinated regulation of stomatal conductance (gs) and leaf water potentials (ΨL) with tree rooting depth, a proxy for water availability. Testing this hypothesis in a seasonal eastern Amazon forest in Brazil, we found that hydraulic strategies indeed depend on height-associated environments. Upper canopy trees, experiencing high VPD, but stable soil water access through deep rooting, exhibited isohydric strategies, defined by little seasonal change in the diurnal pattern of gs and steady seasonal minimum ΨL. By contrast, understory trees, exposed to less variable VPD but highly variable soil water availability, exhibited anisohydric strategies, with fluctuations in diurnal gs that increased in the dry season along with increasing variation in ΨL. Our finding that canopy height structures the coordination between drought-related environmental stressors and hydraulic traits provides a basis for preserving the applicability of the isohydric-to-anisohydric spectrum, which we show here may consistently emerge from environmental context. Our work highlights the importance of understanding how environmental heterogeneity structures forest responses to climate change, providing a mechanistic basis for improving models of tropical ecosystems.

The ballistic articular structure injury classification (BASIC) system: clarifying ballistic articular injuries.

This study aims to (1) devise a classification system to categorize and manage ballistic fractures of the knee, hip, and shoulder; (2) assess the reliability of this classification compared to current classification schemas; and (3) determine the association of this classification with surgical management. We performed a retrospective review of a prospectively collected trauma database at an urban level 1 trauma centre. The study included 147 patients with 169 articular fractures caused by ballistic trauma to the knee, hip, and shoulder. Injuries were selected based on radiographic criteria from plain radiographs and CT scans. The AO/OTA classification system's reliability was compared to that of the novel ballistic articular injury classification system (BASIC), developed using a nominal group approach. The BASIC system's ability to guide surgical decision-making, aiming to achieve stable fixation and minimize post-traumatic arthritis, was also evaluated. The BASIC system was created after analysing 73 knee, 62 hip, and 34 shoulder fractures. CT scans were used in 88% of cases, with 44% of patients receiving surgery. The BASIC classification comprises five subgroups, with a plus sign indicating the need for soft tissue intervention. Interrater reliability showed fair agreement for AO/OTA (k = 0.373) and moderate agreement for BASIC (k = 0.444). The BASIC system correlated strongly with surgical decisions, with an 83% concurrence in treatment choices based on chart reviews. Conventional classification systems provide limited guidance for ballistic articular injuries. The BASIC system offers a pragmatic and reproducible alternative, with potential to inform treatment decisions for knee, hip, and shoulder ballistic injuries. Further research is needed to validate this system and its correlation with patient outcomes. Level III, Diagnostic Study.

Vulnerability detection techniques for smart contracts: A systematic literature review

The number of applications supported by blockchain smart contracts has been greatly increasing in recent years, with smart contracts now being used across several domains, such as the music industry, finance, and retail, to name a few. Despite being used in business-critical contexts, the number of security vulnerabilities in smart contracts has also been increasing, with many of them being exploited and resulting in huge financial and reputation losses. This is despite the enormous effort that is being placed into the research and development of vulnerability detection tools and techniques, which have also greatly increased in number and type in the last few years. Motivated by the recent increase in both vulnerabilities and vulnerability detection techniques, this paper reviews the latest research in smart contract vulnerability detection, emphasizing the techniques being used, the vulnerabilities targeted, and the characteristics of the dataset used for evaluating the technique. We mapped the vulnerabilities against two common vulnerability classification schemes (DASP and SWC) and performed a consolidated analysis. We identified the current research trends and gaps in each technique and highlighted future research opportunities in the field.

Ecological niche modeling of the Leopardus tigrinus complex sheds light on its elusive evolutionary history

Abstract The evolutionary history and taxonomy of the Leopardus tigrinus species complex have been studied based on several approaches, mostly employing genetic and morphological data, leading to distinct classification schemes. We approached this problem from an ecological perspective, with 2 main goals: (i) to evaluate ecological niche differences among regional L. tigrinus populations to determine the extent of ecological divergence among them; and (ii) to identify environmental barriers to historical dispersal that could have driven differentiation among the proposed groups. We modeled the ecological niche of all taxonomic/geographic groups proposed so far to comprise the L. tigrinus complex using the Maximum Entropy algorithm, and evaluated geographic and ecological niche differences among them. Furthermore, we investigated possible environmental barriers to historical dispersal that could have driven differentiation among regional groups. We evaluated 4 hypothetical barriers across 3 time periods to assess their potential historical effect. We found high ecological divergence between northeastern tigrina populations and the northern Andean and Central American tigrinas. Other groups within the L. tigrinus complex are less divergent. In addition, the Guiana Shield tigrina, where the type locality of the species is located, seems to be ecologically similar to populations from northeastern Brazil while also showing some overlap with Andean populations. The Panama center, the Llanos of Colombia and Venezuela, and the Amazon region were identified as historical barriers for tigrina dispersal across all time periods. The inferred historical barriers and ecological divergence observed in this study contribute to the inference of evolutionary differentiation among geographic groups comprising the L. tigrinus complex, revealing areas of consistently low habitat suitability that have likely contributed to divergence among regional populations.

