Mining Research Research Articles

Discovery of interesting frequent item sets in an uncertain database using ant colony optimization

ABSTRACT Applications like business basket analysis, digital service analytics, bio-informatics, and mobile commerce have greatly benefited from the information retrieval of significant features from massive databases for improved decision-making. Item set mining is used to find intriguing patterns in databases. Discovering item sets in an uncertain database is a tedious task. Only mathematical correlations between the elements in an item set are the exclusive subject of recurring item set mining research. The finding is direct to optimal. This article introduces an ant colony that maps the viable solution space to a directed graph with quadratic space complexity. The proposed model evaluates an uncertain transaction database's item set. Compared to the current methods, the findings demonstrate the importance of the proposed model.

Time series clustering based on normal cloud model and complex network

When data mining research is conducted, it is difficult to obtain precise domain knowledge to set a similarity threshold. Furthermore, noise and missing values are inevitable. Missing values and noise without pre-processing are challenges for many algorithms. A time-series clustering method is proposed based on the normal cloud model and complex networks. Matrix profile similarity measurement, normal cloud model generation and filtering, cloud model expectation curve weighting, degree centrality reweighting, and community discovery in complex networks are the five stages of the proposed clustering algorithm. Local features are considered, and the effects of missing values are reduced when performing similarity measurements. The normal cloud model can be used to set thresholds adaptively. The Louvain algorithm accomplishes the clustering task in complex networks without specifying the clusters. Experiments are conducted on 94 datasets and are compared with 8 clustering methods in the UCR time-series clustering benchmark study. Experimental results indicate that the proposed method can perform well on many datasets.

A novel method for identification of disturbance from surface coal mining using all available Landsat data in the GEE platform

Coal mining has contributed to a solid and stable global economy. Surface mining and underground mining are the principal methods for extraction of coal worldwide and given the developments in mining technology and equipment, these methods are increasingly generating more and more coal. Surface mining is also a subject that cannot be overlooked in the context of sustainable development because amongst human activities it causes comparatively strong land disturbance. Much of the damage and assessment of land and ecosystems from surface mining occurs at the site, and at the regional scale; hence access to the mining data is limited. Moreover, it is difficult for remote sensing to extract information on larger-scale areas with high precision. As a consequence, a frequency index of open coal mining can be created by merging the vast high-resolution images acquired by Landsat sensors with the spectral properties of open coal scraped from the overburden layer. Such indices and morphological principles can be used to develop strategies for quickly identifying the open coal boundaries in a vast area without mine-related information. In addition, LandTrendr may be used to reconstruct the spatial and temporal processes of surface disturbance and remediation caused by regional surface coal mining to identify surface mining disturbance and reclamation. Seven typical mining locations worldwide were selected to test and confirm the proposed method's practicality and robustness, including delivering accurate findings based on randomly testing sample points and comparing the mature data outputs. The results showed that: (1) the overall accuracy of the open coal areas identified by the OCFI (Open Coal Frequency Index) was over 85 % (range 0.85–0.96). In all study areas, the accuracy of recognizing disturbance and reclamation events was greater than 76 %. (2) The surface mining disturbed 3046.42 km2 of land and 1487.39 km2 was reclaimed in the seven mining areas investigated between 1986 and 2018, resulting in a reclamation rate of 48.82 %. The proposed method is more accurate and faster in identifying open coal locations than the current commonly used land cover datasets based on comparisons with other data products. The approach can extract surface coal mining in the absence of mining data and offer precise boundary data for mining research, which can be extremely useful for extending mining operations.

GNorm2: an improved gene name recognition and normalization system.

Gene name normalization is an important yet highly complex task in biomedical text mining research, as gene names can be highly ambiguous and may refer to different genes in different species or share similar names with other bioconcepts. This poses a challenge for accurately identifying and linking gene mentions to their corresponding entries in databases such as NCBI Gene or UniProt. While there has been a body of literature on the gene normalization task, few have addressed all of these challenges or make their solutions publicly available to the scientific community. Building on the success of GNormPlus, we have created GNorm2: a more advanced tool with optimized functions and improved performance. GNorm2 integrates a range of advanced deep learning-based methods, resulting in the highest levels of accuracy and efficiency for gene recognition and normalization to date. Our tool is freely available for download. https://github.com/ncbi/GNorm2.