Prevalence and classification of congenital thoracic vertebral body abnormalities in neurologically normal brachycephalic 'screw-tail' dog breeds in referral practice in Australia.

To evaluate the prevalence, type, site and breed predisposition of thoracic congenital vertebral malformations (CVM) in neurologically normal brachycephalic 'screw-tail' dog breeds. Retrospective case series. Neurologically normal French Bulldogs (n = 63), British Bulldogs (n = 42), Boston Terriers (n = 4) and Pugs (n = 86) presenting for brachycephalic obstructive airway syndrome treatment. Either a plain computer tomography scan of the thorax or lateral and dorso-ventral digital radiographs of the thoracic vertebral column of each dog were assessed for any vertebral malformations and classed according to the Modified McMaster vertebral abnormality classification scheme proposed by Guiterrez et al. RESULTS: The prevalence of dogs with one or more abnormal vertebrae was 96.82% in French Bulldogs, 90.47% in British Bulldogs, and 76.74% in Pugs. Pugs had significantly lower instances of CVMs than French Bulldogs. The four Boston Terriers all had vertebral malformations, but the results were not considered to be statistically significant due to their small sample size. In French Bulldogs and British Bulldogs, ventral hypoplasia (Type 3) occurred at greater rates than all other types, followed by ventral and median hypoplasia (Types 7) and symmetrical hypoplasia (Type 8). In Pugs, symmetrical hypoplasia (Type 8) was the most common and then ventral hypoplasia (Type 3). The most affected vertebrae were T6-T9 in Pugs and T5-T10 in French Bulldogs and British Bulldogs. The majority of neurologically normal brachycephalic 'screw-tail' dogs assessed in this paper possessed one or more congenital thoracic vertebral malformations. Dogs with vertebral malformations were found to be more likely to have more than one affected vertebra. Sex had no effect on the prevalence of CVM. A substantial number of neurologically normal brachycephalic 'screw-tail' dogs suffer from congenital vertebral malformations. Thus, the need for selective breeding programmes to eliminate this hereditary condition is crucial.

Robust Containment Queries over Collections of Rational Parametric Curves via Generalized Winding Numbers

Point containment queries for regions bound by watertight geometric surfaces, i.e., closed and without self-intersections, can be evaluated straightforwardly with a number of well-studied algorithms. When this assumption on domain geometry is not met, such methods are either unusable, or prone to misclassifications that can lead to cascading errors in downstream applications. More robust point classification schemes based on generalized winding numbers have been proposed, as they are indifferent to these imperfections. However, existing algorithms are limited to point clouds and collections of linear elements. We extend this methodology to encompass more general curved shapes with an algorithm that evaluates the winding number scalar field over unstructured collections of rational parametric curves. In particular, we evaluate the winding number for each curve independently, making the derived containment query robust to how the curves are arranged. We ensure geometric fidelity in our queries by treating each curve as equivalent to an adaptively constructed polyline that provably has the same generalized winding number at the point of interest. Our algorithm is numerically stable for points that are arbitrarily close to the model, and explicitly treats points that are coincident with curves. We demonstrate the improvements in computational performance granted by this method over conventional techniques as well as the robustness induced by its application.

Multimodal Dictionaries for Traditional Craft Education

We address the problem of systematizing the authoring of digital dictionaries for craft education from ethnographic studies and recordings. First, we present guidelines for the collection of ethnographic data using digital audio and video and identify terms that are central in the description of crafting actions, products, tools, and materials. Second, we present a classification scheme for craft terms and a way to semantically annotate them, using a multilingual and hierarchical thesaurus, which provides term definitions and a semantic hierarchy of these terms. Third, we link ethnographic resources and open-access data to the identified terms using an online platform for the representation of traditional crafts, associating their definition with illustrations, examples of use, and 3D models. We validate the efficacy of the approach by creating multimedia vocabularies for an online eLearning platform for introductory courses to nine traditional crafts.

A brief history of somatostatin interneuron taxonomy or: how many somatostatin subtypes are there, really?