Whole-genome and genome-wide association studies improve key agricultural traits of safflower for industrial and medicinal use.

Safflower (Carthamus tinctorius) is widely cultivated around the world for its seeds and flowers. The presence of linoleic acid (LA) in its seeds and hydroxysafflor yellow A (HSYA) in its flowers are the crucial traits that enable safflower to be used for industrial and medicinal purposes. Understanding the genetic control of these traits is essential for optimizing the quality of safflower and its breeding. To further this research, we present a chromosome-scale assembly of the genome of the safflower variety 'Chuanhonghua 1', which was achieved using an integrated strategy combining Illumina, Oxford Nanopore, and Hi-C sequencing. We obtained a 1.17-Gb assembly with a contig N50 of 1.08Mb, and all assembled sequences were assigned to 12 pseudochromosomes. Safflower's evolution involved the core eudicot γ-triplication event and a whole-genome duplication event, which led to large-scale genomic rearrangements. Extensive genomic shuffling has occurred since the divergence of the ancestor of dicotyledons. We conducted metabolite and transcriptome profiles with time- and part-dependent changes and screened candidate genes that significantly contribute to seed lipid biosynthesis. We also analyzed key gene families that participate in LA and HSYA biosynthesis. Additionally, we re-sequenced 220 safflower lines and carried out a genome-wide association study using high-quality SNP data for eight agronomic traits. We identified SNPs related to important traits in safflower. Besides, the candidate gene HH_034464 (CtCGT1) was shown to be involved in the biosynthesis of HSYA. Overall, we provide a high-quality reference genome and elucidate the genetic basis of LA and HSYA biosynthesis in safflower. This vast amount of data will benefit further research for functional gene mining and breeding in safflower.

An efficient pruning method for mining inter-sequence patterns based on pseudo-IDList

Mining inter-sequence patterns represents a relatively novel research direction, yielding significant contributions to the field of data mining research since its inception. Its versatile applications span various domains, with numerous research avenues emerging as a result. However, it is worth noting that inter-sequence mining algorithms tend to generate a substantial volume of frequent patterns. To date, research efforts in the realm of inter-sequence pattern mining have predominantly focused on creating, copying, and storing all candidate information. This approach has led to potential performance inefficiencies, necessitating more resources than may be inherently required. It is worth highlighting the recent introduction of the pseudo-IDList data structure, which has demonstrated promise in enhancing both runtime efficiency and memory utilization during mining tasks. Nevertheless, this innovation has thus far found application exclusively in clickstream mining and sequential pattern mining contexts.In this paper, we propose an extension of this data structure for the mining of frequent inter-sequence patterns and introduce a novel algorithm named Inter-Sequence Pattern mining based on Pseudo-Index. Additionally, our proposed algorithm incorporates a pruning method known as Inter-Sequence Pattern Mining with Index Intersection Checking, designed to effectively curtail the proliferation of generated candidates. Our experimental results unequivocally demonstrate that the proposed algorithm excels in processing time and storage space utilization when compared to existing state-of-the-art algorithms designed for mining inter-sequence patterns.

Machine Learning Prediction Model for Early Student Academic Performance Evaluation in Video-Based Learning

The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) has created the emergence of educational technology domain for many students to access e-learning platforms. However, there are some drawbacks especially in asynchronous video-based learning. A sense of isolation could occur between teacher and students if the teachers do not interact much with the students in the asynchronous video-based learning. Consequently, the knowledge that is delivered by the teacher may not reach students effectively and cause a drop in student performance in the coming examination. Moreover, the growth of video-based learning has created a huge amount of data on the student learning process on the educational video which may provide a boost for educational data mining research. Therefore, this research study aims to introduce a predictive model that scrutinize the number of video view data based on each chapter in the video as well as student learning style, Felder-Silverman (FS) learning style model to deliver a prediction on individual student early performance in asynchronous video-based learning. This research has tested the different combination of feature selection methods with several handle of imbalance data methods such as Synthetic Minority Oversampling Technique (SMOTE), SMOTE-TOMEK and Adaptive Synthetic (ADASYN) algorithms to build the machine learning model and compare the model performance. As a result, proposed machine learning classifier algorithms with the combination of Maximum Relevance and Minimum Redundancy (MRMR) as feature selection method and SMOTE has been achieved the highest Area Under Curve (AUC) rate of 0.93.

A State-of-the-Art Association Rule Mining Survey and its Rare Application, Challenges, Progress

In recent years, association rule mining is exploring its popularity in the rule mining research communities. In this paper, we present the taxonomy of association patterns analysis approaches in depth. The association patterns analysis is based on support constraints, interestingness, and patterns dimension from different levels of abstraction. An in-depth research statistical view is presented in each category of the association patterns analysis research. Among the identified association rule mining research categories, we have selected rare pattern mining for further exploration of our study. Analysis of rare patterns is very much helpful in determining the rare relationships between patterns. Also, we will identify novel rare data patterns analysis techniques to solve the various problems in real-life applications. The overall research impact in the association rule mining research domain can be found in this review article. It will focus on various research scopes and the future directions of rule mining as well as rare pattern mining research.

Analysis and prospects of common problems in clinical data mining of traditional Chinese medicine prescriptions

Mining data from traditional Chinese medicine(TCM) prescriptions is one of the important methods for inheriting the experience of famous doctors and developing new drugs. However, current research work has problems such as to be optimized research plans and non-standard statistics. The main problems and corresponding solutions summarized by the research mainly include four aspects.(1)The research plan design needs to consider the efficacy and quality of individual cases.(2)The significance of the difference in confidence order of association rules needs to be further considered, and the lift should not be ignored.(3)The clustering analysis steps are complex. The selection of clustering variables should comprehensively consider factors such as the frequency of TCM, network topology parameters, and practical application significance. The selection of distance calculation and clustering methods should be improved based on the characteristics of TCM clinical data. Jaccard distance and its improvement plan should be given attention in the future. A single, unexplained clustering result should not be presented, but the final clustering plan should be selected based on a comprehensive consideration of TCM clinical characteristics and objective evaluation indicators for clustering.(4)When calculating correlation coefficients, algorithms that are only suitable for continuous variables should not be applied to binary variables. This article explained the connotations of the above problems based on the characteristics of TCM clinical research and statistical principles and proposed corresponding suggestions to provide important references for future data mining research work.

A Systematic Literature Review on Identifying Patterns Using Unsupervised Clustering Algorithms: A Data Mining Perspective

Data mining is an analytical approach that contributes to achieving a solution to many problems by extracting previously unknown, fascinating, nontrivial, and potentially valuable information from massive datasets. Clustering in data mining is used for splitting or segmenting data items/points into meaningful groups and clusters by grouping the items that are near to each other based on certain statistics. This paper covers various elements of clustering, such as algorithmic methodologies, applications, clustering assessment measurement, and researcher-proposed enhancements with their impact on data mining thorough grasp of clustering algorithms, its applications, and the advances achieved in the existing literature. This study includes a literature search for papers published between 1995 and 2023, including conference and journal publications. The study begins by outlining fundamental clustering techniques along with algorithm improvements and emphasizing their advantages and limitations in comparison to other clustering algorithms. It investigates the evolution measures for clustering algorithms with an emphasis on metrics used to gauge clustering quality, such as the F-measure and the Rand Index. This study includes a variety of clustering-related topics, such as algorithmic approaches, practical applications, metrics for clustering evaluation, and researcher-proposed improvements. It addresses numerous methodologies offered to increase the convergence speed, resilience, and accuracy of clustering, such as initialization procedures, distance measures, and optimization strategies. The work concludes by emphasizing clustering as an active research area driven by the need to identify significant patterns and structures in data, enhance knowledge acquisition, and improve decision making across different domains. This study aims to contribute to the broader knowledge base of data mining practitioners and researchers, facilitating informed decision making and fostering advancements in the field through a thorough analysis of algorithmic enhancements, clustering assessment metrics, and optimization strategies.