We provide a brief (and unabashedly biased) overview of the pre-transcriptomic history of somatostatin interneuron taxonomy, followed by a chronological summary of the large-scale, NIH-supported effort over the last ten years to generate a comprehensive, single-cell RNA-seq-based taxonomy of cortical neurons. Focusing on somatostatin interneurons, we present the perspective of experimental neuroscientists trying to incorporate the new classification schemes into their own research while struggling to keep up with the ever-increasing number of proposed cell types, which seems to double every two years. We suggest that for experimental analysis, the most useful taxonomic level is the subdivision of somatostatin interneurons into ten or so "supertypes," which closely agrees with their more traditional classification by morphological, electrophysiological and neurochemical features. We argue that finer subdivisions ("t-types" or "clusters"), based on slight variations in gene expression profiles but lacking clear phenotypic differences, are less useful to researchers and may actually defeat the purpose of classifying neurons to begin with. We end by stressing the need for generating novel tools (mouse lines, viral vectors) for genetically targeting distinct supertypes for expression of fluorescent reporters, calcium sensors and excitatory or inhibitory opsins, allowing neuroscientists to chart the input and output synaptic connections of each proposed subtype, reveal the position they occupy in the cortical network and examine experimentally their roles in sensorimotor behaviors and cognitive brain functions.

Classificação dos remineralizadores

An unprecedented classification scheme for stonemeal is proposed in this work, unifying agrogeological and commercial nomenclatures, adopting qualitative criteria and removing ambiguities and innapropriate terms while aiming to facilitate the communication between different fields of knowledge. Stonemeal consists in powdered rocks enriched in K, Ca and/or Mg that are directly applied in the field for crop nutrition. The classification proposal is based both on geochemistry and mineralogy, dividing products in four categories: Primary, for those that have high K and/or P contents; Secondary, for the ones that contain high Ca and/or Mg values; Whole, for those that have high values for both Primary and Secondary classes; and Universal, for the products that have low macronutrient contents, but that still can be commercialized according to legal determinations. The criteria used forthe classification are qualitative, in a sense that they can be adapted to changes in market and legal conditions.

Federated Learning Survey: A Multi-Level Taxonomy of Aggregation Techniques, Experimental Insights, and Future Frontiers

The emerging integration of IoT (Internet of Things) and AI (Artificial Intelligence) has unlocked numerous opportunities for innovation across diverse industries. However, growing privacy concerns and data isolation issues have inhibited this promising advancement. Unfortunately, traditional centralized machine learning (ML) methods have demonstrated their limitations in addressing these hurdles. In response to this ever-evolving landscape, Federated Learning (FL) has surfaced as a cutting-edge machine learning paradigm, enabling collaborative training across decentralized devices. FL allows users to jointly construct AI models without sharing their local raw data, ensuring data privacy, network scalability, and minimal data transfer. One essential aspect of FL revolves around proficient knowledge aggregation within a heterogeneous environment. Yet, the inherent characteristics of FL have amplified the complexity of its practical implementation compared to centralized ML. This survey delves into three prominent clusters of FL research contributions: personalization, optimization, and robustness. The objective is to provide a well-structured and fine-grained classification scheme related to these research areas through a unique methodology for selecting related work. Unlike other survey papers, we employed a hybrid approach that amalgamates bibliometric analysis and systematic scrutinizing to find the most influential work in the literature. Therefore, we examine challenges and contemporary techniques related to heterogeneity, efficiency, security, and privacy. Another valuable asset of this study is its comprehensive coverage of FL aggregation strategies, encompassing architectural features, synchronization methods, and several federation motivations. To further enrich our investigation, we provide practical insights into evaluating novel FL proposals and conduct experiments to assess and compare aggregation methods under IID and non-IID data distributions. Finally, we present a compelling set of research avenues that call for further exploration to open up a treasure of advancement.

Exploring the impact of nocturnal boundary layer stability on wintertime air pollution in a highly polluted basin city using unsupervised learning classification

This study utilizes ten years of wintertime boundary layer meteorological and surface air quality observations to characterize the nocturnal boundary layer (NBL) stability and assess its relationship with air pollution in Taiyuan, a highly polluted basin city in China. An unsupervised learning feature extraction technique known as the self-organizing map (SOM) is applied to objectively classify nocturnal virtual potential temperature (VPT) profiles. The SOM-based classification scheme allows the representation of wintertime day-to-day NBL evolutions by just nine regimes. Special attention is given to four dominant regimes: weak to moderate stability regime (NBL1), cloudy moderate stability regime (NBL3), windy moderate stability regime (NBL7), and strong stability regime (NBL9). These dominant regimes have relatively higher occurrence frequencies (>10%), with the highest frequency associated with the strong stability regime (NBL9) at 25.2%. The diurnal cycles of selected pollutants (CO, NO2, SO2, and PM2.5) exhibit significant distinctions among the different NBL regimes. For instance, in the strong stability regime, CO, NO2, and PM2.5 show explosive growth in the evening due to the accumulation of primary pollutants. However, in the cloudy moderate stability regime, PM2.5 exhibits persistent slow growth throughout the day, likely due to secondary particle formation under high humidity and high SO2 conditions. These findings enhance our understanding of NBL meteorological impacts on surface air pollution in basin cities.