Brief historical review of the geological cartography in Ecuador.

Since 1892, seven versions of the national geological map of the Republic of the Ecuador have been published. In 1892, Dr. Theodor Wolf, of the German Scientific Mission, published the 1st map which included a preliminary geomorphological description. Later, Dr. Walther Sauer with the support of the Universidad Central del Ecuador (Central University of Ecuador), published a 2nd version in 1950, and then an update in 1970, both included oil wells information. In 1969, the Servicio Nacional de Geología y Minería del Ecuador (National Service of Geology and Mining of Ecuador), and Institut Français du Pétrole (French Petroleum Institute), published the 3rd version which included a mineralogical index. The 4th version was developed by the Dirección General de Geología y Minas del Ecuador (General Directorate of Geology and Mines of Ecuador) and Institute of Geological Sciences & Natural Environment Research Council, and was published in 1982, it included a geological model from a plate tectonics approach. Later, in 1993, the Corporación de Desarrollo e Investigación Geológico Minero y Metalúrgica del Ecuador (Geological, Mining and Metallurgical Research and Development Corporation of Ecuador) and the British Geological Survey published a 5th version, which included an overview of mineral occurrences on the Ecuadorian Andes. On the 21st. century, in 2001 was published the 6th version, that consisted of a reviewed national geological map, which included data transferring to the geographical information system ArcGis. Ultimately in 2017, the Instituto Nacional de Investigación Geológico Minero Metalúrgico (National Institute of Geological-Mineral-Metallurgical Research), published the 7th version, incorporating the last updated geological results of previous campaigns. Every version contributed to the understanding of the regional geodynamic framework. Nevertheless, in the last two decades, informal coding of units for the geological cartography, thrived, involving a lack of characterization and descriptions of representative outcrops, along with uncertainties on geochronological control for newly mappable defined units, reduce reliability on the national geological cartography, increasing the uncertainties for the understanding the regional geodynamic framework.

ESG risks to global platinum supply: A case study of Mogalakwena Mine, South Africa

Global demand for metals is expected to continue to grow significantly to 2050, partly driven by the need for climate change mitigation measures that require energy transition metals including platinum group metals (PGMs). The environmental, social and governance (ESG) risks of mining these metals are seldom acknowledged in energy transition scenarios and pose a risk to future metal supply. Multiple ESG drivers at different scales create direct and indirect pressures and risks to a specific mine site and their interactions across scales create complex system dynamics. Although mining companies identify ESG factors as their biggest risks (and opportunities), interdisciplinary mining research is in its infancy and ESG factors are often oversimplified or overlooked. While global studies are important for providing an overview of the industry, and highlighting trends and patterns, they cannot communicate the complexity found at the local scale. This paper describes ESG risks, opportunities, and impacts across nine dimensions (water, energy, biodiversity, waste, air quality, community, social vulnerability, land use, and governance) for Anglo American Platinum’s Mogalakwena PGM mine in South Africa. It provides insight into a developing country African context and an in-depth view of the complex ESG risks around PGM mining in South Africa. Given South Africa hosts 91% of global PGM resources, understanding and addressing these ESG risks is critical for ensuring long-term PGM supply and enabling a just energy transition.

Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems

The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets.

Asteroid Mining research based on the Global Equity Evaluation Model

With the aggravation of the imbalance in world development, people pay more and more attention to global equity. In general, global equity means that the fruits of development are shared by all mankind, and the development gap between countries is reasonable, so that people of all countries can enjoy stability and happiness. Based on the principle of global equity, this paper further studies the problem of asteroid mining. First, we established a global equity evaluation model (two-level) model, which calculates development indicators from economic, scientific and technological, cultural, social and other aspects, and uses analytic hierarchical process (AHP) and entropy weight method (EWM) to determine the dimensions and weight of indicators. We then describe our vision for asteroid mining. Based on the premise that asteroid mining is managed by a unified department, an asteroid mining allocation model (three-level) is established, using the ARIMA model to predict the data, using AHP and EWM to define the weights to reflect the impact of mining on global equity. In the end, we discuss the strengths and weaknesses of our models, leaving room for further studies.

Decision tree classifier based on topological characteristics of subgraph for the mining of protein complexes from large scale PPI networks

The growing accessibility of large-scale protein interaction data demands extensive research to understand cell organization and its functioning at the network level. Bioinformatics and data mining researchers have extensively studied network clustering to examine the structural and operational features of protein protein interaction (PPI) networks. Clustering PPI networks has proven useful in numerous research over the past two decades for identifying functional modules, understanding the roles of previously unknown proteins, and other purposes. Protein complexes represent one of the essential cellular components for creating biological activities. Inferring protein complexes has been made more accessible by experimental approaches. We offer a novel method that integrates the classification model with local topological data, making it more reliable and efficient. This article describes a decision tree classifier based on topological characteristics of the subgraph for mining protein complexes. The proposed graph-based algorithm is an effective and efficient way to identify protein complexes from large-scale PPI networks. The performance of the proposed algorithm is observed in protein–protein interaction networks of yeast and human in the Database of Interacting Proteins (DIP) and the Biological General Repository for Interaction Datasets (BioGRID) using widely accepted benchmark protein complexes from the comprehensive resource of mammalian protein complexes (CORUM) and the comprehensive catalogue of yeast protein complexes (CYC2008). The outcomes demonstrate that our method can outperform the best-performing supervised, semi-supervised, and unsupervised approaches to detecting protein complexes.

Failure mechanism and stability control of surrounding rock in mining roadway with gentle slope and close distance

In this study, theoretical analysis, numerical simulation and field measurement were adopted to analyze the difficult problem of controlling the stability of surrounding rock in gently inclined and close-distance coal seam mining roadways. The results showed that: (1) According to the properties and stress environment of the roadway roof, to determine the specific spatial range of the original roof stage, transition stage and regeneration roof stage of the roadway, and corresponding to the distribution of plastic zones in the spatial stages, was an important basis for analyzing the deformation mechanism of the roadway and proposing a phased collaborative control plan. (2) Under the influence of mining, the plastic zone of the transportation roadway expanded violently by about 10 m ahead of the working face. Within the range of roadway passing the overlying goaf, the plastic zone of the surrounding rock between the layers runed through, especially extending to the roof and two shoulder corners of the roadway. The roof and two shoulder corners of the roadway became the key parts for the stability control of the surrounding rock. (3) The mechanical mechanism of asymmetric expansion of the plastic zone was obtained as follows: the range was determined by the magnitude of the deviatoric stress and the orientation was dominated by the direction of the principal stress. The combined effect of the magnitude of deviatoric stress and the angle of principal stress caused asymmetric expansion of the plastic zone. (4) A phased collaborative control scheme was proposed, and numerical simulation and industrial verification were carried out on the proposed scheme. The deformation of the surrounding rock of the roadway at the engineering site was less than 150 mm, and the control effect of the surrounding rock was good, ensuring the safe and efficient recovery of the fully mechanized mining face. The present study revealed the mechanical mechanism of failure and instability of the surrounding rock of the gently inclined close distance coal seam mining roadway and proposed a phased collaborative control scheme, which could provide valuable information for mining research.