System Design for Sensing in Manufacturing to Apply AI through Hierarchical Abstraction Levels.

Activity recognition combined with artificial intelligence is a vital area of research, ranging across diverse domains, from sports and healthcare to smart homes. In the industrial domain, and the manual assembly lines, the emphasis shifts to human-machine interaction and thus to human activity recognition (HAR) within complex operational environments. Developing models and methods that can reliably and efficiently identify human activities, traditionally just categorized as either simple or complex activities, remains a key challenge in the field. Limitations of the existing methods and approaches include their inability to consider the contextual complexities associated with the performed activities. Our approach to address this challenge is to create different levels of activity abstractions, which allow for a more nuanced comprehension of activities and define their underlying patterns. Specifically, we propose a new hierarchical taxonomy for human activity abstraction levels based on the context of the performed activities that can be used in HAR. The proposed hierarchy consists of five levels, namely atomic, micro, meso, macro, and mega. We compare this taxonomy with other approaches that divide activities into simple and complex categories as well as other similar classification schemes and provide real-world examples in different applications to demonstrate its efficacy. Regarding advanced technologies like artificial intelligence, our study aims to guide and optimize industrial assembly procedures, particularly in uncontrolled non-laboratory environments, by shaping workflows to enable structured data analysis and highlighting correlations across various levels throughout the assembly progression. In addition, it establishes effective communication and shared understanding between researchers and industry professionals while also providing them with the essential resources to facilitate the development of systems, sensors, and algorithms for custom industrial use cases that adapt to the level of abstraction.

Optical solitons for the concatenation model with power–law of self–phase modulation by lie symmetry

This paper investigates the concatenation model under the influence of power-law self-phase modulation through the Lie symmetry. We employ two integration schemes, namely the extended tanh approach and the F-expansion algorithm, to rigorously integrate the reduced ordinary differential equations governing the system. Through this methodological framework, we uncover a diverse array of soliton solutions and systematically classify them, shedding light on their intricate dynamics and characteristics. Our research unveils previously undiscovered soliton solutions, enriching the existing understanding of concatenation models. We introduce a comprehensive classification scheme for these solitons, providing valuable insights into their behavior and interactions. Numerical simulations validate the stability and persistence of the identified soliton solutions across various parameter regimes. Our findings contribute to the theoretical framework of nonlinear wave dynamics and hold potential for innovative applications in fields such as nonlinear optics and information processing.

Evaluation Results and Recommendations of a Novel Tricuspid Regurgitation Classification for Isolated Tricuspid Valve Replacement Surgery

Background: There are still no accepted classification and recommendations for isolated tricuspid valve replacement (ITVR) surgery. So we aim to evaluate the applicability of the tricuspid valve regurgitation classification proposed by Latib in 2018 for ITVR surgery. Methods: We enrolled all patients who underwent ITVR from 2000 to 2021 in our center. Based on a novel classification, the patients were divided into five stages, and in-hospital mortality was used as the primary endpoint to analyze whether this classification scheme was a good way to evaluate the prognosis of patients at different stages and with different surgical options. Results: A total of 254 patients who underwent ITVR were divided into five stages. None of the patients was classified into stage 1, and stages 4/5 accounted for 159 (62.6%). There was no difference in age, gender, or body mass index (BMI). 178 (70.1%) patients underwent traditional open surgery and 76 (29.9%) opted for the transcatheter option. The main etiology was functional tricuspid regurgitation (FTR), with 64.9% of these patients in stage 4 or above. The overall in-hospital mortality rate was 14.2%, with 14.0% in stage 4 vs. 37.8% in stage 5 (p < 0.001). The patients in the intervention group were generally older, and coronary heart disease and atrial fibrillation were also more common (p < 0.05). Interventional mortality for stages 4 and 5 was 35.8% vs. 13.2% in the open group, but there was no significant difference between them after propensity score matching. Conclusions: The tricuspid regurgitation's (TR's) five-stage classifications can predict prognosis for different patients. After this classification, no difference was found between the two procedures, and open surgery is recommended for patients with acceptable general conditions.