Down the Rabbit Hole: Detecting Online Extremism, Radicalisation, and Politicised Hate Speech

Social media is a modern person’s digital voice to project and engage with new ideas and mobilise communities—a power shared with extremists. Given the societal risks of unvetted content-moderating algorithms forExtremism, Radicalisation,andHate speech(ERH)detection, responsible software engineering must understand the who, what, when, where, and why such models are necessary to protect user safetyandfree expression. Hence, we propose and examine the unique research field ofERH context miningto unify disjoint studies. Specifically, we evaluate the start-to-finish design process from socio-technical definition-building and dataset collection strategies to technical algorithm design and performance. Our 2015–2021 51-studySystematic Literature Review (SLR)provides the first cross-examination of textual, network, and visual approaches to detectingextremistaffiliation,hatefulcontent, andradicalisationtowards groups and movements. We identify consensus-driven ERH definitions and propose solutions to existing ideological and geographic biases, particularly due to the lack of research in Oceania/Australasia. Our hybridised investigation on Natural Language Processing, Community Detection, and visual-text models demonstrates the dominating performance of textual transformer-based algorithms. We conclude with vital recommendations for ERH context mining researchers and propose an uptake roadmap with guidelines for researchers, industries, and governments to enable a safer cyberspace.

RFID-based logistics big data asset evaluation and data mining research

Abstract With the rapid rise of e-commerce platforms, in view of the sharp increase in the amount of data in the logistics system, the timely update and processing of relevant logistics information data have assumed a particular relevance. In this paper, we fully draw on the excellent performance of radio frequency identification (RFID) technology and data mining technology, begin by using RFID technology to authenticate logistics commodities, move on to extracting relevant feature information and finally carry out a detailed comparison between k-nearest neighbour algorithm, support vector machine (SVM) algorithm, logistic regression (LR) algorithm and improved LR algorithm. The algorithm provides a solution method for asset information collection channel and data mining classification algorithm. It meets the needs of different customers, and provides a variety of working modes, which helps to improve the time and operation efficiency of data processing algorithms. The results show that the SVM algorithm only achieves 93.8% accuracy when iterating 50 times for the classification results of the classification samples containing the objective functions of x 1 and x 2. The improved LR algorithm stochastic gradient descent algorithm has a classification accuracy of 94.6% after 50 iterations. The RFID-based logistics big data asset evaluation and data mining research identification algorithm has obvious advantages, and the accuracy rate reaches 97.3%.

Event Log Data Quality Issues and Solutions

Process mining is a discipline that analyzes real event data extracted from information systems that support a business process to construct as-is process models and detect performance issues. Process event data are transformed into event logs, where the level of data quality directly impacts the reliability, validity, and usefulness of the derived process insights. The literature offers a taxonomy of preprocessing techniques and papers reporting on solutions for data quality issues in particular scenarios without exploring the relationship between the data quality issues and solutions. This research aims to discover how process mining researchers and practitioners solve certain data quality issues in practice and investigates the nature of the relationship between data quality issues and preprocessing techniques. Therefore, a study was undertaken among prominent process mining researchers and practitioners, gathering information regarding the perceived importance and frequency of data quality issues and solutions and the participants’ recommendations on preprocessing technique selection. The results reveal the most important and frequent data quality issues and preprocessing techniques and the gap between their perceived frequency and importance. Consequently, an overview of how researchers and practitioners solve data quality issues is presented, allowing the development of recommendations.

UTILIZATION OF STATISTICAL ANALYSIS TO IDENTIFY INFLUENTIAL SLOPE PARAMETERS ASSOCIATED WITH ROCKFALL AT OPEN PIT MINES.

The application of statistical analysis software programs has proven useful for the investigation of rockfall runout distance along a designed slope. Rockfall modeling programs are continually being upgraded with more sophisticated analysis tools, such as the use of the rigid body versus lump mass models. Engineers at mine sites utilizing the software may have varied experience related to these models, their associated input parameters, and how to interpret the generated results. To address this concern, researchers at the Spokane Mining Research Division (SMRD) of the U.S. National Institute for Occupational Safety and Health (NIOSH) investigated the influence of slope height, slope angle, slope material, and rock size for both rigid body and lump mass models in a 2-D statistical analysis program. Based on a literature search and industry input, specific ranges common to that of an open pit mining environment were chosen for each of the input parameters to determine 90% rock runout distance as well as their sensitivity to change. Data collected from this numerical analysis and simulation will be compared to empirical rockfall data gathered through the duration of the Highwall Safety project conducted by NIOSH from 2022-2026.