Back to the future: the persistent problems of hybrid war

Abstract What has happened to the spectre of Russian hybrid warfare that once haunted strategists, policy-makers and scholars? Many of the characteristics of Russia's 2014 invasion of Crimea have been absent in Moscow's current war, prompting some to wonder whether Russia has moved beyond ‘hybrid warfare’ as it wages a brutal conventional war in Ukraine. Scholars and practitioners should not pack up their hybrid war handbooks just yet, however. The need to limit conflict escalation between nuclear-armed adversaries suggests that non-military asymmetric tools of will increasingly define great power competition in the twenty-first century. Most urgently, scholars must bring some order to the conceptual debate over what is and is not hybrid war. In this policy paper, we propose a classification scheme that can be applied irrespective of the type of conflict, one that illuminates the wide range of political, social, economic and military weapons states may use in future conflicts. This approach not only allows us to move beyond endless debates over which tactics belong in the definition of hybrid war, but it also allows us to describe changes over time in how countries wage multi-domain wars. By describing the ‘hybridity’ of all conflict along three continuous axes of scale, militarization and centralization, scholars and policy-makers alike can avoid dangerous blind spots that are likely to arise when we try to categorize conflicts into discrete typological bins.

Artificial intelligence literacy: a proposed faceted taxonomy

PurposeThe purpose of this paper is to propose a taxonomy of artificial intelligence (AI) literacy to support AI literacy education and research.Design/methodology/approachThis study makes use of the facet analysis technique and draws upon various sources of data and information to develop a taxonomy of AI literacy. The research consists of the following key steps: a comprehensive review of the literature published on AI literacy research, an examination of well-known AI classification schemes and taxonomies, a review of prior research on data/information/digital literacy research and a qualitative and quantitative analysis of 1,031 metadata records on AI literacy publications. The KH Coder 3 software application was used to analyse metadata records from the Scopus multidisciplinary database.FindingsA new taxonomy of AI literacy is proposed with 13 high-level facets and a list of specific subjects for each facet.Research limitations/implicationsThe proposed taxonomy may serve as a conceptual AI literacy framework to support the critical understanding, use, application and examination of AI-enhanced tools and technologies in various educational and organizational contexts.Practical implicationsThe proposed taxonomy provides a knowledge organization and knowledge mapping structure to support curriculum development and the organization of digital information.Social implicationsThe proposed taxonomy provides a cross-disciplinary perspective of AI literacy. It can be used, adapted, modified or enhanced to accommodate education and learning opportunities and curricula in different domains, disciplines and subject areas.Originality/valueThe proposed AI literacy taxonomy offers a new and original conceptual framework that builds on a variety of different sources of data and integrates literature from various disciplines, including computing, information science, education and literacy research.

Reviews and syntheses: A scoping review evaluating the potential application of ecohydrological models for northern peatland restoration

Abstract. Peatland restoration and rehabilitation action has become more widely acknowledged as a necessary response to mitigating climate change risks and improving global carbon storage. Peatland ecosystems require restoration time spans of the order of decades and, thus, cannot be dependent upon the shorter-term monitoring often carried out in research projects. Hydrological assessments using geospatial tools provide the basis for planning restoration works as well as analysing associated environmental influences. “Restoration” encompasses applications to pre-restoration and post-restoration scenarios for both bogs and fens, across a range of environmental impact fields. The aim of this scoping review is to identify, describe, and categorize current process-based modelling uses in peatlands in order to investigate the applicability and appropriateness of ecohydrological and/or hydrological models for northern peatland restoration. Two literature searches were conducted using the entire Web of Science database in September 2022 and August 2023. Of the final 211 papers included in the review, models and their applications were categorized according to this review's research interests in seven distinct categories aggregating the papers' research themes and model outputs. Restoration site context was added by identifying 229 unique study site locations from the full database, which were catalogued and analysed against raster data for the Köppen–Geiger climate classification scheme. A majority of northern peatland sites were in temperate oceanic zones or humid continental zones that experienced snow. Over one in five models from the full database of papers were unnamed and likely intended for single use. Key themes emerging from topics covered by papers in the database included the following: modelling restoration development from a bog growth perspective, the prioritization of modelling greenhouse gas (GHG) emissions dynamics as a part of policymaking, the importance of spatial connectivity within or alongside process-based models to represent heterogeneous systems, and the increased prevalence of remote sensing and machine learning techniques to predict restoration progress with little physical site intervention. Models are presented according to their application to peatlands or broader ecosystem and organized from most to least complex. This review provides valuable context for the application of ecohydrological models in determining strategies for peatland restoration and evaluating post-intervention development over time.